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Objective: Interventions to support patients' engagement in shared decision making (SDM) are lacking within high-grade glioma (HGG) healthcare. Consultation Planning, Recording and Summarising (CPRS) has shown evidence of increasing patient decision self-efficacy, reducing uncertainty, and regret of decisions. This is the first study of CPRS within a HGG population and delivered over serial medical consultations. Method: A one-arm prospective qualitative longitudinal design was used to evaluate the CPRS intervention and evaluated with participants at sequential clinic appointments depending on their care, in Edinburgh, Scotland. We report on serial semi structured interviews of 16 patients and their partners. Results: Consultation planning before the consultation supported patients to feel known by strengthening the patient voice within the consultation. It prepared patients to actively participate in the consultation, despite the distressing nature of the content. Recording and summarising supported patients to understand their situation. The provision of a consultation record enabled accurate recall, a paced uptake of information and supported the family to feel fully informed. Ultimately, patients understood why decisions were being made rather than being part of making decisions. Conclusions: The CPRS intervention helped patients to understand and to feel known by increasing patient capacity for communication in the consultation, with support before, during, and after the consultation. The intervention focused on preparing patients for SDM but patients did not perceive that they had meaningful choices to make. Further research could look at the inclusion of patient decision aids to support this process.

The ongoing forced reflection on the leading urbanization models' crisis has led to greater attention to marginal areas. In Italy, the scientific and media debate has focused on inner areas that, since 2014, have represented the target of an innovative national cohesion policy aimed at tackling their shrinking dynamics: the National Strategy for Inner Areas (SNAI). Indeed, Italian inner areas are endowed with extraordinary natural capital and settlement models far from urban density. Thus, they seem to respond perfectly to the new raised living needs. However, leaving aside the optimistic rhetoric, strong political and administrative choices are necessary to trigger a `return process' based on this broader attention toward inner areas, thus countering humankind's natural tendency to concentrate on urban realities. In this light, the paper proposes a tool to support SNAI in designing and implementing heritage-based local development strategies to address inner areas' real needs. After a critical reading of the new challenges for planning posed by the pandemic and SNAI's role within them, the contribution moves to frame the THEMA (Tool for Heritage-based Enhancement of Marginal Areas) tool, focusing on specificities of the inner areas as cultural heritage. Finally, the tool's application to a case study, an inner area in Campania Region, allows to outline and discuss its possible benefits for SNAI implementation and its limits.

Each new generation of grassland managers could benefit from an improved understanding of how modification of nitrogen application and harvest dates in response to different weather and soil conditions will affect grass yields and quality. The purpose of this study was to develop a freely available grass yield simulation model, validated for England and Wales, and to examine its strengths and weaknesses as a teaching tool for improving grass management. The model, called LINGRA-N-Plus, was implemented in a Microsoft Excel spreadsheet and iteratively evaluated by students and practitioners (farmers, consultants, and researchers) in a series of workshops across the UK over 2 years. The iterative feedback led to the addition of new algorithms, an improved user interface, and the development of a teaching guide. The students and practitioners identified the ease of use and the capacity to understand, visualize and evaluate how decisions, such as variation of cutting intervals, affect grass yields as strengths of the model. We propose that an effective teaching tool must achieve an appropriate balance between being sufficiently detailed to demonstrate the major relationships (e.g., the effect of nitrogen on grass yields) whilst not becoming so complex that the relationships become incomprehensible. We observed that improving the user-interface allowed us to extend the scope of the model without reducing the level of comprehension. The students appeared to be interested in the explanatory nature of the model whilst the practitioners were more interested in the application of a validated model to enhance their decision making.

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich., This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively., Decision Support Systems (DSS) provide users with information that is combined with their existing knowledge or information from other available sources. We investigate how redundancy of the information from the DSS affects the decision to purchase decision support, as well as the use of and trust in the support. Participants performed a classification task. Depending on the condition, they could purchase advice from either an independent, partly redundant, or fully redundant DSS. Purchase decisions, classification performance and trust were recorded. Participants preferred to purchase the independent system, performed better with it and trusted it more, compared to the partly or fully redundant systems. There was no significant learning effect across blocks. Overall, the results support a model of more normative, rather than confirmative preferences for decision support.

Objectives Reduction in unnecessary services is one strategy for increasing the value of health care. Reference laboratory, or send-out, tests are associated with considerable costs. We investigated whether displaying cost and turnaround time (TAT), or time-to-result, for reference laboratory tests at the time of order entry in the electronic health record (EHR) system would impact provider ordering practices. Methods Reference laboratory test cost and TAT data were randomized prior to the study and only displayed for the intervention group. A 24-month dataset composed of 12 months each for baseline and study periods was extracted from the clinical data mart. A difference-in-differences (DID) analysis was conducted using a linear mixed-effects model to estimate the association between the intervention and changes in test-ordering patterns. Results In the inpatient setting, the DIDs of aggregate test-order costs and volume were not different among the control and intervention groups (p = 0.31 and p = 0.26, respectively). In the ambulatory setting, the DIDs of aggregate test-order costs and volume were not different among the control and intervention groups (p = 0.82 and p = 0.51, respectively). For both inpatient and ambulatory settings, no significant difference was observed in the DID of aggregate test-order costs and volumes calculated in respect to stratified relative cost and TAT groups (p > 0.05). Conclusion Lack of alternative tests, test orders placed at a late step in patient management, and orders facilitated by trainees or mid-level providers may have limited the efficacy of the intervention. Our randomized study demonstrated no significant association between the display of cost or TAT display and ordering frequency.

A major challenge for departments of public health (DPHs) in dealing with the ongoing COVID-19 pandemic is tracing contacts in exponentially growing SARS-CoV-2 infection clusters. Prevention of further disease spread requires a comprehensive registration of the connections between individuals and clusters. Due to the high number of infections with unknown origin, the healthcare analysts need to identify connected cases and clusters through accumulated epidemiological knowledge and the metadata of the infections in their database. Here we contribute a visual analytics dashboard to identify, assess and visualize clusters in COVID-19 contact tracing networks. Additionally, we demonstrate how graph-based machine learning methods can be used to find missing links between infection clusters and thus support the mission to get a comprehensive view on infection events. This work was developed through close collaboration with DPHs in Germany. We argue how our dashboard supports the identification of clusters by public health experts, discuss ongoing developments and possible extensions.

Although algorithmic decision support is omnipresent in many managerial tasks, a lack of algorithm transparency is often stated as a barrier to successful human–machine collaboration. In this paper, we analyze the effects of algorithm transparency on the use of advice from algorithms with different degrees of complexity. We conduct a set of laboratory experiments in which participants receive identical advice from algorithms with different levels of transparency and complexity. Our results indicate that not the algorithm itself, but the individually perceived appropriateness of algorithmic complexity moderates the effects of transparency on the use of advice. We summarize this effect as a plateau curve: While perceiving an algorithm as too simple severely harms the use of its advice, the perception of an algorithm as being too complex has no significant effect. Our insights suggest that managers do not have to be concerned about revealing algorithms that are perceived to be appropriately complex or too complex to decision‐makers, even if the decision‐makers do not fully comprehend them. However, providing transparency on algorithms that are perceived to be simpler than appropriate could disappoint people's expectations and thereby reduce the use of their advice.

Системний моніторинг щільності потоку нейтронів від паливовмісних матеріалів в «Об’єкті Укриття» потребує постійного контролю та визначення параметрів динаміки з метою контролю ядерної безпеки. Запропоновано алгоритм ідентифікації переходів форми трендів динамічних рядів потоків нейтронів в експоненціальну залежність для фіксації подій, які можуть свідчити про зміну стану та поведінки паливовмісних матеріалів. У роботі проводиться моніторинг потужностей статистичних критеріїв, які можуть бути використані при визначенні моментів /часових інтервалів переходу динамічних рядів щільності потоку нейтронів до експоненціального закону розподілу. Запропоновано набір критеріїв, який найбільш доцільно використовувати при перевірці експоненціального закону розподілу, гамма-розподілу, розподілу Пуассона та Вейбула. Оцінка потужностей статистичних критеріїв була проведена на різних об’ємах вибірок, без урахування конкуруючих гіпотез. Встановлено потужності трьох груп критеріїв тестування експоненціальності для різної дискретизації вхідних масивів даних. Рекомендовано мотивоване використання критеріїв для різних об’ємів вибірок. Проведені дослідження є основою для проектування підсистеми тестування експоненціальності в автоматизованій системі підтримки прийняття рішень по забезпеченню та підвищенню рівня ядерної безпеки комплексу «Новий Безпечний Конфайнмент – «Об’єкт Укриття». Отримані результати разом з визначеними наборами оцінки аномальності в дискретизованих масивах даних щільності потоку нейтронів є основою для побудови алгоритмів функціонування автоматизованих систем контролю оцінки відхилень від безпеки в комплексі «Новий Безпечний Конфайнмент – «Об’єкт Укриття». Nuclear safety monitoring of the "New Safe Confinement – Shelter Object" system requires constant monitoring of the dynamics of neutron flux density from clusters of nuclear fuel-containing materials at sub-reactor premises at the Shelter object. The paper proposes an algorithm for identifying transitions of neutron flux time series trends from lineal into exponential dependence. The monitoring of the powers of statistical criteria that can be used in determining the moments of transition of dynamic series to the law of exponential distribution has been carried out. The criteria are classified according to the "power - sample size" parameters. A set of criteria, which is most expedient to use when checking the laws of exponential distribution, is offered. The power of statistical criteria is estimated at different sample sizes, without taking into account competing hypotheses. Powers of three groups of exponentiality testing criteria for different data sets have been established. Motivated use of criteria for different sample sizes is recommended. The obtained results are the basis for the construction of algorithms for automated monitoring systems for assessing the state of fuel-containing materials in the complex "New Safe Confinement – Shelter Object". The scientific novelty of the obtained results consists in clarifying the list of exponentiality testing criteria for discretized time series, specifying the limits of their use, determining optimal sets of monitoring criteria for new classes of objects. The practical significance lies in the use of the results as a basis for designing a subsystem for exponentiality testing and monitoring in an automated control system and decision support system on the research object. The direction of further research is the use of the obtained results to construct algorithms for automated monitoring and decision support systems to assess the state of the New Safe Confinement – Shelter Object system.

—Performance Analysis of contractor in a labor management system is the key idea in this paper. It involves a series of steps like collecting and formatting survey data into a form compatible for analytics. Based on the performance of each contractor, their position can be ranked and top three of the result set is returned as candidates deserving a certificate of appreciation from admin in the system. The user survey is the main trigger for this analysis, through which admin can have an efficient resource planning and future plan management by finding most smart-working contractors among all who is registered. Inherently HR management and decision support can be provisioned effectively with the application of random forest algorithm used in this work.

One promise of computational decision support is enabling better solutions in interactions and social exchange by supporting human strengths (e.g. intuitive decision-making) with formal procedures. This study investigates whether mathematical post-negotiation procedures can guide parties to an efficient negotiation result and thus overcome the shortcomings of human-only approaches in the situation of power asymmetry. The results show that (a) an increase in power by means of a rise in issue authority for one of the two parties does not lead to an increase in humans’ negotiation efficiency, (b) the use of mathematical post-negotiation procedures eminently improves the overall results and (c) the powerful party is likely to benefit most from applying a neutral post-negotiation procedure. These differences highlight that power in commercial negotiations does not compensate for insufficient negotiation skills or efforts in the empowered party. On the contrary, unbalanced power decreases the likelihood of success. Despite the ability to prevail because of power, the post-negotiation procedure reveals options advantageous for both parties.

The actuality of the power to improve the accuracy of the results was determined in order to make a decision about the process of testing the software security. An analysis of the methods of support for making a decision was carried out. The necessity and feasibility of improving the accuracy of the results was determined in case of further software security inconsistencies in the minds of the fuzziness of input and intermediate data. With this method, on the basis of the mathematical apparatus of fuzzy logic, the method of support for making a decision about the security of software security has been developed. The main feature of this method is the synthesis of an improved method of generating the initial vibration in the process of starting a piece of neural string. Within the framework of the model, the next stages of follow-up are reached. For the mathematical formalization of the process of accepting the decision and designation of the input data, the model of forming the vector in the input data was developed. Depending on this model for shaping the input data, an anonymous sign of potential inconsistencies and undeclared possibilities of the PP is valid until the data of PVS-Studio Analysis Results. To improve the accuracy of the classification of data collected, the method of creating a piece of neural array has been improved, which is modified by the method of generating a sample, which is being developed. This generation method includes three equal generations: generation of the initial vibration, generation of the initial butt and generation of a specific value of the safety characteristic. This made it possible to increase the accuracy of classification and acceptance of the solution by 1.6 times for positive elements in the selection by 1.2 times for negative elements in the selection. To confirm the effectiveness of the development of the method of support for the decision on how to ensure software security, a ROC-analysis was carried out over the course of the above procedures. The results of the experiment confirmed the hypothesis about the efficiency of the divided method of support to make a decision about the security of PZ up to 1.2 times equal to the methods, which are based on the position of discriminant and cluster analysis. Визначено актуальність питання підвищення точності результатів прийняття рішення щодо процесу тестування безпеки програмного забезпечення. Проведено аналіз методів підтримки прийняття рішення. Визначено необхідність і можливість підвищення точності результатів прийняття рішення при дослідженні вразливостей програмного забезпечення в умовах нечіткості вхідних і проміжних даних. З цією метою на основі математичного апарату нечіткої логіки розроблено метод підтримки прийняття рішення про безпеку програмного забезпечення. Відмінною особливістю даного методу є синтез удосконаленого способу генерації навчальної вибірки у процес навчання штучної нейронної мережі. В рамках моделювання виконані наступні етапи дослідження. Для математичної формалізації процесу прийняття рішення та визначення вхідних даних розроблено модель формування векторів вхідних даних. Відповідно до даної моделі для формування вхідних даних формується безліч ознак потенційних вразливостей та недекларованих можливостей ПЗ відповідно до даних PVS-Studio Analysis Results. Для підвищення точності класифікації оброблюваних даних удосконалено метод навчання штучної нейронної мережі, що відрізняється способом генерації вибірки, що навчається. Даний спосіб генерації включив три рівні генерації: генерація навчальної вибірки, генерація навчального прикладу і генерація конкретного значення характеристики безпеки. Це дозволило підвищити точність класифікації та прийняття рішення у 1,6 рази для позитивних елементів у вибірці та у 1,2 рази для негативних елементів у вибірці. Для підтвердження ефективності розробки методу підтримки прийняття рішення щодо безпеки програмного забезпечення проведено ROC-аналіз з виконанням відповідних процедур. Результати експерименту підтвердили гіпотезу про ефективність розробленого методу підтримки прийняття рішення про безпеку ПЗ до 1,2 рази порівняно з методами, в основі яких використовуються положення дискримінантного та кластерного аналізу.

Klinisch verfügbare KI(künstliche Intelligenz)-basierte Lösungen dienen der Prozessoptimierung und der Objektivierung bildbasierter Quantifizierung für die kardiale Diagnostik. Damit eröffnet sich eine breitere Anwendung der heutigen Hochleistungsmedizin auch außerhalb von universitären Zentren. In dieser Übersichtsarbeit wird über die Praxisrelevanz sowie die Anwendung der KI in der Bildvorverarbeitung und -segmentierung, der Diagnostik, des Phenotypings für die klinische Entscheidungsunterstützung sowie über den heutigen Stand des Datenschutzes und der interdisziplinären Zusammenarbeit berichtet. Die Weiterentwicklung hochqualitativer KI-basierter Lösungen unter Berücksichtigung aktueller Datenschutzvorgaben erfordert eine multidisziplinäre Zusammenarbeit und den Aufbau geeigneter Forschungsinfrastrukturen. KI-Anwendungen in der diagnostischen kardialen Bildanalyse ohne klinische Daten der Patienten und die letztendlich entscheidende kardiologische Expertise erscheinen für eine valide Befundung und damit Therapieentscheidung bei kardiologischen Patienten nicht sinnvoll.

Background Patients with advanced prostate cancer (PCa) typically undergo numerous lines of treatment leading to large amounts of information in Electronic Health Records (EHRs). The Patient-overview Prostate Cancer (PPC) presents clinical information in a graphical overview. The aim of this study was to measure time spent on retrieving clinical information in PPC compared to EHRs, to assess if retrieved data was correct and to explore usability of PPC. Material and methods Oncologists, urologists and nurses in three hospitals in Sweden were timed when filling out questionnaires about patients using PPC and two different EHRs; Melior and COSMIC. Time and number of errors were analysed using linear mixed models (LMMs). Usability of PPC was measured with the System Usability Scale. Results The LMM showed a significantly shorter time to retrieve information in PPC compared to EHRs. The estimated time to complete one questionnaire was 8 minutes (95% CI = 6-10, p < 0.001) in PPC compared to 25 minutes in Melior and 21 minutes in COSMIC. Compared to PPC, the estimated time difference was 17 minutes longer in Melior (95% CI = 14-20, p < 0.001) and 13 minutes longer in COSMIC (95% CI = 10-17, p < 0.001). The LMM showed significantly fewer errors in PPC compared to Melior. No significant difference in the number of errors was found between PPC and COSMIC. The usability of PPC was rated as excellent by oncologists, urologists and nurses. Conclusion A graphical overview of a patient's medical history, as in PPC, gives health staff rapid access to relevant information with a high degree of usability.

Additive manufacturing (AM) currently evolves from a prototyping process to an alternative manufacturing process for end-use parts, advancing into fabrication of low to medium product volumes. Moreover, increasing awareness of environmental impacts of manufacturing lead to the necessity of holistic evaluation among traditional evaluation criteria in the technical and economic domains. However, existing evaluation approaches seldom consider a complete AM process chain and are lacking a production-oriented notation which enables comparability across technologies and support in identification of improvement potentials on technical and environmental level. To address this gap, this paper highlights the way from structured data acquisition to setup of an energy value stream map (EVSM) for AM process chains in end-use part production, augmenting methods of lean manufacturing by the energy dimension. Consequently, it contributes to a holistic and transparent process chain perspective to assess AM as a manufacturing alternative. The proposed methodology is applied to a case study covering two different process chains, a first based on powder bed fusion via Multi-jet Fusion (MJF) and a second utilizing vat-photopolymerization via Continuous Liquid Interphase Printing (CLIP). While the MJF process chain's energetical hot spot is situated in the printing process itself, the hot spot for CLIP is found in thermal post-processing, exceeding the comparably efficient printing process by magnitudes and ultimately resulting in higher energy intensity per part compared to the MJF process chain. These results highlight the necessity of a holistic evaluation method for complete AM-based process chains and their influence on the product properties. Insights may help engineers, designers and decision makers in pre-selection of suitable manufacturing strategies with a more complete view on AM process chains.

BackgroundCritical decision making in surgical necrotizing enterocolitis (NEC) is highly complex and hard to capture in decision rules due to case-specificity and high mortality risk. In this choice experiment, we aimed to identify the implicit weight of decision factors towards future decision support, and to assess potential differences between specialties or centers.MethodsThirty-five hypothetical surgical NEC scenarios with different factor levels were evaluated by neonatal care experts of all Dutch neonatal care centers in an online environment, where a recommendation for surgery or comfort care was requested. We conducted choice analysis by constructing a binary logistic regression model according to behavioral artificial intelligence technology (BAIT).ResultsOut of 109 invited neonatal care experts, 62 (57%) participated, including 45 neonatologists, 16 pediatric surgeons and one neonatology physician assistant. Cerebral ultrasound (Relative importance = 20%, OR = 4.06, 95% CI = 3.39–4.86) was the most important factor in the decision surgery versus comfort care in surgical NEC, nationwide and for all specialties and centers. Pediatric surgeons more often recommended surgery compared to neonatologists (62% vs. 57%, p = 0.03). For all centers, cerebral ultrasound, congenital comorbidity, hemodynamics and parental preferences were significant decision factors (p Sex (p = 0.14), growth since birth (p = 0.25), and estimated parental capacities (p = 0.06) had no significance in nationwide nor subgroup analyses.ConclusionWe demonstrated how BAIT can analyze the implicit weight of factors in the complex and critical decision for surgery or comfort care for (surgical) NEC. The findings reflect Dutch expertise, but the technique can be expanded internationally. After validation, our choice model/BAIT may function as decision aid.

This is the registration of a protocol for a scoping review to explore the current literature on the design, validation, and implementation of digital technologies for paediatric sepsis prediction. This protocol follows the methodology proposed by Arksey and O'Malley on scoping reviews, along with the Preferred Reporting Items for Systematic Review and Meta-Analysis Extension for a scoping review. We anticipate the findings and analysis from this review will support advancing knowledge towards developing future digital technologies that may support clinical decision-making for sepsis vulnerability and identify areas for further study.

The functions of farm management information systems (FMIS) are mostly examined without considering purchasing statistics as an indicator for their actual use and crucial information for FMIS developers and politics. Thus, the present study analyzes categories and functions of commercial FMIS purchased by farmers within a subsidy program for digital technologies in the German federal state of Bavaria. The FMIS were categorized and linked to eleven general functions. Correlations between supplied FMIS functions and farmers’ purchase decisions were evaluated. Finally, the distribution of FMIS functions and categories were weighted by the number of purchases to reflect their demand. The distribution of categories and functions were also compared to previous studies to show current trends. From October 2018 to June 2020, 52 different FMIS were purchased 569 times, dominated by ten FMIS covering 85 % of purchases. The most purchased FMIS targeted the crop domain. Farmers tend to use office and mobile software versions in combination more often. Although web applications seem to increase, native applications, developed for use on a particular platform or device, were still the most inquired application type. There is evidence that certain functions increase farmers’ willingness to use FMIS when they are available. The most purchased FMIS functions were ‘quality assurance’, ‘inventory’ and ‘finance’. The availability of the functions ‘traceability’ and ‘quality assurance’ clearly increased, confirming a positive trend already predicted in a previous study in 2015. In conclusion, functions providing an automated and site-specific mapping, monitoring, and recording of farm processes and production materials to comply with legislative standards were highly requested by farmers, presumably due to increasing cross-compliance requirements of the Common Agricultural Policy of the European Union. Future FMIS should therefore include functions for recording and evaluating site-specific agri-environmental measures to support result-based payments and related decision-making.

In previous studies, we used Behavioral Artificial Intelligence Technology (BAIT) to develop a decision support tool for doctors of preterm infants with severe necrotizing enterocolitis (NEC). The tool helps doctors in making the decision of advising the patient’s parents to perform surgery or to opt for comfort care in these critically ill neonates. It is built on the knowledge of Dutch neonatal care experts. During development, we asked the experts to perform a choice experiment in which they had to make the decision (surgery or comfort care) in several hypothetical situations of a critically ill neonates with surgical NEC. Doing so, we learned more about the different factors and trade-offs that specialists consider while making this - utmost moral - decision. In the current study, we want to expand on that knowledge by inviting European doctors to provide their expertise as well. These participants will also perform an updated version of the choice experiment, after which we will model on which factors their decisions are made. Then we can examine if the weights per factor, on which these decisions are based, differ across countries and specialties. This will help to gain transparency regarding the decisional process of neonatal specialists, after which the knowledge might even be incorporated into a European decision support model.

International audience; Demand Driven Material Requirements Planning (DDMRP) is a recent method mixing push and pull flow management. Although it claims to be the solution to traditional methods’ limitations, the DDMRP method works at infinite capacity: manufacturing or supply orders are launched according to a logic of replenishment of stocks defined as buffers. This article proposes an evaluation of capacity management using visual charts developed by simulation. These charts correlate the bottleneck resource's loading rate to a service rate by considering one of the DDMRP method parameters, the Decoupled Lead Time (DLT). The charts are a decision support tool. They allow identifying to which loading rate the DLTs are representative of the flow times of manufacturing orders and which capacity level to use. We study different workshops, including a real industrial case. Our results show that it is better to control the flow times by adjusting capacity rather than adjust the DLT parameter.

This paper presents a Public Participation Geographic Information System (PPGIS) application conducted in the Ebro Delta Natural Park, Spain. It aims to support decision-making and management activities. The application is based on an online PPGIS questionnaire using Google Maps API. Participants were asked about the spatial and temporal usage while visiting the Natural Park, about their landscape preferences, and appreciations. A set of eight pairs of bipolar adjectives related to landscape characteristics and experience, four items related to public use, and nine to leisure activities were used. In total, 204 valid answers and 3,969 georeferenced opinions were mapped by this case study. The results of this mapping give insights in the use, perception and appreciation of landscape naturalness and aesthetic beauty, accessibility, facilities, services, and signposting. Furthermore, this study discuss the outcomes of mapping the results and how they support the park management with regard to the identification of conflicts, and the need for action. Finally, this study discuss potentials and limitations of PPGIS as a tool for public participation to capture visitors' experiential knowledge in order to optimize and enhance the management of protected areas.

Subject matter. Informatization of processes of counteraction to man-made emergencies. Goal. Improving the effectiveness of the process of information support for decision-making in overcoming the consequences of man-made emergencies, in terms of its intellectualization, by creating a concept of integration of various software tools within a single, homogeneous space of knowledge about strategic, tactical and operational actions in a wide range of accidents and disasters related to the operation of technical facilities. Tasks. Develop a formal statement of the problem of decision-making in an emergency situation and justify the methodology of its implementation based on the integration of knowledge tools with various analytical models that describe the processes of emergency. Consider the application of the proposed methodology on a scenario that reproduces the situation on the site after the leakage of a highly toxic substance. Methods. System analysis - in the development of a comprehensive process model for decision-making in an emergency; software engineering - when creating the architecture of an integrated intelligent decision support system in emergencies of man-made nature; physics of the processes of distribution of toxic chemicals in the atmosphere - in the development of a scenario of the situation at the site in an emergency situation at an industrial site. Results. The concept of creating an integrated intelligent decision support system for overcoming the consequences of man-made emergencies, in particular, the formal formulation of a typical problem of decision-making in emergencies is described, as well as the basic principles of its solution, and the formulation of the problem of modeling management processes associated with man-made emergencies is developed. Conclusions. The concept of creating an integrated information-analytical system to support decision-making in man-made emergencies is presented. Within the framework of this concept, formal models of decision-making in man-made emergencies and an approach to the integration of diverse software within a single, homogeneous knowledge space on comprehensive measures to overcome the consequences of emergencies related to accidents and disasters at technical facilities infrastructure. The scenario example of the organization of intellectual support of decisions at release into the atmosphere of a toxic chemical is considered., Предмет. Информатизация действий противодействия чрезвычайным ситуациям техногенного характера. Цель. Повышение эффективности процесса информационной поддержки принятия решений при преодолении последствий техногенных чрезвычайных ситуаций, в аспекте ее интеллектуализации, путем создания концепции интеграции разнообразных программных средств в рамках единого, гомогенного пространства знаний о стратегических, тактических и оперативных действиях в условиях широкого спектра аварий и катастроф, связанных с функционированием технических объектов. Задание. Разработать формальную постановку задачи принятия решений в условиях чрезвычайной ситуации и обосновать методологию ее реализации на основе интеграции средств вывода на знаниях с аналитическими моделями, описывающими процессы существования чрезвычайных ситуаций. Рассмотреть применение предлагаемой методологии на сценарном примере, воспроизводящем ситуацию на объекте после утечки сильно действующего ядовитого вещества. Методы. Системный анализ – при разработке комплексной процессной модели принятия решений в условиях чрезвычайной ситуации; программная инженерия – при создании архитектуры встроенной интеллектуальной системы поддержки принятия решений в условиях чрезвычайных ситуаций техногенного характера; физика процессов распространения ядовитых химических веществ в атмосфере при разработке сценарного примера обстановки на объекте в условиях чрезвычайной ситуации на промышленном объекте. Результаты. Концепция создания интегрированной интеллектуальной системы поддержки принятия решений по преодолению последствий чрезвычайных ситуаций техногенного характера, в частности, описана формальная постановка типовой задачи принятия решений в ЧС, а также изложены основные принципы ее решения, и разработана постановка задачи моделирования процессов управления, связанных с ЧС техногенного характера. Выводы. Изложена концепция создания интегрированной информационно-аналитической системы поддержки принятия решений в условиях техногенных чрезвычайных ситуаций. В рамках данной концепции разработаны формальные модели принятия решений в условиях техногенных чрезвычайных ситуаций и подход к интеграции разнородных программных средств в рамках единого, гомогенного пространства знаний о комплексных мерах по преодолению последствий чрезвычайных ситуаций, связанных с авариями и катастрофами на объектах технической инфраструктуры. Рассмотрен сценарный пример организации интеллектуальной поддержки решений при выбросе в атмосферу ядовитого химического вещества., Предмет. Інформатизація процесів протидії надзвичайним ситуаціям техногенного характеру. Мета. Підвищення ефективності процесу інформаційної підтримки прийняття рішень при подоланні наслідків техногенних надзвичайних ситуацій, в аспекті її інтелектуалізації, шляхом створення концепції інтеграції різноманітних програмних засобів в рамках єдиного, гомогенного простору знань про стратегічні, тактичні й оперативні дії в умовах широкого спектру аварій та катастроф, пов’язаних із функціонуванням технічних об’єктів. Завдання. Розробити формальну постановку задачі прийняття рішень в умовах надзвичайної ситуації та обґрунтувати методологію її реалізації на основі інтеграції засобів виведення на знаннях із різноманітними аналітичними моделями, що описують процеси існування надзвичайних ситуацій. Розглянути застосування запропонованої методології на сценарному прикладі, що відтворює ситуацію на об’єкті після витоку сильно діючої отруйної речовини. Методи. Системний аналіз – при розробці комплексної процесної моделі прийняття рішень в умовах надзвичайної ситуації; програмна інженерія – при створенні архітектури інтегрованої інтелектуальної системи підтримки прийняття рішень в умовах надзвичайних ситуацій техногенного характеру; фізика процесів розповсюдження отруйних хімічних речовин в атмосфері – при розробці сценарного прикладу обстановки на об’єкті в умовах надзвичайної ситуації на промисловому об’єкті. Результати. Концепція створення інтегрованої інтелектуальної системи підтримки прийняття рішень щодо подолання наслідків надзвичайних ситуацій техногенного характеру, зокрема, описано формальну постановку типового завдання прийняття рішень у НС, а також викладено основні засади її вирішення, та розроблено постановку завдання моделювання процесів управління, пов'язаних із НС техногенного характеру. Висновки. Викладено концепцію створення інтегрованої інформаційно-аналітичної системи підтримки прийняття рішень в умовах техногенних надзвичайних ситуацій. В рамках даної концепції розроблено формальні моделі прийняття рішень в умовах техногенних надзвичайних ситуацій та підхід до інтеграції різнорідних програмних засобів в рамках єдиного, гомогенного простору знань про комплексні заходи щодо подолання наслідків надзвичайних ситуацій, що пов’язані із аваріями та катастрофами на об’єктах технічної інфраструктури. Розглянуто сценарний приклад організації інтелектуальної підтримки рішень при викиді в атмосферу отруйної хімічної речовини.

The subject of the article is the models of protection of official, commercial, confidential and other information, which is used by the officials of enterprises when making managerial decisions in terms of destabilizing informative influences. The aim of the study is to obtain an integral index, which characterizes the protection of necessary information, which is used by the officials of the enterprise when making managerial decisions in the presence of destabilizing information influences from the social networks, open sources and insiders. The task of this work are determinations of basic parameters, that characterize the system of defense of information of enterprise at the acceptance of administrative decisions a public servant in the conditions of destabilizing informative influences. The methods of researches are a probability theory and quality theory of differential equalizations. Results: on the basis of the observation and research of the activity of officials in the enterprises of different forms of ownership it was established that making managerial decisions by an official of the enterprise under conditions of destabilizing information influence is a relaxation fluctuation in the non-autonomous model of psychological arousal. An analysis of the dynamics of the sequence of influencing impulses on an official from existing information sources was conducted. An integral index, that determines a thresholding at that the system of informative defense of enterprise answers politicians of informative defense of enterprise at the acceptance of administrative decisions, is got. Conclusions: it was found that the threat of successful destabilizing information influence on management decision making by an official of the enterprise is determined by three parameters: the parameter, which determines the leakage of confidential information, the parameter, which determines the violation of the integrity of information and the parameter, which determines the blocking of access to information. It is noticed that the beginning of the emergence of a sequence of influencing destabilizing information influences on the enterprise determined by the content of the information at the initial moment of time (the effect of suggestion) and occurs over a period of time (lag effect).

A voluminous literature addresses the weaknesses of standard stated preference methods used to value non-market environmental goods and services, such as contingent valuation and choice experiment. Deliberative Monetary Valuation (DMV) has emerged as a prominent alternative to the standard versions of these methods. It combines deliberative institutions with preference elicitation. Despite an anchorage in an extensive philosophical literature on deliberative democracy, the theoretical foundations of DMV are underinvestigated. A noteworthy exception is Bartkowski and Lienhoop (2018)’s effort to use Sen’s philosophical views to elaborate such theoretical foundations. The present conceptual contribution pursues this theoretical effort by pointing out two issues left unanswered by the above contribution. The first issue is: how can one ascertain that deliberation has attained its goal in DMV and, more broadly, in decision-making? (We term this the deliberative credentials issue.) The second issue is the role that economists and consultants involved in the proceedings of deliberation are supposed to play. (We term this the role of the analyst issue.) In order to clarify the kind of investigations that DMV or any alternative method should implement to be unequivocal on these issues, we use a formal framework introduced by Cailloux and Meinard (2019), designed to capture the stance that an individual has on a given topic once she has participated in a deliberation: her “deliberated judgment”. This framework allows to identify empirical questions that DMV do not tackle whereas answering these questions would be necessary to clarify the stance that DMV takes on the deliberative credentials issue. When it comes to the role of the analyst issue, our framework advocates an active role of the practitioner in creating what we call models of Deliberated Judgments. This framework thereby helps to characterize the normative stance adopted when implementing a deliberative approach. Une importante littérature critique les faiblesses des méthodes d’évaluation économique fondées sur les préférences déclarées, qui sont utilisées pour évaluer les biens et services environnementaux hors marché, comme l’évaluation contingente et les expériences de choix. L’Évaluation Monétaire Délibérative (ÉMD) est une alternative importante, d’émergence récente, aux versions standards de ces méthodes. Elle consiste à combiner des dispositifs délibératifs avec des techniques d’élicitation de préférences. Malgré son ancrage dans la littérature philosophique profuse qui se consacre à la démocratie délibérative, les fondements théoriques de l’ÉMD restent trop peu étudiés, à l’exception notable des efforts de Bartkowski and Lienhoop (2018), qui s’appuient sur la philosophie de Sen. La présente contribution conceptuelle poursuit cet effort théorique en ciblant deux problématiques laissées en suspens par ces auteurs. La première est la question de savoir comment l’on peut attester du fait que la délibération a bien joué le rôle qui est le sien, dans l’ÉMD et, plus généralement, dans l’aide à la décision (nous parlerons de la problématique de l’effectivité de la délibération) ; la seconde pose la question du rôle que les économistes et consultants impliqués dans le déroulement de la délibération sont supposés jouer (nous parlerons de la problématique du rôle de l’analyste). Afin de clarifier le programme de recherche que l’ÉMD, ou toute autre méthode alternative, devrait poursuivre afin de lever toute ambiguïté sur ces questions, nous nous appuierons sur un cadre formel introduit par Cailloux and Meinard (2019), conçu pour modéliser la posture adoptée par un individu sur un sujet donné à la faveur de sa participation à une délibération : son jugement délibéré. Ce cadre permet d’identifier des questions empiriques auxquelles l’ÉMD ne répond pour l’heure pas, alors même qu’y répondre serait nécessaire pour clarifier le positionnement de l’ÉMD sur la problématique de l’effectivité de la délibération. Quant au rôle de l’analyste, notre cadre suggère de confier à l’analyste le rôle actif de création de ce que nous appelons des modèles de jugement délibéré. Ce cadre aide ainsi à caractériser les positions normatives adoptées lors de l’implémentation d’une approche délibérative.

This study aimed to develop a mobile app that would cater the needs of parents or guardians in finding reliable tutor for their child’s in need of tutoring services. The study focused on developing a mobile app entitled “Grab a Tutor: A Decision Support Mobile App for Student Tutoring.” All features stated on the user story board, use case diagrams and data flow diagrams were provided on the developed mobile app. The Grab a Tutor: A Decision Support Mobile App which was developed from August 1, 2018 up to December 2018 conforms to ISO/IEC 25010 Product Quality Standards and ISO/IEC 25010 Quality in Use Standards. The study was conducted using a developmental type of research. Data were gathered through informal and formal interviews, observations, internet searches, and survey questionnaires. The mobile app was developed using the phases of the Agile Approach Development which includes requirements analysis phase, planning phase, design phase, develop phase, release phase and track and monitor phase. Source code and design were constructed using Ionic framework and XAMPP. The implementation of the various technology used in the mobile app made it easier to meet all the aims that the researcher conceptualized. In order to assess the consistency and reliability of the mobile app, the researcher also used ISO/IEC 25010 as the standard testing tools. As a research standard, it was successful because it meant that the framework produced met the respondents' level of standards. Since it has been standardized, ISO / IEC 25010 describe a well-known consistency attribute and serves as the basis for many approaches to quality control. An error-free application and software reliability would be ensured., CITE AS: Torres, N. (2022). Grab A Tutor: A Decision Support Mobile App for Student Tutoring. The Quest: Journal of Multidisciplinary Research and Development, 1(2)., {"references":["1.\tCarolyne Jacobs, Michael Wilson, Charles Barker. (2015). Using the latest developments in mobile technology to improve students' engagement with assessment feedback. International Conference on Interactive Mobile Communication Technologies and Learning (IMCL), 124-126.","2.\tCrossman, A. (2020, March 19). purposive-sampling-3026727. Retrieved from ThoughtCo.: https://www.thoughtco.com/purposive-sampling-3026727","3.\tFredys Simanca, Rubén González, Luis Rodriguez, Daniel Burgos. (2020). Personalized tutoring model through the application of Learning Analytics phases. IEEE Latin America Transactions, vol. 18, no. 01, 7-15.","4.\tHsien-Hui Tang, Cyun-Meng Jheng, Miao-En Chien, Ni-Miao Lin, Mike Y. Chen. (2013). iCAN: A tablet-based pedagogical system for improving the user experience of children with autism in the learning process. IEEE International Conference on Orange Technologies (ICOT), 177-180.","5.\tPaul, E. A., & Ann, S. R. (2022). Development and Assessment of Online Platform for Crop Production with Decision Support System. The Quest: Journal of Multidisciplinary Research and Development, 1(1).","6.\tJianjun Hou, Kuiyuan Zhang, Xuexiang Yang, Hongpeng Gao. (2014). M-learning Course Development and Practice Based on Tutor-Directed Online Teaching. International Conference of Educational Innovation through Technology (EITT), 21-28.","7.\tKai-Hsiang Yang, Shan-Shan Wu, Wen-Yu Lo, Wan-Ling Chang. (2015). Investigating the Effects of Mobile Learning with Cross-Age Peer Tutoring in English Learning. 2015 IIAI 4th International Congress on Advanced Applied Informatics (IIAI-AAI), 227."]}

Considering the coronavirus disease 2019 (COVID‐19) pandemic, the government and health sectors are incapable of making fast and reliable decisions, particularly given the various effects of decisions on different contexts or countries across multiple sectors. Therefore, leaders often seek decision support approaches to assist them in such scenarios. The most common decision support approach used in this regard is multiattribute decision‐making (MADM). MADM can assist in enforcing the most ideal decision in the best way possible when fed with the appropriate evaluation criteria and aspects. MADM also has been of great aid to practitioners during the COVID‐19 pandemic. Moreover, MADM shows resilience in mitigating consequences in health sectors and other fields. Therefore, this study aims to analyse the rise of MADM techniques in combating COVID‐19 by presenting a systematic literature review of the state‐of‐the‐art COVID‐19 applications. Articles on related topics were searched in four major databases, namely, Web of Science, IEEE Xplore, ScienceDirect, and Scopus, from the beginning of the pandemic in 2019 to April 2021. Articles were selected on the basis of the inclusion and exclusion criteria for the identified systematic review protocol, and a total of 51 articles were obtained after screening and filtering. All these articles were formed into a coherent taxonomy to describe the corresponding current standpoints in the literature. This taxonomy was drawn on the basis of four major categories, namely, medical (n = 30), social (n = 4), economic (n = 13) and technological (n = 4). Deep analysis for each category was performed in terms of several aspects, including issues and challenges encountered, contributions, data set, evaluation criteria, MADM techniques, evaluation and validation and bibliography analysis. This study emphasised the current standpoint and opportunities for MADM in the midst of the COVID‐19 pandemic and promoted additional efforts towards understanding and providing new potential future directions to fulfil the needs of this study field.

Structured reporting has been one of the most discussed topics in radiology for years. Currently, there is a lack of user-friendly software solutions that are integrated into the IT infrastructure of hospitals and practices to allow efficient data entry.Radiological reports are mostly generated as free text documents, either dictated via speech recognition systems or typed. In addition, text components are used to create reports of normal findings that can be further edited and complemented by free text.Software-based reporting systems can combine speech recognition systems with radiological reporting templates in the form of interactive decision trees. A technical integration into RIS ("radiological information system"), PACS ("picture archiving and communication system"), and AV ("advanced visualization") systems via application programming interfaces and interoperability standards can enable efficient processes and the generation of machine-readable report data.Structured and semantically annotated clinical data collected via the reporting system are immediately available for epidemiological data analysis and continuous AI training.The use of structured reporting in routine radiological diagnostics involves an initial transition phase. A successful implementation further requires close integration of the technical infrastructure of several systems.By using a hybrid reporting solution, radiological reports with different levels of structure can be generated. Clinical questions or procedural information can be semi-automatically transferred, thereby eliminating avoidable errors and increasing productivity.KLINISCHES/METHODISCHES PROBLEM: Strukturierte Befundung ist seit Jahren eines der meist diskutierten Themen in der Radiologie. Aktuell herrscht ein Mangel an nutzerfreundlichen Softwarelösungen, welche in die bestehende IT-Infrastruktur der Kliniken und Praxen integriert sind und effiziente Dateneingaben erlauben.Radiologische Befunde werden meist als Freitext über Spracherkennungssysteme diktiert oder per Tastatur eingegeben. Zudem werden Textbausteine für die Erstellung von Normalbefunden verwendet und bei Bedarf durch Freitextinhalte ergänzt.Softwarebasierte Befundungssysteme können Spracherkennungssysteme mit radiologischen Befundvorlagen in Form von interaktiven Entscheidungsbäumen vereinen. Eine technische Integration in RIS(Radiologieinformationssystem)-, PACS(„picture archiving and communication system“)- und AV(„advanced visualization“)-Systeme über Programmierschnittstellen und Interoperabilitätsstandards ermöglicht effiziente Prozesse und die Generierung maschinenlesbarer Befunddaten. LEISTUNGSFäHIGKEIT: Strukturierte, semantisch annotierte klinische Daten, die über ein strukturiertes Befundungssystem erhoben werden, stehen unmittelbar für epidemiologische Datenauswertungen und kontinuierliches KI(Künstliche Intelligenz)-Training zur Verfügung.Der Einsatz der strukturierten Befundung in der radiologischen Routinediagnostik ist mit einer initialen Umstellungsphase verbunden. Eine erfolgreiche Implementierung setzt eine enge Verzahnung der technischen Infrastruktur mehrerer Systeme voraus. EMPFEHLUNG FüR DIE PRAXIS: Durch die Nutzung einer hybriden, softwarebasierten Befundungslösung können radiologische Befunde mit unterschiedlichen Stufen der Struktur generiert werden. Klinische Fragestellungen oder Informationen können aus klinischen Subsystemen semiautomatisch übertragen werden, um vermeidbare Fehler zu eliminieren und die Produktivität zu erhöhen.

PurposeThis study aims to draw the attention of business process management (BPM) research and practice to the textual data generated in the processes and the potential of meaningful insights extraction. The authors apply standard natural language processing (NLP) approaches to gain valuable knowledge in the form of business process (BP) complexity concept suggested in the study. It is built on the objective, subjective and meta-knowledge extracted from the BP textual data and encompassing semantics, syntax and stylistics. As a result, the authors aim to create awareness about cognitive, attention and reading efforts forming the textual data-based BP complexity. The concept serves as a basis for the development of various decision-support solutions for BP workers.Design/methodology/approachThe starting point is an investigation of the complexity concept in the BPM literature to develop an understanding of the related complexity research and to put the textual data-based BP complexity in its context. Afterward, utilizing the linguistic foundations and the theory of situation awareness (SA), the concept is empirically developed and evaluated in a real-world application case using qualitative interview-based and quantitative data-based methods.FindingsIn the practical, real-world application, the authors confirmed that BP textual data could be used to predict BP complexity from the semantic, syntactic and stylistic viewpoints. The authors were able to prove the value of this knowledge about the BP complexity formed based on the (1) professional contextual experience of the BP worker enriched by the awareness of cognitive efforts required for BP execution (objective knowledge), (2) business emotions enriched by attention efforts (subjective knowledge) and (3) quality of the text, i.e. professionalism, expertise and stress level of the text author, enriched by reading efforts (meta-knowledge). In particular, the BP complexity concept has been applied to an industrial example of Information Technology Infrastructure Library (ITIL) change management (CHM) Information Technology (IT) ticket processing. The authors used IT ticket texts from two samples of 28,157 and 4,625 tickets as the basis for the analysis. The authors evaluated the concept with the help of manually labeled tickets and a rule-based approach using historical ticket execution data. Having a recommendation character, the results showed to be useful in creating awareness regarding cognitive, attention and reading efforts for ITIL CHM BP workers coordinating the IT ticket processing.Originality/valueWhile aiming to draw attention to those valuable insights inherent in BP textual data, the authors propose an unconventional approach to BP complexity definition through the lens of textual data. Hereby, the authors address the challenges specified by BPM researchers, i.e. focus on semantics in the development of vocabularies and organization- and sector-specific adaptation of standard NLP techniques.

BACKGROUND AND OBJECTIVES: Platelet transfusions are used across multiple patient populations to prevent and correct bleeding. This scoping review aimed to map the currently available systematic reviews (SRs) and evidence-based guidelines in the field of platelet transfusion. MATERIALS AND METHODS: A systematic literature search was conducted in seven databases for SRs on effectiveness (including dose and timing, transfusion trigger and ratio to other blood products), production modalities and decision support related to platelet transfusion. The following data were charted: methodological features of the SR, population, concept and context features, outcomes reported, study design and number of studies included. Results were synthesized in interactive evidence maps. RESULTS: We identified 110 SRs. The majority focused on clinical effectiveness, including prophylactic or therapeutic transfusions compared to no platelet transfusion (34 SRs), prophylactic compared to therapeutic-only transfusion (8 SRs), dose, timing (11 SRs) and threshold for platelet transfusion (15 SRs) and the ratio of platelet transfusion to other blood products in massive transfusion (14 SRs). Furthermore, we included 34 SRs on decision support, of which 26 evaluated viscoelastic testing. Finally, we identified 22 SRs on platelet production modalities, including derivation (4 SRs), pathogen inactivation (6 SRs), leucodepletion (4 SRs) and ABO/human leucocyte antigen matching (5 SRs). The SRs were mapped according to concept and clinical context. CONCLUSION: An interactive evidence map of SRs and evidence-based guidelines in the field of platelet transfusion has been developed and identified multiple reviews. This work serves as a tool for researchers looking for evidence gaps, thereby both supporting research and avoiding unnecessary duplication. ispartof: VOX SANGUINIS vol:118 issue:1 pages:16-23 ispartof: location:England status: published

Motivated by complying with the EU Water Framework Directive (EU 2000) for ‘Øresund’ to reduce nitrogen emissions as well as to optimize the sewer system across their many shareholders, BIOFOS and DHI joined a large Intereuropean Research and Demonstration project, Digital Water City (DWC), under the Horizon2020 program. The paper presents methods and results of this work by September 2022., This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 820954.

Marxan is the most common decision-support tool used to inform the design of protected-area systems. The original version of Marxan does not consider risk and uncertainty associated with threatening processes affecting protected areas, including uncertainty about the location and condition of species' populations and habitats now and in the future. We described and examined the functionality of a modified version of Marxan, Marxan with Probability. This software explicitly considers 4 types of uncertainty: probability that a feature exists in a particular place (estimated based on species distribution models or spatially explicit population models); probability that features in a site will be lost in the future due to a threatening process, such as climate change, natural catastrophes, and uncontrolled human interventions; probability that a feature will exist in the future due to natural successional processes, such as a fire or flood; and probability the feature exists but has been degraded by threatening processes, such as overfishing or pollution, and thus cannot contribute to conservation goals. We summarized the results of 5 studies that illustrate how each type of uncertainty can be used to inform protected area design. If there were uncertainty in species or habitat distribution, users could maximize the chance that these features were represented by including uncertainty using Marxan with Probability. Similarly, if threatening processes were considered, users minimized the chance that species or habitats were lost or degraded by using Marxan with Probability. Marxan with Probability opens up substantial new avenues for systematic conservation planning research and application by agencies.Marxan es la herramienta de apoyo a las decisiones que más comúnmente se usa para orientar el diseño de los sistemas de áreas protegidas. La versión original de Marxan no considera el riesgo y la incertidumbre asociados con los procesos amenazantes que afectan a las áreas protegidas, incluyendo la incertidumbre sobre la ubicación y la condición de las poblaciones de las especies y su hábitat ahora y en el futuro. Describimos y analizamos la funcionalidad de una versión modificada de Marxan: Marxan con Probabilidad. Este software considera explícitamente cuatro tipos de incertidumbre: probabilidad de que una característica exista en un lugar en particular (estimada con base en los modelos de distribución de especies o con modelos de población espacialmente explícitos); probabilidad de que las características de un sitio se pierdan en el futuro debido a un proceso amenazante, como el cambio climático, las catástrofes naturales y las intervenciones humanas descontroladas; probabilidad de que una característica existirá en el futuro debido a los procesos naturales de sucesión; como los incendios o las inundaciones; y probabilidad de que una característica exista pero haya sido degradada por los procesos amenazantes, como la sobrepesca y la contaminación, y por lo tanto no puede contribuir a los objetivos de conservación. Sintetizamos los resultados de cinco estudios que ilustraron cómo cada tipo de incertidumbre puede usarse para orientar el diseño del área protegida. Si hubiera incertidumbre en la distribución de la especie o de su hábitat, los usuarios podrían maximizar la posibilidad de que estas características estuvieran representadas mediante la inclusión de Marxan con Probabilidad. De manera similar, si los procesos amenazantes estuvieran considerados, los usuarios minimizarían la posibilidad de que se pierda la especie o degrade el hábitat usando Marxan con Probabilidad. Marxan con Probabilidad abre nuevas vías importantes para la investigación sobre la planeación sistemática de la conservación y su aplicación por parte de las agencias.Marxan 软件是指导保护区系统设计的常用决策支持工具。然而, Marxan 的原始版本没有考虑到影响保护区的威胁过程的风险和不确定性, 包括物种种群和栖息地现在及未来位置和各种条件的不确定性。我们设计并检验了改进版 Marxan 软件的功能, 即考虑可能性的 Marxan (Marxan with Probability) 。该软件明确地考虑了四种不确定性:一种特征存在于特定地点的可能性 (基于物种分布模型或空间显式种群模型估计); 一个位点的特征由于未来气候变化、自然灾害, 或不受控制的人类干预等威胁过程而消失的可能性;一种特征由于自然的连续过程, 如火灾或洪水, 在未来存在的可能性;特征虽然存在, 但由于过度捕捞或污染等威胁过程而退化, 因此不能促进保护目标实现的可能性。我们总结了五项研究的结果, 来说明每种类型的不确定性如何用于指导保护区设计。如果物种或栖息地分布存在不确定性, 用户可以通过使用 Marxan with Probability 来纳入不确定性, 从而尽可能地表示出这些特征。类似地, 如果要考虑威胁过程, 用户也可以使用 Marxan with Probability 将物种或栖息地丧失或退化的可能性降至最低。 Marxan with Probability 这一软件为各机构的系统保护规划研究和应用开辟了重要新途径。【翻译: 胡怡思; 审校: 聂永刚】.

A persistent challenge for the Sustainable Development Goals (SDGs) has been a lack of data for indicators to assess progress towards each goal and varying capacities among nations to conduct these assessments. Rapid developments in big data, however, are facilitating a global approach to the SDGs. Tools and data products are emerging that can be extended to and leveraged by nations that do not yet have the capacity to measure SDG indicators. Big Earth Data, a special class of big data, integrates multi-source data within a geographic context, utilizing the principles and methodologies of the established literature on big data science, applied specifically to Earth system science. This paper discusses the research challenges related to Big Earth Data and the concerted efforts and investments required to make and measure progress towards the SDGs. As an example, the Big Earth Data Science Engineering Program (CASEarth) of the Chinese Academy of Sciences is presented along with other case studies on Big Earth Data in support of the SDGs. Lastly, the paper proposes future priorities for developments in Big Earth Data, such as human resource capacity, digital infrastructure, interoperability, and environmental considerations.

It is challenging to communicate uncertainty for high-impact weather events to the public and decision-makers. As a result, there is an increased emphasis and training within the National Weather Service (NWS) for “impact-based decision support.” A Collaborative Science, Technology, And Research (CSTAR) project led by Stony Brook University (SBU) in collaboration with the Alan Alda Center for Communicating Science, several NWS forecast offices, and NWS operational centers held two workshops at SBU on effective forecast communication of probabilistic information for high-impact weather. Trainers in two 1.5-day workshops helped 15–20 forecasters learn to distill their messages, engage audiences, and more effectively communicate risk and uncertainty to decision-makers, media, and the general public. The novel aspect of the first workshop focused on using improvisational techniques to connect with audiences along with exercises to improve communication skills using short, clear, conversational statements. The same forecasters participated in the second workshop, which focused on matching messages to intended audiences and stakeholder interaction. Using a recent high-impact weather event, representatives in emergency management, TV media, departments of transportation, and emergency services provided feedback on the forecaster oral presentations (2–3 min) and a visual slide. This article describes our innovative workshop approach, illustrates some of the techniques used, and highlights participant feedback.

Highlights • We develop nested dynamic network DEA models to measure a portfolio's efficiency. • We measure the multi-period efficiency of a portfolio and its comprised assets. • Target portfolio is benchmarked against all other possible portfolios. • A CVaR optimization problem is included. • A two-stage method is proposed to help investors construct their desired portfolios., Portfolio performance evaluation is a major data envelopment analysis (DEA) application in the finance field. Most proposed DEA approaches focus on single-period portfolio performance assessment based on aggregated historical data. However, such an evaluation setting may result in the loss of valuable information in past individual time periods, and violate real-world portfolio managers’ and investors’ decision making, which generally involves multiple time periods. Furthermore, to our knowledge, all proposed DEA approaches treat the financial assets comprising a portfolio as a “black box”: thus there is no information about their individual performance. Moreover, ideal portfolio evaluation models should enable the target portfolio to compare with all possible portfolios, i.e., enabling full diversification of portfolios across all financial assets. Hence, this research aims at developing nested dynamic network DEA models, an additive model being nested within a slacks-based measure (SBM) DEA model, that explicitly utilizes the information in each individual time period to fully and simultaneously measure the multi-period efficiency of a portfolio and its comprised financial assets. The proposed nested dynamic network DEA models, referred to as NDN DEA models, are linear programs with conditional value-at-risk (CVaR) constraints, and infinitely many decision making units (DMUs). In conducting the empirical study, this research applies the NDN DEA models to a real-world case study, in which Markov chain Monte Carlo Bayesian algorithms are used to obtain future performance forecasts in today's highly volatile investment environments.

BACKGROUND The southern armyworm (SAW) Spodoptera eridania (Stoll) (Lepidoptera: Noctuidae) is native to the tropical Americas where the pest can feed on more than 100 plant species. SAW was recently detected in West and Central Africa, feeding on various crops including cassava, cotton, amaranth and tomato. The current work was carried out to predict the potential spatial distribution of SAW and four of its co‐evolved parasitoids at a global scale using the maximum entropy (Maxent) algorithm. RESULTS SAW may not be a huge problem outside its native range (the Americas) for the time being, but may compromise crop yields in specific hotspots in coming years. The analysis of its potential distribution anticipates that the pest might easily migrate east and south from Cameroon and Gabon. CONCLUSION The models used generally demonstrate that all the parasitoids considered are good candidates for the biological control of SAW globally, except they will not be able to establish in specific climates. The current paper discusses the potential role of biological control using parasitoids as a crucial component of a durable climate‐smart integrated management of SAW to support decision making in Africa and in other regions of bioclimatic suitability. © 2021 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry., Modelled egg, egg‐larval and larval parasitoids might potentially establish in most of agroecologies worldwide and might be good candidates for the biological control of the southern armyworm Spodoptera eridania. © 2021 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Automation is defined in respect to the information it either replaces or augments (Sheridan & Parasuraman, 2005). Based on a four-stage model of human information processing, Parasuraman et. al (2000) suggest to characterize automation in terms of 1) information acquisition, 2) information analysis, 3) decision selection and 4) action implementation. For each stage a different level of control should be allocated towards the system. This may range from no control in a manual task to high control in a fully automated task depending on system, environmental and individual factors (Endsley, 1995; McBride et al., 2014).Contrary to the prediction of normative decision-making models, observed experimental data on dependence and adequate use of automation are mixed due to various underlying factors (e.g. Bartlett & McCarley, 2017; Perkins et al., 2010; Rieskamp & Hoffrage, 2008). For the purpose of this study, we focus on the decision selection stage. We simulate a lower level automation framed as an artificial intelligence (AI) matching the proposed level of automation for high task uncertainty (Parasuraman et al., 2000). Against normative decision models based on Bayesian probability theories this study explores the discrepancy in previous findings for decisions under uncertainty using the expected value method (EV) and signal detection theory (SDT) (Marks, 2020; Meyer, 2004). According to EV the most favorable choice is characterized by the highest pay-off (Sheridan, 2019). The AI recommends a single choice for a mapping detection task in a binary choice scenario. The autonomy and final choice remain with the decision maker who can access the raw data if desired (McBride et al., 2014; Parasuraman et al., 2000). The task is based on uncertain events in real-life data. We control for the need for an AI with task complexity, i.e., complexity of the material that requires ample information mapping and risk-taking. Overall, systems with .60-.70 reliability have been shown to improve operator performance (Wickens & Behnke, 2016). In joint human-system performance, feedback on AI errors is necessary to differentiate between AI and human ability yet not sufficient to calibrate trust (Sheridan & Parasuraman, 2005; Zhang et al., 2020). Expectations towards stakes of the decision itself may shift the trust thresholds in human-system interaction as manifested in response bias (Wickens & Behnke, 2016). Because payoffs outweigh the uncertainty in outcome probabilities in human information processing, biases can cause systematic deviations from normative models. In terms of SDT, dependence means conforming with an AI recommendation; it is associated with system trust and risk of non-dependence (Chancey et al., 2017; John D. Lee & Moray, 1994). Two components, compliance and reliance, define behavioral patterns of dependence. Compliance refers to the trust in signals, i.e., recommendations to take an action. Reliance defines the trust in non-signals, i.e. no recommendation is issued by the AI and therefore no action is taken (J. D. Lee & See, 2004). Reliance and compliance are considered a behavioral consequence of trust and can be observed in answer patterns. Falsely issued AI recommendations have been found to influence compliance rates but may also influence reliance (Chancey et al., 2017). The improvement of human performance by means of AI recommendations seems to offer an untapped potential (Hoff & Bashir, 2015). As a first step, we explore if the response bias can be shifted towards a more liberal threshold by means of an AI recommendation. Using a realistic decision scenario in the form of betting on NBA games, we explore if a fictional decision aid framed as an artificial intelligence may empower more normative decisions under uncertainty at different levels of AI reliability. Additionally, we examine joint human-system performance compared to a human only condition.

Evaluating analytical depletion functions as a tool for estimating streamflow depletion caused by groundwater pumping. Includes data and code associated with the Zipper et al. (2019) 'Rapid and accurate estimates of streamflow depletion caused by groundwater pumping using analytical depletion functions'. Published in Water Resources Research at http://doi.org/10.1029/2018WR024403

Rehabilitation and other long-time care can be complex processes, where interaction and communication can be core determinants of patient-centeredness and satisfaction outcomes. Today, there is no quality-assured integrated ICT system for preparing, planning, implementing, or evaluating rehabilitation where all relevant stakeholders are included. In this scoping review we therefore want to explore what is known about necessary and sufficient data for patient and providers to define goals and measures, get decision support, and support for evaluation of rehabilitation trajectories. We also want to explore how data should be presented (digital user interface), and in what ways better availability of data contribute to experience with, and benefit of, rehabilitation trajectories.

Objective As worldwide population aging is accelerating, innovative technologies are being developed to support independent living among community-dwelling older adults with mild cognitive decline. However, the successful implementation of these interventions is often challenging. Until now, literature on implementation issues related to the specific context of older adults with mild cognitive decline is lacking and the few studies available do not focus specifically on the perspective of professional caregivers. Yet the perspective of these caregivers is important as they can be considered a key facilitator for technology implementation among this population. Therefore, this study was the first to examine technology implementation among community-dwelling older adults with mild cognitive decline from the broader perspective of professional caregivers. Methods In this qualitative study, two focus groups consisting of a heterogeneous pool of professional caregivers were conducted: one in Quebec (Canada, n = 6) and one in Brussels (Belgium, n = 8). Braun and Clarke’ method for thematic analysis, guided by a qualitative descriptive approach was applied to inductively identify themes from the data. Results We identified factors influencing technology implementation in older adults with mild cognitive decline on three levels: an individual level (e.g., characteristics of older adults with mild cognitive decline and professional caregivers’ attitude), an organizational level (e.g., lack of training among professional caregivers) and a level referring to the broader context (e.g., ethical considerations). Conclusions This study contributes to the research gap in knowledge on the needs of professional caregivers to facilitate technology implementation among the population of older adults with cognitive decline. Future directions for research, practice, and policy are given, more specifically to improve knowledge among caregivers and on the development of decision support to retrieve safe and effective technologies that suit patient-centered care.

La biologie de la conservation est une branche engagée de l’écologie qui vise à appliquer les savoirs écologiques à la préservation de la biodiversité. Ce positionnement présuppose que ces savoirs ne sont pas le jouet de pouvoirs. Une telle présupposition fait de cette science une cible de prédilection pour des analyses remettant en cause la distinction savoirs/pouvoirs, dans une dynamique se revendiquant d’une inspiration foucaldienne. Nous pointons des clarifications conceptuelles nécessaires à des analyses de savoirs/pouvoirs, et montrons qu’en leur absence, des contributions majeures perdent de leur force, et certains chantiers de recherche perdent en visibilité. Nous défendons une analyse empirique des relations entre savoirs et pouvoirs et en posons les jalons fondés sur l’analyse des contraintes de l’aide à la décision. Conservation biology is an action-oriented, value-based branch of ecology, aimed at identifying how knowledge can help preserve biodiversity. It is therefore based on the assumption that ecological knowledge is not undermined or manipulated by power. Because it is anchored in this assumption, conservation biology is a prominent target for analyses, inspired by Foucault, aimed at questioning the very distinction between knowledge and power. We point a series of conceptual clarifications needed to develop rigorous and unequivocal analyses of power/knowledge, which have to do with the fundamental projects these analyses pursue and their concept of power. We show that when these clarifications are ignored, contributions to this literature loose part of their force and tend to decrease the visibility of key empirical research frontiers. We introduce elements to strengthen these analyses through an empirical approach based on the analyses of constraints binding decision support interactions.

© 2022 Copyright held by the owner/author(s). Incorporating the preferences of a domain expert, a decision-maker (DM), in solving multiobjective optimization problems increased in popularity in recent years. The DM can choose to use different types of preferences depending on his/her comfort, requirements, or the problem being solved. Most papers, where preference-based and interactive algorithms have been proposed, do not pay attention to the user interfaces and input devices. If they do, they use character or graphics-based preference input methods. We propose the option of using a physical or tactile input device that gives the DM a better sense of control over providing his/her preferences. However, off the shelf hardware devices are not tailored to solve multiobjective optimization problems and provide many controls that may increase the cognitive load on the DM. In this paper, we propose a fully modular physical user interface to input preference information for solving multiobjective optimization problems. The modularity allows to arrange each input module in various ways depending on the algorithm, DM’s requirements, or the problem being solved. The device can be used with any computer and uses web-based visualizations. We demonstrate the potential of the physical interface by solving a real-world problem with an interactive decomposition-based multiobjective evolutionary algorithm. peerReviewed

Mainstreaming energy efficiency financing has been considered a key priority during the last decade among several stakeholders. The capability offered by Multicriteria Decision Analysis to integrate cross-domain financial and energy consumption data, combined with statistical analysis techniques and data abundance, contributes to building the necessary market confidence in energy efficiency projects and make them an attractive investment asset class. In this context, the aim of this paper is to propose a solid methodological framework in order to support the financing procedure of energy efficiency investments, and to identify improved grant financing plans, considering a series of factors which are of vital importance for the sustainability of such actions and the limitation of investment risk. A decision support tool, developed in Python, is presented which implements the suggested methodology, improving the decision making for the investor in terms of the percentage of grant financing per project. The developed methodology has been applied on a reliable dataset of energy efficiency projects from several cities in Latvia, where the actual performance of the investments is exploited. The application of the methodology has resulted in a financing plan which achieves about the same energy savings, while bringing 15% reduction of the energy efficiency investments’ cost.

Screening mammography is a widely used approach for early breast cancer detection, effectively increasing the survival rate of affected patients. According to the Food and Drug Administration’s Mammography Quality Standards Act and Program statistics, approximately 39 million mammography procedures are performed in the United States each year. Therefore, breast cancer screening is among the most common radiological tasks. Interpretation of screening mammograms by a specialist radiologist includes primarily the review of breast positioning quality, which is a key factor affecting the sensitivity of mammography and thus the diagnostic performance. Each mammogram with inadequate positioning may lead to a missed cancer or, in case of false positive signal interpretation, to follow-up activities, increased emotional burden and potential over-therapy and must be repeated, requiring the return of the patient. In this study, we have developed deep convolutional neuronal networks to differentiate mammograms with inadequate breast positioning from the adequate ones. The aim of the proposed automated positioning quality evaluation is to assist radiology technologists in detecting poorly positioned mammograms during patient visits, improve mammography performance, and decrease the recall rate. The implemented models have achieved 96.5% accuracy in cranio-caudal view classification and 93.3% accuracy in mediolateral oblique view regarding breast positioning quality. In addition to these results, we developed a software module that allows the study to be applied in practice by presenting the implemented model predictions and informing the technologist about the missing quality criteria.

In 2018–2020, meteorological droughts over Northwestern Europe caused severe declines in groundwater heads with significant damage to groundwater-dependent ecosystems and agriculture. The response of the groundwater system to different hydrological stresses is valuable information for decision-makers. In this paper, a reproducible, data-driven approach using open-source software is proposed to quantify the effects of different hydrological stresses on heads. A scripted workflow was developed using the open-source Pastas software for time series modeling of heads. For each head time series, the best model structure and relevant hydrological stresses (rainfall, evaporation, river stages, and pumping at one or more well fields) were selected iteratively. A new method was applied to model multiple well fields with a single response function, where the response was scaled by the distances between the pumping and observation wells. Selection of the best model structure was performed through reliability checking based on four criteria. The time series model of each observation well represents an independent estimate of the contribution of different hydrological stresses to the head and is based exclusively on observed data. The approach was applied to estimate the drawdown caused by nearby well fields to 250 observed head time series measured at 122 locations in the eastern part of the Netherlands, a country where summer droughts can cause problems, even though the country is better known for problems with too much water. Reliable models were obtained for 126 head time series of which 78 contain one or more well fields as a contributing stress. The spatial variation of the modeled responses to pumping at the well fields show the expected decline with distance from the well field, even though all responses were modeled independently. An example application at one well field showed how the head response to pumping varies per aquifer. Time series analysis was used to determine the feasibility of reducing pumping rates to mitigate large drawdowns during droughts, which depends on the magnitude and response time of the groundwater system to changes in pumping. This is salient information for decision-makers. This article is part of the special issue “Rapid, Reproducible, and Robust Environmental Modeling for Decision Support: Worked Examples and Open-Source Software Tools”.

Poster describing novel approaches to risk assessment and decision support - Establishment of a risk assessment strategy fornano and microplastic (NMP) - Prospective multi criteria decision support (PMCDS) - Decision support system (DSS)

The success of the company requires careful planning. Perusahaan Daerah Air Minum (PDAM) is a drinking water facility management company that plays an important role in supporting the smooth development of the region with the influence of revenue targets. Prediction of revenue targets is deemed necessary for accurate and effective decision making. Predictions are made by comparing the double moving average (DMA) and double exponential smoothing (DES) methods which refer to the actual data from the previous five (5) years. Measuring forecasting accuracy using mean absolute percentage error (MAPE) and assessing accuracy analysis results using tracking signal. Prediction test uses five (5) order values on DMA and five (5) alpha values on DES. Based on the test, it shows that the DMA has the advantage of a smaller MAPE value

Health care management science (2021). doi:10.1007/s10729-021-09556-2, Published by Springer Science + Business Media B.V., Dordrecht [u.a.]

BackgroundPatient-centredness is an essential quality parameter of modern healthcare. Accordingly, involving patients in decisions about care is required by international laws and an increasing number of medical codes and standards. These directives are based on ethical principles of autonomy. Still, there is limited empirical knowledge about the influence of patient involvement on satisfaction with care.ObjectiveIn a large national vignette survey, we aimed to empirically test healthcare users’ satisfaction with healthcare given different degrees of patient involvement, choices made and outcomes.MethodsA web-based cross-sectional survey distributed to a randomised sample of men in Denmark aged 45–70 years. Case vignettes used prostate-specific antigen (PSA) screening for early detection of prostate cancer as a clinical model. Using a 5-point Likert scale, we measured respondents’ satisfaction with care in scenarios which differed in the amount of patient involvement (ranging from no involvement, through involvement with neutral or nudged information, to shared decision-making), the decision made (PSA test or no PSA test) and clinical outcomes (no cancer detected, detection of treatable cancer and detection of non-treatable cancer).ResultsParticipating healthcare users tended to be more satisfied with healthcare in scenarios illustrating greater levels of patient involvement. Participants were positive towards nudging in favour of the intervention but patient involvement through shared decision-making obtained the highest satisfaction ratings (Likert rating 3.81 without any involvement vs 4.07 for shared decision-making, pConclusionOur study provides empirical support for the hypothesis that greater patient involvement in healthcare decision-making improves satisfaction with care irrespective of decisions made and clinical outcomes. Overall satisfaction with the care illustrated was highest when decisions were reached through shared decision-making.

PurposeThis paper investigates the suitability of fuzzy-logic-based support tools for initial screening of manufacturing reshoring decisions.Design/methodology/approachTwo fuzzy-logic-based support tools are developed together with experts from a Swedish manufacturing firm. The first uses a complete rule base and the second a reduced rule base. Sixteen inference settings are used in both of the support tools.FindingsThe findings show that fuzzy-logic-based support tools are suitable for initial screening of manufacturing reshoring decisions. The developed support tools are capable of suggesting whether a reshoring decision should be further evaluated or not, based on six primary competitiveness criteria. In contrast to existing literature this research shows that it does not matter whether a complete or reduced rule base is used when it comes to accuracy. The developed support tools perform similarly with no statistically significant differences. However, since the interpretability is much higher when a reduced rule base is used and it require fewer resources to develop, the second tool is more preferable for initial screening purposes.Research limitations/implicationsThe developed support tools are implemented at a primary-criteria level and to make them more applicable, they should also include the sub-criteria level. The support tools should also be expanded to not only consider competitiveness criteria, but also other criteria related to availability of resources and strategic orientation of the firm. This requires further research with regard to multi-stage architecture and automatic generation of fuzzy rules in the manufacturing reshoring domain.Practical implicationsThe support tools help managers to invest their scarce time on the most promising reshoring projects and to make timely and resilient decisions by taking a holistic perspective on competitiveness. Practitioners are advised to choose the type of support tool based on the available data.Originality/valueThere is a general lack of decision support tools in the manufacturing reshoring domain. This paper addresses the gap by developing fuzzy-logic-based support tools for initial screening of manufacturing reshoring decisions.

Sensors and control technologies are being deployed at unprecedented levels in both urban and rural water environments. Because sensor networks and control allow for higher-resolution monitoring and decision making in both time and space, greater discretization of control will allow for an unprecedented precision of impacts, both positive and negative. Likewise, humans will continue to cede direct decision-making powers to decision-support technologies, e.g. data algorithms. Systems will have ever-greater potential to effect human lives, and yet, humans will be distanced from decisions. Combined these trends challenge water resources management decision-support tools to incorporate the concepts of ethical and normative expectations. Toward this aim, we propose the Water Ethics Web Engine (WE)2, an integrated and generalized web framework to incorporate voting-based ethical and normative preferences into water resources decision support. We demonstrate this framework with a ‘proof-of-concept’ use case where decision models are learned and deployed to respond to flooding scenarios. Findings indicate that the framework can capture group ‘wisdom’ within learned models to use in decision making. The methodology and ‘proof-of-concept’ system presented here are a step toward building a framework to engage people with algorithmic decision making in cases where ethical preferences are considered. We share our framework and its cyber components openly with the research community. HIGHLIGHTS Water Ethics Web Engine (WE)2 is a web framework to incorporate voting-based ethical preferences into water resources decision support.; We share (WE)2 openly at our project repository: https://github.com/uihilab/waterethicswebengine.; A proof-of-concept use case is presented where decision models are learned and deployed with flooding scenarios.; Results indicate that the framework can capture group ‘wisdom’ in learned models.