Search Results (26,929)

Selecting an appropriate Multi-Criteria Decision-Making (MCDM) algorithm for optimizing marine diesel engine operation presents a complex challenge due to the diversity in mathematical formulations, normalization schemes, and trade-off resolutions across methods. This study systematically evaluates fourteen MCDM algorithms, which are grouped into five primary methodological categories: Scoring-Based, Distance-Based, Pairwise Comparison, Outranking, and Hybrid/Intelligent System-Based methods. The goal is to identify the most suitable algorithm for real-time performance optimization of two stroke marine diesel engines. Using Diesel-RK software, calibrated for marine diesel applications, simulations were performed on a variant of the MAN-B&W-S60-MC-C8-8 engine. A refined five-dimensional parameter space was constructed by systematically varying five key control variables: Start of Injection (SOI), Dwell Time, Fuel Mass Fraction, Fuel Rail Pressure, and Exhaust Valve Timing. A subset of 4454 high-potential alternatives was systematically evaluated according to three equally important criteria: Specific Fuel Consumption (SFC), Nitrogen Oxides (NO_x), and Particulate Matter (PM). The MCDM algorithms were evaluated based on ranking consistency and stability. Among them, Proximity Indexed Value (PIV), Integrated Simple Weighted Sum Product (WISP), and TriMetric Fusion (TMF) emerged as the most stable and consistently aligned with the overall consensus. These methods reliably identified optimal engine control strategies with minimal sensitivity to normalization, making them the most suitable candidates for integration into automated marine engine decision-support systems. The results underscore the importance of algorithm selection and provide a rigorous basis for establishing MCDM in emission-constrained maritime environments. This study is the first comprehensive, simulation-based evaluation of fourteen MCDM algorithms applied specifically to the optimization of two stroke marine diesel engines using Diesel-RK software. Full article

Accurate aircraft trajectory prediction is fundamental to air traffic management, operational safety, and intelligent aerospace systems. With the growing availability of flight data, deep learning has emerged as a powerful tool for modeling the spatiotemporal complexity of 4D trajectories. This paper presents a comprehensive review of deep learning-based approaches for aircraft trajectory prediction, focusing on their evolution, taxonomy, performance, and future directions. We classify existing models into five groups—RNN-based, attention-based, generative, graph-based, and hybrid and integrated models—and evaluate them using standardized metrics such as the RMSE, MAE, ADE, and FDE. Common datasets, including ADS-B and OpenSky, are summarized, along with the prevailing evaluation metrics. Beyond model comparison, we discuss real-world applications in anomaly detection, decision support, and real-time air traffic management, and highlight ongoing challenges such as data standardization, multimodal integration, uncertainty quantification, and self-supervised learning. This review provides a structured taxonomy and forward-looking perspectives, offering valuable insights for researchers and practitioners working to advance next-generation trajectory prediction technologies. Full article

High-overload testing equipment is a key platform for evaluating mechanical performance under extreme conditions, requiring specialized functional design to meet stringent operational demands. The current design process faces numerous challenges, including overreliance on designers’ experience, incomplete requirement analysis, and insufficient automation, resulting in suboptimal solutions. To address these issues, in this study, we propose an integrated method based on axiomatic design (AD) and the Theory of Inventive Problem Solving (TRIZ). This method systematically decomposes technical specifications, maps requirements to a structural framework, and resolves design conflicts using inventive principles. The method employs a comprehensive evaluation framework combining the analytic hierarchy process (AHP) and quality function deployment (QFD) to quantitatively assess candidate designs. It facilitates the development of efficient, standardized high-load testing equipment solutions, enhancing design reliability and innovation capabilities. Full article

In response to the inability to quickly assess wind noise performance during the early stages of automotive styling design, this paper proposes a method for predicting interior wind noise by integrating automotive point cloud models with the Gray Wolf Optimization Residual Network model (GWO-ResNet). Based on wind tunnel test data under typical operating conditions, the point cloud model of the test vehicle is compressed using an auto-encoder and used as input features to construct a nonlinear mapping model between the whole vehicle point cloud and the wind noise level at the driver’s left ear. Through adaptive optimization of key hyperparameters of the ResNet model using the gray wolf optimization algorithm, the accuracy and generalization of the prediction model are improved. The prediction results on the test set indicate that the proposed GWO-ResNet model achieves prediction results that are consistent with the actual measured values for the test samples, thereby validating the effectiveness of the proposed method. A comparative analysis with traditional ResNet models, GWO-LSTM models, and LSTM models revealed that the GWO-ResNet model achieved Mean Absolute Percentage Error (MAPE) and mean squared error (MSE) of 9.72% and 20.96, and 9.88% and 19.69, respectively, on the sedan and SUV test sets, significantly outperforming the other comparison models. The prediction results on the independent validation set also demonstrate good generalization ability and stability (MAPE of 10.14% and 10.15%, MSE of 23.97 and 29.15), further proving the reliability of this model in practical applications. The research results provide an efficient and feasible technical approach for the rapid evaluation of wind noise performance in vehicles and provide a reference for wind noise control in the early design stage of vehicles. At the same time, due to the limitations of the current test data, it is impossible to predict the wind noise during the actual driving of the vehicle. Subsequently, the wind noise during actual driving can be predicted by the test data of multiple working conditions. Full article

The use of molybdenum-based alloys as materials for components operating under high temperatures and significant mechanical loads is widely recognized due to their excellent mechanical properties. However, their low high-temperature resistance remains a critical limitation, which can be effectively mitigated by applying protective coatings. In this study, we investigate the influence of a two-step coating process on the properties and performance of the TZM molybdenum alloy. In the first step, pack cementation was performed. Simultaneous surface saturation with aluminum and silicon, a process known as aluminosiliconizing, was conducted at 1000 °C for 6 h. The saturating mixture comprised powders of aluminum, silicon, aluminum oxide, and ammonium chloride. The second step involved the application of a pre-ceramic coating based on polyhydrosiloxane modified with silicon and boron. This treatment effectively eliminated pores and cracks within the coating. Thermodynamic calculations were carried out to evaluate the likelihood of aluminizing and siliconizing reactions under the applied conditions. Aluminosiliconizing of the TZM alloy resulted in the formation of a protective layer 20–30 µm thick. The multiphase structure of this layer included intermetallics (Al₆₃Mo₃₇, MoAl₃), nitrides (Mo₂N, AlN, Si₃N₄), oxide (Al₂O₃), and a solid solution α-Mo(Al). Subsequent treatment with silicon- and boron-modified polyhydrosiloxane led to the development of a thicker surface layer, 130–160 µm in thickness, composed of crystalline Si, amorphous SiO₂, and likely amorphous boron. A transitional oxide layer ((Al,Si)₂O₃) 5–7 µm thick was also observed. The resulting coating demonstrated excellent structural integrity and chemical inertness in an argon atmosphere at temperatures up to 1100 °C. High-temperature stability at 800 °C was observed for both coating types: aluminosiliconizing, and aluminosiliconizing followed by the pre-ceramic coating. Moreover, additional oxide layers of SiO₂ and B₂O₃ formed on the two-step coated TZM alloy during heating at 800 °C for 24 h. These layers acted as an effective barrier, preventing the evaporation of the substrate material. Full article

Super-supportive CSR behaviors (SSCBs) are integrative actions devised to enhance the effectiveness of CSR initiatives by harmonizing social, environmental, and economic efforts. Despite their strategic role in business operations, SSCBs remain insufficiently addressed, especially within the construction sector. This study utilizes text mining and association rule mining to analyze 211 CSR reports from Chinese construction firms spanning 2010 to 2021. The key findings highlight the pivotal role of 17 SSCBs in strengthening CSR initiatives, revealing three major characteristics: foundational, synergistic, and triggering. Within the construction industry, SSCBs primarily focus on corporate governance, community development, employee welfare, and environmental sustainability, evolving from isolated practices to integrated systems over time. Notably, construction firms tend to adopt SSCB portfolios instead of standalone initiatives. Furthermore, exceeding a certain threshold of SSCBs may increase challenges in coordination and resource allocation. These insights highlight SSCBs as a dynamic, multidimensional construct and provide construction firms with a practical framework to integrate complementary CSR actions, improving coordination, optimizing resources, and strengthening sustainability outcomes in practice. Full article

Integrating bioluminescent organisms as passive lighting sources in the built environment is currently a hot topic. However, there are several limitations facing the implementation and up-scaling of these naturally bioluminescent organisms in the built environment on architectural and urban scales, such as the scale, sensitivity, enclosure, and difficulty of maintenance. Moreover, there are complex technicalities and operational aspects of conventional bioreactors that host these bioluminescent agents, especially in terms of managing their recharge and effluent, not to mention their high maintenance cost. The current work offers a sustainable, stand-alone, bioluminescent urban screen system employing Aliivibrio fischeri CECT 524 bioink on 3D-printed customized scaffolds as bioreceptive panel design based on a field-diffusion pattern to host the bioluminescent bacterial bioink. The field-diffusion pattern was employed thanks to its proven efficiency in entrapment of the various microbial cultures. Three different growth media were tested for culturing Aliivibrio fischeri CECT 524, including Luria Bertani Broth (LB), the Tryptone Soy Broth (TSB), and the standard Marine Broth (MB). The results revealed that the Marine Broth (MB) media achieved the highest bioluminescent intensity and duration. The maximum light emission typically in range of ~490 nm of blue–green light captured by a conventional reflex camera (human eye vision) was observed for 10 consecutive days in complete darkness after 3–10 s, at a room temperature of 25 °C. This was visible mainly at the thin curvilinear peaks of the 3D-printed field pattern. P1 achieved the highest performance in terms of visible blue–green light, and a duration of 10 days of active bioluminescence was achieved without the need for refilling, thanks to the high number of peaks and narrow wells at <0.5 cm of its field-diffusion pattern. This study proves the efficiency of this biomimetic pattern in terms of the bioreceptivity of the bioluminescent bacterial bioink. Furthermore, the proposed 3D-printed urban screens proved their economic sustainability in terms of affordability and their minimized production processes, in addition to their easy maintenance and recharge. These results qualify these 3D-printed bioluminescent urban screens for easy and decentralized adoption and application on an architectural and urban scale. Full article

Modern energy systems are undergoing a profound transformation characterized by the active replacement of conventional fossil-fuel-based power plants with renewable energy sources. This transition aims to reduce the carbon emissions associated with electricity generation while enhancing the economic performance of electric power market players. However, alongside these benefits come several challenges, including reduced overall inertia within energy systems, heightened stochastic variability in grid operation regimes, and stricter demands on the rapid response capabilities and adaptability of emergency controls. This paper presents a novel methodology for selecting effective control laws for low-inertia energy systems, ensuring their dynamic stability during post-emergency operational conditions. The proposed approach integrates advanced techniques, including feature selection via decision tree algorithms, classification using Random Forest models, and result visualization through the Mean Shift clustering method applied to a two-dimensional representation derived from the t-distributed Stochastic Neighbor Embedding technique. A modified version of the IEEE39 benchmark model served as the testbed for numerical experiments, achieving a classification accuracy of 98.3%, accompanied by a control law synthesis delay of just 0.047 milliseconds. In conclusion, this work summarizes the key findings and outlines potential enhancements to refine the presented methodology further. Full article

It is widely accepted that the lightweight design of a grid fin is closely related to its aerodynamic performance and structural integrity, while limited work seeks their balance. This study proposes a lightweight grid fin design method by taking the locally swept-back angle as a variable based on three-dimensional computational fluid dynamics and fluid–thermo–structure coupling analysis for Mach numbers ranging from 0.8 to 5. The effect of the swept-back angle on the relative aerodynamic efficiency profit, weight saving, and structural integrity (with a focus on static strength) was analyzed. The results showed that the locally swept-back configuration maintained structural integrity while enabling simultaneous aerodynamic performance improvement and weight saving across different Mach numbers through swept-back angle adjustment. At Mach 0.8, 1.5, and 2.0, the 20° swept-back configuration achieved a 13.2% weight saving and improved aerodynamic performance. At Mach 0.9, the 15° configuration delivered optimal aerodynamic enhancement with a 10% weight saving. Notably, the 15° configuration demonstrated excellent balance after evaluating all Mach number operating conditions. All these highlight a good attempt for the trade-off design of structures among weight saving, aerodynamic performance, and structural integrity. Full article

Key air route segments refer to route segments that play a hub-supporting role in the air transportation network of a specific airspace, and whose traffic anomalies (such as saturation or delay) are likely to trigger chain reactions through segment correlations. Identifying these segments and implementing resource allocation bias and targeted optimization for them can significantly improve network operational efficiency and alleviate delays. Current key segment identification methods only consider the physical connection status between individual air route segments, ignoring the spatiotemporal correlation of traffic changes among segments. To address this issue, this paper proposes a key segment identification method based on a segment-related network. Firstly, redundant search paths are reduced through the pruning of low-impact indicators, and an improved FP-Growth algorithm is proposed to analyze segment correlations. On this basis, a segment-related network is constructed, where segments serve as nodes and correlations between segments serve as edges. This network integrates the changing relationship between segment flows and the physical connections between segments, thereby improving the accuracy of identification. Finally, in the segment-related network of the Guangdong-Hong Kong-Macao Multi-Airport Terminal Area, key segments such as Pingzhou-Conghua, Pingzhou-Gaoyao, and Zhuhai-Nanlang are identified using degree centrality, PageRank, eigenvector centrality, and global centrality analysis methods. Full article

Stable adhesion on non-magnetic, steep, and irregular underwater surfaces (e.g., concrete dams with cracks or biofilms) remains a challenge for inspection robots. This study develops a novel adsorption mechanism based on the synergistic operation of a Venturi-ejector and a composite suction cup. The mechanism utilizes the Venturi effect to generate stable negative pressure via hydrodynamic entrainment and innovatively adopts a composite suction cup—comprising a rigid base and a dual-layer EPDM sponge (closed-cell + open-cell)—to achieve adaptive sealing, thereby reliably applying the efficient negative-pressure generation capability to rough underwater surfaces. Theoretical modeling established the quantitative relationship between adsorption force (F) and key parameters (nozzle/throat diameters, suction cup radius). CFD simulations revealed optimal adsorption at a nozzle diameter of 4.4 mm and throat diameter of 5.8 mm, achieving a peak simulated F of 520 N. Experiments demonstrated a maximum F of 417.9 N at 88.9 W power. The composite seal significantly reduced leakage on high-roughness surfaces (Ra ≥ 6 mm) compared to single-layer designs. Integrated into an inspection robot, the system provided stable adhesion (>600 N per single adsorption device) on vertical walls and reliable operation under real-world conditions at Balnetan Dam, enabling mechanical-arm-assisted maintenance. Full article

This paper presents a systematic approach to the development and validation of a monitoring and protection system based on the IEC 61850 standard, evaluated through hardware-in-the-loop (HIL) testing. The study utilized an already existing model of a modern power grid consisting of the IEEE 9-bus power system integrated with a large-scale wind power plant (LSWPP). The SEL-487B Relay was configured to protect the PCC using a low-impedance busbar differential monitoring and protection system equipped with adaptive setting group logic that automatically transitions between Group 1 and Group 2 based on system loading conditions. Significant steps were followed for selecting and configuring instrument transformers and implementing relay logic in compliance with IEEE and IEC standards. Real-time digital simulation using Real-Time Digital Simulator (RTDS) hardware and its software, Real-time Simulation Computer-Aided Design (RSCAD), was used to assess the performance of the overall monitoring and protection system, focusing on the monitoring and publishing of the selected electrical and mechanical measurements from a selected wind turbine generator unit (WTGU) on the LSWPP side through the IEC 61850 standard network, and on the behavior of the monitoring and protection system under initial and increased load conditions through monitoring of differential and restraint currents. The overall monitoring and protection system was tested under both initial and increased load conditions, confirming its capability to reliably publish analog values from WTGU13 for availability on the IEC 61850 standard network while maintaining secure protection operation. Quantitatively, the measured differential (operate) and restraint currents were 0.32 PU and 4.38 PU under initial loading, and 1.96 PU and 6.20 PU under increased loading, while total fault clearance times were 606.667 ms and 706.667 ms for faults under initial load and increased load demand conditions, respectively. These results confirm that the developed framework provides accurate real-time monitoring and reliable operation for faults, while demonstrating a practical and replicable solution for monitoring and protection at transmission-level PCCs within renewable-integrated networks. Full article

This paper charts AI’s transformative path toward advancing sustainability within art museums, introducing a Human–AI compass as a conceptual framework for navigating its integration. It advocates for human-centric AI that optimizes operations, modernizes collection management, and deepens visitor engagement—anchored in meaningful human–technology synergy and thoughtful human oversight. Drawing on extensive literature review and real-world museum case studies, the paper explores AI’s multifaceted impact across three domains. Firstly, it examines how AI improves operations, from audience forecasting and resource optimization to refining marketing, supporting conservation, and reshaping curatorial practices. Secondly, it investigates AI’s influence on digital collection management, highlighting its ability to improve organization, searchability, analysis, and interpretation through automated metadata and advanced pattern recognition. Thirdly, the study analyzes how AI elevates the visitor experience via chatbots, audio guides, and interactive applications, leveraging personalization, recommendation systems, and co-creation opportunities. Crucially, this exploration acknowledges AI’s complex challenges—technical-operational, ethical-governance, socioeconomic-cultural, and environmental—underscoring the indispensable role of human judgment in steering its implementation. The Human-AI compass offers a balanced, strategic approach for aligning innovation with human values, ethical principles, museum mission, and sustainability. The study provides valuable insights for researchers, practitioners and policymakers, enriching the broader discourse on AI’s growing role in the art and cultural sector. Full article

The origin of life initiated an evolutionary continuum yielding biologically optimized systems capable of operating under extreme environmental constraints. Biomimetics, defined as the systematic abstraction and transfer of biological principles into engineering domains, has become a strategic design paradigm for addressing the multifactorial challenges of space systems. This study introduces two core contributions to formally establish the discipline of Aerospace Bionic Robotics (ABR): First, it elucidates the relevance of biologically derived functionalities such as autonomy, adaptability, and multifunctionality to enhance the efficiency of space robotic platforms operating in microgravity environments. Second, it proposed the BEAM-D (Biomimetic Engineering and Aerospace Mechatronics Design), a standard for the development of Aerospace Bionic Robotics. By integrating biological abstraction levels (morphological, functional, and behavioral) with engineering protocols including ISO, VDI, and NASA’s TRL, BEAM-D enables a structured design pathway encompassing subsystem specification, cyber–physical integration, in situ testing, and full-scale mission deployment. It is implemented through a modular BEAM-DX framework and reinforced by iterative BIOX design steps. This study thus establishes formalized bio-inspired design tools for advanced orbital and planetary robotic systems capable of sustained autonomous operations in deep space exploration scenarios. Full article

The concept of Multi Q-Fermatean hesitant fuzzy soft sets (MQFHFSS), derived from the integration of multi-Q fuzzy soft sets and Fermatean hesitant fuzzy sets, can be applied in practice to optimise the resolution of complex multi-criteria decision-making problems. The method exceeds traditional approaches such as Fermatean hesitant fuzzy sets, fuzzy soft sets, and Pythagorean fuzzy sets in enhancing the ability to capture higher levels of uncertainty, hesitation, and symmetry in multi-criteria evaluations, thereby supporting more balanced judgments in complex decision-making situations. In this study, we investigate the novel MQFHFSS concept along with the associated operations. The fundamental characteristics of aggregation operators derived from MQFHFSS have been examined to address some complex decision-making issues. Moreover, we discuss some key algebraic features and their different cases, emphasizing the role of symmetry under the influence of MQFHFSS. Finally, we illustrate some numerical examples and solve the real-world decision-making problem by using the proposed technique. Full article