Search Results (107)

The manipulator is a key component for harvesting citrus and other fruit crops. A study of the dynamic characteristics and energy consumption modelling of its joints is the foundation for optimising the manipulator’s load parameters and achieving efficient operation. To address the issues of the 6-DOF citrus-picking manipulator’s high degrees of freedom and complex structure, which lead to complex dynamic characteristics and an unclear energy transfer and consumption mechanism, the electromechanical coupling dynamics and energy consumption of the joint system are systematically studied using bond graphs. Firstly, the bond graph model is constructed by combining it with the joint system’s physical structure. On this basis, the corresponding dynamic characteristic state equation and energy consumption model are established. Secondly, the dynamic response and energy consumption characteristics of the joint system are analysed, revealing the system’s energy consumption and dynamic characteristics under different working conditions. Finally, the effectiveness and precision of the proposed model in describing the dynamic behaviour of the joint system and energy consumption are verified through experiments. The model provides a theoretical basis and a new research perspective for optimizing joint parameters, load solutions, and energy efficiency of the harvesting manipulator. Full article

A knowledge graph is constructed for flight test safety, which is conducive to enhancing the data deduction ability in flight test monitoring and offers efficient and highly complex decision-making support for safety monitoring. Based on this graph, an aircraft attitude predictive model is established by employing neural network technology. This model can accurately predict the changes in aircraft attitude under pilot manipulation, with a mean absolute error of 0.18 degrees in the predicted angle of attack values. By integrating the knowledge graph and the predictive model, an auxiliary decision-making method for abnormal aircraft attitude situations is proposed. This method calculates the safety manipulation space of the aircraft under different flight states through risk quantification technology, providing a theoretical basis for the pilots’ manipulation decisions in abnormal attitude situations. The research is verified based on simulation data, which not only enhances the scientific rigor and practicability of flight test safety monitoring in simulated scenarios but also provides new theoretical support and technical approaches for the field of flight safety. Full article

Market power remains a persistent challenge in liberalized electricity spot markets, where generators can manipulate bids to distort prices and extract rents. Traditional monitoring approaches—such as structural indices or simulation-based models—offer partial insights but fail to capture the nonlinear, spatially correlated propagation of strategic behavior across transmission-constrained networks. This paper develops a diffusion neural learning framework for market power risk assessment that integrates welfare optimization, nodal pricing dynamics, and graph-based deep learning. Specifically, a Graph Diffusion Network (GDN) is trained on simulated spot market scenarios to learn how localized strategic deviations spread through the network, distort locational marginal prices, and alter system welfare. The modeling framework combines a system-wide welfare maximization objective with multi-constraint market clearing, while the GDN embeds network topology into predictive learning. Results from a case study on an IEEE 118-bus system demonstrate that the proposed method achieves an R² of 0.91 in predicting market power indices, outperforming multilayer perceptrons, recurrent neural networks, and Transformer baselines. Welfare analysis reveals that distributionally robust optimization safeguards up to 3.3 million USD in adverse scenarios compared with baseline stochastic approaches. Further, congestion mapping highlights that strategic bidding concentrates distortions at specific nodes, amplifying rents by up to 40 percent. The proposed approach thus offers both predictive accuracy and interpretability, enabling regulators to detect emerging risks and design targeted mitigation strategies. Overall, this work establishes diffusion-based learning as a novel and effective paradigm for electricity market power assessment under high uncertainty and renewable penetration. Full article

Graph-based Recommender Systems (GRSs) model complex user–item relationships. They offer improved accuracy and personalization in recommendations compared to traditional models. However, GRSs also face severe challenges from novel poisoning attacks. Attackers often manipulate GRS graph structures by injecting attack users and their interaction data. This leads to misleading recommendations. Existing detection methods lack the ability to identify such attacks targeting graph-based systems. To address this, we propose AutoDAP, a novel autoencoder-based detection method for poisoning attacks in GRSs. AutoDAP first extracts key statistical features from user interaction data. It fuses them with original interaction information. Then, an autoencoder architecture processes this data. The encoder extracts deep features and connects to an output layer for classification prediction probabilities. The decoder reconstructs graph structure features. By jointly optimizing classification and reconstruction losses, AutoDAP effectively integrates supervised and unsupervised signals. This enhances the detection of attack users. Evaluations on the MovieLens-10M dataset against various poisoning attacks, and on the Amazon dataset with real attack data, demonstrate AutoDAP’s superiority. It outperforms several representative baseline methods in both simulated (MovieLens) and real-world (Amazon) attack scenarios, demonstrating its effectiveness and robustness. Full article

A method for compiling object schemes is proposed, which allows constructing algorithms for calculating the kinematic parameters of robotic manipulators. Examples of compiling object schemes for calculating the velocities and accelerations of points selected on the links of the robotic manipulator are considered. An analysis of the computational complexity of the obtained algorithms is carried out and a method for increasing their computational efficiency is proposed. An increase in computational efficiency is achieved based on the use of the associativity property due to the reduction of additional and multiplication operations performed by the algorithm. Graphs of computational processes illustrating the developed algorithms are presented. The developed algorithms allow parallel calculations; this will further increase the efficiency of calculations when using multiprocessor computing systems. As a result of the study, based on the object approach, an effective universal method for calculating the kinematic parameters of robotic manipulators has been developed. This will improve the quality of robot control. Full article

Cyberattacks on pipeline operational technology systems pose growing risks to energy infrastructure. This study develops a physics-informed simulation and optimization framework for analyzing cyber–physical threats in petroleum pipeline networks. The model integrates networked hydraulic dynamics, SCADA-based state estimation, model predictive control (MPC), and a bilevel formulation for stealthy false-data injection (FDI) attacks. Pipeline flow and pressure dynamics are modeled on a directed graph using nodal pressure evolution and edge-based Weymouth-type relations, including control-aware equipment such as valves and compressors. An extended Kalman filter estimates the full network state from partial SCADA telemetry. The controller computes pressure-safe control inputs via MPC under actuator constraints and forecasted demands. Adversarial manipulation is formalized as a bilevel optimization problem where an attacker perturbs sensor data to degrade throughput while remaining undetected by bad-data detectors. This attack–control interaction is solved via Karush–Kuhn–Tucker (KKT) reformulation, which results in a tractable mixed-integer quadratic program. Test gas pipeline case studies demonstrate the covert reduction in service delivery under attack. Results show that undetectable attacks can cause sustained throughput loss with minimal instantaneous deviation. This reveals the need for integrated detection and control strategies in cyber–physical infrastructure. Full article

Graph Neural Networks (GNNs) have become a cornerstone for graph classification, yet their vulnerability to backdoor attacks remains a significant security concern. While clean-label attacks provide a stealthier approach by preserving original labels, they tend to be less effective in graph settings compared to traditional dirty-label methods. This performance gap arises from the inherent dominance of rich, benign structural patterns in target-class graphs, which overshadow the injected backdoor trigger during the GNNs’ learning process. We demonstrate that prior strategies, such as adversarial perturbations used in other domains to suppress benign features, fail in graph settings due to the amplification effects of the GNNs’ message-passing mechanism. To address this issue, we propose two strategies aimed at enabling the model to better learn backdoor features. First, we introduce a long-distance trigger injection method, placing trigger nodes at topologically distant locations. This enhances the global propagation of the backdoor signal while interfering with the aggregation of native substructures. Second, we propose a vulnerability-aware sample selection method, which identifies graphs that contribute more to the success of the backdoor attack based on low model confidence or frequent forgetting events. We conduct extensive experiments on benchmark datasets such as NCI1, NCI109, Mutagenicity, and ENZYMES, demonstrating that our approach significantly improves attack success rates (ASRs) while maintaining a low clean accuracy drop (CAD) compared to existing methods. This work offers valuable insights into manipulating the competition between benign and backdoor features in graph-structured data. Full article

Graph Neural Networks (GNNs) have shown strong performance in link prediction, a core task in graph analysis. However, recent studies reveal their vulnerability to backdoor attacks, which can manipulate predictions stealthily and pose significant yet underexplored security risks. The existing backdoor strategies for link prediction suffer from two key limitations: gradient-based optimization is computationally intensive and scales poorly to large graphs, while single-node triggers introduce noticeable structural anomalies and local feature inconsistencies, making them both detectable and less effective. To address these limitations, we propose a novel backdoor attack framework grounded in the principle of homophily, designed to balance effectiveness and stealth. For each selected target link to be poisoned, we inject a unique path-based trigger by adding a bridge node that acts as a shared neighbor. The bridge node’s features are generated through a context-aware probabilistic sampling mechanism over the joint neighborhood of the target link, ensuring high consistency with the local graph context. Furthermore, we introduce a confidence-based trigger injection strategy that selects non-existent links with the lowest predicted existence probabilities as targets, ensuring a highly effective attack from a small poisoning budget. Extensive experiments on five benchmark datasets—Cora, Citeseer, Pubmed, CS, and the large-scale Physics graph—demonstrate that our method achieves superior performance in terms of Attack Success Rate (ASR) while maintaining a low Benign Performance Drop (BPD). These results highlight a novel and practical threat to GNN-based link prediction, offering valuable insights for designing more robust graph learning systems. Full article

This paper presents a multi-level annotation model for detecting fake news in Kazakh and Russian languages, aiming to enhance understanding of disinformation strategies in multilingual digital media environments. Unlike traditional binary models, our approach captures the complexity of disinformation by accounting for both linguistic and cultural factors. To support this, a corpus of over 5000 news texts was manually annotated using the Label Studio platform. The annotation scheme consists of seven interrelated categories: CLAIM, SOURCE, EVIDENCE, DISINFORMATION_TECHNIQUE, AUTHOR_INTENT, TARGET_AUDIENCE, and TIMESTAMP. Inter-annotator agreement, evaluated using Cohen’s Kappa, ranged from 0.72 to 0.81, indicating substantial consistency. The annotated data reveals recurring patterns of disinformation, such as emotional manipulation, targeting of vulnerable individuals, and the strategic concealment of intent. Semantic relations between entities, such as CLAIM → EVIDENCE and CLAIM → AUTHOR_INTENT were formalized to represent disinformation narratives as knowledge graphs. This study contributes the first linguistically and culturally adapted annotation model for Kazakh and Russian languages, providing a robust and empirical resource for building interpretable and context-aware fake news detection systems. The resulting annotated corpus and its semantic structure offer valuable empirical material for further research in natural language processing, computational linguistics, and media studies in low-resource language environments. Full article

The paper introduces EVOCA—Explainable Verification Of Claims by Graph Alignment—a hybrid approach that combines NLP (Natural Language Processing) techniques with the structural advantages of knowledge graphs to manage and reduce the amount of evidence required to evaluate statements. The approach leverages the explicit and interpretable structure of semantic graphs, which naturally represent the semantic structure of a sentence—or a set of sentences—and explicitly encodes the relationships among different concepts, thereby facilitating the extraction and manipulation of relevant information. The primary objective of the proposed tool is to condense the evidence into a short sentence that preserves only the salient and relevant information of the target claim. This process eliminates superfluous and redundant information, which could impact the performance of the subsequent verification task and provide useful information to explain the outcome. To achieve this, the proposed tool called EVOCA—Explainable Verification Of Claims by Graph Alignment—generates a sub-graph in AMR (Abstract Meaning Representation), representing the tokens of the claim–evidence pair that exhibit high semantic similarity. The structured representation offered by the AMR graph not only aids in identifying the most relevant information but also improves the interpretability of the results. The resulting sub-graph is converted back into natural language with the SPRING AMR tool, producing a concise but meaning-rich “sub-evidence” sentence. The output can be processed by lightweight language models to determine whether the evidence supports, contradicts, or is neutral about the claim. The approach is tested on the 4297 sentence pairs of the Climate-BERT-fact-checking dataset, and the promising results are discussed. Full article

Deep Reinforcement Learning (DRL) is a powerful framework for solving complex sequential decision-making problems, particularly in robotic control. However, its practical deployment is often hindered by the substantial amount of experience required for learning, which results in high computational and time costs. In this work, we propose a novel integration of DRL with semantic knowledge in the form of Knowledge Graph Embeddings (KGEs), aiming to enhance learning efficiency by providing contextual information to the agent. Our architecture combines KGEs with visual observations, enabling the agent to exploit environmental knowledge during training. Experimental validation with robotic manipulators in environments featuring both fixed and randomized target attributes demonstrates that our method achieves up to 60% reduction in learning time and improves task accuracy by approximately 15 percentage points, without increasing training time or computational complexity. These results highlight the potential of semantic knowledge to reduce sample complexity and improve the effectiveness of DRL in robotic applications. Full article

Graph neural networks (GNNs) have exhibited remarkable performance in various applications. Still, research has revealed their vulnerability to backdoor attacks, where Adversaries inject malicious patterns during the training phase to establish a relationship between backdoor patterns and a specific target label, thereby manipulating the behavior of poisoned GNNs. The inherent symmetry present in the behavior of GNNs can be leveraged to strengthen the robustness of GNNs. This paper presents a quantitative metric, termed Logit Margin Rate (LMR), for analyzing the symmetric properties of the output landscapes across GNN layers. Additionally, a learning paradigm of graph self-distillation is combined with LMR to distill the symmetry knowledge from shallow layers, which can serve as the defensive supervision signals to preserve the benign symmetric relationships in deep layers, thus improving both model stability and adversarial robustness. Experiments were conducted on four benchmark datasets to evaluate the robustness of the proposed Graph Self-Distillation-based Backdoor Defense (GSD-BD) method against three widely used backdoor attack algorithms, demonstrating the robustness of GSD-BD even under severe infection scenarios. Full article

As the energy crisis is leading to energy shortages and constant increases in prices, green energy and renewable energy sources are trending as a viable solution to this problem. One of the most rapidly expanding green energy methods is RF (RadioFrequency) energy harvesting, as RF energy and its corresponding technologies are constantly progressing, due to the introduction of 5G and high-speed telecommunications. The usual system for RF energy harvesting is called a rectenna, and one of its main components is an antenna, responsible for collecting ambient RF energy. In this paper, the optimization process of an ultra-wideband antenna for RF energy harvesting applications was studied, with the main goal of broadening the antenna’s operational bandwidth to include 5G New Radio. For this purpose, the Bezier Search Differential Evolution Algorithm (BeSD) was used along with a novel CST-Matlab API, to manipulate the degrees of freedom of the antenna, while searching for the optimal result, which would satisfy all the necessary dependencies to make it capable of harvesting RF energy in the target frequency band. The BeSD algorithm was first tested with benchmark functions and compared to other widely used algorithms, which it successfully outperformed, and hence, it was selected as the optimizer for this research. All in all, the optimization process was successful by producing an ultra-wideband optimal antenna operating from 1.4 GHz to 3.9 GHz, which includes all vastly used telecommunication technologies, like GSM (1.8 GHz), UMTS (2.1 GHz), Wi-Fi (2.4 GHz), LTE (2.6 GHz), and 5G NR (3.5 GHz). Its ultra-wideband properties and the rest of the characteristics that make this design suitable for RF energy harvesting are proven by its $S_{11}$ response graph, its impedance response graph, its efficiency on the targeted technologies, and its omnidirectionality across its band of operation. Full article

Microfluidic technology is an emerging interdisciplinary field that uses micropipes to handle or manipulate tiny fluids in chemistry, fluid physics, and biomedical engineering. As one of the rapid prototyping methods, the three-dimensional (3D) printing technique, which is rapid and cost-effective and has integrated molding characteristics, has become an important manufacturing technology for microfluidic chips. Polymethyl-methacrylate (PMMA), as an exceptional thermoplastic material, has found widespread application in the field of microfluidics. This paper presents a comprehensive process study on the fabrication of fused deposition modeling (FDM) 3D-printed PMMA microfluidic chips (chips), encompassing finite element numerical analysis studies, orthogonal process parameter optimization experiments, and the application of 3D-printed integrated microfluidic reactors in the reaction between copper ions and ammonium hydroxide. In this work, a thermal stress finite element model shows that the printing platform temperature was a significant printing parameter to prevent warping and delamination in the 3D printing process. A single printing molding technique is employed to fabricate microfluidic chips with square cross-sectional dimensions reduced to 200 μm, and the microchannels exhibited no clogging or leakage. The orthogonal experimental method of 3D-printed PMMA microchannels was carried out, and the optimized printing parameter resulted in a reduction in the microchannel profile to Ra 1.077 μm. Finally, a set of chemical reaction experiments of copper ions and ammonium hydroxide are performed in a 3D-printed microreactor. Furthermore, a color data graph of copper hydroxide is obtained. This study provides a cheap and high-quality research method for future research in water quality detection and chemical engineering. Full article

To address the complexity of constructing traditional topological contracted graphs due to the significant increase in the types and quantities of basic links during the synthesis of complex parallel mechanisms, this paper introduces a novel concept termed “Simplified Contraction Graph (SCG)”. The SCG achieves a deeper level of simplification by omitting the consideration of ternary links on the basis of traditional contracted graphs. Firstly, this paper defines the application of characteristic strings to express the construction rules of SCG, thereby transforming the construction process into an automated generation problem of characteristic strings. Building on this, to mitigate the interference of link arrangement in the construction of conventional SCGs, this paper further proposes the concept of a simplified SCG and investigates its isomorphism properties. A program is designed based on the criteria for generating characteristic strings and isomorphism judgment, successfully generating several special SCGs. Finally, this paper introduces the edge-adding method, which enables the reconstruction of special SCGs into ordinary SCGs and contracted graphs, providing an effective tool for the topological synthesis of parallel mechanisms. Full article