Search Results (723)

Wildfires pose significant threats to the resilience of distribution systems. Furthermore, the phenomenon of global warming is further intensifying their contribution to power outages. Thus, enhancing distribution system resilience against wildfires remains an area of active research. This work presents a probabilistic approach to evaluate the spatio-temporal probability of wildfire occurrence using historical Forest Fire Danger Index (FFDI) data, and its impact on distribution lines and distributed energy resources (DERs) in active distribution networks (ADNs). To enhance system resilience, the deployment of hybrid energy storage systems (HESSs) is assessed, and their effectiveness in mitigating wildfire-induced disruptions is quantified. Furthermore, the proposed probabilistic methodology is compared with two deterministic approaches to demonstrate its superior capability in assessing wildfire risk and resilience improvement. The approach is suitable for large-scale geographical applications, providing a practical framework for resilience assessment and HESS-based mitigation planning in ADNs. Full article

Unobserved fruit crop illnesses are a major threat to agricultural productivity worldwide and frequently cause farmers to suffer large financial losses. Manual field inspection-based disease detection techniques are time-consuming, unreliable, and unsuitable for extensive monitoring. Deep learning approaches, in particular convolutional neural networks, have shown promise for automated plant disease identification, although they still face significant obstacles. These include poor generalization across complicated visual backdrops, limited resilience to different illness sizes, and high processing needs that make deployment on resource-constrained edge devices difficult. We suggest a Hybrid Multi-Scale Neural Network (HMCT-AF with GSAF) architecture for precise and effective fruit crop disease identification in order to overcome these drawbacks. In order to extract long-range dependencies, HMCT-AF with GSAF combines a Vision Transformer-based structural branch with multi-scale convolutional branches to capture both high-level contextual patterns and fine-grained local information. These disparate features are adaptively combined using a novel HMCT-AF with a GSAF module, which enhances model interpretability and classification performance. We conduct evaluations on both PlantVillage (controlled environment) and CLD (real-world in-field conditions), observing consistent performance gains that indicate strong resilience to natural lighting variations and background complexity. With an accuracy of up to 93.79%, HMCT-AF with GSAF outperforms vanilla Transformer models, EfficientNet, and traditional CNNs. These findings demonstrate how well the model captures scale-variant disease symptoms and how it may be used in real-time agricultural applications using hardware that is compatible with the edge. According to our research, HMCT-AF with GSAF presents a viable basis for intelligent, scalable plant disease monitoring systems in contemporary precision farming. Full article

Using lithium batteries to supply electric machinery and/or equipment in underground mines requires an adequate level of security. This is particularly important in coal mines, especially under the threat of methane explosions and/or fire hazards. Lithium battery cells with a BMS should be effectively isolated from the impact of the surrounding mine environment. This can be achieved by storing all battery systems in a certified explosion-proof enclosure (Ex) in accordance with the relevant regulations and standards. Preliminary tests conducted by the authors indicated that use of lithium cells without a BMS in mines is risky and, in practice, unacceptable. BMSs with passive cell balancing are most commonly employed. They allow for the equalization of cell voltages primarily during the charging process. However, the lowest-capacity cell still determines the overall lifetime of a battery. Furthermore, the use of active balancing systems (BMSs) is rare in practice due to their greater complexity and price. Nevertheless, they can significantly extend battery life through the much more efficient redistribution of energy among the cells, including during the discharge process. This article presents the operation of a modified (hybrid) BMS architecture, combining both passive and active balancing methods when employed for the selected suspended mine vehicle. It enables more safe and more effective charging process, as well as discharging process, which results in the longer time of operation of lithium battery packs, for one charge. This system is intended for use in mining machinery and equipment, as well as in selected energy storage systems powered by lithium-based battery modules. Full article

Climate change is advancing, sea levels are rising, and peak river discharges are increasing. Accelerated sea level rise (SLR) may pose a significant threat to the long-term habitability of the Netherlands. In the short term, further reinforcement of flood defenses is required. However, the key long-term question is which adaptation strategy will most effectively manage flood risk in the Netherlands. As part of the SLR Knowledge Programme, research was conducted on various long-term strategies, focusing on the feasibility of three approaches: Protect, Advance, and Accommodate. The Protect and Advance strategies aim to reduce flood risk primarily through the prevention of flooding. The Accommodate strategy, particularly in its more extreme form, emphasizes Managed Retreat, following the precautionary principle, or seeks to mitigate flood consequences rather than invest in Prevention. This study examined the economic implications of two opposing cornerstone strategies, Protect and Managed Retreat, as well as hybrid strategies that integrate elements of both, across different sea level rise scenarios. Additionally, the study includes a forward-looking assessment of the potential impacts on the financial sector, with particular attention to catastrophe insurance and capital requirements aimed at mitigating default risk. The findings indicate that a Managed Retreat strategy represents a last-resort option and cannot be implemented effectively without concurrent protective measures. Furthermore, the annual flood risk is only marginally reduced under the Accommodate strategy, even when combined with protective interventions, while its associated costs significantly exceed those of the Protect strategy. A combined approach integrating protection with localized Accommodate measures that support multi-functional land use, such as nature-based solutions and water storage, appears to offer a more promising strategy, if these values cover the costs. The results can be used to evaluate the effectiveness of possible adaptation strategies to sea level rise. Full article

Clubroot disease, caused by Plasmodiophora brassicae, is a major global threat, causing severe yield losses of up to 100% in heavily infested fields. Interspecific hybridization is essential for the transfer of clubroot resistance genes among the Brassica species. This review aimed to describe the sources of clubroot resistance, categorize their types in Brassica crops, and identify the most effective techniques and underutilized sources for both intergeneric and interspecific hybridization. A systematic literature review served as the foundation for expert analysis, encompassing a comprehensive list of known sources of resistance and a detailed description of their characteristics, including monogenic, polygenic, dominant, and recessive traits. In addition, this review specifies techniques suitable for gene transfer, such as markers, embryo rescue, somatic hybridization, and CRISPR/Cas. Based on the literature, underutilized directions for genetic crosses have been proposed. These conclusions suggest that combining biotechnological methods, including markers, CRISPR/Cas, and embryo rescue, with intergeneric crosses offers the potential to transfer resistance genes from previously untapped sources. Full article

The paper examines the development of forecasting and modeling technologies for environmental processes using classical and quantum data analysis methods. The main focus is on the integration of deep neural networks and classical algorithms, such as AutoARIMA and BATS, with quantum approaches to improve the accuracy of forecasting environmental parameters. The research is aimed at solving key problems in environmental monitoring, particularly insufficient forecast accuracy and the complexity of processing small data with high discretization. We developed the concept of an adaptive system for predicting environmental conditions in urban agglomerations. Hybrid forecasting methods were proposed, which include the integration of quantum layers in LSTM, Transformer, ARIMA, and other models. Approaches to spatial interpolation of environmental data and the creation of an interactive air pollution simulator based on the A* algorithm and the Gaussian kernel were considered. Experimental results confirmed the effectiveness of the proposed methods. The practical significance lies in the possibility of using the developed models for operational monitoring and forecasting of environmental threats. The results of the work can be applied in environmental information systems to increase the accuracy of forecasts and adaptability to changing environmental conditions. Full article

The integration of connected vehicles into smart city infrastructure introduces critical cybersecurity challenges for the Internet of Vehicles (IoV), where resource-constrained vehicles and powerful roadside units (RSUs) must collaborate for secure communication. We propose H-RT-IDPS, a hierarchical real-time intrusion detection and prevention system targeting two high-priority IoV security pillars: availability (traffic overload) and integrity/authenticity (spoofing), with spoofing evaluated across multiple subclasses (GAS, RPM, SPEED, and steering wheel). In the offline phase, deep learning and hybrid models were benchmarked on the vehicular CAN bus dataset CICIoV2024, with the BiLSTM-XGBoost hybrid chosen for its balance between accuracy and inference speed. Real-time deployment uses a TinyML-distilled CNN on vehicles for ultra-lightweight, low-latency detection, while RSU-level BiLSTM-XGBoost performs a deeper temporal analysis. A Kafka–Spark Streaming pipeline supports localized classification, prevention, and dashboard-based monitoring. In baseline, stealth, and coordinated modes, the evaluation achieved accuracy, precision, recall, and F1-scores all above 97%. The mean end-to-end inference latency was 148.67 ms, and the resource usage was stable. The framework remains robust in both high-traffic and low-frequency attack scenarios, enhancing operator situational awareness through real-time visualizations. These results demonstrate a scalable, explainable, and operator-focused IDPS well suited for securing SC-IoV deployments against evolving threats. Full article

Water pollution caused by petroleum-derived volatile organic compounds such as benzene, toluene, ethylbenzene, and xylenes (BTEX), as well as methyl tert-butyl ether (MTBE), poses a growing threat to aquatic ecosystems and human health. These contaminants, together with the organic matter and nutrients present in municipal wastewater, highlight the need for sustainable treatment technologies adapted to tropical conditions. This study evaluated the removal efficiency of BTEX, MTBE, and conventional pollutants using hybrid constructed wetlands (HCWs) that combine vertical subsurface flow (VSSF-CW) and horizontal subsurface flow (HSSF-CW) systems. Two plant species—Heliconia latispatha and Phragmites australis—were tested, along with a polyculture and an unvegetated control. The hybrid systems treated synthetic influents formulated to simulate contaminated municipal wastewater. Parameters including COD, TSS, N–NH₄⁺, N–NO₃⁻, P–PO₄³⁻, BTEX, and MTBE were monitored and analyzed using ANOVA and Tukey’s test (p < 0.05). Vegetated systems achieved COD removal efficiencies exceeding 85%, compared with 72% in the control. Phragmites australis obtained the highest removal of suspended solids (92 ± 3%) and ammonium nitrogen (88 ± 2%), whereas Heliconia latispatha exhibited superior phosphorus removal (84 ± 4%). The polyculture displayed a synergistic effect, achieving removal rates of 93% for benzene, 91% for toluene, and 88% for MTBE, with statistically significant differences relative to the control (p < 0.05). In conclusion, hybrid constructed wetlands planted with Heliconia latispatha and Phragmites australis demonstrated high efficiency and stability in removing BTEX, MTBE, and conventional pollutants under tropical conditions, positioning themselves as a sustainable, low-cost, and esthetically valuable treatment alternative. Full article

Introduction: Globally, microbial infections are projected to be among the leading causes of death by 2050 due to rising drug resistance. Antimicrobials are vital for treating both animals and humans worldwide. However, their overuse and misuse accelerate drug resistance, posing a serious threat to public health. Coumarin is a naturally occurring compound contributing health-beneficial features in drug discovery. Its high solubility in organic solvents, high bioavailability, simple structure, low toxicity, and low molecular weight make it an ideal candidate for combining with other pharmacophores to develop new therapeutic agents. This compound exhibits several biological activities, including antimicrobial, anticancer, anti-inflammatory, antidiabetic, neuroprotective, and anticoagulant effects, motivating medicinal researchers to hybridize it with other compounds to enhance its pharmacological efficacy. Hybridization of different pharmacophores via suitable linkers, including cleavable and non-cleavable ones, is a promising approach in drug development, resulting in new therapeutics with improved biological activity. Therefore, the hybridization of coumarin with other pharmacophores has become an interesting paradigm for medicinal scientists. Aim: This review aims to summarize the existing scientific literature on coumarin-based hybrid compounds with antimicrobial capabilities and discuss the structure–activity relationship (SAR) of these hybrids to potentially guide future research on and development of coumarin-based drugs for microbial treatment. Material and Methods: The review focuses on open-access literature about coumarin hybrid drugs available through searching tools such as Google, Google Scholar, ScienceDirect, and Scopus, published from 2024 to 2025. Results: Coumarin hybrids exhibit promising antimicrobial activity, particularly against S. aureus and C. albicans. The SAR reveals that halogenation, bulky aromatics, nitro, and hydroxyl groups enhance the interaction of the coumarin rings with amino acid residues. Conclusions: The reported coumarin hybrids showed a promising antimicrobial activity, with structural modifications influencing their activity. Hence, more studies, including more pre-clinical and clinical evaluations, are recommended for these hybrid compounds. Full article

Air travel contributed 3.5% to global warming in 2020, with a rising tendency. Only one third of the climate impact is caused by CO₂. Other exhaust gases that cause harm to the climate are nitrogen oxides, soot, and water vapor, creating contrails with a negative impact on earth’s albedo. Hence, it is important to reduce any type of emission. As the effects of global climate change become an unneglectable threat to society, calls for quick changes become prominent. Recognizing the need for disruptive changes in air transport, the LuFo-project 328eHY-TECH was initiated to investigate the potential of regional hybrid-electric aircraft. This article focuses on conceptual aircraft design. An aircraft resembling the D328eco is modeled as a baseline aircraft, on which the sizing of the hybrid-electric propulsion systems is performed. As aircraft are mostly operated on a typical mission, which is shorter than the design mission, a distance of 400 nm is found to be a feasible range for this regional aircraft. In a conducted range study, the potential of state-of-the-art battery properties is being investigated and found to be insufficient. Subsequently conducted trade-off studies show that a 104 kW horsepower electric motor and a battery of 1.8 kWh/kg are needed to save 5% block fuel on a mission with 40 passengers of 95 kg over a distance of 400 nm. It is concluded that changing solely the propulsion system will not yield feasible aircraft designs in the near and midterm future. Full article

Repackaged Android applications pose a significant threat to mobile ecosystems, acting as common vectors for malware distribution and intellectual property infringement. Addressing the challenges of existing repackaging detection methods—such as scalability, reliance on app pairs, and high computational costs—this paper presents a novel hybrid approach that combines supervised learning and symptom discovery. We develop a lightweight feature extraction and analysis framework that leverages only 20 discriminative features, including inter-component communication (ICC) patterns, sensitive API usage, permission profiles, and a structural anomaly metric derived from string offset order. Our experiments, conducted on 8441 Android applications sourced from the RePack dataset, demonstrate the effectiveness of our approach, achieving a maximum F1 score of 85.9% and recall of 98.8% using Support Vector Machines—outperforming prior state-of-the-art models that utilized over 500 features. We also evaluate the standalone predictive power of AndroidSOO’s string offset order feature and highlight its value as a low-cost repackaging indicator. This work offers an accurate, efficient, and scalable alternative for automated detection of repackaged mobile applications in large-scale Android marketplaces. Full article

Parasitic infections represent an important health challenge in falcons (Falconidae), where they may negatively influence survival, reproduction, and performance. This study is the first investigation in Serbia assessing the prevalence and diversity of endoparasites in captive falcons, with special emphasis on Serratospiculum spp. A total of 145 live birds (hybrid falcon, Falco peregrinus, F. cherrug, F. rusticolus) and 1 dead individual were examined. Diagnostic procedures included necropsy, coprological analysis, and molecular detection. Endoparasites were confirmed in 55.2% of fecal samples. The most frequent were Caryospora spp. (41.4%), followed by Porrocaecum spp. (18.6%), Capillaria spp. (3.4%), and Serratospiculum spp. (2.8%). Statistically significant differences were observed between sexes, locations, and preventive health management. Falcons kept under preventive measures showed a markedly lower prevalence compared with those lacking such care. These findings highlight the persistent occurrence of endoparasites in Serbian falcons and underscore the importance of preventive programs. Regular deworming, strict hygiene, and vector control represent essential strategies for minimizing infections, particularly of Serratospiculum spp., which poses a serious respiratory health threat. Full article

Antibiotics are widely used in medicine, livestock and other fields, leading to increasingly prominent enrichment, transformation and potential ecological risks in the global water environment. This poses a serious threat to ecological balance and public health, making the development of efficient and economical treatment technologies a research hotspot for addressing water antibiotic pollution. This paper systematically analyzes the current status of global water antibiotic pollution, migration and transformation characteristics, and research progress in removal technologies. We summarize the main types of antibiotics in water and their spatial distribution across different global water bodies, explore their primary entry pathways into the water environment, and elaborate on transformation behaviors such as migration, adsorption and degradation, as well as residual risks to aquatic ecosystems and human health. We also focus on existing artificial removal technologies, including physical methods like adsorption and membrane separation, chemical methods centered on advanced oxidation, and biological methods utilizing microbial metabolism. And we discuss emerging technologies such as microbial fuel cells and biocatalyst remediation, along with hybrid processes, regarding their development status and application potential. Finally, we outline key challenges in practical application of current technologies, provide an outlook on future research directions and engineering applications, aiming to offer references for water antibiotic pollution control. Full article

The tomato zonate spot virus (TZSV) poses a significant threat to agriculture. Therefore, the elucidation of the functional roles and interactions of its encoded proteins is crucial for the development of effective control strategies. The aim of this study was to investigate the interaction network between the TZSV nucleocapsid (N), the non-structural M-segment (NSm) and the non-structural S-segment (NSs) proteins, with a focus on the functional characterization of the NSm protein. Yeast two-hybrid (Y2H) analysis indicated that both the N protein (N-N) and the NSm protein (NSm-NSm) exhibit self-interaction in vitro, with successful expression of all fusion proteins confirmed by Western blotting. Subsequently, we used bimolecular fluorescence complementation (BiFC) and luciferase complementation imaging (LCI) assays in epidermal cells of Nicotiana benthamiana to confirm that N and NSm proteins self-interact. In addition, heterologous interactions between NSs-N, N-NSm and NSs-NSm were also detected. BiFC and co-localization experiments with fusion proteins elucidated the interaction place of the cell: N-N and NSm-N interactions occurred in both the cytoplasm and nucleus, with NSm-NSm interaction occurring in the nucleus, whereas NSs-N and NSs-NSm interactions only occurred in the cytoplasm. Subcellular localization studies showed that the N protein is distributed in both the cytoplasm and the nucleus, whereas the NSm and NSs proteins are predominantly localized in the cytoplasm. In particular, NSm was found to specifically target plasmodesmata (PD) and co-localize with the known PD marker protein PDLP8. Interestingly, TZSV NSm was demonstrated to mediate the cell-to-cell movement of a cucumber mosaic virus mutant (ΔCMV-GFP) lacking its native movement protein (3a). This was evidenced by the spread of approximately 50 fluorescent foci to neighboring cells observed at 6 dpi. This study comprehensively describes the intricate interaction network between the N, NSm and NSs proteins of TZSV and clarifies their subcellular localizations within plant cells. Crucially, we provide conclusive evidence that the NSm protein of TZSV is a functional movement protein essential for facilitating viral intercellular transport which promotes viral spread within the host during systemic infection. These findings offer important insights into the infection mechanism of TZSV and provide potential targets for the control of TZSV. Full article

Soil degradation in arid ecosystems is a major threat to sustainable development and food security, especially under accelerating climate change. Kazakhstan, where more than 70% of agricultural land suffers from salinisation, erosion, and humus loss, offers a representative case for studying climate-driven degradation. This study quantitatively assessed the influence of air temperature, precipitation, aridity index, and extreme climatic events on soil properties in the arid regions of western Kazakhstan (Atyrau and Mangystau). The analysis integrated long-term meteorological time series (1941–2023) with field and laboratory data (1967–2024) into a harmonised dataset of 1330 records. Principal component analysis (PCA) identified four degradation gradients explaining 73.6% of total variance, while Random Forest and SHAP algorithms quantified variable importance. Mean annual temperature, frequency of arid years, and aridity index were the strongest predictors of humus, salinity, pH, and CO₂ parameters, with climate factors accounting for up to 30% of soil variability. The findings demonstrate that climatic stressors are the main drivers of soil degradation in arid zones, with climate factors explaining up to 30% of the variability in key soil properties (humus, salinity, pH, and CO₂)—a substantial proportion that underscores their dominant role relative to local geochemical and anthropogenic influences. The proposed hybrid PCA—Random Forest/SHAP framework provides a robust tool for analysing climate–soil interactions and supports the design of adaptive land-use strategies to achieve Land Degradation Neutrality (LDN) in Kazakhstan and other arid regions. Full article