Decision-making Process For Selection Research Articles

Evaluation and Comparison of the Performance of Different Hypervisors under Various Workloads of GIADEC

This research study evaluates and compares the performance of Hyper-V, Citrix Xen, and VMware ESXi to aid Ghana Integrated Aluminium Development Corporation (GIADEC) in informed hypervisor selection. The study focuses on identifying the optimal hypervisor for GIADEC, considering factors like effectiveness and efficient resource utilization. It aims to improve resource allocation, hypervisor selection, and performance optimization within GIADEC's IT infrastructure. Using a qualitative approach and extensive literature review, the study supports informed decision-making for hypervisor selection. Through benchmarking with Passmark Performance Test Suite and BootRacer application, each hypervisor underwent rigorous testing across various performance metrics. The study assesses performance aspects including installation and booting times, CPU, memory, disk, and network performance. Results highlight distinct advantages of each hypervisor: Hyper-V for quick virtualization deployment, VMware ESXi for superior CPU, memory, and network performance. Hyper-V excels in memory write and disk sequential read/write speeds, while VMware ESXi demonstrates low memory latency and superior network bandwidth. It is recommended that GIADEC should consider VMware ESXi due to its outstanding overall performance and suitability for diverse workload requirements. This study adds to the knowledge base on hypervisor performance evaluation and supports GIADEC's decision-making process for optimal hypervisor selection.

Developing a hybrid methodology for green-based supplier selection: Application in the automotive industry

The green performance values of businesses are of great importance in terms of sustainability, which includes long-term economic, social, and environmental effects. Thus, today, enterprises and managers are increasingly interested in this issue, and related topics, including supplier selection, have been inserted into decision-making procedures. However, how to predict the effects of green performance criteria, which represent environmental sustainability, on social and economic sustainability remains unclear. In this regard, the main purpose of this research is to develop a supplier selection methodology considering green performance criteria by applying multiple regression analysis and the Evidential Fuzzy Multi-Criteria Decision Making (F-MCDM) method based on Dempster-Shafer Theory (DST), which are both powerful methods in statistical analysis and decision-making under uncertainty. In the first phase of the research, variables that significantly affect green performance have been determined by testing the eight generated hypotheses with multiple regression analysis. Then, the best supplier was determined using those green supplier selection criteria in the Evidential F-MCDM method. Since using environmentally hazardous paints in the production process continues, the automobile paint production sector has been chosen as the application area of this green-based supplier selection methodology. In this respect, green dynamic capacity, green purchasing, eco-design, investment recovery, and green product innovation variables have been inserted into the Evidential F-MCDM method as the determinant variables of green performance. This research reveals that integrating multiple regression and Evidential F-MCDM methods can be a hybrid methodology in supplier selection. Thus, a different perspective is introduced into the green supplier selection decision-making process by considering the effects of criteria in the MCDM model on green performance. This innovation enhances the criteria determination and selection processes in classical MCDM approaches. In addition, green dynamic capacity is the most critical criterion in supplier selection based on their green performance, especially in the scope of this research.

Multi-Time-Scale Markov Decision Process for Joint Service Placement, Network Selection, and Computation Offloading in Aerial IoV Scenarios

Multi-Time-Scale Markov Decision Process for Joint Service Placement, Network Selection, and Computation Offloading in Aerial IoV Scenarios

Social Dynamics in Academic Pathways: A Comprehensive Study of Social Influences on University Program Selection in Kenya

In the dynamic context of Kenyan higher education plus the ever-changing career landscape, the career selection decision-making process regarding university program or course is not only critical but also complex. Failure to make an informed career choice hampers the student’s proper formation of self-esteem into adult life. Hence the need for this study that addresses the gap in understanding the social dynamics influencing Kenyan students’ educational choices, set against an evolving educational landscape. The study is anchored on Ginzberg’s Developmental Theory, Bandura’s Social Learning Theory, and Super’s Self-Concept Theory. The research provides a multifaceted perspective on how students navigate their academic pathways. Surveying 200 undergraduate students at Kibabii University, Kenya, the study employed a descriptive correlational design, integrating both descriptive and inferential statistical analyses to explore the influence of peers, family, teachers, and media. The results indicated that a higher percentage of female students (22%) than male students (12%) make this decision at primary school age. The decision-making is fairly distributed across different stages of secondary education, with the most decisive phase being after secondary school, where 34% of males and 26% of females make their choice. Interestingly, an equal percentage (15%) of both genders report that their decision emerged slowly over time. Further findings indicate a significant impact of peers on program selection, signifying a peer-oriented approach in students’ decision-making. In contrast, parental influence is identified as a major determinant in the timing of these decisions, highlighting the critical role of family in the Kenyan educational setting. These results not only contribute to a nuanced understanding of academic decision-making in Kenya but also underscore the importance of considering these social factors in educational counseling and policy formulation. The study bridges a crucial research gap and provides insights pivotal for educators, counselors, and policymakers, emphasizing the need for comprehensive guidance strategies that accommodate these social influences.

Collagenase Treatment Versus Needle Fasciotomy for Single-Digit Dupuytren Contractures: A Meta-Analysis of Randomized Controlled Trials

The objective of this systematic review and meta-analysis was to synthesize the available randomized controlled trial data comparing needle fasciotomy and collagenase treatment for single-digit Dupuytren contractures with a minimum of 3-year follow-up and determine whether one treatment is superior regarding contracture correction and functional outcomes. A systematic review and meta-analysis was conducted by searching four databases for randomized controlled trials investigating the single-digit treatment outcomes for Dupuytren contracture comparing collagenase treatment and needle fasciotomy with a minimum of 3-year follow-up. The risk of bias of included studies was assessed using the Cochrane risk-of-bias tool. A meta-analysis was performed using a random effects model in anticipation of unobserved heterogeneity. The primary outcome measure was contracture recurrence. Secondary outcome measures included final fixed flexion contracture (FFC), Quick Disabilities of Arm, Shoulder and Hand (QuickDASH) scores, and Unité Rhumatologique des Affections de la Main (URAM) scores. After screening 264 articles, 4 randomized clinical trials were eligible for final inclusion. One trial had a high risk of bias, and two trials had some concern for bias. The final meta-analysis included 347 patients, 169 who underwent collagenase treatment and 178 who underwent needle fasciotomy. No significant differences were noted between the groups in contracture recurrence, FFC, and URAM scores. The pooled data showed a higher QuickDASH score in the collagenase treatment group compared with the needle fasciotomy group, but the observed difference was less than what would be expected to be clinically relevant. Needle fasciotomy and collagenase treatment have similar outcomes with regards to contracture recurrence, final FFC, QuickDASH scores, and URAM scores for the single-digit treatment for Dupuytren contracture at a minimum of 3-year follow-up. Relevant factors that may be considered during the shared decision-making process for treatment selection include surgeon and patient preferences, costs of treatment, and the disparate complication profiles of these two treatments. Therapeutic II.

Decision factors influencing new variety adoption in western Canada by the seed industry

In the agricultural sector, innovation is a vital economic driver for increasing food production. New crop varieties are developed and commercialized, greatly contributing to improved global food security through higher yields, improved nutrition and climate resiliency. Canada is a competitive and innovative actor in the global seed market. This article quantifies the degree of improvement for numerous crop traits required for commercialization success. We use empirical data from seed producers in the prairies to identify their adoption criteria to multiply new seed varieties. Results show that yield potential, disease resistance and lodging resistance are the key traits for pedigreed seed growers regardless of crop type, while other agronomic traits depend on the crop type. Quality factors such as malting or milling properties for cereals, protein content for pulses and oil content for oilseeds are also part of the variety selection decision process for prairie pedigreed seed growers.

Abstract TMP3: Prediction Model For Medical Rescue Treatment Strategies In Patients With Incomplete Reperfusion

Background: After successful reperfusion is achieved (extended Thrombolysis in Cerebral Infarction (eTICI) ≥2b50), decision on pursuing additional treatment strategies in order to achieve complete reperfusion (eTICI=2c/3), is multifactorial and depends on patient’s clinical and imaging characteristics. We have developed and validated a clinical decision tool to provide individualized predictions on achieving delayed reperfusion based on individual patient data. Methods: Single-center registry analysis for all consecutive patients admitted between 02/2015 - 12/2020. Primary variable of interest was perfusion imaging outcome in patients with incomplete reperfusion (eTICI 2a-2c), evaluated on the 24-hour follow-up imaging. This variable was dichotomized into delayed reperfusion, in case of non-observable perfusion deficit, and persistent perfusion deficit, in case of perfusion deficit captured on the final angiography imaging. Final model variable selection was performed via bootstrapped (n=200) stepwise backwards regression. Model was split into a training and testing set (80:20 ratio), with 10-fold cross validation resampling. Results: 372 patients (50.8% female, mean age 74) were included, with 228 (61.2%) of them having delayed reperfusion. Final model identified seven variables of importance including: age, sex, atrial fibrillation, Intervention-to-Follow-Up time, maneuver count, eTICI and collateral status. Model’s discriminative ability for predicting delayed reperfusion was adequate (AUC 0.83, 95% CI 0.74 -0.92), with an overall adjusted calibration (Brier score 0.17, 95% CI 0.15-0.18). Conclusions: Current model presents a tool that may aid clinical decision-making process in selection of patients for pursuing additional treatment strategies after incomplete reperfusion has been achieved. This is an important next step towards personalized treatment of stroke patients undergoing mechanical thrombectomy.

Developing a Decision-Making Process of Location Selection for Truck Public Parking Lots in Korea

As the number of truck registrations and the traffic volume have increased continuously in urban areas, the problem of truck illegal parking on the roadside has become more serious in Korea. Although the government presented the necessity and the construction plan of the truck public parking lots through the ‘4th Comprehensive Plan for Expansion of Truck Resting Facilities’, there is no detailed guideline for the local government of where to construct them. The previous studies on the location selection of the parking lots have mainly focused on passenger cars and buses, but not trucks, and the process for candidate location selection was not mentioned. To fill this gap, this study presents a decision-making process for the location selection of truck public parking lots in urban areas based on mixed-integer programming; it includes a candidate location selection by spatial analysis and an optimal location determination by the application of the competitive p-median algorithm. A case study of Incheon was conducted to validate the presented decision-making process. This study introduced a systematic decision-making process that includes standards establishment, data processing, and methodology application; it is universalistic enough to be utilized as a guideline for the government to efficiently construct truck public parking lots.

VV-ECMO in severe COVID-19: multidimensional perspectives on the use of a complex treatment

VV-ECMO in severe COVID-19: multidimensional perspectives on the use of a complex treatment

Optimal Timing of Anterior Cruciate Ligament Reconstruction in Patients With Anterior Cruciate Ligament Tear

The timing of surgery has been regarded as a key factor in anterior cruciate ligament reconstruction (ACLR), and early vs delayed ACLR remains a controversial topic. To synthesize up-to-date published data from randomized clinical trials (RCTs) comparing early vs elective delayed ACLR for patients with ACL deficiency, in terms of clinical outcomes and complications. The PubMed, Cochrane Library, and Web of Science databases were systematically searched until September 9, 2022. All published RCTs comparing clinical and functional outcomes and complications associated with early ACLR vs elective delayed ACLR. Two reviewers independently extracted relevant data and assessed the methodological quality following the PRISMA guidelines. Due to the clinical heterogeneity, the random-effects model was preferred. The primary outcomes were functional outcomes and complications. The Mantel-Haenszel test was used to evaluate dichotomous variables and the inverse variance method was used to assess continuous variables. This meta-analysis included 972 participants in 11 RCTs stratified by follow-up duration. The following factors did not differ between early and delayed ACLR: operative time (mean difference, 4.97; 95% CI, -0.68 to 10.61; P = .08), retear (OR, 1.52; 95% CI, 0.52-4.43; P = .44), and infection (OR, 3.80; 95% CI, 0.77-18.79; P = .10). There were also no differences between groups in range of motion, knee laxity, International Knee Documentation Committee (IKDC rating scale), and Tegner score. IKDC score (mean difference, 2.77; 95% CI, 1.89-3.66; P < .001), and Lysholm score at 2-year follow-up (mean difference, 2.61; 95% CI, 0.74-4.48; P = .006) significantly differed between early and delayed ACLR. In addition, the timing of surgery was redefined in the included RCTs and subgroup analyses were performed, which validated the robustness of the principal results. This systematic review and meta-analysis found that early ACLR was not superior to delayed ACLR in terms of most factors analyzed, except for IKDC and Lysholm scores. This information should be available to patients with ACL deficiency and clinicians as part of the shared decision-making process of treatment selection.

Decision Support System for The Program Indonesia Pintar Scholarship Using Simple Additive Weighting Method

The Program Indonesia Pintar (PIP) is an educational scholarship from the government for students who lack funds to continue their education, one of which is at the junior high school level. It is still done manually when selecting students who are eligible to receive the PIP scholarship at the junior high school level. This is less efficient because the selection process will take a long to analyze, and the reporting process is not yet computerized. To overcome these problems, a decision support system is needed to assist schools in selecting students who receive PIP assistance. The method used for the development of a decision support system is Simple Additive Weighting (SAW). The choice of this method is because the decision-making process is carried out by searching for the highest alternative from all alternatives so that the assessment is more accurate based on the provisions of the criteria values and preference weights. With the construction of this system, it is expected to be able to provide a solution so that the student selection decision-making process can be carried out quickly and accurately.

Embryo Selection by “Black-Box” Artificial Intelligence: The Ethical and Epistemic Considerations

Background: The combination of time-lapse imaging and artificial intelligence (AI) offers novel potential for embryo assessment by allowing a vast quantity of image data to be analysed via machine learning. Most algorithms developed to date have used neural networks which are uninterpretable (“black-box”) and cannot be understood by doctors, embryologists and patients, which raises ethical and epistemic concerns for embryo selection in a clinical setting. Aim: This study aims to discuss ethical and epistemic considerations surrounding clinical implementation of “black-box” based embryo selection algorithms. Method: A scoping review was performed by evaluating publications reporting “black-box” embryo selection algorithms. Potential ethical and epistemic issues were identified and discussed. Results: No randomised controlled trial was identified in the literature evaluating clinical effectiveness of “black-box” embryo selection algorithms. Several ethical and epistemic concerns were identified. Potential ethical issues included (1) lack of randomised controlled trials, (2) impact on the shared decision-making process in embryo selection between clinicians and patients, (3) misrepresentation of patient values due to hidden reasoning process in “black-box” algorithms, (4) social impacts if algorithm subsequently proven to be biased, and (5) unclear responsibility when algorithm makes obviously poor choices of embryos. Potential epistemic issues included (1) information asymmetries between algorithm developers and doctors, embryologists and patients; (2) risk of biased prediction due to data selection during training process; (3) inability to troubleshoot for data training purposes due to limited interpretability; and (4) the economics of buying into commercial proprietary add-ons. Conclusion: There are significant epistemic and ethical concerns with “black-box” embryo selection. No published randomised controlled trial is available to support its clinical implementation. AI embryo selection in general, however, is potentially useful but must be done carefully and transparently. Interpretable AI would be preferred alternative in causing fewer issues.

Employing Tallying and Take-the-Best Heuristics in the Personnel Selection Decision-Making Process

Managers could benefit from using heuristics in personnel selection, as simple rules reduce time required to decide, without necessarily sacrificing predictive power. The aim of this study is to examine how managers spontaneously employ tallying and take-the-best heuristics in personnel selection and in which ways do they interpret the effects of these heuristics in their decision-making process. We conducted a laboratory experiment with 11 middle managers in a large Estonian financial sector company and 26 economics students. The results show that most managers are not cognizant about the decision-making process used, citing intuition, but the selection decision-making process is better characterized by heuristics. We also find that heuristics can help reduce decision variance. Therefore, managers’ awareness of their decision-making should be instructed to be more aware of their decision-making process and that graphical decision-aids favoring ecologically rational heuristics can be helpful in the personnel selection decision-making process.

Sustainable Supplier Selection in Agri-Food Supply Chain Management

In recent times, sustainability of supply chains becomes the most interested subject by customers, firms, government bodies, academia, and suppliers as the environmental resources reveal as the most valuable and limited asset for human beings. One of the most important elements determining the performance of sustainable agri-food supply chain business is supplier’s commitment. A mainly inspiring feature in this domain is the wide range of factors influencing supplier selection decision-making process in Triple Bottom Line context must be analyzed. However, it is not easy to completely integrate and measure majority of these factors in a simple optimization model. Therefore, this paper starts with a comprehensive review of literature about supplier selection for sustainable supply chains (SSCs) development. Afterwards, a new two-steps hybrid solution method is suggested. In initial step, a set of criteria for supplier selection process is analyzed built on Fuzzy Analytic Network Process (FANP). The result of the first step is fed in to the second step of the analysis to design a model with multiple objectives for selection process of supplier using fuzzy VIKOR. All three dimensions of sustainability; specifically, factors such as weather, water and soil footprint, energy and material consumption, employment, social services and finally financial performance of supply chain are considered concurrently. The suggested approach generates a comprehensive model to help supply chain managers in supplier selection process. Numerical analysis is completed via exploiting data from a Turkish agri-food company case for validating effectiveness, also and success of the proposed solution. Final analytical results of the analysis provide valuable business policy oriented, operational and practical insights on the influence of economic, environmental, and social sustainability for enhanced supplier relationship management module in sustainable supply chain structure. Moreover, by tracking environmental impact indicators such as, emission and pollution rates, footprint rates, against social and cost indicators it would be possible to perform tradeoff analysis for giving more sustainable decisions on new supply chain investments.

Komparasi Penerapan Metode Simple Additive Weighting Dengan Weighted Product Dalam Seleksi Trainer

PT Chandra Asri Petrochemical Tbk (CAP) is the largest Polypropylene producer in Indonesia and was founded in 1984. For the sustainability of the company's operations, PT CAP constantly updates, both in the field of information technology and the capabilities of human resources within the company. Improving the ability of human resources in the field of information technology and aspects of information technology training (IT Training) is important to ensure that skilled human resources are ready to answer challenges in the use of information technology. In ensuring that trainers are selected to train human resources to be reliable and competitive, appropriate and accurate decisions are needed. The Simple Additive Weighting (SAW) and Weighted Product (WP) methods are methods that can be used in the decision-making process. The comparison of the two methods can help maximize the trainer selection decision-making process. The results of the implementation of the two methods are the same ranking order, the first rank in both methods is achieved by the third alternative. The final result obtained from the ranking process using the SAW (Simple Additive Weightingmethod)obtained the largest value as the best alternative with a value of 0.85. The final result obtained from the ranking process using the WP (methodWeighted Product)obtained the largest value as the best alternative with a value of 0.2308.

Selecting the field hospital location for earthquakes: an application for Ankara Province in Turkey

PurposeThe authors present a location selection model for the field hospital to build after a possible earthquake in Ankara, Turkey using the VIKOR method.Design/methodology/approachCompanies or governments that make location selection decisions to improve their performance in new investment decisions for different service industries. On the other hand, disasters, especially earthquakes, force the governments to evaluate their existing potentialities and develop action plans to improve their middle and long-term preparations. This paper proposes a VIKOR method-based location selection model for the field hospital to build after a possible earthquake. Also, the authors present a methodology using the VIKOR method that how government agencies take action for the field hospital's location selection process via VIKOR methodology.FindingsThe modeling and application results show that the field hospital's location selection decision-making process improves considerably using the VIKOR model. This paper shows that the proposed VIKOR-based model can rank alternatives suitability at various criteria targeting to minimize the possible earthquake's impact and obtains a single overall ranking score to select the best alternative.Research limitations/implicationsThe study does not consider the uncertain nature of the field hospital selection problem. The application part is restricted to the Ankara case. But the proposed model can easily extend for different locations in the world.Originality/valueThis paper presents the multi-criteria decision-making (MCDM) framework study of the establishment of field hospitals and demonstrates its importance when criteria diversity is restricted.

Fusion Mode and Style Based on Artificial Intelligence and Clothing Design

With the upgrading of intelligent manufacturing, industrial robots will play an important role in the garment industry. The purpose of this article was to study the pattern and style based on the integration of artificial intelligence and clothing design. In this article, the digital modeling of clothing design and the case analysis of intelligent clothing design are described using the method of comparative experiment. The experimental results are obtained from the analysis of fuzzy number of clothing design language evaluation, three-dimensional human body construction clothing size, clothing design elements and auxiliary functions, and the analysis of the advantages and disadvantages of clothing design system. The popular clothing sample is D4 (0.4862), which is 20% higher than other products. It can be concluded that the model proposed in this article can grasp the needs of consumers and select the right one according to the market positioning. The fabric mass production fashion brand can significantly improve the efficiency and satisfaction of the fabric selection decision-making process. It provides enough technical support and style model for intelligent clothing design.

Multi-criteria spatial analysis for location selection of multi-purpose utility tunnels

Multi-purpose utility tunnels (MUTs) integrate all underground utilities in one accessible tunnel. MUTs reduce the need for excavations and their associated costs, as well as the resulting traffic congestion. Several MUTs have been implemented in different parts of the world. Their locations have either been politically influenced or selected to preserve heritage sites or to meet the conditions of a newly developed city. Nevertheless, selecting the location in an existing city under street segments is affected by several criteria that have different spatial characteristics. Combining these characteristics and managing the trade-offs that exist between them determine the ranking of alternative MUT locations. The use of subjective and objective weights in the decision-making process will offer different perspectives from the decision-maker's perspective and the data itself, respectively. This paper aims to analyze spatial data as an input in the multi-criteria decision-making (MCDM) process of the MUT location selection. The objectives are: (1) defining the criteria that influence the MUT location selection, (2) defining the required GIS datasets for quantifying the criteria as scores for each candidate street segment, (3) analyzing the impacts of the dependencies between the criteria by comparing the ranking results of two MCDM methods (i.e., Analytic Hierarchy Process (AHP) and Analytic Network Process (ANP)) combined with the Technique for Order Preference by the Similarity to Ideal Solution (TOPSIS), (4) analyzing the difference between using subjective weights or objective weights, and (5) developing a prototype system to integrate the MCDM methods in a GIS platform. A vector-based spatial analysis is conducted to identify the suitable locations for MUT construction based on 12 criteria representing physical condition information or affecting social costs. Two subjective MCDM methods (i.e., AHP and ANP) are used to generate each criterion's weights, and the ranking of alternatives is determined using TOPSIS. Another set of weights representing the objective weights are calculated for each criterion using the Shannon Entropy method. These weights are combined with TOPSIS to obtain an objective ranking of the alternatives. Based on the results from the different combinations (AHP + TOPSIS, ANP + TOPSIS, and ENTROPY + TOPSIS), the top alternative is always the same.

Location-Aware Service Recommendations With Privacy-Preservation in the Internet of Things

With the ever-increasing maturity and popularization of the Internet of Things (IoT), tremendous business applications developed by distinct enterprises or organizations have been encapsulated into lightweight web services that can easily be accessed or invoked remotely. However, the big volume of candidate web services places a heavy burden on the users' service selection decision-making process. Under the circumstance, a variety of intelligent recommendation solutions have been developed to reduce the high decision-making cost. Traditional resolutions usually challenge in two aspects. First, the recommendation parameters, i.e., the quality of services (QoS), usually relies on user/service location heavily; therefore, low-quality recommended results may be returned to users if user/service location information is overlooked. Second, historical QoS data often contain partial sensitive information of users; therefore, it becomes a necessity to protect the sensitive QoS data while making accurate recommendation decisions. To tackle the above challenges, we introduce the concepts of user/service location information and locality-sensitive hashing (LSH) in the domain and propose a location-aware recommendation approach with privacy-preservation capability. A wide range of experiments is set up based on the popular WS-DREAM data set, whose results prove the effectiveness and efficiency of our approach.

A spatial genetic framework for koala translocations: where to?

Wildlife translocations are gaining acceptance as a valuable conservation tool for threatened Australian fauna. The 2019–2020 bushfire crisis has significantly affected koala habitat across four states, and translocations, when properly implemented, could facilitate the demographic and genetic recovery of affected populations. Current translocation policies lack an appropriate spatial framework to guide conservation actions and this could lead to unexpected or undesirable outcomes with the potential to hinder population recovery. To address these concerns, we propose development of a spatial framework based on knowledge of population genetic structure and population-specific dispersal patterns estimated from molecular data. At an operational level, application of a spatial genetic framework obviates the need to specify restrictive translocation limits, reduces reliance on subjective interpretations of population structure, and provides the potential to improve translocation success and conservation outcomes. We strongly encourage implementation of a spatial genetic framework and its integration into the decision-making process for selection and prioritisation of release sites for translocated koalas by wildlife carers, researchers and wildlife managers. The proposed framework would also support koala conservation and management more broadly.