Search Results (430)

Quantitative evaluation of public transit networks (PTNs) with complex-network models informs route optimization and operational adjustments. Prior studies emphasize large cities and pay limited attention to small-sized urban systems. This study examines the bus network of Cangzhou City, Hebei Province, China, to broaden the empirical scope and characterize PTNs in smaller cities. The dataset for this study comprises route and stop records, passenger boarding logs, and bus GPS traces. We develop a general workflow for bus data cleaning and completion. To characterize the dynamic bus network and compare it with the static network, we construct a static network and Directed Weighted Dynamic Network I (DWDN I) using the L-space method, and we construct Directed Weighted Dynamic Network II (DWDN II) using the P-space method. We calculated network metrics including degree, weighted degree, clustering coefficient, path length, network diameter, network efficiency, and small-world coefficient. The principal results show that: (1) at the macroscopic level, the dynamic PTN tracks passenger demand, as the average degree, weighted average degree, and clustering coefficient fluctuate in concert with passenger flows; (2) key stations concentrate in the urban core, and stations with high weighted degree display pronounced spatial autocorrelation; (3) the exponential form of the weighted-degree distribution indicates that the examined bus network is not scale-free, while the dynamic network’s small-world coefficient exceeds that of the static network across time periods, reflecting stronger small-world characteristics. This study integrates network and spatial attributes of the PTN to offer an exploratory case for investigating public transit networks in third-tier cities. The findings can inform comparable studies and offer practical guidance for bus operators. Full article

Deployed in distributed organizations and edge networks, contemporary intrusion detection increasingly requires high-performing models without centralizing sensitive traffic logs. This study presents a lightweight federated intrusion detection framework that integrates (i) unidirectional serialization to convert tabular flow records into short sequences, (ii) multi-scale one-dimensional convolutions to capture heterogeneous temporal–statistical patterns at different receptive fields, and (iii) an attention-based reweighting module that emphasizes informative feature channels prior to classification. A sample-size-weighted FedAvg aggregation protocol is used to train a global detector without transferring raw data. Experiments on three widely used benchmarks (UNSW-NB15, KDD Cup 99, and NSL-KDD) under multiple client configurations report consistently high detection effectiveness, with peak accuracies of 99.38% (UNSW-NB15), 99.86% (KDD Cup 99), and 99.02% (NSL-KDD), alongside strong precision, recall, and F1 scores. In addition, the proposed framework is quantitatively benchmarked on UNSW-NB15 against two recent federated intrusion detection baselines, FedMSP-SPEC and a multi-view federated CAE-NSVM model, demonstrating improvements of more than 10 percentage points in macro F1-score while retaining a compact architecture. The manuscript further specifies a concrete threat model, clarifies the client data partitioning strategy and Non-IID quantification, and provides a reproducibility protocol (hyperparameters, random seeds, and evaluation procedures) to facilitate independent verification. Full article

This study presents and systematizes a high-reliability measurement and technological dataset suitable for prediction-based validation of the Spent Fuel Interim Storage Facility (SFISF) of the Paks Nuclear Power Plant. The primary objective of this dataset is not the validation of a general-purpose software tool, but to establish a reproducible experimental basis for the objective and quantitative validation of a three-dimensional, facility-scale heat transfer and buoyancy-driven flow model of the SFISF, developed using the finite difference method (FDM), in a passively cooled system where heat conduction, thermal radiation, and natural convection simultaneously occur. The applied measurement systems (SMAS, CTRS, and the in-house developed CFEPR), their spatial arrangement, accuracy characteristics, as well as data post-processing and the generation of model execution inputs are described in detail. Special emphasis is placed on the functional separation of the available data into initialization data, model execution data, and independent validation datasets, ensuring that model assessment does not rely on calibration or parameter fitting. Furthermore, the estimation of decay heat generated by the stored fuel assemblies is presented using both a standard correlation method (ANSI/ANS-5.1) and isotope inventory-based calculations, and the discrepancies between these approaches are treated as input uncertainties and sensitivity analysis factors. The spectral solar load is considered based on the ASTM G-173 reference spectrum, while during cloudy periods an effective irradiance estimation derived from on-site lux measurements is applied. The results indicate that the available measurement and technological information is sufficient for supporting reproducible, transparent, and quantitative validation studies of the three-dimensional numerical model of the SFISF, as well as for assessing the impact of dominant input uncertainties. Full article

Phase unwrapping is a critical step in interferometric imaging modalities such as holography and synthetic aperture radar, yet conventional analytical algorithms struggle in low signal-to-noise and high-speckle environments. This study presents an artificial intelligence (AI)-based phase-unwrapping framework using a Pix2Pix conditional generative adversarial network (cGAN). A model was designed for robustness under Rayleigh-distributed speckle noise and phase decorrelation, conditions representative of realistic interferometric measurements. Trained on synthetically generated wrapped–unwrapped phase pairs, the AI approach was compared against established analytical phase-unwrapping methods, a quality-guided unwrapping algorithm (Herraez)and a minimum-norm network-flow optimization method (Costantini). Quantitative evaluation using the root mean square error (RMSE), structural similarity index measure (SSIM), and a composite performance index demonstrated that the cGAN was superior under noisy conditions, successfully recovering phase information beyond its training noise range at $\sigma =10$, and accurately unwrapping phases up to $\sigma =20$. This was under a pure unwrapping performance analysis, utility performance was also tested comparing all images to clean noiseless phase. The Pix2Pix model also proved resilient to detector artifacts, despite not being explicitly trained on them, and its worst performance yielded RMSE and SSIM values of 0.089 and 0.927, respectively, with perfect values being 0 and 1. The proposed framework simultaneously unwraps and denoises the phase, offering a simple, open-source, and highly adaptable alternative for phase unwrapping in noisy interferometric systems. Future work will focus on extending the framework to experimental datasets. Full article

Communicable diseases remain a significant public health burden in South Africa, particularly where environmental determinants of health intersect with fragmented surveillance systems. Environmental Health Practitioners (EHPs) are legally mandated to implement the surveillance and prevention of communicable disease services at the municipal level. However, this function is inconsistently operationalised and often remains reactive (outbreak-driven), with limited integration into broader national surveillance systems. This study protocol outlines a mixed-methods investigation to develop a practical framework to strengthen the communicable disease surveillance and prevention function within Environmental Health Services in South Africa. The study will assess existing guiding tools, operational practices, and intersectoral collaboration mechanisms supporting surveillance across metropolitan and district municipalities. Quantitative data will be collected through a national survey of EHPs, while qualitative data will be generated through key informant interviews with national stakeholders, focus group discussions with municipal health managers, and a targeted review of municipal documents. Quantitative data will be analysed using descriptive and inferential statistics, while qualitative data will be thematically analysed and triangulated across data sources. The expected outcome is an integrated framework that clarifies roles, strengthens data flow, and promotes proactive, coordinated surveillance and prevention of communicable diseases within environmental health. The developed framework is anticipated to inform policy discussions and may contribute to efforts aligned with Sustainable Development Goal 3, Target 3.3, on reducing communicable disease burdens, by strengthening municipal communicable disease surveillance and prevention. Full article

This paper addresses the question of how many autonomous aerial vehicles (UAVs or drones) can safely operate within a bounded three-dimensional airspace. First, we derive the absolute mathematical limits on drone density using geometric arguments from sphere packing and covering theory. Then, we verify these limits empirically by simulating a swarm controlled via model predictive control. We incrementally increase the number of drones until motion becomes impossible. Each drone is modeled as a double-integrator system with a bounded speed and acceleration and is surrounded by a radius spherical safety zone $r>0$. The drones are controlled via model predictive control with hard separation constraints. We formalize complete blockage as the loss of any feasible non-trivial trajectory set, either due to geometric crowding or dynamic limitations. Using tools from discrete geometry, we establish absolute upper bounds on a safe population via sphere-packing results and sufficient conditions for total immobilization via sphere-covering arguments. We extend these static bounds by incorporating dynamics through stopping-distance analysis, leading to an inflated exclusion radius that captures the effect of finite control authority. In addition, we prove min-cut style flow-capacity bounds that limit feasible throughput across bottlenecks and derive horizon-dependent conflict-graph conditions that capture MPC infeasibility at high densities. These results provide a rigorous theoretical framework for determining the transition from feasible multi-drone operation to inevitable gridlock, offering explicit quantitative thresholds that can inform airspace design, drone density regulation, and the tuning of predictive controllers. We evaluate our theoretical findings with a simulation environment. Full article

Selecting analytical methods for pesticide residues in food increasingly requires balancing regulatory compliance, analytical performance, and environmental sustainability. This study presents a decision-support tool that evaluates the fitness-for-purpose of pesticide analytical methods by integrating SANTE/11312/2021 v2 validation criteria with Analytical GREEnness (AGREE)-based environmental metrics. Implemented in Excel with VBA macros, the tool guides users through the input of method parameters for both quantitative and screening approaches, scoring each against acceptance criteria. Based on the results, methods are classified as suitable for risk assessment, official control, or self-monitoring. The tool also calculates greenness scores to assess environmental impact. Glyphosate analysis in cereals was selected as a case study, and three approaches were compared: liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS), flow-injection coupled to MS/MS (FI-MS/MS), and lateral flow assay (LFA). LC-MS/MS was identified as the only method suitable for official control, while FI-MS/MS and LFA met requirements for self-monitoring. The greenness assessment highlighted substantial differences, with LFA showing the lowest environmental footprint (AGREE scores of 0.63 and 0.68 for manual and automated LFAs). Overall, the tool provides a practical, user-friendly framework for selecting analytical methods that optimize both analytical performance and environmental sustainability, supporting informed decision-making in food testing. Full article

Signage systems are an integral part of modern urban environments, and they influence both city aesthetics and information flow. But their growing use also adds to the embodied carbon footprint of urban infrastructure, a factor that is often overlooked in sustainable city planning. The present study investigates the environmental impact of signage within the context of urban development and climate-responsive design using two Australian case studies, including one installed at a national bank. The assessment is limited to the cradle-to-site (A1–A4) stages, focusing on material production and transportation impacts only. In each case study, one installed signage unit is used as the functional unit, with the results scaled to a nationwide-deployment scenario in Case Study 2. The results show that aluminium and steel dominate signage materials in both mass and embodied carbon. The study also proposes several mitigation strategies, including the use of low-carbon aluminium, higher-grade steel, and design optimization methods. A quantitative analysis also demonstrates the potential reductions in embodied carbon, ranging from 18% to 80.3%, with low-carbon material substitution achieving up to an 83.4% reduction in one case study. The findings also highlight that targeted material and design choices in the signage sector can significantly advance urban sustainability goals. Full article

This article examines differences between conventional manual measurements of tram operations and data extracted automatically using the REWIZOR program, based on the Yolo10s algorithm. The study addresses the broader question of how artificial intelligence can support analyses of passenger exchange processes in public transport and improve the efficiency of data collection. Measurements conducted in four Polish cities included tram types, stop times, and detailed boarding and alighting durations, while the REWIZOR software enabled automatic detection of stop times and passenger flows based on video recordings. The results show that, although both approaches yield consistent qualitative information regarding doors and passenger counts, significant quantitative discrepancies arise. These differences stem mainly from methodological inconsistencies and varying definitions of boarding, alighting, and stop times, as well as from software-related detection errors. The findings indicate that AI-based measurements require calibration against reference methods to allow reliable comparison with conventional datasets. As currently implemented, REWIZOR can be used effectively for internal analyses of passenger flows, if all compared data come from the same system. Further development—such as implementing simultaneous tracking of people and heads—may considerably improve accuracy and facilitate wider applicability in public transport studies. Full article

Land use change driven by accelerated urbanization in Mongolia has precipitated significant degradation of urban riverine ecosystems over the past two decades. This study investigates hydrological transformations in the Selbe River Catchment of Ulaanbaatar, a cold semi-arid urban system undergoing intensive densification. Using the Site-scale Urban Water Mass Balance Assessment (SUWMBA) framework, we quantified water cycle dynamics across four temporal intervals (2008, 2010, 2018, and 2023), capturing shifts in surface runoff, infiltration, and evapotranspiration associated with land use transitions. Calibration and validation employed discharge records from the Selbe-Dambadarjaa gauging station. Results show that total inflows increased from 223 to 312 mm between 2008 and 2023, driven by a more than twentyfold rise in imported water (from 1 to 22 mm). Evapotranspiration declined by roughly one-third, while infiltration displayed a threshold-type non-linear response—rising sharply between 2010 and 2018 before decreasing again in 2023 as imperviousness intensified. Model performance weakened after 2018, underscoring the limitations of conventional hydrological frameworks in rapidly urbanizing contexts. A redevelopment scenario for the Selbe Sub-Center, aligned with the Ulaanbaatar City Master Plan 2040, projected substantially reduced evapotranspiration (132 mm) and markedly increased stormwater runoff (270 mm), reflecting expanded impervious cover and diminished vegetation. Imported water and wastewater flows (each 386 mm) also increased due to full connection to centralized supply and sewerage infrastructure, indicating a shift toward engineered water pathways and reduced hydrological connectivity to the Selbe River. These findings highlight the urgency of water-sensitive urban design and provide evidence directly informing Mongolia’s 2040 Urban Master Plan and decentralization strategy. The study establishes methodological precedent for applying SUWMBA to cold, semi-arid catchments and contributes quantitative insights for integrated land–water management policies. Full article

Background/Objective: To evaluate the association between optical coherence tomography angiography (OCTA)-derived choriocapillaris flow (CCflow), retinal vascular fractal dimension (FD), and drusen burden in eyes with dry age-related macular degeneration (AMD). Methods: This retrospective study included 113 eyes from 73 patients with dry AMD. Eyes were classified into large and small drusen groups based on median drusen area. OCTA-derived CCflow and FD indices of the superficial and deep capillary plexuses were analyzed. Patient-level clustered analyses were performed using linear mixed-effects and generalized estimating equation models to account for inter-eye correlation. Results: Eyes with large drusen showed significantly lower CCflow compared with those with small drusen (p < 0.001), whereas FDsup did not differ between groups, and FDdeep demonstrated only a near-significant trend toward higher values. CCflow was moderately and negatively correlated with drusen area (ρ = −0.452, p < 0.001), whereas FDdeep showed no significant correlation in unadjusted analyses (ρ = 0.137, p = 0.148). In patient-level age-adjusted multivariable models accounting for inter-eye dependency, CCflow remained independently associated with drusen burden, while FDdeep demonstrated an independent association only after adjustment for age. Conclusions: Reduced CCflow is independently associated with increased drusen burden in dry AMD. FD metrics provide complementary descriptive information regarding microvascular remodeling but do not function as independent biomarkers. CCflow may serve as a robust quantitative indicator of early choroidal compromise in dry AMD. Full article

In the field of construction engineering, utilizing information technology to empower and upgrade traditional construction methods has become an inevitable trend. Intelligent construction sites aim to enhance project management, quality, and safety levels through three layers—digitalization, networking, and intelligentization—by leveraging advanced information technologies such as Building Information Modeling (BIM), the Internet of Things (IoT), intelligent equipment, and big data. To promote the application of intelligent construction site technology, this paper takes intelligent construction sites as the research object, with the analysis of application benefits as the primary research focus. It systematically examines the definition and connotation of intelligent construction sites, reviews the current research status of intelligent construction sites and benefit evaluation theory, and proposes an intelligent construction site application benefit system across five dimensions: economy, product, organization, management, and strategy. Existing benefit assessment studies predominantly adopt single-dimension evaluation approaches, lacking integrated frameworks that combine quantitative and qualitative analysis. Return on Investment (ROI) and Analytic Hierarchy Process (AHP) analysis models are employed to calculate and evaluate the direct and indirect benefits, respectively. Validation was conducted through an actual project, and the results demonstrate that the application of intelligent construction sites yields an ROI of 102.7% based on discounted cash flow analysis (8% social discount rate), with an expert scoring of 9.42 for indirect benefits. The analysis models indicate positive benefits associated with intelligent construction site implementation. While direct causal attribution requires controlled comparison, the observed improvements are consistent with theoretical expectations and industry benchmarks for intelligent construction site adoption. This study verifies the availability of the evaluation system through its application to an actual project. It is hoped that this research will provide a reference for decision-making regarding the application and promotion of intelligent construction sites. Full article

Background: Digital transformation is reshaping healthcare operations, with loyalty programs increasingly used to strengthen patient engagement and streamline administrative workflows. However, fragmented information systems and manual verification routines continue to create bottlenecks, inconsistencies, and extended lead times. Methods: This study applies a mixed-methods approach within the Business Process Management (BPM) lifecycle to redesign the eligibility verification process for a loyalty program at Casa de Saúde São Mateus Hospital. Quantitative time measurements were collected during peak periods, while qualitative insights from staff observations and discussions supported process discovery and bottleneck identification. The proposed solution integrates a centralized SQL database, automated verification routines, and a dedicated administrative interface synchronized with the MedicineOne system. Results: The redesigned process reduced eligibility verification time by approximately 80% and improved Flow Efficiency by around 11.7%. Manual interventions, data fragmentation, and discount-application errors decreased substantially. The centralized database improved data reliability, while automated checks enhanced consistency and reduced staff workload. The system also enabled more accurate beneficiary management and improved coordination across administrative activities. Conclusions: Integrating Healthcare 4.0 principles with BPM enhances internal logistics, reduces lead times, and improves operational reliability. The proposed model offers a replicable framework for modernizing healthcare service delivery. Full article

Salient object detection in optical remote sensing images has attracted extensive research interest in recent years. However, CNN-based methods are generally limited by local receptive fields, while ViT-based methods suffer from common defects in noise suppression, channel selection, foreground-background distinction, and detail enhancement. To address these issues and integrate long-distance contextual dependencies, we introduce GDUFormer, an ORSI-SOD detection method based on the ViT backbone and Gated Differential Units (GDU). Specifically, the GDU consists of two key components—Full-Dimensional Gated Attention (FGA) and Hierarchical Differential Dynamic Convolution (HDDC). FGA consists of two branches aimed at filtering effective features from the information flow. The first branch focuses on aggregating spatial local information under multiple receptive fields and filters the local feature maps via a grouping mechanism. The second branch imitates the Vision Mamba to acquire high-level reasoning and abstraction capabilities, enabling weak channel filtering. HDDC primarily utilizes distance decay and hierarchical intensity difference capture mechanisms to generate dynamic kernel spatial weights, thereby facilitating the convolution kernel to fully mix long-range contextual dependencies. Among these, the intensity difference capture mechanism can adaptively divide hierarchies and allocate parameters according to kernel size, thus realizing varying levels of difference capture in the kernel space. Extensive quantitative and qualitative experiments demonstrate the effectiveness and rationality of GDUFormer and its internal components. Full article

Background/Objectives: SA001, a mofetil-ester prodrug of rebamipide, was developed to enhance gastrointestinal absorption and systemic exposure, which was confirmed in a prior Phase 1 study. Given the limited efficacy of current symptomatic therapies for primary Sjögren’s syndrome (pSS), this trial aimed to assess whether the improved bioavailability of SA001 could translate into clinical benefits. Methods: This multicenter, randomized, double-blind, placebo-controlled Phase 2a study enrolled adults who met the 2016 ACR–EULAR criteria for pSS. The participants were randomly assigned to one of four groups: SA001 at 360, 720, or 1080 mg/day (administered twice daily for 8 weeks) or placebo. Exploratory ocular assessments included tear break-up time, ocular surface staining, the Schirmer test, and the Standard Patient Evaluation of Eye Dryness. Oral endpoints included unstimulated whole salivary flow and the Xerostomia Inventory. Anti-SSA(Ro) antibodies were assessed both quantitatively and qualitatively. Safety evaluations comprised adverse events (AEs), ophthalmic examinations, laboratory tests, and vital signs. The efficacy outcomes were exploratory, and this study was not powered to formally test efficacy hypotheses. Results: Twenty-eight women (mean age 58.54 ± 9.29 years; range 41–75 years) were enrolled in this study and randomly assigned to one of the study groups. SA001 showed no statistically significant improvements versus placebo in ocular or oral endpoints, and no consistent dose–response relationship was observed. The anti-SSA(Ro) findings did not differ meaningfully across the groups. SA001 was generally well-tolerated, with infrequent, mostly mild-to-moderate AEs; however, one serious AE occurred in the placebo group. No clinically relevant ophthalmic or laboratory safety signals were detected. Conclusions: Despite the fact that markedly increased systemic exposure has been demonstrated previously, SA001 did not improve the dryness outcomes in pSS. These findings suggest that systemic exposure alone may be insufficient in established glandular disease and highlight the need for tissue-exposure-driven strategies and biomarker-informed patient selection in future studies. Predefined primary efficacy endpoints and objective, gland-proximal measures of target engagement (e.g., standardized salivary gland ultrasonography and salivary or tear fluid biomarker assessments) may help to better interpret local pharmacodynamic activity and the likelihood of a clinically meaningful benefit. Full article