Search Results (46)

The morphology of rice grains represents one of the most vital agronomic characteristics, significantly impacting both grain productivity and the subsequent milling and nutritional quality of the crop. A comprehensive understanding of the genetic basis and molecular drivers of grain shape is vital for the targeted breeding of high-performance rice lines with consistent yield stability. To pinpoint the genomic regions influencing grain dimensions, we conducted a genome-wide association analysis across a panel of 231 distinct rice accessions, focusing on the discovery of loci associated with length and width. Our analysis revealed four consistent quantitative trait loci (QTLs) distributed across chromosomes 3, 4, and 11. Notably, grain length was associated with qGL3.1, qGL3.2, and qGL11. The first two were co-localized with GS3 and SMG3, respectively, whereas qGL11 likely constitutes a novel locus. One QTL, qGW4, which governs grain width, was found to co-localize with the gene OsOFP14. Haplotype analysis further revealed that the characteristic haplotypes of the candidate genes for qGL3.1, qGL3.2, and qGW4 were enriched in eight germplasm accessions (including Newbonnet, Skybonnet, and Lemont), all of which exhibit a slender-grain phenotype. This finding suggests that the specific combination of these characteristic haplotypes is a common genetic signature of slender-grain rice, serving as a potential gene combination for the targeted improvement of rice grain shape. Our results reveal valuable QTLs and candidate genes and highlight specific germplasm resources that can be readily applied in marker-assisted breeding to improve rice grain shape. Full article

Concrete-filled steel tube (CFST) columns are composite structural elements preferred in various engineering structures due to their superior properties compared to those of traditional structural elements. However, fire resistance analyses are complex due to CFST columns consisting of two components with different thermal and mechanical properties. Significant challenges arise because current design codes and guidelines do not provide clear guidance for determining the time-dependent fire performance of these composite elements. This study aimed to address the existing design gap by investigating the fire behavior of circular long CFST columns under axial compressive load and developing robust, accurate, and reliable design models to predict their fire performance. To this end, an up-to-date database consisting of 62 data-points obtained from experimental studies involving variable material properties, dimensions, and load ratios was created. Analytical design models were meticulously developed using two advanced soft computing techniques: artificial neural networks (ANNs) and genetic expression programming (GEP). The model inputs were determined as six main independent parameters: steel tube diameter (D), wall thickness (t_s), concrete compressive strength (f_c), steel yield strength (f_sy), the slenderness ratio (L/D), and the load ratio (μ). The performance of the developed models was comprehensively compared with experimental data and existing design models. While existing design formulas could not predict time-based fire performance, the developed models demonstrated superior prediction accuracy. The GEP-based model performed well with an R-squared value of 0.937, while the ANN-based model achieved the highest prediction performance with an R-squared value of 0.972. Furthermore, the ANN model demonstrated its excellent prediction capability with a minimal mean absolute percentage error (MAPE = 4.41). Based on the nRMSE classification, the GEP-based model proved to be in the good performance category with an nRMSE value of 0.15, whereas the ANN model was in the excellent performance category with a value of 0.10. Fitness function (f) and performance index (PI) values were used to assess the models’ accuracy; the ANN (f = 1.13; PI = 0.05) and GEP (f = 1.19; PI = 0.08) models demonstrated statistical reliability by offering values appropriate for the expected targets (f ≈ 1; PI ≈ 0). Consequently, it was concluded that these statistically convincing and reliable design models can be used to consistently and accurately predict the time-dependent fire resistance of axially loaded, circular, long CFST columns when adequate design formulas are not available in existing codes. Full article

Wind-induced acceleration represents one of the main challenges in the dynamic behavior of tall buildings. Its estimation can be carried out through experimental wind tunnel tests or using the analytical expressions proposed in various international codes and standards. However, the explicit consideration of uncertainty in structural dynamic properties, wind characteristics, and human-perceived response is limited or nonexistent in most standards. Slender structures like tall buildings can experience excessive acceleration due to wind loading, which can impact the activities of the buildings’ users. To prevent excessive wind-induced vibration, some international codes require that the serviceability limit state, in terms of acceleration, is satisfied. These serviceability limit states require that the wind-induced acceleration is less than or equal to a predefined value, which is taken from perception curves that are developed based on perceived vibration alone. The main objective of this work is to develop acceleration factors for across-wind and torsional acceleration that are calibrated for the selected targeted probability of perception levels by considering the uncertainty in the structural dynamic characteristics and wind characteristics, as well as in the human perception of motion. The acceleration factors are incorporated in a simple-to-use procedure to evaluate the wind-induced acceleration in tall buildings. A numerical example is provided to illustrate the use of the proposed acceleration factors. Full article

Larix gmelinii is the fourth most important tree species in China and a typical zonal climax species in the cold temperate region, with high ecological and resource value. However, intensive logging, high-density afforestation, and insufficient scientific management have led to overly dense, homogeneous, and unstable plantations, severely limiting productivity. To clarify the mechanisms by which structural dynamics regulate productivity, we established a space-for-time sequence (T1–T3, T2-D, CK) under a consistent early-tending background. Using the “1 + 4” nearest-neighbor framework and six spatial structural parameters, we developed tree and forest spatial structure indices (TSSI and FSSI) and integrated nine structural–functional indicators for multivariate analysis. The results showed that TSSI and FSSI effectively characterized multi-level stability and supported stability classification. Along the stand-age gradient, structural stability and spatial use efficiency improved significantly, with FSSI and biomass per hectare (BPH) increasing by 91% and 18% from T1 to T3, though a “structural improvement–functional lag” occurred at T2. Moderate thinning markedly optimized stand configuration, reducing low-stability individuals from 86.45% in T1 to 42.65% in T2-D, while DBH, crown width, FSSI, and BPH (229.87 t·hm⁻²) increased to near natural-forest levels. At the tree scale, DBH, tree height, crown width, and TSSI were positive drivers, whereas a high height–diameter ratio (HDR) constrained growth. At the stand scale, canopy density, species richness, and mean DBH promoted FSSI and BPH, while mean HDR and stand density imposed major constraints. A critical management window was identified when DBH < 25 cm, HDR > 10, and TSSI < 0.25 (approximately 10–30 years post-planting). We propose a stepwise, moderate, and targeted thinning strategy with necessary underplanting to reduce density and slenderness, increase diameter and canopy structure, and enhance diversity, thereby accelerating the synergy between stability and productivity. This framework provides a practical pathway for the scientific management and high-quality development of L. gmelinii plantations. Full article

This study aimed to evaluate the effects of specific LED light spectra on the growth and physiology of Changchuan No. 3 Capsicum annuum L. seedlings. The experimental design involved exposing pepper seedlings to six different spectral light combinations for 7, 14, and 21 days, with the treatments consisting of 2R1B1Y (red/blue/yellow = 2:1:1), 2R1B1FR (red/blue/far-red = 2:1:1), 2R1B1P (red/blue/purple = 2:1:1), 4R2B1G (red/blue/green = 4:2:1), 2R1B1G (red/blue/green = 2:1:1), and 2R1B (red/blue = 2:1). The results demonstrated distinct spectral regulation of seedling development: compared to the white light (CK), the 2R1B1FR (far-red light supplementation) treatment progressively stimulated stem elongation, increasing plant height and stem diameter by 81.6% and 25.9%, respectively, at day 21, but resulted in a more slender stem architecture. The 2R1B1G (balanced green light) treatment consistently promoted balanced growth, culminating in the highest seedling vigor index at the final stage. The 2R1B1P (purple light supplementation) treatment exhibited a strong promotive effect on root development, which became most pronounced at day 21 (126% increase in root dry weight), while concurrently enhancing soluble sugar content and reducing oxidative stress. Conversely, the 2R1B1Y (yellow light supplementation) treatment increased MDA content by 70% and led to a reduction in chlorophyll accumulation, while 2R1B (basic red–blue) resulted in lower biomass accumulation compared to the superior spectral treatments. The 4R2B1G (low green ratio) treatment showed context-dependent outcomes. This study elucidates how targeted spectral compositions, particularly involving far-red and green light, can optimize pepper seedling quality by modulating photomorphogenesis, carbon allocation, and stress physiology. The findings provide a mechanistic basis for designing efficient LED lighting protocols in controlled-environment agriculture to enhance pepper nursery production. Full article

Remote sensing objects often exhibit significant scale variations, high aspect ratios, and diverse orientations. The anisotropic spatial distribution of such objects’ features leads to the conflict between feature representation and boundary regression caused by the coupling of different attribute parameters: previous detection methods based on square-kernel convolution lack the overall perception of large-scale or slender objects due to the limited receptive field; if the receptive field is simply expanded, although more context information can be captured to help object perception, a large amount of background noise will be introduced, resulting in inaccurate feature extraction of remote sensing objects. Additionally, the extracted features face issues of feature conflict and discontinuous loss during parameter regression. Existing methods often neglect the holistic optimization of these aspects. To address these challenges, this paper proposes SODE-Net as a systematic solution. Specifically, we first design a multi-scale fusion and spatially orthogonal convolution (MSSO) module in the backbone network. Its multiple shapes of receptive fields can naturally capture the long-range dependence of the object without introducing too much background noise, thereby extracting more accurate target features. Secondly, we design a multi-level decoupled detection head, which decouples target classification, bounding-box position regression and bounding-box angle regression into three subtasks, effectively avoiding the coupling problem in parameter regression. At the same time, the phase-continuous encoding module is used in the angle regression branch, which converts the periodic angle value into a continuous cosine value, thus ensuring the stability of the loss value. Extensive experiments demonstrate that, compared to existing detection networks, our method achieves superior performance on four widely used remote sensing object datasets: DOTAv1.0, HRSC2016, UCAS-AOD, and DIOR-R. Full article

It has been demonstrated that needle guidance systems can enhance the precision and safety of ultrasound-guided punctures in human medicine. Systems that permit the utilization of commercially available standard needles, instead of those that necessitate the acquisition of costly, proprietary needles, are of particular interest. The objective of this phantom study is to evaluate the reliability and accuracy of magnet-based ultrasound needle guidance systems, which superimpose the position of the needle tip and a predictive trajectory line on the live ultrasound image. We conducted fine-needle aspiration cytology of thyroid nodules. The needles utilized in these procedures are of a slender gauge (21–27G), with lengths ranging from 40 to 80 mm. A dedicated training workstation with integrated software-based analyses of the movement of the needle tip was utilized in 240 standardized phantom punctures (angle: 45°; target depth: 20 mm). No system failures occurred, and the target achieved its aim in all cases. The analysis of the software revealed stable procedural parameters with minor relative deviations from the predefined reference values regarding the distance of needle tip movement (−4.2% to +6.7%), needle tilt (−6.4% to +9.6%), and penetration depth (−7.5% to +4.5%). These deviations appeared to increase with the use of thin needles and, to a lesser extent, long needles. They are attributed to the slight bending of the needle inside the (phantom) tissue. The training workstation we employed is thus suitable for use in educational settings. Nevertheless, in intricate clinical puncture scenarios—for instance, in the case of unfavorable localized small lesions near critical anatomical structures, particularly those involving thin needles—caution is advised, and the system should not be relied upon exclusively. Full article

The field of remote sensing target detection has experienced rapid development in recent years, demonstrating significant value in various applications. However, general detection algorithms still face many key challenges when dealing with directional target detection: firstly, conventional networks struggle to accurately represent features of rotated targets, particularly in modeling the slender shape characteristics of high-aspect-ratio targets; secondly, the mismatch between the static label allocation strategy and the feature space of dynamic rotating targets leads to bias in training sample selection under extreme-aspect-ratio scenarios. To address these issues, this paper proposes a single-stage Shape-Aware Dynamic Alignment Network (SADA-Net) that collaboratively enhances detection accuracy through feature representation optimization and adaptive label matching. The network’s design philosophy demonstrates greater flexibility and complementarity than that of previous models. Specifically, a Dynamic Refined Rotated Convolution Module (DRRCM) is designed to achieve rotation-adaptive feature alignment. An Anchor-Refined Feature Alignment Module (ARFAM) is further constructed to correct feature-to-spatial misalignment. In addition, a Shape-Aware Quality Assessment (SAQA) strategy is proposed to optimize sample matching quality based on target shape information. Experiment results demonstrate that SADA-Net achieves excellent performance comparable to state-of-the-art methods on three widely used remote sensing datasets (i.e., HRSC2016, DOTA, and UCAS-AOD). Full article

Understanding how mammals respond to climate change is critical for predicting future biogeographic shifts and implementing effective conservation strategies. In this study, we applied MaxEnt modeling to identify key determinants of the distribution of the Malabar slender loris (Loris lydekkerianus malabaricus), a nocturnal primate endemic to the Western Ghats of India. Using 416 slender loris sightings, spatially thinned at 0.5 km intervals to reduce spatial autocorrelation, we evaluated 19 present bioclimatic variables alongside 10 additional climatic variables. From these, 14 predictor variables with Pearson correlation values above 0.75 were selected for analysis. Future distribution models employed bioclimatic projections from the CNRM-CM5 global climate models under three Representative Concentration Pathways (RCPs): 2.6, 4.5, and 8.5. The current distribution models identified 23 km² as a suitable habitat for slender lorises, with 3 km² suitable for males and 12 km² for females. Projections for 2070 under RCP 2.6, 4.5, and 8.5 scenarios predict habitat reductions of 52%, 13%, and 8%, respectively, signaling significant vulnerability under changing climatic conditions. Precipitation of the warmest quarter, precipitation of the driest month, distance from roads, and elevation were identified as the most influential variables shaping the species’ distribution. This study underscores the pressing need for targeted conservation efforts to mitigate habitat loss and fragmentation, particularly in the context of climate change. By providing a detailed analysis of current and future habitat suitability, it lays the groundwork for similar predictive studies on nocturnal small mammals. As climate change accelerates, the integration of species–specific ecological insights and advanced modeling techniques will be vital in guiding conservation actions and preserving biodiversity in vulnerable ecosystems like the Western Ghats. Full article

Neglected and Underutilized Species (NUS)—locally available, climate-resilient species—possess significant nutritional, social, and environmental benefits, yet their use, research focus, and market presence have diminished over time. Incorporating NUS into school meal programs can potentially boost childhood nutrition, promote healthy eating, encourage sustainable food production, preserve food culture and heritage, and support biodiversity conservation. School meals offered in Kenya are often monotonous and nutritionally inadequate. We conducted a case study on a school in an informal urban settlement in Nairobi, targeting students between ages 6–12, to demonstrate how incorporating locally grown, nutrient-dense foods into school meals can result in better nutrition for school-age children, while making significant savings for schools. Using the World Food Programme’s School Meal Planner (SMP) PLUS software, the school meals offered were analyzed for nutrient adequacy and optimized including five NUS: African nightshade (Solanum spp.), spider plant (Cleome gynandra), Bambara groundnut (Vigna subterranea), bonavist or hyacinth bean (Lablab purpureus), and slender leaf (Crotalaria spp.). The optimization process was based on the commodity price fluctuations and nutrient composition of the local agrobiodiversity used. The study results show how NUS are a viable and healthy alternative to meet the recommended daily nutrient needs for school-aged children at affordable prices. The tool results showcased the effectiveness of linear programming in enabling national decision making for efficient school feeding program planning, by designing comprehensive, affordable food baskets using local agrobiodiversity. Future research should explore implementing optimized school menus while examining broader aspects, such as school lunch environmental impacts and direct procurement approach opportunities that source local ingredients from smallholder farmers. Full article

The present work was carried out as part of the PRIN 2022JCS2CN project “CoolGal”, which aims to design and manufacture a beryllium target cooled by Galinstan (a liquid metal alloy at room temperature) for the production of neutrons using energetic protons. The objective of the present work is to thermo-structurally design a beam window that encloses the environment in which the target is housed. The window consists of a Havar disk, the thickness of which must be minimized to absorb the least amount of proton beam power, while its diameter must be sufficient to avoid excessive beam loss. The window will then be embedded around its perimeter and will have to withstand two load conditions, applied individually: A mechanical load, due to the atmospheric pressure of 0.11 MPa during vacuuming, and a thermal load, due to heating during irradiation with the proton beam. Once a first-version window geometry was defined, a static structural finite element analysis (FEA) was carried out by activating geometric nonlinearities to assess the structural integrity of the window under mechanical loading. After that, a static thermal–mechanical FEA analysis was carried out to assess the structural integrity of the window under thermal loading. Given the compressive stress state induced by thermal loading and the slenderness of the window itself, a nonlinear buckling structural FEA analysis was also performed. Full article

The precise identification of wheat seedlings in unmanned aerial vehicle (UAV) imagery is fundamental for implementing precision agricultural practices such as targeted pesticide application and irrigation management. This detection task presents significant technical challenges due to two inherent complexities: (1) environmental interference from variable illumination conditions and (2) morphological characteristics of wheat seedlings characterized by slender leaf structures and flexible posture variations. To address these challenges, we propose DHS-YOLO, a novel deep learning framework optimized for robust wheat seedling detection under diverse illumination intensities. Our methodology builds upon the YOLOv11 architecture with three principal enhancements: First, the Dynamic Slender Convolution (DSC) module employs deformable convolutions to adaptively capture the elongated morphological features of wheat leaves. Second, the Histogram Transformer (HT) module integrates a dynamic-range spatial attention mechanism to mitigate illumination-induced image degradation. Third, we implement the ShapeIoU loss function that prioritizes geometric consistency between predicted and ground truth bounding boxes, particularly optimizing for slender plant structures. The experimental validation was conducted using a custom UAV-captured dataset containing wheat seedling images under varying illumination conditions. Compared to the existing models, the proposed model achieved the best performance with precision, recall, mAP50, and mAP50-95 values of 94.1%, 91.0%, 95.2%, and 81.9%, respectively. These results demonstrate our model’s effectiveness in overcoming illumination variations while maintaining high sensitivity to fine plant structures. This research contributes an optimized computer vision solution for precision agriculture applications, particularly enabling automated field management systems through reliable crop detection in challenging environmental conditions. Full article

In this study, an improved target-detection model based on information theory is proposed to address the difficulties of crack-detection tasks, such as slender target shapes, blurred boundaries, and complex backgrounds. By introducing a multi-scale information gain mechanism and a global–local feature coupling strategy, the model has significantly improved feature extraction and expression capabilities. Experimental results show that, on a single-crack dataset, the model’s mAP@50 and mAP@50-95 are 1.6% and 0.8% higher than the baseline model RT-DETR, respectively; on a multi-crack dataset, these two indicators are improved by 1.2% and 1.0%, respectively. The proposed method shows good robustness and detection accuracy in complex scenarios, providing new ideas and technical support for in-depth research in the field of crack detection. Full article

Metal–organic frameworks (MOFs) are also known as porous coordination polymers. This kind of material is constructed with inorganic nodes (metal ions or clusters) with organic linkers and has emerged as a promising class of materials with several unique properties. Well-known applications of MOFs include their use as gas storage and in separation, catalysis, carbon dioxide capture, sensing, slender film gadgets, photodynamic therapy, malignancy biomarkers, treatment, and biomedical imaging. Over the past 15 years, an increasing amount of research has been directed to MOFs due to their advanced applications in fuel cells, supercapacitors, catalytic conversions, and drug delivery systems. Various synthesis methods have been proposed to achieve MOFs with nanometric size and increased surface area, controlled surface topology, and chemical activity for industrial use. In this context, the pharmaceutical industry has been watching the accelerated development of these materials with great attention. Thus, the objective of this work is to study the synthesis, characterization, and toxicity of MOFs as potential technological excipients for the development of drug carriers. This work highlights the use of MOFs not only as delivery systems (DDSs) but also in advanced diagnostics and therapies, such as photodynamic therapy and targeted delivery to tumors. Bibliometric analyses showed a growing interest in the topic, emphasizing its contemporary relevance. Full article

This comprehensive review paper delves into the utilization of Glass Fiber-Reinforced Polymer (GFRP) composites within the realm of concrete column reinforcement, spotlighting the surge in structural engineering applications that leverage GFRP instead of traditional steel to circumvent the latter’s corrosion issues. Despite a significant corpus of research on GFRP-reinforced structural members, questions about their compression behavior persist, making it a focal area of this review. This study evaluates the properties of GFRP bars and their impact on the structural behavior of concrete columns, addressing variables such as concrete type and strength, cross-sectional geometry, slenderness ratio, and reinforcement specifics under varied loading protocols. With a dataset spanning over 250 publications from 1988 to 2024, our findings reveal a marked increase in research interest, particularly in regions like China, Canada, and the United States, highlighting GFRP’s potential as a cost-effective and durable alternative to steel. However, gaps in current knowledge, especially concerning Ultra-High-Performance Concrete (UHPC) reinforced with GFRP, underscore the necessity for targeted research. Additionally, the contribution of GFRP rebars to compressive column capacity ranges from 5% to 40%, but current design codes and standards underestimate this, necessitating new models and design provisions that accurately reflect GFRP’s compressive behavior. Moreover, this review identifies other critical areas for future exploration, including the influence of cross-sectional geometry on structural behavior, the application of GFRP in seismic resistance, and the evaluation of the size effect on column strength. Furthermore, the paper calls for advanced studies on the long-term durability of GFRP-reinforced structures under various environmental conditions, environmental and economic impacts of GFRP usage, and the potential of Artificial Intelligence (AI) and Machine Learning (ML) in predicting the performance of GFRP-reinforced columns. Addressing these research gaps is crucial for developing more resilient and sustainable concrete structures, particularly in seismic zones and harsh environmental conditions, and fostering advancements in structural engineering through the adoption of innovative, efficient construction practices. Full article