Search Results (104)

Driven by 5G/6G and the Internet of Things (IoT), wireless sensor networks (WSNs) are confronted with core challenges such as limited energy constraints, unbalanced resource allocation, and security vulnerabilities. To address these, WSNs are integrated with cognitive radio networks (CRNs) to alleviate spectrum scarcity, and reconfigurable intelligent surfaces (RIS) are adopted to enhance performance, but traditional passive RIS suffers from “double fading” (signal path loss from transmitter to RIS and RIS to receiver), which undermines WSNs’ energy efficiency and the physical layer security (PLS) (e.g., secrecy rate, SR) of primary users (PUs) in CRNs. This study leverages symmetry to develop an active RIS framework for WSN-integrated CRNs, constructing a tripartite collaborative model where symmetric beamforming and resource allocation improve WSN connectivity, reduce energy consumption, and strengthen PLS. Specifically, three symmetry types—resource allocation symmetry, beamforming structure symmetry, and RIS reflection matrix symmetry—are formalized mathematically. These symmetries reduce the degrees of freedom in optimization (e.g., cutting precoding complexity by ~50%) and enhance the directionality of green interference, while ensuring balanced resource use for WSN nodes. The core objective is to minimize total transmit power while satisfying constraints of PU SR, secondary user (SU) quality-of-service (QoS), and PU interference temperature, achieved by converting non-convex SR constraints into solvable second-order cone (SOC) forms and using an alternating optimization algorithm to iteratively refine CBS/PBS precoding matrices and active RIS reflection matrices, with active RIS generating directional “green interference” to suppress eavesdroppers without artificial noise, avoiding redundant energy use. Simulations validate its adaptability to WSN scenarios: 50% lower transmit power than RIS-free schemes (with four CBS antennas), 37.5–40% power savings as active RIS elements increase to 60, and a 40% lower power growth slope in multi-user WSN scenarios, providing a symmetry-aided, low-power solution for secure and efficient WSN-integrated CRNs to advance intelligent WSNs. Full article

Against the backdrop of the full market integration of renewable energy, determining a reasonable proportion between medium- and long-term (MLT) contracts and spot trading has become a core issue in power market reform. Current Chinese policy requires that the share of MLT contracts should not be less than 90%, which helps ensure system security but may suppress the price discovery function of the spot market and limit renewable energy integration. This paper constructs a three-layer model: the first layer describes spot market clearing through Direct Current Optimal Power Flow (DC-OPF), yielding system energy prices and nodal prices; the second layer models bilateral contract decisions between generators and users based on Nash bargaining, incorporating risk preferences via a mean–variance framework; and the third layer introduces two evaluation indicators—contract penetration rate and economic proportion—and applies outer-layer optimization to search for the optimal contract ratio. Parameters are calibrated using coal prices, wind speed, solar irradiance, and load data, with numerical solutions obtained through Monte Carlo simulation and convex optimization. Results show that increasing the share of spot trading enhances overall system efficiency, primarily because renewable energy has low marginal costs and high supply potential, thereby reducing average market prices and mitigating volatility. Simulations indicate that the optimal contract coverage rate may exceed the current policy lower bound, which would expand spot market space and promote renewable energy integration. Sensitivity analysis further reveals that fuel price fluctuations, renewable output, load structure, and risk preferences all affect the optimal proportion, though the overall conclusions remain robust. Policy implications suggest moderately relaxing the constraints on MLT contract proportions, improving contract design, and combining this with transmission expansion and demand response, in order to establish a more efficient and flexible market structure. Full article

Prosumer energy storage behavior alongside national rooftop photovoltaics (RTPV) penetration metrics is essential for decarbonization pathways in buildings. A research gap persists in quantitatively assessing storage strategies under varying regulatory frameworks that integrate both technical and financial dimensions while accounting for behavioral heterogeneity and policy feedback. This study introduces a novel degradation-aware, feedback-preserving framework that optimizes behind-the-meter storage design and operation, enabling realistic modeling of prosumer responses on large-scale RTPV adoption scenarios. Long Short-Term Memory (LSTM) and Compound Annual Growth (CAGR) models applied for the RTPV penetration rates projections in European urban contexts. The increasing rates in the Netherlands, Spain, and Italy respond to second-order regression behavior, with the former to emit signals of saturation and the latter to perform mixed anelastic and reverse elastic curves of elasticities. Accordingly, Germany, France, the United Kingdom (UK), and Greece remain in an inelastic area by 2030. The building RTPV energy storage arbitrage formulation is treated as a linear programming (LP) problem using a convex and piecewise linear cost function, a Model Predictive Control (MPC), Auto Regressive Moving Average (ARMA) and Auto Regressive Integrated Moving Average (ARIMA) statistical forecasts and rolling horizon in order to address the uncertainty of the load and the ratio κ of the sold to purchased electricity price. Weekly arbitrage gains may drop by up to 9.1% due to stochasticity, with maximized gains achieved at battery capacities between 1C and 2C. The weekly gain per cycle performs elastic, anelastic, and reverse behavior of the prosumer across the range of κ values responding to different regulatory mechanisms of pricing. The variability of economic incentives suggests the necessity of flexible energy management strategies. Full article

This paper investigates a simultaneous wireless information and power transfer (SWIPT) decode-and-forward (DF) relay network, where a source node transmits data to a destination node through the assistance of multi-hop passive relays. We employ the time-switching (TS) protocol, enabling the relays to harvest energy from the received previous hop signal to support data forwarding. We first prove that the system throughput monotonically increases with the transmit power of the source node. Next, by employing logarithmic transformations, we convert the non-convex problem of obtaining optimal TS ratios at each relay to maximize the system throughput into a convex optimization problem. Comprehensively taking into account the convergence rate, computational complexity per iteration, and robustness, we selected the log barrier method—a type of interior point method—to address this convex optimization problem, along with providing a detailed implementation procedure. The simulation results validate the optimality of the proposed method and demonstrate its applicability to practical communication systems. For instance, the proposed scheme achieves 1437.3 bps throughput at 40 dBm maximum source power in a 2-relay network—278.6% higher than that of the scheme with TS ratio fixed at 0.75 (379.68 bps). On the other hand, it converges within a 1.36 ms computation time for 5 relays, 6 orders of magnitude faster than exhaustive search (1730 s). Full article

The high-proportion integration of renewable energy sources, represented by wind power and photovoltaics, into active distribution networks (ADNs) can effectively alleviate the pressure associated with advancing China’s dual-carbon goals. However, the high uncertainty in renewable energy output leads to increased system voltage fluctuations and localized voltage violations, posing safety challenges. Consequently, research on optimal dispatch for ADNs with a high penetration of renewable energy has become a current focal point. This paper provides a comprehensive review of research in this domain over the past decade. Initially, it analyzes the voltage impact patterns and control principles in distribution networks under varying levels of renewable energy penetration. Subsequently, it introduces optimization dispatch models for ADNs that focus on three key objectives: safety, economy, and low carbon emissions. Furthermore, addressing the challenge of solving non-convex and nonlinear models, the paper highlights model reformulation strategies such as semidefinite relaxation, second-order cone relaxation, and convex inner approximation methods, along with summarizing relevant intelligent solution algorithms. Additionally, in response to the high uncertainty of renewable energy output, it reviews stochastic optimization dispatch strategies for ADNs, encompassing single-stage, two-stage, and multi-stage approaches. Meanwhile, given the promising prospects of large-scale deep reinforcement learning models in the power sector, their applications in ADN optimization dispatch are also reviewed. Finally, the paper outlines potential future research directions for ADN optimization dispatch. Full article

The inherent intermittency and uncertainty of renewable energy generation pose significant challenges to the safe and stable operation of power grids, particularly when power demand does not match renewable energy supply, leading to issues such as wind and solar power curtailment. To effectively promote the consumption of renewable energy while leveraging electric vehicles (EVs) in virtual power plants (VPPs) as distributed energy storage resources, this paper proposes an ordered scheduling strategy for EVs in campus areas oriented towards renewable energy consumption. Firstly, to address the uncertainty of renewable energy output, this paper uses Conditional Generative Adversarial Network (CGAN) technology to generate a series of typical scenarios. Subsequently, a mathematical model for EV aggregation is established, treating the numerous dispersed EVs within the campus as a collectively controllable resource, laying the foundation for their ordered scheduling. Then, to maximize renewable energy consumption and optimize EV charging scheduling, an improved Particle Swarm Optimization (PSO) algorithm is adopted to solve the problem. Finally, case studies using a real-world testing system demonstrate the feasibility and effectiveness of the proposed method. By introducing a dynamic inertia weight adjustment mechanism and a multi-population cooperative search strategy, the algorithm’s convergence speed and global search capability in solving high-dimensional non-convex optimization problems are significantly improved. Compared with conventional algorithms, the computational efficiency can be increased by up to 54.7%, and economic benefits can be enhanced by 8.6%. Full article

Within the context of global warming, the frequent occurrence of extreme weather may lead to problems, such as a sharp decrease in new energy output, insufficient system backups, and an increase in the amount of energy consumed by users, resulting in large-scale power shortages within the grid for a short period of time. With the increase in the numbers of electric vehicles (EVs) and microgrids (MGs), which are resilient resources, the ability of a system to participate in demand response (DR) is further improved, which may make up for short-term power shortages. In this paper, we first propose a charging and discharging model for EVs during the onset of a cold wave, and then perform load forecasting for EVs during cold wave weather based on user behavioral characteristics. Secondly, in order to accurately portray the flexible regulation capability of microgrids with massively flexible resource access, this paper adopts the convex packet fitting expression based on MGFOR to characterize the flexible regulation capability of MGs. Then, the Conditional Value at Risk (CVaR) is used to quantify the uncertainty of wind and solar power generation, and a two-layer model with the objective of minimizing the operation cost in the upper layer and maximizing the rate of new energy consumption in the lower layer is proposed and solved using Karush–Kuhn–Tucker (KKT) conditions. Finally, the proposed method is verified through examples to ensure the economic operation of the system and improve the new energy consumption rate of the system. Full article

In this study, the ordered flow shop scheduling problem, which is in the class of NP-hard optimization problems, is considered. This problem is used especially to increase the efficiency and prevent delays in the production process. The problem was first identified in the literature during the 1970s. The main objective of this study is to develop an efficient and fast method to overcome the complexity of this problem. For this purpose, the ordered flow shop scheduling problem is explained in detail and a robust meta-heuristic method is proposed. First of all, a genetic algorithm is developed by considering Smith’s convexity criterion. While performing operations such as crossover and mutation in the genetic algorithm, the pyramid structure is integrated to ensure that the solution has certain symmetry. The developed method is compared with other methods, such as the Nawaz–Enscore–Ham (NEH), pair insert, and iterated local search (ILS) methods. In order to increase the reliability of the results, the Pyramid Structure Adapted Tabu Search (PSA-TS) algorithm is also developed. The results are validated by statistical analysis using the Wilcoxon signed-rank test and Friedman test. The proposed genetic algorithm outperforms the methods with which it is compared. To the best of the authors’ knowledge, there is no other method in the literature that preserves the pyramid structure in the ordered flow shop scheduling problem. Therefore, this study is expected to make a significant contribution to the literature in this respect. Full article

A product’s lifespan may be ascertained by analyzing the dependability and aging class of its life distribution. Sustainability metrics are also crucial for assessing the impact on the environment and creating resource-saving strategies. Researchers extensively rely on statistical testing when making judgments; nonparametric tests are particularly useful, since they may be used for a broad variety of datasets and do not require knowledge of the data distribution. To provide effective assessments for well-informed decisions, this study introduces a novel life distribution class called “Exponential Better than Used in Increasing Convex in Laplace Transform Order (EBUCL)”. In this framework, a novel test statistic utilizing the moment inequalities method is introduced to evaluate exponentiality against the EBUCL class. By analyzing its asymptotic behavior, critical values are calculated for sample sizes between 5 and 100. Pitman’s asymptotic efficiency was taken into consideration by calculating the test’s power and comparing it with other tests through the use of simulation studies employing standard reliability distributions. In conclusion, this study addresses the treatment of right-censored data and applied the suggested test approach to real datasets across many domains. Our experimental results have practical implications for sustainability data assessments in engineering and the biological sciences. Full article

In this paper, we propose a joint optimization scheme for a transmit power and power-splitting ratio in device-to-device (D2D)-enabled simultaneous wireless information and power transfer (SWIPT) cellular networks, considering energy signal transmission. This energy signal facilitates the energy cooperation between the D2D transmitter (DT) and the CU. Under the proposed scheme, the D2D rate is maximized while guaranteeing that the cellular user (CU) achieves the same performance as in scenarios without D2D communications. In order to solve the formulated nonconvex problem, we leverage the monotonically increasing property of logarithmic functions to transform it into an equivalent convex problem. As a result, we obtain the optimal solution in closed form. Also, the optimal D2D performance is analyzed, and useful insights into the performance improvements achievable through the proposed scheme are obtained. Numerical results demonstrate that the proposed scheme significantly outperforms the baseline scheme. Full article

In this paper, a class of controlled variational control models is studied by considering the notion of $(q,w)-\pi$-invexity. Our aim is to investigate a solution set in the considered interval-valued controlled models. To achieve this, we establish some characterization results of solutions in the controlled interval-valued variational models. More precisely, necessary and sufficient conditions of optimality are highlighted as part of a feasible solution. To prove that the optimality conditions are sufficient, we impose generalized invariant convexity hypotheses for the involved multiple integral functionals. Finally, a duality result is provided in order to better describe the problem under study. The methodology used in this paper is a combination of techniques from the Lagrange–Hamilton theory, calculus of variations, and control theory. This study could be immediately improved by including an analysis of this class of optimization problems by using curvilinear integrals instead of multiple integrals. The independence of path imposed to these functionals and their physical significance would increase the applicability and importance of the paper. Full article

Seismic inversion is a process of imaging or predicting the spatial and physical properties of underground strata. The most commonly used one is sparse-spike seismic inversion with sparse regularization. There are many effective methods to solve sparse regularization, such as L0-norm, L1-norm, weighted L1-norm, etc. This paper studies the sparse-spike inversion with L0-norm. It is usually solved by the iterative hard thresholding algorithm (IHTA) or its faster variants. However, hard thresholding algorithms often lead to a sharp increase or numerical oscillation of the residual, which will affect the inversion results. In order to deal with this issue, this paper attempts the idea of the relaxed optimal thresholding algorithm (ROTA). In the solution process, due to the particularity of the sparse constraints in this convex relaxation model, this model can be considered as a L1-norm problem when dealt with the location of non-zero elements. We use a modified iterative soft thresholding algorithm (MISTA) to solve it. Hence, it forms a new algorithm called the iterative hybrid thresholding algorithm (IHyTA), which combines IHTA and MISTA. The synthetic and real seismic data tests show that, compared with IHTA, the results of IHyTA are more accurate with the same SNR. IHyTA improves the noise resistance. Full article

The current research aims to analyze the shape and structural features of the eggs of the lepidoptera species Melitaea sp. (Lepidoptera, Nympalidae) and develop design solutions through the implementation of a novel strategy of biomimetic design. Scanning electron microscopy (SEM) analysis of the chorion reveals a medial zone that forms an arachnoid grid resembling a ribbed dome with convex longitudinal ribs and concave transverse ring members. A parametric design algorithm was created with the aid of computer-aided design (CAD) software Rhinoceros 3D and Grasshopper3D in order to abstract and emulate the biological model. A series of physical models were manufactured with variations in geometric parameters like the number of ribs and rings, their thickness, and curvature. Selective laser sintering (SLS) technology and Polyamide12 (nylon) material were utilized for the prototyping process. Quasi-static compression testing was carried out in conjunction with finite element analysis (FEA) to investigate the deformation patterns and stress dispersion of the models. The biomimetic ribbed dome appears to significantly dampen the snap-through behavior that is observed in typical solid and lattice domes, decreasing dynamic stresses developed during the response and preventing catastrophic failure of the structure. Increasing the curvature of the ring segments further reduces the snap-through phenomenon and improves the overall strength. However, excessive curvature has a negative effect on the maximum sustained load. Increasing the number and thickness of the transverse rings and the number of the longitudinal ribs also increases the strength of the dome. However, excessive increase in the rib radius leads to more acute snap-through behavior and an earlier failure. The above results were validated using respective finite element analyses. Full article

Bile acid salts are steroid biosurfactants that build relatively small micelles compared to surfactants with an alkyl chain due to the rigid conformation of the steroid skeleton. In order to increase the capacity of micellar solubilization of the hydrophobic molecular guest, certain C7 alkyl derivatives were synthesized. Namely, introducing an alkyl group in the C7 position of the steroid skeleton results in a more effective increase in the micelle’s hydrophobic domain (core) than the introduction in the C3 position. In comparison, fewer synthetic steps are required than if alkyl groups are introduced into the C12 position of cholic acid in the Grignard reaction. Here, the thermodynamic parameters of micellization (demicellization) of C7 alkyl (number of C atoms in the alkyl group: 2, 3, 4, and 8) derivatives of cholic acid anion in an aqueous solution without additives are examined (which have not yet been determined) in the temperature interval T (10–40) °C. The critical micellar concentration and the change in the standard molar enthalpy of demicellization ($∆h_{\mathrm{d}\mathrm{e}\mathrm{m}\mathrm{i}\mathrm{c}}^0$) are determined by isothermal calorimetric titration (ICT). From the temperature dependence of $∆h_{\mathrm{d}\mathrm{e}\mathrm{m}\mathrm{i}\mathrm{c}}^0$, the change in the standard molar heat capacity of demicellization is obtained ($∆C_{\mathrm{d}\mathrm{e}\mathrm{m}\mathrm{i}\mathrm{c}}^0$), the value of which is proportional to the hydrophobic surface of the monomer, which in the micellar state is protected from hydrophobic hydration. The values of $∆C_{\mathrm{d}\mathrm{e}\mathrm{m}\mathrm{i}\mathrm{c}}^0$ indicate that in the case of C7-alkyl derivatives of cholic acid anion with butyl and octyl chains, parts of the steroid skeleton and alkyl chain remain shielded from hydration after disintegration of the micelle. Conformational analysis can show that starting from the C7 butyl chain in the alkyl chain, sequences with gauche conformation are also possible without the formation of steric repulsive strain between the alkyl chain and the steroid skeleton so that the C7 alkyl chain takes an orientation above the convex surface of the steroid skeleton instead of an elongated conformation toward the aqueous solution. This is a significant observation, namely, if the micelle is used as a carrier of a hydrophobic drug and after the breakdown of the micelle in the biological system, the released drug has a lower tendency to associate with the monomer if its hydrophobic surface is smaller, i.e., the alkyl chain is oriented towards the angular methyl groups of the steroid skeleton (the ideal monomer increases the hydrophobic domain of the micelle, but in aqueous solution, it adopts a conformation with the as small hydrophobic surface as possible oriented towards the aqueous solution)—which then does not disturb the passage of the drug through the cell membrane. Full article

Aim: Electronic prescribing has allowed for the collection of prescription data in real time in Greece for the first time. Hence, the aim of the current study was to present the characteristics of prescriptions for the Greek population during the period from 2015 to 2021. Methods: This retrospective study was based on data extracted from the nationwide Greek electronic prescription database between January 2015 and December 2021. Descriptive statistics methods were used for the needs of the study. As the basic figures examined depend on the size of the population, in order for the results to be comparable, we estimated the corresponding measures per inhabitant, using population data from the Greek Statistical Authority. Appropriate indicators for the comparison of consumption and expenditure over time were estimated. A study of the trend was also carried out using time series and linear regression models. In order to facilitate the design and implementation of specialized policies, it is useful to identify the drug categories with the highest consumption and expenditure, as well as the geographical areas that present similar characteristics. For the first, ABC analysis was used, which helps to identify the most popular categories of drugs, while for the second, cluster analysis was carried out. Agglomerative clustering was used to divide the regions into similar groups. This hierarchical clustering algorithm classifies the population into several clusters, with areas in the same cluster being more similar, and areas in different clusters being dissimilar. The Ward linkage method with Euclidean distance was used. Results: The analysis of prescription drug consumption and expenditure from 2015 to 2021 revealed significant fluctuations and trends across various drug categories, age groups, and geographical areas. Notably, the quantity of prescriptions increased by 20% since 2015, while expenditure surged by over 30%, with significant spikes following the end of the MoU in 2019 and the onset of the pandemic in 2020. In terms of expenditure, antineoplastic and immunomodulation agents (category L) held the largest share, driven by the introduction of new, costly drugs. The expenditure per inhabitant revealed gender and age disparities, with older populations, particularly women, incurring higher costs. Geographically, drug expenditure, and consumption varied significantly, with distinct regional clusters identified. These clusters, while showing some overlap in consumption and expenditure patterns, also highlighted unique regional characteristics. Conclusions: The insights into prescription drug consumption and expenditure trends offer a valuable basis for developing targeted interventions aimed at optimizing healthcare resource allocation. Moreover, the findings underscore the importance of addressing regional and demographic disparities in pharmaceutical use, thereby contributing to more equitable and cost-effective healthcare strategies. More specifically, the age distribution of prescriptions shows the increase in younger ages, which, as a result, anticipates the overall increase in prescriptions. The knowledge of the most convex categories of medicine, as well as the percentages of the use of generic drugs, shows where interventions should be made, with financial incentives and information through new information channels. The geographic disparities recorded should lead to policies that help the residents of hard-to-reach areas to access prescriptions. In addition, the present study provides a strategic framework for policymakers and healthcare managers to guide future studies and inform decision-making processes. Full article