Search Results (613)

Transmission tower guy wires are critical flexible tension members ensuring the stability and safe operation of overhead power transmission networks. However, these components are vulnerable to external impacts from falling rocks, ice masses, and other natural hazards, which can cause excessive deformation, anchorage loosening, and catastrophic failure. Current design standards primarily consider static loads, lacking comprehensive models for predicting dynamic impact responses. This study presents a theoretical model for predicting the peak impact response of guy wires by modeling the impact process as a point mass impacting a nonlinear spring system. Using an energy-based elastic potential method combined with cable theory, analytical solutions for axial force, displacement, and peak impact force are derived. Newton–Cotes numerical integration solves the implicit function to obtain closed-form solutions for efficient prediction. Validated through finite element simulations, deviations of peak displacement, peak impact force, and peak axial force between theoretical and numerical results are within ±4%, ±18%, and ±4%, respectively. Using the validated model, parametric studies show that increasing the inclination angle from 15° to 55° slightly reduces peak displacement by 2–4%, impact force by 1–13%, and axial force by 1–10%. Higher prestress (100–300 MPa) decreases displacement and impact force but increases axial force. Longer lengths (15–55 m) cause linear displacement growth and nonlinear force reduction. Impacts near anchorage points help control displacement risks, and impact velocity generally has a more significant influence on response characteristics than impactor mass. This model provides a scientific basis for impact-resistant design of power grid infrastructure and guidance for optimizing de-icing strategies, enhancing transmission system safety and reliability. Full article

Biomass production in organisms is closely linked to their growth rate, necessitating rapid, in situ, nondestructive, and accurate growth measurement. Existing imaging platforms are often limited by high cost, lack of scalability, wired connections, or insufficient automation, restricting their applicability for high-throughput growth monitoring. Here, we present SIPEREA, a scalable imaging platform built on cost-effective ESP32-CAM modules. SIPEREA comprises three graphical user interface (GUI) based applications: (1) an embedded program for the ESP32-CAM responsible for imaging, (2) an image acquisition program for automatic wireless image transmission from multiple ESP32-CAMs, and (3) an image analysis program that automatically segments organisms in the images using a deep neural network (DNN) and calculates their area. The implementation of asynchronous, sequential wireless image acquisition enables the efficient management of multiple ESP32-CAM modules. To demonstrate the usefulness of this platform, we analyzed images captured over a two-week period using four ESP32-CAM units during Lemna sp. (duckweed) cultivation to compute doubling time. Full article

Poly(vinyl chloride) (PVC) is one of the most widely used polymers due to its strength, low cost, and light weight. Industrial production is mainly conducted by suspension polymerization, which facilitates the control of the emissions of vinyl chloride monomer (VCM), a known carcinogen. However, the process consumes large amounts of water and energy and generates residual compounds such as polyvinyl alcohol (PVA) and polymerization initiators, which must be properly managed to mitigate environmental impacts. To improve sustainability, this study applied mass- and energy-integration strategies together with a zero-liquid-discharge (ZLD) water-regeneration system that uses sequential aerobic and anaerobic reactors to recirculate process water with reduced PVA. Although these measures reduce resource consumption, they can displace or intensify other impacts; therefore, a comprehensive evaluation of the system is necessary. Accordingly, the objective of this study is to quantify and compare the potential environmental impacts (PEIs) of the improved PVC production process through a scenario-based assessment using a waste reduction algorithm (WAR). This is applied to four operating scenarios in order to identify the stages and flows that contribute most to the environmental burden. According to our literature review, there is limited published evidence that simultaneously combines mass/energy integration and a ZLD system in PVC processes; thus, this work provides an integrated assessment useful for industrial design. The environmental performance of the improved process was evaluated using WAR GUI software (v 1.0.17, which quantifies PEIs in categories such as toxicity, climate change, and acidification. Four scenarios were compared: Case 1 (excluding both product and energy), Case 2 (product only), Case 3 (energy only), and Case 4 (product and energy). The total PEI increased from 2.46 PEI/day in Case 1 to 6230 PEI/day in Case 4, with the largest contributions from acidification (5140 PEI/day) and global warming (496 PEI/day), mainly due to natural gas consumption (5184 GJ/day). In contrast, Cases 1 and 2 showed negative PEI values (−3160 and −2660 PEI/day), indicating that converting the toxic VCM (LD₅₀: 500 mg/kg; ATP: 26 mg/L) into PVC (LD₅₀: 2000 mg/kg; ATP: 100 mg/L) can reduce the environmental burden in certain respects. In addition, the ZLD system contributed to maintaining low aquatic toxicity in Case 4 (90.70 PEI/day). Full article

This study develops advanced machine learning (ML) algorithms to predict the tensile strength (Ft) of sustainable recycled aggregate (RA) concrete incorporating supplementary cementitious materials (SCMs—silica fume and fly ash) and polypropylene fibers (PPF). A dataset of 375 Ft results from the literature, characterized by ten input parameters (including cement content, natural and RA contents, SCM dosages, PPF percentage, water–cement ratio, superplasticizer content, and curing period), was used to train and validate two ML algorithms: Random Forest (RF) and Extreme Gradient Boosting (XGBoost). All models demonstrated high predictive accuracy, with results consistently aligning with experimental values, though the XGBoost model outperformed the RF model, achieving superior performance with R² values of 0.9689 and 0.9632 for the training and testing datasets and lower RMSE and MAE values. To interpret the model decisions and uncover black-box insights. SHapley additive explanations (SHAP) analysis was employed, quantifying the global and local importance of each input variable on tensile strength prediction, revealing complex non-linear relationships and interactions. The findings highlight XGBoost as a robust tool for optimizing the mix design of complex sustainable concrete, while SHAP analysis revealed that curing period has the highest positive impact on predicting Ft, and W/C and RA adversely impact Ft, bridging the gap between data-driven predictions and practical engineering applications. The developed XGBoost model outperformed DNN, OGPR, and GEP in predicting. A graphical user interface (GUI) was developed to be used as a tool for predicting Ft of RA concrete containing SCMs and PPF. This approach facilitates the efficient development of high-performance, eco-friendly concrete with reduced experimental effort. Full article

This study develops an automatic dispatch system for quadrotor UAVs that integrates air-writing gesture recognition with a graphical user interface (GUI). The DJI RoboMaster quadrotor UAV (DJI, Shenzhen, China) was employed as the experimental platform, combined with an ESP32 microcontroller (Espressif Systems, Shanghai, China) and the RoboMaster SDK (version 3.0). On the Python (version 3.12.7) platform, a GUI was implemented using Tkinter (version 8.6), allowing users to input addresses or landmarks, which were then automatically converted into geographic coordinates and imported into Google Maps for route planning. The generated flight commands were transmitted to the UAV via a UDP socket, enabling remote autonomous flight. For gesture recognition, a Raspberry Pi integrated with the MediaPipe Hands module was used to capture 16 types of air-written flight commands in real time through a camera. The training samples were categorized into one-dimensional coordinates and two-dimensional images. In the one-dimensional case, X/Y axis coordinates were concatenated after data augmentation, interpolation, and normalization. In the two-dimensional case, three types of images were generated, namely font trajectory plots (T-plots), coordinate-axis plots (XY-plots), and composite plots combining the two (XYT-plots). To evaluate classification performance, several machine learning and deep learning architectures were employed, including a multi-layer perceptron (MLP), support vector machine (SVM), one-dimensional convolutional neural network (1D-CNN), and two-dimensional convolutional neural network (2D-CNN). The results demonstrated effective recognition accuracy across different models and sample formats, verifying the feasibility of the proposed air-writing trajectory framework for non-contact gesture-based UAV control. Furthermore, by combining gesture recognition with a GUI-based map planning interface, the system enhances the intuitiveness and convenience of UAV operation. Future extensions, such as incorporating aerial image object recognition, could extend the framework’s applications to scenarios including forest disaster management, vehicle license plate recognition, and air pollution monitoring. Full article

Advancements in natural language processing (NLP), particularly Large Language Models (LLMs), have greatly improved how we access knowledge. However, in critical domains like biomedicine, challenges like hallucinations—where language models generate information not grounded in data—can lead to dangerous misinformation. This paper presents a hybrid approach that combines LLMs with Knowledge Graphs (KGs) to improve the accuracy and reliability of question-answering systems in the biomedical field. Our method, implemented using the LangChain framework, includes a query-checking algorithm that checks and, where possible, corrects LLM-generated Cypher queries, which are then executed on the Knowledge Graph, grounding answers in the KG and reducing hallucinations in the evaluated cases. We evaluated several LLMs, including several GPT models and Llama 3.3:70b, on a custom benchmark dataset of 50 biomedical questions. GPT-4 Turbo achieved 90% query accuracy, outperforming most other models. We also evaluated prompt engineering, but found little statistically significant improvement compared to the standard prompt, except for Llama 3:70b, which improved with few-shot prompting. To enhance usability, we developed a web-based interface that allows users to input natural language queries, view generated and corrected Cypher queries, and inspect results for accuracy. This framework improves reliability and accessibility by accepting natural language questions and returning verifiable answers directly from the knowledge graph, enabling inspection and reproducibility. The source code for generating the results of this paper and for the user-interface can be found in our Git repository: https://git.zib.de/lpusch/cyphergenkg-gui, accessed on 1 November 2025. Full article

Background/Objectives: Keratoconus (KC) is the most common corneal ectasia. This condition affects quality of vision, especially when it is progressive, and a timely and stage-related treatment is mandatory. Therefore, early diagnosis is crucial to preserve visual acuity. Medical data may be used either in their raw state or in a preprocessed form. Software modifications introduced through updates may potentially affect outcomes. Unlike preprocessed data, raw data preserve their original format across software versions and provide a more consistent basis for clinical analysis. The objective of this study was to distinguish between healthy and KC corneas from raw optical coherence tomography data by using a convolutional neural network. Methods: In total, 2737 eye examinations acquired with the Casia2 anterior-segment optical coherence tomography (Tomey, Nagoya, Japan) were decided by three experienced ophthalmologists to belong to one of three classes: ‘normal’, ‘ectasia’, or ‘other disease’. Each eye examination consisted of sixteen meridional slice images. The dataset included 744 examinations. DenseNet121, EfficientNet-B0, MobileNetV3-Large and ResNet18 were modified for use as convolutional neural networks for prediction. All reported metric values were rounded to four decimal places. Results: The overall accuracy for the modified DenseNet121, modified EfficientNet-B0, modified MobileNetV3-Large and modified ResNet18 is 91.27%, 91.27%, 92.86% and 89.68%, respectively. The macro-averaged sensitivity, macro-averaged specificity, macro-averaged Positive Predictive Value and macro-averaged F1 score for the modified DenseNet121, modified EfficientNet-B0, modified MobileNetV3-Large and modified ResNet18 are reported as 91.27%, 91.27%, 92.86% and 89.68%; 95.63%, 95.63%, 96.43% and 94.84%; 91.58% 91.65%, 92.91% and 90.24%; and 91.35%, 91.29%, 92.85% and 89.81%, respectively. Conclusions: The successful use of a convolutional neural network with raw optical coherence tomography data demonstrates the potential of raw data to be used instead of preprocessed data for diagnosing KC in ophthalmology. Full article

The increasing global production of plastic (P) waste presents a critical environmental challenge, while the construction industry’s demand for sustainable materials continues to grow. The building industry’s reliance on natural aggregates, a contributor to environmental degradation, requires sustainable alternatives. Utilizing plastic waste as a partial aggregate substitute in concrete offers dual advantages: preserving limited resources and redirecting waste from landfills. This research uses advanced machine learning (ML) to forecast the mechanical properties of P waste concrete. Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) models with particle swarm optimization (PSO) were developed to predict compressive and tensile strengths of P waste concrete. A comprehensive dataset comprising 196 datapoints for compressive strength (CS) and 100 datapoints for tensile strength (TS) of P waste concrete was collected from the literature. The input parameters encompassed the plastic (P), cement (C), water-to-cement ratio (W/C), coarse aggregate (CA), fine aggregate (FA), and curing age (Age), while the outputs were CS and TS of P waste concrete. The constructed models were assessed utilizing various statistical metrics. The findings indicate that coefficient of determination of both XGBoost (CS, R² = 0.9911, and TS, R² = 0.9947) and RF (CS, R² = 0.9757, and TS, R² = 0.9737) models performed well, with XGBoost indicating better performance with fewer prediction errors. SHAP analysis emphasizes the substantial effect of P waste on concrete strength properties followed by C and Age. Furthermore, GUIs for predicting TS and CS of concrete containing P waste utilizing both RF and XGBoost models were developed. Overall, this study not only achieves superior accuracy through hybrid PSO-ML models but also contributes to sustainable construction materials and computational material science, offering a data-driven framework for optimizing mix designs that incorporate plastic waste, which can accelerate its adoption in eco-friendly engineering applications. Full article

As global climate warming enhances the navigability of Arctic routes, their navigation value has become prominent, yet ships operating in ice-covered waters face severe threats from sea ice and icebergs. Existing manual observation and radar monitoring remain limited, highlighting an urgent need for efficient target recognition technology. This study focuses on polar environmental target detection by constructing a polar dataset with 1342 JPG images covering four classes, including sea ice, icebergs, ice channels, and ships, obtained via web collection and video frame extraction. The “Grounding DINO pre-annotation + LabelImg manual fine-tuning” strategy is employed to improve annotation efficiency and accuracy, with data augmentation further enhancing dataset diversity. After comparing YOLOv5n, YOLOv8n, and YOLOv11n, YOLOv8n is selected as the baseline model and improved by introducing the CBAM/SE attention mechanism, SCConv/AKConv convolutions, and BiFPN network. Among these models, the improved YOLOv8n + SCConv achieves the best in polar target detection, with a mean average precision (mAP) of 0.844–1.4% higher than the original model. It effectively reduces missed detections of sea ice and icebergs, thereby enhancing adaptability to complex polar environments. The experimental results demonstrate that the improved model exhibits good robustness in images of varying resolutions, scenes with water surface reflections, and AI-generated images. In addition, a visual GUI with image/video detection functions was developed to support real-time monitoring and result visualization. This research provides essential technical support for safe navigation in ice-covered waters, polar resource exploration, and scientific activities. Full article

The construction of a high-order shape function is a key and difficulty for unstructured grid mesh and sliding boundary problems. In this paper, a construction method of space-time absolute nodal coordinate formulation quadrilateral cable (SACQ) is proposed, and the accuracy of the SACQ element is studied and verified with three different applications. First, the shape function of SACQ is constructed with spatiotemporal reduction coordinates, and the action integral of SACQ is composed with the Lagrangian function and discrete with perspective transformation. Second, the numerical convergence region is discussed and determined with the Courant number. Furthermore, a space-time nodal dislocation and its relation with the Courant number are studied. The simulation and verification are focusing on some realistic problems. Finally, a one-sided impact, a free-flexible pendulum, a taut string with a sliding boundary and a deployable guyed mast under an impact transverse wave are simulated. In these problems, an unstructured grid meshed with SACQ has similar energy convergence and accuracy to a structured grid but shows better efficiency. Full article

Flowering often perturbs carbon allocation in sugarcane, yet its transcriptomic–metabolomic basis remains unclear. We profiled two contrasting cultivars, Gui Tang 16-3285 (sugar increases during flowering) and Gui Tang 44 (sugar decreases), sampling apical tissues at five stages (Non-spikelet-bearing stage (NSB), Early booting stage (ESB), Late booting stage (LSB), Tasseling stage (TS), and Flowering stage (FS)). RNA-seq and untargeted LC–MS revealed a strong stage/genotype structure (PCA) with high reproducibility. Pairwise contrasts (FS vs. earlier stages) and time series clustering (Mfuzz) showed extensive, stage-resolved reprogramming with small cross-cultivar overlaps. GO/KEGG indicated that GT16 is enriched for central carbon processes and glucose response, whereas GT44 favors cell-wall remodeling (xylan/xyloglucan), amino/nucleotide sugar, and phenylpropanoid pathways. Integrated analysis identified opposing temporal features across omics layers: in GT16, late-rising metabolites—including sedoheptulose—were consistent with enhanced pentose phosphate/Calvin coupling that regenerates fructose-6-phosphate for sucrose biosynthesis; in GT44, early activation of wall and secondary sinks, together with trehalose/(trehalose-6-phosphate) T6P signatures, paralleled declining soluble sugars. Across cultivars we resolved 11 and 18 genes in reciprocal opposite-trend sets (most with clear temporal order) and eight vs. five metabolites with mirrored dynamics, nominating actionable biomarkers (e.g., sedoheptulose/S7P) and regulatory nodes. These results provide a mechanistic framework linking flowering stage to carbon partitioning and suggest practical levers—timing growth moderation/ripeners, prioritizing sucrose phosphate synthase/Sucrose Phosphate Phosphatase, tempering wall flux, to sustain sucrose during reproductive development and inform breeding for high-sugar, flowering-resilient ideotypes. Full article

To ensure the safety of supercritical CO₂ pipelines and address the limitations of full-scale fracture tests, such as high risk and substantial investment, software for evaluating the crack arrest toughness of CO₂ pipelines containing impurities was developed based on the Battelle Two-Curve Model (BTCM) in this study. The software is programmed in Python (v.3.12.4), with a graphical user interface (GUI) built using PyQt6 (v.6.10.0) and a three-tier architecture design. It integrates the resistance curve model and the decompression wave model. To determine the thermodynamic state of the fluid, a large property database covering pure components and various mixtures is embedded, incorporating state equations such as PR, HEOS, and GERG-2008. The software can generate pressure drop curves, decompression curves, and resistance curves. The pressure plateau can be quickly identified by examining the pressure drop curve. Whether the pipeline can achieve self-crack arrest can be rapidly judged by comparing the positional relationships between the decompression curve and the resistance curve. To verify the accuracy of the software’s calculation results, comparisons were conducted with previous decompression wave experimental data, full-scale burst test data of a CO₂ pipeline, and the international HLP model. The calculation error of the software is within 10%. The development and application of this software provide a convenient, efficient, and accurate practical tool for the calculation of crack arrest toughness and crack arrest evaluation of supercritical CO₂ pipelines. Full article

Background/Objectives: Baihe Dihuang Tang (BDT), a classical herbal formula from Zhang Zhongjing’s Han Dynasty work Jin Gui Yao Lue, is widely used to treat depressive disorder by nourishing Yin, clearing heat, and tonifying the heart and lungs. However, its pharmacological mechanisms remain unclear. This study aims to explore BDT’s antidepressant effects via MAOA-regulated serotonin (5-HT) metabolism and synaptic plasticity, supported by experimental validation, while using network pharmacology to predict MAOA-targeting active components. Methods: Active components and targets of BDT were screened using TCMSP, TCMID, and other databases, and then a component-target-pathway network was constructed. A chronic restraint stress (CRS)-induced depressive mouse model was established. Behavioral tests, including open field test (OFT), elevated plus maze (EPM), forced swimming test (FST) and tail suspension test (TST), were conducted to evaluate antidepressant effects. ELISA, qRT-PCR, and Western blot were employed to assess hippocampal 5-HT metabolism (MAOA, 5-HT/5-HIAA ratio) neurotrophic signaling (BDNF, TrkB) and synaptic plasticity-related proteins (PSD-95, SYN1). Results: BDT significantly reduced FST/TST immobility time and improved anxiety-like behaviors in OFT/EPM. BDT treatment downregulated MAOA expression, elevated hippocampal 5-HT/5-HIAA ratio, activated BDNF/TrkB pathway, and upregulated PSD-95/SYN1. Network pharmacology confirmed MAOA’s central role, identifying MAOA/serotonergic synapse modulation as BDT’s main mechanism and pinpointing Ferulic acid, Caffeate, Stigmasterol, (−)-nopinene, Eugenol, and cis-Anethol as MAOA-targeting bioactive components. Conclusions: BDT ameliorates depressive-like behaviors. This effect is mechanistically linked to suppression of MAOA-mediated 5-HT catabolism—a key validated target. This suppression elevates hippocampal 5-HT bioavailability, thereby activating BDNF/TrkB signaling and promoting synaptic plasticity. Network pharmacology confirmed MAOA as a primary target and identified specific modulatory bioactive components. Full article

The integration of the Scalable Service-Oriented Middleware over IP (SOME/IP) within Automotive Ethernet enables efficient, service-oriented communication in vehicles. This study presents a video stream transmission library using SOME/IP to transfer pre-recorded video data between virtual Electronic Control Units (ECUs). Implemented with vsomeip, OpenCV, and Protocol Buffers, the system handles video serialization, Ethernet transmission, and reconstruction at the receiver side. Experimental evaluation with front and rear dashboard cameras (2560 × 1440 and 1920 × 1080 px) demonstrated that video resolution and file size directly affect processing duration. Optimized 1920 × 1080 videos achieved total processing times of about 400 ms, confirming the feasibility of near-real-time video transmission. A GUI application was also developed to simulate event-based communication by sending object detection updates after video transfer. The proposed framework provides a scalable and modular architecture that can be adapted to real ECU systems, establishing a foundation for future real-time video communication in automotive networks. Full article

Temperature is crucial for fish physiology, as metabolism and related physiological processes are directly influenced by thermal energy. Against the backdrop of global warming, climate-induced variations in water temperature increasingly constrain fish physiology. Consequently, understanding the effects of temperature on fish metabolism is vital for predicting how global warming might impact various fish species. Plateau fish, predominantly cold-water species, exhibit greater sensitivity to temperature fluctuations. However, research on the effects of temperature on plateau fish is currently limited. Consequently, this study employed low-altitude H. nobilis as a reference while D. macrophthalmus and A. grahami were selected from the Yun–Gui Plateau. Following 15 days of temperature acclimation at 10, 15, 20, 25, and 30 °C, organ mass, the resting metabolic rate, and mitochondrial function were measured. The results indicated that high-altitude fish exhibit heightened metabolic sensitivity, demonstrating more pronounced increases or decreases in metabolic rates as temperature increases, along with limited plasticity in organ size. This may render high-altitude fish more vulnerable to the impacts of climate warming. Furthermore, physiological differences between altitudes and species were observed, primarily characterized by higher metabolic rates across all measured temperatures in plateau species. Additionally, plateau fish presented greater masses of heart, red muscle, and liver but smaller masses of brain and kidney. We propose that the trade-off between elevated metabolic rates and organ size may represent an adaptive strategy for fish inhabiting high-altitude environments, involving specific ecological costs and benefits. These findings not only address the knowledge gap regarding the metabolic characteristics of fish on the Yun–Gui Plateau but also provide theoretical and experimental foundations for the conservation of high-altitude fish populations. Full article