Search Results (8,583)

Composite materials such as molybdenum carbides, nitrides, oxides, and mixed anionic compounds like Mo(C,N,O)_x embedded in carbonaceous matrix exhibit promising potential as anode materials for lithium batteries, with a preference for fine-grained morphologies. In this study, we present a novel synthetic approach involving an inorganic–organic hybrid precursor precipitated from aqueous solutions of ammonium heptamolybdate and one of two organic species: 1,8-diaminonaphthalene (1,8-DAN) or hexamethylenediamine (HMD). The precipitation reaction can be carried out in a beaker and in a continuous process using a microjet reactor. This enables the synthesis of precursor material on the gram scale within minutes. The pyrolysis of these precursors yields mixtures of Mo(C,N,O)_x, MoO₂, Mo₂C, Mo₂N, and Mo, with the choice of organic compound significantly influencing the resulting phases and the excess carbon content in the pyrolyzed product. Notably, the pyrolysis process maintains the size and morphology of the micro- to nanometer-sized starting materials. Full article

This paper presents and demonstrates the development of a new lightweight coating for aluminum alloy from a novel multicomponent alloy based on the AlSiMgCu system. The coating was applied using a newly designed approach that combined high velocity oxy-fuel (HVOF) and plasma spraying processes. This hybrid technique enables the deposition of coatings with enhanced performance characteristics. The optical microscopy (OM) and scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM + EDS) revealed a strong adhesion and compaction between the multicomponent coating and the A6061 substrate. The new coating improved hardness by 50% and increased electrical conductivity by approximately 3.3 times compared to the as-cast alloy. Corrosion tests showed a lower corrosion rate, comparable to thermally treated A6061 alloy. Tribological tests indicated over 20% reduction in friction and over 50% reduction in wear rate. This suggests that multicomponent aluminum coatings could improve automotive and parts in contact with hydrogen by enhancing hydrogen fragilization resistance, corrosion resistance, electrical conductivity, and wear properties, with further optimization of thermal spraying potentially boosting performance even further. Full article

This paper introduces a novel hybrid optimization technique aimed at improving the prediction accuracy of solar photovoltaic (PV) outputs using an Improved Hippopotamus Optimization Algorithm (IHO). The IHO enhances the traditional Hippopotamus Optimization (HO) algorithm by addressing its limitations in search efficiency, convergence speed, and global exploration. The IHO algorithm used Latin hypercube sampling (LHS) for population initialization, significantly enhancing the diversity and global search potential of the optimization process. The integration of the Jaya algorithm further refines solution quality and accelerates convergence. Additionally, a combination of unordered dimensional sampling, random crossover, and sequential mutation is employed to enhance the optimization process. The effectiveness of the proposed IHO is demonstrated through the optimization of weights and neuron thresholds in the extreme learning machine (ELM), a model known for its rapid learning capabilities but often affected by the randomness of initial parameters. The IHO-optimized ELM (IHO-ELM) is tested against benchmark algorithms, including BP, the traditional ELM, the HO-ELM, LCN, and LSTM, showing significant improvements in prediction accuracy and stability. Moreover, the IHO-ELM model is validated in a different region to assess its generalization ability for solar PV output prediction. The results confirm that the proposed hybrid approach not only improves prediction accuracy but also demonstrates robust generalization capabilities, making it a promising tool for predictive modeling in solar energy systems. Full article

R-loops, structures that play a crucial role in various biological processes, are integral to gene expression, the maintenance of genome stability, and the formation of epigenomic signatures. When these R-loops are deregulated, they can contribute to the development of serious health conditions, including cancer and neurodegenerative diseases. The detection of R-loops is a complex process that involves several approaches. These include S9.6 antibody- or RNAse H-based immunoprecipitation, non-denaturing bisulfite footprinting, gel electrophoresis, and electron microscopy. Each of these methods offers unique insights into the nature and behavior of R-loops. In our study, we introduce a novel protocol that has been developed based on a single-molecule DNA combing assay. This innovative approach allows for the direct and simultaneous visualization of RNA:DNA hybrids and replication forks, providing a more comprehensive understanding of these structures. Our findings confirm the transcriptional origin of the hybrids, adding to the body of knowledge about their formation. Furthermore, we demonstrate that these hybrids have an inhibitory effect on the progression of replication forks, highlighting their potential impact on DNA replication and cellular function. Full article

In recent years, urban floods have occurred frequently in China. Therefore, there is an urgent need to strengthen urban flood resilience. This paper proposed a hybrid multi-criteria group decision-making method to assess urban flood resilience based on heterogeneous data, group decision-making methodologies, the pressure-state–response model, and social–economic–natural complex ecosystem theory (PSR-SENCE model). A qualitative and quantitative indicator system is formulated using the PSR-SENCE model. Additionally, a new weighting method for indicators, called the synthesis weighting-group analytic hierarchy process (SW-GAHP), is proposed by considering both intrapersonal consistency and interpersonal consistency of decision-makers. Furthermore, an extensional group decision-making technology (EGDMT) based on heterogeneous data is proposed to evaluate qualitative indicators. The flexible parameterized mapping function (FPMF) is introduced for the evaluation of quantitative indicators. The normal cloud model is employed to handle various uncertainties associated with heterogeneous data. The evaluations for Beijing from 2017 to 2021 reveal a consistent annual improvement in urban flood resilience, with a 14.1% increase. Subsequently, optimization recommendations are presented not only for favorable indicators such as regional economic status, drainability, and public transportation service capacity but also for unfavorable indicators like flood risk and population density. This provides a theoretical foundation and a guide for making decisions about the improvement of urban flood resilience. Finally, our proposed method shows superiority and robustness through comparative and sensitivity analyses. Full article

Digital Twins of the Ocean (DTO) are a rapidly emerging topic that has attracted significant interest from scientists in recent years. The initiative, strongly driven by the EU, aims to create a digital replica of the ocean to better understand and manage marine environments. The Iliad project, funded under the EU Green Deal call, is developing a framework to support multiple interoperable DTO using a federated systems-of-systems approach across various fields of applications and ocean areas, called pilots. This paper presents the results of a Water Quality DTO pilot located in the Trondheim fjord in Norway. This paper details the building blocks of DTO, specific to this environmental monitoring pilot. A crucial aspect of any DTO is data, which can be sourced internally, externally, or through a hybrid approach utilizing both. To realistically twin ocean processes, the Water Quality pilot acquires data from both surface and benthic observatories, as well as from mobile sensor platforms for on-demand data collection. Data ingested into an InfluxDB are made available to users via an API or an interface for interacting with the DTO and setting up alerts or events to support ’what-if’ scenarios. Grafana, an interactive visualization application, is used to visualize and interact with not only time-series data but also more complex data such as video streams, maps, and embedded applications. An additional visualization approach leverages game technology based on Unity and Cesium, utilizing their advanced rendering capabilities and physical computations to integrate and dynamically render real-time data from the pilot and diverse sources. This paper includes two case studies that illustrate the use of particle sensors to detect microplastics and monitor algae blooms in the fjord. Numerical models for particle fate and transport, OpenDrift and DREAM, are used to forecast the evolution of these events, simulating the distribution of observed plankton and microplastics during the forecasting period. Full article

In recent years, deep unfolding networks (DUNs) have received widespread attention in the field of compressed sensing (CS) reconstruction due to their good interpretability and strong mapping capabilities. However, existing DUNs often improve the reconstruction effect at the expense of a large number of parameters, and there is the problem of information loss in long-distance feature transmission. Based on the above problems, we propose an unfolded network architecture that mixes Transformer and large kernel convolution to achieve sparse sampling and reconstruction of natural images, namely, a reconstruction network based on Transformer and convolution (TCR-Net). The Transformer framework has the inherent ability to capture global context through a self-attention mechanism, which can effectively solve the challenge of long-range dependence on features. TCR-Net is an end-to-end two-stage architecture. First, a data-driven pre-trained encoder is used to complete the sparse representation and basic feature extraction of image information. Second, a new attention mechanism is introduced to replace the self-attention mechanism in Transformer, and a hybrid Transformer and convolution module based on optimization-inspired is designed. Its iterative process leads to the unfolding framework, which approximates the original image stage by stage. Experimental results show that TCR-Net outperforms existing state-of-the-art CS methods while maintaining fast computational speed. Specifically, when the CS ratio is 0.10, the average PSNR on the test set used in this paper is improved by at least 0.8%, the average SSIM is improved by at least 1.5%, and the processing speed is higher than 70FPS. These quantitative results show that our method has high computational efficiency while ensuring high-quality image restoration. Full article

Models of chemical kinetic processes, comprising systems of stiff ordinary differential equations (ODEs), are essential for modeling important chemical reactions relevant to drinking water chemistry, such as disinfectant decay and disinfection byproduct formation. However, the accuracy of these models can be inhibited by (1) the challenge of fully describing the chemical reaction system, and (2) additional chemical reactions occurring in actual environmental settings that were not accounted for in the laboratory conditions used to develop and calibrate the models. This study proposes the use of a Physics-Informed Neural Network framework, utilizing the eXtreme Theory of Functional Connections (X-TFC) technique to create a hybrid chemical- and data-driven model that incorporates data and the underlying system of ODEs into the trained model in order to increase the accuracy of the predicted chemical concentrations. Full article

A wideband polarization-insensitive bistatic radar cross-section (RCS) reduction design under linear and circular polarization incidence is proposed based on spherical-chessboard metasurfaces. A new metasurface element with wideband characteristics was designed, including a double split-ring structure, single-layer media, and metal board. In the proposed RCS-reduction design, the Pancharatnam–Berry (P-B) phase theory is applied with the designed metasurface element to realize phase distribution mimicking the low-scattering sphere, and thus realizing RCS reduction. In addition, the chessboard configuration is combined with spherical phase distribution to further improve the performance of monostatic and bistatic RCS reduction. Finally, the proposed RCS reduction design can not only realize wideband RCS reduction but also exhibit polarization-insensitive characteristics. It realized 10 dB monostatic and bistatic RCS reduction in a frequency band ranging from 8.5 to 21 GHz (84.8% relative bandwidth) under linear polarization (LP) and circular polarization (CP) incidence. The straightforward and efficient design method of the hybrid spherical chessboard can effectively avoid the complex and time-consuming optimization process in RCS-reduction design. Full article

Achieving cyber-security has grown increasingly tricky because of the rising concern for internet connectivity and the significant growth in software-related applications. It also needs a robust defense system to defend itself from multiple cyberattacks. Therefore, there is a need to generate a method for detecting and classifying cyber-attacks. The developed model can be integrated into three phases: pre-processing, feature selection, and classification. Initially, the min-max normalization of original data was performed to eliminate the impact of maximum or minimum values on the overall characteristics. After that, synthetic minority oversampling techniques (SMOTEs) were developed to reduce the number of minority attacks. The significant features were selected using a Hybrid Genetic Fire Hawk Optimizer (HGFHO). An optimized residual dense-assisted multi-attention transformer (Op-ReDMAT) model was introduced to classify selected features accurately. The proposed model’s performance was evaluated using the UNSW-NB15 and CICIDS2017 datasets. A performance analysis was carried out to demonstrate the effectiveness of the proposed model. The experimental results showed that the UNSW-NB15 dataset attained a higher precision, accuracy, F1-score, error rate, and recall of 97.2%, 98.82%, 97.8%, 2.58, and 98.5%, respectively. On the other hand, the CICIDS 2017 achieved a higher precision, accuracy, F1-score, and recall of 98.6%, 99.12%, 98.8%, and 98.2%, respectively. Full article

Accurate short-term load forecasting is critical for enhancing the reliability and stability of regional smart energy systems. However, the inherent challenges posed by the substantial fluctuations and volatility in electricity load patterns necessitate the development of advanced forecasting techniques. In this study, a novel short-term load forecasting approach based on a two-stage feature extraction process and a hybrid inverted Transformer model is proposed. Initially, the Prophet method is employed to extract essential features such as trends, seasonality and holiday patterns from the original load dataset. Subsequently, variational mode decomposition (VMD) optimized by the IVY algorithm is utilized to extract significant periodic features from the residual component obtained by Prophet. The extracted features from both stages are then integrated to construct a comprehensive data matrix. This matrix is then inputted into a hybrid deep learning model that combines an inverted Transformer (iTransformer), temporal convolutional networks (TCNs) and a multilayer perceptron (MLP) for accurate short-term load forecasting. A thorough evaluation of the proposed method is conducted through four sets of comparative experiments using data collected from the Elia grid in Belgium. Experimental results illustrate the superior performance of the proposed approach, demonstrating high forecasting accuracy and robustness, highlighting its potential in ensuring the stable operation of regional smart energy systems. Full article

The rapid integration of renewable energy sources and the increasing complexity of modern power systems urge the development of advanced methods for ensuring power system stability. This paper presents a novel continuation power flow (CPF) method that combines two well-known parameterization techniques: natural parameterization and arc-length parameterization. The proposed hybrid approach significantly improves computational efficiency, reducing processing time by 32.76% compared to conventional methods while maintaining high accuracy. The method enables faster and more reliable stability assessments by efficiently managing the complexities and uncertainties, particularly in grids with high penetration of renewable energy. Full article

In developing modern intelligent transportation systems, integrated sensing and communication (ISAC) technology has become an efficient and promising method for vehicle road services. To enhance traffic safety and efficiency through real-time interaction between vehicles and roads, this paper proposes a searching–deciding scheme for an alternation radar-communication (radar-comm) system. Firstly, its communication performance is derived for a given detection probability. Then, we process the echo data from real-world millimeter-wave (mmWave) radar into four-dimensional (4D) point cloud datasets and thus separate different hybrid modes of single-vehicle and vehicle fleets into three types of scenes. Based on these datasets, an efficient labeling method is proposed to assist accurate vehicle target detection. Finally, a novel vehicle detection scheme is proposed to classify various scenes and accurately detect vehicle targets based on deep learning methods. Extensive experiments on collected real-world datasets demonstrate that compared to benchmarks, the proposed scheme obtains substantial radar performance and achieves competitive communication performance. Full article

As the metal additive manufacturing (AM) field evolves with an increasing demand for highly complex and customizable products, there is a critical need to close the gap in productivity between metal AM and traditional manufacturing (TM) processes such as continuous casting, machining, etc., designed for mass production. This paper presents the development of the scalable and expeditious additive manufacturing (SEAM) process, which hybridizes binder jet printing and stereolithography principles, and capitalizes on their advantages to improve productivity. The proposed SEAM process was applied to stainless steel 420 (SS420) and the processing conditions (green part printing, debinding, and sintering) were optimized. Finally, an SS420 turbine fabricated using these conditions successfully reached a relative density of 99.7%. The SEAM process is not only suitable for a high-volume production environment but is also capable of fabricating components with excellent accuracy and resolution. Once fully developed, the process is well-suited to bridge the productivity gap between metal AM and TM processes, making it an attractive candidate for further development and future commercialization as a feasible solution to high-volume production AM. Full article

Nowadays, the focus of flow shops is the adoption of customized demand in the context of service-oriented manufacturing. Since production tasks are often characterized by multi-variety, low volume, and a short lead time, it becomes an indispensable factor to include supporting logistics in practical scheduling decisions to reflect the frequent transport of jobs between resources. Motivated by the above background, a hybrid method based on dual back propagation (BP) neural networks is proposed to meet the real-time scheduling requirements with the aim of integrating production and transport activities. First, according to different resource attributes, the hierarchical structure of a flow shop is divided into three layers, respectively: the operation task layer, the job logistics layer, and the production resource layer. Based on the process logic relationships between intra-layer and inter-layer elements, an operation task–logistics–resource supernetwork model is established. Secondly, a dual BP neural network scheduling algorithm is designed for determining an operations sequence involving the transport time. The neural network 1 is used for the initial classification of operation tasks’ priority; and the neural network 2 is used for the sorting of conflicting tasks in the same priority, which can effectively reduce the amount of computational time and dramatically accelerate the solution speed. Finally, the effectiveness of the proposed method is verified by comparing the completion time and computational time for different examples. The numerical simulation results show that with the increase in problem scale, the solution ability of the traditional method gradually deteriorates, while the dual BP neural network has a stable performance and fast computational time. Full article