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Resources for export promotion are scarce, and incorrect market selection can be costly. In the literature, the gravity model, International Trade Center's Export Potential Map, and decision support model all estimate export potential values. Although the same concept is measured, different methodologies are used. This study aimed to compare the rankings assigned to the export potential values by each approach. The results indicated that 45% of the rankings differed by more than five places, while one-third were ranked in the top 10 of only one approach. Given these inconsistencies, alternative approaches for prioritization after identifying export opportunities are recommended. [ABSTRACT FROM AUTHOR]

Recently, traditional manufacturing industries have faced two serious gaps and problems in line with effective product-line sales forecasting methods to balance the negative impacts on the performance of the subjective experience, including (1) arbitrary judgment, such as growth rate of expectancy, manager's experiences, and historical sales data, may cause inaccurately predictive results and severe negative effects, and (2) sales forecasting is a key priority and challenge in the context of considerable product lines that have different properties and need specific models for supporting decision analytics. This study is motivated to propose an advanced hybrid model to utilize the advantages of variation for methods of fuzzy time series (FTS), exponential smoothing (ES), moving average (MA), and regression analysis (RA). To analyze the four product lines—stably growing product (SGP), declining product (DP), irregularly growing product (IGP), and special sales product (SSP)—this study is based on empirical sales data from a leading traditional manufacturer to accurately identify the high potentials of decisive key factors and objectively evaluate the model. Two evaluation standards—the mean absolute percentage error (MAPE) and root mean square error (RMSE), a parameter sensitivity analysis, and comparative analysis—are measured. After implementing the data from the case study, four key reports were conclusively identified. (1) Purely for the RMSE, the best one (10.32) is the ES method in the SGP line. (2) In the DP line, the better one is the RA(2) method, with a relatively low MAPE of 17.78% and RMSE of 26.46. (3) The FTS method is the best choice (MAPE 12.41% and RMSE 18.98) for the IGP line. (4) For the SSP line, the better one (MAPE 24.05 and RMSE 29.34) is the MA method. According to the above reports, although the proposed hybrid model has a general performance for the SSP line, it still has a superior predictor when compared to manager subjective prediction. Interestingly, the proposed model is rarely used, has a new trial with an innovative solution for the traditional manufacturer, and thus realizes applicable values. The study concludes with acceptable and satisfactory results and yields seven important findings and three managerial implications that significantly contribute to decision-making reference for complete sales-production planning for interested parties. Thus, this study benefits and values a conventional industry upgrade from novel application techniques. [ABSTRACT FROM AUTHOR]

Choosing the appropriate project requires good planning based on scientific foundations, especially in Iraq, due to the tremendous development witnessed in the construction industry. Incorrect selection of projects leads to many challenges that may accompany the project throughout its implementation period such as delay in delivery, exceeding the planned cost, overlapping work, and finally, project stopping. In Iraq, focusing on contractor selection, specifically who submits the lowest bid, regardless of the actual feasibility of the project and the integration of its components, led to the spread of random projects that do not achieve their true purpose due to neglect of integration. The following steps were followed to overcome the mentioned problems: (1) Thirty-one influence factors on choosing the appropriate project were identified through an extensive review of the literature; (2) Only seven factors were selected according to the Fuzzy Delphi Technique (FDT); (3) The priority and weight of each factor were determined through pairwise comparisons using the Analytical Network Process (ANP). As a result, a model was developed to select the most appropriate project, and to verify the validity of the proposed model, a case study was applied to three projects for constructing residential complexes belonging to the private sector, which can give an excellent view of the efficiency of the basic project elements, which are structural/organizational, technical, and financial aspects in selecting the best project. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to develop a decision aid tool using "real-world" data within the Australian health system to predict weight loss after bariatric surgery and non-surgical care. Materials and Methods: We analyzed patient record data (aged 16+years) from initial review between 2015 and 2020 with 6-month (n=219) and 9-/12-month (n=153) follow-ups at eight clinical obesity services. Primary outcome was percentage total weight loss (%TWL) at 6 months and 9/12 months. Predictors were selected by statistical evidence (p<0.20), effect size (±2%), and clinical judgment. Multiple linear regression and bariatric surgery were used to create simple predictive models. Accuracy was measured using percentage of predictions within 5% of the observed value, and sensitivity and specificity for predicting target weight loss of 5% (non-surgical care) and 15% (bariatric surgery). Results: Observed %TWL with bariatric surgery vs. non-surgical care was 19% vs. 5% at 6 months and 22% vs. 5% at 9/12 months. Predictors at 6 months with intercept (non-surgical care) of 6% include bariatric surgery (+11%), BMI>60 (–3%), depression (–2%), anxiety (–2%), and eating disorder (–2%). Accuracy, sensitivity, and specificity were 58%, 69%, and 56%. Predictors at 9/12 months with intercept of 5% include bariatric surgery (+15%), type 2 diabetes (+5%), eating disorder (+4%), fatty liver (+2%), atrial fibrillation (–4%), osteoarthritis (–3%), sleep/mental disorders (–2–3%), and ≥10 alcohol drinks/week (–2%). Accuracy, sensitivity, and specificity were 55%, 86%, and 53%. Conclusion: Clinicians may use DACOS to discuss potential weight loss predictors with patients after surgery or non-surgical care. [ABSTRACT FROM AUTHOR]

Sanitation and toilets are important infrastructure for public health and societal stability. However, the adoption of adequate treatment technologies and techniques is a major challenge for both developing and underdeveloped areas. Answering the question of how to improve sanitation and toilet infrastructure in rural areas, for poverty alleviation, inequality mitigation, and good health and well-being under the Sustainable Development Goals, is more challenging compared with urban areas. Decision support models (DSMs) are important for selecting rural sanitation and toilet technologies. However, previous models have not fully respected local standards, needs, and operational environments, and are mainly limited to technological sustainability performance. To overcome such research gaps, this study developed a rural sanitation and toilet technology decision support model (DSM) assessing economic, environmental, and technological sustainability. Both technology and village weighting methods based on 217 general experts and seven local residents, respectively, were adopted to fully tailor indicator weights to rural contexts. The results showed an economic sustainability weight of 0.205, an environmental sustainability weight of 0.466, and a technological sustainability weight of 0.329. The sanitation and toilet technologies were divided into wastewater treatment technologies and toilet technologies, with the former subdivided into primary, secondary, and tertiary wastewater treatment technologies. This study confirmed that the PSO-GWO algorithm outperformed in accuracy and effectiveness. Accordingly, the PSO-GWO algorithm was adopted to demonstrate the optimization of sanitation and toilet technologies in four villages in plateau, mountain, plain, and basin areas. The study can assist local governments in selecting appropriate rural sanitation and toilet technologies during the planning phase. This can enhance the living standards of rural residents and promote sustainable rural development. [ABSTRACT FROM AUTHOR]

With the rapid development of social mode virtualization and electronic component technology, the application of data science and Internet of Things (IoT) technology in the field of e-commerce is gradually increasing. This study aims to explore how these emerging technologies can enhance the advantage of Chinese e-commerce companies in international competition. By comprehensively analyzing the massive data generated by online social networking and the application of IoT sensor technology in logistics and enterprise management, this paper proposes a decision support model based on data analysis. Research methods include data collection, data analysis and case studies. The results of the study show that data analytics and IoT technologies can effectively improve the efficiency of e-commerce operations and customer experience. The conclusion is that these technologies not only contribute to the domestic development of e-commerce enterprises, but also play a non-negligible role in international competition. This research has important implications for understanding the practical applications and potential of new technologies in the field of e-commerce. [ABSTRACT FROM AUTHOR]

The demand for electric vehicles is increasing worldwide, leading to a high demand for lithium-ion batteries, whose supply chains are associated with high environmental and socio-economic impacts. Therefore, legal regulations, such as the EU Battery Regulation, force battery manufacturers to use secondary materials, comply with CO 2 -limits and ensure social conditions along their supply chains. These circumstances make it necessary to assess battery supply chains in terms of their environmental, economic, and social impacts. For this purpose, the life cycle sustainability assessment approach can be used. However, the results of this approach often lead to conflicting goals between and within sustainability dimensions, making it challenging to derive recommendations for more sustainable battery supply chain designs. One approach to solving this problem is to use optimization models that take the three sustainability dimensions into account. To this end, this study aims to combine the results of a Life Cycle Sustainability Assessment of battery supply chains with a decision support model to derive recommendations for action for the design of more sustainable battery supply chains. For this purpose, different spatial-differentiated activities are assessed and included in a decision support model that aims at minimizing the costs of a German battery pack manufacturer, considering environmental, social, and legal restrictions. The decision support model is implemented in Python and solved using Gurobi. The results of the decision support model are compared with the current state of battery supply chains as a benchmark. The result of the study shows that in many of the scenarios studied, the cost, as well as the environmental and social impacts, can be significantly reduced compared to the benchmark. However, if the environmental and social impacts were to be reduced strictly, this would lead to higher costs compared to the benchmark. Furthermore, the results show that a European-centric supply chain would emerge for more environmentally friendly supply chains, while for a more socially friendly supply chain, the focus would be on Japan. [ABSTRACT FROM AUTHOR]

Copyright of Japanese Journal of JSCE / Doboku Gakkai Ronbunshu is the property of Japan Society of Civil Engineers 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.)

Introduction: Identifying and determining target markets is one of the most important ways to diversify non-oil exports and reach global markets. The expansion of export markets is one of the important strategies of the country, considering the decrease in revenues from oil exports and its extreme fluctuations. The agricultural sector has made a significant contribution to the country's non-oil exports in recent years. However, the lack of accurate identification of potential export markets has faced challenges in the export development of this sector. Based on this, the aim of this study is to identify and rank the potential target markets of Iran's export agricultural products in such a way that the potential markets for export products in this sector can be identified and prioritized. The statistical sample of this research is all the countries that had commercial exchanges with Iran in the field of agricultural products during the study period, and the information they needed was available in the World Bank database. Materials and Methods: The data used to calculate the indicators includes the period from 2002 to 2018. In order to identify and rank the market-target products, the decision support model and the implementation of a four-step screening process using Excel and Access software and programming in the SQL software environment were used. Findings: The findings of the research show that there are realistic export opportunities related to nine products (1) other nuts (2) dates, figs, (3) grapes (4) melons, watermelons (5) apples and fresh pears. (6) ginger, saffron, turmeric, thyme and other spices (except pepper) (7) sugary sweets containing cocoa, including white chocolate (8) bread, sweets, cakes, biscuits and (9) other prepared or preserved vegetables but not frozen. [ABSTRACT FROM AUTHOR]

This study aims to change land use by implementing a Decision Support Model (DMS) with the goal of reducing water and fertilizer use. The problem is solved by deriving the necessary results of a set of selected pilot fields that belong to a farmer group located in the region of Central Macedonia. In order to define the pilot farms, the necessary data are collected and then processed using multicriteria weighted goal programming in order to develop a Decision Support Model that is related to the reduction of water and fertilizer use. [ABSTRACT FROM AUTHOR]

The aim of this study is to change land use by applying a decision support model that will contribute to the assimilation of the new cross-compliance rules, to optimal water management, and to the enhancement of the effectiveness and profitability of the farms. The research objective will be achieved by establishing 50-acre pilot fields for five farmer groups through the optimal allocation of limited economic and land resources. The result extracted will lead to the gradual incorporation of the new directives to reduce production costs and recognize the new cross-compliance rules. [ABSTRACT FROM AUTHOR]

Turkey's foreign trade deficit with China has rapidly increased since the 2000s. To reduce the foreign trade deficit with China, Turkey needs to identify and diversify potential commodities to export to China with higher competitiveness. In this aspect, this study identifies potential export opportunities for Turkey in China among the Belt and Road Initiative China-Central Asia-West Asia economic corridor countries to increase Turkey's total export volume and diversification in China. The decision support model with some crucial modifications in accordance with regional practices is implemented to pick the export commodities with the highest export potential value. The results of the modified decision support model indicate that Turkey can have export potential, mostly in textiles, metals, machinery/electrical, and miscellaneous. The export promotion strategies for increasing export diversification and volume in China are recommended for Turkey’s future export strategy. [ABSTRACT FROM AUTHOR]

The world is adopting the Industry 5.0 paradigm to increase human centricity, sustainability, and resilience in efficient, optimized, and profitable manufacturing systems. With benefits, however, come increased risks of economic and physical loss, driving the need for continuous improvement of Industry 5.0 cybersecurity. Implementation and advancement of adequate cybersecurity have created challenges that have been identified in the literature. In this study, key Industry 5.0 cybersecurity challenges and related sub-challenges are highlighted based on a literature review. Graph Theory and Matrix Approach (GTMA) is employed to analyze the challenges and determine relative importance based on permanent values of the variable permanent matrix (VPM). The results identify the most important Industry 5.0 cybersecurity challenges and reveal Industry 5.0 firms should primarily concentrate on supply chain vulnerabilities to decrease data loss and hacking in the organization's supply chain network. This study also recommends that executives and lawmakers acquire knowledge regarding cybersecurity challenges and prepare to deal with them. Addressing these and other subsequently prioritized challenges—the top five rounded out with emergent cybersecurity trends, non-availability of cybersecurity curriculum in education, embedded technical constraints, and absence of skilled employees and training—will lead the methodical development of holistic, robust cybersecurity programs. Firms accepting of this reality may implement such programs to mitigate evolving cyber-risk towards harnessing and sustaining the benefits of novel Industry 5.0 technologies.

In a transboundary river basin, downstream states frequently express concerns regarding the potential utilization of water resources by upstream states as a tool for exerting coercion. This fact contributes to instilling doubt in the applicability of negotiations, even in transboundary basins that possess strong international agreements. In an effort to address the issue, this paper introduces the painted water concept. It divides upstream states' available water into three triage color volumes before reaching downstream states in ascending order of negotiability: green, yellow, and red. Additionally, downstream states must consider the dynamics of transitions of painted water classes over time when developing their negotiation strategies and water policies. In order to assess the concept's contribution in practice, we analyze trilateral riparian negotiations along the Blue Nile River basin, based on a "what-if" analysis approach under four global future scenarios. These results could shed light on part of the complexity of the Blue Nile negotiation and mainstream the water policies and perspectives of riparian states. Here, this paper shows that the painted water concept can provide multidisciplinary insights into proactive water negotiations. The inclusion of such a concept can help to deepen theories, approaches principals, and any disciplines pertinent to transboundary water negotiations. [ABSTRACT FROM AUTHOR]

Safely managed sanitation is indispensable for societies to ensure public health, environmental protection, and economic and social development. This could be achieved, in large areas of the world, through dry sanitation systems. Dry sanitation systems are especially used in water-scarce regions and low-income households. In dense peri-urban areas, the achievement of safe sanitation necessitates a comprehensive fecal sludge management (FSM) service chain, surpassing the mere provision of latrines. This research introduces an automatic Multi-Criteria Decision Analysis (MCDA) approach, which focuses on the particular interface/storage stage of the FSM service chain. The tool aims to support the decision-making process and may be especially useful in the early stages of sanitation planning as it compares different technologies with potential application in low-income countries. It includes different criteria and parameters for the Social, Economic, Technical, and Environmental dimensions of dry sanitation options (SETEds), being adaptable to different contexts and to different priorities. The main key strengths of the tool were found to be its minimal data requirements and ability to customize operation and maintenance cost parameters. These features are particularly relevant in data-scarce contexts, where traditional models may lead to unreliable recommendations or lack of solution ownership by users. The tool was applied to the Ambriz case study, a coastal town in Northern Angola, in West Africa. The obtained results are analyzed and show the tool's application provides technology recommendations aligned with the site and community characteristics. [ABSTRACT FROM AUTHOR]

The increasing traffic and complexity of navigation at sea require advanced decision support systems to ensure greater safety. In this study, we propose a novel decision support system that employs fuzzy logic to improve situational awareness and to assist navigators in collision avoidance during multi-vessel encounters. The system is based on the integration of the rules of the Convention on International Regulations for Preventing Collisions at Sea (COLREGs) and artificial intelligence techniques. The proposed decision model consists of two main modules to calculate the initial encounter conditions for the target vessels, evaluate the collision risk and navigation situation based on COLREG rules, sort the target vessels, and determine the most dangerous vessel. Fuzzy logic is used to calculate the collision avoidance maneuver for the selected ship, considering the closest point of approach, relative bearing, and the ship's own speed. Simulation tests demonstrate the effectiveness of the fuzzy-based decision model in scenarios with two ships. However, in complex situations with multiple ships, the performance of the model is affected by possible conflicts between evasive maneuvers. This highlights the need for a cooperative collision avoidance algorithm for all vessels in high traffic areas. [ABSTRACT FROM AUTHOR]

The lighting system as a crucial urban infrastructure needs to be maintained to uphold good performance. The planning and scheduling of maintenance for geographically distributed lighting systems is a complex research problem as well as a challenge in practice due to resource constraints and the complexity of route planning. This study proposes a multiple-objective decision-support model (MODSM) for lighting maintenance routing, which considers both the carbon emissions generated during the maintenance process and the loss of efficiency due to workload imbalance among maintenance crews. In the proposed model, carbon emissions are converted into a carbon tax index as part of the total cost index (along with the pure cost index), while the workload balance is measured by the standard deviation of workload indexes among the crews. To address the identified problem, an improved genetic algorithm is used to identify the optimal solution for group relamping. A numerical example is provided to demonstrate the suitability of the developed model to the selection of an optimal maintenance scheme, including the number of maintenance crews and their corresponding maintenance routes. Finally, the performance of the proposed model is assessed by sensitivity analysis and comparison with the traditional method. Integrating the interests of multiple stakeholders and environmental protection, the MODSM for lighting maintenance routing developed in this study can provide a helpful tool for researchers and practitioners in the construction engineering and management community to formulate a more comprehensive decision into maintenance management. [ABSTRACT FROM AUTHOR]

Background: Despite the increasing interest and investment in big data analytics (BDA), many organisations find the implementation and use of the tools challenging. This is attributed to the cumbersome nature of some of the tools. Objectives: From both business and academic domains, this study sets out to provide a model that enables, supports, and makes the selection and use of BDA tools easier. Method: The qualitative methods from the perspective of an interpretive approach were employed in the study. The actor-network theory (ANT) was applied as a lens to underpin the phenomenon being studied and gain a deeper understanding of why things happen in the way that they confusedly do, in the selection and subsequent use of BDA tools. Results: The research revealed that five factors, organisational requirements, top-down versus bottom-up approach, the role of stakeholders, the usefulness of BDA, and organisational structure, primarily influence the selection and use of BDA tools in organisations. Conclusion: Empirically, the factors bring fresh perspectives to support the decision in appropriately managing BDA deployment for organisational purposes. Contribution: The main contribution of this study lies in the use of the decisions support model, to practically and theoretically provide a guide for managers in the organisation, in selecting BDA for decision support purposes. From an academic perspective, the study contributes to the advancement in the use of ANT for analysis in information system (IS) research.

Introduction: Clinical prediction models (CPMs) for coronavirus disease 2019 (COVID-19) may support clinical decision making, treatment, and communication. However, attitudes about using CPMs for COVID-19 decision making are unknown. Methods: Online focus groups and interviews were conducted among health care providers, survivors of COVID-19, and surrogates (i.e., loved ones/surrogate decision makers) in the United States and the Netherlands. Semistructured questions explored experiences about clinical decision making in COVID-19 care and facilitators and barriers for implementing CPMs. Results: In the United States, we conducted 4 online focus groups with 1) providers and 2) surrogates and survivors of COVID-19 between January 2021 and July 2021. In the Netherlands, we conducted 3 focus groups and 4 individual interviews with 1) providers and 2) surrogates and survivors of COVID-19 between May 2021 and July 2021. Providers expressed concern about CPM validity and the belief that patients may interpret CPM predictions as absolute. They described CPMs as potentially useful for resource allocation, triaging, education, and research. Several surrogates and people who had COVID-19 were not given prognostic estimates but believed this information would have supported and influenced their decision making. A limited number of participants felt the data would not have applied to them and that they or their loved ones may not have survived, as poor prognosis may have suggested withdrawal of treatment. Conclusions: Many providers had reservations about using CPMs for people with COVID-19 due to concerns about CPM validity and patient-level interpretation of the outcome predictions. However, several people who survived COVID-19 and their surrogates indicated that they would have found this information useful for decision making. Therefore, information provision may be needed to improve provider-level comfort and patient and surrogate understanding of CPMs. Highlights: While clinical prediction models (CPMs) may provide an objective means of assessing COVID-19 prognosis, provider concerns about CPM validity and the interpretation of CPM predictions may limit their clinical use. Providers felt that CPMs may be most useful for resource allocation, triage, research, or educational purposes for COVID-19. Several survivors of COVID-19 and their surrogates felt that CPMs would have been informative and may have aided them in making COVID-19 treatment decisions, while others felt the data would not have applied to them. [ABSTRACT FROM AUTHOR]

Copyright of GeSec: Revista de Gestao e Secretariado is the property of Sindicato das Secretarias e Secretarios do Estado de Sao Paulo (SINSESP) 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.)

In recent decades, digital technologies have seriously changed socioeconomic systems on a global scale. Unfortunately, consequential issues have remained mostly uninvestigated. The literature lacks research into the risks that may arise in the procedure of developing digital capabilities that have considerable impacts on firms’ innovative growth. In addition, inadequate research has been conducted on challenges that may arise when a business is being developed in the context of the digital economy. Moreover, the advent of new risks specific to the digital economy has not been addressed in the overall system of modern economic relations. As a result, the current study aims to investigate the major areas of relevance to transforming companies into the digital economy, considering the impacts of new risks encountered during such transitions. Along this line, this paper develops a decision support model for evaluating risks in the digital economy transformation of the manufacturing industry. This approach is applied to compute the weights and the study ranks the most important risks for digital economy transformation in the manufacturing industry. In addition, the proposed method model is implemented to find industries’ priorities of different risks for the digital economy transformation of the manufacturing industry. Finally, a case study is carried out to assess the most important risk for the digital transformation of the manufacturing industry. The results show that lack of top management involvement (f7), with a weight of 0.0563, an unstable market environment in terms of the uncertainty industry, and market volatility, with a weight of 0.0542, are the most considerable risks for the digital economy transformation (DET) of the manufacturing industry. Additionally, comparison and sensitivity analyses are made to illustrate the advantage of the presented approach.

The application of a plethora of wireless technologies to support real-time food quality monitoring during transportation has significantly improved the performance of fresh food delivery systems. However, deployment of these technologies increases the capital and operational costs of food delivery and, hence, not all food delivery operations need to employ them. This paper looks at the trade-off of the costs involved in utilizing these technologies with the nature of food delivered, the length of transportation, and the perceived costs of food wasted using a linear programming model. The problem is formulated over a bi-echelon network with the possibility of transporting the fresh produce through dry vans, vans with temperature control but without monitoring capability, and vans with temperature control and monitoring capability. Results indicate that under situations of infinite vehicle resource availability, the optimal choice of the van type is independent of the demand levels; however, the optimal choice changes for different travel distances and the value of penalty costs (of allowing food to go waste). For example, technologies that maintain and monitor the temperature of storage conditions will be useful for food items that quickly become waste, especially when transported over longer distances and when the penalty costs are higher. [ABSTRACT FROM AUTHOR]

The purpose of this study was to investigate the efficacy of decision support intervention on treatment knowledge, decision self-efficacy, decisional conflict, and decision satisfaction in patients with hepatocellular cancer. The study was a randomized controlled trial. In all, 69 patients with hepatocellular carcinoma (HCC) were recruited and randomly assigned to a decision support group or a control group. Data were collected at baseline, post-test, and follow-up using self-report questionnaires. After controlling for baseline scores, the between-group difference (95% confidence interval [CI]) for treatment-related knowledge in post-test scores was 11.9 (6.1, 17.8). After controlling for baseline scores, the between-group difference (95% CI) for decisional conflict was −7.0 (−12.0, −2.0). There was no statistically significant between-group difference in decision self-efficacy and decision satisfaction. Findings supported the efficacy of decision support intervention to improve treatment knowledge and reduce decisional conflict but had no significant effect on decision self-efficacy and decision satisfaction in patients with HCC. [ABSTRACT FROM AUTHOR]

To date, information retrieval methods in the medical field have mainly focused on English medical reports, but little work has studied Chinese electronic medical reports, especially in the field of obstetrics and gynecology. In this paper, a dataset of 180,000 complete Chinese ultrasound reports in obstetrics and gynecology was established and made publicly available. Based on the ultrasound reports in the dataset, a new information retrieval method (IKAR) is proposed to extract key information from the ultrasound reports and automatically generate the corresponding ultrasound diagnostic results. The model can both extract what is already in the report and analyze what is not in the report by inference. After applying the IKAR method to the dataset, it is proved that the method could achieve 89.38% accuracy, 91.09% recall, and 90.23% F-score. Moreover, the method achieves an F-score of over 90% on 50% of the 10 components of the report. This study provides a quality dataset for the field of electronic medical records and offers a reference for information retrieval methods in the field of obstetrics and gynecology or in other fields. [ABSTRACT FROM AUTHOR]

Background: Despite the increasing interest and investment in big data analytics (BDA), many organisations find the implementation and use of the tools challenging. This is attributed to the cumbersome nature of some of the tools. Objectives: From both business and academic domains, this study sets out to provide a model that enables, supports, and makes the selection and use of BDA tools easier. Method: The qualitative methods from the perspective of an interpretive approach were employed in the study. The actor-network theory (ANT) was applied as a lens to underpin the phenomenon being studied and gain a deeper understanding of why things happen in the way that they confusedly do, in the selection and subsequent use of BDA tools. Results: The research revealed that five factors, organisational requirements, top-down versus bottom-up approach, the role of stakeholders, the usefulness of BDA, and organisational structure, primarily influence the selection and use of BDA tools in organisations. Conclusion: Empirically, the factors bring fresh perspectives to support the decision in appropriately managing BDA deployment for organisational purposes. Contribution: The main contribution of this study lies in the use of the decisions support model, to practically and theoretically provide a guide for managers in the organisation, in selecting BDA for decision support purposes. From an academic perspective, the study contributes to the advancement in the use of ANT for analysis in information system (IS) research. [ABSTRACT FROM AUTHOR]

Timing and precision are two primary keys for myocardial infarction diagnosis. Even minor errors in diagnosis can dramatically increase the treatment process, increase treatment costs, and put the patient in dangerous health states. This paper presents a decision support system (DSS) based on neural networks and statistical process control charts for diagnosing and controlling myocardial infarction (MI) and continuously monitoring the patient’s blood pressure. To this aim, the data was collected from 175 medical documents of patients with MI and 92 recorded successful diagnoses of MI type. A group of patients was used to validate the effectiveness of the proposed system. The results prove that the proposed hybrid model can diagnose MI with high accuracy and precision compared to machine learning algorithms. Tighter control limits with a confidence level of L=2 (confidence interval of 95.45%) in the control determination stage and wider control limits with a confidence level of L=3 (confidence interval of 99.73%) in the condition determination stage led to higher overall accuracy. This method can help physicians make better decisions on diagnosing cardiovascular diseases.

The increasing traffic and complexity of navigation at sea require advanced decision support systems to ensure greater safety. In this study, we propose a novel decision support system that employs fuzzy logic to improve situational awareness and to assist navigators in collision avoidance during multi-vessel encounters. The system is based on the integration of the rules of the Convention on International Regulations for Preventing Collisions at Sea (COLREGs) and artificial intelligence techniques. The proposed decision model consists of two main modules to calculate the initial encounter conditions for the target vessels, evaluate the collision risk and navigation situation based on COLREG rules, sort the target vessels, and determine the most dangerous vessel. Fuzzy logic is used to calculate the collision avoidance maneuver for the selected ship, considering the closest point of approach, relative bearing, and the ship’s own speed. Simulation tests demonstrate the effectiveness of the fuzzy-based decision model in scenarios with two ships. However, in complex situations with multiple ships, the performance of the model is affected by possible conflicts between evasive maneuvers. This highlights the need for a cooperative collision avoidance algorithm for all vessels in high traffic areas.

The rapid development of globalization and technology has created new needs in human resources. One of the ways to meet those needs and retain qualified workforce is by evaluating personnel performance in institutions. Public institutions manage this evaluation process by considering the criteria affecting efficiency and service quality. In this study, performance evaluation of one hundred personnel working in a municipality was carried out under four main and twenty sub-criteria determined by considering public performance requirements. In the study, Analytical Hierarchy Process (AHP) was used to weight criteria and fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to evaluate personnel performance. In addition, in this study, the current performance evaluation results of the relevant personnel and the results obtained from the model used were compared using linear regression. The findings revealed that the AHP integrated fuzzy TOPSIS model can achieve different and effective results in performance evaluation than the current evaluation. It is expected that this study will contribute to decision makers to make objective decisions in personnel evaluation and to make fair evaluations. [ABSTRACT FROM AUTHOR]

This paper addresses the four essential humanitarian operations: location of emergency facilities, prepositioning of supplies, evacuation, and relief distribution by proposing a hybrid method that combines fuzzy set theory, analytic hierarchy process (AHP), a technique for order preference by similarity to an ideal solution (TOPSIS), and multi-objective programming. The objectives of the proposition are to minimize the evacuation distance, the cost of humanitarian operations, and the suitability of facilities based on qualitative factors while considering the demand, capacity, budget, and utilization constraints. Engineering managers can benefit from the collective expertise of multiple decision-makers, using their knowledge to automatically determine emergency facility location and allocation processes by running the proposed model. The model attempts to achieve a compromise solution for multiple objectives in various humanitarian operations. Finally, the real case study of the Wenchuan earthquake in China is taken to validate the model. [ABSTRACT FROM AUTHOR]

Bridge management includes all actions in the life cycle of the bridge, to ensure its safety, stability, and functionality. Numerous problems have been identified that are primarily related to the organization of planning and the role of decision-making in the reconstruction of the historic pedestrian bridges. The planning process for the reconstruction of these bridges is crucial due to increased traffic load, poor condition, or damage to bridges. Some of these bridges are part of the cultural heritage, while some are unfairly neglected. The motivation for this research arose from the need to establish the priority for the reconstruction of historic pedestrian bridges to achieve their safety, stability, functionality, and cultural preservation. For this reason, a new decision support model based on intuitionistic fuzzy group decision-making to the multi-criteria analysis is created. The model combines multi-criteria method Evaluation Based on Distance from Average Solution and grey relational degree (GRD) with intuitionistic fuzzy theory. Three relevant decision groups of experts are formed, with the knowledge and expertise in the area of research problematic, establishing criteria for the evaluation. A new approach to the consistency of criteria weights is proposed. The intuitionistic fuzzy likelihood function is developed for the aggregation of bridge evaluations. Furthermore, GRD values are calculated to determine the reconstruction priority ranking of bridge for each decision group. The final ranking is defined by integrating Integer Linear Programming (ILP) and Ant Colony Optimization (ACO), determining spatial-functional, time, and financial constraints. [ABSTRACT FROM AUTHOR]

Insurance industry in Indonesia has shown promising result based on premium growth in 2014-2018, as recorded in Indonesia General Insurance Market Update 2019. With the increase of premium, the claim rate also grows. Insurance companies face challenges in processing the claims. Many factors need to be carefully considered before making a claim decision. This paper proposes a decision support model (DSM) to score claim cases and to propose claim risk category (CRC) and claim decision (CD). The model was built with 13 parameters, divided into non-fuzzy group and fuzzy group. The analytic hierarchy process (AHP) method was used to determine the priority weight (PW) among parameters. The Tsukamoto’s fuzzy logic (FL) method was applied to process the fuzzy parameters. A simple mathematics method (SMM) was exercised to calculate the non-fuzzy parameters, and to aggregate the result into claim risk score (CRS). Finally, CRC and CD were derived from the CRS using a rule base. The model was tested using 19611 actual claim history records. The result was: 6171 (31.47%) accepted with CRC= low, 3459 (17.64%) pending (CRC medium), and 9981 (50.89%) pending (CRC high). The DSM model was implemented in python with Google COLAB and Datapane to create various graphics. [ABSTRACT FROM AUTHOR]

Background: Driven by quality outcomes and economic incentives, predicting 30-day hospital readmissions remains important for healthcare systems. The Cleveland Clinic Health System (CCHS) implemented an internally validated readmission risk score in the electronic medical record (EMR). Objective: We evaluated the predictive accuracy of the readmission risk score across CCHS hospitals, across primary discharge diagnosis categories, between surgical/medical specialties, and by race and ethnicity. Design: Retrospective cohort study. Participants: Adult patients discharged from a CCHS hospital April 2017–September 2020. Main Measures: Data was obtained from the CCHS EMR and billing databases. All patients discharged from a CCHS hospital were included except those from Oncology and Labor/Delivery, patients with hospice orders, or patients who died during admission. Discharges were categorized as surgical if from a surgical department or surgery was performed. Primary discharge diagnoses were classified per Agency for Healthcare Research and Quality Clinical Classifications Software Level 1 categories. Discrimination performance predicting 30-day readmission is reported using the c-statistic. Results: The final cohort included 600,872 discharges from 11 Northeast Ohio and Florida CCHS hospitals. The readmission risk score for the cohort had a c-statistic of 0.6875 with consistent yearly performance. The c-statistic for hospital sites ranged from 0.6762, CI [0.6634, 0.6876], to 0.7023, CI [0.6903, 0.7132]. Medical and surgical discharges showed consistent performance with c-statistics of 0.6923, CI [0.6807, 0.7045], and 0.6802, CI [0.6681, 0.6925], respectively. Primary discharge diagnosis showed variation, with lower performance for congenital anomalies and neoplasms. COVID-19 had a c-statistic of 0.6387. Subgroup analyses showed c-statistics of > 0.65 across race and ethnicity categories. Conclusions: The CCHS readmission risk score showed good performance across diverse hospitals, across diagnosis categories, between surgical/medical specialties, and by patient race and ethnicity categories for 3 years after implementation, including during COVID-19. Evaluating clinical decision-making tools post-implementation is crucial to determine their continued relevance, identify opportunities to improve performance, and guide their appropriate use. [ABSTRACT FROM AUTHOR]

This article proposes a decision model to identify the most sustainable solution(s) to ensure the availability of raw water to be subsequently treated to be converted into drinking water as a consequence of the climate change scenario, particularly the drought currently experienced by the Metropolitan Region in Chile, derived from the technical and regulatory requirements associated with the availability of water resources from its capture to its drink ability to meet the future demand of the region. From the perspective of drought, the solution must provide security levels that guarantee the availability of raw water is one of the main concerns of the stakeholders. In turn, the need to adapt current regulations regarding raw water sources, as well as community acceptance of some proposals for converting raw water into potable water and climate dependency, involve qualitative as well as technical aspects that may affect the investment and operating costs of the different solutions required to ensure raw water availability. Therefore, through a multi-criteria approach, it is possible to incorporate quantifiable and intangible aspects and to address conflicting objectives. Through a case study, we present a decision model based on the Analytic Hierarchy Process to define and evaluate the most sustainable solution(s) to secure raw water for drinking. This study proposes to integrate technical and qualitative attributes to identify the challenging criteria and the associated linkage to the problem of selecting proposals for the most sustainable solution(s) to secure raw water, being a guide to decide the implementation of the most appropriate solution. [ABSTRACT FROM AUTHOR]

The choice of the appropriate variety of fruit is one of the most important factors in establishing new orchards. It is necessary to choose the variety that will give the best results in meeting the investment goals. This paper offered an innovative decision support model for plum variety selection, based on expert decision making and fuzzy logic. The fuzzy MARCOS (Measurement Alternatives and Ranking according to COmpromise Solution) method was used. The research was conducted with the aim of improving plum production in Bosnia and Herzegovina (BiH). To achieve this, the knowledge of experts from the Republic of Serbia was used, because this country is currently the third in the world in plum production and have branded many plum varieties. The results obtained using this model showed that two plum varieties stand out - Čačanska rodna and Stanley. These results were also confirmed by the performed sensitivity analysis. The worst results were obtained by the Šumadijka variety. These results will help in the selection of plum varieties when establishing new orchards in BiH to achieve the best results in Bosnian plum production.

Nanomaterials are the most important component of nanosensors, and the selection of the most desirable nanomaterial for nanosensors is a challenge for companies. The classical decision making procedure is very difficult and uncertain to select the desirable nanomaterial. Therefore, we develop a decision making model based on a fuzzy credibility neural network. In this article, we introduce a novel fuzzy credibility neural network using Dombi t-norms and co-norms and also using the score function of fuzzy credibility numbers. Further, the fuzzy credibility neural network applies to the decision making model for the selection of the best nanomaterial for nanosensors. In this approach or decision making model, we first collect data from three experts in the form of fuzzy credibility numbers and then use a neural network to aggregate the data with the help of Dombi t-norm and t-conorm. We consider the expert decision making criteria, which correspond to the input signals of the fuzzy credibility neural network, and calculate the weight of the input signal using distance measure techniques. Next, we compute the hidden layer information and the output layer information by using the fuzzy credibility neural network with the Dombi aggregation operator. The proposed approach is compared with other existing models of decision making and the results of the comparison show that the proposed technique is applicable and reliable for the decision support model. [ABSTRACT FROM AUTHOR]

For several decades, studies and conventions have highlighted the importance of the ecosystem services provided by natural resources and biodiversity for humanity and the need to move their management towards a sustainable model. Inthe United Nations 2030 Agenda for Sustainable Development (2015), among its 17 Sustainable Development Goals (SDGs), there isalsoSDG 15, "Life on Land", specifically dedicated to biodiversity, forests and desertification. The aim is to "sustainably manage forests, combat desertification, halt and reverse land degradation, and halt the loss of bio-diversity". The UNESCO "MAB—Man and Biosphere" Programme could achieve this goal by establishing Biosphere Reserves (BRs). Among them, the establishment of the BR "Etna river valleys" is an opportunity to adopt sustainable development models in the "Etna Park system" (UNESCO site since 2013) for the regeneration of virtuous relationships between cities and rural areas and the promotion of the conservation of natural resources and biodiversity through strategies of territorial enhancement with a multifunctional vision of agriculture and territory. This research aims to provide, through the methodology of "Rough Sets", a useful tool to support the strategic choices that stakeholders will be called upon to make for the management of the complex environmental, cultural and economic mosaic that characterises the BR "Etna river valleys".The results show both weaknesses and strengths in the transition towards a sustainable land management model, which will nonethelesshave to be supported and accompanied by information and financial support. [ABSTRACT FROM AUTHOR]

The implementation of pro-ecological projects, including those in the field of renewable energy sources, should bring the desired effects, not only environmental or social, but also economic and financial. Although the latter should not prejudge the implementation of such projects, investors, who are primarily enterprises, need information on whether financing the project from specific, available sources is effective and when the incurred financial expenses will pay off. The publication presents a model that reflects the procedure in the analysis of financing opportunities for pro-ecological projects, aimed at maximizing their financial effects, with a consideration of specific risk factors and limitations. The result of the application of this model is information that supports making rational decisions about the implementation of a specific pro-ecological project. The model was positively verified within a case study concerning the purchase and assembly of a photovoltaic installation by a medium-sized company in Poland. [ABSTRACT FROM AUTHOR]

Pythagorean fuzzy sets (PFSs) retain the advantages of intuitionistic fuzzy sets (IFSs), while PFSs portray 1.57 times more information than IFSs. In addition, Pythagorean fuzzy preference relations (PFPRs), as a generalization of intuitionistic fuzzy preference relations (IFPRs), are more flexible and applicable. The objective of this paper is to propose a novel decision support model for solving group decision-making problems in a Pythagorean fuzzy environment. First, we define the concepts of ordered consistency and multiplicative consistency for PFPRs. Then, aiming at the group decision-making problem of multiple PFPRs, a consistency improving model is constructed to improve the consistency of group preference relations. Later, a consensus reaching model is developed to reach the degree of group consensus. Furthermore, a decision support model with PFPRs is established to derive the normalized weights and output the final result. Holding these features, this paper builds a decision support model with PFPRs based on multiplicative consistency and consensus. Finally, the described method is validated by an example of financial risk management, and it is concluded that the solvency of a company is an important indicator that affects the financial early warning system. [ABSTRACT FROM AUTHOR]

Sustainability is no longer a new concept. However, applying, measuring and reporting on the sustainability initiative is still a somewhat confused and subjective issue. There is a huge variety of sustainability guidelines and green building schemes of differing natures, meanings and wordings. Despite this, there is no one guideline providing a crossreference between corporate social responsibility and green building performance to help the commercial property market completely incorporate sustainability into their activities. Therefore, this paper intends to link sustainability, real estate environmental, social, and governance (ESG) and sustainable building in order to help companies internalise this concept in all areas of their operations aligning it with their strategic planning. From desk-based research, this proposal examines and compares key aspects of the main schemes in existence, which are currently evolving in the definition, assessment and report of sustainability at the corporate and built environment level. This approach provides key information to help professionals get a better understanding of the specific changes which sustainability brings about in their corporate process, strategies, investment decisions, daily business operations as well as their property management. The insights presented here can support real estate companies to develop comprehensive communication flows and tools for the measurement and disclosure of sustainability data.

Background: Guidelines recommend pharmacologic VTE prophylaxis for acutely ill medical patients at acceptable bleeding risk, but only the International Medical Prevention Registry on Venous Thromboembolism (IMPROVE) model has been validated for bleeding risk assessment., Objectives: We developed and internally validated a risk assessment model (RAM) to predict major in-hospital bleeding using risk factors at admission and compared our model with IMPROVE., Methods: We selected patients admitted to medical services at 10 hospitals in the Cleveland Clinic Health System from 2017 to 2020. We identified major bleeding according to the International Society on Thrombosis and Haemostasis criteria, using a combination of diagnostic codes and laboratory values, and confirmed events with chart review. We fit a least absolute shrinkage selection operator logistic regression model in the training set and compared the discrimination and calibration of our model with the IMPROVE model in the validation set., Results: Among 46 314 admissions, 268 (0.58%) had a major bleed. The final RAM included 16 risk factors, of which prior bleeding (odds ratio [OR] = 4.83), peptic ulcer (OR = 3.82), history of sepsis (OR = 3.26), and steroid use (OR = 2.59) were the strongest. The Cleveland Clinic Bleeding Model had better discrimination than IMPROVE (area under the receiver operating characteristics curve = 0.85 vs 0.70; P < .001) and, at equivalent sensitivity (52%), categorized fewer patients as high risk (7.2% vs 11.8%; P < .001). Calibration was adequate (Brier score = 0.0057)., Conclusion: Using a large population of medical inpatients with verified major bleeding events, we developed and internally validated a RAM for major bleeding whose performance surpassed the IMPROVE model., Competing Interests: Declaration of competing interests Authors have no conflicts of interest to declare., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Due to the many dysfunctions that are currently affecting the healthcare sector in Morocco, it is crucial to manage the Health Emergency Services Regional Network (HESs-RN) as optimally as possible in order to ensure the safety and the quality of patients care at the right time. Our objective is to improve the performance of the network at the regional level. To do that, we based our approach on modelling and simulation, using an Integrated Geographic Information System (GIS). In this paper we propose both the structuring of HESs in the form of the HESs-RN modelled as a graph, and the modelling of the HESs-RN by considering a medical emergency event as the Occurrence of events of Health Emergency (OHE). Moreover, a Decision Support Model (DSM) is proposed to manage and control at best the different HESs-RN's OHE, and in particular to determine, in real time, the fastest path for the patient's transfer. Finally, and in order to verify the validity of the established model, a simulation model is presented to this aim. [ABSTRACT FROM AUTHOR]