Search Results (3,561)

The study of natural spontaneous fertile hybrids, whose parent species is Ae. trivialis Migusch. et Chak (2n = 42), is of great importance for expanding the genetic pool of the genus Triticum L., which is a crucial part of current and future breeding efforts. The number of wild relatives—potential sources of valuable disease resistance genes—is quite large for common wheat: these include species of the genera Tritium, Aegilops, Agropiron, Secale, Haynaldia, Villosa, and others. In addition to disease and pest resistance, wild species offer frost resistance, drought tolerance, salt tolerance, and increased protein quantity and quality. The primary objective of this study was to identify new, genetically diverse source material for common wheat breeding based on botanical and morphological studies, as well as to register new spontaneous Aegilops–wheat hybrids using electrophoretic analysis of storage proteins. To achieve the research objective, the following tasks were set and solved: Aegilops–wheat hybrids were studied and recorded using protein formulas; spontaneous fertile Aegilops–wheat hybrids were analyzed using laser microscopy and tandem mass spectrometry. In this study, we demonstrated differences between the studied spontaneous hybrids using metabolomic analysis and laser microscopy, as well as identified differences in the protein spectra of the spontaneous hybrids and their maternal form, K-1386. These spontaneous Aegilops–wheat hybrids will be used in further work to identify their paternal form. It should be noted that it is advisable to use the studied spontaneous Aegilops–wheat hybrids in future breeding to expand the gene pool of the genus Triticum L. and to obtain new heterogeneous forms. Full article

As data volumes grow and business requirements become increasingly complex, Hybrid Transactional/Analytical Processing (HTAP) technologies, capable of handling both Online Transaction Processing (OLTP) and Online Analytical Processing (OLAP) workloads on a single platform, have gained prominence. HTAP databases typically maintain dual data storage formats and dual query engines: one row-oriented for OLTP, and another column-oriented for OLAP. Query plans, known as hybrid plans, can be segmented and pushed down to execute on these different formats. However, existing HTAP solutions still face challenges in optimizing these hybrid plans, struggling to explore the vast space of potential execution strategies effectively. To address these issues, this study introduces a learning-based query optimizer for row–column dual store HTAP database systems, which automatically generates multiple high-quality query optimizer hints (HINTs) to derive candidate plans. To balance plan generation efficiency with plan quality, a lightweight, learning-based algorithm using Monte Carlo Tree Search (MCTS) for generating hybrid access HINTs is proposed. Moreover, a Transformer-based neural network model coupled with a hybrid plan feature representation method is developed to select the candidate execution plan with the lowest predicted execution time. This work focuses on latency-oriented hybrid-plan selection for analytical queries in a row–column dual-store HTAP architecture; the current evaluation does not cover full mixed OLTP/OLAP workload scheduling, transactional interference, or concurrency control, which are left as future work. Experimental results on AlloyDB Omni, a recent row–column dual-store HTAP database, using the real-world IMDB dataset and JOB benchmark demonstrate that our system reduces execution time by 75.02% compared to the Cost-Based Optimizer (CBO) and by 62.23% compared to the state-of-the-art row-store-based learning query optimizer in this evaluated analytical-query setting. Full article

To deliver freshwater for drip irrigation, our study presents an optimal techno-economic based on a Water Pumping Photovoltaic System (WPPVS) that integrates a Hydrogen Energy Storage System (HySS) to ensure reliable freshwater for agricultural irrigation in remote arid regions. A critical operational challenge in WPPVS is mechanical vibration at low flow rates, which degrades the pump efficiency and lifespan. Our methodology directly addresses this issue by incorporating a vibration-avoidance strategy that ensures that the pump operates only within its stable and, efficient range. To reduce the loss of water supply probability and overall annual costs of the drip irrigation system, a multi-objective optimization framework using Multi-Objective Particle Swarm Optimization (MOPSO) and Gaussian Mixture Model (GMM) clustering to simultaneously minimize the Loss of Water Supply Probability (LWSP), and the system’s total life-cycle cost. The model’s practical applicability is demonstrated through a detailed techno-economic feasibility analysis for a tomato crop drip irrigation project in Sakaka, Saudi Arabia. Sensitivity analysis is performed on dynamic head, crop prices, and interest and inflation rates, confirming the robustness of the system against variable economic indicators. In comparison to 1071 h without HySS, the results revealed that the seasonal irradiation harvest hours are 1863, which represents 21% of the seasonal hours employing the developed hybrid energy storage coordination. This integrated approach provides a holistic and economically viable solution for designing reliable solar irrigation systems with long-term mechanical integrity. Full article

The growing environmental crisis and rapid urbanization have made the shift to renewable energy systems even more important for smart city development. In today’s cities, such renewable energy sources as solar photovoltaics, wind energy, hybrid systems, and battery energy storage are no longer just separate assets. They are now important parts of smart grids, intelligent buildings, and urban infrastructure that work together. However, putting these systems in cities on a large scale makes it harder to monitor, control, integrate, scale, and work with them in real time. In this setting, the Internet of Things (IoT) and edge computing are technologies that make it possible to turn traditional renewable energy systems into smart, responsive, and self-sufficient urban energy systems. IoT-based monitoring and control systems let city operators, utilities, and policymakers gather real-time data, improve grid stability, optimize energy flows, and better integrate distributed renewable energy sources into smart city ecosystems. Edge computing makes these features even better by allowing for low-latency processing, more localized decision-making, and less reliance on centralized cloud infrastructures. This paper offers a thorough and methodical examination of contemporary IoT- and edge-enabled technologies used to monitor, control, and integrate renewable energy systems; specifically highlighting their significance in smart city and smart grid applications. The review combines the most recent research on hardware platforms, communication protocols, data processing architectures, and edge–cloud coordination mechanisms used in solar, wind, and hybrid energy systems. Additionally, this review synthesizes architectural design principles extracted from analyzed studies to guide the development of scalable, resilient, and cost-efficient renewable energy monitoring systems. This study offers a structured foundation for the design of scalable, resilient, and cost-effective renewable energy management systems that align with the sustainability, efficiency, and intelligence goals of future smart cities by analyzing cutting-edge solutions and pinpointing significant technological trends, challenges, and research deficiencies. This review also highlights its contribution vis-à-vis previous surveys by stressing the inter-domain comparison across solar, wind, and hybrid systems. It focuses, in particular, on edge–cloud coordination and architecture-level trade-offs pertinent to smart grid and smart city deployments. Full article

PMSM-based flywheel energy storage systems require fast and robust speed regulation in the presence of parameter uncertainty, load disturbances, and measurement noise, while avoiding the cost and reliability limitations associated with mechanical encoders. This paper proposes a sensorless control framework that combines a fractional-order sliding-mode speed controller with a fractional-order sliding-mode observer. To improve dynamic performance, an improved hybrid Aquila Optimizer–African Vulture Optimization Algorithm (IHAOAVOA) is employed to tune the controller parameters, while the observer follows the proposed robust sensorless design. Simulation results show that at the 1000 rpm operating point under a 20 N·m load disturbance, the proposed method limits the startup overshoot to about 0.24%, compared with 8.02% for the PI control and 9.74% for the conventional sliding-mode control. After the disturbance is introduced at $\mathrm{t}=1.0$ s, the speed drop of the proposed method is limited to 2.80%, whereas those of the PI control and conventional sliding-mode control reach 7.20% and 5.60%, respectively. At the 8000 rpm operating point under an 80 N·m load disturbance, the proposed method maintains the same advantage, with an overshoot of about 0.04% and a speed drop of 1.88%, both lower than those of the two benchmark controllers. In sensorless operation, the sensorless scheme with the IHAOAVOA-tuned speed controller also improves transient estimation performance. At the 1000 rpm operating point, the maximum startup speed estimation error is reduced from 41.8 r/min to 34.8 r/min. At the 8000 rpm operating point, the estimation error enters the ±10 r/min band at 0.0671 s, compared with 0.0718 s for the PSO-tuned case. The electromagnetic torque responses further indicate that the proposed tuning strategy improves transient torque smoothness while maintaining comparable steady-state torque behavior. These results demonstrate that the proposed control framework provides an effective balance among fast dynamic response, disturbance rejection, sensorless estimation accuracy, and electromechanical transient smoothness for PMSM-based flywheel energy storage applications. Full article

This study develops a Fuzzy C-Means-based mathematical framework for the storage-oriented evaluation and classification of hybrid energy system alternatives. The analysis considers fifteen hybrid configurations generated through pairwise combinations of solar, wind, biomass, geothermal, hydropower, and fossil-based energy sources. These alternatives are evaluated with respect to fourteen storage-related criteria, namely energy efficiency, exergy efficiency, entropy, lifetime, cost, CO₂ emissions, recyclability, decarbonization potential, discharge duration, charge duration, power capacity, energy capacity, sustainability, and environmental impact. After constructing and normalizing the decision matrix, the Fuzzy C-Means algorithm is employed to identify latent similarity structures and to determine the degree of membership of each hybrid alternative to multiple clusters. To support the selection of an analytically meaningful partition, alternative cluster structures are compared in terms of partition quality and interpretability. The results indicate that the considered hybrid configurations can be grouped into distinct yet partially overlapping storage-oriented profiles, reflecting differences in technical performance, environmental burden, and sustainability characteristics. In particular, hydropower-supported systems are associated with more stable and infrastructure-compatible profiles, while biomass- and geothermal-related combinations occupy more balanced transitional positions. By extending fuzzy clustering to the storage-oriented analysis of hybrid energy systems, the study provides a mathematically transparent basis for comparative assessment, exploratory classification, and preliminary decision support. Full article

Hydrogen is widely considered a promising clean energy carrier because of its high energy density and environmental benignity, yet the development of safe and reversible hydrogen storage materials remains a major challenge. Two-dimensional materials are particularly attractive for this purpose owing to their large specific surface area, fully exposed active sites, and highly tunable electronic structures. Here, using crystal structure prediction combined with first-principles calculations, we predict a stable metallic Li₃B₂N₂ monolayer as a potential hydrogen storage material. This monolayer can adsorb up to six H₂ molecules per unit cell with an average adsorption energy of ∼0.23 eV/H₂, yielding a high hydrogen storage capacity of ∼7.8 wt.%. Further analysis reveals that hydrogen adsorption is governed by the synergistic effects of electrostatic polarization and orbital hybridization. Moreover, calculations on the temperature- and pressure-dependent hydrogen storage behavior show that all hydrogen-adsorbed structures remain stable at room temperature under a pressure of 3.7 MPa. The van’t Hoff analysis indicates that the maximum desorption temperature at atmospheric pressure is 316 K, suggesting favorable reversibility under near-ambient conditions. These results establish Li₃B₂N₂ as a promising intrinsic two-dimensional material for high-density and reversible hydrogen storage. Full article

Accurate estimation of the state of health (SOH) and prediction of the remaining useful life (RUL) of lithium-ion batteries (LIBs) are critical for ensuring system reliability and safety across diverse energy storage applications. This paper proposes a hybrid deep learning framework that integrates an axial-embedding Transformer (AxEmbTrans) encoder and a bidirectional LSTM (BiLSTM) module for the joint estimation of SOH and RUL. The AxEmbTrans encoder employs axial attention with abstract embeddings to capture global dependencies among multidimensional health features at reduced computational complexity compared to standard self-attention, while the BiLSTM models local temporal dynamics and short-term degradation fluctuations across consecutive cycles, with its bidirectional structure enhancing robustness against transient noise. Informative health features are extracted from charge–discharge curves, grouped into temporal, energy, and thermal categories, and fused using local linear embedding (LLE) for nonlinear dimensionality reduction. An improved Newton–Raphson-based optimizer (INRBO) is introduced to automatically tune the framework’s key hyperparameters, including the hidden dimension, number of attention heads, number of BiLSTM units, and learning rate, incorporating directional similarity modulation and multi-elite guidance to overcome the convergence instability of the standard NRBO. Extensive experiments on NASA and Maryland datasets demonstrate that the proposed method consistently outperforms baselines in both SOH and RUL prediction, achieving higher accuracy, improved robustness, and better cross-condition generalization. Full article

Off-grid electric vehicle (EV) charging infrastructure powered by hybrid solar–wind systems address critical adoption barriers in developing countries, where grid unreliability and sparse charging networks constrain transportation electrification. Despite growing research interest, no comprehensive review has systematically synthesized the interplay between hybrid renewable architectures, intelligent energy management strategies, and techno-economic viability specifically for off-grid EV charging in resource-constrained settings. This systematic review applies the PRISMA methodology to analyze 94 peer-reviewed publications (2013–2026), examining system architectures, intelligent control strategies, power electronics, battery storage, and deployment frameworks for standalone hybrid solar–wind EV charging stations. Key findings indicate that hybrid solar–wind configurations achieve 30–50% reductions in battery storage requirements and 15–25% lower levelized cost of energy (LCOE) (USD 0.08–0.15/kWh) compared with single-source systems, driven by diurnal and seasonal resource complementarity. Among intelligent control methods, the two-stage distributionally robust optimization (TSDRO) framework emerges as the most promising for data-scarce environments, outperforming conventional deterministic and stochastic approaches by 10–20% in managing renewable intermittency without requiring precise probability distributions. Wide-bandgap power semiconductors (SiC, GaN) enable 96–98% conversion efficiency, while lithium iron phosphate batteries provide 3000–5000 cycle lifetimes suited to tropical operating conditions. Critical gaps remain with field validation still predominantly simulation based, long-term operational data exceeding 24 months on equipment degradation and climate resilience are scarce, and scalable financing models for developing country contexts require further development. Nigeria is presented as an exemplar deployment context, with transferable insights for sub-Saharan Africa, South Asia, and Southeast Asia. Full article

Battery Energy Storage Systems (BESSs) are increasingly deployed in grid-scale applications, electric mobility, and renewable integration, where safety, reliability, and longevity are critical. Thermal runaway remains one of the most severe failure modes in lithium-ion batteries, often triggered by complex interactions between electrochemical, thermal, and mechanical phenomena. This paper presents an extended hybrid Physics-Informed Neural Network (PINN) framework for thermal anomaly prediction and early detection of runaway precursors in BESS. The proposed architecture integrates governing physical laws, specifically the Bernardi heat generation equation and Fick’s diffusion law, within a deep learning pipeline composed of a physics module, a temporal Bi-LSTM, and an attention mechanism for explainability, which may represent an obstacle in the application of deep learning algorithms. Beyond the initial formulation, the extended version presented here provides a deeper theoretical background, an expanded methodological justification, a more comprehensive comparison with state-of-the-art approaches, and a detailed discussion on scalability, uncertainty, and deployment challenges. The results for synthetic yet physically consistent datasets represent a proof of concept of the PINN approach, which can achieve superior generalization, robustness to noise, and interpretability compared to purely data-driven baselines, achieving an accuracy above 90% and an AUC of 0.95. The framework contributes to proactive safety management in cyber-physical energy systems and establishes a foundation for real-time, physics-aware anomaly detection in safety-critical BESS applications, e.g., marine transportation contexts and port environments. Full article

Railway traction power supply systems (TPSSs) are evolving from passive grid-fed infrastructures into active energy systems with local photovoltaic (PV) generation capacity, energy storage systems (ESSs), and converter-based regulation. Unlike conventional microgrids, TPSSs feature single-phase, highly dynamic traction loads; short-duration regenerative braking bursts; and strict constraints on voltage quality, stability, and protection. These characteristics make the energy management of distributed PV-storage-integrated TPSSs a distinct research problem. This review examines the field from three coupled perspectives: supply architecture, power electronic interfaces, and energy management strategies. First, representative integration architectures are classified into substation-side, wayside-distributed, and hybrid multi-port schemes. Second, converter interfaces and flexible traction substations are analyzed as the enabling layer for coordinated control of PV, ESS, the utility grid, and traction feeders. Third, major energy management strategies, including rule-based, optimization-based, hierarchical multi-timescale, and uncertainty-aware methods, are compared. The review further discusses power quality, stability, protection, and battery degradation constraints that shape practical deployments. Finally, research gaps and future directions are identified to further the development of more robust, railway-specific, and implementation-oriented PV-storage energy management. Full article

In response to the dual pressures of energy consumption and environmental pollution faced by the global shipping industry, this paper proposes an optimization method for the energy storage capacity of a ship’s hybrid energy system based on a two-layer optimization model, aiming to enhance the energy utilization efficiency and operational stability of the system. A DNN-IPSO optimization framework integrating deep neural networks (DNN) and the improved particle swarm optimization algorithm (IPSO) was constructed, and combined with robust control strategies, it optimized the energy storage capacity configuration problem under complex dynamic conditions. The results show that the proposed method exhibits superior performance in terms of energy utilization efficiency, system dynamic response, and stability. The energy utilization efficiency of the system has been increased to 91.3%, the bus voltage fluctuation has been reduced to 3.98%, the load tracking error has been decreased to 17.6 kW, and the average convergence iteration times have been reduced to 71 times. The 17.6 kW load tracking error accounts for only 1.76% of the rated propulsion power of the 1 MW-level experimental platform, which is approximately 38% lower than that of the GA-PSO method. The experimental results on the real ship show that after using the DNN-IPSO optimization, the unit voyage energy consumption has been reduced to 41.7 kWh/km, the propulsion power stability coefficient has been increased to 0.956, the system transient recovery time has been shortened to 3.2 s, and the power reserve margin has been increased to 18.4%. The proposed method can effectively enhance the energy management capability, dynamic response performance, and operational stability of the ship’s hybrid energy system in the actual operating environment, providing reliable technical support for the engineering application of the integrated energy system of ships. Full article

The Internet of Vehicles (IoV) is rapidly emerging as a core component of intelligent transportation systems, enabling real-time communication among vehicles, infrastructure, and cloud platforms. However, the increasing interconnectivity of vehicular systems and the advancement of quantum computing introduce significant security challenges to existing cryptographic mechanisms. Conventional schemes such as RSA and Elliptic Curve Cryptography (ECC) are vulnerable to quantum attacks and are computationally inefficient for resource-constrained vehicular environments. To address these limitations, this paper proposes a Double-Ring Hybrid Sparse NTRU (DRH-SNTRU) framework, a lightweight and quantum-resistant cryptographic scheme for secure IoV communication. The proposed framework introduces three key enhancements: (i) controlled-support sparse polynomial structures to reduce polynomial multiplication complexity while improving entropy distribution; (ii) a double-ring algebraic architecture that separates key operations from message processing to enhance structural security and minimize coefficient leakage; and (iii) hybrid ephemeral keys derived from contextual entropy to strengthen forward secrecy and adaptive security. An optional ciphertext evaluation mechanism is further incorporated to detect malformed and replayed ciphertexts prior to decryption. Security analysis demonstrates that the proposed framework achieves IND-CPA security under the hardness assumption of the NTRU lattice problem and can be extended to resist chosen-ciphertext attacks through the integrated validation mechanism. Experimental benchmarking across polynomial dimensions N = 512 to 8192 demonstrates that DRH-SNTRU achieves low setup overhead below 3 μs, efficient decryption latency of approximately 305.64 μs at N = 8192, and compact sparse private key representation of only 117 bytes at higher dimensions. Compared with Standard NTRUEncrypt, NTRU-HRSS, and Ring-LWE Encryption, the proposed framework demonstrates improved decryption efficiency, lightweight storage overhead, and enhanced ciphertext integrity protection while maintaining practical scalability for resource-constrained post-quantum IoV environments. Full article

As the core pillar of international trade, the global shipping industry has seen its carbon and pollutant emissions become a key challenge in global environmental governance. Statistics indicate that ship carbon emissions account for 3% of the world’s total anthropogenic CO₂ emissions, while contributing 20% of global NO_x and 12% of SO₂ emissions, posing a serious threat to coastal ecosystems and public health. In response to the International Maritime Organization (IMO) “Net Zero Framework” and national green shipping policies, the transformation of ship power systems toward low-carbon and zero-carbon operation has become an inevitable trend. This paper systematically reviews the research progress and application status of green energy materials for ships, focusing on the working principles, technical characteristics, and engineering application cases of solar photovoltaic (PV) materials, wind energy utilization technologies, fuel cell materials, and alternative clean energy fuels (e.g., liquefied natural gas (LNG), methanol, and hydrogen energy). It also discusses the integration mode and optimization strategy of multi-energy hybrid power systems. The research findings show that solar photovoltaic technology has achieved large-scale application in coastal ships; hydrogen fuel cells are suitable for long-range ocean navigation scenarios due to their high energy density; LNG and methanol have become the current mainstream alternative fuels, relying on mature infrastructure; and hybrid energy systems can significantly improve power supply reliability and emission reduction efficiency through multi-energy complementarity. Finally, aiming at the existing bottlenecks (e.g., cost, energy storage, and safety) of various technologies, future development directions are proposed. This study provides a reference for the technological breakthrough and engineering practice of green energy power systems for ships and contributes to the realization of the “carbon neutrality” goal in the global shipping industry. Full article

The intermittent nature of renewable power sources, nonlinear load effects, and harmonic distortions induced by power electronic converters complicate the maintenance of high energy quality in microgrid-connected hybrid renewable power systems. In a range of operating conditions, conventional strategies-including fractional-order proportional-integral (FOPI) controllers-frequently prove ineffective in delivering both robust harmonic mitigation and expeditious dynamic response. To surmount these constraints, the present paper puts forth an intelligent control solution that is predicated on a fractional-order fuzzy logic (FOFL). The FOFL is integrated into a multi-converter HRPS, comprising a photovoltaic generator, a lithium-ion battery power storage system, and a wind turbine equipped with a permanent magnet synchronous generator. A multifunctional voltage source inverter has been developed to control these parts, which are interfaced via a common DC bus. Through the implementation of MATLAB 2021 simulation studies, the efficacy of the suggested algorithm is verified and evaluated in comparison to the FOPI. The findings indicate that the FOFL enhances system efficacy by minimizing harmonic distortion, improving energy quality, and achieving a faster dynamic response under various circumstances. In the context of grid-connected microgrid environments, the FOFL has been demonstrated to offer superior overall energy management, robustness, and adaptability when compared to other evaluated strategies. Full article