Search Results (21,986)

Efficient parking space management in urban settings represents a growing challenge owing to the sustained increase in the vehicle fleet. This study presents a comparative evaluation of five object detection architectures —YOLOv8s, YOLOv11s, YOLOv12s, RT-DETR-L and Faster R-CNN—applied to real-time intelligent vehicle occupancy monitoring under variable lighting conditions. The models were trained via transfer learning on a custom dataset of 1463 source images (21,944 annotated instances; expanded to 3511 files and 52,664 instances through offline augmentation of the training subset; three classes: free, occupied and unavailable) captured on a university campus located in Juliaca (Puno region), Peru, at 3824 m a.s.l. under daytime and nighttime clear-sky conditions from a single fixed-camera viewpoint. Each architecture was evaluated in ten independent experiments. Six dataset partitioning schemes of increasing strictness—a random control (R0) plus five leakage-controlled partitions—were evaluated. Under the strictest scheme (D3), simultaneously disjoint in acquisition date and camera viewpoint and therefore the most rigorous generalization estimate obtained in this study, accuracy ranges from mAP@0.5:0.95 of 0.9325 for Faster R-CNN to 0.8763 for YOLOv11s. Under the random partitioning conventionally applied to fixed-camera datasets, the same five architectures fell within 0.0055 of one another, all above 0.985, and their ranking was essentially inverted (Spearman $\rho =-0.80$). The differences in computational efficiency across architectures were statistically significant ($H=47.06$, $p<0.001$). YOLOv8s was the fastest of the four non-dominated architectures under the disjoint partition and was selected in 73.3% of weightings, although it ranked fourth in accuracy; its recommendation therefore rests on computational efficiency under a real-time constraint, whereas deployments that prioritize accuracy are better served by Faster R-CNN. The integrated system YOLOv8s + ByteTrack + FastAPI + Next.js 14 achieved per-slot accuracies of 87.5% and 91.8% under daytime and nighttime clear-sky conditions, respectively, using 1395 observations collected in a single university parking lot. For YOLOv8s, the transition from random to disjoint partitioning costs 0.1085 in mAP@0.5:0.95 (0.9913 to 0.8828), indicating that the near-saturated performance obtained under random partitioning substantially reflects the memorization of a fixed spatial configuration rather than generalization. The results support the feasibility of single-stage CNN architectures for intelligent parking monitoring in high-altitude Andean university environments under the evaluated acquisition conditions. Full article

To address large fluctuations in fertilizer flow and the difficulty of real-time quantitative blockage monitoring during fertilization by an integrated tillage, land preparation, and seeding machine, a fertilization monitoring system combining real-time detection and intelligent alarm functions was designed and developed. The system uses an STC32G12K128 microcontroller as the core control unit and integrates fiber-optic sensors, fiber-optic amplifiers, and associated peripheral hardware. Supporting host computer software was also developed on the Python3.13 platform. Based on the light-blocking principle, the optical signal generated by fertilizer particles passing through the sensing area is converted into a digital signal by the fiber-optic amplifier. A quantitative correlation model between the amount of blocked light and the fertilizer discharge rate was then established, enabling indirect and non-contact measurement of the fertilizer discharge rate. Indoor bench tests demonstrated a highly significant positive linear correlation between the amount of blocked light and the fertilizer discharge rate. The overall mean absolute percentage error of the fitted model was below 10%. Based on this detection system, a fertilization monitoring and alarm module was further developed for indoor bench conditions, together with discrimination logic for fertilizer blockage and fertilizer shortage. At rotational speeds of 30–50 r/min, the system achieved an average blockage detection rate of 98%, an average false alarm rate of 4.4%, and an alarm response time of no more than 3 s. Full article

Femtosecond laser processing enables spatially controlled modification of the structure, composition, and functionality of advanced materials through highly localized energy deposition and laser–matter interaction mechanisms. This review discusses the role of ultrafast laser irradiation in the engineering of oxide-based functional systems, including functional oxides, oxide-containing layers, interfaces, and heterogeneous structures whose properties are substantially determined by an oxide component. The mechanisms governing laser-induced oxidation, phase transformation, elemental redistribution, defect generation, and hierarchical micro-/nanostructure formation are considered. Particular attention is given to the ability of femtosecond laser processing to create surfaces with tailored interactions with light, liquids, biological environments, and external stimuli, enabling responsive devices and advanced manufacturing strategies. Laser-modified oxide layers and nanostructured interfaces are analyzed as pathways for controlling surface energy, optical properties, chemical activity, and functional response. The relationship between laser-generated architectures and their applications in sensing, actuation, wetting control, and multifunctional systems is discussed. By connecting ultrafast laser surface engineering with emerging 4D manufacturing concepts, this review highlights femtosecond laser technologies as a versatile platform for designing systems with spatially programmed functionality and, where stimulus-dependent behavior is demonstrated, time-dependent performance. Such approaches provide opportunities for integrating adaptive oxide-based functional systems into advanced manufacturing. Full article

Most active pharmaceutical ingredients (APIs) reach their target by passive systemic distribution, so the dose required for efficacy at the lesion is set by what healthy tissue can tolerate; conventional dosage forms consequently produce pharmacokinetic profiles that oscillate between toxic peaks and sub-therapeutic troughs. Nanoparticulate carriers (liposomes, lipid nanoparticles, polymeric and inorganic systems, and biomimetic carriers) and hydrogels (natural, synthetic, supramolecular, and microgel-assembled) have emerged as the dominant strategies to address this, increasingly combined as hybrid nanoparticle–hydrogel constructs in which the gel provides locoregional retention and the nanoparticles provide cargo protection, intracellular delivery and stimuli responsiveness. Stimuli-responsive chemistries (pH, redox, enzyme, ROS, hypoxia, temperature, light, magnetic, ultrasound, glucose, and multi-stimuli logic) translate the molecular signatures of a disease into spatiotemporally controlled cargo release. This narrative review consolidates the state of the art (prioritizing 2022–2026) and departs from the conventional carrier-type survey in one respect: the literature is read along an explicit chain—disease cue, sensing chemistry, carrier architecture, release mechanism and kinetics, administration route, and clinical readiness—which exposes a variable that classification by carrier type conceals. Across all three material classes, what governs release behavior is not primarily the carrier chemistry but the identity of the released species (dissolved drug, drug from an embedded nanoparticle, an intact nanoparticle, and a matrix fragment) and the transport step that limits it. This is why power-law exponent analysis developed for dissolved drug fits particulate release poorly, why statistical goodness-of-fit cannot by itself establish a release mechanism, and why carrier class predicts clinical readiness less well than administration route and regulatory product type. Translational hurdles—CMC complexity, regulatory fragmentation, anti-PEG immunogenicity, and the structural mismatch between preclinical promise and clinical efficacy—are critically appraised in light of previously reported <1% delivery efficiency analysis. This review identifies converging strategies that could move stimuli-responsive controlled release from an aspirational outcome to a routine clinical reality. Full article

Manual fruit thinning is labor-intensive and inefficient, making the development of intelligent visual detection systems a crucial approach for improving the quality and production efficiency of the pear industry. However, in natural orchard environments, young pear fruits are small in size with slender fruit stalks, and their texture and color characteristics are highly similar to those of tender branches. Furthermore, variations in illumination and occlusions caused by branches and leaves make it difficult for existing detection models to simultaneously and accurately identify fruits and fruit stalks, limiting their application in automated thinning equipment. In this study, Yuluxiang pear was selected as the research object, and image data were collected under diverse field conditions, including forward-lighting, backlighting, close-range shooting, long-range shooting and fruit overlapping. A dedicated dataset containing 3057 images was established. Based on the RT-DETR-R18 network, a lightweight and high-precision fruit–stalk synchronous detection model was proposed. Specifically, the backbone network was reconstructed by integrating GCConv with C2f modules to enhance global feature extraction for slender fruit stalks. The bottleneck structure was optimized using GCConvC3 to reduce feature degradation under occlusion conditions, and an additional 4× down-sampling P2 detection head was introduced to improve the detection capability for small targets. To fully validate the model performance and stability, three types of experiments were conducted in this study: ablation experiments, repeated experiments with different random seeds, and comparative experiments. Ablation experiments verified the cumulative performance improvements brought by the introduced modules. Repeated experiments with different random seeds were performed to explore training randomness-induced performance fluctuations, and the results demonstrated that the proposed model maintains stable overall detection accuracy with minor metric fluctuations. Comparative experiments demonstrated that the proposed model achieved a compact parameter size of only 15.97 M, with a precision of 95.0% for young pear fruit detection and an mAP50 of 83.0% for fruit stalk detection, outperforming all comparative models in overall mAP50. The training convergence curves and Grad-CAM++ visualization results further confirmed the stable optimization process and enhanced feature attention capability of the proposed model. By achieving a favorable balance between detection accuracy and model lightweightness, this approach provides effective technical support for the development of intelligent fruit thinning equipment and vision-based systems for smart pear orchards. Full article

As the primary carrier for oil and gas transportation, pipelines are critical for the entire industry. Pipelines are continuously subjected to corrosion and abrasion in the oil and gas delivery process, leading to gradual wall thickness reduction, shortened service life, and deteriorated operational safety. Ultrasonic testing has been widely adopted for monitoring pipeline wall thickness. Conventional ultrasonic transducers possess rigid configurations, which hinder large-area inspection and exhibit poor adaptability to complex curved components. In contrast, flexible ultrasonic sensors show prominent advantages, with their small size, light weight, and excellent conformal contact with curved surfaces. Flexible piezoelectric thin-film sensors have been used in a wide range of fields. As one of the most representative piezoelectric polymers, poly(vinylidene fluoride–trifluoroethylene) (P(VDF-TrFE)) combines favorable piezoelectric coefficients and intrinsic flexibility, making it popular. Some research groups have investigated the influences of modified filler particles, doping ratios, and fabrication process optimization on the performance of P(VDF-TrFE)-based piezoelectric composites, while others have concentrated on the practical applications of existing flexible piezoelectric sensors. This study emphasizes a rapid customized fabrication strategy for flexible sensors instead of single-specification standardized probes; hence, it does not share the same comparison benchmark as conventional fixed-dimension sensors. Systematic research on flexible piezoelectric thin-film sensors is presented, including piezoelectric material modification, substrate design, laminated structural design, fabrication workflows, establishment of the testing platform, and the development of matched circuit systems. The material preparation and manufacturing processes are optimized, and a scalable technical route for fabricating flexible piezoelectric sensors is proposed. Using this route, flexible piezoelectric thin-film sensors can be rapidly tailored for different application scenarios to satisfy diverse engineering demands. Multiple experiments were conducted on pipeline samples with varying wall thicknesses and curvatures. The results verify that the sensor reaches a measurement precision of 0.01 mm, meeting the demands of high-precision pipeline structural health monitoring. Full article

Metformin is a widely used antidiabetic drug that may enter biological and environmental systems together with micro/nanoplastics; however, their combined effects on protein interactions and microbial responses remain insufficiently understood. This study investigated how polyethylene terephthalate micro/nanoplastics (PET MNPs) influence metformin interactions with bovine serum albumin (BSA) and the subsequent responses of Escherichia coli and Staphylococcus aureus. BSA–metformin systems were conditioned with three PET MNP loads and increasing metformin concentrations. The resulting particle-depleted filtrates were evaluated using fluorescence spectroscopy, ultraviolet–visible spectroscopy, the Bradford assay, Rayleigh light scattering, turbidity, dithiothreitol-based oxidative potential, and reactive oxygen species (ROS) measurements. Bacterial growth, superoxide dismutase activity, glutathione-related thiol antioxidant response, lipid peroxidation, ROS generation, and biofilm formation were also assessed. PET MNP conditioning altered the fluorescence responses of tryptophan and tyrosine, modified BSA-associated absorbance, and produced non-linear changes in protein accessibility, aggregation-related scattering, turbidity, and oxidative indicators. The bacterial responses were species-specific. Escherichia coli showed increased bacterial growth under several exposure conditions, whereas Staphylococcus aureus exhibited reduced growth following metformin addition, particularly at the highest PET MNP load. Staphylococcus aureus also showed consistently elevated biofilm formation and a pronounced transient ROS increase under the high-PET, low-metformin condition. These findings indicate that upstream PET MNP conditioning can modify the physicochemical and biological properties of the filter-passing BSA–metformin phase, leading to concentration-dependent and species-specific bacterial responses. Full article

High-port-count fiber pump combiners are important passive components for scalable laser diode (LD) power combining in high-power fiber-laser systems. However, increasing the number of input ports from 19 to 37 narrows the fabrication window because fiber bundle packing, taper uniformity, splice matching, thermal management, and backward-light tolerance must be controlled simultaneously. In this work, a 37 × 1 tapered fiber bundle pump combiner was fabricated by a tubing-based method using thirty-seven 135/155 µm multimode input fibers and an 800/880 µm output fiber. The input fibers were weakly etched to improve bundle compactness, and the glass-tube-assisted fiber bundle was tapered, cleaved, and fusion-spliced with a tapered output fiber. The fabricated combiner was characterized using thirty-seven 915 nm fiber-coupled LDs. At a total injected power of 4.89 kW, the combiner delivered 4.80 kW output power, corresponding to an overall transmission efficiency of 98.16%. The single-port transmission efficiencies were approximately in the range of 97.3–98.1%, indicating good port-to-port uniformity for the dense 37-fiber bundle. During full-power operation, the highest temperature appeared in the tapered fiber bundle region and reached 103.8 °C, while the fusion-splice region reached 76.2 °C. In addition, the device withstood 500 W backward-propagating light without observable damage, indicating its practical tolerance to reverse-power loading. These results show that the proposed 37 × 1 fiber pump combiner provides an effective all-fiber solution for multi-kilowatt LD power combining. Full article

The use of crossbreeding between dairy and beef breeds has been adopted as a strategy to add value to male calves from dairy systems, improve productive efficiency, and reduce economic and animal welfare concerns. This study evaluated the effects of Holstein × Angus crossbreeding on productive performance, metabolism, ruminal fermentation, nutrient digestibility, carcass characteristics, meat quality, and fatty acid profile during the feedlot finishing phase. Twenty-four uncastrated male cattle (8 Holstein, 8 Angus, and 8 crossbreds) were confined for 130 days and fed an isoenergetic and isonitrogenous diet. No significant differences were observed among breeds for weight gain, dry matter intake, feed efficiency, or nutrient digestibility (p > 0.05). However, Angus and crossbred cattle showed higher carcass yield compared to Holstein cattle (p ≤ 0.05). Angus and crossbred animals presented higher concentrations of total volatile fatty acids, acetate, and propionate in the rumen (p ≤ 0.05). Breed differences were also observed in erythrocyte indices and serum urea concentrations (p ≤ 0.05). Meat from crossbred animals showed superior quality attributes compared to Holstein, including lower ultimate pH, higher lightness, greater water-holding capacity, and an intermediate lipid profile between the parental breeds (p ≤ 0.05). It is concluded that Holstein × Angus crossbreeding improves carcass yield, ruminal fermentation efficiency, and meat quality, without compromising productive performance, representing a viable strategy for adding value to male calves from dairy herds under tropical production conditions. Full article

Landmark recognition for smart tourism is usually validated on curated benchmark images. In deployment, however, the classifier must handle user-generated photographs whose viewpoint, lighting, resolution, occlusion, and compression differ sharply from curated data. This paper evaluates a previously published multi-threshold enhancement and selective YOLO11n-cls ensemble under this shift, and provides a preliminary zero-shot comparison of three general-purpose multimodal large language models (MLLMs) on the same task. To measure the shift, we build Samarkand v2-SNS, a 300-image out-of-distribution test set of social-media photographs of 12 Samarkand landmarks, disjoint from the training and validation data. Under the shift, four supervised baselines fall by 12.73–22.08 percentage points to 73–80% accuracy, and their in-distribution ranking does not hold. The selective ensemble degrades least (99.24% to 93.00%, −6.24 points) and outperforms the strongest baseline by 13 points. A capacity-matched ablation shows that most of this robustness comes from enhancement diversity, not from generic ensembling. In a preliminary comparison, zero-shot MLLMs (GPT-5, Claude Sonnet 4.5, Gemini 2.5) reach only 24.81–54.26%, far below deployment needs. The results argue for reporting out-of-distribution accuracy alongside curated benchmarks, and for hybrid systems that pair compact specialised recognisers with MLLM-based interpretation. Full article

Hyaluronic acid (HA)-based hydrogels are widely used as wound-healing materials and topical delivery systems because of their excellent biocompatibility, water retention capacity, and ability to promote cell migration. However, HA is prone to oxidative chain scission, which reduces molecular weight and compromises formulation stability and functional performance. This study evaluated the feasibility of ergothioneine (EGT) as a candidate antioxidant stabilizing excipient in a model HA-based wound-healing material. CCK-8 assays assessed the biocompatibility of EGT in L929 mouse fibroblasts after 24 h of exposure, and a stress-screening framework including Fenton oxidation, high-temperature/high-humidity treatment, light exposure, and quiescent storage at 4 °C was established. The results showed that Fenton oxidation markedly induced HA degradation, whereas EGT incorporation effectively protected HA structural integrity under oxidative stress. Cell scratch assays further demonstrated that EGT did not interfere with the ability of HA to promote cell migration. EGT may serve as a candidate antioxidant stabilizing excipient for HA-based wound-healing materials, improving HA structural and material stability under oxidative challenge while preserving HA-associated cell-migration function. Full article

Smart meter anomaly detection is critical for ensuring the security and stability of smart grids. However, existing detection methods face the following limitations: insufficient feature extraction, severe class imbalance, inefficient manual hyperparameter tuning, and poor model interpretability. To overcome these limitations, we propose an accurate and interpretable anomaly detection method based on Bayesian-optimized XGBoost and SHAP. Our method integrates multi-dimensional feature extraction to enrich feature information, Borderline-SMOTE to mitigate class imbalance, Bayesian optimization to tune XGBoost hyperparameters, and SHAP to quantify feature contributions and provide model interpretability. Experimental results on the public MAD dataset demonstrate that our method consistently outperforms both classical machine learning models, including decision tree, Random Forest, XGBoost, and LightGBM, as well as representative deep learning models such as CNN, TCN, LSTM, and CNN-LSTM in binary and multi-class classification tasks, achieving superior accuracy, precision, recall, and F1 scores. SHAP analysis further reveals that three-phase unbalance features are the dominant indicators of abnormal samples, a finding highly consistent with the physical mechanisms of power systems. Our method achieves both competitive detection performance and transparent decision-making, providing an actionable solution for smart meter anomaly detection in practical engineering applications. Full article

BiOF has emerged as a promising functional material for photocatalysis, electrochemical energy storage, and ion adsorption due to its unique layered structure, excellent chemical stability, and tunable electronic properties. Density functional theory (DFT) calculations provide in-depth theoretical insights into the crystal structure, intrinsic defects, doping modification, and heterostructure construction of BiOF. This review systematically summarizes the recent DFT research progress of BiOF systems. Computational results reveal the lattice characteristics, bandgap features, built-in electric field distribution and facet anisotropy of BiOF, and highlight the critical influence of Bi semicore states on structural relaxation and electronic modulation. Defect engineering of bismuth and oxygen vacancies can effectively optimize band structure, accelerate carrier separation and improve catalytic performance. Cation and anion doping introduce impurity energy levels to narrow the bandgap and broaden the visible-light response range. Various BiOF-based heterostructures with different band alignment modes are analyzed, and the interfacial built-in electric field dominates charge separation, while excessive formation energy and unfavorable charge transfer still restrict material optimization. This work provides a systematic theoretical reference for the rational design and performance improvement of high-efficiency BiOF-based materials. Full article

Benzophenoxazines are among the most important classes of heterocyclic compounds, with strongly coupled π systems which enable efficient light absorption and emission. Due to its unique photophysical properties, such as high molar absorption coefficient, intense fluorescence, and good photochemical stability, the benzophenoxazine skeleton plays a key role in the design of fluorescent dyes for special applications. The article presents methods for synthesizing benzophenoxazine derivatives, their photophysical properties, and the significance of their applications, highlighting their potential as structural platforms for producing advanced fluorophores. Full article

Light fidelity (LiFi) is progressively evolving as a highly promising communication technology because of its unique benefits, available spectrum, low implementation costs, and adaptive beamforming capabilities. Despite their advantages, existing LiFi networks remain constrained by limited data rates, coverage area, and information security in practical environments. Therefore, a high-speed, high-capacity, and secure quantum key distribution (QKD)-assisted integrated multi-wavelengths (450/532/620 nm) LiFi system using mode division multiplexing (MDM) is proposed. The results demonstrate that the proposed system achieves maximum transmission distances of 20.5–22 m and 19–22 m using different Laguerre–Gaussian (LG) and Hermite–Gaussian (HG) mode indices {[0,0], [0,10], [0,20], [0,30]}, at an aggregate data rate of 40 Gbps. Furthermore, the minimum acceptable transmitter angles of 30–90° for irradiance angles of 20–80° are required to maintain the target bit error rate (BER) of 10⁻⁹. The minimum photodetector detection areas required at transmission distances of 20–30 m are 1–2 cm² at the minimum BER limit. Moreover, the proposed system exhibits optimum performance, achieving an optical loss of −39.47 dB, −49.03 dBm received power, and 45.39 dB signal-to-noise ratio for 1–10 photons/pulse. Compared with existing studies, the proposed system demonstrates enhanced overall performance across various communication metrics. Full article