Search Results (13)

With the increasing adoption of electric vehicles (EV), congestion at charging stations during peak hours has become a prominent issue, imposing significant pressure on station scheduling. Furthermore, the large-scale integration of photovoltaics (PV) introduces dual uncertainties in both generation and load, negatively impacting grid voltage. To tackle the above problems, a strategy for demand response balancing and congestion alleviation of charging stations under grid–road network partition mapping is proposed in this paper. Firstly, a user demand response capability assessment method based on the Fogg Behavior Model is proposed to evaluate the demand response potential of individual users in each zone. The results are aggregated to obtain the demand response participation capability of each zone, thereby realizing capability-based allocation and achieving demand response balancing. Secondly, the road network is divided into several zones and mapped to the power grid, and a two-layer cross-zone collaborative autonomy model is established. The upper layer aims to alleviate inter-zone congestion and balance inter-station power, taking into account the grid voltage level. A tripartite benefit model involving the power grid, charging stations and users is constructed, and an inter-zone mutual-aid model for the upper layer is established and solved optimally. The lower layer establishes an intra-zone self-consistency model, which subdivides different functional zone types within the road network zone, allocates and accommodates the cross-zone power from the upper-layer output inside the zone, and synchronously performs intra-zone cross-zone judgment to avoid congestion at charging stations. Simulation verification is carried out on the IEEE 33-bus system. The results show that the proposed method can effectively alleviate the congestion of charging stations, the balance degree among all zones is increased by 43.58%, and the power grid voltage quality is improved by about 38%. This study offers feasible guidance for exploring large-scale planned participation of electric vehicles in power system demand response. Full article

Background: High-quality anthropometric data are critical for accurately monitoring child nutritional outcomes and informing policy decisions, yet inconsistencies in measurement and reporting across large-scale surveys continue to challenge data reliability. Method: This research assesses the quality of height-for-age (HAZ), weight-for-age (WAZ), and weight-for-height (WHZ) z-scores based on a repeated cross-sectional analysis of two rounds of the National Family Health Survey (NFHS-4, 2015–2016 and NFHS-5, 2019–2021), examining improvements, persistent gaps, and regional disparities. We have used WHO-recommended diagnostics including digit preference, age-heaping, completeness of measurements, biologically implausible values, and distributional properties of z-scores to evaluate the plausibility of anthropometric data and generate state-level rankings to compare transitions across rounds. Results: The results indicate modest national-level improvements in data quality in NFHS-5, particularly reductions in digit preference and implausible values; however, substantial inter-state variation remains, with some states demonstrating clear progress while others continue to exhibit measurement anomalies. The completeness of date of birth improved from 99.0% in NFHS-4 to 99.9% in NFHS-5, while completeness of anthropometric measurements declined from 98.5% to 96.6%. Digit preference for height decreased from 15.2% to 14.4%, and the proportion of biologically implausible HAZ values declined from 3.4% to 2.3%. However, the standard deviation of HAZ increased from 1.77 to 1.85 and that of WHZ from 1.40 to 1.50, indicating persistent measurement variability. Transitions in HAZ rankings further reveal mixed patterns of advancement and stagnation, with regional clustering of improvements more evident in certain parts of the country. Overall, while NFHS-5 reflects progress in anthropometric data quality, key challenges persist related to inconsistent adherence to measurement protocols, variable field performance, and inadequate supervisory oversight. Conclusions: Strengthening training, standardizing procedures, and reinforcing monitoring mechanisms are essential for achieving more reliable anthropometric data, thereby enhancing the accuracy of child nutrition estimates and supporting more evidence-based policy interventions in India. Full article

Distributed Denial-of-Service (DDoS) attacks remain the most pervasive and operationally disruptive cyber threat and are routinely weaponized in interstate conflict (e.g., Russia–Ukraine and Stuxnet). Although attack-chain models are standard for Advanced Persistent Threat (APT) analysis, they have seldom been applied to DDoS, which is often framed as a single-step volumetric assault. However, ubiquitous intelligence and ambient connectivity increasingly enable DDoS campaigns to unfold as multi-stage operations rather than isolated floods. In parallel, large language models (LLMs) create new opportunities to strengthen traditional DDoS defenses through richer contextual understanding. Reviewing incidents from 2019 to 2024, we propose a three-phase DDoS attack chain—preparation, development, and execution—that captures contemporary tactics and their dependencies on novel hardware, network architectures, and application protocols. We classify these patterns, contrast them with conventional DDoS, survey current defenses (anycast and scrubbing, BGP Flowspec, programmable data planes, adaptive ML detection, API hardening), and outline research directions in cross-layer telemetry, adversarially robust learning, automated mitigation orchestration, and cooperative takedown. Full article

Monitoring the horizontal distribution of PM_2.5 within urban areas is of great significance, not only for environmental management but also for providing essential data to understand the distribution, formation, transport, and transformation of PM_2.5 within cities. This study proposes a novel approach—the Spectral Analysis-based PM_2.5 Estimation Machine Learning (SAPML) model. This method uses a machine learning model trained with features derived from multi-azimuth and multi-elevation MAX-DOAS observations, specifically the oxygen dimer (O₄) differential slant column densities (O₄ dSCDs), and labels provided by near-surface ground measurements corresponding to each azimuthal direction, to estimate near-surface PM_2.5 concentrations. This approach does not rely on meteorological data and enables multi-directional near-surface PM_2.5 monitoring using only a single independent instrument. SAPML bypasses the intermediate retrieval of aerosol extinction coefficients and directly estimates PM_2.5 concentrations from spectral analysis results, thereby avoiding the accumulation of errors. Using O₄ dSCD data from multiple MAX-DOAS stations for model training eliminates inter-station conversion differences, allowing a single model to be applied across multiple sites. Station-based k-fold cross-validation yielded an average Pearson correlation coefficient (R) of 0.782, demonstrating the robustness and transferability of the method across major regions in China. Among the machine learning algorithms evaluated, Extreme Gradient Boosting (XGBoost) exhibited the best performance. Feature optimization based on importance ranking reduced data collection time by approximately 30%, while the correlation coefficient (R) of the estimation results decreased by only about 1.3%. The trained SAPML model was further applied to two MAX-DOAS stations in Hefei, HF-HD, and HFC, successfully resolving the near-surface PM_2.5 spatial distribution at both sites. The results revealed clear intra-urban heterogeneity, with higher PM_2.5 concentrations observed in the western industrial park area. During the same observation period, an east-to-west PM_2.5 pollution transport event was captured: PM_2.5 increases were first detected in the upwind direction at HF-HD, followed by the downwind direction at the same station, and finally at the downwind station HFC. These results indicate that the SAPML model is an effective approach for monitoring intra-urban PM_2.5 distributions. Full article

Clouds modulate the net radiative flux that interacts with both shortwave (SW) and longwave (LW) radiation, but the uncertainties regarding their effect in polar regions are especially high because ground observations are lacking and evaluation through satellites is made difficult by high surface reflectance. In this work, sky conditions for six different polar stations, two in the Arctic (Ny-Ålesund and Utqiagvik [formerly Barrow]) and four in Antarctica (Neumayer, Syowa, South Pole, and Dome C) will be presented, considering the decade between 2010 and 2020. Measurements of broadband SW and LW radiation components (both downwelling and upwelling) are collected within the frame of the Baseline Surface Radiation Network (BSRN). Sky conditions—categorized as clear sky, cloudy, or overcast—were determined using cloud fraction estimates obtained through the RADFLUX method, which integrates shortwave (SW) and longwave (LW) radiative fluxes. RADFLUX was applied with daily fitting for all BSRN stations, producing two cloud fraction values: one derived from shortwave downward (SWD) measurements and the other from longwave downward (LWD) measurements. The variation in cloud fraction used to classify conditions from clear sky to overcast appeared consistent and reasonable when compared to seasonal changes in shortwave downward (SWD) and diffuse radiation (DIF), as well as longwave downward (LWD) and longwave upward (LWU) fluxes. These classifications served as labels for a machine learning-based classification task. Three algorithms were evaluated: Random Forest, K-Nearest Neighbors (KNN), and XGBoost. Input features include downward LW radiation, solar zenith angle, surface air temperature ($T_a$), relative humidity, and the ratio of water vapor pressure to $T_a$. Among these models, XGBoost achieved the highest balanced accuracy, with the best scores of 0.78 at Ny-Ålesund (Arctic) and 0.78 at Syowa (Antarctica). The evaluation employed a leave-one-year-out approach to ensure robust temporal validation. Finally, the results from cross-station models highlighted the need for deeper investigation, particularly through clustering stations with similar environmental and climatic characteristics to improve generalization and transferability across locations. Additionally, the use of feature normalization strategies proved effective in reducing inter-station variability and promoting more stable model performance across diverse settings. Full article

The limestone islands of Malta face high levels of water stress due to low rainfall over a small land area and a high population density. We investigate an innovative, cost-effective approach to groundwater monitoring in an island environment by computing auto- and cross-correlations of ambient seismic noise recorded on short-period and broadband seismic stations. While borehole readings give accurate site-specific water level data of the groundwater across the islands, this technique provides a more regional approach to quantitative groundwater monitoring. We perform the moving window cross-spectral method to determine temporal changes in seismic velocity (δv/v). Comparison of the δv/v with groundwater levels from boreholes and precipitation shows comparable patterns. We find that the variations of the δv/v from auto-correlations are more pronounced than the cross-correlation, and that short-period seismic stations are also sensitive. The δv/v signal deteriorates at longer interstation distances, presumably because paths traverse complex geology. We conclude that changes in the groundwater level found beneath very small islands, even as small as 3 km², can be detected seismically. Low-cost, easy-to-deploy seismic stations can thus act as an additional tool for groundwater monitoring, especially in places with limited natural water reservoirs, like rivers and lakes, and infrastructure. Full article

Within the framework of effective mass theory, we investigate the effects of spin–orbit interaction (SOI) and Zeeman splitting on the electronic properties of an electron confined in GaAs single quantum rings. Energies and envelope wavefunctions in the system are determined by solving the Schrödinger equation via the finite element method. First, we consider an inversely quadratic model potential to describe electron confining profiles in a single quantum ring. The study also analyzes the influence of applied electric and magnetic fields. Solutions for eigenstates are then used to evaluate the linear inter-state light absorption coefficient through the corresponding resonant transition energies and electric dipole matrix moment elements, assuming circular polarization for the incident radiation. Results show that both SOI effects and Zeeman splitting reduce the absorption intensity for the considered transitions compared to the case when these interactions are absent. In addition, the magnitude and position of the resonant peaks have non-monotonic behavior with external magnetic fields. Secondly, we investigate the electronic and optical properties of the electron confined in the quantum ring with a topological defect in the structure; the results show that the crossings in the energy curves as a function of the magnetic field are eliminated, and, therefore, an improvement in transition energies occurs. In addition, the dipole matrix moments present a non-oscillatory behavior compared to the case when a topological defect is not considered. Full article

Background and Objectives: The time of the pandemic brought great difficulties, both in state and interstate systems, industry, trade, and with individuals themselves. In addition, numerous studies have shown a drastic increase in mental disorders in people around the world. Therefore, the basic idea of our study was to investigate these disorders in university students in relation to their different lifestyles. Materials and Methods: The cross-sectional study was carried out at the University of Niš (Serbia) from December 2021 to February 2022. All of the participants were assessed by using appropriate questionnaires. The study included 1400 randomly selected students (692 females and 708 males). The statistical analysis of the data included the application of multiple regression analyses and correlation tests. Results: Statistical analysis indicates that extremely severe levels of depression symptoms were reported by 232 students (16.6%). Severe and extremely severe anxiety symptoms were reported by 480 students (34.3%). Multiple linear regression analysis found that for the increased depressive symptoms, the “most deserving” parameters were related to the consumption of alcoholic beverages and psychoactive substances (β = 0.10, and 0.11, respectively), compared to the period before the COVID-19 pandemic. For anxiety symptoms, the main role was played by alcohol consumption (β = 0.11) but also by the use of social networks as an adequate substitute for deprived content during the pandemic (β = 0.13). Alcohol consumption was the most “responsible” for elevated stress levels compared to the period before the pandemic (β = 0.19). Conclusions: Due to the COVID-19 pandemic, symptoms of depression, anxiety, and stress were drastically increased in the university students. There was significantly more frequent consumption of alcoholic beverages and psychoactive substances among them. That is why social support from a close environment is the most important strategy in coping with mental health issues during emergency situations. Full article

Groundwater belongs to the category of strategic minerals, along with hydrocarbon resources, so the supply of drinking water will become one of the urgent problems of modern society. The management of groundwater resources and their protection is a very complicated task, especially in border areas where neighboring states jointly exploit aquifers. The problem of transboundary water resources management, in particular groundwater, has been considered at the international level for more than 30 years. However, despite the adoption of a number of conventions, agreements and programs, both at the global and in the format of interstate relations, an understanding for the approach of a universal solution to the transboundary water issue has not yet been formed. An attempt to study the possibilities of applying the principles of unitization on the example of transboundary oil and gas fields in comparison with groundwater cross-border deposits is made in the paper for the first time. As a successful example, the unitization agreement between Norway and the UK for the development of the Frigg field was chosen. It is established that unitization agreements concluded by states in the joint development of transboundary mineral deposits, actively used in regulating the activities of transboundary oil and gas fields, can be used as one of the possible models of international agreements on the extraction of groundwater in transboundary territories. Full article

Lordsburg Playa, a dry lakebed in the Chihuahuan Desert of southwestern New Mexico (USA), is crossed by Interstate Highway 10 (I-10). Dust from the playa threatens highway safety and has caused dozens of fatal accidents. Two numerical models—the U.S. Department of Agriculture’s Single-Event Wind Erosion Evaluation Program (SWEEP) and the American Meteorological Society and U.S. Environmental Protection Agency Regulatory Model (AERMOD)—were used to simulate and predict the generation and dispersion of windblown soil, dust, and PM₁₀ from playa hotspots and estimate PM₁₀ concentrations downwind. SWEEP simulates soil loss and particulate matter emissions from the playa surface, and AERMOD predicts the concentration of transported dust. The modeling was informed by field and laboratory data on Lordsburg Playa’s properties, soil and land use/land cover databases, and weather data from meteorological stations. The integrated models predicted that dust plumes originating on the playa—including a large, highly emissive area away from the highway and a smaller, less emissive site directly upwind of the interstate—can lead to hourly average PM₁₀ concentrations of tens, to hundreds of thousands, of micrograms per cubic meter. Modeling results were consistent with observations from webcam photos and visibility records from the meteorological sites. Lordsburg Playa sediment contains metals, as will its dust, but human exposures will be short-term and infrequent. This study was the first to successfully combine the SWEEP wind erosion model and the AERMOD air dispersion model to evaluate PM₁₀ dispersion by wind erosion in a playa environment. With this information, land managers will be able to understand the potential levels of dust and PM₁₀ exposure along the highway, and better manage human health and safety during conditions of blowing dust and sand at Lordsburg Playa. Full article

The goals of real business in the context of the digital transformation of international logistics networks and marketing channels have necessitated the application of a scientifically based theoretical approach to the development of a formalized description acceptable for predictive planning based on leading indicators. In the context of globalization and interstate and regional economic unions, this will lead to achieving the maximum end-to-end integration of digital platforms. Based on the analysis, the article presents the integration of digital logistics and marketing approaches with the mathematical models of the ecosystem organization of economic relations. The features of the organization of economic relations between contractors involved in the execution of virtual transactions and the material movement of resources were analyzed. The researchers considered prerequisites for the analytical description of interconnections between the participants of digital platforms in cross border e-commerce. The authors’ approach is based on the idea of both a sales funnel in marketing and a conversion funnel in digital transformation. Considering the integration of logistics and marketing, authors offer the definition of business echelons as stages of the consumer value creation. The theoretical contribution of this article consists in constructing a mathematical description of business echelons along the entire value chain. The developed analytical description of business echelons is acceptable both for embedding a digital management support system into various software products, and for conducting in-depth analysis and finding optimal solutions. Full article

This paper presents a framework to identify critical nodes of a gas pipeline network. This framework calculates a set of metrics typical of the social network analysis considering the topological characteristics of the network. Such metrics are utilized as inputs and outputs of a (Data Envelopment Analysis) DEA model to generate a cross-efficiency index that identifies the most important nodes in the network. The framework was implemented to assess the US interstate gas network between 2013 and 2017 from both the demand and supply-side perspectives. Results emerging from the US gas network case suggest that different analysis perspectives should necessarily be considered to have a more in-depth and comprehensive view of the network capacity and performance. Full article

Interest in understanding the photophysics and photochemistry of thiated nucleobases has been awakened because of their possible involvement in primordial RNA or their potential use as photosensitizers in medicinal chemistry. The interpretation of the photodynamics of these systems, conditioned by their intricate potential energy surfaces, requires the powerful interplay between experimental measurements and state of the art molecular simulations. In this review, we provide an overview on the photophysics of natural nucleobases’ thioanalogs, which covers the last 30 years and both experimental and computational contributions. For all the canonical nucleobase’s thioanalogs, we have compiled the main steady state absorption and emission features and their interpretation in terms of theoretical calculations. Then, we revise the main topographical features, including stationary points and interstate crossings, of their potential energy surfaces based on quantum mechanical calculations and we conclude, by combining the outcome of different spectroscopic techniques and molecular dynamics simulations, with the mechanism by which these nucleobase analogs populate their triplet excited states, which are at the origin of their photosensitizing properties. Full article