Search Results (145)

Under the dual pressures of climate change and rapid urbanization, extreme heat events pose growing risks to densely populated megaregions. The Guangdong–Hong Kong–Macao Greater Bay Area (GBA), a densely populated and economically vital region, serves as a critical hotspot for heat risk aggregation. This study develops a high-resolution multi-dimensional framework to assess the spatiotemporal evolution of its heat risk profile from 2000 to 2020. A Heat Risk Index (HRI) integrating heat hazard and vulnerability components to measure potential heat-related impacts is calculated as the product of the Heat Hazard Index (HHI) and Heat Vulnerability Index (HVI) for 1 km grids in GBA. The HHI integrates the frequency of hot days and hot nights. HVI incorporates population density, GDP, remote-sensing nighttime light data, and MODIS-based landscape indicators (e.g., NDVI, NDWI, and NDBI), with weights determined objectively using the static Entropy Weight Method to ensure spatiotemporal comparability. The findings reveal an escalation of heat risk, expanding at an average rate of 342 km² per year (p = 0.008), with the proportion of areas classified as high-risk or above increasing from 21.8% in 2000 to 33.3% in 2020. This trend was characterized by (a) a pronounced asymmetric warming pattern, with nighttime temperatures rising more rapidly than daytime temperatures; (b) high vulnerability dominated by the concentration of population and economic assets, as indicated by high EWM-based weights; and (c) isolated high-risk hotspots (Guangzhou and Hong Kong) in 2000, which have expanded into a high-risk belt across the Pearl River Delta’s industrial heartland, like Foshan seeing their high-risk area expand from 3.4% to 27.0%. By combining remote sensing and socioeconomic data, this study provides a transferable framework that moves beyond coarse-scale assessments to identify specific intra-regional risk hotspots. The resulting high-resolution risk maps offer a quantitative foundation for developing spatially explicit climate adaptation strategies in the GBA and other rapidly urbanizing megaregions. Full article

Background: Photostability assessment is a critical component in the development of drug products, particularly for antibody–drug conjugates (ADCs) containing light-sensitive small molecules such as camptothecin (CPT) and its derivatives. ADCs conjugated with CPT derivative payloads often require extensive formulation and drug product development to ensure product stability due to their unique light-induced degradation pathways. In this study, we assessed the photostability of two ADC molecules with a CPT derivative payload (deruxtecan, DXd). Methods: Following light exposure, the stability of ADCs was assessed by examining critical quality attributes, such as aggregation and photodegradation products of the antibody, payload, and formulation excipients, using advanced liquid chromatography and mass spectrometry techniques. Results: Our results revealed key degradation pathways, including the formation of high-molecular-weight (HMW) species, payload degradation, and post-translational modifications (PTMs) on amino acid residues in the antibodies. Additionally, the DXd payload amplified the photosensitivity of the formulation solution, leading to histidine degradation in the formulation buffer and subsequent pH changes. To enhance the stability of ADCs for manufacturing and therapeutic use, we developed a robust formulation by systematic buffer screening and a targeted evaluation of selected antioxidant excipients. Further investigations into light conditions revealed that DXd ADCs are particularly sensitive to short-wavelength light. When evaluating the container closure system, it was demonstrated that using amber vials is a viable option for protecting against light-induced degradation. Conclusions: This report outlines a comprehensive strategy to address photo instability in DXd ADC drug product development, focusing on formulation optimization, controlled manufacturing light settings, and the option of using protective containers to ensure product stability. Full article

We present a (Light)weight (S)atellite–(A)erial feature (M)atching framework (LiteSAM) for robust UAV absolute visual localization (AVL) in GPS-denied environments. Existing satellite–aerial matching methods struggle with large appearance variations, texture-scarce regions, and limited efficiency for real-time UAV applications. LiteSAM integrates three key components to address these issues. First, efficient multi-scale feature extraction optimizes representation, reducing inference latency for edge devices. Second, a Token Aggregation–Interaction Transformer (TAIFormer) with a convolutional token mixer (CTM) models inter- and intra-image correlations, enabling robust global–local feature fusion. Third, a MinGRU-based dynamic subpixel refinement module adaptively learns spatial offsets, enhancing subpixel-level matching accuracy and cross-scenario generalization. The experiments show that LiteSAM achieves competitive performance across multiple datasets. On UAV-VisLoc, LiteSAM attains an RMSE@30 of 17.86 m, outperforming state-of-the-art semi-dense methods such as EfficientLoFTR. Its optimized variant, LiteSAM (opt., without dual softmax), delivers inference times of 61.98 ms on standard GPUs and 497.49 ms on NVIDIA Jetson AGX Orin, which are 22.9% and 19.8% faster than EfficientLoFTR (opt.), respectively. With 6.31M parameters, which is 2.4× fewer than EfficientLoFTR’s 15.05M, LiteSAM proves to be suitable for edge deployment. Extensive evaluations on natural image matching and downstream vision tasks confirm its superior accuracy and efficiency for general feature matching. Full article

In security applications, visible-light pedestrian detectors are highly sensitive to changes in illumination and fail under low-light or nighttime conditions, while infrared sensors, though resilient to lighting, often produce blurred object boundaries that hinder precise localization. To address these complementary limitations, we propose a practical multimodal pipeline—Adaptive Energy–Gradient–Contrast (EGC) Fusion with AIFI-YOLOv12—that first fuses infrared and low-light visible images using per-pixel weights derived from local energy, gradient magnitude and contrast measures, then detects pedestrians with an improved YOLOv12 backbone. The detector integrates an AIFI attention module at high semantic levels, replaces selected modules with A2C2f blocks to enhance cross-channel feature aggregation, and preserves P3–P5 outputs to improve small-object localization. We evaluate the complete pipeline on the LLVIP dataset and report Precision, Recall, mAP@50, mAP@50–95, GFLOPs, FPS and detection time, comparing against YOLOv8, YOLOv10–YOLOv12 baselines (n and s scales). Quantitative and qualitative results show that the proposed fusion restores complementary thermal and visible details and that the AIFI-enhanced detector yields more robust nighttime pedestrian detection while maintaining a competitive computational profile suitable for real-world security deployments. Full article

Background/Objectives: Forced degradation studies are critical for identifying potential degradation pathways of monoclonal antibodies (mAbs), particularly under thermal stress. Due to their structural complexity and sensitivity to elevated temperatures, mAbs are prone to fragmentation, aggregation, and post-translational modifications. This study aimed to evaluate and compare the degradation profiles of biosimilar anti-VEGF mAb and its originator counterparts sourced from both the United States (U.S.) and the European Union (EU) under thermal stress conditions. To our knowledge, this represents one of the few studies conducting a direct head-to-head comparability assessment across biosimilar and two geographically sourced originators, integrating orthogonal analytical approaches. Methods: Biosimilar candidate and originator products (U.S. and EU) were incubated at 37 °C and 50 °C for 3, 7, and 14 days. Fragmentation profiles were assessed using validated non-reduced and reduced capillary electrophoresis–sodium dodecyl sulfate (CE-SDS) methods. Additionally, size-exclusion ultra-performance liquid chromatography (SE-UPLC) and liquid chromatography–tandem mass spectrometry (LC-MS/MS) assays were performed on samples stressed for 14 days to provide deeper insights into degradation pathways. Results: Non-reduced CE-SDS analysis indicated a time- and temperature-dependent increase in low-molecular-weight fragments and a corresponding decrease in the intact form, with more pronounced effects observed at 50 °C. Reduced CE-SDS revealed a more rapid increase in total impurity levels at 50 °C, accompanied by a decrease in total light and heavy chain content. SE-UPLC showed enhanced aggregation under thermal stress, more pronounced at 50 °C. LC-MS/MS analysis identified increased asparagine deamidation in the PENNY peptide and pyroglutamic acid formation (pE) at the N-terminus of the heavy chain. Conclusions: The degradation profiles of the biosimilar and originator mAbs were highly comparable under thermal stress, with no significant qualitative differences detected. By applying a multi-tiered analytical characterization technique, this study provides a comprehensive comparability assessment that underscores the robustness of biosimilarity even under forced degradation conditions. Full article

This study proposes a machine learning-based framework for classifying occupational stress levels among nurses using physiological time-series data collected from wearable sensors. The dataset comprises multimodal signals including electrodermal activity (EDA), heart rate (HR), skin temperature (TEMP), and tri-axial accelerometer measurements (X, Y, Z), which are labeled into three categorical stress levels: low (0), medium (1), and high (2). To enhance the usability of the raw data, a resampling process was performed to aggregate the measurements into one-minute intervals, followed by the application of the Synthetic Minority Over-sampling Technique (SMOTE) to mitigate severe class imbalance. Subsequently, a comparative classification analysis was conducted using four supervised learning algorithms: Random Forest, XGBoost, k-Nearest Neighbors (k-NN), and LightGBM. Model performances were evaluated based on accuracy, weighted F1-score, and confusion matrices to ensure robustness across imbalanced class distributions. Additionally, temporal pattern analyses by the day of the week and the hour of the day revealed significant trends in stress variation, underscoring the influence of circadian and organizational factors. Among the models tested, ensemble-based methods, particularly Random Forest and XGBoost with optimized hyperparameters, demonstrated a superior predictive performance. These findings highlight the feasibility of integrating real-time, sensor-driven stress monitoring systems into healthcare environments to support proactive workforce management and improve care quality. Full article

Images captured under complex lighting conditions often suffer from local under/ overexposure and detail loss. Existing methods typically process illumination and texture information in a mixed manner, making it difficult to simultaneously achieve precise exposure adjustment and preservation of detail. To address this challenge, we propose LapECNet, an enhanced Laplacian pyramid network architecture for image exposure correction and detail reconstruction. Specifically, it decomposes the input image into different frequency bands of a Laplacian pyramid, enabling separate handling of illumination adjustment and detail enhancement. The framework first decomposes the image into three feature levels. At each level, we introduce a feature enhancement module that adaptively processes image features across different frequency bands using spatial and channel attention mechanisms. After enhancing the features at each level, we further propose a dynamic aggregation module that learns adaptive weights to hierarchically fuse multi-scale features, achieving context-aware recombination of the enhanced features. Extensive experiments with public benchmarks on the MSEC dataset demonstrated that our method gave improvements of 15.4% in PSNR and 7.2% in SSIM over previous methods. On the LCDP dataset, our method demonstrated improvements of 7.2% in PSNR and 13.9% in SSIM over previous methods. Full article

Traditional concrete has low tensile strength, is prone to cracking, and has poor durability, which limits its scope of application. Basalt Fiber Textile–Reinforced Concrete (BTRC), a new type of fiber-reinforced cement material, offers advantages such as light weight, increased strength, improved crack resistance, and high durability. It effectively addresses the limitations of traditional concrete. However, the tensile properties of BTRC have not been fully studied, especially with fine aggregate concrete as the matrix, and there are few reports on this topic. Therefore, this study conducted uniaxial tensile tests of Basalt Textile–Reinforced Fine Aggregate Concrete (BTRFAC) and systematically investigated the effects of two mesh sizes (5 mm × 5 mm and 10 mm × 10 mm) and two to four layers of fiber mesh on the tensile strength, strain hardening behavior, crack propagation, and ductile tensile mechanical properties of BTRFAC thin slabs. The tests revealed that an increase in the number of fiber mesh layers significantly reinforced the material’s tensile strength and ductility. The tensile strength of the 5 mm mesh specimen (four-layer mesh) reached 2.96 MPa, which is 193% higher than plain concrete, and the ultimate tensile strain increased by 413%. The tensile strength of the 10 mm mesh specimen (four-layer mesh) was 2.12 MPa, which is 109% higher than plain concrete, and the ultimate tensile strain increased by 298%. The strength utilization rate of the 5 mm and 10 mm mesh fibers was 41% and 54% respectively, mainly due to the weakening effect caused by interface slippage between the fiber mesh and the matrix. An excessively small mesh size may lead to premature debonding from the matrix, but its denser fiber distribution and larger bonding area exhibit better strain hardening characteristics. More than three layers of fiber mesh can significantly improve the uniformity of crack distribution and delay propagation of the main crack. A calculation formula for the tensile bearing capacity of BTRFAC thin slabs is proposed, and the error between the theoretical value and the experimental value was very small. This research provides a theoretical basis and reference data for the design and application of basalt fiber mesh–reinforced concrete thin slabs. Full article

The real-time monitoring of animal postures through computer vision techniques has become essential for modern precision livestock management. To overcome the limitations of current behavioral analysis systems in balancing computational efficiency and detection accuracy, this study develops an optimized deep learning framework named YOLOv8-BCD specifically designed for ovine posture recognition. The proposed architecture employs a multi-level lightweight design incorporating enhanced feature fusion mechanisms and spatial-channel attention modules, effectively improving detection performance in complex farm environments with occlusions and variable lighting. Our methodology introduces three technical innovations: (1) Adaptive multi-scale feature aggregation through bidirectional cross-layer connections. (2) Context-aware attention weighting for critical region emphasis. (3) Streamlined detection head optimization for resource-constrained devices. The experimental dataset comprises 1476 annotated images capturing three characteristic postures (standing, lying, and side lying) under practical farming conditions. Comparative evaluations demonstrate significant improvements over baseline models, achieving 91.7% recognition accuracy with 389 FPS processing speed while maintaining 19.2% parameter reduction and 32.1% lower computational load compared to standard YOLOv8. This efficient solution provides technical support for automated health monitoring in intensive livestock production systems, showing practical potential for large-scale agricultural applications requiring real-time behavioral analysis. Full article

Amphiphilic block copolymers are essential for developing advanced polymer nanomaterials with applications in bioimaging, drug delivery, and nanoreactors. In this study, we successfully synthesized functional block copolymer assemblies at high concentrations through redox-initiated reversible addition–fragmentation chain transfer (RAFT) emulsion polymerization of 2-(acetoacetoxy)ethyl methacrylate (AEMA), a β-ketoester functional monomer. Utilizing a redox initiation system at 50 °C, we produced poly(poly(ethylene glycol) methyl ether methacrylate)-b-PAEMA (PPEGMA_n-PAEMA_m). Kinetic studies demonstrated rapid monomer conversion exceeding 95% within 30 min, with distinct polymerization phases driven by micelle formation and monomer depletion. Transmission Electron Microscopy (TEM) and Dynamic Light Scattering (DLS) revealed the formation of diverse morphologies, including worm-like, vesicular structures, and spherical micelles, depending on the macro-CTA molecular weight and monomer concentration. Additionally, post-polymerization modification with aggregation-induced emission (AIE) luminogens, such as 1-(4-aminophenyl)-1,2,2-tristyrene (TPE-NH₂), resulted in AIE-active polymer assemblies exhibiting strong fluorescence in aqueous dispersions. These AIE-active polymer assemblies also exhibited good biocompatibility. These findings demonstrate the efficacy of redox-initiated RAFT emulsion polymerization in fabricating functional, scalable block copolymer assemblies with potential applications in the field of life sciences. Full article

The construction industry is a fundamental sector for the development of countries; however, it produces negative environmental impacts due to the demand for natural resources and the generation of construction and demolition waste (CDW). Therefore, the pursuit of solutions to recycle and reintegrate these wastes, which often accumulate in poorly regulated areas, becomes not only an environmental priority but also an opportunity to transform a problem into an advantage. Utilizing these residues contributes to reducing the pressure on natural resources, minimizes the environmental footprint of the construction sector, and promotes a more sustainable and responsible model that can serve as an example for future generations. The properties of recycled concrete aggregates (RCA) and recycled asphalt pavement (RAP) were determined in order to subsequently obtain the properties of different permeable recycled concrete (RPC) elaborated from a factorial design 2³ with these aggregates. The properties studied were workability, permeability, volumetric weight, compression uniaxial, and bending. Finally, they were studied and correlated with their matrix microstructure by means of TGA and SEM tests, which allowed determining the compounds contained in the various mixtures and their impact on physical–mechanical behavior. The results indicate that RCA and RAP are feasible alternatives for making porous pavements in pedestrian or light traffic areas when recycled aggregates of 3/4” size are included in their matrix, resulting in the optimum dosage of the M5 3/4” mix in this research, whose mechanical properties are: uniaxial compressive strength: 15.39 MPa; flexural strength: 3.12 MPa; permeability: 0.375 cm/s. Full article

Image super-resolution (SR) is a formidable challenge due to the intricacies of the underwater environment such as light absorption, scattering, and color distortion. Plenty of deep learning methods have provided a substantial performance boost for SR. Nevertheless, these methods are not only computationally expensive but also often lack flexibility in adapting to severely degraded image statistics. To counteract these issues, we propose a dynamic structure-aware modulation network (DSMN) for efficient and accurate underwater SR. A Mixed Transformer incorporated a structure-aware Transformer block and multi-head Transformer block, which could comprehensively utilize local structural attributes and global features to enhance the details of underwater image restoration. Then, we devised a dynamic information modulation module (DIMM), which adaptively modulated the output of the Mixed Transformer with appropriate weights based on input statistics to highlight important information. Further, a hybrid-attention fusion module (HAFM) adopted spatial and channel interaction to aggregate more delicate features, facilitating high-quality underwater image reconstruction. Extensive experiments on benchmark datasets revealed that our proposed DSMN surpasses the most renowned SR methods regarding quantitative and qualitative metrics, along with less computational effort. Full article

Anionic thermo- and pH-responsive copolymers were synthesized by photoiniferter reversible addition–fragmentation chain transfer polymerization (PI-RAFT). The thermo-responsive properties were provided by oligo(ethylene glycol)-based macromonomer units containing hydrophilic and hydrophobic moieties. The pH-responsive properties were enabled by the addition of 5–20 mol% of strong (2-acrylamido-2-methylpropanesulfonic) and weak (methacrylic) acids. Upon initiation by visible light at 470 nm and in the absence of radical initiators, yields from the ternary copolymers reached 94% in 2.5 h when the process was carried out in continuous flow mode using 4-cyano-4-[(dodecylsulfanylthiocarbonyl)sulfanyl]pentanoic acid as a light-sensitive RAFT agent. The polymers were characterized using size exclusion chromatography, IR and NMR spectroscopy, and differential scanning calorimetry. The copolymers featured a sufficiently high molecular weight (93–146 kDa) consistent with theoretical values and satisfactory dispersities in the range of 1.18–1.45. The pH-responsive properties were studied in deionized water, saline, and buffer solutions. Dramatic differences in LCST behavior were observed in strong and weak acid-based polyelectrolytes. The introduction of sulfonic acid units, even in very small amounts, completely suppressed the LCST transition in deionized water while maintaining it in the saline and buffer solutions, with a negligible LCST dependence on the pH. In contrast, the incorporation of weak methacrylic acid demonstrated a pronounced pH dependence. The peculiarities of micelle formation in aqueous solutions were investigated and critical micelle concentrations and their ability to retain pyrene, a hydrophobic drug model, were determined. It was observed that anionic molecular brushes formed small micelles with aggregation numbers of 1–2 at concentrations in the order of 10⁻⁴ mg/mL. These micelles have a high ability to entrap pyrene, which makes them a promising tool for targeted drug delivery. Full article

Recycling’s value in conserving scarce resources, avoiding environmental damage to the land, and reducing energy consumption is well known. This research aims to develop a composite that uses recycled reinforcement that was formed through an in situ method to build confidence in the usage of recycled materials. Thus, in connection with defense and aerospace industry applications, aluminum composite alloys receive more interest due to their light weight and high strength with improved mechanical properties; therefore, this research focuses on the fabrication of in situ-developed recycled TiC (r-TiC)-reinforced AlZnMgCu composites, i.e., new recycled materials. Experiments were conducted to determine the synthesized composites’ microstructural, mechanical, tribological, and corrosion properties. The microstructural study showed that r-TiC was distributed uniformly along the grain boundaries until the addition of 12% r-TiC. However, the accumulation of reinforcements began at 14% r-TiC addition and became more aggregated with subsequent increases in the percentage addition of r-TiC. The mechanical and tribological tests showed that the composite with 14% r-TiC was superior to all other compositions, with 60% improved mechanical qualities and the lowest wear rate of 0.0007 mm3/m. Composites containing 2% r-TiC showed the best corrosion resistance, an increase of 22% over AlZnMgCu, without reinforcement. Full article

Foamed concrete is increasingly utilized in engineering due to its light weight, excellent thermal insulation, fire resistance, etc. However, its low strength has always been the most crucial factor limiting its large-scale application. This study introduced an innovative method to enhance the strength of foamed concrete by using nano-Al₂O₃ (NA) as a foam stabilizer. NA was introduced into a foaming agent containing sodium dodecyl sulfate (SDS) and hydroxypropyl methylcellulose (HPMC) to prepare a highly stable foam. This approach significantly improved the foam stability and the strength of foamed concrete. Its drainage volume, settlement distance, microstructure, and stabilizing action were investigated, along with the strength, microstructure, and hydration products of foamed concrete. The presence of NA effectively reduced the drainage volume and settlement distance of the foam. NA is distributed at the gas–liquid interface and within the liquid film to play a hindering role, increasing the thickness of the liquid film, delaying the liquid discharge rate from the liquid film, and hindering bubble aggregation, thereby enhancing foam stability. Additionally, due to the stabilizing effect of NA on the foam, the precast foam forms a fine and uniform pore structure in the hardened foamed concrete. At 28 d, the compressive strength of FC0 (0% NAs in foam) is 2.18 MPa, while that of FC3 (0.18% NAs in foam) is 3.90 MPa, increased by 79%. The reason for this is that NA promotes the formation of AFt, and its secondary hydration leads to the continuous consumption of Ca(OH)₂, resulting in a more complete hydration reaction. This study presents a novel method for significantly improving the performance of foamed concrete by incorporating NA. Full article