Search Results (729)

Wheat exhibits moderate tolerance to salinity. The increasing salinization of arable land poses a significant risk to future wheat production. Therefore, it is imperative to expedite the genetic breeding of wheat for enhanced salt tolerance. This study investigates the genetic and phenotypic diversity of 90 wheat varieties under salt stress, utilizing a comprehensive approach involving trait distribution analysis, hierarchical clustering, kinship estimation, and low-density association analysis. The phenotypic analysis of key agronomic traits revealed significant variability in traits such as leaf area index, canopy temperature, grain area, dry weight, harvest index, grain yield, and tiller number. Most traits exhibited a near-normal distribution, with a few parameters showing skewed or bimodal distributions, indicating the presence of subpopulations with distinct trait profiles. The hierarchical clustering analysis identified five distinct genetic clusters among the wheat varieties, highlighting the complex genetic relationships and variations in salt stress tolerance. Kinship estimates further confirmed the presence of genetic divergence among the accessions, with a majority showing weak or null relationships. Statistical models for association analysis revealed the effectiveness of the Generalized Linear Mixed Model (GLMM) in detecting a greater number of significant genetic markers associated with key agronomic traits, with the GLMM explaining a higher proportion of phenotypic variation. The findings underline the importance of genetic diversity in wheat breeding programs aimed at improving salt stress tolerance and agronomic performance. These results provide valuable insights for future breeding strategies, focusing on the optimization of key traits and marker-assisted selection for the development of salt-tolerant wheat cultivars. Full article

The rapid growth in natural gas consumption by gas-fired generators and the emergence of power-to-hydrogen (P2H) technology have increased the interdependency of natural gas and power systems, presenting new challenges to energy system operators due to the heterogeneous uncertainties associated with power loads, renewable energy sources (RESs), and gas loads. These uncertainties can easily spread from one infrastructure to another, increasing the risk of cascading outages. Given the erratic nature of RESs, P2H technology provides a valuable solution for large-scale energy storage systems, crucial for the transition to economic, clean, and secure energy systems. This paper proposes a new approach for the co-optimized operation of gas and electric power systems, aiming to reduce combined operating costs by 10–15% without jeopardizing gas and energy supplies to customers. A mixed integer non-linear programming (MINLP) model is developed for the optimal day-ahead operation of these integrated systems, with a case study involving the IEEE 24-bus power system and a 20-node natural gas system. Simulation results demonstrate the model’s effectiveness in minimizing total costs by up to 20% and significantly reducing renewable energy curtailment by over 50%. The proposed approach supports UN Sustainable Development Goals by ensuring sustainable energy (SDG 7), fostering innovation and resilient infrastructure (SDG 9), enhancing energy efficiency for resilient cities (SDG 11), promoting responsible consumption (SDG 12), contributing to climate action (SDG 13), and strengthening partnerships (SDG 17). It promotes clean energy, technological innovation, resilient infrastructure, efficient resource use, and climate action, supporting the transition to sustainable energy systems. Full article

Background/Objectives: Traditional diet management for type 2 diabetes (T2DM) is often complex and effortful to sustain. Nudging offers low-effort and automatic approaches to dietary behaviour change yet remains underexplored in T2DM. This study evaluated the independent and combined 6-month effects of nudging education (NE) and nudging tableware (NT) on HbA1c, along with other secondary health outcomes, among adults with T2DM and overweight/obesity, compared to their non-nudge counterparts (control education, CE; control tableware, CT). Methods: A 2 × 2 factorial cluster RCT was conducted in 12 primary healthcare settings in China (pre-registered as ChiCtr2100044471). Participants were randomly assigned to the nudging education group (NE + CT), the nudging tableware group (CE + NT), the combined group (NE + NT) or the full-control group (CE + CT) for 1 month. The primary outcome was HbA1c. Secondary outcomes included dietary behaviours, metabolic indicators, and psychological health. Generalized linear mixed models were used for analysis. Results: A total of 284 participants (mean age, 52.28 years; 54.3% male) were randomly assigned and included in the analysis. After 6 months, NE and NT independently led to HbA1c reductions (−0.76%, p < 0.001; −0.33%, p = 0.042, vs. controls), with an additive but non-interactive effect when combined, resulting in a 1.04% reduction (p < 0.001) in the combined group. They also improved total calorie, macronutrient, and vegetable intake, FBG, plasma lipids, and BMI. NE additionally reduced diabetes distress and enhanced self-efficacy. Conclusions: Both NE and NT improved dietary and metabolic outcomes without increasing the psychological burden. The combined group showed the greatest benefits. Findings highlighted the importance of considering automatic processes in diabetes management. Full article

Background/Objectives: Numerous research studies link the improvement in executive functions and school success. However, there is hardly any research analyzing the transfer of this improvement to behavioral problems. This study analyzed whether improving executive functions, through contextualized daily activities, decreases these behaviors. Methods: Fifty third-year kindergarten students participated, divided into experimental and active control groups, with pre- and post-intervention measurements. The students in the experimental group were trained with the EFE-P program and the students in the control group received regular curriculum activities. The EFE-P program (i) has been designed with the aim of improving their executive functions, using a game-based approach; (ii) not only involves cognitive activities, but also behavioral and emotional activities, related to the warm aspects of executive functions; and (iii) consists of three units (inhibitory control, working memory and cognitive flexibility), with each unit involving 7 sessions (21 sessions in total), with an approximate duration of 30 min each. Results: Analysis of the data using a generalized linear mixed effects model revealed that students in the experimental group scored lower for behavioral problems than those in the active control group and the effect sizes were large for all of them: aggressiveness (d = 1.25); hyperactivity (d = 0.77); attention deficit (d = 1.12); anxiety (d = 0.82); and depression (d = 1.51). Conclusions: After discussing the results, it is concluded that intervention in executive functions induces, by way of distant transfer, a decrease in behavioral problems in preschool; the role of contextualized activities in real situations is emphasized; and several implications for practice and research are discussed. Full article

Climate change has emerged as a significant driver of the increasing frequency and severity of power outages. Rising global temperatures place additional stress on electrical grids that must meet substantial electricity demands, while extreme weather events such as hurricanes, floods, heatwaves, and wildfires frequently damage vulnerable electrical infrastructure. Ensuring the resilient operation of distribution systems under these conditions poses a major challenge. This paper presents a comprehensive two-stage techno-economic strategy to enhance the resilience of modern distribution systems. The approach optimizes the scheduling of distributed energy resources—including distributed generation (DG), wind turbines (WTs), battery energy storage systems (BESSs), and electric vehicle (EV) charging stations—along with the strategic placement of remotely controlled switches. Key objectives include preventing damage propagation through the isolation of affected areas, maintaining power supply via islanding, and implementing prioritized load shedding during emergencies. Since improving resilience incurs additional costs, it is essential to strike a balance between resilience and economic factors. The performance of our two-stage multi-objective mixed-integer linear programming approach, which accounts for uncertainties in vulnerability modeling based on thresholds for line damage, market prices, and renewable energy sources, was evaluated using the IEEE 33-bus test system. The results demonstrated the effectiveness of the proposed methodology, highlighting its ability to improve resilience by enhancing system robustness, enabling faster recovery, and optimizing operational costs in response to high-impact low-probability (HILP) natural events. Full article

Within the 21st century, in the Maritime Silk Road, wave energy, a clean renewable source, is drawing more interest, especially in areas with power shortages. This paper investigates wave energy in ships, particularly in a hybrid electric bulk carrier, by designing a system that supplements the existing power setup with oscillating buoy wave energy converters. The system includes diesel generators (DGs), a wave energy generation system, heterogeneous energy storage (consisting of battery storage (BS) and thermal storage (TS)), a combined cooling heat and power (CCHP) unit, and a power-to-thermal conversion (PtC) unit. To ensure safe and reliable navigation despite uncertainties in wave energy output, onboard power loads, and outdoor temperature, a robust coordination method is adopted. This method employs a two-stage robust optimization (RO) strategy to coordinate the various onboard units across different time scales, minimizing operational costs while satisfying all operational constraints, even in the worst-case scenarios. By applying constraint linearization, the robust coordination model is formulated as a mixed-integer linear programming (MILP) problem and solved using an efficient solver. Finally, the effectiveness of the proposed method is validated through case studies and comparisons with existing ship operation benchmarks, demonstrating significant reductions in operational costs and robust performance under various uncertain conditions. Notably, the simulation results for the Singapore–Trincomalee route show an 18.4% reduction in carbon emissions compared to conventional systems. Full article

Background: Percutaneous vertebral augmentation techniques, including vertebroplasty, kyphoplasty, and bone tumor radiofrequency ablation (BT-RFA), are commonly used to treat painful vertebral compression fractures (VCFs). While generally safe and effective, they carry risks, including cement extravasation, which can lead to pulmonary embolism or spinal cord compression. This study aims to compare the rate of cement extravasation across different vertebral augmentation techniques and identify potential risk factors. Methods: A retrospective cohort study was conducted at a comprehensive cancer center on 1002 procedure encounters in 888 patients who underwent vertebral augmentation for painful VCFs. Data were collected on patient demographics, fracture pathology, procedure type, imaging guidance, and pain scores. Intraoperative and postoperative imaging were manually reviewed to assess cement extravasation. Statistical analyses were performed using pairwise comparisons with Tukey’s Honest Significant Difference adjustment to compare cement extravasation rates across the procedure groups and generalized linear mixed models to assess the association between the cement extravasation with other variables. Results: Cement extravasation occurred in 573 (57.2%) encounters. Kyphoplasty had the lowest rate of cement extravasation (46.2%) with significantly lower odds compared to vertebroplasty (OR: 0.42, 95% CI: 0.30–0.58; p < 0.0001) and BT-RFA (OR: 0.57, 95% CI: 0.42–0.77; p = 0.0009). Pathologic fractures and multilevel augmentations were linked to a 64% (p = 0.001) and 63% (p = 0.0003) increased odds of cement extravasation, respectively. Male sex and older age were protective factors. Conclusions: Cement extravasation is a common but largely asymptomatic complication of percutaneous vertebral augmentation. It is crucial to consider patient-specific risk factors when selecting an augmentation technique to optimize outcomes. Kyphoplasty may be the optimal choice for patients at increased risk of cement extravasation. Full article

Under cavitation conditions, hydraulic turbines can suffer from mechanical damage, which will shorten their useful life and reduce power generation efficiency. Timely detection of cavitation phenomena in hydraulic turbines is critical for ensuring operational reliability and maintaining energy conversion efficiency. However, extracting cavitation features is challenging due to strong environmental noise interference and the inherent non-linearity and non-stationarity of a cavitation hydroacoustic signal. A multi-index fusion adaptive cavitation feature extraction and cavitation detection method is proposed to solve the above problems. The number of decomposition layers in the multi-index fusion variational mode decomposition (VMD) algorithm is adaptively determined by fusing multiple indicators related to cavitation characteristics, thus retaining more cavitation information and improving the quality of cavitation feature extraction. Then, the cavitation features are selected based on the frequency characteristics of different degrees of cavitation. In this way, the detection of incipient cavitation and the secondary detection of supercavitation are realized. Finally, the cavitation detection effect was verified using the hydro-acoustic signal collected from a mixed-flow hydro turbine model test stand. The detection accuracy rate and false alarm rate were used as evaluation indicators, and the comparison results showed that the proposed method has high detection accuracy and a low false alarm rate. Full article

This study demonstrates that small-scale liquefied natural gas (SS LNG) is a viable and cost-effective alternative to High-Speed Diesel (HSD) for power generation in remote areas of Indonesia. An integrated supply chain model is developed to optimize total costs based on LNG inventory levels. The model minimizes transportation costs from supply depots to demand points and handling costs at receiving terminals, which utilize Floating Storage Regasification Units (FSRUs). LNG distribution is optimized using a Multi-Depot Capacitated Vehicle Routing Problem (MDCVRP), formulated as a Mixed Integer Linear Programming (MILP) problem to reduce fuel consumption, CO₂ emissions, and vessel rental expenses. The novelty of this research lies in its integrated cost optimization, combining transportation and handling within a model specifically adapted to Indonesia’s complex geography and infrastructure. The simulation involves four LNG plant supply nodes and 50 demand locations, serving a total demand of 15,528 m³/day across four clusters. The analysis estimates a total investment of USD 685.3 million, with a plant-gate LNG price of 10.35 to 11.28 USD/MMBTU at a 10 percent discount rate, representing a 55 to 60 percent cost reduction compared to HSD. These findings support the strategic deployment of SS LNG to expand affordable electricity access in remote and underserved regions. Full article

Background: Cooking and dietary intake may affect psychological well-being. Objective: We evaluated the effects of a virtual culinary medicine teaching kitchen intervention on psychosocial health. Methods: In a randomized crossover trial implementing a vegan diet high or low in extra virgin olive oil, adults with ≥5% atherosclerotic cardiovascular disease risk participated in eight weekly group cooking classes. Psychosocial survey assessments of perceived stress, positive and negative affect, and quality of life before and after the intervention were compared using paired t-tests and post hoc linear mixed models. Results: Pre-post analysis among 40 participants (75% female, 64.4 ± 8.6 years) indicated a 19% decrease in perceived stress (p < 0.01), 6–8% increase in positive affect (p < 0.04), and 13% decrease in negative affect (p = 0.02). Energy/fatigue and general health-related quality of life improved post-intervention (both p ≤ 0.02). Conclusions: Participation in a group culinary medicine intervention improved mood, stress, and health-related quality of life, warranting larger, diverse studies. Benefits may relate to social support, improved health status, diet factors, and emerging psychosocial influences of cooking. Full article

Background: This study aimed to develop a population pharmacokinetic (PK) model that characterized the plasma concentration–time profiles of the total and unbound pamiparib, a PARP inhibitor, in glioblastoma patients and identified patient factors influencing the PK. Methods: The total and unbound pamiparib plasma concentration data were obtained from 41 glioblastoma patients receiving 60 mg of pamiparib twice daily. Nonlinear mixed-effects modeling was performed using Monolix (2024R1) to simultaneously fit the total and unbound drug plasma concentration data. The covariate model was developed by covariate screening using generalized additive modeling followed by stepwise covariate modeling. Model simulations were performed following oral doses of 10–60 mg BID. Results: The total and unbound pamiparib plasma concentration–time profiles were best described by a one-compartment model with first-order absorption and elimination. Creatinine clearance and age were the significant covariates on the apparent volume of distribution (V/F) and apparent clearance (CL/F), respectively, explaining ~22% and ~5% of IIV of V/F and CL/F. Population estimates of the absorption rate constant (Ka), V/F, CL/F, and unbound fraction for the total drug were 1.58 h⁻¹, 44 L, 2.59 L/h, and 0.041. Model simulations suggested that doses as low as 20 mg BID may be adequate for therapeutic effects in a general patient population, assuming that a target engagement ratio (i.e., unbound C_ss,min/IC50) of 5 or above is sufficient for full target engagement. Conclusions: The total and unbound pamiparib plasma PK are well characterized by a linear one-compartment model, with creatinine clearance as the significant covariate on V/F. Model simulations support further clinical investigation into dose reduction to optimize the benefit-to-risk ratio of pamiparib, particularly in combination therapies. Full article

The increasing penetration of renewable energy sources (RESs) has reduced the inertia and reserve levels of microgrids, posing challenges to frequency security during power imbalances. To address these challenges, this paper proposes a multi-objective distributionally robust frequency-constrained economic dispatch (DRFC-ED) model. First, the model aims to jointly optimize generation dispatch, reserve deployment, and the virtual inertia and damping constants of inverter-based resources to achieve a comprehensive optimization of both economic efficiency and frequency response performance. Then, the model further considers the distinctions between inertia and damping in the frequency response for more effective parameter deployment. Furthermore, the model leverages deep neural networks (DNNs) to convexify non-convex frequency constraints and employs a distributionally robust chance-constrained approach with Wasserstein distance-based ambiguity sets to handle RES uncertainty. Additionally, a method of directly obtaining the compromise optimal solution is used to transform the multi-objective problem into a single-objective one. Finally, the model is formulated as a mixed-integer linear programming problem and validated through case studies, demonstrating (1) an 8.03% reduction in the frequency integral time absolute error (ITAE) with only a 2.1% increase in economic cost compared to single-objective approaches, while (2) maintaining maximum frequency deviation (MFD) < 0.5 Hz during disturbances. Full article

The harm of marine debris (MD) to the environment and human beings has been paid more and more attention. At present, the most effective way to collect macro-MD floating on the sea is to send vessels. We employ vessels equipped with a hybrid energy system (HES) composed of photovoltaic (PV), battery and diesel to carry out MD cleanup. We propose a two-stage optimization approach for vessel routing and energy management strategy. In the first stage, the vessel routing problem with a drifting time window is modeled to minimize the vessel travel time considering continuous speed. The drifting time window means that multiple time windows are set on the MD trajectory, which is used to depict its dynamic nature. An adaptive large neighborhood search algorithm considering an elitist strategy coupled with speed optimization is designed to solve this problem. In the second stage, a mixed integer linear programming model for energy management strategy is established to minimize the total cost, including the power generation cost of diesel and PV, the battery charge, and discharge and carbon tax costs. The model takes the power load balance, the power limit of each part of the hybrid energy system and the battery charge and discharge state as constraints. The correctness of the proposed models and the effectiveness of the proposed algorithm are verified by a numerical example. The results not only show the advantages of hybrid energy vessels in energy saving and emission reduction but also show that the drifting time window can provide a rich and effective route selection solution. Some suggestions for rational utilization of hybrid energy vessels with long and short trips are put forward. Full article

Relative to other renewable energy technologies, concentrated solar power (CSP) is only in the beginning phases of large-scale deployment. Its incorporation into national grids is steadily growing, with anticipation of its substantial contribution to the energy mix. A number of emerging economies are situated in areas that receive abundant amounts of direct normal irradiance (DNI), which translates into expectations of significant effectiveness for CSP. However, any assessment related to the planning of CSP facilities is challenging because of the complexity of the associated criteria and the number of stakeholders. Additional complications are the differing concepts and configurations for CSP plants available, a dearth of related experience, and inadequate amounts of data in some developing countries. The goal of the work presented in this paper was to evaluate the practical CSP implementation options for such parts of the world. Ambiguity and imprecision issues were addressed through the application of multi-criteria decision-making (MCDM) in a fuzzy environment. Six technology combinations, involving dry cooling and varied installed capacity levels, were examined: three parabolic trough collectors with and without thermal storage, two solar towers with differing storage levels, and a linear Fresnel with direct steam generation. The in-depth performance analysis was based on 4 main criteria and 29 sub-criteria. Quantitative and qualitative data, plus input from 44 stakeholders, were incorporated into the proposed fuzzy analytic hierarchy process (AHP) model. In addition to demonstrating the advantages and drawbacks of each scenario relative to the local energy sector requirements, the model’s results also provide accurate recommendation guidelines for integrating CSP technology into national grids while respecting stakeholders’ priorities. Full article

Microgrids facilitate the complementary and collaborative operation of various distributed energy resources. Implementing effective day-ahead scheduling strategies can significantly enhance the economic efficiency and operational stability of microgrid systems. In this study, the long short-term memory (LSTM) neural network is first employed to forecast photovoltaic (PV) power generation and load demand, using operational data from a full-scale microgrid system. Subsequently, an optimization model for a full-scale PV–energy storage microgrid is developed, integrating a PV power generation system, a battery energy storage system, and a specific industrial load. The model aims to minimize the total daily operating cost of the system while satisfying a set of system operational constraints, with particular emphasis on the safety requirements for grid exchange power. The formulated optimization problem is then transformed into a mixed-integer linear programming (MILP) model, which is solved using a computational solver to derive the day-ahead economic scheduling scheme. Finally, the proposed scheduling scheme is validated through field experiments conducted on the full-scale PV–energy storage microgrid system across various operational scenarios. By comparing the simulation results with the experimental outcomes, the effectiveness and practicality of the proposed day-ahead economic scheduling scheme for the microgrid are demonstrated. Full article