Search Results (2,186)

In today’s energy landscape, defined by the growing demand for sustainable energy generation technologies and the parallel need to advance internal combustion engine (ICE) architectures toward cleaner and more efficient solutions, the adoption of Free-Piston Linear Generator (FPLG) engines emerges as a highly promising approach. This innovative system enables the direct conversion of combustion-induced piston motion into electrical energy, eliminating the need for traditional crankshaft and connecting rod mechanisms. The FPLG concept facilitates efficient utilization of a broad spectrum of fuels—including methane, ethanol, LPG, gasoline, biodiesel, and hydrogen—by supporting variable compression ratio operation. This feature enhances operational flexibility and fuel adaptability, positioning the technology as a viable candidate for future energy transition scenarios. The absence of rotating mechanical components significantly reduces frictional losses, contributing to an overall increase in system efficiency. To accurately characterize and optimize engine performance, an extensive series of one-dimensional (1D) numerical simulations was performed under both free and controlled operating conditions. The resulting data enabled the development of semi-empirical models capable of predicting the dynamic behavior of the engine across a wide range of working scenarios. Finally, through a detailed parametric analysis, the optimal operating conditions were identified to maximize both net electric efficiency and electrical power output. These findings provide a solid ground for the design and implementation of FPLG engine systems in advanced power generation applications. Full article

Synchronous Alternating Current (AC) power systems are increasingly supported by embedded High-Voltage Direct Current (HVDC) links to enhance operational flexibility and ensure security of supply. However, the loss of High-Voltage Alternating Current (HVAC) interconnections in these synchronous areas may lead to transmission network splitting, posing serious challenges to frequency stability due to the reduction in overall system inertia and stiffness. In this paper, a supplementary control layer is proposed to enable embedded HVDC systems, particularly those based on modern Voltage Source Converters (VSCs), to support frequency stability under post-splitting conditions. The proposed control strategy combines Angle-Difference Control (ADC), Frequency-Difference Control (FDC), and feedforward action, enabling fast and coordinated active-power modulation. A single-bus, dynamic multi-area Load Frequency Control (LFC) model is developed, combining the regulation of thermal units, Renewable Energy Sources’ (RESs’) Fast Frequency Response (FFR) with Synthetic Inertia (SI), and VSC-HVDC modulation. The effectiveness of the proposed control layer is demonstrated by applying it to the East Tyrrhenian Link (ETL), an embedded VSC-HVDC interconnection connecting Sicily with the mainland of Italy, under a post-splitting low-inertia condition in which Sicily operates as an islanded synchronous system, i.e., after losing synchronism with the mainland of Italy, in a 2030 scenario condition. The simulation results demonstrate that the proposed controller enables embedded VSC-HVDC systems to actively participate in post-splitting frequency containment and damping, as well as coordinated active power reallocation, thereby enhancing overall system stability and resilience. Full article

Artificial Intelligence of Things (AIoT) technologies shifted the structure of production systems, enabling the development of more intelligent, connected and sustainable manufacturing environments. However, some industrial sectors, such as aerospace manufacturing industry, fell behind in the adoption of these new technologies, mainly because of the high safety standards, strict reliability requirements and long lifespan of aircraft components. Due to low production volumes and complex manufacturing processes, this sector relies heavily on weakly automated legacy machines and production systems. This article proposes a methodology to ease the integration of AIoT technologies for retrofitting legacy industrial equipment in the aeronautical domain in order to achieve the requirements of modern industrial production systems, enabling the development of more flexible, efficient and interconnected manufacturing environments. The proposed methodology is validated through a case study where the Smart Retrofitting of a legacy aeronautical industrial machine is carried out. The case study focuses on the development of an AIoT-based architecture to implement a predictive maintenance system through vibration and infrared thermography monitoring. A three layer architecture is proposed based on Edge/Fog/Cloud Computing paradigms. A hybrid communication architecture is used, combining wired technologies for critical real-time control tasks and wireless technologies for enhanced flexibility and scalability. The results demonstrate the viability of the proposed methodology for retrofitting legacy aircraft manufacturing systems. Full article

This study developed a Visitor Attraction Quality Checklist to evaluate amenity infrastructure. Seoullo 7017, an elevated linear park located in the Seoul Station area, is the study region. Drawing on the literature on urban regeneration and tourism, as well as amenity-based approaches and the quality evaluation of elevated linear parks, this study develops evaluation criteria that incorporate the physical environment and safety, accessibility and convenience, landscape and identity, and social usage and experience. By applying a longitudinal analysis, on-site qualitative evaluations were conducted between August 2017 and January 2026. The findings show that Seoullo 7017 functions well as a visitor attraction, offering high-standard safety infrastructure, cleanliness, and good esthetic value to accommodate diverse visitors. However, there is a seasonal disparity in cultural programming and limited connections to the surrounding local economy. This study also reveals that rigid planter designs, a lack of tree maintenance, and insufficient shaded areas limit spatial flexibility and visitor comfort. For Seoullo 7017 to pursue sustainability, it must refine its horticultural management, integrate with local businesses, and improve its design. The Visitor Attraction Quality Checklist serves as a longitudinal diagnostic tool for managing elevated urban linear parks as an outcome of global regeneration projects. Full article

Intelligent bearing fault diagnosis is essential for maintaining the reliability of rotating machinery. However, deploying deep learning models in industrial environments is often constrained by a lack of labeled data, environmental noise, and strict hardware limits. To address these connected challenges, this paper proposes 1D-CNN-Trans, a flexible and resource-efficient hybrid framework. Designed for supervised diagnosis with restricted data, the configurable model combines a compact one-dimensional convolutional neural network (1D-CNN) for local feature extraction, a Transformer encoder for capturing long-range temporal dependencies, and an optional squeeze-and-excitation (SE) module for channel recalibration under favorable conditions. The method is evaluated on two standard mechanical benchmarks under limited sample conditions, controlled additive white Gaussian noise (AWGN), and dynamic non-stationary interference. Experimental results indicate that 1D-CNN-Trans shows improved robustness under interference compared to selected baselines, notably improving accuracy against a standard CNN backbone. Furthermore, findings indicate that while the Transformer ensures noise robustness, channel recalibration (via SE) introduces optimization instability under extreme sparsity and noise. Consequently, we reposition the architecture as a configurable framework where recalibration is conditionally activated. Finally, theoretical complexity analysis is provided to validate the model’s low computational burden, indicating its general feasibility for resource-constrained scenarios. Full article

This project focuses on the development of a 2U CubeSat intended for potential integration into an LEO constellation. The CubeSat is designed to deliver space-based CNS services, supporting the evolving needs of next-generation airspace and global communication networks. The primary objective is to enhance global connectivity and demonstrate how compact satellite platforms can contribute to modern ICNS systems. By leveraging the flexibility, scalability, and cost-efficiency of CubeSat technology, the mission aims to validate the role of small satellites in delivering reliable and responsive CNS capabilities. This approach provides a foundation for future advancements in satellite constellations tailored for airspace management and communication services. Full article

The rapid diffusion of industry 4.0 technologies has substantially transformed the maritime transportation sectors by enabling data-driven operations, enhanced connectivity, and more intelligent decision-making processes. Digital technologies such as the Internet of Things (IoT), simulation systems, and advanced data analytics are increasingly reshaping operational structures in maritime logistics, positioning technological transformation as a strategic priority for firms. However, the weighting and prioritization of components emerging with industry 4.0 technologies remain an underexplored area in the literature. The primary motivation of this study is to determine the weights of these industry 4.0 components using the Bayesian Best Worst Method (BWM) and to reveal their corresponding credal ranking levels. In this context, the present study aims to evaluate and prioritize the critical industry 4.0 components influencing technological transformation processes using the Bayesian BWM. Bayesian BWM is preferred over alternative Multi Criteria Decision Making (MCDM) approaches due to its ability to explicitly model uncertainty within a probabilistic framework, generate more consistent weighting results, and flexibly incorporate decision-makers’ judgments. The findings reveal that safety and security (0.2945) constitute the most influential main component, underscoring the necessity of robust digital infrastructures and reliable systems within highly digitalized operational environments. Among the sub-components, data privacy (0.1301) demonstrates the highest global weight, highlighting the growing importance of safeguarding sensitive information in data-intensive digital systems. The results further indicate that autonomous operation and coordination play significant roles in facilitating efficient digital operations, particularly through real-time equipment monitoring and IoT-based operational visibility. Moreover, sustainability (0.1968) emerges as the second most important component, suggesting that organizations increasingly assess technological investments not only in terms of operational efficiency but also with respect to long-term resilience. Within this dimension, continuous training (0.0614) is identified as the most influential component, indicating that the success of digital transformation depends not only on technological infrastructure but also on the development of human capabilities. With the increasing digitalization of the maritime industry, protection against cyber threats has become essential for ensuring operational continuity and safeguarding data integrity. In this regard, adopting proactive cybersecurity strategies and continuously monitoring and updating systems are of critical importance. In the digital transformation of maritime transportation, integrating sustainability considerations is essential to ensure long-term operational efficiency and environmental responsibility. These practical implications are particularly relevant for policymakers, port authorities, and shipping companies seeking to enhance both digital capabilities and sustainable performance. Full article

High-sensitivity rapid detection of ammonia (NH₃) in environmental monitoring, industrial safety, early diagnosis, and other fields is of great significance. Covalent organic frameworks (COFs) have shown great potential in the field of gas sensing due to their designable porous structure and active sites. However, the traditional solvothermal synthesis method of COFs has problems such as cumbersome steps, high energy consumption and serious environmental pollution. Therefore, it is of great significance to invent a new method for COF synthesis that is green and efficient and makes it easy to conduct flexible ammonia gas sensing. This study first reported a solvent-free synthesis of imine connection 1,3,5-Triformylbenzene (TFB) and p-Phenylenediamine (PDA)—a new strategy for COF. This method innovatively employs zinc trifluoromethyl sulfonate (Zn(OTf)₂) as a bifunctional catalyst. This catalyst not only efficiently catalyzes para-phenylenediamine, but its zinc ions also play a unique structural guiding role, guiding the reactants to be arranged in a directional manner, thereby constructing a highly ordered porous crystal structure. A series of characterizations confirmed that the obtained TFB-PDA-COF had good crystallinity and a high proportion of imine bonds (C=N). The powder material was coated onto a flexible polyimide (PI) substrate, successfully constructing a resistive ammonia gas sensor that operates at room temperature. The test results show that this sensor has a high response value, rapid response/recovery capability, and good selectivity for ammonia gas. More importantly, based on a flexible PI substrate, the device can maintain stable sensing performance even under repeated bending conditions, demonstrating its great potential in practical flexible electronic applications. This work not only provides a brand-new “zinc ion-guided” paradigm for the green and controllable synthesis of COF but also lays a material foundation for their application in the next-generation flexible sensing field. Full article

While direct current (DC) ice-melting is currently adopted for some transmission lines, its application to 10 kV distribution transformers—often located in remote and rugged terrain—presents significant operational challenges. Disconnecting these transformers prior to ice-melting is a complex procedure that incurs substantial labor, material, and financial costs. Leaving transformers connected risks DC current flowing into idle windings, potentially causing damage. Furthermore, existing mobile DC ice-melting power supplies are bulky and impose stringent transportation requirements, rendering them unsuitable for use on mountain roads. To overcome these limitations, this paper proposes a compact, lightweight variable-frequency ice-melting device. The operating principle and output characteristics of the variable-frequency method are investigated in detail. Using Simulink, system modeling and simulation analyses are performed to obtain the voltage and current output characteristics, along with harmonic spectra. Simulation results demonstrate that the proposed device achieves significant miniaturization compared with conventional solutions: within the typical parameter range of conventional devices, the volume can be reduced by 44–58% and the weight by 43–52%. In addition, the selected LC filter parameters (L = 10.39 mH, C = 86.62 μF) represent an optimized compromise solution that effectively suppresses input harmonics while maintaining the output current total harmonic distortion (THD) within an acceptable limit of 3.6%. Experimental results further validate the feasibility of the variable-frequency ice-melting current. Based on a matrix converter topology, the proposed device enables flexible adjustment of the output melting voltage and frequency, exhibits excellent low-frequency performance and dynamic response, and maintains low output harmonic content—fully meeting the application requirements for variable-frequency ice-melting. The key novelty lies in a compact matrix-converter-based de-icing device with systematic low-frequency performance analysis, offering superior portability and adaptability over traditional DC solutions. Full article

The sixth-generation (6G) mobile communication systems are anticipated to be operational by 2030, prompting extensive research efforts by governments and private entities. Designed to meet societal, economic, and technological demands unaddressed by fifth-generation (5G) networks, 6G integrates scalability, security, and reliability with ubiquity and resource-intensive artificial intelligence. Envisaged as multi-band, decentralized, autonomous, flexible, and user-centric, 6G networks incorporate innovative technologies, including cell-free (CF), three-dimensional heterogeneous networks (3D HetNet), reconfigurable intelligent surfaces (RIS), integrated sensing and communication (ISAC), as well as artificial intelligence/machine learning (ML). In 6G 3D HetNets, the densification of access points (APs) continues, accommodating increased connections and traffic volumes, alongside the use of higher frequency bands. Although 6G networks are not fully standardized, they target demanding Quality of Service (QoS) standards, such as a peak data rate of 1.0 Tbps and latency of 0.1 ms. This paper conducts a comprehensive literature review on radio resource management (RRM) in 6G cell-free and 3D HetNet systems, emphasizing challenges such as interference mitigation. It presents a taxonomy of RRM approaches, systematically studying, categorizing, and qualitatively analyzing recent techniques, outlining the current state, and indicating future trends, technologies, and challenges shaping 6G systems. Full article

With advancements in Earth observation capabilities, the demand for large-scale mapping using remote sensing images has increased significantly. However, selecting an optimal image set for the area of interest (AOI) from a large collection of remote sensing images remains challenging. On the one hand, it is crucial to select images with minimal redundancy and low cloud cover to enhance production efficiency and the effective coverage of mapping products. On the other hand, adjacent selected images should transition naturally so that the resulting mapping products appear visually cohesive. Unfortunately, most existing remote sensing image selection algorithms focus only on the former, with little attention to visual consistency. Meanwhile, images from the same swath inherently offer advantages in both redundancy reduction and visual consistency. However, a larger coverage area also carries the potential for greater variation in cloud cover, and cloud distribution within a swath can be highly complex. Managing the relationships among swaths, images, and cloud cover is also challenging. To address these issues, this paper proposes a novel image selection model, SwathSel. Candidate images are grouped through a composite grouping strategy based on swaths, cloud cover, and topological connectivity, thereby expanding the fundamental unit for image selection from individual scenes to connected image subsets. A dynamic adjustment mechanism is introduced to enhance grouping flexibility. Additionally, local and global swath consistency constraints are designed to strengthen visual consistency among images, and a subset evaluation module is used to comprehensively assess swath consistency, coverage, cloud cover, and metadata information. Through a greedy strategy combined with a rapid refinement technique, the final selected image set is obtained. Experiments were conducted on four datasets, and four quantitative metrics were designed to evaluate the visual consistency of the results. Compared with baseline models, SwathSel achieves lower redundancy and cloud cover while delivering superior visual consistency. Full article

Beyond their classical role as “cellular powerhouses”, mitochondria are increasingly recognized as dynamic and interconnected networks whose architecture, quality control, and intercellular communication influence cellular and organismal homeostasis. Mitochondrial dynamics—including fusion–fission balance, mitophagy–biogenesis coupling, intracellular organization, and intercellular transfer via tunneling nanotubes, extracellular vesicles, or transient cell fusion—contribute to tissue adaptation and functional decline during aging. Focusing on cardiac muscle, skeletal muscle, and the nervous system, this narrative review synthesizes current evidence describing how aging disrupts mitochondrial network integrity through altered dynamics, impaired organelle positioning and transport, reduced mitophagy, mtDNA instability, and compromised metabolic coupling between cells. These alterations propagate across tissues, limiting energetic flexibility, stress resilience, and regenerative capacity. Building on these mechanisms, we discuss a systems-level perspective in which aging is associated with progressive loss of mitochondrial network coherence rather than solely cumulative molecular damage. Within this framework, mitochondrial connectivity functions as an integrative descriptor of cellular resilience: well-organized networks counteract metabolic perturbations, whereas functionally decoupled networks amplify stress and promote maladaptive aging trajectories. Emerging evidence indicates that physiological and pharmacological interventions, including endurance exercise, caloric restriction or mimetics, fusion-supporting pathways, and mitophagy-enhancing strategies, can partially restore network organization even later in life. Molecular, cellular, and tissue-level insights are integrated to highlight mitochondrial network dynamics as both a mechanistic contributor to aging and a potentially modifiable target for future preventive and therapeutic interventions. Full article

This study proposes an adaptive variable structure control strategy for charging stacks to address the issues of reduced conversion efficiency during wide-voltage-range operation and insufficient module allocation flexibility in multi-vehicle scenarios. By dynamically adjusting the number and series/parallel configurations of modules, the strategy ensures that modules consistently operate in high-efficiency regions, thereby achieving high energy conversion efficiency across a wide voltage range. First, the operational characteristics of the three-phase PWM rectifier and the dual active bridge (DAB) converters are analyzed, and their corresponding mathematical and loss models are established. Subsequently, the charging demands acquired by the charging stack are analyzed, and an adaptive variable structure control strategy is designed based on the module margin of the charging stack. When modules are surplus, the feasible range of series/parallel configurations for each port is constrained, and module combinations are optimized with the objective of minimizing system losses. When modules are insufficient, an adaptive module reservation scheduling strategy is employed to ensure temporal fairness in vehicle connection order while supplying power to multiple vehicles, effectively reducing the average charging time. Finally, the effectiveness of the proposed control strategy is validated through simulations conducted on the Matlab/Simulink platform. Results demonstrate that compared to traditional fixed-structure systems, the proposed strategy improves peak efficiency by up to 2.53% at 400 V and 1.12% at 800 V, while reducing the average charging time by 3.07% in the disconnection scenario and 12.1% in the asynchronous access scenario. Full article

The integration of electric mobility and energy systems has emerged as a key research domain in the transition toward sustainable energy and decarbonized transport, yet the literature is lacking systematic quantitative overviews of its scientific development. This study addresses this gap by conducting a bibliometric analysis of research activities across five domains central to electric vehicle–energy system integration: central energy management systems; renewable energy, hydrogen production, and large-scale storage; industrial applications; smart energy communities, virtual power plants, and vehicle-to-X; and urban high-power charging parks with local storage. Using publication data from Web of Science and Scopus, performance analysis and science mapping techniques were applied to examine publication dynamics, thematic structures, and intellectual linkages. Results indicate strong growth and consolidation around smart grids and decentralized flexibility solutions, particularly within energy management, renewable integration, and community-based energy systems, while industrial applications and high-power charging infrastructures remain comparatively underrepresented. The findings suggest a maturing interdisciplinary field characterized by expanding connections between mobility and energy research, alongside emerging opportunities related to industrial integration, charging infrastructure, and vehicle-to-grid deployment. The study provides a structured, multi-domain perspective on the convergence of electric mobility and energy systems, enabling a differentiated understanding of research dynamics. The study provides a structured, multi-domain perspective on the convergence of electric mobility and energy systems. The findings highlight priority areas for future research, particularly industrial integration and scalable charging infrastructure, and offer insights for policymakers and industry stakeholders. Full article

To address the vibration and noise issues induced by inertial forces in marine V-type air compressors during operation, this study systematically investigates inertial force balancing and optimization. Based on dynamic analysis, analytical expressions for the first- and second-order reciprocating inertial forces and the rotating inertial force under unbalanced conditions are precisely derived. Considering the characteristics of a V-type air compressor with a V-angle of γ = 60°, the synthesis model of the first-order reciprocating inertial force is modified. The positive–negative rotating wheel system method is employed for preliminary balancing design, and the rigid–flexible coupling dynamics theory is innovatively introduced to construct a high-precision multi-body dynamics model that accounts for the flexible deformation of the crankshaft and connecting rod. Through joint simulation using ANSYS (2024R1) and Adams (2024.2), the dynamic responses of the pure rigid-body model and the rigid–flexible coupling model are compared to determine the optimal balancing configuration. The Adams/Insight module is utilized to perform multi-objective optimization of the balance iron mass. Results indicate that the rigid–flexible coupling model more accurately reflects the dynamic characteristics of the air compressor compared to the pure rigid-body model, significantly enhancing simulation accuracy. The optimized balance iron configuration effectively suppresses system vibration, with the peak X-direction bearing reaction force decreasing from 3750 N to 3610 N (a reduction of 3.7%), the vibration intensity reducing by 45.3%, and the radiated noise sound power level decreasing by 7.45%. This study provides a systematic theoretical approach and technical pathway for vibration and noise reduction, as well as for structural reliability design of marine air compressors. Full article