Search Results (11,528)

The ovaries are crucial reproductive organs that regulate the menstrual cycle and support pregnancy through the production of steroid hormones. They are highly susceptible to various environmental pollutants, which can lead to ovarian disorders. Luteal phase defect (LPD) and premature ovarian failure (POF) are common ovarian disorders in women. In this study, we integrate network toxicology with molecular docking and molecular dynamics simulations to elucidate the toxicological mechanisms of Benzo(a)pyrene (BaP), a widespread endocrine disruptor, in LPD and POF. Through systematic data mining of the GeneCards and OMIM databases, we identified 1336 targets associated with LPD and 2066 targets related to POF, as well as 220 BaP targets. Venn diagram analysis revealed 36 potential targets for BaP-induced LPD and 43 for BaP-induced POF. GO and KEGG enrichment analyses suggest that BaP-induced LPD and POF may share toxicological mechanisms. PPI network visualization indicated that EGFR, ESR1, and STAT3 are critical common targets for BaP-induced LPD and POF. Molecular docking and molecular dynamics simulations revealed that BaP exhibits strong binding affinity with all three core genes. In KGN cells modeling LPD and POF phenotypes, cellular experiments confirmed that BaP downregulated EGFR and ESR1 expression while upregulating STAT3 expression, thereby supporting the reliability of these targets in BaP-induced ovarian dysfunction. These findings provide insights into BaP-induced reproductive toxicity and offer a foundation for targeted clinical interventions to mitigate the effects of environmental pollutants on women’s reproductive health. Full article

Reliability is a fundamental aspect of modern structural engineering due to the inherent randomness of materials, loads, and environmental conditions. However, as system complexity increases, a substantial computational cost is typically required to evaluate the failure probability, often involving 10⁵–10⁶ limit state function evaluations in a conventional Monte Carlo simulation. To address this challenge, this study presents an active-learning-driven deep neural network (ALDNN) meta model algorithm to improve both efficiency and accuracy in reliability analysis. To substantially reduce the computational costs, a multi-phase active learning framework incorporating weighted sampling and adaptive threshold-based candidate filtering is implemented by iteratively selecting more important points and adaptively training deep neural networks. Thresholds for candidate sample points and training datasets are gradually adjusted based on feedback from estimated responses. The proposed method reduces the number of true limit state evaluations to the order of 10² in the benchmark problems considered, while maintaining high accuracy. Its performance is assessed using widely referenced benchmark problems, and finite-element-method-based implicit examples for frame structures are further employed to verify applicability. The results demonstrate the high efficiency, accuracy, and scalability of the ALDNN meta model as system complexity increases. Full article

With the advancement of UAV (Unmanned Aerial Vehicle) technology, flying ad-hoc networks (FANETs), composed of multiple coordinating UAVs, demonstrate tremendous application potential in disaster relief, environmental monitoring and intelligent logistics. However, inherent resource constraints and unpredictable operating environments make UAV failures a frequent and critical challenge. Particularly in mission-critical applications, simultaneous or consecutive failures of multiple UAVs can severely disrupt network topology, leading to catastrophic consequences such as network fragmentation and service interruptions. Furthermore, traditional topology reconstruction algorithms suffer from high computational overhead and significant communication delays. Primarily designed for single-node failure recovery, they are ill-equipped to address the challenge of concurrent multi-node failures. To address these challenges, this paper proposes a topology reconstruction algorithm tailored for multi-node failure scenarios in FANETs. The core objective of this algorithm is to minimize communication overhead and secondary damage to the network during the reconstruction process while ensuring basic reconstruction results, thereby improving the system’s energy efficiency and robustness. The proposed framework integrates three key phases: First, overlapping communication coverage areas among neighbors of failed nodes are leveraged to define first and second regions, enabling rapid identification of connection restoration candidate positions and avoiding computationally intensive global calculations. Second, a comprehensive importance evaluation mechanism is constructed based on the topological and functional attributes of node, categorizing nodes into different importance types. For failed nodes of varying importance, differentiated search ranges and retry strategies are employed to ensure the most suitable nodes are selected for reconstruction tasks. Third, the inflexibility of repulsion ranges in traditional artificial potential field (APF) method is addressed by introducing dynamic repulsion influence zones and a composite repulsion model. The improved APF algorithm enhances safety in high-speed scenarios and reduces the probability of UAVs becoming trapped in local minima. Finally, extensive simulations validate that the proposed algorithm accurately identifies critical network nodes and promptly implements effective reconstruction measures to minimize network damage. Full article

This work presents a compact, self-powered wireless CO₂ sensing node for autonomous environmental monitoring. The system integrates a high-efficiency multijunction photovoltaic (PV) module, a 4000 F hybrid supercapacitor operating at 3.6–4.2 V, and a custom power management system in a LiPo-sized form factor. The PV module, composed of nine parallel triple-junction solar cells, achieves an average efficiency of 27% and delivers peak power at 4.26 V under 600 W/m² irradiance. The sensing unit includes miniaturized CO₂, humidity, and temperature sensors with LoRa-based wireless communication. The low-power NDIR CO₂ sensor provides a resolution of 15–20 ppm and a response time of ~45 s. Week-long tests demonstrated fully autonomous operation with reliable 5 min data transmission, capturing diurnal CO₂ variations associated with plant activity even under low irradiance. Energy storage occurs for irradiance levels ≥65 W/m², and long-term simulations confirm stable supercapacitor voltage over yearly cycles. This work demonstrates a compact multijunction solar–hybrid supercapacitor platform capable of sustaining WSN for long-term, maintenance-free CO₂ monitoring under real-world and low-irradiance conditions. Our results demonstrate that the sensing node can reliably monitor plant-driven CO₂ dynamics, clearly resolving the expected photosynthesis–respiration cycles and their dependence on incident solar radiation, while simultaneously sustaining its energy budget under highly challenging illumination and transmission conditions. Full article

Accurate identification of hydrodynamic parameters is essential for high-fidelity modeling and control of unmanned underwater vehicles (UUVs). Compared with towing tank experiments and computational fluid dynamics simulations, system identification based on free-running trial data offers a cost-effective and scalable alternative. However, in real ocean environments, unmodeled lumped disturbances—such as shear currents, stratification-induced buoyancy variations, and wave-induced drift forces—strongly couple with the vehicle’s intrinsic dynamics. Conventional least-squares estimators and physics-informed neural networks tend to absorb environmental effects into the physical parameters, leading to physically inconsistent estimates. To address this challenge, this paper proposes a physics-guided hybrid network (PG-HyNet) with input-domain structural decoupling. The architecture explicitly separates the intrinsic rigid-body dynamics from spatially varying environmental disturbances by assigning dynamics-related states to a physics-constrained branch and position-dependent variables to a residual disturbance branch. A staged training strategy is introduced to stabilize identification and suppress parameter drift during optimization. The framework is validated using high-fidelity simulations incorporating shear currents, density stratification, and wave drift effects, as well as real-world lake trial data. The results demonstrate that PG-HyNet significantly improves robustness against disturbance-induced parameter compensation, enabling physically consistent hydrodynamic parameter recovery while accurately capturing spatially varying environmental disturbance effects. Full article

This paper presents an analysis of the impact of full and partial curfews on water demand and production, as imposed in Kuwait during the meteorological spring (March, April, and May) of 2020, in response to the COVID-19 pandemic. We consider all desalination technologies used in Kuwait: Multi-Stage Flash (MSF), Multi-Effect Thermal Vapor Compression (MED-TVC), and Reverse Osmosis (RO). Historical data and predictive models are combined and analyzed via a statistical genetic algorithm. The environmental and economic implications of the lockdown measures were assessed through quantitative evaluation, comparing actual 2020 water demand and production data with values predicted under normal operating conditions. During the 2020 COVID-19 pandemic, water consumption surged, with maximum daily consumption climbing by 3.6%, and average daily consumption by 5.2%. These values were significant increases relative to 2019, for which the corresponding figures were 2.1% and 1.6%. The study assesses the economic and environmental consequences quantitatively, specifically the increase in CO, CO₂, and NO_x emissions, due to the increase in fuel consumption at desalination and power plants. Water demand and production across the national water network were simulated using mathematical models specifically designed for this purpose, developed from data provided by the Meteorological Department of Civil Aviation and the Ministry of Electricity, Water, and Renewable Energy. Full article

Accurate measurement of robotic pose is indispensable for large-scale precision manufacturing and robotic calibration, particularly because traditional robotic kinematic models often fall short owing to environmental disturbances and structural uncertainties. Laser tracker systems offer high-precision, large-volume measurement capabilities and are therefore appealing as external references for robot pose estimation; however, their practical efficacy is heavily reliant on optical tracking stability, sensor noise levels, and system robustness. This paper introduces a laser tracker-based framework for measuring robot pose, which integrates PSD-based optical spot sensing, multi-sensor fusion, and simulation-based system analysis. A prototype PSD sensing subsystem has been developed utilizing analog signal conditioning, high-speed A/D sampling, and FPGA-based centroid computation. Bench experiments validate the linearity, geometric sensitivity, and robustness of the PSD sensing chain under controlled spot translations and various ambient illumination conditions. Results demonstrate that the PSD response is nearly linear within a ±0.9 mm spot displacement and that the implementation of an interference optical filter significantly enhances measurement repeatability under background light. At the system level, a comprehensive simulation framework is established wherein PSD measurements are fused with inertial and encoder data via an extended Kalman filter. The simulations explore the effects of process noise tuning, time synchronization, systematic error sources, and control strategies on pose estimation accuracy. Ranging-related effects and error-compensation mechanisms are analyzed within the context of modeling and simulation, providing insights into the interferometric ranging principle underlying the complete laser tracker system. The validation of the prototype alongside simulation results demonstrates that PSD-based optical tracking, combined with multi-sensor fusion and layered error compensation, can effectively improve robustness and positional accuracy. The proposed framework offers valuable guidance for the development and phased validation of laser tracker-oriented robot pose measurement systems in complex industrial environments. Full article

Non-point source pollution has become one of the most widespread environmental degradation problems in recent years. This study aimed to investigate how hydrological processes regulate nitrogen and phosphorus losses under simulated rainfall conditions through in situ rainfall experiments in karst farmland. We conducted a field-scale plot experiment, recorded rainfall and runoff, and measured the nutrient concentration in the runoff of nine experimental plots on the slope toe, middle slope and upper slope. Simulated rainfall intensity was 90 mm/h for 60 min. The results showed nitrogen losses were dominated by subsurface flow in small-scale studies, which accounted for 55.19% (2.50 kg/ha), 71.35% (3.88 kg/ha), and 93.85% (1.39 kg/ha) of TN losses at the toe, middle, and upper slope positions, respectively. The middle slope exhibited the highest losses of N mainly due to its larger subsurface runoff volume. NH⁴⁺ dominated TN in surface flow, contributing up to 97.5% (0.0092 kg/ha) at the slope toe, whereas NO³⁻ was the dominant N form in subsurface flow, with little variation across the three slope positions, averaging 0.062 kg/ha. In contrast, phosphorus losses are primarily associated with surface flow, with TP concentrations in surface flow being 5–60 times higher than those in subsurface flow, with average surface TP losses of approximately 0.04 kg/ha. These results imply that nutrient management in karst farmland should adopt differentiated control strategies, with greater emphasis on reducing subsurface nitrogen leaching while limiting surface runoff and erosion to mitigate phosphorus losses. However, the conclusions are based solely on small-scale rainfall simulation experiments, and nutrient loss may also be influenced by factors such as karst terrain heterogeneity, prior soil moisture content, soil properties, and rainfall characteristics. Full article

Leptospirosis is a neglected zoonotic disease of global importance and remains a persistent public health challenge in Fiji, where outbreaks frequently occur following extreme weather events. This study developed a One Health System Dynamics model to simulate interacting human, rodent, and environmental subsystems over a 24-month outbreak-relevant period, parameterised using published epidemiological and ecological data. Model validity and robustness were assessed through the reproduction of known epidemiological patterns, including seasonal trends. Results indicate that leptospirosis incidence is primarily driven by the interaction between rodent population dynamics and water systems, with standing water acting as a critical ecological enabler for both Leptospira persistence and rodent habitat suitability. Extreme scenario testing indicated that eliminating water contamination drove cases to near zero within 14 months, whereas soil contamination had minimal effect. Sensitivity analyses identified rodent birth and death rates as the most influential parameters governing system behaviour. While an integrated One Health intervention produced the greatest cumulative reduction in cases (21.07%), intervention effects were additive rather than synergistic, reflecting the dominance of underlying ecological feedback structures. This study offers a novel system-based framework for understanding leptospirosis burden in Fiji. It advances beyond static risk models by capturing endogenous dynamics, feedback loops, and non-linear interactions. The findings highlight the need for genuine cross-sectoral coordination and ecologically based rodent management near water bodies to sustainably reduce Fiji’s disease burden. Full article

Ammonia has great potential as a clean energy alternative and can contribute to reducing carbon emissions from conventional fossil fuels. To investigate the combustion characteristics of ammonia-doped natural gas and to evaluate its feasibility for practical applications, this study experimentally and numerically examined the temperature and pressure variations of ammonia-doped natural gas mixtures under different initial pressures. In addition, the combustion products corresponding to different ammonia doping ratios were simulated and analyzed. The results indicate that, with increasing ammonia doping ratio, both combustion temperature and pressure decrease to varying degrees. Under atmospheric pressure, the combustion temperature generally decreases by approximately 25%, while the peak pressure reduction reaches up to 87.85% in certain cases. Furthermore, under negative pressure conditions, a relatively low ammonia doping ratio enhances the combustion intensity of the mixture, and the peak combustion temperature occurs at lower ammonia concentrations. From an environmental perspective, the variation in combustion products with ammonia doping ratio was further analyzed. The results show that the CO concentration in the combustion products decreases progressively by approximately 71.11% as the ammonia doping ratio increases. In contrast, the NO concentration increases to a maximum value and then remains nearly constant, whereas the NO₂ concentration initially increases and subsequently decreases after reaching a peak value of 0.813 ppm. Overall, these findings provide experimental and theoretical support for understanding the combustion characteristics of mixed gaseous fuels and offer a scientific basis for the application and safety assessment of ammonia-doped natural gas. Full article

Background/Objectives: Bacteriophages offer a promising alternative to conventional antibiotics. However, their therapeutic efficacy is often limited by instability in harsh environmental conditions, particularly within the gastrointestinal tract. This study aimed to isolate lytic bacteriophages from wastewater and evaluate the protective capacity of sodium alginate encapsulation against various stressors to enable effective oral delivery. Methods: Four distinct lytic phages (As, Ec, Pa, Gc) were isolated from wastewater and characterized by Transmission Electron Microscopy (TEM) and PCR, confirming their families (Siphoviridae, Podoviridae, Myoviridae). These phages demonstrated potent lytic activity against diverse bacterial pathogens, including Aeromonas hydrophila, Escherichia coli, Pseudomonas aeruginosa, and Glutamicbacter creatinolyticus. The phages were encapsulated in 5% sodium alginate via an extrusion method. Stability was assessed under extreme pH (2.0 and 13), at elevated temperature (up to 80 °C), and in simulated gastrointestinal transit. Results: Encapsulation efficiency exceeded 95%. Unencapsulated phages were completely inactivated at pH 2.0 within 10 min, whereas encapsulated phages maintained significant viability (3.06–3.43 log PFU/mL). Encapsulation also significantly enhanced phage survival under extreme alkaline conditions and elevated temperatures. In simulated gastrointestinal transit, encapsulated phages exhibited superior recovery (2.50 log PFU/mL) compared to their free counterparts (≤1 log PFU/mL). Long-term storage evaluations over three months further confirmed the robust stability of the encapsulated formulations at both 4 °C and 21 °C. Conclusions: Sodium alginate encapsulation effectively shields bacteriophages from severe environmental degradation, particularly acidic gastric stress, enhancing their potential for oral delivery. These findings support the development of stable, formulated phage products for diverse practical applications in phage therapy to combat antimicrobial resistance. Full article

The proliferation of urban substations situated near residential areas has intensified the need for effective noise control, particularly in the mid-to-high frequency range. Traditional sound barriers often rely on mass-increasing strategies, which are constrained by the mass law and practical installation limitations. This study investigates a lightweight sound barrier solution utilizing an embedded Acoustic Black Hole (ABH) structure to address this challenge. Numerical simulations predict a significant improvement in the Sound Transmission Loss (STL) of the ABH plate compared to uniform plates. Experimental validation conducted in a specific cavity setup demonstrates that the damped ABH plate (2.97 mm thick, 3.47 kg) achieves a superior noise reduction performance, matching or even exceeding that of a significantly heavier uniform plate (4 mm thick, 5.00 kg) above its characteristic frequency (254 Hz), while realizing a 30% weight reduction. The superior performance is explained by two synergistic mechanisms: the ABH’s power-law profile concentrates bending wave energy for highly efficient damping at the thin tip; it compresses the structural wavelength, reducing radiation efficiency synchronously. The findings confirm the ABH structure as a promising, lightweight technology for controlling substation equipment noise, with broad application prospects in urban acoustic environmental protection. Full article

Land use and land cover change (LULC) driven by agricultural expansion has become a major environmental challenge in tropical regions, particularly in coffee-producing landscapes, where economic growth often conflicts with forest conservation. This study integrates multi-temporal land cover analysis and future scenario modeling to assess LULC dynamics associated with coffee expansion in the district of Ocumal, in the Amazona Peru. Land cover classes were identified using a Random Forest classification approach applied to Landsat imagery from 2000, 2010, and 2020 processed in Google Earth Engine (GEE), while future scenarios for 2030 and 2040 were simulated using the MOLUSCE plugin in QGIS 2.18. Cross-tabulation matrices and annual rates of change were calculated using IDRISI SELVA 17.0. The results show increases of 12.6% and 7.4% in coffee crop area during 2000–2010 and 2010–2020, respectively, alongside a significant reduction in forest and grassland cover (−5.06% and −2.10% during 2010–2020), mainly driven by agricultural expansion facilitated by transportation infrastructure and market accessibility. This study contributes to the international literature by providing empirical evidence from the Peruvian Amazon on the long-term impacts of coffee expansion on land use and land cover, supporting land-use planning and sustainable agriculture in tropical regions. Full article

Organic mixed ionic–electronic conductors (OMIECs) are pivotal for bioelectronics; however, the microscopic origins of doping-dependent charge transport remain elusive. In this study, we established a multi-scale computational framework to quantify the distinct intramolecular electronic coupling ($H_{ab}$) distributions in systems with 25% and 75% doping levels. Our protocol employs automated quantum chemical calculations to exhaustively identify intrinsic local minima, ensuring thermodynamically stable initial conformations. Subsequent Molecular Dynamics (MD) simulations characterize the equilibration timescales and counter-ion dispersion behaviors. The simulation results reveal that the 75% doped system exhibits significantly stronger counter-ion confinement and a distinct electrostatic landscape compared to the 25% system. Finally, hybrid QM/MM calculations integrated with Constrained Density Functional Theory (CDFT) were utilized to evaluate $H_{ab}$ within these specific environments. The computed coupling distributions show a clear correlation with local electrostatic fluctuations induced by differing counter-ion arrangements. These findings indicate that doping-induced environmental disorder is a critical factor modulating intramolecular transport efficiency, providing a theoretical basis for optimizing OMIEC performance through electrostatic engineering. Full article

Predicting the future parking locations and durations of private vehicles using vehicular edge devices is critical for real-time intelligent transportation services, ranging from instant point-of-interest recommendations to dynamic route planning. Advanced deep neural networks like Transformers demonstrate exceptional performance in mobility prediction; however, their heavy reliance on dense matrix multiplication makes them unsuitable for real-time applications on vehicular edge devices. Spiking neural networks offer a potential solution due to their asynchronous event-driven characteristics and low power consumption. However, existing spiking neural networks face three fundamental challenges: (1) handling heterogeneous inter-event intervals; (2) mitigating quantization errors in regression tasks under limited simulation steps; and (3) efficiently regulating information flow based on external contexts. To address these challenges, we propose an event-driven spiking neural network for private vehicle parking prediction called Spark. First, we design a Time-Adaptive Leaky Integrate-and-Fire neuron with a lookup table-based decay mechanism to efficiently model variable inter-event intervals. Second, an accumulate-based readout strategy is introduced to mitigate quantization errors by integrating discrete spike trains into continuous output values for high-precision regression. Third, a Spiking Contextual Gating module is proposed to selectively regulate spiking information flow across channels based on environmental context. These components are integrated into a unified architecture that maintains high prediction accuracy while remaining computationally efficient. Extensive experiments on real-world datasets demonstrate that Spark achieves an effective balance between prediction accuracy and computational efficiency compared to baselines. Full article