Search Results (197)

The integrity of real-time monitoring data is paramount to the accuracy of scientific research and the reliability of decision-making. However, data incompleteness arising from transmission interruptions or extreme weather disrupting equipment operations severely compromises the validity of statistical analyses and the stability of modelling. From a mathematical view, real-time monitoring data may be regarded as continuous functions, exhibiting intricate correlations and mutual influences between different indicators. Leveraging their inherent smoothness and interdependencies enables high-precision data imputation. Within the functional data analysis framework, this paper proposes a Diversity Constraint and Adaptive Graph Multi-View Functional Matrix Completion (DCAGMFMC) method. Integrating multi-view learning with an adaptive graph strategy, this approach comprehensively accounts for complex correlations between data from different views while extracting differential information across views, thereby enhancing information utilization and imputation accuracy. Random simulation experiments demonstrate that the DCAGMFMC method exhibits significant imputation advantages over classical methods such as KNN, HFI, SFI, MVNFMC, and GRMFMC. Furthermore, practical applications on meteorological datasets reveal that, compared to these imputation methods, the root mean square error (RMSE), mean absolute error (MAE), and normalized root mean square error (NRMSE) of the DCAGMVNFMC method decreased by an average of 39.11% to 59.15%, 54.50% to 71.97%, and 43.96% to 63.70%, respectively. It also demonstrated stable imputation performance across various meteorological indicators and missing data rates, exhibiting good adaptability and practical value. Full article

This study focuses on the impact of wastewater discharges from the Besòs treatment plant on the coastal water quality of Barcelona, particularly under adverse weather conditions. A simplified mathematical model was developed to predict, in real time, the concentration of bacterial indicators (Enterococci and E. coli) along nearby beaches. This model aims to quickly detect contamination events and trigger alerts to evacuate swimming areas before water quality tests are completed. The simulator uses meteorological data—such as wind direction and speed, rainfall intensity, and solar irradiance, among others—to anticipate pollution levels without requiring immediate water sampling. The model was tested against real-world scenarios and validated with historical meteorological and bacteriological data collected over six years. The results show that bacterial pollution occurs mainly during intense rainfall events combined with specific wind conditions, particularly when winds blow from the southeast (SE) or east–southeast (ESE) at moderate to high speeds. These wind patterns carry under-treated wastewater toward the coast. Conversely, winds from the north or northwest tend to disperse the contaminants offshore, posing little to no risk to swimmers. This study confirms that pollution events are relatively rare—about two per year—but pose significant health risks when they do occur. The simulator proved reliable, accurately predicting contamination episodes without producing false alarms. Minor variables such as water temperature or suspended solids showed limited influence, with wind and sunlight being the most critical factors. The model’s rapid response capability allows public authorities to take swift action, significantly reducing the risk to beachgoers. This system enhances current water quality monitoring by offering a predictive, cost-effective, and preventive tool for beach management in urban coastal environments. Full article

The precision of ERA5 reanalysis datasets and their applicability in the mountainous regions of central China are essential for weather forecasting and climate change research in the transitional zone between northern and southern China. This study employs three months of continuous measurements collected from a high-precision remote sensing platform located in a representative mountainous valley (Xinyang city) in central China, spanning December 2024 to February 2025. Our findings indicate that both horizontal and vertical wind speeds from the ERA5 dataset exhibit diminishing deviations as altitude increases. Significant biases are observed below 500 m, with horizontal mean wind speed deviations ranging from −4 to −3 m/s and vertical mean wind speed deviations falling between 0.1 and 0.2 m/s. Conversely, minimal biases are noted near the top of the boundary layer. Both ERA5 and observations reveal a dominance of northeasterly and southwesterly winds at near-surface levels, which aligns with the valley orientation. This underscores the substantial impact of heterogeneous mountainous terrain on the low-level dynamic field. At an altitude of 1000 m, both datasets present similar frequency patterns, with peak frequencies of approximately 15%; however, notable discrepancies in peak wind directions are evident (north–northeast for observations and north–northwest for ERA5). In contrast to dynamic variables, ERA5 temperature deviations are centered around 0 K within the lower layers (0–500 m) but show a slight increase, varying from around 0 K to 6.8 K, indicating an upward trend in deviation with altitude. Similarly, relative humidity (RH) demonstrates an increasing bias with altitude, although its representation of moisture variability remains insufficient. During a typical cold event, substantial deviations in multiple ERA5 variables highlight the needs for further improvements. The integration of machine learning techniques and mathematical correction algorithms is strongly recommended as a means to enhance the accuracy of ERA5 data under such extreme conditions. These findings contribute to a deeper understanding of the use of ERA5 datasets in the mountainous areas of central China and offer reliable scientific references for weather forecasting and climate modelings in these areas. Full article

Intensity–duration–frequency (IDF) curves, which relate rainfall intensity (i), storm duration (d), and return period (T), are cornerstone tools for planning, designing, and operating hydraulic works. Since Sherman’s pioneering formulation in 1931, many modern implementations have systematically omitted the duration-shifting parameter C, causing predicted intensities to diverge to infinity as $d\to 0$. This mathematical paradox becomes especially problematic under extreme hydrological regimes and convective storms exceeding 300 mm/h, where an accurate curve fit is critical. Here, we first review conventional IDF curve fitting techniques and their limitations. We then introduce IDF-GtzLo, a novel, intuitive formulation that reinstates and calibrates C directly from observed storm statistics, ensuring finite intensities for all durations. Applied to 36 automatic weather stations across Mexico, our method reduces the root mean square error by 23 % compared to the classical model. By eliminating the infinite intensity paradox and improving statistical performance, IDF-GtzLo offers a more reliable foundation for hydrological risk assessment and the design of infrastructure resilient to climate-driven extremes. Full article

Total Electron Content (TEC) is a fundamental parameter characterizing the electron density distribution in the ionosphere. Traditional global TEC modeling approaches predominantly rely on mathematical methods (such as spherical harmonic function fitting), often resulting in models suffering from excessive smoothing and low accuracy. While the 1° high-resolution global TEC model released by MIT offers improved temporal-spatial resolution, it exhibits regions of data gaps. Existing ionospheric image completion methods frequently employ Generative Adversarial Networks (GANs), which suffer from drawbacks such as complex model structures and lengthy training times. We propose a novel high-resolution global ionospheric TEC modeling method based on a Dual-Branch Convolutional Neural Network (DB-CNN) designed for the completion and restoration of incomplete 1°-resolution ionospheric TEC images. The novel model utilizes a dual-branch input structure: the background field, generated using the International Reference Ionosphere (IRI) model TEC maps, and the observation field, consisting of global incomplete TEC maps coupled with their corresponding mask maps. An asymmetric dual-branch parallel encoder, feature fusion, and residual decoder framework enables precise reconstruction of missing regions, ultimately generating a complete global ionospheric TEC map. Experimental results demonstrate that the model achieves Root Mean Square Errors (RMSE) of 0.30 TECU and 1.65 TECU in the observed and unobserved regions, respectively, in simulated data experiments. For measured experiments, the RMSE values are 1.39 TECU and 1.93 TECU in the observed and unobserved regions. Validation results utilizing Jason-3 altimeter-measured VTEC demonstrate that the model achieves stable reconstruction performance across all four seasons and various time periods. In key-day comparisons, its STD and RMSE consistently outperform those of the CODE global ionospheric model (GIM). Furthermore, a long-term evaluation from 2021 to 2024 reveals that, compared to the CODE model, the DB-CNN achieves average reductions of 38.2% in STD and 23.5% in RMSE. This study provides a novel dual-branch input convolutional neural network-based method for constructing 1°-resolution global ionospheric products, offering significant application value for enhancing GNSS positioning accuracy and space weather monitoring capabilities. Full article

The strategic development of the low-altitude economy requires efficient urban logistics solutions. The existing Unmanned Aerial Vehicle (UAV) truck delivery system faces severe challenges in dealing with dynamic weather constraints and multi-agent coordination. This article proposes a neural network-based optimisation framework that integrates constrained K-means clustering and a three-stage neural architecture. In this work, a mathematical model for heterogeneous vehicle constraints considering time windows and UAV energy consumption is developed, and it is validated through reference to the Solomon benchmark’s arithmetic examples. Experimental results show that the Truck–Drone Cooperative Traveling Salesman Problem (TDCTSP) model reduces the cost by 21.3% and the delivery time variance by 18.7% compared to the truck-only solution (Truck Traveling Salesman Problem (TTSP)). Improved neural network (INN) algorithms are also superior to the traditional genetic algorithm (GA) and Adaptive Large Neighborhood Search (ALNS) methods in terms of the quality of computed solutions. This research provides an adaptive solution for intelligent low-altitude logistics, which provides a theoretical basis and practical tools for the development of urban air traffic under environmental uncertainty. Full article

Current simulation training for pilots wearing night vision goggles (NVGs) (e.g., night landings and tactical reconnaissance) faces fidelity limitations from conventional displays. This study proposed a novel dynamic NIR-LED direct-view display system for authentic nighttime scene simulation. Through comparative characterization of NVG response across LED wavelengths under ultra-low-current conditions, 940 nm was identified as the optimal wavelength. Quantification of inherent nonlinear response in NVG observation enabled derivation of a mathematical model that provides the foundation for inverse gamma correction compensation. A prototype NIR-LED display was engineered with 1.25 mm pixel pitch and 1280 × 1024 resolution at 60 Hz refresh rate, achieving >90% uniformity and >2000:1 contrast. Subjective evaluations confirmed exceptional simulation fidelity. This system enables high-contrast, low-power NVG simulation for both full-flight simulators and urban low-altitude reconnaissance training systems, providing the first quantified analysis of NVG-LED nonlinear interactions and establishing the technical foundation for next-generation LED-based all-weather visual displays. Full article

Soybean (Glycine max L.) is one of the world’s most important sources of plant-based protein, with a protein content exceeding 35–40% (dry basis), along with other essential nutritional benefits. Ideally, soybeans are field-dried to approximately 13% moisture content (wet basis, wb); however, adverse weather conditions can necessitate harvesting at elevated moisture levels sometimes exceeding 20% (wb). In such cases, mechanized drying systems, particularly in northern U.S. regions, become essential for safe storage and quality preservation. This study investigated the effects of drying temperature, airflow rate, and initial moisture content on drying kinetics and kernel integrity using mathematical modeling. Drying behavior was modeled using fractional calculus and compared to the empirical Page model, while kernel cracking and breakage were analyzed using logistic regression. Both fractional and Page models exhibited strong agreement with experimental data (R² = 0.903–0.993). The fractional model achieved superior predictive accuracy, improving RMSE and MAE by 83.7% and 81.2%, respectively, compared to the Page model. Cracking and breakage were more strongly influenced by drying temperature than by initial moisture content, with the greatest quality degradation occurring at high temperatures. Optimal drying conditions were identified as temperatures below 27 °C and initial moisture contents between 19 and 20% (wb), which best preserved kernel quality. Logistic models more accurately predicted breakage than cracking, confirming their effectiveness in assessing mechanical damage during drying. The results affirm the suitability of fractional order models for accurately capturing drying kinetics, while logistic models offer robust performance for evaluating physical quality degradation. These modeling approaches provide a framework for efficient and quality-preserving soybean drying strategies in regions reliant on off-field drying systems. Full article

Lidar technology is a key sensor for autonomous driving due to its precise environmental perception. However, adverse weather and atmospheric conditions involving fog, rain, snow, dust, and smog can impair lidar performance, leading to potential safety risks. This paper introduces a comprehensive methodology to simulate lidar systems under such conditions and validate the results against real-world experiments. Existing empirical models for the extinction and backscattering of laser beams are analyzed, and new models are proposed for dust storms and smog, derived using Mie theory. These models are implemented in the CARLA simulator and evaluated using Robot Operating System 2 (ROS 2). The simulation methodology introduced allowed the authors to set up test experiments replicating real-world conditions, to validate the models against real-world data available in the literature, and to predict the performance of the lidar system in all weather conditions. This approach enables the development of virtual test scenarios for corner cases representing rare weather conditions to improve robustness and safety in autonomous systems. Full article

This article examines the modeling of sustainable development in transport logistics, focusing on the impact of climate factors, changing weather conditions, and digitalization processes. The study analyzes the complex influence of adverse weather phenomena, such as fog, rain, snow, extreme temperatures, and strong winds, whose frequency and intensity are increasing due to climate change, on the efficiency, safety, and reliability of transport systems across all modes except pipelines. Special attention is paid to the integration of weather-resilient sensor technologies, including LiDAR, thermal imaging, and advanced monitoring systems, to strengthen infrastructure resilience and ensure uninterrupted transport operations under environmental stress. The methodological framework combines comparative analytical methods with economic–mathematical modeling, particularly Leontief’s input–output model, to evaluate the mutual influence between the transport sector and sustainable economic growth within an interconnected ecosystem of economic and technological factors. The findings confirm that data-driven management strategies, the digital transformation of logistics, and the strengthening of centralized hubs contribute significantly to increasing the resilience and flexibility of transport systems, mitigating the negative economic impacts of climate risks, and promoting long-term sustainable development. Practical recommendations are proposed to optimize freight flows, adapt infrastructure to changing weather risks, and support the integration of innovative digital technologies as part of an evolving ecosystem. Full article

As cities grow denser, the demand for efficient parking systems becomes more critical to reduce traffic congestion, fuel consumption, and environmental impact. This paper proposes a smart parking solution that combines deep learning and algorithmic sorting to identify the nearest available parking slot in real time. The system uses several pre-trained convolutional neural network (CNN) models—VGG16, ResNet50, Xception, LeNet, AlexNet, and MobileNet—along with a lightweight custom CNN architecture, all trained on a custom parking dataset. These models are integrated into a mobile application that allows users to view and request nearby parking spaces. A merge sort algorithm ranks available slots based on proximity to the user. The system is validated using benchmark datasets (CNR-EXT and PKLot), demonstrating high accuracy across diverse weather conditions. The proposed system shows how applied mathematical models and deep learning can improve urban mobility through intelligent infrastructure. Full article

The increasing frequency and severity of extreme weather events pose significant threats to power systems, particularly at the distribution level. The most detrimental consequence of such events is observed in critical loads due to high outage costs. As a result, there is a pressing need for utilities to invest in enhancing system resilience, which requires a comprehensive resilience investment framework and metrics to evaluate system performance. This paper proposes a distribution system resilience assessment framework to guide strategic investment decisions. The framework incorporates a mathematical model that estimates system restoration time after an extreme event, considering the criticality of loads, the interdependence of component failures and repair sequences, and the availability of repair crews. In addition, two new resilience metrics—disconnected load point hours (DLH) and normalized DLH (NDLH)—are introduced, which provide a more comprehensive view of system resilience by reflecting both vulnerability and the ability to withstand and recover from extreme events. Case studies are performed on a modified IEEE 69-bus test system utilizing the developed framework. The results evaluate the effectiveness of different resilience investment strategies, including infrastructure hardening, distributed energy resources management, and repair process coordination, in improving the system resilience for maintaining the critical loads and the overall distribution system. Full article

Wind power forecasting is challenging because of complex, nonlinear relationships between inherent patterns and external disturbances. Though much progress has been achieved in deep learning approaches, existing methods cannot effectively decompose and model intertwined spatio-temporal dependencies. Current methods typically treat wind power as a unified signal without explicitly separating inherent patterns from external influences, so they have limited prediction accuracy. This paper introduces a novel framework GCN-EIF that decouples external interference factors (EIFs) from inherent wind power patterns to achieve excellent prediction accuracy. Our innovation lies in the physically informed architecture that explicitly models the mathematical relationship: $P\left(t\right)=P_{\mathrm{inherent}}\left(t\right)+\mathrm{EIF}\left(t\right)$. The framework adopts a three-component architecture consisting of (1) a multi-graph convolutional network using both geographical proximity and power correlation graphs to capture heterogeneous spatial dependencies between wind farms, (2) an attention-enhanced LSTM network that weights temporal features differentially based on their predictive significance, and (3) a specialized Conv2D mechanism to identify and isolate external disturbance patterns. A key methodological contribution is our signal decomposition strategy during the prediction phase, where an EIF is eliminated from historical data to better learn fundamental patterns, and then a predicted EIF is reintroduced for the target period, significantly reducing error propagation. Extensive experiments across diverse wind farm clusters and different weather conditions indicate that GCN-EIF achieves an 18.99% lower RMSE and 5.08% lower MAE than state-of-the-art methods. Meanwhile, real-time performance analysis confirms the model’s operational viability as it maintains excellent prediction accuracy (RMSE < 15) even at high data arrival rates (100 samples/second) while ensuring processing latency below critical thresholds (10 ms) under typical system loads. Full article

Wastewater treatment plants (WWTPs) generate significant emissions of gaseous substances, such as H₂S, NH₃, and VOCs, which cause discomfort and pose health risks to residents in surrounding areas. The objective of this study was to estimate pollutant concentrations under various scenarios through a mathematical modeling of the pollutant dispersion in the surrounding air using the AERMOD View software platform, version 11.2.0. In this study, four mathematical models with two different scenarios were conducted to illustrate the odor concentrations both on site and in nearby areas under the most unfavorable weather conditions. The “1st Highest Values” and “98th Percentile” metrics were used to represent the peak concentrations and to exclude the 2% of conditions with the worst-case dispersion, respectively. In the first scenario, under normal operating conditions with all treatment equipment functioning, the maximum on-site odor concentration was estimated at 36.8 ouE/m³ using the 1st highest value function, and it was 20.4 ouE/m³ using the 98th percentile function. The second scenario considered all emission sources, with the grease collection system of the de-sanding/grease separation Unit Line 1 and the sludge collection system of the primary settling decanter (Unit 4) out of service. In this case, the maximum on-site odor concentration reached 749 ouE/m³ over 98% of a one-year period and 956.5 ouE/m³ using the 1st highest value function. These findings underscore the necessity for ongoing monitoring, strict adherence to environmental regulations, and stakeholder engagement to improve mitigation techniques and foster community trust in environmental management. Regular inspections are essential to ensure that all equipment operates within normal parameters, supporting both regulatory compliance and improved operational efficiency, including the control of odor emissions. Full article

To promote sustainable development in urban environments, minimising the reflected light pollution from glass curtain walls is critical. This study investigates numerical evaluation methods for assessing the impact of curtain wall-reflected light on road traffic light pollution. While existing research focuses on indoor glare and static target pollution, limited attention has been given to the dynamic impacts on moving traffic participants. This research evaluates light pollution (discomfort glare) induced by triple-layer hollow glass curtain walls in green buildings. A mathematical model predicting the solar reflection characteristics (reflectivity and brightness) was established using optical equations, with the accuracy verified through field experiments and numerical simulations. Subsequently, a driver discomfort glare (DDG) evaluation model was developed, incorporating the dynamic relationships between reflected light sources and drivers, including relative position variations, vertical eye illumination, and correlations between sightlines, driving speed, and road terrain. A numerical simulation system was implemented using Rhino’s Ladybug + Honeybee tools, demonstrated through a case analysis of high-rise buildings in Dalian. The system simulated glare effects under sunny/snowy conditions while examining thickness-related variations. The results revealed significant correlations between the glass thickness, weather conditions, and discomfort glare intensity. The proposed DDG model and simulation approach offer practical tools for assessing dynamic light pollution impacts, supporting the theoretical evaluation of outdoor light environments in green buildings. This methodology provides an effective framework for analysing the moving-target light pollution from architectural reflections, advancing sustainable urban design strategies. Full article