Search Results (315)

Continuous discharges from diverse industrial activities have severely degraded the water quality of the Lerma River, turning it into a major environmental, social, and public health concern. Conventional wastewater treatment processes are often insufficient for eliminating persistent and refractory organic pollutants; therefore, the implementation of advanced oxidation processes (AOPs) is increasingly required to restore water quality. In this context, the present study systematically evaluated the individual and combined effects of ozonation and electrochemical oxidation using boron-doped diamond (BDD) electrodes for the treatment of contaminated river water. Ozonation alone achieved an 89% reduction in turbidity and a 19% decrease in total organic carbon (TOC), while electrochemical oxidation reduced turbidity by 82% and TOC by 57%. Remarkably, the simultaneous application of both treatments resulted in a 98% reduction in turbidity and an 80% decrease in TOC, clearly demonstrating a strong synergistic effect. Regarding true color at 436 nm, associated with yellow chromophore compounds, removal efficiencies of 98.9%, 94.7%, and 67.3% were obtained for the combined process, electrochemical oxidation, and ozonation, respectively. Phytotoxicity tests with Lactuca sativa seeds showed no statistically significant difference in toxicity in water treated with the O₃–EO System compared to raw water. These results highlight, for the first time under real river water conditions, the superior performance of the integrated O₃–EO system as an effective strategy for the intensified degradation and partial mineralization of persistent organic contaminants, thereby underscoring its strong potential for advanced remediation of heavily polluted surface waters. Full article

Background: Body Dysmorphic Disorder (BDD) is characterized by an intense preoccupation with perceived flaws in physical appearance, which can influence choices related to aesthetically driven healthcare. In orthodontics, this may cause a mismatch between a person’s subjective concern about their appearance and the treatment need determined by established indices. Therefore, orthodontic treatment indices are crucial to ensure that interventions are clinically justified rather than primarily motivated by disproportionate appearance-related distress. Objective: To systematically review and appraise the existing evidence on the connection between BDD and orthodontic treatment need as assessed by established indices. Materials and Methods: A systematic search of five electronic databases was conducted for studies published up to March 2026 that examined the association between BDD and orthodontic treatment need. Eligible studies included individuals undergoing orthodontic treatment or seeking orthodontic care, in whom BDD was evaluated using validated instruments and treatment need was assessed using established orthodontic indices. Risk of bias was assessed using the ROBINS-E tool. Results: A total of 2743 records were identified, and four observational studies met the inclusion criteria. Due to heterogeneity in study design, assessment methods and outcomes, findings were synthesized narratively. Orthodontic treatment need was assessed using the Dental Health Component of the Index of Orthodontic Treatment Need (IOTN-DHC), the Aesthetic Component of the Index of Orthodontic Treatment Need (IOTN-AC), and the Index of Complexity, Outcome and Need (ICON). Two studies using IOTN-DHC reported a negative association between BDD and orthodontic treatment need, whereas studies using IOTN-AC and ICON found no significant relationship. Associations with sex, age, education, depression, and anxiety were inconsistent across studies. Conclusions: Current evidence suggests an inconsistent relationship between Body Dysmorphic Disorder and orthodontic treatment need, highlighting the relevance of personalized assessment in orthodontic decision-making. Given the limited number of studies and the high risk of bias, the findings should be considered preliminary, and further standardized studies are needed to clarify this association. Full article

Per- and polyfluoroalkyl substances (PFASs) in fluorinated firefighting wastewater (FFW), which are difficult to remediate using conventional technologies, represent a critical environmental hazard due to the extreme persistence and bioaccumulation potential of soil–groundwater systems. Niobium-supported boron-doped diamond (BDD) anodes were synthesized by microwave plasma chemical vapor deposition, and their performance in the electrochemical advanced oxidation processes (EAOPs) of FFW were systematically investigated. Under optimized conditions (100 mM Na₂SO₄ electrolyte with 100 mM peroxymonosulfate (PMS), current density of 33.3 mA/cm², pH = 6), the BDD anode achieved near-complete mineralization, with 92.5% total organic carbon (TOC) removal and significant defluorination (77.5% F⁻ release) within 240 min in simulated FFW-contaminated groundwater. For FFW-contaminated soil remediation, 90.2% TOC removal and 41.6% defluorination were achieved after 720 min under optimal treatment (water-to-soil ratio of 20:1). Quenching experiments and electron paramagnetic resonance (EPR) tests revealed that hydroxyl radicals (·OH) and singlet oxygen (¹O₂) were the predominant reactive species. Liquid chromatography–mass spectrometry/mass spectrometry (LC-MS/MS) analysis indicated that PFASs were removed by shortened carbon chains, ultimately mineralizing to CO₂ and F⁻. Toxicity assessment using Vibrio fischeri luminescence demonstrated a reduction in toxicity (from 99.8% to 20.9%), confirming the effective detoxification of BDD-based EAOPs. This work establishes BDD-based EAOPs as a promising technology for eliminating PFASs in groundwater and soil, offering theoretical insights into EAOPs and engineering solutions for PFAS remediation. Full article

Drone-based smart sensing increasingly relies on Vision–Language Models (VLMs) for real-time scene interpretation, obstacle detection, and autonomous navigation reasoning. Deploying such systems at scale demands not only high perceptual accuracy but also energy efficiency, a critical constraint on battery-powered Unmanned Aerial Vehicle (UAV) platforms, and linguistic flexibility for multinational operational contexts. We present a systematic benchmarking framework that jointly evaluates perception performance and inference energy for five open-source VLMs across thirteen languages spanning six language families, including three low-resource varieties (Arabic, Basque, and Luxembourgish). Using imagery sampled from the Berkeley DeepDrive 10K (BDD10K), each model is evaluated on four sensing tasks of increasing difficulty scored via a sentence-transformer backbone, with energy measured following the AI Energy Score methodology (Wh per 1000 queries) through continuous NVML-based GPU power sampling. Across 65 language–model observations, LLaVA-1.6 achieves the highest perception score ($\overline{S}=0.160$) while Phi-3-Vision attains the best energy efficiency ($66.3$ Wh/1000 queries); energy consumption and task accuracy are statistically uncorrelated (Spearman $\rho =0.001$; $p=0.995$). A formal UAV inference energy model instantiated for four commercial platforms confirms LLaVA-1.6 as Pareto-optimal on heavy-lift platforms (DJI Matrice 300/350 RTK) and LLaVA-1.5 on the energy-constrained Matrice 30; compact UAVs such as the Mavic 3 Enterprise exceed the budget of all evaluated models at standard query rates. Friedman tests reveal significant cross-language variability in energy demands (${\chi }^2=40.43$; $p=3.5\times {10}^{-8}$) and navigation reasoning performance (${\chi }^2=13.35$; $p=0.010$). Critically, we document a double penalty for low-resource languages, which simultaneously incur higher inference energy costs and lower task accuracy, with direct implications for equitable multilingual UAV deployments. Full article

Electrochemical oxidation (EO) possesses numerous advantages and great potential for organic pollutant degradation. However, traditional plate anodes for EO are limited by pollutant mass transfer, leading to low oxidation efficiency and high energy consumption. Herein, a three-dimensional (3D) polyacrylonitrile-based carbon fiber brush (PAN-CFB) anode was employed to enhance mass transfer and improve oxidation efficiency. The oxidation capacity of the PAN-CFB anode was compared with those of boron-doped diamond (BDD) and Ti/IrO₂-Ta₂O₅ plate anodes using oxalic acid (OA), phenol, and perfluorooctanoic acid (PFOA) as target pollutants, respectively. Experimental results demonstrated that the 3D PAN-CFB anode exhibits superior direct oxidation capacity compared to BDD and the Ti/IrO₂-Ta₂O₅ plate anode in degrading OA, which is attributed to the significantly enhanced mass transfer of OA toward the brush anode surface. Under a constant current of 400 mA for 240 min, the total organic carbon (TOC) removal from 50 mmol/L OA reached 90.5%, 57.5% and 6.6% for PAN-CFB, BDD and the Ti/IrO₂-Ta₂O₅ anode, respectively, and the energy consumption followed the order of PAN-CFB (5.5~8.9 kWh/kg_TOC) < BDD (11.2~19.3 kWh/kg_TOC) < Ti/IrO₂-Ta₂O₅ (76.1~120.7 kWh/kg_TOC). However, the 3D PAN-CFB anode exhibited poor stability at high potential and failed to promote phenol and PFOA degradation due to the weak direct oxidation capacity toward the two pollutants and the poor generation capacity of reactive oxygen species, associated with its low oxygen evolution potential. Therefore, future efforts should focus on developing stable 3D brush electrodes with a higher oxygen evolution potential to enable non-selective oxidation of a broader range of pollutants. Full article

To address the challenges of insufficient feature fusion and incomplete multi-scale information capture in complex traffic scenarios, we propose a vehicle type recognition network based on feature comparison and the Mixture of Experts (MoE) model. Specifically, the MobileNetV4 backbone is introduced to enhance deep feature extraction for vehicle targets. Meanwhile, we design a Multi-scale Interleaving Fusion Module (MSIFM), which progressively transmits feature channels via an interleaving structure to capture multi-scale features while enhancing vehicle feature representation. Moreover, we devise a Feature Compare Enhancement Module (FCEM) to efficiently fuse feature maps with different semantic information. By performing feature comparison, it strengthens strongly correlated features while suppressing weakly correlated ones. Finally, we design a Mixture of Experts Feature Enhancement Module (MOEFEM) to aggregate multi-scale feature maps and adaptively capture detailed vehicle features through multiple expert units. Experimental results demonstrate that our method achieves mAP improvements of 2.2% and 2.4% over YOLOv11 on UA-DETRAC and BDD100K, respectively. The proposed method not only improves detection accuracy significantly but also maintains real-time efficiency, providing a practical solution for high-precision vehicle type recognition. It offers valuable technical support for intelligent transportation systems, smart city management, and autonomous driving safety. Full article

Vehicle detection is a fundamental perception task in intelligent transportation systems and autonomous driving. Although state-of-the-art detectors achieve competitive performance under normal conditions, their robustness degrades substantially under adverse conditions such as rain, fog, low illumination, and sensor noise. To address this challenge, we propose RD-DETR, a vehicle detector that incorporates reaction–diffusion mechanisms into deep feature learning. The RDNet backbone adopts a pyramid-based enhancement strategy in which shallow layers preserve fine-grained texture details while deep layers employ reaction–diffusion-inspired dynamics to suppress noise and enhance target representations. The Phase-Guided Spatial Attention (PGSA) module leverages phase-related structural cues that are relatively less sensitive to global illumination and contrast variations, helping recover vehicle boundaries when appearance cues become unreliable under adverse imaging conditions. The Content-Aware Adaptive Fusion Module (CA-AFM) dynamically aggregates multi-scale features according to scene complexity, improving detection across diverse traffic scenarios. Experiments on BDD100K and DAWN show that RD-DETR yields mAP@0.5 improvements of 3.2 and 4.0 percentage points over RT-DETR, respectively, while reducing model parameters by 27.6%, indicating a favorable balance between accuracy and efficiency under the evaluated settings. Full article

In autonomous driving and intelligent transportation systems, the degradation of image quality under low-light conditions severely impacts the reliability of subsequent object detection. Existing methods predominantly employ data-driven deep learning models for image enhancement, often lacking physical interpretability and struggling to maintain robustness in complex lighting-varying traffic scenarios. To address this, this paper proposes a Physically Guided Transformer–CNN Hybrid Network (Physically Guided Transformer–CNN Hybrid Network, PGT-Net) for end-to-end joint optimization of low-light enhancement and object detection. PGT-Net innovatively integrates the atmospheric scattering physical model with deep learning architecture: first, a learnable physical guidance branch estimates the scene’s atmospheric illumination map and transmittance map, providing explicit physical priors for the network; second, a dual-branch enhancement backbone is designed, where the local CNN branch (based on an improved UNet) restores fine textures, while the Global Transformer Branch (based on Swin Transformer) models long-range dependencies to correct global uneven illumination, with features adaptively combined via a Physical Fusion Module to ensure enhancement results align with physical laws while retaining rich visual features; finally, the enhanced images are directly fed into a lightweight detection head (e.g., YOLOv7) for joint training and optimization. Comprehensive experiments on public datasets (ExDark, BDD100K-night, etc.) demonstrate that PGT-Net significantly outperforms mainstream methods (e.g., RetinexNet, KinD, Zero-DCE) in both low-light image enhancement quality (PSNR/SSIM) and object detection accuracy (mAP), while maintaining high inference efficiency. This research offers an interpretable, high-performance solution for visual perception tasks under adverse lighting conditions, holding strong theoretical significance and practical value. Full article

Vulnerable road users in informal urban environments confront a distinct set of hazards that standard computer vision datasets are ill-equipped to represent: artisanal speed bumps constructed without regulatory compliance, deteriorated road markings, and the mototaxi—a three-wheeled motorized vehicle that constitutes the primary informal transport mode in intermediate Andean cities yet is absent from all major international repositories. This paper presents QHAWAY—from Quechua qhaway, a transitive verb meaning “to look; to observe”—an Advanced Driver Assistance System (ADAS) predicated on instance segmentation, monocular distance estimation via the pinhole camera model, and Time-to-Collision (TTC) computation, developed for the road environment of Ayacucho, Peru (2761 m a.s.l.), a city recognised by UNESCO as a Creative City of Crafts and Folk Art since 2019. A hybrid dataset comprising 25,602 images with 127,525 annotated instances across 12 classes was assembled by combining an original local collection of 4598 images (10,701 instances) captured through four complementary acquisition methods across the five urban districts of the Huamanga province with three established international datasets (BDD100K, BSTLD, RLMD; 21,004 images, 116,824 instances). A three-phase progressive training strategy with monotonically increasing resolution (640, 800, and 1024 pixels) was evaluated as an ablation study. A multi-architecture comparison spanning YOLOv8L-seg and the YOLO26 family (nano, small, large) identified YOLO26L-seg as the best-performing model, attaining mAP50 Box of 0.829 and mAP50 Mask of 0.788 at epoch 179. The integration of ByteTrack multi-object tracking with the pinhole equation $D=(H_{\mathrm{real}}\times f)/h_{\mathrm{px}}$ delineates operational risk zones aligned with the NHTSA forward collision warning standard (danger: <3 m; caution: 3–7 m; TTC threshold ≤ 2.4 s). The system sustains processing rates of 19.2–25.4 FPS on an NVIDIA RTX 5080 GPU. A systematic field survey established that 96% of the audited speed bumps fail to comply with MTC Directive No. 01-2011-MTC/14, constituting the first quantitative record of informal road infrastructure non-compliance in the Andean region. Validation was conducted under naturalistic driving conditions without staged scenarios. Grad-CAM explainability analysis, encompassing three complementary visualisation algorithms (Grad-CAM, Grad-CAM++, and EigenCAM), confirmed that model attention concentrates consistently on safety-critical objects. Full article

Semantic segmentation for autonomous driving demands balancing high-fidelity perception with real-time latency. While Transformers achieve state-of-the-art results, their quadratic complexity bottlenecks high-resolution processing. State Space Models (SSMs) like Mamba offer linear complexity but often suffer from local detail loss and inefficient scanning strategies. We introduce AutoMamba, a tailored Hybrid-SSM architecture. We propose a Hybrid-SSM block incorporating Depthwise Convolutions to inject local spatial priors and a Stage-Adaptive Mixed-Scanning strategy. This strategy prioritizes horizontal context in early stages for road layouts while only activating vertical scanning in deep layers to preserve anisotropic structures like poles. Furthermore, we reveal that unlike Transformers, Mamba architectures require Auxiliary Supervision and Online Hard Example Mining (OHEM) to address “long-tail forgetting.” Experiments on Cityscapes and BDD100K under a training-from-scratch setting demonstrate AutoMamba’s superiority. Notably, AutoMamba-B0 achieves 67.79% mIoU on Cityscapes with 31.3% fewer FLOPs than SegFormer-B0. Moreover, while the larger SegFormer-B2 fails with Out-Of-Memory errors at $2048\times 2048$ resolution, AutoMamba-B2 scales efficiently, validating its linear complexity advantage for next-generation perception systems. Full article

Recent advancements in intelligent transportation systems have enabled smart vehicles to autonomously detect, predict, and respond to potential hazards in real time. However, achieving sub-second reaction performance remains challenging due to computational latency in sensor data processing. This paper presents an adaptive parallel processing framework that integrates multi-core concurrency and adjustable spatial down-sampling (compression) for real-time multi-vehicle collision prevention. We benchmark four operating modes (sequential/parallel × compressed/uncompressed) on a 22-thread CPU platform. Compared to the sequential uncompressed baseline, the proposed fork-compress mode reduces end-to-end pipeline latency by approximately 66%. Compared to the sequential compressed baseline, the reduction is smaller (≈24%), highlighting the importance of explicitly stating the baseline for headline claims. The scalability analysis is based on Amdahl’s Law and indicates an effective parallelizable fraction of about 25% under our implementation, with the remaining time dominated by I/O, synchronization, and coordination overhead. We define compression factor k as linear spatial down-sampling where both image width and height are divided by k (pixel area reduced to 1/k²). Empirical results show that moderate down-sampling (around k ≈ 4–6) provides the best latency–accuracy trade-off. A supporting detection study using YOLOv4-tiny on BDD100K demonstrates that down-sampling can significantly reduce mAP if the model is not retrained, and that compression-aware fine-tuning partially recovers the lost accuracy. Full article

In autonomous driving, deep convolutional neural networks face a core conflict between computational efficiency and safety-critical robustness on resource-constrained onboard computing units. Dominant structural pruning, based on weight magnitude or geometric statistics, fails in long-tailed traffic scenarios by equating parameter magnitude with feature importance and pruning critical filters in the tail classes. To address this, we propose a structural pruning framework that evaluates the semantic utility of features using weighted copula entropy rather than relying solely on their magnitude. Our novel approach integrates Elastic Net regularization for inducing sparsity and weighted copula entropy for unbiased information-theoretic feature selection. By incorporating inverse class frequency weighting into empirical Copula estimation, we decouple feature relevance from sample abundance, ensuring the preservation of rare-class discriminators based on their information content rather than occurrence frequency. Furthermore, this metric is embedded into an enhanced max-relevance and min-redundancy algorithm to eliminate semantic redundancy while maintaining representational diversity. Extensive experiments on the BDD100K dataset with YOLOv5l and YOLOv8l architectures demonstrate that, at a 50% pruning rate, the proposed method reduces FLOPs and parameters by nearly 50%, with only 0.09% mAP@0.5 loss for YOLOv5l and 0.14% mAP@0.5 loss for YOLOv8l, while significantly improving the mAP of the extreme tail class Train from 0% to 3.84% and 2.76% to 5.12%, respectively. It achieves a more favorable trade-off between detection accuracy and computational efficiency than mainstream pruning approaches. This work provides a lightweight scheme for autonomous driving perception models and a new information-theoretic perspective for structured network pruning. Full article

This study presents the successful electrodeposition of polyaniline (PANI) and TiO₂/PANI composites on boron-doped diamond (BDD) and fluorine-doped tin oxide (FTO) substrates via cyclic voltammetry. Using 20 scan cycles in 0.5 M H₂SO₄, we synthesized thin films with tailored electrochemical properties. The formation of PANI was confirmed by characteristic redox peaks in the voltammograms, while FTIR spectroscopy identified key functional groups and bonding interactions between TiO₂ and PANI. Morphological analysis via optical and scanning electron microscopy revealed uniform but cracked surfaces influenced by TiO₂ loading. Composite electrodes with molar ratios of 2:1, 4:1, and 6:1 (TiO₂:PANI) were compared, showing increased titanium content with higher ratios, as confirmed by EDS. This work offers a reproducible route for designing modified electrodes with enhanced interfacial properties. Full article

Ciprofloxacin (CIP) is an antibiotic that is widely used in humans and animals. However, the compound has been detected in animal-derived products and the environment due to its extensive use, causing serious concern for public health and environmental safety. The issue raises the urgent need to develop innovative techniques to monitor CIP. Therefore, this study aims to develop a simple and sensitive CIP sensor called the boron-doped diamond nanoparticle-modified screen-printed electrode (BDD NPs/SPE) and the nickel oxide nanoparticle-modified BDD NPs/SPE (NiO NPs/BDD NPs/SPE). NiO NPs were synthesized via green synthesis using Spatholobus littoralis Hassk. root extract as the reducing agent. The formation and characteristics of NiO NPs were then confirmed through a UV-Vis spectrophotometer, XRD, PSA, FT-IR, and XPS. The successful modification of SPE was confirmed through SEM-EDX, followed by measurements using square-wave voltammetry. The results showed that the modified SPE could detect CIP over a concentration range of 0.1–100 µM and produced a low detection limit of 0.109 µM for BDD NPs/SPE and 0.054 µM for NiO NPs/BDD NPs/SPE. The proposed method was successfully applied to the determination of CIP in commercial tablets, milk, and human urine, with a satisfactory % recovery from 95 to 100%. The current study successfully developed a simple yet highly sensitive sensor that enabled robust, reliable, and efficient detection of CIP, showing its strong potential for practical applications. Full article

This study presents the development of a highly sensitive and selective electrochemical method for the determination of etamsylate (ETM) using a Boron-Doped Diamond (BDD) electrode. Analytical conditions were optimized using Square Wave Voltammetry (SWV), establishing an orthophosphoric acid (V) solution as the ideal supporting electrolyte. Under optimized instrumental parameters, the sensor demonstrated a superior linear response with a correlation coefficient of 0.999. The calculated limit of detection (LOD) was 0.10 µmol L⁻¹ (0.026 mg L⁻¹), with very good repeatability, indicated by an RSD of 3.17%. The practical utility of the BDD electrode was confirmed through the analysis of pharmaceutical and spiked biological samples, yielding recovery values between 95% and 102%. The results indicate that this method serves as a robust, cost-effective, and efficient alternative for routine quality control, clinical diagnostics, and environmental monitoring of etamsylate. Full article