Search Results (10)

Effective task allocation is fundamental to the success of heterogeneous multi-robot cooperative missions in smart manufacturing workshops, yet real-world operational uncertainties pose severe challenges to solution feasibility and mission robustness. Addressing these challenges, this paper focuses on the inspection and maintenance task allocation problem for heterogeneous mobile robot teams operating under fluctuating equipment maintenance time windows, variable task execution durations, and uncertain robot travel speeds caused by workshop congestion and payload variations. First, the aforementioned uncertain parameters are characterized using triangular fuzzy numbers, upon which a fuzzy chance-constrained programming model is constructed with the objective of minimizing total operational cost while ensuring constraint satisfaction under uncertainty. The proposed model simultaneously handles two types of critical constraints: the service time window constraint, which requires each task to be completed before its latest allowable service deadline, and the time sequence constraint, which enforces that each equipment inspection task must be completed prior to the corresponding maintenance task. Then, to tackle the inherent NP-hardness of this problem, a multi-strategy hybrid Harris Hawks Optimization algorithm incorporating differential evolution, termed MSHHODE, is proposed. In detail, three targeted enhancement mechanisms are introduced: a hunting enthusiasm factor that governs the dynamic balance between global exploration and local exploitation throughout the search process; an elite-assisted guidance strategy that stabilizes convergence by leveraging high-quality solutions to direct population evolution; and an adaptive differential evolution mechanism that reinforces global search diversity and mitigates premature convergence to local optima. Finally, simulation experiments conducted across multiple workshop-scale scenarios demonstrate that MSHHODE consistently outperforms benchmark algorithms across different key performance metrics under varied uncertain conditions, which validates the effectiveness and robustness of the proposed approach in solving complex, constrained allocation problems, offering a practical and reliable framework for real-world heterogeneous multi-robot task planning in smart manufacturing environments. Full article

The NP-hard coordination of heterogeneous robots for time-windowed warehouse tasks remains challenging: metaheuristics are precise but slow, whereas neural methods cannot handle heterogeneous constraints, leading to infeasible allocations. This paper presents the Constraint-Aware Heterogeneous Transformer (CAHT), a lightweight encoder–decoder architecture that performs end-to-end task assignment and sequencing in a single forward pass. The central innovation is a dynamic feasibility masking mechanism that enforces capacity and energy constraints directly within the softmax computation, eliminating infeasible allocations at the architectural level. This is complemented by a spatial-bias Transformer encoder and a two-stage supervised–reinforcement learning training paradigm using ALNS-generated labels. Experiments across four problem scales (5–20 robots, 50–200 tasks) demonstrate that CAHT achieves objective values within 7–13% of the ALNS reference while being 29–91× faster (23–104 ms vs. 2–3 s). Constraint violation rates remain below 6%, with time-window satisfaction above 94%. Ablation analysis identifies dynamic masking as the dominant contribution (+213% degradation upon removal), and cross-scale generalization reveals that the optimality gap decreases from 13.0% to 10.7% as the problem scale grows. With only 0.91 M parameters, CAHT occupies a new trade-off point on the Pareto frontier, offering a practical path toward real-time autonomous warehouse coordination. Full article

Background: The aim of this study was to derive and internally validate a prescriptive clinical prediction rule (CPR) for identifying baseline factors associated with short-term clinical improvement in patients with neck pain (NP) undergoing a manual therapy (MT)-based physiotherapy program. Methods: A prospective cohort study was conducted, including 71 patients with NP (18–65 years). Participants received six MT-based sessions over three weeks. Baseline assessments included Pain Intensity Numeric Rating Scale (PI-NRS), Neck Disability Index (NDI), Body Mass (BM), Body Mass Index (BMI), International Physical Activity Questionnaire-Short Form (IPAQ-SF), Hospital Anxiety and Depression Scale (HADS), Minnesota Satisfaction Questionnaire-Short Form (MSQ), and Craniovertebral Angle (CVA). Clinical improvement was defined using the Global Perceived Effect Scale (GPES-7). Univariate analyses, receiver operating characteristic (ROC) curve analysis, and forward stepwise logistic regression were performed to derive the predictive model. Results: Fifty-six participants (78.9%) reported moderate to complete improvement. BM ≥ 76.5 kg and MSQ score ≤ 42.5 were retained in the final regression model. When both predictors were present, the probability of clinical improvement increased to 96.43% (positive likelihood ratio = 7.58). The model demonstrated adequate fit (Nagelkerke R² = 0.247; Hosmer–Lemeshow p = 0.804). Internal validation yielded an optimism-corrected AUC of 0.741, suggesting minimal overfitting. Conclusions: Higher BM and lower MSQ score were associated with greater short-term improvement following MT in patients with NP. These findings highlight the relevance of integrating physical and psychosocial factors in prescriptive rehabilitation approaches. External validation of this CPR is required before clinical implementation. Full article

Despite the growing use of machine learning (ML) for analyzing service quality and customer satisfaction, empirical studies based on Brazilian broadband telecommunications data remain scarce. This is especially true for those who leverage publicly available nationwide datasets. To address this gap, this study investigates customer satisfaction with broadband internet services in Brazil using supervised ML and explainable artificial intelligence (XAI) techniques applied to survey data collected by ANATEL between 2017 and 2020. Customer satisfaction was operationalized using the Net Promoter Score (NPS) reference scale, and three modifications in the scale were evaluated: (i) a binary model grouping ratings ≥ 8 as satisfied and ≤7 as dissatisfied (portion of the neutrals as satisfied and another as dissatisfied); (ii) a binary model excluding neutral responses (ratings 7–8) and retaining only detractors (≤6) and promoters (≥9); and (iii) a multiclass model following the original NPS categories (detractors, neutrals, and promoters). Nine ML classifiers were trained and validated on tabular data for each formulation. Model interpretability was addressed through SHAP and feature importance analysis using tree-based models. The results indicate that Histogram Gradient Boosting and Random Forest achieve the most robust and stable performance, particularly in binary classification scenarios. The analysis of neutral customers reveals classification ambiguity, showing scores of “7” tend toward dissatisfaction, while scores of “8” tend toward satisfaction. XAI analyses consistently identify browsing speed, billing accuracy, fulfillment of advertised service conditions, and connection stability as the most influential predictors of satisfaction. By combining predictive performance with model transparency, this study provides computational evidence for explainable satisfaction modeling and highlights the value of public regulatory datasets for reproducible ML research. Full article

Emergency material scheduling (EMS) is a core component of post-disaster emergency response, with its efficiency directly impacting rescue effectiveness and the satisfaction of affected populations. However, due to severe road damage, limited availability of resources, and logistical challenges after disasters, current EMS practices often suffer from uneven resource distribution. To address these issues, this paper proposes a priority-based, multi-stage EMS approach with drone coordination. First, we construct a three-level EMS network “storage warehouses–transit centers–disaster areas” by integrating the advantages of large-scale transportation via trains and the flexible delivery capabilities of drones. Second, considering multiple constraints, such as the priority level of disaster areas, drone flight range, transport capacity, and inventory capacities at each node, we formulate a bilevel mixed-integer nonlinear programming model. Third, given the NP-hard nature of the problem, we design a hybrid algorithm—the Tabu Genetic Algorithm combined with Branch and Bound (TGA-BB), which integrates the global search capability of genetic algorithms, the precise solution mechanism of branch and bound, and the local search avoidance features of Tabu search. A stage-adjustment operator is also introduced to better adapt the algorithm to multi-stage scheduling requirements. Finally, we designed eight instances of varying scales to systematically evaluate the performance of the stage-adjustment operator and the Tabu search mechanism within TGA-BB. Comparative experiments were conducted against several traditional heuristic algorithms. The experimental results show that TGA-BB outperformed the other algorithms across all eight test cases, in terms of both average response time and average runtime. Specifically, in Instance 7, TGA-BB reduced the average response time by approximately 52.37% compared to TGA-Particle Swarm Optimization (TGA-PSO), and in Instance 2, it shortened the average runtime by about 97.95% compared to TGA-Simulated Annealing (TGA-SA).These results fully validate the superior solution accuracy and computational efficiency of TGA-BB in drone-coordinated, multi-stage EMS. Full article

Background and Objectives: Chronic rhinosinusitis (CRS) is a complex inflammatory condition of the nasal passages that severely impairs quality of life. Type 2 CRS is characterized by eosinophilic inflammation, driven by cytokines like IL-4, IL-5, and IL-13. These cytokines are key to CRS pathogenesis and contribute to a heavy disease burden, especially with comorbidities. This study assessed dupilumab, a monoclonal antibody targeting IL-4 and IL-13 signaling, to evaluate its efficacy in reducing the disease burden in patients with CRS with nasal polyps (CRSwNP). Materials and Methods: The patients received subcutaneous dupilumab for 42 weeks. The outcomes included Nasal Polyp Score (NPS); Sino-Nasal Outcome Test (SNOT-22), Numeric Rating Scale (NRS), and Visual Analog Scale (VAS) scores; total IgE; and olfactory function. Results: Significant improvements were observed across the NPS and SNOT-22, NRS, and VAS scores after 42 weeks. Their total IgE levels were reduced, though a transient increase in peripheral eosinophilia appeared at 16 weeks. The patients also reported substantial improvements in olfactory function and high satisfaction with the treatment, supporting dupilumab’s potential in reducing both symptom severity and inflammation in CRSwNP. Conclusions: These results indicate that dupilumab may be an effective treatment for CRSwNP, offering significant symptom relief, improved olfactory function, and enhanced quality of life. High satisfaction levels suggest that dupilumab may provide therapeutic advantages over the conventional CRS treatments, though further studies are warranted to confirm its long-term benefits. Full article

As the quality of public space has become significant for urban development, the creation of high-quality public spaces is becoming increasingly important. Since the implementation of urban renewal policies, an increasing number of buildings have emerged, creating new types of public spaces. Compared to original public spaces, new public spaces are more open, flexible, and diverse. The design of public spaces is closely related to users and the flâneur can precisely serve as a user and observer to conduct in-depth research. So, our study was conducted under the identity of the flâneur, focusing on two cases in Guangzhou. The flâneur completes the data collection through two methods. Static research involves observing and taking photos, whereas dynamic research involves interviews and questionnaires. This study analysed three aspects: the group category, behavioural diversity, and activity time and evaluated the public space using the NPS scale. The study found that the recommendation rate of new urban public spaces is higher than that of original public spaces. The study also found that original public spaces need to be improved in four ways: equipping furniture facilities, improving traffic congestion, increasing blue-green spaces, and establishing artistic spaces. New urban public spaces need to make efforts to create more interactive spaces and increase stagnation points. Full article

General practitioners (GPs) played a vital role during the COVID-19 pandemic. Little is known about GPs’ view of their role, leadership, participation in regional services and preferences for future pandemic preparedness. This representative study of German GPs comprised a web-based survey and computer-assisted telephone interviewing (CATI). It addressed GPs’ satisfaction with their role, self-perceived leadership (validated C-LEAD scale), participation in newly established health services, and preferences for future pandemic preparedness (net promotor score; NPS; range −100 to +100%). Statistical analyses were conducted using Spearman’s correlation and Kruskal–Wallis tests. In total, 630 GPs completed the questionnaire and 102 GPs the CATI. In addition to their practice duties, most GPs (72.5%) participated in at least one regional health service, mainly vaccination centres/teams (52.7%). Self-perceived leadership was high with a C-LEAD score of 47.4 (max. 63; SD ± 8.5). Overall, 58.8% were not satisfied with their role which correlated with the feeling of being left alone (r = −0.349, p < 0.001). 77.5 % of respondents believed that political leaders underestimated GPs’ potential contribution to pandemic control. Regarding regional pandemic services, GPs preferred COVID-19 focus practices (NPS +43.7) over diagnostic centres (NPS −31). Many GPs, though highly engaged regionally, were dissatisfied with their role but had clear preferences for future regional services. Future pandemic planning should integrate GPs’ perspectives. Full article

A P300-based speller can be used to control a home automation system via brain activity. Evaluation of the visual stimuli used in a P300-based speller is a common topic in the field of brain–computer interfaces (BCIs). The aim of the present work is to compare, using the usability approach, two types of stimuli that have provided high performance in previous studies. Twelve participants controlled a BCI under two conditions, which varied in terms of the type of stimulus employed: a red famous face surrounded by a white rectangle (RFW) and a range of neutral pictures (NPs). The usability approach included variables related to effectiveness (accuracy and information transfer rate), efficiency (stress and fatigue), and satisfaction (pleasantness and System Usability Scale and Affect Grid questionnaires). The results indicated that there were no significant differences in effectiveness, but the system that used NPs was reported as significantly more pleasant. Hence, since satisfaction variables should also be considered in systems that potential users are likely to employ regularly, the use of different NPs may be a more suitable option than the use of a single RFW for the development of a home automation system based on a visual P300-based speller. Full article

Shift scheduling problems (SSPs) are advanced NP-hard problems which are generally evaluated with integer programming. This study presents an applicable shift schedule of workers in a large-scale natural gas combined cycle power plant (NGCCPP), which realize 35.17% of the total electricity generation in Turkey alone, as at of the end of 2018. This study included 80 workers who worked three shifts in the selected NGCCPP for 30 days. The proposed scheduling model was solved according to the skills of the workers, and there were nine criteria by which the workers were evaluated for their abilities. Analytic network process (ANP) is a method used for obtaining the weights of workers’ abilities in a particular skill. These weights are used in the proposed scheduling model as concepts in goal programming (GP). The SSP–ANP–GP model sees employees’ everyday preferences as their main feature, bringing high-performance to the highest level, and bringing an objective functionality, and lowering the lowest success of daily choice. At the same time, the model introduced large-scale and soft constraints that reflect the nature of the shift requirements of this program by specifying the most appropriate program. The required data were obtained from the selected NGCCPP and the model solutions were approved by the plant experts. The SSP–ANP–GP model was resolved at a reasonable time. Monthly acquisition time was significantly reduced, and the satisfaction of the employees was significantly increased by using the obtained program. When past studies were examined, it was determined that a shift scheduling problem of this size in the energy sector had not previously been studied. Full article