Selection Model Research Articles

Fuzzy task assignment in heterogeneous distributed multi-robot system

This study addresses the problem of coordination in cooperative multi-robot systems performing complex tasks. An analysis of cooperative behavior in mobile multi-robot systems in terms of task execution accuracy by heterogeneous robots is carried out. In addition, we evaluate the capacity and compatibility of tasks assigned to robots to optimize task execution without using direct communication with the robots or a central decision-making unit. A model for task selection in heterogeneous distributed multi-robot systems is proposed. It is based on two processes: the first decomposes complex tasks into elementary tasks, and the second assigns elementary tasks to mobile robots for real-time execution. The distribution of elementary tasks is NP-hard, which leads us to recommend approximate solutions. A fuzzy system called Fuzzy Decision Making in Task Selection is proposed, which uses fuzzy logic to solve this problem. This system allows robots to choose to perform any task in the future. An approach is presented that uses two cascading fuzzy systems. The first calculates the utility of the robot and then activates the second fuzzy system to calculate the utility of the task. By using the output of the fuzzy decision system in our model, each robot will be able to decide for itself which tasks to perform. The results of a simulation of mobile robots transporting goods demonstrate the effectiveness of this fuzzy decision-maker.

Using Gerontology Theory to Guide the Development of Artificial Intelligence to Support Aging-in-Place

If artificial intelligence (AI) is to support aging-in-place, determining how, when, and why to apply AI is a crucial endeavor. A seminal gerontology meta-theory called Se-lection, Optimization, and Compensation (SOC) model has promise to conceptualize how AI can play a role in aging-in-place. The model posits that successful aging re-quires selecting goals/domains to apply resources, opti-mizing means to best achieve those goals, and compen-sating for losses by attaining new resources or tapping into unused resources for alternative means of pursuing those goals. In this short paper, we describe the SOC model, and draw links to domains in which AI can sup-port aging in place. For example, AI can assist with health-related decision making (selection), cognitive training and reminders (optimization), and domestic task assistance (compensation). Human-centered considera-tions are provided for implementation of AI in the home.

Mixed method evaluation of factors influencing the adoption of organic participatory guarantee system certification among Vietnamese vegetable farmers

AbstractIn markets where vegetables are commonly cultivated with heavy use of synthetic pesticides, it is particularly important for consumers to be able to identify genuine organic produce. Organic Participatory Guarantee Systems (PGS) certification offers smallholder farmers an affordable way to build trust among consumers and secure premium prices for their organic produce. In Vietnam, the demand for vegetables with no, or low, pesticide residues is growing. The attractiveness of PGS certification should increase accordingly, but the number of organic PGS certified farmers in Vietnam are stagnating or even decreasing. The aim of this study is to explain this paradox by investigating the factors that influence the adoption of organic PGS certification among Vietnamese vegetable farmers. We follow a mixed methods approach, combining a qualitative analysis of farmer interviews (n = 62) and a quantitative analysis of a farm survey data (n = 434) using a sample selection model that estimates adoption contingent on farmers’ awareness of PGS certification. Drawing on the Unified Theory of Acceptance and Use of Technology, we found that social relationships and positive attitudes towards farming and the environment drive the adoption of organic PGS. Barriers include insufficient knowledge of organic farming techniques, labour shortages, and ageing farmers. At the systemic level, support by (non-)governmental institutions facilitates adoption, but challenges such as lower yields and limited access to premium markets remain. To promote organic PGS among Vietnamese vegetable farmers, efforts should focus on disseminating organic farming techniques that boost yields, alleviate labour demands, and ensure secure markets for premium-priced organic vegetables.

Usefulness of decision tree analysis of MRI features for diagnosis of placenta accreta spectrum in cases with placenta previa.

Placenta previa complicated by placenta accrete spectrum (PAS) is a life-threatening obstetrical condition; therefore, preoperative diagnosis of PAS is important to determine adequate management. Although several MRI features that suggest PAS has been reported, the diagnostic importance, as well as optimal use of each feature has not been fully evaluated. The occurrence of 11 PAS-related MRI features was investigated in MR images of 145 patients with placenta previa. The correlation between each MRI feature and pathological diagnosis of PAS was evaluated using univariate analysis. A decision tree model was constructed according to a random forest machine learning model of variable selection. Eight MRI features showed a significant correlation with PAS in univariate analysis. Among these features, placental/uterine bulge and myometrial thinning showed high odds ratios: 138.2 (95% CI: 12.7-1425.6) and 66.0 (95% CI: 18.01-237.1), respectively. A decision tree was constructed based on five selected MRI features: myometrial thinning, placental bulge, serosal hypervascularity, placental ischemic infarction/recess, and intraplacental T2 dark bands. The decision tree predicted the presence of PAS in the randomly assigned validation cohort with significance (p < 0.001). The sensitivity and the specificity of the decision tree for detecting PAS were 90.0% (95%CI: 53.2-98.9) and 95.5% (95%CI: 89.9-96.8), respectively. Among PAS-related MRI features, placental/uterine bulge and myometrial thinning showed high diagnostic values. In addition, the present decision tree model was shown to be effective in predicting the presence of PAS in cases with placenta previa.

UOWC spatial diversity techniques over hostile maritime environments: an approach under imperfect CSI and per-source power constraints

Optical communication in submarine environments has emerged as a novel technology that enables high bandwidth and high data rate links. However, the unique characteristics of the underwater channel impose new challenges, such as mitigating the remarkable absorption and scattering of hostile maritime environments. For the first time, we consider a per-source optical power constraint based on eye-safety regulations, which has never before been taken into account in Multiple-Input/Single-Output (MISO) systems within underwater optical wireless communication (UOWC) scenarios. Hence, we introduce an innovative spatial repetition coding (SRC) system model, which enables the analysis of an SRC scheme operating under either a per-source or a per-transmitter power constraint. In addition, a tractable generalized transmit laser selection (GTLS) model is presented in order to consider the impact of erroneous selections of the best laser source due to imperfect channel state information (CSI) at the transmitter, prevalent in underwater scenarios with dynamic fluctuations from water currents. Novel bit error rate closed-form expressions and asymptotic results are derived. The presented results demonstrate that an SRC system, when appropriately designed under a per-source power constraint, outperforms the TLS system by effectively mitigating the adverse effects of underwater links. Conversely, in situations where compact transmitters necessitate constraints that significantly modify eye-safety, TLS schemes are superior.

Transfer Learning with Hybrid Firefly Butterfly Optimization Feature Selection Model for Early Parkinson Disease Prediction

Transfer Learning with Hybrid Firefly Butterfly Optimization Feature Selection Model for Early Parkinson Disease Prediction

Optimization of the rural domestic waste recycling network based on the path planning site selection model

IntroductionIn the context of rural revitalization strategies, effectively managing rural waste is a critical task for achieving ecological environmental sustainability. The current state of garbage collection and transportation is examined in this paper, focusing on the rural waste management practices in Dongfeng County, Liaoyuan City, Jilin Province, through mathematical modeling.MethodsBy analyzing the rural domestic waste collection network, the volume of waste generated at various points is fuzzified using the fuzzy mathematical theory.ResultsThe results of path optimization for waste collection and transportation were derived from a case study, demonstrating the feasibility and practicality of the rural domestic waste recycling network model.DicussionAdditionally, sensitivity analysis was conducted to assess the impact of changes on the total cost. Potential solutions for rural waste recycling and treatment are offered in this paper, providing valuable insights for the development of a rural domestic waste recycling network in Jilin Province.

Robustifying Conditional Portfolio Decisions via Optimal Transport

We propose a data-driven portfolio selection model that integrates side information, conditional estimation, and robustness using the framework of distributionally robust optimization. Conditioning on the observed side information, the portfolio manager solves an allocation problem that minimizes the worst-case conditional risk-return tradeoff, subject to all possible perturbations of the covariate-return probability distribution in an optimal transport ambiguity set. Despite the nonlinearity of the objective function in the probability measure, we show that the distributionally robust portfolio allocation with a side information problem can be reformulated as a finite-dimensional optimization problem. If portfolio decisions are made based on either the mean-variance or the mean-conditional value-at-risk criterion, the reformulation can be further simplified to second-order or semidefinite cone programs. Empirical studies in the U.S. equity market demonstrate the advantage of our integrative framework against other benchmarks. Funding: The material in this paper is based on work supported by the Air Force Office of Scientific Research [Award FA9550-20-1-0397]. Additional support is gratefully acknowledged from the National Science Foundation [Grants 1915967, 1820942, and 1838676], the Natural Sciences and Engineering Research Council of Canada [Grant RGPIN-2016-05208], and the China Merchant Bank. V. A. Nguyen gratefully acknowledges the generous support from the Chinese University of Hong Kong [Improvement on Competitiveness in Hiring New Faculties Funding Scheme] and the Chinese University of Hong Kong [Direct Grant 4055191]. S. Wang is partially supported by the National Natural Science Foundation of China [Grant 72371022]. Finally, this research was enabled in part by support provided by Compute Canada. Supplemental Material: The computer code and data that support the findings of this study and the online appendix are available within this article’s supplemental material at https://doi.org/10.1287/opre.2021.0243 .

Significant Effect of Carbon-Ion Radiation Therapy Combined With Androgen Deprivation on Biochemical Recurrence Rates in High-Risk Prostate Cancer Patients: A Two-Center Controlled Trial Compare With X-Ray External Beam Radiation Therapy.

To compare the effects of carbon-ion radiation therapy (CIRT) and external beam radiotherapy (EBRT) on the prognosis of patients with prostate cancer. The effects of initial prostate-specific antigen (iPSA), clinical Tumor (cT) stage, radiotherapy method, and other clinical factors on the prognosis of 577 patients with radiotherapy were analyzed. Cox regression analysis showed that CIRT (RR: 0.49, p = 0.0215), cT stage ≥ 3 (RR: 2.72, p = 0.0003), and iPSA ≥ 16 ng/mL (RR: 1.74, p = 0.0347) were independent predictors of biochemical recurrence (BCR). After propensity score matching (PSM), CIRT (RR: 0.42, p = 0.0147), cT stage ≥ 3 (RR: 2.55, p = 0.0092), and iPSA ≥ 16 ng/mL (RR: 2.12, p = 0.0366) were still the predictors of univariate analysis. In multivariate analysis, CIRT (RR: 0.42, p = 0.015) and cT stage≥ 3 (RR:2.21, p = 0.0332) were independent predictors of BCR. Among them, we used iPSA and cT stages to establish a new radiotherapy selection model based on BCR risk. Patients who met more than one factor (score ≥ 1) and underwent CIRT had significantly better BCR progression-free survival (PFS) than those who received EBRT (p ≤ 0.01). This was also confirmed by Kaplan-Meier analysis after PSM. CIRT patients exhibited lower 5-year BCR rates compared to the EBRT group. Patients with a risk score of our model ≥ 1 undergoing CIRT were more likely to experience BCR benefits compared to those receiving EBRT.

Resource selection by a megaomnivore in a marine foraging habitat

AbstractHabitat‐based approaches to animal conservation are bolstered by an understanding of resource selection, that is, use of resources (i.e., habitat features) relative to their availability in the environment. Quantifying resource selection is especially valuable when data characterizing animal space use are limited, as is often the case with mobile and/or cryptic species. Documenting associations with habitat features can better inform management in space in time, while also revealing key insight into movement ecology and behavior. Here, we evaluate resource selection by a megaomnivore whose highly mobile nature within marine habitats has resulted in an incomplete understanding of drivers of space use. We used satellite telemetry to track 29 green turtles (Chelonia mydas) from an eastern Pacific foraging aggregation in San Diego Bay, California, USA during 2013–2023. Tracking produced 5023 Fastloc‐GPS points which we used to model selection for local environmental resources relative to their availability. We employed logistic models to evaluate associations with seagrass, bathymetry, and water temperatures, implementing a framework that additionally allowed us to explore the roles of season, diel period, and turtle body size. Our methods demonstrate an approach for down‐weighting observations according to assumed telemetry error and autocorrelation. Results from fine‐scale resource selection models provide evidence that green turtles in San Diego Bay select for eelgrass meadows (Zostera marina), particularly during the warmest months of the year, but the strength of this selection changes from day to night. We additionally found day–night shifts in depth and temperature selection that changed with turtle body size and season. We discuss these findings in the context of diel patterns in resting and foraging behavior in addition to seasonal changes in thermally sensitive metabolic rates. Our study documents resource associations and provides quantitative information for the management of sea turtle foraging populations and their habitats. We offer key insight into habitat use by green turtles in the eastern Pacific at a pivotal time when multiple indicators point to population growth and expansion within the region.

Digital models for the selection of party organs and election candidates in Spanish political parties: the impact of intraparty democracy

Processes for selecting party officials and election candidates are key aspects of the internal democracy of political parties. This is not only because they reveal who is appointed, but also because they provide insight into intra-party dynamics and the levels of internal democracy within the parties. The aim of this article is to offer a general overview of the internal selection models used by Spanish political parties with representation in the Congress of Deputies, in order to determine whether online procedures are as widespread as is often believed, and, whether they have any real impact on intra-party democracy. We look into whether political parties are making widespread use of digital tools to promote new selection processes, whether the establishment of online selection mechanisms impacts the internal democracy of political parties and what types of parties are primarily using these tools. For this purpose we have conducted a taxonomy of the selection processes in Spanish political parties represented in the Congress of Deputies, based on the development of a model of indicators drawing on some previous studies.

The study of active support for energy storage system in system frequency regulation

Abstract As the penetration of new energy sources increases, the proportion of traditional energy units continues to decline, reducing the inertia and primary frequency regulation capability of the power system. Therefore, exploring the frequency support capabilities of energy storage systems with low inertia is crucial. Based on this, this paper first proposes an active frequency support method for energy storage system based on a Voltage Source Virtual Synchronous Generator (VSG), aiming to enhance system inertia and reduce response delay. Based on this issue, a parameter tuning optimization selection model based on the Transient Search Optimization (TSO) algorithm is proposed to determine the optimal power output and best frequency regulation parameters for the energy storage system. Finally, the model proposed in this paper is applied to the IEEE 14-bus system to verify its effectiveness.

A novel method for tracing gasoline using GC-IRMS and Relief-Stacking fusion model

Due to China’s vast geographical expanse and the complexity of its natural environment, traditional methods are not suitable for tracing the origins of gasoline samples from different regions of China. The aim of this study is to trace the origins of gasoline samples through the analysis of carbon stable isotopic characteristics from 60 gasoline samples collected across four major regions in China. In this study, a novel method that combines gas chromatography-isotope ratio mass spectrometry (GC-IRMS) with Relief-Stacking was proposed for the first time, significantly enhancing the precision of the provenance results. Firstly, eight characteristic components were identified by Relief feature selection model for the first time and applied to the assessment of carbon stable isotope ratios across different regions. The discovery of these components effectively reduces data redundancy, thereby enhancing the accuracy of provenance analysis. Subsequently, five effective source tracing models (XGBoost, Random Forest, CatBoost, LightGBM, and KNN) were selected based on the CEI values. To further enhance the learning efficiency of the sourcing models, this study integrated the five selected source tracing models using three different methods(voting, stacking, and blending) and validated the performance of the optimal sourcing model. The results indicated that the Stacking fusion model achieves a CEI value of 0.923, significantly outperforming other fusion models. This advantage is attributed to the Stacking fusion model combining the s trengths of Boosting and Bagging ensemble methods and leveraging the advantages of individual classifiers within the model. The method proposed in this study can effectively address the limitations of previous tracing methods and offer valuable insights for the development of platforms related to stable isotope source tracing. In future research, through appropriate parameter adjustments and feature adaptations, the Relief-Stacking model can likewise be applied to source tracing studies in other countries characterized by extensive geographic ranges and variable environmental conditions.

Comparative Analysis of Decision Tree and Logistic Regression Models in Employee Recruitment and Selection for Enterprise Success

Enterprise success is determined by competent Human Resources (HR). The recruitment and selection process of employee candidates plays an important role in producing competent human resources as an effective initial selection increases the chances of finding the right candidate for a particular role. This research predicts the likelihood of a candidate being further selected in the interview phase based on behavioral and functional recruitment and selection which are important aspects of a candidate's potential fit and contribution to the enterprise. The research uses a comparison of decision tree analysis models and logistic regression to make predictions with several measurement metrics to see the accuracy and confusion matrix of each model used. Based on evaluation and validation, the decision tree analysis model is superior in prediction even though the results tend to be the same as the logistic regression model. The accuracy value of the classification using the decision tree model was 86.67% with correct prediction results of 78 data from 90 testing data and the accuracy value of the logistic regression model was 85.55% with correct prediction results of 77 data from 90 testing data. The results of the comparison of the two models show that the performance of the decision tree classifier model tends to be better.

A Deep Learning Approach for Enhanced Real-Time Prediction of Winter Road Surface Temperatures in High-Altitude Mountain Areas

Low temperatures and icing in winter are significant factors that severely affect highway safety and traffic mobility. To enhance the precision and reliability of real-time winter road surface temperature (RST) prediction, a short-term prediction model is developed that harnesses both feature selection and deep learning. Leveraging meteorological data from a mountain highway in Yunnan, China, the key environmental variables affecting road surface temperature were first extracted using a random forest (RF) model for feature selection. These features were then combined with RST data to construct multiple groups of input variable combinations for the prediction model. A short-term prediction model with a 10-minute update frequency was built using a long short-term memory neural network (LSTM), namely RF-LSTM. The best input variable combination and preset parameters for the prediction model were determined through comparative testing with prevalent machine learning models, and the transferability of the prediction model was verified. The results showed that the best input variable combination for the RF-LSTM prediction model was road surface temperature and air temperature. The model recognised that the short-term RST was affected by long and short-term memory characteristics within a two-hour timeframe. When compared to the RF model, backpropagation (BP) neural network model and the standard LSTM model, the proposed model reduces prediction errors by 59.15%, 31.10% and 20.26%, respectively, while the prediction accuracy is 99.13% within an error margin of ±0.5℃. On the verification dataset, the proposed model maintains its time transferability with an average prediction absolute error of 0.0478. In all, the proposed model not only achieves a higher level of precision in real-time RST predictions but also ensures a more consistent and reliable performance under the challenging conditions of high altitude and mountainous terrain, offering enhanced support for traffic safety and road maintenance decision-making.

The influence of neighbor selection on self-organized UAV swarm based on finite perception vision.

Recently, vision-based unmanned aerial vehicle (UAV) swarming has emerged as a promising alternative that can overcome the adaptability and scalability limitations of distributed and communication-based UAV swarm systems. While most vision-based control algorithms are predicated on the detection of neighboring objects, they often overlook key perceptual factors such as visual occlusion and the impact of visual sensor limitations on swarm performance. To address the interaction problem of neighbor selection at the core of self-organizing UAV swarm control, a perceptually realistic finite perception visual (FPV) neighbor selection model is proposed, which is based on the lateral visual characteristics of birds, incorporates adjustable lateral visual field widths and orientations, and is able to ignore occluded agents. Based on the FPV model, a neighbor selection method based on the Acute Angle Test (AAT) is proposed,&#xD;which overcomes the limitation that the traditional neighbor selection mechanism can only interact with the nearest neighboring agents. A large number of Monte Carlo simulation comparison experiments show that the proposed FPV+AAT neighborselection mechanism can reduce the redundant communication burden between large scale self-organized UAV swarms, and outperforms the traditional neighbor selection method in terms of order, safety, union, connectivity, and noise resistance.

Research and Discussion on the Teaching Model of Cross-School Course Selection Based on the Internet

With the rapid development of internet technology, internet-based cross-school course selection has become a new trend in the field of education. This paper the teaching model of internet-based cross-school course selection, analyzes its advantages and challenges, and explores the practical strategies of this teaching model through actual cases. It aims provide useful references for promoting the sharing of educational resources, improving the quality of teaching, and cultivating innovative talents.

Challenges and Opportunities in the Adoption of Flexible Manufacturing Systems in Indian Manufacturing Industries

The manufacturing sector is increasingly adopting automation technologies to enhance productivity and flexibility. Among these technologies, Flexible Manufacturing Systems (FMS) offer substantial benefits, such as reduced lead times, improved production flexibility, and lowered operational costs. Despite its advantages, FMS adoption in Indian manufacturing industries has been slow due to several barriers, including high capital costs, technological complexity, and inadequate government support. This study investigates the current status of FMS adoption in India, identifies the key barriers hindering its implementation, and proposes a decision-making model to facilitate its wider adoption. The research gap highlights the lack of a comprehensive framework to quantify the barriers and assess the performance of FMS adoption. To address this, a two-phase Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methodology was used. This approach allowed for the classification and prioritization of barriers, as well as the development of a model for FMS selection based on statistical analysis. A case study was conducted using this methodology to evaluate various FMS models and provide a systematic decision-making framework for manufacturers. The results revealed that the most significant barriers to FMS adoption in India include high upfront costs, insufficient infrastructure, and the challenges posed by rapidly advancing FMS technology. By applying the AHP-TOPSIS approach, this study offers insights into overcoming these barriers and presents actionable recommendations for manufacturers and policymakers. The findings emphasize the need for increased government support and industry collaboration to accelerate FMS adoption in India. This research contributes to the development of a structured decision-making framework, which could help manufacturers implement FMS more effectively, thereby enhancing competitiveness in an increasingly dynamic global market. Keywords— Flexible Manufacturing Systems, Barriers to FMS, Indian Manufacturing, AHP-TOPSIS, Decision-Making Model

A multi-criteria decision-making model for sustainable selection of coastal protection structures

Although nature-based and hybrid (i.e., a combination of hard and nature-based) solutions provide well-documented benefits to both the environment and people living nearby, they cannot be implemented in all coastal zones. Thus, the selection process of coastal protection structures (CPSs) requires an in-depth and comprehensible assessment with an effective and robust decision-support framework. Existing frameworks in the literature, however, do not focus on the selection of nature-based solutions and environmental and social factors were not considered thoroughly in these frameworks. In this respect, this research aims to develop a decision-support model that guides decision-makers in selecting the most appropriate and sustainable CPSs. The framework was designed in a way that includes a variety of environmental and social factors along with economic and technical aspects. Then, fuzzy AHP and TOPSIS analyses were conducted to determine the relative importance of the selection factors and to select the most appropriate CPS. Factors such as sediment dynamics, geotechnical issues, construction cost, transportation and navigation effects, and technological know-how were among the top-rated factors. TOPSIS analysis results revealed that mangrove forests and artificial reefs were the top two most sustainable CPSs. The proposed framework can minimize the social and environmental impacts of CPSs and expedite the green transition in the coastal environment.

Towards Practical Antenna Selection Based on Multilabel CNN for Large‐Scale MIMO System in an Indoor Scenario

ABSTRACTLarge‐scale multiple input multiple output (MIMO), also known as massive MIMO, serves as a transformative technology that effectively addresses the surging need for data‐intensive applications in the realm of 5G mobile networks and beyond. With a multitude of antennas at its disposal, a large‐scale MIMO base station possesses the capability to concurrently improve by orders of magnitude system spectral and energy efficiencies. Nevertheless, hardware cost and power consumption arise as serious challenge. To circumvent this latter, we investigate the potential of multilabel convolutional neural network (ML‐CNN) to develop an antenna selection (AS) model that based on the available channel state information (CSI) at the transmitter, it dynamically selects the optimal subset of antennas in real‐time. We evaluate the model performance using real indoor channel measurements under three different antenna array configurations. The obtained results demonstrate that the proposed ML‐CNN approach performs similarly to the convex relaxation‐based method, a mathematical technique often used for AS problems that provides optimal solutions with high computation cost, with the advantage of significantly reduced computation time. Moreover, it exhibits strong resilience across various antenna array configurations. Additionally, assessing the ML‐CNN's performance in scenario with imperfect channel estimation confirms its robustness.