Search Results (3,964)

Recent European regulations promote distributed energy resources as alternatives to centralized generation. This study compares utility-scale and distributed photovoltaic (PV) systems coupled with Battery Energy-Storage Systems (BESSs) in the Italian electricity market, analyzing different battery sizes. A multistage stochastic mixed-integer linear programming model, using Monte Carlo PV production scenarios, optimizes day-ahead and intra-day market offers while incorporating PV forecast updates. In real time, battery flexibility reduces imbalances. Here we show that, to ensure dispatchability—defined as keeping annual imbalances below 5% of PV output—a 1 MW PV system requires 220 kWh of storage for utility-scale and 50 kWh for distributed systems, increasing the levelized cost of electricity by +13.1% and +1.94%, respectively. Net present value is negative for BESSs performing imbalance netting only. Therefore, a multiple service strategy, including imbalance netting and energy arbitrage, is introduced. Performing arbitrage while keeping dispatchability reaches an economic optimum with a 1.7 MWh BESS for utility-scale systems and 1.1 MWh BESS for distributed systems. These results show lower PV firming costs than previous studies, and highlight that under a multiple-service strategy, better economic outcomes are obtained with larger storage capacities. Full article

As the global energy system accelerates its transition toward a low-carbon economy, renewable energy sources (RESs), such as wind and photovoltaic power, are rapidly replacing traditional fossil fuels. These RESs are becoming a critical element of deeply decarbonized power systems (DDPSs). However, the inherent non-stationarity, multi-scale volatility, and uncontrollability of RES output significantly increase the risk of source–load imbalance, posing serious challenges to the reliability and economic efficiency of power systems. Scenario generation technology has emerged as a critical tool to quantify uncertainty and support dispatch optimization. Nevertheless, conventional scenario generation methods often fail to produce highly credible wind and solar output scenarios. To address this gap, this paper proposes a novel renewable energy scenario generation method based on a multi-resolution diffusion model. To accurately capture fluctuation characteristics across multiple time scales, we introduce a diffusion model in conjunction with a multi-scale time series decomposition approach, forming a multi-stage diffusion modeling framework capable of representing both long-term trends and short-term fluctuations in RES output. A cascaded conditional diffusion modeling framework is designed, leveraging historical trend information as a conditioning input to enhance the physical consistency of generated scenarios. Furthermore, a forecast-guided fusion strategy is proposed to jointly model long-term and short-term dynamics, thereby improving the generalization capability of long-term scenario generation. Simulation results demonstrate that MDDPM achieves a Wasserstein Distance (WD) of 0.0156 in the wind power scenario, outperforming DDPM (WD = 0.0185) and MC (WD = 0.0305). Additionally, MDDPM improves the Global Coverage Rate (GCR) by 15% compared to MC and other baselines. Full article

The development and implementation of microbial consortium-based biofertilizers represent a promising direction in sustainable agriculture, particularly in the context of the ongoing global ecological and agricultural crisis. This article examines the agroecological and economic impacts of applying microbial consortiums and explores the mechanisms of technology transfer using the example of two countries with differing levels of scientific and technological advancement–China and Kazakhstan. The analysis of the Chinese experience reveals that the successful integration of microbial biofertilizers into agricultural practice is made possible by a well-established institutional framework that includes strong governmental support for R&D, a robust scientific infrastructure, and effective coordination with the private sector. In contrast, Kazakhstan, despite its favorable agroecological conditions and growing interest among farmers in environmentally friendly technologies, faces several challenges from limited funding to a fragmented technology transfer system. The comparative study demonstrates that adapting Chinese models requires consideration of local specificities and the strengthening of intergovernmental cooperation. The article concludes by emphasizing the need to establish a multi-level innovation ecosystem encompassing the entire cycle of development and deployment of microbial biofertilizers, as a prerequisite for improving agricultural productivity and ensuring food security in countries at different stages of economic development. Full article

Meat quality is influenced by factors such as age, breed, slaughter weight, and nutrition. This study investigated the growth performance, slaughter performance, and meat quality of ducks across different breeds and ages. Results indicated that at the same age, significant differences in body weight were observed among breeds (p < 0.05), with the weight ranking in descending order as follows: Cherry Valley ducks (C) > Wuqin 10 meat ducks (W) > Mianyang Partridge ducks (M) > Liancheng White ducks (L). A comparison of the same breed across different ages revealed that the pectoral muscle ratio tended to increase with age, whereas the leg muscle ratio showed the opposite trend; however, total meat production gradually rose. At all three growth stages, C ducks exhibited higher body weight and meat yield than the other breeds. W ducks demonstrated excellent meat quality traits and appropriate meat production, with indices such as shear force, water-holding capacity, and fat content all higher than those of the other breeds. L ducks and M ducks had relatively lower body weight and meat production compared to the other breeds, yet their shear force and water-holding capacity were superior to those of C ducks. The analysis of meat quality at different times showed that across all breeds, shear force, meat color, muscle fiber diameter, crude protein content, and fat content increased with age, while drip loss rate and muscle fiber density decreased. A comprehensive multi-index evaluation model for duck meat quality under different breeds was established, along with a four-factor principal component model (Z1, Z2, Z3, Z4). Using the comprehensive ranking equation K, the meat quality performance of different breeds at various ages, in descending order, was as follows: 63-day-old W > 90-day-old M > 63-day-old C > 90-day-old L > 63-day-old M > 90-day-old C > 63-day-old L > 90-day-old W > 42-day-old C > 42-day-old W. This study not only provides a theoretical basis for evaluating meat quality traits in different duck breeds but also offers insights for breed selection and age-related quality optimization. Full article

Accurately revealing the sliding wear mechanisms of mechanical surfaces is crucial for enhancing the performance of mechanical surfaces. This study reveals the mechanism of stage transitions in three-dimensional surface wear processes from a microscopic contact perspective. Firstly, according to the fractal theory, a mathematical model for the critical area scale controlling debris particle formation is established. Secondly, incorporating the contact area scale, a mathematical expression for the wear coefficient of surfaces, is proposed based on the multi-stage contact theory. Finally, the influences of fractal parameters on critical contact load, wear rate, and wear coefficient are systematically examined. The experimental findings substantiate that the proposed wear model exhibits an explicit deterministic formulation and demonstrates high predictive accuracy for the wear rate. Full article

2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) exposure has long been associated with reproductive dysfunction in males and females even at miniscule levels, which can persist across generations. Given the continued industrial use and detection of other aryl hydrocarbon receptor (AhR) agonists in the general population and the demonstrated heritable phenotypes of TCDD exposure, further work is justified to elucidate reproductive pathologies and minimize exposure risk. In females, multi- and transgenerational subfertility has been demonstrated in a zebrafish (Danio rerio) model exposed to 50 pg/mL TCDD once at 3 and 7 weeks post fertilization (wpf). We further characterize the histopathologic, hormonal and transcriptomic outcomes of the mature female zebrafish ovary following early-life TCDD exposure. Exposure was associated with significantly increased ovarian atresia in the F0 and F1, but not F2 generation. Other oocyte staging and vitellogenesis were unaffected in all generations. Exposed F0 females showed increased levels of whole-body triiodothyronine (T3) and 17β-estradiol (E2) levels, but not vitellogenin (Vtg), 11-ketotestosterone (11-KT), cortisol, thyroxine (T4), or testosterone (T). Ovarian transcriptomics were most dysregulated in the F2. Both F0 and F2, but not F1, showed changes in epigenetic-related gene expression. Rho signaling was the top pathway for both F0 and F2. Full article

To solve the issue that existing yield prediction methods do not fully capture the interaction between multiple factors, we propose a winter wheat yield prediction framework with triple cross-attention for multi-source data fusion. This framework consists of three modules: a multi-source data processing module, a multi-source feature fusion module, and a yield prediction module. The multi-source data processing module collects satellite, climate, and soil data based on the winter wheat planting range, and constructs a multi-source feature sequence set by combining statistical data. The multi-source feature fusion module first extracts deeper-level feature information based on the characteristics of different data, and then performs multi-source feature fusion through a triple cross-attention fusion mechanism. The encoder part in the production prediction module adds a graph attention mechanism, forming a dual branch with the original multi-head self-attention mechanism to ensure the capture of global dependencies while enhancing the preservation of local feature information. The decoder section generates the final predicted output. The results show that: (1) Using 2021 and 2022 as test sets, the mean absolute error of our method is 385.99 kg/hm², and the root mean squared error is 501.94 kg/hm², which is lower than other methods. (2) It can be concluded that the jointing-heading stage (March to April) is the most crucial period affecting winter wheat production. (3) It is evident that our model has the ability to predict the final winter wheat yield nearly a month in advance. Full article

To address traditional methods’ limitations in neglecting the interaction between random shock loads and progressive degradation, as well as failure correlations, this study proposes a dynamic reliability framework integrating Gamma processes, homogeneous Poisson processes (HPP), and mixed Copula functions. The framework develops a wind turbine gearbox reliability model under shock–degradation coupling while quantifying failure correlations. Gamma processes characterize continuous degradation, with parameters estimated from P-S-N curves. Based on stress–strength interference theory, random shocks within damage thresholds are integrated to form a coupled reliability model. A Gumbel–Clayton–Frank mixed Copula with a multi-layer nested algorithm quantifies failure correlations, with correlation parameters estimated via the RSS principle and genetic algorithms. Validation using a 2 MW gearbox’s planetary gear-stage system covers four scenarios: natural degradation, shock–degradation coupling, and both scenarios with failure correlations. The results show that compared to independent assumptions, the model accelerates reliability decline, increasing failure rates by >37%. Relative to natural degradation-only models, failure rates rise by >60%, validating the model’s effectiveness and alignment with real-world operational conditions. Full article

Accurate and timely wheat yield prediction is valuable globally for enhancing agricultural planning, optimizing resource use, and supporting trade strategies. Study addresses the need for precision in yield estimation by applying machine-learning (ML) regression models to high-resolution Unmanned Aerial Vehicle (UAV) multispectral (MS) and Red-Green-Blue (RGB) imagery. Research analyzes five European wheat cultivars across 400 experimental plots created by combining 20 nitrogen, phosphorus, and potassium (NPK) fertilizer treatments. Yield variations from 1.41 to 6.42 t/ha strengthen model robustness with diverse data. The ML approach is automated using PyCaret, which optimized and evaluated 25 regression models based on 65 vegetation indices and yield data, resulting in 66 feature variables across 400 observations. The dataset, split into training (70%) and testing sets (30%), was used to predict yields at three growth stages: 9 May, 20 May, and 6 June 2022. Key models achieved high accuracy, with the Support Vector Regression (SVR) model reaching R² = 0.95 on 9 May and R² = 0.91 on 6 June, and the Multi-Layer Perceptron (MLP) Regressor attaining R² = 0.94 on 20 May. The findings underscore the effectiveness of precisely measured MS indices and a rigorous experimental approach in achieving high-accuracy yield predictions. This study demonstrates how a precise experimental setup, large-scale field data, and AutoML can harness UAV and machine learning’s potential to enhance wheat yield predictions. The main limitations of this study lie in its focus on experimental fields under specific conditions; future research could explore adaptability to diverse environments and wheat varieties for broader applicability. Full article

The dynamic non-equilibrium effect (DNE) describes the non-unique character of saturation–capillary pressure relationships observed under static, steady-state, or monotonic hydrodynamic conditions. Macroscopically, the DNE manifests as variations in soil hydraulic characteristic curves arising from varying hydrodynamic testing conditions and is fundamentally governed by soil matrix particle size distribution. Changes in the DNE across porous media with discrete particle size fractions are investigated via stepwise drying experiments. Through quantification of saturation–capillary pressure hysteresis and DNE metrics, three critical signatures are identified: (1) the temporal lag between peak capillary pressure and minimum water saturation; (2) the pressure gap between transient and equilibrium states; and (3) residual water saturation. In the four experimental sets, with the finest material (Test 1), the peak capillary pressure consistently precedes the minimum water saturation by up to 60 s. Conversely, with the coarsest material (Test 4), peak capillary pressure does not consistently precede minimum saturation, with a maximum lag of only 30 s. The pressure gap between transient and equilibrium states reached 14.04 cm H₂O in the finest sand, compared to only 2.65 cm H₂O in the coarsest sand. Simultaneously, residual water saturation was significantly higher in the finest sand (0.364) than in the coarsest sand (0.086). The results further reveal that the intensity of the DNE scales inversely with particle size and linearly with wetting phase saturation (Sw), exhibiting systematic decay as Sw decreases. Coarse media exhibit negligible hysteresis due to suppressed capillary retention; this is in stark contrast with fine sands, in which the DNE is observed to persist in advanced drying stages. These results establish pore geometry and capillary dominance as fundamental factors controlling non-equilibrium fluid dynamics, providing a mechanistic framework for the refinement of multi-phase flow models in heterogeneous porous systems. Full article

This paper presents a digital twin-driven multi-objective optimization approach for enhancing the performance and productivity of a multi-product manufacturing system under complex operational challenges. More specifically, the concept of digital twin is applied to virtually replicate a physical system that leverages real-time data fusion from Internet of Things devices or sensors. JaamSim serves as the platform for modeling the digital twin, simulating the dynamics of the manufacturing system. The implemented digital twin is a manufacturing system that incorporates a three-stage production line to complete and stockpile two gear types. The production line is subject to unpredictable events, including equipment breakdowns, maintenance, and product returns. The stochasticity of these real-world-like events is modeled using a normal distribution. Manufacturing control strategies, such as CONWIP and Kanban, are implemented to evaluate the impact on the performance of the manufacturing system in a simulation environment. The evaluation is performed based on three key indicators: service level, the amount of work-in-progress items, and overall system profitability. Multiple objective functions are formulated to optimize the behavior of the system by reducing the work-in-progress items and improving both cost-effectiveness and service level. To this end, the proposed approach couples the JaamSim-based digital twins with evolutionary and swarm-based algorithms to carry out the multi-objective optimization under varying conditions. In this sense, the present work offers an early demonstration of an industrial digital twin, implementing an offline simulation-based manufacturing environment that utilizes optimization algorithms. Results demonstrate the trade-offs between the employed strategies and offer insights on the implementation of hybrid production control systems in dynamic environments. Full article

To noninvasively and precisely discriminate among the growth stages of multiple cultivars of navel oranges simultaneously, the fusion of the technologies of near-infrared (NIR) hyperspectral imaging (HSI) combined with machine vision (MV) and deep learning is employed. NIR reflectance spectra and hyperspectral and RGB images for 740 Gannan navel oranges of five cultivars are collected. Based on preprocessed spectra, optimally selected hyperspectral images, and registered RGB images, a dual-branch multi-modal feature fusion convolutional neural network (CNN) model is established. In this model, a spectral branch is designed to extract spectral features reflecting internal compositional variations, while the image branch is utilized to extract external color and texture features from the integration of hyperspectral and RGB images. Finally, growth stages are determined via the fusion of features. To validate the availability of the proposed method, various machine-learning and deep-learning models are compared for single-modal and multi-modal data. The results demonstrate that multi-modal feature fusion of HSI and MV combined with the constructed dual-branch CNN deep-learning model yields excellent growth stage discrimination in navel oranges, achieving an accuracy, recall rate, precision, F1 score, and kappa coefficient on the testing set are 95.95%, 96.66%, 96.76%, 96.69%, and 0.9481, respectively, providing a prominent way to precisely monitor the growth stages of fruits. Full article

Continuous multi-day extremely low or high new energy outputs have posed significant challenges in relation to power supply and new energy accommodations. Conventional reservoir hydropower, with the advantage of controllability and the storage ability of reservoirs, can represent a reliable and low-carbon flexibility resource to safeguard against continuous extreme new energy fluctuations. This paper proposes a mid-term scheduling method for reservoir hydropower to enhance our ability to regulate continuous extreme new energy fluctuations. First, a data-driven scenario generation method is proposed to characterize the continuous extreme new energy output by combining kernel density estimation, Monte Carlo sampling, and the synchronized backward reduction method. Second, a two-stage stochastic hydropower–new energy complementary optimization scheduling model is constructed with the reservoir water level as the decision variable, ensuring that reservoirs have a sufficient water buffering capacity to free up transmission channels for continuous extremely high new energy outputs and sufficient water energy storage to compensate for continuous extremely low new energy outputs. Third, the mathematical model is transformed into a tractable mixed-integer linear programming (MILP) problem by using piecewise linear and triangular interpolation techniques on the solution, reducing the solution complexity. Finally, a case study of a hydropower–PV station in a river basin is conducted to demonstrate that the proposed model can effectively enhance hydropower’s regulation ability, to mitigate continuous extreme PV outputs, thereby improving power supply reliability in this hybrid renewable energy system. Full article

The co-rearing model for young silkworms (Bombyx mori) utilizing artificial feed is currently undergoing significant promotion within the sericulture industry in China. Within this model, accurately counting the number of young silkworms serves as a crucial foundation for achieving precision rearing and high-quality breeding. Currently, manual counting remains the prevalent method for enumerating young silkworms, yet it is highly subjective. A dataset of young silkworm bodies has been constructed, and the Young Silkworm Counting (YSC) method has been proposed. This method combines an improved detector, incorporating an optimized multi-scale feature fusion module and the Efficient Multi-Scale Attention Fusion Cross Stage Partial (EMA-CSP) mechanism, with an optimized tracker (based on ByteTrack with improved detection box matching), alongside the implementation of a ‘detection line’ approach. The experimental results demonstrate that the recall, precision, and average precision (AP_50:95) of the improved detection algorithm are 87.9%, 91.3% and 72.7%, respectively. Additionally, the enhanced ByteTrack method attains a multiple-object tracking accuracy (MOTA) of 88.3%, an IDF1 of 90.2%, and a higher-order tracking accuracy (HOTA) of 78.1%. Experimental validation demonstrates a counting accuracy exceeding 90%. The present study achieves precise counting of young silkworms in complex environments through an improved detection-tracking method combined with a detection line approach. Full article

This study addresses the challenge of non-uniform fracture propagation in multi-cluster staged fracturing of horizontal wells by proposing a three-dimensional dynamic simulation method for temporary plugging fracturing, grounded in a fully coupled fluid–solid damage theory framework. A Tubing-CZM (cohesive zone model) coupling model was developed to enable real-time interaction computation of flow distribution and fracture propagation. Focusing on the Xinjiang X Block reservoir, this research systematically investigates the influence mechanisms of reservoir properties, engineering parameters (fracture spacing, number of perforation clusters, perforation friction), and temporary plugging parameters on fracture propagation morphology and fluid allocation. Our key findings include the following. (1) Increasing fracture spacing from 10 m to 20 m enhances intermediate fracture length by 38.2% and improves fracture width uniformity by 21.5%; (2) temporary plugging reduces the fluid intake heterogeneity coefficient by 76% and increases stimulated reservoir volume (SRV) by 32%; (3) high perforation friction (7.5 MPa) significantly optimizes fracture uniformity compared to low-friction (2.5 MPa) scenarios, balancing flow allocation ratios between edge and central fractures. The proposed dynamic flow diversion control criteria and quantified temporary plugging design standards provide critical theoretical foundations and operational guidelines for optimizing unconventional reservoir fracturing. Full article