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In this paper, the effects of different designs of pressure vessels on hydrodynamic parameters and the performance of seawater RO systems are investigated using computer simulations. Simulation results showed that designs using pressure vessels with four membranes in two-pass RO systems have a lower energy consumption, 5.29 and 0.33 kWh/m3 for the first and second passes, respectively, than those with two or three membranes. Also, designs No. 4 and No. 10 were the most efficient, 41 and 56% for the first and second passes, respectively, compared to other ones. The first and second passes required a lower permeate and feed pressure when using designs No. 3, No. 6, No. 9, and No. 12 where pressure vessels with two membranes were used. In addition, hybrid designs of two-pass RO systems were found to have the best efficiency and the lowest energy consumption among various other designs. Findings showed that in two-pass RO systems, the first pass consumes more energy than the second pass. Thus, the efficiency of the second pass in all designs of two-pass RO systems is more than the first pass. The results presented in this paper can be used to optimize the design of two-pass RO systems to ensure the optimum operation of such systems. [ABSTRACT FROM AUTHOR]

Several existing research studies on the modeling of photovoltaic (PV) systems feeding a resistive load through a DC-DC converter have proven that the use of the small-signal approach using analytical or graphical methods for the design of a linear model is failing. This is due to the fact that the simplified linear model, usually presented by a transfer function, is unable to reveal all the existing information about the actual solar system behavior when subjected to the Standard Test Conditions (STC). Since it does not take into account: (i) the nonlinearity of the current–voltage (I-V) characteristic of the solar panel, (ii) the presence of elementary components in both the solar panel and the DC-DC converter, which are often unmodeled and cannot be easily considered in the modeling, (iii) the existence of extra components in the DC-DC converter and more dynamics that must be neglected at high frequencies, for example those describing the thermal energies dissipated in the IGBT part of the DC-DC boost converter, the presence of cross-coupling between some electrical quantities occurring either in the DC-DC boost converter or in the solar panel, etc. For these reasons, this paper proposes a new design of a small-signal linear model to accurately describe the actual solar system behavior operating at STC. This is achieved by considering its internal functioning as a black box where only its input–output measurements are available in the form of frequency response data (FRD), generated by running a free stand-alone Plecs® software package. As this software has the ability to perfectly model the behavior of boost DC-DC converters, previously connected by variable resistive loads, it is possible thanks to the existence of equivalent electrical circuits, available in its own library. Unfortunately, this library lacks an equivalent circuit for the KC200GT photovoltaic panel where its design is needed through evaluating its current–voltage characteristics. The resulting global model is then simulated in the modified Plecs® software, providing the desired FRDs. These are used to estimate the parameters of the proposed linear model, which then used for tuning the PID controller. Here, this controller is combined with the standard Maximum Power Point Tracking (MPPT) strategy based on the direct implementation of the Incremental Conductance (INC) algorithm, highlighting therefore the proposed improved INC-MPPT strategy. It perfectly overcomes the ripple problems encountered in some electrical components of the solar system when controlled by the standard INC-MPPT strategy. As the proposed improvements depend on the model accuracy, the main contribution of this paper lies therefore on the necessary steps to design this proposed small-signal model. [ABSTRACT FROM AUTHOR]

Memristors have several chaotic dynamic models and have been used successfully in various fields, including secure communication systems, information storage, and artificial neural networks. The memristor-based four-dimensional chaotic (FDMC) systems generate unpredictable and intricate time domain signals. Parameter fluctuations in the FDMC system may give birth to chaos, making it difficult to suppress. Stabilizing chaos in the FDMC system improves the circuit's performance. This paper synthesizes a novel time-efficient chatter-free nonlinear robust adaptive control (NLRAC) technique that stabilizes chaos in the FDMC system affected by time-varying unknown bounded exogenous disturbances and model uncertainties. The proposed NLRAC strategy decimates the time-varying unknown bounded exogenous disturbances and model uncertainties effects; it establishes a faster, smoother state-variable trajectories convergence to the zero vicinity. The theoretical analysis and mathematical proofs are based on the Lyapunov stability technique. Computer simulation results show that the proposed NLRAC technique effectively brings the FDMC system's state-variable trajectories to zero with reduced fluctuations for control input signals and state-variable trajectories. This feedback controller's attribute enhances closed-loop stability performance, improves precision, and reduces risk overshoot. The paper includes comparative computer simulation results to endorse the proposed controller performance. [ABSTRACT FROM AUTHOR]

Different sounds have various effects on human health, and by introducing the ones that are therapeutic, a healing environment can be created. This paper describes the process to train and test a machine learning algorithm to describe and explore the therapeutic nature of Quranic verse. Using a dataset containing four emotional states namely happy, sad, angry, and relaxed, we trained a model and classified different recitations of the Quran into one of these states. This paper proposes the use of Mel-frequency cepstral coefficients (MFCC) to extract features from Quranic audio and classify it with respect to a known dataset. Based on the experiments conducted on Quranic verses, we summarize our results. [ABSTRACT FROM AUTHOR]

Numerous studies have introduced a variety of synchronization techniques that involve many structures. However, since the majority of synchronization methods influence or increase the system states from a stability perspective, these methods necessitate retuning the controller parameters, reconstructing a converter controller, or employing a different control construction. This paper proposes a novel, yet simple and straightforward, method for implementing a synchronization technique concept based on the conventional synchronization method known as the "dark lamp" process. Since the proposed method is based on logic functions, there is no additional state in the entire system of the model, which is the cornerstone of this paper. The proposed method is validated and studied in a time–domain simulation environment, specifically a PSCAD/EMTDC. The proposed new synchronization technique in this paper overcomes the discussed synchronization techniques in the literature by dispensing with the need either to use a phase-locked loop (PLL) or to retune the controller parameters and, furthermore, does not increase the system state equation. Meanwhile the proposed techniques in the literature increase the system state equation by at least two states, which affects the entire system performance and stability. The results of this paper show promising and important outcomes that will further the study of the synchronization technique. [ABSTRACT FROM AUTHOR]

The incorporation of net-zero technology into preexisting energy networks is crucial for facilitating the shift toward an ecologically conscious and sustainable energy infrastructure. The primary objective of this integration is to effectively decrease carbon footprints and to provide a comprehensive understanding of the current approaches and trends related to the design and management frameworks of integrated energy networks. The initial section of this study establishes the foundation for a comprehensive examination of the particular challenges associated with decarbonization in the strategic and operational aspects of integrated energy networks. The subsequent analysis proceeds to elucidate the fundamental framework and technological architecture upon which these energy networks are constructed. This provides significant insights into the operational complexity and efficacy of the system. In addition, the paper provides a concise examination of prominent frameworks and alternative approaches that tackle the issue of low-carbon design and administration. The degree of accuracy facilitates individuals when selecting systems that align with the specific requirements of unique circumstances. Furthermore, this study provides explicit suggestions for future research based on an examination of the distinct attributes and framework of integrated energy networks. The anticipated outcome of implementing these recommendations is to enable the advancement of sustainable development and expedite the shift toward energy infrastructure with reduced carbon emissions. This will make a significant contribution to the collaborative endeavor of mitigating climate change and fostering a sustainable energy future. This study further elucidates the significant contribution of integrated energy networks in addressing climate change and enhancing energy efficiency. It achieves this by synthesizing a complete range of concepts sourced from many academic papers, industry reports, and case studies. This statement offers an examination of the multifaceted technological, legislative, and planning factors that contribute to the attainment of net-zero objectives. [ABSTRACT FROM AUTHOR]

This paper presents an active fault-tolerant flight control system (FCS) design for the dual-system/hybrid VTOL UAV configuration that maximally exploits its inherent over-actuation abilities to provide higher levels of fault-tolerance and reliability compared to that of the other VTOL UAVs configurations up to this point. The significance of this study is to convert the main drawback of having the vertical rotors as dead weights during the cruise to a key advantage where all the UAV's controls are kept active during all the flight phases raising the UAV's fault-tolerance and maneuverability abilities. The FCS proposed elaborates on a new architecture for the hierarchical automatic control loops using the dynamic inversion control to design the trajectory tracking virtual commands and employing optimal dynamic control allocation to optimally resolve the controls redundancy. Regarding fault-tolerance, the presented FCS handles simultaneous failures and jamming of all the control surfaces during flight where the control commands to the healthy controls (vertical rotors) were automatically adapted such that the trimming and trajectory tracking were successfully maintained till the mission end. Additionally, this paper addresses for the first time the employment of the controls redundancy to enhance the VTOL UAV's maneuverability in fault-free cases, where we present a study of reducing the minimum turn radius of the UAV when the vertical rotors are employed with the control surfaces during the maneuver. A reduction of 38% in the coordinated steady-level minimum turn radius turn at 35 m/sec flight speed was achieved. [ABSTRACT FROM AUTHOR]

Electric vehicles with proper battery charging mechanism are essential to achieve superior performance with good dynamic response and high efficiency. This paper comes up with the analysis of interval type-2 fuzzy logic controller (IT2FLC) for interleaved Cuk converter for battery charging in electric vehicles. The key intention of this paper is to obtain excellent controller parameters such as improved accuracy and stability with good power quality attributes of less harmonic distortion and unity power factor at the supply side using a robust and intelligent control approach. IT2FLC is developed effectively to acquire the optimal proportional integral (PI) parameters for the constant current and constant voltage charging controllers to enrich the operation of the battery charging system. This in turn achieves excellent transient parameters with less settling time of 0.01 s, reduced overshoot of 1% and efficiency of about 93.85%. The so-called interval type-2 (IT2) controller is therefore accomplished to alleviate uncertainties and improvise the dynamic stability of the charging solution. The behavioural traits of the intelligent controller are examined and compared with Ziegler–Nicholas tuned PI and T1FL-based PI using MATLAB/Simulink. A hardware prototype of 350 W, 48 V/5 A charger is built and verified with dsPIC33F to evaluate the working principle of the converter using IT2. [ABSTRACT FROM AUTHOR]

For solving the storage and indexing problem of big data, this paper proposes a big data storage algorithm and an indexing algorithm, respectively. Simultaneously, the data classification and segmentation algorithm is given, and then, the classified data are segmented and sliced based on features of data. Moreover, the calculation of the service queue length on data slicing is given to achieve fast storage of slices in the data server. A data reconstruction method is given to index each slice of the original data and implement the data reconstruction according to the given reconstruction method. At the same time, an evaluation method for data reconstruction is also given to achieve the minimum difference between the reconstructed data and the original data. Simulation results show that the accurate rate of the data reconstruction method proposed in this paper is higher than 93%. In addition, compared with the existing data reconstruction methods, it is found that it not only has higher reconstruction accurate rate, faster reconstruction process, stronger anti-interference ability, but also lower information loss. These researches in this paper provide the powerful methods for protecting important documents, reducing losses, reducing waste of resources, and improving the convenience, speed, and safety in work, life, and so on. [ABSTRACT FROM AUTHOR]

The finite element limit analysis solution presented in this paper offers novel approaches for estimating the ultimate bearing capacity of annular foundations on Hoek–Brown criterion. The study examines the effects of five of dimensionless parameters, including the ratio of internal to external radii, the depth ratio, the adhesive factor, the yield parameter, and the geological strength index, on the findings of bearing capacity as well as the processes of collapse. Furthermore, one of the soft-computing regression methodologies, the multi-objective evolutionary polynomial regression analysis (MOGA-EPR) method, is utilized along with the requirement of the FELA outcomes as input data. This paper provides accurate limit-state predictions for annular footings on diverse rock masses using the MOGA-EPR model. By using the MOGA-EPR approach, the findings are highly precise and trustworthy, empowering designers to choose the best annular foundation design for various Hoek–Brown material varieties. Moreover, this study extends its scope by encompassing the application of multiple linear regression, multiple nonlinear regression, and artificial neural network models for an extensive comparative analysis. The amalgamation of these models widens the evaluative framework, fostering a more comprehensive exploration of predictive capabilities and insights into the stability assessment of annular footings across rock mass conditions. Through this multifaceted approach, a holistic comprehension emerges, thereby enhancing the decision-making process pertaining to the design of annular foundations within diverse Hoek–Brown material contexts. [ABSTRACT FROM AUTHOR]

Historical masonry structures are the most important features that distinguish our cultural heritage, from here comes the importance of protection this type of masonry structures from collapse under earthquakes effect or other dynamic loading such as blast loading. Therefore, Seismic Base Isolation is used to reduce earthquakes damages and protect structures from collapse. This paper aims to study the Effect of Seismic Base Isolation, using High Damping Rubber Bearing (HDRB) on Existing Masonry Structures subjected to dynamic loads such as earthquakes, through utilizing numerical modeling of masonry structures. This paper includes definition of seismic base isolation mechanical characteristics, and definition of numerical modeling approaches for structural masonry analysis. The studied masonry structure is the old AWQAF Directorate Building in Aleppo, which is islamic government building affiliated to the Syrian Ministry of AWQAF; In order to perform the numerical modeling, equivalent masonry properties used for this structure were also determined. This structure was analyzed applying nonlinear dynamic analysis-time history method, and reanalyzing it by using seismic base isolation, type HDRB applying the same method. The results of the two analyses were discussed and compared, and the conclusions of this paper showed reduction in shear force when using seismic base isolation, and reduction in stresses that appear in structure because the stresses are located in the level of base isolation system. [ABSTRACT FROM AUTHOR]

Since its inception, salient object detection has been widely used in various image processing tasks. However, previous approaches have mainly focused on how to optimize the accuracy of detection, ignoring the computational overhead of the model itself. This makes it difficult to be used in many devices with limited hardware conditions. To address the above problems, this paper proposes a lightweight saliency detection network based on background denoising and feature generation. First, we build a lightweight feature extraction network by improving the MobileNetV3 feature extraction part and with the help of a multidimensional focus module. Subsequently, in order to fully explore the difference between the background and the salient objects. We insert a noise suppression subnet at the end of the encoder. This subnetwork uses the learned background features to reverse fuse the information from the backbone network, effectively decreasing the amount of background noise and encouraging the model to better distinguish subtle differences between salient objects and the background. Finally, in the decoding stage this paper constructs a feature generator that allows the model to efficiently extend the feature map with little increase in the number of parameters. Thereby, it compensates for the lack of expression capability of lightweight model features. The experiment results on six publicly available benchmark datasets prove that our method is more efficient and accurate than existing lightweight saliency detection methods. [ABSTRACT FROM AUTHOR]

Cancelable biometrics serves as an effective countermeasure against various template attacks launched by intruders, safeguarding the biometric system. This paper proposes a cancelable approach with a novel feature extraction technique for iris recognition, known as the hybrid architecture of the convolutional neural network (CNN) and GRU (gated recurrent unit). To provide cancelability to the system, the paper makes use of a random projection technique. The proposed method has the best outcome in terms of accurate identification. The method is validated on two Iris datasets IITD and MMU, which show promising results on the equal error rate (EER) and accuracy. The proposed model provides 0.02 and 0.045 EER for IITD and MMU, respectively, and accuracy 0.98 and 0.933%, for IITD and MMU Iris dataset, respectively, which is very high compared to other methodologies. The proposed hybrid architecture is being used for a cancelable biometric system for the first time based on literature review. The efficiency of the proposed method is high when validated on the datasets. [ABSTRACT FROM AUTHOR]

Since the emergence of big data, the popularity of deep learning models has increased and they are being implemented in a wide range of applications, including people detection and counting in congested environments. Detecting and counting people for human behavior analysis in retail stores is a challenging research problem due to the congested and crowded environment. This paper proposes a deep learning approach for detecting and counting people in the presence of occlusions and illuminance variation in a crowded retail environment, utilizing deep CNNs (DCNNs) for semantic segmentation of top-view depth visual data. Semantic segmentation has been implemented using (DCNNs) in recent years since it is a powerful approach. The objective of this paper is to design a novel architecture that consists of an encoder–decoder architecture. We were motivated to use transfer learning to solve the problem of insufficient training data. We used ResNet50 for the encoder, and we built the decoder part as a novel contribution. Our model was trained and evaluated on the TVHeads dataset and the people counting dataset (PCDS) that are available for research purposes. It consists of depth data of people captured from a top-view RGB-D sensor. The segmentation results indicate high accuracy and demonstrate that the proposed model is robust and accurate. [ABSTRACT FROM AUTHOR]

At present, in end-to-end network combined with deep learning in stereo matching, how to perceive ample image details within an acceptable range of computational cost, so as to improve the final matching accuracy, has become a hot research topic. In this paper, we design a cascaded disparity correction network based on DispNet, which can refine the initial disparity image by combining the process output results in the trunk network. The method proposed in this paper is named ETE–DC–CNN (end to end–disparity correction–convolutional neural network). In the trunk network, the mixed dilated convolution module is employed to extract the features of the original image, and the feature image is constructed into a 3D disparity space volume through depth-separable convolution, after which the initial disparity image can be obtained through 2D encoder–decoder module. In the correction network, the gradient information of the initial feature image is combined to reconstruct the modified matching cost body guided by the initial disparity image and then a smaller encoder–decoder module is trained and processed to make the final result at the sub-pixel level. The algorithm in this paper has been verified in SceneFlow and KITTI 2012/2015 data sets. The method proposed in this article achieves 3.46, 2.12, 1.19, and 0.6 results in the non-occluded area and 4.04, 2.55, 1.58, and 0.7 results in the global area on the Er (> 2 px), Er (> 3 px), Er (> 5 px), and Epe indicators in the KITTI 2012 test set, which are higher than those of the backbone network DispNet. [ABSTRACT FROM AUTHOR]

A major research subject in recent times is Alzheimer's disease (AD) due to the growth and considerable societal impacts on health. So, the detection of AD is essential for medication care. Early detection of AD is critical for effective treatment, and monitoring the time period between normal aging's unavoidable cognitive loss and dementia's more catastrophic degradation is common practice. The deep learning method for early diagnosis and automated categorization of AD has suddenly gained a lot of attention since rapid advancement in the field of GANs approaches has now been used in the clinical research sector. Many recent studies using brain MRI images and convolutional neural networks (CNNs) to identify Alzheimer's disease have yielded promising results. Instead of adequately engaging with the lack of real data, many research papers have focused on prediction. The main purpose of this paper is to do this by generating synthetic MRI images using a series of DCGANs. This paper demonstrates the effectiveness of this concept by cascading DCGANs that imitate different stages of Alzheimer's disease and utilizing SRGANs to enhance the resolution of MRI scans. The purpose of this research is to come forward and tell if an individual might just get Alzheimer's disease. CNN, DCGANs, and SRGANs are used in this paper to present a deep learning-based approach that improves classification and prediction accuracy to 99.7% and also handles the lack of data and the resolution of data. [ABSTRACT FROM AUTHOR]

Existing blockchain approaches exhibit a diverse set of dimensions, and on the other hand, IoT-based health care applications manifest a wide variety of requirements. The state-of-the-art analysis of blockchain concerning existing IoT-based approaches for the healthcare domain has been investigated to a limited extend. The purpose of this survey paper is to analyze current state-of-the-art blockchain work in several IoT disciplines, with a focus on the health sector. This study also attempts to demonstrate the prospective use of blockchain in healthcare, as well as the obstacles and future paths of blockchain development. Furthermore, the fundamentals of blockchain have been thoroughly explained to appeal to a diverse audience. On the contrary, we analyzed state-of-the-art studies from several IoT disciplines for eHealth, and also the study deficit but also the obstacles when considering blockchain to IoT, which are highlighted and explored in the paper with suggested alternatives. [ABSTRACT FROM AUTHOR]

Automatic question generation and automatic question answering from text is a fundamental academic tool that serves a wide range of purposes, including self-study, coursework, educational assessment, and many more. Manual construction of questions is a time-consuming and complicated process that requires experience, whereas automating the process diminishes the costs of manual question creation and fulfills the need for a persistent supply of questions for the tutors and self-evaluators. This paper uses an encoder–decoder architecture-based text-to-text transfer transformer (T5) intending to generate several types of question–answer pairs over a given context, including subjective question–answers having short and long answers, fill-in-the-blanks-type question–answers, Boolean answer (yes-or-no)-type questions, and multiple-choice question-answers. The model has been evaluated on benchmark datasets—SQuAD, QuAC, and BoolQ; over automated metrics—BLEU, ROUGE, METEOR, F1, and accuracy. The model outperformed previous baseline models, with 18.87 and 25.24 scores over BLEU-4 and METEOR metrics, respectively. The paper also demonstrates that the proposed system efficiently generates question–answer pairs where the baseline approaches struggled. The evaluation analysis also shows that the generated question–answer pairs are comparable with existing systems and even better in terms of diversity. Also, the generated questions are grammatically and contextually correct, and the answer generated matches the question in the textual context. [ABSTRACT FROM AUTHOR]

In this paper, an economic life cycle assessment of hydrogen-fueled gas turbine power plant is developed, where hydrogen is produced via electrolysis with electricity generated from renewable resources (green hydrogen). By this way, the continuous green electricity without fluctuation can be generated. With a great potential in solar irradiation, Iran can be a major country for producing green hydrogen. For this purpose, first, we simulate a 100 MW solar PV plant in Fars province and calculate annual electricity generation. Considering capital cost (Capex) and operations and maintenance costs (Opex) of plants including PV, electrolyzer, hydrogen storage and distribution facilities, and gas turbine, we estimate levelized cost of electricity from green H2-fueled gas turbine power plant. Because hydrogen technology has not matured yet, the analysis for both 2021 and 2040 will be conducted. Due to lower marginal production costs, the increase in renewable energy sources decreases electricity wholesale prices. In this paper, we also estimate long-term merit order effect (MOE) of electricity generated by green hydrogen on electricity wholesale price. The results show that in renewable/hydrogen-based forecast for electricity mix of 2040, electricity wholesale price is estimated around 6.38 c$/kWh, nearly 38% less than present wholesale price. [ABSTRACT FROM AUTHOR]

The paper takes the Jidong Nanbu high temperature oil reservoir as the research object and establishes the comprehensive numerical model of CO2 storage and displacement based on the CO2 displacement mechanism, CO2 heat recovery mechanism, and CO2 geological storage mechanism. On this basis, the injection and production optimization model is established by combining the theory of multi-objective and single-objective optimization algorithm respectively. The objective function of the multi-objective injection and production optimization model is NPV and CO2 heat recovery. The model is solved using the NSGAII and MOPSO algorithm. The results show that the objective function value obtained by the MOPSO algorithm is better than the NSGAII algorithm. The optimal values of NPV and CO2 heat recovery were 4.781 × 108 CNY and 1.078 × 1016 J respectively. The objective function of the single objective injection and production optimization model is the NPV. GA and DE optimization algorithms are used to solve the model. The results show that the DE algorithm gets the better objective function value than the GA algorithm. The optimal value of NPV is 3.69 × 1010 CNY. [ABSTRACT FROM AUTHOR]

While emerging pollutants are being detected in water bodies globally, a significant amount of solid waste that could be used for effluent remediation is being discarded. This not only reduces the half-life of landfills but also contributes to environmental pollution. Of these, eggshells (ES) are one of the most discarded food waste items worldwide. This paper presents the development and characterization of ES-based adsorbent for the removal of oxytetracycline (OTC), an emerging pharmaceutical pollutant, from aqueous solutions. The sorption of OTC on ES and ES-derived materials has not been previously studied. The ES was modified by removing the organic layer and both materials were used to grow Fe oxides, which can impart magnetic response to allow indirect manipulation or the addition of new sorption sites. Two methodologies were employed to synthesize Fe oxides: alkaline oxidation in the presence of nitrate and impregnation–pyrolysis (IP), which have not been used previously for developing magnetic ES. The materials developed by IP exhibit the highest total specific surface area and display a magnetic response due to the presence of magnetite and maghemite. Moreover, they exhibit a negative zeta potential over a wide range of pH values. All materials were able to adsorb OTC at pH 3, 7, or 9, indicating that ES (the simplest material) and ESIP (composite with good enough magnetic response) are suitable for removing OTC from aqueous solution. ES is recommended when indirect manipulation is not necessary, whereas ESIP is recommended when it is required. In order to explore the potential for reuse of the composite-pollutant materials, their antibacterial capacity against E. coli and E. faecium was evaluated. The findings of the present work contribute to the development of a circular economy by reducing waste generation, minimizing the consumption of natural resources, and reducing greenhouse gas emissions, while improving environmental protection. [ABSTRACT FROM AUTHOR]

Electrodialysis (ED) and the continuous evolution of ion exchange membranes are one of the reasons behind its diverse applications. Known to be a high-water recovery process with limited uses of chemicals gives it the advantage of sustainable treatment technology. Water softening plays a major role in water industry; it can ensure clean, safe, and efficient water use but it affects water equilibrium. Mainly the lack of calcium carbonate causes a real issue in water distribution networks made of concrete, pipes, and reservoirs by decreasing their lifetime. This paper addresses the selectivity of softening using ED to reduce the negative impact of water hardness and to maintain both the quality and equilibrium of water. This latter should be neither too hard nor too soft, and the calco-carbonic equilibrium is of major interest. In this work, ED operations were carried out at a laboratory scale using different water initial concentrations. The propensity of the water towards calcium carbonate scaling was addressed by monitoring the Langelier saturation index (LSI) generally used to assess water scaling potential. At a demineralization rate (DR) of 80%, all the dilutes obtained from all the feed water reached the status of soft water. ED gave good results in terms of hardness reduction, and LSI corresponded to the equilibrium state at a DR of 40% in all the cases studied. [ABSTRACT FROM AUTHOR]

In this study, we present an exact derivation of a meso-scale mathematical model for analyzing heat transfer in nanofluids through variational convergence, targeting the reduction of discrepancies between theoretical predictions and experimental data. The model derives distinct sets of partial differential equations for microscale, mesoscale, and macroscale simulations, taking into account several influencing factors across different scales. By employing the weak convergence of distributions and the theory of distributions, we establish variational convergence between the PDEs at different scales. Significantly, the mesoscale model leads to a derivation of the quadratic dependence of thermal conductivity on the particle volume fraction. Our numerical evaluations reveal a marked improvement in prediction accuracy, with the proposed model reducing prediction error in some cases by as much as 92% compared to some existing linear models. Additionally, when tested against available experimental data, the model shows a closer alignment reinforcing its potential for enhancing the prediction and understanding of nanofluid heat transfer. Hence, this paper represents a valuable contribution to nanofluid heat transfer research, providing a promising direction for future advancements in the field. [ABSTRACT FROM AUTHOR]

Gas lift is one of the most common artificial lift methods used to enhance oil well production. Oscillations in production flow rate, known as casing heading instability, are a common issue in gas lift systems. Designing an automatic control system is a recommended solution to prevent casing heading and improve the performance of a gas lift system. One of the major challenges in designing such a control system is the uncertainty of the system parameters and state variables. In this paper, the analytical gas lift model presented in previous studies was modified to account for the real gas flow in the annulus. Then, a parameter estimator and a state observer were designed using a square root Kalman filter to estimate the uncertain parameter and state variables. The simulation results have shown that the improved model is more reliable. The designed Linear Quadratic Gaussian (LQG) control, along with the estimator and observer, can stabilize the gas lift system under uncertain conditions. The main contribution of this work is the development of an integrated system for controlling and estimating the gas lift system. This system ensures reliable performance even in the presence of changes in wellbore and reservoir conditions. [ABSTRACT FROM AUTHOR]

It is the key technology to reduce the pollution of production layer by using retrievable bridge plug to plug production string and carry out snubbing operation. This paper establishes the simulation calculation model of the small diameter bridge plug rubber tube through the rubber thermal aging experiment. The influence of rubber combination on sealing performance and safety under high temperature and high pressure was analyzed, and the rubber structure was optimized. The results show that the sealing performance of the three-rubber tube structure is about 8.7% higher than that of the double-rubber tube structure at 120 °C and 55 MPa. In order to take into account the deformation and sealing requirements of the rubber tube, the preset trapezoidal ring groove structure (groove number n, groove width l, groove depth δ) in the rubber tube is optimized. Under the condition of 35 MPa setting load, the optimized A-structure rubber tube (n = 3, l = 10.5 mm, δ = 0.5 mm) improves the sealing performance by about 21.15% and reduces the Mises stress by 4.88% compared with that before optimization. It is suitable for oil and gas wells with long-term sealing and high safety requirements. The sealing performance of the optimized B-structure rubber tube (n = 3, l = 10.5 mm, δ = 1.5 mm) can be improved by about 43.94%. Although the Mises stress is increased by 15.97%, it is still within the safe range and can be applied to the working conditions with high sealing pressure and short-term sealing. [ABSTRACT FROM AUTHOR]

Insufficient hole cleaning in deviated eccentric annulus yields many drilling problems due to excessive accumulation of cuttings. The majority of the implemented studies in such issues are computational or experimental making them complicated, expensive, and time-consuming. This paper presents a statistical data-driven model to predict cuttings concentration (CA), as a hole cleaning efficiency indicator, under different presumed eccentricity (ε) values of 0.0, 0.4, and 0.8. The model was constructed on the basis of Buckingham Pi theorem utilizing oil field data collected from six offshore deviated wells for developing reliable CA predictive tool. Least linear regression approach was employed for each ε value considering seven dimensionless groups of drilling parameters, rheological parameters, cuttings density, and hole cleaning efficiency indicators, such as transport velocity ratio (VTR), carrying capacity index (CCI), and equivalent circulating density (ECD). The field data were filtered and divided into 75% for training and 25% for validation. Furthermore, a sensitivity study was conducted by parametric differentiation of independent variables regarding the developed CA correlations. Results showed that, VTR, CCI followed by ECD had the highest significance, especially in eccentric annuli. In contrast, cuttings density had the lowest significance in all cases. The proposed method outperformed Duan and Khaled models with root mean square error of 1.45, 1.77, 2.89, and mean absolute percentage error of 34.6, 41.3, 54.5% for ε = 0.0, 0.4, and 0.8, respectively. This study could provide promising and practical methodology in estimating CA and improving deviated drilling efficiency up to 61.8° from vertical. [ABSTRACT FROM AUTHOR]

Waterflooding is a widely-used secondary oil recovery technique employed in the oil industry. In mature oil fields, waterflooding becomes increasingly essential in order to maximize oil recovery and extend field life, but optimizing its performance remains a complex and challenging task. In recent years, there has been growing interest in developing integrated approaches combining reservoir simulation, well modeling, and data-driven techniques to improve waterflood performance. The Capacitance–Resistance Model (CRM) has been proven to be a fast and effective tool for predicting waterflooding and reservoir characterization. Previous studies have successfully applied CRM to waterflood management to increase oil recovery. This paper develops a novel integrated and iterative workflow for waterflooding optimization in mature fields using the CRM for multilayer reservoirs equipped with Interval Control Valves (ICVs). The proposed approach, which integrates geological and well data with CRM results, was validated using a benchmark field model named the Olympus. This new workflow will help to put connected injection and production wells in different groups to reduce computational costs. In addition, this workflow can be used to determine the optimized number and proper location of the ICVs inside production wells. We determined the workover programs for existing wells, such as installing sensors and ICVs, deepening the wells, or plug-backs. Finally, it can be used for determining optimal water injection rates and well control strategies, such as valve openings in different production layers. As a result, the oil recovery factor increased, and the NPV was maximized, respecting the Olympus field's economic and operational constraints. [ABSTRACT FROM AUTHOR]

As the power component of the oscillator, the screw motor often fails at the bonding interface, and the mechanical mechanism of bonding failure has not been fully studied. Therefore, this paper establishes a shell-bushing cohesive zone model to explore the mechanical behavior and failure factors of the bonding interface. It is found that the mechanical model can well simulate the bonding failure process, the error is less than 5%, and the finite element simulation results are consistent with the failure position of screw motor in Daqing Oilfield. In addition, the effects of rotor position, drilling fluid pressure, interference, wall thickness and pressure difference on the bonding interface were studied. The results show that the rotor extrusion on the bushing in the circular arc section is the main position that causes the damage of the shell-bushing interface. The interference and pressure difference are the main factors causing the bonding interface failure of the even thickness screw motor, and the position of the maximum initial damage is given. When the drilling fluid pressure is 35 MPa, the maximum radial displacement of the conventional screw motor reaches 0.37 mm, and it is not recommended to be used in deeper Wells. The interference amount has a significant effect on the initial damage of the bonding interface of the even thickness screw motor, and it is not recommended to use a large interference amount for the even thickness screw motor. With the increase in wall thickness, the initial damage of the bonding interface of the screw motor decreases linearly. The research results provide a reference for the design of fixed rotor parameters and the optimization of vulcanized bonding process. [ABSTRACT FROM AUTHOR]

Inter-turn faults (ITFs) are one of the most common types of faults that affect the operation of induction motors. This paper presents an ABC frame-based model to simulate the induction motor with ITFs. The proposed model is capable of representing the induction motor in both health and faulty conditions considering the introduced unbalance by ITFs in the machine circuit. The performance of the proposed model is thoroughly investigated using a detailed simulation in MATLAB/Simulink environment. Different fault cases with a variety of motor loading conditions are considered and compared with the experimental results obtained from a specially wounded 3-phase induction motor. The simulation current response of the proposed model shows good correlation with the experimentally obtained response under the same conditions. Also, a comparison between the proposed ABC model and a DQ-model is established demonstrating ABC model superior performance in simulating ITFs in induction motor. [ABSTRACT FROM AUTHOR]

This paper proposes a unique sensor for detecting total ionizing dose (TID) on metal–oxide–semiconductor (MOS) devices. The proposed capacitive radiation sensor is based on commercial 180 nm complementary metal-oxide semiconductor (CMOS) technology. The sensor parameters of 180 nm length (L) and 20 nm oxide thickness (TOX) have been finalized based on simulation and mathematical analysis. Low and high radiation doses ranging from 100 rad to 1 Mrad are used to characterize it. The sensor's sensitivity for 0–10 krad is 20 mV/krad, 10 krad–100 krad is 3.9 mV/krad, and 100 krad–1 Mrad is 0.6 mV/krad when threshold shift is considered into account. Analysis of fixed oxide charge and interface trap charge generation due to ionizing radiation is done. Because interface traps are crucial to device performance, this device is evaluates using traps between 1 E06 cm−3 and 1 E14 cm−3 for realistic performance. Every interface trap exhibits a threshold voltage (VT) shift. Visual technology computer-aided design (TCAD) simulator was used to build, study, and evaluate a capacitive radiation sensor that might be used as a dosimeter for TID monitoring. [ABSTRACT FROM AUTHOR]

This paper proposes a novel stabilizing control method aimed at overcoming the instability challenges posed by the negative incremental resistance characteristics of a constant power load (CPL) within an autonomous microgrid (MG). The proposed stabilization technique integrates a power derivative-integral term with conventional droop control, strategically applied to enhance the MG's dynamic stability in the presence of CPL. The considered MG model, encompassing three inverter-based distributed generations (DGs), a constant impedance load (CIL), and CPL, is meticulously developed and simulated using MATLAB environment. Phase Locked Loop (PLL) is employed to synchronize the CPL with MG. The proposed controller is a key highlight, featuring optimally designed and tuned controller parameters for all DGs, CPL, and PLL. Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are employed to address the challenges linked to the laborious tuning process of controllers. The simulation results, which include scenarios involving three-phase faults and step changes, provide compelling evidence of the proposed controller's superior performance compared to conventional droop scheme. Furthermore, a comparative analysis is conducted to affirm and quantify the enhancements achieved through the proposed modified droop PSO-based MG controller concerning transient response. The results obtained emphasize the effectiveness of the proposed approach in simultaneously minimizing both overshoot and settling time. In comparison with the conventional controller, the proposed controller demonstrates a substantial decrease in percentage overshoot for the active power of DG3 and DC voltage of the CPL, with values of 93.89 and 99.9%, respectively. The corresponding improvements in settling time are notable, showcasing reductions of 83.11% for the active power of DG3 and 66.1% for the DC voltage of the CPL. When compared to the GA-based controller, the proposed controller exhibits significant percentage overshoot reductions for and DC voltage of the CPL, achieving 79.42 and 99.8%, respectively. Additionally, the settling time records noteworthy improvements, with reductions of 76.19% for the active power of DG3 and 57.57% for the DC voltage of the CPL. To further validate the real-world applicability and effectiveness of the proposed method, a real-time digital simulator (RTDS) is employed. The RTDS experiments results confirm the proposed scheme's ability to enhance MG stability, substantiating the simulation findings. This holistic approach, encompassing theoretical modeling, simulation studies, and real-time validation, establishes the proposed stabilizing control method as a promising and effective solution for mitigating instability issues associated with CPL in autonomous MGs. [ABSTRACT FROM AUTHOR]

According to recent power quality studies, voltage sags and voltage unbalance in medium-voltage and low-voltage distribution grids are the most common type of power quality problems. Power quality is an essential concern in the modern power system that can affect consumers and utility. In modern age, with an increase in sensitive and expensive electronic equipment, power quality is essential for the power system's reliable and safe operation. This paper proposes a hybrid technique-based unified power quality conditioner (UPQC) of microgrid in an EV charging station (EVCS), and it is the combination of recalling-enhanced recurrent neural network (RERNN) and sand cat swarm optimization (SCSO), together called as RERNN-SCSO. The SCSO creates the converter control signal, and the RERNN method forecasts the control signal from SCSO method. The proposed technique consists of series-active power filter (Se-APF), shunt-active power filter (Sh-APF), and DC-side capacitor as the parts of proposed technique. The major purpose of RERNN-SCSO technique is used to increase the power quality. Many loads, like highly inductive NLL, nonlinear loads (NLL) as well as unbalanced NLL (UNLL), are all linked at the point of common coupling (PCC). When the major structure of UPQC is built into the EVCS, an additional alternating current (AC)/DC converter will help with initialization. At this, the charging position provides charging service to the EVs as well as discharges the EVs into the power grid. The novelty of the proposed work consists in connecting EVs in parallel to the UPQC DC-side using double-layer microgrid (MC) and DC–DC converter. The proposed method ensures EV charging while efficiently handling quality of power issues like harmonic current, voltage disturbance, voltage sag and swell and voltage unbalance. RERNN-SCSO method performance is performed on the MATLAB/Simulink software to analyze the working performances and related with present techniques. RERNN-SCSO method-based THD under voltage sag condition as 2.03% and swell as 2.01%, which is lower than the existing methods. [ABSTRACT FROM AUTHOR]

Recently, interest in wireless sensor networks (WSNs) and applications has grown significantly. The path loss caused by buildings and tall trees is a major obstacle in the distribution of sensor nodes. Because of absorption, dispersion, and attenuation by the structures or trees, a degradation occurs in the reliability of the communication link. In this paper, four path-loss models (PLMs) were developed for a LoRa (long-range) wireless sensor node mounted on the drone as a part of a WSN for monitoring air pollution over a distance of approximately 1,000 m utilising the received signal strength indicator (RSSI). Four novel PLMs were created using MATLAB's curve-fitting tools, employing polynomial (POLY), exponential (EXP), Gaussian, and power equations. To improve the accuracy of these equations, their coefficients were fine-tuned using the particle swarm optimisation (PSO) algorithm, resulting in the development of hybrid algorithms—EXP-PSO, POLY-PSO, Gaussian-PSO, and power-PSO. The results demonstrated that PSO-enhanced PLMs achieved an outstanding correlation coefficient (R2) value of 1, indicating their exceptional ability to represent PLMs accurately in the tested air population environment utilising WSNs. Notably, the POLY-PSO algorithm outperformed other algorithms, achieving the lowest root-mean-square error (RMSE) of 1.945 × 10−4 and sum of squares error (SSE) of 1.853 × 10−26. The PLMs based on hybridisation algorithms outperformed existing PLMs regarding the correlation coefficient. These findings hold significant implications for designing and optimising WSNs in air pollution applications, offering valuable insights into radio path-loss modelling in this area. [ABSTRACT FROM AUTHOR]

This paper is focused on the modeling and simulation of a hysteresis current-mode regulated high-gain DC–DC converter intended for solar applications. First, the study effort outlined the working principle of a novel high-gain DC–DC converter scheme called a double-switch SEPIC-buck converter (DSSB). This work also presents the dual hysteresis current control technique, which aims to enhance the system's performance and lower the harmonic content of its voltages and currents. Two dual hysteresis current controllers independently control the source and load currents of the proposed converter. The frequency and hysteresis bandwidth are also simultaneously set using the two controllers. Compared to other standard DC–DC converters (buck, SEPIC, boost, and buck-boost), DSSB has better efficiency, a larger voltage gain, and superior dynamic characteristics. The suggested topology has a peak efficiency of 95% and a voltage gain of 1 + D D / 1 - D . The suggested DSSB converter has a THD of 6.6% and an efficiency of 97–98% when compared to various DC–DC converters, while the conventional SEPIC converter, along with boost, buck, and buck-boost converters, have a THD of 10% and an efficiency of 95–97%. Additionally, the DSSB converter has a far smaller crest factor than all other DC–DC converters, including boost, buck, buck-boost, and SEPIC. The suggested converter's performance is theoretically assessed using simulations based on Matlab/Simulink software under various circumstances. The results of simulation work in Matlab/Simulink corroborate the findings of the analysis and investigation. [ABSTRACT FROM AUTHOR]

In this paper, based on reversibility and approximation concepts, full adder (FA) circuits are designed for fault-tolerant applications. Area saving along with power reduction is the main objectives in the design and implementation of the proposed FAs. So, the basic reversible gates including Toffoli, Fredkin, Feynman, and Thapliyal Ranganathan (TR) are realized based on the gate diffusion input (GDI) technique. Subsequently, three approximate reversible FAs are designed with 3, 2, and 1 gates, respectively, which have special features such as 0 constant input (CI) and minimum garbage output and transistor counts. The circuitry performance and the quality of output images are extracted, when the FAs are placed in the approximate ripple carry adder (RCA) for image addition. The results show the superiority of the proposed FAs. Comprehensive results are extracted from the defined figure of merits (FoMs), which consists of area, power-delay product (PDP), peak signal-to-noise ratio (PSNR), and normalized mean error distance (NMED). The Pro-2 FA is selected as the best option due to its significant superiority compared to other circuits. The Pro-2 has 61.47% and 73.51% savings of PDP and PDP-area product (PDAP), respectively, compared to the exact circuit. [ABSTRACT FROM AUTHOR]

In this paper, a new approach to analyzing graphene-based antennas in the terahertz frequency range using the characteristic mode analysis method is presented. By the proposed method, graphene antenna parameters are obtained with simpler analysis and better physical visibility compared to numerical methods. In this regard, two structures of a graphene-based rectangular microstrip patch antenna for resonance in the terahertz frequency range are considered. The first structure consists of a metal ground plane and a substrate on which a layer of graphene is placed instead of a 10 μm × 7 μm metal patch. The second structure includes a metal ground plane with dimensions 19 μm × 16 μm, a substrate, a graphene layer on the substrate and a 10 μm × 7 μm metal patch placed on a graphene layer. Characteristic mode analysis method has been applied to the mentioned structure with different substrates, and the effect of graphene chemical potential on the modal behavior of the structure has been analyzed. For the first structure, there are two modes in the frequency range of 1–3 THz, but they do not have good resonance and they can be considered as non-resonant mode. Due to the conductivity of graphene in low frequency range of terahertz, which causes graphene to be a dielectric in this area. For the second structure, the 7.6 THz resonant frequency corresponds to the first mode of the structure and the 10 THz resonant frequency corresponds to the second mode of the structure, respectively, in the vertically and horizontally feeding stimulated. [ABSTRACT FROM AUTHOR]

This paper proposes a novel free-weighting matrix (FWM) approach for the event-triggered (ET) consensus and formation control of multi-agent systems over a directed graph. A new FWM equality for the consensus error is proposed. This FWM is applied to attain coherent behavior of systems via a fully distributed ET approach. The proposed approach is also extended for ET formation control to attain a specific pattern of agents. Furthermore, an FWM-based consensus controller design with practical ET cooperative control is considered to address consensus and formation control problems by using the in-degree information of a node rather than the algebraic connectivity of a graph or adaptation of parameters. Additionally, the controller gain is formulated using a more general transformation compared to that used in classical approaches. Moreover, the proposed ET scheme for the designed controller is applied on the transmission side to preserve computational resources by eliminating the Zeno behavior. In contrast to the existing methods, the proposed approach (i) provides a fully distributed protocol without using central information of network or adaptive gain; (ii) provides a practical ET mechanism to assure effective bandwidth and fewer energy consumption; and (iii) can be applied to the ET formation problem for mobile agents. Simulations involving six aerial vehicles are performed to demonstrate the efficacy of the proposed cooperative control strategy. [ABSTRACT FROM AUTHOR]

Energy prices are booming to high levels with the ongoing global events and massive energy demand post-COVID, triggering panic among-st the general public. In this regard, energy demand management can play an important role here by letting both the entities (utility and customer) take control over the consumption pattern long enough until renewables take over the energy supply from the conventional resources using real-time device scheduling algorithms. To achieve this phenomenon, both participating entities need proper prioritization and their interests to be properly taken care of. In this paper sensitivity analysis of varying priorities to the objectives (customer cost and peak-to-average power ratio) representing both the entities has been carried out. A real-time device scheduling algorithm is implemented to improve the consumption pattern of appliances consisting of multiple objectives. Proper way of normalization is developed to retain the original essence of these objectives. The analysis embeds novel swap operations that are incorporated with standard particle swarm optimization (PSO) and its variants under varying pricing schemes. In order to demonstrate the proposed DR-based real-time optimization, a residence in Chicago, Illinois, USA is taken as a case study for this work. The results demonstrate the quickness and proper optimizations of the PSO variants with butterfly-PSO performing the best among-st the rest. Continuous real-time pricing obtained the best solution with apt quickness among-st its counterparts for each time-slot and for cumulative time of the day. An overall reduction in each entity's metrics is obtained in this work. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid forecasting model combining auto-regressive integrated moving average (ARIMA) and artificial neural network (ANN) techniques to improve the software fault forecasting and hence improvising the reliability of software. Software reliability forecasting plays a critical role in software development and maintenance, as it helps to identify potential errors and improve the overall performance of software systems. The proposed model leverages the strengths of both ARIMA and ANN, allowing for more accurate predictions and better handling of complex and dynamic software systems. The effectiveness of the hybrid model is evaluated using real-world software data, demonstrating its superiority over traditional forecasting methods. This research contributes to the development of more robust and reliable software systems, which are essential in today's rapidly evolving technological landscape. [ABSTRACT FROM AUTHOR]

The major challenges of the distribution system are optimal location and sizing of distributed generation (OLSDG), as these significantly influence the system losses, voltage profile and voltage stability issues. In this paper, the OLSDG problem is solved by a newly developed artificial rabbits optimization (ARO) algorithm. The objective of an OLSDG problem is to minimize real power loss and total voltage deviation (TVD) and enhance the voltage stability index (VSI) in radial distribution systems (RDSs) while satisfying all the constraints. The effectiveness of the ARO has been validated on IEEE 33-bus and large-scale 118-bus RDSs for various power factors of distributed generation (DG), like optimal power factor (OPF), fixed power factor, and unity power factor (UPF) under four different scenarios. It has been found that RDSs' performance in terms of TVD, VSI, and mainly power loss is much improved when DGs have been operated with an OPF. In the case of DGs operating with UPF, the power loss decreases by 65.50% and 60.24% in an IEEE-33 bus and 118-bus RDSs, respectively, whereas DGs operating with OPF, the power loss reduces to 94.44% and 90.28%, respectively. The results obtained by ARO are validated by comparing them with the results of other optimization methods in the recent literature. The results prove that the proposed ARO is more efficient, has less computational time and can provide better solutions for an OLSDG problem. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) power generation prediction is a significant research topic in photovoltaics due to the clean and pollution-free characteristics of solar energy, which have contributed to its popularity worldwide. Photovoltaic data, as a type of time series data, exhibit strong periodicity and volatility. Researchers typically employ time–frequency signal processing methods, like empirical mode decomposition (EMD), to smooth the data during the feature engineering stage. However, improper operations at this stage could result in information leakage. Unfortunately, many existing studies on photovoltaic prediction fail to provide sufficient details on how signal processing methods are used during model training. To address this issue, this paper proposes the similarity-day extension EMD that avoids information leakage. The proposed method is validated through experiments conducted on the PV dataset of the Desert Knowledge Australia Solar Center, using mainstream models such as GRU, LSTM, CNN-LSTM, LSTN-CNN, and Bi-LSTM. The experimental results demonstrate an average improvement of 3.67% in MAE and 5.71% in RMSE when using this method, thus verifying its feasibility and effectiveness. Moreover, the proposed method can be applied to other data processing methods that may suffer from information leakage. [ABSTRACT FROM AUTHOR]

In this paper, a two-stage, single-phase, multilevel dc/ac converter for PV system is proposed. The typical two-stage inverter in PV systems requires a high dc-link voltage, and a step-up dc/dc converter is used. However, a two-stage power conversion requires a high number of power electronic components, resulting in higher cost and lower efficiency. Compared to the similar seven-level inverter, the proposed topology provides three times step-up voltage ratio and a smaller number of circuit components. The introduced topology consists of two stages, the front stage is a step-up dc/dc converter-based switched capacitor circuit. This stage provides high voltage boasting ratio and self-balancing dc-link voltage capacitors capability. Furthermore, a soft switching operation for the power switching devices is implemented using the LC-resonant circuit; thus, peak current amplitude can be regulated, electromagnetic noise and switching losses are significantly decreased. The second stage is a seven-level dc/ac inverter and has the advantages of fewer numbers of semiconductor devices. The operating principles and the modulation scheme of both stages are analyzed and discussed. Simulation and experimental results are provided to prove the effectiveness of the presented topology under different operating conditions. [ABSTRACT FROM AUTHOR]

This paper proposes a synchrophasor measurement-assisted integrated scheme to enhance power grid stability via transient stability assessment and emergency control of power systems in real-time. The synchrophasor measurements are time-stamped network information available from Phasor Measurement Units (PMUs). The Transient Stability Assessment (TSA) is based on measured generator bus angles obtained directly by PMUs at the control center. These synchronized angle measurements are utilized to construct features, and a Random Forest classifier is implemented to infer the TSA in real-time. The TSA results are determined within the first three cycles following the fault clearance. For operating scenarios that are unstable, an effective emergency control scheme is developed to avoid system degradation. The control strategy utilizes wide-area measurements to formulate a proportional sharing principle-based methodology in real-time. The composite scheme estimates the location and magnitude of the emergency measures to enhance grid stability. The performance of the proposed composite scheme is tested on an IEEE 39-bus test system. The results highlight that the scheme is computationally efficient and robust to topological changes. [ABSTRACT FROM AUTHOR]

Despite unprecedented quality improvement and defect prevention measures, producing a complete lot of perfect items is still highly unlikely due to the degradation of machines and processes over time. The previous studies on supply chains with defective/imperfect items have explored options such as process quality improvement investments, reworking, selling at a salvage value, or disposing of these items at an additional cost. This paper, contrary to existing literature, categorizes defective items into three types based on the level of imperfection: slightly, moderately, and highly defective items, and determines the optimal lot size for perfect and defective items. The focus is to develop a supply chain model for a single manufacturer and multiple retailers in the presence of defective items and manage defective products based on quality to generate profit from low-defective products rather than traditional defect rate reduction methods. Numerical examples and sensitivity analyses are presented to demonstrate the effectiveness of the proposed model in improving the total profit of the supply chain by optimizing production quantities. [ABSTRACT FROM AUTHOR]

Nonorthogonal multiple access (NOMA) superposes several user signals for simultaneous transmission, considerably ameliorating spectral efficiency. To exploit efficiently radio frequency energy sources surrounding NOMA transmitters for improved energy efficiency, NOMA networks further employ energy harvesting, which practically possesses nonlinear characteristic. In addition, multiple antennas can be deployed at users to transfer-and-harvest energy efficiently and to transmit/receive information reliably with antenna array processing. As such, this paper evaluates numerically the throughput and the outage probability of NOMA networks accounting for practical factors such as nonlinear energy harvesting, all multi-antenna users, and imperfect channel information. Illustrative results expose that nonlinear energy harvesting and channel information imperfection considerably degrade system performance, which is governed flexibly by multiple parameters. Notably, NOMA networks can prevent a total outage and achieve the best performance with a proper parameter selection. Moreover, their performance is considerably ameliorated with accreting the number of antennas. [ABSTRACT FROM AUTHOR]

This paper proposes a novel, robust flight control system using adaptive nonlinear sliding mode control for a quadrotor UAV in the presence of parametric uncertainties and aerodynamical disturbances. The proposed control system is based on a proportional derivative sliding surface combined with a non-singular fast terminal sliding mode control to enhance the tracking accuracy and reduce the chattering influence. Moreover, an adaptive mechanism is proposed to approximate the unknown upper limit of external disturbances/uncertainties. The Lyapunov criteria is used to prove the closed-loop stability and calculate the adaptive mechanism. The proposed adaptive PD-NFTSMC (APD-NFTSMC) can cope with the negative influence of modeling uncertainties, external disturbances, and measurement noise, allows null error in the steady state, and solves the chattering problem. Moreover, intensive simulation experiments under various external conditions are carried out to highlight the sovereignty of the developed control approach. Finally, comparisons with some well-known control techniques are performed to show the usefulness, smoothness, and robustness of the proposed APD-NFTSMC strategy. [ABSTRACT FROM AUTHOR]

Development of a reliable and intelligent traffic monitoring system is highly desired to improve the transportation safety and establish future transportation plans due to the fast growth of vehicle population. Vehicle classification is one of the most critical subsystems where existing ones suffer from privacy concerns, requirements of complicated systems, and high maintenance cost. This paper reports a novel vehicle classification method by utilizing a triboelectric sensor to accurately identify vehicles. Novelty of this method originates from using triboelectric sensor and machine learning method with important advantages over current alternatives by providing an easy installation, simple operation, noninvasive measurement, cost-effective manufacturing, and highly accurate classification. To make a classification, vehicle toys' signals were acquired from triboelectric sensor and then applied to a deep learning algorithm. The 1932 sensor output data were grouped into a set of seven vehicle toys with different wheelbases, and number of tires passing on are used to train and optimize 1D-CNN model. The utilized 1D-CNN model achieved accuracy, f1-score, precision, and recall as 96.38%, 0.9638, 0.9658, and 0.9637, respectively. [ABSTRACT FROM AUTHOR]

Integrated photonics (IP) is an emerging technology in photonics in which optical waveguides (WGs) and devices are fabricated as an integrated structure onto the surface of a flat substrate. IP technology has the great potential for reshaping the high-speed optical communication, sensing, and imaging exploiting the benefits of miniaturization, low cost, and high efficiency. Erbium-doped waveguide technology has become a popular area of research as it is compatible with CMOS technology. This is largely due to the success of erbium-doped fiber amplifiers (EDFAs) in achieving high gain, high saturation power, and low noise figure (NF) in high-speed long-haul optical fiber links. In this paper, we report the performance enhancement of Er–Yb: co-doped waveguide amplifier (EYCDWA) for C-band (1530–1565 nm) employing backward pumping with a standard 980-nm pump in the presence of energy transfer upconversion (UC). The performance of EYCDWA is evaluated in terms of signal enhancement (relative gain), net internal gain (NIG), and NF using numerical simulations. The length of WG, ion densities of Er 3 + and Yb 3 , and excess losses are adjusted to achieve record high NIG of 11 dB/cm and signal enhancement of 15.5 dB/cm at signal wavelength of 1530 nm. Also, NF values in the range of 4.9–5.1 dB are observed for C-band wavelengths for input signal power of − 15 dBm. [ABSTRACT FROM AUTHOR]

Among all technologies applied in three-dimensional printing (3DP), fused deposition modeling (FDM) is the most developing one because it is capable of producing parts with geometrically complex figures. Despite its wide applications, there are some drawbacks to its extension, for instance, weak mechanical characteristics and low dimensional accuracy. In this paper, the influence of four FDM process parameters (layer height, printing speed, infill density, and the number of top and bottom layers) on four criteria (impact strength, dimensional accuracy, consumed raw material, and production time) of polylactic acid (PLA) parts is studied. Unlike previous research, this research not only optimizes the properties of produced parts but also minimizes production costs. First, each criterion is analyzed singly; then, in an investigation, all criteria are combined and optimized simultaneously. In other words, a comprehensive decision is made considering both products' qualities and the production costs. The applied methodology for multi-criterion decision-making in this research is also usable in other fields of industry. With the help of this methodology, the best selection of process parameters' levels is attainable. According to the results, layer height = 0.3 mm, number of top and bottom layers = 2, infill density = 60% and print speed = 45.28 mm/s are the best choice while considering all four criteria. Layer height is found the most effective parameter. An increase in layer height leads to a stronger part with a shorter production time but a heavier one with less dimensional accuracy. [ABSTRACT FROM AUTHOR]

Achieving three-dimensional (3D) omnidirectional wireless power transfer (OWPT) without any active control is challenging. This paper proposes a square polyhedron transmitter (SPTx) based on the reverse current of parallel coil, which can create a 3D omnidirectional magnetic field. To minimize the counteracting of the reverse magnetic field, each coil is equipped with magnetic shielding material. The coils of SPTx are connected in series and excited by a single power supply without any current amplitude or phase control circuits. The performance of SPTx system is validated with different receivers (Rx) by experiments. In the experiment with dual Rxs, the maximum transmission efficiency is 79.6% with an efficiency stability rate of 49%. In addition, the output power does not affect the efficiency, and more Rxs will increase transmission efficiency. [ABSTRACT FROM AUTHOR]