Search Results (1,536)

Contemporary visual assistive devices often lack immersive user experience due to passive control systems. This study introduces a neuronally controlled visual assistive device (NCVAD) that aims to assist visually impaired users in performing reach tasks with active, intuitive control. The developed NCVAD integrates computer vision, electroencephalogram (EEG) signal processing, and robotic manipulation to facilitate object detection, selection, and assistive guidance. The monocular vision-based subsystem implements the YOLOv8n algorithm to detect objects of daily use. Then, audio prompting conveys the detected objects’ information to the user, who selects their targeted object using a voluntary trigger decoded through real-time EEG classification. The target’s physical coordinates are extracted using ArUco markers, and a gradient descent-based path optimization algorithm (POA) guides a 3-DoF robotic arm to reach the target. The classification algorithm achieves over 85% precision and recall in decoding EEG data, even with coexisting physiological artifacts. Similarly, the POA achieves approximately 650 ms of actuation time with a 0.001 learning rate and 0.1 cm² error threshold settings. In conclusion, the study also validates the preliminary analysis results on a working physical model and benchmarks the robotic arm’s performance against human users, establishing the proof-of-concept for future assistive technologies integrating EEG and computer vision paradigms. Full article

Ship repair is hazardous, often presenting unsuitable working areas and risks due to the ship’s configuration. Welding tasks are particularly dangerous due to the high temperatures generated, high enough to melt the metal in structural elements, bulkheads, linings, and tanks. This study investigates the consequences of temperature distribution during the welding of naval plates and proposes some accident prevention measures. Industry working conditions were reproduced, including the materials, procedures, and tools used, as well as the certified personnel employed. DH 36-grade naval steel, with a composition of C max. 0.18%, Mn 0.90–1.60%, P 0.035%, S 0.04%, Si 0.10–0.50%, Ni max 0.4%, Cr max 0.25%, Mo 0.08%, Cu max 0.35%, Cb (Nb) 0.05%, and V 0.1%, was welded via FCAW-G (Gas-Shielded Flux-Cored Arc Welding), selected for this study because it is one of the most widely practiced in the naval industry. The main sensor used in the experiments was an FLIR model E50 thermographic camera, and thermal waxes were employed. The results for each thickness case are presented in both graphical and tabular form to provide accurate and actionable guidelines, prioritizing safety. After studying the butt jointing of naval plates of various thicknesses (8, 10, and 15 mm), safe distances to maintain were proposed to avoid risks in the most unfavorable cases: 350 mm from the welding seam to avoid burn injuries to unprotected areas of the body and 250 mm from the welding seam to avoid producing flammable gases. These numbers are less accurate but easier to remember, which prevents errors in the face of hazards throughout a long working day. Full article

Improving yield stability under water-limited conditions is a key objective of wheat breeding programmes. One trait of particular interest is carbohydrate accumulation and remobilisation. This study assessed the genetic basis of aspects of yield and flag leaf sugar contents under drought and well-watered conditions using QTL mapping in a population of 90 doubled haploid lines derived from the cross Chinese Spring × SQ1. As well as soluble sugar content, glucose, fructose, sucrose, and maltose, the traits grain yield (Yld), biomass (Bio), and thousand grain weight (TGW) were also analysed. Analysis of variance showed that genotype, environment and their interactions significantly influenced all the traits studied, with environmental effects explaining up to 74.4% of the total variation. QTL analysis identified 40 QTLs for Yld, TGW, and Bio as well as 53 QTLs for soluble carbohydrates, accounting for up to 40% of phenotypic variation. QTLs coincident for more than one trait were identified on 21 chromosome regions, associated with carbohydrate metabolism and yield performance under drought, particularly on chromosomes 2D, 4A, 4B, 5B, 5D, 6B, and 7A. Candidate genes for several yield-related QTLs were identified. These results provide useful genetic markers for the development of more drought-resistant wheat cultivars. Full article

This work presents a comprehensive evaluation of corrosion progression in DH36 naval steel through the integration of electrochemical impedance spectroscopy (EIS), weight loss, scanning electron microscopy (SEM), and advanced magnetic non-destructive techniques under artificial seawater (ASW, ASTM D1141) and natural marine conditions. Quantitative correlations are established between corrosion layer growth, electrochemical parameters, and magnetic permeability, demonstrating the magnetic sensor’s capacity for the real-time, non-invasive assessment of marine steel degradation. Laboratory exposures reveal a rapid initial corrosion phase with the formation of lepidocrocite and goethite, followed by the densification of the corrosion product layer and a pronounced decline in corrosion rate, ultimately governed by diffusion-controlled kinetics. Notably, changes in magnetic permeability closely track both the thickening of non-magnetic corrosion products and microstructural deterioration, with declining μmax and increased hysteresis widths (FWHM) sensitively indicating evolving surface conditions. A direct comparison with in situ marine immersion at Rafina confirms that the evolution of corrosion morphology and the corresponding magnetic response are further modulated by biofilm development, which exacerbates the attenuation of measured surface permeability and introduces greater variability linked to biological activity. These findings underscore the robustness and diagnostic potential of magnetic non-destructive sensors for the predictive, condition-based monitoring of naval steels, bridging laboratory-controlled observations and complex real-world environments with high quantitative fidelity to corrosion kinetics, phase evolution, and microstructural transformations, thus guiding the strategic deployment of protection and maintenance regimens for naval fleet integrity. Full article

Dehydroshikimate (DHS) dehydratase (DSD) catalyzes the conversion of DHS into 3,4-dihydroxybenzoic acid (3,4-DHBA), a compound with promising applications across various industries. The DSD from Podospora anserina (DSD_Pa) was characterized and its catalytic properties were compared with those of previously investigated enzymes, AsbF (Bacillus thuringiensis), Qa-4 (Neurospora crassa), and QsuB (Corynebacterium glutamicum), both in vitro and in vivo using tube fermentation. Escherichia coli and C. glutamicum were used as platforms to construct model 3,4-DHBA producers. To increase DHS availability in both hosts, shikimate dehydrogenase AroE was inactivated, and the plasmid pVS7-aroG4, encoding 3-deoxy-D-arabinoheptulosonate 7-phosphate synthase (E. coli), was introduced. In E. coli, heterologous 3,4-DHBA synthesis was achieved through chromosomal integration of dsd genes. The fungal genes were codon-optimized for this bacterium. The same genes were cloned into the pVK9 vector and introduced into C. glutamicum, where 3,4-DHBA degradation was disrupted (ΔpcaHG). AsbF (k_cat ~ 1 s⁻¹) showed poor 3,4-DHBA accumulation in both hosts (1–1.5 g/L). The enzymes with better catalytic characteristics, QsuB (k_cat ~ 60 s⁻¹), DSD_Pa (k_cat ~ 125 s⁻¹), and Qa-4 (k_cat ~ 220 s⁻¹), provided 5 g/L 3,4-DHBA in E. coli and 3 g/L 3,4-DHBA in C. glutamicum, except for Qa-4. The low production (~1.5 g/L) observed for Qa-4 in C. glutamicum might be attributed to a non-optimal nucleotide sequence rich in codons rare for C. glutamicum. Full article

In addition to being governed by genetic factors, environmental factors also play a crucial role in influencing the fragrance of rice. In this research, the high-quality rice variety Dalixiang was selected as the experimental material to investigate the impacts of soil nutrients in Guiyang and Meitan on its fragrance. The results indicated that the levels of ammonium nitrogen, organic matter, total nitrogen, available nitrogen, and the pH value in the soil of Meitan were lower compared to those in Guiyang. Conversely, the contents of total potassium, available phosphorus, and available potassium were higher in Meitan. Specifically, the concentrations of 2-acetyl-1-pyrroline (2AP) in the leaves of Dalixiang at the heading stage and in the grains at the maturity stage at the Meitan planting site were 0.13 mg/kg and 0.56 mg/kg, respectively. These values were significantly lower than the 0.17 mg/kg and 0.64 mg/kg measured at the Guiyang planting site. This phenomenon is associated with the higher expression levels of the betaine aldehyde dehydrogenase (OsBadh2) gene, enhanced enzyme activities, and a greater content of γ-aminobutyric acid (GABA) in the leaves of Dalixiang at the Meitan planting site. In contrast, the expression levels of genes related to triose phosphate isomerase (OsTPI), proline dehydrogenase (OsProDH), ornithine aminotransferase (OsOAT), and Delta1-pyrroline-5-carboxylic acid synthetase (OsP5CS), along with their corresponding enzyme activities, as well as the contents of methylglyoxal, proline, and ornithine, were lower. In conclusion, due to the influence of the Guiyang environment, the biosynthesis of Dalixiang 2AP was promoted, which made the Dalixiang planted in Guiyang stronger than that planted in Meitan. This study provides a theoretical basis for the selection of the best planting area of Dalixiang and the improvement of Dalixiang flavor through agronomic cultivation techniques. Full article

To address the complex pile–soil interaction mechanisms in predicting the compressive bearing capacity of HSCM piles (Helix Stiffened Cement Mixing piles) in marine soft soil regions, this study proposes an intelligent prediction method based on a GA-BPNN (Genetic Algorithm-Optimized Back Propagation Neural Network). A high-quality database comprising 1243 data points was established through finite element numerical simulations. By integrating data preprocessing techniques and the GA-BPNN model, the study systematically investigated the influence of helical blade spacing H₁ and H₂, strength ratio C_ref/S_u, and diameter ratio D_sc/D_H on bearing capacity. The results demonstrate that the GA-BPNN model achieves a prediction accuracy of 99.07%, with a mean squared error (MSE) of 7.20 × 10⁻³ and a coefficient of determination R² of 0.990. SHAP value analysis reveals that the strength ratio and diameter ratio are the dominant factors, exhibiting nonlinear relationships with bearing capacity characterized by saturation effects and threshold-dependent behavior. Laboratory tests further confirm strong correlations between cement–soil strength C_ref, formed pile diameter D_sc, and bearing capacity. The findings indicate that the GA-BPNN model provides an efficient and accurate approach for predicting the bearing capacity of HSCM piles, offering a reliable basis for engineering parameter optimization. Full article

To enable intelligent inspection of underground cable systems, this study presents a gecko-inspired quadruped robot that integrates multi-degree-of-freedom motion with a deep learning-based visual detection system. Inspired by the gecko’s flexible spine and leg structure, the robot exhibits strong adaptability to confined and uneven tunnel environments. The motion system is modeled using the standard Denavit–Hartenberg (D–H) method, with both forward and inverse kinematics derived analytically. A zero-impact foot trajectory is employed to achieve stable gait planning. For defect detection, the robot incorporates a binocular vision module and an enhanced YOLOv8 framework. The key improvements include a lightweight feature fusion structure (SlimNeck), a multidimensional coordinate attention (MCA) mechanism, and a refined MPDIoU loss function, which collectively improve the detection accuracy of subtle defects such as insulation aging, micro-cracks, and surface contamination. A variety of data augmentation techniques—such as brightness adjustment, Gaussian noise, and occlusion simulation—are applied to enhance robustness under complex lighting and environmental conditions. The experimental results validate the effectiveness of the proposed system in both kinematic control and vision-based defect recognition. This work demonstrates the potential of integrating bio-inspired mechanical design with intelligent visual perception to support practical, efficient cable inspection in confined underground environments. Full article

In general, the silkworm, Bombyx mori, has a diapause trait in its eggs. Therefore, transgenic silkworm can be produced by embryonic microinjection using eggs laid by a non-diapause strain in B. mori. In this study, we performed microinjection using eggs of diapause strains which have good characteristics for industrial use, such as a big cocoon, thin and smooth silk, and tolerance against disease due to the growing industrial use of transgenic silkworms. For the conversion of egg diapause traits from diapause to non-diapause types, we used anti-serum against the diapause hormone of B. mori (BmDH), which was injected into maternal pupae, producing non-diapause eggs at a high rate. Finally, we attempted microinjection using three diapause strains with different voltinism (i.e., number of generations of an organism in a year) and were able to successfully produce transgenic silkworms in all three of them, demonstrating that our method is applicable to a wide range of silkworm strains with a diapause trait. Full article

Background: Dentinal hypersensitivity (DH) significantly impacts oral health-related quality of life. While global prevalence estimates range from 10–15%, region-specific data from Saudi Arabia remain limited. This study also aligns with Saudi Vision 2030’s mental health initiatives, as DH-associated anxiety impacts overall well-being. This study assessed DH prevalence and quality of life impact among Saudi adults. Methods: A cross-sectional online survey was conducted among 748 Saudi adults aged ≥ 18 years between April and May. Data were collected using a validated Arabic Dentinal Hypersensitivity Experience Questionnaire (DHEQ) alongside socio-demographic variables. Participants reporting DH symptoms within 12 months were included in impact analyses. Descriptive statistics and one-way ANOVA examined associations between DHEQ scores and participant characteristics. Results: Self-reported DH prevalence was 54.3% (n = 406), substantially exceeding global estimates. Among affected individuals, mean DHEQ score was 0.56 ± 0.19, indicating moderate-to-substantial quality-of-life impact. Functional limitations were most affected, particularly enjoyment of eating and drinking (0.72 ± 0.21). Significant associations were identified between higher DHEQ scores and age extremes (<18 and >35 years; p < 0.001), higher income levels (p = 0.032), fewer teeth (p = 0.040), and dental pain presence (p = 0.009). Sex, residence, education, and employment showed no significant associations. Conclusions: More than half of Saudi adults reported DH symptoms, representing a significant public health concern with substantial quality of life implications. Prevalence substantially exceeds global estimates, highlighting the need for targeted interventions. Age, income, tooth count, and pain presence emerged as key factors. These findings support developing population-specific prevention strategies, particularly targeting younger and older adults with tooth loss. Full article

Deforestation and land-use changes lead to significant soil degradation and erosion, particularly in Amazonian ecosystems, due to the region’s climate and geology. This study characterizes soil quality using physical, chemical, and biological parameters across different land uses. It uses a soil quality index (SQI) based on a minimum data set (MDS), from 19 evaluated parameters. The land uses evaluated were cacao monoculture (CMC), agroforestry systems associated with fruit and timber species (FAFS and TAFS, respectively), and a secondary forest. The SQI was composed of six variables, bulk density (BD), soil organic matter (SOM), urease activity (UR), pH, dehydrogenase activity (DH), and leaf litter, which are considered relevant indicators that allow for an adequate evaluation of soil quality. According to the SQI assessment, FAFS has a moderate-quality rating (0.40), followed by secondary forest (0.35), TAFS (0.33), and CMC (0.30), the last three categorized as low-quality. The methods used are replicable and efficient for evaluating changes in soil properties based on different land uses and management systems in landscapes similar to those of the Ecuadorian Amazon. Also worth mentioning is the potential of agroforestry as a sustainable land-use strategy that can enhance above- and below-ground biodiversity and nutrient cycling. Therefore, implementing agroforestry practices can contribute to long-term soil conservation and the resilience of tropical ecosystems. Full article

This study presents the results of axial tension (uplift) and compression tests evaluating the capacity of helical piles installed in Shahriyar dense sand using the UTM apparatus. Thirteen pile load experiments involving single-, double-, or triple-helix piles with shaft diameters of 13 mm were performed, including six compression tests and seven tension tests with different pitches (D_h =13, 20, and 25 mm). The tested helical piles with a helix diameter of 51 mm were considered, and the interhelix spacing approximately ranged between two and four times the helix diameter. Through laboratory testing techniques, the Shahriyar dense sand properties were identified. Alongside theoretical analyses of helical piles, the tensile and compressive pile load tests outcomes in dense sand with a relative density of 70% are presented. It was found that the maximum capacities of the compressive and tensile helical piles were up to six and eleven times that of the shaft capacity, respectively. With an increasing number of helices, the settlement reduced, and the bearing capacity increased. Consequently, helical piles can be manufactured in smaller sizes compared to steel piles. Overall, the compressive capacities of helical piles were higher than the tensile capacities under similar conditions. Single-helices piles with a pitch of 20 mm and double-helices piles with a pitch of 13 mm were more effective than others. Therefore, placing helices at the shallower depths and using smaller pitches result in better performance. In this study, when compared to values from the L1–L2 method, the theoretical method slightly underestimates the ultimate compression capacity and both overestimates and underestimates the uplift capacity for single- and double-helical piles, respectively, due to the individual bearing mode and cylindrical shear mode. Full article

Stevia rebaudiana Bertoni, a semi-perennial herb from the Asteraceae family, is native to the Paraguay–Brazil border region. The growing industrial interest in this species is due to its natural sweetening properties, such as steviol and its derivatives, which offer sweetness without adding calories. Morphological traits are crucial for assessing genetic variability and ensuring distinctness, homogeneity, and stability (DHS) for cultivar protection. This study characterized 19 elite Stevia genotypes from Embrapa Cerrados’ Active Germplasm Bank (BAG) using 21 morphological descriptors from Brazil’s Ministry of Agriculture, Livestock, and Supply (MAPA). Genetic distances were calculated using the simple coincidence index complement method, and clustering was performed via the Unweighted Pair-Group Method with Arithmetic Mean (UPGMA). The results showed that 17 of the 21 descriptors (>80%) effectively differentiated the genotypes, revealing significant genetic variability. Dendrogram analysis identified at least four major similarity groups, highlighting the potential of these genotypes for Stevia breeding programs. These findings underscore the suitability of these elite genotypes for developing superior varieties adapted to Cerrado conditions, supporting future cultivation and genetic improvement efforts. Full article

Digital hoarding (DH) is an emerging behavior with potential implications for psychological well-being and daily functioning. While traditionally associated with physical hoarding disorder, DH presents unique challenges in digital environments, particularly among university students increasingly immersed in technology. This study examines the relationship between DH and academic performance, proposing a theoretical model in which academic engagement and academic burnout act as mediating mechanisms. Drawing on the Job Demands–Resources Theory, we provide evidence that DH contributes to a health impairment process that negatively affects student outcomes. Our findings reveal DH as a novel predictor of academic burnout, highlighting its detrimental impact on academic performance. These results carry significant theoretical and practical implications, offering new insights into the role of technology-related anxiety disorders in educational settings. From a practical perspective, our study underscores the need for higher education institutions to implement targeted interventions focused on emotional regulation and learning strategies to mitigate the negative effects of DH. Despite limitations related to sample specificity and cross-sectional data, this research opens avenues for future longitudinal studies and interventions aimed at addressing DH in both academic and professional contexts. By linking digital behaviors to mental health and performance, this work aligns with public health interests in understanding technology’s impact on youth well-being. Full article

Accurate heat load forecasting is essential for the efficiency of District Heating Systems (DHS). Still, it is challenged by the need to model long-term temporal dependencies and nonlinear relationships with weather and other factors. This study proposes a hybrid deep learning framework combining an attention-enhanced Neural Basis Expansion Analysis for Time Series (N-BEATS) model and eXtreme Gradient Boosting (XGBoost). The N-BEATS component, with a multi-head self-attention mechanism, captures temporal dynamics, while XGBoost models non-linear impacts of external variables. Predictions are integrated using an optimized weighted averaging strategy. Evaluated on a dataset from 103 heating units, the model outperformed 13 baselines, achieving an MSE of 0.4131, MAE of 0.3732, RMSE of 0.6427, and R² of 0.9664. This corresponds to a reduction of 32.6% in MSE, 32.0% in MAE, and 17.9% in RMSE, and an improvement of 5.1% in R² over the best baseline. Ablation studies and statistical tests confirmed the effectiveness of the attention mechanism and ensemble strategy. This model provides an efficient solution for DHS load forecasting, facilitating optimized energy dispatch and enhancing system performance. Full article