Search Results (106)

Designing optimal heritage tourism routes in secondary cities involves complex trade-offs between cultural richness, travel time, carbon emissions, spatial coherence, and group satisfaction. This study addresses the Personalized Group Trip Design Problem (PGTDP) under real-world constraints by proposing DRL–IMVO–GAN—a hybrid multi-objective optimization framework that integrates Deep Reinforcement Learning (DRL) for policy-guided initialization, an Improved Multiverse Optimizer (IMVO) for global search, and a Generative Adversarial Network (GAN) for local refinement and solution diversity. The model operates within a digital twin of Warin Chamrap’s old town, leveraging 92 POIs, congestion heatmaps, and behaviorally clustered tourist profiles. The proposed method was benchmarked against seven state-of-the-art techniques, including PSO + DRL, Genetic Algorithm with Multi-Neighborhood Search (Genetic + MNS), Dual-ACO, ALNS-ASP, and others. Results demonstrate that DRL–IMVO–GAN consistently dominates across key metrics. Under equal-objective weighting, it attained the highest heritage score (74.2), shortest travel time (21.3 min), and top satisfaction score (17.5 out of 18), along with the highest hypervolume (0.85) and Pareto Coverage Ratio (0.95). Beyond performance, the framework exhibits strong generalization in zero- and few-shot scenarios, adapting to unseen POIs, modified constraints, and new user profiles without retraining. These findings underscore the method’s robustness, behavioral coherence, and interpretability—positioning it as a scalable, intelligent decision-support tool for sustainable and user-centered cultural tourism planning in secondary cities. Full article

Developmental theories posit that social perspective-taking, the social-cognitive process of adopting another person’s viewpoint to understand the person’s thoughts and feelings, is important for youths’ successful functioning in close relationships, yet this idea has received little empirical attention. Guided by a social-emotional adjustment trade-offs framework, the current study tested the proposal that adolescents’ (N = 300, M age = 14.76) social perspective-taking would be linked with positive aspects of friendship in terms of friendship quality but also maladaptive aspects of friendship, namely co-rumination (i.e., excessive problem discussion between friends). This study used a multi-method design including surveys, laboratory tasks, and observations and extended past work by considering multiple dimensions of social perspective-taking including ability, tendency, and accuracy. Results provided support for friendship adjustment trade-offs of social perspective-taking. Full article

This systematic review explores the role of smartwatches in stress management, mental health monitoring, and overall well-being. Drawing from 61 peer-reviewed studies published between 2016 and 2025, this review synthesizes empirical findings across diverse methodologies, including biometric data collection, machine learning algorithms, and user-centered design evaluations. Smartwatches, equipped with sensors for physiological signals such as heart rate, heart rate variability, electrodermal activity, and skin temperature, have demonstrated promise in detecting and predicting stress and mood fluctuations in both clinical and everyday contexts. This review emphasizes the need for interdisciplinary collaboration to advance technological precision, ethical data handling, and user experience design. Moreover, it highlights how different algorithms—such as Support Vector Machines (SVMs), Random Forests, Deep Neural Networks, and Boosting methods—perform across various physiological signals (e.g., HRV, EDA, skin temperature). Furthermore, it identifies performance trends and challenges across lab-based vs. real-world deployments, emphasizing the trade-off between generalizability and personalization in model design. Full article

Computational models of peritoneal dialysis (PD) are increasingly useful for optimizing treatment in patients with kidney disease requiring dialysis (KDRD). However, although several mathematical models have been developed in the past few decades, a direct comparison of the models’ accuracy with respect to predicting in vivo data is needed to further create robust personalized models. Here, we used a dataset obtained in a previous in vivo experimental model of PD in pigs (23 sessions of 4 h 2 L dwells in four pigs) and humans (20 sessions in 20 patients) to compare six computational models of PD: the Graff model (UGM), the three-pore model (TPM), the Garred model (GM), and the Waniewski model (WM), as well as two variations of these (UGM-18, SWM). We conducted this comparison to predict the dialysate concentrations of key uremic toxins and electrolytes (four in humans) throughout a 4 h dwell. The model predictions can provide insight into inter-individual differences in ultrafiltration, which are critical for tailoring PD regimens in KDRD. While TPM offered improved physiological reality, its computational cost suggests a trade-off between model complexity and clinical applicability for real-time or portable kidney support systems. In future applications, such models could provide adaptive PD regimens for tailored care based on patient-specific toxin kinetics and fluid dynamics. Full article

Mass customization (MC) has become a pivotal manufacturing strategy for addressing the growing demand for personalized products without compromising cost efficiency and scalability. The emergence of Industry 4.0 (I4.0) has further expanded the potential of MC by enabling intelligent, flexible, and interconnected production systems. This paper presents a systematic literature review covering the period from 2011 to 2024, aimed at examining how I4.0 technologies influenced the conceptual evolution, technological enablers, and supply chain implications of MC. A total of 3441 publications were retrieved from Scopus and analyzed using a combination of bibliometric mapping and qualitative synthesis. The review identifies three primary research streams: (1) MC conceptual frameworks and performance metrics, (2) enabling technologies and methods across the product lifecycle, and (3) supply chain strategies tailored to MC environments. Key enablers such as product modularity, customer co-design platforms, additive manufacturing, and reconfigurable production systems are discussed, along with barriers related to complexity, integration challenges, and sustainability trade-offs. The study highlights a gradual convergence toward mass personalization, supported by real-time data, artificial intelligence, and predictive analytics. The findings offer a structured understanding of MC in the I4.0 context and point toward future research opportunities involving digital twin integration, cross-disciplinary implementation models, and sustainability-driven customization frameworks. Full article

We propose Phase-Adaptive Federated Learning (PAFL), a novel framework for privacy-preserving personalized travel itinerary generation that dynamically balances privacy and utility through a phase-dependent aggregation mechanism inspired by phase-change materials. (1) PAFL’s primary objective is to dynamically optimize the privacy–utility trade-off in federated travel recommendation systems through phase-adaptive anonymization. The phase parameter φ ∈ [0, 1] operates as a tunable control variable that continuously adjusts the latent space geometry between differentially private (φ→1) and utility-optimized (φ→0) representations via a thermodynamic-inspired transformation. Conventional federated learning approaches often rely on static privacy-preserving techniques, which either degrade recommendation quality or inadequately protect sensitive user data; PAFL addresses this limitation through three key innovations: a latent-space phase transformer, a differential privacy-gradient inverter with mathematically provable reconstruction bounds (εt ≤ 1.0), and a lightweight sequential transformer. (2) PAFL’s core innovation lies in its phase-adaptive mechanism that dynamically balances privacy preservation through differential privacy and utility maintenance via gradient inversion, governed by the tunable phase parameter φ. Experimental results demonstrate statistically significant improvements, with 18.7% higher HR@10 (p < 0.01) and 62% lower membership inference risk compared to state-of-the-art methods, while maintaining εtotal < 2.3 over 100 training rounds. The framework advances federated learning for sensitive recommendation tasks by establishing a new paradigm for adaptive privacy–utility optimization. Full article

Background/Objectives: While a preference for an equal distribution of health gains is common, there are situations where individuals may opt to concentrate health gains for a select few. This study investigates how distributive preferences, defined as societal valuations of alternative allocations of fixed total health benefits, vary with the magnitude of individual health gains. Methods: Using the person trade-off (PTO) method, we conducted an online survey with a nationally representative sample of Chinese adults (N = 500). The respondents evaluated five allocation programs differing in both individual health gain magnitude and number of beneficiaries. Distributive preferences are classified into five distinct types: diffusion, concentration, maximization, extreme egalitarianism and extreme inequality seeking. Threshold regression analysis identified critical transition points in preference patterns. Results: Non-maximizing tendencies were dominant (79% of the respondents). The health gain threshold was estimated to be 4.6 years (95% CI: [4.28, 4.85]): below this threshold, respondents tend to allocate smaller benefits to more patients (diffusion preference); above the threshold, people are inclined to allocate larger benefits to fewer patients (concentration preference). The income level and self-reported health status of the participants were identified as potential factors influencing distributive preferences. Conclusions: This study provides the first quantitative evidence from China that distributive preferences exhibit a non-linear shift based on the magnitude of health benefits. The identified 4.6-year threshold provides policymakers with an empirically based instrument to strike a balance between efficiency and the reduction in inequality in resource allocation. These findings advocate for incorporating social value weights into health technology assessments, especially for interventions that offer substantial individual benefits. Full article

In the Internet of Things (IoT), data distribution among diverse terminals exhibits substantial statistical heterogeneity. This imbalance can lead to skewness and accuracy degradation, ultimately affecting the generalization ability and robustness of Federated Learning (FL) models. Our work addresses these critical challenges by proposing a novel method, Meta-Equilibrized Federated Learning (MEFL), which integrates meta-learning with gradient-descent preservation and an equilibrated optimization aggregation mechanism based on gradient similarity and variance weighted adjustment. By alleviating the gradient biases caused by multi-step local updates from the source, MEFL effectively resolves the issues of inconsistency between global and local optimization objectives. MEFL optimizes trade-offs between local and global models, and provides an efficient solution for cross-domain data security deployment in IoT scenarios. Comprehensive experiments conducted on real-world datasets demonstrate that MEFL achieves at least 3.26% improvement in final test accuracy, and substantially lowers communication overhead, compared to the existing state-of-the-art baseline methods. The results demonstrate that MEFL exhibits superior performance and generalization capability in addressing personalization challenges with imbalanced non-IID data distributions. Full article

Background/Objectives: Urinary tract infections (UTIs) are frequently diagnosed empirically, often leading to overtreatment and rising antimicrobial resistance. This study aimed to develop and evaluate machine learning (ML) models that predict urine culture outcomes using routine urinalysis and demographic data, supporting more targeted empirical antibiotic use. Methods: A real-world dataset comprising 8065 urinalysis records from a hospital laboratory was used to train five ensemble ML models, including random forest, XGBoost (eXtreme gradient boosting), extra trees, voting classifier, and stacking classifier. Models were developed using 10-fold stratified cross-validation and assessed via clinically relevant metrics including specificity, sensitivity, likelihood ratios, and diagnostic odds ratios (DORs). To enhance screening utility, threshold optimization was applied to the best-performing model (XGBoost) using the Youden index. Results: XGBoost and random forest demonstrated the most balanced diagnostic profiles (AUROC: 0.819 and 0.791, respectively), with DORs exceeding 21. The voting and stacking classifiers achieved the highest specificity (>95%) and positive likelihood ratios (>10) but exhibited lower sensitivity. Feature importance analysis identified positive nitrites, white blood cell count, and specific gravity as key predictors. Threshold tuning of XGBoost improved sensitivity from 70.2% to 87.9% and reduced false negatives by 82%, with an associated NPV of 96.4%. The adjusted model reduced overtreatment by 56% compared to empirical prescribing. Conclusions: ML models based on structured urinalysis and demographic data can support clinical decision-making for UTIs. While high-specificity models may reduce unnecessary antibiotic use, sensitivity trade-offs must be considered. Threshold-optimized XGBoost offers a clinically adaptable tool for empirical treatment decisions by improving sensitivity and reducing overtreatment, thus supporting the more personalized and judicious use of antibiotics. Full article

Urban air mobility (UAM) is expected to provide environmental benefits while enhancing transportation for citizens and businesses, particularly in commercial and emergency medical applications. The rapid development of electric vertical take-off and landing (eVTOL) aircraft has demonstrated the potential to introduce new technological capabilities to the market, fostering visions of widespread and diverse UAM applications. This paper reviews state-of-the-art occupant safety for personal drones and examines existing occupant protection methods in the aircraft. The study serves as a guide for stakeholders, including regulators, manufacturers, researchers, policymakers, and industry professionals—by providing insights into the regulatory landscape and safety assurance frameworks for eVTOL aircraft in UAM applications. Furthermore, we present a functional hazard assessment (FHA) conducted on a reference concept, detailing the process, decision-making considerations, and key variations. The analysis illustrates the FHA methodology while discussing the trade-offs involved in safety evaluations. Additionally, we provide a summary and a featured description of current eVTOL aircraft, highlighting their key characteristics and technological advancements. Full article

Spike neural networks (SNNs) perform excellently in various domains. However, SNNs based on differential privacy (DP) protocols introduce uniform noise to the gradient parameters, which may affect the trade-off between model efficiency and personal privacy. Therefore, the adaptive differential private SNN (ADPSNN) is proposed in this work. It dynamically adjusts the privacy budget based on the correlations between the output spikes and labels. In addition, the noise is added to the gradient parameters according to the privacy budget. The ADPSNN is tested on four datasets with different spiking neurons including leaky integrated-and-firing (LIF) and integrate-and-fire (IF) models. Experimental results show that the LIF neuron model provides superior utility on the MNIST (accuracy 99.56%) and Fashion-MNIST (accuracy 92.26%) datasets, while the IF neuron model performs well on the CIFAR10 (accuracy 90.67%) and CIFAR100 (accuracy 66.10%) datasets. Compared to existing methods, the accuracy of ADPSNN is improved by 0.09% to 3.1%. The ADPSNN has many potential applications, such as image classification, health care, and intelligent driving. Full article

The widespread use of mobile devices has led to the continuous collection of vast amounts of user-generated data, supporting data-driven decisions across a variety of fields. However, the growing volume of these data raises significant privacy concerns, especially when they include personal information vulnerable to misuse. Differential privacy (DP) has emerged as a prominent solution to these concerns, enabling the collection of user-generated data for data-driven decision-making while protecting user privacy. Despite their strengths, existing DP-based data collection frameworks are often faced with a trade-off between the utility of the data and the computational overhead. To address these challenges, we propose the differentially private fractional coverage model (DPFCM), a DP-based framework that adaptively balances data utility and computational overhead according to the requirements of data-driven decisions. DPFCM introduces two parameters, α and β, which control the fractions of collected data elements and user data, respectively, to ensure both data diversity and representative user coverage. In addition, we propose two probability-based methods for effectively determining the minimum data each user should provide to satisfy the DPFCM requirements. Experimental results on real-world datasets validate the effectiveness of DPFCM, demonstrating its high data utility and computational efficiency, especially for applications requiring real-time decision-making. Full article

Federated learning (FL) enables privacy-preserving collaborative model training across edge devices without exposing raw user data, but it is vulnerable to privacy leakage through shared model updates, making differential privacy (DP) essential. Existing DP-based FL methods, such as fixed-noise DP, suffer from excessive noise injection and inefficient privacy budget allocation, which degrade model accuracy. To address these limitations, we propose an adaptive differential privacy mechanism that dynamically adjusts the noise based on gradient sensitivity, optimizing the privacy–accuracy trade-off, along with a hierarchical privacy budget management strategy to minimize cumulative privacy loss. We also incorporate communication-efficient techniques like gradient sparsification and quantization to reduce bandwidth usage without sacrificing privacy guarantees. Experimental results on three real-world datasets showed that our adaptive DP-FL method improved accuracy by up to 8.1%, reduced privacy loss by 38%, and lowered communication overhead by 15–18%. While promising, our method’s robustness against advanced privacy attacks and its scalability in real-world edge environments are areas for future exploration, highlighting the need for further validation in practical FL applications such as personalized recommendation and privacy-sensitive user behavior modeling. Full article

Chinese cities have made significant progress in fostering low-carbon societies and piloting a variety of low-carbon measures. Nonetheless, the effective implementation of these initiatives and the long-term upkeep of related amenities rely heavily on resident support. The existing studies provide limited insight into how local residents perceive and endorse different types of low-carbon measures, which often involve varying trade-offs. Addressing this gap, the present study surveyed the willingness to pay (WTP) of residents in Hangzhou—an early adopter of low-carbon practices in China—across five representative low-carbon measures. Survey data were collected from 13 distinct residential neighborhoods. The results indicate that Hangzhou residents are more inclined to financially support measures offering direct personal benefits compared to those benefiting the collective good, with this tendency being notably pronounced among highly educated individuals. Further findings include the following: (1) respondents aware of ongoing low-carbon measures were more willing to pay for them; (2) male respondents, recent migrants (within the past five years), high-income groups, and residents in aging communities tended to contribute higher amounts; (3) providing detailed information on carbon mitigation effects markedly increased both the likelihood and the magnitude of WTP; (4) the promotion of new energy vehicles (NEVs) remains contentious, particularly between NEV owners and gasoline vehicle owners. These findings highlight the need for targeted policies and educational programs to strengthen public awareness and support for low-carbon interventions, thereby advancing sustainability in fast-growing urban centers like Hangzhou. Overall, these findings provide key insights for the formulation of low-carbon city policies and sustainable urban planning, emphasizing the global importance of local socioeconomic dynamics and offering a valuable reference for cities worldwide seeking to advance sustainability transitions and meet international climate targets. Full article

Background/Objectives: Determining appropriate cellular objectives is crucial for the system-scale modeling of biological networks for metabolic engineering, cellular reprogramming, and drug discovery applications. The mathematical representation of metabolic objectives can describe how cells manage limited resources to achieve biological goals within mechanistic and environmental constraints. While rapidly proliferating cells like tumors are often assumed to prioritize biomass production, mammalian cell types can exhibit objectives beyond growth, such as supporting tissue functions, developmental processes, and redox homeostasis. Methods: This review addresses the challenge of determining metabolic objectives and trade-offs from multiomics data. Results: Recent advances in single-cell omics, metabolic modeling, and machine/deep learning methods have enabled the inference of cellular objectives at both the transcriptomic and metabolic levels, bridging gene expression patterns with metabolic phenotypes. Conclusions: These in silico models provide insights into how cells adapt to changing environments, drug treatments, and genetic manipulations. We further explore the potential application of incorporating cellular objectives into personalized medicine, drug discovery, tissue engineering, and systems biology. Full article