Search Results (4,624)

Background: Dengue infection is an expanding public health threat in Indonesia, increasingly reported in semi-urban areas undergoing rapid demographic and environmental change, where household-level determinants remain poorly characterised. Methods: We conducted a case–control study in the Deli Serdang district, North Sumatra, evaluating sociodemographic and environmental risk factors for dengue. Patients admitted to the district referral hospital (July–September 2024) were screened via medical records. Laboratory-confirmed dengue cases were compared with non-dengue febrile controls. Housing conditions and sociodemographic characteristics were assessed using a validated electronic questionnaire with photographic documentation. Multivariable logistic regression identified independent risk factors. Results: Of 238 individuals screened, 39 dengue cases and 78 controls were enrolled. Male sex (aOR 6.7, 95% CI 1.3–33.7), student status (aOR 7.8, 95% CI 1.1–56.5), absence of window screens (aOR 12.9, 95% CI 3.1–53.8), and surrounding vegetation (aOR 7.3, 95% CI 1.7–31.9) were independently associated with dengue infection. Rural residence was overrepresented among cases, suggesting expansion beyond traditional urban boundaries. Conclusions: Dengue risk in a transitional setting is shaped by demographic exposure and modifiable structural vulnerabilities. Integrated prevention strategies, including window screening, covered water storage, environmental management, and school-based vector control, are needed in rapidly urbanising districts. Full article

Multi-sensor fusion of GNSS, IMU, and vision sensors has been extensively applied in urban Internet of Things systems and automated driving to improve positioning accuracy in complex environments. However, conventional FGO algorithms are based on fixed sensor weights, which limit their adaptability to fluctuations in sensor errors caused by environmental changes, thereby compromising positioning performance. To overcome this limitation, a novel multi-sensor adaptive weighted localization algorithm based on joint residuals detection was proposed in this study. The algorithm computes joint residuals by the sliding window accumulation of GNSS, IMU, and vision sensor measurements. By integrating a global weight decay factor into the M-estimation framework, the weights of each sensor were dynamically adjusted, thereby suppressing the effects of outliers on the state estimation. This approach enables high-precision and robust estimation of position, velocity, and attitude. Experimental results demonstrate that, based on validation with the GNSS–Visual–Inertial Navigation System (GVINS) public datasets sports field and complex environments, the proposed method exhibits superior performance in challenging low-altitude economic scenarios such as weak GNSS signals and significant IMU drift—specifically, it improves positioning accuracy by 32.3% and reduces velocity error by 32% compared to traditional FGO algorithms. In scenarios with GNSS signal interference, the system effectively mitigates error accumulation and maintains the stability of position and velocity estimation. The proposed algorithm demonstrates exceptional positioning accuracy and robustness in complex and dynamic environments, making it highly suitable for advanced urban IoT and automated driving applications. Full article

Urban Unmanned Aerial Vehicle (UAV) logistics operations are frequently constrained by the intersection of limited battery endurance and dynamic ground traffic. When mobile depots are delayed by congestion, onboard drone fleets experience extended idling periods, leading to constrained sortie generation and reduced asset utilization. To address this bottleneck, this paper introduces a traffic-predictive multi-UAV dispatch framework for deterministic day-ahead planning under modeled urban operating conditions. By coupling a count-derived macroscopic speed surrogate learned using XGBoost with a Particle Swarm Optimization (PSO)–Mixed-Integer Linear Programming (MILP) optimization architecture, the framework synchronizes mobile depot trajectories with forecasted low-congestion windows and pre-allocates endurance-feasible parallel aerial sorties. Controlled computational experiments across 30 synthetic routing instances demonstrate the potential value of this approach within the stated modeling assumptions. Compared to baseline clustered deployments, the traffic-aware framework raises mean fleet utilization from 0.43 to 0.63—a 46.2% relative improvement driven by temporal compression of the mission window rather than an absolute increase in flight hours. Furthermore, the proposed framework reduces total mission completion time by 69.87% relative to the conventional truck-only baseline, while achieving a 29.58% incremental gain over static speed drone deployments. These findings suggest that incorporating predictive ground traffic information into day-ahead UAV scheduling can improve modeled fleet efficiency; however, field validation with measured route-level speeds, real delivery demand, and operational constraints remains necessary before deployment-level claims can be made. Full article

Introduction: Lutein+Zeaxanthin (L+Z) are the major constituents of macular pigments of the retina. There is a lack of information on the bioavailability of the two compounds in the presence and absence of omega-3 fatty acids in L+Z supplements which are commonly prescribed to treat macular degeneration. Despite growing interest in L+Z supplementation, there remains a limited understanding of their short-term bioavailability dynamics and the potential added value of omega-3 co-supplementation. This pilot study reports on the bioavailability of serum responses to L+Z supplements in the presence of omega-3 fatty acids and evaluates time-resolved analytical approaches using Area Under the Curve. Subjects/Methods: A total of 10 men and six women with an average age of 31.38 ± 1.27 years participated in this randomised, non-blinded, controlled study for a total of 19 days (7-day wash-out period plus 12-day intervention period). The control group (n = 9) consumed the L+Z supplement (12 mg/d) only, while the intervention group (n = 7) consumed the L+Z supplement along with 900 mg/d of an omega-3 supplement (540 mg EPA + DHA 360 mg). Each group adhered to a comprehensive low-carotenoid and omega-3 diet list (LCOD) for the 7-day wash-out period and the 12-day intervention period. The participants reported the foods they consumed daily in their diet logbooks, online logs, and the ASA 24 diet assessment log over the study period. The body composition of each subject in the two groups was assessed before and after the study using a SECA body composition analyser, and the relative serum L+Z response in both groups was determined using Area Under the Curve (AUC and incremental AUC) by trapezoidal approximation. Results: The mean ± SEM baseline serum lutein+zeaxanthin (L+Z) concentrations measured at the end of the wash-out period (Day 7) were 2.23 ± 0.65 µg/mL in the control group and 1.20 ± 0.53 µg/mL in the intervention group. Following wash-out, serum L+Z concentrations increased in both groups, reaching 2.81 ± 0.90 µg/mL (control) and 2.63 ± 1.21 µg/mL (intervention) at Day 13, and 2.98 ± 0.69 µg/mL (control) and 3.02 µg/mL (intervention) at Day 19. Total exposure assessed by AUC₇–₁₃ and AUC₁₃–₁₉ did not differ significantly between the groups (p > 0.05). Incremental exposure analyses identified the post-wash-out period as the primary biologically responsive window, with higher mean incremental L+Z bioavailability in the intervention group (4.36 µg/day/mL) compared with the control group (3.00 µg/day/mL), although this difference was not statistically significant (p > 0.05). No significant effect of omega-3 co-supplementation on oxidative stress biomarkers was observed (p > 0.05). Conclusion: Omega-3 co-supplementation did not demonstrate a consistent additional benefit on L+Z bioavailability or oxidative stress markers. Day-resolved analyses using iAUC revealed temporal patterns not captured by conventional AUC measures. These exploratory findings should be interpreted with caution and confirmed in larger, longer-term studies. Full article

Agricultural supply chains operating under rural infrastructure constraints face persistent logistical inefficiencies that reduce producer income and weaken territorial sustainability. This paper assesses how collaborative and coordinated distribution architectures reshape economic performance, efficiency, and equity in dispersed networks of cocoa producers in El Carmen de Bolívar, Colombia. The unified optimization framework compares three regimes: decentralized non-collaborative individual shipments, collaborative consolidation based on distribution centers, and coordinated distribution with time-window synchronization. The findings show a reduction in average logistics costs from $0.688/kg in decentralized distribution to $0.323/kg with collaborative distribution centers, and even further to $0.282/kg in coordinated distribution, representing an overall reduction of approximately 59%. A sensitivity analysis across 64 accessibility configurations shows that the advantage of coordination increases as time rigidity increases. These structural improvements translate into a 13.97% increase in total producer utility, raising average utility from $278 to $317 per producer. In addition, the distributional assessment based on Lorenz curves and Gini coefficients indicates that inequality remains stable despite gains in welfare. These results demonstrate that spatial consolidation combined with temporal synchronization is a decisive lever for resilient and inclusive rural supply systems. Full article

The successful application of extrusion-based 3D-printed geopolymer mortars largely depends on precursor chemistry, activator composition, mixture proportions, and fresh-state behavior, which is highly sensitive to time-dependent structural build-up. This review examines the relationships among mix design, geopolymerization chemistry, rheological properties, and printability requirements for 3D-printed geopolymer mortars. Particular emphasis is placed on the effects of precursor type, alkaline activator characteristics, liquid-to-solid ratio, additives, and fibers on flowability, yield stress, viscosity, extrudability, buildability, shape retention, and interlayer bonding. The review further discusses how geopolymerization kinetics influence the evolution of fresh-state properties, the printable time window, and the transition from extrusion to structural stability. In addition, early-age performance is evaluated in terms of setting behavior, green strength development, and layer-interface integrity. Current challenges, including the lack of standardized test methods, limited comparability among published studies, and the complex coupling between material design and process parameters, are also highlighted. Finally, the review identifies key research gaps and proposes future directions for developing robust, printable, and sustainable geopolymer mortar systems for additive manufacturing in construction. Full article

Fault location in power transmission lines (PTLs) relies on impedance or traveling wave (TW) principles. TW approaches offer superior accuracy and high robustness against fault resistance. While multi-ended methods require precise terminal synchronization, single-ended TW (SETW) methods utilize measurements from one terminal, requiring accurate distinction of reflected waves. This study employs the computationally efficient approximate derivative (AD)—the difference between consecutive samples—for SETW fault detection and location. Normally near zero, the AD of modal signals produces sharp transitions during faults. Comparing AD output to a threshold achieves fault detection. The AD then identifies arrival times of the incident and reflected TWs. When using TW theory to distinguish reflections from the fault point and remote end, the fault distance is calculated from their arrival time difference. Validated through 293 diverse ATP simulated fault scenarios, the approach delivered highly accurate results despite using a lower sampling rate than established methods, utilizing an exceptionally short data window—only 2.03 ms for a 300 km line. Finally, operational boundaries for the signal-to-noise ratio (SNR) in noisy conditions are established. Full article

Background: Over 60,000 patients are diagnosed annually with pancreatic cancer in the United States, most with pancreatic ductal adenocarcinoma (PDAC). Despite multimodality treatment, prognosis remains poor, underscoring the need to better define factors associated with improved survival in resectable disease. The aim of this study was to propose “completeness of therapy” in resectable PDAC based on chemotherapy type, relative dose intensity (RDI), duration, and timing of initiation, and to evaluate their association with overall survival (OS). Methods: A systematic review of PubMed identified 34 studies. Hazard ratios (HRs) were extracted and synthesized in forest plots. Outcomes focused on OS in relation to chemotherapy completion (cumulative cycles), time to adjuvant chemotherapy initiation (TTA), RDI, and regimen type. The goal was to conceptualize an evidence-based completeness-of-therapy framework for resected PDAC. Results: Completion of chemotherapy was associated with a 42% reduction in the hazard of death compared with incomplete therapy (HR = 0.58, 95% CI 0.47–0.71, p < 0.01). No significant OS difference was observed for longer TTA within a ≤12-week window after surgery (HR = 1.22, 95% CI 0.95–1.56, p = 0.10). Higher RDI demonstrated a large but non-significant trend toward improved OS (HR = 0.51, p = 0.24). Non-significant trends favoring gemcitabine-based regimens (HR = 0.87, p = 0.26) and FOLFIRINOX (HR = 0.79, p = 0.26) were observed, with FOLFIRINOX suggesting a 21% relative reduction in mortality. Conclusions: Chemotherapy completion is strongly associated with improved OS in resectable PDAC. Initiation of adjuvant therapy within 12 weeks appears sufficient, allowing recovery from surgery. Higher RDI and specific chemotherapy regimens demonstrated numerically favorable hazard ratios; however, these associations were not statistically significant and should be interpreted cautiously. Full article

Benefiting from tunable emission from ultraviolet to near-infrared windows, long luminescence lifetimes, and exceptional photostability, rare earth (RE)-doped nanomaterials overcome the limitations of conventional dyes and quantum dots, enabling deep-tissue, high-resolution, and low-background imaging. As multifunctional fluorescent probes, RE-doped nanomaterials are driving the development of next-generation biomedical imaging. This review summarizes recent advances in the structural design of RE-doped nanomaterials, surface engineering for biocompatibility, and targeting strategies for improved performance, and highlights their integration into advanced imaging modalities, including NIR-I/II fluorescence, FLIM, PAI, super-resolution STED, multimodal FL/MRI/CT, X-ray-excited luminescence, and persistent luminescence. Meanwhile, mechanistic insights, material innovations, and comparative advantages are discussed. Furthermore, challenges related to quantum yield, scalable synthesis, imaging resolution, and clinical translation are considered, while future directions—centered on multifunctional probe design, NIR-II imaging, and AI-assisted data analysis—are proposed, offering a versatile platform for precise multimodal imaging with significant potential to advance early diagnosis, personalized therapy, and clinical applications. Full article

Medium- and long-term dispatching of renewable energy bases is an important method for ensuring large-scale transmission and consumption. However, most existing medium- and long-term dispatching methods ignore the uncertainties of wind and photovoltaic power output, resulting in excessive maintenance-window margins and insufficient regulation reserves. However, relevant studies that consider such uncertainties are mostly limited to short-term scheduling and are therefore inadequate for medium- and long-term dispatching needs. To this end, a two-stage robust optimal dispatch method for renewable energy bases that considers the impacts of wind and photovoltaic output uncertainties and unit maintenance is proposed. Firstly, the first stage decision variables consist of the on/off and maintenance statuses of thermal power units. Next, the output of each power source is taken as the conventional decision variables in the second stage, while the curtailed wind/photovoltaic power and load shedding are taken as the unconventional decision variables when the balance cannot be achieved by adjusting the power source output under the given wind and solar power output scenarios. In the end, a polyhedron set based on an uncertainty budget was adopted to describe the fluctuations in wind and photovoltaic output, and the minimum scheduling cost in the worst scenarios was solved using the column and constraint algorithm. A renewable energy base in Northwest China was selected as a case to validate the proposed model’s effectiveness. The results show that the proposed model significantly reduces the operating cost in actual operation compared to deterministic optimization and pre-maintenance robust optimization. Full article

Methicillin-resistant Staphylococcus aureus (MRSA) is a multidrug-resistant pathogen. Long-term clinical use of linezolid readily induces bacterial resistance in MRSA. This study explored resistance evolution and related mechanisms of MRSA to linezolid under subinhibitory concentrations of rifampicin, as well as the antibacterial activity and anti-resistance potential of the combination. Synergistic effects were confirmed via the broth microdilution method, checkerboard method, and time-kill curve assay. The mutant prevention concentration (MPC) was determined to assess suppression of resistant mutant enrichment. We used a 28-day adaptive evolution model and compared resistance dynamics between linezolid monotherapy and its combination with subinhibitory concentrations of rifampicin. We analyzed the growth characteristics, biofilm formation, virulence phenotypes, and resistance-related mutations of induced strains. The combination exerted synergistic or additive effects, reducing the MPC of linezolid, narrowing the mutant selection window, and delaying resistance development. Strains induced by the combination exhibited slower growth, a greater reduction in biofilm formation, and significantly lower hemolytic activity and attenuated in vivo virulence in the Galleria mellonella infection model. Sanger sequencing revealed specific mutations in the 23S rRNA gene and the ribosomal protein gene (rplC). Linezolid combined with rifampicin synergistically suppresses resistant mutant enrichment and delays resistance evolution, providing experimental support for optimizing anti-MRSA therapeutic regimens. Full article

Accurate short-term forecasting of residential electricity demand is increasingly important for smart distribution systems, particularly in the context of demand-side management and flexibility-oriented grid operation. In this study, a high-resolution forecasting framework is proposed in which household electricity demand is classified into fixed, shiftable, and adjustable load categories and forecasted together with total load. A one-minute-resolution synthetic residential load dataset is generated using the Centre for Renewable Energy Systems Technology (CREST) demand model for households with two to five occupants over a 31-day winter period in January. The appliance-level demand data are grouped according to operational characteristics and integrated into a representative four-bus distribution feeder. Minute-level power flow analysis is then performed to calculate technical losses, which are incorporated into the forecasting dataset together with meteorological variables (temperature, wind speed, and solar irradiance) and temporal descriptors. Using this multi-input structure, random forest (RF), support vector machine (SVM), feed-forward neural network (FFNN), and long short-term memory (LSTM) models are comparatively evaluated for the prediction of fixed, shiftable, adjustable, and total residential loads. Model performance is assessed using root mean square error (RMSE) and Pearson correlation coefficient (R), while mean absolute error (MAE) is additionally reported for the final test set. The results show that the LSTM model provided the most consistent overall forecasting performance, particularly for shiftable, adjustable, and total load estimation, while RF yielded competitive results for fixed-load correlation and short-window forecasting in Buses 1 and 2. In contrast, SVM and FFNN exhibited weaker generalization performance across several load categories. The proposed framework provides a practical foundation for the development of dynamic pricing mechanisms that consider load-type-based controllability levels. Overall, the findings demonstrate that integrating load categorization with meteorological, temporal, and technical loss information provides a robust and reproducible framework for smart grid applications such as demand-side management, peak load mitigation, and flexibility-aware residential load analysis. Full article

Empty-vehicle repositioning is a persistent challenge in long-haul road freight because carriers must reduce empty mileage without sacrificing service reliability under lead times, appointment windows, and uncertain load realization. This paper formulates empty-vehicle repositioning on freight corridors as a stochastic control problem with explicit space–time feasibility and a stated within-epoch event order. Lead times couple current dispatch decisions to future capacity, pickup windows impose reachability constraints, and stochastic match feasibility captures information and market frictions. We develop dynamic lower bounds from time-expanded relaxations, showing that dual prices of inventory-balance constraints can be interpreted as space–time scarcity values. We further introduce an order-dependent nested friction decomposition that separates excess empty movement into spatial imbalance, temporal mismatch induced by lead times and time windows, and information frictions. Guided by this structure, we propose price-guided rolling-horizon and generalized-cost policies and evaluate them on synthetic corridor experiments organized around the three friction families. The results reveal service–empty-mileage trade-offs, a pronounced knee in the Pareto frontier, lower service loss under widened tight pickup windows, and strong sensitivity to match feasibility. The PG-RH policy reduces empty-distance exposure and total cost relative to static balancing in the main scenarios while maintaining comparable, but not uniformly dominant, service performance. The framework provides a diagnostic basis for identifying the sources of deadhead and for designing operational interventions that reduce empty mileage without undermining reliability. Full article

Background and Purpose: Tenecteplase (TNK) within 4.5 h from symptom onset is not inferior to alteplase in treating ischemic stroke. In recent years, some randomized controlled trials (RCTs) have investigated the efficacy of extending the therapeutic window up to 24 h. This updated meta-analysis aims to synthesize the results of these RCTs comparing TNK to the best medical treatment (BMT) with or without endovascular thrombectomy. Methods: In accordance with PRISMA guidelines, all RCTs comparing TNK with BMT in adult patients between 4.5 and 24 h were systematically searched. The primary endpoint was good functional outcome at 90 days (mRS 0–2). Secondary endpoints included excellent outcome (mRS 0–1), symptomatic intracerebral hemorrhage (sICH), 90-day mortality, complete reperfusion at 24 h. Odd and Hazard ratios (ORs; HRs) were pooled using meta-analytic methods. Results: A total of seven RCTs involving 1754 patients were included. The rates of the primary endpoint were higher in TNK-treated patients (HR: 1.15; 95% CI: 1.03–1.27), as were rates of excellent functional outcome (HR: 1.29; 95% CI: 1.08–1.55). In the subgroup receiving intravenous therapy (IVT) alone, the primary endpoint was significantly more frequent in the TNK group than in the BMT group (OR: 1.47; 95% CI: 1.17–1.84; p for heterogeneity < 0.0001). TNK treatment was also associated with higher reperfusion rates compared with BMT, reflecting a greater proportion of saved ischemic penumbra as assessed via perfusion imaging. Although symptomatic intracranial hemorrhage (sICH) occurred more frequently in TNK-treated patients, the difference did not reach statistical significance, and mortality rates were comparable between treatment groups. Conclusions: Tenecteplase administered between 4.5 and 24 h is associated with improved rates of both good and excellent functional outcomes compared with BMT, especially in patients treated with IVT alone. Additionally, TNK is linked to higher rates of reperfusion. Full article

Microsatellites operate in highly dynamic thermal environments due to severe physical constraints, making temperature telemetry a critical onboard health indicator. Conventional threshold-based monitoring fails to distinguish normal operational mode transitions from genuine faults, causing excessive false alarms. To address this, we propose a two-stage framework integrating unsupervised thermal mode discovery with mode-specific deep learning prediction. Raw temperature telemetry is downsampled and segmented into orbital cycles. Unsupervised clustering identifies two nominal thermal regimes and four canonical fault-type libraries (step, spike, drift, and noise), each corresponding to distinct in-orbit failure mechanisms. For each nominal mode, a Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) is trained on 7-day historical windows to forecast 3-day temperature evolution. Post-downlink, incoming cycle mode is inferred via nearest-neighbor DTW classification; anomalies are flagged when prediction residuals exceed mode-adaptive thresholds. Validation on Macau Science Satellite-1B (MSS-1B, COSPAR 2023-069-B, NORAD 56732) in-orbit telemetry from a 41° inclination low-Earth orbit—where solar illumination dominates external thermal loading and internal heat from the data-communication module and scientific payload constitutes the primary internal thermal source—shows the method reduces anomaly flags by 96.6% and improves prediction mean absolute error by 51.3% compared to a non-classified global baseline under nominal operating conditions, correctly detecting a known operational transient while suppressing spurious alarms. A synthetic fault injection experiment with four anomaly types and five baseline methods further confirms the framework’s detection capability, achieving an overall F1 score of 0.725 vs. 0.258 for the global baseline—a 2.8× improvement driven primarily by a 4× precision gain. Sensitivity analysis reveals that the two-stage advantage is most pronounced for low-magnitude and short-duration faults, where mode-specific context is essential. This work advances microsatellite autonomous health management by providing reliable anomaly detection with quantified fault detection performance. Full article