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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]

With the rapid development of pure electric vehicles, how to improve the range has become the focus of research. Due to the large shell mass of the battery pack shell (BPE), it is necessary to optimize its structure in turn. In this paper, the BPE model was reconstructed by improving the upper shell material and adding reinforcements following the direction of the leaf vein fibers of the Magnolia Grandiflora. A quasi-Monte Carlo method based on Sobol sequences and a Latin hypercube design with variance sensitivity analysis was used to determine the seven design variables. The 122 data sets were trained and predicted using the basic gradient descent algorithm combined with the conjugate direction method, and the predictions were compared with static mechanical simulations for sharp cornering conditions on bumpy roads. The results showed that the BPE weight reduction ratio was 19.5%, and the maximum stress reduction ratio was 27.49%, and the displacement reduction ratio was 29.29% respectively, which satisfied the material requirements. It had a 20.88% increase in first-order mode frequency, which effectively prevented resonance. [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]

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]

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]

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]

Superhydrophobic surfaces have been widely studied for their self-cleaning properties. However, most of the constructed superhydrophobic surfaces had problems of changing the surface morphology and color of paper/cloth. An extremely dilute superhydrophobic solution was prepared by hybrid assembly of aminated nano-SiO2 and high fluorine epoxy polymer P(FOEMA-r-GMA). The preparation of superhydrophobic cotton (SHC) and superhydrophobic book paper (SHBP) was studied through the optimal construction conditions of solution impregnation method. The optimal construction conditions for SHC were as follows: The concentration of fluorinated epoxy polymer was 3 mg/mL, the soaking time was 5 h, the drying time was 8 h, and the drying temperature was 120 °C. The maximum WCA was 158° ± 3°, and the minimum WRA was 4° ± 3°. SHC surface had good hydrophobic effect, acid and alkali resistance, and self-cleaning effect. Additionally, its surface morphology still remained basically unchanged. SHC could be used for oil–water separation with a maximum oil–water separation rate of 98.4%. The optimal construction conditions for SHBP were: The concentration of fluorinated epoxy polymer was 3 mg/mL, the soaking time was 9 h, the drying time was 6 h, the maximum WCA was 155° ± 3°, and the minimum WRA was 6° ± 1°. The hydrophobicity of SHBP was greatly improved; it would extract selective adsorbing oil from the oil–water mixture and slightly reduce its smoothness. [ABSTRACT FROM AUTHOR]

In this paper, the capillary is divided into several regions, and the effective pore radius is proposed. The relationship between the effective hole radius and the maximum radius is analyzed. The fractal models of fractal flow rate, average velocity and effective permeability of power-law fluid flow in fractal soil composed of circular cross section capillaries are derived. The effects of different power exponent, viscosity coefficient and fractal dimension on the average flow velocity and effective permeability model are analyzed, and the comparative analysis of different sections is carried out. The reasonable calculation results of the fractal model given in this paper verify the correctness of the model. The introduction of the effective pore radius concept and the derivation of fractal models have advanced our ability to characterize and predict flow behavior. We anticipate that these findings will stimulate further research and practical applications in various domains, benefiting both scientific knowledge and technological advancements. [ABSTRACT FROM AUTHOR]

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]

Word Sense Disambiguation (WSD) is a key step for many natural language processing tasks such as information search, automatic translation, and sentiment analysis. WSD is the process that identifies appropriate senses of ambiguous words in the text. With the increasing number of words to be disambiguated in large amount of text data, WSD becomes very challenging and that is why an exhaustive search for the best set of senses may be unpractical. Recently, several metaheuristic approaches have been proposed for different complex optimization problems and have achieved good results. Therefore, in order to improve the WSD process, in this paper, the WSD problem is modeled as a combinatorial optimization problem, and the Discrete Student Psychology-Based Optimization (DSPBO) metaheuristic is proposed and used to selecting appropriate senses. A DSPBO-based WSD is proposed to disambiguate more ambiguous words together in function to their contexts in the target text, and a Lesk-based fitness function is used to guide the DSPBO metaheuristic to optimize the general semantic similarity of selected senses. The proposed approach is evaluated and compared to several recent WSD approaches on the well-known corpuses SensEval-2, SensEval-3, SemEval-2007, SemEval-13, and SemEval-15. The comparison is made in terms of F-Measure, precision, and recall. Experiments show a significant improvement both over existing knowledge lexicon-based approaches and metaheuristic-based approaches, with a higher F-measure of 84.21%, 83.33%, 87.5%, 77.58%, and 81.08% on SensEval-2, SensEval-3, SemEval-2007, SemEval-13, and SemEval-15, respectively. [ABSTRACT FROM AUTHOR]

The main objective of blind image deblurring is to restore a high-quality sharp image from a blurry input through estimation of unknown blur kernel and latent sharp image. This is an ill posed problem as a range of additional heuristics are required to accurately estimate the blur kernel. These methods have a major drawback that estimated kernel doesn't result in minimum cost function for truly sharp image. In this paper, an improved blind image deblurring approach named Scharr Edge-based regularization (SEBR) based on combination of state-of-the-art Scharr edge based (SEB) gradient operator and a normalized regularization is proposed that accurately estimates the blur kernel and provides minimum cost function for sharp images. Initially, the SEB operator is applied to blurry input to preserve the spatial relations between the initial pixel values of the image. Moreover, the isotropic nature of the Scharr operator ensures the rotation symmetry effectively while optimizing the localization of salient edges to produce a gradient image. Finally, after applying normalized regularization, the blur kernel is estimated with high precision, which then recovers a sharp image. Experimental evaluations are performed on publicly available datasets of synthetic and real blur data. The proposed SEBR algorithm is very fast and robust in nature. The experimental result on synthetic data shows that SEBR improves the values of PSNR and SSIM. The SEBR also achieves favorable results in terms of visual quality against state-of-the-art deblurring techniques on real blur images having complex backgrounds with uniform and non-uniform blur, and images taken in low lighting conditions. [ABSTRACT FROM AUTHOR]

In this study, novel Spectro-Temporal Energy Ratio features based on the formants of vowels, linearly spaced low-frequency, and logarithmically spaced high-frequency parts of the human auditory system are introduced to implement single- and cross-corpus speech emotion recognition experiments. Since the underlying dynamics and characteristics of speech recognition and speech emotion recognition differ too much, designing an emotion-recognition-specific filter bank is mandatory. The proposed features will formulate a novel filter bank strategy to construct 7 trapezoidal filter banks. These novel filter banks differ from Mel and Bark scales in shape and frequency regions and are targeted to generalize the feature space. Cross-corpus experimentation is a step forward in speech emotion recognition, but the researchers are usually chagrined at its results. Our goal is to create a feature set that is robust and resistant to cross-corporal variations using various feature selection algorithms. We will prove this by shrinking the dimension of the feature space from 6984 down to 128 while boosting the accuracy using SVM, RBM, and sVGG (small-VGG) classifiers. Although RBMs are considered no longer fashionable, we will show that they can achieve outstanding jobs when tuned properly. This paper discloses a striking 90.65% accuracy rate harnessing STER features on EmoDB. [ABSTRACT FROM AUTHOR]

With the colossal development that the multimedia applications have currently reached, it is possible to manipulate the data contents easily. Thus, a receiver may get incorrect data compared to what the sender transmits. As a result, it is required to come up with an effective solution that checks the integrity of the received data, mainly images. In this context, we attempt through this paper to present a semi-fragile blind watermarking method using ABC, BRISK features and DCT. The method works on embedding the watermark data into specific area. As for the extraction stage, an authentication key is used to decide if the watermarked image was changed during transferring or not. Our findings show the efficiency of the proposed technique in terms of imperceptibility, robustness against some attacks, and tamper detection accuracy. [ABSTRACT FROM AUTHOR]

The proliferation of social networks, the Internet of Things, and economic mobility has led to an exponential increase in data. New data having high volume, high velocity, high variety, and high value are called big data. Big data present additional requirements in terms of storage and computation resources. Various enterprises aim to outsource their big data services to the cloud because of its cost efficiency, less management, resource pooling, and resilient computing. However, outsourcing the storage of sensitive data can expose them to potential security risks. Encryption presents a straightforward solution for data privacy preserving. In traditional encryption mechanisms, such as advanced encryption standard, the data owner and users must share an exact key for both data encryption and decryption. Currently, these mechanisms do not provide a scalable and secure solution for big data storage and analysis. Furthermore, they need to be more efficient to support big data velocity. Unfortunately, securing outsourced big data storage to a public cloud environment to later maintain efficient and secure processing over encrypted data by cloud servers cannot be ensured using traditional encryption mechanisms. In this paper, we propose a security approach for this issue by which honest but curious users or cloud service providers cannot reach complete information from the stored data. From the analysis, the proposed approach can provide secure cloud-assisted big data. Meanwhile, the performance evaluation shows the efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

Backlit image enhancement is a challenging research problem in image processing and computer vision. The complexity of enhancing backlit images is that they simultaneously contain bright and dark intensity regions. Existing state-of-the-art methods usually have low contrast enhancement in the dark regions, are prone to color distortion in the background or noise amplification, and have high computational complexity. Inspired by its potentiality, this paper applies region segmentation and a transfer learning-based approach to enhance the contrast of the dark regions in backlit images using multi-illumination mappings. In the proposed framework, gamma and log transform functions are applied to achieve a varied set of illumination-map to improve the brightness and contrast of the backlit image. The average results of the validation metrics of the proposed method for contrast measure (CM), entropy (E), CM/E ratio, and SSIM are 8.10, 7.52, 1.07, and 0.58, respectively. The experimental results corroborate that the proposed method demonstrates superior performance to state-of-the-art methods both subjectively and objectively. [ABSTRACT FROM AUTHOR]

Vision-based autonomous underwater vehicle localization, navigation, and mapping is an attractive field of research in recent years. Despite the development of vision-only and vision-inertial-based algorithms, particularly in the terrestrial and aerial robotics groups, applying such approaches directly to underwater environments remains challenging due to visual degradation in the medium and further due to the slow crusade of the underwater vehicles. This paper introduces U-VIP-SLAM, a unique visual-inertial-pressure SLAM system that combines data from a monocular camera, a low-cost IMU, and a pressure sensor to create a SLAM system focused to the underwater region. All of the key components of a comprehensive SLAM system are included in the U-VIP-SLAM, including loop closure capabilities tailored to the underwater environment. Further, we introduce forward and backward projection strategies to fuse pressure measurement in the local bundle adjustment. Furthermore, a unique initialization strategy is proposed to effectively bootstrap the system by fusing pressure and IMU data. Several datasets were used to compare the performance of the proposed methods against the state-of-the-art monocular VI systems. Source Code is available at https://github.com/chintha/U-VIP-SLAM.git. [ABSTRACT FROM AUTHOR]

Due to recent technical breakthroughs in wireless communication and the Internet of Things (IoT), the smart grid (SG) has been recognized as a next-generation network for intelligent and efficient electric power transmission. Electric vehicle charging is becoming one of the most popular SG application. However, in SG environment the communication between a vehicle user and smart meter is mostly performed using insecure channel for managing demand response during peak hours. This raises serious security and privacy issues. Motivated from the aforementioned challenges, this paper presents a secure and privacy-preserving framework for IoT-enabled SG environment. The proposed framework first uses a secure mutual authentication scheme to register and exchange session key among SG participants. Second, a deep learning method that uses a stacked sparse denoising autoencoder to convert data into a new encoded format is suggested. The attention-based truncated long short-term memory uses this modified data to identify intrusions. The proposed blockchain architecture uses the proof of authentication consensus mechanism to propagate regular transactions in order to validate data integrity and prevent data poisoning attacks. The suggested framework outperforms several current state-of-the-art solutions in terms of security and numerical discoveries. [ABSTRACT FROM AUTHOR]

K-means is one of the most algorithms used in unsupervised machine learning. Numerous metrics are adapted to k-means for estimating the optimal number of clusters k-optimal. In this paper, a comparative study is carried out to select similarities and contrast the differences between four clustering metrics: weighted sum of squares, Calinski–Harabasz index, Rao diversity index, and Silhouette method. Data normalization's effect was also assessed in this work. To illustrate the methodology, six 2D-datasets were studied and labelled 'likely Gaussian' and 'non-Gaussian' depending on how data are distributed within their domain of definition; this characteristic was assigned as data shape. Hence, k-means clustering was carried out before and after normalization; the adopted metrics were then computed and k-optimal were selected for each combination. Results showed that the accuracy of the clustering, regarding the known optimal clusters, is very sensitive to the interplay between the components of threefold {dataset distribution, normalization form, and metrics/method}. Among several remarks, it was statistically proved that likely Gaussian datasets showed quite negligible RMSE values compared to the non-Gaussian datasets; diversity index was well adapted to the two data shapes; WSS-elbow method reached an appreciable level of accuracy for non-Gaussian ones; Silhouette and Calinski–Harabasz scores were highly sensitivity to data shape compared to normalization forms. In another hand, normal standardization improved the accuracy of clustering especially for likely Gaussian data. Finally, this experimental study proved that a pre-analysis of data according to data shape and normalization should be considered before the adoption of a clustering method/metrics. [ABSTRACT FROM AUTHOR]

The Internet of Things is an ecosystem that connects billions of smart devices, meters, and sensors. These devices and sensors collect and share data for use and evaluation by organizations in different industry sectors. Humans may use the IoT to live and work more intelligently and gain total control over their lives. Consequently, IoT can be used to connect devices and integrate them with new digital technologies for customers. On the other hand, smart utility companies in the electric, gas, and water sectors need to deliver services more efficiently and analyze their operations in a way that can help optimize performance, detect growing problems in real time, and initiate fixes to avoid unplanned service interruptions. Building actual smart metering networks is costly and time-consuming. Therefore, in this paper, a new Smart Utilities Traffic Scheduling Algorithm (SUTSA) is proposed. To minimize the system complexity, the model is based on narrowband power line communication, in which a wired hidden network sends data across power lines. A simulation is performed using OPNET Modeler 14.5 to evaluate the proposed model. The results proved that the proposed model is highly scalable and achieves full network-bandwidth utilization in different situations based on different application requirements. [ABSTRACT FROM AUTHOR]

Attention deficit hyperactivity disorder (ADHD) is a neurological disorder that affects an individual's behavior. The rising cases of ADHD among children and adolescents worldwide have raised the concern and require techniques for its early diagnosis and identification. The symptoms of ADHD are characterized by patterns of hyperactivity, inattention, and impulsivity. Recent advances in neuroimaging have allowed researchers to obtain the functional and structural patterns of the brain affected by ADHD. This work considers the resting state functional magnetic imaging (rs-fMRI) data and analyzes the functional connectivity of 40 subjects (20 ADHD and 20 healthy controls) through voxel size blood-oxygen-level-dependent (BOLD) signal. These BOLD signals are functionally relevant to the corresponding resting state networks (RSN). In this paper, we have proposed a modified deep learning-based bidirectional long short-term memory (BLSTM) model that automates the classification of ADHD through the identified voxels within the active region of the RSN. Initially, we have visualized the 28 active regions of RSN and time series of behavioral data of 40 subjects with 176 time stamps. Then, the proposed modified BLSTM has been trained by using the feature vector (40 × 261 × 28) for each subject and Adam hyper-parameter for optimization. The experimental results represent that the proposed model outperforms the many other models by achieving the classification accuracy of 87.50 % . We have also provided a detailed comparative analysis of the proposed model with the different existing state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

AISI 304 stainless steel has extensive applications in different industries such as medical, automobile and molding due to its excellent mechanical features of poor thermal conductivity, high strength at high temperatures, and high corrosion resistance. However, it is difficult-to-process by traditional machining. Therefore, non-traditional surface treatment and finishing processes such as shot peening and abrasive-based methods are more applicable for this material. Considering the advantages of strength improvement and surface quality for shot peening and abrasive flow machining respectively, better advantages of these two processes are combined into a newly developed and patented process called as GOV (flow peening) process. In this paper, the effects of GOV process parameters (number of cycles, steel ball size, media ratio) on the surface quality of the AISI 304 in terms of surface roughness, white layer and material removal already pre-processed by electric discharge machining (EDM) were investigated. Surface finishing with GOV process decreases surface roughness Ra value from 2.34 µm to 0.40 µm by removing micro-level chips from specimen surface up to 11.5 mg. This material removal amount is supported by SEM images. It is obvious to understand that white layer formed owing to nature of EDM process is entirely removed from surface. [ABSTRACT FROM AUTHOR]

The study of the complex model associated with chaotic models is always the most complicated and fundamental in the current scientific environment. The primary goal of the current paper is to provide an illustration of the fundamental theory while analysing dynamical systems and validating the chaotic behaviour of the Lorenz–Haken (LH) equations, a system of fractional differential equations. The LH equations are used to describe the 4D chaotic laser. The Adams–Bashforth numerical method is used to extract the numerical solutions projected model. The classical model introduces the bifurcation of the parameter linked with the system. The system's uniqueness and existence are confirmed using the fixed-point hypothesis with the Caputo–Fabrizio fractional operator, followed by boundedness and dynamical analysis. Furthermore, the chaotic character of the numerical solutions with different orders obtained at different beginning circumstances is presented. [ABSTRACT FROM AUTHOR]

In this paper, an optimal nonlinear attitude controller is designed and implemented as hardware in the loop for a spacecraft simulator under various failures of reaction wheels. The proposed controller is developed in the closed form based on predicting the nonlinear continuous responses of spacecraft simulator. The special case of the controller when all uncertainties are ignored leads to feedback linearization. However, the stability of the controller in the presence of parametric uncertainties and unmodeled dynamics of the platform is analyzed, and the effect of the prediction time on the boundedness of system responses is presented. A redundant reaction wheel is located in the platform to compensate for failures of three main reaction wheels. How to distribute torque between healthy wheels under different types of failure including stuck and oscillatory failures is addressed and experimentally implemented. The laboratory results that are consistent with computer simulations show the accuracy and validity of the designed controller. It is seen that the spacecraft attitude converges in a limited time in the presence of system uncertainties and actuator failures. [ABSTRACT FROM AUTHOR]

This paper deals with the detection of cutout shapes and the prediction of their location using machine learning algorithms. To this end, a series of simulation studies were performed for constructing a dataset containing different cutout shapes in different locations. Firstly, free vibration analysis of sandwich structures was performed using COMSOL. Natural frequencies of sandwich structures with different-shaped cutouts at different locations are obtained as a result of conducted simulation studies. Since the location and shape of cutouts significantly cause a change in the natural frequencies of sandwich structures, the first six natural frequencies of the sandwich structure chosen to be used as input for the machine learning algorithms. Afterward, different machine learning algorithms were employed to classify and predict the cutout shapes and locations using MATLAB. Furthermore, the Bayesian optimization algorithm has been adopted in order to tune the hyperparameters of the machine learning classification algorithms and improve their performance. The results of this work showed that the best cutout shape detection can be conducted by the tuned SVM machine learning algorithm with classification accuracy reaching 98.4%. Moreover, multiple machine learning algorithms have been adopted for predicting the location of the cutouts. The location prediction study gave an outperformed performance where the R-Squared metric was 1 for all tested machine learning algorithms. Also, the results of the location prediction study showed that the best RMSE value was 0.00049 that has been obtained using the Gaussian process. In summary, this study can be used as a reference by the designers to develop an opinion about the created SS before design and production, which can help in saving time and money. [ABSTRACT FROM AUTHOR]

This paper numerically investigates the thermo-hydraulic performance and irreversibility characteristics in a single-pass duct with attached and detached ribs subjected to pulsating turbulent flow. The pulsation frequency is varied from 15.3 to 70.3 Hz with the inlet Reynolds number (Re) ranging from 5000 to 20,000. The variation of Nusselt number, friction factor, Thermal Performance Index (TPI) and Augmented Entropy Generation Number (AEG) with frequency and Re has been evaluated using Finite Element Method (FEM). The simulation results reveal that the heat transfer characteristics of both attached and detached ribs increase significantly with pulsating flow at the inlet of the channel. Detached ribs with pulsating flow at the inlet depict superior heat transfer augmenting characteristics and relatively smaller Augmented Entropy Generation Numbers in comparison with attached ribs. At an inlet pulsation frequency of 15.3 Hz, a 78.43% increase in overall Thermal Performance Index is observed in detached ribs with an AEG of 0.71. Attached ribs exhibit a 49.9% enhancement in heat transfer at a pulsation frequency of 30.1 Hz and a lowest AEG of 0.79 at a pulsation frequency of 15.3 Hz. The detached ribs with pulsating flow at inlet outperform the attached ribs within a certain frequency range. [ABSTRACT FROM AUTHOR]

In this paper, the thermodynamic performance of state-of-the-art silicon carbide fiber-reinforced silicon carbide matrix composite (SiCf/SiC)-bladed gas turbines (GTs) are presented. Two different GT power systems are comparatively considered namely; the basic gas turbine (BGT) cycle and the intercooled gas turbine cycle (ICGT). The work analyzes the thermodynamic advantages of adoption of ceramic matrix composite turbine blades to existing GTs featuring traditional single-crystal superalloy-blades. Mathematical models and governing equations pertaining to the interdependent performance parameters namely: blade coolant flow rates, 1st law, and 2nd law thermodynamic efficiencies, and component exergy destruction were converted into computer codes. It was observed that SiCf/SiC-bladed GTs will require 84–85% lesser blade coolant at 1900 K leading to superior thermodynamic performance. The enhancement in the 1st law efficiency and 2nd law efficiency of the SiCf/SiC-bladed BGT systems are observed to be 3% compared to conventional superalloy-bladed systems. Similarly, enhancement in the 1st law efficiency and the 2nd law efficiency of the SiCf/SiC-bladed ICGT systems are observed to be 3%. The SiCf/SiC-bladed GT systems are found to be superior to the single-crystal superalloy-bladed GT as they exhibit lower component exergy destruction. The performance maps suggest that replacing the existing single-crystal superalloy blades GTs with the novel ceramic matrix composite blades would exhibit superior thermodynamic performance even when GT firing temperatures are more than 1800 K. These may be used by utility developers to select suitable technology and sizing of GT systems. [ABSTRACT FROM AUTHOR]

Extrusion-based additive manufacturing (EB-AM) is a 3D fabrication process in which the material is heated and extracted through a nozzle before being deposited one layer at a time. It uses two types of technologies: filament and pellet extrusion. Filament extrusion machines entail transforming raw plastic pellets into a spool of filament; hence, these machines use limited materials. Pellet extrusion printers are more complex machines and use a screw inside a barrel. Screw feeds, transports, and melts the material before forcing it to be deposited at the required temperatures. Besides the complexities, a pellet extruder offers lower costs, higher speeds, and a wide variety of materials that can be used. This paper presents the development in extrusion-based 3D printing addressing filament and pellet-based printing issues, including filament breakage, nozzle clogging, in-line rheological monitoring, multi-color printing, material retraction, and cost-effectiveness of pellet-based extrusion additive manufacturing. To improve the material domain for 3D printing and utilize the advantages of filament and pellet-based extrusion, a hybrid form of 3D printer can be developed, capable of printing both filaments and pellet forms of material. This review work is intended to help young researchers in the field of EB-AM to understand the advances of this technology and its related complexities. [ABSTRACT FROM AUTHOR]

Photochromic pattern with dual-mode fluorescence authentication was developed using a new self-healable nanocomposite hydrogel. A hybrid hydrogel of tricarboxy cellulose (TCC), sodium alginate (SA) and polyvinyl alcohol (PVA) was admixed with rare-earth doped aluminate (REA) nanoparticles (NPs) to produce inorganic–organic nanocomposite inks. The freezing and thawing procedures were used to prepare the TCC/SA/PVA composite hydrogel. TCC containing carboxylic substituents per anhydroglucose moiety was synthesized using one-pot oxidation technique, and PVA was dissolved in TCC aqueous solutions. To create transparent photochromic paper surface that changes color to green under UV illumination, a homogeneous film of REA@TCC/SA/PVA was imprinted directly onto paper surface. The luminous prints had a fluorescence peak at 519 nm once stimulated at 370 nm. The particles of the synthesized lanthanide-doped aluminate were measured to be between 9 and 33 nm in size using transmission electron microscope (TEM). X-ray powder diffraction was also employed to examine the structure of lanthanide-doped strontium aluminate. The morphological characteristics of stamped sheets were determined by a variety of analysis methods. The printed sheets displayed non-fatiguing reversible photochromism. The cytotoxicity of the prepared photoluminescent hydrogels was investigated. The mechanical performance of prints and hydrogel rheological properties were investigated. The prepared REA@TCC/SA/PVA hydrogel provides an efficient technique for a variety of authentications. [ABSTRACT FROM AUTHOR]

Information technology applications are crucial to the proper utilization of manufacturing equipment in the new industrial age, i.e., Industry 4.0. There are certain fundamental conditions that users must meet to adapt the manufacturing processes to Industry 4.0. For this, as in the past, there is a major need for modeling and simulation tools in this industrial age. In the creation of industry-driven predictive models for machining processes, substantial progress has recently been made. This paper includes a comprehensive review of predictive performance models for machining (particularly analytical models), as well as a list of existing models' strengths and drawbacks. It contains a review of available modeling tools, as well as their usability and/or limits in the monitoring of industrial machining operations. The goal of process models is to forecast principal variables such as stress, strain, force, and temperature. These factors, however, should be connected to performance outcomes, i.e., product quality and manufacturing efficiency, to be valuable to the industry (dimensional accuracy, surface quality, surface integrity, tool life, energy consumption, etc.). Industry adoption of cutting models depends on a model's ability to make this connection and predict the performance of process outputs. Therefore, this review article organizes and summarizes a variety of critical research themes connected to well-established analytical models for machining processes. [ABSTRACT FROM AUTHOR]

In the realm of additive manufacturing, powder bed fusion (PBF) is recognized as an innovative and highly effective technique for manufacturing titanium-based materials. With an understanding of and regulation for the complex microstructural evolution that occurs during the PBF process, several previous studies have been conducted to enhance the reliability, efficiency, and performance of the PBF through investigation and optimization of microstructure evolution in PBF. These studies have examined various aspects, including feedstock materials, process parameters, and post-processing techniques, in order to gain a comprehensive understanding and control over the progression of microstructure in the PBF. Process parameters are widely acknowledged as critical determinants in the PBF process for titanium-based materials, significantly influencing the quality in 3D printed components. Previous studies have provided an in-depth discussion of the effects of process parameters, such as laser power, scanning speed, and hatching space, on the microstructure evolution of Ti-based materials. The primary objective of this review paper is, therefore, to provide a comprehensive and clear explanation of recent efforts, with a particular focus on investigating the complex evolution of microstructures in Ti-based materials during the PBF process. This thorough discussion is devoted to providing a comprehensive understanding of the effects of process parameters on the evolution of microstructures in Ti-based materials. [ABSTRACT FROM AUTHOR]

Hybrid nanofluids are a type of nanofluid that is created by combining two different types of nanoparticles with a traditional fluid. These nanofluids have unique physicochemical properties that make them more effective at transferring heat than traditional nanofluids. This research paper focuses on predicting thermal and energy transport in non-Newtonian biomagnetic hybrid nanofluids that contain gold and silver nanoparticles, using Gaussian process regression (GPR). The study uses blood as the traditional fluid and incorporates the effects of thermal radiation, thermophoresis, Brownian motion and activation energy into the model equation. The governing nonlinear partial differential equations are simplified to a set of ordinary differential equations using similarity replacements. The shooting method, along with the Runge–Kutta-Fehlberg fourth–fifth-order scheme, is used to solve the transformed equations using MATLAB. The results of the study are presented through figures and tables, which include the coefficient of skin friction, Nusselt number, Sherwood number and motile microbe's flux, illustrated with surface plots. The GPR model is developed using four basic function kernels (squared exponential, exponential, rational quadratic and matern32 functions) and evaluated using statistical indicators such as RMSE, MSE, MAE and R. The predicted results and simulated numerical values are in good agreement with the coefficient of determination (R2) of 0.999999 for all parameters. The study also finds that GPR models with exponential kernel functions outperform other kernel functions in both the Oldroyd-B and Casson hybrid nanofluid data sets. However, the findings indicate that nanofluids and hybrid nanofluids have superior thermal qualities and stability, making them promising candidates for various thermal applications including solar thermal systems, automotive cooling systems, heat sinks, engineering, medical areas and thermal energy storage. [ABSTRACT FROM AUTHOR]

Using high-order shear and normal deformation theory (HSNDT), this study analyzes the dynamic response and time history of the impact force of the sandwich plate with the auxetic core under low-velocity impact. The impact was modeled using a two-degree-of-freedom mass and spring model, and the Hertz linearized model was utilized to derive the contact force's time history. The rectangular sandwich panel has simple supported boundary conditions and consists of three layers: two aluminum top face sheets and one auxetic core layer with a negative Poisson's ratio. Using the energy technique, the system's governing equations are derived. The equilibrium equations were solved by the analytic approach of the Navier method in the space domain and the numerical method of Newmark in the time domain. The use of HSNDT distinguishes this article from others on similar topics, and the flexibility of the thick core in the thickness direction is considered. The Effects of different geometric and material properties have been investigated, and the results have been compared with those of other similar papers and studies for validation. The data indicate that the greater the degree of inclination of the cell, the longer the impact period and the lower the peak impact force. Moreover, the larger angle of the auxetic cell reduces the deflection at the impact site. In terms of minimizing deflection, the auxetic honeycomb sandwich panel is 25% superior to the non-auxetic honeycomb panel. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the influence of variable thickness on the mechanical properties of thin-walled twelve right-angle section tubes (TTRSTs) through numerical characterization. This paper introduced the TTRSTs with different thicknesses for sides and webs. The three-point bending finite element model correlated with the physical test was built up and modified to find the lowest initial peak force (IPF), maximum specific energy absorption (SEA), and highest mean crushing force (MCF). Effects of variable thickness as well as impact location on the energy absorption performance of TTRSTs were investigated, suggesting that the energy absorption performance was more sensitive to side plate thickness changes than web plate thickness changes in web plate impacts. In addition, the TTRSTs with about 2.5 mm thickness demonstrated the highest SEA. Optimal Latin hypercube design (Opt LHD), response surface method (RSM), and NSGA-II were employed to optimize the TTRST multiobjectively under different impact loadings. The optimized results highlight that the TTRSTs can reach a 46.92% lower initial peak force or 2.61% higher MCF with minor SEA by selecting the appropriate thickness for impacts at different locations. [ABSTRACT FROM AUTHOR]

This paper investigates the aerodynamic performance of a system of multiple vehicles (three) propelled by an electric fan/compressor system in a closed low-pressure environment. The study was conducted on a 2-D axisymmetric model of multiple vehicles traveling at the same cruising speed inside a tube. The flow field was computed using the unsteady RANS model. The results demonstrate a significant improvement in aerodynamic performance when using a multiple-vehicle system equipped with an air-breathing electrical engine. The combined use of multiple-vehicle and the air-breathing propulsion system eliminated the piston effect at low Mach numbers and reduced transonic range effects at higher Mach numbers. Consequently, a considerable enhancement in aerodynamic performance over the single-vehicle model was attained. A minimum of 11% and a maximum of 57% reduction of further in average system drag was achieved for M = 0.3 and 0.7, respectively. An increase in thrust was also observed in the multiple-vehicle system over the single-vehicle system. The results of the study demonstrated the advantage of using an air-breathing multiple-vehicle system as an efficient and environmentally friendly high-speed transportation system. [ABSTRACT FROM AUTHOR]

Drilling precise micro-holes in glass material has brought a new challenge, primarily due to its application in microfluidic devices. It is required to reduce machining time and simultaneously achieve repeatability of the process. The machined hole should have minimum overcut to get desired hole diameter. Maximum hole circularity and minimum heat-affected zone are the essential hole characteristics to achieve controlled machining. The multi-criteria decision-making (MCDM) method is quite effective in selection of best possible combination of outputs from several alternative solutions. The experimental data from the previously published paper is used in the current study. As input parameters, the experiment data include voltage, tool feed rate, and machining time. The radial overcut (ROC), circularity of the machined hole and heat-affected zone (HAZ) were calculated as output responses. The experiments were conducted using copper and nickel-coated copper tools in previous study and response data were used in current work. The present study uses the methods which include hybrid grey relational analysis (GRA), technique for order performance by similarity to ideal solution (TOPSIS), and VIšekriterijumsko KOmpromisno Rangiranje (VIKOR) methods for the selection of the best combination of process parameters in drilling to obtain the optimal geometric characteristic of a hole, i.e. optimal values of ROC, circularity and HAZ, in glass using electrochemical discharge machining (ECDM). In this study, weight calculation for MCDM was proposed using entropy and analytic hierarchy process (AHP) methods and combined to get final weights using the fuzzy logic tool, which determines the importance of AHP and entropy weights using the expert opinion. The final weights were used to calculate the ranks from each MCDM method and determine optimal process parameter selection in ECDM hole drilling with copper and nickel-coated copper tools. The fuzzy logic method determined that the weight contribution from AHP is 0.586 in the present study. Genetic algorithm-based multi-objective optimization was conducted, and a non-dominated Pareto front was generated. The Spearman correlation method was used to determine the relationship between different MCDM methods. The current study will be helpful in selecting the best combination of process parameters in drilling holes during ECDM machining using MCDM methods. [ABSTRACT FROM AUTHOR]

Wave energy converters (WECs), particularly oscillating water column (OWC) systems, offer a range of economic and ecological benefits that make them an attractive and promising renewable energy source. To improve energy extraction in medium wave power regions, such as the Mediterranean beaches, the concept of an OWC device with an optimized oscillation chamber according to a sequential process is considered. In this regard, experimental tests, using sequential optimization procedure, are performed in this paper on an onshore OWC device focusing on its overall hydraulic effectiveness. Major emphasis is given to analyze the effects of the underwater front-wall vertical and angular positions, the oscillating chamber geometry, wave conditions and the power take-off (PTO) shape on hydrodynamic performances. The variation of the front-wall was studied according to two criteria: vertical type and angular type where the range of variation is between the angles 0° and 35° with a step of 5°. The chamber volume varying is ensured by inserting a pair of air-guide plates between the roof and the fluid free-surface. The obtained results revealed that changing the chamber front-wall direction affects the OWC oscillation energetic characteristics and the aerodynamic behavior of the turbine driving flow. Water elevation ratio "η/h", absorbed power, hydraulic efficiency, inlet air velocity upstream of the turbine and turbine rotating speed showed a gradual growth with increasing the wall opening angle up to FWA = 20°. The dimensionless turbine coefficients exhibited also an improvement in their behavior with this angle value. The air-guide plates addition leads to a raise of inlet air velocity, turbine speed and device efficiency by 5.5%, 12.3% and 7.5%, respectively. The developed process can greatly help to enhance the performance of real-size OWCs. [ABSTRACT FROM AUTHOR]

This paper critically reviews various energy-saving models used for enhancing the energy efficiency of hydrostatic power transmission drives. The system layout, component design, and controllers used are discussed in detail in its sub-category. Based on the detailed review of various energy-saving models, a novel energy-efficient hydrostatic power transmission drive of the chairlift transport system (CTS) is proposed. The rise in fluid temperature in the existing hydraulic arrangement of the CTS, owing to its continuous recirculation, is controlled by incorporating the flushing valve, while the boost pump replenishes the fluid volume loss. However, because of its loading nature, the CTS's existing power transmission drive encounters energy losses and uncontrolled fluid cooling. In the present study, the hydraulic circuit design of the existing power transmission drive of the CTS is modified to reduce energy losses. Using a variable displacement auxiliary pump in the system helps minimize the energy losses through the pressure relief valve. Also, the proposed design enhances the cooling efficiency of the flushing valve unit controlled through a suitably designed Fuzzy logic controller having a dependency on fluid temperature. The simulation model is made in MATLAB/ Simulink® environment, and the result shows that, with the increase in speed, the orifice area opening of the proportional flow control valve increases to prevent oil temperature from exceeding the operating range and the same decreases for a reduced speed. Furthermore, the proposed system exhibits a significant energy saving of 32 kJ over 300 s, demonstrating its improved efficiency compared to the existing system. [ABSTRACT FROM AUTHOR]