Search Results (8,553)

This study investigated the influence of the cooling process on the microstructure and mechanical properties of high-strength, high-ductility ship plate steel. The transformation temperature ranges for ferrite (F) and bainite (B) for the experimental steel were determined through thermal simulation experiments. Based on these findings, hot-rolling experiments in laboratory were designed to elucidate the influence of three different cooling paths on the resultant microstructure and mechanical properties. The results demonstrate that the two-stage (air cooling + water cooling) and three-stage (water cooling + air cooling + water cooling) processes after rolling enhance the strength through phase transformation and precipitation strengthening mechanisms. The three-stage process provides an additional fine-grain strengthening effect. Compared to the F+Pearlite (P) or B microstructures produced by single-stage cooling, the F+B dual-phase steel obtained through these multi-stage cooling routes exhibits superior ductility at a comparable yield strength grade. Notably, the two-stage cooling mode proves particularly effective in enhancing ductility. These findings provide a theoretical foundation for designing cooling processes for high-strength, high-ductility ship plate steel. Full article

The scalability of coordination policies is a critical challenge in swarm robotics, where agent numbers may vary substantially between deployment scenarios. Reinforcement learning (RL) offers a promising avenue for learning decentralized policies from local interactions, yet a fundamental question remains: can policies trained on one swarm size transfer to different population scales without retraining? This zero-shot transfer problem is particularly challenging because the traditional RL approaches learn fixed-dimensional representations tied to specific agent counts, making them brittle to population changes at deployment time. While existing work addresses scalability through population-aware training (e.g., mean-field methods) or multi-size curricula (e.g., population transfer learning), these approaches either impose restrictive assumptions or require explicit exposure to varied team sizes during training. Graph Neural Networks (GNNs) offer a fundamentally different path. Their permutation invariance and ability to process variable-sized graphs suggest potential for zero-shot generalization across swarm sizes, where policies trained on a single population scale could deploy directly to larger or smaller teams. However, this capability remains largely unexplored in the context of swarm coordination. For this reason, we empirically investigate this question by combining GNNs with deep Q-learning in cooperative swarms. We focused on well-established 2D navigation tasks that are commonly used in the swarm robotics literature to study coordination and scalability, providing a controlled yet meaningful setting for our analysis. To address this, we introduce Deep Graph Q-Learning (DGQL), which embeds agent-neighbor graphs into Q-learning and trains on fixed-size swarms. Across two benchmarks (goal reaching and obstacle avoidance), we deploy up to three times larger teams. The DGQL preserves a functional coordination without retraining, but efficiency degrades with size. The ultimate goal distance grows monotonically (15–29 agents) and worsens beyond roughly twice the training size ($\approx 20$ agents), with task-dependent trade-offs. Our results quantify scalability limits of GNN-enhanced DQL and suggest architectural and training strategies to better sustain performance across scales. Full article

This study addresses the technical challenges of cracking and surface crack initiation in Ni60 alloy cladding layers fabricated by laser cladding additive manufacturing on FeMnNiSi alloys. An innovative FeMnNiSi-Inconel625-Ni60 laminate design was proposed, achieving metallurgical bonding of the dissimilar materials through an Inconel625 transition layer. This effectively addresses the interfacial stress concentration issue caused by differences in thermal expansion coefficients in conventional processes. The results demonstrate that the interfacial microstructure is regulated by synergistic Nb-Mo element segregation, promoting the precipitation of γ″ phase and the formation of a nanoscale Laves phase. This phase not only inhibits carbide aggregation and growth, refining grain size, but also deflects crack propagation paths by pinning dislocations, achieving a dual mechanism of stress reduction and crack arrest. The Ni60 cladding layer in the laminated structure exhibits an average surface microhardness of 641.31 HV_0.3, 3.88 times that of the substrate (165.22 HV_0.3), while the Inconel625 base layer shows 340.71 HV_0.3, 2.06 times the substrate’s value. Wear testing reveals the laminated cladding layer has a wear volume of 0.086 mm³ (0.243 mm³ less than the substrate’s 0.329 mm³) and a wear rate of 0.86 × 10⁻² mm³/(N·m), 73.86% lower than the substrate’s 3.29 × 10⁻² mm³/(N·m), indicating superior wear resistance. The electrochemical test results show that under the same corrosion conditions, the self-corrosion potential and polarization resistance of the FeMnNiSi-Inconel625-Ni60 cladding layer are significantly higher than those of the substrate, while the corrosion current density is significantly lower than that of the substrate. The frequency stability region at the highest impedance modulus |Z| is wider than that of the substrate, and the corrosion rate is 71.86% slower than that of the substrate, demonstrating excellent wear resistance. This study not only reveals the mechanism by which Laves phases improve interfacial properties through microstructural regulation but also provides a scalable interface design strategy for heterogeneous material additive manufacturing, which has important engineering value in promoting the application of laser cladding technology in the field of high-end equipment repair. Full article

Additive Manufacturing (AM) has emerged as a transformative technology for rapid prototyping and fabrication of geometrically complex structures. However, the inherent thermal cycling and rapid solidification in processes such as Selective Laser Sintering (SLS) frequently induce deformation and residual stresses, leading to dimensional deviations and potential premature failure. This paper presents OpenAM-SimCCX, an open-source workflow for finite element-based thermo-mechanical simulation of AM processes using CalculiX 2.21. The framework employs a time-dependent thermo-mechanical model with layer-by-layer element activation to capture key aspects of SLS, including laser–material interaction and scanning strategy effects. Systematic comparisons of different scanning strategies demonstrate clear correlations between path planning, residual stress distributions, and distortion, while computational time analyses confirm the framework’s efficiency. By providing comprehensive documentation, implementation guides, and open repositories, OpenAM-SimCCX offers an accessible and economically viable alternative to commercial software, particularly for academic institutions and small- to medium-sized enterprises. This framework advances open-source simulation tools for AM and promotes broader adoption in both research and industry. Full article

This paper presents a comparative study on the suitability of free-boundary surface parameterization techniques for generating trajectories on 3D surfaces. The approach maps a 3D surface to a 2D parametric domain through four parameterization methods: Least-Squares Conformal Mapping, Boundary First Flattening, As-Rigid-As-Possible, and Conformal Equivalence of Triangular Meshes. Structured trajectory patterns are generated in the 2D domain and projected back to 3D. We introduce center-to-boundary geodesic deviation measure, which yields a deviation profile over the boundary loop and reflects how well central alignment is preserved under each parameterization method. The results highlight differences in distortion and geodesic preservation, reflecting the suitability of methods for path generation. Full article

As the integrated circuit process nodes are continuously reduced, higher complexity and accuracy requirements are imposed on the design rule checking (DRC) of standard cell layouts. Traditional manual repair methods are inefficient and prone to errors. A standard cell layout DRC automatic repair strategy based on an improved simulated annealing algorithm is proposed to address this issue. The proposed method quantifies the degree of graphic conflict by dynamically adjusting the annealing parameters; the high-conflict areas and repair paths are optimized. Meanwhile, the proposed method supports the repair of DRC rules at different process nodes ranging from MOSFET (28 nm) to FinFET (14 nm). Experiments results demonstrate that the proposed method outperforms traditional methods in both repair time and quality. Compared to manual repair, about 70% (MOSFET process) and 80% (FinFET process) of time can be saved by the proposed method, and new violations can be avoided during the repair process. Compared with traditional simulated annealing algorithms, approximately 40% (MOSFET process) and 50% (FinFET process) of the running time can be saved, and 100% elimination rate of DRC violations is achieved. The proposed method provides a fully automated and highly reliable DRC repair solution for integrated circuit layout design. Full article

A transition from fossil fuels to renewable energy is underway to achieve net-zero emissions. The institutional arrangements in Indonesia’s energy transportation sector are crucial for various stakeholders involved in the energy transition. This study combines historical institutionalism with a multi-level perspective to analyze how policy formulation, critical junctures, and path dependence shape institutional changes toward sustainable mobility. The evolution of institutional arrangements can be categorized into three phases: the establishment of fuel-oil-based infrastructure and dependency (1970–2003); the diversification of cleaner fuels through compressed natural gas and biofuels (2004–2014); and the development of affordable and clean energy, focusing on biofuels and electrification (2015 to present). In parallel, a quantitative total cost of ownership analysis of vehicles using different fuel types demonstrates how institutional reforms, fiscal incentives, and regulatory support reshape the economic feasibility of low-carbon technologies. Landscape pressures—such as global decarbonization, fuel import dependence, and energy security challenges—interact with niche innovations, including biofuels, electric vehicles, and hybrid systems, to drive systemic transformation. The findings indicate that institutional changes, supported by quantitative economic evidence and technology diffusion, play a pivotal role in realigning Indonesia’s transport energy regime toward a more resilient, inclusive, and sustainable transition. Full article

Detection of changes in remote sensing relies on clean multi-temporal images, but cloud cover may considerably degrade image quality. Cloud removal, a critical image-restoration task, demands effective modeling of long-range spatial dependencies to reconstruct information under cloud occlusions. While Transformer-based models excel at handling such spatial modeling, their quadratic computational complexity limits practical application. The recently proposed Mamba, a state space model, offers a computationally efficient alternative for long-range modeling, but its inherent 1D sequential processing is ill-suited to capturing complex 2D spatial contexts in images. To bridge this gap, we propose the multi-pattern scanning Mamba (MPSM) block. Our MPSM block adapts the Mamba architecture for vision tasks by introducing a set of diverse scanning patterns that traverse features along horizontal, vertical, and diagonal paths. This multi-directional approach ensures that each feature aggregates comprehensive contextual information from the entire spatial domain. Furthermore, we introduce a dynamic path-aware (DPA) mechanism to adaptively recalibrate feature contributions from different scanning paths, enhancing the model’s focus on position-sensitive information. To effectively capture both global structures and local details, our MPSM blocks are embedded within a U-Net architecture enhanced with multi-scale supervision. Extensive experiments on the RICE1, RICE2, and T-CLOUD datasets demonstrate that our method achieves state-of-the-art performance while maintaining favorable computational efficiency. Full article

India’s aviation sector, crucial for connectivity, economic growth, and national integration, faces sustainability measurement challenges focused solely on carbon emissions. This study proposes the Aviation Sustainability Performance Index (ASPI-India), spanning four pillars: Environmental Stewardship, Social Responsibility, Governance Maturity, and Economic Resilience. Measurable indicators are derived from regulatory filings, commercial flight databases, geospatial tracking, and targeted surveys. Data sources include DGCA safety audits, AAI operational statistics, ADS-B flight path data, and passenger satisfaction surveys from 2010 to 2024. Fixed-effects panel models link ASPI-India to operational and financial outcomes like load factor stability, CASK, and credit rating resilience. Quasi-experimental designs exploit policy shocks through difference-in-differences estimation. Factor analysis validates the four-pillar structure, and robustness checks compare entropy, PCA, and equal weighting. Results show that a one-standard-deviation increase in ASPI-India improves load factor stability, ancillary revenue share, and credit terms, especially for carriers with diversified route networks. The framework provides actionable insights for airlines, regulators, and investors to embed sustainability in aviation management. Full article

In this segment of the Palliative Care Unit case series, we introduce a patient with a long history of oncological treatments for recurrent breast cancer. After active treatments and a global control of the neoplasm, disease progression made the patient access different lines of chemotherapies, even asking for them in anticipation of a few advantages in the balance between benefits and risks. When the patient decided to permanently discontinue chemotherapy, she felt she had disrupted her values. Also, as a reaction to breaking bad news without estimating alternative paths, she considered her deteriorating condition as the sole criterion for assisted dying in another country. Could this be a self-consistent choice for this patient, so determined to find and pursue possibilities in treatment previously? Should this clue respond precisely to the patient’s needs? This contribution’s objective is to debate possibilities of patient self-determination and dignity at the end of life by integrating psychological support, palliative care, and legal–ethical awareness. This case study presents multidisciplinary team work through some key turning points. This team work was carried out in a national context that is currently inconclusive regarding assisted suicide, since active euthanasia is illegal. At the same time, the national Constitutional Court (242/2019) recently opened the possibility of eventual medically assisted suicide under certain circumstances. In this case, health professionals considered this context and tried to delve deeply into respecting the patient’s identity in order to determine when and if the exceptional circumstances were met. This case highlights the ethical sense of end-of-life accompaniment, which when conducted by physicians, nurses, and psychologists together can lead to effective support and allow patients to maintain their identity and to express themselves respecting not only their fears but also their vision of themselves as human beings. A first key turning point was, for instance, taking into account the patient’s history and values, and a subsequent one was supporting the patient in exploring healthcare services and related end-of-life support. In a further key turning point, the patient was helped in engaging with physicians in order to understand types of continuous care, as well as the timing and expected results of sedation. Finally, she chose a healthcare service where she could spend the end of her life in fulfillment of her values. Overall, this case report illustrates how integrating psychological support, palliative care, and legal–ethical awareness can promote patient self-determination and dignity at the end of life. Full article

Attention and brain focus are essential in human activities that require learning. In higher education, a popular means of acquiring knowledge and information is through serious games. The need for integrating digital learning tools, including serious games, into university curricula has been demonstrated by the students’ preferences that are oriented more towards engaging and interactive alternatives than traditional education. This study examines real-time attention measurement in serious games using wearable brain–computer interfaces (BCIs). By capturing electroencephalography (EEG) signals non-invasively, the system continuously monitors players’ cognitive states to assess attention levels during gameplay. The novel approach proposes adaptive attention measurements to investigate the ability to maintain attention during cognitive tasks of different durations and intensities, using a single-channel EEG system—NeuroSky Mindwave Mobile 2. The measures have been achieved on ten volunteer master’s students in Computer Science. Attention levels during short and intense tasks were compared with those recorded during moderate and long-term activities like watching an educational lecture. The aim was to highlight differences in mental concentration and consistency depending on the type of cognitive task. The experiment was designed following a unique protocol applied to all ten students. Data were acquired using the NeuroExperimenter software 6.6, and analytics were performed in RStudio Desktop for Windows 11. Data is available at request for further investigations and analytics. Experimental results demonstrate that wearable BCIs can reliably detect attention fluctuations and that integrating this neuroadaptive feedback significantly enhances player focus and immersion. Thus, integrating real-time cognitive monitoring in serious game design is an efficient method to optimize cognitive load and create personalized, engaging, and effective learning or training experiences. Beta and attention brain waves, associated with concentration and mental processing, had higher values during the gameplay phase than in the lecture phase. At the same time, there are significant differences between participants—some react better to reading, while others react better to interactive games. The outcomes of this study contribute to the design of personalized learning experiences by customizing learning paths. Integrating NeuroSky or similar EEG tools can be a significant step toward more data-driven, learner-aware environments when designing or evaluating educational games. Full article

Conducting measurements is a daily and time-consuming process that is critical to the manufacturing industry. The most widespread way to carry out the measuring process is using a Coordinate Measuring Machine (CMM). In this paper, a methodology is presented to accelerate the measuring procedure by optimally programming a CMM. The proposed methodology utilizes the information from a computer-aided design (CAD) file and the capabilities of CMMs in order to optimize the measurement process. An improved artificial fish swarm algorithm was modified to meet the requirements of the measurement process and the capabilities of the CMMs. In addition, the ant colony optimization method is applied to extract the optimal sequence of measurements throughout the multiple areas on the component. The resulting optimal path also utilizes the free areas between the different manufactured features of the component. Finally, the resulting path is collision-free, ensuring the integrity and the safety of the CMM. The proposed methodology is verified through real-world experiments. Full article

Garnet-type Li₇La₃Zr₂O₁₂ (LLZO) is a solid electrolyte candidate for ASSLBs, owing to its wide electrochemical window and intrinsic safety. Yet phase-pure LLZO remains difficult because secondary phases form, and the transition towards the tetragonal phase, aliovalent doping, mitigates these issues. Still, the phase formation pathway is not fully understood. Here, we present comparative in situ and ex situ studies of Nb- and Ta-doped LLZO (LLZNO and LLZTO) that were synthesized by a solid-state reaction. In situ/ex situ XRD reveals that the lithium precursor dictates the reaction path: differing decomposition temperatures of the lithium precursor define reaction windows that control cubic-phase purity and particle morphology. In air, limited Li diffusion favors oxycarbonates and pyrochlore, necessitating 950–1050 °C to achieve phase-pure cubic LLZO. Under N₂, faster Li availability and diffusion enable uniform nucleation and a route to cubic LLZO without detectable secondary phases. These findings demonstrate the coupled effects of temperature, precursor, dopant, and atmosphere, guiding process optimization and scalable production. Full article

The rational utilization of cultivated land resources is central to ensuring both ecological and food security in the Yangtze River Economic Belt (YREB), holding strategic significance for regional sustainable development. Using panel data from 2010 to 2023 for 130 cities in the YREB, this study examines a spatial correlation network (SCN) for non-grain land use (NGLU) and its driving forces via a modified gravity model, social network analysis (SNA), and quadratic assignment procedure regression. The results show the following: (1) The risk of NGLU continues to increase, with the spatial pattern evolving from a “single-peak right deviation” pattern to a “multi-peak coexistence” pattern featuring three-level polarization and gradient transmission, primarily driven by economic potential disparities. (2) The SCN has increased in density, but its pathways are relatively singular. Node functions exhibit significant differentiation, with high-degree nodes forming “control poles”, high-intermediate nodes dominating cross-regional risk transmission, and low-proximity nodes experiencing “protective marginalization”. Node centrality distribution is highly connected with the regional development gradient. (3) The formation of the spatial network is jointly driven by multiple factors. Geographical proximity, economic potential differences, comparative benefit differences, non-agricultural employment differences, and factor mobility all positively contribute to the spillover effect. Conversely, implementing cultivated land protection policies and the regional imbalance in local industrial development path dependence significantly inhibit the non-grain trend. This study further reveals that a synergistic governance system characterized by “axial management, node classification, and edge support” should be recommended to prevent the gradient risk transmission induced by economic disparities, providing a scientific basis for achieving sustainable use of regional cultivated land resources and coordinated governance of food security. Full article

The rapid emergence of Generative Artificial Intelligence (GenAI) has sparked a growing debate about its ethical, methodological, and epistemological implications for qualitative research. This study aimed to examine and deeply understand researchers’ perceptions regarding the use of GenAI tools in different phases of the qualitative research process. The study involved a sample of 214 researchers from diverse disciplinary areas, with publications indexed in Web of Science or Scopus that apply qualitative methods. Data collection was conducted using an open-ended questionnaire, and analysis was carried out using coding and thematic analysis procedures, which allowed us to identify patterns of perception, user experiences, and barriers. The findings show that, while GenAI is valued for its ability to optimize tasks such as corpus organization, initial coding, transcription, translation, and information synthesis, its implementation raises concerns regarding privacy, consent, authorship, the reliability of results, and the loss of interpretive depth. Furthermore, a dual ecosystem is observed, where some researchers already incorporate it, mainly generative text assistants like ChatGPT, while others have yet to use it or are unfamiliar with it. Overall, the results suggest that the most solid path is an assisted model, supported by clear ethical frameworks, adapted methodological guidelines, and critical training for responsible and humanistic use. Full article