Search Results (56)

The operation range of the adaptive cycle engine (ACE) compression system is constrained by both the compression components and the bypass ducts, resulting in intricate matching mechanisms. Conventional analysis methods struggle to adequately evaluate the feasible operating range or the coupled constraints between components. This study employs an integrated hybrid-dimensional approach, combining zero-dimensional bypass analysis with one-dimensional/quasi-two-dimensional component analysis, to systematically investigate the matching effects of a triple-bypass compression system. The influence of key matching parameters, including the compression component operating points, high-pressure (HP) and low-pressure (LP) shaft speeds, and the core-driven fan stage (CDFS) variable inlet guide vane (VIGV) angles, is investigated. Results indicate that compression component matching primarily influences adjacent downstream bypass ratios, while HP/LP shaft speeds and the CDFS VIGV angle predominantly regulate the first and second bypass ratios. The feasible operating envelope is determined by the superimposed effects of these control parameters. To maximize the total bypass ratio, optimal operation requires increasing the front fan stall margin, elevating LP shaft speed, reducing HP shaft speed, and implementing partial CDFS VIGV closure to enhance pre-swirl. These findings provide critical guidance for control logic refinement and design optimization in advanced variable-cycle compression systems. Full article

China’s media governance policies play a crucial role in shaping media ecology and promoting the modernization of national governance capacity. This study employed the Latent Dirichlet Allocation (LDA) model and co-occurrence network analysis to systematically analyze the thematic content of national-level media governance policies issued in China between 1996 and 2024, and to examine the evolution of policy themes from a triple logical synergy perspective. In consideration of the socio-economic context and governance issues, this study has categorized the evolution of media governance policies into four distinct phases. This study used the LDA model to extract high-frequency words and built a co-occurrence network to explore the structural relationship among these words, with a synergy framework to analyze the thematic evolution across periods. The findings indicate that China’s media governance policies over the past three decades have been the result of stage-by-stage adjustments under the synergistic influences of technological drivers, social demands, and governance philosophies. Media governance constitutes a pivotal component in the modernization of China’s national governance capacity. A comprehensive analysis of the evolution of policy themes reveals the internal pattern of media governance in China. Full article

In architectural heritage conservation, fragmented data practices and heterogeneous formats hinder knowledge extraction, limiting the translation of raw data into actionable conservation insights. This study proposes a knowledge-centric framework integrating smart data methodologies to bridge this gap. The framework synergizes Heritage Building Information Modeling (HBIM), semantic knowledge graphs, and knowledge bases, prioritizing three interconnected dimensions: geometric digitization through 3D laser scanning and parametric HBIM reconstruction, semantic enrichment of historical texts via NLP and rule-based entity extraction, and knowledge graph-driven discovery of spatiotemporal patterns using Neo4j and ontology mapping. Validated through dual case studies—the Historical Educational Sites in South China (humanistic narratives) and the Dong ethnic drum towers (structural logic)—the framework demonstrates its capacity to automate knowledge generation, converting 20.5 GB of multi-source data into 2652 RDF triples that interconnect 1701 nodes across HBIM models and archival records. By enabling real-time visualization of semantic relationships (e.g., educator networks, mortise-and-tenon typologies) through graph queries, the system enhances interdisciplinary collaboration. Furthermore, the proposed smart data framework facilitated the generation of domain-specific knowledge through systematic data valorization, yielding actionable insights for architectural conservation practice. This research redefines conservation as a knowledge-to-action paradigm, where smart data methodologies unify tangible and intangible heritage values, fostering data-driven stewardship across cultural, historical, and technical domains. Full article

In many areas of operation, application-specific logic implemented in FPGAs (Field Programmable Gate Arrays) is critical. In these situations, various mitigation methods are used to reduce or completely eliminate malfunctions in the circuit resulting from undesired physical phenomena (e.g., ionizing radiation). Such phenomena may occur, among others, in medicine, the military, nuclear power, and space systems. One of the most popular methods is the use of triple modular redundancy (TMR). Here, the FPGA provides a good basis for building TMR-based safety-critical systems due to its concurrent processing. This paper presents an overview of the implementation of logic structures using TMR. In this paper, the authors focus on different concepts for the implementation of FSMs. The different concepts differ in the way TMR voters are attached and the extent of redundancy of the individual FSM components. The article compares the efficiency of the different solutions. In order to evaluate this efficiency, it is crucial to determine the logic utilization or the power consumption of a given implementation. In the experimental part of the article, the authors show the results of the synthesis of FSM benchmarks, for different mitigation models. The synthesis was carried out for both commercial and academic tools. Full article

Some knowledge bases (KBs) extracted from Wikipedia articles can achieve very high average precision values (over 95% in DBpedia). However, subtle mistakes including inconsistencies, outliers, and erroneous relations are usually ignored in the construction of KBs by extraction rules. Automatic detection and correction of these subtle errors is important for improving the quality of KBs. In this paper, an inductive logic programming with exceptional information (EILP) is proposed to automatically detect errors in large knowledge bases (KBs). EILP leverages the exceptional information problems that are ignored in conventional rule-learning algorithms such as inductive logic programming (ILP). Furthermore, an inductive logical correction method with exceptional features (EILC) is proposed to automatically correct these mistakes by learning a set of correction rules with exceptional features, in which respective metrics are provided to validate the revised triples. The experimental results demonstrate the effectiveness of EILP and EILC in detecting and repairing large knowledge bases, respectively. Full article

Numerous anti-diabetic medications, including metformin, have been explored for their anticancer effects because of the substantial correlation between diabetes and cancer incidence. Metformin has recently gained interest for its anticancer effects against malignancies such as breast cancer, one of the leading causes of death among women worldwide. The cancer-related characteristics of cell proliferation, invasion, migration, and apoptosis are all targeted by metformin. Among breast cancer patients, triple-negative breast cancer (TNBC) is linked to an increased risk of early recurrence and metastases and has poor prognosis. In addition, TNBC has fewer treatment options compared to other breast cancer subtypes because it lacks hormone receptors and human epidermal growth factor receptor 2 (HER2), and it often develops resistance to available treatment options. The current review highlights the recent updates on the mechanistic insights and the efficacy of metformin and metformin-based approaches for the treatment of TNBC. We logically discuss the experimental evidence from the in vitro and in vivo studies exploring metformin’s effects on metabolic pathways, and then its combination with other therapeutic agents, targeting cell signaling pathways, and approaches to enhance metformin’s effects. We also present clinical studies that underscore the beneficial outcomes of metformin or its combination with other agents in TNBC patients. Full article

Today, many electronic circuits are required to be able to work effectively, even in environments exposed to ionizing radiation. This work examines the effects of ionizing radiation on shift registers realized in a bulk 16 nm FinFET technology, focusing on Single-Event Upset (SEU). An SEU occurs when a charged particle ionizes a sensitive node in the circuit, causing a stored bit to flip from one logical state to its opposite. This study estimates the saturation cross-section for the 16 nm FinFET technology and compares it with results from a 28 nm planar CMOS technology. The experiments were conducted at the SIRAD facility of INFN Legnaro Laboratories (Italy). The device under test was irradiated with the ion sources ⁵⁸$Ni$ and ²⁸$Si$, both with different tilt angles, to assess the number of SEUs with different LET and range values. Additionally, the study evaluates the effectiveness of the radiation-hardened by design technique, specifically the Triple Modular Redundancy (TMR), which is a technique commonly employed in planar technologies. However, in this particular case study, TMR proved to be ineffective, and the reasons behind this limitation are analyzed along with potential improvements for future designs. Full article

The increasing energy demand and initiatives to lower carbon emissions have elevated the significance of renewable energy sources. Photovoltaic (PV) systems are pivotal in converting solar energy into electricity and have a significant role in sustainable energy production. Therefore, it is critical to implement maximum power point tracking (MPPT) controllers to optimize the efficiency of PV systems by extracting accessible maximum power. This research investigates the performance and comparison of various MPPT control algorithms for a standalone PV system. Several cases involving individual MPPT controllers, as well as hybrid combinations using two and three controllers, have been simulated in MATLAB/SIMULINK. The sensed parameters, i.e., output power, voltage, and current, specify that though individual controllers effectively track the maximum power point, hybrid controllers achieve superior performance by utilizing the combined strengths of each algorithm. The results indicate that individual MPPT controllers, such as perturb and observe (P&O), particle swarm optimization (PSO), and fuzzy logic (FL), achieved tracking efficiencies of 97.6%, 90.3%, and 90.1%, respectively. In contrast, hybrid dual controllers such as P&O-PSO, PSO-FL, and P&O-FL demonstrated improved performance, with tracking efficiencies of 96.8%, 96.4%, and 96.5%, respectively. This research also proposes a new hybrid triple-MPPT controller combining P&O-PSO-FL, which surpassed both individual and dual-hybrid controllers, achieving an impressive efficiency of 99.5%. Finally, a comparison of all seven cases of MPPT control algorithms is presented, highlighting the advantages and disadvantages of individual as well as hybrid approaches. Full article

Rule-based reasoning with different kinds of uncertain information has been identified in numerous applications within the real world. Any reasoning method must be able to coherently obtain the inference result by composing the given if–then rule with the assertion of the given input. The symmetric quintuple-implication principle was established by introducing symmetry into the five implication operators included. For example, the first, third and fifth implication operators exhibit symmetric properties, i.e., the three implication operators are taken as the same kind of operator and the second and fourth implication operators satisfy symmetry, that is, the two implication operators take the same kind of operator. So, the reasoning method induced by this principle possesses significant advantages in terms of its logical foundation and reductivity. This paper derives and studies reasoning methods for the mixture of fuzzy information and intuitionistic fuzzy information based on the symmetric quintuple-implication principle where all five implication operators satisfy symmetry (also called the quintuple-implication principle). Such reasoning methods are based on the ideas that the input and the given if–then rule can be combined for calculation only when their information representations exhibit consistency. An inconsistent given if–then rule with two different representations should be regarded as the composition of two different consistent given if–then rules with their own unique representations. This paper then elaborates on the methods by employing the possibility and necessity operators and the quintuple-implication principle from the perspective of whether the representation of rule antecedent and rule consequent is consistent or not. The reductivity of all the proposed reasoning methods is also analyzed in detail. This paper mainly contributes to the development of a novel mixed information reasoning framework, along with the introduction of the quintuple-implication principle into reasoning with mixed information. The proposed methods have also been applied to pattern recognition, and several experiments demonstrate that the mixed information reasoning methods based on the quintuple-implication principle are superior to the corresponding methods based on the triple I principle. Full article

Knowledge graph (KG) reasoning aims to obtain new knowledge based on existing data. Utilizing large language models (LLMs) through in-context learning for KG reasoning has become a significant direction. However, existing methods mainly extract in-context triples by manually defined standards (such as the neighbors that are directly linked with the query triple), without considering whether they are useful for LLM reasoning. Furthermore, the triples beyond the neighbors can also provide important clues for reasoning. Therefore, it is necessary to extract more useful in-context triples of LLMs for KG reasoning. This paper proposes a rule-and-reinforce in-context triple extraction method to enhance the in-context learning of LLMs for KG reasoning. First, we collect the in-context triples specific to each query triple with the guidance of logical rules, and a neural extractor is pre-trained by the collected triples. Subsequently, the feedback of LLMs is collected as rewards to further optimize the extractor, where the policy gradient is utilized to encourage the extractor to explore more useful triples that yield higher rewards. The experimental results on five different knowledge graphs demonstrate that the proposed method can effectively improve the reasoning performance of LLMs. Compared to the traditional reasoning method AnyBURL, the greatest improvement is 0.147 on Hits@10, FB15k-237. Full article

Knowledge graphs, as an important research direction in artificial intelligence, have been widely applied in many fields and tasks. The relations in knowledge graphs have explicit semantics and play a crucial role in knowledge completion and reasoning. Correctly measuring the inferential value of relations and identifying important relations in a knowledge graph can effectively improve the effectiveness of knowledge graphs in reasoning tasks. However, the existing methods primarily consider the connectivity and structural characteristics of relations, but neglect the semantics and the mutual influence of relations in reasoning tasks. This leads to truly valuable relations being difficult to fully utilize in long-chain reasoning. To address this problem, this work, inspired by information entropy and uncertainty-measurement methods in knowledge bases, proposes a method called Relation Importance Measurement based on Information Entropy (RIMIE) to measure the inferential value of relations in knowledge graphs. RIMIE considers the semantics of relations and the role of relations in reasoning. Specifically, based on the values of relations in logical chains, RIMIE partitions the logical sample set into multiple equivalence classes, and generates a knowledge structure for each relation. Correspondingly, to effectively measure the inferential values of relations in knowledge graphs, the concept of relation entropy is proposed, and it is calculated according to the knowledge structures. Finally, to objectively assess the effectiveness of RIMIE, a group of experiments are conducted, which compare the influences of the relations selected according to RIMIE and other patterns on the triple classifications by knowledge graph representation learning. The experimental results confirm what is claimed above. Full article

The logic structures implemented in Field Programmable Gate Arrays (FPGAs) are often critical and their correct operation is vital. FPGA devices are often used in areas where there is increased ionising radiation (space, medical diagnostics, aviation or nuclear power). There is therefore a need for mechanisms to correct radiation-induced errors. A common approach is the redundant implementation of particularly critical parts of the logic structure. By triplicating selected fragments, it is possible not only to detect potential errors but also to correct them. Such an approach is called triple modular redundancy (TMR), and its essence lies in the use of specialised voting circuits called voters, which allow the erroneous results of individual subcircuits to be eliminated by voting. The triplicate circuit under consideration, together with the voter, constitutes the mitigation structure. It becomes necessary to develop a test environment to assess the correct operation of these circuits. Also key is the efficiency of the implementation of these structures, which can be related to the occupation of logical resources or the power consumption of a given implementation. This paper demonstrates the essence of implementing a test environment to test the correctness of the mitigation of logic structures using TMR voters. An error injector mechanism using the Pseudo-Random Bit Sequence (PRBS) register is proposed, which introduces an element of randomness into the testing process. The aim of this research is to determine the implementation efficiency of the proposed test environment. In the experimental part, the implementation costs of the proposed solution were examined. The results indicate that between 66 and 109 LUT blocks were required to implement the error injector, corresponding to a relatively small increase in dynamic power consumption: by 22% for combinational circuits and by 37% for sequential circuits. Full article

In terms of energy generation and consumption, ships are autonomous isolated systems, with power demands varying according to the type of ship: passenger or commercial. The power supply in modern ships is based on thermal engines-generators, which use fossil fuels, marine diesel oil (MDO) and liquefied natural gas (LNG). The continuous operation of thermal engines on ships during cruises results in increased emissions of polluting gases, mainly CO/CO₂. The combination of renewable energy sources (REs) and triple-fuel diesel engines (TFDEs) can reduce CO/CO₂ emissions, resulting in a “greener” interaction between ships and the ecosystem. This work presents a new control method for balancing the power generation and the load demands of a ship equipped with TFDEs, fuel cells (FCs), and REs, based on a real and accurate model of a super-tanker and simulation of its operation in real cruise conditions. The new TFDE technology engines are capable of using different fuels (marine diesel oil, heavy fuel oil and liquified natural gas), producing the power required for ship operation, as well as using compositions of other fuels based on diesel, aiming to reduce the polluting gases produced. The energy management system (EMS) of a ship is designed and implemented in the structure of a finite state machine (FSM), using the logical design of transitions from state to state. The results demonstrate that further reductions in fossil fuel consumption as well as CO₂ emissions are possible if ship power generation is combined with FC units that consume hydrogen as fuel. The hydrogen is produced locally on the ship through electrolysis using the electric power generated by the on-board renewable energy sources (REs) using photovoltaic systems (PVs) and wind energy conversion turbines (WECs). Full article

The emergence of eVTOL (electrical Vertical Takeoff and Landing) aircraft necessitates the development of safe and efficient systems to meet stringent certification and operational requirements. The primary state-of-the-art technology for flight control actuation in eVTOL aircraft is electro-mechanical actuators (EMAs), which heavily rely on multiple redundancies of critical components to achieve fault tolerance. However, challenges persist in terms of insufficient reliability, immaturity, and a lack of a measurable evaluation method. This research addresses these issues by elucidating the design requirements for EMAs in eVTOL aircraft and proposing a systematic design and evaluation approach for EMA architecture. A key enhancement involves the incorporation of decentralized voting and monitoring (VoDeMo) mechanisms within the Electronic Control Units (ECUs) to improve the overall safety of the EMA. The paper introduces an innovative triple-dual redundant architecture for aircraft control effectors, comprising three dissimilar lanes of ECUs and two similar redundant parallel channels of power electronics and motors. The design is synergistically supported by a comprehensive evaluation that incorporates quantifiable Model-Based Safety Assessment (MBSA), utilizing both physical simulation and logical safety models. Hardware-In-the-Loop (HIL) tests are conducted on a constructed prototype to validate the proposed architecture. Full article

A temporal knowledge graph represents temporal information between entities in a multi-relational graph. Its reasoning aims to infer and predict potential links among entities. Predicting time-aware entities is a challenging task due to significant differences in entity appearances over time, such as different timestamps and frequencies. Current embedding-based similarity-matching methods have been introduced for predicting temporal facts. However, they lack deterministic logical explainability and cannot model the dynamic evolution of entities over time. To address these challenges, we propose a novel framework for temporal knowledge graph reasoning based on multi-view feature fusion (MVFF). First, MVFF extracts logical rules and uses the Gumbel-Softmax trick to sample high-quality rules. Second, it integrates logical rules, temporal quadruples, and factual triples to capture semantic features, temporal information, and structural information to solve link prediction tasks. Through experiments on four benchmark datasets, we show that MVFF outperforms state-of-the-art methods, providing not only better performance but also interpretable results. Full article