Search Results (6,814)

Over the past two decades, the meat industry has faced increasing pressure to prevent foodborne outbreaks and reduce economic losses associated with delayed detection of spoilage. This demand has accelerated the development of on-site, real-time sensing tools capable of identifying early signs of contamination. Electrospun nanofiber (NF) platforms have emerged as particularly promising due to their large surface area, tunable porosity, and versatile chemistry, which make them ideal scaffolds for immobilizing enzymes, antibodies, or aptamers while preserving bioactivity under field conditions. These NFs have been integrated into optical, electrochemical, and resistive devices, each enhancing response time and sensitivity for key targets ranging from volatile organic compounds indicating early decay to specific bacterial markers and antibiotic residues. In practical applications, NF matrices enhance signal generation (SERS hotspots), facilitate analyte diffusion through three-dimensional networks, and stabilize delicate biorecognition elements for repeated use. This review summarizes major NF fabrication strategies, representative sensor designs for meat quality monitoring, and performance considerations relevant to industrial deployment, including reproducibility, shelf life, and regulatory compliance. The integration of such platforms with data networks and Internet of Things (IoT) nodes offers a path toward continuous, automated surveillance throughout processing and cold-chain logistics. By addressing current technical and regulatory challenges, NF-based biosensors have the potential to significantly reduce waste and safeguard public health through early detection of contamination before it escalates into costly recalls. Full article

To address the issue of material spillage and equipment wear caused by conveyor belt deviation in complex industrial scenarios, this study proposes a detection method based on an improved U-Net. The approach adopts U-Net as the backbone network, with a ResNet34 encoder to enhance feature extraction capability. At the skip connections, a Multi-scale Adaptive Guidance Attention (MASAG) module is embedded to strengthen the fusion of semantic and detailed features. In the loss function design, a boundary loss is incorporated to improve edge segmentation accuracy. Furthermore, the segmentation results are refined via edge detection and RANSAC regression, and a reference line is constructed based on the physical stability of rollers in the image to enable quantitative measurement of deviation. Experiments on a self-constructed dataset demonstrate that the proposed method achieves higher accuracy (99.77%) compared with the baseline U-Net (99.65%) and also surpasses other categories of approaches, including detection-based (YOLOv5s), anchor-point-based (UFLD), and segmentation-based approaches represented by SEU-Net and DeepLabV3+, thereby exhibiting strong robustness and real-time performance across diverse complex operating conditions. The results validate the effectiveness of this method in practical applications and provide a reliable technical pathway for the development of intelligent monitoring systems for mining conveyor belts. Full article

The operational stability of organic solar cells critically depends on the excited-state characteristics of electron acceptor materials. Through systematic quantum chemical calculations on four representative acceptors (PCBM, ITIC, Y6, and TBT-26), this study reveals fundamental spectra–stability relationships. Non-fullerene acceptors demonstrate superior light-harvesting with systematically tuned energy levels and significantly lower exciton binding energies (2.05–2.12 eV) compared to PCBM (2.97 eV), facilitating efficient charge separation. Structural dynamics analysis uncovers distinct stability mechanisms: ITIC maintains exceptional structural integrity (anionic RMSD = 0.023, S₁ RMSD = 0.134) with superior bond preservation, ensuring balanced performance–stability. Y6 exhibits substantial structural relaxation in excited states (S₁ RMSD = 0.307, T₁ RMSD = 0.262) despite its low exciton binding energy, indicating significant non-radiative losses. TBT-26 employs selective bond stabilization, preserving acceptor–proximal bonding despite considerable anionic flexibility. These findings establish that optimal molecular design requires both favorable electronic properties and structural preservation in photoactive states, providing crucial guidance for developing efficient and stable organic photovoltaics. Full article

Accurate extrinsic calibration between optical sensors, such as camera and LiDAR, is crucial for multimodal perception. Traditional methods based on specific calibration targets exhibit poor robustness in complex optical environments such as glare, reflections, or low light, and they rely on cumbersome manual operations. To address this, we propose a fully unsupervised, end-to-end calibration framework. Our approach adopts a dual-Transformer architecture: a Vision Transformer extracts semantic features from the image stream, while a Point Transformer captures the geometric structure of the 3D LiDAR point cloud. These cross-modal representations are aligned and fused through a neural network, and a regression algorithm is used to obtain the 6-DoF extrinsic transformation matrix. A multi-constraint loss function is designed to enhance structural consistency between modalities, thereby improving calibration stability and accuracy. On the KITTI benchmark, our method achieves a mean rotation error of 0.21° and a translation error of 3.31 cm; on a self-collected dataset, it attains an average reprojection error of 1.52 pixels. These results demonstrate a generalizable and robust solution for optical-sensor extrinsic calibration, enabling precise and self-sufficient perception in real-world applications. Full article

Domain generalization (DG) aims to develop models that perform robustly on unseen target domains, a critical but challenging objective for real-world fault diagnosis. The challenge is further complicated in compound fault diagnosis, where the rigidity of hard labels and the simplicity of label smoothing under-represent inter-class relations and compositional structures, degrading cross-domain robustness. While current domain generalization methods can alleviate these issues, they typically rely on multi-source domain data. However, considering the limitations of equipment operational conditions and data acquisition costs in industrial applications, only one or two independently distributed source datasets are typically available. In this work, an adaptive label refinement network (ALRN) was designed for learning with imperfect labels under source-scarce conditions. Compared to hard labels and label smoothing, ALRN learns richer, more robust soft labels that encode the semantic similarities between fault classes. The model first trains a convolutional neural network (CNN) to obtain initial class probabilities. It then iteratively refines the training labels by computing a weighted average of predictions within each class, using the sample-wise cross-entropy loss as an adaptive weighting factor. Furthermore, a label refinement stability coefficient based on the max-min Kullback–Leibler (KL) divergence ratio across classes is proposed to evaluate label quality and determine when to terminate the refinement iterations. With only one or two source domains for training, ALRN achieves accuracy gains exceeding 22% under unseen operating conditions compared with a conventional CNN baseline. These results validate that the proposed label refinement algorithm can effectively enhance the cross-domain diagnostic performance, providing a novel and practical solution for learning with imperfect supervision in cross-domain compound fault diagnosis. Full article

Globally, intensive groundwater extraction has led to widespread land subsidence, posing severe threats to urban infrastructure, structural safety, and flood control capacity, and resulting in substantial economic losses and ecological degradation. Based on dynamic monitoring data and a poroelastic fluid–solid coupling model developed using COMSOL Multiphysics 6.2, this study systematically investigates the characteristics and evolution of land subsidence in Decheng District before and after the implementation of a groundwater extraction ban. Furthermore, recommendations and strategies for the sustainable management of regional groundwater resources are proposed. The results indicate that after the ban was enforced in 2020, the extraction volumes of deep and shallow groundwater in Decheng District decreased from 830,000 m³/a and 33,070,000 m³/a to 178,000 m³/a and 20,775,000 m³/a, respectively. The ban significantly influenced groundwater levels, with the recovery rate of deep groundwater increasing markedly from approximately 0.5 m/a before the ban to about 5 m/a afterward. Groundwater levels directly govern the rate of land subsidence; their decline increases the effective stress within the strata, leading to aquifer compaction and subsequent subsidence. Following the ban, the subsidence rate in Decheng District decreased significantly, with the annual subsidence volume reduced by more than 80% compared to the pre-ban period. Predictive analysis using the fluid–solid coupling model reveals that extraction from deep confined aquifers is the main driver of regional subsidence, with a time lag of approximately five years between groundwater level changes and subsidence response. After the implementation of the extraction ban, the subsidence rate slowed considerably. Over the long term, the subsiding strata tend to stabilize, although most of the subsidence that has already occurred is irreversible, making it difficult for the strata to return to their original state. In summary, the groundwater extraction ban has effectively facilitated groundwater recovery and mitigated land subsidence in Decheng District, though the response exhibits both temporal lag and spatial variability. Future work should focus on establishing an integrated monitoring and regulation system for land subsidence and groundwater dynamics to ensure the coordinated security of both water resources and the geological environment. These findings provide a scientific basis for informing land subsidence prevention and guiding the rational exploitation of groundwater resources in Decheng District. Full article

The inverted bucket steam valve is a key piece of equipment in steam systems. Optimizing its noise reduction performance via intelligent algorithms is crucial for enhancing the stability of steam systems. In this study, the nutcracker optimization algorithm (NOA) was investigated and improved. A simulation method coupling computational fluid dynamics (CFD) with acoustic software was employed to characterize the acoustic properties of inverted bucket steam valves equipped with noise-reducing elements of different structures. Subsequently, the structural dimensions of the valve’s noise-reducing element were optimized using a support vector machine (SVM)-based surrogate model and the improved NOA. Concurrently, experimental tests were conducted on the inverted bucket steam valve before and after optimization to validate the simulation accuracy. The experimental results demonstrate that the SVM-NOA increases the maximum transmission loss (TL) of the valve’s noise-reducing element by 44.14 dB, with the error between experimental and simulation results being less than 3%. This verifies the accuracy of the acoustic simulation method and confirms the practicality and versatility of the SVM-NOA for solving real-world engineering problems. Full article

Lipid droplets (LDs) are emerging as key regulators of metabolism and inflammation, with their buildup in microglia linked to aging and neurodegeneration. Perilipin-2 (Plin2) is a ubiquitously expressed LD-associated protein that stabilizes lipid stores; in peripheral tissues, its upregulation promotes lipid retention, inflammation, and metabolic dysfunction. Yet, its role in microglia remains unclear. Using CRISPR-engineered Plin2 knockout (KO) BV2 microglia, we examined how Plin2 contributes to lipid accumulation, bioenergetics, and immune function. Compared to wild-type (WT) cells, Plin2 KO microglia showed markedly reduced LD burden under basal and oleic acid-loaded conditions. Functionally, this was linked to enhanced phagocytosis of zymosan particles, even after lipid loading, indicating improved clearance capacity. Transcriptomics revealed genotype-specific responses to amyloid-β (Aβ), especially in mitochondrial metabolism pathways. Seahorse assays confirmed a distinct bioenergetic profile in KO cells, with reduced basal respiration and glycolysis but preserved mitochondrial capacity, increased spare reserve, and a blunted glycolytic response to Aβ. Together, these findings establish Plin2 as a regulator of microglial lipid storage and metabolic state, with its loss reducing lipid buildup, enhancing phagocytosis, and altering Aβ-induced metabolic reprogramming. Targeting Plin2 may represent a strategy to reprogram microglial metabolism and function in aging and neurodegeneration. Full article

In dynamic, high-interference industrial and logistics environments, multi-AGV cooperative tasks are often affected by communication delays and data loss, leading to information staleness and reduced control accuracy. Traditional handshake frequency hopping communication strategies introduce additional overhead in high-load environments, and channel selection strategies struggle to adapt to dynamic changes. To address challenges related to communication delay, task coordination, and real-time information exchange, we propose a control and communication co-optimization method based on a nonlinear Age of Information (AoI) penalty and an adaptive handshake frequency hopping mechanism. The method constructs a coupled control-communication model, designs an adaptive handshake period and multi-channel frequency hopping strategy to reduce channel conflicts, and introduces a nonlinear AoI penalty function that prioritizes the update of critical timely information, improving communication success rates and path control accuracy. Furthermore, by integrating the differential dynamics model, state estimation under communication delay and control error modeling, we propose a cooperative optimization algorithm for perception control and communication based on nonlinear AoI optimization (PPO-CCBNA). The algorithm achieves efficient solution based on approximate policy optimization (PPO). Simulation results demonstrate that PPO-CCBNA significantly outperforms benchmark algorithms in communication success rates, control stability, and energy efficiency, validating its effectiveness and feasibility in complex multi-AGV cooperative tasks. Full article

Fractured lost circulation remains a major drilling challenge due to low compatibility between conventional plugging materials and fractures. By utilizing thermosetting resin emulsification and high-temperature crosslinking coalescence, this study developed a temperature-activated nanomaterial enhanced liquid–solid phase transition plugging emulsion. The system adapts to varying fracture apertures, forming plugging particles with a broad size distribution and high strength upon thermal activation. The structural characteristics, mechanical properties, and fracture-plugging performance of the plugging particles were systematically investigated. Results demonstrate that the optimized system, comprising 8 wt.% emulsifier, 0.16 wt.% dispersant, 0.4 wt.% crosslinker, 0.4 wt.% viscosifier, 70 wt.% distilled water, and 2 wt.% nano-silica (all percentages relative to epoxy resin content), can produce particles with a size of 1–5 mm at formation temperatures of 80–120 °C. After 16 h of thermal aging at 180 °C, the particles exhibited excellent thermal stability and compressive strength, with D(90) degradation rates of 3.07–5.41%, and mass loss of 0.63–3.40% under 60 MPa. The system exhibits excellent injectability and drilling fluid compatibility, forming rough-surfaced particles for stable bridging. Microscopic analysis confirmed full curing in 140–180 min. Notably, it sealed 1–5 mm fractures with 10 MPa pressure, enabling adaptive plugging for unknown fracture apertures. Full article

Long-tailed image classification remains challenging for vision–language models. Head classes dominate training while tail classes are underrepresented and noisy, and short prompts with weak text supervision further amplify head bias. This paper presents TASA, an end-to-end framework that stabilizes textual supervision and enhances cross-modal fusion. A Semantic Distribution Modulation (SDM) module constructs class-specific text prototypes by cosine-weighted fusion of multiple LLM-generated descriptions with a canonical template, providing stable and diverse semantic anchors without training text parameters. Dual-Space Cross-Modal Fusion (DCF) module incorporates selective-scan state–space blocks into both image and text branches, enabling bidirectional conditioning and efficient feature fusion through a lightweight multilayer perceptron. Together with a margin-aware alignment loss, TASA aligns images with class prototypes for classification without requiring paired image–text data or per-class prompt tuning. Experiments on CIFAR-10/100-LT, ImageNet-LT, and Places-LT demonstrate consistent improvements across many-, medium-, and few-shot groups. Ablation studies confirm that DCF yields the largest single-module gain, while SDM and DCF combined provide the most robust and balanced performance. These results highlight the effectiveness of integrating text-driven prototypes with state–space fusion for long-tailed classification. Full article

Bearing fault diagnosis encounters limitations including insufficient accuracy, elevated model complexity, and demanding hyperparameter optimization. This research introduces a diagnostic framework combining variational mode decomposition (VMD) and fast Fourier transform (FFT) for extracting comprehensive temporal–spectral characteristics from vibration data. The methodology employs a hybrid deep learning architecture integrating convolutional neural networks (CNNs) with Transformers, where CNNs identify local features while Transformers capture extended dependencies. Meta-learning-enhanced Bayesian optimization and HyperBand (Meta-BOHB) is utilized for efficient hyperparameter selection. Evaluation on the Case Western Reserve University (CWRU) dataset using 5-fold cross-validation demonstrates a mean classification accuracy of 99.91% with exceptional stability (±0.08%). Comparative analysis reveals superior performance regarding precision, convergence rate, and loss metrics compared to existing approaches. Cross-dataset validation using Mechanical Fault Prevention Technology (MFPT) and Paderborn University (PU) datasets confirms robust generalization capabilities, achieving 100% and 98.75% accuracy within 5 and 7 iterations, respectively. Ablation studies validate the contribution of each component. Results demonstrate consistent performance across diverse experimental conditions, indicating significant potential for enhancing reliability and reducing operational costs in industrial fault diagnosis applications. The proposed method effectively addresses key challenges in bearing fault detection through advanced signal processing and optimized deep learning techniques. Full article

Maritime power systems must reduce fuel use and emissions while improving resilience. We study a shipboard PV–battery subsystem interfaced with a DC–DC converter running maximum power point tracking (MPPT) and curve-scanning GMPPT to manage partial shading. Dynamic average-value models capture irradiance steps and show GMPPT sustains operation near the global MPP without local peak trapping. We compare converter options—conventional single-port stages, high-gain bidirectional dual-PWM converters, and three-level three-port topologies—provide sizing rules for passives, and note soft-switching in order to limit loss. A Fourier framework links the switching ripple to power quality metrics: as irradiance falls, the current THD rises while the PCC voltage distortion remains constant on a stiff bus. We make the loss relation explicit via $I_{rms}^{2}R$ scaling with THDi and propose a simple reactive power policy, assigning VAR ranges to active power bins. For AC-coupled cases, a hybrid EMT plus transient stability workflow estimates ride-through margins and critical clearing times, providing a practical path from modeling to monitoring. Full article

Closed-loop geothermal systems (CLGSs) avoid groundwater production and offer stable deep heat supply, but their long-term performance hinges on reliable coupling between the wellbore, the near-well interface and the surrounding formation. Using the D22 well in the Xiongan New Area (deep carbonate reservoir), we built a three-domain thermo-hydraulic framework that updates CO₂ properties with temperature and pressure and explicitly accounts for wellbore-formation thermal resistance. Two geometries (U-tube and single-well coaxial) and two working fluids (CO₂ and water) were compared and optimized under field constraints. With the coaxial configuration, CO₂ delivers an average thermal power of 186.3 kW, exceeding that of water by 44.9%, while the fraction of wellbore heat loss drops by 3–5%. Under field-matched conditions, the predicted outlet temperature (76.8 °C) agrees with the measured value (77.2 °C) within 0.52%, confirming the value of field calibration for parameter transferability. Long-term simulations indicate that after 30 years of continuous operation the outlet temperature decline remains <8 °C for CO₂, outperforming water and implying better reservoir utilization and supply stability. Sensitivity and Pareto analyses identify a practical operating window, i.e., flow velocity of 0.9–1.1 m s⁻¹ and depth of 3000–3500 m, favoring the single-well coaxial + CO₂ scheme. These results show how field-calibrated modeling narrows uncertainty and yields implementable guidance on geometry, operating conditions, and wellbore insulation strategy. This study provides quantitative evidence that CO₂-CLGSs in deep carbonate formations can simultaneously increase thermal output and limit long-term decline, supporting near-term engineering deployment. Full article

Natural shampoos are increasingly designed to provide multifunctional benefits beyond cleansing, including hair conditioning, scalp protection, and reduced irritation potential. POLEVAN^®, a proprietary levan-based polysaccharide produced enzymatically from sugar, offers a combination of oligo- and polysaccharide fractions with potential cosmetic applications. This study evaluated POLEVAN^® in shampoo formulations for three targeted effects: improving hair glossiness, enhancing scalp moisturization, and boosting foam while enabling reduced surfactant levels. Glossiness was assessed ex vivo using damaged hair tresses. Moisturization was assessed in a randomized clinical trial, comparing the test formulation with hyaluronic acid (HA), employing corneometer readings and Trans Epidermal Water Loss (TEWL) measurements. The study was subject-blinded, and all outcomes were determined solely through quantitative, device-based measurements, minimizing observer bias. Foaming performance was tested using the Shaking Cylinder Method. Shampoos containing 2% POLEVAN^® significantly increased hair glossiness by 24% (p = 0.0375) versus a non-significant increase without POLEVAN^®. Moisturization studies showed no significant difference between POLEVAN^® and HA in maintaining hydration or preventing TEWL over 4 weeks. Foam analysis demonstrated that addition of POLEVAN^® allowed up to 50% reduction in surfactant content without compromising foam generation or stability. These results highlight POLEVAN^® as a multifunctional natural ingredient capable of improving sensory and performance attributes of shampoos while supporting gentler formulations. Full article