Search Results (101)

In most commercial lithium-ion batteries, graphite remains the primary anode material. However, its theoretical specific capacity is only 372 mAh∙g⁻¹, which falls short of meeting the demands of high-performance electronic devices. Silicon anodes, despite boasting an ultra-high theoretical specific capacity of 4200 mAh∙g⁻¹, suffer from significant volume expansion (>300%) during cycling, leading to severe capacity fade and limiting their commercial viability. Currently, silicon-based polymer-derived ceramics have emerged as a highly promising next-generation anode material for lithium-ion batteries, thanks to their unique nano-cluster structure, tunable composition, and low volume expansion characteristics. The maximum capacity of the ceramics can exceed 1000 mAh∙g⁻¹, and their unique synthesis routes enable customization to align with diverse electrochemical application requirements. In this paper, we present the progress of silicon oxycarbide (SiOC), silicon carbonitride (SiCN), silicon boron carbonitride (SiBCN) and silicon oxycarbonitride (SiOCN) in the field of LIBs, including their synthesis, structural characteristics and electrochemical properties, etc. The mechanisms of lithium-ion storage in the Si-based anode materials are summarized as well, including the key role of free carbon in these materials. Full article

Steel is an important product in many engineering sectors; however, steelmaking remains one of the largest ${\mathrm{CO}}_2$ emitters. Therefore, new governmental policies drive the steelmaking industry toward a cleaner and more sustainable operation such as the gas-based direct reduction–electric arc furnace process. To become carbon neutral, utilizing more scrap is one of the feasible solutions to achieve this goal. Addressing knowledge gaps regarding scrap heterogeneity (size, shape, and composition) is essential to evaluate the effects of increased scrap ratios in basic oxygen furnace (BOF) operations. This review systematically examines heat and mass transfer correlations relevant to scrap melting in BOF steelmaking, with a focus on low Prandtl number fluids (thick thermal boundary layer) and dense particulate systems. Notably, a majority of these correlations are designed for fluids with high Prandtl numbers. Even for the ones tailored for low Prandtl, they lack the introduction of the porosity effect which alters the melting behavior in such high temperature systems. The review is divided into two parts. First, it surveys heat transfer correlations for single elements (rods, spheres, and prisms) under natural and forced convection, emphasizing their role in predicting melting rates and estimating maximum shell size. Second, it introduces three numerical modeling approaches, highlighting that the computational fluid dynamics–discrete element method (CFD–DEM) offers flexibility in modeling diverse scrap geometries and contact interactions while being computationally less demanding than particle-resolved direct numerical simulation (PR-DNS). Nevertheless, the review identifies a critical gap: no current CFD–DEM framework simultaneously captures shell formation (particle growth) and non-isotropic scrap melting (particle shrinkage), underscoring the need for improved multiphase models to enhance BOF operation. Full article

This paper addresses the challenge of maximizing power extraction in offshore wind energy systems through the development of an enhanced maximum power point tracking (MPPT) control strategy. Offshore wind energy is inherently intermittent, leading to discrepancies between power generation and electricity demand. To address this issue, we propose three advanced control algorithms to perform a comparative analysis: sliding mode control (SMC), the Integral Backstepping-Based Real-Twisting Algorithm (IBRTA), and Feed-Back Linearization (FBL). These algorithms are designed to handle the nonlinear dynamics and aerodynamic uncertainties associated with offshore wind turbines. Given the practical limitations in acquiring accurate nonlinear terms and aerodynamic forces, our approach focuses on ensuring the adaptability and robustness of the control algorithms under varying operational conditions. The proposed strategies are rigorously evaluated through MATLAB/Simulink 2024 A simulations across multiple wind speed scenarios. Our comparative analysis demonstrates the superior performance of the proposed methods in optimizing power extraction under diverse conditions, contributing to the advancement of MPPT techniques for offshore wind energy systems. Full article

Phase change materials (PCMs) have significant potential for utilization due to their high energy storage density and excellent safety in energy storage. In this research, a flexible heat storage device using the stable supercooling of sodium acetate trihydrate composite is developed, enabling on-demand heat release through controlled solidification initiation. The solidification and heat release characteristics are investigated in experiments. The results indicate that the heat release characteristics of this heat storage device are closely linked to the crystallization process of the PCM. During the experiment, based on whether external intervention was needed for the solidification process, the PCM manifested two separate solidification modes—specifically, spontaneous self-solidification and triggered-solidification. Meanwhile, the heat release rates, temperature changes, and crystal morphologies were observed in the two solidification modes. Compared with spontaneous self-solidification, triggered-solidification achieved a higher peak surface temperature (53.6 °C vs. 46.2 °C) and reached 45 °C significantly faster (5 min vs. 15 min). Spontaneous self-solidification exhibited slower, uncontrollable heat release with dendritic crystals, while triggered-solidification provided rapid, controllable heat release with dense filamentous crystals. This controllable switching between modes offers key practical advantages, allowing the device to provide either rapid, high-power heat discharge or slower, sustained release as required by the application. According to the crystal solidification theory, the different supercooling degrees are the main reasons for the two solidification modes exhibiting different solidification characteristics. During solidification, the growth rate of SAT crystals exhibits substantial disparities across diverse experiments. In this research, the maximum axial growth rate is 2564 μm/s, and the maximum radial growth rate is 167 μm/s. Full article

The rapid development of sixth-generation mobile communication systems has brought about significant advancements in both Quality of Service (QoS) and Quality of Experience (QoE) for users, largely due to the extremely high data rates and a diverse range of service offerings. However, these advancements have also introduced challenges, especially concerning the growing demand for a wireless spectrum and the limited availability of resources. Various efforts have been made and research has attempted to tackle this issue such as the use of Cognitive Radio Networks (CRNs), which allows opportunistic spectrum access and intelligent resource management. This work demonstrate a new method in the optimization of allocation resource in CRNs based on the Snake Optimizer (SO) along with reinforcement learning (RL), which is an effective meta-heuristic algorithm that simulates snake cloning behavior. SO is tested over three different scenarios with varying numbers of secondary users (SUs), primary users (PUs), and frequency bands available. The obtained results reveal that the proposed approach is able to largely satisfy the aforementioned requirements and ensures high spectrum utilization efficiency and low collision rates, which eventually lead to the maximum possible spectral capacity. The study also demonstrates that SO is versatile and resilient and thus indicates its capability of serving as an effective method for augmenting resource management in next-generation wireless communication systems. Full article

The Gompertz distribution has long been a cornerstone for analyzing growth processes and mortality patterns across various scientific disciplines. However, as the intricacies of real-world phenomena evolve, there is a pressing need for more versatile probability distributions that can accurately capture a wide array of data characteristics. In response to this demand, we introduce the Marshall–Olkin Power Gompertz (MOPG) distribution, an innovative and powerful extension of the traditional Gompertz model. The MOPG distribution is crafted by enhancing the Power Gompertz cumulative distribution function through the Marshall–Olkin transformation. This distribution yields two pivotal contributions: a power parameter (c) that significantly increases the model’s adaptability to diverse data patterns and the Marshall–Olkin transformation, which modifies tail behavior to enhance predictive accuracy. Furthermore, we derived the distribution’s essential statistical properties and evaluate its performance through extensive Monte Carlo simulations, along with a maximum likelihood estimation of model parameters. Our empirical validation, utilizing three real-world data sets, compellingly demonstrated that the MOPG distribution not only surpasses several well-established lifetime distributions but is also superior in terms of flexibility and tail behavior characterization. The results highlight that the proposed MOPG stands out as a superior choice, delivering the most precise fit to the data when compared to various competing models, and its performance makes it a compelling option worth considering. Full article

As global climate change and energy crises intensify, the pursuit of low-carbon integrated energy systems (IESs) has become increasingly important. This paper proposes an improved genetic algorithm (IGA) designed to optimize the multi-objective low-carbon operations of IESs, aiming to minimize both operating costs and carbon emissions. The IGA incorporates circular crossover and polynomial mutation techniques, which not only preserve advantageous traits from the parent population but also enhance genetic diversity, enabling comprehensive exploration of potential solutions. Additionally, the algorithm selects parent populations based on individual fitness and dominance, retaining successful chromosomes and eliminating those that violate constraints. This process ensures that subsequent generations inherit superior genetic traits while minimizing constraint violations, thereby enhancing the feasibility of the solutions. To evaluate the effectiveness of the proposed algorithm, we tested it on three different IES scenarios. The results demonstrate that the IGA successfully reduces equality constraint violations to below 0.3 kW, representing less than 0.2% deviation from the IES’s power demand in each time slot. We compared its performance against a multi-objective genetic algorithm, a multi-objective particle swarm algorithm, and a single-objective genetic algorithm. Compared to conventional genetic algorithms, the IGA achieved maximum 5% improvement in both operational cost reduction and carbon emission minimization objectives compared to the unimproved single-objective genetic algorithm, demonstrating its superior performance in multi-objective optimization for low-carbon IESs. These outcomes underscore the algorithm’s reliability and practical applicability. Full article

Optimal water resource allocation in agricultural irrigation districts constitutes a core strategy for achieving coordinated regional water–food–ecosystem development. However, current studies rarely integrate inter-basin water diversion projects into the allocation, and the prolonged operation of diversion systems fails to adequately consider their ecological impacts in the irrigation districts. This study incorporates inter-basin water diversion into supply–demand dynamics and considers its influence on groundwater table changes in terrestrial ecological targets. Inexact two-stage stochastic programming (ITSP) was applied for optimal water allocation to address uncertainties from fluctuations in future water availability and interval ambiguity in socioeconomic information. Taking the densely populated agricultural irrigation district of Huaibei as a case study, we established a multi-stakeholder allocation model, considering the Yangtze-to-Huai water diversion project, to maximize comprehensive benefits under multiple scenarios of water availability for the years of 2030 and 2040. The results demonstrate that the district will face escalating water scarcity risks, with demand–supply gaps widening when available water resources decrease. The water redistribution in the second stage reduces scarcity-induced losses, achieving maximum comprehensive benefits. The water diversion project enhances supply capacity and boosts economic gains. The project can also decrease the fluctuation range of the total benefits by 5 × 10⁶ CNY (2030) and 3.4 × 10⁷ CNY (2040), compared with the scenario without the project. From 2030 to 2040, limited water resources will progressively shift toward sectors with higher economic output per unit water, squeezing agricultural allocations. Therefore, for irrigation districts in developing countries, maintaining a minimum guaranteed rate of agricultural water proves critical to safeguarding food security. Full article

Second-hand housing transactions are an important part of the housing market. Due to the dual influence of location and price, the sales cycle of second-hand housing has shown significant diversity. As a result, when residents sell or buy second-hand houses, they often cannot accurately and quickly evaluate the cycle of the second-hand house; thus, the transaction fails. For this reason, this paper develops a prediction model of the second-hand housing sales cycle based on the hybrid kernel extreme learning machine (HKELM) optimized using the Improved Crested Porcupine Optimizer (CPO), which has achieved rapid and accurate prediction. Firstly, this paper uses a Stimulus–Organism–Response model to identify 33 factors that affect the second-hand housing sales cycle from three aspects: policy factors, economic factors, and market supply and demand. Then, in order to solve the problems of slow convergence, easy-to-fall-into local optimum, and insufficient optimization performance of the traditional CPO, this paper proposes an improved optimization algorithm for crowned porcupines (Cubic Chaos Mapping Crested Porcupine Optimizer, CMTCPO). Subsequently, this paper puts forward a prediction model of the second-hand housing sales cycle based on an improved CPO-HKELM. The model has the advantages of a simple structure, easy implementation, and fast calculation speed. Finally, this paper selects 400 second-hand houses in eight cities in China as case studies. The case study shows that the maximum relative error based on the model proposed in this paper is only 0.0001784. A ten-fold cross-test proves that the model does not have an over-fitting phenomenon and has high reliability. In addition, this paper discusses the performances of different chaotic maps to improve the CPO and proves that the algorithm including chaotic maps, mixed mutation, and tangent flight has the best performance. Compared with the classical meta-heuristic optimization algorithm, the improved CPO proposed in this paper has the smallest calculation error and the fastest convergence speed. Compared with a BPNN, LSSVM, RF, XGBoost, and LightGBM, the HKELM has advantages in prediction performance, being able to handle high-dimensional complex data sets more effectively and significantly reduce the consumption of computing resources. The relevant research results of this paper are helpful to predict the second-hand housing sales cycle more quickly and accurately. Full article

Background/Objectives: The stone pine (Pinus pinea L.) is an evergreen coniferous species valued for its edible seeds, which provide significant economic benefits to local populations. Remarkable phenotypic plasticity but low genetic variation characterizes the species. In Greece, natural populations of P. pinea are part of the Natura 2000 network and are protected under Annex I Priority Habitat type 2270. These populations, located across six Natura 2000 sites (including two islands), face increasing threats from tourism and climate change, leading to ecosystem degradation. Genetic and epigenetic studies are critical for the conservation of forest species because they provide insights into the genetic diversity, adaptive potential, and resilience of species, helping to inform effective management strategies and protect biodiversity in changing environments. This study aims to assess the genetic and epigenetic diversity of P. pinea in four Natura 2000 sites using molecular markers and to propose conservation strategies to ensure the species’ long-term sustainability. Additionally, a preliminary investigation of water potential under maximum daily water demand was conducted to evaluate the species’ adaptive response. Methods: Genetic analysis was performed using Amplified Fragment Length Polymorphism (AFLP) markers, while epigenetic analysis was conducted using Methylation-Susceptible Amplified Polymorphism (MSAP) markers. Sampling was carried out in four Natura 2000 areas, where genetic and epigenetic diversity patterns were examined. Furthermore, a preliminary study on water potential under peak daily water demand conditions was conducted to assess the species’ physiological adaptation to environmental stress. Results: The results of this study provide valuable insights into conservation strategies by highlighting the potential role of epigenetic variation in the adaptability of P. pinea, despite its low genetic variability. Understanding the species’ epigenetic flexibility can inform conservation efforts aimed at enhancing its resilience to environmental stressors, such as climate change. Additionally, the preliminary water potential analysis contributes to identifying physiological traits that may help predict the species’ survival under varying environmental conditions, guiding the development of more targeted conservation practices and management plans. Further research could refine these findings and strengthen their application in conservation efforts. Conclusions: The conclusions emphasize the critical importance of this research in informing conservation efforts for P. pinea in Greece, particularly considering climate change and human pressures. The results highlight the need for both in-situ and ex-situ conservation strategies to ensure the long-term sustainability of the species. The key recommendations include the protection of natural habitats, the implementation of controlled seed collection practices, and further research into the epigenetic mechanisms that may enhance the species’ resilience to environmental stress. Future studies should focus on deepening our understanding of these epigenetic factors and their role in the adaptability of P. pinea, which will be essential for developing more effective conservation measures. Full article

The finite-difference time-domain (FDTD) method is a robust numerical approach for the three-dimensional forward modeling of airborne ground-penetrating radar responses of complex geological structures, particularly landslides. However, standard FDTD implementations encounter significant memory demands as aircraft altitude increases and when modeling high-permittivity subsurface media (e.g., water-saturated soils), often exceeding ordinary computational resources. Existing subgridding FDTD methods, tailored for simple localized target models, are also inadequate for simulating landslide models. To overcome these limitations, we thus propose a novel high-order FDTD-based subgridding algorithm that applies coarse grids to the air layer and fine grids to the subsurface medium, enabling the simulation of arbitrarily complex landslide models with significantly reduced memory consumption. This study achieves the first implementation of the high-order FDTD(2,4) method in both coarse- and fine-grid regions, which enables larger grid sizes in both regions. As a result, the proposed approach not only preserves high-order spatial accuracy but also achieves significant memory savings. To mitigate the challenges posed by higher-order difference stencils, we introduce a specialized grid configuration with an overlap zone between coarse and fine grids, supplemented by surrounding virtual nodes. The algorithm accommodates various grid refinement factors, ensuring adaptability to dielectric models with diverse permittivity values and structural complexities. By optimizing the grid refinement factor based on the subsurface medium’s maximum permittivity, simulations can be performed with minimal memory usage. Field updates within the overlapping region are followed by weighted corrections to ensure numerical stability, whereas simulations without these novel measures exhibit oscillatory artifacts. Wavefield snapshots reveal seamless transitions across grid boundaries without spurious artifacts. Numerical experiments on deposition-type landslide models and water-bearing media confirm the validity and stability of the proposed method. Notably, using the optimal grid refinement factor reduces memory consumption to less than 8% of the standard FDTD method for aquifer model simulations. Full article

The field of unmanned aerial vehicles (UAVs) has experienced substantial growth, with applications expanding across diverse domains. Missions increasingly demand higher autonomy, reducing human intervention and relying more on advanced onboard systems. However, integrating hybrid power sources, especially micro-turboprop engines, into UAVs poses significant challenges due to their complexity, hindering the development of effective power management control systems. This research aims to design a control algorithm for dynamic power allocation based on UAV operational needs. A fuzzy logic-based control algorithm was implemented on the Single-Board Computer (SBC) of a micro-turbogenerator test bench, which was previously developed in an earlier study. After implementing and testing the algorithm, voltage stabilization was achieved at improved levels by tightening the membership function constraints of the fuzzy logic controller. Automating the throttle control of the Electric Ducted Fan (EDF), the test platform’s primary power consumer, enabled the electric generator’s maximum capacity to be reached. This result indicates the necessity of replacing the current electric motor with one that is capable of higher power outputs to support the system’s enhanced performance. Full article

The mapping of land use/cover (LULC) types is a crucial tool for natural resource management and monitoring changes in both human and physical environments. Unmanned aerial vehicles (UAVs) provide high-resolution data, enhancing the capability for accurate LULC representation at potentially very high spatial resolutions. In the present study, two widely used supervised classification methods, namely the Maximum Likelihood Classification (MLC) and Mahalanobis Distance Classification (MDC), were applied to analyze image data collected by UAVs from a typical Mediterranean site located in Greece. The study area, characterized by diverse land uses (urban, agricultural, and natural areas), served as an ideal field for comparing the two classification methods. Although both methods produced comparable results, MLC outperformed MDC, with an overall accuracy of 96.58% and a Kappa coefficient of 0.942, compared to MDC for which an overall accuracy of 92.77% and a Kappa coefficient of 0.878 were reported. This study highlights the advantages of using UAVs to produce robust information on the geospatial variability of land use/cover in a given area at very high spatial resolution in a cost-efficient, timely, and on-demand manner. Such information can help in decision- and policy-making for ensuring a more sustainable physical environment. This study’s limitations, including the small and relatively homogeneous study area, are acknowledged. Future research could potentially focus on exploring the use of advanced classification techniques, such as deep learning and more diverse Mediterranean landscapes, which would assist in enhancing the present’s approach applicability. Full article

As the adoption of electric vehicles (EVs) continues to rise, the pressure on charging station resources has intensified, particularly under high-load conditions, where limited charging infrastructure struggles to meet the growing demand. Issues such as uneven resource allocation, prolonged charging wait times, fairness concerns among different user groups, and inefficient scheduling strategies have significantly impacted the overall operational efficiency of charging infrastructure and the user experience. Against this backdrop, the effective management of charging infrastructure has become increasingly critical, especially in balancing the diverse mobility needs and service expectations of users. Traditional charging scheduling methods often rely on static or rule-based strategies, which lack the flexibility to adapt to dynamic load environments. This rigidity hinders optimal resource allocation, leading to low charging pile utilization and reduced charging efficiency for users. To address this, we propose an Adaptive Charging Priority (ACP) strategy aimed at enhancing charging resource utilization and improving user experience. The key innovations include (1) dynamic adjustment of priority parameters for optimized resource allocation; (2) a dynamic charging station reservation algorithm based on load status and user arrival rates to prioritize high-priority users; (3) a scheduling strategy for low-priority vehicles to minimize waiting times for non-reserved vehicles; and (4) integration of real-time data with the DDPDQN algorithm for dynamic resource allocation and user matching. Simulation results indicate that the ACP strategy outperforms the FIFS and RFWDA strategies under high-load conditions (High-priority vehicle arrival rate: 22 EV/h, random vehicle arrival rate: 13 EV/h, maximum parking duration: 1200 s). Specifically, the ACP strategy reduces charging wait times by 96 s and 28 s, respectively, and charging journey times by 452 s and 73 s. Additionally, charging station utilization increases by 19.5% and 11.3%. For reserved vehicles, the ACP strategy reduces waiting times and journey times by 27 s and 188 s, respectively, while increasing the number of fully charged vehicles by 104. For non-reserved vehicles, waiting and journey times decrease by 213 s and 218 s, respectively, with a 75 s increase in fully charged vehicles. Overall, the ACP strategy outperforms traditional methods across several key metrics, demonstrating its advantages in resource optimization and scheduling. Full article

The rapid rise in the power conversion efficiency (PCE) of perovskite solar cells (PSCs) has opened the door for diverse potential applications in powering indoor Internet of Things (IoT) devices. An energy harvesting system (EHS) powered by a PSC module with a backup Li-ion battery, which stores excess power at moments of high irradiances and delivers the stored power to drive the load during operation scenarios with low irradiances, has been designed. A DC-DC boost converter is engaged to match the voltage of the PSC and Li-ion battery, and maximum power point tracking (MPPT) is achieved by a perturb and observe (P&O) algorithm, which perturbs the photovoltaic (PV) system by adjusting its operating voltage and observing the difference in the output power of the PSC. Furthermore, the charging and discharging rate of the battery storage is controlled by a DC-DC buck–boost bidirectional converter with the incorporation of a proportional–integral (PI) controller. The bidirectional DC-DC converter operates in a dual mode, achieved through the anti-parallel connection of a conventional buck and boost converter. The proposed EHS utilizes DC-DC converters, MPPT algorithms, and PI control schemes. Three different case scenarios are modeled to investigate the system’s behavior under varying irradiances of 200 W/m², 100 W/m², and 50 W/m². For all three cases with different irradiances, MPPT achieves tracking efficiencies of more than 95%. The laboratory-fabricated PSC operated at MPP can produce an output power ranging from 21.37 mW (50 W/m²) to 90.15 mW (200 W/m²). The range of the converter’s output power is between 5.117 mW and 63.78 mW. This power range can sufficiently meet the demands of modern low-energy IoT devices. Moreover, fully charged and fully discharged battery scenarios were simulated to study the performance of the system. Finally, the IoT load profile was simulated to confirm the potential of the proposed energy harvesting system in self-sustainable IoT applications. Upon review of the current literature, there are limited studies demonstrating a combination of EHS with PSCs as an indoor power source for IoT applications, along with a bidirectional DC-DC buck–boost converter to manage battery charging and discharging. The evaluation of the system performance presented in this work provides important guidance for the development and optimization of new-generation PV technologies like PSCs for practical indoor applications. Full article