Explore the vast range of titles available.

Photovoltaic (PV) systems play a vital role in the global transition to renewable energy, yet their efficiency is often compromised by surface defects such as dust accumulation, bird droppings, and cracks. Traditional inspection methods are inefficient, while existing deep learning-based detection models struggle with limited adaptability, large model sizes, and inadequate performance under real-world conditions. To address these challenges, we propose the DCD-YOLOv8s algorithm—an enhanced version of the YOLOv8 architecture that integrates deformable convolutional networks (DCNv3), coordinate attention (CA), and dynamic head (DyHead) modules. These enhancements are designed to strengthen feature extraction, object localization, and detection accuracy while minimizing computational overhead. A custom dataset was constructed by combining a public PV panel defect database with field-collected images, further expanded through data augmentation and self-training strategy. Experimental results demonstrated that DCD-YOLOv8s achieved superior results, with an F1-score of 92.8%, mAP@50 of 95.0%, and mAP@50–95 of 82.3%, while maintaining a high inference speed of 45.9 FPS. Comparative evaluations against YOLOv5s, YOLOv6s, YOLOv7s, YOLOv8s, YOLOv10s, RT-DETR-R18, and YOLOv11s confirm its superior performance of DCD-YOLOv8s in identifying PV surface defects. Ablation studies validated the individual and combined efficacy of the integrated modules. Although real-time UAV-based deployment was not conducted, a mission planning framework was proposed. These results highlight DCD-YOLOv8s’s strong potential for integration into real-time UAV-based inspection systems, contributing to cost-effective and reliable PV system maintenance.

Openings are often introduced in continuous reinforced concrete (RC) deep beams to accommodate utility services, which can compromise their structural capacity. This paper presents a numerical investigation—via nonlinear finite element (FE) modeling—into the effects of post-construction rectangular openings in continuous high-strength concrete (HSC) deep beams. A previously tested two-span continuous HSC deep beam with rectangular openings was used for model validation and subsequently adopted in a parametric study, maintaining consistent beam and opening dimensions. The study focuses on the influence of opening location, both symmetric and asymmetric, at mid-depth within critical shear and flexural zones of the two-span continuous deep beam. Key parameters analyzed include load-carrying capacity, support reactions, initial and post-cracking stiffness, reinforcement stresses, and concrete stress distribution. Results indicate that mid-depth openings located in flexure-critical regions have minimal impact, causing only a 3–5% reduction in load-carrying capacity and negligible changes in stress behavior. However, when openings intersect the primary strut paths, reductions in capacity ranged from 17% to 53%, depending on the number and location of the openings (i.e., crossing external or internal struts). Furthermore, symmetric placement of openings was found to significantly mitigate performance degradation compared to asymmetric configurations. These findings provide design insights that enable safe incorporation of service openings without excessive material use, thereby promoting more sustainable and resource-efficient concrete construction.

Batik industrial effluent wastewater (BIE) contains toxic dyes that, if directly channeled into receiving water bodies without proper treatment, could pollute the aquatic ecosystem and, detrimentally, affect the health of people. This study is aimed at assessing the adsorptive efficacy of a novel low-cost sewage-sludge-based biochar (SSB), in removing color from batik industrial effluent (BIE). Sewage-sludge-based biochar (SSB) was synthesized through two stages, the first is raw-material gathering and preparation. The second stage is carbonization, in a muffle furnace, at 700 °C for 60 min. To investigate the changes introduced by the preparation process, the raw sewage sludge (RS) and SSB were characterized by the Brunauer–Emmett–Teller (BET) method, Fourier-transform infrared spectroscopy (FTIR), and scanning electron microscopy. The surface area of biochar was found to be 117.7 m2/g. The results of FTIR showed that some functional groups, such as CO and OH, were hosted on the surface of the biochar. Continuous fixed-bed column studies were conducted, by using SSB as an adsorbent. A glass column with a diameter of 20 mm was packed with SSB, to depths of 5 cm, 8 cm, and 12 cm. The volumes of BIE passing through the column were 384 mL/d, 864 mL/d, and 1680 mL/d, at a flow rate of 16 mL/h, 36 mL/h, and 70 mL/h, respectively. The initial color concentration in the batik sample was 234 Pt-Co, and the pH was kept in the range of 3–5. The effect of varying bed depth and flow rate over time on the removal efficiency of color was analyzed. It was observed that the breakthrough time differed according to the depth of the bed and changes in the flow rates. The longest time, where breakthrough and exhausting points occurred, was recorded at the highest bed and slowest flowrate. However, the increase in flow rate and decrease in bed depth made the breakthrough curves steeper. The maximum bed capacity of 42.30 mg/g was achieved at a 16 mL/h flowrate and 12 cm bed height. Thomas and Bohart–Adams mathematical models were applied, to analyze the adsorption data and the interaction between the adsorption variables. For both models, the correlation coefficient (R2) was more than 0.9, which signifies that the experimental data are well fitted. Furthermore, the adsorption behavior is best explained by the Thomas model, as it covers the whole range of breakthrough curves.

A significant issue for the ecosystem is the presence of boron in water resources, particularly in produced water. Batch and dynamic experiments were used in this research to extract boron in the form of boric acid from aqueous solutions using boron selective resins, DIAION CRB05. DIAION™ CRB05 is an adsorbent that is effective in extracting boron from aqueous solutions due to its high binding capacity and selectivity for boron ions, and it is also regenerable, making it cost-effective and sustainable. Field Emission Scanning Electron Microscopy (FESEM), X-ray diffraction (XRD), and FTIR analysis for DIAION CRB05 characterization. To increase the adsorption capacity and find the ideal values for predictor variables such as pH, adsorbent dose, time, and boric acid concentration, the Box–Behnken response surface method (RSM) was applied. The dosage was reported to be 2000 mg/L at pH 2 and boron initial concentration of 1115 mg/L with 255 min for the highest removal anticipated from RSM. According to the outcomes of this research, the DIAION CRB05 material enhanced boron removal capability and has superior performance to several currently available adsorbents, which makes it suitable for use as an adsorbent for removing boric acid from aqueous solutions. The outcomes of isotherm and kinetic experiments were fitted using linear methods. The Temkin isotherm and the pseudo-first-order model were found to have good fits after comparison with R2 of 0.998, and 0.997, respectively. The results of the study demonstrate the effectiveness of DIAION™ CRB05 in removing boron from aqueous solutions and provide insight into the optimal conditions for the adsorption process. Thus, the DIAION CRB05 resin was chosen as the ideal choice for recovering boron from an aqueous solution because of its higher sorption capacity and percentage of boron absorbed.

Reliable detection of traffic signs and lights (TSLs) at long range and under varying illumination is essential for improving the perception and safety of autonomous driving systems (ADS). Traditional object detection models often exhibit significant performance degradation in real-world environments characterized by high dynamic range and complex lighting conditions. To overcome these limitations, this research presents FED-YOLOv10s, an improved and lightweight object detection framework based on You Only look Once v10 (YOLOv10). The proposed model integrates a C2f-Faster block derived from FasterNet to reduce parameters and floating-point operations, an Efficient Multiscale Attention (EMA) mechanism to improve TSL-invariant feature extraction, and a deformable Convolution Networks v4 (DCNv4) module to enhance multiscale spatial adaptability. Experimental findings demonstrate that the proposed architecture achieves an optimal balance between computational efficiency and detection accuracy, attaining an F1-score of 91.8%, and mAP@0.5 of 95.1%, while reducing parameters to 8.13 million. Comparative analyses across multiple traffic sign detection benchmarks demonstrate that FED-YOLOv10s outperforms state-of-the-art models in precision, recall, and mAP. These results highlight FED-YOLOv10s as a robust, efficient, and deployable solution for intelligent traffic perception in ADS.

In recent years, the country has spent significant workforce and material resources to prevent traffic accidents, particularly those caused by fatigued driving. The current studies mainly concentrate on driver physiological signals, driving behavior, and vehicle information. However, most of the approaches are computationally intensive and inconvenient for real-time detection. Therefore, this paper designs a network that combines precision, speed and lightweight and proposes an algorithm for facial fatigue detection based on multi-feature fusion. Specifically, the face detection model takes YOLOv8 (You Only Look Once version 8) as the basic framework, and replaces its backbone network with MobileNetv3. To focus on the significant regions in the image, CPCA (Channel Prior Convolution Attention) is adopted to enhance the network’s capacity for feature extraction. Meanwhile, the network training phase employs the Focal-EIOU (Focal and Efficient Intersection Over Union) loss function, which makes the network lightweight and increases the accuracy of target detection. Ultimately, the Dlib toolkit was employed to annotate 68 facial feature points. This study established an evaluation metric for facial fatigue and developed a novel fatigue detection algorithm to assess the driver’s condition. A series of comparative experiments were carried out on the self-built dataset. The suggested method’s mAP (mean Average Precision) values for object detection and fatigue detection are 96.71% and 95.75%, respectively, as well as the detection speed is 47 FPS (Frames Per Second). This method can balance the contradiction between computational complexity and model accuracy. Furthermore, it can be transplanted to NVIDIA Jetson Orin NX and quickly detect the driver’s state while maintaining a high degree of accuracy. It contributes to the development of automobile safety systems and reduces the occurrence of traffic accidents.

Unmanned Aerial Vehicles (UAVs) are indispensable assets for missions in dynamic and complex environments, requiring highly efficient path planning that simultaneously optimizes the often-conflicting objectives of minimizing flight distance, energy consumption, and mission time. While Ant Colony Optimization (ACO) is a recognized and effective metaheuristic for this domain, its performance is significantly constrained by a static, empirically-derived pheromone update mechanism, which prevents the algorithm from adaptively learning or optimally managing the search process. To overcome this critical limitation, this study introduces a novel DRL-Assisted ACO framework where a Deep Reinforcement Learning (DRL) agent is seamlessly integrated with the ACO to strategically determine the optimal paths under multi-objective constraints. This intelligent agent is tasked with learning the optimal, mission-specific pheromone update strategy. It achieves this by observing the performance of generated paths and receiving a sophisticated reward signal meticulously derived from the Analytic Hierarchy Process (AHP), which systematically weights the mission objectives. Validated through a simulated case study conducted in Khartoum State, Su-dan, the DRL-Assisted ACO approach has demonstrably achieved superior performance, exhibiting marked gains in convergence speed and generating paths with a significantly higher overall multi-objective utility score, thereby delivering a robust and adaptive solution essential for high-stakes autonomous UAV operations.

In recent years, the demand for high-strength concrete (HSC) for buildings has been steadily increasing. Continuous HSC deep beams are frequently employed in various structural applications, including high-rise buildings, bridges, and parking garages, due to their superior load capacity. Some cases require the addition of openings after the construction for passing utilities such as drainage and electricity. This study experimentally examines four two-span HSC deep beams: one control solid beam, one beam with circular openings, and two beams that utilized different strengthening schemes. The openings were asymmetrical circular openings, with one positioned in each span. This study sought to regain the full capacity of beams with openings by employing two types of strengthening schemes. The first one used bolted steel plates, while the second was a hybrid scheme that combined bolted steel plates with externally bonded fiber-reinforced polymer (FRP) sheets. Test findings demonstrated that both methods effectively restored the load capacity of the strengthened beams. The strengthened beam with steel plates achieved a load capacity of 125% compared to the solid beam. Likewise, the beam retrofitted with hybrid steel/FRP composites reached 117%. Additionally, the energy dissipation and ductility index of the strengthened beam with steel plates were 32% and 77%, respectively, compared to the strengthened beam with hybrid steel/FRP composites. The findings emphasize the effectiveness of the applied retrofitting techniques in restoring the lost capacity due to the cutting of post-construction openings in deep beams.

Doxorubicin is a widely used chemotherapeutic agent; however, its clinical utility is limited by dose-dependent cardiotoxicity. Existing cardioprotective strategies are insufficient, showing that there is a need for safer and more effective alternatives. This study evaluated the cardioprotective effects of metformin and allicin, individually and in combination, against doxorubicin-induced cardiotoxicity in rats. Fifty adult male Wistar albino rats were randomized into five groups (n = 10 each): The control group was administered normal saline (2 mL/kg, intraperitoneally, on days 7, 14, and 21); the DOX-only group received doxorubicin (6 mg/kg, intraperitoneally, on days 7, 14, and 21; cumulative dose 18 mg/kg); the DOX + Allicin group was given allicin (40 mg/kg/day, orally), the DOX + Metformin group received metformin (300 mg/kg/day, orally), and the DOX + Allicin+ Metformin group received both agents at these doses. Treatments were given orally once daily for 21 days. On day 22, blood samples and cardiac tissues were collected for biochemical and histopathological evaluation. Parameters assessed included body and heart weights, serum cardiac biomarkers (CK-MB, LDH, cTn I), antioxidant defenses (GSH, CAT, GPx, SOD), and oxidative stress indices (MDA, NO). Both allicin and metformin significantly attenuated DOX-induced elevation of cardiac enzymes, with greater protection observed under combined therapy. Antioxidant markers (GSH, GPx, SOD, CAT, NO) increased significantly, whereas MDA levels decreased. Dual treatment produced superior effects compared to either agent alone, a finding further supported by marked histopathological improvement in cardiac tissues. Metformin and allicin each conferred significant cardioprotection against doxorubicin-induced cardiotoxicity, evidenced by the restoration of cardiac enzymes, reduction of oxidative stress, and improvement in myocardial histoarchitecture. Notably, combined therapy produced greater biochemical and structural recovery than either monotherapy, highlighting its enhanced overall cardioprotective efficacy.

Since 2016, Yemen has been experiencing the largest cholera outbreak in modern history. Multidrug resistance (MDR) emerged among Vibrio cholerae isolates from cholera patients in 2018. Here, to characterize circulating genotypes, we analysed 260 isolates sampled in Yemen between 2018 and 2019. Eighty-four percent of V. cholerae isolates were serogroup O1 belonging to the seventh pandemic El Tor (7PET) lineage, sub-lineage T13, whereas 16% were non-toxigenic, from divergent non-7PET lineages. Treatment of severe cholera with macrolides between 2016 and 2019 coincided with the emergence and dominance of T13 subclones carrying an incompatibility type C (IncC) plasmid harbouring an MDR pseudo-compound transposon. MDR plasmid detection also in endemic non-7PET V. cholerae lineages suggested genetic exchange with 7PET epidemic strains. Stable co-occurrence of the IncC plasmid with the SXT family of integrative and conjugative element in the 7PET background has major implications for cholera control, highlighting the importance of genomic epidemiological surveillance to limit MDR spread. Genomic epidemiology of Vibrio cholerae isolates recovered between 2016 and 2019 during the Yemen cholera outbreak reveals acquisition of multidrug resistance and patterns of plasmid transmission between endemic and epidemic lineages.