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Abstract Detecting ship targets in remote sensing images within complex scenarios faces numerous challenges. The limited feature information of small-scale targets and their random orientation angles often result in missed and false detections. To address these issues, this paper proposes a Multi-Scale Rotated Detection Network (MSRO-Net) for detecting rotated ship targets in remote sensing images. The network adopts a CNN-Transformer hybrid architecture for collaborative feature extraction and integrates our proposed Coordinate-Aware Pyramid Feature Aggregation module (CAPP). This backbone network retains the capability of local feature extraction while also connecting global context and capturing long-range dependencies. The model can gather information from any position in the sequence, extract contextual information of targets at different scales, and enhance global feature representation. Additionally, this paper proposes an Upsampling Feature Reconstruction Pyramid (ARFPN-C), based on Adaptive Rotated Convolution (ARC). This network combines ARC adaptive rotating convolution with a multi-scale feature fusion mechanism to enhance the model’s perceptual capability, addressing the issues of limited target feature information and random orientation angles. The proposed algorithm is validated on two public remote sensing image datasets, HRSC2016 and DOTA. The mAP07, mAP12 on the HRSC2016 dataset, as well as the mAP in the ship category of the DOTA dataset, show a significant advantage over other commonly used object detection algorithms, with accuracies of 90.70%, 98.98%, and 89.46%, respectively. These results further validate the accuracy and effectiveness of the proposed MSRO-Net in object detection.

Abstract The existing UAV inspection images are faced with many challenges for insulator defect recognition. A new multi-resolution Context Cluster CenterNet++ model is proposed. First, this paper proposes the Context Cluster method to solve the problem of low recognition accuracy caused by non-uniform distribution of targets. The cluster region is used to identify and predict the location of the target, and the improved loss function is used to modify the cluster center. Secondly, this paper uses deformable convolution operator (DCNv2) combined with path aggregation network (PAN) to carry out deformable convolution operation on the image, and accurately predicts the regression box and key point triplet (KP), so as to improve the accurate positioning of the target position of any shape and any scale. The sensitivity of the model to target scale change and deformation is reduced, and the recognition accuracy of the model is improved. Then, Bhattacharyya distance is used to calculate the triplet prediction loss of key points and the target center point offset loss, so as to significantly improve the positioning accuracy of the same target in different frames. Finally, experiments are carried out on the MS-COCO dataset and the National Grid standardized UAV inspection insulator image dataset. Our code is at https://github.com/mengbonannan88/CC-CenterNet .

Abstract Public health emergencies are critical to people’s lives and health, economic development and social stability. Understanding how to respond correctly to public health emergencies is the focus of societal attention. This paper focuses on the tripartite entities of public health emergencies: local governments, pharmaceutical enterprises and the public. On the basis of the assumption of finite rationality, it delves into the game-theoretic interaction among these groups during such crises. By constructing an evolutionary game model, this paper analyses the dynamic adjustment process of the decision-making behaviors of these three parties, leading to the identification of evolutionarily stable strategies for the local government, pharmaceutical enterprises, and the public under different conditions. Moreover, MATLAB is used to carry out simulation experiments to analyse the influence of the local government’s reward and punishment mechanism on the strategic choices of the involved parties in the game. The research findings indicate that (1) For the tripartite entities of public health emergencies, the key for strategy choices is to reduce the gain obtained from illegal production and non-cooperation with prevention and control. (2) The strength of the initial willingness to participate has a significant effect on the evolution strategies of each subject. (3) For pharmaceutical companies and the public, the incentives and penalties of local governments can promote the former’s compliance and the latter’s cooperation in prevention and control. Based on these results, countermeasure suggestions to promote mutual collaboration among local governments, pharmaceutical enterprises, and the public to jointly respond to public health emergencies are proposed.

Abstract Power quality (PQ) disturbances, such as voltage sags, are significant issues that can lead to damage in electrical equipment and system downtime. Detecting and classifying these disturbances accurately is essential for maintaining reliable power systems. This paper introduces a novel approach to voltage sag analysis by employing wavelet packet analysis combined with energy-based feature extraction to enhance PQ monitoring. The study decomposes voltage sag signals into different frequency bands to extract key features for disturbance detection. We compare six commonly used mother wavelets (db1, db4, db10, dmey, sym5, and coif5) to identify the most suitable wavelet for voltage sag detection. The energy distribution curve analysis is used to evaluate the energy characteristics of each wavelet’s decomposition, with a focus on identifying the most effective signal features for PQ monitoring. The paper presents a thorough error analysis and compares the energy values extracted by different wavelet functions to demonstrate the reliability and accuracy of the proposed method. The results show that wavelet packet analysis significantly improves the detection and classification of voltage sag disturbances, providing a robust and efficient tool for real-time PQ monitoring. This study contributes to the development of advanced PQ monitoring systems by offering a more precise and computationally efficient method for voltage sag analysis, ultimately helping to protect electrical systems from potential damage and reducing operational costs. Wavelet packet analysis is applied as a novel feature extraction method for voltage sag detection by offering improved time-frequency analysis over traditional methods. Energy features are extracted from wavelet packet coefficients for sag identification. This work utilizes wavelet packet analysis to extract energy-based features for voltage sag detection.

Abstract Nowadays, Egypt is treating the Nile River Water to produce drinking water, and this process generates large amounts of waste, around 635 million m3 annually, which is called water treatment plant sludge (WTPS). This WTPS cost the government around 30 million US dollars to return it back to the Nile River in addition to negatively affecting the environment. Therefore, there is an urgent need to find environmentally friendly alternatives that reduce the impact of such an issue. This paper focuses on treating WTPS by drying, grinding and calcining to develop it as an alternative binder for use in alkali-activated concrete. This approach would not only provide green construction material but also reveal an environmental disposal route for the sludge produced in Egypt or in any country has the same issue. The treatment methodology used in this study was based on finding the optimum calcination temperature regime for WTPS after drying and grinding. Fifteen specimens of WTPS powder were used to investigate the optimum calcination temperature and duration by applying different temperatures ranging from 500 °C to 800 °C for various exposure durations of 30, 60 and 90 min. XRD and Chapelle tests were employed to chemically investigate the efficiency of the obtained calcined WTPS specimens, while strength activity index and compressive strength tests were used to mechanically verify the findings of the chemical tests. The results indicated that the calcination regime, which involved applying a maximum temperature of 650 °C for 90 min, achieved the best chemical characteristics and a strength activity index of 145%. Moreover, this regime resulted in a compressive strength of 21 MPa when WTPS powder was used as a precursor in alkali-activated concrete. Additionally, this paper presented a brief comparison of the production cost and energy consumption between cement and WTPS. The comparison demonstrated the efficiency of using WTPS as a replacement for cement, showing that the production of WTPS costs 50% less and consumes 92% less energy than cement.

Abstract This paper proposes the YOLOv8n_H method to address issues regarding parameter redundancy, slow inference speed, and suboptimal detection precision in contemporary helmet-wearing target recognition algorithms. The YOLOv8 C2f module is enhanced with a new SC_Bottleneck structure, incorporating the SCConv module, now termed SC_C2f, to mitigate model complexity and computational costs. Additionally, the original Detect structure is substituted with the PC-Head decoupling head, leading to a significant reduction in parameter count and an enhancement in model efficiency. Moreover, the original Detect structure is replaced by the PC-Head decoupling head, significantly reducing parameter count and enhancing model efficiency. Finally, regression accuracy and convergence speed are boosted by the dynamic non-monotonic focusing mechanism introduced through the WIoU boundary loss function. Experimental results on the expanded SHWD dataset demonstrate a 46.63% reduction in model volume, a 44.19% decrease in parameter count, a 54.88% reduction in computational load, and an improvement in mean Average Precision (mAP) to 93.8% compared to the original YOLOv8 algorithm. In comparison to other algorithms, the model proposed in this paper markedly reduces model size, parameter count, and computational load while ensuring superior detection accuracy.

Abstract Drug discovery and development is a challenging and time-consuming process. Laboratory experiments conducted on Vidarabine showed IC50 6.97 µg∕mL, 25.78 µg∕mL, and ˃ 100 µg∕mL against non-small Lung cancer (A-549), Human Melanoma (A-375), and Human epidermoid Skin carcinoma (skin/epidermis) (A-431) respectively. To address these challenges, this paper presents an Artificial Intelligence (AI) model that combines the capabilities of Deep Learning (DL) to identify potential new drug candidates, Fuzzy Rough Set (FRS) theory to determine the most important chemical compound features, Explainable Artificial Intelligence (XAI) to explain the features’ importance in the last layer, and medicinal chemistry to rediscover anticancer drugs based on natural products like Vidarabine. The proposed model aims to identify potential new drug candidates. By analyzing the results from laboratory experiments on Vidarabine, the model identifies Sulfur and magnesium oxide (MgO) as new potential anticancer agents. The proposed model selected Sulfur and MgO based on Interpreting their promising features, and further laboratory experiments were conducted to validate the model’s predictions. The results demonstrated that, while Vidarabine was inactive against the A-431 cell line (IC50 ˃ 100 µg∕mL), Sulfur and MgO exhibited significant anticancer activity (IC50 4.55 and 17.29 µg/ml respectively). Sulfur displayed strong activity against A-549 and A-375 cell lines (IC50 3.06 and 1.86 µg/ml respectively) better than Vidarabine (IC50 6.97 and 25.78 µg/ml respectively). However, MgO showed weaker activity against these two cell lines. This paper emphasizes the importance of uncovering hidden chemical features that may not be discernible without the assistance of AI. This highlights the ability of AI to discover novel compounds with therapeutic potential, which can significantly impact the field of drug discovery. The promising anticancer activity exhibited by Sulfur and MgO warrants further preclinical studies.

Abstract The application of artificial neural networks (ANNs) can be found in numerous fields, including image and speech recognition, natural language processing, and autonomous vehicles. As well, intrusion detection, the subject of this paper, relies heavily on it. Different intrusion detection models have been constructed using ANNs. While ANNs are relatively mature to construct intrusion detection models, some challenges remain. Among the most notorious of these are the bloated models caused by the large number of parameters, and the non-interpretability of the models. Our paper presents Convolutional Kolmogorov-Arnold Networks (CKANs), which are designed to overcome these difficulties and provide an interpretable and accurate intrusion detection model. Kolmogorov-Arnold Networks (KANs) are developed from the Kolmogorov-Arnold representation theorem. Meanwhile, CKAN incorporates a convolutional computational mechanism based on KAN. The model proposed in this paper is constructed by incorporating attention mechanisms into CKAN’s computational logic. The datasets CICIoT2023 and CICIoMT2024 were used for model training and validation. From the results of evaluating the performance indicators of the experiments, the intrusion detection model constructed based on CKANs has an attractive application prospect. As compared with other methods, the model can predict a much higher level of accuracy with significantly fewer parameters. However, it is not superior in terms of memory usage, execution speed and energy consumption.

Abstract This paper aims to construct a green environmental protection system by advancing database energy-saving techniques and optimizing the energy-saving mechanism against the backdrop of blockchain integration. The protocol classification of wireless sensor networks is examined within the context of the rapid growth of information technology. The analysis draws upon the database storage and sharing model and recent research examples that connect blockchain and database technology. Additionally, the paper investigates the blockchain data structure and storage path by analyzing the spatial–temporal correlation properties of blockchain data. The findings demonstrate that increasing the number of network nodes in the database system within a reasonable range can reduce the overall execution time. Additionally, as time progresses, the query traffic of the system database continues to increase. The research has practical implications in the domains of databases and the Internet of Things.

Abstract The ability to conduct research and utilize its findings has been a fundamental competency for medical students, often assessed through the observable research outcomes. This study aims to compare the research competency of medical students before, during, and after the COVID-19 pandemic to explore the future direction of research education in medical schools. We collected information on research outcomes submitted by medical students as graduation requirements during the pre-COVID-19, pandemic, and post-COVID-19 periods based on the duration of social distancing. The research outcomes were classified into two categories: publication of a paper and presentation at a conference. We compared variables such as authorship, type of journal or conference, and type of literature or presentation. The research outcomes submitted by all medical school graduates at their senior year from 2018 to 2023 were included in the analysis. The graduation requirements were usually achieved by publishing a paper (67.5%) with a faculty of clinical medicine (63.5%) in the fourth year (50.0%), regardless of COVID-19 pandemic. The findings highlighted the significant impact of the COVID-19 pandemic on the research outcomes of medical students. Post hoc analysis revealed that while the quality of published papers improved during the COVID-19 pandemic, there was a regression in author contributions in the post-pandemic period. The impact of the COVID-19 pandemic on medical students’ experiences with research is likely that the weakened student initiative observed in participating in and conducting research will persist, especially after the pandemic. Therefore, it is imperative to implement strategies to increase motivation and empower medical students to research.

Abstract Symbolic anchoring is an important topic in robotics, as it enables robots to obtain symbolic knowledge from the perceptual information acquired through their sensors and maintain the link between that knowledge and the sensory data. In cognitive-based robots, this process of transforming sub-symbolic data generated by sensors to obtain and maintain symbolic knowledge is still an open problem. To address this issue, this paper presents SAILOR, a framework for symbolic anchoring integrated into ROS 2. SAILOR aims to maintain the link between symbolic data and perceptual data in real robots over time. It provides a semantic world modeling approach using two deep learning-based sub-symbolic robotic skills: object recognition and matching function. The object recognition skill allows the robot to recognize and identify objects in its environment, while the matching function enables the robot to decide if new perceptual data corresponds to existing symbolic data. This paper describes the proposed method and the development of the framework, as well as its integration in MERLIN2 (a hybrid cognitive architecture fully functional in robots running ROS 2) and the validation of SAILOR using public datasets and a real-world scenario.

Abstract This study presents a novel approach to identifying meters and their pointers in modern industrial scenarios using deep learning. We developed a neural network model that can detect gauges and one or more of their pointers on low-quality images. We use an encoder network, jump connections, and a modified Convolutional Block Attention Module (CBAM) to detect gauge panels and pointer keypoints in images. We also combine the output of the decoder network and the output of the improved CBAM as inputs to the Object Heatmap-Scalarmap Module to find pointer tip heat map peaks and predict pointer pointing. The method proposed in this paper is compared with several deep learning networks. The experimental results show that the model in this paper has the highest recognition correctness, with an average precision of 0.95 and 0.763 for Object Keypoint Similarity and Vector Direction Similarity, and an average recall of 0.951 and 0.856 in the test set, respectively, and achieves the best results in terms of efficiency and accuracy achieve the best trade-off, and performs well in recognizing multiple pointer targets. This demonstrates its robustness in real scenarios and provides a new idea for recognizing pointers in low-quality images more efficiently and accurately in complex industrial scenarios.

Abstract In order to reduce turnout rail wear, the paper establishes a coupled dynamics model and a turnout rail wear model that consider the true profile of the turnout rail, the vehicle’s continuous traction force while passing, and the operational resistance. Comparative analysis of various models for predicting turnout rail wear indicates that the wear energy model is better suited for this purpose. The ideal profile update step for the turnout rail is 0.05 mm, and the adaptive filtering algorithm, tailored to the turnout characteristics, smooths wear distribution effectively while retaining crucial data features. The wear model developed in the paper predicts wear depth that is 89.91% consistent with the measured data. The grinding model introduced in the paper significantly enhances the wheel-rail contact conditions in the turnout, with lateral and vertical vibration accelerations of the vehicle reduced by 52.56% and 30.43%, respectively, during turnout passage. The research offers theoretical support for mitigating rail wear in high-speed turnouts.

Abstract Pakistan’s geographic location makes it an important land hub between Central Asia, Middle East-North Africa, and China. However, the railways, roads, farmland, riverways, and residential quarters in the Piedmont plains of Baluchistan province in northwestern Pakistan are under serious threat of flooding in the summer of 2022. The urgency and severity of climate change’s impact on humanity are underscored by the significant threats posed to human life and property in Piedmont Plains environments through extreme flood events, which has garnered widespread concerns. In flood scenarios, accurately predicting the extent of flooding is crucial for disaster assessment, emergency response, and the efficient allocation of resources. Previous research has primarily predicted flooding likelihood based on topographical factors or integrated annual rainfall data, failing to account for the extent of flooding from short-term rainfall before and after an event. Flood disasters are not caused by a single factor but are influenced by a variety of elements, including terrain and climate. Therefore, current research still lacks a comprehensive consideration of these influencing factors to accurately predict both the range and severity of flood impacts. In this paper, in response to the inability to accurately predict the flood damage in the pre-hill plains region in previous studies, combined with the current Pakistan mega-flood disaster, will couple the impacts of various flood-inducing factors on flooding, construct a prediction model for the degree of inundation of the Pakistani pre-hill plains flood disaster, and combined with the distribution of regional bearers, analyze the risk-resistant capacity of different types of bearers, and draw a comprehensive risk map piece under the flooding disaster. This paper bridges the gap of not integrating various factors in previous studies. Our research results provide strong evidence for flood prediction in Pakistan and similar regions, which is of great significance in reducing the loss of life and property of people around the world.

Abstract Due to the low contrast of abdominal CT (Computer Tomography) images and the similar color and shape of the liver to other organs such as the spleen, stomach, and kidneys, liver segmentation presents significant challenges. Additionally, 2D CT images obtained from different angles (such as sagittal, coronal, and transverse planes) increase the diversity of liver morphology and the complexity of segmentation. To address these issues, this paper proposes a Detail Enhanced Convolution (DE Conv) to improve liver feature learning and thereby enhance liver segmentation performance. Furthermore, to enable the model to better learn liver features at different scales, a Multi-Scale Feature Fusion module (MSFF) is added to the skip connections in the model. The MSFF module enhances the capture of global features, thus improving the accuracy of the liver segmentation model. Through the aforementioned research, this paper proposes a liver segmentation network based on detail enhancement and multi-scale feature fusion (DEMF-Net). We conducted extensive experiments on the LiTS17 dataset, and the results demonstrate that the DEMF-Net model achieved significant improvements across various evaluation metrics. Therefore, the proposed DEMF-Net model can achieve precise liver segmentation.

Abstract Integrating the Internet of Things (IoT) in smart grids has revolutionized the energy sector, enabling real-time data collection and efficient energy distribution. However, this integration also introduces significant security challenges, particularly data encryption. Traditional encryption algorithms used in IoT are vulnerable to various attacks, and the advent of quantum computing exacerbates these vulnerabilities. To address the above challenges, this paper proposes a novel encryption mechanism, the Quantum-Resistant Hybrid Encryption for IoT (QRHE-IoT), designed to enhance the security of communications in IoT-enabled smart grids. QRHE-IoT combines the strengths of symmetric and asymmetric encryption algorithms and incorporates quantum-resistant algorithms to provide robust security. This paper explains the QRHE-IoT mechanism, its theoretical basis, and how it addresses the security challenges in smart grid communications. It also presents the results of tests conducted to evaluate the effectiveness of QRHE-IoT in a simulated smart grid environment. The proposed QRHE-IoT mechanism presents a promising solution to these challenges, offering robust security for smart grid communications in the face of emerging threats, including quantum computing.

Abstract In this paper, a miniaturized 2 × 2 MIMO dual-wideband ground radiation antenna targeting Wi-Fi 6/6E/7 standards using 2.4 GHz, 5 GHz, and 6 GHz frequency bands with sufficient antenna performance was designed. The proposed antenna system contains four identical 4 mm × 6 mm antennas of the internal loop type and two identical 6 mm × 6 mm isolators containing lumped LC elements. This paper considers maximizing the impedance bandwidth using an advanced impedance control technique and isolation enhancement through T-shaped isolators. To achieve dual-wideband resonance, an impedance control method using LC parameter value control is employed by utilizing the coupling between the ground plane, the voltage source, the feed loop, and the radiation loop in each radiation band. The operating principle of the T-shaped isolator was proposed using the scattering matrix and impedance matching. Simulated results showed that the proposed antenna can cover 96 MHz at the 2.4 GHz band and 2,232 MHz at both the 5 GHz and 6 GHz bands with an impedance bandwidth of -10 dB and isolation performance of less than − 20 dB across all frequency bands. Average measured efficiencies of 52%, 74%, and 80% were obtained for the corresponding operating bands.

Abstract The production stage of an automated job shop is closely linked to the automated guided vehicle (AGV), which needs to be planned in an integrated manner to achieve overall optimization. In order to improve the collaboration between the production stages and the AGV operation system, a two-layer scheduling optimization model is proposed for simultaneous decision making of batching problems, job sequences and AGV obstacle avoidance. Under the AGV automatic path seeking mode, this paper adopts a data-driven Bayesian network method to portray the transportation time of AGVs based on the historical operation data to control the uncertainty of the transportation time of AGVs. Meanwhile, a time window is established to control the risk of AGV delay, and a data-driven Bayesian network is constructed to optimize the two-layer scheduling model of automated job shop and AGV. To solve the model, we design an improved particle swarm algorithm combining genetic operators, crossover operators and elite retention operator. The results show that the model in this paper can effectively improve the collaboration between the production stage and AGV operation system within the shop floor, and successfully solve the actual operation scale case to enhance the effectiveness of the production and transportation system.

Abstract Independent pitch control (IPC) is a crucial technology for enhancing the performance of wind turbines, optimizing the power output, and reducing the loads by managing each blade. This paper explores the primary vibration modes of semi-submersible wind turbines under wind-wave coupling. Given the effectiveness of pitch control in vibration suppression, this paper addresses the limitations of conventional collective pitch control (CPC) by designing an independent pitch control method based on an equivalent wind speed model (EWIPC). This model constructs an effective representation of the actual wind speed’s influence on pitch angle by comprehensively considering the spatial distribution of wind speeds. This way, the control accuracy and response speed are significantly improved, making the control strategy more intuitive and efficient in complex wind speed environments. The proposed independent pitch control method is validated through simulations on the International Energy Agency (IEA) 15 MW wind turbine. The simulation results indicate that the EWIPC stabilizes wind turbine power output and reduces structural loads. Additionally, it demonstrates significant effectiveness in reducing vibrations of the blades and tower, as well as in eliminating 1P oscillations in the blade root bending moment.

Abstract The integration of radar technology into smart furniture represents a practical approach to health monitoring, circumventing the concerns regarding user convenience and privacy often encountered by conventional smart home systems. Radar technology’s inherent non-contact methodology, privacy-preserving features, adaptability to diverse environmental conditions, and high precision characteristics collectively establish it a compelling alternative for comprehensive health monitoring within domestic environments. In this paper, we introduce a millimeter (mm)-wave radar system positioned strategically behind a seat, featuring an algorithm capable of identifying unique cardiac waveform patterns for healthy subjects. These patterns are characterized by two peaks followed by a valley in each cycle, which can be correlated to Electrocardiogram (ECG), enabling effective cardiac waveform monitoring. The provided algorithm excels in discerning variations in heart patterns, particularly in individuals with prolonged corrected QT intervals, by minimizing high frequency breathing interference and ensuring accurate pattern recognition. Additionally, this paper addresses the influence of body movements in seated individuals, conducting a comprehensive study on heart rate variability and estimation. Experiment results demonstrate a maximum interbeat intervals (IBI) error of 30 milliseconds and an average relative error of 4.8% in heart rate estimation, showcasing the efficacy of the proposed method utilizing variational mode decomposition and a multi-bin approach.

Abstract Trade policy differences among different countries are important factors affecting international trade cooperation. In this paper, we build an evolutionary game model of international trade in which complex networks portray game relationships and trade policy differences are game strategies of players. Compared with the fully coupled game relationship and two-strategies game, the game relationship dynamic adjustment, trade policy differences and stochastic game payoffs in this paper are more in line with the real international trade context. We use mathematical analyses and computer simulation methods to reveal the impact of trade policy differences on the game payoffs of countries and the overall international trade networks. The results show that the reduce of the tolerance “t” of fair-trade is difficult to eliminate the opportunism trade policy and lead to a decline in the game payoffs of all countries and the global trading network. Dynamic adjustment of trade relations can not only improve the game payoffs of all countries and the trade network, but also offset the negative impact of the decline of the tolerance “t”. In terms of long-term, only the win-win trade policy can maximize the payoffs of the global trade network and achieve a balanced distribution of trade benefits.

Abstract Food security is the foundation of national security, which is related to economic development and social stability. Shanxi Province, as China’s grain production and marketing balance area, has rich agricultural resources and profound farming civilization, its food security situation is not only related to its own economic development, but also has a strategic position that can not be ignored in the national food security system. Based on the concept and goal of the development of food security in Shanxi Province, from the five aspects of quantity security, economic security, policy security, resource sustainability and ecological sustainability, this paper constructs the evaluation index system of food security in Shanxi Province, including the fluctuation rate of grain production and the sown area of grain, and establishes the entropy TOPSIS model to evaluate the development level of food security in Shanxi Province. The results show that the overall level of food security in Shanxi Province from 2001 to 2022 shows a trend of first decline and then rise, and the importance of food economic security, policy security and resource sustainability is increasing year by year. In the process of improving the level of food security development in Shanxi Province, it is also affected by structural imbalance, scientific and technological level, grain storage and transportation. Finally, the paper puts forward some policy suggestions to ensure food security in Shanxi Province.

Abstract The paper introduces a method for predicting damage intensity in masonry residential buildings situated in mining areas, focusing on the impact of large-scale continuous ground deformation. The research utilizes in situ data collected in a database, encompassing structural and material features, as well as information on maintenance quality and building durability. In addition to this information, the database collected data on the intensity of continuous deformation of the mining area at the location of the building, as well as the range and intensity of damage identified in buildings. The information included in the database was the result of many years of observations of buildings during the disclosure of impacts from mining exploitation and was based on: the results of in-situ building inventory, analysis of available building documentation and information provided by mining companies. The archived data were categorized variables labeled. The transformation of the data to a labeled value was dictated directly by the assumptions of the GOBNILP algorithm. Ultimately, a predictive model, represented by an optimal Bayesian network structure, is established. The optimisation of the network structure is achieved through the adaptation of the GOBNILP Bayesian network learning algorithm from data. This optimisation process is executed through the Gurobi Optimizer. It is worth noting that this interdisciplinary approach represents one of the first applications of such a methodology in the field of civil and environmental engineering. The results obtained can therefore be of significant value given the fact that the methodology of detecting the structure of Bayesian networks from data is still developing intensively in other scientific fields. In the course of the analyses, metric scores are examined, and various network structures are assessed based on their complexity. Great values of classification accuracies over 91% were obtained. This meticulous evaluation allows for the selection of the optimal Bayesian network that best generalises the knowledge acquired during the learning process. The paper also demonstrates the potential application of the obtained model in diagnosing damage causes and predicting future occurrences, highlighting the versatility of the proposed approach for addressing issues in the field.

Abstract In field of location prediction, trajectory recognition is one of the most widely research issues. Since trajectory includes various information such as position, time, and speed, many scientific methods are applied to extracting meaningful features, and discovering valuable knowledges. This paper pays more attention on case study of in-store trajectory, and proposes a series of recurrent neural network (RNN) for location prediction based on trajectory. The trajectory is provided by a indoor location system (ILS) in supermarket, and used radio frequency identification (RFID) technique to collect customer mobility data namely RFID data. After reviewing relatively previous studies, scholars mostly pursue customer segmentation and classification tasks based on trajectory, this paper briefly focus on regression analysis and pattern recognition of original trajectory itself. This paper also includes two improvements of experimental and methodological design. In ILS experiments, we select crossing sections as universal background to filtering customers and their trajectories, and choose fish, vegetable, and meat sections as experiment target, so as to train a general prediction model for heterogeneous shopping behaviors. In methodologies, we propose several RNNs with hybrid gate units on pre-defined trajectory based on RFID data, and also investigate their advances on time-series regression task for trajectory prediction. According to comparative and numerical results, the proposed models show higher performance on in-store trajectory prediction than other benchmark methods and other classic neural networks.

Abstract With the advancement of this digital era and the emergence of DApps and Blockchain, secure, robust and transparent network transaction has become invaluable today. These traditional methods of securing the transactions and maintaining transparency have encountered many challenges. It includes some such issues as follows: data privacy, centralized vulnerability, inefficiency in fraud detection and much more. To that effect, and to address such limitations, this paper provides a blockchain technology framework that is driven by advanced machine learning techniques, which will enhance security and transparency throughout the network of transactions. We begin with a design framework based on Federated Learning for Blockchain Integration where distributed datasets across blockchain nodes contribute to a global machine learning model but do not share raw data samples. Different nodes learn their own models. After that, these local models are aggregated towards a common, global model using secure aggregation methods, which makes sure that there is nozza of data privacy and hence, in the process making sure that more accurate models can be obtained due to diversified data sets. With LSTMs Autoencoders, more excellent security protocols are created for anomaly detection and fraud. So, by training the autoencoder on normal transaction data, the system can alert transactions with high reconstruction errors, meaning real-time anomalies. This proactive detection of anomalies reduces fraudulent activities significantly as most of the threats are recognized early. To this end, this paper proposes Smart Contract-based Model Management for machine learning models in a decentralized environment. Smart contracts are responsible for the submission, validation, and execution of the locally updated models in a decentralized fashion such that the management process is transparent and tamper resistant. Integrity and authenticity requirements are fulfilled by enforcing consensus mechanisms. Privacy in Machine Learning is guaranteed through Differential Privacy and Homomorphic Encryption. Differential privacy techniques, so as to ensure individual transaction data privacy in the updates of the local model before aggregation. In homomorphic encryption, computations are made in the encrypted form so when forming privacy preserving global model, privacy is preserved. The Real-time analysis of the transactions can be done with CNNs to detect fraud. Streaming transaction data is analyzed by CNNs leveraging the privacy-preserving global model and producing immediate alerts and actions for detected fraud. This real timing makes the network even more reliable and trustworthy. Our proposed framework is effective according to the interim outcomes where the aggregation of local models occurred without data leakage, detected anomalies very efficiently, managed models very transparently, with privacy of data at a very high level, and easily detected fraudulent transactions. The work presented here provides a great boost to send secure and very easily transparent transactions across the network, and thus resulted in enhanced network trust and decentralization.

Abstract Forecast verification is very important in the nowcasting operation and technical development of strong convective weather. The current conventional verification method for nowcasting uses a binary classification event verification method, which exists with double punishment, leading to low scoring issues. In order to make up for the shortcomings of conventional verification methods and explore the potential value of forecasting, based on the characteristics and requirements of strong convective weather nowcasting operations, this paper proposes a neighborhood verification method that considers spatial scale, time scale, and intensity error information simultaneously, based on the spatial neighborhood fraction skill score (FSS) verification method. The paper designs an intensity neighborhood correction scheme, a time neighborhood correction scheme, and a comprehensive correction scheme that considers both intensity and time neighborhoods. By introducing spatial neighborhood probability, the strict spatial matching requirement is weakened. Based on the Gaussian membership function in fuzzy logic, the value of forecasting grid below the threshold is explored. Time neighborhood adaptive weighting strategy is adopted to solve the problem of early and delayed forecasts. The evaluation and verification of the optical flow radar extrapolation nowcasting results show that compared to the traditional “point-to-point” verification indicators, the proposed indicators can increase the tolerance of verification, provide more useful information, and are more in line with the needs of practical application.

Abstract Skin cancer is a significant global health concern, with timely and accurate diagnosis playing a critical role in improving patient outcomes. In recent years, computer-aided diagnosis systems have emerged as powerful tools for automated skin cancer classification, revolutionizing the field of dermatology. This survey analyzes 107 research papers published over the last 18 years, providing a thorough evaluation of advancements in classification techniques, with a focus on the growing integration of computer vision and artificial intelligence (AI) in enhancing diagnostic accuracy and reliability. The paper begins by presenting an overview of the fundamental concepts of skin cancer, addressing underlying challenges in accurate classification, and highlighting the limitations of traditional diagnostic methods. Extensive examination is devoted to a range of datasets, including the HAM10000 and the ISIC archive, among others, commonly employed by researchers. The exploration then delves into machine learning techniques coupled with handcrafted features, emphasizing their inherent limitations. Subsequent sections provide a comprehensive investigation into deep learning-based approaches, encompassing convolutional neural networks, transfer learning, attention mechanisms, ensemble techniques, generative adversarial networks, vision transformers, and segmentation-guided classification strategies, detailing various architectures, tailored for skin lesion analysis. The survey also sheds light on the various hybrid and multimodal techniques employed for classification. By critically analyzing each approach and highlighting its limitations, this survey provides researchers with valuable insights into the latest advancements, trends, and gaps in skin cancer classification. Moreover, it offers clinicians practical knowledge on the integration of AI tools to enhance diagnostic decision-making processes. This comprehensive analysis aims to bridge the gap between research and clinical practice, serving as a guide for the AI community to further advance the state-of-the-art in skin cancer classification systems.

Abstract Hydrogen-based electric vehicles such as Fuel Cell Electric Vehicles (FCHEVs) play an important role in producing zero carbon emissions and in reducing the pressure from the fuel economy crisis, simultaneously. This paper aims to address the energy management design for various performance metrics, such as power tracking and system accuracy, fuel cell lifetime, battery lifetime, and reduction of transient and peak current on Polymer Electrolyte Membrane Fuel Cell (PEMFC) and Li-ion batteries. The proposed algorithm includes a combination of reinforcement learning algorithms in low-level control loops and high-level supervisory control based on fuzzy logic load sharing, which is implemented in the system under consideration. More specifically, this research paper establishes a power system model with three DC-DC converters, which includes a hierarchical energy management framework employed in a two-layer control strategy. Three loop control strategies for hybrid electric vehicles based on reinforcement learning are designed in the low-level layer control strategy. The Deep Deterministic Policy Gradient with Twin Delayed (DDPG TD3) is used with a network. Three DRL controllers are designed using the hierarchical energy optimization control architecture. The comparative results between the two strategies, Deep Reinforcement Learning and Fuzzy logic supervisory control (DRL-F) and Super-Twisting algorithm and Fuzzy logic supervisory control (STW-F) under the EUDC driving cycle indicate that the proposed model DRL-F can ensure the Root Mean Square Error (RMSE) reduction for 21.05% compared to the STW-F and the Mean Error reduction for 8.31% compared to the STW-F method. The results demonstrate a more robust, accurate and precise system alongside uncertainties and disturbances in the Energy Management System (EMS) of FCHEV based on an advanced learning method.

Abstract Mongolian patterns are easily damaged by various factors in the process of inheritance and preservation, and the traditional manual restoration methods are time-consuming, laborious, and costly. With the development of deep learning technology and the rapid growth of the image restoration field, the existing image restoration methods are mostly aimed at natural scene images. They do not apply to Mongolian patterns with complex line texture structures and high saturation-rich colors. In order to solve this problem, this paper proposes a Mongolian pattern restoration model with a multi-stage network. In the first stage, a pyramid context encoder network is introduced to learn the contextual features of the image for global restoration; in the second stage, a local restoration network is constructed by combining the RIC convolutional layer and the MPD down-sampling module; and in the third stage, the global refinement restoration is carried out by using the U-Net network that incorporates the attention mechanism. The experimental results show that this paper’s method achieves remarkable results in the Mongolian pattern repair task, using four evaluation metrics such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), LPIPS, and L1 Loss to compare with the existing method. The results show that this paper’s method performs well in the Mongolian pattern repair task. Also, the performance of the model on public datasets verifies its wide applicability. The method in this paper provides an efficient solution for the digital restoration of Mongolian motifs and has important application prospects.

Abstract This paper purposed to prepare poly (lactic acid)/continuous cotton thread (PLA /CCT) filaments by using prepreg method, and investigated the properties of PLA/CCT filament and their 3D printed composites. Firstly, a prepreg device was home-made to immerse CCT with PLA melts. The effects of the dragging speed and tensioning equipment on the quality of PLA/CCT filament was investigated. The functions of the immersion temperature, diameter of CCT and alkali treatment of CCT to the tensile properties of PLA/CCT filament were inspected. The results showed that when the dragging speed was 12 r/min, the diameter of the PLA/CCT filament equaled to the standard 3D printed diameter (1.75 mm). Increasing the CCT diameter from 0.6 to 1.0 mm, the tensile strength was improved from 44.26 to 52.81 MPa. The alkali treatment reduced the contact angle of PLA dramatically, and further improved the interfacial compatibility between PLA and CCT, and enhanced the tensile properties of filament composites. Secondly, for achieving CCT without any cutting during the 3D printing, an un-cutting off route was planned. Taguchi experiment was used to investigate how the 3D printing processing parameters affect the flexural properties of the 3D printed PLA/CCT composites. Meanwhile, the impact properties of the 3D printed PLA/CCT composites were analyzed. The results showed that the flexural strength and strain of PLA were improved to 135 MPa and 4.39% with 3D parameters (210℃ of nozzle temperature and 6 mm/s of printing speed). The impact strength of PLA was enhanced dramatically by 5.5% times from 2.75 to 18.87 J/mm2. Finally, a thermal experiment was carried out and the results showed that PLA did not degrade during the processing and its molecular structure stayed the same at various states. The results of this paper demonstrated that the natural fiber CCT can be used to enforce PLA and lower the cost of PLA, and the PLA/CCT composites have the potential to be applied in fields of 3D printing of light-weight parts.

Abstract A well-designed scheduling plan that meets the practical constraints of the workshop is crucial for enhancing production efficiency in ship plane block assembly. Unlike traditional flow line scheduling problems, the scheduling optimization problem for ship plane block flow line involves dual resource constraints, including work teams and spare parts supply limitations. This can be seen as a Dual Resource Constrained Blocked Flow Shop Scheduling Problem (DRCBFSP). This paper presents a scheduling optimization method for this kind of problem to minimize the maximum completion time. To address the dual constraints, chromosomes are encoded as a two-dimensional array composed of positive integers representing the assembly order of blocks and the allocation of work teams. An improved Grey Wolf Optimization Algorithm (IGWO) is proposed to solve the problem, and the Rank Order Value (ROV) rule is used to transform the discrete scheduling solution with the continuous individual position vector. The IGWO algorithm also incorporates nonlinear search factors, dynamic inertia weight factors, and Gaussian mutation perturbation strategies to enhance its development and exploration capabilities. The experimental results suggest that the mathematical model and the IGWO algorithm established in this paper can effectively solve the DRCBFSP encountered in ship block building.

Abstract With the rapid development of the semiconductor industry, Hardware Trojans (HT) as a kind of malicious function that can be implanted at will in all processes of integrated circuit design, manufacturing, and deployment have become a great threat in the field of hardware security. Side-channel analysis is widely used in the detection of HT due to its high efficiency, non-contact nature, and accuracy. In this paper, we propose a framework for HT detection based on contrastive learning using power consumption information in unsupervised or weakly supervised scenarios. First, the framework augments the data, such as creatively using a one-dimensional discrete chaotic mapping to disturb the data to achieve data augmentation to improve the generalization capabilities of the model. Second, the model representation is learned by comparing the similarities and differences between samples, freeing it from the dependence on labels. Finally, the detection of HT is accomplished more efficiently by categorizing the side information during circuit operation through the backbone network. Experiments on data from nine different public HTs show that the proposed method exhibits better generalization capabilities using the same network model within a comparative learning framework. The model trained on the dataset of small Trojan T100 has a detection efficiency advantage of up to 44% in detecting large Trojans, while the model trained on the dataset of large Trojan T2100 has a detection efficiency advantage of up to 10% in detecting small Trojans. The results in data imbalanced and noisy environments also show that the contrastive learning framework in this paper can better fulfill the requirements of detecting unknown HT in unsupervised or weakly supervised scenarios.

Abstract In this paper, a two-level search strategy fused with an improved no-fit polygon algorithm and improved bat algorithm is proposed to obtain the layout points of multiple vehicles. Additionally, a space–time scheduling strategy is proposed using the Improved D*Lite Algorithm (ID*Lite) and improved Bezier curve to generate the trajectories of individual vehicles. Furthermore, a conflict resolution strategy is introduced to address the collision conflict problem during multi-vehicle scheduling. The proposed strategies aim to achieve the objectives of shortest scheduling time and smallest array space in order to reduce the time and space cost of vehicle loading on ro-ro carriers. Through a comparison with other algorithms (e.g., at high loading pressure, Scenario 3), the strategies presented in this paper demonstrate advantages such as higher deck utilization (92.234%), shorter path length, fewer collision conflicts, and more balanced weighting. Mainstream methods only focus on a certain part of vehicle transportation, we not only use more stable parking spot generation algorithm and parking order generation algorithm, but also more high-speed multi-vehicle path-finding algorithm, path optimization strategy, multi-vehicle scheduling strategy, which innovatively form a complete vehicle planning process, and at the same time, as an algorithmic framework that can be generalized to any 2d or 3d scenarios.

Abstract The accurate classification of seismic events into natural earthquakes (EQ) and quarry blasts (QB) is crucial for geological understanding, seismic hazard mitigation, and public safety. This paper proposes a machine-learning approach to discriminate seismic events, particularly differentiating between natural EQs and man-made QBs. The core of this study is to integrate different features into a unified dataset to train some linear and nonlinear supervised machine learning (ML) models. The proposed approach considers a collection of 837 events (EQs and QBs) with local magnitudes of $$1.5 \le M_{L} \le 3.3$$ 1.5 ≤ M L ≤ 3.3 from the Egyptian National Seismic Network (ENSN) seismic event catalog between 2009 and 2015. This paper’s principal contribution is applying feature selection techniques and feature importance analysis to identify the best features leading to the best events’ discrimination. In other words, the proposed approach enhances classification accuracy and provides insights into which features are most crucial for distinguishing between EQ and QB events. The results show that with only three features, corner frequency, power of event, and spectral ratio, the best-developed ML model accomplishes a discrimination accuracy of 100% among several benchmarks of linear and non-linear models.

Abstract In recent years, inkjet digital printing technology has become a popular research area. This paper focuses on the spreading behavior of single ink drops on coated paper in digital inkjet printing. It explores the impact of ink drop spreading on monochromatic spectral reflectance, providing new insights for the theoretical development of spectral prediction models. The study involves constructing a spreading behavior model through theoretical modeling. Numerical simulations were conducted using the Volume of Fluid (VOF) method in Fluent software to assess the feasibility of the theoretical model. Finally, experimental fitting was used to validate the accuracy of the theoretical model. Considering the feathering effect in practical printing, a comprehensive monochromatic extended Clapper-Yule spectral prediction model was developed based on the spreading behavior of single ink drops. The prediction results of this model showed significant accuracy improvements in monochromatic spectral prediction compared to the traditional segmented Clapper-Yule model. This research offers new perspectives for spectral prediction and provides theoretical guidance for enhancing print image quality.

Abstract Lightweight double leaf walls have been extensively employed in assembly and large-space buildings. Due to the complex and varied stud configurations in double leaf walls, accurately and efficiently predicting the sound transmission loss (STL) of such structures poses a significant challenge. To support performance-based design workflows, this paper presents an analytical model based on sound transmission path decoupling, enabling architects to quickly predict the STL of commonly used lightweight double leaf wall types, including wooden, steel, and acoustical stud constructions. The paper systematically discusses the impact of different stud configurations on sound insulation performance and reveals the underlying mechanisms of sound bridge effects. Results show that the sound bridge effect arises from the structural sound transmission path introduced by various types of studs in the wall, and optimizing stud configurations is essential for decoupling the two leaves of the wall acoustically. Traditional wooden studs, considered as rigid frames, contribute more to the sound bridge effect compared to steel studs of the same structure. A promising approach involving acoustical studs with rubber sound isolation inserts is proposed, which achieves high-level sound insulation performance while offering significant spatial and construction efficiency advantages. This study provides valuable insights into advancing high-performance, lightweight building partitions and contributes to enhancing indoor soundscapes.

Abstract The widespread use of mobile applications (apps) offers a new platform for sustaining traditional culture, yet insufficient focus on interface design has hindered user experience. This paper focuses on traditional Chinese medicine (TCM) apps, examining user preferences for interface design elements and their combinations across four dimensions: visual effects, functional attributes, layout, and interaction modes. Utilizing Conjoint Analysis Method (CAM), this study quantitatively explores user preferences for the combination schemes of 18 orthogonal designs. The study collected 464 users’ data through questionnaires and processed the data using CAM and Multivariate Analysis of Variance (MANOVA). The findings indicate that users have a high demand for theme colors (35.09%), text styles (27.32%), and icon styles (14.78%) in medical consultation apps. For health education apps, users exhibit preferences for text styles (24.95%), theme colors (24.58%), and functional architecture (16.30%). In professional learning apps, the preferred elements are theme colors (24.07%), text styles (18.54%), and icon styles (18.03%). Based on these results, the paper proposes corresponding design strategies for each type of TCM app. This study reveals the characteristics of user preferences for different categories of TCM apps, enriching the theories and methods of interaction design for similar mobile applications. Ultimately, this facilitates the integration of traditional Chinese medicine culture with modern digital technology, promoting the digital and sustainable transmission of traditional culture.

Abstract The physical and mental health development of college students has gradually become an important topic of social concern. The academic community focuses on different ways of physical exercise to improve the mental health of college students. On the basis of sports psychology, this paper discusses the interaction mechanism between physical activity and social support, interpersonal relationship quality and loneliness of college students, that is, physical and mental health effects of physical activities to promote the psychological level of college students. By random sampling, 784 college students were investigated and analyzed with physical exercise scale, loneliness scale, interpersonal relationship scale and social support scale, based on the general characteristics of the demographics variables, and through the analysis of multiple regression path model, this paper further explored the mechanism of the effects of physical activity, social support, loneliness and interpersonal relationship quality of college students. The results showed that physical exercise had a negative predictive effect on loneliness(β = − 0.246, p

Abstract The emergence of 6G cognitive radio IoT networks introduces both opportunities and complexities in spectrum access and security. Blockchain technology has emerged as a viable solution to address these challenges, offering enhanced security, transparency, and efficiency in spectrum management. This survey paper offers a thorough analysis of recent advancements in blockchain-enabled security mechanisms specifically for spectrum access within 6G cognitive radio IoT networks. Covering literature from 2019 to the present, the paper highlights significant contributions and developments in integrating blockchain technology with cognitive radio and IoT systems. It reviews spectrum access security and shows how blockchain’s decentralized approach can solve related issues. Key areas of focus include secure authentication systems, tamper-resistant spectrum sensing, decentralized databases, and smart contracts for spectrum management. The paper also addresses ongoing challenges like interoperability, scalability, and the need for comprehensive security frameworks. Future research directions are proposed, emphasizing the development of advanced blockchain protocols, integration with machine learning, and addressing regulatory and standardization concerns. This paper provides valuable insights for researchers and practitioners aiming to leverage blockchain technology, alongside ML/AI, to enhance security and efficiency in next-generation cognitive radio IoT networks.

Abstract Tea bud detection technology is of great significance in realizing automated and intelligent plucking of tea buds. This study proposes a lightweight tea bud identification model based on modified Yolov5 to increase the picking accuracy and labor efficiency of intelligent tea bud picking while lowering the deployment pressure of mobile terminals. The following methods are used to make improvements: the backbone network CSPDarknet-53 of YOLOv5 is replaced with the EfficientNetV2 feature extraction network to reduce the number of parameters and floating-point operations of the model; the neck network of YOLOv5, the Ghost module is introduced to construct the ghost convolution and C3ghost module to further reduce the number of parameters and floating-point operations of the model; replacing the upsampling module of the neck network with the CARAFE upsampling module can aggregate the contextual tea bud feature information within a larger sensory field and improve the mean average precision of the model in detecting tea buds. The results show that the improved tea bud detection model has a mean average precision of 85.79%, only 4.14 M parameters, and only 5.02G of floating-point operations. The number of parameters and floating-point operations is reduced by 40.94% and 68.15%, respectively, when compared to the original Yolov5 model, but the mean average precision is raised by 1.67% points. The advantages of this paper’s algorithm in tea shot detection can be noticed by comparing it to other YOLO series detection algorithms. The improved YOLOv5 algorithm in this paper can effectively detect tea buds based on lightweight, and provide corresponding theoretical research for intelligent tea-picking robots.

Abstract As one of the primary precast components in prefabricated construction, composite slabs have increasingly attracted interest for their costs as well as carbon footprint in production and installation stages. Conventional methods for separating composite slabs can lead to a building project necessitating multiple specifications of composite slabs. Due to the requirement to customize molds for different modulus of composite slabs, the production process experiences a substantial rise in energy consumption and resource waste. This not only increases production complexity and expenses but also indirectly raises carbon emissions. This paper conducts secondary development of a plugin based on the Building Information Modeling (BIM) technology platform, C# language and Visual Studio 2017 to optimize the modular design of composite slabs with the help of Revit software. This plugin can quickly and intelligently recommend the most suitable combination of composite slabs specifications based on the actual size of the floor slabs, thereby greatly reducing the types of composite slabs required. The modular optimization process developed in this paper is applied to an actual engineering project, the results show that the types of composite slabs molds after optimization are reduced from 9 to 4, and the mold cost is reduced by 69.60%. At the same time, the carbon emissions of the optimized composite slabs in the production stage are reduced by 7.03% compared with those before optimization. It is observed that this optimization scheme not only significantly reduces the production costs of composite slabs and improves production efficiency but also effectively reduces carbon emissions during the production phase of composite slabs.

Abstract Reflector antenna has been widely used in deep space exploration, radar warning, and other fields, all of which requires high pointing accuracy. The antenna elevation bearings are the key component that guarantees its pointing accuracy, while any degradation or fault can seriously affect the antenna’s performance, leading to deviations in antenna pointing and instability during operation. However, the relationship between the antenna elevation bearing fault and its pointing accuracy remains unclear because there is insufficient experimental faulty transmission data and pointing error collected from the test-rig simultaneously. Therefore, this paper aims to establish a deep learning model-based relationship to reveal the underlying relationship between the antenna transmission faults and its pointing accuracy. By linking the two, transmission faults in key components can serve as a substitute for pointing accuracy as one of the criteria for antenna maintenance decisions, vibration signals, serving as a basis for fault diagnosis, can be collected and processed in real-time without the need for equipment shutdowns, undoubtedly bringing convenience to antenna maintenance providing a theoretical basis for the development of antenna maintenance strategies. In order to overcome the problem of insufficient data, this paper has established an antenna elevation system dynamic simulation model containing pre-defined transmission faults. Furthermore, to link antenna fault diagnosis with antenna pointing errors, a mathematical model for antenna axis error analysis has been established. Finally, labeled fault data and antenna pointing errors have been put into the deep neural network model for training to obtain the prediction model for predicting antenna axis error. The results showed that faults in the key transmission components have a significant impact on antenna pointing errors and the proposed deep neural network learning model exhibits a high predictive accuracy.

Abstract Nanofluids have the capacity to reduce interfacial tension (IFT) of crude oil and water for enhanced oil recovery (EOR) operations, but traditional nanoparticles are limited in tight reservoirs due to their inappropriate size for micro-nano pores and their tendency to aggregate. In this paper, Graphene Quantum Dots (GQDs) with simple and favorable properties are developed, and their performance and mechanism for reducing IFT are evaluated. The paper also aims to explore the effects of GQD precursor type, synthesis duration, and molar percentages of precursors on reducing IFT. For this purpose, citric acid was used as a carbon source, and ethylenediamine, urea, and thiourea were used as nitrogen sources to synthesize different GQDs. FTIR, XPS, HR-TEM, XRD, UV visible, and PL photoluminescence were used to identify the GQDs’ characteristics. The highest IFT reduction value is achieved by using 1000 ppm ethylenediamine-derived GQDs, which reduces the IFT from 19.03 to 0.70 mN/m at 200,000 ppm NaCl concentrations without using any surfactants. The XPS analysis revealed that ethylenediamine-derived GQDs exhibit higher pyrrolic nitrogen content and a relative intensity ratio of sp3 C/sp2 C. It has been identified that the presence of more polar atomic bonds on the surfaces of GQDs decreases the ability of GQDs to reduce IFT. In this way, urea-derived and thiourea-derived GQDs exhibit less capability for IFT reductions from 19.03 to 12.33 and 18.04 mN/m at 1000 ppm GQDs and 200,000 ppm NaCl concentration, respectively. Furthermore, ethylenediamine-derived and urea-derived GQDs perform optimally at approximately 5 and 10 h, respectively, with a precursor molar ratio of 3 (mole ethylenediamine/urea per mole citric acid), while thiourea-derived GQDs show no significant IFT change with varying synthesis times or precursor molar ratio. The developed GQDs provide a promising solution for EOR operations in unconventional reservoirs by significantly decreasing the IFT between crude oil and nanofluids.

Abstract Information dissemination is vital to human production and life. Identifying and analyzing the modes and mechanisms of information dissemination under different conditions is conducive to playing the role of positive information and reducing the impact of negative information. Considering that multiple pieces of information coexist and conflict with each other, in this paper, a competitive information dissemination model is established and the proposed system is analyzed. The next generation matrix method is used to determine the basic reproduction number R 0. The equilibrium conditions of the system are presented, and the Routh–Hurwitz stability criterion and the second method of Liapunov are used for performing stability analysis. Based on Pontryagin’s extreme value principle, the influence rate and the isolation rate of the boot mechanism are chosen as control parameters. In addition, the optimal control parameter expression of the system and optimal control system are presented. The numerical simulation shows various states and changes in information dissemination. The results presented in this work show that the competitive information suppresses other competitive information during the process of dissemination. The results presented in this paper can serve as a reference for relevant departments to manage information dissemination.

Abstract Technology plays a pivotal role in shaping cultural identities and practices across the globe. This paper explores the complex relationship between technology and cultural transformation, recognizing both its opportunities and challenges in diverse cultural contexts. We employ key theoretical frameworks, including cultural diffusion, technological determinism and the digital divide, to examine how technological advancements contribute to cultural development. Our findings reveal that technology acts as a catalyst for cultural exchange, innovation and adaptation, enabling unprecedented global communication and the exchange of ideas. For example, the rapid spread of the internet and mobile technologies has facilitated cross-cultural interactions, leading to the blending of cultural values and practices on a global scale. However, the research also highlights the challenges posed by technology, such as the digital divide, which exacerbates inequalities in access to digital resources, and the risk of cultural homogenization, where dominant global cultures overshadow local identities. This study underscores the dual nature of technological influence: while it fosters cultural enrichment and connectivity, it also poses risks to cultural diversity and equity. By analyzing empirical case studies, we show how these dynamics unfold in real-world contexts. Ultimately, this paper advocates for inclusive, mindful approaches to technological integration that respect and preserve cultural diversity. Our findings offer valuable insights for policymakers, educators and technologists, emphasizing the need for culturally sensitive strategies to ensure that technological progress enhances, rather than erodes, cultural heritage in the digital age.

Abstract The paper proposes a multi-rigid-body system state identification method based on self-healing model in order to improve the accuracy and reliability of CNC machine tools. Firstly, considering the influence of the joint surface, the Lagrange method is used to establish the mechanical model of the multi-rigid-body system. We input acceleration information and use the second-order modulation function to complete the online real-time identification of the joint surface parameters, thereby establishing the self-healing mechanical model of the multi-rigid-body system. Then, the system state equation is established by Kalman filter method based on the self-healing mechanical model, and the full information identification of multi-rigid-body system is realized by using acceleration information as input. Finally, the VMC850 vertical machining center is taken as an example to verify that the above method has high accuracy. The online state identification method proposed in this paper can provide real-time data basis for machine tool accuracy compensation and health state estimation.

Abstract With the development of information technology, massive traffic data-driven short-term traffic situation analysis of urban road networks has become a research hotspot in urban traffic management. Accurate vehicle trajectory and traffic flow prediction can provide technical support for vehicle path planning and road congestion warning. Unlike most studies that use GPS data to predict vehicle trajectories, this paper combines the broad coverage, high reliability, and lighter weight of traffic checkpoint data to propose a method that uses trajectory prediction technology to forecast the traffic flow in urban road networks accurately. The method adopts a checkpoint data-driven approach for data collection, combines graph convolutional neural network (GCN) and gated recurrent unit (GRU) models to more effectively learn and extract spatiotemporal correlation features of vehicle trajectories, which significantly improves the accuracy of vehicle trajectory prediction, and uses the output of the trajectory prediction model to forecast traffic flow more accurately. Firstly, transforming the checkpoint data into daily vehicle trajectories with time series characteristics, realizing the vehicle trajectory travel chain division. Secondly, the adjacency matrix is established by using the spatial relationship of each checkpoint, and the feature matrix of the vehicle’s driving trajectory over time is established, which is used as the input of GCN to learn the spatial characteristics of the vehicle while driving on the road network, and then GRU is added to further process the data after GCN training, constructing a GCN-GRU vehicle trajectory prediction model for vehicle trajectory prediction. Finally, the traffic flow of each checkpoint is calculated based on the prediction result of vehicle trajectory and compared with the real checkpoint flow. This paper conducts many experiments on the Qingdao City Shinan district checkpoint dataset. The results show that compared with the single models GCN, GRU, BiGRU, and BiLSTM, the GCN-GRU model has reduced the MAE by 0.75, 0.46, 0.52, and 0.57, and the RMSE by 0.76, 0.52, 0.58, and 0.68, respectively, demonstrating stronger spatial and temporal correlation characteristics and higher prediction accuracy. The MAPE between the forecasted flow and the real flow is 0.18, which verifies the reliability of the proposed method.

Abstract Wireless Sensor Networks present a significant issue for data routing because of the potential use of obtaining data from far locations with greater energy efficiency. Networks have become essential to modern concepts of the Internet of Things. The primary foundation for supporting diverse service-centric applications has continued to be the sensor node activity of both sensing phenomena in their local environs and relaying their results to centralized Base Stations. Malware detection and inadequate Cluster Heads node selection are issues with the current technology, resulting in a drastic decrease in the total Internet of Things-based performance of sensor networks. The paper proposes an Enhanced Lion Swarm Optimization (ELSO) and Elliptic Curve Cryptography (ECC) scheme for secure cluster head selection and malware detection in IoT-based Wireless Sensor Networks (WSNs). The paper includes network models, choice of Cluster Head (CH) and attack detection procedures. The proposed method chooses the Cluster Head with the best fitness function values, increasing data transmission speeds and energy efficiencies. Minimum Hop Detection has been implemented to provide the best routing paths against attack nodes. Security level for quick data transmissions via the Internet of Things using Wireless Sensor Networks strengthen sinkhole attacks and black hole nodes, which are successfully removed using this method. The proposed method integrates the use of Lion Swarm Optimization and Elliptic Curve Cryptography (ECC) enhances network security by ensuring secure data transmission and preventing unauthorized access, which is particularly important in IoT-WSN environments. The proposed method achieves less End delay, increased throughput of 93%, lower energy utilization of 4%, increased network lifetime of up to 96%, Packet Delivery Ratio of up to 98% and 97% of malicious node detection efficiently compared to existing methods.

Abstract To improve the safety of ship navigation in complex sea areas and reduce planning time while achieving optimal path planning. The paper proposes an improved A* algorithm that incorporates ship collision risk assessment. The paper utilizes multi-scale raster maps to divide the sea chart in the context of complex sea areas, and combines the Line-of-sight (LOS) algorithm to solve the zigzag paths that may appear in this planning context. Moreover, in order to improve the efficiency of optimal path planning in the context of complex sea areas while ensuring path safety, the paper proposes a collision risk function to optimize the determination of the cost of A* algorithm nodes, thereby enhancing the heuristic function of the A* algorithm. The improved A* algorithm can consider both path length and collision risk to plan the optimal path and to enhance the overall quality of the planning results. To verify the advantages of the improved algorithm proposed in the paper, the Zhoushan Islands sea area with complex environment is selected as the planning background for simulation study. The results show that the improved algorithm with the introduction of the collision risk function reduces the path planning time, the number of expanded nodes, and the path length by 30%, 11%, and 5.8%, respectively, compared with the original algorithm, which can effectively reduce the computational burden of the algorithm. This study provides a relatively complete and scientific route planning strategy for the study of the safe navigation of smart ships in complex sea areas.

Abstract Mangroves are special vegetation that grows in the intertidal zone of the coast and has extremely high ecological and environmental value. Different mangrove species exhibit significant differences in ecological functions and environmental responses, so accurately identifying and distinguishing these species is crucial for ecological protection and monitoring. However, mangrove species recognition faces challenges, such as morphological similarity, environmental complexity, target size variability, and data scarcity. Traditional mangrove monitoring methods mainly rely on expensive and operationally complex multispectral or hyperspectral remote sensing sensors, which have high data processing and storage costs, hindering large-scale application and popularization. Although hyperspectral monitoring is still necessary in certain situations, the low identification accuracy in routine monitoring severely hinders ecological analysis. To address these issues, this paper proposes the UrmsNet segmentation network, aimed at improving identification accuracy in routine monitoring while reducing costs and complexity. It includes an improved lightweight convolution SCConv, an Adaptive Selective Attention Module (ASAM), and a Cross-Layer Feature Fusion Module (CLFFM). ASAM adaptively extracts and fuses features of different mangrove species, enhancing the network’s ability to characterize mangrove species with similar morphology and in complex environments. CLFFM combines shallow details and deep semantic information to ensure accurate segmentation of mangrove boundaries and small targets.Additionally, this paper constructs a high-quality RGB image dataset for mangrove species segmentation to address the data scarcity problem. Compared to traditional methods, our approach is more precise and efficient. While maintaining relatively low parameters and computational complexity (FLOPs), it achieves excellent performance with mIoU and mPA metrics of 92.21% and 95.98%, respectively. This performance is comparable to the latest methods using multispectral or hyperspectral data but significantly reduces cost and complexity. By combining periodic hyperspectral monitoring with UrmsNet-supported routine monitoring, a more comprehensive and efficient mangrove ecological monitoring can be achieved.These research findings provide a new technical approach for large-scale, low-cost monitoring of important ecosystems such as mangroves, with significant theoretical and practical value. Furthermore, UrmsNet also demonstrates excellent performance on LoveDA, Potsdam, and Vaihingen datasets, showing potential for wider application.