Search Results (163)

High-synchrotron-peaked (HSP) BL Lac objects are extreme particle accelerators whose synchrotron emission peaks at high frequencies, typically in the UV-to-X-ray band (${\nu }_{\mathrm{peak}}>{10}^{15}$ Hz; ${\nu }_{\mathrm{peak}}\ge {10}^{17}$ for EHSPs), implying electron Lorentz factors of order ${10}^5$–${10}^6$. Their relative proximity ($z\lesssim 0.5$), clean radiation environments, and favorable Hillas parameters make them prime candidates for ultra-high-energy cosmic ray (UHECR) acceleration beyond ${10}^{19}$ eV and for neutrino production above 100 TeV. The 2017 association of IceCube-170922A with the flaring blazar TXS 0506+056 provided compelling evidence for blazars as neutrino sources, while an archival neutrino flare from 2014–2015 with no clear electromagnetic counterpart (13 events) revealed additional complexity in the emission mechanism. This review examines HSP physical properties, identifies them through WISE-based infrared selection (the 2WHSP and 3HSP catalogs, ∼2000 sources), and contrasts leptonic synchrotron self-Compton models with hadronic alternatives. We assess the observational evidence linking HSPs to high-energy neutrinos and UHECRs, finding that extreme baryonic loading ($L_p/L_e\sim {10}^3$–${10}^5$) strains energetic budgets, Auger composition measurements favor heavy nuclei over proton-dominated scenarios, and the near-isotropy of UHECR arrival directions is difficult to reconcile with rare beamed sources. Potential resolutions involving magnetic reconnection, structured jets, and duty cycle effects are discussed. Next-generation facilities, including IceCube-Gen2, KM3NeT, CTAO, IXPE, and AugerPrime/TA × 4, will probe key observables to either establish HSP BL Lacs as sources of the highest-energy cosmic particles or redirect the search toward alternative accelerator classes. Full article

Autonomous cleaning robots operating in semi-structured dynamic environments must execute task-oriented motions while safely interacting with surrounding agents. These agents include pedestrians, vehicles, and other robots. In such environments (e.g., interaction-rich campus environments), reliable self-decision-making requires anticipating the future motions of surrounding agents rather than relying solely on reactive obstacle avoidance. This paper presents a trajectory prediction-enabled self-decision-making framework for autonomous cleaning robots in campus environments. A learning-based multi-agent trajectory prediction model is trained offline using public benchmarks and real-world operational data to capture typical interaction patterns in corridor-following, edge-cleaning, and intersection scenarios. The predicted trajectories are then incorporated as forward-looking priors into the robot’s online decision-making and planning process, enabling prediction-aware yielding, detouring, and task continuation decisions. The proposed framework is evaluated using real-world data-driven scenario reconstruction on a high-fidelity simulation platform that incorporates realistic vehicle dynamics and heterogeneous traffic participants. This evaluation focuses on short-horizon prediction performance and its impact on downstream decision-making stability. The results show that integrating trajectory prediction into the decision-making loop leads to more stable motion behavior and fewer abrupt adjustments in interaction scenarios. Under short-term prediction horizons, the evaluation results show that the proposed model achieves ADERate and FDERate exceeding 90% under predefined error thresholds, while lane-change prediction accuracy remains around 79%. In addition, the robot maintains stable speed tracking with only minor fluctuations under medium-density traffic conditions. Full article

Battery-powered electric unmanned surface vehicles (e-USVs) and electrified surface-cleaning platforms require reliable onboard vision under strict compute and power constraints. In reflective water environments, tiny floating debris is often obscured by specular highlights, reflection bands, ripples, motion blur, and camera jitter, while label noise further degrades training stability. To improve robustness without increasing onboard inference burden, this paper proposes YOLOv11-IMP, a lightweight detector for reflective water-surface scenes and embedded edge inference. The method integrates a transformer-enhanced backbone stage, a Global Channel–Spatial Attention module in the neck, and a median-enhanced channel–spatial module in the neck to improve global-context modeling, cross-scale interaction, and weak-boundary representation. WIoU-v3 is adopted to improve localization, and a train-time-only noise-aware screening strategy based on the small-loss principle is introduced to suppress unreliable labels without extra inference cost. Experiments on the CAS dataset and a self-built debris dataset show gains of 3.3% in AP@0.75 and 6.5% in AP for small objects over YOLOv11, while maintaining 7.3 GFLOPs and real-time inference on Jetson Nano, demonstrating practical potential for energy-constrained onboard missions. Full article

Current Unmanned Aerial Vehicle (UAV) swarm designs prioritize physical reliability over network security, leaving systems vulnerable to increasingly sophisticated cyber threats in complex environments. Existing defense methods are mostly limited to peripheral network security technologies, such as encryption, authentication, and firewalls. Consequently, they lack deep integration at the formation architecture level. This separation results in a disconnect between system reliability design and security protection mechanisms, making it difficult to effectively deal with high-level security threats such as internal backdoor vulnerabilities. To this end, this paper proposes an endogenous security architecture for UAV swarm based on dynamic heterogeneous redundancy (DHR) and cooperative supervision. Firstly, a theoretical model of DHR system for UAV swarm was constructed, and discrete nodes are abstracted as dynamic heterogeneous resource pools. Through the formal definition of the heterogeneous executor space, redundancy adjudication mechanism, and dynamic scheduling method, we demonstrate how this architecture suppresses common mode failures by introducing internal and external uncertainties, thereby realizing the coordination and unification of safety and security. Secondly, a distributed security control strategy based on cooperative supervision is proposed, which uses cross-validation between neighbors to replace the centralized adjudication of traditional DHR, solves the problem of anomaly detection in a decentralized environment, and combines reactive cleaning and periodic disturbance scheduling to give the system the ability to self-heal against unknown threats. Simulations in various attack scenarios demonstrate the proposed method’s superiority over traditional architectures. Especially in the simulated dormant multi-mode Advanced Persistent Threat (APT) scenario, the system can still maintain availability of more than 81%, which effectively verifies the key role of the coordination mechanism of heterogeneity, redundancy and dynamics in enhancing the safety and security of UAV swarms. Full article

Firefighters are exposed to complex combustion products and to contaminants carried on personal protective equipment (PPE). Occupational exposure as a firefighter is classified as carcinogenic. This review summarizes the current evidence on exposure environments, routes of uptake, contamination and secondary exposure from PPE, and the effectiveness and limits of decontamination approaches. Across incident types, smoke composition varies with the fuels and combustion conditions, but fine and ultrafine particles and semi-volatile organic chemicals are common. Biomonitoring confirms uptake after incidents. Self-contained breathing apparatus reduces inhalation exposure during active suppression, yet exposures persist through dermal absorption at ensemble interfaces and post-incident tasks. Protective ensembles can retain soot-bound polycyclic aromatic hydrocarbons, additive chemicals, and metals; volatiles and particles resuspension in vehicles and stations can extend exposure. Studies show that on-scene preliminary exposure reduction and laundering can lower contaminant burdens on PPE; however, removal remains incomplete and decreases when cleaning is delayed or when gear is aged. Emerging evidence raises additional concern for per- and polyfluoroalkyl substances from foams and coating materials, with limited data on exposure metrics and removability. The field lacks standardized, realistic contamination platforms and a dose-based definition of clean PPE. Integrated intervention studies linking exposure, secondary exposure pathways, biomarkers, and decontamination methods are needed to set performance-based targets and evaluate emerging hazards. Full article

Urban air pollution presents significant and escalating challenges to the long-term performance, safety, and durability of aluminium alloy façade systems. This perspective article proposes a conceptual framework to improve the durability of curtain walls in urban environments by exploring the interactions between airborne pollutants and their effect on aluminium materials. This paper synthesizes cross-disciplinary evidence and introduces a design concept, the Pollution Degradation Modifier (PDM), to conceptually integrate environmental stressors into standard code criteria. While not yet empirically validated, the PDM model outlines input parameters to guide future research and potential applications. Additionally, the study explores emerging mitigation strategies, including self-cleaning coatings, IoT-enabled monitoring systems, and smart façade technologies. The findings offer practical guidance for architects and structural engineers seeking to enhance façade resilience in high-pollution regions. Central to this research is the introduction of the Pollution Degradation Modifier (PDM), a new environmental load coefficient designed to support performance-based façade design responsive to site-specific pollution exposure. Full article

As the demand for clean water grows, the strategic management of water resources has become increasingly critical. However, the depletion of these resources is being accelerated by anthropogenic pollutants and resultant internal pipe corrosion within distribution networks. Conventional water treatment methods are characterized by high energy consumption, rendering them impractical in environments lacking a continuous external power supply. Consequently, innovative, self-sustained technologies for simultaneously monitoring fluid conditions and purifying water are a necessity. In this work, we present a water-driven triboelectric nanogenerator (W-TENG) used for energy harvesting and water-quality monitoring within pipe networks. Composed of a silicone rubber tube and aluminum electrodes, the optimized W-TENG achieved an open-circuit voltage of 58 V, short-circuit current of 1.1 µA, and 59.5 mW/m² at a 10 MΩ load. The W-TENG distinguishes pH levels and liquid types based on electrical outputs. Notably, a parallel connection of two W-TENGs enhanced electrical energy by 214% compared to the sum of two units. As an application, a self-powered electrochemical deposition was conducted and copper ions were successfully removed using energy stored in a 1 mF capacitor. These results indicate that the W-TENG is expected to be utilized as a self-powered platform for simultaneous water purification and real-time infrastructure monitoring. Full article

In marine hot–humid and salt spray environments, shipborne snap-action limit switches operating under micro-current loads are prone to triggering failures caused by the accumulation of heterogeneous films on electrical contact interfaces, which can induce abnormal behavior in electromechanical systems. To address this issue, this study systematically investigates the failure mechanisms of micro-current limit switches using multimodal diagnostic approaches. The results demonstrate that the migration and accumulation of corrosion products and foreign contaminants within the microswitch unit promote the formation of high-resistance heterogeneous films at the electrical contact interfaces, severely impairing reliable electrical conduction. Electrical contact experiments further reveal that the contact behavior is strongly dependent on the current magnitude. When the current exceeds 2A, arc discharge generated during contact closure can effectively disrupt and remove the heterogeneous films, thereby restoring the electrical functionality of previously failed switches under subsequent micro-current operating conditions. Based on the identified failure mechanism, and inspired by the natural eye-cleaning behavior of crabs, a biomimetic press-and-wipe self-cleaning dual-redundant limit switch design is proposed. The design enables autonomous surface cleaning through controlled reciprocal wiping between the moving and stationary electrical contacts, effectively suppressing the formation and accumulation of high-resistance films at the source. Comparative salt spray and damp heat storage tests demonstrate that the proposed self-cleaning limit switch maintains stable and reliable electrical contact performance in simulated marine environments, significantly improving operational reliability and service life under micro-current loads. This work provides both mechanistic insights and a practical structural solution for enhancing the reliability of electrical contact components operating under low-current conditions in harsh marine environments. Full article

The progressive degradation of surface waters should become one of the most important problems requiring an urgent solution. One of the methods developed is filtering water through loose, degraded sediments, blooms of cyanobacteria or algae, or a bed of hemp (Cannabis sativa L.) waste or hemp fibers. The conducted tests on the percolation of water samples and/or water with sediment from surface waters at sites with different ecological statuses indicate the possibility of using hemp waste for the reclamation of water reservoirs and rivers. The effect of filtration is a rapid improvement in water quality and, consequently, an improvement in the ecological status. The best result was achieved for a small freshwater reservoir with a large number of algae and loose degraded sediment. The initial turbidity value was at the limit of the device’s measurement capability, reaching 9991 NTU. After filtration through the hemp waste bed, the turbidity dropped to 42.52 NTU, a 99.57% decrease. The remaining parameters, C, TDS, and pH, were not subject to significant variability as a result of filtering. Excessive amounts of organic matter, which create a problem for surface waters, are removed. Due to the carrier (hemp waste), which is organic waste, any possible release of small amounts into the aquatic environment will not pose a threat. After applying filtration, a decision can be made on further actions regarding the water reservoir or river: Self-renewal of the reservoir or further percolation using, for example, mill gauze or cleaning the reservoir with other, non-invasive methods. After the filtering procedure, the hemp waste, enriched with organic matter and water remaining in the waste, can be used for composting or directly for soil mulching (preliminary tests have yielded positive results). A hemp waste filter effectively removes Chronomus aprilinus larvae (Chrinomidae) from water. This result indicates the possibility of removing mosquito larvae in malaria-affected areas. The use of hemp filters would reduce the amount of toxic chemicals used to reduce mosquito larvae. Improving the ecological status of surface waters by filtering contaminants with hemp waste filters can reduce the need for chemical treatment. The use of natural, biological filters enables sustainable surface water management. This is crucial in today’s rapidly increasing chemical pollution of surface waters. Full article

Fluorinated colloidal nanosystems have attracted significant attention for their advantageous properties and potential applications in the biomedical field, especially in ¹⁹F magnetic resonance imaging. These nanosystems are known for their high specificity, excellent biocompatibility, and ease of functional modification. Furthermore, they offer unique advantages for functional surface coating due to their surface performance and chemical resistance. This paper discusses recent developments in fluorinated colloidal nanosystems, including applications in biological detection (such as enzymes, proteins, pH levels, ions, reducing environments, and reactive oxygen species) and surface coating (such as self-cleaning, self-healing, antibacterial properties, anti-fogging, antifouling, and oil–water separation). This article also highlights current challenges and provides suggestions for future research directions in the field of fluorinated colloidal nanosystems. Full article

Autonomous vehicles (AVs) rely on LiDAR and camera sensors to perceive their environment. However, adverse weather conditions, such as rain, snow, and fog, negatively affect these sensors, reducing their reliability by introducing unwanted noise. Effective denoising of multimodal sensor data is crucial for safe and reliable AV operation in such circumstances. Existing denoising methods primarily focus on unimodal approaches, addressing noise in individual modalities without fully leveraging the complementary nature of LiDAR and camera data. To enhance multimodal perception in adverse weather, we propose a novel Adaptive Cross-Modal Denoising (ACMD) framework, which leverages modality-specific self-denoising encoders, followed by an Adaptive Bridge Controller (ABC) to evaluate residual noise and guide the direction of cross-modal denoising. Following this, the Cross-Modal Denoising (CMD) module is introduced, which selectively refines the noisier modality using semantic guidance from the cleaner modality. Synthetic noise was added to both sensors’ data during training to simulate real-world noisy conditions. Experiments on the WeatherKITTI dataset show that ACMD surpasses traditional unimodal denoising methods (Restormer, PathNet, BM3D, PointCleanNet) by 28.2% in PSNR and 33.3% in CD, and outperforms state-of-the-art fusion models by 16.2% in JDE. The ACMD framework enhances AV reliability in adverse weather conditions, supporting safe autonomous driving. Full article

A method for classifying floating garbage in rivers using a modified YOLOv11 algorithm is proposed to solve the problem of poor recognition of river floating objects using the conventional object detection algorithms. This approach first integrates a stronger CBAM that applies multi-scale channel attention to extract the features of floating objects of different sizes, as well as boundary-enhanced spatial attention to highlight target edge features. Second, an enhanced scenario-adapted SIoU Loss Function (saSIoU) is presented, which contains an angle-sensitive increase for large targets, shape-adaptive coefficients for irregular floating objects, and dynamic boundary blur tolerance for complex aquatic environments. Experimental validation on a self-collected dataset of river floating objects-containing six categories and 12,000 images, shows that the improved model has an mAP@0.5 of 86.48%, an mAP@0.95 of 56.44%, a precision of 80.43%, and a recall of 84.36%. Compared with the original YOLOv11, the improved model has an increase of 2.65 percentage points in mAP@0.5, and an increase of 4.27 percentage points in mAP@0.95, while remaining lightweight (2.60 M parameters, 6.44 giga floating-point operations (GFLOPs)). The proposal method has relatively better detection accuracy and real-time performance in terms of detection accuracy and real-time performance, which can provide a relatively reliable technical approach to achieve intelligent cleaning of river float garbage and water environment management. Full article

Nature-Based Solutions (NBSs) have proven to be effective and reliable for climate change adaptation and risk reduction. Among these, Floating Treatment Wetlands (FTWs) have recently gained significant attention. FTWs are floating NBS systems that enhance the biological self-cleaning capacity of aquatic environments. Since the performance of FTWs is derived from the rhizosphere suspended beneath a buoyant frame and the interactions between biofilm and macrophytes (rhizosphere), it is crucial to operate and design FTWs in a way that supports the specific pollutant removal pathways of FTWs. Key parameters to consider are plant selection, choice of planting medium, length of plant establishment phase, treatment medium depth, surface coverage ratio, hydraulic retention time (HRT), and placement of FTWs. Despite recent advances, there is a lack of established guidelines for FTW development, which has led to diverse construction and operational practices. This review aims to collate the latest advances in FTW research, identify gaps, and suggest a coherent classification and construction framework. By highlighting best practices, performance factors, and operational parameters, this review seeks to guide the future development and implementation of FTWs. Full article

Plant diseases are currently a major threat to agricultural economies and food availability, having a negative environmental impact. Despite being a promising line of research, current approaches struggle with poor cross-site generalization, limited labels and dataset bias. Real-field complexities, such as environmental variability, heterogeneous varieties or temporal dynamics as are often overlooked. Numerous studies have been conducted to address these challenges, proposing advanced learning strategies and improved evaluation protocols. Synthetic data generation and self-supervised learning reduce dataset bias, while domain adaptation, hyperspectral, and thermal signals improve robustness across sites. However, a large portion of current methods are developed and validated mainly on clean laboratory datasets, which do not capture the variability of real-field conditions. Existing AI models often lead to imperfect detection results when dealing with field images complexities, such as dense vegetation, variable illumination or changing symptom expression. Although augmentation techniques can approximate real-world conditions, incorporating field data represents a substantial enhancement in model reliability. Federated transfer learning offers a promising approach to enhance plant disease detection, by enabling collaborative training of models across diverse agricultural environments, using in-field data but without disclosing the participants data to each others. In this study, we collaboratively trained a hybrid Graph–SNN model using federated learning (FL) to preserve data privacy, optimized for efficient use of participant resources. The model achieved an accuracy of 0.9445 on clean laboratory data and 0.6202 exclusively on field data, underscoring the considerable challenges posed by real-world conditions. Our findings demonstrate the potential of FL for privacy preserving and reliable plant disease detection under real field conditions. Full article

Silica-based multifunctional coatings hold great promise for applications in optical devices, lenses, and solar panels. Herein, we report a facile, low-temperature route to integrate super-hydrophobicity with high transparency and low haze. By precisely controlling particle gradation and applying fluorine passivation, a multi-scale structure with micro-scale uniformity and nano-scale asperity was constructed. This unique architecture, combined with low surface energy, effectively reduces light scattering and enhances air trapping. Consequently, the coated glass achieves a high optical transmittance of 95.24% with a low haze of 0.97%, alongside a water contact angle of 153° and a sliding angle of 3°. The coating also exhibits distinct anti-reflection (an improvement of ~5.0% relative to the bare substrate) and self-cleaning properties. Furthermore, it demonstrates impressive robustness and durability, withstanding extreme conditions including cryogenic temperatures (−50 °C), hygrothermal environments, and long-term outdoor exposure. This work demonstrates the versatile potential of our strategy for fabricating highly transparent and superhydrophobic surfaces. Full article