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Development of forming methods for surface-coated metals is a current concern due to their economic and environmental advantages. For a successful forming operation, it is necessary that both components, the substrate and the coating, are able to withstand stress without damage until the final shape and dimensions are reached. This goal can be achieved through good knowledge of the elastic and plastic properties of the substrate and the coating, the compatibility between them, the appropriate surface treatment, and the rigorous control of technological forming parameters. Our study was carried out with flat specimens of pre-painted Al 99.0 sheet that were electromagnetically formed by bulging. Forming behavior was investigated as depending on the initial thickness of the substrate, on the aluminum sheet pretreatment, as well as on the plastic deformation path of the metal–paint structure. To verify the damage to the paint layer, tests with increasing strains were performed, and the interface between the metal and the coating layer was investigated by scanning electron microscopy. The obtained results indicate that electromagnetic forming of pre-painted sheets can be a feasible method for specific applications if the forming degree of the substrate is tightly correlated with the type of desired coating and with the pretreatment method used for the metal surface. Full article

The retina offers a unique window into both ocular and systemic health, motivating the development of AI-based tools for disease screening and risk assessment. In this study, we present a comprehensive evaluation of six state-of-the-art deep neural networks, including convolutional neural networks and vision transformer architectures, on the Brazilian Multilabel Ophthalmological Dataset (BRSET), comprising 16,266 fundus images annotated for multiple clinical and demographic labels. We explored seven classification tasks: Diabetes, Diabetic Retinopathy (2-class), Diabetic Retinopathy (3-class), Hypertension, Hypertensive Retinopathy, Drusen, and Sex classification. Models were evaluated using precision, recall, F1-score, accuracy, and AUC. Among all models, the Swin-L generally delivered the best performance across scenarios for Diabetes (AUC = 0.88, weighted F1-score = 0.86), Diabetic Retinopathy (2-class) (AUC = 0.98, weighted F1-score = 0.95), Diabetic Retinopathy (3-class) (macro AUC = 0.98, weighted F1-score = 0.95), Hypertension (AUC = 0.85, weighted F1-score = 0.79), Hypertensive Retinopathy (AUC = 0.81, weighted F1-score = 0.97), Drusen detection (AUC = 0.93, weighted F1-score = 0.90), and Sex classification (AUC = 0.87, weighted F1-score = 0.80). These results reflect excellent to outstanding diagnostic performance. We also employed gradient-based saliency maps to enhance explainability and visualize decision-relevant retinal features. Our findings underscore the potential of deep learning, particularly vision transformer models, to deliver accurate, interpretable, and clinically meaningful screening tools for retinal and systemic disease detection. Full article

This study sought to quantify and characterize diverse rootstock scion interactions in cacao around graft compatibility, disease resistance, nutrient use efficiency, vigor traits, and translocation of nonstructural carbohydrates. In total, 106 grafts were performed with three scion cultivars (Matina 1/6, Criollo 22, Pound 7) and nine diverse open-pollinated seedling populations (BYNC, EQX 3348, GNV 360, IMC 14, PA 107, SCA 6, T 294, T 384, T 484). We found evidence for both local and translocated graft incompatibility. Cross sections and Micro-XCT imaging revealed anatomical anomalies, including necrosis and cavitation at the junction and accumulation of starch in the rootstock directly below the graft junction. Scion genetics were a significant factor in explaining differences in graft take, and graft take varied from 47% (Criollo 22) to 72% (Pound 7). Rootstock and scion identity both accounted for differences in survival over the course of the 30-month greenhouse study, with a low of 28.5% survival of Criollo 22 scions and a high of 72% for Pound 7 scions. Survival by rootstocks varied from 14.3% on GNV 360 to 100% survival on T 294 rootstock. A positive correlation of 0.34 (p = 0.098) was found between the graft success of different rootstock–scion combinations and their kinship coefficient, suggesting that relatedness of stock and scion could be a driver of incompatibility. Significant rootstock–scion effects were also observed for nutrient use efficiency, plant vigor, and resistance to Phytophthora palmivora. These findings, while preliminary in nature, highlight the potential of rootstock breeding to improve plant nutrition, resilience, and disease resistance in cacao. Full article

In this paper, we study the classification problem of short-time-window steady-state visual evoked potentials (SSVEPs) and propose a novel deep convolutional network named EEGResNet based on the idea of residual connection to further improve the classification performance. Since the frequency-domain features extracted from short-time-window signals are difficult to distinguish, the EEGResNet starts from the filter bank (FB)-based feature extraction module in the time domain. The FB designed in this paper is composed of four sixth-order Butterworth filters with different bandpass ranges, and the four bandwidths are 19–50 Hz, 14–38 Hz, 9–26 Hz, and 3–14 Hz, respectively. Then, the extracted four feature tensors with the same shape are directly aggregated together. Furthermore, the aggregated features are further learned by a six-layer convolutional neural network with residual connections. Finally, the network output is generated through an adaptive fully connected layer. To prove the effectiveness and superiority of our designed EEGResNet, necessary experiments and comparisons are conducted over two large public datasets. To further verify the application potential of the trained network, a virtual simulation of brain computer interface (BCI) based quadrotor control is presented through V-REP. Full article

Background and Objectives: Lower limb defects often present significant reconstructive challenges due to limited soft tissue availability and exposure of critical structures. Perforator-based flaps offer reliable solutions, with minimal donor site morbidity. This study aimed to evaluate the efficacy of infrared thermography (IRT) in preoperative planning and postoperative monitoring of perforator-based flaps, assessing its accuracy in identifying perforators, predicting complications, and optimizing outcomes. Materials and Methods: A prospective observational study was conducted on 76 patients undergoing lower limb reconstruction with fascio-cutaneous perforator flaps between 2022 and 2024. Perforator mapping was performed concurrently with IRT and Doppler ultrasonography (D-US), with intraoperative confirmation. Flap design variables and systemic parameters were recorded. Postoperative monitoring employed thermal imaging on days 1 and 7. Outcomes were correlated with thermal, anatomical, and systemic factors using statistical analyses, including t-tests and Pearson correlation. Results: IRT showed high sensitivity (97.4%) and positive predictive value (96.8%) for perforator detection. A total of nine minor complications occurred, predominantly in patients with diabetes mellitus and/or elevated glycemia (p = 0.05). Larger flap-to-defect ratios (A/C and B/C) correlated with increased complications in propeller flaps, while smaller ratios posed risks for V-Y and Keystone flaps. Thermal analysis indicated significantly lower flap temperatures and greater temperature gradients in flaps with complications by postoperative day 7 (p < 0.05). CRP levels correlated with glycemia and white blood cell counts, highlighting systemic inflammation’s impact on outcomes. Conclusions: IRT proves to be a reliable, non-invasive method for perforator localization and flap monitoring, enhancing surgical planning and early complication detection. Combined with D-US, it improves perforator selection and perfusion assessment. Thermographic parameters, systemic factors, and flap design metrics collectively predict flap viability. Integration of IRT into surgical workflows offers a cost-effective tool for optimizing reconstructive outcomes in lower limb surgery. Full article

The classification of submarine topography and geomorphology is essential for marine resource exploitation and ocean engineering, with wide-ranging implications in marine geology, disaster assessment, resource exploration, and autonomous underwater navigation. Submarine landscapes are highly complex and diverse. Traditional visual interpretation methods are not only inefficient and subjective but also lack the precision required for high-accuracy classification. While many machine learning and deep learning models have achieved promising results in image classification, limited work has been performed on integrating backscatter and bathymetric data for multi-source processing. Existing approaches often suffer from high computational costs and excessive hyperparameter demands. In this study, we propose a novel approach that integrates pruning-enhanced ConDenseNet with label smoothing regularization to reduce misclassification, strengthen the cross-entropy loss function, and significantly lower model complexity. Our method improves classification accuracy by 2% to 10%, reduces the number of hyperparameters by 50% to 96%, and cuts computation time by 50% to 85.5% compared to state-of-the-art models, including AlexNet, VGG, ResNet, and Vision Transformer. These results demonstrate the effectiveness and efficiency of our model for multi-source submarine topography classification. Full article

In today’s volatile supply chain environment, organizations require flexible and collaborative procurement strategies. Swap contracts, originally developed as financial instruments, have recently been adopted to address inventory imbalances—such as the 2021 COVID-19 vaccine swap between South Korea and Israel. Despite its increasing adoption in the real world, theoretical studies on swap-based procurement remain limited. This study proposes an integrated model that combines buyer-to-buyer swap agreements with long-term wholesale contracts under demand uncertainty. The model quantifies the expected swap quantity between parties and embeds it into the profit function to derive optimal order quantities. Numerical experiments are conducted to compare the performance of the proposed strategy with that of a baseline wholesale contract. Sensitivity analyses are performed on key parameters, including demand asymmetry and swap prices. The numerical analysis indicates that the swap-integrated procurement strategy consistently outperforms procurement based on long-term wholesale contracts. Moreover, the results reveal that under the swap-integrated strategy, the optimal order quantity must be adjusted—either increased or decreased—depending on the demand scale of the counterpart and the specified swap price, deviating from the optimal quantity under traditional long-term contracts. These findings highlight the potential of swap-integrated procurement strategies as practical coordination mechanisms across both private and public sectors, offering strategic value in contexts such as vaccine distribution, fresh produce, and other critical products. Full article

With the rapid development of information technology and semiconductor technology, the iteration speed of electronic devices has accelerated in an unprecedented manner, and the market demand for miniaturized, highly integrated, and highly intelligent devices continues to rise. But when these electronic devices operate at high power, the electronic components generate a large amount of integrated heat. Due to the limitations of existing heat dissipation channels, the current heat dissipation performance of electronic packaging materials is struggling to meet practical needs, resulting in heat accumulation and high temperatures inside the equipment, seriously affecting operational stability. For electronic devices that require high energy density and fast signal transmission, improving the heat dissipation capability of electronic packaging materials can significantly enhance their application prospects. In order to improve the thermal conductivity of composite materials, hexagonal boron nitride (h-BN) was selected as the thermal filling material in this paper. The BMI resin was structurally modified through molecular structure design. The results showed that the micro-branched structure and h-BN synergistically improved the thermal conductivity and insulation performance of the composite material, with a thermal conductivity coefficient of 1.51 W/(m·K) and a significant improvement in insulation performance. The core mechanism is the optimization of the dispersion state of h-BN filler in the matrix resin through the free volume in the micro-branched structure, which improves the thermal conductivity of the composite material while maintaining high insulation. Full article

This study examines how Environmental, Social, and Governance (ESG) reporting is communicated and perceived on YouTube. A dataset of 553 relevant videos and 5060 user comments was extracted on 2 April 2025 ranging between 2014 and 2025, and sentiment, topic, and stance analyses were applied to both transcripts and comments. The majority of video content strongly endorsed ESG reporting, emphasizing themes such as transparency, regulatory compliance, and financial performance. In contrast, viewer comments revealed diverse stances, including skepticism about methodological inconsistencies, accusations of greenwashing, and concerns over politicization. Notably, statistical analysis showed minimal correlation between video sentiment and audience sentiment, suggesting that user perceptions are shaped by factors beyond the tone of the videos themselves. These findings underscore the need for more rigorous ESG frameworks, enhanced standardization, and proactive stakeholder engagement strategies. The study highlights the value of online platforms for capturing stakeholder feedback in real time, offering practical insights for organizations and policymakers seeking to strengthen ESG disclosure and communication. Full article

Monitoring position and movements of preterm infants is important to ensure their well-being and optimal development. This study evaluates the feasibility of a pressure-sensitive fiber-optic mattress (FM), made entirely of plastic, for two-dimensional analysis of preterm infant movements and positioning. Before clinical use, we developed a simple, replicable, and cost-effective test protocol to simulate infant movements and positions, enabling early identification of technical limitations. Using data from 20 preterm infants, we assessed the FM’s potential to monitor posture and limb motion. FM-derived pressure patterns were compared with camera-based manual annotations to distinguish between different positions and out-of-bed moments, as well as limb-specific movements. Bench-test results demonstrated the FM’s sensitivity to motion and pressure changes, supporting its use in preclinical validation. Clinical data confirmed the FM’s reliability in identifying infant positions and movement patterns, showing an accuracy comparable to camera annotations. However, limitations such as calibration, sensitivity to ambient light, and edge-related artifacts were noted, indicating areas for improvement. In conclusion, the test protocol proved effective for early-stage evaluation of smart mattress technologies. The FM showed promising clinical feasibility for non-obtrusive monitoring of preterm infants, though further optimization is needed for robust performance in neonatal care. Full article

Next Point-of-Interest (POI) recommendation is aimed at predicting users’ future visits based on their current status and historical check-in records, providing convenience to users and potential profits to businesses. The Graph Neural Network (GNN) has become a common approach for this task due to the capabilities of modeling relations between nodes in a global perspective. However, most existing studies overlook the more prevalent heterogeneous relations in real-world scenarios, and manually constructed graphs may suffer from inaccuracies. To address these limitations, we propose a model called Heterogeneous Graph Structure Learning for Next POI Recommendation (HGSL-POI), which integrates three key components: heterogeneous graph contrastive learning, graph structure learning, and sequence modeling. The model first employs meta-path-based subgraphs and the user–POI interaction graph to obtain initial representations of users and POIs. Based on these representations, it reconstructs the subgraphs through graph structure learning. Finally, based on the embeddings from the reconstructed graphs, sequence modeling incorporating graph neural networks captures users’ sequential preferences to make recommendations. Experimental results on real-world datasets demonstrate the effectiveness of the proposed model. Additional studies confirm its robustness and superior performance across diverse recommendation tasks. Full article

With the increasing uncertainty introduced by the large-scale integration of renewable energy sources, traditional power dispatching methods face significant challenges, including severe frequency fluctuations, substantial forecasting deviations, and the difficulty of balancing economic efficiency with system stability. To address these issues, a deep learning-based dispatching framework is proposed, which integrates spatiotemporal feature extraction with a stability-aware mechanism. A joint forecasting model is constructed using Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) to handle multi-source inputs, while a reinforcement learning-based stability-aware scheduler is developed to manage dynamic system responses. In addition, an uncertainty modeling mechanism combining Dropout and Bayesian networks is incorporated to enhance dispatch robustness. Experiments conducted on real-world power grid and renewable generation datasets demonstrate that the proposed forecasting module achieves approximately a 2.1% improvement in accuracy compared with Autoformer and reduces Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) by 18.1% and 14.1%, respectively, compared with traditional LSTM models. The achieved Mean Absolute Percentage Error (MAPE) of 5.82% outperforms all baseline models. In terms of scheduling performance, the proposed method reduces the total operating cost by 5.8% relative to Autoformer, decreases the frequency deviation from 0.158 Hz to 0.129 Hz, and increases the Critical Clearing Time (CCT) to 2.74 s, significantly enhancing dynamic system stability. Ablation studies reveal that removing the uncertainty modeling module increases the frequency deviation to 0.153 Hz and raises operational costs by approximately 6.9%, confirming the critical role of this module in maintaining robustness. Furthermore, under diverse load profiles and meteorological disturbances, the proposed method maintains stable forecasting accuracy and scheduling policy outputs, demonstrating strong generalization capabilities. Overall, the proposed approach achieves a well-balanced performance in terms of forecasting precision, system stability, and economic efficiency in power grids with high renewable energy penetration, indicating substantial potential for practical deployment and further research. Full article

In intelligent vehicular networks, the accuracy of semantic segmentation in road scenes is crucial for vehicle-mounted artificial intelligence to achieve environmental perception, decision support, and safety control. Although deep learning methods have made significant progress, two main challenges remain: first, the difficulty in balancing global and local features leads to blurred object boundaries and misclassification; second, conventional convolutions have limited ability to perceive irregular objects, causing information loss and affecting segmentation accuracy. To address these issues, this paper proposes a global–local collaborative attention module and a spider web convolution module. The former enhances feature representation through bidirectional feature interaction and dynamic weight allocation, reducing false positives and missed detections. The latter introduces an asymmetric sampling topology and six-directional receptive field paths to effectively improve the recognition of irregular objects. Experiments on the Cityscapes, CamVid, and BDD100K datasets, collected using vehicle-mounted cameras, demonstrate that the proposed method performs excellently across multiple evaluation metrics, including mIoU, mRecall, mPrecision, and mAccuracy. Comparative experiments with classical segmentation networks, attention mechanisms, and convolution modules validate the effectiveness of the proposed approach. The proposed method demonstrates outstanding performance in sensor-based semantic segmentation tasks and is well-suited for environmental perception systems in autonomous driving. Full article

The expression of recombinant proteins in heterologous hosts is a common strategy to obtain larger quantities of the “protein of interest” (POI) for scientific, therapeutic or commercial purposes. However, the experimental success of such an approach critically depends on the choice of an appropriate host system to obtain biologically active forms of the POI. The correct folding of the molecule, mediated by disulfide bond formation, is one of the most critical steps in that process. Here we describe the recombinant expression of hirudin, a leech-derived anticoagulant and thrombin inhibitor, in the yeast Komagataella phaffii (formerly known and mentioned throughout this publication as Pichia pastoris) and in two different strains of Escherichia coli, one of them being especially designed for improved disulfide bond formation through expression of a protein disulfide isomerase. Cultivation of the heterologous hosts and expression of hirudin were performed at different temperatures, ranging from 22 to 42 °C for the bacterial strains and from 20 to 30 °C for the yeast strain, respectively. The thrombin-inhibitory potencies of all hirudin preparations were determined using the thrombin time coagulation assay. To our surprise, the hirudin preparations of P. pastoris were considerably less potent as thrombin inhibitors than the respective preparations of both E. coli strains, indicating that a eukaryotic background is not per se a better choice for the expression of a biologically active eukaryotic protein. The hirudin preparations of both E. coli strains exhibited comparable high thrombin-inhibitory potencies when the strains were cultivated at their respective optimal temperatures, whereas lower or higher cultivation temperatures reduced the inhibitory potencies. Full article

Obstructive sleep apnea syndrome (OSAS) is characterized by cycles of decreased blood oxygen saturation followed by reoxygenation due to transient apnea. Cognitive dysfunction is a complication of OSAS, but its mechanisms remain unclear. Eight-week-old C57BL/6J mice were exposed to intermittent hypoxia (IH) to model OSAS, and cognitive function and hippocampal gene expression were analyzed. Three groups were maintained for 28 days: an IH group (oxygen alternating between 10 and 21% in 2 min cycles, 8 h/day), sustained hypoxia group (SH) (10% oxygen, 8 h/day), and control group (21% oxygen). Behavioral tests and RNA sequencing (RNA-seq) analysis were performed. While Y-maze test results showed no differences, the IH group demonstrated impaired memory and learning in passive avoidance tests compared to control and SH groups. RNA-seq revealed coordinated suppression of mitochondrial function genes and oxidative stress response pathways, specifically in the IH group. RT-qPCR showed decreased Lars2, Hmcn1, and Vstm2l expression in the IH group. Pathway analysis showed the suppression of the KEAP1-NFE2L2 antioxidant pathway in the IH group vs. the SH group. Our findings demonstrate that IH induces cognitive dysfunction through suppression of the KEAP1-NFE2L2 antioxidant pathway and downregulation of mitochondrial genes (Lars2, Vstm2l), leading to oxidative stress and mitochondrial dysfunction. These findings advance our understanding of the molecular basis underlying OSAS-related cognitive impairment. Full article