Search Results (188,963)

Studies on two-dimensional materials (such as topological insulators or transition metal dichalcogenides) have shown that they exhibit unique properties, including high charge carrier mobility and tunable bandgaps, making them attractive for next-generation electronics. Some of these materials (e.g., HfSe${}_2$) also offer thickness-dependent bandgap engineering. However, the standard device fabrication techniques often introduce processing contamination, which reduces device efficiency. In this paper, we present a modified mechanical exfoliation technique, the Reversed Structuring Procedure, which enables the fabrication of hybrid systems based on 2D microflakes with improved interface cleanness and contact quality. Hall effect measurements on Bi${}_2$Se${}_3$ and HfSe${}_2$ devices confirm enhanced electrical performance, including the decrease in the measured total resistance. We also introduce a novel Star-Shaped Electrode Structure, which allows for accurate Hall measurements and the exploration of geometric magnetoresistance effects within the same device. This dual-purpose geometry enhances the flexibility and demonstrates broader functionality of the proposed fabrication method. The presented results validate the Reversed Structuring Procedure method as a robust and versatile approach for laboratory test-platforms for electronic applications of new types of layered materials whose fabrication technology is not yet compatible with CMOS. Full article

This paper proposes an event-triggered extension of duty-ratio-based model predictive direct speed control (DR-MPDSC) for permanent magnet synchronous motor (PMSM) drives in electric vehicle (EV) applications. The main contribution is the development of an event-triggered execution framework specifically tailored to DR-MPDSC, in which control updates are performed only when the speed tracking error violates a prescribed condition, rather than at every sampling instant. Unlike conventional MPDSC and time-triggered DR-MPDSC schemes, the proposed strategy achieves a significant reduction in control execution frequency while preserving fast dynamic response and closed-loop stability. An optimized duty-ratio formulation is employed to regulate the effective application duration of the selected voltage vector within each sampling interval, resulting in reduced electromagnetic torque ripple and improved stator current quality. An extended Kalman filter (EKF) is integrated to estimate rotor speed and load torque, enabling disturbance-aware predictive speed control without mechanical torque sensing. Furthermore, a unified field-weakening strategy is incorporated to ensure wide-speed-range operation under constant power constraints, which is essential for EV traction systems. Simulation and experimental results demonstrate that the proposed event-triggered DR-MPDSC achieves steady-state speed errors below 0.5%, limits electromagnetic torque ripple to approximately 2.5%, and reduces stator current total harmonic distortion (THD) to 3.84%, compared with 5.8% obtained using conventional MPDSC. Moreover, the event-triggered mechanism reduces control update executions by up to 87.73% without degrading transient performance or field-weakening capability. These results confirm the effectiveness and practical viability of the proposed control strategy for high-performance PMSM drives in EV applications. Full article

The rapid advancement of Artificial Intelligence (AI) technologies has significantly transformed teaching, learning, and research practices within higher education institutions (HEIs). Although a growing body of literature has examined the application of AI in higher education, existing studies remain fragmented, often focusing on isolated tools or outcomes, with limited synthesis of best practices, core functionalities, and implementation challenges across diverse contexts. To address this gap, this systematic review aims to comprehensively examine the best practices, functionalities, and challenges associated with the integration of AI in HEIs. A comprehensive literature search was conducted across major academic databases, including Google Scholar, Scopus, Taylor & Francis, and Web of Science, resulting in the inclusion of 35 peer-reviewed studies published between 2014 and 2024. The findings suggest that effective AI integration is supported by best practices, including promoting student engagement and interaction, providing language support, facilitating collaborative projects, and fostering creativity and idea generation. Key AI functionalities identified include adaptive learning systems that personalize educational experiences, predictive analytics for identifying at-risk students, and automated grading tools that improve assessment efficiency and accuracy. Despite these benefits, significant challenges persist, including limited knowledge and skills, ethical concerns, inadequate infrastructure, insufficient institutional and management support, data privacy risks, inequitable access to technology, and the absence of standardized evaluation metrics. This review provides evidence-based insights to inform educators, institutional leaders, and policymakers on strategies for leveraging AI to enhance teaching, learning, and research in higher education. Full article

Pesticide residues from agriculture pose persistent threats to ecosystems and human health. Precipitation and surface runoff facilitate the transport of pesticide residues, leading to their subsequent accumulation in lakes and rivers. Microalgae-based bioremediation offers a promising and environmentally friendly approach for degrading and detoxifying these residues. This study employed liquid chromatography–mass spectrometry (LC-MS) to determine pesticide residues in various microalgal solutions. Using three-dimensional excitation-emission matrix (3D-EEM) spectroscopy and fluorescence regional integration (FRI), we quantified the dynamics of dissolved organic matter (DOM) and its relationship with pesticide degradation in the microalgal system. Over time, Tolypothrix tenuis exhibited the highest degradation rate for THX (95.7%), while Anabaena showed the most effective degradation for ATZ (53.8%). Based on structural analysis of degradation products, three potential degradation pathways for THX and ATZ under microalgae action were proposed. Moreover, the degradation process may also involve reactive oxygen species and intracellular enzymes. Hydroxylation and carboxylation were the primary reactions involved in THX degradation, leading to ring opening and subsequent mineralization. In ATZ, the initially removed groups included methyl and carbonyl groups, with the final products undergoing hydroxylation and subsequent mineralization to water and carbon dioxide. This study, conducted within the context of aquatic environmental protection, investigates the threat of pesticide residues to aquatic ecosystems. It further elucidates the associated environmental impacts and degradation mechanisms from a microalgal perspective. Full article

The growing global demand for critical raw materials has highlighted the need for autonomous systems in abandoned underground mines. We propose a multi-robot coordination architecture using Hierarchical Finite State Machines (HFSMs) for sequential task execution in GPS-denied, infrastructure-less environments. Unlike existing centralized approaches, our system enables each robot to execute its own HFSM behavior triggered through inter-robot communication, eliminating dependency on persistent connectivity. We implemented and validated this architecture using a Deployer robot and a Stinger robot within the EU Horizon PERSEPHONE project. Experimental validation demonstrated successful coordination both with persistent connectivity and during network interruptions, proving the system’s fault tolerance capabilities. The system successfully executed sequential deployment and anchoring tasks, demonstrating that this coordination approach enables multi-robot coordination without requiring persistent connectivity, thereby addressing critical limitations for autonomous operations in underground environments. Full article

This review explores the advancements and applications of β-hairpin peptide hydrogels, starting from the paradigmatic case of MAX1 and its highly versatile analogue MAX8. MAX1 (H-VKVKVKVKV^DPPTKVKVKVKV-NH₂) has been identified as the first synthetic β-hairpin peptide for the preparation of stimuli-responsive peptide-based hydrogels. At low ionic strength or neutral pH, MAX1 remains unfolded and soluble. However, under physiological conditions, it folds into a β-hairpin structure, then producing a self-supporting matrix within minutes. The formed gel is shear-thinning and self-healing, making it suitable for injectable therapies. To explore MAX1 molecular space and enhance its practical clinical use, the primary sequence was engineered via chemical modification, with specific single amino acid substitution and relative net charge alteration. This approach generates MAX1 analogues, differing in gelation kinetics, mechanical response and biological performances. The β-hairpin peptide hydrogels are categorized into five different groups: MAX1, MAX1 analogues, MAX8, MAX8 analogues and non-MAX peptides sequences. Collectively, the review outcomes demonstrate the use of β-hairpin peptide matrices as tunable platforms for the development of predictable and stable biomaterials for advanced tissue engineering and drug delivery applications. Full article

This study experimentally investigates the cooling performance of a single-opening wind catcher model under varying orientations and wind speeds. The wind catcher was connected to a horizontal cavity representing an indoor space, with a rear outlet simulating a window opening. Electric resistors were installed at the catcher shaft and in the middle of the cavity length to simulate the building’s heat loads. Experiments were conducted in a wind tunnel, where K-type thermocouples were employed to record temperature variations for both closed and open cavity ends. Five wind speeds (4–9 m/s) and five orientations (0–180°) were examined. Under the closed-cavity configuration, the maximum temperature reduction (cooling) of 4 °C occurred at an orientation of 180°, at which the catcher opening was positioned on the leeward side. This orientation created a low-pressure region at the catcher’s inlet, located within the wake of the model, which, combined with a favorable vertical temperature gradient, enhanced suction-driven cooling. In the open-cavity configuration, cooling was observed for all orientations and wind speeds. The greatest temperature reduction of 6 °C occurred at the 180° orientation, whereas other orientations produced lower temperatures changes, down to 2 °C. Full article

This article introduces the design and implementation of an Unmanned Surface Vehicle (USV), named “AquaMIB”, which introduces a novel and integrated approach for real-time and autonomous water quality monitoring in aquatic environments. The system integrates modular hardware and software, combining sensors for temperature, pH, conductivity, dissolved oxygen, and oxidation reduction potential with GPS, LiDAR, a digital compass, communication modules, and a dedicated power unit. Software components include Python on a Raspberry Pi for navigation and control, C on an Atmega 324P for sensing, C++ on an Arduino Uno for remote control, and C#/JavaScript for the web-based control center. Users assign task points, and the USV autonomously navigates, collects data, and transmits it via RESTful API. Field trials showed 96.5% navigation accuracy over 2.2 km, with 66% of task points reached within 3 m. A total of 120 measurements were processed in real time and visualized as GIS-based spatial maps. The system demonstrates a cost-effective, modular solution for aquatic monitoring. The system’s ability to generate real-time GIS maps enables immediate identification of environmental anomalies, transforming raw sensor data into an actionable decision-support tool for aquatic management. Full article

In fields such as rock and soil grouting and petroleum extraction, the flow of water driven by an immiscible fluid (or vice versa) within a porous medium is frequently encountered. Due to the presence of an interface between the two fluids, whose position changes over time and needs to be solved concurrently with the fluid pressure field, this issue represents a special two-phase moving boundary problem. In this paper, fundamental governing equations for this moving boundary problem in one-dimensional Cartesian, cylindrical, and spherical coordinate systems are developed. Analytical solutions for the pore pressure distribution and interface movement are obtained through the method of similarity transformation. By disregarding the pressure variation in the original underground water, this two-phase moving boundary problem can be reduced into a one-phase moving boundary problem. Consequently, analytical solutions for this one-phase problem are also obtained. The analytical solutions mainly address specific boundary conditions. For cases with general boundary conditions, numerical solutions are provided through a combination of finite volume method and moving node approach. By assuming the instantaneous establishment of a steady-state pore pressure distribution within the medium, the transient two-phase flow model is transformed into a quasi-steady model. Subsequently, an approximate solution for the quasi-steady model is also established. After verifying the model solutions, computational examples are presented to evaluate the effectiveness of the one-phase approximation and the quasi-steady approximation. The one-phase model tends to underestimate fluid pressure within the porous medium under pressure boundary conditions, thereby overestimating the movement speed of the two-phase interface. Additionally, under flow rate boundary conditions, the one-phase model tends to underestimate the pressure required to achieve the design flow rate. As the stiffness of the porous medium increases, the influence of the pressure variation rate term in the transient model equations gradually diminishes. Consequently, the interface movement and pore pressure distribution obtained from the quasi-steady solutions are essentially consistent with those obtained from the transient model, and the quasi-steady solutions are convenient to apply under these circumstances. Full article

The present study describes the ten-year (2014–2024) validation of a Class 100,000ISO 8 qualified air system used in the manufacture of non-sterile pharmaceutical dosage forms in a GMP-certified facility. The lifecycle evaluation included design, installation, qualification, continuous operation, environmental monitoring, cleaning and disinfection verification, and annual third-party validation. The system was assessed for critical parameters, including air renewal rates, airflow directionality, the integrity of high-efficiency particulate air (HEPA) filters and ultra-low penetration air (ULPA) filters, environmental recovery times, and non-viable particle counts. Particle monitoring focused on 0.5 $\mathsf{\mu }$m and 1.0 $\mathsf{\mu }$m channels within the 0.5–5 $\mathsf{\mu }$m range specified by ISO 14644-1 for ISO 8 areas. The 0.5–1.0 $\mathsf{\mu }$m range was prioritized because it provides higher statistical representativeness for evaluating filter performance and controlling fine particulate dispersion, which is particularly relevant in non-sterile pharmaceutical production, while larger particles (>5 $\mathsf{\mu }$m) are more critical in aseptic processes. The influence of personnel and air exchange rates on cleanliness was also assessed during the final years of the study. Results demonstrate that continuous, systematic validation ensures the controlled environmental conditions required for pharmaceutical production and supports the sustained quality and safety of the finished products. This study provides a technical reference for engineers, pharmacists, and quality professionals involved in cleanroom design, qualification, and regulatory compliance. Full article

Ground-source heat pump (GSHP) systems are widely regarded as an energy-efficient solution for building heating and cooling. However, their actual performance in large commercial buildings is often limited by rigid control strategies, insufficient equipment coordination, and suboptimal load matching. In the Liuzhou Fengqing Port commercial complex, the seasonal coefficient of performance (SCOP) of the GSHP system remains at a relatively low level of 3.0–3.5 under conventional operation. To address these challenges, this study proposes a gray-box-model-based cooperative optimization and group control strategy for GSHP systems. A hybrid gray-box modeling approach (YFU model), integrating physical-mechanism modeling with data-driven parameter identification, is developed to characterize the energy consumption behavior of GSHP units and variable-frequency pumps. On this basis, a multi-equipment cooperative optimization framework is established to coordinate GSHP unit on/off scheduling, load allocation, and pump staging. In addition, continuous operational variables (e.g., chilled-water supply temperature and circulation flow rate) are globally optimized within a hierarchical control structure. The proposed strategy is validated through both simulation analysis and on-site field implementation, demonstrating significant improvements in system energy efficiency, with annual electricity savings of no less than 3.6 × 10⁵ kWh and an increase in SCOP from approximately 3.2 to above 4.0. The results indicate that the proposed framework offers strong interpretability, robustness, and engineering applicability. It also provides a reusable technical paradigm for intelligent energy-saving retrofits of GSHP systems in large commercial buildings. Full article

We present an advanced statistical framework for estimating the relative intensity of astrophysical event distributions (e.g., Gamma-Ray Bursts, GRBs) on the sky tofacilitate population studies and large-scale structure analysis. In contrast to the traditional approach based on the ratio of Kernel Density Estimation (KDE), which is characterized by numerical instability and bandwidth sensitivity, this work applies a logistic regression embedded in a Bayesian framework to directly model selection effects. It reformulates the problem as a logistic regression task within a Generalized Additive Model (GAM) framework, utilizing isotropic Splines on the Sphere (SOS) to map the conditional probability of redshift measurement. The model complexity and smoothness are objectively optimized using Restricted Maximum Likelihood (REML) and the Akaike Information Criterion (AIC), ensuring a data-driven bias-variance trade-off. We benchmark this approach against an Adaptive Kernel Density Estimator (AKDE) using von Mises–Fisher kernels and Abramson’s square root law. The comparative analysis reveals strong statistical evidence in favor of this Preconditioned (Precon) Estimator, yielding a log-likelihood improvement of $\Delta \mathcal{L}\approx 74.3$ (Bayes factor $>{10}^{30}$) over the adaptive method. We show that this Precon Estimator acts as a spectral bandwidth extender, effectively decoupling the wideband exposure map from the narrowband selection efficiency. This provides a tool for cosmologists to recover high-frequency structural features—such as the sharp cutoffs—that are mathematically irresolvable by direct density estimators due to the bandwidth limitation inherent in sparse samples. The methodology ensures that reconstructions of the cosmic web are stable against Poisson noise and consistent with observational constraints. Full article

To improve the pumping performance of biomimetic flapping-wing devices in small river channels, this study introduces high-frequency disturbances in the heave direction based on traditional pitch–heave motion. A systematic investigation of the forces and hydrodynamic performance is conducted using numerical simulations, with vortex contour analysis to explore the evolution mechanism of the wake vortex structure. The results show that high-frequency disturbances cause the instantaneous thrust to exhibit an amplitude modulation feature, with thrust oscillating approximately f_p/f_b times within one base frequency cycle. As the disturbance frequency increases, the average thrust also increases. There is a significant frequency-dependent difference in performance: at low disturbance frequencies (f_p/f_b ≤ 16), changes in thrust, pressure difference, and flow rate are limited, with little improvement in pumping efficiency; at intermediate frequencies (16 < f_p/f_b ≤ 32), wake coherence and jet momentum flux are significantly enhanced, and both thrust and pumping efficiency reach their maximum (up to 47%); at high disturbance frequencies (f_p/f_b > 32), although the vortex structure is further strengthened, input power increases sharply, leading to a decrease in efficiency. Overall, moderate disturbance frequencies can effectively enhance the thrust and pumping performance of the flapping wing, while excessively high frequencies do not offer an advantage due to the high energy cost. Full article

The advent of generative AI (GenAI) and its growing use in education has sparked a renewed wave of school digital transformation. School principals are pivotal in advancing and shaping school digital transformation, yet little is known about how they understand and lead digital transformation in the age of GenAI, particularly within China’s complex educational system. This study employed Q methodology to identify the perceptions and leadership styles of Chinese K–12 school principals toward school digital transformation in the age of GenAI. An analysis of a 30-item Q set with a P sample of 23 principals revealed four leadership types: Cautious Observation–Technological Gatekeeping Leadership, Moderate Ambition–Culturally Transformative Leadership, Moderate Ambition–Emotionally Empowering Leadership, and High Aspiration–Strategy-Driven Leadership. Overall, principals’ stances on GenAI formed a continuum, ranging from cautious observation and skeptical optimism to active embrace. These perceptions and leadership styles were shaped by Confucian cultural values, a flexible central–local governance arrangement, and parents’ high expectations for students’ academic achievement. Furthermore, structural constraints in resource provision further heightened principals’ reliance on maintaining guanxi-based relationships. This study enhances the understanding of the diversity of principals’ leadership practices worldwide and offers actionable insights for governments and principals to more effectively advance AI-enabled school digital transformation. Full article

The microstructure of the Ti-Al binary system is an area of great interest, as it affects material properties and plasticity. Phase transformations induce microstructural changes; therefore, accurately modeling the phase transformations of the Ti-Al system is necessary to describe plasticity. Interatomic potentials can be a powerful tool to model how materials behave; however, existing potentials lack accuracy in certain aspects. While classical potentials like the Modified Embedded Atom Method (MEAM) perform adequately for modeling a dilute Al solute within Ti’s $\alpha$ phase, they struggle with accurately predicting plasticity. In particular, they struggle with stacking fault energies in intermetallics and to some extent elastic properties. This hinders their effectiveness in investigating the plastic behavior of formed intermetallics in Ti-Al alloys. Classical potentials also fail to predict the $\alpha$-to-$\beta$ phase boundary. Existing machine learning (ML) potentials reproduce the properties of formed intermetallics with density functional theory (DFT) but do not accurately capture the $\alpha$-to-$\beta$ or $\alpha$-to-D0₁₉ phase boundaries. This work uses a rapid artificial neural network (RANN) framework to produce a neural network potential for the Ti-Al binary system. This potential is capable of reproducing the Ti-Al binary phase diagram up to 30% Al concentration. The present interatomic potential ensures stability and allows results near the accuracy of DFT. Using Monte Carlo simulations, the RANN potential accurately predicts the $\alpha$-to-$\beta$ and $\alpha$-to-D0₁₉ phase transitions. The current potential also exhibits accurate elastic constants and stacking fault energies for the L1₀ and D0₁₉ phases. Full article