Search Results (1,141)

Large language models demand substantial computational and data resources, motivating approaches that improve the training efficiency of small language models. While curriculum learning methods based on linguistic difficulty measures have been explored as a potential solution, prior approaches that rely on complex linguistic indices are often computationally expensive, difficult to interpret, or fail to yield consistent improvements. Moreover, existing methods rarely incorporate the cognitive and linguistic efficiency observed in human language acquisition. To address these gaps, we propose a readability-driven curriculum learning method based on the Flesch Reading Ease (FRE) score, which provides a simple, interpretable, and cognitively motivated measure of text difficulty. Across two dataset configurations and multiple curriculum granularities, our method yields consistent improvements over baseline models without curriculum learning, achieving substantial gains on BLiMP and MNLI. Reading behavior evaluations also reveal human-like sensitivity to textual difficulty. These findings demonstrate that a lightweight, interpretable curriculum design can enhance small language models under strict data constraints, offering a practical path toward more efficient training. Full article

Rapid urbanization and global climate change are leading to intensified Urban Heat Island (UHI) in tropical regions. This study examined and analyzed urban ventilation corridors to mitigate UHI, paying particular attention to the building arrangement and wind environment. The comprehensive review emphasizes the importance of macro-scale urban planning, including the orientation of street grids and the design of breezeways and air paths. After analyzing these strategies, CFD simulations were applied to the design of high-rise buildings in Semarang and residential areas in Jakarta. These studies revealed that in high-rise building areas in Semarang, the proposed design configuration resulted in a 62% increase in ground-level wind speeds. A further analysis of residential areas in Jakarta revealed that the most comfortable location within a house was in the second row, facing the wind, where the distance between houses was 8.5 m, and the average velocity was 2.78 m/s. Research conducted in this area may contribute to the development of more sustainable and resilient urban areas in tropical climates, as well as assist local governments in planning for these areas. Full article

Productive and healthy soils are essential in agriculture and other economic uses of land which depend on plant growth, and are under increasing pressure globally. The physical properties of soil, its porosity and pore structure, also have a significant impact on a wide range of environmental factors, such as surface water runoff and greenhouse gas exchange. Methods exist for evaluating soil porosity that are applied in a laboratory environment or by inserting sensors into soil in the field. However, such methods do not readily sample adequately in space or time and are labour-intensive. The purpose of the current study is to investigate the potential for estimation of soil porosity and pore size using the strength of reflection of audio pulses from natural soil surfaces. Estimation of porous material properties using acoustic reflections is well established. But because of the complex, viscous interactions between sound waves and pore structures, these methods are generally restricted to transmissions at low audio frequencies or at ultrasonic frequencies. In contrast, this study presents a novel design for an integrated broad band sensing system, which is compact, inexpensive, and which is capable of rapid, non-contact, and in situ sampling of a soil structure from a small, moving, farm vehicle. The new system is shown to have the capability of obtaining soil parameter estimates at sampling distances of less than 1 m and with accuracies of around 1%. In describing this novel design, special care is taken to consider the challenges presented by real agriculture soils. These challenges include the pasture, through which the sound must penetrate without significant losses, and soil roughness, which can potentially scatter sound away from the specular reflection path. The key to this new integrated acoustic design is an extension of an existing theory for acoustic interactions with porous materials and rigorous testing of assumptions via simulations. A configuration is suggested and tested, comprising seven audio frequencies and three angles of incidence. It is concluded that a practical, new operational tool of similar design should be readily manufactured. This tool would be inexpensive, compact, low-power, and non-intrusive to either the soil or the surrounding environment. Audio processing can be conducted within the scope of, say, mobile phones. The practical application is to be able to easily map regions of an agricultural space in some detail and to use that to guide land treatment and mitigation. Full article

Controlled environment agriculture (CEA) pivots food production from an outdoor field setting to the indoors where growing conditions can be calibrated to fit crop needs. This research investigates vertical farms as a type of CEA. In particular, using the St. Louis area as a case study, it provides data-driven support for optimizing a vertical farm’s business model including its supply chain. The methodology presented here informs agri-preneurs about what crops to grow in a vertical farm, how much to grow given local market demand, and what vertical farm configuration (e.g., Dutch bucket, nutrient film technique, deep water culture) a facility should use. Based on the case study’s base scenario, the simulated vertical farm business would record an economic loss. However, the study did find several paths to improving profitability. First, reducing fixed and variable costs benefits profitability. Proper facility-level production and resource planning helps with managing the fixed costs. Second, increasing market prices may benefit profitability, but it has diminishing returns. As a result, firms can justify making investments that enhance their reputation and market competitiveness, though the advantage these marketing activities provide will decline as prices increase. Third, growing demand or increasing market share does not necessarily improve profitability. Full article

With the rapid advancement of artificial intelligence technology, the role of Artificial Intelligence Generated Content (AIGC) applications within digital learning communities has become increasingly significant. Enhancing the level of knowledge innovation through the integration of human and artificial intelligence has emerged as a critical issue. Grounded in social cognitive theory, this study utilizes a sample of 407 Super Star Learn community learners as a case study. It applies the Fuzzy Set Qualitative Comparative Analysis (fsQCA) method to investigate the synergistic effects of technological environment, cultural context, and individual cognitive factors in promoting learners’ knowledge innovation capabilities. The results show the following: (1) No single condition constitutes a prerequisite for learners to achieve high-level knowledge innovation when acting in isolation. However, enhancing technical capabilities has a relatively universal impact on promoting learners to achieve these results. (2) The multiple concurrency of the technological environment, cultural environment, and individual cognitive conditions has generated multiple configuration patterns that promote knowledge innovation, indicating that the paths leading to learners’ high-level innovation exhibit the characteristic of numerous concurrency. Therefore, it is suggested that digital learning communities actively explore new paths for sustainable knowledge innovation and development driven by generative artificial intelligence technology, thereby injecting sustainable impetus into the development and innovation process of learners, contributing to the goals of sustainable education. Full article

Generative Artificial Intelligence (GAI) has become a topic of increasing societal and academic relevance, with its rapid diffusion reshaping public debate, policymaking, and scholarly inquiry across diverse disciplines. Building on this context, the present study explores the factors influencing GAI adoption among Spanish digital natives (Millennials and Zoomers), using data from a large national survey of 1533 participants (average age = 33.51 years). The theoretical foundation of this research is the Theory of Planned Behavior (TPB). Accordingly, the study examines how perceived usefulness (USEFUL), innovativeness (INNOV), privacy concerns (PRI), knowledge (KNOWL), perceived social performance (SPER), and perceived need for regulation (NREG), along with gender (FEM) and generational identity (GENZ), influence the frequency of GAI use. A mixed-methods design combines ordered logistic regression to assess average effects and fuzzy set qualitative comparative analysis (fsQCA) to uncover multiple causal paths. The results show that USEFUL, INNOV, KNOWL, and GENZ positively influence GAI use, whereas NREG discourages it. PRI and SPER show no statistically significant differences. The fsQCA reveals 17 configurations leading to GAI use and eight to non-use, confirming an asymmetric pattern in which all variables, including PRI, SPER, and FEM, are relevant in specific combinations. These insights highlight the multifaceted nature of GAI adoption and suggest tailored educational, communication, and policy strategies to promote responsible and inclusive use. Full article

This study introduces a novel adaptive Mechanical Wave Energy Converter (MWEC) designed to efficiently capture nearshore wave energy for sustainable electricity generation along the southeast surf coast of NEOM (135° longitude). The MWEC system features a polyvinyl chloride (PVC) cubic buoy integrated with a mechanical power take-off (PTO) mechanism, optimized for deployment in shallow waters for a depth of around 1 m. Three buoy volumes, V1: 6000 cm³, V2: 30,000 cm³, and V3: 72,000 cm³, were experimentally evaluated under consistent PTO and spring tension configurations. The findings reveal a direct relationship between buoy volume and force output, with larger buoys exhibiting greater energy capture potential, while smaller buoys provided faster and more stable response dynamics. The energy retention efficiency of the buoy–PTO system was measured at 20% for V1, 14% for V2, and 10% for V3, indicating a trade-off between responsiveness and total energy capture. Notably, the largest buoy (V3) generated a peak power output of 213 W at an average wave amplitude of 65 cm, confirming its suitability for high-energy conditions along NEOM’s surf coast. In contrast, the smaller buoy (V1) performed more effectively during periods of reduced wave activity. Wave climate data collected during November and December 2024 support a hybrid deployment strategy, utilizing different buoy sizes to adapt to seasonal wave variability. These results highlight the potential of modular, wave-adaptive mechanical systems for scalable, site-specific renewable energy solutions in coastal environments like NEOM. The proposed MWEC offers a promising path toward low-cost, low-maintenance wave energy harvesting in shallow waters, contributing to Saudi Arabia’s sustainable energy goals. Full article

Offshore platforms need to be made, from the start of their construction, to withstand the extreme environmental conditions they will be facing. This study investigates the welding-induced residual stress and distortion in a Y-shaped tubular joint extracted from an offshore wind turbine jacket substructure. While similar joints are commonly used in offshore platforms, their welding behavior remains underexplored in the existing literature. The joint configuration is representative of critical load-bearing connections commonly used in offshore platforms exposed to harsh marine environments. A finite element model has been developed to simulate the welding process in a typical offshore tubular joint through thermal and mechanical simulation. Validation of the model has been achieved with results against reference experimental data, with temperature and distortion errors of 3.9 and 5.3%, respectively. Residual stress and distortions were analyzed along predefined paths in vertical, transverse, and longitudinal directions. A mesh sensitivity study was conducted to balance computational efficiency and result accuracy. Furthermore, clamped and free displacement boundary conditions are analyzed, demonstrating reduced deformation and stress for the second case. Full article

Molecular communication (MC) has emerged as a promising paradigm for nanoscale information exchange in Internet of Bio-Nano Things (IoBNT) environments, offering intrinsic biocompatibility and potential for real-time in vivo monitoring. This study proposes a cascaded MC channel framework for vascular stenosis detection, which integrates non-Newtonian blood rheology, bell-shaped constriction geometry, and adsorption–desorption dynamics. Path delay and path loss are introduced as quantitative metrics to characterize how structural narrowing and molecular interactions jointly affect signal propagation. On this basis, a peak response time-based delay inversion method is developed to estimate both the location and severity of stenosis. COMSOL 6.2 simulations demonstrate high spatial resolution and resilience to measurement noise across diverse vascular configurations. By linking nanoscale transport dynamics with system-level detection, the approach establishes a tractable pathway for the early identification of vascular anomalies. Beyond theoretical modeling, the framework underscores the translational potential of MC-based diagnostics. It provides a foundation for non-invasive vascular health monitoring in IoT-enabled biomedical systems with direct relevance to continuous screening and preventive cardiovascular care. Future in vitro and in vivo studies will be essential to validate feasibility and support integration with implantable or wearable biosensing devices, enabling real-time, personalized health management. Full article

Oxide semiconductor gas sensors are widely used due to their low cost, rapid response, small footprint, and ease of integration. However, in complex gas mixtures their selectivity is often limited by inherent cross-sensitivity. To address this, we developed a reconfigurable sensor-array system that supports up to 12 chemiresistive sensors with four- or six-electrode configurations, independent thermal control, and programmable gas paths. As a representative case study, we designed a customized array for fish-spoilage biomarkers, intentionally leveraging the cross-sensitivity and broad-spectrum responses of metal-oxide sensors. Following principal component analysis (PCA) preprocessing, we evaluated convolutional neural network (CNN), random forest (RF), and particle swarm optimization–tuned support vector machine (PSO-SVM) classifiers. The RF model achieved 94% classification accuracy. Subsequent channel optimization (correlation analysis and feature-importance assessment) reduced the array from 12 to 8 sensors and improved accuracy to 96%, while simplifying the system. These results demonstrate that deliberately leveraging cross-sensitivity within a carefully selected array yields an information-rich odor fingerprint, providing a practical platform for complex gas-mixture identification and food-freshness assessment. Full article

Multilayer heterostructures of [(BiFeO₃)_m/(SrTiO₃)_n]_p were synthesized on ITO-coated quartz substrates via pulsed laser deposition, with varying thickness ratios (r = n/m) and periodicities (p = 1–3). Structural, electrical, and piezoelectric properties were systematically investigated using X-ray diffraction, AFM, and PFM. The BiFeO₃ layers crystallized in a distorted rhombohedral phase (R3c), free of secondary phases. Compared to single-layer BiFeO₃ films, the multilayers exhibited markedly lower leakage current densities and enhanced piezoelectric response. Electrical conduction transitioned from space-charge-limited current at low fields (E < 100 kV/cm) to Fowler–Nordheim tunneling at high fields (E > 100 kV/cm). Optimal performance was achieved for r = 0.30, p = 1, with minimal leakage (J = 8.64 A/cm² at E = 400 kV/cm) and a peak piezoelectric coefficient (d₃₃ = 55.55 pm/V). The lowest coercive field (Ec = 238 kV/cm) occurred in the configuration r = 0.45, p = 3. Saturated hysteresis loops confirmed stable ferroelectric domains. These findings demonstrate that manipulating layer geometry in [(BiFeO₃)_m/(SrTiO₃)_n]_p stacks significantly enhances functional properties, offering a viable path toward efficient, lead-free piezoelectric nanodevices. Full article

Unmanned surface vehicles (USVs) rely on multi-sensor perception, such as radar, LiDAR, GPS, and vision, to ensure safe and efficient navigation in complex maritime environments. Traditional ant colony optimization (ACO) for path planning, however, suffers from premature convergence, slow adaptation, and poor smoothness in cluttered waters, while the dynamic window approach (DWA) without global guidance can become trapped in local obstacle configurations. This paper presents a sensor-oriented hybrid method that couples an improved ACO for global route planning with an enhanced DWA for local, real-time obstacle avoidance. In the global stage, the ACO state–transition rule integrates path length, obstacle clearance, and trajectory smoothness heuristics, while a cosine-annealed schedule adaptively balances exploration and exploitation. Pheromone updating combines local and global mechanisms under bounded limits, with a stagnation detector to restore diversity. In the local stage, the DWA cost function is redesigned under USV kinematics to integrate velocity adaptability, trajectory smoothness, and goal-deviation, using obstacle data that would typically originate from onboard sensors. Simulation studies, where obstacle maps emulate sensor-detected environments, show that the proposed method achieves shorter paths, faster convergence, smoother trajectories, larger safety margins, and higher success rates against dynamic obstacles compared with standalone ACO or DWA. These results demonstrate the method’s potential for sensor-based, real-time USV navigation and collision avoidance in complex maritime scenarios. Full article

Crack monitoring in concrete structures is essential to maintaining structural integrity. Therefore, this paper proposes a mobile ground robot equipped with a 2-DoF manipulator and stereo vision sensors for autonomous crack monitoring and mapping. To facilitate crack detection over large areas, a 2-DoF motorized manipulator providing linear and rotational motions, with a stereo vision sensor mounted on the end effector, was deployed. In combination with a manual rotation plate, this configuration enhances accessibility and expands the field of view for crack monitoring. Another stereo vision sensor, mounted at the front of the robot, was used to acquire point cloud data of the surrounding environment, enabling tasks such as SLAM (simultaneous localization and mapping), path planning and following, and obstacle avoidance. Cracks are detected and segmented using the deep learning algorithms YOLO (You Only Look Once) v6-s and SFNet (Semantic Flow Network), respectively. To enhance the performance of crack segmentation, synthetic image generation and preprocessing techniques, including cropping and scaling, were applied. The dimensions of cracks are calculated using point clouds filtered with the median absolute deviation method. To validate the performance of the proposed crack-monitoring and mapping method with the robot system, indoor experimental tests were performed. The experimental results confirmed that, in cases of divided imaging, the crack propagation direction was predicted, enabling robotic manipulation and division-point calculation. Subsequently, total crack length and width were calculated by combining reconstructed 3D point clouds from multiple frames, with a maximum relative error of 1%. Full article

Yield stress and thixotropy are critical rheological properties for enabling successful 3D printing of magnetic colloidal systems. However, conventional magnetic colloids, typically composed of a single dispersed phase, exhibit insufficient rheological tunability for reliable 3D printing. In this study, we developed a novel magnetic colloidal system comprising a carrier liquid, magnetic nanoparticles, and organic modified bentonite. A direct-ink-writing 3D-printing platform was specifically designed and optimized for thixotropic materials, incorporating three distinct extruder head configurations. Through an in-depth rheological investigation and printing trials, quantitative analysis revealed that the printability of magnetic colloids is significantly affected by multiple factors, including magnetic field strength, pre-shear conditions, and printing speed. Furthermore, we successfully fabricated 3D architectures through the precise coordination of deposition paths and magnetic field modulation. This work offers initial support for the material’s future applications in soft robotics, in vivo therapeutic systems, and targeted drug delivery platforms. Full article

Frame Replication and Elimination for Reliability (FRER) in Time-Sensitive Networking (TSN) enhances fault tolerance by duplicating critical traffic across disjoint paths. However, always-on FRER configurations introduce persistent redundancy overhead, even under nominal network conditions. This paper proposes a predictive FRER activation framework that anticipates faults using a Key Performance Indicator (KPI)-driven bidirectional Long Short-Term Memory (BiLSTM) model. By continuously analyzing multivariate KPIs—such as latency, jitter, and retransmission rates—the model forecasts potential faults and proactively activates FRER. Redundancy is deactivated upon KPI recovery or after a defined minimum protection window, thereby reducing bandwidth usage without compromising reliability. The framework includes a Python-based simulation environment, a real-time visualization dashboard built with Streamlit, and a fully integrated runtime controller. The experimental results demonstrate substantial improvements in link utilization while preserving fault protection, highlighting the effectiveness of anticipatory redundancy strategies in industrial TSN environments. Full article