Search Results (381)

Micro-combustion for micro-thermophotovoltaic (MTPV) and micro-thermoelectric (MTE) systems is gaining renewed interest as a pathway toward compact power generation with high energy density. This review examines how emerging artificial intelligence (AI) methodologies can accelerate the development of such systems by addressing longstanding modeling, optimization, and design challenges. We analyze four major research areas: artificial neural network (ANN)-based design optimization, AI-driven prediction of micro-scale flow variables, Physics-Informed Neural Networks for combustion modeling, and surrogate models that approximate high-fidelity computational fluid dynamics (CFD) and detailed chemistry solvers. These approaches enable faster exploration of geometric and operating spaces, improved prediction of nonlinear flow and reaction dynamics, and efficient reconstructions of thermal and chemical fields. The review outlines a wide range of future research directions motivated by advances in high-fidelity modeling, AI-based optimization, and hybrid data-physics learning approaches, while also highlighting key challenges related to data availability, model robustness, validation, and manufacturability. Overall, the synthesis shows that overcoming these limitations will enable the development of micro-combustors with higher energy efficiency, lower emissions, more stable and controllable flames, and the practical realization of commercially viable MTPV and MTE systems. Full article

Enhancing the explosive power output of the knee joints is critical for improving the agility and obstacle crossing of humanoid robots. However, a mismatch between the knee-to-CoM transmission ratio and jumping demands, together with power-loss–induced motor performance degradation at high speeds, shortens the high-power operating window and limits jump performance. To address this, this paper introduces a variable-reduction-ratio knee-joint paradigm in which the reduction ratio is coupled to the joint angle and decreases during extension. Analysis of motor output and knee kinematics motivates coupling the reduction ratio to the joint angle. A high initial ratio increases the takeoff torque, and a gradual decrease limits motor speed and power losses, extending the high-power window. A linear-actuator-driven guide-rod mechanism realizes this strategy, and parameter optimization guided by explosive jump control is employed to select the design parameters. Experimental validation demonstrates a high jump of 0.63 m on a single-joint platform (a theoretical improvement of $31.9\%$ over the optimal fixed-ratio baseline under the tested conditions). Integrated into a humanoid robot, the proposed design enables a 1.1 m long jump, a 0.5 m high jump, and a 0.5 m box jump. Full article

Building on multiple theoretical views, this paper aimed to investigate how traits and their specific mechanisms transfer into realized entrepreneurial behaviors. Thus, this paper seeks to address various apparent gaps through an integrative theoretical framework that examines the serial mediation between Individual Entrepreneurial Orientation, Entrepreneurial Alertness, and Entrepreneurial Intentions, and their influence on Entrepreneurial Behavior. Based on a quantitative method with a survey strategy, this paper applied partial least squares-based structural equation modeling on a sample of 405 aspiring entrepreneurs in Saudi Arabia. The paper’s findings confirmed the positive and significant relationships between Individual Entrepreneurial Orientation and Entrepreneurial Alertness, Entrepreneurial Alertness and Entrepreneurial Intentions, and Entrepreneurial Intentions and Entrepreneurial Behavior. In addition, the results supported three indirect hypotheses, corroborating that Individual Entrepreneurial Orientation could affect Entrepreneurial Behavior indirectly through Entrepreneurial Alertness and Entrepreneurial Intentions. Likewise, the results supported the serial mediation hypothesis, in which Individual Entrepreneurial Orientation influenced Entrepreneurial Behavior through a sequential process, with both Entrepreneurial Alertness and Entrepreneurial Intentions as mediators. This paper offers theoretical and practical implications for the literature and practice of entrepreneurship. The study contributes to our understanding of the traits and cognitions that can motivate individuals to start a business. In addition, this study responded to many previous calls to examine not only the direct effects of EI antecedents but also the mediating roles of key factors. Full article

The use of neural network technologies is becoming more widespread today, from automating routine office tasks to developing new medicines. However, at the same time, the load on power grids and generation systems increases significantly, which, alongside the desire to increase equipment performance, further motivates the development of specialized architectures for hardware implementation and training of neural networks. Memristor-based systems are considered one of the promising areas for creating energy-efficient platforms for artificial intelligence (AI) due to their ability to implement in-memory computing at the hardware level. A crucial step towards the realization of such systems is the comprehensive characterization of memristive devices. This work presents the implementation of a hardware platform for the automated measurement of key memristor characteristics, including current-voltage (I-V) curves, retention time, and endurance. The developed device features a modular architecture for validating the functionality of individual subsystems and incorporates a unipolar pulse switching scheme to mitigate the risk of gate-oxide breakdown in 1T1R active arrays that can occur when applying negative voltages during synaptic weight programming. Full article

In typical executive compensation structures, higher corporate ranks are associated with greater pay. However, the reform of state-owned enterprises (SOEs) in China introduced strict salary caps for top executives, while lower-tier managers continued to receive market-based compensation, resulting in a phenomenon of pay-rank inversion—where subordinates earn more than their superiors. Leveraging this anomaly as a quasi-natural experiment, this study investigates the specific impact and underlying mechanism of pay-rank inversion on mergers and acquisitions (M&A) decisions and subsequent value realization within Chinese SOEs, thereby addressing the broad academic discourse on optimal executive compensation design. Employing a difference-in-differences (DID) approach with panel data spanning from 2007 to 2022, our analysis reveals that pay-rank inversion significantly reduces firms’ M&A intentions. Mechanistic analysis suggests that this negative effect arises primarily from diminished executive risk-taking. Furthermore, we find that the adverse impact is attenuated when CEOs possess longer tenures or receive equity-based incentives, but it ultimately undermines the realization of value post-M&A. These findings highlight the unintended consequences of high-level compensation reforms and emphasize the critical role of a well-structured pay hierarchy in sustaining executive incentives for strategic decision-making. Despite providing robust evidence, this study is subject to limitations, including its focus on measuring inversion only between the first and second management tiers. Future research should extend the analysis to the pay inversion between the listed firm and its controlling SOE group and explore alternative causal pathways beyond risk-taking, such as CEO work motivation, to deepen the understanding of high-level executive behavior. Full article

Flow quality at the engine face, especially total pressure recovery and swirl, is central to the performance and stability of external compression supersonic inlets. Steady-state RANS-based numerical computations are performed to quantify bleed/swirl trade-offs in a single-ramp intake. The CFD simulations were performed first without a bleed system over M_∞ = 1.4–1.9 to locate the practical onset of a bleed requirement. The deterioration in pressure recovery and swirl beyond M_∞ ≈ 1.6, which is consistent with a pre-shock strength near the turbulent separation threshold, motivated the use of a bleed system. The comparisons with and without the bleed system were performed next at M_∞ = 1.6, 1.8, and 1.9 across the operation map parameterized by the flow ratio. The CFD simulations were performed using ANSYS Fluent, with a pressure-based coupled solver with a realizable k-ε turbulence model and enhanced wall treatment. The results provide engine-face distortion metrics using a standardized ring to sector swirl ratio alongside pressure recovery. The results show that bleed removes low-momentum near-wall fluid and stabilizes the terminal–shock interaction, raising pressure recovery and lowering peak swirl and swirl intensity across the map, while extending the stable operating range to a lower flow ratio at a fixed M_∞. The analysis delivers a design-oriented linkage between shock/boundary-layer interaction control and swirl: when bleed is applied at and above M_∞ = 1.6, the separation footprints shrink and the organized swirl sectors weaken, yielding improved operability with modest bleed fractions. Full article

This study investigates different types of self-identification in terms of religiosity and/or spirituality and some psychosocial correlates of these categorizations. An Italian adult sample (N = 594) was divided into four groups: Religious and Spiritual (RS), Spiritual but not Religious (SnR), Religious but not Spiritual (RnS), and neither Religious nor Spiritual (nRnS). Participants completed measures assessing centrality of religion, spiritual orientation, religious orientations, and main motives in life. Statistical analyses (ANOVAs, t-tests) showed that RS individuals scored highest across all religiosity and spirituality dimensions, with a predominantly intrinsic orientation and strong focus on all life motives, especially self-realization. SnR individuals reported low religiosity but high spirituality, especially concerning meaning and sacredness of life, along with attributing importance to different life motives, particularly to self-realization and meaning. RnS participants showed limited engagement in both religiosity and spirituality, valuing primarily ideological and meaning-related aspects, while nRnS reported minimal scores in religiosity and spirituality, though the pursuit of meaning remained salient. Overall, meaning emerged as a central dimension across all groups, suggesting its role as a universal human motivation. Findings underscore the non-overlapping yet interrelated nature of spiritual and religious identities and their different implications in individual experiences and motives in life. Full article

This article examines the development of Islamic education in Islamic primary schools in the Netherlands from 1988 to 2025. Since the early 1970s, the Muslim population in the Netherlands has grown significantly—initially due to labor migrants from Turkey and Morocco, and later as a result of asylum seekers from countries such as Somalia, Iraq, and Syria. The desire to practice and pass on their faith led to the establishment of mosques, educational centers, boarding schools, and eventually Islamic primary schools. In 1987, some of the founders of Islamic primary schools aspired to establish institutions similar to Madrasas, focusing heavily on Islamic instruction such as Qur’an recitation and Hadith studies. However, these ambitions could not be realized due to funding requirements. Others were inspired by the Imam Hatip schools in Turkey, which offer religious subjects such as Qur’an, Hadith, and Sira (the life of the Prophet Muhammad) alongside the national curriculum. Ultimately, a Dutch model of Islamic education emerged—partly influenced by the Imam Hatip concept, yet possessing a distinct identity. This study investigates how Islamic education has evolved in practice through semi-structured interviews, school observations, document analysis, and a national survey of religion teachers. The findings indicate that the desire to provide Islamic religious education was the primary motive behind the founding of the first Islamic primary school in 1988. Since then, this objective has remained central to school boards and parents alike. Religious education has progressed from fragmented teaching materials rooted in Arabic and Turkish contexts to coherent, Dutch-language curricula. By 2025, the teaching materials of Worden wie je bent (“Becoming Who You Are”) and the Amana have become dominant. Instruction encompasses not only religious knowledge and Qur’an recitation but also social-emotional development, citizenship, and sexuality education within an Islamic framework. Full article

This work was motivated by the premise that new competitive advantages in the international economy are increasingly enabled by the collaborative industrial system rather than working alone. Construction firms are transforming from contractors to integration service providers. However, existing studies on collaborative processes ignore the value attributes of the firm. This study aims to explore a comprehensive framework by complementing the value attribute perspective and empirically reveals the impact of six necessary collaboration factors on competitive advantage. Data of 192 respondents from seven leading Chinese construction Groups based in China are collected. The results show that the two macro elements (i.e., Value Reconfiguration and Strategy Congruence) act together on the remaining four endogenous variables of Resource Sharing, Information Sharing, Organizational Integration and External Integration. The realization of enterprise collaboration has a significant positive impact on the improvement of its competitive advantage, and 13 critical paths are identified in this paper. This paper provides a new perspective on the theoretical system of collaboration and practical guidance for enterprise to provide a higher-quality package of services. Full article

Quantum machine learning (QML) integrates quantum computing with classical machine learning. Within this domain, QML-CQ classification tasks, where classical data is processed by quantum circuits, have attracted particular interest for their potential to exploit high-dimensional feature maps, entanglement-enabled correlations, and non-classical priors. Yet, practical realizations remain constrained by the Noisy Intermediate-Scale Quantum (NISQ) era, where limited qubit counts, gate errors, and coherence losses necessitate frugal, noise-aware strategies. The Data Re-Uploading (DRU) algorithm has emerged as a strong NISQ-compatible candidate, offering universal classification capabilities with minimal qubit requirements. While DRU has been experimentally demonstrated on ion-trap, photonic, and superconducting platforms, no implementations exist for spin-based quantum processing units (QPU-SBs), despite their scalability potential via CMOS-compatible fabrication and recent demonstrations of multi-qubit processors. Here, we present a pulse-level, noise-aware DRU framework for spin-based QPUs, designed to bridge the gap between gate-level models and realistic spin-qubit execution. Our approach includes (i) compiling DRU circuits into hardware-proximate, time-domain controls derived from the Loss–DiVincenzo Hamiltonian, (ii) explicitly incorporating coherent and incoherent noise sources through pulse perturbations and Lindblad channels, (iii) enabling systematic noise-sensitivity studies across one-, two-, and four-spin configurations via continuous-time simulation, and (iv) developing a noise-aware training pipeline that benchmarks gate-level baselines against spin-level dynamics using information-theoretic loss functions. Numerical experiments show that our simulations reproduce gate-level dynamics with fidelities near unity while providing a richer error characterization under realistic noise. Moreover, divergence-based losses significantly enhance classification accuracy and robustness compared to fidelity-based metrics. Together, these results establish the proposed framework as a practical route for advancing DRU on spin-based platforms and motivate future work on error-attentive training and spin–quantum-dot noise modeling. Full article

Using big data for risk analysis of construction projects is a largely unexplored area. In this traditional industry, risk identification is often based either on so-called domain expert knowledge, in other words on experience, or on different statistical and quantitative analysis of individual past projects. The motivation of this research is based on the implemented and evaluated data-driven and AI-based DARIA approach to identify financial risks in the execution phase of transportation infrastructure construction projects that shows exceptional results at an early stage of the project execution phase and has already been deployed into enterprise-wide production within the STRABAG group. Due to DARIA’s productive use, concern and doubts about the trustworthiness of its ML algorithm are certainly possible, especially when DARIA identifies risky projects while all conventional metrics within the STRABAG controlling system do not identify any problems. “If AI systems do not prove to be worthy of trust, their widespread acceptance and adoption will be hindered, and the potentially vast societal and economic benefits will not be fully realized”. Thus, and based on the results of a user study during DARIA’s successful deployment into enterprise-wide production, this paper focuses on the identification of suitable indicators to measure the trustworthiness of the DARIA ML algorithm in the interaction between individuals and systems as well as on the modeling of the reproducibility of the internal state of DARIA’s ML model. Full article

Motivated by the seminal discoveries in graphene, the exploration of novel physical phenomena in alternative two-dimensional (2D) materials has attracted tremendous attention. In this work, through theoretical investigation using first-principles calculations, we reveal that Mo-intercalated ${\mathrm{CrI}}_3$ bilayer exhibits ferromagnetic semiconductor behavior with a small easy-plane magnetocrystalline anisotropy energy (MAE) of 0.618 meV/Cr(Mo) between (100) and (001) magnetizations. The spin–orbit coupling (SOC) opens a narrow band gap at the Fermi level for both magnetization orientations with nonzero Chern number for realizing the quantum anomalous Hall effect (QAHE) in the former and with trivial topology in the latter. The small MAE implies the efficient experimental manipulation of magnetization between distinct topologies through an external magnetic field. Our findings provide compelling evidence that the QAHE in this system originates from the quantum spin Hall effect (QSHE), driven by intrinsic magnetism under broken time-reversal symmetry. These unique properties position Mo-intercalated ${\mathrm{CrI}}_3$ as a promising candidate for tunable spintronic applications. Full article

This paper studies a multi-period strip cutting problem motivated by the paper industry. The focus is on multi-cutter slitting machines, which allow the simultaneous production of items with different lengths and provide higher cutting flexibility compared to conventional single-cutter machines. We propose a pattern-based mixed-integer programming formulation to evaluate the benefits of multi-cutter machines and compare it with heuristic strategies and a single-cutter benchmark. To address demand uncertainty, we extend the model using robust optimization with budgeted uncertainty sets and derive a tractable reformulation. Computational experiments with real-world data show that multi-cutter machines can substantially reduce raw material usage costs compared to the single-cutter setting. Under demand uncertainty, the budgeted robust model provides lower realized costs and smaller variability than both deterministic and box-type robust models. Full article

Background/Objectives: The stability of caregiver manpower plays a crucial role in the operation of long-term care institutions. This study adopts Social Exchange Theory as the theoretical foundation to construct the psychological mechanism through which organizational commitment and work value influence retention intention via job involvement. Against the backdrop of Taiwan’s intensifying aging society and the increasing service demands of the veterans’ support system, Veterans Homes have gradually become indispensable within the long-term care system. Therefore, the primary objective of this study is to explore the formation mechanism of retention intention among caregivers in Veterans Homes. Methods: Data analysis was conducted using structural equation modeling, with 447 valid samples collected from caregivers across 16 Veterans Homes in Taiwan. Results: The results indicate that, in the process of forming retention intention, job involvement serves as a mediator between organizational commitment and work value on retention intention and demonstrates significant mediating effects. Conclusions: These findings suggest that when caregivers perceive value realization and organizational identification in their work, they are more likely to exhibit active engagement, thereby strengthening their tendency to remain employed. Furthermore, the study reveals that the effect of organizational commitment on job involvement is stronger than that of work value, indicating that exchange motives triggered by emotional bonds carry greater implications for retention. In conclusion, organizational support and personal value perceptions stimulate emotional engagement, which further influences caregivers’ decisions to remain in long-term service and ultimately shape their retention behavior. Full article

Small and medium-sized enterprises (SMEs) often face severe resource constraints and operational fragility during crises. However, little is known about how managerial resilience (MR) translates into performance through time-related psychological resources and innovation—two capabilities that are both scarce and critical under such conditions. Drawing on Temporal Motivation Theory (TMT), this study develops and tests a dual-mediation model in which employee temporal psychological capital (TPC) and employee innovative behavior (EIB) transmit the effects of MR on performance. As a core methodological innovation, we adopt a multi-method analytical strategy to provide robust and complementary evidence rather than a hierarchy of results: Partial Least Squares Structural Equation Modeling (PLS-SEM) is used to examine sufficiency-based causal pathways and quantify the mediating mechanisms; Support Vector Machine (SVM) classification offers a non-parametric predictive validation of how MR and its mediators distinguish high- and low-performance cases; and Necessary Condition Analysis (NCA) identifies non-compensatory conditions that must be present for high performance to occur. These three methods address different research questions—sufficiency, classification robustness, and necessity—therefore serving as parallel, equally important components of the analysis. A total of 455 SME managers and employees were surveyed, and results show that MR significantly enhances all three dimensions of TPC (temporal control, temporal fit, time pressure resilience) and EIB (idea generation, idea promotion, idea realization), which in turn improve employee performance. SVM classification confirms that high MR, strong TPC, and active innovation align with high performance, while NCA reveals temporal control, idea generation, and idea realization as necessary bottleneck conditions. By integrating sufficiency–necessity logic with predictive classification, our findings suggest that SMEs should prioritize leadership resilience training to strengthen managers’ adaptive capacity, while simultaneously implementing time management interventions—such as temporal control workshops, workload balancing, and innovation pipeline support—to enhance employees’ ability to align tasks with organizational timelines, execute ideas effectively, and sustain performance during crises. Full article