Search Results (771)

This paper examines how the current state of affective computing is limited by its reliance on theories that treat emotions as static, isolated states, and argues that the holistic and process-oriented theory of mind from Yogācāra Buddhism offers a more sophisticated alternative, viewing emotion as an experience deeply integrated with cognition, volition, and somatic awareness. As a case study, this paper proposes a framework for sentiment analysis inspired by Yogācāra principles, based upon the Chinese Buddhist text Mahāyāna Treatise on the Hundred Dharmas Illuminating the Gate. This multi-aspect annotation system analyzes emotional expressions across five key dimensions corresponding to Yogācāra’s “ever-present” Mental Factors. By mapping emotions in this compositional manner, the framework provides a more granular and context-rich understanding of human sentiment than current methods allow. This paper thus serves as a call to diversify AI’s theoretical foundations, demonstrating through this Yogācāra case study how engagement with insights from different traditions can resist the top-down “theoretical monopoly” of Western psychological models, which flattens the rich diversity of human affective experience into a single, dominant paradigm. Full article

Static electricity-induced luminescence (SEL) materials exhibit luminescence in response to minute electrical charges and therefore have potential for application in self-powered charge-detection sensors that operate without an external power source. However, important aspects of their luminescence mechanism and the associated material properties remain insufficiently understood. In this study, SEL films based on SrAl₂O₄:Eu²⁺ were evaluated, and the effects of SrAl₂O₄:Eu²⁺ concentration and applied voltage on the luminescence behavior were quantitatively investigated. The results showed that the SEL intensity increased in proportion to the square of the applied voltage, while the SEL luminescence area increased monotonically with increasing voltage. These results suggest that the SEL intensity and SEL area may reflect the amount of discharge–charge from the needle electrode and the charge distribution on the film surface, respectively. In addition, increasing the SEL phosphor content enhanced the luminescence intensity, whereas no significant effect was observed on the relative change in luminescence area with applied voltage. Collectively, these findings provide fundamental insights for the design of charge-detection sensors based on SrAl₂O₄:Eu²⁺. Full article

The manuscript deals with the bending behavior of beams with relatively less investigated cellular topologies based on triply periodic minimal surfaces (TPMSs). Three types of sandwich-type specimens (namely Schoen IWP, Fischer–Koch S, and Schoen F-RD) with five different volume fractions of 10, 15, 20, 25, and 35% (±1%) made of aluminum alloy AlSi10Mg by selective laser melting (SLM) technology were investigated. Three-point bending tests were performed at room temperature on a Zwick/Roell 1456 universal testing machine. The force–deflection dependences were plotted, while in addition to nominal stresses, the effective flexural stiffness and energy absorption to failure were evaluated to compare the properties of the investigated cellular beams. In the preparatory phase, critical aspects of possible premature failure of the samples with the smallest and highest selected volume fractions were addressed, while the manufacturability and fracture surfaces of the samples were assessed in order to improve the input conditions of the setup. By comparing the results obtained in the experimental testing in the second phase, it was found that the highest nominal bending stresses were achieved by the Schoen F-RD structure (although not significantly higher than Fischer–Koch S), but in terms of stiffness and amount of absorbed energy, the Fischer–Koch S structure showed the highest values. The improvement of input parameters led to an increase in the achieved nominal bending stresses by at least 100 MPa for all types of investigated structures compared to the first phase. The combined use of preliminary SLM process optimization, bending tests, and fracture surface/EDX analysis made it possible to relate the flexural response of the investigated TPMS topologies to manufacturing-related defects and premature-failure mechanisms in thin-walled AlSi10Mg cellular structures. The presented specimen configuration is intended as a comparative experimental benchmark for flexural performance of sandwich-type TPMS beams under quasi-static loading. Full article

Introduction: Currently, there is an ongoing debate regarding the benefits of kinematic alignment (KA) versus mechanical alignment (MA) in total knee arthroplasty (TKA). Robotic-assisted TKA has been shown to improve implant positioning and precision of the KA technique, enabling successful kinematic alignment. However, its impact on early postoperative and functional outcomes remains unclear. This study aims to examine how imageless, table-mounted, robotic-assisted KA-TKA compares with conventional MA-TKA. Methods: Registry data of all primary TKAs using ATTUNE™ cruciate-retaining implants (January 2021–December 2024) performed by a single, experienced surgeon in a high-volume arthroplasty center were retrospectively reviewed. A total of 64 patients who underwent robotic-assisted KA-TKA were compared to 39 patients who underwent conventional MA-TKA. The mean age was 70.3 ± 7.71 and 69.3 ± 9.47 in the KA-TKA group and the MA-TKA group, respectively, while the male proportion was 32.8% and 30.7%, respectively. Early postoperative outcomes (static/dynamic pain score, ambulation distance, length of stay) and 6-month functional outcomes (range of motion, Knee Society Score, Oxford Knee Score, SF-36, patient expectation/satisfaction scores) were analyzed. Delta changes in outcome scores and proportion of patients attaining a minimum clinically important difference (MCID) were studied. Results: Robotic-assisted KA-TKA displayed benefits in the majority of the early postoperative outcomes, with significant improvements in ambulation distance (23.3 vs. 14.7 m, p = 0.002) compared to conventional MA-TKA. Both groups showed significant improvements in the majority of the functional outcomes at 6 months. Robotic-assisted KA-TKA also shows significant improvements in selected mental health aspects of SF-36, namely vitality (p = 0.001), mental health (p = 0.048), mental component summary (MCS) (p = 0.004), and a larger proportion attaining SF-36 vitality MCID (p = 0.045). Following false discovery rate correction for multiple comparisons, postoperative ambulation distance, SF-36 vitality, and MCS remained statistically significant between groups. No significant differences in KSS, OKS, and satisfaction/expectation fulfillment were noted. Conclusions: Robotic-assisted KA-TKA demonstrated early rehabilitation and select mental health-related quality of life improvements compared to conventional MA-TKA. Further studies are needed to examine its long-term clinical outcomes. Full article

Carbon nanotube (CNT)-reinforced composites are promising advanced materials due to their exceptional mechanical properties. This paper presents a comprehensive investigation of the mechanical behavior of CNT/epoxy composites through theoretical modeling and experimental validation. An equivalent cylindrical fiber model was developed to transform CNTs into effective reinforcement phases, enabling the application of classical composite mechanics. Three reinforcement configurations were analyzed: two unidirectional short fiber models (aligned and staggered) and a three-dimensional four-directional braided long-fiber model. The effects of geometric parameters, including the diameter-to-thickness ratio (D/t) and fiber aspect ratio, on the effective elastic moduli were systematically evaluated. Static and dynamic compression experiments were conducted using an MTS 810 testing system and a Split Hopkinson Pressure Bar (SHPB) to examine the influence of loading rate, vacuum treatment, and reinforcement type (CNT, SiC, and hybrid SiC/CNT) on composite strength. The results indicated that 3 wt% CNT reinforcement increases the Young’s modulus by 30% under static loading and enhanced the dynamic compressive strength under impact loading. The vacuum degassing process significantly affected composite quality, with insufficient vacuum leading to strength degradation due to void formation. Theoretical predictions using Mori–Tanaka and dilute methods showed good agreement with experimental results at low reinforcement volume fractions. Scanning electron microscopy revealed uniform CNT dispersion and provided insights into failure mechanisms, including CNT pull-out and breakage. This work contributes to the understanding of structure–property relationships in CNT-reinforced polymer composites and provides guidelines for achieving their optimal design. Full article

Machine learning approaches to wildfire spread prediction are constrained by the lack of standardized, multi-source, spatiotemporal datasets that fuse terrain, weather, and fire-state information into a single ML-ready format. We present WildfireCube, a reproducible event-centric pipeline and methodology for constructing dense fourth-order spatiotemporal tensors of shape (T, C, H, W) at 30 m spatial and 3 h temporal resolution. Following the analysis-ready data convention established in the Earth Observation community, the pipeline fuses four open data sources: the Copernicus GLO-30 Digital Elevation Model for static terrain derivatives, ERA5-Land reanalysis for hourly weather forcing, Sentinel-2 Level-2A imagery for spectral vegetation and burn-severity indices, and NASA FIRMS active-fire hotspot detections for fire-state reconstruction via ordinary kriging. The resulting 13-channel normalized tensor separates causal drivers into three physically motivated groups: static landscape controls (elevation, slope, aspect, fuel load), dynamic atmospheric forcings (wind components, temperature, precipitation), and evolving fire state (fire-front mask, burn severity, fractional burn, observation confidence). A physics-informed normalization framework maps all channels to bounded ranges using fixed physical constants rather than sample statistics, ensuring cross-event comparability and exact invertibility. We demonstrate the pipeline on 13 wildfire events across the United States, Canada, and Greece (2017–2023), producing a processed catalog exceeding 300 GB compressed and spanning a 14-fold range in burned area, a 27 °C range in mean temperature, and different fire regimes. Event tensors are stored in chunked Zarr archives with Zstandard compression, achieving a 2.58× compression ratio. As future work, the pipeline will be applied to a 40-event target catalog projected to exceed 2 TB of raw data, providing the multi-regime diversity and scale required for training robust deep learning models for spatiotemporal wildfire prediction. Full article

In this work, we develop machine-learned moment tensor potentials (MTPs) to simulate the static and dynamic structural properties in Al_xGa_1−xN and related materials. The potentials are trained on DFT-calculated data for forces, stresses, and energies obtained from random atomic displacements and cell deformations. MTP-calculated physical properties, including lattice parameters and elastic constants, thermal expansion, harmonic and anharmonic vibrational properties, and the thermal conductivity, are benchmarked against first-principles results and experimental data. The comparisons testify to the very high accuracy achieved by the machine-learned potentials despite the massively reduced computational effort. Additionally, the impact of various aspects of the MTP training procedure is examined. Full article

The seismic fragility of reinforced concrete (RC) bridge piers is critical for urban rail transport systems, as severe pier damage may interrupt post-earthquake operation and threaten network safety. Compared with conventional highway bridge piers, urban rail transport RC solid piers usually have lower axial load ratios, larger cross-sections, and stricter serviceability requirements. However, the combined effects of geometric parameters, reinforcement detailing, and material strength on their cyclic behavior, dynamic response, and seismic fragility remain insufficiently understood. To address this issue, seven 1/4-scale RC solid pier specimens were tested under quasi-static cyclic loading to examine the effects of pier height, transverse reinforcement ratio, and longitudinal reinforcement ratio on damage evolution, hysteretic response, skeleton curves, and energy dissipation. A fiber-based OpenSees model considering bond-slip effects was then established, validated against the tests, and extended to a full-scale prototype pier for parametric analysis. The effects of aspect ratio, axial load ratio, longitudinal reinforcement ratio, stirrup ratio, steel yield strength, and concrete strength were evaluated under cyclic loading and nonlinear dynamic time-history excitations. An incremental dynamic analysis-based probabilistic seismic demand model was further developed using 30 near-fault ground motions, with peak ground acceleration as the intensity measure and displacement ductility as the engineering demand parameter. The results showed that increasing the aspect ratio changed the failure mode from flexure-shear-dominated to flexure-dominated behavior, increasing the ultimate displacement from 122 mm to 155 mm while reducing the peak lateral strength from 263 kN to 248 kN. Increasing the longitudinal reinforcement ratio improved both peak strength and ultimate displacement, from 226 kN to 262 kN and from 120 mm to 160 mm, respectively. The numerical results indicated that aspect ratio, axial load ratio, and longitudinal reinforcement ratio had more pronounced effects on seismic demand and fragility than stirrup ratio. Increasing steel yield strength generally reduced seismic fragility, whereas increasing concrete strength enhanced lateral resistance but did not necessarily improve fragility performance. These findings suggest that the seismic performance of urban rail transport RC solid piers should be evaluated by combining cyclic response, dynamic demand, and fragility-based performance, rather than by maximizing any single design parameter. Full article

Refractory high-entropy alloys (RHEAs) have attracted increasing attention for structural applications under extreme conditions; however, the uniformity and reliability of their mechanical properties remain critical challenges, particularly when processed by additive manufacturing. In this work, the microstructural heterogeneity and mechanical uniformity of a selective laser melting (SLM)-fabricated MoNbZrTaW RHEA were systematically investigated. Microstructural characterization revealed a dual-phase BCC structure with dendritic and interdendritic regions distributed along the build direction. Statistical analyses were employed to quantify variations in microstructure and mechanical properties, including hardness, fracture strength, yield strength, and fracture strain. The effects of strain rate and specimen aspect ratio on mechanical behavior were further examined through compression testing. Weibull statistical analysis demonstrated that strength-related properties exhibit high uniformity despite pronounced microstructural heterogeneity, whereas fracture strain shows comparatively greater scatter. The results indicate that solid-solution strengthening governs the mechanical response and helps mitigate the influence of microstructural non-uniformity. These findings provide important insights into the mechanical reliability of SLM-fabricated RHEAs under room-temperature quasi-static loading, and support their potential for further investigation in advanced structural applications. Full article

The introduction of the special and general theory of relativity had significant implications for the notion of time, especially the relativity of clock time. Many physicists and philosophers concluded that the theory also showed that time was unreal and that the universe was a four-dimensional block universe. The argument focused on particular aspects of the theory—relative simultaneity and general covariance, respectively—to arrive at this conclusion. But while it is true that views about time can be inferred from the theory of relativity, the unreality of time is not a deductive consequence of the theory. It is therefore possible to ask whether the theory is compatible with the reality of time. If invariant and asymmetric features of the theory are taken into account—the space-time interval ds and entropic relations—as will be argued in this paper, a dynamic notion of time emerges as a philosophical consequence of the theory of relativity. This paper defends a Heraclitean, dynamic view, against the predominant Parmenidean, static view of time. Full article

With the rapid development of intelligent shipping and the autonomy of marine engineering equipment, numerous studies have focused on the advancement of Autonomous Surface Vehicles (ASVs). As a fundamental component of ASV automation systems, path planning directly determines the safety and economy of ship navigation. This paper systematically reviews recent research progress in ASV path planning. First, five key issues are identified for ASV path planning: navigation environment, environment modeling, ship motion model, collision avoidance for safety, and optimization. Second, existing algorithms are classified into four categories: graph search-based, sampling-based, numerical optimization-based, and artificial intelligence-based. The improvement directions and application scenarios of each category are elaborated. Finally, the four types of algorithms are evaluated against three indicators: path quality, scalability and extensibility, and algorithm performance. Analysis of the reviewed literature shows that traditional graph search and sampling algorithms perform well in various aspects under static environments, but are insufficient in adapting to multiple constraints and generalizing to different environments. In contrast, artificial intelligence algorithms represented by deep reinforcement learning exhibit significant advantages in dynamic collision avoidance decision-making, multi-agent coordination, and environmental generalization, and have become the mainstream direction of current research. This paper summarizes the existing challenges in safety and optimization in current ASV path planning research and prospects future development directions. Full article

India has been witnessing a high growth rate of aggregate income in the current era of globalization. Even though the per capita income is yet to catch up, this led to an improved global status in 2025, with India becoming the fifth largest economy in terms of aggregate GDP. However, the economic gains have been accompanied by a host of environmental problems. In particular, the increase in carbon emissions is emerging as the biggest challenge in achieving the Sustainable Development Goals by 2030. While some national policy initiatives exist, Indian states have also started implementing new public policies for a contextualized environmental management at a sub-national level to curtail the negative impact of carbon emissions on sustainable development. In this context, this study seeks to explore three aspects: first, the characteristics of the series for per capita CO₂ (PCCO₂) emissions, per capita state domestic product (PCGSDP), and per capita R&D (PCR&D) spending aimed at safeguarding the environment in Indian states; second, the prevalence of both enduring and near-term linkages among the three variables in distinct panels; and third, the constantly changing interplay involving income and carbon emissions in the midst of R&D spending for the environment in the Indian states from 2008–2025. While the series for PCGSDP and PCR&D is seen rising along with PCCO₂ in most states, there are some exceptional states like Delhi and Kerala where trends of PCCO₂ are falling. The panel cointegration and VECM results show that the three indicators, viz., income, PCCO₂ and R&D spending, have a stable long-run relationship, and that income and R&D cause CO₂ emissions in all states’ panels and the panel of developed states. Using several polynomials between the income and CO₂ emission nexus over several panels of states and using panel cointegration techniques, the study reveals that static panel fixed effects models are most appropriate in the case of all states’ panels and the panel of developed states to establish an inverted Environmental Kuznets Curve (EKC), and that R&D spending has worked as a significant control variable to justify the declining shape of the EKC. The study recommends a continuous increase in R&D spending by all states of any development stature to achieve sustainable development in the earliest possible time. Full article

As the aviation industry explores sustainable solutions for next-generation aircraft, the strut-braced wing (SBW) concept has emerged as a promising configuration, combining the enhanced aerodynamic efficiency of high-aspect-ratio (HAR) wings with a significant reduction in wing structural weight compared to conventional cantilever designs. Given the inherent aerodynamics and structural complexities of SBW concepts, developing innovative design methodologies is essential for fully investigating their potential. This work presents a low-fidelity, two-fold design methodology combining an overall aircraft design framework with finite element structural analysis. The approach enables overall aircraft design (OAD) sizing, exploration, and optimization of regional strut-braced wing configurations and assessing the effects of strut connections and jury on the wing’s static and buckling behavior. Trade-off and optimization studies based on the reference ATR-72 aircraft led to an optimal SBW configuration with an aspect ratio of 17.64 and a strut position ratio of 0.543, achieving reductions of about 24% in wing weight and 6.78% in fuel burn. The structural analysis of the optimized SBW indicates that a clamped–clamped strut connection provides superior buckling performance, and incorporating a jury strut effectively mitigates buckling issues while achieving approximately 20% wing weight reduction compared to the configuration without a jury. Full article

Short-fiber-reinforced composite materials are increasingly being used in a wide variety of fields, including medicine, the automotive industry, and aviation. This growing demand has driven the development of new manufacturing technologies, numerical modeling and topology optimization methods, and techniques for assessing the internal structure of such products, among others. This review provides a comprehensive examination of the characteristics and mechanical properties of short-fiber-reinforced composite materials, focusing on the key aspects of their design and manufacturing processes. We analyze the consideration of anisotropy in material modeling, the methods for the non-destructive testing of material structure, and the multidisciplinary approach to product design. The review also addresses advanced design techniques, including topology optimization and bimaterial optimization for designing products with embedded lattice structures, as well as adhesion modeling. In contrast to existing reviews, this work presents an overview of multidisciplinary studies dedicated to all stages of designing and manufacturing structures from short-fiber-reinforced composites, unifying the engineering pipeline from material property and molding modeling to topology-optimized design and static analysis under operation loads. Full article

This study proposes a novel pneumatically driven mechanically prestressed rhombic bistable composite laminate with asymmetric cylindrical curvature, which exhibits two weakly-coupled cylindrical shapes where each shape is influenced by planform and geometry parameters. A reduced-order analytical model is developed to predict the laminate’s quasi-static equilibrium shapes and snap-through transitions of the laminate under pneumatic work loading, which is triggered by the internal pressure applied to the fluidic channels. A sensitivity study based on the model investigates the influence of key planform and geometric parameters (the internal angle α and aspect ratio E) on the laminate’s out-of-plane deflection and snap-through pressure. The results show that increasing α reduces the critical prestrain required to achieve bistability and amplifies the out-of-plane deflection, while excessive α may lead to monostable curvature. Variations in aspect ratio modify the coupling stiffness between orthogonal PEMC layers, thereby influencing the bistable domain and critical snap-through pressure. These findings provide methods for the design of bistable composite structures. Full article