Search Results (5,191)

With the increasing demand for low-carbon energy, automated defect detection using unmanned aerial vehicle (UAV)-based thermal inspection has become essential for maintaining the reliability of photovoltaic systems. However, existing methods still suffer from low-contrast thermal imagery, large-scale variations of defects, and subtle thermal anomalies. To address these challenges, this study proposes Grouped-Hypergraph-Modulation DEIM (GHM-DEIM), a robust end-to-end detection framework based on an improved DEIM architecture. Specifically, a grouped multi-scale aggregation attention network is introduced to enhance global thermal perception and recover discriminative features from blurred backgrounds. In addition, an enhanced encoder incorporating a hypergraph-based context encoding mechanism is designed to model high-order non-local relationships and improve feature representation across different defect scales. Furthermore, a modulation fusion module is employed to adaptively refine multi-scale feature responses and suppress environmental noise interference. Extensive experiments conducted on the ThermoSolar-PV and PV-HSD-2025 datasets demonstrate that the proposed method consistently outperforms state-of-the-art detectors, achieving mAP@50 values of 88.6% and 74.2%, respectively, with improvements of 4.7% and 2.9% over the baseline. These results demonstrate the effectiveness and robustness of GHM-DEIM for UAV-based PV thermal defect inspection. Full article

Grounding connectors critically influence the safety and long-term reliability of earthing systems through coupled electro-thermal, mechanical, and corrosion behaviors, yet no standardized quantitative framework exists for jointly evaluating these performance dimensions across diverse deployment scenarios. This study introduces a unified multi-criteria evaluation framework applied to six grounding connector configurations spanning four alloy families and three joining technologies. Electro-thermal response was characterized by coupled finite element simulations (0–100 A), mechanical reliability by quasi-static tensile testing (n = 10 per configuration), and corrosion durability by accelerated salt-spray exposure with image-based corroded area fraction quantification. Performance metrics were normalized and aggregated using equal-weight, Analytic Hierarchy Process, and Shannon entropy weighting schemes, with the Technique for Order of Preference by Similarity to Ideal Solution applied for multi-scenario ranking. One-way analysis of variance confirmed statistically significant effects of connector type on tensile performance (F(5, 54) = 3154.90, p < 0.001). The exothermic welded joint achieved the highest mean ultimate tensile load (61.5 ± 1.5 kN), while copper mechanical connectors exhibited the lowest steady-state temperature rise (~2 K above ambient at 100 A). Compression-crimped connectors ranked first under both equal and Analytic Hierarchy Process weighting (closeness coefficients 0.737 and 0.807, respectively), while stainless steel connectors ranked first under corrosion-critical deployment scenarios. Scenario-weighted analyses demonstrate that the optimal material–process combination shifts with environmental severity, current duty, and mechanical demand, providing a reproducible, evidence-based basis for context-dependent connector specification. Full article

This study aimed to systematically compare the functional properties of the four major components (albumin, globulin, prolamin, and glutelin) of protein from Yunnan deep-veined walnuts to screen for protein-based carrier materials with good processing adaptability and the ability to efficiently encapsulate the active ingredient rutin. In addition, the binding and molecular interactions between the preferred protein and rutin were analyzed. The results indicated that albumin exhibited superior performance compared to the other three components in solubility, emulsifying properties, foaming properties, and gel properties, and demonstrated the strongest processing applicability. Further analysis revealed that albumin possessed an excellent amino acid composition (essential amino acid content accounting for 42.30%) and antioxidant activity (with the highest ABTS scavenging rate reaching 85.71 ± 0.26%), which indicated its considerable potential as a functional carrier. Loading rutin onto albumin yielded a walnut albumin–rutin complex (WA@Rut), which significantly enhanced the thermal stability of albumin (with the thermal denaturation temperature elevated to 108.72 °C) and the storage stability of rutin (66.16 ± 5.05% retention after 22 days of storage). Combined analyses of FT-IR spectroscopy, intrinsic fluorescence spectroscopy, molecular docking, and molecular dynamics simulations confirmed that rutin primarily bound to albumin via hydrogen bonding and electrostatic interactions, and formed a stable complex structure. SEM images revealed that the composite surface was smooth and exhibited a flake-like morphology. In conclusion, walnut albumin is a protein resource with significant functional potential in Yunnan deep-veined walnuts, and it exhibits strong processing applicability and enables efficient encapsulation and protection of active ingredients. This study provides novel strategies and theoretical foundations for the high-value utilization of walnut protein. Full article

GeSi alloy quantum dots (QDs) are a promising candidate for a light source implemented in Si-based monolithic optoelectronic integrated circuits (MOEICs) thanks to their telecom-wavelength emission and the compatibility with the Si integration technology. Herein, the engineering properties of GeSi alloy QDs are demonstrated via rapid thermal annealing (RTA). The PL spectra of GeSi alloy QDs exhibits remarkably enhanced intensity and an initial red shift followed by a blue shift with increasing annealing temperature. Particularly, it can be characterized as a single narrow peak at ~1.55 µm of the intensity enhanced by ~20 times after the RTA at 1100 °C. These features are attributed to the progressively enhanced intermixing and the abnormal transition from compressive strain to tensile strain in QDs with increasing annealing temperature, which are demonstrated by Raman spectra and transmission electron microscopy (TEM) images. Moreover, a large polycrystalline-domain appears around QD at a sufficiently high annealing temperature. It facilitates the tensile strain in QDs, which arises during the RTA due to the thermal expansion coefficient mismatch between Ge and Si. These results demonstrate that high-temperature annealing can efficiently modulate the properties of GeSi alloy QDs, particularly for emission at 1.55 µm, which may have great potential for an efficient Si-based light source. Full article

Molecular imprinting is a powerful strategy for fabricating synthetic materials with selective recognition toward specific biomolecules. In this work, molecularly imprinted (MIM) membranes based on poly (ethylene-co-vinyl alcohol) (EVAL) were developed for selective protein recognition and conformational modulation using α-amylase as a model template. Membranes were prepared by phase inversion, generating porous structures suitable for mass transport and adsorption. Template extraction, measured using UV–Vis spectroscopy, showed a rapid and effective removal of α-amylase while preserving membrane morphology, as confirmed by SEM. FTIR-ATR and chemical imaging confirmed template removal from the membrane and a uniform surface distribution of rebound α-amylase after successive template incubation. Rebinding experiments showed a concentration-dependent uptake of α-amylase and an apparent saturation trend at higher concentrations. Selectivity tests using bovine serum albumin as an analog confirmed preferential recognition of α-amylase. Enzymatic assays showed partial recovery of catalytic activity after rebinding of thermally denatured α-amylase, indicating that imprinted cavities may promote protein conformational reorganization. These results highlight the potential of EVAL-based imprinted membranes as biomimetic platforms for selective protein recognition and functional modulation. Full article

Passively mode-locked lasers, as essential tools for generating ultrashort pulses, have found widespread applications in industrial manufacturing, optical communications, biomedical imaging, and fundamental scientific research. Saturable absorbers serve as the key components governing the performance of such laser systems. Conventional saturable absorber materials, including semiconductor saturable absorber mirrors, carbon nanotubes, and graphene, however, suffer from inherent limitations in operational wavelength range, damage threshold, and environmental stability. In recent years, two-dimensional transition metal carbides and nitrides, known as MXenes, have emerged as a promising class of materials to address these challenges. Their unique metallic conductivity, broadband saturable absorption, ultrafast carrier dynamics, excellent thermal management capability, and versatile chemical tunability offer unprecedented opportunities for advanced saturable absorber applications. This review systematically summarizes the recent progress of MXene-based saturable absorbers, with an emphasis on their distinctive advantages in extending the mode-locked wavelength range, enhancing output pulse stability, and increasing the optical damage threshold. Furthermore, strategies for performance optimization through surface terminal group engineering, defect modulation, and heterostructure design are discussed in depth. Finally, the future prospects and key challenges toward industrial implementation of MXenes in ultrafast photonics are outlined, aiming to stimulate further advancements in high-performance ultrafast laser technology. Full article

Cloud detection is a fundamental task in atmospheric science and satellite remote sensing. While numerous algorithms utilizing multiple visible and infrared channels have been developed, the absence of visible light at night forces most current methods to rely on multi-channel thermal infrared (TIR) observations. Consequently, detection accuracy is significantly reduced due to the minimal thermal contrast between low clouds and the ground. Furthermore, distinguishing clouds under strictly moonless conditions remains a critical challenge. Leveraging the low-light observation capability of the Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS/DNB), this study proposes a single-channel cloud detection algorithm. Based on the physical scattering of ground-based artificial lights by clouds, the algorithm integrates a feature-engineering layer with a Random Forest machine learning model. This moonlight-independent approach can rapidly determine cloudy conditions, offering a novel method for high-precision nighttime cloud detection. Validation experiments using a single fixed radar site in Longmen, China, with 97 rigorously synchronized satellite-radar sample pairs, demonstrate that the proposed algorithm achieves an overall accuracy of 86.6% (95% CI: 78.4–92.0%) against millimeter-wave cloud radar observations. While strictly reliant on stable artificial ground lights—making it primarily applicable to urban and artificially lit regions—this method provides a valuable supplementary tool for nighttime monitoring. Full article

Deep learning has significantly improved the quality of infrared and visible image fusion. However, existing mainstream deep fusion networks often come with complex architectures and a large number of parameters. While general lightweight techniques can effectively reduce model complexity, they often weaken feature interactions during the lightweighting process, resulting in the loss of complementary texture and thermal information in fused images and making it difficult to balance fusion performance and model efficiency. To address these issues, this paper constructs an infrared and visible image fusion network based on a self-compensating lightweight convolution mechanism, named LWC-DenseFuse. The core of the network lies in a self-compensating lightweight convolution module, which goes beyond conventional convolution replacement and explicitly addresses feature degradation introduced by lightweight design. The module decouples spatial and channel correlations of standard convolution through depthwise convolution and pointwise convolution, while incorporating a channel attention mechanism to adaptively enhance salient features. Additionally, channel shuffle technology is employed to promote information exchange between groups, thereby enhancing feature interaction and compensating for the loss of critical information caused by lightweight design. To further improve the representation capability of the lightweight network during optimization, a staged training strategy with progressive loss weighting is introduced. Experimental evaluations demonstrate that the proposed fusion network significantly reduces the number of model parameters while ensuring real-time inference performance. Meanwhile, it effectively alleviates the performance degradation typically associated with lightweight architectures, as evidenced by improvements in information entropy and visual fidelity. Full article

Exhaust piping in diesel engines is subject to severe thermal stress arising from high-temperature, high-pressure gas flows, and spray cooling with atomizing nozzles has become a widely adopted method to safeguard structural reliability. However, at present, the understanding of the spray fragmentation mechanism of two-phase flow under low inlet pressure is still not comprehensive. This study establishes a three-dimensional model of a gas–liquid impinging-jet nozzle and applies a coupled Volume-of-Fluid to Discrete-Phase-Model (VOF–DPM) approach to resolve the liquid breakup process in detail. High-speed imaging experiments were carried out to validate the numerical results. Orthogonal tests were conducted at five pressure levels for both gas and water—0.28, 0.24, 0.20, 0.16, and 0.12 MPa—producing 25 data pairs of spray cone angle and Sauter Mean Diameter (SMD). Within the 0–0.3 MPa air inlet pressure range explored here, raising the pressure consistently reduced the SMD and widened the cone angle, although both trends weakened as the pressure increased. Water inlet pressure exhibited a nonlinear influence, with local extrema appearing in the higher-pressure region. The overall SMD reached a minimum of 34.12 μm and a maximum of 149.04 μm. Using these 25 data points, a genetic algorithm was employed to optimize the pressure ratio under the constraint of total hydraulic power, yielding optimization strategies for different power budgets. An additional outcome of the simulation was the identification of a structural weakness: by reshaping the original flat impingement surface into a full conical surface, atomization quality improved by 29.36% under identical boundary conditions. These findings clarify the atomization mechanism of gas–liquid impinging jets under low inlet pressure and offer practical guidance for nozzle optimization. Full article

Wire arc additive manufacturing of aluminum generates coarse, anisotropic solidification microstructures that limit mechanical performance, and interlayer friction stir processing (FSP) is increasingly applied to refine them. This study reports the layer-resolved grain morphology and the recrystallized crystal evolution in MIG + FSP-fabricated aluminum alloy 4043 walls, pairing the FSP spindle torque recorded from the CNC controller with multi-descriptor grain morphology in a coupling that, to the authors’ knowledge, has not been previously reported in the WAAM + FSP literature. Methodologically, two four-bead, three-layer walls were co-fabricated under identical deposition conditions on a HAAS VF-3 CNC platform, one by MIG deposition alone and one by the complete MIG + FSP route; the FSP spindle torque was measured at three positions per layer (118 ± 6 N·m at 600 RPM for L1, and 19.1 ± 1.0 and 26.6 ± 1.3 N·m at 1200 RPM for L2 and L3), and quantitative image analysis of 10,091 grains provided the layer-resolved mean grain area, equivalent diameter, aspect ratio, perimeter-to-area ratio, and circularity. The results show that the mean grain area increased from 8.55 μm² (L1) to 12.96 μm² (L3) while the aspect ratio decreased monotonically (1.389 to 1.323), indicating progressive grain equiaxiality with build height; the P/A ratio followed a non-monotonic layer dependence (2.54 to 2.11 to 2.50 μm⁻¹), with the L2 minimum consistent with reduced boundary line density under the combined thermal influence of two adjacent FSP events. The MIG + FSP route produced grain areas 29–48× smaller per layer than the MIG wall and a 45.8% higher hardness (75.8 ± 7.7 versus 52.0 ± 1.3 HV; n = 6; p = 0.0027). In conclusion, the L3 torque exceeds the L2 torque at equal 1200 RPM, qualitatively consistent with the d^−p term in the grain-size-explicit creep framework $\stackrel{.}{\gamma }$ = C·(τⁿ/d^p)·exp(−Q/RT), although temperature, strain rate, and grain size cannot be fully decoupled from the present three-layer dataset. The morphology and the distributional evidence are consistent with dynamic recrystallization (DRX); discrimination between continuous and discontinuous DRX requires EBSD. Full article

After leakage from buried hydrogen-blended natural gas pipelines, gas may seep through the soil into quasi-closed enclosures and form buoyancy-driven non-uniform combustible clouds. The effect of ignition delay on such clouds remains insufficiently understood, particularly regarding the relationship between visible flame development and local thermal response. In this study, 44 soil-seepage combustion experiments were conducted in a 1.5 m × 1.5 m × 1.5 m enclosure. The methane and hydrogen concentrations at three heights, flame evolution, and transient temperatures were measured using gas sensors, high-speed imaging, and thermocouples. The ignition delay ranged from 27 s to 5429 s, with hydrogen blending ratios of 10–30 vol% and ignition positions at the floor, middle, and ceiling levels. The results show that longer ignition delays generally weakened the visible flame luminosity and propagation extent. However, the peak temperature measured by the central thermocouple did not decrease. For the long-delay subset with t_d > 307 s, the central peak temperature increased with the ignition delay, with R² = 0.74. Concentration measurements indicated that preferential hydrogen migration and slower methane redistribution continuously reconfigured the local flammability state before ignition. These findings suggest that, in enclosed soil-seepage HBNG scenarios, prolonged ignition delay may weaken visible flame development but does not necessarily reduce local thermal exposure. Full article

Laser Powder Bed Fusion (LPBF) is a complex manufacturing process that depends on precise control of printing parameters and melt pool geometry, which directly influence defect formation and final part quality. This study evaluated the reliability of a simplified thermal conduction-based melt pool model by combining post-process metallographic analysis with in situ dual-wavelength infrared thermal imaging. Experimental data were obtained through single-track printing on 316L, IN625, and CoCr alloys across a wide range of parameters. The simulated melt pool length showed strong agreement with thermal camera measurements (R²_adj > 0.78), while the width showed moderate but consistent correlation (R²_adj > 0.52). For melt pool depth, the model systematically deviated due to its inability to capture keyhole melting, although a strong linear correlation was still observed (R²_adj > 0.86). Cross-validation between metallographic measurements and thermal imaging revealed only a 6–9% discrepancy, confirming the reliability of both methods and the potential of dual-wavelength cameras for industrial process monitoring. Overall, the model proves to be a reliable tool for predicting melt pool surface geometry specifically within the conduction melting regime, while its predictive capability degrades significantly in the keyhole regime, where simulated peak temperatures reach up to 7000 °C and melt pool depth errors escalate due to the disregard of recoil pressure, liquid and vapor dynamics. Full article

The COVID-19 pandemic exposed critical limitations in conventional screening protocols, particularly in high-traffic environments where rapid, accurate, and contactless health assessment became essential to mitigate transmission risks. In response, this study presents the development of an Artificial Intelligence-Enhanced Contactless Screening Kiosk (AICS-K) that integrates multimodal sensing, embedded systems engineering, and machine learning into a unified workflow. Utilizing a Raspberry Pi platform with computer vision, thermal sensing, QR-based contact tracing, and intelligent control logic, the system enables efficient real-time screening while minimizing human intervention. The proposed architecture demonstrates the potential of extensible, affordable AI-driven solutions for early signs detection and institutional health resilience. Full article

Maritime piracy poses an ongoing operational threat to commercial shipping in high-risk regions, where fast-approach attack scenarios leave vessel crews with critically limited reaction time. Automated threat detection technologies—including radar, electro-optical, and thermal imaging sensors—are increasingly integrated into maritime security architectures; however, their operational effectiveness has rarely been evaluated through quantitative engineering frameworks. This study presents a technical analysis of mechatronic detection systems, focusing on detection range, reaction time constraints, and classification reliability under representative piracy conditions. A kinematic time-to-contact model is introduced to quantify how detection distance directly governs the available defensive response window: extending reliable detection from 1 NM to 3 NM expands the reaction margin from approximately 171 s to over 440 s, a difference that may determine whether protective measures can be executed in time. Classification performance is assessed using standard metrics, with recall identified as the operationally critical indicator in asymmetric threat environments. Model-based simulations indicate that, under the assumed scenario parameters, automated detection systems can reduce operational risk by up to 45%, illustrating the sensitivity of survivability outcomes to early detection capability. The findings translate directly into design thresholds for sensor range, algorithmic sensitivity, and processing latency, providing actionable engineering recommendations for practitioners responsible for maritime security system design and vessel protection planning. Full article

This study investigates the coupling between flow dynamics, acoustic response, and convective heat transfer in a rectangular impinging jet striking on a heated slotted plate at two closely spaced Reynolds numbers (Re = 3550 and Re = 3750). Velocity fields were obtained using Particle Image Velocimetry (PIV), and coherent structures were analyzed using Proper Orthogonal Decomposition (POD) while acoustic measurements were used to characterize the tonal behavior. Infrared thermography was employed to determine local and mean Stanton numbers. The mean Stanton number increased by 6.6% when the Reynolds number increased from Re = 3550 to Re = 3750, while the sound pressure level decreased from 78 dB to 71 dB. At Re = 3550, the acoustic spectrum exhibited multi-tone behavior associated with distributed modal energy. In contrast, at Re = 3750, a single dominant frequency governed the flow dynamics. The energy of the first POD mode nearly doubled when passing from Re = 3550 to Re = 3750. The cross-correlation coefficients between the first POD mode and the acoustic field increase from 0.76 to 0.93 when changing from Re = 3550 to Re = 3750. These findings show that the dominant vortex mode which contains nearly 20% of the fluctuating energy (for Re = 3750), significant influences the energy transfer from the dynamic field to the acoustic field resulting in a strong noise reduction. Simultaneously, convective heat transfer increases, highlighting the key role of coherent flow organization on both acoustic and thermal behavior of the system. Full article