Search Results (5,937)

Renewable Energy Communities (RECs) are emerging as significant in the global paradigm shift towards a smart and sustainable energy environment. By empowering energy consumers to actively participate in local energy generation, and sharing, using renewable energy sources, energy storage, and flexible loads, REC participants can reduce costs, and also contribute to low-carbon objectives, providing the flexibility needed to address modern smart grid challenges. This article presents a mixed integer linear programming model for optimal sizing of the solar PVs and battery energy storage systems (BESS) of REC participants who engage in P2P energy exchange. The model is formulated using a two-stage stochastic optimization to address load and PV uncertainty, and unsupervised clustering to structure the data for the stochastic optimization process. The model enables sizing solar PVs for different rooftop geometries and the objective function includes comprehensively defined electricity, operational, and scaled investment costs for solar PV and BESS, where economic fairness constraints are analyzed and implemented. The model is validated on real solar and atmospheric measured data from Zagreb, Croatia, and publicly available household consumption data from Northern Germany. The article also analyzes how tariff models, and electricity prices affect PV and BESS sizes, cost reductions, and P2P energy exchange for different REC participants with varying consumption and production profiles. Full article

Prenatal lead (Pb) and manganese (Mn) exposure can impair neurodevelopment, targeting the central nervous system. This study investigated the effects of prenatal exposure to Pb and Mn on neurodevelopment in children at 18 months of age, using data from 607 Italian mother–child pairs enrolled in the Northern Adriatic Cohort II (NAC-II). All children born at term (≥37 weeks) were assessed with the Bayley Scales of Infant and Toddler Development, third edition. Cord blood concentrations of Mn and Pb were categorized as low or high exposures based on the 75th percentile of their distribution. Sociodemographic and lifestyle information was collected via questionnaires. Using simple and multiple linear regressions, the study examined the relationship between the cognitive composite score (COGN) and Mn and Pb co-exposure, including their interaction. Stratified regressions explored how Mn exposure influenced the effect of Pb, in the whole cohort and by the child’s sex. Beta coefficients (β) and the 90% confidence interval (90% CI) were estimated. Boys showed an interaction effect between Mn and Pb, with a reduction in COGN (β = −5.78, 90% CI: −11.17; −0.40), further described as a negative effect of high Pb on cognition when Mn exposure was also high (β = −6.98, 90% CI: −10.93; −3.04). No clear effects were observed in girls or the entire cohort at these levels of exposure. The findings highlight the harmful impact of combined prenatal Pb and Mn exposure on cognitive development in boys. Full article

Objectives: To determine if psychological factors, such as anxiety, depression, fear of movement and fear of rupture are associated with increased tendon-related disability, quantified by the Tendinopathy Severity Assessment-Achilles (TENDINS-A). Design: Cross-sectional. Setting: Online Qualtrics survey. Participants: Sixty-eight participants (54% female) with Achilles tendinopathy and a mean (standard deviation) age of 40.1 (12.6) years. Main Outcome Measures: The TENDINS-A (including subscales of pain; symptoms such as stiffness; physical function), Patient Health Questionnaire-9, General Anxiety Disorder-7, Tampa Scale for Kinesiophobia and fear of tendon rupture. Associations were evaluated using generalised linear models (adjusting for age and sex), with significance accepted when p < 0.05. Results: Anxiety symptoms were positively associated with Achilles pain (p = 0.035), symptoms (p = 0.045) and physical function (p = 0.019). Depressive symptoms were negatively associated with symptoms (p = 0.045) but not pain (p = 0.078) or physical function (p = 0.429). Fear of movement was not associated with pain (p = 0.479), symptoms (p = 0.915) or physical function (p = 0.064). Fear of rupture was associated with pain (p = 0.042), but not symptoms (p = 0.797) or physical function (p = 0.509). Conclusions: Our research demonstrated significant correlations between anxiety and depression with the severity of tendon-related disability. Fear of movement was only associated with physical function, whereas fear of rupture was associated with pain and physical function. Full article

The accurate forecasting of vegetable prices is crucial for policy formulation, market decisions, and agricultural market stability. Traditional time-series models often require manual parameter tuning and struggle to effectively handle the complex non-linear characteristics of vegetable price data, limiting their predictive accuracy. This study conducts a comprehensive analysis of the performance of traditional methods, deep learning approaches, and cutting-edge large language models in vegetable-price forecasting using multiple predictive performance metrics. Experimental results demonstrate that large language models generally outperform other methods, but do not have consistent performance for all kinds of vegetables across different time scales. As a result, we propose a novel vegetable-price forecasting method based on mixture of expert models (VPF-MoE), which combines the strengths of large language models and deep learning methods. Different from the traditional single model prediction method, VPF-MoE can dynamically adapt to the characteristics of different vegetable types, dynamically select the best prediction method, and significantly improve the accuracy and robustness of the prediction. In addition, we optimized the application of large language models in vegetable-price forecasting, offering a new technological pathway for vegetable-price prediction. Full article

This study presents novel and generalizable sufficient conditions for determining the oscillatory behavior of solutions to higher-order half-linear neutral delay dynamic equations on time scales. Utilizing the Riccati transformation technique in combination with Taylor monomials, we derive new and comprehensive oscillation criteria that cover a wide range of cases, including super-linear, half-linear, and sublinear equations. These results extend and improve upon existing oscillation criteria found in the literature by introducing more general conditions and providing a broader applicability to different types of dynamic equations. Furthermore, the study highlights the role of symmetry in the underlying equations, demonstrating how symmetry properties can be leveraged to simplify the analysis and provide additional insights into oscillatory behavior. To demonstrate the practical relevance of our findings, we include illustrative examples that show how these new criteria, along with symmetry-based perspectives, can be effectively applied to various time scales. Full article

Background: A valuable outcome measure to monitor amyotrophic lateral sclerosis (ALS) disease progression is crucial in clinical trials. Rasch-Built Overall Amyotrophic Lateral Sclerosis Disability Scale (ROADS) is a novel questionnaire assessing ALS disability. Currently, there are no studies on the relationship between ROADS and ALS survival. This study explored the value of Chinese ROADS as a novel tool for measuring disease progression and the correlation between ROADS and ALS survival. Methods: A total of 170 ALS participants were included in this study. Clinical characteristics and baseline ROADS, ΔROADS, ALSFRS-R, and ΔFRS of patients were collected. Participants were followed for 18 months to assess time to tracheostomy and survival. Scales were collected every 3 to 6 months. We evaluated the association of baseline ROADS and ΔROADS with survival using Cox regression analyses. Linear mixed effects models were used to assess changes over time in ROADS and ALSFRS-R. Results: Multivariate Cox models confirmed that baseline ROADS positively correlated with ALS survival (HR = 0.95, p < 0.001), while baseline ΔROADS negatively correlated with survival (HR = 1.26, p < 0.001). Additionally, linear mixed effects models suggested that ROADS, similar to ALSFRS-R, declined significantly over time, but there was no significant difference between these two. Conclusions: Our study indicates that Chinese ROADS is strongly related to ALS survival. Changes in ROADS with disease progression are similar to those in ALSFRS-R. These findings support Chinese ROADS as a reliable outcome measure for clinical trials, potentially enhancing the dimension of evaluating treatment effectiveness in ALS trials. Full article

Background: Interactive learning environments have emerged as transformative tools in education, enhancing engagement, academic performance, and addressing challenges like learning anxiety. This study examines the influence of multiple variables, including anxiety, internet usage for problem-solving, attitude towards a history course, metacognitive awareness, and interactive learning environments, on seventh-grade students’ academic performance. Methods: Using the Exploration of Attitudes Towards History Scale (EDIS) scale to measure attitudes and the Metacognitive Awareness of Reading Strategies Inventory-Revised Two-Factor Version (MARSI-2fR) to assess metacognitive awareness, the study evaluated historical knowledge across three stages, namely pre-intervention, post-intervention, and a one-month-later retest. A comparative analysis was conducted between the control group and the intervention group. The statistical analyses involved the calculation of correlation coefficients, the implementation of general linear models, and the performance of Wilcoxon signed-rank tests. Results: The findings indicated that prior to the intervention, factors such as learning anxiety and the extratextual component of metacognition were statistically significant predictors of achievement. However, the aforementioned factors ceased to be statistically significant when the parameter of study strategies was incorporated into the statistical model. The impact of the interactive learning environment on students’ achievement is highly statistically significant in terms of post-test scores, while the influence of all other predictors becomes insignificant. The retest confirmed the continued maintenance of the achieved results as evaluated following the intervention. Conclusions: The study confirms previous research demonstrating that interactive learning environments are an effective method of enhancing students’ academic performance and reducing the negative impact of learning anxiety. Full article

Population aging and urbanization are two of the most significant social transformations of the 21st century. Against the backdrop of rapid aging in China, developing age-friendly community environments, particularly through the renovation of legacy residential communities, not only supports active and healthy aging but also promotes equity and sustainable development. This study focuses on residential communities built in the 1980s in Hangzhou, exploring strategies for the age-friendly renovation of outdoor public spaces. The residential communities that flourished during the construction boom of the 1980s are now confronting a dual challenge: aging populations and deteriorating facilities. However, existing renovation efforts often pay insufficient attention to the comprehensive age-friendly transformation of outdoor public spaces within these neighborhoods. Following a structured research framework encompassing investigation, evaluation, design, and discussion, this study first analyzes linear grid layouts and usage patterns of these communities. Then, the research team uses post-occupancy evaluation (POE) to assess the age-friendliness of outdoor public spaces. Semi-structured interviews with elderly residents identify key concerns and establish a preliminary evaluation framework, while a Likert-scale questionnaire quantifies the satisfaction with age-friendly features across four communities. The assessment reveals that key age-friendliness issues, including poor traffic safety, dispersed activity spaces, and insufficiently adapted facilities, are closely linked to the linear usage patterns within the spatial framework of the grid layouts. Based on the findings, the study develops tiered renovation goals, renovation principles and implemented an age-friendly design in the Hemu Community. The strengths, weaknesses, and feasibility of the renovation plan are discussed, while three recommendations are made to ensure successful implementation. The study is intended to provide a valuable reference for advancing age-friendly residential renewal efforts in Hangzhou and contributing to the broader objective of sustainable, inclusive city development. Full article

Background/Objectives: This study investigates the impact of maternal glycemic levels during early and late pregnancy on offspring neurodevelopment in China. Methods: Fasting plasma glucose (FPG) and triglyceride (TG) levels were measured in maternal blood during pregnancy, and the TyG index was calculated to assess insulin resistance. Hyperglycemia was defined as FPG > 5.1 mmol/L. Neurodevelopmental outcomes in offspring aged 6–36 months were evaluated using the China Developmental Scale for Children, focusing on developmental delay (DD) and developmental quotient (DQ). Mothers were categorized into four glycemic groups: healthy glycemia group (HGG), early pregnancy hyperglycemia group (EHG), late pregnancy hyperglycemia group (LHG), and full-term hyperglycemia group (FHG). Linear and logistic regression models were applied. Results: Among 1888 mother–child pairs, hyperglycemia and FPG were associated with an increased risk of overall DD (aOR = 1.68; 95% CI 1.07–2.64) and lower DQ (aBeta = −1.53; 95% CI −2.70 to −0.36). Elevated FPG was linked to DD in fine motor and social behaviors. Compared to HGG, LHG and FHG significantly increased the risk of overall DD (aOR = 2.18; 95% CI 1.26–3.77; aOR = 2.64; 95% CI 1.38–5.05), whereas EHG did not. Male offspring were particularly vulnerable to early pregnancy hyperglycemia (aBeta = −2.80; 95% CI −4.36 to −1.34; aOR = 2.05; 95% CI 1.10–3.80). Conclusions: Maternal glycemic levels during pregnancy influence offspring neurodevelopment, with persistent hyperglycemia significantly increasing DD risk. Early pregnancy hyperglycemia particularly affects male offspring, underscoring the need for glycemic management during pregnancy. Full article

Conventional approaches for the structural health monitoring of infrastructures often rely on physical sensors or targets attached to structural members, which require considerable preparation, maintenance, and operational effort, including continuous on-site adjustments. This paper presents an image-driven hybrid structural analysis technique that combines digital image processing (DIP) and regression analysis with a continuum point cloud method (CPCM) built on a particle-based strong formulation. Polynomial regressions capture the boundary shape change due to the structural loading and precisely identify the edge and corner coordinates of the deformed structure. The captured edge profiles are transformed into essential boundary conditions. This allows the construction of a strongly formulated boundary value problem (BVP), classified as the Dirichlet problem. Capturing boundary conditions from the digital image is novel, although a similar approach was applied to the point cloud data. It was shown that the CPCM is more efficient in this hybrid simulation framework than the weak-form-based numerical schemes. Unlike the finite element method (FEM), it can avoid aligning boundary nodes with regression points. A three-point bending test of a rubber beam was simulated to validate the developed technique. The simulation results were benchmarked against numerical results by ANSYS and various relevant numerical schemes. The technique can effectively solve the Dirichlet-type BVP, yielding accurate deformation, stress, and strain values across the entire problem domain when employing a linear strain model and increasing the number of CPCM nodes. In addition, comparative analysis with conventional displacement tracking techniques verifies the developed technique’s robustness. The proposed technique effectively circumvents the inherent limitations of traditional monitoring methods resulting from the reliance on physical gauges or target markers so that a robust and non-contact solution for remote structural health monitoring in real-scale infrastructures can be provided, even in unfavorable experimental environments. Full article

Sub-surface soil hydrological characterization is one of the challenging tasks for engineers and soil scientists, especially the complex hydrological processes that combine key variables such as soil moisture, matric suction, and soil temperature. The ability to infer these variables through a singular measurable soil property, soil resistivity, can potentially improve sub-surface characterization. This research leverages various machine learning algorithms to develop predictive models trained on a comprehensive dataset of sensor-based soil moisture, matric suction, and soil temperature obtained from prototype ET covers, with known resistivity values. Different types of sensors were installed at multiple depths in the ET covers, and resistivity tests were conducted periodically at the same location. Cross-validation and feature selection methods were used to optimize model performance and identify key variables that most significantly impact soil resistivity. Strong inverse correlations between soil moisture and resistivity (r = −0.88) and weak positive correlations with temperature (r = 0.41) and suction (r = 0.34) were observed. Among the machine learning models evaluated, artificial neural networks and support vector machines demonstrated superior predictive performance, achieving a coefficient of determination (R²) above 0.77 and lower root mean square error (RMSE) values (less than 0.14). Linear regression and decision tree models exhibited suboptimal performance because of their limitations in capturing non-linear relationships and overfitting, respectively. Random forest demonstrated superior generalization capabilities compared to decision trees; however, it encountered challenges with mid-range data variability. The findings demonstrate the effectiveness of artificial neural networks in predicting field-scale soil resistivity by utilizing hydrological variables. Full article

Profiting from the surprising non-linear expressive capacity, deep convolutional neural networks have inspired lots of progress in low illumination (LI) remote sensing image enhancement. The key lies in sufficiently exploiting both the specific long-range (e.g., non-local similarity) and short-range (e.g., local continuity) structures distributed across different scales of each input LI image to build an appropriate deep mapping function from the LI images to their corresponding high-quality counterparts. However, most existing methods can only individually exploit the general long-range or short-range structures shared across most images at a single scale, thus limiting their generalization performance in challenging cases. We propose a multi-scale long–short range structure aggregation learning network for remote sensing imagery enhancement. It features flexible architecture for exploiting features at different scales of the input low illumination (LI) image, with branches including a short-range structure learning module and a long-range structure learning module. These modules extract and combine structural details from the input image at different scales and cast them into pixel-wise scale factors to enhance the image at a finer granularity. The network sufficiently leverages the specific long-range and short-range structures of the input LI image for superior enhancement performance, as demonstrated by extensive experiments on both synthetic and real datasets. Full article

Modern electrical grids are evolving towards distributed architectures, necessitating efficient and reliable state synchronization mechanisms to maintain structural and functional consistency. This paper investigates the application of conflict-free replicated data types (CRDTs) for representing and synchronizing the states of distributed electrical grid systems (DEGSs). We present a general structure for DEGSs based on CRDTs, focusing on the Convergent Replicated Data Type (CvRDT) model with delta state propagation to optimize the communication overhead. The Observed Remove Set (ORSet) and Last-Writer-Wins Register (LWW-Register) are utilized to handle concurrent updates and ensure that only the most recent state changes are retained. An actor-based framework, “Vigilant Hawk”, leveraging the Akka toolkit, was developed to simulate the asynchronous and concurrent nature of DEGSs. Each electrical grid node is modelled as an independent actor with isolated state management, facilitating scalability and fault tolerance. Through a series of experiments involving 100 nodes under varying latency degradation coefficients (LDK), we examined the impact of network conditions on the state synchronization efficiency. The simulation results demonstrate that CRDTs effectively maintain consistency and deterministic behavior in DEGSs, even with increased network latency and node disturbances. An effective LDK range was identified (LDK effective = 2 or 4), where the network remains stable without significant delays in state propagation. The linear relationship between the full state distribution time (FSDT) and LDK indicates that the system can scale horizontally without introducing complex communication overhead. The findings affirm that using CRDTs for state synchronization enhances the resilience and operational efficiency of distributed electrical grids. The deterministic and conflict-free properties of CRDTs eliminate the need for complex concurrency control mechanisms, making them suitable for real-time monitoring and control applications. Future work will focus on addressing identified limitations, such as optimizing message routing based on the network topology and incorporating security measures to protect state information in critical infrastructure systems. Full article

In this work, we elaborate further on a (3+1)-dimensional cosmological Running-Vacuum-type-Model (RVM) of inflation based on string-inspired Chern-Simons(CS) gravity, involving axions coupled to gravitational-CS(gCS) anomalous terms. Inflation in such models is caused by primordial-gravitational-waves(GW)-induced condensation of the gCS terms, which leads to a linear-axion potential. We demonstrate that this inflationary phase may be metastable, due to the existence of imaginary parts of the gCS condensate. These are quantum effects, proportional to commutators of GW perturbations, hence vanishing in the classical theory. Their existence is quantum-ordering-scheme dependent. We argue in favor of a physical importance of such imaginary parts, which we compute to second order in the GW (tensor) perturbations in the framework of a gauge-fixed effective Lagrangian, within a (mean field) weak-quantum-gravity-path-integral approach. We thus provide estimates of the inflation lifetime. On matching our results with the inflationary phenomenology, we fix the quantum-ordering ambiguities, and obtain an order-of-magnitude constraint on the String-Mass-Scale-to-Planck-Mass ratio, consistent with previous estimates by the authors in the framework of a dynamical-system approach to linear-axion RVM inflation. Finally, we examine the role of periodic modulations in the axion potential induced by non-perturbative effects on the slow-roll inflationary parameters, and find compatibility with the cosmological data. Full article

As wind turbines increase in size, blades become longer, thinner, and more flexible, making them more susceptible to large geometric nonlinear deformations, which pose challenges for aeroelastic simulations. This study presents a nonlinear aeroelastic model that accounts for large deformations of slender, flexible blades, coupled through the Actuator Line Method (ALM) and Geometrically Exact Beam Theory (GEBT). The accuracy of the model is validated by comparing it with established numerical methods, demonstrating its ability to capture the bending–torsional coupled nonlinear characteristics of highly flexible blades. A bidirectional fluid–structure coupling simulation of the IEA 15MW wind turbine under uniform flow conditions is conducted. The effect of blade nonlinear deformation on aeroelastic performance is compared with a linear model based on Euler–Bernoulli beam theory. The study finds that nonlinear deformations reduce predicted angle of attack, decrease aerodynamic load distribution, and lead to a noticeable decline in both wind turbine performance and blade deflection. The effects on thrust and edgewise deformation are particularly significant. Additionally, nonlinear deformations weaken the tip vortex strength, slow the momentum exchange in the wake region, reduce turbulence intensity, and delay wake recovery. This study highlights the importance of considering blade nonlinear deformations in large-scale wind turbines. Full article