Search Results (69)

Cyberattacks on pipeline operational technology systems pose growing risks to energy infrastructure. This study develops a physics-informed simulation and optimization framework for analyzing cyber–physical threats in petroleum pipeline networks. The model integrates networked hydraulic dynamics, SCADA-based state estimation, model predictive control (MPC), and a bilevel formulation for stealthy false-data injection (FDI) attacks. Pipeline flow and pressure dynamics are modeled on a directed graph using nodal pressure evolution and edge-based Weymouth-type relations, including control-aware equipment such as valves and compressors. An extended Kalman filter estimates the full network state from partial SCADA telemetry. The controller computes pressure-safe control inputs via MPC under actuator constraints and forecasted demands. Adversarial manipulation is formalized as a bilevel optimization problem where an attacker perturbs sensor data to degrade throughput while remaining undetected by bad-data detectors. This attack–control interaction is solved via Karush–Kuhn–Tucker (KKT) reformulation, which results in a tractable mixed-integer quadratic program. Test gas pipeline case studies demonstrate the covert reduction in service delivery under attack. Results show that undetectable attacks can cause sustained throughput loss with minimal instantaneous deviation. This reveals the need for integrated detection and control strategies in cyber–physical infrastructure. Full article

Honey is a valuable natural product prized for its nutritional and therapeutic properties, including antioxidant, antimicrobial, and anti-inflammatory effects. However, in addition to health-promoting compounds, honey may also contain plant-derived toxins, contaminants, and degradation products. Certain phytotoxins—such as pyrrolizidine alkaloids, grayanotoxins, triptolide, celastrol, gelsedine-type alkaloids, and tutin—can be transferred to honey from specific plant sources and pose health risks, particularly at high doses or with long-term exposure. Furthermore, compounds like 5-hydroxymethylfurfural, trace metals, pesticide residues, and Clostridium botulinum spores may present additional risks, especially to sensitive groups such as infants. Consumers often assume that natural products are inherently safe, which may lead to unintentional exposure to harmful substances. Adverse effects can range from chronic toxicity to, in extreme cases, death. Therefore, raising awareness among consumers and vendors is essential to reduce the intake of honey from unverified sources. Continuous monitoring of honey composition and further studies on the toxicodynamics of rare contaminants are crucial to ensuring safety while preserving the therapeutic benefits of this remarkable natural substance. Full article

Although many deep learning-based image restoration networks have emerged in various image restoration tasks, most can only perform well in a specific type of restoration task and still face challenges in the general performance of image restoration. The fundamental reason for this problem is that different types of degradation require different frequency features, and the image needs to be adaptively reconstructed according to the characteristics of input degradation. At the same time, we noticed that the previous image restoration network ignored the reconstruction of the edge contour details of the image, resulting in unclear contours of the restored image. Therefore, we proposed an edge-aware guided adaptive frequency navigation network, EAFormer, which extracts edge information in the image by applying different edge detection operators and reconstructs the edge contour details of the image more accurately during the restoration process. The adaptive frequency navigation perceives different frequency components in the image and interactively participates in the subsequent restoration process with high- and low-frequency feature information, better retaining the global structural information of the image and making the restored image more visually coherent and realistic. We verified the versatility of EAFormer in five classic image restoration tasks, and many experimental results also show that our model has advanced performance. Full article

Though global efforts are increasingly being urged to expand Protected Areas (PAs), PA Downgrading, Downsizing, and Degazettement (PADDD) events have been widespread worldwide to date. However, such events may often be poorly understood, as is the case in Myanmar, due to limited awareness of PADDD’s role and inadequate reporting. To fill this information gap, this study aimed to examine the enacted PADDD events and their impacts. A comprehensive dataset was developed for the enacted Myanmar PADDD events by compiling relevant PA documents. We identified 73 enacted PADDD events in 20 PAs (in Myanmar), affecting 1231.4 km² between 1989 and 2020, with downsizing as the sole type of PADDD in Myanmar. While rural settlements, other proximate causes, and infrastructure were highly associated with PADDD events, degradation contributed to the highest reduction in PA extent. Case studies demonstrated that PA habitats were more fragmented and deforested in the post-PADDD era. Land cover changes were more severe in PADDDed areas than in unPADDDed areas, while ranges of threatened species remained unprotected in PADDDed areas. Our results underscore the importance of comprehensive evaluations of the proposed PADDD and firm PADDD policy in safeguarding the PA estate and mitigating future potential PADDD events. Full article

In the face of the challenge of urban water resource degradation, green infrastructure construction has become a core strategy in modern urban water resource management. Urban aquatic horticulture (UAH), as an important component of this strategy, possesses the dual value of ecological purification and landscape aesthetics. However, its practical implementation is often constrained by public awareness and acceptance. This study aims to address the mismatch between the dual values of urban aquatic horticulture and public perception, and to develop an optimised plant selection strategy that integrates purification functions with public perception. Based on literature reviews, 18 images of aquatic plant landscapes showcasing different ornamental forms, species richness, and life types were created. A questionnaire survey was conducted on 320 participants to assess their perceptions of landscape aesthetic appeal and visual preferences, and a quantitative relationship model was established using multiple stepwise linear regression analysis. The public’s aesthetic perception of aquatic plant landscapes with different ornamental forms and species richness varies significantly, with flowering plant landscapes more likely to evoke aesthetic perception than non-flowering landscapes. The public’s visual preferences for landscape attributes significantly influence their aesthetic perception of aquatic plant landscapes. A multiple stepwise linear regression equation was established to model the relationship between the aesthetic perception of aquatic plant community landscapes and the public’s visual preferences for landscape attributes. There is no significant association between species richness and perceived landscape aesthetic appeal. The study developed an optimised selection strategy for aquatic plants that integrates purification functions with public perception, providing theoretical basis and practical guidance for the scientific configuration of aquatic horticultural systems in urban green infrastructure. In landscape design, flowering plants with ornamental value should be prioritised, with emphasis on landscape layers, colour, and spatial shaping to enhance public acceptance and promote the sustainable development of urban water resource management. Full article

When faced with diverse types of image tampering and image quality degradation in real-world scenarios, traditional image tampering localization methods often struggle to balance boundary accuracy and robustness. To address these issues, this paper proposes a symmetric guided dual-branch image tampering localization network—FENet (Fusion-Enhanced Network)—that integrates adaptive feature fusion and edge attention mechanisms. This method is based on a structurally symmetric dual-branch architecture, which extracts RGB semantic features and SRM noise residual information to comprehensively capture the fine-grained differences in tampered regions at the visual and statistical levels. To effectively fuse different features, this paper designs a self-calibrating fusion module (SCF), which introduces a content-aware dynamic weighting mechanism to adaptively adjust the importance of different feature branches, thereby enhancing the discriminative power and expressiveness of the fused features. Furthermore, considering that image tampering often involves abnormal changes in edge structures, we further propose an edge-aware coordinate attention mechanism (ECAM). By jointly modeling spatial position information and edge-guided information, the model is guided to focus more precisely on potential tampering boundaries, thereby enhancing its boundary detection and localization capabilities. Experiments on public datasets such as Columbia, CASIA, and NIST16 demonstrate that FENet achieves significantly better results than existing methods. We also analyze the model’s performance under various image quality conditions, such as JPEG compression and Gaussian blur, demonstrating its robustness in real-world scenarios. Experiments in Facebook, Weibo, and WeChat scenarios show that our method achieves average F1 scores that are 2.8%, 3%, and 5.6% higher than those of existing state-of-the-art methods, respectively. Full article

Plastic pollution in marine environments is a globally recognized concern that poses ecological and economic threats. While 80% of plastic originates from land, 20% comes from sea-based sources like shipping and fishing. Comprehensive assessments of fishing-related plastics are limited but crucial for mitigation. This study analyzed the distribution and temporal evolution of three fishing-related items (EPS fish boxes, fragments, and buoys) along the Bocassette spit in the northern Adriatic Sea, a region with high fishing and aquaculture activity. UAV monitoring (November 2019, June/October 2020) and structured interviews with Po Delta fishermen were conducted. The collected debris was mainly EPS, with boxes (54.8%) and fragments (39.6%). Fishermen showed strong awareness of degradation, identifying plastic as the primary litter type and reporting gear loss. Litter concentrated in active dunes and the southern sector indicates human and riverine influence. Persistent items (61%) at higher elevations suggest longer residence times. Mapped EPS boxes could generate billions of micro-particles (e.g., ~10¹³). The results reveal a complex interaction between natural processes and human activities in litter distribution. This highlights the need for integrated management strategies, like improved waste management, targeted cleanup, and community involvement, to reduce long-term impacts on vulnerable coastal ecosystems. Full article

Processing ancient text images presents significant challenges due to severe visual degradation, missing glyph structures, and various types of noise caused by aging. These issues are particularly prominent in Chinese historical documents and stone inscriptions, where diverse writing styles, multi-angle capturing, uneven lighting, and low contrast further hinder the performance of traditional OCR techniques. In this paper, we propose a unified neural framework, UniText, for the detection, recognition, and glyph restoration of Chinese characters in images of historical documents and inscriptions. UniText operates at the character level and processes full-page inputs, making it robust to multi-scale, multi-oriented, and noise-corrupted text. The model adopts a multi-task architecture that integrates spatial localization, semantic recognition, and visual restoration through stroke-aware supervision and multi-scale feature aggregation. Experimental results on our curated dataset of ancient Chinese texts demonstrate that UniText achieves a competitive performance in detection and recognition while producing visually faithful restorations under challenging conditions. This work provides a technically scalable and generalizable framework for image-based document analysis, with potential applications in historical document processing, digital archiving, and broader tasks in text image understanding. Full article

With the widespread adoption of encryption technologies, encrypted traffic classification has become essential for maintaining network security awareness and optimizing service quality. However, existing deep learning-based methods often rely on fixed-length truncation during preprocessing, which can lead to the loss of critical information and degraded classification performance. To address this issue, we propose a Multi-Feature Fusion (MFF) model that learns robust representations of encrypted traffic through a dual-path feature extraction architecture. The temporal modeling branch incorporates a Squeeze-and-Excitation (SE) attention mechanism into ResNet18 to dynamically emphasize salient temporal patterns. Meanwhile, the global statistical feature branch uses an autoencoder for the nonlinear dimensionality reduction and semantic reconstruction of 52-dimensional statistical features, effectively preserving high-level semantic information of traffic interactions. MFF integrates both feature types to achieve feature enhancement and construct a more robust representation, thereby improving classification accuracy and generalization. In addition, SHAP-based interpretability analysis further validates the model’s decision-making process and reliability. Experimental results show that MFF achieves classification accuracies of 99.61% and 99.99% on the ISCX VPN-nonVPN and USTC-TFC datasets, respectively, outperforming mainstream baselines. Full article

The ecological assessment of rivers under the Water Framework Directive (WFD) requires the use of biological quality elements (BQEs) across defined abiotic types of rivers. However, limited evidence exists on how well biological indices perform across multiple typological classes, particularly under the influence of complex, overlapping stressors. This study evaluated the diagnostic performance of four biological indices (IO—diatoms, MIR—macrophytes, MMI_PL—benthic macroinvertebrates, and EFI + PL—fish) in 16 river sites in southern Poland. These were classified into four abiotic types (5, 6, 12, and 17) and subjected to varying levels of human pressure. Biological, physical and chemical, and hydromorphological data were collected along environmental gradients including conductivity, nutrient enrichment, and habitat modification. Statistical analyses were used to evaluate patterns in community composition and index responsiveness. The IO and MMI_PL indices were the most consistent and sensitive in distinguishing between reference and degraded river conditions. MIR and EFI + PL were more variable, especially in lowland rivers, and showed stronger associations with habitat structure and oxygen levels. Conductivity emerged as a key driver of biological responses across all BQEs, with clear taxonomical shifts observed. The results support the need to consider both typological context and local environmental variation in ecological classification. The findings underscore the need for typology-aware, pressure-specific biomonitoring strategies that combine multiple organism groups and integrate continuous environmental variables. Such approaches can enhance the ecological realism and diagnostic accuracy of river assessment systems, supporting more effective water resource management across diverse hydroecological contexts. Full article

Water scarcity is a critical challenge in arid and semi-arid regions, where agricultural water consumption accounts for a significant portion of freshwater use. Conventional agriculture (CA) methods with high reliance on chemical and mechanical inputs often exacerbate this issue through soil degradation and water loss. This review aims to examine how different organic practices, such as mulching, cover cropping, composting, crop rotation, and no-till (NT) in combination with precision technologies, can contribute to water optimization, and it discusses the opportunities and challenges for the adoption and implementation of those practices. Previous findings show that organic agriculture (OA) may outperform CA in drought conditions. However, the problems of weed management in organic NT, trade-offs in cover crop biomass and moisture conservation, limited access to irrigation technologies, lack of awareness, and certification barriers challenge agricultural resilience and sustainability. Since the outcomes of OA practices depend on the crop type, local environment, and accessibility of knowledge and inputs, further context-specific research is needed to refine a scalable solution that maintains both productivity and resilience. Full article

Coastal habitats are crucial in mitigating the impact of coastal hazards on society. However, the shortage of information about the role of these habitats in reducing floods in Rivers State, Nigeria, is limited. This study aims to assess the contribution of mangrove habitats in protecting coastal communities from flooding using the InVEST coastal vulnerability model (version 3.10.2). The model analyzes various data inputs and assigns relative numbers, ranging from 1 to 5, indicating different levels of exposure. Data on population, bathymetry, shoreline type, land use land cover, and continental shelf were obtained from relevant websites and the InVEST model package. The findings indicate that the mangrove habitats in Rivers State offer minimal protection against coastal flooding due to their degraded state caused by oil spills and over-exploitation. Additionally, sandy beaches provide little to no protection, and the socio-economic conditions in the communities contribute to increased vulnerability to flooding. The study recommends awareness programs to educate the public about the importance of mangroves for coastal protection in addition to their conservation and restoration. Full article

Nowadays, as a result of the increasing awareness of European societies and new legal regulations, the role of renewable energy sources in individual heating is growing. One of the forms of renewable heat and electricity production is the use of biomass pellet burners coupled with Stirling engines. To ensure high system efficiency, the combustion process of this type of fuel requires an appropriate design of the burners, which can provide high-temperature flue gases. This requirement may be challenging, as the long operation of such a burner may cause the thermal degradation of its components, mainly the upper burner wall. The subject of this analysis was a burner with a nominal power of 10 kW. As the analysis tool, a previously validated CFD model was used. In this work, two ways of thermal degradation prevention are presented. The first one is geometry optimization via secondary air hole distribution. The results show that an appropriate geometrical design of the burner may be an efficient way of shifting the high-temperature zone to the burner axis, which may mitigate the thermal degradation risk. Secondly, the inlet air mass flow is changed to show its impact on the presence and location of the high-temperature zone. Both methods can be treated as interesting ways for solving the challenge of the long-term operation of high-temperature biomass burners by avoiding thermal degradation. Full article

The rapid adoption of electric vehicles (EVs) and the increasing reliance on renewable energy sources necessitate innovative charging infrastructure solutions to address key challenges in energy efficiency, grid stability, and sustainable transportation. Dynamic wireless charging systems, which enable EVs to charge while in motion, offer a transformative approach to mitigating range anxiety and optimizing energy utilization. However, these systems face significant operational challenges, including dynamic traffic conditions, uncertain EV arrival patterns, energy transfer efficiency variations, and renewable energy intermittency. This paper proposes a novel quantum computing-assisted optimization framework for the modeling, operation, and control of wireless dynamic charging infrastructure in urban highway networks. Specifically, we leverage Variational Quantum Algorithms (VQAs) to address the high-dimensional, multi-objective optimization problem associated with real-time energy dispatch, charging pad utilization, and traffic flow coordination. The mathematical modeling framework captures critical aspects of the system, including power balance constraints, state-of-charge (SOC) dynamics, stochastic vehicle arrivals, and charging efficiency degradation due to vehicle misalignment and speed variations. The proposed methodology integrates quantum-inspired optimization techniques with classical distributionally robust optimization (DRO) principles, ensuring adaptability to system uncertainties while maintaining computational efficiency. A comprehensive case study is conducted on a 50 km urban highway network equipped with 20 charging pad segments, supporting an average traffic flow of 10,000 EVs per day. The results demonstrate that the proposed quantum-assisted approach significantly enhances energy efficiency, reducing energy losses by up to 18% compared to classical optimization methods. Moreover, traffic-aware adaptive charging strategies improve SOC recovery by 25% during peak congestion periods while ensuring equitable energy allocation among different vehicle types. The framework also facilitates a 30% increase in renewable energy utilization, aligning energy dispatch with periods of high solar and wind generation. Key insights from the case study highlight the critical impact of vehicle alignment, speed variations, and congestion on wireless charging performance, emphasizing the need for intelligent scheduling and real-time optimization. The findings contribute to advancing the integration of quantum computing into sustainable transportation planning, offering a scalable and robust solution for next-generation EV charging infrastructure. Full article

Erosion is a significant environmental challenge in Serbia, shaped by natural and human factors. Pronounced relief, fragile geological substrate, a developed hydrographic network, and a climate characterized by an uneven distribution of precipitation throughout the year make this area prone to activating erosion processes and flash floods whenever there is a significant disruption in ecological balance, whether due to the removal of vegetation cover or inadequate land use. Researchers have recorded approximately 11,500 torrents in Serbia, most of which were activated during the 19th century, a period of significant social and political change, as well as intensive deforestation and the irrational exploitation of natural resources. By the mid-19th century, the effects of land degradation were impossible to ignore. As the adequate assessment of soil erosion intensity is the initial step in developing a prevention and protection strategy and the type and scope of anti-erosion works and measures, this article presents the path that the anti-erosion field in Serbia has taken from the initial observations of erosion processes through the first attempts to create the Barren Land Cadastre and Torrent Cadastre to the creation of the Erosion Potential Method (EPM) and its modification by Dr. Lazarević that resulted in the creation of the first Erosion Map of SR Serbia in 1971 (published in 1983). In 2020, a new Erosion Map of Serbia was created with the application of Geographic Information System (GIS) technologies and based on the original method by Professor Slobodan Gavrilović—the EPM—without the modifications introduced by Lazarević. We compared the 1983 and 2020 erosion maps in a GIS environment, where the change in soil erosion categories was analyzed using a confusion matrix. The updated erosion maps mirror the shift in methodology from a traditional approach (Lazarević’s modification) to the modern GIS-based method (Gavrilović’s original EPM) and reflect technological improvements and changes in land use, conservation practices, and environmental awareness. Full article