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Novel Hybrid Unequal-Sized WENO Scheme Employing Trigonometric Polynomials for Solving Hyperbolic Conservation Laws on Structured Grids
No full textThis study presents a novel fifth-order unequal-sized trigonometric weighted essentially non-oscillatory (US-TWENO) scheme and a novel hybrid US-TWENO (HUS-TWENO) scheme with a novel troubled cell indicator in a finite difference framework to address hyperbolic conservation laws on structured grids. Firstly, we propose three unequal-degree reconstruction polynomials in the new trigonometric polynomial space to devise a novel fifth-order US-TWENO scheme. Then, we devise a novel troubled cell indicator capable of accurately identifying troubled cells containing strong discontinuities: the existence of extreme points of the trigonometric polynomials within the smallest interval (the target cell itself) is determined by whether the estimated minimum and maximum values of their derivative trigonometric polynomials have opposite signs. To the best of our knowledge, this is the first troubled cell indicator devised specifically within the target cell interval. The HUS-TWENO scheme is improved, offering greater efficiency, lower dissipation, and higher resolution. Numerical experiments demonstrate the effectiveness of the HUS-TWENO scheme
Robust Parameter Interval Identification for a Logistic-Type Fractional Difference System
No full textClassical integer-order chaotic maps usually exhibit chaotic degradation under prolonged iterations or finite-precision computation, which may compromise the reliability of chaos-based algorithms. Fractional difference chaotic systems with memory effects offer a promising alternative; however, existing studies rarely provide a systematic and quantitative understanding of how the nonlinear gain parameter, memory strength, and initial condition collectively influence the emergence and robustness of complex dynamics under finite-time iterations. It should be noted that memory effects do not inherently guarantee robust chaotic behavior under finite-precision computation, and appropriate parameter and initial-condition selection remains essential. In this paper, we conduct a systematic numerical dynamical analysis of a logistic-type fractional difference system with power-law memory by leveraging bifurcation diagrams and Lyapunov exponent mappings. Rather than aiming to select optimal parameter points, we propose a quantitative composite chaos evaluation (CCE) framework to identify admissible parameter intervals within which robust finite-time chaotic dynamics can be consistently sustained. Numerical results demonstrate the effectiveness and reliability of the proposed framework, which may facilitate future applications in chaos-enhanced optimization, nonlinear control, and secure communication
DRL-TinyEdge: Energy- and Latency-Aware Deep Reinforcement Learning for Adaptive TinyML at the 6G Edge
No full textVarious TinyML models face a constantly challenging environment when running on emerging sixth-generation (6G) edge networks, with volatile wireless environments, limited computing power, and highly constrained energy use. This paper introduces DRL-TinyEdge, a latency- and energy-sensitive deep reinforcement learning (DRL) platform optimised for the 6G edge of adaptive TinyML. The suggested on-device DRL controller autonomously decides on the execution venue (local, partial, or cloud) and model configuration (depth, quantization, and frequency) in real time to trade off accuracy, latency, and power savings. To assure safety during adaptation to changing conditions, the multi-objective reward will be a combination of p95 latency, per-inference energy, preservation of accuracy and policy stability. The system is tested under two workloads representative of classical applications, including image classification (CIFAR-10) and sensor analytics in an industrial IoT system, on a low-power platform (ESP32, Jetson Nano) connected to a simulated 6G mmWave testbed. Findings indicate uniform improvements, with up to a 28 per cent decrease in p95 latency and a 43 per cent decrease in energy per inference, and with accuracy differences of less than 1 per cent compared to baseline models. DRL-TinyEdge offers better adaptability, stability, and scalability when using a CPU < 5 and a decision latency < 10 ms, compared to Static-Offload, Heuristic-QoS, or TinyNAS/QAT. Code, hyperparameter settings, and measurement programmes will also be published at the time of acceptance to enable reproducibility and open benchmarking
Establishment of an Effective Gene Editing System for ‘Baihuayushizi’ Pomegranate
No full textPomegranate (Punica granatum L.) is a popular fruit tree with high edible and ornamental values. However, the traditional breeding strategies are lacking in efficiency for the improvement of agronomic traits of pomegranate. Gene editing technologies offer a solution for promoting desired growth or metabolic processes in pomegranate trees. In this study, we established a CRISPR-mediated gene editing system for pomegranate, using Agrobacterium tumefaciens as the delivery vehicle and unlignified stems of the ‘Baihuayushizi’ cultivar as explants. The editing efficiency of our system was inferred to be 38.00%, which is substantially higher than those in some other plant species. The impacts of different culture conditions on the system were further assessed. Pre-culture duration was found to have the largest influence on the success of genetic transformation, followed by A. tumefaciens infection time and concentration. The optimal pre-culture time for this system is 3 days, and the A. tumefaciens concentration, infection time, and co-culture duration are OD600 = 0.8, 10 min, and 2 days, respectively. With the help of our system, we successfully knocked the PgBZR1 gene out from ‘Baihuayushizi’ pomegranate, which encodes a key transcription factor that regulates the growth and development of pomegranate. Given these advantages, we anticipate that our gene editing system is a useful tool for future studies on pomegranate gene functions and genetic improvement
Research on Design and Control Method of Flexible Wing Ribs with Chordwise Variable Camber
No full textTo improve the continuous chordwise bending performance of morphing wings, this study proposes a rigid–flexible coupled wing rib structure and its control strategy. Initially, the optimal rigid–flexible hybrid configuration was optimized via the mean camber line parameterization and genetic algorithm. For the flexible segment, topology optimization was conducted using the load path method, followed by subspace-based shape–size alternating optimization; bionic “longbow” curved beams and ‘S’-shaped substructures were adopted to enhance deformability. Biomimetic pneumatic muscles were used as actuators, and a fuzzy-adjusted PI sliding mode controller was designed to address the issue that traditional PI sliding mode controllers cannot achieve precise control under non-optimal parameters or when there is a significant difference in deformation targets. Experimental results show that when the flexible rib deflects by 15°, the three-rib wing box achieves a 30° deflection, with stresses within the allowable limit of 7075Al-T6 (540 MPa) and a deformation error of only 7.6%. For the 15° downward bending control, the adjustment time is 6.06 s, the steady-state error is 0.19°, and the overshoot is 1.8%. This study verifies the feasibility of the proposed rigid–flexible coupled structure and fuzzy PI-SMC, providing a technical reference for morphing aircraft
Overview of the Municipal Emission Reduction Plan Landscape in Greece in Terms of Policy Framework and Procurement Patterns
No full textGreece’s National Climate Law, enacted under L. 4936, mandates the development of Municipal Emission Reduction Plans (MERPs) by local authorities. Publicly available MERP procurement data contains valuable information that can be utilized to provide an overview and insights into MERP procurement and development. The main objective of this study is to perform a comparative analysis of Greek MERP procurement data and identify patterns in the contract cost estimation of mitigation action plans in Greek municipalities. For this purpose, MERP procurement data was collected from the official procurement register, KIMDIS, and subsequently analyzed through a bivariate approach comparing the collected data with selected independent variables. The results are stratified by population range and official municipal classification to enable comparison between different sizes and types of municipalities. The results indicate that a total of 44% of municipalities in Greece procured their MERP, with significant delays in adherence to official deadlines and only after the MERP became a prerequisite for funding-related matters. Additionally, the procurement process was highly characterized by single bidding. Average contract duration ranged from 110 to 220 days, with an average contract value between EUR 18,000 and EUR 33,000. The difference between tender budget and contract value averaged between 0 and 5%
Design and Research on an Active Contract Signing Mechanism for Demand Response in Community Electric Vehicle Orderly Charging Considering User Satisfaction
No full textTo address grid security issues such as load fluctuation and transformer overloading caused by increasing community EV charging demand, this study proposes two active demand response mechanisms to encourage users to voluntarily participate in orderly charging: a single-signup mechanism and a hybrid mechanism integrating signing willingness with user satisfaction. A hierarchical user satisfaction model is developed, integrating incentive perception and dispatch satisfaction, to characterize nonlinear user responses under varying incentive and dispatch levels. A genetic algorithm is then applied to determine the optimal contract portfolio that maximizes community-wide satisfaction. Simulation results show that the hybrid mechanism achieves the highest average satisfaction (0.8788), significantly outperforming both the single-signup and traditional passive schemes, effectively enhancing user participation and grid flexibility. This study provides a new theoretical framework and optimization pathway for mechanism innovation in orderly electric vehicle charging under centralized construction and unified operation scenarios in residential communities and offers valuable insights for the coordinated development of vehicle–grid interaction and demand-side management models in community-based new power systems
CO2 Valorization by CH4 Tri-Reforming on Al2O3-Supported NiCo Nanoparticles
No full textCO2 valorization from real feedstocks through CH4 tri-reforming (CH4-TR), combining steam reforming (SR), dry reforming (DR), and partial oxidation (CPO) of methane in a single process, is a desirable strategy for greenhouse gas mitigation and syngas (CO + H2) production. NiCo/γ−Al2O3 catalysts prepared by impregnation at different relative metal contents (Ni50Co50 and Ni30Co70) were investigated for CH4-TR in a fixed-bed reactor under conventional heating and characterized by XRD, FESEM, and Raman spectroscopy after catalytic runs. This study focused on the role of the Ni/Co ratio and feed composition on selectivity for CO2 valorization, syngas yield, and deactivation resistance. Both the catalysts showed high activity, with a superior performance of Ni50Co50 confirming Ni metal species as the active sites. While in DR, a slow deactivation occurred due to coke deposition, in CH4-TR, the addition of small O2 and/or H2O amounts stabilized activity and selectivity due to surface carbon removal. Large O2 and H2O amounts strongly inhibited CO2 conversion due to competition with CPO and SR, in the order CPO ≥ DR > SR. Interestingly, the stoichiometric CH4-to-oxidants ratio favored the DR pathway, giving very high CO2 conversion. Modulating CH4 addition into real flue mixtures renders CH4-TR on NiCo/γ-Al2O3 catalysts a favorable strategy for effective valorization of CO2 industrial or biomass-derived streams
Credit Risk Management Dynamics: Evidence from Indonesian Rural Banks
No full textThis paper investigates credit risk management as a dynamic system. Panel Vector Autoregression (PVAR) is employed to model interrelationships among four key components: Non-Performing Loans (NPLs), Loan Loss Provision (LLP), loan charge-off (LCO) and capital. The Cost-to-Income ratio (CIR) and Size and Net Profit-to-Equity ratio (ROE) are used as control variables. The panel dataset comprises 1461 conventional rural banks in Indonesia with a quarterly frequency from June 2010 to March 2024. There are several key findings of this study. First, credit risk management practices in rural banks predominantly follow an incurred loss approach, although the expected loss model appears to be more commonly adopted by larger institutions. Second, capital serves a critical function as a buffer against credit losses. Third, subsample investigation reveals a significant role of accounting discretionary. This study offers significant implications for both policy development and academic research in microfinance
Autonomous Gas Leak Detection in Hazardous Environments Using Gradient-Guided Depth-First Search Algorithm
No full textGas leak detection in industrial environments poses critical safety challenges that require algorithms capable of balancing rapid source identification with comprehensive spatial coverage. Conventional approaches using fixed sensor networks provide limited coverage, while manual inspection methods expose personnel to hazardous conditions. This paper presents a novel Gradient-Guided Depth-First Search (GG-DFS) algorithm designed for autonomous mobile robots, which integrates gradient-following behavior with systematic exploration guarantees. The algorithm utilizes local concentration gradient estimation to direct movement toward leak sources while implementing depth-first search with backtracking to ensure complete environmental coverage. We assess the performance of GG-DFS through extensive simulations comprising 160 independent runs with varying leak configurations (1–4 sources) and starting positions. Experimental results show that GG-DFS achieves rapid initial source detection (9.3±7.3steps;mean±SD), maintains 100% coverage completeness with 100% detection reliability, and achieves 50% exploration efficiency. In multi-source conditions, GG-DFS requires 70% fewer detection steps in four-leak scenarios compared to single-leak environments due to gradient amplification effects. Comparative evaluation demonstrates a substantial improvement in detection speed and efficiency over standard DFS, with GG-DFS achieving a composite performance score of 0.98, compared to 0.65 for standard DFS, 0.64 for the lawnmower pattern, and 0.53 for gradient ascent. These findings establish GG-DFS as a robust and reliable framework for safety-critical autonomous environmental monitoring applications