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Concrete engineers are always looking for materials that can be employed to improve the mechanical performance and lower the carbon footprint of construction products. In this regard, cellulose, a biobased material presents a massive opportunity for its application in the construction industry. Office paper waste (OPW) is cellulose-rich feedstock and a low-cost source to produce cellulose nanocrystals (CNCs). In this study, response surface methodology (RSM) was used for the production of CNCs from OPW using acid hydrolysis technique at different hydrolysis conditions; time (30–90 min), temperature (40–50 °C), and OPW/acid ratio (1:15-1:25 w/v). The physicochemical properties of CNCs were analyzed using various techniques. All the investigated responses mainly well-fitted RSM cubic models (R2 = 0.9460 to 0.9969) with insignificant lack-of-fit (p-value < 0.05). The highest yield (> 83%) was achieved at the lowest process time, temperature, and high OPW: acid ratio. The increase in process time and deinked OPW: acid ratio from 30 to 90 min and 1:15 to 1:25 resulted in higher crystallinity of CNCs. However, higher process temperature (50 °C) showed a reduction in crystallinity and length of CNCs. The optimization results of CNC production for meeting application of the CNCs as a construction material are time 90 min, temperature 40 °C, and OPW: acid ratio 1:25. The resulting CNC-mortar was tested under mechanical loadings and yielded 37.4 MPa compressive strength and 5.1 MPa of flexure strength. The study revealed that CNCs isolated from OPW has a great potential to use as a green additive for fabricating strong structural concrete. [ABSTRACT FROM AUTHOR]

The continuous increase in power requirement has created new challenges for insulating materials. In a construction of a power transformer, the paper insulation is most vulnerable to power leakage. The condition of the insulation system in a power transformer directly affects its stability and reliability. The emergence of nanodielectrics for specialized high-voltage applications sparked off a variety of research activities, which proved that nanocomposites are capable of improving the electrical, thermal and mechanical properties of polymers. This paper primarily investigates the effect of layering of alkyl phenolic resin nanocomposite onto a basic cellulose paper by increasing resin's content from 3.0–7.0%. Assessment is done to examine various physical, mechanical and dielectric properties. Analysis of the results indicates that alkyl phenolic resin-modified paper has uncommon properties that can become a promising replacement against basic cellulose paper. Also, the modification of the phenolic resin with the addition of silicon carbide (nSiC) nanoparticles (nanofillers) can greatly enhance the mechanical flexural strength, visco-elastic and thermal stability of the composite. The paper outlines the characteristics and properties of modified insulating paper in comparison with other existing unmodified insulating papers such as basic kraft paper, electra paper, nomex paper. [ABSTRACT FROM AUTHOR]

Physiological signals commonly suffer from contamination by various types of noise, ones of the most foremost are baseline wander (BLW) and power line interference (PLI). The removal of these interferences is a crucial in biomedical signal processing and diseases diagnosis. This paper introduces a digital fractional notch filter derived from the corresponding anti-notch one and specifically designed for the removal of BLW and PLI from physiological signals. The salient feature of the proposed filter is its capability to eliminate any frequency range only by adjusting the single parameter ν which defines the central frequency of the anti-notch filter. The performance of the filter is closely linked to the fractional order α and the number of samples L used in approximating the ideal fractional filter. Genetic algorithms were employed to determine the optimal values for these parameters (α, L). The proposed filter has been implemented on noisy ECG, EEG, and EMG signals, exhibiting its efficiency in removing unwanted noise. Comparative analysis with existing BLW and PLI removal techniques indicates that the proposed filter outperforms them based on the evaluation metrics employed. [ABSTRACT FROM AUTHOR]

This study investigates the process conditions of the adsorption of copper (II) ion onto paper mill sludge (PMS) in a batch process. These conditions are: concentration of initial solution, contact time, temperature and quantity of the adsorbent. Characteristic properties of PMS employed as an adsorbent in the experiments were defined using Fourier transform infrared spectroscopy (FT-IR) scanning electron microscopy (SEM) and elemental analyses. According to the obtained results, while the amount of removed copper (II) ion increased with an increase in the rate of the adsorbent and contact time, it decreased as a result of an increase in the temperature and initial solution concentration. Langmuir, Freundlich and Dubinin-Radushkevich (D-R) isotherms were implemented for the determination of the most appropriate isotherm model for the experimental data, and it was found that the process is in concordance with Langmuir equation. The maximum adsorption capacity of PMS was calculated as 114.42 mg g-1. In kinetic studies, the adsorption process of copper (II) ion onto PMS was controlled by the pseudo-second-order kinetic model. The calculated activation value (Ea) was 38.61 kJ mol-1 and demonstrates that the process occurred by physical adsorption mechanism. The values of the thermodynamic parameters such as enthalpy (ΔHO)(-21.19kJmol-1), free energy (ΔGO) (−8.883 kJ mol-1) and entropy (ΔSO) (0.101 kJ mol-1 K-1) changes were determined to estimate the nature of the process. The results clearly showed that the process was of exothermic and spontaneous nature and that PMS could be utilized as an adsorbent for the removal of copper (II) ion from wastewater. [ABSTRACT FROM AUTHOR]

Paper de-inking is one of the critical processes in pulp and paper industry as it is ecofriendly and energy saving. This process requires microbial enzymes such as xylanases with ability to withstand harsh bioprocess conditions. Microbulbifer is a halophilic genus with ability to produce hydrolytic enzymes that could be applied in the biotechnological industry. So far, none of the xylanases from this genus have been studied, particularly in paper de-inking process. Therefore, in this study, the xylanase of a new halophilic bacterium, Microbulbifer sp. strain CL37, was characterized. Strain CL37 produced maximum amount of xylanase at 14th hour of incubation at 30 °C. The xylanase demonstrated optimal activity at 70 °C and pH 7. The xylanase was stable at wide range of NaCl (0–14%, w/v), in the presence of Al3+, Ca2+, Co2+, Cu+, Cu2+, Fe2+, Fe3+, Mn2+, Zn2+, acetone, chloroform, ethanol, sodium deoxycholate, Triton X-100, Tween 20, 40, 60, and 80, indicating that it is a halotolerant enzyme with high stability in various additives. The xylanase also demonstrated its ability to de-ink paper with considerably high efficiency (159%) as compared to other strains. The valuable characteristics possessed by xylanase of strain CL37 could potentially benefit to de-inking process in paper industry. [ABSTRACT FROM AUTHOR]

In this work, a fine correlation among destructive and diagnostic parameters of transformer's paper insulation is presented. The degree of polymerization (DP) directly assesses the condition of the paper insulation strength and is measured through destructive procedure. Generally, 2-FAL, CO2 and CO are the aging products of paper decomposition and referred as diagnostic parameters. An Adaptive Neuro Fuzzy Inference System (ANFIS) is proposed and developed to estimate the value of DP as the function of amount of diagnostic parameters. The proposed system has an advantage of diagnosing the health of solid insulation without performing destructive tests. The diagnostic parameters are taken as inputs to the system to determine the value of DP. The system uses 630 data points for training the ANFIS model and follows a ten-fold cross-validation approach. The average validation error has been determined to be. 0.0029. Further, the model's performance has been assessed using experimental data. The optimal ANFIS model has been achieved by suitably selecting the number and type of membership function. The estimated value of DP has been found to conform to the experimental measurements for every case under test. The performance of this model has also been compared with a fuzzy inference system (FIS) model in the reported literature. The comparison shows that the ANFIS model determines the DP values with a greater degree of accuracy than the FIS model. [ABSTRACT FROM AUTHOR]

As a new energy resource with great development potential, the exploitation of hot dry rock geothermal resources is increasing around the world. However, the drilled formations, such as granite and other rocks with high hardness and wear resistance, seriously restrict the drilling efficiency. Polycrystalline diamond compact (PDC) bit, cone bit and PDC-cone composite bit are mainly used for geothermal drilling, but there is a lack of research on the drilling conditions of these bits to compare their advantages and disadvantages horizontally. This paper conducted rock-breaking experiment of granite under normal temperature and pressure using full-sized bit and analyzed weight on bit, torque and axial, radical and circumferential acceleration of three types of bits. This paper analyzed the cutting marks at the bottom of the rock and the wear of the bits, which can reflect the possible failure mode of bits in the process of drilling. Moreover, the monitored data from sandstone to granite in two wells numbered XX34-3-1 and XXB-1-1 and the wear and failure degree of three types of bits are discussed. Indoor experiments and field applications have proved that PDC bit has the highest rate of penetration (ROP) with the largest wear degree in granite formation. The wear of tri-cone bit is smallest but its drilling efficiency is low. The ROP and wear of PDC-cone composite bit are between the two kinds of bits mentioned above. This paper can be used to analyze the adaptability of drill bits and recommend drill bit types when drilling in granite formation. [ABSTRACT FROM AUTHOR]

As the increasing trend of global consumption of waste electrical and electronic equipment (WEEE), the recycling of WEEE has been thought highly of. In order to solve the problems of high economic cost, environmental detrimental caused by improper recycling, ignorance of social benefits of recycling, uncertain recycling amount and single recycling channel in recycling process of WEEE, this paper proposes a sustainable multi-objective WEEE double recycling channel fuzzy programming model based on triple bottom line (TBL) theory. First of all, TBL is used to develop sustainable multi-objective recycling models concerning economy, environment and society. Secondly, the model is fuzzed. This process includes two steps: the first step is to take WEEE recycling quantity as a triangular fuzzy parameter, and use fuzzy chance constraint method to transform fuzzy constraint into an equivalent clear condition; the second step is to fuzzify suboptimal objective in the model by defining objective membership degree, and transform multi-objective optimization problem into a single-objective optimization problem based on the maximum satisfaction. Finally, taking a recycling enterprise in Shantou, China, as an example, genetic algorithm (GA) and particle swarm optimization (PSO) are comparatively used to solve a calculation case related to the model and the contribution of the model is verified by comparing economic cost, environmental impact and social benefits of the dual recycling channels. [ABSTRACT FROM AUTHOR]

Genomic signal processing is a development field in medicine and agriculture. Numerous research areas are processing the genomics of living organism such as animals and particularly human beings. In this paper, the microarray data set for the biological organism which includes a large number of gene data has taken for the processing. The microarray data are a powerful technology practised in the research field for validating the gene discovery and diagnosis of diseases. The data are processed to a large number with plenty of genes. The proposed Kalman filter-based fuzzy c-means cluster and artificial neural network (KF-FANN) enhance the genomic signal processing to the optimal level. The Kalman filter proposed in this paper to remove the noise and smoothen the data for signal processing. An ideal clustering process is carried out for the classification of the microarray data. The fuzzy c-means clustering was proposed in this paper for grouping the microarray after removing the noise. The artificial neural network is a biologically inspired model proposed in this work for the classification of microarray data to point out the normal and abnormal genes in the microarray data. The proposed work has compared with existing techniques such as c-means, k-means clustering, and multi-SVM, respectively. The proposed method is carried out in the MATLAB platform, and results are evaluated in terms of Calinski–Harabasz index, separation index, Xie and Beni's index, partition index, accuracy, precision, recall, and F-score. The analysed result shows that the proposed KF-FANN is an efficient method for the classification of microarray data than existing approaches in genomic signal processing. [ABSTRACT FROM AUTHOR]

Internet of things (IoT) devices should be designed taking security requirements into consideration, so that they can be used securely in open environments. Designing secure IoT devices requires the ability to design fast and secure cryptography modules. A component in these modules is the pseudorandom number generator (PRNG), which can be built using different strategies. Some of these strategies use chaotic maps, and in such cases, the chaotic map that is selected must be simple and feasible to implement in a digital device by using the IEEE-754 floating-point standard. The chaotic map must also generate number sequences whose statistical distribution looks uniform. In this way, this paper shows the digital implementation of a PRNG by using a non-scaled non-discretized skew tent map (STM). The proposed PRNG can produce uniformly distributed number sequences when the annulling chaos conditions are identified and avoided on the chaotic map. Furthermore, the pseudorandom sequences are generated in few milliseconds. Compared to similar PRNGs recently reported, the proposed PRNG has been successful, based on tests, such as the correlation coefficient, key sensitivity, statistical analysis, entropy analysis, key space and randomness. [ABSTRACT FROM AUTHOR]

The various benefits of biofuels versus fossil fuels due to recent global challenges and issues are the best approach toward low-cost economic production of renewable energy. This study is trying to obtain economic catalysts with easy fabrication technology. The synthesized catalysts were obtained using calcium oxide/nanoclay catalysts by an initial ion-exchange reaction of calcium oxide and nanoclays (montmorillonite). These catalysts have been synthesized for the first time by being stirred for 5 h at a temperature of 80 °C, and the colloidal supernatant is obtained and kept in an ultrasonic bath for 20 min. The solution was filtered, washed several times, the residual mixture on filter paper was dried in the oven at 50 °C for few hours, and the powder was calcined for 8 h in a furnace at 600 °C. After identification and characterization, using XRD, BET, and SEM, the results approved the formation of a new nanostructure in synthesized catalysts, which were suitable to be used in biodiesel production from waste oils with high free fatty acids content. The results of this study indicate that the catalysts production process is not complicated, and methyl ester production rates in all biodiesel samples were more than 97% (97.1–98.8%). [ABSTRACT FROM AUTHOR]

In this study, an attempt is made to investigate the load transfer in pile groups constructed in saturated and unsaturated soil using aluminum model piles of 20 × 20 mm in cross section and 200 mm in length with six group configurations, single, 2 × 1 , 3 × 1 , 2 × 2 , 3 × 2 , and 3 × 3 , groups in addition to pull-out test. The samples are prepared at three different soil properties: two models with the same degree of saturation and two with different dry unit weights and the third is unsaturated soil model with. A relationship between the soil suction and water content is introduced using a digital monitor, accompanied by eight suction probes planted in CBR molds. This relationship is used to predict the value of suction when conducting tests on models of the unsaturated soil. Further, the soil suction is measured using a filter paper method. Results of ultimate load capacity obtained from the load–displacement curves reveal an increase in the ultimate load with increasing the number of piles in the group for the same soil properties, and also an increase in the soil dry unit weight. Further, the results indicate an increase in pile capacity when the soil becomes in unsaturated state compared with saturated soil for the same pile group tested. On the other hand, the pull-out test gives a maximum load carrying capacity when the single pile tested was in saturated soil with high dry density, and minimum results are obtained when tested in unsaturated condition with the percentage of increase being about 463 and 34% for two saturated soils, compared with that for unsaturated soil. [ABSTRACT FROM AUTHOR]

Packaging material is one of a vital feeding industries for many food and non-food industries. Active packaging based on antimicrobial efficiency has a great attention to save food, sterile medical and pharmaceutical packing items. Synergetic antimicrobial effect of metal oxide and linear polyethylenimine represented as a unique active packaging material. Sr(Ti0.7Fe0.3)1-xScxO3 system was prepared using modified Pechini method where (x = 0, 0.01, 0.05, 0.1). The prepared samples were characterized using FTIR, TEM, DLS and surface area measurements. The characteristic bands for Sr–O, Ti–O vibrations were detected for the undoped SrTi0.7Fe0.3O3 samples at about 615, 589 cm−1. For the Sc-doped samples (Sr(Ti0.7Fe0.3)0.99Sc0.01O3, Sr(Ti0.7Fe0.3)0.95Sc0.5O3 and Sr(Ti0.7Fe0.3)0.9Sc0.1O3), the characteristic band for Sc-O vibration was detected at about 666 cm−1. The electron diffraction of the prepared samples verified all the samples are well crystallized. From TEM, Sr(Ti0.7Fe0.3)0.95Sc0.5O3 and Sr(Ti0.7Fe0.3)0.9Sc0.1O3 samples have well-defined dense particles than that of SrTi0.7Fe0.3O3 and Sr(Ti0.7Fe0.3)0.99Sc0.01O3 samples with irregular shape. From the electron diffraction of the prepared samples, all the samples are well crystallized. Particle size and zeta potential indicated good particle size distribution with nice dispersion stability. In addition, the surface area measurement indicated excellent agreement between particle size and surface area of prepared metal oxides. Moreover, the addition of inorganic complexes individually to PEI produced broad spectrum antimicrobial paper sheets. The excellent antimicrobial properties were remarked for Sr(Ti0.7Fe0.3)0.9Sc0.1O3/PEI samples. [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]

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]

How to slow down the casing deformation of shale gas wells is one of the problems to be solved urgently. Fault slip is inevitable, so how to prevent casing deformation has become an important research hotspot. In this paper, a new casing deformation control method based on solid expandable tubular (SET) technology is proposed to reduce casing deformation. This paper establishes a three-dimensional dynamic model of casing deformation considering formation pressure, frictional contact, and nonlinear loads, analyzes the advantages of using SET technology in cementing, and researches the influence of SET and annular cement parameters on casing deformation. The research results show that: on the basis of the existing cementing cement, the use of SET technology is more effective. The inner diameter of the casing after deformation will increase by more than 10 mm on the current basis, which is more conducive to subsequent construction. The expansion ratio of SET has different slowing effects on the casing deformation. In addition, the study also found that the effect of using "flexible cement + SET" is better. If only flexible cement is used, the elastic modulus of ring cement is 0.01 GPa. In order to achieve the same effect, in the case of the "flexible cement + SET" process, only annular cement with an elastic modulus of 0.1 GPa is required, which is equivalent to reducing the difficulty of cement construction. The research results of this paper provide new ideas and methods for casing deformation. [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]

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]

Aiming the problem that the spatial-temporal hierarchical continuous sign language recognition (CSLR) model with video as input is computationally intensive, thus limiting the real-time application, this paper proposes a temporal super-resolution network (TSRNet) to reduce the model computation while keeping the loss of accuracy to a minimum, achieving the best compromise between the real-time performance and accuracy. The TSRNet-based CSLR constructed in this paper consists of three main parts: frame-level feature extraction, temporal feature extraction and the proposed TSRNet, where the TSRNet is located between them, and consists of two branches: detail and coarse descriptors. The extracted frame-level features are first sparse, after which they are passed through the two branches designed for feature reconstruction; the fused dense sequence is subjected to temporal feature extraction. In order to better recover the semantic-level information, this paper also proposes a self-generating adversarial network training method, which treats the TSRNet as the generator and the frame-level and temporal processing parts as the discriminator. In addition, to unify the criteria for judging the loss of model accuracy under different benchmarks, this paper proposes word error rate deviation (WERD), where the error rate between estimated WER and reference WER obtained by reconstructed frame-level feature sequence and complete original frame-level feature sequence, respectively. Experiments on two large-scale sign language datasets demonstrate the effectiveness of the model. The method proposed in this paper is not only applicable to CSLR, but is general to spatial-temporal hierarchical models where the input is video data. Code is available at https://github.com/woshisad159/CSLR.git. [ABSTRACT FROM AUTHOR]

Muslims aim to recite and memorize the Holy Quran correctly. However, traditional recitation verification approaches depend on humans who may not be available. On the other hand, Artificial Intelligence (AI) capabilities assist in developing intelligent recitation verification tools based on speech recognition techniques. This study aims to overview the current state of the intelligent Quran recitation recognition and verification solutions and highlight the related open issues. A systematic literature review was performed on the published paper since 2006 up to date to answer six research questions. The research questions covered the speech recognition techniques and methods used to develop Quran recitation recognition and verification models, the database and tools used, and the existing mobile application supporting real-time intelligent Quran recitation verification services. Based on the review results, a taxonomy of the Quran recitation recognition and verification techniques was generated, including traditional and end-to-end speech recognition methods. Moreover, the limitations of the existing AI-based Quran recitation verification applications were reported. Additionally, the available Quran audio datasets and tools capable of dealing with Quranic speech were identified. In conclusion, several open issues can be addressed in future research, e.g., considering AI-based approaches to ensure sequence recitation and recognize diacritics-based errors. [ABSTRACT FROM AUTHOR]

Fault slip will directly affect the technical performance of solid expandable tubular (SET). However, there are few researches on the mechanical behavior of shear failure when SET crosses the slip fault. In response to the above problems, this paper builds a three-dimensional dynamic tube-rock system numerical model that considers nonlinear frictional contact and fault slip based on the SET material constitutive model, Coulomb friction, and shale layer rock. The purpose is to reveal the influence of fault slip on the mechanical behavior of SET and to analyze the risk point of SET failure. The results of the study show that as the amount of fault slip increases, the maximum value of the Mises stress of SET appears in the direction perpendicular to the amount of fault slip (90°). With the increase in the number of faults, the shear stress distribution on the cross section hardly changes, and the shear stress distribution on the fault plane presents a "8" shape. When the included angle is 60°, the shear stress on the shear plane is the largest and the shear distribution is the most uneven. The research results of this paper provide a theoretical basis for the slip shear of SET. [ABSTRACT FROM AUTHOR]

Arabian Journal for Science and Engineering (AJSE) is a rapidly growing multidisciplinary academic journal that publishes original contributions in the Science disciplines of Biological sciences, Chemistry, Earth Sciences, and Physics, and in the Engineering disciplines of Civil, Chemical, Electrical, Mechanical, Petroleum Engineering, Computer Science and Engineering, Systems Engineering, and along with a Theme Issue on specific topic. This study uses bibliometric tools to present a retrospective analysis of research productivity during 1977–2021. The bibliometric method used to analyze AJSE's published papers based on annual growth, citation pattern, prolific authors, country, organization, funding agencies, author keyword, authorship patterns, theme analysis, most cited papers, cited references, and international collaborations. The result shows that AJSE has grown noticeably from 1977 to 2021 in total publications and citations, reaching 7658 papers and 33,846 citations. India (TP: 1324) is the country with the more number of papers contributed. King Fahd University of Petroleum and Minerals (TP: 838) found as the most contributing organization to the AJSE publications. Zhang L identified as the most prolific author with 28 publications. The most important author keywords are Optimization, Adsorption, Genetic algorithm, and Mechanical properties. Saudi Arabia and Egypt collaborated in the maximum number of AJSE research papers. This is the first bibliometric investigation of AJSE scientific literature since its establishment. Overall, the findings provide useful information to the AJSE editorial board. Its retrospective review will likely encourage readers of the journal and help the editorial team formulate research agendas and inform research strategies that will enable research scientists to contribute high-quality research papers to the AJSE. [ABSTRACT FROM AUTHOR]

Traditional current and voltage strategies proposed for the single-phase rectifier with pulse width modulation (PWM) used in the AC charging system for electric vehicles, have a large response time and the percentage of total harmonic distortion (THD) of the line current is more than 2%, which causes a reduction of the power factor (PF). The present paper focuses on the robust sliding mode current–voltage control (SMCVC) design for the single-phase rectifier with PWM. This SMCVC strategy used the grid current directly without any rotating coordinate transformations and reduced the complexity computational of the implementation algorithm and allows to satisfy the following desired performances, low THD of the input current and the PF is around one in a faster response time. In this paper, the proposed control strategy is compared and verified with proportional integral (PI) control and proportional integral sliding mode current control (PI-SMCC) by Matlab/Simulink. At the end of this paper, simulation examples are given to illustrate our approach and to show that our proposed method provides very good results with respect to some literature results. [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]

Plant fibers have enormous potential for enhancing concrete's impact resistance due to their exceptional toughness and ductility. Nevertheless, theoretical studies on the impact properties of plant fiber-reinforced concrete (PFRC) are still relatively few. Thus, it is crucial to investigate from a theoretical standpoint how plant fibers affect concrete's impact properties. Based on the modified Griffith fracture theory, this study established the relationship between plant fiber type and strain hysteresis factor. In order to simulate the damage process of PFRC, a micro-spring system was used. Based on this, a dynamic constitutive model considering the influence of plant fibers was proposed by combining the methods of damage mechanics and fracture theory. Parameter fitting and inverse analysis were used to validate the accuracy of the proposed model. Additionally, the mechanical behavior of four types of PFRC (Jute, Flax, Ramie and Sisal Fiber Concrete) under impact loading was simulated by the finite element method (FEM) using the proposed model. The results show that the predictions of the proposed model maintain a good of fit R2 more than 0.98 with the experimental results. The parameter fitting and inversion results also show that the proposed model has a good prediction. Simulation results show that sisal fiber-reinforced concrete exhibited the best performance among the four types of PFRC under identical loading conditions, offering more significant advantages for engineering applications. Theoretical and simulation studies on the impact properties of PFRC were conducted in this paper, and the findings are believed to provide a valuable methodological basis for the engineering application of using less expensive plant fibers to enhance concrete impact properties. [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]

Deep learning methods have shown significant potential in computer vision applications within the field of civil engineering. One important area of research is the detection and classification of construction machines using convolutional neural networks (CNNs). However, a major challenge in adopting machine learning models for vision systems lies in hyperparameter optimization. Therefore, the objective of this paper is to propose a meticulous statistical approach to hyperparameter tuning for deep learning in the visual classification of construction machines. It is noteworthy that the method for selecting hyperparameters utilizes statistical concepts, including analysis of variance and the Tukey test. Moreover, three research questions were formulated to address statistical differences in performance when varying the CNN architecture, optimizer and configuration (architecture and optimizer). Two additional research questions were posed to determine the recommended hyperparameters for the task and assess performance differences for non-selected hyperparameters. For this purpose, 18 hyperparameter combinations were analyzed, encompassing three CNN architectures (DenseNet, Mobilenet and VGG16) and six optimizers (adadelta, adagrad, rmsprop, adam, adamax and sgd). The Alberta Construction Image Dataset, consisting of 2850 images of three construction machines (excavator, dump truck and concrete mixer truck), was used in the experiments. The results demonstrate that the hyperparameter configurations exhibit statistically significant differences in image classification. Furthermore, the recommended combination (DenseNet × adagrad) yielded the highest average accuracy results in both binary tests (90.0%) and multiclass classification (77.8%). [ABSTRACT FROM AUTHOR]

This review paper examines the advancements in solid-state power amplifiers (SSPAs) for wireless communication technology. As mobile devices rely on efficient power amplifiers to maintain battery life and ensure clear signal transmission, fabrication technologies like complementary metal–oxide–semiconductor (CMOS) and gallium nitride (GaN) are revolutionizing power amplifier (PA) design. The choice of material depends on the working frequency, with gallium arsenide (GaAs) and GaN suitable for frequencies under 100 GHz, and indium phosphide reaching up to 500 GHz. However, cost is a crucial factor in industrial manufacturing, making CMOS technology advantageous for on-chip system integration. Millimeter-wave chips have different requirements based on their application scenarios. In the Ka-band (26.5–40 GHz), high-power GaN and GaAs chips are preferred for satellite and long-distance communication. In contrast, the 60 GHz band is suited for short-distance high-speed communication and consumer electronics, making lower-cost CMOS and germanium silicon devices the preferred choice. This paper explores critical design considerations for SSPAs, focusing on common structures like envelope tracking, Doherty amplifiers, envelope elimination and restoration, and various linearization methods. We provide a clear comparison of their strengths and weaknesses to empower readers to select the optimal SSPA structure for their needs. Our review aims to facilitate informed decisions in the development of efficient and cost-effective SSPAs for advancing wireless communication technology. [ABSTRACT FROM AUTHOR]

Long-term accurate forecasting of the various sources for the electric energy production is challenging due to unmodelled dynamics and unexpected uncertainties. This paper develops non-parametric source models with higher-order polynomial bases to forecast the 16 sources utilized for the electric energy production. These models are optimized with the modified iterative neural networks and batch least squares, and their prediction performances are compared. In addition, for the first time in the literature, this paper quantifies the unseen uncertainties like the drought years and watery years affecting especially the hydropower and natural gas-based electric energy productions. These uncertainties are incorporated into the parametric imported-local source models whose unknown parameters are optimized with a modified constrained particle swarm optimization algorithm. These models are trained by using the real data for Türkiye, and the results are analysed extensively. Finally, 10 years ahead estimates of the 16 imported-local sources for the energy production have been obtained with the developed models. [ABSTRACT FROM AUTHOR]

Predicting wind speed accurately is essential for the efficient generation of wind power. To enhance the precision of wind speed forecasting, this paper proposes a multivariate combinatorial model based on a deep learning neural network, which incorporates not only historical wind speed data but also relevant meteorological features. Initially, the feature extraction of meteorological features related to wind speed is first performed using an autoencoder and singular value decomposition. Subsequently, the complementary ensemble empirical mode decomposition and wavelet transform method is utilized to mitigate noise in the wind speed series. Finally, this paper utilizes a gated recurrent unit (GRU) deep learning neural network for predicting the wind speed series. The optimization of the GRU's four hyperparameters is accomplished through the implementation of the improved gray wolf algorithm. This paper evaluates and validates the predictive performance of the model using two datasets. The experimental results demonstrate that the mean absolute percentage error of the proposed model's 1-step predictions on the two datasets is 0.7532% and 0.5263%, with corresponding root mean square error values of 0.0283 and 0.0227, respectively. These results indicate a significant improvement over those achieved by other models under comparison. [ABSTRACT FROM AUTHOR]

This paper presents a unique five-level inverter consisting of one DC voltage source and one capacitor. Here the capacitor acts like another source and is fed to the inverter along with the DC voltage source. The DC voltage source is also used to charge the capacitor with the help of an additional switch used inside the converter circuit. An inductor is used in the charging path to reduce the peak of the charging current. The in-phase disposition sinusoidal pulse-width modulation technique is used to generate the gate pulse for the devices of the presented converter. The proposed topology operates in two modes. One is energy stored mode and another is energy release mode. By using both modes of operation, the additional switch is triggered to charge the capacitor. The capacitor's charging and discharging mode and inverter power loss have been described thoroughly in this paper. The proposed five-level inverter topology has been developed and verified inside the laboratory. The controller of the converter has been implemented in the FPGA platform. The experimental results are obtained to evaluate the efficacy of the proposed inverter. [ABSTRACT FROM AUTHOR]

The modern power systems incorporate high penetration of renewable is a large, composite, interconnected network with dynamic behavior. The small disturbances occurring in the system may induce low-frequency oscillations (LFOs) in the system. If the (LFOs) are not suppressed within a stipulated time, it may cause system islanding or even blackouts. Hence, it is essential to investigate the behavior of the system under various levels of disturbances and control action must be taken to damp these oscillations. The established approach to damping the LFOs is by installing power system stabilizers (PSS). PSS uses the local signals from generators to control the oscillations. The dominant source of inter-area oscillations in power systems is due to overloaded weak interconnected lines, converter-interfaced generation, and the action of the high gain exciter present in the system. Consequently, wide area control is needed to control the inter-area oscillations existent in the system. This paper developed a coordinated design of conventional PSS, static compensator, renewable converters, and wide area controller for damping the local and inter-area oscillations in renewable incorporated power systems. The performance of the developed controller is evaluated through the time domain analysis and eigenvalue analysis. A comparison of the introduced controller has been done with other standard conventional methods. The choice of input signals for the wide area controller from the wide-area measurement system is done based on the controllability index. Additionally, the location of the controller must be identified to dampen the inter-area oscillations in the system. In this paper, the controllability index is calculated to find out the highly affected wide area signals for considering it as the feedback signal to a developed controller. The location of the controller is recognized by computing the participation factor. The developed controller has experimented on renewable incorporated large study power systems when time delay and noise are present in wide area signals. [ABSTRACT FROM AUTHOR]

Industrialization and population growth have made surface areas more valuable, thereby the multi-story buildings have become an absolute necessity. At this point, numeric models became the fastest and simplest way to evaluate the response of soils and structures. The issued factor in the current paper is related to the way of transferring the multi-story building loads to an alluvial stratum and evaluate the accuracy of different cases, in order to save time and economy. For load transfer, the first case (case i) includes uniform distributed load, the second case (case ii) includes the transfer from the basement columns and walls, and the third case (case iii) includes modeling the real state of the building. Mainly, all three cases gave close results in terms of settlement magnitudes of 2.21, 1.96, and 1.81 cm, respectively. It was inspected that case (i) showed 12.8% more deformation than case (ii) and 22.1% more deformation than case (iii). However, the situation is not the same for the settlement pattern, and the under-column and corner effects are neglected in uniform load. Additionally, the bending moments, which is a critical parameter for the design of a reinforced concrete foundation, have developed different results. In case (ii) and (iii) a bending moment of 500 kNm/m is observed in the center column, while in case (i) the moments converge to 0. Therefore, this study highlights the importance of outstanding decision making when assessing the load-transferring mechanism in modeling with numerical methods. The necessity of the determination of the convenient load transfer way depending on the parameter that is crucial in the evaluation of the soil–structure interaction comes to the fore with current paper. [ABSTRACT FROM AUTHOR]

In this paper, an electric servo actuator implementation scheme with an energy recovery link is proposed to address the potential energy being wasted when a load falls. The new electric servo actuator recovers the potential energy dissipated during the load-falling progress by using an accumulator. The accumulator can also provide extra thrust when the load is lifted. Moreover, a dynamic model is established based on the theory of power bond graphs to analyze the dynamic and energy consumption characteristics of the new electric servo actuator. The simulation results show that the new electric servo actuator can significantly reduce the required motor power and effectively recover the potential energy of the load. This achievement is very significant for improving the energy efficiency of electric servo actuators and mitigating global warming. In addition, the modeling method used in this paper has important reference significance for the energy consumption analysis of other mechanical systems with complex energy domains. [ABSTRACT FROM AUTHOR]

As the center of the financial market, the stock market is popular with the public attention of investors. It is of great significance for investors that an effective analytic method of stock public opinion is proposed. As the main communication platform, the forum not only provides the investors with investment information but also comments related to the stock market. In view of the defects of text emotions and investment problems, this paper proposes a framework based on web crawler and deep learning technologies including one-dimensional convolutional neural networks (1DCNN) and long short-term memory (LSTM), to evaluate the stock market volatility. Among them, the extracted features include not only the stock price but also the text information. Firstly, we develop the crawler technology to grab large-scale text data from the internet and they are manually labeled their emotions by analyzing the relevant financial knowledge. Secondly, as the character-level text classification method, the 1DCNN is designed for text sentiment classification to detect the reliability of text annotation. Finally, considering the time sequence of price and the continuity of post influence, the emotional and technical features are combined to estimate the fluctuation of the stock market in different industries by the LSTM model. We test four evaluation indexes, the classification accuracy of the model is 74.38%, the accuracy rate is 76.83%, the recall rate is 70%, and the F1 value is 72.8%. The results show that the combination of characteristics of internet public opinion more effectively evaluates the changes in the stock market. [ABSTRACT FROM AUTHOR]

As COVID-19 has spread rapidly, detection of the COVID-19 infection from radiology and radiography images is probably one of the quickest ways to diagnose the patients. Many researchers found the necessity to utilize chest X-ray and chest computed tomography imaging to diagnose COVID-19 infection. In this paper, our objective is to minimize the false negatives and false positives in the detection process. Reduction in the number of false negatives minimizes community spread of the COVID-19 pandemic. Reducing false positives help people avoid mental trauma and wasteful expenses. This paper proposes a novel weighted consensus model to minimize the number of false negatives and false positives without compromising accuracy. In the proposed novel weighted consensus model, the accuracy of individual classification models is normalized. While predicting, different models predict different classes, and the sum of the normalized accuracy for a particular class is then considered based on a predefined threshold value. We used traditional Machine Learning classification algorithms like Linear Regression, Support Vector Machine, k-Nearest Neighbours, Decision Tree, and Random Forest for the weighted consensus experimental evaluation. We predicted the classes, which provided better insights into the condition. The proposed model can perform as well as the existing state-of-the-art technique in terms of accuracy (99.64%) and reduce false negatives and false positives. [ABSTRACT FROM AUTHOR]

The communication between the human brain and the external devices can be established using Electroencephalograms (EEG)-based Brain–Computer Interface by converting the neural activities of the brain into electric signals. The EEG signals were isolated into an energy–frequency–time spectrum with Hilbert Huang transform that was used by the Deep Learning (DL)-based model to learn discriminative spectro-temporal patterns of the raw EEG signals of ten digits. This paper has two major contributions: first, create a novel dataset known as BrainDigiData of EEG signals of ten digits from (0–9) using a multi-channel EEG device. Second to propose a DL-based one-dimensional Convolutional neural network model BrainDigiCNN to classify the BrainDigiData of EEG signals of digits. The publicly available Mind Big Dataset (MBD) of digits was also used to evaluate the performance of the proposed model. The research done in this paper showed that the band-wise analysis of EEG signals in a complex scenario resulted in improved results as compared to the scenario used in the previously existing work for digit classification using EEG signals. The proposed BrainDigiCNN model achieved the highest average accuracy of 96.99%. The average classification accuracy of 98.27% was achieved for the MBD dataset of 14 channel device EMOTIV EPOC+ and 89.62% on the MBD dataset of 5-channel EMOTIV Insight. The statistical analysis of the proposed model on traditional Machine Learning (ML) classifiers using paired t-test resulted in a p-value less than 0.05 which shows the significant difference between the proposed model and ML classifiers. [ABSTRACT FROM AUTHOR]

In the current situation of worldwide pandemic COVID-19, which has infected 62.5 Million people and caused nearly 1.46 Million deaths worldwide as of Nov 2020. The profoundly powerful and quickly advancing circumstance with COVID-19 has made it hard to get precise, on-request latest data with respect to the virus. Especially, the frontline workers of the battle medical services experts, policymakers, clinical scientists, and so on will require expert specific methods to stay aware of this literature for getting scientific knowledge of the latest research findings. The risks are most certainly not trivial, as decisions made on fallacious, answers may endanger trust or general well being and security of the public. But, with thousands of research papers being dispensed on the topic, making it more difficult to keep track of the latest research. Taking these challenges into account we have proposed COBERT: a retriever-reader dual algorithmic system that answers the complex queries by searching a document of 59K corona virus-related literature made accessible through the Coronavirus Open Research Dataset Challenge (CORD-19). The retriever is composed of a TF-IDF vectorizer capturing the top 500 documents with optimal scores. The reader which is pre-trained Bidirectional Encoder Representations from Transformers (BERT) on SQuAD 1.1 dev dataset built on top of the HuggingFace BERT transformers, refines the sentences from the filtered documents, which are then passed into ranker which compares the logits scores to produce a short answer, title of the paper and source article of extraction. The proposed DistilBERT version has outperformed previous pre-trained models obtaining an Exact Match(EM)/F1 score of 80.6/87.3 respectively. [ABSTRACT FROM AUTHOR]

It is of great theoretical value and practical significance to study the location of cascading failures in chemical material networks with large and complex characteristics. Previous mainly used the method of obtaining all information of nodes, maximum likelihood estimation function, and distance centrality to locate faults. These methods need to determine the status of all nodes in the network or perform operations on the nodes in the entire network related to the observation node, the localization process is more complicated and costly, and the results are not accurate enough. This paper proposes a positioning method based on the breadth-first search strategy (BFS)-time-reversal backpropagation algorithm to study the cascading fault location of chemical material networks. Firstly, the cascade fault propagation model of the chemical material network is constructed according to the relevant theory of the complex network, and the observation node selection strategy selects the observation node. Secondly, use the BFS to search out the fault area, then make assumptions about the time delay of the propagation process, and use the time-reversal backpropagation algorithm to filter the nodes in the fault area to realize the cascade fault location of the chemical material network. Finally, change the observation node selection strategy, analyze the positioning accuracy under each strategy, and select the optimal selection strategy for positioning using the method in this paper. Case analysis shows that this method can effectively realize the cascading fault location in the chemical material network. [ABSTRACT FROM AUTHOR]

A Base Station (BS) is a gateway between a wireless sensor network (WSN) and the network administrator (NA). It is responsible for gathering information from the sensors and forwarding it to the NA while collecting instruction from him and communicating it to the WSN. Consequently, the BS has become the prime target of attackers. One form of attack on the BS is tempering. The attacker finds the physical location and destroys it. An adversary can easily find the BS since (in a typical WSN) it is the node with the highest traffic. In this paper, we hide the BS using proportional–integral–derivative (PID) controlled fake packet injection and data aggregation. The data aggregation strategy helps combine data from the sensor nodes into a super-packet, thus reducing the actual traffic. On the other hand, the PID-controlled fake packet injection technique compensates for the difference in traffic between the BS and other nodes in the network. The simulation results demonstrate that the proposed approach can protect the BS from packet-tracing and traffic analysis attacks. [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]

Ultraprecision positioning to better than nanometer in accuracy is of a great interest in various applications. The paper reports on recent pre-rolling experimental measurements of the moment friction during the phase of pre-rolling, i.e., at the start and at the finish. To discuss the case properly, the experiments are set for pure rolling without any external effect nor any slippage at the contact patch. The pendulum was designed to secure pure pre-rolling without slippage. Beside the fact that the experiments have shown extremely low values of rolling coefficients in the order of (× 10–7), it is possible to establish the appearance of real hysteresis curves showing the dependance of the friction moment on the displacement. The sensitivity of the measurement of rolling friction moments is in the order of 10–10 Nm, the measurement error does not exceed 10% with 125 nm minimum displacement of the ball peel adhesion density evaluated as 2.2 × 10–4 J/m2. [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]

In order to improve the accuracy of crosslinked polyethylene (XLPE) cable status diagnosis and eliminate redundant features, this paper proposes a feature selection method for XLPE cable status diagnosis based on particle swarm optimization algorithm to optimize support vector machine. Firstly, this paper constructs 31 binary coded feature combinations based on five commonly used feature parameters for XLPE cable detection. Secondly, the optimal feature subset is selected based on the average accuracy of 20 cross-validation of PSO-SVM under 31 different feature combinations. Finally, train the diagnostic model with the optimal feature subset and its corresponding parameters, and verify the diagnostic performance of the selected features with test set samples and XLPE cable field data. The experimental results show that the PSO-SVM feature selection method can effectively find the optimal feature subset. The number of features selected in this paper is 2, and the classification accuracy of the test set is 98%. Compared with the preferred features of traditional feature selection methods, the features selected in this paper have good generalization ability. [ABSTRACT FROM AUTHOR]

There are usually two types of slopes in nature: soil and rock. However, the granular slope has gradually come into view and has caused concern. In this paper, a modified Mohr–Coulomb (MC) criterion has been proposed to estimate the strength parameters of medium materials using a new parameter ( τ 0 ) instead of the traditional MC parameter (c). Meanwhile, there is a new stability chart of a granular slope whose core is the dimensionless stability number Ns and dimensionless coefficient DR based on a modified MC criterion to estimate the safety factor of a granular slope preliminarily when the geometric and strength parameters of the slope are determined. A practical case is introduced to test the results, showing that the stability chart proposed in this paper is applicable to granular slopes. The research content and the proposed new viewpoints are intended to offer preliminary judgments on the stability analysis of granular slopes and provide guidance for the stability analysis and construction design of granular slope engineering. [ABSTRACT FROM AUTHOR]