Search Results (209)

Sugar content is pivotal in determining the flavor quality of tomato, and numerous genes related to tomato fruit quality have been identified. The distribution of sugar sources in plants primarily relies on the functionality of sugar transporters. Despite this, the specific role of SUT2, a sucrose transporter family member, in sugar accumulation within tomato fruits is still unclear. This study demonstrates that SUT2 is localized to the plasma membrane and possesses the function of transporting sucrose from the extracellular side to the intracellular side of the plasma membrane. Its expression level progressively decreases during fruit development. SUT2 knockout resulted in a significant increase in sugar content in tomato fruits. Further investigation revealed that the elevated sugar levels in knockout lines were accompanied by alterations in the expression of the sugar accumulation related genes STP1 and CDPK26/27. These findings provide new insights into the biological role of SUT2 in regulating sugar content in tomato fruits, improve our understanding of sugar accumulation mechanisms in tomato fruits, and offer valuable perspectives for quality improvement in tomato. Full article

A monocular deflectometric system comprises a camera and a screen that collaboratively facilitate the reconstruction of a specular surface under test (SUT). This paper presents a methodology for solving the slope distribution of the SUT utilizing pose estimation derived from reflections, based on vision ray calibration (VRC). Initially recorded by the camera, an assisted flat mirror in different postures reflects the patterns displayed by a screen maintained in a constant posture. The system undergoes a calibration based on the VRC to ascertain the vision ray distribution of the camera and the spatial relationship between the camera and the screen. Subsequently, the camera records the reflected patterns by the SUT, which remains in a constant posture while the screen is adjusted to multiple postures. Utilizing the VRC, the vision ray distribution among several postures of the screen and the SUT is calibrated. Following this, an iterative integrated calibration is performed, employing the calibration results from the preceding separate calibrations as initial parameters. The integrated calibration amalgamates the cost functions from the separate calibrations with the intersection of lines in Plücker space. Ultimately, the results from the integrated calibration yield the slope distribution of the SUT, enabling an integral reconstruction. In both the numeric simulations and actual measurements, the integrated calibration significantly enhances the accuracy of the reconstructions when compared to the reconstructions with the separate calibrations. Full article

The ability to perform instantaneous frequency measurement (IFM) of unknown microwave signals holds significant importance across various application domains. This paper presents a power-independent microwave photonic IFM system. The proposed system implements frequency measurement through the construction of an amplitude comparison function (ACF) curve, achieved by introducing a frequency-dependent time delay via an optical tunable delay line (OTDL) for the signal under test (SUT). System simulation demonstrates the measurement capability across a wide bandwidth of 0.1–40 GHz with high precision, exhibiting frequency errors ranging from −0.03 to 0.04 GHz. The scheme also maintains consistent performance under varying input power levels. Key implementation aspects, including single-sideband modulation selection and system extension methods, are analyzed in detail to optimize measurement accuracy. Notably, the proposed architecture features a simple and compact design with excellent integration potential. These characteristics, combined with its wide operational bandwidth and high measurement precision, make this approach particularly suitable for demanding applications in electronic reconnaissance and communication. Full article

Accurate prediction of reservoir temperature is critical for optimizing geothermal energy systems, yet the complexity of geothermal data poses significant challenges for traditional modeling approaches. This study conducts a comprehensive comparative analysis of advanced machine learning models, including support vector regression (SVR), random forest (RF), Gaussian process regression (GP), deep neural networks (DNN), and graph neural networks (GNN), to evaluate their predictive performance for reservoir temperature estimation. Enhanced feature engineering techniques, including accumulated local effects (ALE) and SHAP value analysis, are employed to improve model interpretability and identify key hydrogeochemical predictors. Results demonstrate that RF outperforms other models, achieving the lowest mean squared error (MSE = 66.16) and highest R² score (0.977), which is attributed to its ensemble learning approach and robust handling of nonlinear relationships. SVR and GP exhibit moderate performance, while DNN and GNN show limitations due to overfitting and sensitivity to hyperparameter tuning. Feature importance analysis reveals that SiO₂ concentration as the most influential predictor, aligning with domain knowledge. The study highlights the interplay between model complexity, dataset size, and predictive accuracy, offering actionable insights for optimizing geothermal energy systems. By integrating advanced machine learning with enhanced feature engineering, this research provides a robust framework for improving reservoir temperature prediction, contributing to the sustainable development of geothermal energy in alignment with sustainable energy development. Full article

This research evaluates four hydrogen (H₂) production technologies via water electrolysis (WE): alkaline water electrolysis (AWE), proton exchange membrane electrolysis (PEME), anion exchange membrane electrolysis (AEME), and solid oxide electrolysis (SOE). Two scoring and ranking methods, the MACBETH method and the Pugh decision matrix, are utilized for this evaluation. The scoring process employs nine decision criteria: capital expenditure (CAPEX), operating expenditure (OPEX), operating efficiency (SOE), startup time (SuT), environmental impact (EI), technology readiness level (TRL), maintenance requirements (MRs), supply chain challenges (SCCs), and levelized cost of H₂ (LCOH). The MACBETH method involves pairwise technology comparisons for each decision criterion using seven qualitative judgment categories, which are converted into quantitative scores via M-MACBETH software (Version 3.2.0). The Pugh decision matrix benchmarks WE technologies using a baseline technology—SMR with CCS—and a three-point scoring scale (0 for the baseline, +1 for better, −1 for worse). Results from both methods indicate AWE as the leading H₂ production technology, which is followed by AEME, PEME, and SOE. AWE excels due to its lowest CAPEX and OPEX, highest TRL, and optimal operational efficiency (at ≈7 bars of pressure), which minimizes LCOH. AEME demonstrates balanced performance across the criteria. While PEME shows advantages in some areas, it requires improvements in others. SOE has the most areas needing enhancement. These insights can direct future R&D efforts toward the most promising H₂ production technologies to achieve the net-zero goal. Full article

Yam (Dioscorea cayennensis subsp. rotundata,hereafter referred to as Dioscorea rotundata) is a staple tropical tuber crop with notable nutritional and economic value. Its development and yield depend on efficient sucrose allocation from source tissues. Sucrose transporters (SUTs), a conserved family of membrane proteins, mediate sucrose loading, translocation, and unloading. Although well-studied in model plants and cereals, SUTs in yam remain largely uncharacterized. This study aims to identify and characterize the SUT gene family in yam and explore their roles in sucrose transport and tuber development. We conducted a genome-wide analysis of yam SUT genes, including gene structure, subcellular localization, and phylogeny. Molecular docking was used to predict sucrose-binding residues, and qRT-PCR assessed gene expression across tissues and tuber developmental stages. Eight SUT genes were identified and classified based on sequence similarity and domain structure. Docking analysis revealed key residues involved in sucrose binding and possible conformational shifts influencing transport. Expression profiling showed that most SUT genes, especially in the tuber apex, were progressively upregulated during development, suggesting roles in sucrose unloading and cell expansion. Additionally, functional validation of DrSUT1 in Arabidopsis thaliana confirmed its involvement in sucrose transport, supporting its role in yam sucrose partitioning. Yam SUT genes, especially those highly expressed in sink tissues, are involved in sucrose partitioning and tuber development. These findings provide structural and functional insights into SUT-mediated sugar transport and lay a foundation for improving sucrose utilization and yield in yam and other tuber crops. Full article

The importance of forest ecosystems in tourism and recreation to sustainable tourism (SUT) in protected areas (PARs) has been the subject of research in numerous studies. People are increasingly turning to nature and its values. To prevent impacts on forest ecosystems, protected area managers plan for SUT and recreation use. The SUT in the PARs is based on four main pillars of sustainability: ecological, economic, sociocultural, and institutional dimensions. In this study, the authors examined the significance of social and ecological elements in the evolution of sustainable tourism for residents and visitors of the Karadjordjevo Special Nature Reserve (Karadjordjevo), where the primary resource is the forest ecosystem. The aim of this research was to examine the state and prospects for the development of sustainable tourism in the nature reserve by observing the dimensions of sustainability. In addition, the aim was to determine whether sustainable tourism affects the satisfaction of residents and visitors of this nature reserve. The research employs a quantitative methodology and applies the Prism of Sustainability model, continuing the authors’ previous research of nature-based tourism on the Balkan Peninsula. Based on the analysis of 1240 replies, it may be inferred that the four facets of sustainability significantly influence the state of tourism and that SUT greatly affects respondents’ sentiments. The new insights from this research indicate that the respondents recognized the importance of sustainable tourism, although not all significant destination factors exist that can impact the protection of nature on the one hand and the development of tourism with no adverse environmental consequences on the other. The research results are important for the various SUT plans and documents related to the management of destinations with forest ecosystems. Full article

Understanding the contributions of source–sink relationships to photosynthesis will help achieve high wheat grain yields. A single-factor field experiment was conducted to quantify the regulatory effects of different sink–source ratios on wheat photosynthetic characteristics, including two wheat cultivars with different source–sink relationships as materials for detailed source–sink manipulations through flag leaf removal (LR) and removal of spikelets on one side of each spike (SR). Compared with a control (CK), LR increased the sink–source ratio (23.84%) and significantly reduced the yield (16.17%), 1000-kernel weight (11.73%), and kernels per spike (7.33%). LR increased the leaves’ net photosynthetic rate (Pn) (4.27–15.82%), the electron transfer rate (3.97–14.93%), and the Rubisco activity (2.16–12.25%) in the short term, and LR increased sucrose synthesis-related enzyme activities (3.96–19.95%) and gene expressions (SPS1, SUS1, CIN1, and SUT1). Compared with CK, SR reduced the sink–source ratio (44.12%) and significantly increased the 1000-kernel weight (10.02%) but reduced the yield (43.93%) and kernels per spike (49.31%). SR reduced the leaves’ Pn (8.54–21.41%), the electron transfer rate (3.51–16.71%), and the Rubisco activity (5.96–21.51%), and the photosynthetic process was limited. SR decreased sucrose synthesis-related enzyme activities (5.12–29.09%) and gene expressions (SPS1, SUS1, CIN1, and SUT1). Therefore, a higher sink–source ratio is an important indicator of high photosynthetic efficiency, which can be used as a screening and judgment index in variety selection and cultivation regulation. Full article

The progression of marine resource exploration into deepwater and ultra-deepwater regions has intensified the requirement for precise quantification of the undrained shear strength of clay. Although diverse in situ testing methodologies—including the vane shear test (VST), cone penetration test (CPT), T-bar penetration test (TPT), and ball penetration test (BPT)—are widely utilized for the assessment of clay strength, systematic discrepancies and correlations between their derived measurements remain inadequately resolved. The aim of this work is to provide a systematic comparison of strength interpretations across different in situ testing methods, with emphasis on identifying method-specific biases under varying soil behaviors. To achieve this, a unified numerical simulation framework was developed to simulate these four prevalent testing techniques, employing large-deformation finite element analysis via the Coupled Eulerian–Lagrangian (CEL) approach. The model integrates critical constitutive behaviors of marine clays, specifically strain softening and strain rate dependency, to replicate in situ shear strength evolution. Rigorous sensitivity analyses confirm the model’s robustness. The results indicate that, when the stain rate and softening effects are neglected, the resistance factors from the CPT and VST remain largely insensitive to shear strength variations. However, T-bar and ball penetrometers tend to underestimate strength by up to 15% in high-strength soils due to the incomplete development of a full-flow failure mechanism. As a result, their application in high-strength soils is not recommended. With both the strain rate and softening effects considered, the interpreted strength value S_ut from the CPT increases by 13.5% compared to cases excluding these effects, while other methods exhibit marginal decreases of 4–5%. The isolated analysis of strain softening reveals that, under identical softening parameters, the CPT demonstrates the least sensitivity to strain softening among the four methods examined, with the factor reduction ratio N_s/N₀ ranging from 0.76 to 1.00, while the other three methods range from 0.65 to 0.88. The results indicate that the CPT is well suited for strength testing in soils exhibiting pronounced softening behavior, as it reduces the influence of strain softening on the measured results. These findings provide critical insights into method-specific biases in undrained shear strength assessments, supporting a more reliable interpretation of in situ test data for deepwater geotechnical applications. Full article

In operational testing contexts, testers face dual challenges of constrained timeframes and limited resources, both of which impede the generation of reliability test data. To address this issue, integrating data from similar systems with test data can effectively expand data sources. This study proposes a systematic approach wherein the mission of the system under test (SUT) is decomposed to identify candidate subsystems for data combination. A phylogenetic tree representation is constructed for subsystem analysis and subsequently mapped to a mixed-integer programming (MIP) model, enabling efficient computation of similarity factors. A reliability assessment model that combines data from similar subsystems is established. The similarity factor is regarded as a covariate, and the regression relationship between it and the subsystem failure-time distribution is established. The joint posterior distribution of regression coefficients is derived using Bayesian theory, which are then sampled via the No-U-Turn Sampler (NUTS) algorithm to obtain reliability estimates. Numerical case studies demonstrate that the proposed method outperforms existing approaches, yielding more robust similarity factors and higher accuracy in reliability assessments. Full article

Polyester-glass laminates are widely used to reinforce underground steel fuel tanks due to their excellent corrosion resistance and mechanical performance. However, the management of these composites at the end of their service life poses significant challenges, particularly in terms of material recovery and environmental impact. This study investigates both the structural benefits and recyclability of polyester-glass laminates. Numerical simulations confirmed that reinforcing corroded steel tank shells with a 5 mm GFRP (Glass Fiber Reinforced Polymer) coating reduced the maximum equivalent stress by nearly 50%, significantly improving mechanical integrity. In parallel, thermogravimetric and microscopic analyses were conducted on waste GFRP samples subjected to pyrolysis, gasification, and combustion. Among the methods tested, pyrolysis proved to be the most favorable, allowing substantial organic degradation while preserving the structural integrity of the glass fiber fraction. However, microscopy revealed that the fibers were embedded in a dense char matrix, requiring additional separation processes. Although combustion leaves the fibers physically loose, pyrolysis is favored due to better preservation of fiber mechanical properties. Combustion resulted in loose and morphologically intact fibers but exposed them to high temperatures, which, according to the literature, may reduce their mechanical strength. Gasification showed intermediate performance in terms of energy recovery and fiber preservation. The findings suggest that pyrolysis offers the best trade-off between environmental performance and fiber recovery potential, provided that appropriate post-treatment is applied. This work supports the use of pyrolysis as a technically and environmentally viable strategy for recycling polyester-glass laminates and encourages further development of closed-loop composite waste management. Full article

In recent years, Opuntia stricta (Cactaceae family) has garnered considerable attention due to its promising nutritional and medicinal properties. This study aims to investigate the chemical composition and bioactivity of Sicilian Opuntia stricta fruit pulp and seeds. Liquid chromatography–mass spectrometry analysis revealed the presence of betalain derivatives, especially isobetanin and betanin, as the main pigments in the freeze-dried pulp and its hydroalcoholic extract. Other constituents, namely, piscidic acid, isorhamnetin-3-O-glucoside, and isorhamnetin-3-O-rutinoside, were identified. Linoleic acid (41.95%) was the main abundant fatty acid followed by palmitic acid (19.32%) in the seed’s fixed oil as analyzed by gas chromatography–mass spectrometry. The antioxidant activity was assessed using a multi-target approach using 2,2-azinobis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS), 2,2-diphenyl-1-picrylhydrazyl (DPPH), ferric reducing antioxidant power assay (FRAP), and β-carotene bleaching tests. The ABTS test showed greater sensitivity to the action of the samples with significant half-maximal inhibitory concentrations (IC₅₀) of 13.24 and 14.82 mg/mL for the hydroalcoholic extract and the freeze-dried fruit pulp, respectively. Opuntia stricta’s extracts were also assessed for the carbohydrate-hydrolyzing enzyme and lipase inhibitory effect. The freeze-dried fruit pulp exhibited the highest effect against lipase (IC₅₀ of 33.54 μg/mL). Collectively, our results contribute to the characterization of this traditionally consumed Sicilian edible plant and suggest its use as a source of bioactive compounds useful for the prevention of obesity linked to hyperglycemia. Full article

This study investigated the impact of nighttime temperature and elevation on the oil and erucic acid content of rapeseed (Brassica napus L.) seeds, focusing on the role of sugar synthesis in the silique wall as a substrate for oil synthesis. Field experiments across different altitudes and controlled low nighttime temperature (LNT) treatments (20/18 °C and 20/13 °C) were conducted. Transcriptome analysis of the silique walls was performed to explore gene expression changes. The results showed that higher altitudes and lower nighttime temperatures significantly increased seed oil and erucic acid content, particularly in strong temperature-sensitive line (STSL) seeds. LNT conditions promoted sucrose synthesis and transport in the silique wall by upregulating genes involved in sugar transport (SUT, SWEET, SUC1) and transcription factors (WRKY51, NAC104). This, in turn, enhanced the substrate availability for oil synthesis in the seeds. Furthermore, genes associated with oil biosynthesis (SAD, FAD2, KAS) were significantly upregulated under LNT, promoting oil accumulation. In conclusion, nighttime temperature is a critical factor influencing oil content in rapeseed seeds. Low nighttime temperatures enhance sucrose transport and gene expression in the silique wall, leading to increased oil synthesis. These findings provide insights for breeding strategies aimed at improving seed oil content under varying climatic conditions. Full article

Counting fetal movements is essential for assessing fetal health, but manually recording these movements can be challenging and inconvenient for pregnant women. This study presents a wearable device designed to detect fetal movements across various settings, both within and outside medical facilities. The device integrates accelerometer and gyroscope sensors with Internet of Things (IoT) technology to accurately differentiate between fetal and non-fetal movements. Data were collected from 35 pregnant women at Suranaree University of Technology (SUT) Hospital. This study evaluated ten signal extraction methods, six machine learning algorithms, and four feature selection techniques to enhance classification performance. The device utilized Particle Swarm Optimization (PSO) for feature selection and Extreme Gradient Boosting (XGB) with PSO hyper-tuning. It achieved a sensitivity of 90.00%, precision of 87.46%, and an F1-score of 88.56%, reflecting commendable results. The IoT-enabled technology facilitated continuous monitoring with an average latency of 423.6 ms. It ensured complete data integrity and successful transmission, with the capability to operate continuously for up to 48 h on a single charge. The findings substantiate the efficacy of the proposed approach in detecting fetal movements, thereby demonstrating a practical and valuable technology for fetal movement detection applications. Full article

Soils may not always be suitable to fulfill their intended function. Soil improvement can be achieved by mechanical or chemical methods, especially in transportation facilities. L and FA additives are frequently used as chemical improvement additives. In this study, two natural clay samples with extreme and very high plasticity were improved by using L and FA admixtures, and their properties under static and repeated loads were investigated by ML methods. Two soil samples from two different sites were analyzed. In this study, eight datasets were used. There are 14 inputs, including specific gravity (Gs), void ratio (eo), sieve analysis (+No.4, −No.200), clay size, LL, plastic limit (PL), plasticity index (PI), linear shrinkage (Ls), shrinkage limit (SL), cure day, agent, clay type, and agent percentage. The outputs are index and swelling properties (compressive, percent), compressive strengths, modulus of elasticity, and compressibility properties in soaked and non-soaked conditions. Prediction is attempted with different ML (ML) techniques. ML techniques used for regression (such as Decision Tree Regression (DTR) and K-nearest neighbors (KNN)). SHapley Additive Explanations (SHAP), the impact of inputs on outputs were observed, and it was generally found that PL and LL had the highest impact on outputs. Different performance metrics are used for evaluation. The results showed that these ML techniques can predict the static and cyclic properties of extremely high plasticity clays with high performance (R² > 0.99). These results highlight the general applicability of the used ML models on different datasets containing soil properties. Full article