Search Results (13,740)

This research proposes integrating a combined system from a supercritical Brayton cycle (SBC) at extremely high temperatures and pressures and a conventional ORC cycle. The ORC cycle was evaluated with three working fluids: acetone, toluene, and cyclohexane. Of these, the cyclohexane, thanks to its dry fluid condition, obtained the best result in the sensitivity analysis for the energetic and exergetic evaluations with the most relevant (net power and exergy destruction) for the variation in the most critical performance parameter of the system for both the configuration with reheat and the configuration with recompression. Between the two proposed configurations, the most favorable performance was obtained with a binary system with reheat and recompression; with reheat, the SBC obtained first- and second-law efficiencies of 45.8% and 25.2%, respectively, while the SBC obtained values of 54.8% and 27.9%, respectively, with reheat and recompression. Thus, an increase in overall system efficiency of 30.3% is obtained. In addition, the destroyed exergy is reduced by 23% due to the bypass before the evaporation process. The SBC-ORC combined hybrid system with reheat and recompression has a solar radiation of 950 W/m² K, an exhaust heat recovery efficiency of 0.85, and a turbine inlet temperature of 1008.15 K. The high pressure is 25,000 kPa, the isentropic efficiency of the turbines is 0.8, the pressure ratio is 12, and the pinch point of the evaporator is initially 20 °C and reaches values of 45 °C in favorable supercritical conditions. Full article

Self-emulsifying drug delivery systems (SEDDS) represent an innovative approach to improving the solubility and bioavailability of poorly water-soluble drugs, addressing significant challenges associated with oral drug delivery. This review highlights the advancements and applications of SEDDS, including their transition from liquid to solid forms, while addressing the formulation strategies, characterization techniques, and future prospects in pharmaceutical sciences. The review systematically analyzes existing studies on SEDDS, focusing on their classification into liquid and solid forms and their preparation methods, including spray drying, hot-melt extrusion, and adsorption onto carriers. Characterization techniques such as droplet size analysis, dissolution studies, and solid-state evaluations are detailed. Additionally, emerging trends, including 3D printing, hybrid systems, and supersaturable SEDDS (Su-SEDDS), are explored. Liquid SEDDS (L-SEDDS) enhance drug solubility and absorption by forming emulsions upon contact with gastrointestinal fluids. However, they suffer from stability and leakage issues. Transitioning to solid SEDDS (S-SEDDS) has resolved these limitations, offering enhanced stability, scalability, and patient compliance. Innovations such as personalized 3D-printed SEDDS, biologics delivery, and targeted systems demonstrate their potential for diverse therapeutic applications. Computational modeling and in silico approaches further accelerate formulation optimization. SEDDS have revolutionized drug delivery by improving bioavailability and enabling precise, patient-centric therapies. While challenges such as scalability and excipient toxicity persist, emerging technologies and multidisciplinary collaborations are paving the way for next-generation SEDDS. Their adaptability and potential for personalized medicine solidify their role as a cornerstone in modern pharmaceutical development. Full article

Lithium-ion batteries are major drivers to decarbonize road traffic and electric power systems. With the rising number of electric vehicles comes an increasing number of lithium-ion batteries reaching their end of use. After their usage, several strategies, e.g., reuse, repurposing, remanufacturing, or material recycling can be applied. In this context, remanufacturing is the favored end-of-use strategy to enable a new use cycle of lithium-ion batteries and their components. The process of remanufacturing itself is the restoration of a used product to at least its original performance by disassembling, cleaning, sorting, reconditioning, and reassembling. Thereby, disassembly as the first step is a decisive process step, as it creates the foundation for all further steps in the process chain and significantly determines the economic feasibility of the remanufacturing process. The aim of the disassembly depth is the replacement of individual cells to replace the smallest possible deficient unit and not, as is currently the case, the entire battery module or even the entire battery system. Consequently, disassembly sequences are derived from a priority matrix, a disassembly graph is generated, and the obstacles to non-destructive cell replacement are analyzed for two lithium-ion traction battery systems, to analyze the distinctions between battery electric vehicle (BEV) and plug-in hybrid electric vehicle (PHEV) battery systems and identify the necessary tools and fundamental procedures required for the effective management of battery systems within the circular economy. Full article

With the rapid expansion of urban subway networks, vibrations induced by subway operations have become an increasingly significant concern for nearby structures. To assess the influence of subway-induced vibrations on nearby structures, it is essential to predict the vibration effects accurately prior to the construction of the subway system. By combining an improved Long Short-Term Memory (LSTM) model with a spectral analysis, this paper proposes a hybrid method to enhance the accuracy and efficiency of predicting structural vibrations induced by subway operations. The improved LSTM model is composed of BiLSTM, an attention mechanism, and the DBO algorithm. The symmetry inherent in the vibration propagation paths and the structural layouts of subway systems is leveraged to improve the feature extraction and modeling accuracy. Additionally, the hybrid method utilizes the symmetric properties of vibration signals in the spectral domain to enhance prediction robustness and efficiency. Then, the hybrid method is utilized to rapidly achieve highly accurate vibration responses induced by subway operations. The verification results demonstrate the following: (1) The improved LSTM model enhances the ability to recognize patterns in time-series vibration data, leading to improved model convergence and generalization. The improved LSTM mode has a significant improvement in prediction accuracy compared to the standard LSTM network. For numerical simulation and real-world measured signals, values of R² increased by 3% and 49.37%. (2) The proposed hybrid method significantly reduces computational time while ensuring results consistent with those obtained from the time-history analysis method. Applying the proposed hybrid method for data augmentation enhances the accuracy of the spectral analysis. The hybrid method achieves an improvement of 7% for the prediction accuracy. Full article

Signal analysis is a fundamental field in engineering and data science, focused on the study of signal representation, transformation, and manipulation. The accurate estimation of harmonic vibration components and their associated parameters in vibrating mechanical systems presents significant challenges in the presence of very similar frequencies and mode mixing. In this context, a hybrid strategy to estimate harmonic vibration modes in weakly damped, multi-degree-of-freedom vibrating mechanical systems by combining Empirical Mode Decomposition and Variational Mode Decomposition is described. In this way, this hybrid approach leverages the detection of mode mixing based on the analysis of intrinsic mode functions through Empirical Mode Decomposition to determine the number of components to be estimated and thus provide greater information for Variational Mode Decomposition. The computational time and dependency on a predefined number of modes are significantly reduced by providing crucial information about the approximate number of vibratory components, enabling a more precise estimation with Variational Mode Decomposition. This hybrid strategy is employed to compute unknown natural frequencies of vibrating systems using output measurement signals. The algorithm for this hybrid strategy is presented, along with a comparison to conventional techniques such as Empirical Mode Decomposition, Variational Mode Decomposition, and the Fast Fourier Transform. Through several case studies involving multi-degree-of-freedom vibrating systems, the superior and satisfactory performance of the hybrid method is demonstrated. Additionally, the advantages of the hybrid approach in terms of computational efficiency and accuracy in signal decomposition are highlighted. Full article

In this paper, we study the appearance of topological p-wave superconductivity of the type S=1, Sz=0 in a layer of adatoms. This unconventional superconductivity arises due to an anti-symmetric hybridization between the orbitals of the adatoms and those of the atoms in the superconducting BCS substrate. This two-dimensional system is topologically non-trivial only in the absence of a magnetic field and belongs to class DIII of the Altland–Zirnbauer classification. We obtain the Pfaffian that characterizes the topological phases of the system and its phase diagram. We discuss the differences between the two-dimensional case and a chain with the same type of superconductivity. Full article

The development of the new energy vehicle (NEV) market in China has promoted the sustainability of the automotive industry, but has also brought pressures to NEV charging infrastructure. This paper aims to determine the strategic role of charging stations, particularly on whether they should provide service for plug-in hybrid electric vehicles (PHEVs) in the highway service area during peak holidays. Firstly, the charging service resource allocation for a charging station that provides services for both electronic vehicles (EVs) and PHEVs is studied. Secondly, different queueing disciplines are compared. At last, a comparison between scenarios where charging services are limited to EVs and those where services extend to both EVs and PHEVs is conducted. A queueing system considering customer balking and reneging is developed. The impacts of parameters, such as the NEV arrival rate and patience degree of different NEV drivers, on the optimal allocation plan, profit, and comparison results are discussed. The main conclusions are as follows: (1) If the EV arrival rate is greater than the charging service rate, the charging station should not provide charging services for PHEVs. Providing service only for EVs derives more revenues and profits and results in a shorter waiting queue. Conversely, if the total arrival rate of NEVs (including EVs and PHEVs) is lower than the charging service rate, then the charging station should also serve PHEVs. (2) If providing service for PHEVs, a mixed queueing discipline should be applied when the total arrival rate approximates the service rate. When the total NEV arrival rate is significantly lower than the charging service rate, the separate queueing discipline should be adopted. (3) When applying a separate queueing discipline, if a certain type of NEV has a higher arrival rate and the drivers exhibit greater patience, then more charging resources should be allocated to this type of NEV. If the charging service is less busy, the more patient the drivers are, the less service resources should be allocated to them, whereas, during peak times, the more patient the drivers are, the more service resources should be allocated to them. Full article

The prediction of debris flows is essential for safeguarding infrastructure and minimizing the economic losses associated with the hazards. Traditional empirical and theoretical models, while providing foundational insights, often struggle to capture the complex and nonlinear behaviors inherent in debris flows. This study aims to enhance debris flow prediction by integrating theoretical modeling with data-driven approaches. We model debris flow as a viscoplastic fluid, employing the Herschel–Bulkley rheological model to describe its behavior. By combining the kinematic wave model with lubrication theory, we develop a comprehensive theoretical framework that encapsulates the mechanical physics of debris flows and identifies key governing parameters. Numerical solutions of this theoretical model are utilized to generate an extensive training dataset, which is subsequently used to train a support vector regression (SVR) model. The SVR model targets slide depth and velocity upon impact, using explanatory variables including yield stress, material density, source area depth and length, and slope length. The model demonstrates high predictive accuracy, achieving coefficients of determination R2 of 0.956 for slide depth and 0.911 for slide velocity at impact. Additionally, the relative residuals σ are primarily distributed within the range of −0.05 to 0.05 for both slide depth and slide velocity upon impact. These results indicate that the proposed hybrid model not only incorporates the fundamental physical mechanisms governing debris flows but also significantly enhances predictive performance through data-driven optimization. This study underscores the critical advantage of merging physical models with machine learning techniques, offering a robust tool for improved debris flow prediction and risk assessment, which can inform the development of more effective early warning systems and mitigation measures. Full article

Conventional endoscopy is limited in its ability to examine the small bowel and perform long-term monitoring due to the risk of infection and tissue perforation. Wireless Capsule Endoscopy (WCE) is a painless and non-invasive method of examining the body’s internal organs using a small camera that is swallowed like a pill. The existing active locomotion technologies do not have a practical localization system to control the capsule’s movement within the body. A robust localization system is essential for safely guiding the WCE device through the complex gastrointestinal (GI) tract. Moreover, having access to the capsule’s trajectory data is highly desirable for drug delivery and surgery, as well as for creating accurate user profiles for diagnosis and future reference. Therefore, a robust, real-time, and practical localization system is imperative to advance the field of WCE and make it desirable for clinical trials. In this work, we have identified salient features of different localization techniques and categorized studies in comprehensive tables. This study is self-contained as it offers a comprehensive overview of emerging localization techniques based on magnetic field, radio frequency (RF), video, and hybrid methods. A summary at the end of each method is provided to point out the potential gaps and give directions for future research. The main point of this work is to present an in-depth review of the most recent localization techniques published in the past five years. This will assist researchers in comprehending current techniques and pinpointing potential areas for further investigation. This review can be a significant reference and guide for future research on WCE localization. Full article

The transformation of biomass into renewable fuels and chemicals has gained remarkable attention as a sustainable alternative to fossil-based resources. Metal-based catalysts, encompassing transition and noble metals, are crucial in these transformations as they drive critical reactions, such as hydrodeoxygenation, hydrogenation, and reforming. Transition metals, including nickel, cobalt, and iron, provide cost-effective solutions for large-scale processes, while noble metals, such as platinum and palladium, exhibit superior activity and selectivity for specific reactions. Catalytic advancements, including the development of hybrid and bimetallic systems, have further improved the efficiency, stability, and scalability of biomass transformation processes. This review highlights the catalytic upgrading of lignocellulosic, algal, and waste biomass into high-value platform chemicals, biofuels, and biopolymers, with a focus on processes, such as Fischer–Tropsch synthesis, aqueous-phase reforming, and catalytic cracking. Key challenges, including catalyst deactivation, economic feasibility, and environmental sustainability, are examined alongside emerging solutions, like AI-driven catalyst design and lifecycle analysis. By addressing these challenges and leveraging innovative technologies, metal-based catalysis can accelerate the transition to a circular bioeconomy, supporting global efforts to combat climate change and reduce fossil fuel dependence. Full article

This study investigates the development of epoxy–resin composites reinforced with coral-derived calcium carbonate (CaCO₃) fillers for enhanced radiation shielding and mechanical properties. Leveraging the high calcium content and density of coral, composites were prepared with filler weight fractions of 0%, 25%, and 50%. SEM and EDS analyses revealed that higher filler concentrations (50%) increased particle agglomeration, affecting matrix uniformity. Mechanical testing showed that while the tensile and flexural strengths decreased with the increased filler content, the compressive strength significantly improved, reaching 135 MPa at a 50% coral content. Radiation shielding evaluations demonstrated enhanced attenuation with a higher filler content, achieving 39.63% absorption at 60 kVp for the 50% coral composite. However, the shielding efficiency was notably lower compared to lead, which achieves over 99% absorption at similar energy levels. These quantitative comparisons highlight the material’s limitations in high-radiation environments but emphasize its suitability for moderate shielding applications. Despite their lower shielding efficiency, the composites provide an environmentally friendly and non-toxic alternative to lead, aligning with sustainability goals. Future work should focus on optimizing filler dispersion, mitigating agglomeration, and exploring hybrid systems to enhance the shielding efficiency and mechanical properties. The further quantitative evaluation of parameters such as Zeff and cross-sections is recommended to comprehensively assess the material’s performance. Full article

Fuel blending plays a very important role in petroleum refineries, because it directly affects the quality of the end products, as well as the overall profitability of the refinery. This process of blending involves a combination of various hydrocarbon streams to make fuels that meet specific performance standards and comply with regulatory guidelines. For many decades, most refineries have been dependent on linear programming (LP) models for developing recipes for blending optimization. However, most LP models normally fail to capture the complex nonlinear interaction of blend components with fuel properties, leading to off-specification products that may necessitate re-blending. This work discusses a case study of a hybrid artificial intelligence (AI)-based method for gasoline blending based on a genetic algorithm (GA) combined with an artificial neural network (ANN). AI-based blending systems are more flexible and will enable the refineries to meet the product specifications regularly and result in cost reduction owing to the fall in quality giveaways. The AI-powered process discussed can predict, with much better accuracy, critical combustion properties of gasoline such as the Research Octane Number (RON), Motor Octane Number (MON), and Antiknock Index (AKI), compared to the classical LP models, with the added advantage of optimization of the blend ratio in real time. The results showed that the AI-integrated fuel blending system was able to produce fuel recipes with a mean absolute error (MAE) of 1.4 for the AKI. The obtained MAE is close to the experimental uncertainty of 0.5 octane. A high coefficient of determination (R²) of 0.99 was also obtained when the system was validated with a new set of 57 fuels comprising primary reference fuels and real gasoline blends. The study highlights the potential of AI-integrated systems in transforming traditional fuel blending practices towards sustainable and economically viable refinery operations. Full article

Vehicle Ad hoc Networks (VANETs) play an essential role in intelligent transportation systems (ITSs) by improving road safety and traffic management through robust decentralized communication between vehicles and infrastructure. Yet, decentralization introduces security vulnerabilities, including spoofing, tampering, and denial-of-service attacks, which can compromise the reliability and safety of vehicular communications. Traditional centralized security mechanisms are often inadequate in providing the real-time response and scalability required by such dispersed networks. This research promotes a shift toward distributed and real-time technologies, including blockchain and secure multi-party computation, to enhance communication integrity and privacy, ultimately strengthening system resilience by eliminating single points of failure. A core aspect of this study is the novel D-CASBR framework, which integrates three essential components. First, it employs hybrid machine learning methods, such as ElasticNet and Gradient Boosting, to facilitate real-time anomaly detection, identifying unusual activities as they occur. Second, it utilizes a consortium blockchain to provide secure and transparent information exchange among authorized participants. Third, it implements a fog-enabled reputation system that uses distributed fog computing to effectively manage trust within the network. This comprehensive approach addresses latency issues found in conventional systems while significantly improving the reliability and efficacy of threat detection, achieving 95 percent anomaly detection accuracy with minimal false positives. The result is a substantial advancement in securing vehicular networks. Full article

Motion control of modern and advanced aircraft has to be provided under conditions of incomplete and inaccurate knowledge of their parameters and characteristics, possible flight modes, and environmental influences. In addition, various abnormal situations may occur during flight, in particular, equipment failures and structural damage. These circumstances cause the problem of a rapid adjustment of the used control laws so that the control system can adapt to the mentioned changes. However, most adaptive control schemes have a model of the control object, which plays a crucial role in adjusting the control law. That is, it is required to solve also the identification problem for dynamical systems. We propose an approach to solving the above-mentioned problems based on artificial neural networks (ANNs) and hybrid technologies. In the class of traditional neural network technologies, we use recurrent neural networks of the NARX type, which allow us to obtain black-box models for controlled dynamical systems. It is shown that in a number of cases, in particular, for control objects with complicated dynamic properties, this approach turns out to be inefficient. One of the possible alternatives to this approach, investigated in the paper, consists of the transition to hybrid neural network models of the gray box type. These are semi-empirical models that combine in the resulting network structure both empirical data on the behavior of an object and theoretical knowledge about its nature. They allow solving with high accuracy the problems inaccessible by the level of complexity for ANN models of the black-box type. However, the process of forming such models requires a very large consumption of computational resources. For this reason, the paper considers another variant of the hybrid ANN model. In it, the hybrid model consists not of the combination of empirical and theoretical elements, resulting in a recurrent network of a special kind, but of the combination of elements of feedforward networks and recurrent networks. Such a variant opens up the possibility of involving deep learning technology in the construction of motion models for controlled systems. As a result of this study, data were obtained that allow us to evaluate the effectiveness of two variants of hybrid neural networks, which can be used to solve problems of modeling, identification, and control of aircraft. The capabilities and limitations of these variants are demonstrated on several examples. Namely, on the example of the problem of aircraft longitudinal angular motion, the possibilities of modeling the motion using the NARX network as applied to a supersonic transport aircraft (SST) are first considered. It is shown that under complicated operating conditions this network does not always provide acceptable modeling accuracy. Further, the same problem, but applied to a maneuverable aircraft, as a more complex object of modeling and identification, is solved using both a NARX network (black box) and a semi-empirical model (gray box). The significant advantage of the gray box model over the black box one is shown. The capabilities of the hybrid model realizing deep learning technologies are demonstrated by forming a model of the control object (SST) and neurocontroller on the example of the MRAC adaptive control scheme. The efficiency of the obtained solution is illustrated by comparing the response of the control object with a failure situation (a decrease in the efficiency of longitudinal control by 50%) with and without adaptation. Full article

Electrically driven legged robots have become popular in recent years. However, the development of reliable energy supply systems and effective energy management strategies for legged robots with dramatically varying power requirements still needs to be explored. This article proposes a learning-based model predictive control (MPC) energy management strategy for legged robots with battery–supercapacitor hybrid energy storage systems containing a power prediction unit and an MPC with learning-based adaptive weights. Firstly, the mathematical model of the legged robot is established, and a dual-layer long short-term memory network is constructed to predict the load power demand, providing the model and measurable disturbance for the MPC. Secondly, a multi-objective optimization objective function is established for the MPC-based energy management strategy. Three normalized terms, battery capacity loss, battery power fluctuation, and supercapacitor state-of-charge regulation, are balanced in the objective function. Finally, a deep learning algorithm is proposed to adaptively adjust the three weighting factors to meet the diverse operation conditions. Hardware-in-the-loop experimental implementations demonstrate that the proposed method can improve the kinematic performance of the legged robot by maintaining the supercapacitor state of charge at a relatively high level and reducing the battery capacity loss by 12.7% compared with the conventional MPC method. Full article