Search Results (6,796)

This paper presents a symmetry-driven broadband circularly polarized magnetoelectric dipole antenna with bandpass filtering response, where the principle of symmetry is strategically employed to enhance both radiation and filtering performance. The antenna’s circular polarization is achieved through a symmetrical arrangement of two orthogonally placed metallic ME dipoles combined with a phase delay line, creating balanced current distributions for optimal CP characteristics. The design further incorporates symmetrical parasitic elements—a pair of identical inverted L-shaped metallic structures placed perpendicular to the ground plane at −45° relative to the ME dipoles—which introduce an additional CP resonance through their mirror-symmetric configuration, thereby significantly broadening the axial ratio bandwidth. The filtering functionality is realized through a combination of symmetrical modifications: grid slots etched in the metallic ground plane and an open-circuited stub loaded on the microstrip feed line work in tandem to create two radiation nulls in the upper stopband, while the inherent symmetrical properties of the ME dipoles naturally produce a radiation null in the lower stopband. This comprehensive symmetry-based approach results in a well-balanced bandpass filtering response across a wide operating bandwidth. Experimental validation through prototype measurement confirms the effectiveness of the symmetric design with compact dimensions of 0.96${\lambda }_0$ × 0.55${\lambda }_0$ × 0.17${\lambda }_0$ (${\lambda }_0$ is the wavelength at the lowest operating frequency), demonstrating an impedance bandwidth of 66.4% (2.87–5.05 GHz), an AR bandwidth of 31.9% (3.32–4.58 GHz), an average passband gain of 5.5 dBi, and out-of-band suppression levels of 11.5 dB and 26.8 dB at the lower and upper stopbands, respectively, along with good filtering performance characterized by a gain-suppression index (GSI) of 0.93 and radiation skirt index (RSI) of 0.58. The proposed antenna is suitable for satellite communication terminals requiring wide AR bandwidth and strong interference rejection in L/S-bands. Full article

Hybrid modeling techniques are increasingly important for improving predictive accuracy and control in biomanufacturing, particularly in data-limited conditions. This study develops and experimentally validates a hybrid deep learning model predictive control (MPC) framework for recombinant P. pastoris fed-batch fermentations. Bayesian optimization and grid search techniques were employed to identify the best-performing hybrid model architecture: an LSTM layer with 2 hidden units followed by a fully connected layer with 8 nodes and ReLU activation. This design balanced accuracy (NRMSE 4.93%) and computational efficiency (AICc 998). This architecture was adapted to a new, smaller dataset of bacteriophage Qβ coat protein production using transfer learning, yielding strong predictive performance with low validation (3.53%) and test (5.61%) losses. Finally, the hybrid model was integrated into a novel MPC system and experimentally validated, demonstrating robust real-time substrate feed control in a way that allows it to maintain specific target growth rates. The system achieved predictive accuracies of 6.51% for biomass and 14.65% for product estimation, with an average tracking error of 10.64%. In summary, this work establishes a robust, adaptable, and efficient hybrid modeling framework for MPC in P. pastoris bioprocesses. By integrating automated architecture searching, transfer learning, and MPC, the approach offers a practical and generalizable solution for real-time control and supports scalable digital twin deployment in industrial biotechnology. Full article

To combat the catastrophic effects of climate change, the usage of renewable energy sources (RESs) has increased dramatically in recent years. The main drivers of the increase in solar photovoltaic (PV) system grid integrations in recent years have been lowering energy costs and pollution. Active and reactive powers are controlled by a proportional–integral controller, whereas energy storage batteries improve the quality of energy by storing both current and voltage, which have an impact on steady-state error. Since traditional controllers are unable to maximize the energy output of solar systems, artificial intelligence (AI) is essential for enhancing the energy generation of PV systems under a variety of climatic conditions. Nevertheless, variations in the weather can have an impact on how well photovoltaic systems function. This paper presents an intelligent power management controller (IPMC) for obtaining power management with load and electric-vehicle applications. The architecture combines the solar PV, battery with electric-vehicle load, and grid system. Initially, the PV architecture is utilized to generate power from the irradiance. The generated power is utilized to compensate for the required load demand on the grid side. The remaining PV power generated is utilized to charge the batteries of electric vehicles. The power management of the PV is obtained by considering the proposed control strategy. The power management controller is a combination of the twisting sliding-mode controller (TSMC) and Modified Pufferfish Optimization Algorithm (MPOA). The proposed method is implemented, and the application results are matched with the Mountain Gazelle Optimizer (MSO) and Beluga Whale Optimization (BWO) Algorithm by evaluating the PV power output, EV power, battery-power and battery-energy utilization, grid power, and grid price to show the merits of the proposed work. Full article

The Internet of Things (IoT) holds transformative potential in fields such as power grid optimization, defense networks, and healthcare. However, the constrained processing capacities and resource limitations of IoT networks make them especially susceptible to cyber threats. This study addresses the problem of detecting intrusions in IoT environments by evaluating the performance of deep learning (DL) models under different data and algorithmic conditions. We conducted a comparative analysis of three widely used DL models—Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Bidirectional LSTM (biLSTM)—across four benchmark IoT intrusion detection datasets: BoTIoT, CiCIoT, ToNIoT, and WUSTL-IIoT-2021. Each model was assessed under balanced and imbalanced dataset configurations and evaluated using three loss functions (cross-entropy, focal loss, and dual focal loss). By analyzing model efficacy across these datasets, we highlight the importance of generalizability and adaptability to varied data characteristics that are essential for real-world applications. The results demonstrate that the CNN trained using the cross-entropy loss function consistently outperforms the other models, particularly on balanced datasets. On the other hand, LSTM and biLSTM show strong potential in temporal modeling, but their performance is highly dependent on the characteristics of the dataset. By analyzing the performance of multiple DL models under diverse datasets, this research provides actionable insights for developing secure, interpretable IoT systems that can meet the challenges of designing a secure IoT system. Full article

This study introduces an enhanced Intrusion Detection System (IDS) framework for Denial-of-Service (DoS) attacks, utilizing network traffic inter-arrival time (IAT) analysis. By examining the timing between packets and other statistical features, we detected patterns of malicious activity, allowing early and effective DoS threat mitigation. We generate real DoS traffic, including normal, Internet Control Message Protocol (ICMP), Smurf attack, and Transmission Control Protocol (TCP) classes, and develop nine predictive algorithms, combining traditional machine learning and advanced deep learning techniques with optimization methods, including the synthetic minority sampling technique (SMOTE) and grid search (GS). Our findings reveal that while traditional machine learning achieved moderate accuracy, it struggled with imbalanced datasets. In contrast, Deep Neural Network (DNN) models showed significant improvements with optimization, with DNN combined with GS (DNN-GS) reaching 89% accuracy. However, we also used Recurrent Neural Networks (RNNs) combined with SMOTE and GS (RNN-SMOTE-GS), which emerged as the best-performing with a precision of 97%, demonstrating the effectiveness of combining SMOTE and GS and highlighting the critical role of advanced optimization techniques in enhancing the detection capabilities of IDS models for the accurate classification of various types of network traffic and attacks. Full article

As power grids continue to expand in scale, the occurrence of cascading failures within them can lead to significant economic losses. Therefore, assessing the criticality of grid nodes is crucial for ensuring the secure and stable operation of power systems and for mitigating losses when cascading failures occur. The classical Local Link Similarity (LLS) algorithm in complex networks evaluates the importance of network nodes from a neighborhood topology perspective, but it suffers from issues such as the excessive weighting of node degrees and the neglect of electrical parameters. Based on the classical algorithm, this paper first develops the Improved Local Link Similarity (ILLS) algorithm by substituting alternative similarity metrics and comparatively evaluating their performance. Building upon the ILLS, we then propose the Electrical LLS (ELLS) algorithm by integrating node power flow and electrical coupling connectivity as multiplicative factors, with optimal combinations determined via simulation experiments. Compared to classical approaches, ELLS demonstrates superior adaptability to power grid contexts and delivers enhanced accuracy in power system node importance assessments. These algorithms are applied to rank the node importance in the IEEE 300-bus system. Their performance is evaluated using the loss-of-load-size metric, comparing ELLS, ILLS, and the classical algorithm. The results demonstrate that under the loss-of-load-size metric, the ELLS algorithm achieves approximately 25% higher accuracy compared to both the ILLS and the classical algorithm, validating its effectiveness. Full article

The energy sector uses artificial intelligence (AI) as a crucial instrument to achieve environmental sustainability targets by improving resource efficiency and decreasing emissions while minimizing waste production. This paper establishes an industry-specific executive playbook that guides energy sector leaders by implementing AI technologies for sustainability management with approaches suitable for industrial needs. The playbook provides an industry-specific framework along with strategies and AI-based solutions to help organizations overcome their sustainability challenges. Predictive analytics combined with smart grid management implemented through AI applications produced 15% less energy waste and reduced carbon emissions by 20% according to industry pilot project data. AI has proven its transformative capabilities by optimizing energy consumption while detecting inefficiencies to create both operational improvements and cost savings. The real-time monitoring capabilities of AI systems help companies meet strict environmental regulations and international climate goals by optimizing resource use and waste reduction, supporting circular economy practices for sustainable operations and enduring profitability. Leaders can establish impactful technology-based sustainability initiatives through the playbook which addresses the energy sector requirements for corporate goals and regulatory standards. Full article

Bupleurum scorzonerifolium Willd. is a perennial herbaceous plant of the genus Bupleurum in the Apiaceae family. Also known as red Bupleurum, it is mainly distributed in Northeast China, North China and other regions and is a commonly used medicinal plant. It is difficult for the wild plant resources of Bupleurum scorzonerifolium Willd. to meet the market demand. In artificial cultivation, there are problems such as a low yield per plant, low quality, weakened stress resistance and variety degradation. The contents of bioactive components and metabolites in traditional Chinese medicinal materials vary significantly across different growth years. The growth duration directly impacts their quality and clinical efficacy. Therefore, determining the optimal growth period is one of the crucial factors in ensuring the quality of traditional Chinese medicinal materials. In this study, Gas Chromatography–Mass Spectrometry (GC-MS) and High-performance liquid chromatography (HPLC) were comprehensively applied to analyze the metabolically differential substances in different parts of Bupleurum scorzonerifolium Willd. By comparing the compositions and content differences of chemical components in different growth years and different parts, the chemical components with significant differences were accurately screened out. In order to further explore the dynamic change characteristics and internal laws of metabolites, a metabolic network was constructed for a visual analysis and, finally, to see the optimal growth years of Bupleurum scorzonerifolium Willd. This result showed that with the accumulation of the growth cycle, the height, root width, fresh mass and saikosaponins content of Bupleurum scorzonerifolium Willd. increased year by year. Except for sodium and calcium elements in the main shoot, the other elements were significantly reduced. In addition, 59 primary metabolites were identified by GC-MS, with the accumulation of the growth cycle, the contents of organic acids, sugars, alcohols and amino acids gradually decreased, while the contents of alkyl, glycosides and other substances gradually increased. There were 53 positive correlations and 18 negative correlations in the triennial Bupleurum scorzonerifolium Willd. grid, all of which were positively correlated with saikosaponins. Therefore, the triennial Bupleurum scorzonerifolium Willd. was considered to be the suitable growth year. It not only provided a new idea and method for the quality evaluation of Bupleurum scorzonerifolium Willd., but also provided a scientific basis for the quality control of Chinese herbs. Full article

Battery-swapping stations (BSSs) are pivotal for supplying energy to electric heavy-duty trucks. However, their operations face challenges in accurate demand forecasting for battery-swapping and fair revenue allocation. This study proposes an optimization strategy for the economic operation of BSSs that optimizes revenue allocation and load balancing to enhance financial viability and grid stability. First, factors including geographical environment, traffic conditions, and truck characteristics are incorporated to simulate swapping behaviors, supporting the construction of an accurate demand-forecasting model. Second, an optimization problem is formulated to maximize the weighted difference between BSS revenue and squared load deviations. An economic operations strategy is proposed based on an adaptive Shapley value. It enables precise evaluation of differentiated member contributions through dynamic adjustment of bias weights in revenue allocation for a strategy that aligns with the interests of multiple stakeholders and market dynamics. Simulation results validate the superior performance of the proposed algorithm in revenue maximization, peak shaving, and valley filling. Full article

Rolling bearings are indispensable but also the most fault-prone components of rotating machinery, typically used in fields such as industrial aircraft, production workshops, and manufacturing. They encounter diverse mechanical stresses, such as vibration and friction during operation, which may lead to wear and fatigue cracks. From this standpoint, the present study combined a 1-D convolutional neural network (1-D CNN) with a long short-term memory (LSTM) algorithm for classifying different ball-bearing health conditions. A physics-guided method that adopts fault characteristics frequencies was used to calculate an optimal input size (sample length). Moreover, grid search was utilized to optimize (1) the number of epochs, (2) batch size, and (3) dropout ratio and further enhance the efficacy of the proposed 1-D CNN-LSTM network. Therefore, an attempt was made to reduce epistemic uncertainty that arises due to not knowing the best possible hyper-parameter configuration. Ultimately, the effectiveness of the physics-guided optimized 1-D CNN-LSTM was tested by comparing its performance with other state-of-the-art models. The findings revealed that the average accuracies could be enhanced by up to 20.717% with the help of the proposed approach after testing it on two benchmark datasets. Full article

As power grids scale and aging assets edge toward obsolescence, grounding grid corrosion has become a critical vulnerability. Conventional diagnosis must fit high-dimensional electrical data to a physical model, typically yielding a nonlinear under-determined system fraught with computational burden and uncertainty. We propose the Enhanced Biomimetic Hippopotamus Optimization (EBOHO) algorithm, which distills the river-dwelling hippo’s ecological wisdom into three synergistic strategies: a beta-function herd seeding that replicates the genetic diversity of juvenile hippos diffusing through wetlands, an elite–mean cooperative foraging rule that echoes the way dominant bulls steer the herd toward nutrient-rich pastures, and a lens imaging opposition maneuver inspired by moonlit water reflections that spawn mirror candidates to avert premature convergence. Benchmarks on the CEC 2017 suite and four classical design problems show EBOHO’s superior global search, robustness, and convergence speed over numerous state-of-the-art meta-heuristics, including prior hippo variants. An industrial case study on grounding grid corrosion further confirms that EBOHO swiftly resolves the under-determined equations and pinpoints corrosion sites with high precision, underscoring its promise as a nature-inspired diagnostic engine for aging power system infrastructure. Full article

Laser-induced breakdown spectroscopy (LIBS) has played a critical role in Mars exploration missions, substantially contributing to the geochemical analysis of Martian surface substances. However, the complex nonlinearity of LIBS processes can considerably limit the quantification accuracy of conventional LIBS chemometric methods. Hence chemometrics based on artificial neural network (ANN) algorithms have become increasingly popular in LIBS analysis due to their extraordinary ability in nonlinear feature modeling. The hidden layer activation functions are key to ANN model performance, yet common activation functions usually suffer from problems such as gradient vanishing (e.g., Sigmoid and Tanh) and dying neurons (e.g., ReLU). In this study, we propose a novel LIBS quantification method, named the Bayesian optimization-based tunable Softplus backpropagation neural network (BOTS-BPNN). Based on a dataset comprising 1800 LIBS spectra collected by a laboratory duplicate of the MarSCoDe instrument onboard the Zhurong Mars rover, we have revealed that a BPNN model adopting a tunable Softplus activation function can achieve higher prediction accuracy than BPNN models adopting other common activation functions if the tunable Softplus parameter β is properly selected. Moreover, the way to find the proper β value has also been investigated. We demonstrate that the Bayesian optimization method surpasses the traditional grid search method regarding both performance and efficiency. The BOTS-BPNN model also shows superior performance over other common machine learning models like random forest (RF). This work indicates the potential of BOTS-BPNN as an effective chemometric method for analyzing Mars in situ LIBS data and sheds light on the use of chemometrics for data analysis in future planetary explorations. Full article

This study explores modernization strategies for existing district heating (DH) networks to enhance their efficiency and sustainability, focusing on achieving net-zero emissions in urban heating systems. Building upon a literature review and expert interviews, we developed a strategic decision-support framework that outlines distinct strategies for retrofitting district heating grids and includes a portfolio analysis. This framework serves as a tool to guide DH operators and stakeholders in selecting well-founded modernization pathways by considering technical, economic, and social dimensions. The review identifies several promising measures, such as reducing operational temperatures at substations, implementing optimized substations, integrating renewable and waste heat sources, implementing thermal energy storage (TES), deploying smart metering and monitoring infrastructure, and expanding networks while addressing public concerns. Additionally, the review highlights the importance of stakeholder engagement and policy support in successfully implementing these strategies. The developed strategic decision-support framework helps practitioners select a tailored modernization strategy aligned with the local context. Furthermore, the findings show the necessity of adopting a comprehensive approach that combines technical upgrades with robust stakeholder involvement and supportive policy measures to facilitate the transition to sustainable urban heating solutions. For example, the development of decision-support tools enables stakeholders to systematically evaluate and select grid modernization strategies, directly helping to reduce transmission losses and lower greenhouse gas (GHG) emissions contributing to climate goals and enhancing energy security. Indeed, as shown in the reviewed literature, retrofitting high-temperature district heating networks with low-temperature distribution and integrating renewables can lead to near-complete decarbonization of the supplied heat. Additionally, integrating advanced digital technologies, such as smart grid systems, can enhance grid efficiency and enable a greater share of variable renewable energy thus supporting national decarbonization targets. Further investigation could point to the most determining context factors for best choices to improve the sustainability and efficiency of existing DH systems. Full article

This study evaluates the performance of nine satellite and model-based daily surface soil moisture products, encompassing sixteen algorithm versions across mainland China to support sustainable land and water management. The assessment utilizes 2018 in situ measurements from over 2400 stations in China’s Automatic Soil Moisture Monitoring Network. All products were standardized to a 0.25° × 0.25° grid in the WGS-84 coordinate system through reprojection and resampling for consistent comparison. Daily averaged station observations were matched to product pixels using a 10 km radius buffer, with the mean station value as the reference for each time series after rigorous quality control. Results reveal distinct performance rankings, with SMAP-based products, particularly the SMAP_IB descending orbit variant, achieving the lowest unbiased root mean square deviation (ubRMSD) and highest correlation with in situ data. Blended products like ESA CCI and NOAA SMOPS, alongside reanalysis datasets such as ERA5 and MERRA2, outperformed SMOS and China’s FY3 products. The SoMo.ml product showed the broadest spatial coverage and strong temporal consistency, while FY3-based products showed limitations in spatial reliability and seasonal dynamics capture. These findings provide critical insights for selecting appropriate soil moisture datasets to enhance sustainable agricultural practices, optimize water resource allocation, monitor ecosystem resilience, and support climate adaptation strategies, therefore advancing sustainable development across diverse geographical regions in China. Full article

Against the backdrop of the increasing penetration rate of new energy year by year, power systems face a continuously growing demand for flexibility. Under the structure of such a new power system, it is essential not only to introduce diverse flexible power sources but also to explore the flexible regulation capabilities of existing conventional power sources. To fully utilize the flexibility of thermal power units (TPUs), this study proposes a real-time optimal scheduling strategy for a wind–thermal energy-storage integrated system with an adaptive time division and variable objectives. Based on the evaluation results of the real-time flexible supply–demand relationship within a regional power grid, the operation modes of TPUs are categorized into three types: economic mode, peak shaving mode, and coordination mode. For each operation mode, corresponding optimization objectives are defined, and an energy storage control strategy is developed to assist in the peak shaving of TPUs. While effectively harnessing the flexibility of TPUs, the proposed method reduces both the frequency and capacity of TPUs entering deep peak shaving. Using data from a province in Northwest China as a case study, simulation calculations and analyses demonstrate that the proposed method increases renewable energy consumption by 314.37 MWh while decreasing system economic benefits by CNY 129,000. Compared with traditional scheduling methods for TPUs to accommodate renewable energy, the system benefit increases by CNY 297,000, and an additional 13.53 MWh of peak wind power is accommodated. These results confirm that the proposed scheduling strategy can significantly enhance the system’s ability to integrate new energy while maintaining its economic efficiency. Full article