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Purpose: From the buying club's perspective, the transfer of a player can be interpreted as an investment from which the club expects uncertain future benefits. This paper aims to develop a decision-oriented approach for the valuation of football players that could theoretically help clubs determine the subjective value of investing in a player to assess its potential economic advantage. Design/methodology/approach: We build on a semi-investment-theoretical risk-value model and elaborate an approach that can be applied in imperfect markets under uncertainty. Furthermore, we illustrate the valuation process with a numerical example based on fictitious data. Due to this explicitly intended decision support, our approach differs fundamentally from a large part of the literature, which is empirically based and attempts to explain observable figures through various influencing factors. Findings: We propose a semi-investment-theoretical valuation approach that is based on a two-step model, namely, a first valuation at the club level and a final calculation to determine the decision value for an individual player. In contrast to the previous literature, we do not rely on an econometric framework that attempts to explain observable past variables but rather present a general, forward-looking decision model that can support managers in their investment decisions. Originality/value: This approach is the first to show managers how to make an economically rational investment decision by determining the maximum payable price. Nevertheless, there is no normative requirement for the decision-maker. The club will obviously have to supplement the calculus with nonfinancial objectives. Overall, our paper can constitute a first step toward decision-oriented player valuation and for theoretical comparison with practical investment decisions in football clubs, which obviously take into account other specific sports team decisions. [ABSTRACT FROM AUTHOR]

The global elderly population is on the rise, posing significant challenges in both developed and developing nations. These challenges include issues related to mobility, falls, and the performance of daily tasks. However, the demands of today's fast-paced lifestyles make it difficult to provide constant care for the elderly, driving them towards a preference for independent living. To address this need for independent living, the concept of Ambient assisted living (AAL) has been developed. AAL is an intelligent system that employs sensors and devices to monitor and assist elderly individuals in their daily activities. Nevertheless, conflicts can arise when multiple sensors report conflicting activities concurrently, leading to device argumentation. To tackle this issue, this paper introduces a novel Long Short-Term Memory (LSTM)-based learning technique for aiding decision-making during device argumentation concerning activity occurrences. The LSTM model classifies user activities and sensor status values, employing an innovative approach to identify device argumentation within these classified activities. This LSTM decision-making (LDM) model effectively resolves device argumentation and accurately identifies the target activity. An experiment using a combined dataset from the ARAS and CASE datasets demonstrates the superiority of the proposed model, achieving an accuracy of 90.30%, precision of 85.66%, recall of 95.23%, and an F1-Score of 90.22%. [ABSTRACT FROM AUTHOR]

Abstract Background Decision models are increasingly used to inform policy-making processes, and there is a need to improve their credibility. The estimation of health and economic outcomes generated from decision models is influenced by the development process itself. This paper aims to present the conceptual model development process of cardiometabolic disease (CMD) policy models in the UK setting. Methods This conceptual model followed the International Society of Pharmacoeconomics and Outcomes Research-Society of Medical Decision Making (ISPOR-SMDM) Modelling Good Research Practices Task Force-2. Results First, for the conceptualisation of the problem, the CMD disease staging, progression and current clinical guidelines were summarised, followed by a systematic review of published policy models. We critically appraised policy models such as cardiovascular disease and type 2 diabetes. Key messages from the review emphasised the importance of understanding various determinants influencing model development, including risk factors, model structure, models’ parameters, data utilisation, economic perspective, equality/equity consideration, transparency and validation process. Second, as a sequential process, is model conceptualisation, to determine which modelling types and their attributes best represent the defined problem. Expert opinions, including a clinician and experienced modellers, provided input on the state transition model to ensure the structure is clinically relevant. From this stage, the consideration and agreement to establish a disease state in a state transition model was discussed. Conclusion This conceptual model serves as a basis for representing the systematic process for structuring a CMD policy model to enhance its transparency and credibility.

Background: Decision models are increasingly used to inform policy-making processes, and there is a need to improve their credibility. The estimation of health and economic outcomes generated from decision models is influenced by the development process itself. This paper aims to present the conceptual model development process of cardiometabolic disease (CMD) policy models in the UK setting. Methods: This conceptual model followed the International Society of Pharmacoeconomics and Outcomes Research-Society of Medical Decision Making (ISPOR-SMDM) Modelling Good Research Practices Task Force-2. Results: First, for the conceptualisation of the problem, the CMD disease staging, progression and current clinical guidelines were summarised, followed by a systematic review of published policy models. We critically appraised policy models such as cardiovascular disease and type 2 diabetes. Key messages from the review emphasised the importance of understanding various determinants influencing model development, including risk factors, model structure, models' parameters, data utilisation, economic perspective, equality/equity consideration, transparency and validation process. Second, as a sequential process, is model conceptualisation, to determine which modelling types and their attributes best represent the defined problem. Expert opinions, including a clinician and experienced modellers, provided input on the state transition model to ensure the structure is clinically relevant. From this stage, the consideration and agreement to establish a disease state in a state transition model was discussed. Conclusion: This conceptual model serves as a basis for representing the systematic process for structuring a CMD policy model to enhance its transparency and credibility. [ABSTRACT FROM AUTHOR]

Objective Background Methods Results Conclusions To propose a rational basis for donor testing in cases of suspected antibody‐mediated transfusion‐related lung injury (AMT).Anti‐leukocyte antibodies in donated blood are established causes of transfusion‐related lung injury (TRALI). However, the question of whether to test donors for antibodies is not identical to whether the case meets definition criteria for TRALI. There is a balance between the potential benefits of testing and the costs of donor deferral and investigation. We propose that a decision‐making process based on optimising the balance between risk and benefit requires a subjective choice of the relative value of different outcomes of testing.We have developed a formal decision model to illustrate how these choices affect testing decisions.Using a Bayesian probability model, we show that the diagnostic benefit and TRALI prevention benefit of testing donors have a complex interrelationship with the number of implicated donors and clinical suspicion of antibody‐mediated TRALI (AMT) and that rational testing choices vary according to value assigned to outcomes.The challenges to the use of a formal decision model for clinical testing are discussed and conclude that a formal model is a useful consensus‐building tool for improving consistency and openness in decision making. [ABSTRACT FROM AUTHOR]

With the rapid advancement of information technology, its integration into modern education has become essential, with a focus on enhancing teaching outcomes and the student learning experiences through innovative teaching methods. However, existing studies predominantly suffer from a lack of systematic evaluation indicators and scientific decision models. The selection of evaluation indicators is often subjective, incomplete, and primarily qualitative, lacking quantitative data support. A systematic decision analysis method was proposed in this study to address these issues. This study comprises three components: the construction of an evaluation indicator system for innovative teaching methods in information technology education, a scale analysis based on gray relational degree, and the computational steps for the decision model concerning innovative teaching methods. This study provides scientific bases and practical pathways for the selection of innovative teaching methods in information technology education, aiming to enhance teaching effectiveness and educational quality. [ABSTRACT FROM AUTHOR]

Introduction: Habitat loss and degradation pose significant threats to global fish and wildlife populations, prompting substantial investments in habitat creation and restoration efforts. Not all habitats provide equal benefits, leading to challenges in prioritizing restoration actions. For example, juvenile anadromous salmonids require high quality rearing aquatic habitats to achieve the physiological requirements needed to successfully migrate to the ocean. However, there are profound disagreements among anadromous salmon restoration managers whether it is best to focus efforts on restoring in-channel habitats that are available for the entire rearing period or floodplain habitats that, while facilitating greater growth and survival than in-channel habitats, are only available for a few weeks at a time and are typically only activated every two-to-three years. Methods: We used an existing fall-run Chinook salmon decision-support model to evaluate under what conditions floodplain restoration would provide greater benefits than in-channel habitat restoration. The simulations included a wide range of floodplain inundation frequencies and durations and floodplain benefits in the form of increased survival and growth relative to in-channel habitats. Results: The simulations results indicated that in-channel habitat restoration was always the best habitat restoration action when there was no existing in-channel habitat despite simulating a wide range of flood frequency, duration, and growth and survival benefits. Floodplain restoration was generally best when there was sufficient in-channel habitat available to successfully rear most of the juveniles produced by the returning adult salmon. Discussion: We hypothesize that in-channel and floodplain habitats have different roles in salmon population maintenance with in-channel habitats regulating the overall population size and floodplains acting as recurrent resource pulses. Our study provides a quantitative framework to evaluate the benefit of these two habitat types and provides generalizable rulesets that can be used by managers when implementing habitat restoration strategies for species that inhabit both in-channel and floodplain habitats. [ABSTRACT FROM AUTHOR]

Abstract Background Prostate cancer (PCa) causes a substantial health and financial burden worldwide, underscoring the need for efficient mass screening approaches. This study attempts to evaluate the Net Cost-Benefit Index (NCBI) of PCa screening in Iran to offer insights for informed decision-making and resource allocation. Method The Net Cost-Benefit Index (NCBI) was calculated for four age groups (40 years and above) using a decision-analysis model. Two screening strategies, prostate-specific antigen (PSA) solely and PSA with Digital Rectal Examination (DRE), were evaluated from the health system perspective. A retrospective assessment of 1402 prostate cancer (PCa) patients’ profiles were conducted, and direct medical and non-medical costs were calculated based on the 2021 official tariff rates, patient records, and interviews. The monetary value of mass screening was determined through Willingness to Pay (WTP) assessments, which served as a measure for the benefit aspect. Result The combined PSA and DRE strategy of screening is cost-effective, yields up to $3 saving in costs per case and emerges as the dominant strategy over PSA alone. Screening for men aged 70 and above does not meet economic justification, indicated by a negative Net Cost-Benefit Index (NCBI). The 40–49 age group exhibits the highest net benefit, $13.81 based on basic information and $13.54 based on comprehensive information. Sensitivity analysis strongly supports the cost-effectiveness of the combined screening approach. Conclusion This study advocates prostate cancer screening with PSA and DRE, is economically justified for men aged 40–69. The results of the study recommend that policymakers prioritize resource allocation for PCa screening programs based on age and budget constraints. Men’s willingness to pay, especially for the 40–49 age group which had the highest net benefit, leverages their financial participation in screening services. Additionally, screening services for other age groups, such as 50–54 or 55–59, can be provided either for free or at a reduced cost.

Many Blockchain-based approaches have been published in the field of health data management applications (HDMAs). However, no comprehensive guideline exists to guide the multiple and interdependent design decisions to develop such systems. This paper aims to support the HDMA system design processes by introducing a novel decision model. The model considers all relevant requirements, from regulatory context to user needs and trust considerations. To generate the decision model, we define a taxonomy that organizes previously published approaches by their technical design features and combines it with the trust assumptions of the participating actors according to the STRIDE method. The model aims to support a cohesive overall system design by addressing Blockchain type, off-chain storage, identity and access management, security decisions, and the specific use case of data donation. A group of experts evaluated the decision tree and its utility is demonstrated in three representative use cases. Special attention is paid to the use case of data donation via a data trustee, which is examined in detail. [ABSTRACT FROM AUTHOR]

Background: Prostate cancer (PCa) causes a substantial health and financial burden worldwide, underscoring the need for efficient mass screening approaches. This study attempts to evaluate the Net Cost-Benefit Index (NCBI) of PCa screening in Iran to offer insights for informed decision-making and resource allocation. Method: The Net Cost-Benefit Index (NCBI) was calculated for four age groups (40 years and above) using a decision-analysis model. Two screening strategies, prostate-specific antigen (PSA) solely and PSA with Digital Rectal Examination (DRE), were evaluated from the health system perspective. A retrospective assessment of 1402 prostate cancer (PCa) patients' profiles were conducted, and direct medical and non-medical costs were calculated based on the 2021 official tariff rates, patient records, and interviews. The monetary value of mass screening was determined through Willingness to Pay (WTP) assessments, which served as a measure for the benefit aspect. Result: The combined PSA and DRE strategy of screening is cost-effective, yields up to $3 saving in costs per case and emerges as the dominant strategy over PSA alone. Screening for men aged 70 and above does not meet economic justification, indicated by a negative Net Cost-Benefit Index (NCBI). The 40–49 age group exhibits the highest net benefit, $13.81 based on basic information and $13.54 based on comprehensive information. Sensitivity analysis strongly supports the cost-effectiveness of the combined screening approach. Conclusion: This study advocates prostate cancer screening with PSA and DRE, is economically justified for men aged 40–69. The results of the study recommend that policymakers prioritize resource allocation for PCa screening programs based on age and budget constraints. Men's willingness to pay, especially for the 40–49 age group which had the highest net benefit, leverages their financial participation in screening services. Additionally, screening services for other age groups, such as 50–54 or 55–59, can be provided either for free or at a reduced cost. [ABSTRACT FROM AUTHOR]

Advanced analytics offers new means by which to increase efficiency. However, real-world applications of advanced analytics in manufacturing are scarce. One reason is that the management task of selecting advanced analytics technologies (AATs) for application areas in manufacturing is not well understood. In practice, choosing AATs is difficult because a myriad of potential techniques (e.g. diagnostic, predictive, and prescriptive) are suitable for different areas in the value chain (e.g. planning, scheduling, or quality assurance). It is thus challenging for managers to identify AATs that yield economic benefit. We propose a multi-criteria decision model that managers can use to select efficient AATs tailored to company-specific needs. Based on a data envelopment analysis, our model evaluates the efficiency of each AAT with respect to cost drivers and performance across common application areas in manufacturing. The effectiveness of our decision model is demonstrated by applying it to two manufacturing companies. For each company, a customized portfolio of efficient AATs is derived for a sample of use cases. Thereby, we aid management decision-making concerning the efficient allocation of corporate resources. Our decision model not only facilitates optimal financial allocation for operations in the short-term but also guides long-term strategic investments in AATs. [ABSTRACT FROM AUTHOR]

IntroductionHabitat loss and degradation pose significant threats to global fish and wildlife populations, prompting substantial investments in habitat creation and restoration efforts. Not all habitats provide equal benefits, leading to challenges in prioritizing restoration actions. For example, juvenile anadromous salmonids require high quality rearing aquatic habitats to achieve the physiological requirements needed to successfully migrate to the ocean. However, there are profound disagreements among anadromous salmon restoration managers whether it is best to focus efforts on restoring in-channel habitats that are available for the entire rearing period or floodplain habitats that, while facilitating greater growth and survival than in-channel habitats, are only available for a few weeks at a time and are typically only activated every two-to-three years.MethodsWe used an existing fall-run Chinook salmon decision-support model to evaluate under what conditions floodplain restoration would provide greater benefits than in-channel habitat restoration. The simulations included a wide range of floodplain inundation frequencies and durations and floodplain benefits in the form of increased survival and growth relative to in-channel habitats.ResultsThe simulations results indicated that in-channel habitat restoration was always the best habitat restoration action when there was no existing in-channel habitat despite simulating a wide range of flood frequency, duration, and growth and survival benefits. Floodplain restoration was generally best when there was sufficient in-channel habitat available to successfully rear most of the juveniles produced by the returning adult salmon.DiscussionWe hypothesize that in-channel and floodplain habitats have different roles in salmon population maintenance with in-channel habitats regulating the overall population size and floodplains acting as recurrent resource pulses. Our study provides a quantitative framework to evaluate the benefit of these two habitat types and provides generalizable rulesets that can be used by managers when implementing habitat restoration strategies for species that inhabit both in-channel and floodplain habitats.

The field environment is complex and variable, and multiple factors constrain the effectiveness of UAV applications, and a single flow applications may result in over- or under-use of pesticides in plots with different requirements. Therefore, it is crucial to study a decision-making model of flow rate for plant protection UAVs under multi-factor interaction. In this paper, based on a large amount of experimental data, combined with Pearson correlation analysis and random forest variable importance score ranking, screening the features obtained from the experiment increases the correlation between input and output, making the output results more reliable. The model evaluation results showed that the GA-BP neural network model has a correlation coefficient of 0.99 between the true value, predicted value, and a coefficient of determination of 0.98, which is better than the general regression model. A validation test was conducted to test the effectiveness of the model for new data. The final result yields an error value within ±20% for the GA-BP model to predict the flow rate. At the same time, the BP neural network fluctuated more for some of the predicted values, which caused a 50% error in fitting results. It proves the feasibility of the BP neural network optimized based on feature screening and genetic algorithm in plant protection UAV flow rate decision-making, which can provide a reference basis and scientific guidance for precise variable spraying operation of plant protection UAVs.

Abstract Background Accurate clinical structural variant (SV) calling is essential for cancer target identification and diagnosis but has been historically challenging due to the lack of ground truth for clinical specimens. Meanwhile, reduced clinical-testing cost is the key to the widespread clinical utility. Methods We analyzed massive data from tumor samples of 476 patients and developed a computational framework for accurate and cost-effective detection of clinically-relevant SVs. In addition, standard materials and classical experiments including immunohistochemistry and/or fluorescence in situ hybridization were used to validate the developed computational framework. Results We systematically evaluated the common algorithms for SV detection and established an expert-reviewed SV call set of 1,303 tumor-specific SVs with high-evidence levels. Moreover, we developed a random-forest-based decision model to improve the true positive of SVs. To independently validate the tailored ‘two-step’ strategy, we utilized standard materials and classical experiments. The accuracy of the model was over 90% (92–99.78%) for all types of data. Conclusion Our study provides a valuable resource and an actionable guide to improve cancer-specific SV detection accuracy and clinical applicability.

Business process modeling is a crucial aspect of domains such as Business Process Management and Software Engineering. The availability of various BPM languages in the market makes it challenging for process modelers to select the best-fit BPM language for a specific process modeling task. A decision model is necessary to systematically capture and make scattered knowledge on BPM languages available for reuse by process modelers and academics. This paper presents a decision model for the BPM language selection problem in research projects. The model contains mappings of 72 BPM features to 23 BPM languages. We validated and refined the decision model through 10 expert interviews with domain experts from various organizations. We evaluated the efficiency, validity, and generality of the decision model by conducting four case studies of academic research projects with their original researchers. The results confirmed that the decision model supports process modelers in the selection process by providing more insights into the decision process. Based on the empirical evidence from the case studies and domain expert feedback, we conclude that having the knowledge readily available in the decision model supports academics in making more informed decisions that align with their preferences and prioritized requirements. Furthermore, the captured knowledge provides a comprehensive overview of BPM languages, features, and quality characteristics that other researchers can employ to tackle future research challenges. Our observations indicate that BPMN is a commonly used modeling language for process modeling. Therefore, it is more sensible for academics to explain why they did not select BPMN than to discuss why they chose it for their research project(s). [ABSTRACT FROM AUTHOR]

The coffee sector, one of the top-traded commodity groups worldwide, seeks to overcome its sustainability challenges through different cultural and technical practices, including shaded-coffee plantations, common in Central America and recognized as an opportunity to overcome such challenges. However, there is limited literature explaining the elements that influence decision-making processes for sustainable coffee production; therefore, this study aimed at identifying and modeling the factors driving this sector toward decisions for sustainable coffee practices. The Interpretive Structural Modeling (ISM) method allowed to represent a comprehensive model of the factors. In addition, a case study of six shaded-coffee farms using Life Cycle Assessment (LCA) and Environmental Life Cycle costing (E-LCC) provided a contextualized analysis. Main findings stress that the farm stage is the highest contributor to the cost and environmental impacts in green coffee production. Moreover, the model undertook outcomes of the LCA, E-LCC and literature reviews, detecting knowledge and costs as key driving factors for farmers' decisions, followed by the certification schemes, policies and the cooperative system as elements that influence decisions. Emissions, biodiversity and climate change adaptation behave as linking factors, while the use of water resources is the most dependent factor. This research establishes a first model to understand and address the factors that influence how decisions are taken in small-coffee farms when moving toward more sustainable coffee production, opening opportunities for further research as well as improved and tailored policy interventions in similar contexts. [ABSTRACT FROM AUTHOR]

In manufacturing, the essential product characteristics are often created through multiple stages. Coupling product data obtained through inspection and controllers based on decision models with prediction capabilities enables quality control loops, enhancing both feedback and feedforward mechanisms. This paper proposes a methodology to merge the formulation of feedback and feedforward quality control loops into a performance evaluation model for multi-stage manufacturing systems. This approach evaluates quality control loop impacts system-wide, aiding in configuring and reconfiguring quality gates. A case study illustrates how allocating inspection technologies and efficient decision models improves overall system performance through effective feedback and feedforward control loops. [ABSTRACT FROM AUTHOR]

This paper used a new interior graphic modeling research based on CAD and depth enhancement teaching models. A massive database for graphic design has been established. An optimization method is proposed based on intelligent decision making, intelligent monitoring, panoramic vision, professional cooperation and intelligent planning. This system can make many systems of different dimensions share and integrate horizontally. The graphic design of CAD is introduced into 3D CAD. The Boolean method is introduced into the smooth grid instruction to obtain the smooth surface of the target surface. Combining the object of plane decomposition with other geometric shapes by form-fitting instruction achieves object control. Experiments show the effectiveness of the method. The system has good running performance, stability and safety.

Clarifying the construction concept of network ideological and political education platforms in colleges and universities is a requirement to grasp the fundamental direction of network ideological and political education. This paper combines the Actor-critic algorithm with the state representation model and decision-making model to construct a network ideological and political education platform with the functions of education, management, service, dissemination, socialization, and practice. After the practical application of the Civic and Political Education Platform, the recognition and satisfaction of the platform are analyzed through questionnaire surveys. Finally, the Civic and Political Learning Achievement Quiz is conducted for college students at different stages to analyze the positive effect of the platform on the Civic and Political Learning Effect. The results show that in the four dimensions such as recognition, the p-value is less than 0.05, indicating that the difference between the experimental class and the control class is significant. Specifically, analyze the indicators of each dimension. Perceived resource richness, Guangzhou region satisfaction 3.8 is higher than the other three regions. Perceived resource quality perceived Shenzhen region is 3.68, higher than the other three regions, followed by Guangzhou. By the third stage, the average scores of the experimental group and the control group were 90.8 and 84.3, respectively, with a p-value of 0.008, indicating that the online education platform is becoming more and more effective in the learning of Civics and Politics for students in colleges and universities with time.

In the rapidly evolving domain of global e-commerce, operational organizations are increasingly grappling with unprecedented competitive pressures and challenges in operational efficiency. This study investigates the integration of artificial intelligence (AI) and information management as a transformative approach to augment operational efficiency in e-commerce organizations. The research delves into the optimization of organizational structures, leveraging the predictive capabilities of key information facilitated by AI. A novel decision-making model, integrating AI techniques, is developed to address the limitations inherent in existing decision-making technologies. The model's efficacy is demonstrated through a detailed case study of an e-commerce platform, where objectives and constraints for optimizing e-commerce operational organizations are meticulously constructed. The research identifies critical optimization points, including resource allocation, inventory management, logistics distribution, and customer relationship management. A distinctive organizational operation decision-making model, synergizing with the established Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), is proposed. This model focuses on optimizing variables pivotal to operational efficiency, with the dual goals of elevating efficiency and enhancing customer satisfaction. The findings underscore the significant role of AI technology in amplifying decision quality and boosting operational efficiency in e-commerce operational organizations, while also presenting potential applicability to a broader spectrum of organizational types. [ABSTRACT FROM AUTHOR]

A decision model is developed by adopting two control techniques, combining cultural methods and pesticides in a hybrid approach. To control the adverse effects in the long term and to be able to evaluate the extensive use of pesticides on the environment and nearby ecosystems, the novel decision model assumes the use of pesticides only in an emergency situation. We, therefore, formulate a rice-pest-control model by rigorously modelling a rice-pest system and including the decision model and control techniques. The model is then extended to become an optimal control system with an objective function that minimizes the annual losses of rice by controlling insect pest infestations and simultaneously reduce the adverse impacts of pesticides on the environment and nearby ecosystems. This rice-pest-control model is verified by analysis, obtains the necessary conditions for optimality, and confirms our main results numerically. The rice-pest system is verified by stability analysis at equilibrium points and shows transcritical bifurcations indicative of acceptable thresholds for insect pests to demonstrate the pest control strategy. [ABSTRACT FROM AUTHOR]

The strategic scientific management of cultural and research institution is put under scrutiny in the paper The strategic management of scientific research [Managementul cercetării științifice] by the author Meda Gâlea. To better improve the strategic scientific management, the author dives into the history of an important Romanian research institute, the Romanian Academy, and into its management style in relation to the research developed. To substantiate the results, and to support the development of the formula that underpins the decision-making process in the management of a scientific institution, the author uses actual scientific literature, corelated with the research carried out among the research and technical staff of the Romanian Academy in Iași. The most important takeaway of this scientific paper is the PRA-nX formula used to facilitate decision making in terms of economic management for better scientific performance.

Background: The fulfilment of the emergency response to the disaster area not only prioritises foods or medicines but also helps in mobilisation of equipment, facilities, and infrastructures in the form of logistics. The suitable warehouses will be able to provide every logistic needed and this will have a positive impact on the government as one of the relief institutions that is responsible for helping the refugees as quickly as possible. Objectives: The objectives of the study are to determine the logistic needs from disaster area, analyse and evaluate all assets from the 35 warehouses, identify the key indicators to generate the parameter, and to generate several mathematical formulas to be used in the simulation case. Method: Euclidean-based methodology is applied to calculate every fitness distance as a gap value of each parameter. All stock quantity from 35 warehouses will be the source of the data, with the logistic needs play role as the initial input for the calculation processes. Results: The logistic needs and the distance resulted in this study became the key indicators to determine the criteria. The study also generates seven equations for the disaster’s case simulation. Conclusion: To conclude, the outcomes resulted from the simulation indicate that from 35 warehouses alternatives, the fitness gap calculation completed at the 31st warehouse and the public hydrant became the fastest logistic, which completed all demand by two warehouses. Contribution: The study will be useful for the decision makers as the recommendation and guideline to improve the acceleration of emergency response of the logistic mobilisation from selected and prioritised warehouses.

A decision model is developed by adopting two control techniques, combining cultural methods and pesticides in a hybrid approach. To control the adverse effects in the long term and to be able to evaluate the extensive use of pesticides on the environment and nearby ecosystems, the novel decision model assumes the use of pesticides only in an emergency situation. We, therefore, formulate a rice-pest-control model by rigorously modelling a rice-pest system and including the decision model and control techniques. The model is then extended to become an optimal control system with an objective function that minimizes the annual losses of rice by controlling insect pest infestations and simultaneously reduce the adverse impacts of pesticides on the environment and nearby ecosystems. This rice-pest-control model is verified by analysis, obtains the necessary conditions for optimality, and confirms our main results numerically. The rice-pest system is verified by stability analysis at equilibrium points and shows transcritical bifurcations indicative of acceptable thresholds for insect pests to demonstrate the pest control strategy.

Raw aquatic food is regarded as an important source of proteins. However, the presence of various bacteria can increase the risk of disease in humans. As a result, a crucial step in the processing of aquatic products is the disinfection of raw aquatic food. An intelligent ultraviolet c radiation (UVC) sterilization system was established and evaluated in this study. On the automated production line, the UVC lamp's sterilization intensity can be automatically adjusted to achieve intelligent sterilization of the various aquatic product categories. The system was implemented and evaluated for oyster surface sterilization experiments in the production line. The quality indicators of oysters, such as volatile salt nitrogen content and aerobic bacterial count, were measured before and after sterilization. The average sterilization rate of the intelligent system is 96.4%. The results showed that the system had an excellent sterilization effect and improved the shelf life of aquatic products. [ABSTRACT FROM AUTHOR]

The sustainability issues surrounding conventional agriculture motivate the need for exploring new sustainable methods of farming, critical for global sustainable development. Vertical farming is a potentially underexplored component of sustainable food production portfolio. This paper offers the first quantitative model in the environmental economics and policy literature that evaluates the economic prospect of vertical farming systems in a competitive market setting. Our framework identifies the principal factors to assess the economic and risk aversion potential of vertical farming and utilize a decision model quantify the trade-off between the two alternative farming practices. The model is utilized to evaluate the competitive economic prospect of vertical farming in seven locations with heterogeneous climate and economic conditions within the USA. The results quantify the value proposition of vertical farming in various conditions. Consequently, we leverage these results to evaluate the current and future prospect of the vertical farming industry. [ABSTRACT FROM AUTHOR]

Breakfast processed products are remarkably at risk of fungal contamination. This research surveyed the fumonisins concentration in different breakfast products and carried out in vitro experiments measuring fumonisins content in different substrates inoculated with Fusarium verticillioides. The pipeline started with the identification of combinations of ingredients for 58 breakfast products. Twenty-three core ingredients, seven nutritional components and production types were analyzed using a Pearson correlation, k-means clustering, and principal component analysis to show that no single factor is responsible for high fumonisins detection in processed cereals products. Consequently, decision tree regression was used as a means of determining and visualizing complex logical interactions between the same factors. We clustered the association of ingredients in low, medium, and high risk of fumonisin detection. The analysis showed that high fumonisins concentration is associated with those products that have high maize concentrations coupled especially with high sodium or rice. In an in vitro experiment, different media were prepared by mixing the ingredients in the proportion found in the first survey and by measuring fumonisins production by Fusarium verticillioides. Results showed that (1) fumonisins production by F. verticillioides is boosted by the synergistic effect of maize and highly ready carbohydrate content such as white flour; (2) a combination of maize > 26% (w/w), rice > 2.5% (w/w), and NaCl > 2.2% (w/w) led to high fumonisins production, while mono-ingredient products were more protective against fumonisins production. The observations in the in vitro experiments appeared to align with the decision tree model that an increase in ingredient complexity can lead to fumonisins production by Fusarium. However, more research is urgently needed to develop the area of predictive mycology based on the association of processing, ingredients, fungal development, and mycotoxins production. [ABSTRACT FROM AUTHOR]

Aim: Diabetes health economic (HE) models play important roles in decision making. For most HE models of diabetes 2 diabetes (T2D), the core model concerns the prediction of complications. However, reviews of HE models pay little attention to the incorporation of prediction models. The objective of the current review is to investigate how prediction models have been incorporated into HE models of T2D and to identify challenges and possible solutions. Methods: PubMed, Web of Science, Embase, and Cochrane were searched from January 1, 1997, to November 15, 2022, to identify published HE models for T2D. All models that participated in The Mount Hood Diabetes Simulation Modeling Database or previous challenges were manually searched. Data extraction was performed by two independent authors. Characteristics of HE models, their underlying prediction models, and methods of incorporating prediction models were investigated. Results: The scoping review identified 34 HE models, including a continuous-time object-oriented model (n = 1), discrete-time state transition models (n = 18), and discrete-time discrete event simulation models (n = 15). Published prediction models were often applied to simulate complication risks, such as the UKPDS (n = 20), Framingham (n = 7), BRAVO (n = 2), NDR (n = 2), and RECODe (n = 2). Four methods were identified to combine interdependent prediction models for different complications, including random order evaluation (n = 12), simultaneous evaluation (n = 4), the 'sunflower method' (n = 3), and pre-defined order (n = 1). The remaining studies did not consider interdependency or reported unclearly. Conclusions: The methodology of integrating prediction models in HE models requires further attention, especially regarding how prediction models are selected, adjusted, and ordered. [ABSTRACT FROM AUTHOR]

Shuttle-based storage and retrieval systems (SBS/RSs), which are designed to increase throughput capacity and flexibility, are a type of automated storage and retrieval system used for lightweight loads. SBS/RSs can increase throughput capacity by using multiple shuttles and elevators as storage and retrieval machines (SRMs). They can also facilitate improvements in flexibility since they are able to adjust the number of SRMs according to transaction demands. Thus, determining the number of shuttles is an important issue in tier-to-tier SBS/RSs. In this paper, a decision model to determine the number of shuttles is proposed. The model is based on the travel time model, and it considers parameters such as the physical configuration, velocity profile and the probability that the shuttle operates a dual command. Finally, the throughput capacity from the travel time model is compared with that from a simulation-based approach in order to verify the effects of the model. In addition, a critical discussion regarding the characteristics of the tier-to-tier system is provided. [ABSTRACT FROM AUTHOR]

In order to solve the problem of the cognitive mechanism between user emotion and styling image in the process of electric vehicle design, improve the company's decision-making ability on styling imagery, and reduce the risk of electric vehicle development, an electric vehicle styling image decision model based on user evaluation data was proposed. The decision model first uses the web crawler method to obtain user evaluation data of research sample, and performs preprocessing, keyword extraction, and weight calculation on evaluation data; then uses sentiment analysis algorithm to obtain the sentiment value of the keywords in user evaluation data, as the user identification emotion evaluation score; encode and parameterize the modeling feature elements of research samples, and then construct the mapping relationship between the modeling feature elements and user's cognitive emotion. The “fashion” modeling image was taken as an example to design a case to verify the feasibility of the decision-making model. The research results show that the decision-making model can not only help companies quickly find user's attention to the different attributes of electric vehicle styling images, but also assist designers to quickly decide on styling image design goals, improve design efficiency, and reduce project period.

Context: Decentralized autonomous organizations are a new form of smart contract-based governance. Decentralized autonomous organization platforms, which support the creation of such organizations, are becoming increasingly popular, such as Aragon and Colony. Selecting the best fitting platform is challenging for organizations, as a significant number of decision criteria, such as popularity, developer availability, governance issues and consistent documentation of such platforms, should be considered. Additionally, decision-makers at the organizations are not experts in every domain, so they must continuously acquire volatile knowledge regarding such platforms. Objective: Supporting decision-makers in selecting the right decentralized autonomous organization platforms by designing an effective decision model is the main objective of this study. We aim to provide more insight into their selection process and reduce time and effort significantly by designing a decision model. Method: This study presents a decision model for the decentralized autonomous organization platform selection problem. The decision model captures knowledge regarding such platforms and concepts systematically. This model is based on an existing theoretical framework that assists software engineers with a set of multi-criteria decision-making problems in software production. Results: We conducted three industry case studies in the context of three decentralized autonomous organizations to evaluate the effectiveness and efficiency of the decision model in assisting decision-makers. The case study participants declared that the decision model provides significantly more insight into their selection process and reduces time and effort. Conclusion: We observe in the empirical evidence from the case studies that decision-makers can make more rational, efficient, and effective decisions with the decision model. Furthermore, the reusable form of the captured knowledge regarding decentralized autonomous organization platforms can be employed by other researchers in their future investigations.

Aim: While the European Union (EU) has approved several COVID-19 vaccines, new variants of concern may be able to escape immunity. The purpose of this study is to project the cost-effectiveness of future lockdown policies in conjunction with a variant-adapted vaccine booster. The exemplary scenario foresees a 25% decline in the vaccine protection against severe disease. Methods: A decision model was constructed using, for example, information on age-specific fatality rates, intensive care unit (ICU) costs and outcomes, and herd protection threshold. The costs and benefits of a future lockdown strategy were determined from a societal viewpoint under three future scenarios—a booster shot's efficacy of 0%, 50%, and 95%. Results: The cost-effectiveness ratio of a lockdown policy in conjunction with a booster dose with 95% efficacy is €44,214 per life year gained. A lockdown is cost-effective when the probability of approving a booster dose with 95% efficacy is at least 48% (76% when considering uncertainty in input factors). Conclusion: In this exemplary scenario, a future lockdown policy appears to be cost-effective if the probability of approving a variant-adapted vaccine booster with an efficacy of 95% is at least 48%. [ABSTRACT FROM AUTHOR]

Due to increasing globalization and market volatility, value creation systems are becoming progressively vulnerable to unpredictable crises. Organizations are forced to design their processes more crisis-proof to secure their existence in the dynamic market environment. The ability to prepare for, respond to and recover from random disruptive events is described by the term resilience. One possible way to increase resilience of value creation systems is by adapting flexible technologies like additive manufacturing. Due to its multiple and broad ranging applications and characteristic potentials, additive manufacturing provides the ability to radically change the design of value creation systems. This paper proposes a decision model to assist organizations in improving their system resilience by integrating additive manufacturing processes into their value creation system. The Design Science Research Paradigm was adopted to align practical research with existing problems. The model follows a knowledge-based approach, for which a qualitative research study was designed and conducted to derive model requirements. Subsequently, the model was conceptually developed considering the identified requirements and validated on a laboratory scale. [ABSTRACT FROM AUTHOR]

Background: The fulfilment of the emergency response to the disaster area not only prioritises foods or medicines but also helps in mobilisation of equipment, facilities, and infrastructures in the form of logistics. The suitable warehouses will be able to provide every logistic needed and this will have a positive impact on the government as one of the relief institutions that is responsible for helping the refugees as quickly as possible. Objectives: The objectives of the study are to determine the logistic needs from disaster area, analyse and evaluate all assets from the 35 warehouses, identify the key indicators to generate the parameter, and to generate several mathematical formulas to be used in the simulation case. Method: Euclidean-based methodology is applied to calculate every fitness distance as a gap value of each parameter. All stock quantity from 35 warehouses will be the source of the data, with the logistic needs play role as the initial input for the calculation processes. Results: The logistic needs and the distance resulted in this study became the key indicators to determine the criteria. The study also generates seven equations for the disaster's case simulation. Conclusion: To conclude, the outcomes resulted from the simulation indicate that from 35 warehouses alternatives, the fitness gap calculation completed at the 31st warehouse and the public hydrant became the fastest logistic, which completed all demand by two warehouses. Contribution: The study will be useful for the decision makers as the recommendation and guideline to improve the acceleration of emergency response of the logistic mobilisation from selected and prioritised warehouses. [ABSTRACT FROM AUTHOR]

Purpose: Outsourcing information security has proven to be an efficient solution for information security management; however, it may not be the most suitable approach for every organization. This research aimed to develop a multi-criteria decision-making model that would enable organizations to determine which approach to information security management (outsourcing or internal management) is more suitable for their needs and capabilities.

Background. New interventions are available for the prevention of respiratory syncytial virus (RSV) disease in young infants. We aimed to assess the potential impact and cost-effectiveness of using a long-acting monoclonal antibody (RSV mAb) or maternal RSV vaccine in the Argentine context. Methods. We used a static proportionate outcomes model to calculate the costs and consequences of using RSV mAb or maternal RSV vaccine over a ten-year period (2025–2034) in Argentina, assuming both year-round and seasonal administration. We compared each intervention to no pharmaceutical RSV intervention. The primary outcome was the discounted cost per disability-adjusted life year (DALY) averted from a societal perspective. We assumed willingness-to-pay of US$ 12,285 per DALY averted (0.9 times the national gross domestic product per capita). We used population study data on costs and disease burden and the efficacy of clinical trials of both interventions as inputs. We ran deterministic and probabilistic uncertainty analyses. Findings. Either strategy (RSV mAb or maternal RSV vaccine) could prevent >25% of RSV deaths aged <5 years and ∼30% aged <6 months (the age group where most intervention impact occurs). With a dose price of $US 50, both products have a 100% probability of being cost-effective compared to no intervention (US$ 5283 [95%CI $5203–$5363] and US$ 5522 [95%CI $5427 – $5617] per DALY averted for year-round use of RSV mAb and maternal RSV vaccine, respectively). Similar health impact could be achieved by a six-month seasonal strategy, which could improve cost-effectiveness by around 45% (assuming the dose price is unchanged). Interpretation. Either RSV mAb or maternal RSV vaccine are worth consideration in Argentina when priced at ≤US$ 50 per dose. A seasonal strategy could improve cost-effectiveness. [ABSTRACT FROM AUTHOR]

Abstract Background The decision to outsource an activity is one of the most complex organizational decisions. This decision is also influenced by several factors and components. In order to facilitate and optimize it, for the first time in this study, a decision model for outsourcing medical service delivery in public hospitals has been developed. Methods We conducted this cross-sectional study in 3 stages: 1) We identified the factors affecting the outsourcing decisions, 2) an expert panel identified the influential factors. After standardization, we distributed 220 questionnaires among university staff managers and heads, nursing managers, and managers of the research units, and 3) Structural Equation Model applied to evaluate the relationship between the variables on AMOS22, at 0.05 significant level. Results Findings indicated the optimal level of all fit indices. The path coefficient between all identified factors with the outsourcing decision was positive (t > 1.96). Factors ranging from the most effective to least effective included monitoring and control, service type, human resource, economic and financial, executive capability, external environment, and terms and conditions. Conclusion The proposed model provides unit evaluation to make the appropriate decision on outsourcing or non-outsourcing. Control and monitoring were the most determining factors. We recommend performing monitoring continuously as a guide and deterrent to error. We also recommend continuous monitoring and control over the quality of outsourced units and stakeholder satisfaction.

Purpose: The objective of this study was to compare the cost-effectiveness of preoperative octreotide therapy followed by surgery versus the standard treatment modality for growth-hormone secreting pituitary adenomas, direct surgery (that is, surgery without preoperative treatment) from a public third-party payer perspective. Methods: We developed an individual-level state-transition microsimulation model to simulate costs and outcomes associated with preoperative octreotide therapy followed by surgery and direct surgery for patients with growth-hormone secreting pituitary adenomas. Transition probabilities, utilities, and costs were estimated from recent published data and discounted by 3% annually over a lifetime time horizon. Model outcomes included lifetime costs [2020 United States (US) Dollars], quality-adjusted life-years (QALYs) and incremental cost-effectiveness ratios (ICERs). Results: Under base case assumptions, direct surgery was found to be the dominant strategy as it yielded lower costs and greater health effects (QALYs) compared to preoperative octreotide strategy in the second-order Monte Carlo microsimulation. The ICER was most sensitive to probability of remission following primary therapy and duration of preoperative octreotide therapy. Accounting for joint parameter uncertainty, direct surgery had a higher probability of demonstrating a cost-effective profile compared to preoperative octreotide treatment at 77% compared to 23%, respectively. Conclusions: Using standard benchmarks for cost-effectiveness in the US ($100,000/QALY), preoperative octreotide therapy followed by surgery may not be cost-effective compared to direct surgery for patients with growth-hormone secreting pituitary adenomas but the result is highly sensitive to initial treatment failure and duration of preoperative treatment. [ABSTRACT FROM AUTHOR]

Anorexia Nervosa (AN) is a severe psychiatric disorder and knowledge about the cost-effectiveness of potential interventions is limited. The aim of this paper is to introduce the Trimbos Institute health economic cost-effectiveness model for Anorexia Nervosa (AnoMod-TI), a flexible modeling tool for assessing the long-term cost-effectiveness of interventions for AN in late adolescent and adult patients, which could support clinical decision making. AnoMod-TI is a state-transition cohort simulation (Markov) model developed from a Dutch societal perspective, which consists of four health states – namely full remission (FR), partial remission (PR), AN and death. Results are expressed as total healthcare costs, QALYs and incremental cost-effectiveness ratio. For the purpose of demonstrating AnoMod-TI and how it could be used to estimate cost-effectiveness over a 20-year time horizon, it was applied to a hypothetical treatment scenario. Results illustrate how a relatively costly intervention with only modest effects can still be cost-effective in the long term. AnoMod-TI can be used to examine long-term cost-effectiveness of various interventions aimed at either treating AN or preventing relapse from a state of partial or full remission. AnoMod-TI is freely available upon request to the authors. [ABSTRACT FROM AUTHOR]

How to recruit, test, and train the intelligent recommendation system users, and how to assign the archive translation tasks to all intelligent recommendation system users according to the intelligent matching principles are still a problem that needs to be solved. With the help of proper names and terms in China's Imperial Maritime Customs archives, this manuscript aims to solve the problem. When the corresponding translation, domain or attributes of a proper name or term is known, it will be easier for some archive translation tasks to be completed, and the adaptive archive intelligent recommendation system will also improve the efficiency of intelligent recommendation quality of archive translation tasks. These related domains or attributes are different labels of these archives. To put it simply, multi-label classification means that the same instance can have multiple labels or be labelled into multiple categories, which is called multi-label classification. With the multi-label classification, archives can be classified into different categories, such as the trade archives, preventive archives, personnel archives, etc. The system users are divided into different professional domains by some tests, for instance, system users who are good at economic knowledge and users who have higher language skills. With these labels, the intelligent recommendation system can make the intelligent match between the archives and system users, so as to improve the efficiency and quality of intelligent archive translation tasks. In this manuscript, through multilabel classification, the intelligent recommendation system can realize the intelligent allocation of archive translation tasks to the system users. The intelligent allocation is realized through the construction of intelligent control model, and verifies that the intelligent recommendation system can improve the performance of task allocation over time without the participation of task issuers. [ABSTRACT FROM AUTHOR]

Featured Application: In this study, an effective multi-objective optimization method (I-NSGA-III-VLC) is proposed and applied to the rotation and echelon usage problem of aircraft equipment under uncertainty, proving the effectiveness of the method. Optimizing the aircraft equipment usage scheme of different units according to their task intensity has great significance in improving aircraft reliability and health management. This paper studied the modeling and solving methods of the rotation and echelon usage problems of aircraft equipment measured by dual-life indexes, one of which cannot be controlled. In order to maximize the waste rate of the rotation quantity, echelon uniformity index, life matching index and life utilization index, a decision-making model of the equipment rotation and echelon usage problem under uncertainty was constructed, and an improved NSGA-III with a variable length chromosome was proposed. An improved segmented coding method and operators were proposed, and the repeated individual control mechanism was used to improve the population diversity. When the scale of the problem was large, this method could search a wider range in a short time and obtain more feasible solutions, which verified the feasibility of this method. [ABSTRACT FROM AUTHOR]