Search Results (3,268)

With the development of artificial intelligence and automated driving technologies, traffic signal control is evolving toward greater flexibility and faster response. From the perspective of the Transportation Cyber-Physical System (T-CPS), this paper focuses on mixed traffic scenarios involving connected and automated vehicles (CAVs) and human-driven vehicles (HVs). It proposes integrating a Large Language Model (LLM) into signal control: roadside devices perceive traffic states, prompt engineering is constructed, and the LLM is driven to reason and generate control signals. On this basis, a CAV speed guidance algorithm is proposed. Controlled SUMO simulations of a single isolated intersection under ideal V2X communication assumptions show that the proposed method improves delay performance under the tested mixed-traffic conditions. As the CAV penetration rate increases, traffic performance is further improved. Additional experiments under emergency-vehicle priority, road-construction constraints, different traffic-demand levels, perception noise, and different decision intervals and guidance ranges provide simulation-based evidence of training-free scenario adaptability and robustness within the examined scope. Although inference latency and remote-API delays constrain the timely availability of fresh LLM actions, the hard-deadline policy and deterministic fallback mechanism maintain continuous signal execution and favorable traffic performance in the controlled SUMO simulations. Full article

Background/Objectives: Management of locally advanced laryngeal squamous cell carcinoma remains controversial. This study compared definitive chemoradiotherapy (CRT) with total laryngectomy followed by postoperative radiotherapy with or without chemotherapy (TL + PORT/CRT) in T3–T4aN0–N1 disease. Methods: This multicenter retrospective cohort included 311 patients treated between 2008 and 2025 at two centers: 160 received definitive CRT and 151 underwent TL + PORT/CRT. Survival was estimated using Kaplan–Meier and Cox regression analyses. Propensity score matching (PSM) and inverse probability of treatment weighting (IPTW) were used as adjusted analyses. The primary endpoint was disease-specific survival (DSS). Results: Median follow-up was 98 months. In the unmatched cohort, 5- and 10-year DSS rates were 92.1% and 92.1% after CRT versus 93.7% and 83.5% after TL + PORT/CRT (p = 0.351), while 5-year local control favored TL + PORT/CRT (98% vs. 79%; p < 0.001). After PSM (59 patients per group), cluster-robust Cox analysis showed inferior overall survival (OS) after CRT (hazard ratio [HR], 2.08; 95% confidence interval [CI], 1.12–3.86; p = 0.021) and inferior local control (HR, 12.83; 95% CI, 3.75–43.87; p < 0.001). In the 2015–2025 intensity–modulated radiotherapy (IMRT)-restricted analysis, OS also favored TL + PORT/CRT (HR, 3.64; 95% CI, 1.09–12.22; p = 0.036), whereas full-cohort IPTW showed no significant OS difference (HR, 0.93; 95% CI, 0.64–1.36; p = 0.715). Functional laryngeal preservation was achieved in 126/160 patients (78.8%) after CRT. Conclusions: Definitive CRT was associated with inferior local control, while DSS remained comparable. OS estimates varied across adjusted analyses and did not support a stable treatment-specific survival effect. Definitive CRT remains an organ-preservation option in appropriately selected patients. Full article

Low-Dose Computed Tomography (LDCT) reduces radiation exposure but introduces severe quantum noise and streak artifacts that degrade image quality. To address these challenges, we propose the Physics-Guided Sequential State Space Transformer ($P{S}^{3}T$), a projection-domain denoising framework that combines sequential state-space modeling with a photon-aware attention mechanism to capture long-range dependencies across projection angles with linear computational complexity. A differentiable Filtered Backprojection (FBP) layer further enforces reconstruction-domain consistency during training. The proposed framework was evaluated on the Mayo Clinic LDCT and Projection Dataset using patient-level dataset partitioning. Experimental results demonstrate that $P{S}^{3}T$ consistently outperforms state-of-the-art methods, including DRL, SADiff, and GEDFormer, across the abdomen, head, and chest datasets. On the abdomen dataset, $P{S}^{3}T$ reached a peak PSNR of 42.40 dB, an SSIM of 0.9020, and the lowest RMSE of 0.0076 across anatomical regions. Statistical analysis using 95% confidence intervals and paired Wilcoxon signed-rank tests confirmed that these improvements were significant ($p<0.05$). Furthermore, $P{S}^{3}T$ achieved the lowest reconstruction consistency loss (0.0128 at epoch 50), demonstrating stable convergence and the effectiveness of incorporating acquisition physics into projection-domain LDCT denoising. Full article

Heritage buildings sit outside most national energy codes, yet Palestine alone hosts over 3000 historic structures whose 80–120 cm two-leaf stone construction is absent from any published, measurement-grounded retrofit dataset. A 19th-century three-storey heritage building (Qaser Morcos, Bethlehem; net floor area 406 m², volume 2100 m³) was investigated through (i) in situ measurements (heat-flux meter to ISO 9869-1; infrared thermography; illuminance survey); (ii) a Heritage Building Information Model (H-BIM) built in DesignBuilder v7 with EnergyPlus 25.1 and the Jerusalem-centre typical meteorological year weather file, with end-uses other than heating, ventilation and air conditioning (HVAC) calibrated to three years of measured electricity records and the HVAC baseline comfort-normalised; (iii) a Heritage Impact Assessment per EN 16883:2017; and (iv) a 5000-run Monte Carlo uncertainty analysis on five envelope and system inputs. Two retrofit stages were evaluated: Stage 1 (passive and renewable measures—roof insulation, shading elements, LED lighting with motion sensors, solar water heating, and a 20 kWp photovoltaic system) and Stage 2 (deep envelope and system upgrade—Stage 1 plus triple-glazing, Variable Refrigerant Flow system, and a 17.5 kWp photovoltaic system). Predicted final (delivered) electricity intensity falls from 94.5 to 71.2 kWh/m²·y under Stage 1 (−24.7%) and to 63.3 kWh/m²·y under Stage 2 (−33.0%; 90% Monte Carlo uncertainty interval 59.0–70.4 kWh/m²·y), both well below the Palestinian Energy Building Code limit of 120 kWh/m²·y. On-site generation exceeds post-retrofit electrical demand under both stages, yielding a net-positive electrical balance. Direct carbon dioxide emissions fall by 6.84 t/y (−33%) at Stage 2. Discounted paybacks are 4.6 y (Stage 1) and 10.4 y (Stage 2), with internal rates of return of 24.7% and 11.7%. All proposed interventions received project-team consensus scores of ≥3/5 against EN 16883:2017 heritage-impact criteria. This study delivers a replicable simulation-based workflow for the energy assessment and heritage-compatible retrofit of thick-walled Palestinian stone buildings and one of the first field-informed heritage-retrofit datasets from the Levant. Full article

Background/Objective: Insufficient research has examined the muscle damage and inflammatory effects of training performed in a fasted state with reduced (low) glycogen reserves. Methods: This study aimed to investigate the acute effects of exercise sessions performed under fasting (FST) and liquid nutrient-supplemented postprandial (PPD) conditions, as well as with full and reduced body glycogen stores, on muscle damage markers creatine kinase (CK) and lactate dehydrogenase (LDH), and the inflammatory marker interleukin-6 (IL-6). Eleven male amateur football players (mean age 19.91 ± 1.64 years) were included in the study. Participants performed a single 60 min aerobic endurance exercise session at 70% VO₂max under the liquid nutrient-supplemented PPD condition, and two 60 min sessions with a 60 min recovery interval under 10–12 h FST conditions. Data were analyzed using paired samples t-test, ANOVA, Wilcoxon, and Friedman tests, depending on parametric and nonparametric assumptions. Results: In pre-exercise (PRE-FE) measurements, no significant differences were observed between the FST and PPD conditions for GLU, CK, LDH, and IL-6 levels. In postexercise (POST-FE) measurements, GLU levels were significantly higher in the PPD condition than in the FST condition (p < 0.05), whereas LA, CK, LDH, and IL-6 levels did not differ significantly between the two conditions. In the FST condition, following the second exercise session performed with default reduced glycogen availability (POST-SE), CK levels significantly increased from 279.36 ± 16.7 U/L to 335.63 ± 20.3 U/L, LDH levels from 178.72 ± 38.1 U/L to 208.09 ± 50.7 U/L, and IL-6 levels from 2.60 ± 2.4 pg/mL to 8.69 ± 5.8 pg/mL (p < 0.05). In the PPD condition, no significant changes were observed in any of the parameters following a single exercise session (p > 0.05). Conclusions: The two-a-day training approach performed with default reduced glycogen availability may increase muscle damage and inflammatory responses. These findings are considered as an inference that low-glycogen training should not be implemented during the pre-competition period because of the risk of inducing muscle damage. Full article

Background/Objectives: Cholesterol sulfate (CS) is a multifunctional signaling molecule implicated in diverse physiological and pathological processes. Its clinical translation as a biomarker is hindered by the lack of established reference intervals, unclear disease-specific alterations, and undefined relationships with routine laboratory parameters. This study aimed to establish a serum CS reference range for Chinese populations, evaluate its associations with various diseases and clinical laboratory parameters, and define its clinical diagnostic utility. Methods: A total of 642 subjects were enrolled, comprising 372 healthy controls and 270 patients with one of six diseases: Alzheimer’s disease, osteoporosis, obesity, type 2 diabetes mellitus (T2DM), non-alcoholic fatty liver disease (NAFLD), or Crohn’s disease. Serum CS concentrations were quantified using LC-MS/MS. Reference intervals were established using non-parametric methods. Univariate analyses included Spearman’s rank correlation, Mann–Whitney U test, and receiver operating characteristic (ROC) curve analysis; multivariate analysis employed a gamma-family generalized linear model (GLM) with a log-link function, incorporating age, sex, clinical laboratory parameters, and disease status as covariates. The age-by-sex interaction was examined to assess synergistic effects. Incremental diagnostic value of CS was evaluated using the DeLong test and likelihood ratio test. Results: The serum CS reference interval for healthy individuals was 0.55–2.24 mg/L (males: 0.60–2.25 mg/L; females: 0.54–2.20 mg/L), with a significant sex difference. The age-by-sex interaction effect was significant (two-way ANOVA interaction p = 0.005; GLM interaction β = 0.102, p = 0.002). Stratified analysis revealed a significant positive correlation between age and CS in males, but no such association in females. In univariate analysis, CS correlated significantly with total cholesterol (TC), non-HDL-C, hemoglobin A1c (HbA1c), and other biomarkers; CS levels were elevated in Alzheimer’s disease and reduced in Crohn’s disease. In the multivariate GLM, only TC and male sex emerged as independent significant determinants of CS, whereas HbA1c and NAFLD showed borderline effects; no disease group retained independent significance. After adjustment for TC and sex, the residual area under the curves (AUCs) of CS for all diseases were close to 0.50, and DeLong’s test indicated no incremental diagnostic value beyond the baseline model (all p > 0.70). Conclusions: This study is the first to establish a serum CS reference range for the Chinese population. Serum CS levels are independently regulated by TC and sex, with a significant age-by-sex interaction. The associations between CS and disease status were largely mediated by confounding from lipid profiles and age. CS is not suitable as an independent disease biomarker but may serve as a supplementary indicator for assessing lipid metabolism—particularly in male populations, where it may indirectly reflect age-related changes in cholesterol metabolism. Full article

Background/Objectives: To characterize the degenerative vocabulary of 500 consecutive lumbar MRI reports using a transparent, reproducible text extraction pipeline; to quantify which descriptors distinguish included from excluded reports and which factors predict text-derived severity; and to specify a reproducible image analysis pipeline whose formal validation against radiologist Pfirrmann grading is defined as the next step. Methods: We analyzed 500 lumbar MRI reports (386 patients; December 2018–November 2025), extracting morphological and severity keywords, segmental levels (L1–L2 to L5–S1), Modic mentions, and a custom text-derived ordinal severity (0–3). Keyword prevalence differences were tested with the Fisher exact test under Benjamini–Hochberg false discovery rate control, with 95% confidence intervals (CIs) for all odds ratios (ORs) and risk differences; predictors of severity were examined by ordinal logistic regression. Robustness to within-patient clustering was assessed with patient-clustered robust estimation and one-report-per-patient analyses. A MATLAB pipeline derived per-disc candidate imaging features (predicted Pfirrmann grade, normalized disc height, T2 signal index, quality control) across 316 studies. Results: In total, 385/500 reports (77.0%) met the inclusion criteria. The structured morphological keyword field was missing in 49.4% of records, varying by year (58.6% in 2020, 68.7% in 2023, 41.9% in 2024, 33.5% in 2025), indicating systematic reporting drift. Bulge was the dominant descriptor (53.8%); L4–L5 and L5–S1 dominated level mentions. After correction, bulge (OR 6.70, 95% CI 3.86–11.65), mild (5.08, 2.39–10.78), dehydration (9.82, 2.36–40.87) and central (8.63, 2.07–36.01) were enriched among the included reports. All four remained significant in clustering-aware sensitivity analyses. Older age independently predicted higher severity (OR 1.36 per 10 years, 95% CI 1.19–1.56). The image pipeline produced per-disc candidate biomarkers across 316 studies. Conclusions: Free-text lumbar MRI reports encode a recognizable but heterogeneous degenerative vocabulary, sufficient for cohort construction yet inconsistent for quantitative grading; the text-derived severity is a noisy proxy. The reproducible image analysis pipeline yields candidate biomarkers whose formal validation against an adjudicated radiologist Pfirrmann reference standard is the explicit, pre-specified next step. Full article

This study presents SenseMat, a modular sensing infrastructure based on recycled end-of-life tire (ELT)-derived rubber flooring that integrates continuous body-weight (BW) estimation, environmental monitoring and Internet-of-Things (IoT) connectivity into a single structural livestock infrastructure. Designed as a modular engineering platform, SenseMat provides a structural framework that can be extended with additional sensing modules and adapted to different monitoring applications requiring resilient flooring and distributed sensing. The technical feasibility of the weighing module was evaluated through a 51-day proof-of-concept study conducted under commercial buffalo farming conditions, involving two buffalo calves and generating 4872 BW measurements acquired at 30 min intervals. Following a dedicated preprocessing workflow, continuous BW estimates showed good consistency with weekly reference measurements obtained using a professional livestock scale (R² = 0.975 and 0.948), with mean relative errors of 0.88% and 1.03% and root mean square errors of 2.15 and 3.09 kg for the two animals, respectively. Simultaneously, the integrated environmental module continuously monitored air temperature and relative humidity, suggesting the capability of the platform to provide synchronized environmental information alongside continuous BW acquisition within a unified monitoring framework. These findings demonstrate the technical feasibility of integrating sensing, environmental monitoring and IoT connectivity into recycled ELT-derived livestock flooring, supporting its development as a modular smart platform for continuous monitoring in precision livestock farming. Future validation under larger-scale commercial conditions will further assess its scalability and broader applicability. Full article

Objective: To determine healthcare professionals’ attitudes toward migrant patients and explore the sociodemographic and migration-related factors associated with these attitudes. Methods: This descriptive, cross-sectional, exploratory study was conducted with 390 healthcare professionals reached through a Facebook-based professional community and subsequently via other social media platforms using snowball sampling. Data were collected using a Demographic Information Form and the Healthcare Professionals’ Attitudes Toward Migrants Scale. Data Analysis involved the use of independent-samples t-tests or one-way ANOVAs, or Mann–Whitney U or Kruskal–Wallis tests, based on distribution characteristics; effect sizes and 95% confidence intervals were calculated for each comparison, and the Benjamini–Hochberg false discovery rate (FDR) correction was applied to all 96 comparisons reported. Additionally, a multiple linear regression analysis was performed using conceptually justified predictors to identify independent and interrelated factors. Results: The mean total attitude score was 38.47 ± 10.71 (the total score was calculated by reversing the scores on the positive attitude subscale; thus, a higher score indicates a more negative overall attitude; see Methods). In pairwise comparisons, attitude scores differed according to marital status, job satisfaction, the number of migrant patients encountered, language barriers, cultural differences, access to interpreters, perceived competence in providing care, perceptions of migrants’ attitudes, perceived social prejudice, workload, and willingness to receive training on migrant health (raw p < 0.05 in 42 of 96 comparisons; 39 after FDR correction; 22 of these 42 also met a stricter α = 0.001 threshold applied given the large number of repeated comparisons). Multivariable regression (R² = 0.398) showed that perceived social prejudice (standardized β = 0.300), reluctance to pursue education (β = 0.287), and increased workload (β = 0.181) were independently associated with higher total attitude scores. Inaccessibility of interpreters (p = 0.010) and negative perception of migrants’ attitudes (p = 0.029) showed associations at the conventional p < 0.05 level but did not meet the stricter α = 0.001 threshold. Conclusions: In this exploratory sample, healthcare professionals’ attitudes toward migrant patients were associated not only with individual characteristics but also with communication barriers, cultural competence, and working conditions. Given that the study design was cross-sectional and based on non-probability sampling, these associations should be interpreted with caution and not assumed to be causal. Strengthening professional interpreter services, expanding cultural competence training, and evaluating institutional support strategies through future controlled or longitudinal studies may help improve healthcare professionals’ attitudes and promote migrant-friendly, equitable healthcare services. Full article

Accurate estimation of aboveground biomass (AGB) is essential for sustainable pasture management, but remains challenging in heterogeneous mountain environments. This study evaluated a remote-sensing framework for estimating AGB across five landscape zones of the Aragats Volcanic Massif, Armenia, using PlanetScope imagery, terrain variables and machine learning. Biomass was measured in 30 plots comprising 90 nested quadrats during five field campaigns from April to July 2025. Predictor selection, algorithm comparison, preprocessing and tuning were performed within a fully nested leave-one-plot-out cross-validation framework. The model achieved an out-of-fold R² of 0.596 (RMSE = 54.0 g m⁻²; MAE = 33.1 g m⁻²) at the 20 × 20 m plot level, decreasing to 0.503 when the same predictions were evaluated against individual quadrats, which isolates the effect of field-observation support. Variance partitioning showed that weak zone-level performance arose from two distinct causes: dominant within-plot variability in the Meadow Steppe, and a strong soil background under sparse early-season cover in the Dry Steppe. Fine-resolution UAV imagery, acquired without reflectance calibration, did not improve prediction in a matched cross-sensor comparison. Applied to 18 pastures covering 1822.5 ha, the model yielded an aggregate campaign-date stock of 1651.45 t (95% uncertainty interval 1207.70–2092.80 t). Full article

We study the estimation of the offspring mean of a supercritical branching process in a random environment when multiple conditionally independent populations evolve in a common environment. Extending the single-population self-normalized Cramér moderate deviation theory to this multi-population setting, we introduce a pooled Lotka–Nagaev estimator and construct its associated martingale difference sequence. The core of the analysis is an exact decomposition of the pooled conditional variance, which, owing to the shared environment and conditional independence of the populations, separates the environmental and demographic sources of variability. This decomposition reveals a structural dichotomy: the environmental variance is undiluted by pooling, while the demographic variance is attenuated at a rate proportional to the inverse square of the number of populations. Verifying the two conditions of the martingale moderate-deviation theorem yields self-normalized Cramér moderate deviations for the pooled Student t-statistic, together with a Berry–Esseen bound, a moderate deviation principle, and confidence intervals for the offspring mean. The resulting pooling efficiency gain, in which the demographic variance decays inversely with the number of populations while the environmental variance forms an irreducible floor, has no analogue in any single-population framework and is confirmed by Monte Carlo simulation. Full article

We studied flux–fractional compartment models with Caputo memory terms and their associated state sensitivity systems. The state, classical parameter sensitivities, and fractional-order sensitivity are formulated within a unified linear Volterra framework. Using a Sobolev-space formulation and an equivalent Bielecki norm, we establish existence and uniqueness in $W^{1,p}(0,T;H)$ on arbitrary finite time intervals. We further prove continuous differentiability of the parameter-to-solution map and derive a dimensionally consistent fractional-order sensitivity involving a dimensionless logarithmic kernel. The theory is illustrated by a one-compartment model with explicit Mittag–Leffler representations and relative sensitivity analysis. Full article

The growing penetration of renewable energy sources introduces significant uncertainties into integrated energy systems (IESs). Conventional single-timescale management strategies, typically designed for static power balance, fail to address the symmetry of source-load uncertainties arising from both supply and demand sides. To address this challenge, this paper proposes a multi-timescale optimal scheduling framework that integrates demand response (DR) and multi-energy flow coupling. The framework adopts a hierarchical progressive strategy across day-ahead, intra-day, and real-time stages. The day-ahead stage optimizes the economic baseline with an hourly resolution. The intra-day stage conducts rolling correction at 15 min intervals to activate slow-response equipment flexibility, boosting combined heat and power (CHP) generation by 40.70% and increasing waste-heat cooling consumption by 41.12%. The real-time stage employs energy storage at 5 min resolution to suppress fluctuations, maintaining electricity, heat, and cooling load deviations, respectively, at remarkably low levels of 0.17%, 0.10%, and 0.06%. Comparative results show that with power-to-gas (P2G) integration, the system purchases off-peak electricity for synthetic natural gas production, cutting gas procurement costs by 12.70% and reducing net carbon emissions from 5.14 t to 4.91 t. DR mechanisms enable a gas–electricity substitution strategy that lowers electricity purchase costs by 9.97%, reduces evening peak electric vehicle (EV) charging load by 8.32%, and decreases charging expenses by 15%. Full article

Nowadays, the volume of heterogeneous data in situational analysis centers is growing exponentially, leading to information overload for decision makers (DMs) and a decrease in the effectiveness of traditional decision support systems (DSS). Intelligent DSSs (ISDSS) demonstrate potential, but face challenges in explainability, heterogeneous data integration, cognitive load, and scalability. This paper proposes a method for intelligent decision support focused on identifying and generating options for resolving emergency situations—conditions that have no exact precedents in the knowledge base. The method includes formalizing the situation using a vector of normalized parameters $S\left(t\right)$, separating it into independent and dependent variables with the construction of a dependency tree, neural network classification of three types of conditions (normal, abnormal, and emergency) with a forecast for a lead interval τ, the synthesis of solutions for emergency situations based on an analysis of proximity graphs to known emergency precedents and evolutionary optimization. A computational experiment was conducted on the open dataset of the Tennessee Eastman Process simulation model with 28 failure types and 200 repeated simulations. The neural network classifier achieved an accuracy of 0.88 and a macro-averaged F1-score of 0.87 on a test set of 200 situations. Graphs of nearby emergency precedents were constructed for 50 synthetic emergency situations; analysis demonstrated the stability of topological characteristics (vertex degree 5.62 ± 1.18, closeness centrality 0.43 ± 0.09), substantiating the applicability of graph neural networks for accelerated control action synthesis. The proposed method reduces dependence on expert assessments and improves the adaptability and explainability of decisions, while the demonstrated stability of the graph-based precedent retrieval lays the groundwork for future full-scale validation of control-action synthesis in next-generation hybrid IDSS. Full article

Reliable structural monitoring for smart buildings and construction sites requires more than sensor acquisition; it requires sensing and communication paths that remain traceable, recoverable, and compatible with platform-side data processing under heterogeneous field constraints. This study presents a dual-path IoT sensing and communication framework that deliberately separates high-data-rate vibration monitoring from low-data-rate inclination-status monitoring while maintaining common requirements for preservation of available time information, data-source identification, and backend interpretability. The smart-building path integrates an ADXL355 triaxial accelerometer, ESP32-S3, Power over Ethernet (PoE), and Message Queuing Telemetry Transport (MQTT) for 200 Hz vibration acquisition, together with a second-order 10 Hz low-pass filter, 40-record batching, and a Flash LittleFS-based store-and-recovery mechanism that interleaves live and replayed records after reconnection. The construction-site path combines an SCL3300-D01 inclinometer with LoRaWAN, baseline-referenced relative-angle estimation, and a hysteresis state machine with distinct alarm and recovery thresholds. In a 24 h validation, four vibration nodes delivered all 69,120,000 expected records, and four forced-outage trials recovered all offline records while live transmission continued. Frequency-domain analysis confirmed attenuation of high-frequency components while retaining the dominant low-frequency response. The inclination path demonstrated quantifiable angle accuracy, correct alarm/recovery transitions, continuous LoRaWAN frame delivery over the observed interval, and correct backend decoding. The results show that path-specific communication design, combined with a common traceability concept, supports prototype functionality under the reported test conditions, not immediate construction-site deployment. Full 3D visual synchronization, BIM/GIS asset mapping, and digital-twin platform interfacing were not implemented and remain future development tasks. Full article