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The need to make evidence-based decisions in conservation planning for threatened species in the face of limited resources and knowledge is widely recognised as a growing challenge. Increasingly sophisticated decision-support tools and approaches are available to conservation programs. The ability of conservation planners to effectively implement these tools will be key to incorporating complex information into threatened species management. The development of effective decision science approaches does not end when they are made available to planners. Planner and practitioner input into their use and outputs is an important part of incorporating these tools into on-ground conservation. The New South Wales Saving our Species program is a large-scale conservation program with jurisdiction over more than 1100 threatened species, ecological communities and populations. We discuss why co-design is key to successful implementation of decision science in program-level planning; this approach has supported the Saving our Species program to account for forms of knowledge that may otherwise be ignored by data driven optimisation. This paper focuses on the role of conservation planners in developing and applying decision tools. We present three case studies that deployed tools co-developed for the Saving our Species program. Through these case studies, we suggest that effective conservation planning can be best achieved through (1) narrowing down the number of options under consideration, by eliminating sub-optimal choices (2) supporting decision-makers to understand the relative advantages and disadvantages of the choices under consideration and (3) enhancing the effectiveness of decision-support tools by integrating practitioner expertise into their application. [ABSTRACT FROM AUTHOR]

Copyright of Forestry Chronicle is the property of Canadian Institute of Forestry and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The application of educational big data analytics holds significant importance in enhancing decision-making processes for school teaching quality. This study explores the effective utilization of educational big data analytics technologies to support the improvement of teaching quality in schools. Initially, the challenges and needs faced by current school teaching quality decision-making were analyzed, highlighting the critical role of educational big data analytics in this context. Subsequently, the limitations and gaps in existing study were identified through a review of related studies, underscoring the study value of this study. Based on this foundation, this study progresses through an examination of the decision-making factors that influence school teaching quality, problem description and model assumptions, construction of decision models, and model solutions using genetic algorithms. By analyzing key factors and constraints in the decision-making process for school teaching quality and integrating optimization algorithms, a viable decision support model was proposed and empirically analyzed. This study aims to provide a scientific basis for school administrators and decision-makers, thereby promoting continuous improvement in school teaching quality [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) systems have been increasingly applied to breast ultrasonography. They are expected to decrease the workload of radiologists and to improve diagnostic accuracy. The aim of this study is to evaluate the performance of an AI system for the BI-RADS category assessment in breast masses detected on breast ultrasound. A total of 715 masses detected in 530 patients were analyzed. Three breast imaging centers of the same institution and nine breast radiologists participated in this study. Ultrasound was performed by one radiologist who obtained two orthogonal views of each detected lesion. These images were retrospectively reviewed by a second radiologist blinded to the patient's clinical data. A commercial AI system evaluated images. The level of agreement between the AI system and the two radiologists and their diagnostic performance were calculated according to dichotomic BI-RADS category assessment. This study included 715 breast masses. Of these, 134 (18.75%) were malignant, and 581 (81.25%) were benign. In discriminating benign and probably benign from suspicious lesions, the agreement between AI and the first and second radiologists was moderate statistically. The sensitivity and specificity of radiologist 1, radiologist 2, and AI were calculated as 98.51% and 80.72%, 97.76% and 75.56%, and 98.51% and 65.40%, respectively. For radiologist 1, the positive predictive value (PPV) was 54.10%, the negative predictive value (NPV) was 99.58%, and the accuracy was 84.06%. Radiologist 2 achieved a PPV of 47.99%, NPV of 99.32%, and accuracy of 79.72%. The AI system exhibited a PPV of 39.64%, NPV of 99.48%, and accuracy of 71.61%. Notably, none of the lesions categorized as BI-RADS 2 by AI were malignant, while 2 of the lesions classified as BI-RADS 3 by AI were subsequently confirmed as malignant. By considering AI-assigned BI-RADS 2 as safe, we could potentially avoid 11% (18 out of 163) of benign lesion biopsies and 46.2% (110 out of 238) of follow-ups. AI proves effective in predicting malignancy. Integrating it into the clinical workflow has the potential to reduce unnecessary biopsies and short-term follow-ups, which, in turn, can contribute to sustainability in healthcare practices. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has been recently shown to improve clinical workflows and outcomes - yet its potential in pediatric surgery remains largely unexplored. This systematic review details the use of AI in pediatric surgery. Nine medical databases were searched from inception until January 2023, identifying articles focused on AI in pediatric surgery. Two authors reviewed full texts of eligible articles. Studies were included if they were original investigations on the development, validation, or clinical application of AI models for pediatric health conditions primarily managed surgically. Studies were excluded if they were not peer-reviewed, were review articles, editorials, commentaries, or case reports, did not focus on pediatric surgical conditions, or did not employ at least one AI model. Extracted data included study characteristics, clinical specialty, AI method and algorithm type, AI model (algorithm) role and performance metrics, key results, interpretability, validation, and risk of bias using PROBAST and QUADAS-2. Authors screened 8178 articles and included 112. Half of the studies (50%) reported predictive models (for adverse events [25%], surgical outcomes [16%] and survival [9%]), followed by diagnostic (29%) and decision support models (21%). Neural networks (44%) and ensemble learners (36%) were the most commonly used AI methods across application domains. The main pediatric surgical subspecialties represented across all models were general surgery (31%) and neurosurgery (25%). Forty-four percent of models were interpretable, and 6% were both interpretable and externally validated. Forty percent of models had a high risk of bias, and concerns over applicability were identified in 7%. While AI has wide potential clinical applications in pediatric surgery, very few published AI algorithms were externally validated, interpretable, and unbiased. Future research needs to focus on developing AI models which are prospectively validated and ultimately integrated into clinical workflows. 2A. [Display omitted] • What is currently known about this topic? Artificial intelligence has significant potential to transform child and adolescent health • What new information is contained in this article? More than 100 predictive, diagnostic, and decision support AI models have been developed to assist in the care of children undergoing surgery. The present study is the first comprehensive review that details the use of artificial intelligence in pediatric surgery External validation, interpretability, and performance vary across models, and only few models were externally validated and interpretable. [ABSTRACT FROM AUTHOR]

Brazil is among the 30 countries with the highest tuberculosis prevalence, and many studies are trying to understand and fight the harmful outcomes of this disease. In that sense, the following project proposes the development of a mobile application prototype that centralizes diagnosis aid tools for tuberculosis, making the process more transparent for the patient, using the Single Patient Application (SPA) concept. The application was built based on JavaScript and the frameworks chosen were Vue Framework along with Framework7 due to its focus on interface components. Communication with the back-end was done through AJAX calls. As a result, a prototype was built with the five main screens that the user will interact with: authentication/login screen, home screen, list of available algorithms, outcomes algorithm home screen and patient history. The interface design is expected to facilitate the planning of other application areas and contribute to defining business rules. Once implemented on the server side, these rules can support an organized, patient-centered tuberculosis database. [ABSTRACT FROM AUTHOR]

The pursuit of environmental sustainability and resource conservation has underscored the growing significance of the circular economy paradigm across industries. This transformation is particularly pertinent within the automotive industry, necessitating a profound restructuring of the value chain, with a specific focus on end-of-life considerations. Significant potential for circular economy arises from the thorough examination of end-of-life vehicles and processes, involving strategies such as component reuse, material recycling and primary resource reduction. Providing process-, product-, component-, and material-specific information at the end-of-life of automotive vehicles can improve decision-making regarding circular strategies. It also demands for new digital solutions. This paper addresses an integrated approach of data flow analysis and Life Cycle Assessment (LCA) to enable a comprehensive monitoring of the environmental impact of these processes. First, the use case of end-of-life dismantling processes is presented - including pyrotechnics neutralization, vehicle dehydration, disassembly of reuse components as well as vehicle compaction. Secondly, the data demand respective information and material flows is depicted. As a main result, the LCA data flow model specifically tailored to the end-of-life vehicle dismantling processes is described. This model serves as a fundamental basis for analyzing the environmental impact of end-of-life processes combined with the underlying data flow. [ABSTRACT FROM AUTHOR]

The manufacturing industry faces numerous challenges: increasing energy and material costs, limited resources availability, customers' and legal requirements on product quality, climate action, social standards. An example for a political and regulative push is the European Green Deal and the Industry 5.0 strategy announced by the European Commission, which requires manufacturing enterprises to take actions to improve sustainability. Especially small and medium enterprises (SMEs) require support to identify potentially suitable actions to implement. This paper proposes a guided decision support method for SMEs to help them on the sustainability pathway. With a multi-step procedure, the current status of a specific company regarding sustainability actions, their development goals, challenging fields of action, plans and interests in application of advanced technologies and social or environmental services can be identified. Starting from a questionnaire-based self-assessment, relevant fields of actions are derived and potential advanced technologies, social innovations and environmental services are identified. The developed approach combines evaluation of the assessment results, as well as online workshops with SMEs to refine the obtained information, align the suggested set of actions with needs of the companies and prioritize them. As result of this procedure, participating SMEs receive an advanced sustainability action plan (ASAP), which helps them to prioritize available measures to implement on their individual sustainability pathway. The ASAP has been implemented for 41 companies within an online community hub system designed to match and join technology and service providers with manufacturing SMEs. "Processes and Production system" was identified as the most important action field for 41% of SMEs, highlighting the need for efficiency improvement in production processes. [ABSTRACT FROM AUTHOR]

Financial institutions face many challenges in managing modern financial transactions and vast data volumes. To overcome these challenges, they are increasingly harnessing advanced data management technologies such as artificial intelligence and cloud computing. This paper presents a comprehensive review of how these tools transform financial decision-making in various domains and applications. We analyzed both foundational and recent advancements using a rigorous methodology based on the PRISMA 2020 guideline. Our findings indicate that many major financial institutions are adopting AI-driven solutions to potentially enhance real-time risk assessment, transactional efficiency, and predictive analytics. While they bring benefits like faster decision-making and reduced operational costs, they also pose challenges like data security and integration complexities that require further research and development. Looking ahead, we envision a more integrated, responsive, and secure financial ecosystem that leverages the convergence of AI, cloud computing, and advanced data storage. This synthesis underscores the significance of contemporary data management solutions in shaping the future of data-driven financial services, offering a guideline for stakeholders in this evolving domain. [ABSTRACT FROM AUTHOR]

In intelligent decision support, the integration of fuzzy logic systems with data classification has emerged as a promising avenue. This cooperative approach seeks to enhance decision-making processes by leveraging the strengths of both fuzzy logic and data classification techniques. However, a critical gap exists in the literature concerning the seamless integration of fuzzy logic systems and data classification for effective decision support. Existing approaches often treat these methodologies in isolation, overlooking the synergies that can arise from their collaborative utilization. Bridging this gap is essential for developing robust decision support systems capable of handling the intricacies of modern datasets. The research aims to address this gap by proposing a cooperative approach that seamlessly integrates fuzzy logic systems and data classification methods. By doing so, it seeks to overcome the limitations of traditional decision support systems and enhance their adaptability to real-world scenarios characterized by uncertainty and complexity. The method involves the development of a hybrid system that combines fuzzy logic rules and data classification algorithms. The fuzzy logic component captures and processes imprecise information, while the data classification component identifies patterns and trends within the data. The cooperative nature of the approach ensures that each method complements the other, resulting in a more robust and effective decision support system. The results demonstrate the improved performance of the proposed cooperative approach compared to traditional decision support systems. The system exhibits enhanced accuracy and adaptability, showcasing its potential to address the challenges posed by modern datasets. [ABSTRACT FROM AUTHOR]

This study presents an integrated multi-attribute decision-making (MADM) and data envelopment analysis (DEA) framework for solving problems with heterogeneous attributes. We classify the heterogeneous attributes into desirable and undesirable classes and provide a model for aggregating the attributes' weights and the alternatives' scores. The proposed model is initially designed as a Multiple Objective Decision Making (MODM) problem with a Data Envelopment Analysis (DEA) policy and then reformulated as a linear programming model tackled through a goal programming approach. We apply the proposed model to a set of European countries based on their fulfillment of the 17 Sustainable Development Goals (SDGs) defined by the United Nations. We show the proposed approach minimizes computational efforts and complexities and maximizes the participation and satisfaction of decision-makers. We compare the rankings derived from our model with those obtained from standard MADM techniques such as Euclid and TOPSIS. We illustrate how the different normalization methods are applied to condition the discrimination power of the models and analyze the reversals triggered by TOPSIS relative to the other techniques. We conclude by noting that our model does not rely on the weights defined by the experts to determine the ranking, which constitutes a significant advantage over the standard MADM techniques in strategic evaluation environments. • We present an integrated MADM and DEA model for problems with heterogeneous attributes. • Heterogeneous attributes are classified into desirable and undesirable categories. • Our model does not rely on the weights defined by the experts to determine the ranking. • The rankings derived remain stable independently of the normalization formula applied. • We study the fulfillment of Sustainable Development Goals among European countries. [ABSTRACT FROM AUTHOR]

Military stealth technology is crucial in modern combat, allowing military vehicles to be more survivable and less detectable. The primary objective of this study is to map and visualise the global research output on military stealth technology in order to acquire insights into the research landscape, identify relevant trends, and facilitate informed decision-making. This study was mainly focused on the mapping and visualisation of global research output on military stealth technology. For conducting the study, data was collected from the Scopus database covering the period from 2001-2021. A total of 299 articles with 1764 citations were found during the search. The majority of papers (74.25 %) were published in a multi-authored mode. The overall collaborative index was 2.84. Professors Zhang X and Zhang Y of China were named top contributors, each with seven papers and 139 and 121 citations, respectively. China leads the list of contributing countries, with 108 papers and 731 citations, followed by the United States (51), India (19), the United Kingdom (12), and South Korea (7). As per keyword analysis, "military applications," "radar," and "military vehicles" were the biggest hotspots. The findings revealed that Asian countries are increasingly dominating their western counterparts in global research publications on stealth technology. This scientometric analysis contributes to a better understanding of how countries are implementing new technologies for military assets. Furthermore, it may assist scientists in identifying core areas of interest and future research directions in the field, as well as library professionals in selecting appropriate resources. [ABSTRACT FROM AUTHOR]

According to the demand of sports athletes' scientific training, combined with data mining technology, a set of sports training model which is suitable for China's national conditions and has a higher level of scientific research is established. Starting from the data mining technology, this paper expounds its connotation and several common methods in detail. Based on the demand of sports training, this paper studies the quality control of sports training and the statistical system of tactical combat. On this basis, an improved output method of Aprilio algorithm is introduced to enhance the correctness of systematic evaluation. On this basis, three algorithms, classic, DC-Apriori and modified Apriori, are simulated and compared. The results show that the improved Apriori algorithm studied in this project can provide better decision support for the training model. [ABSTRACT FROM AUTHOR]

The safety of people's lives and property is the primary factor for the success of urban construction. Therefore, in order to better maintain social stability and harmony, relying on computer technology to effectively detect violence and to make decision support has important theoretical and practical significance. Aiming at the shortcomings of traditional manual design feature extraction methods, this paper proposes a super automatic violence detection method based on the combination of Deep Learning and trajectory in AI systems. Firstly, aiming at the problem of complex time and high accuracy of traditional manual feature extraction, a deep spatiotemporal violence detection method based on three-dimensional convolution and trajectory in AI systems is proposed. We improve the IDT algorithm to extract the target trajectory, and carry out three-dimensional convolution and pooling operation to calculate the deep-seated temporal and spatial information in the video frame, so as to realize peer-to-peer detection in AI systems. Secondly, in order to further improve the acquired deep-seated time and space information and utilization rate and achieve high detection rate, the feature fusion of double stream convolution and three-dimensional convolution is proposed, and the feature extraction of continuous video frame sequence is carried out by three-dimensional convolution neural network (C3D), which can effectively extract the fusion feature information of time and space in the classification layer, so as to obtain the final classification result. Finally, in order to solve the problem of too deep network level and slow convergence, dense convolution is introduced, which reduces the parameters of the network model and time complexity. Experimental results show that compared with other mainstream algorithms, this method is more effective and stable, and can be applied to the detection of violent abnormal behavior in video. Meanwhile, the method proposed in this paper has important theoretical value and practical significance for decision support of video surveillance system in AI systems. [ABSTRACT FROM AUTHOR]

Wearable sensors enable down range data collection of physiological and cognitive performance of the warfighter. However, autonomous teams may find the sensor data impractical to interpret and hence influence real-time decisions without the support of subject matter experts. Decision support tools can reduce the burden of interpreting physiological data in the field and incorporate a systems perspective where noisy field data can contain useful additional signals. We present a methodology of how artificial intelligence can be used for modeling human performance with decision-making to achieve actionable decision support. We provide a framework for systems design and advancing from the laboratory to real world environments. The result is a validated measure of down-range human performance with a low burden of operation. [ABSTRACT FROM AUTHOR]

Coastal ecosystems are complex and often support a broad spectrum of functions with competing objectives. In addition to their ecological value, they offer socio-economic benefits (i.e., ecosystem services) to coastal communities. One potential way to help address this complexity is to use decision support systems to help natural resources managers understand system dynamics and evaluate strategies to maintain the health and integrity of these ecosystems. This paper presents a roadmap and detailed application of co-production strategies where managers and researchers are fully engaged in a collaborative manner in the design of a decision support tool for coastal ecosystems. It also emphasizes the importance of capturing end-users' (i.e., natural resource managers) priorities to refine the conceptual design of the decision support tool, while maintaining a sound scientific and modeling framework. The case study presented here centers on the Northern Gulf of Mexico, but the concept can be exported globally to other systems. This effort highlights foundational co-production strategies, including transdisciplinary team assembly, a knowledge sharing workshop, Toolbox Dialogue Initiative workshops to facilitate working across disciplines, core team and focus group meetings, and design charrettes. Further, this paper articulates the benefits and difficulties of executing a co-production process through virtual collaborations. • Coastal ecosystem management by co-productive, interdisciplinary design methods. • Co-production design strategies for decision support systems. • Northern Gulf of Mexico natural resource management decision support tool. [ABSTRACT FROM AUTHOR]

Methods inspired by an observation of biological organisms constitute a dynamically growing area of research. Such approaches were proved to be efficient in solving many real-life tasks. This paper introduces an application of two Swarm Intelligence methods for solving the ship collision avoidance problem. The Firefly Algorithm population-based approach has been compared with the Ant Colony Optimization-based method, developed as a constructive algorithm. The definition of the problem to be solved, an explanation of the methods and simulation tests results have been presented in the paper and followed by a discussion of the obtained solutions. The methods can be applied in the Decision Support Systems of manned ships or in the Autonomous Navigation Systems of Unmanned Surface Vessels or fully autonomous ships - Maritime Autonomous Surface Ships. [ABSTRACT FROM AUTHOR]

This article presents the implementation of a multi-criteria decision support method in the management of multi-source switching systems. We implement the Analytic Hierarchy Process (AHP) in order to solve problems relating to multi-source switching systems. This method reduces undesired multiple switchings bothering the output signal and perceived as electrical perturbations for sensitive charges. The advantage of this approach is that it makes it possible to compare several criteria that are difficultly customisable, using a sole optimisation function. As solution to curb issues relating to the implementation of AHP in real time as descrribed in the literature, we propose an algorithm for aggregating criteria, sub-criteria and alternatives, of which the ultimate goal is to switch to the alternative source offering the best compromise amongst a variety of contradictory. To approve the proposed AHP method, we implement an expert source management system in a Labview environment, which reproduces the same switching conditions in real time. [ABSTRACT FROM AUTHOR]

Guidance in visual analytics aims to support users in accomplishing their analytical goals and generating insights. Different approaches for guidance are widely adopted in many tools and frameworks for various purposes – from helping to focus on relevant data subspaces to selecting suitable visualization techniques. With each of these different purposes come specific considerations on how to provide the needed guidance. In this paper, we propose a generic method for making these considerations by framing the guidance problem as a decision problem and applying decision making theory and models toward its solution. This method passes through three stages: (1) identifying decision points; (2) deriving and evaluating alternatives; (3) visualizing the resulting alternatives to support users in comparing them and making their choice. Our method is realized as a set of practical worksheets and illustrated by applying it to a use case of providing guidance among different clustering methods. Finally, we compare our method with existing guidance frameworks to relate and delineate the respective goals and contributions of each. [ABSTRACT FROM AUTHOR]

In this research, a fuzzy rule–based decision support (FRBDS) tool is developed to assist selection of ground treatment techniques for road embankments. Using input variables such as embankment height, geological condition and soft soil thickness, the FRBDS evaluates the ground treatment level, based upon which the highway engineer determines a suitable treatment method taking into account project constraints. In the case study, the FRBDS is applied for a 10-km package of HaNoi–HaiPhong expressway construction project and is evaluated in comparison to expert judgements. The results show that the treatment methods provided by the two approaches are similar for 36 out of 52 sections of the package, but the FRBDS provides more consistent and rational solutions. In particular, the FRBDS significantly reduces technological fragmentation. It can be concluded that the FRBDS is a useful tool that establishes structured innovative solutions to improve the quality of decision-making for large transportation projects. [ABSTRACT FROM AUTHOR]

The use of occupational exoskeletons (OE) promises improved ergonomics, empowerment of users and prevention of musculoskeletal disorders. However, selecting an OE for a specific application is difficult, the process is complex and a holistic approach that will guide decision-makers in companies is missing. The goal of this paper is to describe a framework that will assist the selection of an OE for a given use case. Based on a systematic literature review existing implementation plans for OE are identified and 99 relevant criteria collected. As the criteria are described in different level of abstractions, a categorisation is proposed, that include human and task-centred dimensions (Task & Workplace, User, Human-Machine-Interface) as well as macro-economic dimensions based on the PEST framework (Political, Economic, Socio-cultural, Technological). The derived framework with its criteria and dimension can be implemented in existing selection processes and provides a key step for increasing the market penetration of OE. [ABSTRACT FROM AUTHOR]

With the EU Green Deal and the UN Sustainable Developments Goals, the vision of a greenhouse gas-neutral and resource-efficient economy is already firmly anchored in world politics. In this context, the automotive industry faces many challenges including the increasing scarcity of natural resources, a rising demand in terms of sustainable vehicle design, production and materials sourcing. Due to all this, end-of-life decisions regarding dismantling have become increasingly important. A high proportion of secondary materials will be required in the vehicles of the future. To response to these challenges, companies have turned their focus towards the circular economy as a central approach to close material loops. In the German research project "Catena-X" a new data ecosystem with digital twins is one enabler that is being developed. The digital twins represent a promising approach to the circular economy by ensuring transparent, product-specific and end-to-end data exchange throughout the entire product lifecycle, from the material sourcing to the eventual dismantling and recycling. As one particular and unique solution, a decision framework that facilitates the best circular strategy at the end of a vehicle's life is developed and presented in this paper. The underlying data-driven decision support framework is based on circular economy KPIs. This includes material, components and specific vehicle KPIs to best identify the most suitable circular strategy. The framework was methodologically developed by an interdisciplinary team of partners, who are stakeholders throughout the value chain and participants in the Catena-X project. An integrated approach of user-centered design, an adapted version of the V-model and the Scaled Agile Framework were used for the methodology in the development of the solution. The paper presents the concept of a digital twin for a decision support system, that includes a central decision logic that also includes the relevant KPIs and a survey for evaluating the decision logic utilised with a chosen dismantling company. [ABSTRACT FROM AUTHOR]

This paper described the application of a terminology server (TS) within a health institution in Uruguay, whose architecture is based on SNOMED CT and graph databases (NoSQL). The aim of this project is to apply the use of terminological service from a clinical, statistical, managerial, decision support and research perspective, among others, without compromising performance. Particularly, this article exposes the use of the TS as support for alert systems associated to diabetes mellitus patients. [ABSTRACT FROM AUTHOR]

The article presents the way financial and non-financial information are valued, as well as a case study with respondents from Romanian entities, by category of interested parties. The main tools with an impact on the companies' performance and importance of the financial-accounting system are the accounting value and the market value of the entities. The study is based on answers obtained from respondents located in the city of Buhuți, Bacău County, Romania, to a questionnaire distributed to legal entities from various fields of activity. [ABSTRACT FROM AUTHOR]

This research was conducted based on a case study at PT SIER in Indonesia. Programmer team at PT SIER had a very important role in supporting the company's operations. Due to the increasing number of requests for application development from various departments to the Programmer team, the company intended to add one Programmer to Programmer team so that the Programmer team could carry out their tasks faster. Therefore, the company opened a vacancy for one Programmer. Then the company got many candidates for Programmer position. After conducting selection, the company found top three candidates. But the company had to choose one of them because the company's budget at that time was only to recruit one candidate. However, the company found it difficult to choose one of them as the best candidate. To help this company in determining the best candidate for Programmer position, this research offered the use of Elimination and Choice Expressing Reality method or ELECTRE method as a decision support. The ELECTRE method was used in this research because this method was a method to make decision that could choose the best option from alternatives that had several criteria or factors. The result showed that ELECTRE Method could help in determining the best candidate for Programmer position by incorporating factors or criteria of the candidate. [ABSTRACT FROM AUTHOR]

In recent aerospace missions, space logistics have proven essential in storing, delivering and returning crew and materials between terrestrial facilities and space stations. Unlike classical commercial logistics, space logistics operations are cost-prohibitive and mission-driven, and its replenishment cycle for essential materials is relatively long. Therefore, the complete utilisation of spacecraft payload is of utmost importance. The theory of the inventory packing problem is extended in this study to build autonomous agents that interact with one another within a space logistics decision support system to reinforce the replenishment decision, chunk loading optimisation, and quality inspection. With the long replenishment cycle time, an agent embedded with interval type-2 fuzzy logic is explored to support chaotic time-series demand forecasting to derive re-order quantities in the desired period. Afterwards, the second agent solves the space chunk loading problem using the differential evolution algorithm to utilise payloads and capacities, particularly cylindrical chunks fully. The third agent measures actual item dimensions and quality to deploy the three-dimensional object scanning devices. Feedback is provided to the second agent to derive optimal chunk-loading instructions. Thanks to the autonomous interactions among the above agents, mission-critical decisions for space logistics are supported to achieve operational excellence. [ABSTRACT FROM AUTHOR]

This paper aims to perform a literature review on the use of multi-criteria methods for assessing agricultural sustainability, focusing on the distribution of papers according to year, journal, and countries, the most productive authors, the most frequently used multi-criteria methods and their characteristics, the type of system and the type of agriculture in which these methods are being performed, the methodological approaches and assessment types, and the sustainability dimensions considered. The data collection has been carried out through the Web of Science™ platform on September 3rd, 2021. After a refinement process, 41 papers were selected. The descriptive analysis was carried out through Bibliometrix tool, while content analysis was performed using Nvivo. The descriptive analysis shows that from 2016 to 2021 the scientific production addressing multi-criteria methods to assess agricultural sustainability started to grow markedly in a very rapid matter, reaching an average of 6 papers per year. France and China are the most scientifically productive countries. The content analysis points out that the most used multi-criteria method is the AHP that was used 11 times. The outranking methods, instead, were used only 3 times. In 68% of the papers the Triple Bottom Line was used as dimensions, and in 41% of the papers the spatial applicability was the farming system. The results highlight that doesn't exist many MCDM/A methods for assessing agricultural sustainability, and most of them are compensatory. These results point out that the use of multi-criteria methods in assessing agricultural sustainability is still underexplored and can be improved. [Display omitted] • Multicriteria methods are powerful tools for assessing agricultural sustainability. • A literature review on agricultural sustainability evaluation was performed. • The number of studies has been increasing in the last five years. • Most of the approaches are based on compensatory multicriteria methods. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Applied Ecology / Yingyong Shengtai Xuebao is the property of Chinese Journal of Applied Ecology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Residual biomass from agricultural production and processing, such as citrus pulp and olive pomace, is an important resource for energy production. In particular, this is the case in regions where transformation industries are concentrated. Current biomass estimates often focus on actual production data, that may not fully capture the biomass potential across all suitable cultivation areas. To bridge this gap, the study predicts the overall potential biomass available for energy production, taking into account the total area suitable for citrus cultivation. The research is focused on the study of citrus species in the province of Syracuse, Sicily, Italy. The methodology combines Geographic Information System (GIS) tools, for data interpolation and map overlays, with Software for Assisted Habitat Modelling (SAHM) for local level simulations. The results of the different models showed accurate and spatially coherent predictions, with AUC values ranging from 0.85 to 0.90, and highest potentialities in the northern and eastern regions of the study area. The results highlighted potential citrus cultivation on 47,706 ha and estimated 184,340 t of biomass, 16,461,520.82 Nm3 of biogas, and 8110 t of digestate. The results of the study identified potential areas for both increasing biomass production and optimising the distribution of digestate, thus demonstrating the utility of these thematic maps as a decision support tool for land management. The simulations and the methodology applied in this study indicated potential economic and environmental benefits to be gained from sustainable by-product management. This approach facilitates the optimisation of decision-making processes for land planning, thereby contributing to the broader objective of improving resource efficiency and sustainability in the agricultural production and processing sectors. [Display omitted] • The proposed methodology produces the best environmental niche for species growth. • The methodology includes six phases from data collection to algorithms comparison. • The SDM-based potential surface allowed estimation of potential species production. • The potential biomass was estimated from species production at territorial level. • In citrus application, predictive models produced robust and accurate results. [ABSTRACT FROM AUTHOR]

• Goal: Strategic decision support that recommends appropriate microgrid design. • Using a multi-step design science-oriented process including expert evaluation. • Morphological analysis of literature to deduce possible microgrid design options. • Categorization of 62 real-world microgrids and algorithmic archetype derivation. • Decision tree generation and design principle derivation to form decision support. Microgrids are custom-designed, but their extensive design options hinder their dissemination. Consequently, microgrid-interested parties need strategic support to identify suitable design options for their use case. This paper develops a decision support artifact in the form of a decision tree for recommending the most suitable microgrid design for a project. A multi-step design science-oriented process was used. First, a morphological analysis of academic literature was conducted to deduce all possible microgrid design options and visualize them in a morphological box. Once done, 62 real-world microgrids of diverse types and locations were classified according to the morphological box. The produced dataset was used to derive five microgrid design archetypes algorithmically using cluster analysis. The dataset and their associated archetypes were then fed into a rule-mining algorithm to generate a decision tree that recommends the appropriate microgrid archetype based on up to four questions about, for instance, the microgrid's objective and the connectivity to the main grid. Furthermore, design principles for each microgrid archetype recommendation were formulated. The developed design artifact serves as applicable knowledge and a benchmark framework for researchers, as well as a comprehensive and simultaneously simplified decision-making framework for practitioners. [ABSTRACT FROM AUTHOR]

Augmented reality (AR) has been applied in many situation as the development of visualization and communication technologies. In coal mine industry, it is essential to improve the decision making process in the underground front line with the assistance of AR. In this paper, we propose a framework to implement an AR based decision support system, which includes knowledge support, real time expert connection and AR communication platform. The proposed framework has been applied in a coal mine in China and the user feedback indicate that the proposed framework can improve the communication and knowledge access efficiency in the coal mine industry. [ABSTRACT FROM AUTHOR]

The paper describes an intellectual model of clinical risk management (CRM) for a general hospital based on Failure Mode and Effects Analysis (FMEA), Route Cause Analysis (RCA) and Systems Engineering Initiative in Patient Safety (SEIPS) model. Input and output parameters of the CRM model, management resources and corrective interventions were determined. The main output parameter was a cumulative burden of healthcare-related adverse events (HAEs) that reflects both the severity and the rate of each HAE. The model was tested in a 300-bed urban hospital. It was shown that effectiveness of interventions used to prevent HAE may vary depending on the HAE type. In resource-constrained environment, implementation of the CRM model in routine practice allows for the selection of interventions aimed at preventing healthcare safety failures that will provide the greatest benefit both for patients and for the hospital. It opens up prospects for cognitive support of patient safety improvements and for the healthcare costs reduction. [ABSTRACT FROM AUTHOR]

Establishing insulin dosages for diabetic patients is getting a challenge right after a patient's admission to a hospital. This is mainly due to the physicians' limited information on the current health state of a newly diagnosed patient. Specifically, for juvenile patients, high variability of body response to insulin causes the necessity of continuous adjustment of dosages during the patient treatment. To support the physicians in applying the initial insulin dose and adjusting the following doses, we propose a forecasting approach in this paper. We propose a forecasting model based on a specifically designed neural network to predict basal insulin. In the experimental part of this paper, we provide evidence for the high efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

Companies aim to spur quality, effectiveness, efficiency, and support growth. At the same time, however, conditions change significantly, imposing demands on companies to be flexible and agile with regard to their production environment. Changes involved are based on the business strategy, internal and external stressors, and impact operational and tactical level. The adaptability of the digital infrastructure determines the flexibility, control, and possibility of supporting decision-making in production processes. This paper specifies requirements and a blueprint for an adaptable digital infrastructure. Furthermore, components such as middleware are described that are needed to enable not only a flexible digital infrastructure, but to get the right information, at the right moment, to the right entity, system, person, and decision-maker. [ABSTRACT FROM AUTHOR]

Due to the increasing implementation of cyber physical systems and the internet of things in industry, there is a trend towards flexible, multi-variant production with a very high degree of automation to strengthen the resilience of the company. Each variant is subject to high quality requirements that must be checked to ensure quality. A common means for this is quality assurance through visual inspections, which are carried out manually. Visual inspections are time-consuming yet prone to wrong decisions, since they are subjective, inconsistent, and susceptible to uncertainties. The quality of the inspection relies heavily on the experience of the personnel. This work addresses this issue through the concept and design of a system for objective decision making in visual inspection by integrating Deep Learning (DL) models with a Belief Rule Based Expert System (BRBES) inside a smart devices application. Smart devices like tablets and smartphones serve to generate information by recording and evaluating image material of the components being inspected. Based on this data, DL models are trained and used to classify defects on new image material to automate part of the inspection process. Furthermore, smart devices serve to provide context-dependent decision recommendations in the visual inspection process, which were calculated by the BRBES with the inclusion of uncertainties. The knowledge base of the BRBES is fed by the expert knowledge of experienced visual inspectors using knowledge elicitation techniques. In this way, the system can enable optimized and objectified visual inspection based on the data-driven and knowledge-driven approaches used. This paper outlines the concept of integrating DL and BRBES into a smart devices decision support system. [ABSTRACT FROM AUTHOR]

Normative and descriptive multi-criteria decision analysis methods were compared to understand their differences in providing decision insights, with a wetland restoration planning study, Chesapeake Bay watershed, USA. Excessive nutrient and sediment runoff is causing impaired water quality and degradation of aquatic habitat in the Chesapeake Bay. A team at The Nature Conservancy evaluated a decision to prioritize large-scale wetland restoration opportunities. A total of 964 potentially restorable wetland alternatives were delineated using spatial analysis. The alternatives were evaluated on seven water quality and climate resilience criteria. Normative methods included ranking the alternatives based on scaling and averaging criteria values per alternative using value and distance functions. Descriptive methods included grouping the alternatives based on their statistical similarities and dissimilarities using principal component analysis. Sensitivity analysis incorporated different data transformations based on risk and riskless decision contexts, different criteria weighting scenarios, and two formats for grouping alternatives. The descriptive method provided inherent information about tradeoffs that normative methods did not, whereas the normative methods provided a similar type of information about tradeoffs. Inherent tradeoff information can provide useful decision insights without complicated criteria scaling and weighting methods, which can be favorable to researchers that desire a more objective analysis. However, descriptive methods do not guarantee favorable results and should be considered along with preferences and judgments. Multiple methods can guide a more comprehensive approach to thinking about tradeoffs in other similar contexts. Supporting data are available on request: David.Martin@tnc.org. • Multi-criteria decision analysis used to evaluate a wetland restoration decision. • Normative and descriptive methods are compared for similarities and dissimilarities. • Risk and riskless contexts evaluated. • Results emphasize differences between normative and descriptive methods. • Case study in Chesapeake Bay watershed, USA. [ABSTRACT FROM AUTHOR]

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Objectives: Naloxone is a harm reduction tool for mitigating the rising rate of opioid overdose deaths. We sought to develop and implement an alert in the electronic health record outlining which patients are at higher risk of opioid overdose and should be coprescribed naloxone. Our aim was to increase coprescribing of naloxone to qualified patients. We also endeavored to evaluate naloxone prescription volume, fill rates, and statewide dispenses before and after alert implementation. Methods: We developed the electronic alert according to a state opioid safety initiative specifying under which conditions it should activate. We collected data on naloxone prescriptions ordered in the 5 months before and after alert implementation and unique patients with a naloxone dispense statewide. We used internal pharmacy data to evaluate the percentage of fills and used a χ2 test to assess changes in percentage of fills. We used descriptive statistics and t tests to analyze changes in the number of prescriptions and changes in unique patients dispensed naloxone. Results: We found a 2144% increase in the number of monthly naloxone prescriptions written after the alert became active. There was no statistically significant change in the percentage of fills. There was a 402.8% increase in unique patients statewide with a naloxone dispense after alert implementation. Conclusions: Designing and implementing an electronic alert prompting naloxone coprescription are feasible and were associated with substantial increases in numbers of naloxone prescriptions and patients with naloxone dispenses statewide. Our findings expand on prior literature about electronic decision support for naloxone coprescription. [ABSTRACT FROM AUTHOR]

Prediction markets provide a promising approach for future event prediction. Most existing prediction market approaches are based on auction mechanisms. Despite their theoretical appeal and success in various application settings, these mechanisms suffer from several major drawbacks. First, opinions from experts and amateurs are treated equally. Second, continuous attention from participants is assumed. Third, such mechanisms are subject to various forms of market manipulation. To alleviate these limitations, we propose to employ the classic fixed odds betting as an alternative prediction market mechanism. We build a structural model based on a belief–decision framework as the event probability estimator. This belief–decision framework models bettors’ beliefs with mixed beta distributions and bettors’ decisions with prospect theory. A maximum likelihood approach is applied to estimate the model parameters. We conducted experiments on three real-world betting datasets to evaluate our proposed approach. Experimental results show that fixed odds betting based prediction outperforms the reduced form models based on odds and betting results, and achieves a comparable performance with auction-based prediction markets. The results suggest the possibility of employing fixed odds betting as a prediction market in a variety of application contexts where the assumptions made by auction-based approaches do not hold. [ABSTRACT FROM AUTHOR]

Decision Support Indicators (DSIs) are metrics designed to inform local and regional stakeholders about the characteristics of a predicted (or ongoing) event to facilitate decision-making. In this paper, the DSI concept was developed to clarify the different aims of different kinds of indicators by naming them, and a framework was developed to describe and support the usage of such DSIs. The framework includes three kinds of DSI: hydroclimatic DSIs which are easy to calculate but hard to understand by non-experts; impact-based DSIs which are often difficult to calculate but easy to understand by non-experts; and event-based DSIs , which compare a current or projected state to a locally well-known historical event, where hydroclimatic and impact-based DSIs are currently mainly used. Tables and figures were developed to support the DSI development in collaboration with stakeholders. To develop and test the framework, seven case studies, representing different hydrological pressures on three continents (South America, Asia, and Europe), were carried out. The case studies span several temporal and spatial scales (hours-decades; 70–6,000 km2) as well as hydrological pressures (pluvial and riverine floods, drought, and water scarcity), representing different climate zones. Based on stakeholder workshops, DSIs were developed for these cases, which are used as examples of the conceptual framework. The adaptability of the DSI framework to this wide range of cases shows that the framework and related concepts are useful in many contexts. • Decision Support Indicators (DSI) inform stakeholders of future hydrological hazards. • Based on local knowledge about past events, event-based DSIs are highly informative. • A novel framework for indicator development has been developed. • The framework has been tested in seven case studies on three continents. • The framework can be seen as a decision-support tool, facilitating indicator development. [ABSTRACT FROM AUTHOR]

The onset of the COVID-19 pandemic drove a widespread, often uncoordinated effort by research groups to develop mathematical models of SARS-CoV-2 to study its spread and inform control efforts. The urgent demand for insight at the outset of the pandemic meant early models were typically either simple or repurposed from existing research agendas. Our group predominantly uses agent-based models (ABMs) to study fine-scale intervention scenarios. These high-resolution models are large, complex, require extensive empirical data, and are often more detailed than strictly necessary for answering qualitative questions like "Should we lockdown?" During the early stages of an extraordinary infectious disease crisis, particularly before clear empirical evidence is available, simpler models are more appropriate. As more detailed empirical evidence becomes available, however, and policy decisions become more nuanced and complex, fine-scale approaches like ours become more useful. In this manuscript, we discuss how our group navigated this transition as we modeled the pandemic. The role of modelers often included nearly real-time analysis, and the massive undertaking of adapting our tools quickly. We were often playing catch up with a firehose of evidence, while simultaneously struggling to do both academic research and real-time decision support, under conditions conducive to neither. By reflecting on our experiences of responding to the pandemic and what we learned from these challenges, we can better prepare for future demands. • The COVID-19 pandemic created major challenges for modelers. • Here we detail several lessons-learned, and approaches that may be useful to others. • Collaborative scenario modeling efforts like SMH help to mitigate model flaws. • Harmonization requirements can make it hard for new groups to join collaborations. [ABSTRACT FROM AUTHOR]

Mathematical models are useful for public health planning and response to infectious disease threats. However, different models can provide differing results, which can hamper decision making if not synthesized appropriately. To address this challenge, multi-model hubs convene independent modeling groups to generate ensembles, known to provide more accurate predictions of future outcomes. Yet, these hubs are resource intensive, and how many models are sufficient in a hub is not known. Here, we compare the benefit of predictions from multiple models in different contexts: (1) decision settings that depend on predictions of quantitative outcomes (e.g., hospital capacity planning), where assessments of the benefits of multi-model ensembles have largely focused; and (2) decisions settings that require the ranking of alternative epidemic scenarios (e.g., comparing outcomes under multiple possible interventions and biological uncertainties). We develop a mathematical framework to mimic a multi-model prediction setting, and use this framework to quantify how frequently predictions from different models agree. We further explore multi-model agreement using real-world, empirical data from 14 rounds of U.S. COVID-19 Scenario Modeling Hub projections. Our results suggest that the value of multiple models could be different in different decision contexts, and if only a few models are available, focusing on the rank of alternative epidemic scenarios could be more robust than focusing on quantitative outcomes. Although additional exploration of the sufficient number of models for different contexts is still needed, our results indicate that it may be possible to identify decision contexts where it is robust to rely on fewer models, a finding that can inform the use of modeling resources during future public health crises. • Results from multiple models can better support public health policy. • Agreement between models may be more likely for ranking alterative epidemic scenarios than estimating epidemic outcomes. • If multiple models are not available, decision makers may focus on scenario ranking. [ABSTRACT FROM AUTHOR]

In order to remain competitive many companies are forced to exploit the existing potential for improvement across their entire manufacturing network in terms of efficiency, cost reduction, robustness and agility. In particular, the lack of a systematic decision-making process and the fact that decisions are often based on individual experience and imitative behavior lead to unfavorably structured manufacturing networks. Additionally, in increasingly volatile business environments, companies are forced to constantly rethink and thus continuously reconfigure their existing manufacturing network. Today, there are several planning approaches on factory and network level that build on modern decision support tools to enable targeted and transparent reconfiguration of manufacturing networks despite high complexity. However, the subsequent migration process (transformation of the existing network to a new target state) is mostly neglected in literature. This contribution presents an original framework for a systematic reconfiguration process of manufacturing networks. The framework combines existing approaches in literature with identified challenges in the context of three conducted manufacturing network reconfiguration use cases. To close identified gaps in literature the focus is put on the migration process. The elaborated framework is based on a rolling planning and control approach, with decision support tools (i.e. simulation & optimization) taking the leading role due to the operational, complex and dynamic nature of migration processes. [ABSTRACT FROM AUTHOR]

Digitalized processes offer various advantages: uniform information flow, traceability of process progress, and enhanced analyzability. While these represent initial benefits from the digitalization of the process, a recurring evaluation and optimization of the process are required to sustain and improve process efficiency. For both non-digitalized and digitalized processes, the challenges for process owners are comparable. However, for digitalized processes, convenient tools facilitate process modifications. Particularly no-code digitalized processes exploit the opportunity for quick and agile process adaption with low efforts but require a structured and systematic method to ensure a quality-driven approach. This paper conceptualizes a rule-based decision support model for process improvement that takes advantage of digital traces recorded during process execution of digitalized processes. On the one hand, process mining techniques support data analysis and assist the extraction of process performance indicators as precursors to predict low process performance. On the other hand, process experts compile a list of recommendations for action in workshops and interviews to counteract low process efficiency. The approach to rule-based decision support developed in the scope of this research merges these two dimensions and constitutes an assistive tool for the continuous improvement of digitalized processes. Initial results of the model in practical application support this assessment and demonstrate further research needs. [ABSTRACT FROM AUTHOR]

Orientation is increasingly in demand, especially today in the age of digital and recent technologies, with the disappearance of some jobs and the emergence of others, and with the variety of choices in front of the students who have led professionals to seek a new method and digital solution to help them solve these issues and achieve their personal and professional success. There are a lot of institutions and associations that are organized orientation sessions for students and try to fill the gaps in that area, but it is always a poor fit in the face of a lot of students. And to make an effective decision related to guidance, we need to consider several factors such as grades, preferences, interests, and personality characteristics... which gives a sharp vision of the student and helps to know which category he belongs to. The purpose of this paper is to present a review of studies on distinctive approaches used in the orientation field such as the K-Nearest-Neighbor Method (KNN), Naïve Bayes, Fuzzy logic, Fuzzy Analytic Hierarchy Process (FAHP), and the Internet of things (IoT) in order to discover the best approach which helps students to pick and succeed in their orientation. [ABSTRACT FROM AUTHOR]

Objectives: The planning and control of team sport training activities is an extremely important aspect of athletic development and team performance. This research introduces a novel system which leverages techniques from the fields of control system theory and artificial intelligence to construct optimal future training plans when unexpected disturbances and deviations from a training plan goal occur.Design: Simulation-based experimental design.Methods: The adaptation of training load prescriptions was formulated as an optimal control problem where we seek to minimize the difference between a desired training plan goal and an observed training outcome. To determine the most suitable approach to optimize future training loads the performance of an artificial intelligence-based feedback controller was compared to random and proportional controllers. Computational simulations (N = 1800) were conducted using a non-linear training plan spanning 60 days over a 12-week period, and the control strategies were assessed on their ability to adapt future training loads when disturbances and deviations from an optimal planning policy have occurred. Statistical analysis was conducted to determine if significant differences existed between the three control strategies.Results: The results of a repeated measures analysis of variance demonstrated that an intelligent feedback controller significantly outperforms the random (p < .001, ES = 7.41, very large) and proportional control (p < .001, ES = 7.41, very large) strategies at reducing the deviations from a training plan goal.Conclusions: This system can be used to support the decision making of practitioners across several areas considered important for the effective planning and adaption of athletic training. [ABSTRACT FROM AUTHOR]

The purpose of this study was to explore parents' and health care professionals' perception of parents' experiences in making decisions between acute and palliative therapies along the trajectory of their child's life-limiting condition. An interpretive description qualitative study was conducted. Semistructured interviews were completed with 6 parents and 6 health care professionals. Qualitative thematic analysis was used to identify, analyze, and report 4 themes: (1) "Going by your heart and gut": the process of making a unique decision; (2) "Not black and white": experiencing decisional conflict when making difficult decisions; (3) "Widening the circle of care": various sources of decision-making support; and (4) "Always a worry": concerns regarding parents' decision quality and outcomes. Parents described experiencing decisional conflict when making health care decisions for their child with a life-limiting condition. Decision support provided by health care professionals in an interprofessional manner was preferred and supplemented by a parent-based support network. Reassurance regarding their good parenting from health care professionals was described as supportive throughout the decision-making experience. [ABSTRACT FROM AUTHOR]

Near‐term iterative forecasting is a powerful tool for ecological decision support and has the potential to transform our understanding of ecological predictability. However, to this point, there has been no cross‐ecosystem analysis of near‐term ecological forecasts, making it difficult to synthesize diverse research efforts and prioritize future developments for this emerging field. In this study, we analyzed 178 near‐term (≤10‐yr forecast horizon) ecological forecasting papers to understand the development and current state of near‐term ecological forecasting literature and to compare forecast accuracy across scales and variables. Our results indicated that near‐term ecological forecasting is widespread and growing: forecasts have been produced for sites on all seven continents and the rate of forecast publication is increasing over time. As forecast production has accelerated, some best practices have been proposed and application of these best practices is increasing. In particular, data publication, forecast archiving, and workflow automation have all increased significantly over time. However, adoption of proposed best practices remains low overall: for example, despite the fact that uncertainty is often cited as an essential component of an ecological forecast, only 45% of papers included uncertainty in their forecast outputs. As the use of these proposed best practices increases, near‐term ecological forecasting has the potential to make significant contributions to our understanding of forecastability across scales and variables. In this study, we found that forecastability (defined here as realized forecast accuracy) decreased in predictable patterns over 1–7 d forecast horizons. Variables that were closely related (i.e., chlorophyll and phytoplankton) displayed very similar trends in forecastability, while more distantly related variables (i.e., pollen and evapotranspiration) exhibited significantly different patterns. Increasing use of proposed best practices in ecological forecasting will allow us to examine the forecastability of additional variables and timescales in the future, providing a robust analysis of the fundamental predictability of ecological variables. [ABSTRACT FROM AUTHOR]

Autism Spectrum Disorder (ASD) is a deleterious neurodevelopmental disorder affecting 1 in 54 children. The complex interdisciplinary nature of ASD research, however, introduces challenges to the spread and accessibility of new discoveries among researchers in different disciplines; furthermore, this highly complex research environment makes it even harder for practicing physicians and primary care providers to keep up with recent advances, which would profoundly impact the extent toward which recent research impacts standard medical and caretake practices. In order to contribute toward bridging the gaps between researchers in different fields, practicing physicians, and primary caretakers, we have created the most expansive autism ontology up to date (AutimsOnt) through utilizing the Protégé ontological framework. With 676 classes and more than 124 properties, AutismOnt can serve as the foundation to support a wide range of applications ranging from decision support systems for practicing physicians to text annotation processes that would allow for the creation of an interdisciplinary research platform where investigators can easily share and retrieve scientific findings. The Ontology is available in the NCBO BioPortal. [ABSTRACT FROM AUTHOR]

It is known that selecting competent employees according to the company's profile is a recurring difficulty among all professionals and sectors of human resources, where decision making requires a high level of cognitive effort to make a correct and clear selection about the attributes competed. The most common ways of selecting a new employee is mostly based on several interviews, theorical and practical tests, forms and group dynamics, in the expectation that the data provided by the candidates will be the starting point for a great selection, but, in most cases, the selection is made in a way that do not use some logical think, just the empirical thinking, liable to make unjust selections or that do not meet expectations properly. Based on this fact, the objective of this work is to report the use of the multicriteria method to support decision making AHP-TOPSIS-2N through the web software Three Decision Methods to support the choice of intern for the IoT-Based startup "Riverdata". After interviews with the chosen one, it was noticed that the results were satisfactory and met the expectations of those responsible for the selection. [ABSTRACT FROM AUTHOR]