Search Results (529)

In the evolving landscape of next-generation wireless networks, ensuring seamless mobility and high-quality service delivery for millions of devices and end users in dynamic scenarios, where the speed of a wireless device keeps changing with time, is important. The mobility, seamless and continuous connectivity, and ultra-dense deployment of wireless networks pose a significant challenge. Seamless and successful transition of a wireless device from point A to point B in variable-speed scenarios is one of the major challenges in future networks. This paper presents a novel Deep Q-Network (DQN)-based reinforcement learning (RL) framework integrated with Software-Defined Networking (SDN) for intelligent mobility management in hybrid 5G cellular networks consisting of macro and small base stations. The proposed system architecture utilizes a SDN controller to receive real-time user measurement reports, including Reference Signal Received Power (RSRP), Signal-to-Interference Noise Ratio (SINR), and user velocity, thereby classifying user mobility into distinct subclasses and dynamically determining optimal handover parameters. Leveraging the DQN’s capability to learn adaptive strategies, the model enables seamless transitions between macro and small cells based on mobility profiles, thereby enhancing Quality of Service (QoS) metrics such as latency, throughput, and handover efficiency. Simulation results demonstrate consistent performance improvements over baseline and existing models in ultra-dense network environments, with handover success rates 10–15% higher across SINR and different speed scenarios, while maintaining a packet failure rate of 9% across different speed scenarios, allowing more users to transition during various environmental changes seamlessly. Our proposed model is compared with our previous work and Learning-based Intelligent Mobility Management (LIM2) models. Specifically, our previous work focused on adaptive handover management primarily for high-speed train scenarios using a learning-assisted approach tailored to fixed high-mobility scenarios, with a limitation to single mobility conditions. This work contributes to the field of merging SDN’s centralized control with the predictive power of RL, paving the way for more resilient and responsive mobile networks in high-mobility scenarios. The proposed approach incorporates subclass-based mobility action abstraction, joint optimization of TTT and hysteresis margin, and dynamic target cell selection using global network information available at the SDN controller. Full article

To address the issue of insufficient accuracy in wind power forecasting arising from intermittency and volatility, this paper proposes a short-term wind power prediction model integrating MIC (Maximal Information Coefficient) feature selection with adaptive noise-complete set empirical mode decomposition, convolutional neural networks, and a bidirectional long short-term memory network hybrid architecture. The main innovations of this work lie in the following: Firstly, MIC quantifies the strength of the nonlinear correlation between meteorological features and the MAE (Mean Absolute Error) in power generation, thereby enabling the identification of highly correlated features to reduce the input dimensionality. Secondly, CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) performs adaptive modal decomposition on raw power sequences. Combining sample entropy with K-means clustering reconstructs IMFs (Intrinsic Mode Functions), while the introduction of VMD (Variational Mode Decomposition) for quadratic optimisation significantly improves the quality of signal decomposition, enabling a more refined separation of fluctuation characteristics across different time scales. Finally, the optimised meteorological features and reconstructed components are input into a CNN (Convolutional Neural Network)-BiLSTM (Bidirectional Long Short-Term Memory) module. Power regression prediction is achieved through the synergistic effect of spatial feature extraction and bidirectional temporal dependency modelling. Case study results demonstrate that compared to the TCN (Temporal Convolutional Network)-Transformer, the proposed method achieves a 0.4022 improvement in the coefficient of determination R², a 13.2598 reduction in MAE, a 19.864 decrease in RMSE (Root Mean Square Error). At the same time, it maintains stable performance even when faced with unreliable data scenarios involving random missing features, demonstrating excellent generalisation ability. Furthermore, the model training time has been reduced to 77.6469 s, with a single prediction response time of just 0.0659 s. Full article

L605 cobalt-based superalloy is a typical difficult-to-machine material because of its high strength, pronounced work hardening, and low thermal conductivity. To improve the microgroove machining performance of this alloy, a self-developed water-jet-guided laser (WJGL) system equipped with a multi-focus lens was employed, and single-factor experiments together with a Box–Behnken response surface design were conducted to investigate the effects of laser power, pulse frequency, water pressure, and feed speed on microgroove depth. The results showed that microgroove depth increased with laser power, decreased with pulse frequency and feed speed, and first increased and then decreased with water pressure. Analysis of variance demonstrated that the developed quadratic regression model was significant and fit the data well. A recommended parameter combination of 274.9 W laser power, 3334.9 Hz pulse frequency, 1.636 MPa water pressure, and 0.107 mm/s feed speed corresponded to a predicted microgroove depth of 621.2 μm. Validation experiments yielded an average microgroove depth of 600.2 μm, with a relative error of 3.4%, indicating that the model can be used for microgroove depth prediction and parameter selection in WJGL machining of L605 alloy and may provide guidance for future multi-objective optimization considering both machining quality and efficiency. Full article

GDSL esterase/lipase (GELP) proteins constitute an evolutionarily conserved yet functionally diversified hydrolase family in land plants. They participate in cuticle and secondary cell wall biosynthesis, seed lipid remobilization, reproductive development, and hormone-mediated responses to biotic and abiotic stresses. Despite extensive genome-wide and comparative genomic studies that have categorized large GELPs across numerous crops and model species, only a fraction of members have been functionally characterized in plants, and their catalytic mechanisms and regulatory architectures remain poorly understood. Recent population genomics and cross-species orthogroup analyses in 46 angiosperms have uncovered substantial natural variation within GELP coding sequences and regulatory regions, providing a powerful framework to link allelic diversity to evolutionary trajectories and physiological functions. This review synthesizes current knowledge on GELP evolution, biochemical properties, and roles in development and stress adaptation, and critically evaluates how these insights can be translated into biotechnology and molecular breeding strategies. It highlights emerging resources and concepts from orthogroup-based classification and multi-species datasets that enable systematic discovery of GELP alleles affecting agronomic traits. It further outlines research exploiting GELPs in crop improvement, emphasizing the integration of reverse and forward genetics with multi-omics profiling, biochemical and structural characterization, and gene regulatory network reconstruction. Systematic assessment of the phenotypic impacts of single and combinatorial GELP perturbations on yield, quality, and stress resilience is proposed as a key step toward translating basic insights into breeding and engineering strategies. Full article

Qinghai Province shoulders the heavy responsibility of serving as China’s ecological security barrier. In the process of implementing the “ecological priority” strategy, the green development of grassland animal husbandry in Qinghai Province plays an especially important driving role. To systematically reveal the temporal and spatial evolution characteristics and core driving mechanism of Green Total Factor Productivity (GTFP) and provide a decision-making basis for the green transformation and high-quality development of regional animal husbandry, this paper, based on relevant data from 2010 to 2024 in Qinghai Province, constructs a measurement and influencing factor index system for the GTFP of grassland animal husbandry. Then, it conducts a systematic analysis of the temporal evolution and spatial differentiation characteristics of the GTFP of grassland animal husbandry in Qinghai Province using methods such as trend surface analysis and standard deviation ellipse. Subsequently, the influencing factors are discussed through the geographic detector model. The research findings are as follows: (1) During the study period, the GTFP of grassland animal husbandry in Qinghai Province shows an overall upward trend. Spatially, it presents a regional pattern of “strong in the north and stable in the south,” with the migration center moving towards the northeast and the distribution becoming more concentrated. (2) The level of fiscal support for agriculture, accessibility of transportation, the degree of environmental governance and the degree of digitalization play core driving roles in the process of GTFP climbing in grassland animal husbandry. (3) Interaction analysis shows that the explanatory power of any two influencing factors in the study area is higher than that of a single factor, and the interaction between the level of fiscal support for agriculture and the degree of environmental governance is the most significant. Therefore, the key to improving the GTFP of grassland animal husbandry in Qinghai Province lies in the coordinated allocation and linkage of financial support for agriculture and environmental governance. At the same time, this study can provide reference value for the green transformation and high-quality development of plateau grassland animal husbandry. Full article

The connection of large-capacity loads at nodes in a radial distribution network can readily lead to severe voltage sag phenomena, thereby significantly deteriorating power supply quality. To ensure the safe operation of both voltage-sensitive equipment and the power grid, the deployment of Dynamic Voltage Restorers (DVR) and Static Var Compensators (SVC) is recognized as one of the most effective countermeasures for addressing voltage sag issues. Considering the inherent topological characteristics of the radial distribution network, a dimensional collaborative governance strategy is proposed, which takes longitudinal dimension collaborative governance as the primary approach and horizontal dimension collaborative governance as a supplementary measure. Based on sensitivity analysis, the concepts of horizontal sensitivity and longitudinal sensitivity are defined. Furthermore, considering the response time of governance equipment, the voltage sag governance process is divided into two distinct stages: in the first stage, governance is primarily reliant on DVR, and a longitudinal dimension collaborative optimization algorithm is proposed to solve the corresponding optimization model; in the second stage, governance mainly utilizes SVC, where a standard particle swarm optimization (PSO) algorithm is employed to solve its optimization model. A case study conducted on a 42-node radial distribution network validates that the proposed approach effectively governances the voltage sag problem in the distribution network. Through analysis, the number of nodes experiencing voltage sag was reduced from 29 to 0 in both the first and second governance stages. In the first stage, the total compensation voltage of the DVR is 0.581 p.u. With the coordinated participation of SVC in the second stage, the total DVR compensation voltage decreases to 0.100 p.u., corresponding to a significant reduction of 82.79%. Given the higher capital cost of DVR relative to SVC, this substantial decrease in required DVR capacity effectively lowers the overall governance cost. Full article

Non-intrusive load monitoring (NILM), as a key technology for decomposing power loads by analyzing aggregate electrical signals, holds significant importance for advancing refined energy management and achieving carbon peaking and carbon neutrality goals. This paper systematically reviews the technical processes of event-based and state-based NILM methods. It focuses on analyzing key technical challenges in typical application scenarios, such as real-time feedback, energy efficiency optimization, and demand response. These challenges include balancing high real-time performance with accuracy, leveraging edge computing while ensuring privacy protection, and addressing issues like unknown load identification and user behavior modeling. Furthermore, this paper discusses cross-cutting challenges related to data quality, algorithm transferability, system integration, and cost. This review aims to provide a systematic, scenario-based analytical framework to facilitate the transition of NILM from theoretical research to practical application, offering insights for subsequent technological development and engineering implementation. Full article

The sorting of coal gangue is of great significance for improving coal quality, avoiding environmental pollution, and reducing labor costs. The image-based coal gangue sorting method has been proposed by a large number of researchers, but the complexity of the environment, the speed and accuracy of coal gangue detection and recognition methods, and the performance of hardware equipment all pose challenges to the accuracy of coal gangue sorting. This paper discusses the research and application of deep-learning methods in the field of coal gangue detection and proposes an improved YOLOv7 coal gangue detection model for ordinary GPU devices with large computing power and memory. In response to the feature redundancy problem of the YOLOv7 model in coal gangue detection tasks, FasterNet was introduced to improve the backbone network of YOLOv7, reducing redundant calculations and memory access, making the model more effective in extracting features. In response to the requirements for detection speed in high-speed motion of belt conveyors, VoVGSCSP was introduced to improve the efficient layer aggregation network (ELAN) of YOLOv7 neck, further enhancing the detection speed of the model. The experimental results show that when the belt speed is 0.6 m/s, the improved model’s mAP0.5 is similar to YOLOv7, FPS increases from 9 frames per second to 18 frames per second, coal gangue sorting rates reach 91.1%, and coal misselection rates are 4.8%. The proposed coal gangue detection and recognition method based on improved YOLOv7 has increased the detection speed of the recognition model and promoted the improvement of coal gangue sorting efficiency. Full article

Morocco’s recent legalization of industrial and medicinal cannabis has created a rapidly expanding seed-oil sector whose sustainability has yet to be fully assessed. This study applies an environmental life cycle assessment (LCA) in accordance with ISO 14040:2006 and ISO 14044:2006, complemented by a qualitative social responsibility assessment based on ISO 26000:2010, aiming to evaluate the life cycle sustainability of Moroccan cannabis seed oil. Three representative processing chains, traditional artisanal presses, producer cooperatives and regulated industrial plants are compared using a functional unit of 1 kg of cold-pressed oil packaged for local distribution. Inventory data were drawn from field measurements and interviews and were modeled in OpenLCA with background datasets from Ecoinvent 3.8 and Agribalyse v3.1. Impact assessment used the ReCiPe 2016 (H) method at the midpoint level across nine categories (climate change, fossil resource scarcity, water use, freshwater eutrophication, terrestrial acidification, land occupation, carcinogenic, non-carcinogenic human toxicity, and fine particulate matter formation). Sensitivity analyses varied seed yield, electricity mix and transport distances by ±20% to gauge uncertainty. Results show that the cooperative scenario achieves the lowest impacts across nearly all categories because of higher extraction yields (3 kg seed per kg oil), lower energy use (0.54 kWh kg⁻¹ oil) and more effective co-product recovery. In contrast, artisanal extraction requires approximately 1 kg of additional seed input per functional unit compared to optimized scenarios, significantly increasing upstream environmental burdens and causing upstream agricultural burdens to multiply. Industrial facilities perform comparably to cooperatives if powered by renewable electricity. Integrating a semi-quantitative social responsibility assessment reveals that legalization has markedly improved organizational governance, labor conditions, consumer protection and community involvement. Cooperatives display the most balanced social performance, whereas industrial plants excel in governance and quality control. A set of recommendations, including drip irrigation, cultivar improvement, co-product valorisation, renewable energy adoption, eco-designed packaging and cooperative governance, is proposed to enhance the environmental and socio-economic sustainability of Morocco’s emerging cannabis seed-oil industry. Full article

Cultivated lettuce (Lactuca sativa L.) is a major leafy crop and an emerging model for functional genomics within the Asteraceae family, supported by high-quality reference genomes and efficient transformation systems. Although CRISPR/Cas technology offers powerful opportunities for crop improvement, editing efficiency depends on optimized construct architecture and reliable guide RNA (gRNA) validation. However, a rapid platform for evaluating CRISPR reagents in lettuce is still lacking. Here, we developed an efficient hairyroot-based system to accelerate CRISPR/Cas genome editing optimization in L. sativa. Four Agrobacterium rhizogenes strains were compared for hairy root induction in two cultivars, ‘Saladin’ and ‘Osiride’, identifying strain ATCC15834 as the most effective based on transformation frequency and root production. Using this platform, we evaluated multiple CRISPR construct configurations, including alternative promoters for nuclease and gRNA expression. A plant-derived promoter combined with At-pU6-26 variant significantly improved editing efficiency. As a proof of concept, we targeted LsHB2, the putative ortholog of Arabidopsis thaliana ATHB2, a key regulator of the shade avoidance response using SpCas9, SaCas9, and LbCas12a nucleases. The system enabled rapid genotyping and quantitative indel profiling. Overall, this workflow provides a robust framework for efficient guide selection and construct optimization in lettuce genome editing. Full article

The large-scale integration and grid connection of renewable energy sources and charging stations introduce a multitude of nonlinear and impact loads, resulting in more severe distortion and higher complexity of disturbance signals in power systems. As a consequence, power quality disturbances (PQDs) in active distribution networks, including overvoltage and harmonics, display greater randomness and diversity, which increases the challenge of PQD identification. To tackle this problem, this study presents a dual-channel early-fusion approach for PQD recognition based on Spatial Post-MultiScale Fusion Entropy (SMFE). SMFE is used as an entropy-based feature-construction pipeline in which a time–frequency representation is formed prior to spatial post-multiscale aggregation to produce a compact complexity map complementary to waveform morphology. Subsequently, a dual-channel model is constructed by integrating waveform-morphology input with SMFE-derived complexity features for joint learning. By leveraging the ConvNeXt architecture and a Squeeze-and-Excitation (SE) mechanism, a multimodal channel-recalibration model is implemented to emphasize informative feature responses during PQD recognition. Experimental verification with simulated signals shows that the proposed approach achieves an identification accuracy of 97.83% under an SNR of 30 dB, indicating robust performance under the tested noise settings. Full article

Early prediction of semen quality in young boars is crucial to reduce operational costs associated with low-productivity boars at boar studs, where high genetic merit is key. B-ultrasound imaging, a non-invasive method using echo signals to visualize tissue density, has been evaluated as a potential tool for this purpose. Stronger echoes indicate denser tissues, such as the seminiferous tubules responsible for sperm production. This study focuses on investigating the predictive power of using B-ultrasound imaging from boars around 6 to 9 months old to predict semen quality over the next six months in industrial settings. Several scanning factors were considered, including image brightness, imaging area of the testicle, and the imaging angle of the probe. The studied dataset included 1417 images and 3254 semen records from 107 boars. Results showed that the model’s performance was significantly influenced by the imaging area, the angle of the testicle, and the pixel brightness of the image without being standardized. While the accuracy of B-ultrasound imaging is not yet sufficient to replace traditional assessments, this study highlights key features in testicular images that may significantly impact model predictions, providing practical guidance for leveraging B-ultrasound imaging in predicting semen quality in young boars. Full article

High-quality training data are essential for the performance and generalization of artificial intelligence systems, particularly in dynamic environments such as adaptive stream processing for disaster response. However, constructing large and representative datasets remains costly and time-consuming, especially in domains where real data are scarce or difficult to obtain. Large Language Models (LLMs) provide powerful capabilities for synthetic text generation, yet the quality of generated data strongly depends on the design of input prompts. Prompt engineering is therefore critical, but it remains largely manual and difficult to scale, particularly in black-box settings where model internals are inaccessible. This work introduces EVOLMD-MO, a multi-objective evolutionary framework for automated prompt learning aimed at generating high-quality synthetic text datasets using black-box LLMs. The proposed approach formulates prompt optimization as a multi-objective search problem in which candidate prompts evolve through genetic operators guided by two complementary objectives: semantic fidelity to reference data and generative diversity of the produced samples. To support scalable optimization, the framework integrates a modular multi-agent architecture that decouples prompt evolution, LLM interaction, and evaluation mechanisms. The evolutionary process is implemented using the NSGA-II algorithm, enabling the discovery of diverse Pareto-optimal prompts that balance semantic preservation and diversity. Experimental evaluation using large-scale disaster-related social media data demonstrates that the proposed approach consistently improves prompt quality across generations while maintaining a stable trade-off between fidelity and diversity. Compared with a single-objective baseline, EVOLMD-MO explores a significantly broader semantic search space and produces more diverse yet semantically coherent synthetic datasets. These results indicate that multi-objective evolutionary prompt learning constitutes a promising strategy for black-box LLM-driven data generation, with potential applicability to adaptive data analytics and real-time decision-support systems in highly dynamic environments, pending broader validation across domains and models. Full article

This study aimed to comparatively optimize and evaluate the quality characteristics of Washington Navel orange slices using vacuum microwave drying (VMD) and conventional hot air drying (HAD) systems. Response Surface Methodology based on the Box–Behnken design was applied to both systems. For the models developed in the VMD system, the coefficient of determination (R²) was found to be in the range of 0.96–0.97, and the optimum conditions were determined as 4 kW power, 60 °C temperature, and 2 mm slice thickness. For HAD, the optimum conditions were determined as 78 °C temperature, 1.57 m/s air velocity, of 2.3 mm slice thickness. VMD showed superior performance compared to hot air drying in terms of total phenolic preservation, retention of bioactive compounds, and rehydration capacity. Hydroxymethylfurfural (HMF) formation was higher during hot-air drying. The effective moisture diffusivity (D_eff) was significantly higher in VMD (8.38 × 10⁻¹⁰ m²/s) than in HAD (1.49 × 10⁻¹⁰ m²/s), indicating enhanced internal moisture transport under vacuum microwave conditions. The results revealed that VMD is an efficient technology for producing high-quality dried citrus products with improved bioactive retention and reduced processing time. Full article

Grid-forming (GFM) battery energy storage system (BESS) inverters are becoming a cornerstone of resilient microgrids, where severe voltage sags and abrupt operating shifts can challenge both voltage regulation and controller stability. Finite-set model predictive control (FS-MPC) offers fast transient response and multi-objective coordination, yet conventional designs rely on static cost-function weights that are typically tuned offline and may become suboptimal under disturbance-driven regime changes. This paper proposes a forecast-guided KAN-adaptive FS-MPC framework that (i) formulates the inner-loop predictive control in the stationary αβ frame, thereby avoiding PLL dependency and mitigating loss-of-lock risk under extreme sags, and (ii) introduces an Operating Stress Index (OSI) that fuses load forecasts with reserve-margin or percent-operating-reserve signals to quantify grid vulnerability and trigger resilience-oriented control adaptation. A lightweight Kolmogorov–Arnold Network (KAN), parameterized by learnable B-spline edge functions, is embedded as an online weight governor to update key FS-MPC weighting factors in real time, dynamically balancing voltage tracking and switching effort. Experimental validation under high-frequency microgrid scenarios shows that, under a 50% symmetrical voltage sag, the proposed controller reduces the worst-case voltage deviation from 0.45 p.u. to 0.16 p.u. (64.4%) and shortens the recovery time from 35 ms to 8 ms (77.1%) compared with static-weight FS-MPC. In the islanding-like transition case, the proposed method restores the PCC voltage within 18 ms, whereas the static baseline fails to recover within 100 ms. Moreover, the deployed KAN governor requires only 6.2 μs per inference on a 200 MHz DSP, supporting real-time embedded implementation. These results demonstrate that forecast-guided adaptive weighting improves transient resilience and power quality while maintaining DSP-feasible computational complexity. Full article