Search Results (21,581)

The external relations of small towns are an important dimension in the regional urban system. However, the “metropolitan bias” in existing studies results in a lack of empirical verification of their characteristics, hindering effective regional policymaking. Applying Central Flow Theory (CFT), mobile phone data, and a multiscale geographically weighted regression (MGWR) model, this study investigates the spatially non-stationary associations between built environment factors and the “city-ness” and “town-ness” of small towns in the Yangtze River Delta. The results show: (1) Enterprise density in metropolitan shadow areas is positively associated with cross-city jobs–housing separation; in peripheral areas, both enterprise density and housing prices exhibit a strong correlation with intra-municipal jobs–housing separation. (2) Middle schools consistently correlate with localized intra-municipal flows, suggesting a plausible spatial anchoring role; around metropolises, medical and commercial facilities link to recreational flows and commuting town-ness, while in distal small towns, medical facilities coincide with intratown jobs–housing balance, and commercial facilities correlate with localized consumption and cross-town employment mobility. (3) Higher road network density corresponds to a shrinking commuting radius near metropolises and intra-municipal intertown interconnection in distal towns, rather than mere external relation channels. This study empirically supports CFT at the small-town scale, explores plausible mechanisms, and informs differentiated planning strategies. Full article

Background/Objectives: Therapeutic drug monitoring (TDM) of immunosuppressants is essential in treating pediatric kidney diseases; however, repeated venipuncture is burdensome in children. We evaluated whether minimally invasive fingerstick capillary sampling combined with liquid chromatography–tandem mass spectrometry (LC-MS/MS) provides results analytically comparable to those of conventional venous sampling. Methods: Capillary whole blood (2.8 µL) was collected via fingersticks from pediatric patients receiving mycophenolate mofetil, with or without tacrolimus (TAC) or cyclosporine A (CsA). Drug concentrations were quantified using a previously validated simultaneous LC-MS/MS method and compared with conventional venous sampling using linear regression and Bland–Altman analyses. Results: Seventy-four paired samples from 21 patients were analyzed. Strong correlations were observed between capillary and venous samples for mycophenolic acid (MPA), TAC, and CsA (R² > 0.90). Hematocrit correction improved agreement for MPA. Bland–Altman analyses demonstrated acceptable bias across analytes. Conclusions: Fingerstick-based microvolume sampling combined with LC-MS/MS provides analytically reliable immunosuppressant quantification in pediatric patients. Although larger clinical validation is required, this minimally invasive approach may reduce procedural burden and may support future outpatient or home-based TDM strategies. Full article

Background/Objectives: Nutritional research emphasizes evaluating food processing levels alongside nutrient content. The Nova system categorizes foods as minimally processed foods (MPFs), processed culinary ingredients (PCIs), processed foods (PFs), and ultra-processed foods (UPFs). High UPF consumption is linked to adverse health outcomes in older adults. Traditional Food Frequency Questionnaires (FFQs) often fail to capture processing differences. This study evaluated the reproducibility and relative validity of a Nova-based FFQ (NFFQ-Elderly) in Italian healthy older adults aged ≥65 years. Methods: A total of 111 older adults (73.7 ± 5.9 years; 56.8% women) completed the NFFQ-Elderly twice (4–6 weeks interval). Relative validity was compared with a three-day weighed food record. Foods were categorized by Nova groups and analyzed for absolute intake, energy and weight percentages. Pearson correlation (r), intraclass correlation coefficients (ICCs), and Bland–Altman plots were used. Results: Reproducibility was satisfactory for MPFs (r = 0.75; ICC = 0.74), UPFs (r = 0.87; ICC = 0.85), and PFs (r ≈ 0.73; ICC ≈ 0.66–0.67). Relative validity was moderate for MPFs (r = 0.57; ICC = 0.53) and UPFs (r = 0.48; ICC ≈ 0.37), but lower for PCIs. Accuracy generally improved when intakes were expressed as percentages of total energy or weight. Bland–Altman analyses showed limited mean bias for MPFs and PFs, but higher variability for PCIs and absolute energy intake. Conclusions: The NFFQ-Elderly appears to be a suitable tool for ranking older adults according to their relative intake of MPFs and UPFs. Estimates for PCIs are less reliable, indicating caution when interpreting absolute intake values. Full article

IoT-based smart greenhouse sensing, real-time signal conditioning and abnormality detection are still predominantly executed at gateway or cloud levels, limiting responsiveness and increasing vulnerability to noise-induced false alarms. This study proposes and experimentally validates a mobile-edge signal processing and abnormality detection framework executed entirely within an Android-based smartphone application, eliminating dependence on continuous cloud-side analytics. Environmental data from 27 wireless sensor nodes measuring temperature, relative humidity, CO₂ concentration, and light intensity were processed in real time using a sliding-window moving-average filter (N = 6) implemented with O(1) computational complexity. Abnormal conditions were determined via thresholding combined with temporal majority voting validation to suppress transient violations. Performance was also evaluated with direct threshold-based detection on raw signals to assess the effect of mobile-side filtering and temporal majority validation on abnormal sample counts, event fragmentation, and detection consistency. Mobile application side signal conditioning reduced short-term variance by 35–55% while maintaining an effective delay below two sampling intervals. Event-level analysis demonstrated substantial consolidation of noise-induced detections, reducing abnormal event frequency by up to 69% and increasing median event duration from 5 to 38 min for temperature, with negligible detection bias (±1.1%). End-to-end processing latency remained bounded under sustained multi-node streaming, with median delays of 1.0–1.6 s and 95th-percentile delays below 4.0 s. These results demonstrate that lightweight mobile-edge signal conditioning can significantly enhance detection robust-ness, reduce false alarms, and achieve low-latency environmental monitoring in green-houses. The proposed framework provides scalable and computationally efficient architecture for real-time abnormality detection in precision agriculture systems. Full article

This study evaluates GNSS water-vapor tomography across the Lisbon metropolitan area and explores how increasing network density with low-cost receivers improves three-dimensional humidity fields for meteorological applications. Three configurations were tested for December 2022, a month characterized by several rainfall events, including a severe urban-impacting one: (i) a hybrid setup combining permanent and low-cost stations (TOMO_PL), (ii) a dense network of only low-cost stations (TOMO_L), (iii) a sparse arrangement using only permanent stations (TOMO_P). Tomographic water vapor density fields were compared with independent references from the Weather Research and Forecasting (WRF) model, ERA 5 reanalysis, and radiosonde data. All products show the expected exponential decline in water vapor with increasing altitude. Tomography consistently underestimates moisture in the lowest 2.0 to 2.5 km and tends to overestimate it at higher levels, with a weaker correlation above mid-tropospheric heights. Vertical RMSE remains below 2 g m⁻³ for all solutions, but TOMO_P performs the worst due to weak and uneven spatial geometry. Time–height analysis reveals that densified setups capture the changing moisture in the lower atmosphere, including increased near-surface humidity during December 11–13, when rainfall exceeded 120 mm in 24 h, although mid-level intrusions and dry layers observed by radiosondes are not captured. Mean PWV patterns show realistically low points over the Sintra mountain range and align best with TOMO_PL (spatial RMSE 0.6 g m⁻³, bias 0.4 g m⁻³, correlation 0.9), while TOMO_P creates artifacts that mimic mesoscale gradients. Categorized skill analysis shows the highest accuracy under high-moisture conditions and limited ability to detect dry conditions, with TOMO_PL showing the best overall performance against both ERA5 and WRF. Overall, low-cost densification significantly enhances boundary-layer humidity and PWV retrievals, supporting their use for urban heavy-rain monitoring and, with error-aware integration, for short-term forecasting. Full article

Background/Objectives: Mild cognitive impairment (MCI), the prodromal stage of Alzheimer’s disease, may be influenced by nutritional status and genetic susceptibility. This systematic review synthesised evidence on how nutritional biomarkers interact with genetic variants, particularly APOE ε4, to influence cognitive outcomes in individuals with MCI. Methods: Following PRISMA 2020 guidelines, seven studies were included (three longitudinal, two randomised controlled trials, and two cross-sectional) involving adults aged ≥55 years with MCI. Nutritional exposures comprised plasma or serum concentrations of vitamins A, D, E, the vitamin B group, lipids, selenium, and ketogenic medium-chain triglycerides. Genetic risk was assessed primarily through APOE ε4 status. Risk of bias was assessed using RoB 2 and ROBINS-I, and certainty of evidence using GRADE. Due to heterogeneity in biomarkers, cognitive tools, and study designs, findings were synthesised narratively. Results: Across nutrient categories, higher concentrations of vitamin D, selenium, and antioxidants were associated with better cognitive outcomes. kMCT supplementation improved episodic memory and brain energy metabolism. Evidence for nutrient–gene interactions was mixed: APOE ε4 modified responses to vitamin B group and selenium but showed limited influence on vitamin D, lipids, or kMCT effects. Heterogeneity in biomarker assays, cognitive tools, and genetic stratification limited comparability across studies. Conclusions: Nutritional biomarkers appear to influence cognitive trajectories in MCI, and some associations may differ by APOE ε4 status. However, small samples and limited genetic stratification constrain interpretation. Future research should prioritise standardised biomarker measurement, genetically stratified cohorts, and individual participant data meta-analyses to clarify nutrient–gene interactions in MCI. Full article

Background: Oral biofilms are a major etiological factor in dental caries, periodontal disease, peri-implantitis, and endodontic infections. Increasing antimicrobial resistance and the limitations of conventional therapies have intensified interest in antimicrobial photodynamic therapy (aPDT). Hypericin, a natural photosensitizer derived from Hypericum perforatum, demonstrates potent reactive oxygen species generation and broad antimicrobial activity; however, its dental applications remain insufficiently synthesized. Objective: To systematically evaluate the antimicrobial efficacy, treatment parameters, safety, and clinical potential of hypericin-mediated aPDT against oral biofilms and infections in dentistry. Methods: This systematic review was conducted according to PRISMA 2020 and registered in PROSPERO CRD42024617727. Electronic searches of PubMed/MEDLINE, Embase, Scopus, and the Cochrane Library (January 2010 to December 2025) were performed. Studies assessing hypericin-mediated aPDT in oral or dental contexts were included. Methodological quality was evaluated using a predefined nine-domain risk-of-bias tool. Results: Eleven studies met the inclusion criteria. Hypericin-mediated aPDT demonstrated strong antimicrobial effects, achieving up to 99% planktonic inactivation and significant biofilm reduction across bacterial and fungal species. Activity was particularly pronounced against Gram-positive organisms, including Staphylococcus aureus and Enterococcus faecalis. However, efficacy against mature biofilms was variable and often dependent on formulation and irradiation parameters. Most studies showed moderate methodological quality, with frequent deficiencies in reporting light calibration and dosimetry. Advanced delivery systems, including liposomal and nanoparticle formulations, improved photodynamic performance. Conclusions: Hypericin-mediated aPDT shows promising antimicrobial activity against oral pathogens and biofilms, with favorable selectivity and safety profiles. Nevertheless, the evidence remains predominantly preclinical and heterogeneous. Standardized protocols and well-designed clinical trials are required before routine dental implementation can be recommended. Full article

Accurate measurement of atmospheric temperature profiles in the mesopause region is crucial for understanding the atmospheric dynamics and climate processes. To address this challenge, a sodium Doppler lidar based on the resonance fluorescence scattering mechanism was recently developed to precisely detect atmospheric temperatures in the mesopause region in Qingdao (36.1°N, 120.1°E), China. For the first time, high-resolution observations of atmospheric temperature in the mesopause region (80–105 km) were achieved by the self-developed Na Doppler lidar in Qingdao under the complex atmospheric conditions of the mid-latitude coastal zone. A systematic cross-validation between the self-developed lidar and SABER satellite observations was conducted, and the temperature bias between the two detection methods in the mesopause region and its altitude-dependent characteristics were quantitatively assessed. The temperature profiles measured by lidar exhibited good agreement when compared with the satellite data yielding estimations of RMSE and mean absolute deviation of 9.2 K and 7.3 K, respectively, from 80 km to 100 km altitudes. A correlation analysis conducted between the lidar temperature data and satellite data showed that the closer the satellite passed over Qingdao, the better the correlation demonstrated by the data. The correlation coefficient of the closer comparison data can reach 0.86, which means that the self-developed lidar system in Qingdao has a good ability to detect temperature profiles in the middle and upper atmosphere. The nocturnal evolution details and short-period fluctuations of the temperature field in the mesopause region over Qingdao were observed, revealing the local temperature structural characteristics under the complex atmospheric conditions at the land–sea interface in the Qingdao area. Full article

Business development unfolds within complex adaptive environments marked by nonlinear interaction, structural asymmetry, and recurrent instability. Sustained performance under such conditions requires regulatory structures that preserve coherence while enabling structural transformation. This study advances symmetry evolution as a systems principle that explains the emergence of balance through interaction among decision bias, structural symmetry, and regulatory intensity. An evolutionary regulation framework represents this interaction as a closed-loop dynamic that drives coevolution of regulation and symmetry through recursive feedback. Stability emerges as a property of proportional coupling rather than correction of deviations. Multi-modal simulations representing turbulent decision landscapes demonstrate formation of bounded oscillatory equilibrium under perturbation while preserving exploratory capacity, with a mean recovery interval of 1.01 iterations, compared with 9.56 under fixed regulatory intensity and 47.29 under exogenous adjustment, indicating a substantial reduction in recovery time. Coordinated evolution of regulatory gain and structural symmetry sustains adaptive stability without suppressing innovation dynamics. The study establishes a systemic foundation for resilience and endogenous governance in complex business systems and reframes decision optimization as structural adaptation within evolving regulatory architectures. Full article

This study investigates Downlink/Uplink Decoupling (DUDe) in 5G networks, a framework that allows user equipment to select its uplink serving cell independently of the downlink anchor. This approach is designed to alleviate the “macro bias” and pathloss issues that typically degrade performance for Internet of Things (IoT) traffic. We propose a framework managed by Mobile Edge Computing (MEC) that operates on a per-Transmission Time Interval (TTI) basis, incorporating stability mechanisms such as hysteresis and Time to Trigger to prevent frequent, unnecessary handovers. The performance is evaluated using a system-level simulator across two scenarios: a high-density urban IoT deployment and an Industry 4.0 smart factory environment. Our results demonstrate that the proposed framework significantly improves uplink throughput and reduces tail latency compared to traditional coupled association methods. Furthermore, an ablation study confirms that these performance gains are derived from the structural decoupling of links, providing a scalable path for improving connectivity in 5G and beyond. Full article

Capacitive micromachined ultrasonic transducers (CMUTs) have been developed over the past 30 years and achieved practical applications in both medical imaging and industrial non-destructive testing. This article presents the fundamental principles of CMUTs and surveys fabrication technologies, offering a comprehensive review of major advances and challenges in medical ultrasound and photoacoustic imaging applications. The article further reviews and analyzes three primary challenges currently confronting CMUTs in medical imaging applications: lower output acoustic pressure, dielectric charging effects, and the need for high bias voltage. It also presents and discusses a potential combined approach to comprehensively address these challenges, with the aim of enhancing CMUT performance and broadening clinical adoption. Full article

In industrial environments, robust Temporal Action Localization (TAL) is essential; however, frequent occlusions often compromise the reliability of skeletal data, leading to negative transfer in multimodal fusion. To address this challenge, we propose a Gated Skeleton Refinement Module (Gated SRM), a universal front-end preprocessing module that explicitly incorporates OpenPose confidence scores into the network architecture. By applying these scores as a logarithmic bias within a self-attention mechanism, our method achieves soft suppression—dynamically attenuating the attention weights assigned to unreliable joints—before adaptively fusing the refined skeletal features with RGB representations through a learnable gating network. Extensive experiments on the heavily occluded IKEA ASM dataset demonstrate that our approach effectively prevents the catastrophic accuracy degradation typical of naive and established multimodal fusion strategies, improving the mean Average Precision (mAP) to 21.77%, maintaining parity with the RGB-only baseline while demonstrating superior robustness. Furthermore, the system maintains a practical end-to-end inference speed of approximately 9.2 frames per second (FPS), which is sufficient for monitoring macro-level industrial workflows. By prioritizing confidence-based data selection over data restoration, this sensor-metadata-driven architecture offers a robust and principled approach acting as a critical fail-safe and safety-net for real-world action recognition under occlusion. Full article

This study examines the impact of explainable artificial intelligence (XAI) features on user satisfaction and purchase intention in Saudi mobile shopping applications, utilising the stimulus–organism–response (S–O–R) framework. With the increasing reliance on AI-driven decision support in e-commerce, enhancing transparency, fairness, trustworthiness, and interpretability has become crucial for shaping consumer perceptions and behavioural responses. The research employed a quantitative methodology using partial least squares structural equation modelling (PLS-SEM) to examine the relationships among stimulus factors, cognitive and affective states, consumer satisfaction, and purchase intention. In a survey of 597 respondents from Jeddah and Makkah, Saudi Arabia, the findings highlight that fairness and bias detection, trustworthiness, and transparency significantly influence consumers’ cognitive and affective states, which in turn enhance satisfaction and intention to purchase. Consumer satisfaction emerged as a critical mediator, reinforcing the role of positive emotional and cognitive experiences in driving purchase behaviours. However, interpretability showed limited impact, suggesting that consumers may prioritise fairness and trustworthiness over technical clarity of explanations. Theoretically, this study contributes to advancing knowledge on the role of XAI in consumer behaviour by integrating fairness, transparency, and affective responses into the S–O–R paradigm. From a managerial perspective, the results underscore the importance for mobile shopping platforms to design AI systems that foster trust, reduce perceived bias, and ensure transparency, thereby improving consumer engagement and purchase outcomes. By addressing gaps in interpretability and transparency, businesses can strengthen user trust and loyalty, ultimately enhancing competitive advantage in Saudi Arabia’s rapidly growing e-commerce sector. Full article

Background: Treatment adherence and outcomes for drug-resistant tuberculosis (DR-TB) continue to be subpar in rural South Africa, where structural health system limitations, comorbid conditions, and diverse resistance patterns make clinical management more challenging. This study aimed to assess how demographic, clinical, and programmatic factors, including a Community Engagement–Clinical Governance (CE–CG) implementation period, affect DR-TB treatment outcomes using explanatory predictive modeling. Methods: A retrospective cohort study was conducted using routine program data from 694 DR-TB patients. A complete-case analysis was performed for multivariable modeling (n = 282). Logistic regression and decision tree models were used to examine the relationships between treatment success and selected predictors, including age, sex, treatment regimen, resistance phenotype, comorbidities, and the CE–CG implementation period. Model discrimination and performance were evaluated using receiver operating characteristic (ROC) curves, pseudo-R² statistics, likelihood ratio tests, and multicollinearity diagnostics. Results: The cohort had a mean age of 40.7 years, and 58.8% of patients were male. Overall treatment success was 59.9%. Severe resistance phenotypes were rare (1.7%) but clinically significant. Comparative analysis showed no notable demographic or outcome differences between included and excluded patients, indicating minimal selection bias. In adjusted models, treatment initiation during the CE–CG implementation period was significantly linked to lower odds of treatment success (adjusted odds ratio [aOR] = 0.443; 95% CI: 0.240–0.818; p = 0.009). Severe resistance phenotypes were strongly negatively associated with treatment success (aOR = 0.303; p = 0.056). Logistic regression models had limited discriminatory ability (AUC: 0.523–0.548), while the decision tree model showed modest improvement (AUC: 0.626). Overall, the model’s explanatory power was limited (pseudo-R² = 0.029), although no evidence of multicollinearity was found. Conclusions: Programmatic implementation periods and resistance severity were important factors associated with treatment outcomes in this rural DR-TB cohort. Although model discrimination was modest and explanatory power was limited, the findings provide useful insights into structural and programmatic vulnerabilities that affect treatment success in real-world settings. Strengthening clinical governance, improving routine program documentation, and incorporating more granular adherence, social, and governance indicators into routine data systems may improve both program evaluation and future predictive modeling. Full article

Low earth orbit (LEO) satellite orbit prediction is one of the key measures to compensate for position errors and ensure position accuracy, which plays an important role in the aerospace communication network for undertaking functions such as routing relay, real-time communication, and signal forwarding. However, existing learning-based satellite orbit prediction models that are recognized as the best measurement inevitably face the problem of distribution bias. Orbit predictions can lead to a decrease in model performance due to different types of satellites (LEO and SSO) and different time scales. In this article, a new method is explored to overcome these shortcomings. Unlike previous methods that explore the temporal correlation of orbit data, this novel orbit prediction method converts satellite orbit data into the frequency domain via Fourier transformation, using a third-order Fourier-derivative convolution framework. Specifically, the proposed Fourier dilation convolution (FDC) model demonstrates better generalization ability across different types of satellites and different time scales by combining frequency domain analysis and dilated convolution. Two real datasets are applied for experimental validation, and the results show the effectiveness of our proposed FDC model. Meanwhile, the proposed FDC model shows a decrease in mean absolute error (MAE) values compared to the temporal convolutional network based seasonal and trend decomposition using a Loess (STL-TCN) model. Quantitative comparisons demonstrate that compared to the STL-TCN model, the FDC model reduces the mean absolute error (MAE) by approximately 10% to 85% across different orbital dimensions. Finally, we conducted further analysis of the interpretability of the model. Full article