Search Results (92)

Background/Objectives: Wrist cock-up orthoses are standard for work-related musculoskeletal disorders, yet consensus is lacking on whether commercial orthoses (COs) or custom-made thermoplastic orthoses (THs) better preserve function. While COs offer availability, THs provide a superior anatomical fit. This study evaluated dexterity and satisfaction in healthy female employees to establish a functional baseline for preventive strategies. Methods: Healthy female office workers with no prior musculoskeletal or neurological conditions participated in this randomized cross-over study. Manual dexterity was assessed at baseline and after each of two consecutive workdays, during which participants wore, in a randomized order, either a CO or a TH made by an expert physiotherapist. Outcome measures included the Functional Dexterity Test (FDT), recording time and errors, and the Client Satisfaction with Device (CSD-It) scale. Results: Twenty right-handed women (mean age 45.6 ± 11 years) participated. A significant difference in FDT completion times across conditions (χ² = 12.6, p = 0.002) was found. While both orthoses slowed performance compared to baseline (p < 0.01), the CO allowed for faster dexterity than the TH (p < 0.01). No differences were found in error rates. Regarding satisfaction, the CO achieved significantly better CSD-It scores than the TH (p = 0.0047), despite 60% of users reporting increased skin temperature with the CO. Final preferences were nearly evenly split (55% CO vs. 45% TH). Conclusions: Both orthoses impact manual dexterity without compromising precision. While the CO offered better execution speed and overall satisfaction, the TH version was preferred for prolonged skin tolerability. Selection should be individualized, balancing mechanical efficiency with the superior fit of custom-fabricated solutions in office environments. Full article

Water utilities are increasingly expected to combine technical reliability with social inclusion, risk communication, and service continuity. This empirical paper reports a cross-sectional mixed-mode household survey conducted in Rzeszów, Poland, based on 384 complete questionnaire records. For a city of approximately 200,000 inhabitants, this sample size matched the conventional planning benchmark associated with a 95% confidence level and a 5% maximum error under simple-random-sampling assumptions; however, because recruitment was mixed-mode and non-probabilistic, the results are interpreted as evidence from the realized sample rather than as formally weighted population estimates. The questionnaire covered routine service evaluation, interruption experience, preparedness, communication preferences, vulnerability-related burden, and willingness to support reliability enhancement. The analytical workflow combined descriptive statistics, reliability analysis, Bartlett’s test of sphericity, the Kaiser–Meyer–Olkin measure, principal component analysis, Mann–Whitney U tests, Kruskal–Wallis tests, chi-square tests, Spearman correlation, binary logistic regression, correspondence analysis, and CHAID-type segmentation. The highest ratings were recorded for continuity of supply (mean = 4.18) and pressure stability (mean = 4.15), whereas price fairness received the lowest mean score (3.17). Interruptions were reported by 40.1% of respondents and were associated with lower overall satisfaction. Logistic regression showed that continuity rating (OR = 4.029) and water quality rating (OR = 2.305) increased the odds of high satisfaction, whereas longer interruptions reduced them (OR = 0.354). Additional analyses showed that interruptions lasting 12 h or more markedly increased the odds of high nuisance among affected households (OR = 5.914), while respondents aged 51 years or more had lower odds of declaring emergency-information awareness (OR = 0.468). Internal bootstrap validation indicated only mild optimism (optimism-corrected AUC = 0.825). The findings indicate that customer satisfaction in urban water services is shaped primarily by continuity, perceived water quality, and disruption burden, while communication and preparedness needs remain socially differentiated. Full article

The increasing adoption of electric vehicles in residential buildings creates challenges for charging infrastructure management, particularly in pricing services to balance revenue, user satisfaction, and grid stability. Traditional pricing methods, such as fixed rates and time-of-use tariffs, cannot adapt to the dynamic nature of charging demand. We propose a reinforcement learning framework for dynamic pricing of residential EV charging stations. The framework formulates the pricing problem as a Markov decision process and employs proximal policy optimization to learn a pricing policy based on real-time conditions. The state representation includes ten features covering temporal indicators, charging loads, grid status, traffic, and weather. A multi-objective reward function balances revenue, station utilization, grid stability, and user satisfaction. The system is trained on 6878 charging sessions from a residential complex in Trondheim, Norway. Compared with fixed pricing and time-of-use pricing, the proposed method achieves an overall score of 0.569, representing improvements of 32.9% and 48.9%, respectively. Sensitivity analysis confirms that the model remains robust across different demand response assumptions. The main contributions include a custom reinforcement learning environment for residential EV charging and empirical evidence that learned policies outperform traditional pricing approaches. Full article

The aviation industry operates in a security-sensitive environment where customer feedback may contain not only expressions of satisfaction or dissatisfaction but also threatening or violent language with potential security implications. While conventional sentiment analysis effectively captures customer opinions, it remains insufficient for identifying security-relevant linguistic cues that could signal risks requiring proactive intervention. This study addresses this gap by introducing a security-aware ambient intelligence framework for detecting violent language in airline customer reviews. This framework supports intelligent internet-based monitoring systems and real-time threat detection. We present the first annotated dataset of airline reviews specifically labeled for violent and threatening content, derived from 3629 reviews and balanced through manual resampling to achieve equal representation across positive, neutral, negative, and violent classes. The proposed framework employs VADER-based sentiment analysis for initial polarity estimation, combined with a validated annotation process to identify violent or threat-related content, followed by comprehensive feature engineering combining TF-IDF (2000 features) with text statistics and sentiment scores. We systematically evaluate individual classifiers (Random Forest, Decision Tree, SVM, Naive Bayes) against ensemble methods (Voting, Stacking, Boosting) using accuracy, precision, recall, F1-score, and ROC AUC metrics. Results demonstrate that Stacking achieves the highest raw performance (98.57% accuracy, F1-macro 0.9856), while Naive Bayes offers an optimal balance between effectiveness and computational efficiency (81.79% accuracy, F1-macro 0.8172, training time 0.03 s). This is the first dataset and framework designed for security-aware analysis of airline reviews. The selected Naive Bayes model achieves per-class F1-scores of 0.9978 for neutral, 0.7814 for negative, 0.7482 for violent, and 0.7415 for positive reviews, with a macro-average ROC AUC of 0.7123. The framework is deployed with serialized components enabling real-time prediction, supporting both single-review analysis and batch processing for integration into airline security monitoring systems. This work establishes a foundation for security-aware natural language processing in critical infrastructure contexts, bridging the gap between conventional sentiment analysis and proactive threat detection. Full article

Background and Objectives: Severe alveolar atrophy may pose significant challenges for dental rehabilitation. Recent advances in digital planning and CAD/CAM technology have renewed the interest in patient-specific subperiosteal implants as a treatment option for anatomically challenging cases. This cohort study evaluated changes in oral health-related quality of life and patient satisfaction following rehabilitation with customized subperiosteal implants in severe alveolar atrophy. Materials and Methods: This cohort study included all consecutive adult patients with severe alveolar atrophy who underwent reconstruction with patient-specific subperiosteal implants at the Department of Oral and Maxillofacial Surgery of “Evangelismos” General Hospital, Athens, Greece, in 2025. Oral health-related quality of life was assessed using the validated OHIP-14 questionnaire preoperatively and 12 months postoperatively. Patient satisfaction was evaluated using a numerical rating scale (NRS). Secondary outcomes included postoperative complications, implant exposure, implant stability, and need for reoperation. Comparisons between baseline and 12-month scores were performed using the Wilcoxon signed-rank test. Results: Nine patients who had completed 12-month follow-up were included. Five were male, and all implants were placed in the maxilla. Significant improvement was observed in oral health-related quality of life, with the median OHIP-14 total score decreasing from 41 preoperatively to 1 at the 12-month follow-up. Patient satisfaction also improved significantly, with the median NRS total score increasing from 17 to 58. Improvements were consistent across all OHIP-14 domains and all NRS items. No major complications were recorded. One patient developed early wound dehiscence, and one patient presented with implant exposure at the anterior palate. At the final follow-up twelve months postoperatively, all implants remained clinically and radiographically stable. Conclusions: These preliminary short-term findings suggest that customized subperiosteal implants may be a promising option for selected patients with severe alveolar atrophy in whom placement of conventional endosseous implants is not feasible; however, the results should be interpreted cautiously given the very small sample size and observational design. Full article

Patellar maltracking is among the most common causes of anterior knee pain after total knee arthroplasty (TKA), underscoring the need for accurate prevention and treatment. Therefore, the purpose of this narrative review is to provide a comprehensive overview of current evidence on post-TKA tracking, focusing on component alignment, preoperative patient assessment, and revision treatment options. A PubMed database search was performed, leveraging the literature from the last 20 years, and the results were qualitatively synthesized. According to current studies, several precautions should be taken to prevent patellofemoral stress and, consequently, patellar maltracking, such as avoiding internal rotation, valgus alignment, and excessive flexion of the femoral component and internal rotation of the tibial component. Regarding alignment strategies, kinematic alignment appears to offer potential benefits over mechanical alignment in certain functional outcomes and patient satisfaction scores. However, these differences should be interpreted cautiously as they may not always exceed the minimal clinically important difference. Furthermore, recent evidence indicates that quadriceps biomechanics influence TKA outcomes, potentially suggesting that conventional surgical approaches may need to be individualized, though these preliminary findings require prospective validation. Currently, robotic-assisted surgery represents a developmental direction for patient-tailored interventions and offers great promise for better prosthesis customization to the individual patient. Integration of imaging data with dynamic soft-tissue assessment enables more predictable reconstruction of joint kinematics. Regarding surgical treatment, the selection of specific methods requires a prior clinical and radiographic assessment. Indications range from patellar maltracking direction and component malrotation to patient preferences and rehabilitation potential. Ultimately, the future of TKA relies on personalized interventions to prevent complications and improve patient outcomes. This evolution is driven by the shift from mechanical alignment to kinematic alignment, alongside quadriceps tendon assessment and intraoperative robotic-assisted measurement, all aimed at optimizing the accuracy of implant positioning. Full article

Amid the wave of digital transformation and customer service automation, traditional chatbots are increasingly challenged by their inability to handle unstructured data and complex queries. This issue is particularly critical in the import trade industry, where customer service representatives must respond promptly to diverse inquiries involving quality anomalies, order tracking, and product substitution. Existing rule-based or keyword-driven chatbots often fail to provide accurate responses, resulting in reduced customer satisfaction and increased operational burdens. This study proposes and implements a “Retrieval-Augmented Generation (RAG)-based Customer Service Chatbot,” integrating the RAG framework with a Neo4j-based knowledge graph, specifically tailored for the import trade domain. The system constructs a dedicated QA dataset, knowledge graph, and dynamic learning mechanism. It semantically vectorizes internal documents, meeting records, quality assurance procedures, and historical dialogues, establishing interrelated knowledge nodes to enhance the chatbot’s comprehension and response accuracy. The study also incorporates GPT-based response evaluation and a high-score caching strategy, enabling dynamic learning and knowledge enhancement. Experiments were conducted using 101 representative enterprise-level queries across six categories, reflecting real-world operational scenarios and inquiry needs. The results demonstrate that the combination of knowledge graphs and RAG technology effectively reduces AI hallucinations and improves response coverage and accuracy, thereby addressing complex problems in customer service applications. This paper not only presents a feasible AI implementation model for the import trading industry but also offers a practical architectural reference for domain-specific knowledge management in the import trade and allied sectors. Full article

Background: The integration of advanced 3D scanning and additive manufacturing technologies in stroke rehabilitation offers promising advancements in the design and production of ankle-foot orthoses. These technological innovations are progressively recognized for their potential to provide more precise and customized orthotic solutions for individuals with stroke-related impairments. Objectives: The primary aim of this study was to biomechanically test and validate the effectiveness of custom ankle-foot orthoses produced through additive manufacturing technology using data captured by a novel photogrammetric scanning system. The customized orthosis was compared with a standard prefabricated orthosis to assess their relative effectiveness in improving gait dynamics and patient satisfaction in stroke rehabilitation. Methods: Participants with equinovarus deformity, a common consequence of stroke, were fitted with custom ankle-foot orthoses, alongside conventional prefabricated orthoses. The study utilized the Qualisys^® motion analysis system for comprehensive biomechanical gait analysis, and the QUEST questionnaire was employed to capture participant feedback on both types of orthoses. Detailed comparisons of gait dynamics were conducted using Statistical Parametric Mapping with each orthosis. Results: The study revealed notable kinematic and kinetic differences between the custom and prefabricated orthoses. The custom orthoses demonstrated superior performance in enhancing gait efficiency, symmetry, and safety. Patient feedback favored the customized orthoses over the prefabricated variants, with higher scores in comfort, fit, and overall effectiveness. Conclusions: This research underscores the effectiveness of custom orthoses produced through additive manufacturing technology for stroke rehabilitation. By offering a comprehensive evaluation of orthotic interventions and establishing a comparative framework, the study serves as a reference point for future research, advocating for a more personalized and evidence-based approach in orthotic design for improving the quality of life of stroke survivors. Full article

To address surging and uncertain electricity customer demands, this paper proposes a data-driven electricity customer service scheduling (ECSS) optimization model to improve customer service quality and alleviate agent scheduling pressure. The method begins by building a demand analysis model based on customer feature extraction using the maximal information coefficient (MIC). An agent workforce sizing model is then developed by integrating the AHP–fuzzy comprehensive evaluation and Z-score standardization, accounting for call-volume proportion, hourly call-handling capacity, and time-period length. Furthermore, a demand–skill matching method is introduced between customer calls and agent skills. A particle swarm optimization (PSO)-based intelligent scheduling algorithm is established, with queuing time, skill level, and handling time as key objectives and constraints. Case-study validation shows that the model improves operational efficiency by approximately 26.28% and reduces annual labor costs by about 6.13%, thereby enhancing customer satisfaction, service center efficiency, and scheduling system economy. 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

Background/Objectives: Primary health care (PHC) is the cornerstone of any high-quality healthcare system. For PHC to work well, healthcare professionals need to be skilled in critical thinking, self-reflection, and patient-centered care. However, few studies have explored the potential interplays between these factors. Therefore, this cross-sectional study evaluated the critical thinking disposition and training needs of PHC professionals, alongside patient experiences and satisfaction with PHC services. Methods: The study involved 54 PHC professionals and 100 patients from sixteen PHC facilities in Crete, Greece. Professionals completed the Critical Thinking Disposition Scale (CTDS) and Training Needs Assessment (TNA) questionnaires, while patients filled out the Quality-of-Life Instrument of Chronic Conditions in Primary Health Care (QUALICOPC) questionnaire. Results: Our findings indicated that PHC professionals exhibited high critical thinking levels (CTDS, mean score of 46.46 ± 4.24). However, TNA scores suggested moderate training needs, particularly in relationships/investigations [median: 0.50 (0, 1.50)], communication/patient-centered [median: 0.30 (0, 1.1)], and flexibility and application of knowledge [median: 0.40 (0, 1.0)]. Nevertheless, no significant correlation was found between CTDS and TNA (ρ = 0.08, p > 0.05). Patients mostly rated their health as poor (40%), and 26% lacked a family physician. Although patients were highly satisfied with communication and patient-centered care (>95% reporting positive experiences), continuity and empowerment had room for improvement. Only 37% felt their GP knew their living conditions, and 26% lacked a personal physician. Patients with chronic conditions reported significantly different experiences. Specifically, patients with chronic conditions had better continuity of care (84% vs. 59%, p = 0.01) and more comprehensive care (70% vs. 43%, p = 0.01) compared to controls. Conclusions: Our findings suggest that targeted training is needed for PHC professionals to address skill gaps. These initial findings could guide the creation of customized professional development initiatives and point to areas where PHC services could be structurally improved. Additional studies, including longitudinal ones, are required to further validate these associations. Full article

Failures in clean water service lines can disrupt supply, increase operational costs, and reduce customer satisfaction. This study develops a machine learning framework to predict such failures, providing a proactive tool for utility asset management. A case study was conducted on a water distribution network in Tehran, serving approximately 205,000 customers, with 11 years of service line data and over 88,000 recorded failures. Service line attributes, including length, diameter, material, age, demand, and pressure, were combined with historical failure data to train Random Forest, Extreme Gradient Boosting, and Long Short-Term Memory models. Model performance was assessed using F1-score, AUC-ROC, and AUC-PRC. A novel metric was introduced to quantify failure reduction when prioritising replacements. The results demonstrate that machine learning can effectively capture complex relationships between service line features and failures, offering significant benefits for tactical maintenance planning. This research underscores the potential of predictive approaches to improve reliability and reduce costs. Full article

The concept of sustainability in the supermarket sector has emerged as a strategic priority, as companies are required to reduce their environmental footprint and enhance their social and economic performance. The aim of this literature review is to identify, document, and analyze the key performance indicators (KPIs) applied in the sector, with emphasis on environmental, social, and economic dimensions, and to investigate the extent to which technical energy interventions are linked to business and consumer benefits. The methodology was inspired by the general logic of organized search and selection procedures, and for this reason, elements of the PRISMA framework were used, with a search conducted across multiple international scientific databases and selection criteria ensuring the validity and relevance of the sources. The analysis classified the indicators into the following three categories: environmental (e.g., CO₂ emissions, energy consumption), social (e.g., customer satisfaction, corporate image), and economic (e.g., ESG score, return on investment). The study revealed substantial progress made by supermarket chains globally in adopting energy-efficiency technologies, such as LED lighting and renewable energy with proven benefits in reducing consumption and consequently, improving environmental performance. However, a lack of holistic integration between technical interventions and social-economic indicators was identified, limiting the use of KPIs as a strategic tool for guiding specific sustainability strategies. This research concludes that there is a need to develop unified, sector-specific measurement frameworks that integrate environmental, social, and economic parameters, as well as empirical research that quantitatively connects energy strategies with business and consumer performance through comparable indicators in the context of supermarket operations, thereby opening ground for further exploration of the field. Full article

Customer satisfaction (CS) is a cornerstone of competitiveness in the hospitality sector, particularly in restaurants, where service interactions are highly sensory and time-sensitive. Traditional measurement instruments, such as SERVQUAL, SERVPERF, and the American Customer Satisfaction Index, provide valuable diagnostic insights but remain limited by recall bias, social desirability, and delayed feedback. Advances in deep learning now enable non-intrusive, real-time monitoring of customer experience. This study evaluates the feasibility of using a convolutional neural network (CNN) to automatically classify customer satisfaction based on facial expressions captured at the point of payment in a restaurant. From an initial dataset of over 5000 images, 2969 were validated and labeled through a binary self-report mechanism. The CNN, implemented with transfer learning (MobileNetV2), achieved robust performance, with 93.5% accuracy, 92.8% recall, 91.0% F1-score, and an area under the ROC curve of 0.93. Comparative benchmarks with Support Vector Machine and Random Forest classifiers confirmed the superiority of the CNN across all metrics. The findings highlight CNNs as reliable and scalable tools for continuous CS monitoring, complementing rather than replacing classical survey-based approaches. By integrating implicit, real-time signals with traditional instruments, restaurants can strengthen decision-making, enhance service quality, and co-create personalized experiences while addressing challenges of explainability, external validity, and data ethics. Full article

Effective website usability assessment is crucial for improving user experience, driving customer satisfaction, and ensuring business success, particularly in the competitive e-commerce sector. Traditional methods, such as expert reviews and user testing, are resource-intensive and often fail to fully capture the complex interplay between a site’s aesthetic design and its technical performance. This paper introduces an end-to-end multimodal deep learning framework that automates the usability assessment of fashion e-commerce websites. The framework fuses structured numerical indicators (e.g., load time, mobile compatibility) with high-level visual features extracted from full-page screenshots. The proposed framework employs a comprehensive set of visual backbones—including modern architectures such as ConvNeXt and Vision Transformers (ViT, Swin) alongside established CNNs—and systematically evaluates three fusion strategies: early fusion, late fusion, and a state-of-the-art cross-modal fusion strategy that enables deep, bidirectional interactions between modalities. Extensive experiments demonstrate that the cross-modal fusion approach, particularly when paired with a ConvNeXt backbone, achieves superior performance with a 0.92 accuracy and 0.89 F1-score, outperforming both unimodal and simpler fusion baselines. Model interpretability is provided through SHAP and LIME, confirming that the predictions align with established usability principles and generate actionable insights. Although validated on fashion e-commerce sites, the framework is highly adaptable to other domains—such as e-learning and e-government—via domain-specific data and light fine-tuning. It provides a robust, explainable benchmark for data-driven, multimodal website usability assessment and paves the way for more intelligent, automated user-experience optimization. Full article