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Energy efficient transactions for blockchain networks using adaptive global best–worst particle swarm optimization
Design and evaluation of a home based upper limb rehabilitation training device for stroke patients
A new method based on AVOA-optimized support vector machine for predicting performance characterisitcs during turning aluminium 7068 under graphene nanofluid
Automatic measurements of volar tilt and radial inclination of the distal radius on 3D models: validation against manual methods
Abstract Assessing distal radius malunions traditionally relies on measuring volar tilt and radial inclination in 2D radiographs. Reliability of these parameters is limited by projection errors and landmark ambiguity. Using 3D models allows more precise quantification of these parameters. This study validated an algorithm for measuring volar tilt and radial inclination on 3D virtual models of malunited and healthy radii. The algorithm establishes a 3D coordinate system and anatomical landmarks replicating conventional 2D definitions to maintain clinical compatibility. Automatic 3D measurements on models from 16 participants were compared with manual measurements on 2D radiographs (by three surgeons in consensus) and on 3D models (by two independent raters). Agreement was assessed using Bland–Altman analysis, and landmark discrepancies were quantified. Mean differences between automatic 3D and manual 2D measurements were small: for volar tilt, 2.0° in malunited and 3.5° in healthy radii; for radial inclination, 1.5° and 2.3°, respectively. Strong agreement was also found between automatic and manual 3D measurements. Landmark deviations ranged from 0.5 to 2.8 mm, with an average longitudinal axis difference of 2.5°. This open-source algorithm provides a reproducible 3D assessment of distal radius alignment. By closely matching 2D standards, the algorithm has a high potential for clinical integration.
Social network analysis for crime prediction under social computing and deep learning technology
Development and characterization of SiC nanofiber and hybrid reinforced composites for dental restorations
Abstract Enhancing the mechanical reliability of dental restorative materials is essential for improving long-term clinical performance. This study examined the mechanical, tribological, morphological, and thermal properties of Bis-GMA/TEGDMA (50/50 wt%) composites reinforced with silicon carbide (SiC) nanofibers and nanoparticles. Seven formulations were prepared: a control, three nanofiber composites (0.1–0.3 wt%), and three nanohybrid systems. All samples were photo-cured using strong, flashing, and gradually strong LED modes. Mechanical behavior was evaluated via Shore hardness and compression testing, while tribological performance was assessed using pin-on-disc wear analysis. SEM, XRD, and DSC provided structural and thermal characterization. SiC incorporation produced clear composition-dependent effects. Hardness increased by 3.5% in the 0.2% nanofiber composite relative to the control. The same formulation showed the greatest mechanical enhancement, with a 13.2% increase in compression strength, whereas the 0.3 wt% hybrid composite exhibited a 34% decrease, indicating overloading effects at higher hybrid content. Tribologically, both the 0.2% nanofiber and 0.3% hybrid composites demonstrated improved resistance to wear, exhibiting minimal weight loss. Curing mode significantly influenced all measured properties, with strong-mode curing yielding the highest overall performance. These findings highlight the potential and limitations of SiC-based reinforcement strategies for developing next-generation dental composites.
Quantitative determination of al (III) traces in soft drink, pharmaceutical products and biological fluids of kidney failure and alzheimer disease patients using carbon sensor
Abstract Carbon paste sensor based on p -chlorophenyl maleanilic acid ( p Cl-MA) as an ionophore was synthesized and investigated as an Al(III) ion selective sensor. The developed sensor shows Nernstian slope of 20.32 ± 1.18 mV decade − 1 over the working concentration range of 1.0 × 10 − 6 − 1.0 × 10 − 1 mol L − 1 of Al (III) ion with a detection limit of 3.3 × 10 − 7 mol L − 1 . The sensor exhibited a stable potential response in the pH range from 4.0 to 6.0 and a fast response time of 8 s. The prepared sensor indicated a very good selectivity to Al (III) over a variety of cations. The sensor could be easily employed for determination of Al (III) ions in pharmaceutical products, soft drink and biological fluids of kidney failure and Alzheimer disease patients. The reaction mechanism between p CL-MA and Al (III) was confirmed by FT-IR spectroscopic analysis, SEM and EDX.
Nanotechnology strategies for enhancing productivity, sulphur use efficiency and soil fertility in groundnut-mustard cropping systems
Abstract This study aimed to evaluate the impact of sulfur application, particularly sulfur nanoparticles (SNPs), on yield, nutrient content, uptake, and soil sulfur dynamics in a groundnut-mustard cropping system. Two year (2021–2022) study was conducted at Anand Agricultural University, Gujarat, India. Eleven treatments of SNPs and elemental sulfur were applied via fertigation, and their effects on groundnut-mustard cropping system performance and soil properties were analyzed. The highest chlorophyll content, seed, haulm, and stover yield, as well as increased sulfur, iron, zinc, manganese, and copper content in plant parts, were observed under recommended dose of fertilizer (RDF) + SNPs at 3.0 mg S/kg soil (split application: half at sowing, half at 1 month after sowing). Sulfur fractions and Diethylene Triamine Penta Acetic acid extractable micronutrients were highest at RDF + SNPs at 4.0 mg S/kg soil, split similarly. The lowest values were recorded under control treatment. The optimized application of SNPs significantly improved yield, nutrient uptake, and sulfur use efficiency in the groundnut-mustard system, offering a sustainable alternative to conventional sulfur fertilizers. RDF + SNPs at 3.0 mg S/kg soil in a split dose is recommended as an optimum sulfur management strategy under controlled conditions. This approach supports sustainable agricultural practices and efficient nutrient management, ensuring long-term soil health and productivity.
Exploring the relationship between artificial intelligence and resilience in manufacturing industrial chains: mechanisms, effects and empirical evidence
Abstract Using panel data from 30 Chinese provinces for the period 2012–2023, this study systematically examines the mechanisms, nonlinear characteristics, and spatial heterogeneity of artificial intelligence’s impact on the resilience of manufacturing industrial chains. The results indicate that AI exerts a significant and robust direct positive effect on industrial chain resilience. Furthermore, AI indirectly enhances resilience by promoting regional economic development. The urbanization rate positively moderates this relationship, with a higher urbanization level amplifying AI’s enabling effect. A threshold analysis reveals that the influence of AI exhibits nonlinear characteristics based on the development level of data elements; beyond a certain threshold, its positive effect displays a pattern of “marginal increase.” Heterogeneity analysis shows that AI’s enabling effect varies regionally, being strongest in the east, followed by the west, and least pronounced in the central region. Moreover, this effect intensifies with higher levels of supply chain resilience, suggesting a “Matthew effect” whereby stronger chains benefit more. This study provides theoretical and empirical insights into how digital technologies enhance industrial resilience and offers policy implications for designing differentiated and coordinated AI promotion strategies.
Cluster-derived profiles of suicidal ideation and quality of life among older adults living alone in South Korea
Language assisted learnable hyperdimensional computing framework for retinal disease classification
Examining awareness, implementation, and challenges of sustainable development goal 6 in rural Osun State, Nigeria
Recipe optimization and SRF test of Cu-compatible Nb$${}_{3}$$Sn films by DC magnetron sputtering from a stoichiometric target
Pulmonary toxicity of polymethyl methacrylate nanoplastics via intratracheal intubation in mice
Daily briefing: Animals without brains sleep too — hinting at why we sleep at all
Parameter Estimation in photovoltaic systems using a hybrid Bat and crow metaheuristic algorithm
When the Target Is Not the Driver: Rethinking LDL in Cardiac Allograft Vasculopathy
Improving dental disease diagnosis using a cross attention based hybrid model of DeiT and CoAtNet
Abstract Accurate dental diagnosis is essential for effective treatment planning and improving patient outcomes, particularly in identifying various dental diseases, such as cavities, fillings, implants, and impacted teeth. This study proposes a new hybrid model that integrates the strengths of the data-efficient image transformer (DeiT) and convolutional attention network (CoAtNet) to enhance diagnostic accuracy. Our approach’s first step involves preprocessing dental radiographic images to improve their quality and enhance feature extraction. The model employs a cross-attention fusion mechanism that aligns and merges feature representations from DeiT and CoAtNet, leveraging their unique capabilities to capture relevant patterns in the data. A stacking classifier, comprising base classifiers such as support vector machines (SVM), eXtreme gradient boosting (XGBoost), and multilayer perceptron (MLP), optimizes classification performance by combining predictions from multiple models. The proposed model demonstrates superior performance, achieving an accuracy of 96%, a precision of 96.5%, 96.1% for sensitivity, 96.4% for specificity, and 96.3% for Dice similarity coefficient, thus showcasing its effectiveness in the automatic diagnosis of dental diseases.
Deep Learning–Based Continuous QT Monitoring to Identify High-Risk Prolongation Events After Class III Antiarrhythmic Initiation
BACKGROUND: Drug-induced QT prolongation after successful inpatient loading of class III antiarrhythmics may occur during routine outpatient care. Insertable cardiac monitors offer continuous signals but are limited by single-lead configuration. We hypothesized that a spatially aware deep learning system (3DRECON-QT) can reconstruct spatial information from a single lead vector to quantify QT/QTc and identify high-risk prolongation. METHODS: We developed 3DRECON-QT using a multitask encoder–decoder that ingests a 10-s single-lead signal, reconstructs 12 leads, and predicts QT/QTc. The model was developed using 12-lead ECGs with clinician-adjudicated QT/RR from a large health system and tested in an external center with different ECG hardware. Continuous monitoring performance was assessed in a public dofetilide‐loading data set with serial ECGs. In a real-world cohort of outpatients on dofetilide or sotalol presenting to the hospital or emergency room for any reason, rates of ventricular arrhythmias and QT prolongation were assessed. Device validation was tested in patients with insertable cardiac monitor recordings paired with clinical 12-lead ECGs. RESULTS: 3DRECON-QT classified prolonged QTc from single-lead signals with area under the receiver operating characteristics curve, 0.942 (mean absolute error, 17.5 ms) in the internal test set and 0.943 (mean absolute error, 21.1 ms) externally. During continuous dofetilide monitoring, predictions correlated with ground truth ( r , 0.851; mean absolute error, 17.8 ms; area under the receiver operating characteristics curve, 0.936 for prolonged QTc, 0.816 for ≥15% QTc rise). QTc prediction from true insertable cardiac monitor recordings showed r =0.824 and mean absolute error, 17.5 ms. In outpatients on class III antiarrhythmics (n=1676), 16.5% had high-risk QTc prolongation. Ventricular arrhythmia events were 3.97% versus 0.86% without prolongation (adjusted odds ratio, 4.24 [95% CI, 1.81–9.90]). 3DRECON-QT detected these events with area under the receiver operating characteristics curve 0.94 (F1 score, 0.60). CONCLUSIONS: A single-lead, deep-learning approach can achieve guideline-level measurement accuracy, enable continuous QTc surveillance from nonstandard ECG vectors, and identify clinically meaningful outpatient QTc prolongation associated with a >4-fold increase in serious ventricular arrhythmias. This strategy may enhance safety monitoring after class III antiarrhythmic initiation and support targeted intervention.