Search Results (2,859)

Ulcerative colitis (UC) is a chronic inflammatory bowel disease characterized by a complex etiology and a protracted disease course. Active ingredients from traditional Chinese medicine (TCM), by leveraging the holistic regulatory advantages of anti-inflammatory activity, immune barrier preservation, and gut microbiota regulation, have shown unique therapeutic potential in the intervention of UC. Although bottlenecks such as unclear targets, fragmented mechanisms of action, and poor druggability constrain the clinical translation of TCM active ingredients, current research efforts are dedicated to overcoming these obstacles. This article reviews the latest research progress (2021–2026) on TCM active ingredients for UC treatment. It analyzes the anti-UC mechanisms from three core dimensions: chemical diversity and pharmacodynamic characteristics, validation of direct targets, and indirect regulation through the “gut microbiota–metabolite” axis. Moreover, it emphasizes recent breakthroughs in druggability optimization technologies, including carrier-based nano drug delivery systems (NDDS), carrier-free NDDS, co-delivery NDDS, and prodrug design strategy. Research demonstrates that TCM active ingredients achieve therapeutic effects by modulating inflammatory signaling networks, restoring intestinal immune homeostasis, repairing the mucosal barrier, and remodeling the gut microenvironment. Simultaneously, the application of novel delivery strategies effectively resolves issues such as poor solubility, low oral bioavailability, and insufficient colon targeting. Finally, this review suggests that future research on TCM active ingredients for UC therapy should concentrate on systematically clarifying multi-level mechanisms and designing clinically translatable smart drug delivery strategies, aiming to provide a theoretical basis and practical reference for promoting TCM modernization and innovative UC drug development. Full article

Plastic pollution is a problem for many municipalities, water authorities, and industries, including transportation, energy, agriculture, fisheries, real estate, tourism, hospitality, insurance, and healthcare. Efforts to understand and mitigate plastic pollution would benefit from a dedicated map satisfying basic criteria including traceability, scalability, spatio-temporal resolution, and data flexibility. This article details and demonstrates how several existing pollution maps satisfy these criteria and makes recommendations on their use for specific activities, including temporal monitoring, root-cause analysis (RCA), cleanups, and tourism guides. Advantages of using plastic density rather than piecewise logs as the primary data format are highlighted, in particular feasible memory requirements and access to cloud data. Environmental plastic mapping by passive optical sensors, which offer the potential of comprehensive qualified data, is also surveyed, including demonstration of an original shortwave infrared (SWIR) polarization imager, and dynamic plastic pollution monitoring is demonstrated through the application-programming interface (API) of the Google Maps platform utilizing both sensor and published survey data. Full article

Background/Objectives: Peri-implant diseases, encompassing peri-implant mucositis and peri-implantitis, affect 43% and 18.8–23% of implant-bearing patients, respectively, representing significant clinical challenges in implant dentistry. While mechanical debridement remains foundational, biologically active materials offer promising adjunctive regenerative strategies. This narrative review synthesises current evidence regarding five biologically active materials: enamel matrix derivative (EMD), platelet-rich fibrin (PRF), fibroblast growth factor-2 (FGF-2), recombinant human platelet-derived growth factor-BB (rhPDGF-BB/GEM 21S^®), and polynucleotide–hyaluronic acid combinations (Regenfast^®). Methods: The relevant literature was identified using electronic databases, including MEDLINE, PubMed, Scopus, and Google Scholar. This review focused on clinical studies and randomised controlled trials with a minimum follow-up of six months investigating biologically active materials in peri-implant disease management. Material mechanisms, clinical efficacy, therapeutic limitations, and evidence quality were systematically evaluated. Attention was directed toward identifying genuine biological distinctions between peri-implant and periodontal disease contexts. Results: EMD demonstrates efficacy exclusively within multimodal surgical protocols, with isolated application yielding limited benefits. rhPDGF-BB shows superior periodontal regenerative capacity; however, dedicated peri-implantitis trials remain absent. FGF-2 exhibits paradoxical osteogenic suppression despite bone fill achievement, limiting peri-implant applicability. PRF and Regenfast^® demonstrate a mechanistically sound rationale yet lack substantive peri-implant disease validation. The critical findings revealed that peri-implant regeneration fundamentally differs from periodontal regeneration: implants lack periodontal ligament anatomy, rendering ligamentogenic differentiation-promoting agents biologically inappropriate. Conclusions: Contemporary biologically active materials demonstrate compelling periodontal efficacy yet remain inadequately validated for peri-implantitis management. This disparity reflects authentic biological distinctions rather than insufficient investigation. Until multicentre randomised controlled trials stratify efficacy across distinct peri-implant disease presentations, practitioners must prioritise evidence-based surgical fundamentals—meticulous decontamination, strategic grafting, and optimised wound healing—integrating biologically active materials judiciously within comprehensive, anatomy-respecting treatment protocols. Full article

Detecting objects in drone-captured aerial imagery is particularly formidable due to challenges such as the prevalence of numerous small targets and their dense spatial distribution. To bridge this gap, this paper introduces YOLO-UTD (YOLO-UAV Traffic Detection), a dedicated small object detector tailored for drone traffic surveillance. Built upon the YOLOv8 framework, the proposed model incorporates three principal enhancements. First, a specialized small-object detection head replaces the original large-object head to increase the sensitivity to fine-grained features. Second, we introduce a shallow-augmented feature pyramid network (SFPN) into the neck module. The SFPN enriches the semantic content of high-resolution shallow features via dense multiscale interactions and CARAFE upsampling, boosting performance on small targets. Finally, a C2fA layer is integrated into the deep backbone stages to adaptively fuse spatial details and semantic context through a dual-path architecture and a cross-attention mechanism, thereby dynamically refining features critical for small objects. Extensive experiments on the VisDrone2019 dataset validate that YOLO-UTD achieves a 3.6% higher mean average precision (mAP) than YOLOv8 while preserving a low parameter footprint, with a particularly significant gain of 5.3% in vehicle detection accuracy. These findings confirm the model’s efficacy and strong potential for application in smart city drone surveillance. Full article

Integration of LiDAR and thermal sensing has become increasingly important in robotics, infrastructure diagnostics, environmental monitoring, and autonomous perception systems. LiDAR sensors provide accurate three-dimensional geometric information but do not directly capture thermal properties of observed objects, whereas thermal cameras provide temperature distributions without explicit spatial structure. Fusion of both sensing modalities enables thermally augmented 3D scene reconstruction and spatial localization of temperature anomalies. This paper presents a practical LiDAR–thermal fusion framework for three-dimensional localization of heat sources using an Ouster OS1 LiDAR sensor and a FLIR A70 thermal camera. The proposed framework includes intrinsic thermal-camera calibration, extrinsic LiDAR–thermal calibration, multimodal data synchronization, projection of LiDAR points onto the thermal image plane, and assignment of temperature values to spatial points. Additionally, a dedicated thermally distinguishable calibration target is proposed to enable reliable multimodal feature extraction under low-contrast LWIR imaging conditions. The developed framework was experimentally validated using real radiometric thermal data and LiDAR point clouds acquired under laboratory conditions. Quantitative evaluation demonstrated reprojection errors below 1 pixel and a mean hottest-point localisation error of approximately 4.1 cm at a distance of 12.3 m. The results confirm that accurate spatial localisation of thermal anomalies can be achieved using a geometry-based multimodal fusion approach without relying on computationally expensive learning-based methods. The proposed framework emphasises practical deployment, deterministic calibration, and applicability in scenarios where limited training data or constrained computational resources make learning-based approaches difficult to apply. The proposed system may be applied to building energy diagnostics, industrial inspection, technical infrastructure monitoring, and robotic perception systems that require reliable spatial localisation of heat sources under real measurement conditions. Full article

Volatile fatty acids (VFAs) are essential precursors in chemical synthesis for various chemicals, polymers, pharmaceuticals, and fragrance compounds. Acidogenic anaerobic digestion (or arrested methanogenesis) is a promising method to stabilize organic wastes and convert them to value-added products such as VFAs. However, the VFAs’ accumulation could in turn suppress the fermentation process through product inhibition and limit the titer of VFA in the digestate. Therefore, in situ separation and recovery of VFAs from the fermentate is crucial to constructing an effective continuous VFA-producing system. Recent research has been dedicated to addressing these issues and advancing the utilization of biobased VFAs, particularly through process-intensified strategies employing novel green solvents such as natural deep eutectic solvents. Furthermore, in situ conversion of VFAs into esters is another potential strategy for VFA removal. However, VFA esterification in an aqueous medium is challenging due to the abundant water driving the reaction toward hydrolysis. Recent advances in free or immobilized enzyme catalysis in solvents have demonstrated improved ester yield by providing a hydrophobic space for the esterification reaction in aqueous solution. In this review, we present an overview of critical aspects on the state-of-the-art of green solvent-based process intensification strategies, including feedstock selection and pretreatment, operating condition optimization, advances in membrane- and solvent-based recovery methods, and biocatalytic in situ esterification. Lastly, we provide perspectives toward cost-effective, continuous, high-solid, environmental-benign, and industrial-relevant VFA production applications. Full article

The use of insect-based protein sources in aquaculture is gaining increasing attention with Hermetia illucens (black soldier fly, BSF) larvae meal representing a promising substitute to fishmeal (FM). This study evaluated the effect of partial dietary inclusion of BSF meal (BSF0, BSF2.5, BSF5, BSF10%) on the volatilome of rainbow trout (Oncorhynchus mykiss) fillets, before and after cooking, using gas chromatography–mass spectrometry (GC–MS) and a metal oxide sensor-(MOX)-based device. Fish were fed diets with increasing BSF inclusion, and both raw and cooked fillets were analyzed to assess changes in volatile organic compounds (VOCs). GC–MS enabled the identification and semi-quantitative analysis of VOC classes, while MOX sensor responses were processed using Linear Discriminant Analysis (LDA) to assess discrimination among dietary treatments. Results showed that BSF inclusion influenced the volatile profile, with clearer separation at higher inclusion levels (BSF5–BSF10%), especially in cooked fillets. Thermal processing enhanced these differences. GC–MS analysis revealed a reduction in aldehydes and ketones and an increase in carboxylic acids with higher BSF inclusion. Key compounds such as hexanal and heptanal decreased, indicating changes in lipid-derived volatile pathways. Overall, the integration of GC–MS and MOX sensors proved effective in detecting diet-induced changes, supporting their application as effective and reliable tools for quality assessment in aquaculture products, with potential implications for sensory quality that should be further confirmed through dedicated sensory studies. Full article

To address the insufficient voltage boost capability and excessive device stress of DC–DC converters at hydrogen fuel cell output ports—issues that hinder the safe and stable operation of megawatt-level grid-connected systems—this paper proposes a two-stage, multi-unit interconnected grid-connection topology. This topology features a single-switch boost capability with a double Z-source network, accompanied by a dedicated grid-connection control strategy. First, the switching device in the quasi-Z-source converter is positioned at the front end to mitigate the device stress in the DC boost stage of the grid-connected topology. Second, the inductors in the Z-source converter are replaced with quasi-Z-source networks to form a double Z-source structure, thereby enhancing the boost capability of the front-end DC–DC Boost converter for hydrogen fuel cells, reducing the duty cycle, and suppressing inductor ripple current. Subsequently, an AC/DC grid-connection regulation strategy is designed to achieve stable power output from the hydrogen fuel cells. The results show that compared with the traditional Z-source Boost converter scheme, the proposed topology increases the output voltage by 21.2% and reduces the voltage stress of the switching device by 8.3% at the rated output power, making it highly suitable for high-power applications. Finally, the correctness of the theoretical analysis and the effectiveness of the proposed topology are verified through simulations and experiments. Full article

Glyphosate, the most extensively used herbicide worldwide, is frequently detected in aquatic environments due to its high solubility, persistence, and intensive agricultural application. Its occurrence, together with that of its principal metabolite aminomethylphosphonic acid (AMPA), raises substantial environmental and public health concerns. Conventional water treatment technologies generally exhibit limited efficiency in achieving complete removal and mineralization of this compound. In recent years, advanced electrochemical oxidation processes, and particularly anodic oxidation, have emerged as promising alternatives owing to their ability to generate highly reactive hydroxyl radicals in situ. This review provides the first contaminant-specific and mechanistic assessment dedicated exclusively to the anodic electro-oxidation of glyphosate. In contrast to previous reviews offering broad surveys of electrode materials or generalized evaluations of glyphosate treatment technologies, this work synthesizes all mechanistic, kinetic, and material-dependent insights reported between 2016 and 2025. A comparative analysis of major anode families (including boron-doped diamond (BDD), ${\mathrm{P}\mathrm{b}\mathrm{O}}_2$, mixed-metal oxides, and Magnéli-phase Ti₄O₇) is presented, highlighting glyphosate-specific degradation pathways, intermediate formation, and the operational parameters controlling mineralization efficiency and energy demand. By establishing a structured framework that links electrode properties, radical-generation mechanisms, and pollutant-specific degradation chemistry, this review addresses a critical gap in the literature and provides a scientific basis for designing next-generation electrochemical processes for the efficient and sustainable removal of glyphosate and related organophosphorus contaminants. Full article

Since 2005, continuous research has been dedicated to unraveling the relationship between the sea lamprey (Petromyzon marinus) life cycle and fatty acid profiles. This mini review synthesizes the key findings of these investigations, transitioning from a chronological overview to a thematic approach focused on the utility of fatty acids as multifaceted biomarkers. Specifically, this work examines their application in identifying population and stock structures, decoding trophic ecology across ontogenetic stages, and tracking feeding strategies during the marine phase. Finally, the role of fatty acids in modulating cellular and physiological responses to environmental stressors—such as elevated salinity and pollutants—is addressed, highlighting the challenges lamprey juveniles overcome during their downstream trophic migration to the ocean. Full article

A Bayesian convolutional neural network (BCNN) quantifies prediction uncertainty by introducing randomness into weights or activations, which is important for safety-critical applications such as medical diagnosis and autonomous driving. However, BCNN inference typically relies on Monte Carlo sampling requiring multiple forward passes, leading to computation and energy consumption far beyond standard CNN hardware acceleration. FPGA, with its parallel processing, reconfigurability, and high-energy efficiency, are ideal platforms for dedicated BCNN accelerators. This paper designs and implements an FPGA acceleration method for BCNN-using high-level synthesis. First, convolution, pooling, and fully connected modules are individually optimized. Then, a mean/variance dual-path parallel expansion is adopted, combined with mixed-precision quantization and global scaling compensation, local reparameterization sampling, parameter reordering, and ping-pong buffering, achieving low resource usage and high-energy efficiency while enabling uncertainty evaluation. Experimental results on Bayes VGG16 show resource utilization of 24,776 LUT, 23,378 FF, 115 BRAM, and 129 DSP, with total power of 2.049 W. Compared with an unoptimized Bayesian implementation, the proposed design reduces inference latency to about one-third, and its latency is only 17% higher than that of the classical VGG16. Compared with PC-based floating-point models, the accuracy loss on four BCNN models (tested on CIFAR-10) is within 1%. The predictive entropy effectively distinguishes normal, noisy, and out-of-distribution (OOD) samples, validating the uncertainty quantification capability of the BCNN FPGA accelerator. Full article

The increasing adoption of Plant Factories with Artificial Lighting (PFAL) has intensified the reliance on Internet of Things (IoT) technologies for real-time monitoring and control of environmental and operational variables. While IoT-based architectures enable precise resource management and productivity optimization, PFAL systems remain highly vulnerable to component failures, sensor malfunctions, communication faults, and energy disruptions, which may compromise crop integrity and system reliability. These risks are particularly critical in low-cost and small-scale PFAL implementations, where maintenance capacity and redundancy are often limited. Existing IoT-based PFAL monitoring systems typically address either hardware or software redundancy in isolation and rarely incorporate a dedicated maintenance-oriented fault detection layer validated under realistic multi-failure scenarios. This study addresses these challenges by proposing a low-cost redundant system architecture for PFAL applications that simultaneously integrates (1) hardware redundancy through multi-sensor configurations; (2) analytical redundancy based on residual generation and threshold-based fault isolation; and (3) a maintenance-oriented fault detection layer capable of identifying abnormal internal device conditions. Experimental validation was conducted using four hardware configurations—Arduino Nano with Ethernet, ESP32, STM32 with Wi-Fi, and STM32 with Ethernet—evaluated across five fault scenarios: dust accumulation, water exposure, high temperature, fire detection, and physical impact. The STM32 with Ethernet configuration consistently achieved the fastest fault detection response times across all tested scenarios. Future work will focus on the integration of machine learning-based predictive maintenance algorithms, multi-node PFAL network deployments, and long-term field validation. Full article

Rotated ship detection in complex synthetic aperture radar (SAR) scenes remains a critical yet challenging task for maritime remote sensing applications. Existing methods are plagued by three core bottlenecks: inconsistent directional responses across multi-scale features, unstable rotation angle regression, and non-uniform supervision quality of positive samples during training, which collectively lead to elevated false alarms, missed detections, and severe localization degradation, especially under high IoU thresholds in complex inshore environments. To address these challenges, we propose CORE-Net, a collaborative optimization framework integrating three dedicated modules in the forward detection stage: a Rotation-Consistent Feature Pyramid (RCFP) to alleviate cross-scale directional mismatch, a Progressive Cascade Rotation Head (PCR Head) to improve progressive angle prediction stability, and an Orientation-Aware Regression Enhancement Unit (OAREU) to strengthen directional geometric representation in regression features, alongside an Uncertainty-Aware Sample Reliability Steering (UARS) module for training-stage optimization to softly downweight the regression contribution of positive samples with high classification confidence but low geometric consistency. Extensive experiments on three public SAR ship detection datasets (RSDD-SAR, SSDD+, and RSAR) demonstrate that the proposed method consistently improves AP_50:95 while maintaining high Recall and Precision, validating that joint optimization of feature representation, rotated regression, and sample reliability is an effective strategy to enhance both the robustness and fine-grained localization capability of rotated ship detection in complex SAR scenes. In addition, large-scene inference experiments on uncropped Sentinel-1 and RSDD-SAR images further demonstrate that CORE-Net can be extended from patch-based evaluation to high-resolution SAR scene interpretation using a sliding-window inference strategy. Full article

The massive generation of coal-based solid wastes (CBSWs) poses severe environmental challenges globally, while widespread soil degradation threatens food security and ecosystem stability. This review critically evaluates the technical feasibility and agro-ecological benefits of valorizing CBSWs—including coal gangue, fly ash, gasification slag, and desulfurization gypsum—as soil amendments within a circular economy framework. We systematically examine the physicochemical characteristics of major CBSW types, analyze modification methods that enhance their performance and safety, and assess their multifaceted effects on soil physical structure, chemical properties, nutrient dynamics, heavy metal immobilization, and microbial communities. A dedicated section addresses environmental risks, particularly toxic element leaching, and outlines integrated control strategies from source selection to post-application monitoring. Critical knowledge gaps persist regarding long-term contaminant stability under climate change scenarios, molecular-scale immobilization mechanisms, and economic scalability. Future research must prioritize advanced low-energy modification technologies, robust long-term field studies, and harmonized international regulations. We conclude that with scientifically guided modification and stringent risk management, CBSWs can be transformed into safe, multifunctional soil conditioners, simultaneously addressing industrial waste management and contributing to global restoration of degraded soil health. Full article

Real-time monitoring of lithium-ion capacitors (LICs) is crucial for ensuring reliability and predictive maintenance in dynamic applications such as electric transportation. However, traditional electrochemical impedance spectroscopy (EIS) techniques are complex and costly for onboard diagnostics due to their reliance on external excitation signals and dedicated hardware. Therefore, this paper presents an innovative framework for online state of health (SOH) estimation that bypasses these limitations by utilizing fast Fourier transform (FFT)-based passive impedance extraction directly from operational current and voltage signals. From experimental data, the equivalent circuit model (ECM) is developed, as well as its parameters, such as ohmic resistance, charge-transfer resistance, and Warburg diffusion. These parameters are identified through the extraction of impedance points in the low frequency region through FFT and the series resistance point using ohmic measurement, then performing a periodic curve fitting to these points. These curve fittings provide extracted ECM parameters. These parameters are used with a trained model to estimate the SOH of the monitored cell and are updated online. The proposed method was experimentally validated on five LIC cells aged under various C-rates (1C, 4C, 7C) and temperatures (35 °C, 40 °C, 50 °C), showing consistent impedance evolution with capacity fade. Validation of the utilized machine learning models, such as Polynomial Regression (PR), principal components analysis (PCA), and random forest (RF) regression, achieved SOH prediction errors as low as 2.23% compared to experimental results. The developed framework is particularly suitable for applications such as flash-charged electric buses but is broadly applicable across other energy storage systems as well. This advanced method enables real-time diagnostics without hardware modification, offering significant potential for integration into existing battery management systems (BMSs). Full article