Search Results (15,139)

Digital technologies are increasingly transforming teaching and learning, particularly through technology-enhanced assessment and feedback systems. This study examines the feasibility and pedagogical utility of the Kit-Build Concept Map (KBCM) system as a technology-supported approach for systematizing serial concept mapping in a human–computer interaction course. A three-week study was conducted with 258 undergraduate students, integrating a re-composition framework with real-time feedback to support continuous refinement of students’ externalized conceptual representations. Pre-tests, post-tests, and concept map analytics were used to evaluate learning gains and concept map structures across instructional sessions. The results show that the KBCM system enabled lecturers to identify individual and class-level map gaps and provide timely, data-informed feedback to support instructional monitoring and pedagogical decision-making. Students showed statistically significant improvements in learning outcomes, consistent progress across instructional weeks, along with a measurable reduction in discrepancies between student-generated maps and the expert map. These findings suggest that serial concept mapping with re-composition and feedback support may help students refine their externalized conceptual representations to become more closely aligned with target knowledge over time. Overall, this study highlights the potential of technology-enhanced concept mapping systems to support continuous instructional feedback, assessment, and data-informed pedagogical practices in higher education. The findings should be interpreted within the context of a short-term, three-week implementation focusing on changes in externalized conceptual representations rather than direct measurement of internal cognitive processes. Full article

Real-time, non-contact velocity measurement of hot-rolled bars is critical for metallurgical process control, but conventional laser Doppler velocimetry (LDV) systems often fail in these environments. The intense broadband thermal radiation from targets up to 1000 °C, coupled with severe surface depolarization, overwhelms weak scattered signals in high-speed (up to 40 m/s) rolling zones. To address this issue, we developed a fully integrated, thermal-radiation-resistant LDV sensing system. Hardware optimization was achieved by eliminating polarized-light transmission and adopting a parallel-beam design, which significantly enlarges the laser overlap area and increases detection depth. Furthermore, a 1550 nm laser (100 mW) was coaxially combined with a 10 nm narrow-band filter to isolate the thermal background and boost signal strength. A customized workflow utilizing continuous Fourier transform (CFT) spectral refinement and energy centroid estimation was implemented to precisely extract the true Doppler shift. Performance evaluations show the system achieves an excellent signal-to-noise ratio (SNR) of 29,532. Allan variance analysis confirms a stable detection sensitivity of 0.003 m/s (0.1 s integration time), a local short-to-medium-term optimal limit of 1.6 × 10⁻⁴ m/s, and a statistical accuracy of 0.005 m/s. Finally, the system was successfully deployed on an industrial rolling mill production line. It provided reliable velocity feedback for mill speed adjustment, achieving a near-zero-tension rolling process and fundamentally resolving workpiece dragging, squeezing, and steel pile-up. Full article

Typhoons are major physical disturbances in marginal seas, yet their event-scale impacts on microbial processes and carbon cycling remain poorly constrained. Here, we investigated the biogeochemical responses to Super Typhoon Maria (2018) in the East China Sea using combined field observations and satellite data. While surface temperature, nutrients, and chlorophyll-a (Chl-a) showed no significant changes, depth-integrated nutrients and Chl-a increased markedly, revealing a clear decoupling between surface and depth-integrated responses driven by vertical mixing and upwelling. Satellite observations further showed that phytoplankton enhancement was short-lived, with Chl-a returning to background levels within one week. This rapid attenuation likely reflects transient nutrient supply and strong grazing pressure. In contrast, microbial responses were characterized by increased bacterial specific growth rate without significant changes in biomass or production, indicating enhanced microbial turnover. Together, these results suggest that typhoon forcing promotes rapid and vertically structured carbon processing through the microbial loop without increasing biomass accumulation. This highlights the importance of temporal resolution and vertical structure in understanding ecosystem responses to episodic disturbances in marginal seas. Full article

Resistant starch type 3 (RS3) exhibits physiological benefits in regulating post-meal blood sugar levels and enhancing gut microbiota balance. In this study, mung bean starch was isolated and modified through debranching, annealing (ANN) and heat-moisture treatment (HMT). The multi-scale structures investigated by SEM, FT-IR, and XRD unveiled the formation of short-range ordered, helix, and crystalline structures. Notably, RS3 formed through debranching and HMT exhibited both a remarkably high RS content of 54.71% and a low estimated glycemic index (eGI) of 51.78. Statistical evaluation through correlation and stepwise regression analyses suggested that short-range molecular order was the primary factor associated with the resistance of RS3 to enzymatic hydrolysis, while the chain length of B-chains exerted secondary yet notable influences. This work provided novel insights into the interplay between processing methodologies, ordered molecular structures, and starch digestibility resistance. Full article

This study presents a damage-based comparative assessment of reinforced concrete buildings affected by the 1992 Erzincan earthquake (Mw 6.8) and the 2023 Kahramanmaraş earthquake sequence (Pazarcık, Mw 7.7; Elbistan, Mw 7.6), two destructive earthquake events in Türkiye separated by nearly three decades. A distinctive contribution of the study is the presentation of original color photographs from the 1992 Erzincan earthquake, systematically documented and comparatively evaluated for the first time and directly compared with post-earthquake field observations from Malatya following the 2023 earthquake sequence. To complement the field-based evidence, representative strong ground motion records from both earthquake events were processed and compared using standard seismic intensity and spectral response parameters. The spectral evaluation indicates that the 1992 Erzincan ground motion and the 2023 Elbistan-related motion recorded in Malatya imposed comparable seismic demands relevant to typical reinforced concrete buildings, thereby providing a rational basis for cross-event damage interpretation. Despite substantial advances in Turkish seismic design codes, recurrent damage mechanisms were observed in both building stocks, particularly soft-story formation, short-column effects, inadequate transverse reinforcement, poor beam–column joint performance, and deficiencies in material quality and detailing. The findings demonstrate that seismic safety cannot be improved through code development alone unless design provisions are consistently translated into construction quality, detailing practice, inspection, and field implementation. Full article

Lithium has become one of the key raw materials for the energy transition due to the central role of lithium-ion batteries in electromobility, energy storage, and the integration of renewable energy sources. However, the rapid increase in demand reveals growing environmental, social, geopolitical, and market tensions. The aim of the paper is a critical synthesis of global lithium utilization from the perspective of challenges, technological innovations, and integrative strategies supporting a more sustainable material–energy system. A broad, systematic literature review covering the entire value chain was applied: resources, extraction, processing, end-use applications, second life of batteries, recycling, and governance. The analysis shows that the strategic importance of lithium arises from the increasing demand pressure from electric vehicles and stationary storage, while the sustainability of the current model is constrained by supply concentration, uneven control over downstream stages, the water–carbon footprint of extraction and processing, social conflicts, and incomplete integration of secondary loops. At the same time, innovations such as direct lithium extraction (DLE), recovery from geothermal brines, design for recycling, second life, and battery passports can partially alleviate these tensions, but they do not eliminate the need for primary supply in the short term. The conclusion of the work is that sustainable global lithium utilization requires simultaneous diversification of sources, development of circular value chains, and multi-level governance integrating resource security, environmental efficiency, and social legitimacy. Full article

Driven by the high-end furniture industry’s demand for skin-tactile decorative boards, UV inkjet printing shows potential for wood-based surface finishing. Using primer-treated inkjet-printed melamine-faced panels, this study compared traditional UV varnish coatings with different thicknesses and UV curing intensities and 254 nm UV excimer-cured coatings with different radiant energies. Varnish thickness significantly affected surface roughness, 20° gloss, 85° gloss, and color difference, indicating a trade-off between matte tactile appearance and color fidelity. Thinner varnish coatings exhibited higher roughness and lower gloss but larger color differences, whereas thicker coatings better preserved color fidelity but resulted in higher gloss. For the UV excimer-cured system, one-way ANOVA showed significant treatment effects on acrylate conversion, water contact angle, 85° gloss, surface roughness, and abrasion mass loss. The coating prepared at an excimer radiant energy of 827.9 mJ/cm² showed the lowest 85° gloss of 5.28 GU and a pencil hardness of 3H, but also exhibited the highest abrasion mass loss in the short-cycle abrasion screening test. For both coating systems, three independently prepared specimens were tested for each processing condition. The UV varnish system was analyzed using two-way ANOVA, whereas the UV excimer-cured system was analyzed using one-way ANOVA. Friedman tests of sensory evaluation data showed significant differences among the eight selected samples for fineness, smoothness, and elasticity, with the excimer-cured coatings generally receiving higher fineness and smoothness scores than the UV varnish coatings. These results indicate that 254 nm UV excimer curing is a promising route for producing low-gloss, micro-wrinkle-induced skin-tactile surfaces on inkjet-printed melamine-faced panels, although optimization should balance tactile quality, gloss reduction, and abrasion resistance. Full article

Efficient management of soil–water resources is critical for global food security under intensifying climatic and demographic pressures. This review provides a comprehensive synthesis of artificial intelligence (AI) and distributed deep learning methodologies applied to soil–water interactions in precision agriculture. The physical and hydraulic foundations of soil–water systems—including water retention, unsaturated flow governed by the Richards equation, and soil degradation processes—are examined and situated within a unified framework of AI-based modeling and decision support. Classical machine learning (ML) algorithms (Random Forests, Support Vector Machines, gradient boosting) and deep learning architectures (convolutional neural networks, long short-term memory networks, transformers) are evaluated with respect to their capacity to predict soil moisture dynamics, estimate hydraulic properties, support smart irrigation scheduling, and generate digital soil maps at field-to-regional scales. Distributed training paradigms, federated learning for privacy-preserving multi-farm analytics, and edge AI deployment on low-power IoT hardware are assessed as enabling infrastructures for scalable agricultural intelligence. This review further addresses explainability, uncertainty quantification, and ethical dimensions inherent to AI-driven agricultural systems. Key challenges—including training data scarcity in data-poor regions, model interpretability, integration with physics-based hydrological models, and real-time deployment constraints—are critically discussed. Prospective research directions encompass physics-informed neural networks, foundation models for earth observation, autonomous digital twins of soil–water systems, and federated learning architectures aligned with data sovereignty frameworks. The synthesis underscores AI’s transformative potential for sustainable agricultural water management while delineating the technical and sociotechnical barriers that must be resolved to realize this potential at a global scale. Full article

Lactoferrin (LF)-derived peptides (LDPs) are short cationic and amphipathic fragments generated primarily from the N-terminal lobe of LF through pepsin-mediated proteolytic processes. The best-characterized LDPs include lactoferricin (LFcin), lactoferrampin (LFampin), and LF1‒11. In addition to these native peptides, a growing range of engineered LDPs has been developed by modifying the LFcin-derived RRWQWR motif through the incorporation of non-natural amino acids, cyclization, multimerization, and conjugation with chemotherapeutic agents. LDPs have garnered significant interest as potential anticancer peptides due to their ability to preferentially engage with the surfaces of malignant cells and initiate various tumor-suppressive mechanisms. This review article provides an overview of the principal classes of LDPs and elucidates how structural features influence membrane interaction, selectivity, intracellular targeting, apoptotic pathways, and immune modulation. It also discusses current mechanistic insights and examines the major challenges and opportunities for translating innovative LDPs into clinically useful cancer therapeutics. Full article

Millimeter-wave radar enables non-contact monitoring of cardiac activity and therefore has the potential to reconstruct electrocardiogram signals without surface electrodes. However, existing radar-based electrocardiogram reconstruction methods still suffer from incomplete extraction of heartbeat-related information and insufficient modeling of electrocardiogram-related features, which limits reconstruction accuracy. To address these issues, this study proposes a millimeter-wave radar-based electrocardiogram reconstruction method that integrates a respiratory-harmonic-suppressed multi-channel signal-processing frontend with the proposed CA-WTBNet deep reconstruction network. First, based on maximal overlap discrete wavelet transform-based multi-resolution analysis, respiratory harmonics mixed into heartbeat-related components are suppressed by combining respiratory harmonic detection with a heart-rate frequency protection strategy, while cardiac-related information is preserved as much as possible. A multi-channel input representation is then constructed. Meanwhile, the proposed deep reconstruction network is developed to jointly model complementary channel-wise features, local waveform morphology, and temporal dependencies by integrating channel-attention mechanisms, convolutional residual modules, window-based Transformer blocks, and bidirectional long short-term memory. Experiments conducted on the public dataset show that our method achieves an average Pearson correlation coefficient of 0.9641, a mean normalized root mean square error of 0.0458, an average R-peak F1 score of 0.9956, and an average R-peak timing error of 3.13 ms on the test set. In comparison with related studies on the same public Resting dataset, the proposed method achieves the best overall performance among the compared methods, with a 0.53% improvement in Pearson correlation coefficient and a 10.20% reduction in normalized root mean square error over the best-performing compared method. Full article

Under low-temperature and humid conditions, icing on airfoil surfaces, such as wind turbine blades, deteriorates the aerodynamic performance and decreases the power generation efficiency. To shorten the de-icing time and reduce the de-icing energy consumption, an ultrasonic de-icing method was used by coupling the longitudinal vibration of a piezoelectric transducer and the bending deformation of an iced plate. The simulation method was used to investigate the distributions and the variations of the stresses at the bond interface. An experimental system for ultrasonic de-icing tests was developed and built, and the de-icing experiments were carried out. The experimental results showed that the present ultrasonic de-icing method had a short de-icing time and low de-icing energy consumption, and the de-icing processes agreed with the simulation results. In the present research, the ice layer with a diameter of 20 mm was removed in the shortest de-icing time and the lowest energy consumption because its diameter was close to that of the transducer, which resulted in the highest shear stress at the bond interface. The present study provides theoretical and experimental foundations for deep research on the surface anti- and de-icing method with ultrasonic techniques. Full article

The increasing occurrence of pharmaceutical contaminants in aquatic environments has intensified the demand for sustainable and cost-effective water treatment technologies. This study investigated the conversion of potato peel waste into carbonaceous adsorbents through hydrothermal carbonization (HTC) and conventional pyrolysis (PRYR) for the removal of carbamazepine (CBZ) from synthetic wastewater. Hydrochars and biochars were synthesized under varying processing conditions and characterized using scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), elemental analysis, and Brunauer–Emmett–Teller (BET) surface area analysis. Adsorption experiments were conducted using a 50 mg/L CBZ solution at pH 6, room temperature, and an adsorbent dosage of 1 g/L. The adsorption performance was evaluated after short contact times to assess rapid-removal capability. HTC-derived hydrochars exhibited significantly superior performance compared with pyrolysis-derived biochars, achieving up to 97% CBZ removal and adsorption capacities approaching 50 mg g⁻¹ within 1 min of contact. In contrast, pyrolysis-derived biochars achieved removal efficiencies between approximately 7 and 55% under similar conditions. Correlation analysis between adsorption behaviour and physicochemical properties revealed that adsorption performance was more strongly influenced by surface chemistry, aromaticity, and mesoporosity than by BET surface area alone. FTIR analysis suggested that hydrogen bonding, π–π electron donor–acceptor interactions, and pore filling contributed to CBZ adsorption. HTC hydrochars retained abundant oxygen-containing functional groups that promoted rapid and stable adsorption, whereas pyrolysis-derived biochars exhibited weaker adsorption interactions despite possessing higher surface areas. The findings demonstrate that hydrothermal carbonization provides an effective low-temperature route for valorising potato peel waste into efficient adsorbents for rapid pharmaceutical removal from water and highlight the critical role of adsorbent surface chemistry in determining adsorption performance. Full article

The prediction of student engagement characteristics involves forecasting and analyzing student interaction with educational materials using engagement prediction models. This process encompasses the prediction of cognitive, behavioral, and emotional dimensions of engagement. The existing student engagement prediction models have some limitations, including poor convergence, less generalizability, complexity issues, overfitting, false errors, and limited resources. Hence, the research proposes the Multi-Instance Localization-based Gradient Boosted Long Short-Term Memory (MIL-GBLTM) model to tackle the challenge of predicting student engagement characteristics in online classes. The integration of effective MIL with a Triplet Attention mechanism focuses on the significant features that help with engagement prediction; LSTM layers capture intricate sequential patterns, and fractional gradient boosting is used for fine-tuning for accurate prediction, alongside ensemble-based learning. The LSTM layers with the Triplet Attention module refine temporal attention, and Fractional Gradient Boosting ensures the model’s adaptability and robustness. By combining these components, the proposed model is able to predict accurate student engagement with high convergence. This integrated approach enhances the capabilities of engagement prediction models in educational contexts, facilitating more effective interventions and personalized student support in online learning environments. Experimental results demonstrate that the proposed MIL-GBLTM model outperforms other existing models by achieving the highest accuracy of 96.55% with a k-fold of 10, utilizing the wacv2016 dataset. Full article

Predicting landslide displacement is important for geological-hazard early warning. In reservoir areas, displacement evolution is affected by rainfall, reservoir water level, vegetation variation, and the intrinsic non-stationarity of the displacement sequence, which makes accurate prediction difficult for conventional single-sequence models. To address this problem, this study proposes a residual-increment-oriented landslide displacement prediction framework that fuses multi-source monitoring variables. The displacement sequence is first processed into trend and periodic-related fluctuation representations, and the residual increment is used as the prediction target. Rainfall, reservoir water level, and the normalized difference vegetation index (NDVI) are incorporated as external monitoring variables. A cross-branch attention mechanism models interactions among heterogeneous feature branches, and a sparse MoE-based fusion module is introduced to adaptively adjust branch contributions under different deformation conditions. The model predicts the displacement residual increment, from which the final displacement is reconstructed. A case study using the Baishui River (Baishuihe) landslide monitoring dataset was conducted, together with additional validation on the related Bazimen Z110 landslide monitoring dataset and comparisons against conventional recurrent, convolutional, statistical, and Transformer-based baselines. The results show that the proposed model achieves lower RMSE and MAE than the compared methods on the tested datasets. These findings suggest that residual-increment modeling, multi-source monitoring variables, and condition-dependent branch fusion can improve short-term displacement prediction for the tested reservoir-area landslide cases. Full article

Accurate waveform classification in noisy environments is an important task in modern communications, radar signal analysis, biomedical signal interpretation, industrial monitoring and other signal processing systems. This paper investigates the performance of three neural network architectures: Multilayer Perceptron (MLP), one-dimensional Convolutional Neural Network (1D-CNN), and Long Short-Term Memory (LSTM) for multiclass waveform classification in the presence of Additive White Gaussian Noise (AWGN). A time series dataset consisting of multiple waveform classes is generated and corrupted with AWGN across a wide range of signal-to-noise ratio (SNR) levels to simulate noisy signal distortion conditions. The three models are trained and evaluated under identical conditions to ensure a fair comparison. Their classification performance is evaluated in terms of accuracy, Confusion Matrix (CM), Receiver Operating Characteristic (ROC) curve and the Area Under the ROC curve (AUC) across varying SNR values. Simulation results demonstrate that the 1D-CNN effectively captures local temporal patterns and achieves superior robustness in classification at moderate and high SNR levels. The LSTM model demonstrates the ability to capture temporal dependencies in sequential waveform data but exhibits sensitivity to waveform variations due to amplitude, phase and frequency changes and noise at lower SNR values. The MLP, although computationally simpler, shows comparatively limited performance in low-SNR conditions due to its lack of temporal feature extraction capability. For the case of multiclass deterministic waveforms, the accuracy of classification for the 1D-CNN and LSTM is nearly 100% at SNR = 5 dB showing their robustness in classification, whereas the accuracy of MLP is approximately 70% that shows poor classification in noisy conditions. When there is random amplitude, frequency and phase variations in the waveforms, the accuracy of the 1D-CNN and MLP increases with SNR, and 1D-CNN superior to MLP. However, the LSTM accuracy fails to improve with SNR, resulting in poor classification performance in such a scenario. The results provide an insight into the suitability of different neural architectures for waveform classification tasks in noisy communication or other time series applications and highlight the advantages of convolutional feature extraction for robust signal recognition. Full article