Search Results (16,359)

Location estimation is significant in this era of the Internet of Things (IoT). Satellite and cellular signals are often blocked indoors, prompting researchers to explore alternative wireless technologies for indoor positioning. Among these, Wi-Fi Received Signal Strength (RSS) with fingerprinting is dominant in large, multi-floor buildings due to its existing infrastructure, acceptable accuracy, low cost, easy deployment, and scalability. This study aims to systematically search and review the literature on the use of real Wi-Fi RSS fingerprints for indoor localization or positioning in large, multi-floor buildings, in accordance with PRISMA guidelines, to identify current trends, performance, and gaps. Our findings highlight three main public datasets in this fields (covering areas over 10,000 sq.m). Recent trends indicate the widespread adoption of Deep Learning (DL) techniques, particularly Convolutional Neural Networks (CNNs) and Stacked Autoencoders (SAEs). While buildings (in the same vicinity) and their respective floors are accurately identified, the maximum average error remains around 7 m. A notable gap is the lack of public datasets with detailed room or zone information. This review intends to serve as a guide for future researchers looking to improve indoor location estimation in large, multi-floor structures such as universities, hospitals, and malls. Full article

Background: Fine particulate matter (PM_2.5) is an established systemic toxicant, yet its association with clinical skin aging remains incompletely characterized. Although pigmentary changes and wrinkles are commonly attributed to ultraviolet exposure, experimental and epidemiologic evidence suggests that long-term PM_2.5 exposure may contribute to extrinsic skin aging through oxidative, inflammatory, and aryl hydrocarbon receptor-mediated pathways. However, human studies specifically quantifying PM_2.5 exposure in relation to validated skin aging outcomes are sparse, and no prior meta-analysis has systematically synthesized this evidence. Objective: To conduct a systematic review and meta-analysis of epidemiologic studies reporting measured or modeled long-term PM_2.5 exposure and extractable quantitative associations with clinical skin aging outcomes. Methods: We performed a comprehensive PRISMA 2020-guided search of PubMed, Embase, Web of Science, and Scopus (inception to 18 November 2025). Eligible studies included human participants, quantified long-term PM_2.5 exposure, validated clinical or imaging-based skin aging outcomes, and extractable effect estimates. Ratio-type effect measures (arithmetic mean ratios, geometric mean ratios, and odds ratios) were transformed to the natural-log scale, standardized to a common exposure contrast of per 10 µg/m³ PM_2.5, and synthesized as generic relative association metrics. Random-effects models with DerSimonian–Laird estimation and Hartung–Knapp adjustment were applied for pigmentary outcomes. VISIA imaging β-coefficients were synthesized narratively. Results: Four epidemiologic cohorts met predefined eligibility criteria. From these, we extracted seven PM_2.5-specific pigmentary effect estimates, one clinically assessed wrinkle estimate, and two VISIA imaging outcomes. The pooled relative association for pigmentary aging corresponded to a ratio of 1.11 per 10 µg/m³ PM_2.5 (95% CI, 0.82–1.50), indicating a directionally positive but statistically imprecise association compatible with both increased and unchanged pigmentary aging. All individual pigmentary estimates were directionally positive. A single cohort reported a 3.2% increase in wrinkle severity per 10 µg/m³ PM_2.5 (ratio 1.032). VISIA imaging showed significant worsening of brown spot severity (+9.5 percentile per 10 µg/m³), while wrinkle percentiles showed a non-significant change. Conclusions: Based on a comprehensive PRISMA-guided search, the available epidemiologic evidence suggests a consistent directionally positive association between long-term PM_2.5 exposure and pigmentary skin aging outcomes, with limited and uncertain evidence for wrinkle-related phenotypes. The current evidence base remains small, heterogeneous, and of low certainty. Accordingly, these findings should be interpreted as hypothesis-generating and underscore the need for larger, longitudinal, and methodologically harmonized studies. (Registration: PROSPERO CRD420251231462) Full article

Background: Eating habits such as fruit and vegetable (FV) consumption and breakfast frequency are well recognized for their contribution to overall health and well-being. However, the psychological mechanisms that explain the link between these habits and life satisfaction remain poorly explored among university students in the health sciences. Objective: To examine whether positive affect mediates the relationship between FV consumption, breakfast frequency, and life satisfaction among health sciences students. Methods: A cross-sectional study was conducted with 511 students. FV consumption, breakfast frequency, positive affect, and life satisfaction were assessed using self-report measures. Mediation models were applied to estimate direct and indirect associations. Results: FV consumption and breakfast frequency were positively associated with both positive affect and life satisfaction. Although the direct associations with life satisfaction were not significant, the indirect associations through positive affect were significant (FV: β = 0.114, 95% CI [0.055, 0.173]; breakfast: β = 0.133, 95% CI [0.073, 0.192]). The model accounted for 51.4% of the variance in life satisfaction. Conclusions: The results highlight the role of positive affect as a psychological mechanism linking everyday eating habits to life satisfaction, emphasizing the need to integrate emotional components into strategies for promoting healthy lifestyles among university populations. Full article

In recent years, e-bikes have rapidly gained popularity in China. However, riders frequently engage in aberrant behaviors, posing significant traffic safety concerns. Field observation combined with traffic conflict techniques offer an effective approach for identifying risky riding behaviors that significantly affect traffic safety. This study aims to address two major limitations in existing research that can lead to estimation biases: the unsystematic and incomplete inclusion of potential risky riding behaviors, and the insufficient consideration of unobserved heterogeneity in conflict data. Data on 437 e-bike–motor vehicle conflicts were collected at four signalized intersections in Hefei, covering 21 variables including illegal, negligent, and error-prone riding behaviors, as well as sociodemographic factors. Appropriate conflict risk indicators were selected for straight-line and angle conflicts, respectively. A random parameters binary logit model with heterogeneity in means and variances (RPBL-HMV) was developed and compared against binary logistic and mixed logit models. The results indicate that the RPBL-HMV model provides a significantly better goodness-of-fit than the other two models. Six factors with fixed parameters are positively associated with high-risk conflicts, while two factors exhibit random parameters—one of which decreases in mean when riders fail to slow down before turning. The identified risky behaviors and the corresponding targeted countermeasures offer practical insights for regulating unsafe e-bike riding and improving intersection safety. Full article

Medical personnel wearing barrier clothing protecting against infectious agents are at risk of heat stress resulting from limited heat exchange with the environment. The aim of the study was to assess the impact of changing underwear on the thermal parameters of protective clothing sets and on the expected safe working time. The study used a Newton thermal manikin to determine the thermal insulation and water vapor resistance of clothing sets consisting of three types of underwear (standard medical underwear and short and long thermal underwear) worn under two types of barrier suits. The obtained data were used to conduct physiological simulations in the Predicted Heat Strain (PHS) program, estimating the time it takes for core body temperature to rise to 38 °C in conditions of 22 °C and 35 °C. The results showed that replacing medical underwear with thermal underwear at 22 °C extended safe working time by 24%. In hot conditions (35 °C), the positive impact was smaller, extending working time by a maximum of 4%. Changing the inner layer is an effective method of improving comfort and safety in barrier clothing, especially in thermoneutral conditions. Full article

Background: Skin cancer is known to be associated with aging, oxidative stress, and inflammation. The present study aimed to explore the association between PhenoAge, dietary inflammatory index (DII), and dietary oxidative balance index (DOBS) with skin cancer risk. Methods: A total of 474 individuals aged over 20 years who had information on DII, DOBS, PhenoAge, socioeconomic and demographic factors, and self-reported skin cancer, and 16,154 without skin cancer were included in the National Health and Nutrition Examination Survey database (2005–2018). The combination of DII/DOBS was categorized into 3 categories: inflammation- and oxidation-promoting diet, inflammation- and oxidation-reducing diet, and composite diet. We applied logistic regression to estimate odds ratios (ORs) for the association of DII/DOBS and PhenoAge with skin cancer risk, after adjusting for covariates and survey year. Results: PhenoAge was associated with an increased likelihood of skin cancer (OR 1.07, 95% CI 1.06 to 1.08, p < 0.001). DII and DOBS were associated with PhenoAge advancement of OR 1.28 (95% CI 1.20 to 1.36), OR 0.95 (95% CI 0.94 to 0.96), respectively (p < 0.001). After adjusting for all covariates, the comparison between the inflammation–oxidation-promoting diet and the inflammation–oxidation-reducing diet had a positive relationship with skin cancer (OR 2.19, 95% CI 1.29 to 3.72, p = 0.004). PhenoAge mediated 28.06% of the associations between DII/DOBS and skin cancer risk (p < 0.05). The association remained in the subgroup analysis. Conclusion: Our results suggest that an inflammation- and oxidation-promoting diet is related to increased skin cancer risk and may be partly mediated by PhenoAge. Full article

In naturally ventilated dairy barns, many questions regarding airflow, indoor air quality, and emissions are still unanswered, often resulting in inaccurate environmental control of the housing. Particularly, limited understanding of the implications of the constantly changing outdoor weather conditions in interaction with the building design and the role of the characteristics of the animals’ movement inside the building enhances uncertainties in the estimation of airflows within and across the barns. Computational fluid dynamics (CFD) have been used in the past to better understand the dynamics of barn climate, but the models are typically too slow to be used for real-time prediction and control. We investigated the effect of animal characteristics (i.e., animal location, orientation, body posture, and dimensions) on the pressure drop in the animal occupied zone considering inlet wind speed from 0.1 m s⁻¹ to 5 m s⁻¹ and wind direction of 0° and 90° in a CFD model. The cow position in general had little impact on the pressure drop at low wind speeds, but became relevant at higher wind speeds. Cows distributed in a more organized alignment showed less airflow resistance, and, therefore, a lower pressure drop and higher air velocities. Moreover, the cow breed affected the pressure drop, with higher withers resulting in a higher pressure drop and air resistance. In contrast, the effects of cow lying–standing ratio on the pressure drop and airflow resistance coefficients were negligible for both investigated wind directions. Our study aims to provide guidance for optimizing parametrizations of the animal occupied zone in order to enhance the speed of simulations without significant loss in model accuracy. In addition, the conclusions drawn from our study may support the adaption of building design and herd management to improve the effectiveness of ventilation concepts of naturally ventilated dairy barns. Full article

The lack of explicit negative labels issue is a prevalent challenge in numerous domains, including CV, NLP, and Recommender Systems (RSs). To address this challenge, many negative sample completion methods are proposed, such as optimizing sample distribution through pseudo-negative sampling and confidence screening in CV, constructing reliable negative examples by leveraging textual semantics in NLP, and supplementing negative samples via sparsity analysis of user interaction behaviors and preference inference in RS for handling implicit feedback. However, most existing methods fail to adequately address the Missing-Not-At-Random (MNAR) nature of the data and the potential presence of unmeasured confounders, which compromise model robustness in practice. In this paper, we first formulate the prediction task in RS with implicit feedback as a positive-unlabeled (PU) learning problem. We then propose a two-phase debiasing framework consisting of exposure status imputation, followed by debiasing through the proposed doubly robust estimator. Moreover, our theoretical analysis shows that existing propensity-based approaches are biased in the presence of unmeasured confounders. To overcome this, we incorporate a robust deconfounding method in the debiasing phase to effectively mitigate the impact of unmeasured confounders. We conduct extensive experiments on three widely used real-world datasets to demonstrate the effectiveness and potential of the proposed methods. Full article

We propose a dual-branch deep learning framework for reconstructing standard 12-lead electrocardiograms (ECGs) from a single-lead input. The model integrates waveform information from Lead I ECG signals with clinically interpretable metadata to enhance reconstruction fidelity and introduces predictive uncertainty estimation to improve interpretability and reliability. A publicly available dataset of 10,646 ECG records was utilized. The model combined Lead I signals with clinical metadata through two processing branches: a CNN–BiLSTM branch for time-series data and a fully connected branch for metadata. Monte Carlo dropout was applied during inference to generate uncertainty estimates. Reconstruction performance was evaluated using Pearson’s correlation coefficient and root mean square error. Metadata consistently contributed to performance improvements, particularly in the QRS complexes and T-wave segments, and the proposed framework outperformed U-Net when metadata were included. Predictive uncertainty showed moderate to strong positive correlations with reconstruction errors, especially in the chest leads, and heatmaps revealed waveform regions with reduced reliability in arrhythmic and morphologically atypical cases. To the best of our knowledge, this is the first study to incorporate predictive uncertainty into ECG reconstruction. These findings suggest that combining waveform data with metadata and uncertainty quantification offers a promising approach for developing more trustworthy and clinically useful wearable ECG systems. Full article

This study develops a value co-creation-oriented analytical framework to evaluate the performance and evolutionary dynamics of China’s national-level quality policies from 1979 to 2023. A comprehensive categorization and scoring system is established to measure policy intensity, coordination, and comprehensiveness. Policy texts are systematically coded through content analysis, and indicator weights are determined using the Analytic Hierarchy Process (AHP). The resulting composite effect values are further analyzed through punctuated-equilibrium testing, breakpoint analysis, and a Vector Autoregression (VAR) model to estimate the temporal lag of policy implementation. Based on 10,962 policy documents retrieved from the Peking University Law Database, the results reveal clear evolutionary stages and cyclical upward trends in policy performance since the reform and opening-up, while the insufficient supply of demand-side policies remains a long-term structural weakness. The overall evolution path shows a transition from unilateral government provision centered on public value to dual government–market regulation driven by mixed commercial value, and finally toward pluralistic quality governance under value co-creation. Empirical evidence also indicates that quality policies act as short-term stimulus instruments that generate positive but sectorally differentiated effects across the three major industries. These findings highlight the need to expand policy coverage, enhance coordination and comprehensiveness, and rebalance the supply structure. Strengthening short-term stimulus effects while promoting inclusive, co-governed, and sustainable quality policy systems can further improve long-term effectiveness and provide useful insights for international discussions on value co-creation-based governance. Full article

Brushless DC motors are widely used for their high power density and efficiency. However, sensorless control remains challenging due to the difficulty of accurate rotor position detection, especially at low speeds. This paper proposes a novel sensorless trapezoidal control method based on Maximum Likelihood Estimation (MLE) for rotor sector detection. Unlike conventional back-EMF zero-crossing techniques, the proposed method uses a statistical algorithm to generate a probability map from prior motor state data, enabling accurate rotor position estimation without sensors. The MLE method operates with a typical computation time of 50–100 $\mathrm{\mu }$s, offering a balanced tradeoff between speed and accuracy. It is significantly faster than Kalman filter-based approaches (200–1000 $\mathrm{\mu }$s) and comparable to observer-based methods (20–80 $\mathrm{\mu }$s), while being more robust than zero-crossing techniques (<5 $\mathrm{\mu }$s). This makes it a practical and cost-effective solution for applications demanding high efficiency and reliability, such as electric mobility systems. Full article

Background: Accurate prediction of survival in septic patients remains a major challenge in intensive care medicine. Established severity scores such as the Acute Physiology and Chronic Health Evaluation II (APACHE II) and the Sequential Organ Failure Assessment (SOFA) are widely used to estimate prognosis, while biochemical markers such as serum lactate may provide complementary information. However, the prognostic interplay between these scores, lactate dynamics, vasopressor requirement, and infection focus has not been fully elucidated in septic populations. Methods: We conducted a retrospective observational study of 146 adult patients with sepsis admitted to the intensive care unit (ICU) of the Hospital Clínico Universitario de Valladolid (HCUV), Spain, between 2022 and 2024. Demographic data, APACHE II and SOFA scores at admission, lactate levels at admission and 24 h, albumin, and procalcitonin were recorded. Vasopressor use (categorized by intensity) and infection focus (urinary vs. non-urinary) were documented. The primary outcome was ICU mortality. Correlation analyses (Pearson or Spearman as appropriate) were performed separately for urinary and non-urinary subgroups. Multivariable logistic regression models were constructed using APACHE II, SOFA, log-transformed lactate at 24 h, vasopressor use, and urinary focus as predictors. Model performance was assessed using Nagelkerke R², area under the ROC curve (AUC), and classification accuracy. Results: ICU mortality was 23.3%. APACHE II (OR 1.092; p = 0.004) and SOFA (OR 1.185; p = 0.023) were independent predictors of ICU mortality, while log-transformed lactate at 24 h showed a positive trend (OR 1.920; p = 0.066). The addition of urinary focus (protective effect, OR 0.19; p = 0.035) and vasopressor requirement (OR 2.20; p = 0.04) modestly improved model discrimination (Nagelkerke R² = 0.395). ROC analyses showed AUCs of 0.800 for APACHE + SOFA + log-lactate, 0.824 for the vasopressor model, and 0.833 for the urinary focus model. The best-performing models achieved >85% overall accuracy, with specificity consistently above 95%. Conclusions: In septic ICU patients, APACHE II and SOFA scores remain independent predictors of ICU mortality, and lactate at 24 h adds prognostic value—particularly in non-urinary infections. Vasopressor requirement and infection focus modestly improved model discrimination, underscoring their clinical relevance. These findings suggest that integrating severity scores with selected metabolic and clinical variables may modestly refine survival prediction in septic patients. Full article

This study conceptualizes transformational leadership not as an evaluation by others but as the leader’s self-recognition—self-perceived transformational leadership (STFL)—and examines the roles of two psychological resources, Vigor and challenge-oriented coping (Overcoming), together with subjective well-being (SWB). Using validated scales, we surveyed approximately 600 employees from large Japanese corporations. After confirming the validity of a four-factor measurement model via confirmatory factor analysis (CFA), we tested structural relationships using structural equation modeling (SEM) with maximum likelihood estimation; indirect effects were assessed with Monte Carlo confidence intervals. The results showed that the strongest direct effect on STFL was from Overcoming, with a moderate and significant direct effect from Vigor. In contrast, the direct effect of SWB on STFL was small and marginal; however, the indirect effects of Vigor and Overcoming on STFL via SWB were small but significant, indicating a pattern of partial mediation. Overall, the primary pathway to STFL is the direct effect of psychological resources, while SWB contributes secondarily as an affective route. These findings refine the dynamics proposed by the broaden-and-build framework: positive affect broadens behavioral repertoires and fosters resource formation, and those resources, in turn, are reflected in self-recognition as a transformational leader—yet with a dominance of the direct resource pathway. Given the cross-sectional, self-report design and the focus on employees of large Japanese firms, additional longitudinal and intervention studies are needed to enhance the generalizability of the conclusions. Full article

Accurate parameter estimation of photovoltaic (PV) models is fundamentally a challenging nonlinear optimization problem, characterized by strong nonlinearity, high dimensionality, and multiple local optima. These characteristics significantly hinder the convergence accuracy, stability, and efficiency of conventional metaheuristic algorithms when applied to PV parameter identification. Although the enterprise development (ED) optimization algorithm has shown promising performance in various optimization tasks, it still suffers from slow convergence, limited solution precision, and poor robustness in complex PV parameter estimation scenarios. To overcome these limitations, this paper proposes a multi-strategy enhanced enterprise development (MEED) optimization algorithm for high-precision PV model parameter estimation. In MEED, a hybrid initialization strategy combining chaotic mapping and adversarial learning is designed to enhance population diversity and improve the quality of initial solutions. Furthermore, a historical trend-guided position update mechanism is introduced to exploit accumulated search information and accelerate convergence toward the global optimum. In addition, a mirror-reflection boundary control strategy is employed to maintain population diversity and effectively prevent premature convergence. The proposed MEED algorithm is first evaluated on the IEEE CEC2017 benchmark suite, where it is compared with 11 state-of-the-art metaheuristic algorithms under 30-, 50-, and 100-dimensional settings. Quantitative experimental results demonstrate that MEED achieves superior solution accuracy, faster convergence speed, and stronger robustness, yielding lower mean fitness values and smaller standard deviations on the majority of test functions. Statistical analyses based on Wilcoxon rank-sum and Friedman tests further confirm the significant performance advantages of MEED. Moreover, MEED is applied to the parameter estimation of single-diode and double-diode PV models using real measurement data. The results show that MEED consistently attains lower root mean square error (RMSE) and integrated absolute error (IAE) than existing methods while exhibiting more stable convergence behavior. These findings demonstrate that MEED provides an efficient and reliable optimization framework for PV model parameter estimation and other complex engineering optimization problems. Full article

During constant-force operations in complex marine environments, underwater manipulators are affected by hydrodynamic disturbances and unknown, time-varying environment stiffness. Under classical impedance control (IC), this often leads to large transient contact forces and steady-state force errors, making high-precision compliant control difficult to achieve. To address this issue, this study proposes a Bayesian recursive least-squares-based fuzzy adaptive impedance control (BRLS-FAIC) strategy with displacement correction for underwater manipulators. Within a position-based impedance-control framework, a Bayesian Recursive Least Squares (BRLS) stiffness identifier is constructed by incorporating process and measurement noise into a stochastic regression model, enabling online estimation of the environment stiffness and its covariance under noisy, time-varying conditions. The identified stiffness is used in a displacement-correction law derived from the contact model to update the reference position, thereby removing dependence on the unknown environment location and reducing steady-state force bias. On this basis, a three-input/two-output fuzzy adaptive impedance tuner, driven by the force error, its rate of change, and a stiffness-perception index, adjusts the desired damping and stiffness online under amplitude limitation and first-order filtering. Using an underwater manipulator dynamic model that includes buoyancy and hydrodynamic effects, MATLAB simulations are carried out for step, ramp, and sinusoidal stiffness variations and for planar, inclined, and curved contact scenarios. The results show that, compared with classical IC and fuzzy adaptive impedance control (FAIC), the proposed BRLS-FAIC strategy reduces steady-state force errors, shortens force and position settling times, and suppresses peak contact forces in variable-stiffness underwater environments. Full article