Search Results (1,262)

This study investigates the optimization and prediction of mechanical properties in 3D-printed PLA composites reinforced with graphene nanoplatelets (GNP). The effects of GNP content (0, 2, and 5 wt.%), nozzle temperature (190–210 °C), print speed (20–60 mm/s), and layer thickness (0.15–0.35 mm) on tensile strength, Young’s modulus, and hardness were analyzed using a central composite design, at three print orientations (0°, 45°, and 90°). Compared to pure PLA, the incorporation of 5 wt.% GNP led to a 67% improvement in tensile strength, a 205% increase in Young’s modulus, and a 44% enhancement in hardness. Advanced machine learning models, such as XGBoost and Gaussian Process Regression, were employed for prediction, with R² values exceeding 0.99 and MAPE below 4%. The models were validated using K-Fold Cross-Validation (K = 5), ensuring reliable and robust predictions while preventing overfitting. SHAP (Shapley Additive exPlanations) analysis indicated that GNP composition and layer thickness were the most influential factors, with SHAP values ranging between ±0.75. The Gaussian Process model outperformed both Linear Regression and XGBoost, achieving the highest R² of 0.9900 ± 0.0021, the lowest MSE (0.6593 ± 0.1054), RMSE (0.812 ± 0.323), MAE (0.6755 ± 0.1123), MAPE (3.157% ± 0.320), and RRMSE (3.409% ± 0.513), highlighting its superior predictive accuracy and stability. This integrated methodology, combining experimental optimization, ANOVA, and interpretable machine learning, presents a promising and potentially robust strategy for optimizing the mechanical performance of GNP-reinforced PLA composites, emphasizing their potential for high-performance engineering applications. Full article

Background: Albania is undergoing rapid nutrition transition, yet cardiometabolic (CM) risk in young adults is poorly characterized. We report baseline, cross-sectional findings from a WHO-aligned sentinel study examining diet, physical activity and early CM phenotypes, with fat quality examined as a modifiable exposure. Methods: Young adults recruited on campus (n = 262; median age, 21 years; 172 women, 90 men) underwent standardized anthropometry, seated blood pressure (BP) and fasting glucose (FG). Diet was assessed by two interviewer-administered 24 h recalls and activity outlined by the IPAQ-short form. We derived potential renal acid load (PRAL) and a MASLD-oriented nutrient score, computed a composite CM risk score (cCMRS: sex-standardized mean of WHtR, mean arterial pressure, FG) and fitted prespecified energy-partition models for isocaloric +5% of energy substitutions (SFA → PUFA; SFA → MUFA) with Benjamini–Hochberg false discovery rate (FDR) control. Results: Despite normal average BMI (23.4), risk clustering was common: elevated BP in 63% of men and 30% of women, impaired FG (100–125 mg/dL) in almost one third and central adiposity (WHtR ≥ 0.5) in 51% of men and 24% of women. Diets were SFA-rich (~17–19%E), sodium-dense and low in fiber and several micronutrients (e.g., vitamin D, folate, potassium). In isocaloric models, SFA → PUFA was associated with more favorable nutrient signatures: MASLD-oriented score −28% (p < 0.001; FDR-significant) and PRAL −33% (p = 0.007; FDR-borderline/suggestive). Conclusions: A waist-centric CM subphenotype—central adiposity co-occurring with upward BP shifts and intermittent dysglycemia—was detectable in young adults despite normal average BMI, against a background of poor diet quality and low activity. These baseline surveillance signals are not causal effects. Integration into routine with WHO-aligned NCD surveillance is feasible. Prospective follow-up (biomarker calibration, device-based activity, repeated waves) will refine inferences and inform scalable proactive prevention. Full article

Drug-resistant tuberculosis (DR-TB) threatens global TB control. We investigated the prevalence and molecular characteristics of second-line drug resistance among rifampicin (RIF)- and/or isoniazid (INH)-resistant Mycobacterium tuberculosis complex (MTBC) isolates in São Paulo, Brazil, using the MTBDRsl v. 2.0 line-probe assay. MTBC isolates RIF- and/or INH-resistant by GenoType MTBDRplus or phenotypic testing (2019–2021) were subsequently tested by MTBDRsl for fluoroquinolone (FQ) and injectable drugs (capreomycin, amikacin, kanamycin) resistance. Isolates with inferred mutations underwent Sanger sequencing. Of 13,557 isolates, 728 (5.4%) were RIF- and/or INH-resistant (297 INH-R, 235 RIF-R, 196 MDR). Among them, 623 (85.6%) were tested by MTBDRsl; 582 (93.4%) showed no additional resistance, while 41 (6.6%) carried mutations. FQ resistance was detected in 38 isolates (92.7%), mostly in gyrA (n = 35). Three isolates with gyrB mutations were wild-type by sequencing. Two MDR isolates harbored the rrs a1401g mutation, and one also harbored gyrA D94G. Sequencing confirmed resistance in 38 of 41 isolates. Most MDR strains with second-line mutations (n = 32/33; 97%) were pre-XDR. Affected patients were predominantly male (68.4%), with pulmonary TB (92.1%), and unfavorable outcomes (39.5%). Second-line resistance prevalence was low overall, but FQ resistance was high among MDR isolates. Findings support integrating molecular and sequencing-based tools for accurate detection and management of DR-TB. Full article

Insufficient harvesting of visible photons, limited adsorption, and fast recombination of photogenerated electron-hole pairs restrict the application of graphitic carbon nitride (g-C₃N₄). Here, we propose a straightforward solid-phase synthesis method for fabricating 2D/2D graphitic carbon nitride/reduced graphene oxide (SCN/GR) hybrid photocatalysts. The synthesis process involves the thermal condensation of three precursors: dicyandiamide (as the g-C₃N₄ source), NH₄Cl (as a pore-forming agent), and graphene oxide (GO, which is in situ reduced to rGO during thermal treatment). The incorporation of reduced graphene oxide (rGO) into the g-C₃N₄ matrix not only narrows the bandgap of the material but also expedites the separation of photogenerated carriers. The photocatalytic activity of the SCN/GR hybrid was systematically evaluated by degrading ciprofloxacin in aqueous solution under different light conditions. The results demonstrated remarkable degradation efficiency: 72% removal within 1 h under full-spectrum light, 81% under UV light, and 52% under visible light. Notably, the introduction of rGO significantly improved the visible light absorption capacity of g-C₃N₄. Additionally, SCN/GR exhibits exceptional cyclic stability, maintaining its structural integrity and photocatalytic properties unchanged across five successive degradation cycles. This study offers a simple yet effective pathway to synthesize 2D/2D composite photocatalysts, which hold significant promise for practical applications in water treatment processes. Full article

Detecting available road space is a fundamental task for autonomous driving vehicles, requiring robust image feature extraction methods that operate reliably across diverse sensor-captured scenarios. However, existing approaches process each input independently without leveraging Accumulated Experiential Knowledge (AEK), limiting their adaptability and reliability. In order to explore the impact of AEK, we introduce MemRoadNet, a Memory-Augmented (MA) semantic segmentation framework that integrates human-inspired cognitive architectures with deep-learning models for free road space detection. Our approach combines an InternImage-XL backbone with a UPerNet decoder and a Human-like Memory Bank system implementing episodic, semantic, and working memory subsystems. The memory system stores road experiences with emotional valences based on segmentation performance, enabling intelligent retrieval and integration of relevant historical patterns during training and inference. Experimental validation on the KITTI road, Cityscapes, and R2D benchmarks demonstrates that our single-modality RGB approach achieves competitive performance with complex multimodal systems while maintaining computational efficiency and achieving top performance among single-modality methods. The MA framework represents a significant advancement in sensor-based computer vision systems, bridging computational efficiency and segmentation quality for autonomous driving applications. Full article

Gross primary productivity (GPP) serves as a critical indicator of carbon uptake in agricultural and natural ecosystems, quantifying the extent of carbon dioxide fixation through photosynthesis. Understanding the influence of climate, phenology, and elevation on GPP is essential for achieving carbon neutrality and ensuring sustainable agricultural and ecosystem management. This study adopts a novel methodology that integrates the Shapley Additive Explanations analysis framework with the XGBoost model (R 4.3.3 package xgboost 1.7.7.1) to elucidate complex nonlinear interactions among the factors under investigation. The results show that from 2001 to 2022, GPP increased at an average rate of 6.77 g C/m²/year, with forests exhibiting the highest productivity (>900 g C/m²) compared to grasslands and croplands (300–600 g C/m²). Phenological changes, such as a 0.44 d/year extension in the growing season and a 0.20 d/year advancement in its peak, highlight the significant impact of climate change on vegetation growth. SHAP analysis further identifies precipitation as the primary driver for croplands, growing season length for forests, and temperature for grasslands. These findings support global initiatives aimed at achieving sustainable development goal 13 (Climate Action) by offering actionable insights for adaptive land use policies and carbon-neutrality strategies. Full article

Labor shortages in seedling nurseries, particularly in manual inspection and replanting, hinder operational efficiency despite advancements in automation. This study aims to develop a cost-effective, GPU-free machine vision system to automate the detection of deficient seedlings in plug trays, specifically for small-scale nursery operations. The proposed Deficiency Detection and Replanting Positioning (DDRP) machine integrates low-cost components including an Intel RealSense Depth Camera D435, Raspberry Pi 4B, stepper motors, and a programmable logic controller (PLC). It utilizes OpenCV’s Haar cascade algorithm, HSV color space conversion, and Otsu thresholding to enable real-time image processing without GPU acceleration. The proposed Deficiency Detection and Replanting Positioning (DDRP) machine integrates low-cost components including an Intel RealSense Depth Camera D435, Raspberry Pi 4B, stepper motors, and a programmable logic controller (PLC). It utilizes OpenCV’s Haar cascade algorithm, HSV color space conversion, and Otsu thresholding to enable real-time image processing without GPU acceleration. Under controlled laboratory conditions, the DDRP-Machine achieved high detection accuracy (96.0–98.7%) and precision rates (82.14–83.78%). Benchmarking against deep-learning models such as YOLOv5x and Mask R-CNN showed comparable performance, while requiring only one-third to one-fifth of the cost and avoiding complex infrastructure. The Batch Detection (BD) mode significantly reduced processing time compared to Continuous Detection (CD), enhancing real-time applicability. The DDRP-Machine demonstrates strong potential to improve seedling inspection efficiency and reduce labor dependency in nursery operations. Its modular design and minimal hardware requirements make it a practical and scalable solution for resource-limited environments. This study offers a viable pathway for small-scale farms to adopt intelligent automation without the financial burden of high-end AI systems. Future enhancements, adaptive lighting and self-learning capabilities, will further improve field robustness and including broaden its applicability across diverse nursery conditions. Full article

Addressing climate change and achieving carbon neutrality are urgent global responsibilities, with China’s “dual carbon” goals presenting a significant challenge and opportunity for its energy sector. Green finance, as a pivotal driver for fostering low-carbon and high-quality development in the energy industry, significantly accelerates its green transition. Employing an integrated micro-macro framework, this study first develops a tripartite evolutionary game model involving government, local energy enterprises, and external energy enterprises to analyze the micro-mechanisms of corporate low-carbon decision-making under green finance policies. Subsequently, utilizing panel data from 30 Chinese provinces (2013–2021), it empirically examines the macro impact of green finance on the industry’s low-carbon, high-quality development using a spatial Durbin model (SDM). Key findings include the following: (1) Game analysis reveals that local enterprises’ low-carbon transition propensity and emission reduction returns increase with R&D investment but are negatively moderated by the tax rate level within green finance policies. (2) Spatial econometric results demonstrate that green finance significantly facilitates local energy industry low-carbon transition via technological progress, confirming a significant negative spatial spillover effect on neighboring regions, with notable regional heterogeneity. (3) The effectiveness of green finance policy exhibits significant regional disparity, being markedly stronger in eastern China compared to central and western regions. The findings provide a theoretical and practical foundation for improving market mechanisms and regional coordination in China’s green finance policies, offering a valuable reference for the design of green finance systems in other major emerging and developing economies. Full article

As an economically important species endemic to the upper tributaries of Yangtze River in China, long-finned gudgeon fish (Rhinogobio ventralis) has been classified as endangered due to habitat destruction and population decline. In this study, we constructed a chromosome-level genome assembly of R. ventralis by integration of MGI, PacBio and Hi-C sequencing technologies. The final genome assembly was 1015.9 Mb in length (contig N50: 25.91 Mb; scaffold N50: 39.99 Mb), and 97.19% of the haplotypic genome sequences were anchored onto 25 chromosomes. Repetitive elements accounted for 51.00% of the entire genome assembly. A total of 23,220 protein-coding genes were predicted for the assembled genome, of which 99.79% were functionally annotated. Genome evaluation revealed 99.72% completeness for the genome assembly. Through genome-wide prediction of antimicrobial peptides (AMPs), we identified and localized 561 putative AMP-containing genes in the R. ventralis genome. These genes were further classified into 185 distinct functional categories based on public databases, with the top ten components of Penetratin (21.74%), Histone (5.70%), E6AP (4.09%), Scolopendin 1 (2.67%), D38 (2.31%), WBp-1 (2.13%), Defensin (2.13%), Claudin 1 (1.96%), Azurocidin (AZU1, 1.78%), and Ubiquitin (1.60%). Our data presented here provide a potential genetic resource for promoting fundamental research and wild population conservation of this endangered fish species. Full article

Diaphorina citri Kuwayama is a key pest of citrus and insect vector of Huanglongbing (HLB), also known as citrus greening disease, causing significant losses in Florida and other regions. The naturally occurring effective ladybeetle predators and their impact on D. citri reduced from years of insecticide use against this pest and are not available commercially. Additionally, most species are large-sized, while most eggs and neonates of D. citri are in hard-to-reach locations such as unopened leaves, which makes access difficult for them. We evaluated a commercially available small-sized predatory ladybeetle Rhyzobius lophanthae Blaisdell against D. citri immatures. A single adult consumed an average of 24.9 eggs and 8.7 first and second instar nymphs of D. citri within 24 h. Beetles exhibited Type II functional response against nymphs with an attack rate of 0.92 h⁻¹ and a handling time of 0.08 h. Their consumption rate increased with nymphal density up to twenty per shoot. In the field test, beetles lived 10 days longer when confined with new shoots infested with D. citri immatures in a voile fabric sleeve cage in citrus trees every two days, versus seven days. In an open field release of R. lophanthae in a citrus orchard, these ladybeetles were found foraging in sentinel and neighboring trees infested with D. citri. The consumption rate of R. lophanthae on D. citri immatures and its survival in Florida orchards suggest its potential for biological control and Integrated Pest Management. Full article

A deflection prediction approach based on an adaptive MEC–RBF neural network was developed in this study. By dynamically optimizing the centres, widths, and weights of the RBF network, the proposed method substantially increases the prediction accuracy, and it achieves an R² of 0.9789 and an RMSE of 1.4978 on the training dataset. It effectively resolves the stability challenges that are associated with nonlinear construction conditions in traditional models. An orthogonal experimental design analysis revealed that the girder block length and the cantilever-to-span length ratio (d/L) were the most influential factors that affected deflection, whereas the effects of uniformly distributed loads and temperature were negligible, thereby providing a sound basis for parameter simplification. The application of the model to the Hannan Yangtze River Bridge yielded a maximum discrepancy of only 5.56 mm (17.7% error rate) between the predicted and measured values, thus demonstrating its practical engineering reliability. By innovatively integrating intelligent optimization techniques with neural networks, this approach overcomes the limitations in terms of real-time responsiveness and long-term stability of conventional methods and offers an efficient and reliable technical tool for the control of large-scale bridge construction. Full article

As a systematic project, corporate green innovation involves technological, organizational, and environmental dimensions. Therefore, its effective functioning is contingent on guidance from internal leadership. STEM represents an integration of science, technology, engineering, and mathematics education. A STEM CEO is a chief executive officer holding a degree in science, engineering, agriculture, or medicine. However, research on the impact of STEM CEOs on green innovation is limited. Using data from Chinese listed manufacturing firms from 2010 to 2023, panel fixed effects models reveal that STEM CEOs positively influence corporate green innovation. Further analysis indicates that alleviating financing constraints, fostering external collaboration, increasing R&D investment, and improving the efficiency of innovation resource allocation are key pathways through which STEM CEOs enhance green innovation output. Furthermore, this impact is positively moderated by the level of green finance development and the intensity of market competition. Finally, heterogeneity tests demonstrate that these positive effects are more pronounced for firms with high public environmental concern, in non-heavily polluting industries, with strong ESG performance, and in highly competitive industries. These findings underscore the role of STEM leaders in enhancing the output of green innovation systems, offering actionable insights into the interaction between STEM CEOs and the external environment. Full article

Background: Bioelectrical Impedance Vector Analysis (BIVA) is a qualitative method that standardizes resistance and reactance relative to stature (R/H and Xc/H) and plots them as vectors on an R-Xc graph. This equation-free approach assesses body composition, allowing for the evaluation of hydration status and cellular integrity through tolerance ellipses. This study aimed to systematically map BIVA reference ellipses across general, pediatric, pathological, and athletic populations. Methods: A scoping review was conducted according to PRISMA-ScR guidelines. Five databases were searched. Extracted data included (a) sample characteristics (sample size, age, sex, BMI, country, ethnicity), (b) population type, (c) analyzer specifications, and (d) R/H and Xc/H means, standard deviations, and correlation values. Results: A total of 53 studies published between 1994 and July 2025 were included. From these, 508 tolerance ellipses were identified: 281 for the general population (18–92 years), 133 for children/adolescents (0–18 years), 49 for athletes, and 45 for pathological groups. Studies were primarily conducted in Europe and the Americas, using 11 analyzers with variations in measurement protocols, including body side, posture, and electrode placement. Conclusions: This scoping review categorizes the existing BIVA tolerance ellipses by population type, sex, age, BMI, device used, and measurement protocol. The structured presentation is intended to guide researchers, clinicians, nutritionists, and sports professionals in selecting appropriate reference ellipses tailored to specific populations and contexts. Full article

Enhancing interlayer bond strength remains a critical challenge in the extrusion-based 3D printing of cementitious materials. This study investigates the optimisation of interlayer bond strength in extrusion-based 3D-printed cementitious materials through a combined application of Response Surface Methodology (RSM) and Artificial Neural Networks (ANNs). Using a concise yet comprehensive dataset, RSM provided interpretable main effects, curvature, and interactions, while the ANN captured non-linearities beyond quadratic forms. Comparative analysis revealed that the RSM model achieved higher predictive accuracy ($R^2=0.95$) compared to the ANN model ($R^2=0.87$). Desirability-based optimisation confirmed the critical importance of minimising casting delays to mitigate interlayer weaknesses, with RSM suggesting a water-to-cement (W/C) ratio of approximately 0.45 and a minimal time gap of less than 5 min, while ANN predicted slightly lower optimal W/C values but with reduced reliability due to the limited dataset. Sensitivity analysis using partial dependence plots (PDPs) further highlighted that ordinary Portland cement (OPC) content and W/C ratio are the dominant factors, contributing approximately 2.0 and 1.8 MPa respectively to the variation in predicted bond strength, followed by superplasticiser dosage and silica content. Variables such as water content, viscosity-modifying agent, and time gap exhibited moderate influence, while sand and fibre content had marginal effects within the tested ranges. These results demonstrate that RSM provides robust predictive performance and interpretable optimisation guidance, while ANN offers flexible non-linear modelling but requires larger datasets to achieve stable generalisation. Integrating both methods offers a complementary pathway to advance mix design and process control strategies in 3D concrete printing. Full article

Wearable devices (WDs) are increasingly integrated into clinical workflows to enable continuous, non-invasive vital signs monitoring. Combined with Artificial Intelligence (AI), these systems can shift clinical monitoring from being reactive to predictive, allowing for earlier detection of deterioration and more personalized interventions. The value of these technologies lies not in absolute measurements, but in detecting physiological parameters trends relative to each patient’s baseline. Such a trend-based approach enables real-time prediction of deterioration, enhancing patient safety and continuity of care. However, despite their shared multiparametric capabilities, WDs are not interchangeable. This narrative review analyzes nine clinically validated devices, Radius VSM^® (Masimo Corporation, Irvine, CA, USA), BioButton^® (BioIntelliSense Inc., Redwood City, CA, USA. Distributed by Medtronic), Portrait Mobile^® (GE HealthCare, Chicago, IL, USA), VitalPatch^® (VitalConnect Inc., San Jose, CA, USA), CardioWatch 287-2^® (Corsano Health B.V., The Hague, The Netherlands. Distributed by Medtronic), Cosinuss C-Med Alpha^® (Cosinuss Gmb, Munich, Germany), SensiumVitals^® (Sensium Healthcare Limited, Abingdon, Oxfordshire, UK), Isansys Lifetouch^® (Isansys Lifecare Ltd., Abingdon, Oxfordshire, UK), and CheckPoint Cardio^® (CheckPoint R&D LTD., Kazanlak, Bulgaria), highlighting how differences in sensor configurations, battery life, connectivity, and validation contexts influence their suitability across various clinical environments. Rather than establishing a hierarchy of technical superiority, this review emphasizes the importance of context-driven selection, considering care setting, patient profile, infrastructure requirements, and interoperability. Each device demonstrates strengths and limitations depending on patient population and operational demands, ranging from perioperative, post-operative, emergency, or post-Intensive Care Unit (ICU) settings. The findings support a tailored approach to WD implementation, where matching device capabilities to clinical needs is key to maximizing utility, safety, and efficiency. Full article