Search Results (265)

Metal oxide nanoparticles serve as crucial drivers in modern biomedical, catalytic, environmental, and energy technologies due to their high surface-to-volume ratios and quantum confinement properties. Traditional chemical and physical synthesis methods remain limited by significant energy footprints, high costs, and the use of hazardous reagents. To address these challenges, bioinspired (“green”) synthesis has emerged as a sustainable paradigm that employs biological systems as nature nanofactories. This critical review provides a provides a comprehensive and systematic analysis of the green synthesis of major metal oxide systems (ZnO, TiO₂, Fe₃O₄/Fe₂O₃, CuO, Co₃O₄, CeO_2, and MnO₂) using diverse biological templates, including plant extracts, bacteria, fungi, algae, and biopolymers. Moving beyond simple descriptive summaries, we critically evaluate the foundational electron-transfer and nucleation mechanism, systematically correlate processing parameters with physical outcomes, and offer a rigorous comparative analysis across different biological kingdoms. Finally, we directly address the underlying challenges facing the field: reproducibility bottlenecks, scalability limits, environmental safety variations, and regulatory hurdles necessary for industrial translation. Full article

Background/Objectives: The accuracy and reliability of automated clinical analyzers are fundamental to patient safety and effective medical decision-making. Traditional quality control (QC) methods, which rely on periodic manual calibration and reactive maintenance, are inherently limited by high latency and labor costs; furthermore, they fail to provide continuous, real-time monitoring. This paper introduces a novel hybrid ensemble learning framework for the automated quality inspection of medical devices through the analysis of time-series reaction curves. Methods: Our system integrates three heterogeneous anomaly detection paradigms: an Enhanced Dynamic Time Warping (DTW) detector for robust non-linear pattern matching, a Shape Template Matching (STM) detector that mimics expert clinical logic by analyzing morphological features in a normalized shape space, and a specialized Time-series Variational Autoencoder (TimeVAE) for deep representation learning. The outputs of these detectors are fused using a weighted ensemble strategy, which is specifically designed to prioritize the minimization of false negatives—a critical requirement in medical diagnostics. Results: We evaluate our framework on a comprehensive, multi-center real-world dataset comprising seven distinct biochemical assays. Experimental results demonstrate that our proposed method achieves superior performance, attaining a $0\%$ false negative rate on CRE and DBIL assays and outperforming all baseline methods on the other five datasets. An ablation study confirms the model’s robustness even with limited training data, and a comparative analysis against eight state-of-the-art baseline methods further validates the effectiveness of our domain-optimized ensemble approach. Conclusions: The system provides a robust, interpretable, and highly automated solution for transitioning from reactive maintenance to proactive, real-time quality assurance in clinical laboratories. Full article

Artificial intelligence (AI) has reshaped higher education over six decades, evolving from drill-and-practice programs to adaptive cognitive tutors and, most recently, transformer-based generative models. This article presents a critical review of publisher-built generative AI assistants, adopting an explicitly socio-technical perspective that combines a technological lens with a pedagogical one. It makes three contributions. First, it synthesizes the technical and algorithmic evolution of educational AI, from rule-based and expert systems through knowledge tracing and learning analytics to large language models and retrieval-augmented generation, and organizes these mechanisms into a taxonomy. Second, it introduces a reproducible evaluation framework that couples the TRIAD rubric (Trust, Relevance, Impact, Adoption, and Design) with a Jobs-to-Be-Done (JTBD) lens, complete with anchored scoring criteria, an evidence-and-confidence grading scheme, and reported inter-rater reliability. Third, it applies the framework to eleven assistants released by U.S. publishers, distinguishing peer-reviewed evidence from institutional reports and commercial claims. The analysis reflects a mid-2025 snapshot and is presented as a reusable template rather than a static ranking. Findings reveal substantial variation in privacy safeguards, curricular alignment, documented impact, adoption, and usability. The review identifies application scenarios and recommendations for researchers and institutional leaders seeking to guide the responsible integration of AI in higher education. Full article

Target tracking for Unmanned Aerial Vehicles (UAVs) can be significantly impacted by environmental factors such as lighting variations, background clutter, and target occlusion. To address these challenges, we developed a target tracking method that integrates both frequency-domain and spatial perception capabilities in UAV vision (FSTrack). Specifically: (1) we utilized the Swin Transformer as the core network to extract features from both the template and search images; (2) we introduced a Transformer-based module to enhance both frequency and spatial information, improving tracking accuracy under varying illumination conditions; (3) we designed a spatio-temporal feature fusion module with multiple multi-head self-attention mechanisms to precisely model the tracking state, thus increasing reliability in cluttered and occluded environments; and (4) we created a hybrid loss function to boost accuracy in both classification and regression tasks. Our experimental results on the UAV123, DTB70, and UAVDT datasets show that our approach not only surpasses current state-of-the-art methods in success rates and precision but also operates more swiftly. Full article

Molecular dynamics simulations were conducted to investigate the thermal stability and structural evolution of Li-Mg alloys subjected to thermal cycling between 100 K and 400 K. Alloy compositions containing 0, 5, 10, and 20 at.% Mg were analyzed using a modified embedded-atom method interatomic potential. Structural characterization was performed through radial distribution functions, Polyhedral Template Matching (PTM), and mean squared displacement (MSD) calculations. The results showed that heating promoted the temporary formation of HCP, FCC, and other local atomic environments, indicating partial loss of crystalline ordering even below the melting temperature of Li. Nevertheless, the BCC structure remained dominant for all compositions, and the structural changes were reversible during cooling. Increasing Mg concentration improved the thermal stability of the alloys by reducing the formation of non-BCC atomic structures and decreasing atomic mobility during thermal cycling. In particular, the 20 at.% Mg alloy preserved more than 90% of the BCC population throughout the simulations. In addition, the energy variations between cycles remained very small, indicating stable thermodynamic behavior during heating and cooling. These findings provide atomistic insight into the temperature-dependent behavior of Li-Mg alloys that may be useful in works related to lithium-metal battery applications. Full article

A silk fibroin/cellulose inverse-opal photonic crystal composite with robust mechanical properties was fabricated by blending a silk fibroin solution with methylcellulose, utilizing a 3D poly(methyl methacrylate) (PMMA) photonic crystal array as a template, via sequential infiltration, curing, and etching processes. Leveraging the intrinsic water sensitivity of both silk fibroin and methylcellulose, the resulting composite exhibits exceptional moisture-sensing capabilities across gaseous, liquid, and solid phases. Specifically, for atmospheric humidity, the film delivers a distinct optical response to a relative humidity variation in merely 5%. In liquid systems, owing to the material’s excellent affinity for low-polarity organic solvents and the disruptive effect of highly polar solvents (e.g., water) on the photonic periodic structure, the structural color of the film can sensitively report trace water contents down to 0.025%. Furthermore, in solid matrices, the composite enables the precise detection of not only free water but also water of crystallization. Full article

Direct utilization of biomass-derived silica in neutral surfactant-templated mesoporous synthesis remains underexplored with respect to mesostructure control and functional integration. High-purity silica extracted from acid-treated rice husk ash (~98.4 wt% SiO₂) was employed as the sole precursor in a fluoride-assisted sol–gel route to synthesize MSU-X frameworks without chemical modification. Systematic parametric variation—pH, Si/surfactant ratio, hydrothermal temperature, and aging duration—establishes quantitative structure–processing correlations. Under optimized conditions (pH 2, Si/Tergitol = 8, 60 °C, 96 h), the resulting material exhibits a wormhole-like mesoarchitecture with a BET surface area of 816 m² g⁻¹, mean pore diameter of ~3.6 nm, and three-dimensionally interconnected channels, confirmed by SAXS, TEM, and N₂ sorption. EDXRF analysis confirms effective impurity removal and high silica incorporation efficiency (~95–96%); thermal stability persists to 700 °C, with incipient crystallization near 800 °C. As a functional demonstration, MSU-X served as an anti-agglomeration scaffold for ZIF-8 crystallization during DDT adsorption. Despite attenuated kinetics relative to pristine ZIF-8—where severe agglomeration occludes active imidazole nodes—the Z8/MSU-X composite achieved near-quantitative DDT removal (74.10 mg g⁻¹). This performance stems from the mesoporous matrix driving size-confined, highly dispersed ZIF-8 growth, thereby maximizing active-site exposure. Operating within a reagent-limited regime rather than a capacity-saturated boundary, this efficient depletion confirms that the scaffold successfully suppresses site loss. Ultimately, these findings validate biogenic silica as a directly integrable precursor for tailored mesostructure assembly, positioning agricultural waste as a high-performance feedstock for hierarchical adsorption architectures. Full article

Robotic sorting-and-filling tasks in flexible manufacturing require robots to reproduce specified in-box arrangements while adapting to variations in container poses, object availability, sensing conditions, and external interventions. This paper proposes a box-centric one-shot teaching framework for robotic packing tasks with relative pose constraints. In the teaching stage, a human operator demonstrates the desired packing layout only once. The system uses reference-prompted SAM-based contour refinement to extract box and in-box object contours, object categories, quantities, and relative position and orientation constraints. These constraints are then converted from pixel-plane measurements into box-local pose constraints, forming a reusable box-centric packing template that preserves both translational and angular layout information. During execution, the recorded template is transferred to detected box instances with different global poses, and executable pick-and-place commands are generated through a task-level perception-to-command pipeline. A mechanism for continuous assignment and state updates is further introduced to maintain residual target slots, update object-to-slot allocation, and report missing or redundant objects across execution rounds. Single-box template transfer experiments achieved mean placement errors of 7.16 mm and 7.57 mm for two recorded templates, while representative post-execution images further showed that the relative object orientations were visually preserved with respect to the taught template footprints. Multi-box experiments demonstrated that unfinished residual slots could be preserved and completed after scene updates without re-teaching. Additional validation with different container types and object shapes showed the feasibility of extending the framework beyond cube-only cases. Ablation tests under nine exposure settings further showed that SAM refinement improved template-acquisition robustness compared with the previous recognition method. These results verify that the proposed framework enables one-shot template acquisition, box-centric layout transfer, relative pose preservation, and persistent task-level execution for constrained robotic packing tasks. Full article

Existing multi-modal UAV tracking methods typically rely on fixed-interval dynamic template update strategies to capture diverse target appearances, together with predefined thresholds to select high-quality search regions for template update. However, due to the irregular motion of targets and the complexity of real-world scenarios, such passive update mechanisms suffer from notable limitations. Fixed sampling intervals often fail to adequately capture appearance variations, while fixed threshold-based selection is insufficient to accommodate diverse imaging conditions, leading to ineffective updates or the introduction of noisy templates, thereby degrading tracking robustness and accuracy. To address these issues, we propose a search region-guided adaptive dynamic template update framework for robust multi-modal UAV tracking, aiming to improve both scene adaptability and target matching capability. Specifically, we design a Guided Template Selection Transformer, which dynamically matches templates conditioned on the current search region, enabling the tracker to autonomously select the most suitable template for the target’s current state. Furthermore, we introduce a Dynamic Threshold Module that adaptively adjusts template selection criteria according to different tracking scenarios, ensuring the reliability and contextual relevance of candidate templates. In addition, we develop a Dynamic Template Memory Module to maintain an ordered repository of target templates under different target states, providing a structured and high-quality template pool for the proposed selection mechanism. Extensive experiments on a standard multi-modal UAV tracking benchmark demonstrate that the proposed method significantly outperforms existing approaches, effectively overcoming the limitations of conventional fixed update strategies. Moreover, the proposed approach exhibits strong generalization capability across three additional multi-modal tracking datasets from typical surveillance scenarios. Full article

The sustainable management of water supply reservoirs requires analysis of spatiotemporal variations in nutrient levels, phytoplankton composition, and trophic status. The Katse Dam (KD) is a strategic raw water supply source that generates hydropower and sustains aquaculture. However, it is exposed to nutrient enrichment from mining and aquaculture, whose impact on its trophic status necessitates monitoring. This study applies the organic pollution index (OPI), the modified pollution index (MPI), and Carlson’s trophic state index (CTSI) to assess the trophic status of KD. The results from the first decade (FD) (2003–2013), when the intensity of mining and aquaculture activities was minimal, were compared with the results from the second decade (SD) (2014–2024) when there was higher activity. The MPI revealed that KD transitioned from a contaminated status during the FD to a greatly contaminated status during the SD. KD shifted from mesotrophic to eutrophic in the transitional zone and from eutrophic to hypereutrophic in the lacustrine zone. The cyanobacteria Radiocystis sp. replaced Asterionella sp. and became the most abundant algae in the SD, followed by the diatom Flagilaria sp. Principal component analysis (PCA) indicated stronger correlations between NH₄, PO₄, NO₃, and NO₂, while canonical correspondence analysis (CCA) indicated a strong correlation between PO₄ and Fragilaria sp. in the SD. The OPI classified KD water quality as excellent, with the exception of the lacustrine zone, where the water quality was polluted in 2016 and 2021. The data analysis revealed how long-term variations in KD water chemistry and phytoplankton influenced trophic status. This study thus provides water managers with a template for assessing water quality to secure the strategic value of the KD. Full article

Siamese tracking is widely used in object tracking due to its efficient dual-branch symmetric structure, deep feature matching mechanism, and flexible template strategy. Existing mainstream Siamese tracking algorithms typically employ static template matching or linear combination-based template updating to localize the target in the next frame. However, these mechanisms often struggle to ensure template accuracy in complex environments involving changes in target appearance, scale, occlusion, and motion blur, thereby compromising robustness and stability. To address these issues, this paper proposes a confidence-guided adaptive template update with a re-detection (CATUR) network. CATUR constructs a tracking confidence assessment module that uses average peak-to-correlation energy (APCE) and a dynamic threshold mechanism to determine the current tracking state, providing a basis for template updates and target re-detection. It also designs an adaptive template update network that effectively combines the initial, historical, and current-frame templates, enhancing adaptation to target appearance variations. By integrating a global search module and a re-detection module, CATUR achieves precise target re-localization, rapid template updating, and tracking recovery. Extensive experiments and ablation studies on LaSOT and TrackingNet demonstrate that CATUR improves AUC, PNorm, and P by 4.0%, 4.0%, and 3.2%, respectively, significantly enhancing tracking accuracy and robustness in complex environments. Full article

Background/Objectives: This study describes the development and validation of the Danish Sentence Test (DAST), a Danish-language, adaptive speech-in-noise test constructed from a linguistically balanced corpus using a template-based method. This approach enables controlled linguistic variation while maintaining lexical consistency and may serve as a model for developing similar speech materials in other languages. Methods: Sentences spoken by one female talker from the DAST corpus were sorted into 44 balanced lists of 20 sentences using a psychometric optimization procedure. Speech reception thresholds (SRTs) were measured in 20 normal-hearing participants using headphone playback with speech-shaped noise. Results: Across the 44 sentence lists, the mean SRT was −5.3 dB SNR, with list means within ±0.5 dB of the grand average under the tested configuration. The average within-subject standard deviation was 0.7 dB, and the grand-average psychometric slope was 18.5%/dB. A statistically significant within-session training effect of approximately 0.02 dB per measurement. Conclusions: This study provides normative speech reception threshold (SRT) data for the adaptive Danish Sentence Test (DAST) in normal-hearing listeners under a defined headphone-based speech-in-noise paradigm and demonstrates that the resulting sentence lists yield comparable performance across lists. The template-based construction and optimization approach offers a framework for developing linguistically rich sentence-in-noise tests in other languages. Full article

Background: Traditional refills of intrathecal infusion pumps rely on manual palpation and the use of external templates, a method that can be challenging in patients with anatomical variations or a high body mass index. Ultrasound guidance has emerged as a precision-based alternative. This study aimed to evaluate the impact of the ultrasound-guided technique versus the conventional template-based technique on patient satisfaction. Methods: A quasi-experimental before-and-after study was conducted on a cohort of 45 chronic pain patients. Immediate satisfaction with procedure duration (IPP-SQ), overall treatment efficacy (CRES-4), and pain interference via the Brief Pain Inventory (BPI) were assessed. Results: The use of ultrasound was associated with significantly higher satisfaction regarding procedure duration, with a mean score of 5.00 (95% CI: 4.35–5.65) compared to 3.22 (95% CI: 2.70–3.75) with the traditional method (p < 0.001). Overall satisfaction (CRES-4) also improved significantly (12.4 vs. 11.3; p = 0.001). Regarding patient-reported outcome measures (PROMs), the mean pain intensity in the subsequent week was lower following the ultrasound technique (mean difference −0.48; p = 0.040). Technically, no first-attempt failures were recorded under ultrasound guidance in this sample, compared to a 20% re-attempt rate observed with the manual method. Conclusions: The transition from the traditional method to ultrasound-guided refill optimizes technical precision and substantially enhances the patient experience. By reducing pain and increasing satisfaction, ultrasound guidance proves to be a valuable resource for improving procedural precision, representing an advancement toward a more personalized medicine approach. Full article

While the petroleum industry undergoes structural adjustments in supply and demand alongside a green and low-carbon transition, water drilling mud generated during oil extraction poses severe environmental challenges. Consequently, addressing the solid waste pollution and disposal issues associated with drilling mud has become critical. In this study, ZSM-5 zeolite was synthesized using water drilling mud as a silicon and aluminum source, inexpensive n-butylamine as a template agent, and a combined approach of alkali-melting activation pre-treatment and seed-directed hydrothermal synthesis. By adjusting key parameters such as water content, template agent dosage, and seed addition, optimal synthesis conditions were determined. Based on these conditions, a series of ZSM-5 zeolites with varying silicon-to-aluminum ratios were synthesized. Characterization results from XRD, TEM, SEM, and N₂ adsorption–desorption experiments revealed that all prepared samples exhibited high crystallinity, regular morphology, and high specific surface area. ²⁷Al MAS NMR results indicated that almost aluminum species were located at the framework structures with four-coordination. In the 1,3,5-triisopropylbenzene cracking reaction, the conversion rate increased with decreasing silicon-to-aluminum ratio, consistent with variations in acid amount. These findings achieve high-value utilization of waste drilling mud, offering a novel pathway for low-cost synthesis of high-performance ZSM-5 zeolite. This breakthrough injects fresh momentum into the petroleum refining industry’s green sustainable development, fostering a win–win scenario that harmonizes ecological conservation with industrial profitability. Full article

Accurate, standardized dental model measurements remain labor-intensive and difficult to scale in orthodontics. This technical development study aimed to develop and preliminarily evaluate a semiautomated three-dimensional (3D) dental cast measurement system using standardized measurement templates (patent pending). The workflow integrates robotic handling of models, X-ray CT acquisition of volumetric data, optional intraoral-scan polygonal data (e.g., STL), template generation from 3D data, and orthodontist-guided landmark placement, after which dedicated software retrieves 3D coordinates and performs automated measurements and visualization. The system was demonstrated on four standard models scanned by X-ray CT. It produced automated aggregation of measurements and 3D visual outputs, and enabled calculation of conventional indices as well as template-based metrics such as palatal volume and cusp height variation. This semiautomated approach combines mechanical efficiency with expert oversight, providing a standardized alternative to manual measurement and a foundation for broader applications in orthodontic, prosthodontic, and forensic contexts. Full article