Search Results (9,046)

The operation envelope of distribution networks can obtain the independent p-q controllable range of each active node, providing an effective means to address the issues of different ownership and control objectives between distribution networks and distributed energy resources (DERs). Existing research mainly focuses on deterministic operation envelopes, neglecting the operational status of the system. To ensure the maximization of the envelope operation domain and the feasibility of decomposition, this paper proposes a modified hyperellipsoidal dynamic operation envelopes (MHDOEs) method for distribution networks based on adjustable “Degree of Squareness” hyperellipsoids. Firstly, an improved convex inner approximation method is applied to the non-convex and nonlinear model of traditional distribution networks to obtain a convex solution space strictly contained within the original feasible region of the system, ensuring the feasibility of flexible operation domain decomposition. Secondly, the embedding of the adjustable “Degree of Squareness” maximum hyperellipsoid is used to obtain the total p-q operation domain of the distribution network, facilitating the overall planning of the distribution network. Furthermore, the calculation of the maximum inscribed hyperrectangle of the hyperellipsoid is performed to achieve p-q decoupled operation among the active nodes of the distribution network. Subsequently, a correction coefficient is introduced to penalize “unknown states” during the operation domain calculation process, effectively enhancing the adaptability of the proposed method to complex stochastic scenarios. Finally, Monte Carlo methods are employed to construct various stochastic scenarios for the IEEE 33-node and IEEE 69-node systems, verifying the accuracy and decomposition feasibility of the obtained p-q operation domains. Full article

Structural planes are one of the key factors controlling the stability of rock masses. A comprehensive understanding of the spatial distribution characteristics of structural planes is essential for accurately identifying key blocks, analyzing rock mass stability, and addressing various rock engineering challenges. This study compares the effectiveness of four stochastic structural plane generation methods—the Monte Carlo method, the Copula-based method, generative adversarial networks (GAN), and denoised diffusion models (DDPM)—in generating stochastic structural planes and capturing potential correlations between structural plane parameters. The Monte Carlo method employs the mean and variance of three parameters of the measured factual structural planes to generate data that follow the same distributions. The other three methods take the entire set of measured factual structural planes as the overall input to generate structural planes that exhibit the same probability distributions. Five sets of structural planes on four rock slopes in Norway are examined as an example. The validation and analysis were performed using histogram comparison, data feature comparison, scatter plot comparison, and linear regression analysis. The results show that the Monte Carlo method fails to capture the potential correlation between the dip direction and dip angle despite the best fit to the measured factual structural planes. The Copula-based method performs better with smaller datasets, and GAN and DDPM are better at capturing the correlation of measured factual structural planes in the case of large datasets. Therefore, in the case of a limited number of measured structural planes, it is advisable to employ the Copula-based method. In scenarios where the dataset is extensive, the deep generative model is recommended due to its ability to capture complex data structures. The results of this study can be utilized as a valuable point of reference for the accurate generation of stochastic structural planes within rock masses. Full article

This research aims to develop a mathematical model and a solution approach for jointly optimizing a global inventory service level and order sizes for a single-commodity supply chain network with multiple warehouses or distribution centers. The latter face stochastic demands, such as most real-world supply chains do nowadays, yielding significant model complexity. The studied problem is of high relevance for inventory management, inventory location, and supply chain network design-related literature, as well as for logistics and supply chain managers. The proposed optimization model minimizes the total costs associated with cycle inventory, safety stock, and stock-out-related events, considering a global inventory service level and differentiated order sizes for a fixed and known set of warehouses. Subsequently, the model is solved by employing the Newton–Raphson algorithm, which is developed and implemented assuming stochastic demands with a normal approximation. The algorithm reached optimality conditions and the convergence criterion in a few iterations, within less than a second, for a variety of real-world sized instances involving up to 200 warehouses. The model solutions are contrasted with those obtained with a previous widely employed approximation, where safety stock costs were further approximated and order sizes were optimized without considering stock-out-related costs. This comparison denotes valuable benefits without significant additional computational efforts. Thus, the proposed approach is suitable for managers of real-world supply chains, since they would be able to attain system performance improvements by simultaneously optimizing the global inventory service level and order sizes, thereby providing a better system cost equilibrium. Full article

Nucleation is a fundamental and general process at the initial stage of first-order phase transition. Although various models based on the classical nucleation theory (CNT) have been proposed to explain the energetics and kinetics of nucleation, detailed understanding at nanoscale is still required. Here, in view of the homogeneous bubble nucleation, we focus on cavity formation, in which evaluation of the size dependence of free energy change is the key issue. We propose the application of a formula in stochastic thermodynamics, the Jarzynski equality, for data analysis of molecular dynamics (MD) simulation to evaluate the free energy of cavity formation. As a test case, we performed a series of MD simulations with a Lennard-Jones (LJ) fluid system. By applying an external spherical force field to equilibrated LJ liquid, we evaluated the free energy change during cavity growth as the Jarzynski’s ensemble average of required works. A fairly smooth free energy curve was obtained as a function of bubble radius in metastable liquid of mildly negative pressure conditions. Full article

This study examines the current state of China’s agricultural exports to ASEAN countries using a segmented export structure analysis via a stochastic frontier gravity model, based on panel data from 2007 to 2020. The results indicate that: (1) China’s primary agricultural exports to ASEAN countries include plant products, food and beverages, and tobacco, with animal products mainly exported to Thailand, plant products mainly exported to Vietnam, and animal and plant fats, food, beverages, and tobacco mainly exported to Malaysia. (2) The economic scale and population size of China and ASEAN countries have differing impacts on various markets, while distance significantly negatively affects the exports of animal products, plant products, food, beverages, and tobacco. Additionally, ASEAN countries’ per capita carbon emissions positively influence the exports of these product categories. (3) The liner shipping connectivity index is significantly negatively correlated with trade inefficiency. The influences of financial freedom, trade freedom, investment freedom, and government expenditure on trade inefficiency vary across ASEAN countries. (4) China’s export efficiency for animal products, plant products, food, beverages, and tobacco has increased rapidly to Thailand and Vietnam, with Malaysia and Singapore showing high export efficiency, while that to Cambodia lags. (5) China exhibits significant export potential to Thailand, Indonesia, and Vietnam, with substantial expansion opportunities in Indonesia. Moreover, China’s export potential and opportunities in Cambodia are steadily increasing. Full article

There is an increasing emphasis on creating healthier living spaces and improving quality of life, making the planning and establishment of healthy cities a pivotal policy and a developmental goal worldwide. This study adopted WHO-recommended indicators for healthy cities and employed stochastic frontier analysis to estimate the correlation between influencing factors and efficiency in developing healthy cities across 22 counties and cities in Taiwan from 2001 to 2022. This study yielded several key findings: (1) there was significant room for improvement in the development of healthy cities in Taiwan; (2) western metropolitan areas demonstrated higher efficiency compared to eastern counties, cities, and outlying islands; and (3) key indicators of a healthy city included nursing manpower, air quality, employment rates, income levels, and the availability of kindergartens. Developing healthy cities requires integrating various factors including policy, environmental conditions, societal aspects, and economic considerations. Collaboration between the public and private sectors is essential for fostering sustainable, healthy cities. Full article

This study aims to comprehensively investigate the dynamic characteristics of the tower of a scaled wind turbine model through wind tunnel tests. A model was scaled from the NREL 5 MW prototype wind turbine with a geometric scale ratio of 1/75, based on the similarity rules in thrust coefficient and dynamic characteristics. A series of wind tunnel tests were carried out on the scaled wind turbine model under different operating conditions and parked conditions with different yaw angles, and the modal parameters of the scaled model were identified by the stochastic subspace identification method and rotor stop tests. It was found that the vibration response of the tower in the fore–aft direction achieved its maximum value when the yaw angle was 90° with feathered blades, while the tower vibration response in the side–side direction was relatively severe with the yaw angle ranging from 10° to 50°. These observations are found to be well aligned with the aerodynamic characteristics of the airfoil. Moreover, the experimental results indicate that the scaled wind turbine model can reflect the vibration responses of its full-scale counterpart in the fore–aft direction. The natural frequencies and mode shapes of the scaled model can be accurately identified by different methods, but the identified damping ratios are relatively scattered. Full article

A typical wind system captures wind energy and converts it into electricity, which is then converted to DC for battery storage using an AC/DC converter; an inverter then supplies AC electricity at the grid frequency. However, this solution involves losses in electronic components and incurs costs and environmental impacts associated with battery storage. To address these issues, a different wind system layout configuration is considered, where the energy storage duties are assumed by a hydro storage system employing a reversible hydraulic pump (referred to as a Pump as Turbine). This solution utilises an elevated reservoir connected to the Pump as Turbine to compensate for fluctuations in wind and load; this approach offers lower costs, a longer lifespan, reduced maintenance, and a smaller waste management cost. This study focuses on a comprehensive sensitivity analysis of the new wind system power layout, considering multiple objectives. Specifically, the analysis targets the net change in the mass of water (potential energy) stored in the pumped hydro system, the captured wind energy, and the torque provided in hydraulic turbine mode. On the other hand, the design variables are represented by the Pump as Turbine-specific speed, the hydraulic system gearbox ratio, and the pump head. To assess how solutions are affected by random changes in wind speed and external load, the sensitivity analysis considers the multi-objective optimisation problem showing for both the wind speed and the external load a stochastic contribution. Full article

Ground-penetrating radar (GPR) has been widely used for subsurface detection and testing. Numerical simulations of GPR signal are commonly performed to aid the interpretation of subsurface structures and targets in complex environments. To enhance the accuracy of GPR simulations on heterogeneous medium, this paper proposes a hybrid modeling method that combines the discrete element method with a component fusion strategy (DEM–CFS). Taking the asphalt pavement as an example, three 3D stochastic models with distinctly different porosities are constructed by the DEM–CFS method. Firstly, the DEM is utilized to establish the spatial distribution of random coarse aggregates. Then, the component fusion strategy is employed to integrate other components into the coarse aggregate skeleton. Finally, the GPR response of the constructed asphalt models is simulated using the finite-difference time-domain method. The proposed modeling method is validated through both numerical and laboratory experiments and demonstrates high precision. The results indicate that the proposed modeling method has high accuracy in predicting the dielectric constant of heterogeneous media, as generated models are closely aligned with real-world conditions. Full article

Accurate risk assessment is crucial for predicting potential financial losses. This paper introduces an innovative approach by employing expected risk models that utilize risk samples to capture comprehensive risk characteristics. The innovation lies in the integration of classical credibility theory with expected risk models, enhancing their stability and precision. In this study, two distinct expected risk models were developed, referred to as Model Type I and Model Type II. The Type I model involves independent and identically distributed random samples, while the Type II model incorporates time-varying stochastic processes, including heteroscedastic models like GARCH(p,q). However, these models often exhibit high variability and instability, which can undermine their effectiveness. To mitigate these issues, we applied classical credibility theory, resulting in credible expected risk models. These enhanced models aim to improve the accuracy of Value-at-Risk (VaR) forecasts, a key risk measure defined as the maximum potential loss over a specified period at a given confidence level. The credible expected risk models, referred to as CreVaR, provide more stable and precise VaR forecasts by incorporating credibility adjustments. The effectiveness of these models is evaluated through two complementary approaches: coverage probability, which assesses the accuracy of risk predictions; and scoring functions, which offer a more nuanced evaluation of prediction accuracy by comparing predicted risks with actual observed outcomes. Scoring functions are essential in further assessing the reliability of CreVaR forecasts by quantifying how closely the forecasts align with the actual data, thereby providing a more comprehensive measure of predictive performance. Our findings demonstrate that the CreVaR risk measure delivers more reliable and stable risk forecasts compared to conventional methods. This research contributes to quantitative risk management by offering a robust approach to financial risk prediction, thereby supporting better decision making for companies and financial institutions. Full article

Uncertain fractional differential equations (UFDEs) are excellent tools for describing complicated dynamic systems. This study analyzes the valuation problems of currency options based on UFDE under the optimistic value criterion. Firstly, a new uncertain fractional currency model is formulated to describe the dynamics of the foreign exchange rate. Then, the pricing formulae of European, American, and Asian currency options are obtained under the optimistic value criterion. Numerical simulations are performed to discuss the properties of the option prices with respect to some parameters. Finally, a real-world example is provided to show that the uncertain fractional currency model is superior to the classical stochastic model. Full article

The output of renewable energy sources is characterized by random fluctuations, and considering scenarios with a stochastic renewable energy output is of great significance for energy storage planning. Existing scenario generation methods based on random sampling fail to account for the volatility and temporal characteristics of renewable energy output. To enhance photovoltaic (PV) absorption capacity and reduce the cost of planning distributed PV and energy storage systems, a scenario-driven optimization configuration strategy for energy storage in high-proportion renewable energy power systems is proposed, incorporating demand-side response and bidirectional dynamic reconfiguration strategies into the planning model. Firstly, this paper designs a time series scenario generation method for renewable energy output based on a Deep Belief Network (DBN) to fully explore the characteristics of renewable energy output. Then, considering various cost factors of PV and energy storage, a capacity determination model is established by analyzing the relationship between annual planning costs, PV connection capacity, energy storage installation capacity, and power. Case studies are conducted on the IEEE-33 node system to compare and analyze the impact of active distribution network strategies on the planning results of PV and energy storage equipment under different scenarios. The results show that by incorporating demand-side response and bidirectional dynamic reconfiguration strategies into the active distribution network, the selection and sizing of PV energy storage can significantly improve the PV absorption capacity, achieve the lowest planning cost, and address the issue of low voltage levels during periods of excess PV output due to bidirectional reconfiguration. This improves the economic efficiency and reliability of the operation of power distribution networks with a high proportion of PV, providing a solution for energy storage planning that considers the randomness of renewable energy output. Full article

Maize is an important crop, and the detection of maize diseases is critical for ensuring food security and improving agricultural production efficiency. To address the challenges of difficult feature extraction due to the high similarity among maize leaf disease species, the blurring of image edge features, and the susceptibility of maize leaf images to noise during acquisition and transmission, we propose a maize disease detection method based on ICPNet (Integrated multidimensional attention coordinate depthwise convolution PSO (Particle Swarm Optimization)-Integrated lion optimisation algorithm network). Firstly, we introduce a novel attention mechanism called Integrated Multidimensional Attention (IMA), which enhances the stability and responsiveness of the model in detecting small speckled disease features by combining cross-attention and spatial channel reconstruction methods. Secondly, we propose Coordinate Depthwise Convolution (CDC) to enhance the accuracy of feature maps through multi-scale convolutional processing, allowing for better differentiation of the fuzzy edges of maize leaf disease regions. To further optimize model performance, we introduce the PSO-Integrated Lion Optimisation Algorithm (PLOA), which leverages the exploratory stochasticity and annealing mechanism of the particle swarm algorithm to enhance the model’s ability to handle mutation points while maintaining training stability and robustness. The experimental results demonstrate that ICPNet achieved an average accuracy of 88.4% and a precision of 87.3% on the self-constructed dataset. This method effectively extracts the tiny and fuzzy edge features of maize leaf diseases, providing a valuable reference for disease control in large-scale maize production. Full article

Coconut plantations throughout the Asia–Pacific region are generally characterised by the presence of low-productivity senile palms over the age of 60, which have negative impacts on farming communities, coconut processors, and the wider economy. In Fiji, despite numerous senile coconut replacement programs, 60% of coconut palms are considered senile. The purpose of this study is to provide preliminary estimates of the financial viability of a market-based approach to senile coconut palm replacement in Fiji by utilising the palms as a feedstock, for the manufacture of rotary peeled veneer, along with plantation pine and mahogany. A mathematical model capable of supporting deterministic and stochastic dynamic optimisation was developed with an objective function to maximise the gross margin of marketable veneer manufacture per hour ($G{M}_{pz}$) by procuring the optimal allocation of logs throughout the landscape. The majority of facility location and log processing scale scenarios evaluated found that utilising large volumes of senile coconut palms for the manufacture of veneer was optimal, whilst veneering mills situated near the coconut plantations in Vanua Levu were found to maximise $G{M}_{pz}$. Overall, the results indicate that a coconut veneer and engineered wood product (EWP) value chain could present a financially viable opportunity to support large-scale senile coconut palm replacement in Fiji. Full article

With increasing pressures on big businesses to expand performance objectives beyond financial metrics and to include social and environmental objectives, business organizations experience rising tension in balancing these various objectives. Oftentimes, subjective narratives can weigh in on the relative importance of competing objectives. This subjectivity is a contributing factor to findings of inconsistent and mixed results for the financial impact of an organization’s environmental performance in the prior literature. Our research effort seeks to provide a positivist perspective on the relationship between environmental performance and financial performance of companies. Also, given the importance of efficient operations for corporate success, we examine the influence of operational productivity on the environmental and financial performance relationship. Using a global dataset compiled from reputable sources, including 1738 unique firms spanning between the years 2011 and 2020, we find statistically significant results that indicate that lower carbon emissions are associated with higher profitability when a firm has competitively high operational productivity. Companies with operational productivity that is competitively low do not perform well financially when carbon emissions are low. Thus, our study fills a research gap in this domain by relying exclusively on a broad set of purely objective data and illuminating the importance of operational efficiency on the relationship between the environmental performance and financial performance of firms. Full article