Search Results (3,985)

Grain quality in bread wheat is a complex trait determined by multiple genetic factors and their interaction with environmental conditions. This study investigated the genetic architecture of key grain quality traits in the Avalon × Cadenza double haploid (DH) population under contrasting climatic conditions in Kazakhstan. A set of 101 spring-type DH lines was evaluated over three years in three major wheat-growing regions of Kazakhstan, representing northern, central, and southern environments. Grain yield and nine grain quality traits were assessed, including amylose content (Amc, %), test weight per liter (TWL, g/L), grain protein content (GPC, %), gliadin content (Gli, %), glutenin content (Glu, %), grain hardness (GH, %), grain vitreousness (GV, %), falling number (FN, s), and sedimentation value determined in a 2% acetic acid solution (SV, mL). The objectives were to characterize phenotypic variation, examine trait relationships, and identify major and environmentally stable quantitative trait loci (QTLs) controlling grain quality. QTL mapping identified 89 QTLs associated with the nine studied traits, including 82 major QTLs explaining more than 10% of phenotypic variation and 16 stable QTLs detected in two or more environments. The largest numbers of QTLs were found for GPC, SV, and TWL. Stable QTLs were distributed across all three wheat genomes, with important regions detected on chromosomes 1A, 1B, 2D, 4A, 4D, 5A, 6A, and 7D. Several stable QTLs co-localized with genomic regions previously associated with grain quality and developmental regulation, including loci near Wx-B1, Rht-D1, and Ppd-D1, suggesting biologically meaningful links among gluten composition, starch biosynthesis, plant development, and grain physical properties. These results improve understanding of the genetic control of wheat grain quality across diverse environments in Kazakhstan and provide promising targets for marker-assisted selection to combine improved end-use quality with wide environmental adaptation. Full article

Co-pyrolysis of polyolefins (LDPE, PP, PS) mixed with silicone rubber (SR) was investigated using a laboratory-scale pyrolysis apparatus to evaluate product composition, synergistic interactions, and siloxane recovery potential. Synergistic effects were assessed by comparing experimental mass balances and product distributions with calculated values derived from individual polymer pyrolysis. Co-pyrolysis resulted in a reduction in liquid yield and an increase in gaseous products and solid residue compared to calculated values, with liquid yields decreasing by up to ≈15 wt% at high SR content. This shift was accompanied by an enrichment in lighter hydrocarbons in both phases, reaching up to a ≈18% relative increase at high SR content, and by a redistribution towards smaller cyclic siloxanes. Chromatographic analysis confirmed that no new compounds were formed, but the proportion of low molecular weight species increased with silicone content. These effects are attributed to the distinct thermal behavior of the polymers, as silicone rubber does not melt but becomes brittle, allowing molten polyolefins to infiltrate surface cracks and prolong residence time, thereby promoting secondary cracking. Furthermore, recovery of hexamethylcyclotrisiloxane (D₃), the primary silicone pyrolysis product, was demonstrated from the liquid co-pyrolysis products via solvent-assisted filtration using ethanol, achieving purities above 99.5% and recovery rates up to ≈75% compared to other possible methods. These findings provide insights into co-pyrolysis behavior and offer a basis for developing strategies for the recovery of siloxane and advanced recycling of mixed polymer waste. Full article

Brain tumor classification from magnetic resonance imaging, which is also known as MRI, plays a sensitive role in computer-assisted diagnosis systems. In recent years, deep learning models have achieved high classification accuracy. However, their sensitivity to adversarial perturbations has become an important reliability concern in medical applications. This study suggests a robust brain tumor classification framework that combines non-negative matrix factorization (NNMF or NMF), lightweight convolutional neural networks (CNNs), and diffusion-based feature purification. Initially, MRI images are preprocessed and converted into a non-negative data matrix, from which compact and interpretable NNMF feature representations are extracted. Statistical metrics, including AUC, Cohen’s d, and p-values, are used to rank and choose the most discriminative components. Then, a lightweight CNN classifier is trained directly on the selected feature groups. To improve adversarial robustness, a diffusion-based feature-space purification module is introduced. A forward noise method followed by a learned denoiser network is used before classification. System performance is estimated using both clean accuracy and robust accuracy under powerful adversarial attacks created by AutoAttack. The experimental results show that the proposed framework achieves competitive classification performance while significantly enhancing robustness against adversarial perturbations. The findings presuppose that combining interpretable NNMF-based representations with a lightweight deep approach and diffusion-based defense technique supplies an effective and reliable solution for medical image classification under adversarial conditions. Full article

Almond (Prunus dulcis (Mill.) D.A. Webb) is one of the most economically important nut crops worldwide, valued for its nutritional properties and adaptability to diverse agroecological environments. This review summarizes current knowledge on almond domestication, genetic diversity, production trends, and improvement strategies, with a focus on drought tolerance under climate change. Archaeobotanical and molecular evidence indicate central Asia and the eastern Mediterranean as key centers of origin, where recurrent introgression from wild Prunus species contributed to the high genetic variability of cultivated almond. Global production trends reveal increasing challenges due to prolonged drought, climate variability, and rising water and energy costs, particularly affecting major producers such as the United States. Mediterranean regions are transitioning from traditional low-density orchards to intensive systems, where cultivar and rootstock choice are crucial for sustainability. Self-fertile and late-blooming cultivars improve yield stability, while interspecific hybrid rootstocks enhance water use efficiency and tolerance to drought and poor soils. Drought stress impacts almond physiology and yield, although moderate deficit irrigation can maintain productivity and improve kernel quality. Future improvement relies on germplasm conservation, marker-assisted selection, and genomic tools to develop climate-resilient cultivars integrated with sustainable water management strategies. Full article

Surface Acoustic Wave (SAW) technology, long central to analog signal processing and RF filtering, is undergoing a major renewal. Driven by advances that decouple SAWs from traditional piezoelectric materials and fixed-function devices, the field is gaining unprecedented control over acoustic, optical, and electronic interactions at the micro and nanoscale. This review synthesizes these developments across four fronts: new physical mechanisms for SAW manipulation, emerging material platforms, ranging from thin films to 2D systems, along with reconfigurable device architectures and circuits, and the expanding landscape of applications they enable. Optical methods are reshaping how SAWs are generated and controlled, bypassing the limits of conventional electromechanical coupling. Coherent optical excitation of high-Q SAW cavities via Brillouin-like optomechanical interactions now grants access to modes in non-piezoelectric substrates such as diamond and silicon, while on-chip SAW excitation in photonic waveguides through backward stimulated Brillouin scattering opens new integrated sensing routes. In parallel, magneto-acoustic experiments have revealed nonreciprocal SAW diffraction from resonant scattering in magnetoelastic gratings. On the device side, ZnO thin-film transistors integrated on LiNbO₃ exploit acoustoelectric coupling to realize voltage-tunable phase shifters; UHF Z-shaped delay lines achieve high sensitivity in a compact footprint; and parametric synthesis of wideband, multi-stage lattice filters targets 5G-class performance. Atomistic simulations show that SAW propagation in 2D MXene films can be engineered via surface terminations, while aerosol jet printing and SAW-assisted particle patterning provide agile, cleanroom-light fabrication of microfluidic and magnetic components. These advances enable applications ranging from hybrid quantum systems and quantum links to lab-on-a-chip particle control, SBS-based and UHF sensing, reconfigurable RF front-ends, and soft robotic actuators based on patterned magnetic composites. At the same time, optical techniques offer non-contact probes of dissipation, and MXenes and other emerging materials open new regimes of acoustic control. Conclusively, they are transforming SAW technology into a versatile, programmable platform for mediating complex interactions in next-generation electronic, photonic, and quantum systems. Full article

Accurate fruit localization and efficient harvesting are key challenges for agricultural robots, especially in dynamic orchard environments, where platform vibration, fruit occlusion, and computational resource limitations of embedded devices significantly impact system performance. To address these issues, this paper proposes a lightweight “fruit detection + harvesting” framework. First, by integrating MobileNetV4 and Triplet Attention mechanisms, an improved YOLOv8n network is designed, with the improved YOLOv8n Precision reaching 98.148% and FPS reaching 30 FPS on Jetson Nano, achieving a good balance between detection accuracy and computational efficiency suitable for edge deployment. Second, a strawberry three-dimensional coordinate reconstruction method based on weighted 3D centroid reconstruction is proposed, utilizing depth bias adjustment coefficients to improve spatial accuracy. Third, to address localization errors caused by vibration and platform motion, a dynamic compensation and temporal fusion strategy based on an Inertial Measurement Unit (IMU) is proposed. The rotation matrix estimated from IMU data is first used to correct camera pose variations. Then, an adaptive sliding window is employed to smooth the coordinate sequence. Finally, an Extended Kalman Filter (EKF) is applied to further refine the fused results by incorporating temporal dynamics, ensuring that the reconstructed three-dimensional coordinates in the robotic arm reference frame achieve higher stability and continuity. Experimental results in orchard scenarios show that compared with traditional methods, the system has higher localization accuracy, stronger robustness to dynamic disturbances, and higher harvesting efficiency. This work provides a practical and deployable solution for advancing intelligent fruit-harvesting robots. Full article

Accurate needle insertion during epidural anesthesia is challenging due to strong dependence on clinician experience and the limited integration of guidance modalities that simultaneously provide visual feedback and physical motion constraints. Current approaches, including ultrasound guidance and augmented reality visualization, mainly offer passive assistance and do not actively regulate insertion trajectory and depth, which may lead to variability in accuracy and increased risk of complications. This work presents a multimodal human–machine assistance system that combines augmented reality guidance with virtual fixtures to support lumbar epidural needle placement. A Tuohy needle is coupled to a haptic device interacting with a patient-specific L3–L4 lumbar phantom fabricated using 3D printing and ballistic gel. A model-based force profile reproduces the mechanical response of anatomical layers during insertion. Three experimental conditions are evaluated: freehand execution, augmented reality guidance with trajectory and depth visualization, and cooperative guidance using virtual fixtures defined by a cylindrical corridor and a depth-limiting plane. Results show a progressive reduction in mean depth error from 6.82 ± 3.46 mm (freehand) to 4.96 ± 2.41 mm (augmented reality) and 2.21 ± 1.73 mm (virtual fixtures). These findings indicate that the integration of visual and haptic guidance significantly enhances insertion precision and control. The proposed approach highlights the potential of multimodal human–machine cooperation for safer training and assisted interventions. Full article

Background/Objectives: Bud sports (somatic mutations) offer a quick way to develop new bougainvillea varieties by altering specific traits while keeping the desirable genetic background of the original cultivar. However, we still lack a comprehensive understanding of their genomic architecture and the molecular mechanisms behind their formation. This study aimed to characterize the population genomic characteristics of bud sports derived from the commercial variety Bougainvillea × buttiana ‘Miss Manila’. Methods: We employed genotyping by sequencing (GBS) on 39 accessions, including 27 bud sports and 12 conventional varieties. Population genomic analyses, such as principal component analysis (PCA), phylogenetic reconstruction, ADMIXTURE, and diversity statistics (π, He, Tajima’s D), were performed on 64,810 high-quality SNPs. Genome-wide scans for differentiation (FST) and selective sweeps (XP-CLR) were also conducted. Results: Bud sports showed significantly lower genetic diversity (π and He) than conventional varieties, which matches their clonal origin. PCA, phylogenetic, and ADMIXTURE analyses (optimal K = 4) revealed clear genetic differentiation and distinct population structures between the two groups. The bud sport population possessed fewer private alleles and a less negative Tajima’s D value. Genomic scans identified regions under selection in bud sports, with functional annotation pointed to genes involved in ubiquitin-mediated proteolysis and RNA transport. Notably, Bou_119143 (UDP-rhamnose rhamnosyltransferase 1) showed a high mutation frequency specifically in bud sports. Conclusions: We provide the first population-genomic evidence that bud sports of ‘Miss Manila’ are genetically distinct clonal lineages, shaped by somatic mutation and selection. These findings support bud sports as efficient sources for germplasm innovation. The identified genomic regions and candidate genes lay a foundation for future marker-assisted selection and molecular breeding in bougainvillea. Full article

Background/Objectives: Transcatheter ablation assisted by three-dimensional (3D) electroanatomical mapping (EAM) is the elective treatment for atrioventricular nodal reentrant tachycardia (AVNRT) in children and adolescents. In this population of patients, the most frequently employed EAM strategies are the low-voltage bridge (LVB) strategy and sinus rhythm propagation mapping (SRPM). However, the exact pathophysiology and anatomy of the AVNRT reentrant circuits are still poorly understood. The aim of this study was to investigate the relationship between SRPM and LVB and to shed light on nodal physiology in children and adolescents affected by AVNRT. Methods: We retrospectively collected data on pediatric patients who underwent cryoablation for AVNRT assisted by high-density 3D EAM by using the LVB strategy; maps were reviewed by two independent electrophysiologists and the SRPM was described. SRPM was defined as typical when only one collision area was identified and atypical whenever either no or ≥ two collision areas were localized. Results: Twenty-eight consecutive patients (11.3 ± 3.3 years) were enrolled. All procedures were acutely successful. Overall, atypical SRPM was present in 10 patients (35.7%), and it did not correlate with the presence of multiple SPs or electrophysiological data. Moreover, we observed an imperfect concordance between SRPM and LVB (only in 10/18 patients). When SRPM and LVB were assessed in different locations, the LVB identified the effective cryoablation site in more cases than SRPM (4/8 vs. 1/8). Lastly, in cases of double collision, one collision area co-localized with the LVB and the effective cryoablation spot, whereas the other was located superiorly, closer to the His bundle. Conclusions: Atypical sinus rhythm propagation in the Koch’s triangle is a frequent finding in pediatric AVNRT patients. In this series, LVB showed closer concordance with the successful cryolesion site than retrospectively reconstructed SRPM. Full article

We present a Tagalog lip-reading system designed to enhance communication accessibility for individuals with hearing impairments. Existing lip-reading models focus on English and other major languages and cannot recognize Tagalog visual speech patterns. To address this gap, we implemented 3D Convolutional Neural Network combined with Bidirectional Long Short-Term Memory network, supported by a custom Tagalog dataset of common words. This architecture achieved an average character error rate of 10.09%, word error rate of 24.08%, and overall word accuracy of 76.27%, demonstrating promising recognition accuracy for Tagalog lip movements. By introducing the Tagalog-specific lip-reading framework, the potential of deep learning-based visual speech recognition was validated to support inclusive technologies, with applications in daily communication, education, and assistive tools for the Filipino deaf community. Full article

Locked-in Syndrome (LIS) is a neurological condition in which individuals remain conscious but experience complete paralysis of voluntary muscles, except for eye movements—highlighting the need for reliable assistive communication technologies. This study presents the design and evaluation of an Arabic electrooculogram (EOG)-based communication system with adaptive classification capabilities for LIS applications. A custom-designed EOG acquisition circuit incorporating filtering and amplification stages was implemented and compared with the OpenBCI Cyton board. The system employed a hybrid classification approach combining amplitude, temporal, and statistical features to distinguish between blinks and voluntary vertical eye movements. Testing with ten healthy subjects yielded a mean classification accuracy of 83.96% ± 4.59% and an information transfer rate of 10.43 letters per minute, corresponding to a 30.38% improvement over conventional approaches. The custom-designed circuit achieved a signal-to-noise ratio of 25.21 dB, outperforming the OpenBCI Cyton board by 8% while reducing system cost by 62%. The integration with a Morse code-based interface enabled Arabic letter composition, while the system incorporated auto-completion and text-to-speech functionalities to further enhance communication efficiency. This cost-effective solution addresses a critical gap in assistive technologies for Arabic-speaking individuals with LIS and shows strong potential for enhancing their communication abilities and overall quality of life. Full article

Ankle dorsiflexion range of motion (ADF-ROM) deficits has been linked to impaired function, altered gait, and injury risk. This study’s objective was to examine the acute effects of static self-stretching (SSS), foam rolling (FR), and instrument-assisted soft tissue mobilization (IASTM) of the posterior lower-leg on ADF-ROM and functional ankle outcomes in individuals with ADF-ROM deficits. Thirteen healthy, physically active college students with active ADF-ROM ≤ 13°, assessed in a non-weight-bearing position, completed all three interventions in a randomized, within-subject repeated-measures design. Pre- and post-intervention assessments included ADF-ROM, ankle plantar flexor isometric strength (APF-IS), single-leg countermovement vertical jump (SLCVJ), anterior reach distance in the Y-Balance Test (A-YBT), and gait parameters (contact time and plantar pressure). A two-way repeated-measures ANOVA with Bonferroni post hoc tests was used. Effect sizes reported as partial eta squared (${\eta }_p^2$) and Cohen $d_{\mathrm{z}}$. All interventions significantly improved ADF-ROM (p < 0.001; ${\eta }_p^2$ = 0.885), with IASTM showing the largest increase (50.7%, $d_{\mathrm{z}}$= 2.15), followed by FR (35.4%, $d_{\mathrm{z}}$ = 2.20) and SSS (21.5%, $d_{\mathrm{z}}$ = 1.82). Differences between IASTM and FR (p > 0.05, $d_{\mathrm{z}}$ = 0.40) and between FR and SSS (p > 0.05, $d_{\mathrm{z}}$ = 0.69) were nonsignificant, while IASTM was significantly greater than SSS (p < 0.05, $d_{\mathrm{z}}$ = 0.92). Significant gains were also seen in A-YBT (p < 0.05; ${\eta }_p^2$ = 0.302) and rearfoot plantar pressure (p < 0.01; ${\eta }_p^2$ = 0.482), although pairwise comparisons were nonsignificant and demonstrated small-to-moderate effect sizes ($d_{\mathrm{z}}$ = 0.35–0.52). No significant changes occurred in APF-IS, SLCVJ, or contact time and mid- and forefoot plantar pressures during roll-off. In conclusion, all interventions improved ADF-ROM, with IASTM and FR being comparably effective. However, only slight improvements in dynamic balance and certain gait parameters were noted, with no effect on strength or power. Full article

This study investigates whether metacognitive prompting for responsible generative AI (GenAI) use can enhance students’ psychological sustainability in AI-assisted learning. Using a face-to-face classroom experiment (N = 148; 74 prompting, 74 control), we examined how metacognitive prompts embedded in a GenAI-assisted academic task influence learning anxiety and academic self-efficacy, and whether anxiety mediates the effect on self-efficacy. Manipulation checks indicated that the prompting condition produced significantly higher metacognitive engagement than the control condition (t(146) = 7.50, p < 0.001, d = 1.23). Hypothesis tests showed that metacognitive prompting reduced learning anxiety (b = −0.68, p < 0.001) and increased academic self-efficacy (b = 0.40, p = 0.008). Learning anxiety was negatively associated with self-efficacy (b = −0.42, p < 0.001). Mediation analyses using bootstrap confidence intervals revealed a significant indirect effect of prompting on self-efficacy via reduced anxiety (ab = 0.26, 95% CI [0.12, 0.43]), indicating partial mediation. These findings suggest that responsible GenAI use can be supported through instructional design. Brief metacognitive prompts may help students regulate AI use, reduce learning anxiety, and maintain academic self-efficacy. More broadly, the study contributes to sustainable education and educational technology research by showing that classroom scaffolds can support student agency and well-being in AI-assisted learning. Full article

Directly modulated VCSEL transmitters are widely deployed in short-reach optical interconnects. However, further scaling of per-lane symbol rates in AI/HPC data center fabrics requires modulation schemes beyond the practical limits of direct current modulation. We demonstrate a laterally integrated VCSEL–electro-absorption modulator (EAM) transmitter enabled by resonance-detuned coupling on an oxide-confined half-VCSEL platform. A localized 20 nm surface etch produces > 5 nm resonance detuning, confirmed by measured spectra and supported by transfer-matrix and mode-matching simulations, which indicate strong slow-light-assisted lateral coupling into the modulator. Experimentally, the measured spectra confirm a 5 nm resonance separation. Static characterization shows a coupling ratio of 63% extracted from near-field profiles and an extinction ratio of 4 dB (based on modulator-side power) under a −2 V modulator bias, with an apparent 1 mW absorption at a 6 mA VCSEL drive current. Dynamic measurements demonstrate a small-signal 3 dB bandwidth of approximately 23 GHz and clear NRZ eye openings at 25 Gbps and 30 Gbps. These results validate resonance-detuned lateral integration as a compact and manufacturable approach to VCSEL-based externally modulated transmitters for next-generation short-reach interconnects. Full article

The growing accumulation of microplastics in marine environments demands fast and accurate analytical methods for polymer identification. This study presents a new canonical spectral transformation (CST) strategy designed to extract the most relevant information of Raman spectra and enhance the performance of artificial intelligence (AI) models in the classification of microplastics. Using the Marine Plastic Database (MPDB) as the source of Raman spectra, five supervised models—k-Nearest Neighbor (KNN), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Multilayer Perceptron (MLP), and a one-dimensional Convolutional Neural Network (CNN-1D)—were trained and evaluated under both typical (conventional methodology) and CST workflows using 500 noisy samples per category. The CST consists of representing a Raman spectra in a vector where only the magnitude peaks of the most relevant frequency bands of the spectra are retained and the remaining values are null. This CST minimizes the inclusion of non-target data reaching the AI models. All models achieved higher accuracy with CST, where CNN-1D achieved the most significant performance, increasing accuracy to 0.90. In addition, CNN-1D identified Polystyrene (PS) and Poly(methyl methacrylate) (PMMA) with a score of 100% and 99%, respectively. The results demonstrate that CST effectively enhances spectral feature extraction and can be generalized to other spectroscopic techniques, providing a scalable framework for AI-assisted microplastic identification in seawater samples. Full article