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Purpose: The aim of this work is to investigate the free vibration study of sandwich cylindrical and spherical shells made of laminated composite by applying the finite element method utilizing the computer code ANSYS 19 and the revised shear deformation theory with four-variable. Methods: The study uses two approaches that combine analytical and finite element methods to fully understand the vibration response in these complex structures. The suggested theoretical model’s kinematics is determined by an undetermined integral component, and it incorporates the effects of transverse shear stresses through the thickness of the plate/shell using the hyperbolic form function. The present model guarantees the fulfillment of zero shear stress conditions at the upper and lower surfaces of the plate/shell without the need for a shear correction factor, in contrast to typical first order shear deformation theories (FSDTs). The equations of motion are identified and solved using the Navier method and the Hamilton’s principle. Results: The current results are compared with other higher-order theories found in the literature to verify the veracity of the current hypothesis. It has been noted that the fundamental frequency values derived using the current theory are very similar to those found in the literature. In addition, the effects of the radius of curvature aspect ratio and mode vibration are considered. Conclusions: Based on the analysis, it can be said that the kinematics based on the indeterminate integral component are very efficient and, when used in the study of laminated plates and shells, lead to higher accuracy than conventional approaches.

Sustainable development is threatened by soil erosion. Since it takes over 300 years for a centimeter of soil to form, preventing soil erosion is essential to safeguarding priceless resources. In any hydrological analysis, morphometric analysis is a relevant scientific method that is essential to the development and management of drainage basins. The main objectives of this study were (i) to analyze the morphometric parameters of the Oued Amter Basin using geographic information systems (GIS) and remote sensing (RS) (geospatial tools) and (ii) to prioritize sub-basins and identify the major morphometric parameters that influence soil erosion in the Oued Amter Basin. Morphometric analysis of the Oued Amter Basin (300 km2) was performed to prioritize sub-basins based on their susceptibility to erosion by water using geospatial tools (remote-sensing-based data and a GIS). The linear, relief, and shape morphometric parameters of the drainage network were calculated using data from the Advanced Land Observing Satellite (ALOS) phased-array L-type synthetic-aperture radar (PALSAR) digital elevation model (DEM) with a spatial resolution of 12.5 m. These parameters revealed the network’s texture, morpho-tectonics, geometry, and relief characteristics (including stream length (Lu) and mean stream length (Lsm), stream length ratio (RL), bifurcation ratio (Rb), average bifurcation ratio (RBM), drainage density (Dd), drainage texture (T), flow frequency (Fs), elongation rate (Re), circularity ratio (Rc), form factor (Ff), relief, and relief ratio). Using the array of compound (Cp) values that were computed, we set the priority ranks and divided the sub-watershed into four priority-rank groups: (i) low, which represents 5% of the basin surface; (ii) moderate, which represents 30%; (iii) high, which represents 35%; and (iv) extremely high, which represents 30% of the basin surface. The results show that this classification is valuable for prioritizing and planning interventions focused on addressing soil erosion and mitigating natural hazard risks within the watershed. The method tested here has been shown to be an effective tool to improve sustainable soil management.

Purpose: The purpose of this paper aims to design a robust wind turbine emulator (WTE) based on a three-phase induction motor (3PIM). Design/methodology/approach: The 3PIM is driven by a soft voltage source inverter (VSI) controlled by a specific space vector modulation. By adjusting the appropriate vector sequence selection, the desired VSI output voltage allows a real wind turbine speed emulation in the laboratory, taking into account the wind profile, static and dynamic behaviors and parametric variations for theoretical and then experimental analysis. A Mexican hat profile and a sinusoidal profile are therefore used as the wind speed system input to highlight the electrical, mechanical and electromagnetic system response. Findings: The simulation results, based on relative error data, show that the proposed reactive power control method effectively estimates the flux and the rotor time constant, thus ensuring an accurate trajectory tracking of the wind speed for the wind emulation application. Originality/value: The proposed architecture achieves its results through the use of mathematical theory and WTE topology combine with an online adaptive estimator and Lyapunov stability adaptation control methods. These approaches are particularly relevant for low-cost or low-power alternative current (AC) motor drives in the field of renewable energy emulation. It has the advantage of eliminating the need for expensive and unreliable position transducers, thereby increasing the emulator drive life. A comparative analysis was also carried out to highlight the online adaptive estimator fast response time and accuracy. [ABSTRACT FROM AUTHOR]

This paper proposes an improved Wind Turbine Emulator (WTE) structure using Sliding Mode Control (SMC) technique based on the Two-Phase Induction Motor (TPIM) for software simulation laboratory purposes. Since the power electronic converters application with various control strategies in the AC machine drive field facilitates the wind turbine characteristics imitation, an advanced SVM controlled-2ϕ-inverter is adopted. Considering the asymmetry between the TPIM main and auxiliary windings, mathematical equations were studied allowing control of the speed and the torque under different conditions. The 2-phase-inverter is controlled through an appropriate adjustment of four space voltage vectors. In order to enhance the speed and flux regulation loops and to increase the system robustness against the motor parameters variation and the wind sudden fluctuations, the SMC theory, which contains the adaptive rotor flux observer is used here to develop a control law that governs the system to track the reference speed set by the WT mathematical model to obtain the desired output. Several parameters, such as electromagnetic torque, rotor fluxes, stator currents, and rotor speed, were analyzed. Static and dynamic characteristics and the WTE transient and steady-state responses are satisfactorily reproduced by the proposed emulator scheme.

Aircrafts may encounter various issues such as the reduction of control surface effectiveness. This might lead to a considerable danger for lives and materials. Such a problem requires the application of a Fault-Tolerant Control (FTC). This study aims to achieve a "trajectory-following" mission of an airplane, affected by multiple actuator failures. Two different fault scenarios are considered: a consecutive degradation in the effectiveness of control surfaces (one at a time), and an abrupt partial degradation in all control surfaces. A modified FTC strategy, known as Progressive Accommodation, is put forward. It combines the State-Dependent Riccati Equation and the Forward-Propagation Riccati-Equation. This study considers a nonlinear model of an airplane DC-8 with coupled longitudinal and lateral motions. The proposed strategy takes into account the complexity of the model and the control objectives. It preserves the stability of the airplane and allows it to follow a specific trajectory, despite its damaged control surfaces. Thus, the designed method can be a useful alternative to hardware redundancy in airplanes.

Purpose: The direct torque control (DTC) of induction motor (IM) drive is featured by high ripples in the electromagnetic torque and stator flux profiles because they are controlled by two hysteresis regulators. Furthermore, the machine flux is not directly measurable. Hence, it is better to reconstitute it from the instantaneous electrical equations of the machine. Once the stator flux is estimated, we can guarantee a reliable sensorless DTC control. Thus, the purpose of this research work is to ensure fast response and full reference tracking of the IM under sensorless DTC strategy with desired dynamic behavior and low ripple levels. Design/methodology/approach: In this work, an improved DTC strategy, which is DTC_SVM_3L, is suggested. The first step of the designed approach is to substitute the conventional inverter feeding the motor with a three-level inverter because it guarantees reduced switching losses, improved quality of voltage waveform and low-current total harmonic distortion rate. The second aim of this paper is to make the IM operate at a constant switching frequency by using the nearest three vectors-based space vector modulation (SVM) technique rather than hysteresis controllers. The third objective of this study is to conceive a sliding-mode stator flux observer, which can improve the control performances by using a sensorless algorithm to get an accurate estimation, and consequently, increase the reliability of the system and decrease the cost of using sensors. The stability of the proposed observer is demonstrated based on the Lyapunov theory. To overcome the load change disturbance in the proposed DTC control strategy, this paper exhibits a comparative assessment of four speed regulation methods: classical proportional and integral (PI) regulator, fuzzy logic PI controller, particle swarm optimization PI controller and backstepping regulator. The entire control algorithm was tested under different disturbances such as stator resistance and load torque variations. Findings: It was ascertained that the IM, controlled with three-level inverter, exhibits good performances under the proposed DTC-SVM strategy based on a sliding-mode observer. The robustness of the suggested approach against parameter variations is also proved. Originality/value: The theoretical development of the proposed control strategy is thoroughly described. Then, simulations using Matlab/Simulink software are launched to investigate the merits of the sensorless DTC-SVM command of three-level inverter-fed IM drive with different speed regulators. [ABSTRACT FROM AUTHOR]

Nanofluids are employed in variety of thermal energy systems owing to their modified properties and ability of enhancement in heat transfer. Among the properties of nanofluids, thermal conductivity and dynamic viscosity have significant role in the heat transfer characteristics and fluid flow specifications. Among the intelligent techniques, Support Vector Machines (SVMs) are attractive choices regarding their significant exactness in prediction and modeling. In this article, research works on the forecasting and modeling of the mentioned properties of nanofluids with SVMs are reviewed and their outcomes are presented. In accordance with the results of the reviewed works it can be stated that the developed approaches based on the SVM technique have shown significant performance in accurate modeling of both thermal conductivity and dynamic viscosity and can have superior performance in comparison with other intelligent methods in some cases. Furthermore, it can be pointed out that the performance of these systems is dependent on some elements like the employed optimization algorithms. Some recommendations are represented for future studies to reach models with more favorable performance.

This investigation reports Casson liquid stretching flow persuaded by convectively heated permeable surface. Porous medium effects under Darcy–Forchheimer relation are scrutinized. Nonlinear version of thermo-solutal buoyant forces (i.e., nonlinear mixed convection) is introduced. Nanoliquid model features thermophoresis and Brownian diffusions. Characteristics of heat transfer are elaborated considering heat absorption, thermal radiation, Robin conditions and heat generation. Mass transfer effects are addressed considering chemical reaction. Similarity approach assists to simplify governing expressions (partial differential equations) into the ordinary ones, and these ordinary differential expressions are tackled analytically (via homotopy analysis scheme) and numerically (via bvp4c scheme). The obtained results via homotopy and bvp4c methods are also validated. Besides, the consequence of multiple emerging parameters on dimensionless profiles is investigated via graphs and tables. It is scrutinized that velocity is strongly enhanced with increasing nonlinear mixed convection parameter while it diminishes with increasing Casson fluid parameter, inertia coefficient parameter and porosity parameter.

Malgré la reconnaissance de l’ampleur dévastatrice du tabagisme sur le cancer bronchopulmonaire, les subtilités de la dépendance à la nicotine chez les patients atteints de cette maladie restent largement méconnues et sous-étudiées. Dans cette optique, notre étude visait à évaluer les caractéristiques du cancer bronchopulmonaire chez les patients présentant une forte dépendance nicotinique.

Effective thermal management of photovoltaic (PV) systems is crucial for mitigating temperature-induced efficiency losses and enhancing overall performance. This study proposes a novel approach to address this challenge by employing multi-layer phase change materials (PCMs) with strategically degraded nanoparticle concentrations integrated into photovoltaic-thermal (PVT) systems. The transient heat-transfer dynamics, temperature distributions, and conversion efficiencies were numerically investigated for three configurations: a uniform single-layer nano-PCM, a two-layer nano-PCM with degraded concentrations, and a three-layer nano-PCM with degraded nanoparticle concentrations. Notably, the three-layer degraded configuration consistently outperformed other arrangements, maintaining PV temperatures up to 5 °C lower and electrical efficiencies up to 4 % higher over 100 min of operation. This configuration also achieved the highest thermal efficiency of 0.682, surpassing the single PCM case by 6.7 %. By combining multi-layer PCMs with degraded nanoparticle loadings, the proposed approach enabled more uniform heat distribution within the nano-PCM composite, overcoming the inherent limitation of decreasing thermal interaction between nanoparticles and PCM regions farther from the heat source observed in uniform nano-PCMs. The proposed approach for thermal management can significantly enhance the heat absorption, temperature regulation, and thermoelectric performance of PVT systems under continuous operation scenarios.

The aim of current paper is to explore the dynamics of Tangent hyperbolic hybrid nanofluid flow and heat transfer over a stretchy surface, combined effect of thermal radiation and Cattaneo-Christov heat flux effects also incorporated in the energy equation. Hybrid nanofluids are advanced class of nanofluid have several engineering applications due to their enhanced thermal, electrical, and rheological properties. Here we developed a mathematical model for two-dimensional hybrid nanofluid containing particles copper (Cu)and alumina oxide (Al2O3)suspended with base fluid H2O. Variable viscosity and viscous dissipation aspect also amalgamated in the present study. After applying the dimensionless variables, the governing PDE's are converted into ODE's. The numerical computations are performed with the help of MATLAB software. The most important outcomes predefined pertinent parameters like magnetic parameter, Weissenberg number, relaxation time, thermal radiation parameter, prosperity parameter, convective parameter, Prandtl number, heat source parameter, Eckert number, on velocity and temperature profile are thoroughly examined graphically and physically. There occur increment in the temperature profile of hybrid nanofluid than regular nanofluid. Also over current outcomes shows close agreement for a particular cases.

A continuous floc image analyser (C-FIA) was developed for tracking the dynamics of floc particle formation, growth and settling during conventional drinking water treatment processes, comprising coagulation, flocculation and settling. For this, a script was developed to (1) extract the video frames and sync these with reaction time (t), (2) transform the video frames to an equivalent matrix of pixel RGB data, (3) optically filter the video frames for background effects, (4) calculate the filtered (corrected for background) flocculation index (C-FIA FIt) for the frame belonging to each process time through RGB data and (5) correct the C-FIA FItfor baseline RGB signal (raw water frame, C-FIA FI0). The video frames collected by C-FIA have potential to provide direct information on floc size and concentration. The C-FIA FI data were acquired from jar tests using a range of alum doses performed on three natural surface water samples with highly contrasting water quality. The C-FIA FI data acquired, displayed lower variance in comparison with available data of photometric dispersive analysis (PDA). The indices previously developed for PDA analysis were utilized in this study for C-FIA FI data, including plateau index. In experiments conducted, this index showed an inverse relationship with residual turbidity, UVA254and colour following coagulation. This indicates a potential for C-FIA to be applied for coagulant dose control at conventional drinking water treatment plants. On the other hand, from the experimental work performed, impacts of light intensity and light radiation angle to the photography cell on the resulting FI value were found. These can impact on the pixels' red/green/blue (RGB) values of the video frames/images, and consequently can impact on the resultant C-FIA FI data.

In the current study, three machine learning (ML) models, i.e. Gaussian process regression (GPR), generalized regression neural network (GRNN), and multigene genetic programming (MGGP), were developed for predicting the discharge coefficient (Cd) of a radial gate under two different flow conditions, i.e. free and submerged. The modeling development of the flow and geometry input variables for the Cdwas determined based on statistical correlations. We also performed a sensitivity analysis of the input variables for the Cd. The modeling results indicated that the developed ML models attained acceptable predictable performance; however, the prediction accuracy of the models was better under the free flow condition. In quantitative terms, the minimum root mean square error (RMSE) value was 0.010 using the GPR model and 0.019 using the MGGP model for the submerged and free flow conditions, respectively. The sensitivity analysis evidenced that the ratio of the gate opening height to the depth of water in the upstream (W:Yo) was the influential variable for the Cdunder the free flow condition, whereas the ratio of the depth of water in the upstream to the depth of water in downstream (Yo:YT) was the influential variable for the Cdunder the submerged flow condition.

AbstractThis study employs five genetic algorithm (GA)-based machine learning (ML) models, namely the Decision Tree (DT), k-Nearest Neighbors (kNN), NaïveBayes (NB), Support Vector Machine (SVM), and Extreme Learning Machine (ELM), to build a novel ensemble algorithm that is founded on the Bagging method for landslide susceptibility mapping (LSM) in Jerash Governorate, north of Jordan. The GA-based wrapper feature selection (FS) was done based on the five individual models and in the initial stages of modeling, an inquiry for the best feature for each of the five models was made. Finally, five hybrid models, namely DT-GA, kNN-GA, NB-GA, SVM-GA, and ELM-GA were constructed and combined to create Bagging-based ensemble model. The FS outcomes uncovered that rainfall depth, distance to roads, the Stream Power Index, the Normalized Difference Vegetation Index, slope, geology, and aspect are the most influential determinants of landslides. After the significant variables were identified, they were selected as input predictors and entered into the models. GA-based Bagging ensemble model with the area under the receiver operating characteristic curve (AUROC) of 0.85 achieved the highest accuracy in the validation run, followed by SVM-GA (AUROC = 0.80), NB-GA (AUROC = 0.76), DT-GA (AUROC = 0.72), kNN-GA (AUROC = 0.70), and ELM-GA (AUROC = 0.48).

Flat plate solar collectors (FPSCs) are the most often used as solar collectors due to their easiness of installation and usage. The current research investigates the energy efficiency of FPSC using different mass concentration with varied base fluids containing Graphene nanofluids (T-Gr). Mass concentration of 0.1%-wt., 0.075%-wt., 0.050%-wt. and 0.025%-wt. were mixed with ethylene glycol (EG) and distilled water (DW) in different rations. The operating conditions were volumetric flowrate (1.5, 1 and 0.5) LPM 50 °C-input fluid temperature and 800 W/m2-global solar irradiation. Scanning electron microscope (SEM) and energy dispersive X-ray (EDX) were used to synthesize the thermally treated nanomaterial. The theoretical investigation indicated that using T-Gr nanosuspensions increased the FPSC efficiency in comparison with the host fluid for all examined mass concentrations and volumetric flowrates. In quantitative terms, the maximum thermal effectiveness improvement for the EG, (DW:70 +EG:30) and DW:EG (DW:50 +EG:50) and using flowrates of (1.5, 1 and 0.5) LPM were 12.54%, 12.46% and 12.48%. In addition, the research results pointed that the essential parameters (i.e., loss energy (FRUL)) and gain energy (FR(τα)) of the T-Gr nanofluids were increased significantly.

Phase-change materials (PCMs) have a remarkable potential for use as efficient energy storage means. However, their poor response rates during energy storage and retrieval modes require the use of heat transfer enhancers to combat these limitations. This research marks the first attempt to explore the potential of dimple-shaped fins for the enhancement of PCM thermal response in a shell-and-tube casing. Fin arrays with different dimensions and diverse distribution patterns were designed and studied to assess the effect of modifying the fin geometric parameters and distribution patterns in various spatial zones of the physical domain. The results indicate that increasing the number of dimple fins in the range of 8–32 results in faster heat storage rates by up to 8.7% faster than they would be without the dimple fins. Further improvements of approximately 1.4, 1.2, 1.1, and 1.0% can be obtained by optimizing the position of the first fin section, the spacing between other fin sections, the fin spacing based on the aromatic algorithm, and the use of the staggered fin distribution. The heat storage rate is improved by almost 12% for the best case compared with that of the no-fin case.Graphical Abstract

Abstract: The application of Artificial Intelligence (AI) techniques has become popular in science and engineering applications since the middle of the twentieth century. In this present study, three AI techniques (ANFIS, GP and ANN) have been used for forecasting streamflow into Shakkar watershed (Narmada Basin), India. The models have been used considering previous streamflow and cyclic terms in the input vector to provide a suitable time series model for streamflow forecasting. To evaluate the model performance, RMSE, MAE, CORR and CE were employed. Results showed that the ANFIS has the best performance in forecasting streamflow time series for Shakkar watershed. The GP and ANN are in the 2nd and 3rd ranks, respectively. According to the results, in all the AI methods (ANFIS, GP and ANN), the model with cyclic terms had better performance compared to those models not considering periodic nature and being applied by only considering the previous streamflow. Graphical Abstract:

Natural disasters like floods are happening worldwide. Due to their negative impact on different social, economic and environmental aspects need to monitor and map these phenomena have increased. In fact, to access the zones affected by the flood, we use open source remote sensing (RS) images acquired by optical and radar sensors. Furthermore, we present a method using Sentinel-1 images; we suggest applying Ground Range Detected (GRD) images. For this purpose, pre-processed built and provided by the European Space Agency (ESA), preserved by free software Sentinel Application Platform (SNAP) for data extraction around appropriate demand. Moreover, the principal objective of this article is to assess the capability of Sentinel-1 Synthetic Aperture Radar (SAR) images in order to visualize flood areas in the Inaouene watershed located in north-eastern of Morocco. The origin of this natural hazard is the combination of natural and anthropogenic factors that makes the watershed vulnerable with a sub-annual frequency. The results of this work help decision-makers and managers in the field of natural risk management and land-use planning to implement a strategy and action plan for the protection of the populations and the environment against the negative impact of floods.

This article deals with the determination of the multi-stars induction machine (MSIM) equivalent circuit parameters based on the geometrical and electrical data using two methods namely analytic and finite element method (FEM). Additionally, this paper proposes a general dynamic study of MSIM. This latter is fed by a multi-phase inverter (MPI) controlled by a space vector pulse width modulation (SVPWM) and a sinusoidal pulse width modulation (SPWM). However, when this machine is fed by the voltage inverter for the case of 30° electrical shift between stator stars, large harmonic currents are observed. Thus, the main goal of this paper is to reduce the effect of these harmonic currents. Therefore, to overcome these issues, the machine stars number has been increased to three and four and the inverter has been controlled by the SVPWM. The effectiveness of this association and its capability to reduce the effect of the output harmonic current and electromagnetic torque ripple has been proved by simulation results.

ABSTRACTThe fauna of Tunisian CercerisLatreille, 1802 is summarised. It is presented by a total of 42 species and subspecies and an unidentified taxon. Among them, Cerceris clytiade Beaumont, 1959, C. disparDahlbom, 1845, C. flavilabris(Fabricius, 1793), C. priesneriMochi, 1938 and C. quadricincta segregatade Beaumont, 1970 are reported for the first time in Tunisia. The knowledge of the genus is well-developed by new provincial records for all newly identified species excluding C. chromaticaSchletterer, 1887 and C. nitrariaeMorice, 1911.

In this paper, stability analysis of thin-walled functionally graded (FG) sandwich box beams under combined bending and axial forces was carried out. Based on higher order theory that includes sectional distortion and warping, a nonlinear displacement field of closed cross-section is adopted. The energy principle is applied in the context of elastic behavior of materials. Simple power-law is used to adjust the material properties in the thickness direction of each wall of FGM box beam. Ritz's method is adopted to obtain the nonlinear coupled equilibrium equations, and then the critical loads are obtained by means of the corresponding tangent stiffness matrix. A Finite Element simulation performed with the code ABAQUS software is used to verify the efficiency and accuracy of the present approach in lateral torsional buckling predictions. The effects of the power-law index and skin–core-skin thickness ratios on the critical loads of FG sandwich box beams are presented through several case studies. Moreover, the numerical solutions show that the previous effects play a significant role in the stability analysis of FG sandwich box beams.

This research paper presents a precise three-dimensional (3D) pavement model designed to accurately account for the detrimental effects of the asphalt layer while representing various layers within a pavement structure. The primary objectives of this study include the development of an accurate 3D pavement model, the integration of asphalt concrete's viscoelastic properties into the analysis, and the improvement of pavement distress prediction accuracy. To achieve these goals, a comprehensive approach was undertaken, involving a stress relaxation test to assess the viscoelastic properties of asphalt concrete. The study incorporates Prony series coefficients into the analysis, employing Finite Element Modeling Software Abaqus for an accurate representation of asphalt concrete behavior. The realistic geometry of asphalt concrete is captured using the Simpleware application, enhancing precision compared to Abaqus mesh geometry. Additionally, a user-defined material subroutine (UMAT) based on the Kelvin viscoelastic model is integrated into the finite element analysis within Abaqus. This approach significantly enhances predictions of vertical displacements and horizontal strains associated with common pavement distresses, surpassing conventional linear pavement modeling methods. Moreover, it offers improved accuracy in representing the actual geometry of asphalt concrete and enhances the prediction of wearing course performance, especially in high-temperature regions. By customizing the UMAT subroutine to simulate flexible pavement behavior, this research contributes to the advancement of pavement analysis and design.

Latent heat thermal energy storage (LHTES) is a promising technology, but thermal response limitations caused by the relatively poor thermal conductivity of phase change materials (PCMs) remains an issue. In recent years, computational fluid dynamics (CFD) has proven to be a powerful and reliable tool in design optimization of different systems and problems in the energy sector. Building on that, the present work will use CFD to investigate the thermal performance enhancements achieved using vertical double-tube LHTES systems with multi-channel sinusoidal wavy geometries. The main parameters investigated here are the wavy channel height (H), pitch (P) and the number of channels (N) and the results are assessed in terms of melting rates and heat transfer coefficients. The analysis found that increasing the height from 1 mm to 3 mm, accelerated the phase change by 21 %, while reducing the pitch from 12.5 mm to 5 mm, enhanced the heat storage rates by 28 %. In addition, increasing the number of channels from 3 to 7 improved the charging rate by 64 %. Compared to a conventional straight channel, the enhanced wavy channel (N = 7, H = 3 mm, P = 5 mm) achieved 80 % higher thermal storage rate and 42 % faster charging time. The above findings provide new insight into the potential of utilizing compact vertical LHTES systems via customized wavy channels, with significant impact towards enhancing the existing thermal energy storage systems.

This work deals with determination of rapid and precise methods to predict the amount of sewing thread needed to sew a garment using different chain stitches of the class 400 (from 401 to 407 chain stitches). At first, to avoid unused stocks, sewing consumption value was determined using a geometrical method (based on different chain stitch shapes). The prediction of the sewing thread consumption was proposed as a function of the studied input parameters, which are fabric thickness, stitch density, yarn linear density, and stitch width. Then, a statistical method based on the multilinear regression was studied. Geometrical and statistical results were discussed. Based on the R2range, we concluded that the geometrical method is more accurate than the statistical one (from 98.16 to 99.19% and from 97.30 to 98.51%, respectively). Thus, this result encourages industrialists to use geometrical models to predict thread consumption.

AbstractThe purpose of the present study was to predict the areas affected by flood hazard in the Talar watershed, Mazandaran province, Iran, using Adaptive Boosting (AdaBoost), Boosted Generalized Linear Models (BGLM), Extreme Gradient Boosting (XGB) ensemble models, and the novel ensemble framework of deep decision trees include the Deep Boosting (DB) model. For this purpose, 14 flood conditioning variables were used as independent variables in flood hazard modeling. In addition, 130 flood points in the region were identified by field visits and available flood information, which were used as the dependent variable in modeling. The results showed that all used models have a good efficiency in predicting flood hazard. The area under curve (AUC) of BGLM, XGB, AdaBoost and DB models were 0.88, 0.87, 0.89 and 0.91, respectively, which indicated the highest efficiency of the DB model in flood hazard modeling in the study area. Relative importance of the variables showed that they have different effects in each model. Altitude and distance from the river are more important than other variables. However, these two variables have been selected as the most important variables based on machine learning models, but other variables may be influential in flood hazards.

AbstractThe Aerosol Optical Depth (AOD) has measured using remote sensing and GIS methods, with MODIS data collected in Jharkhand from 2011 to 2017. The state's eastern and northern borders have greater aerosol loadings (AOD: >0.5) while the southern and western parts have lower aerosol loadings (AOD: <0.3). Primary, secondary, tertiary, and quaternary aerosol sources have been identified and categorized using the Analytic Hierarchical Process (AHP). Only 1.29% of the study area, which still emits the most aerosols, is covered by primary sources. Industrial zones, mining regions, thermal power plants, cement industries, high road density, and stone crushers are found in many locations throughout the country. Secondary sources of aerosols account for 5.23% of the study and are located near the main sources. The quaternary (54.08%) and tertiary (39.4%) aerosol sources mainly covered the Southern, Western, and North-Western portions of the state, which is enveloped by a heavily vegetated region. AOD, sources of aerosols, wind direction, and velocity were examined here. There were non-separable connections in this area and also AOD distribution is connected to aerosol sources, wind direction, and wind velocity. Finally, it employs the AOD values to identify different aerosol kinds and source heterogeneity to elucidate their influence.

AbstractLandslides are dangerous events which threaten both human life and property. The study aims to analyze the landslide susceptibility (LS) in the Kysuca river basin, Slovakia. For this reason, previous landslide events were analyzed with 16 landslide conditioning factors. Landslide inventory was divided into training (70% of landslide locations) and validating dataset (30% of landslide locations). The heuristic approach of Fuzzy Decision Making Trial and Evaluation Laboratory (FDEMATEL)-Analytic Network Process (ANP) was applied first, followed by bivariate Frequency Ratio (FR), multivariate Logistic Regression (LR), Random Forest Classifier (RFC), Naïve Bayes Classifier (NBC) and Extreme Gradient Boosting (XGBoost), respectively. The results showed that 52.2%, 36.5%, 40.7%, 50.6%, 43.6% and 40.3% of the total basin area had very high to high LS corresponding to FDEMATEL-ANP, FR, LR, RFC, NBC and XGBoost model, respectively. The analysis revealed that RFC was the most accurate model (overall accuracy of 98.3% and AUC of 97.0%). Besides, the heuristic approach of FDEMATEL-ANP model (overall accuracy of 93.8% and AUC of 92.4%) had better prediction capability than bivariate FR (overall accuracy of 86.9% and AUC of 86.1%), multivariate LR (overall accuracy of 90.5% and AUC of 91.2%), machine learning NBC (overall accuracy of 76.3% and AUC of 90.9%) and even deep learning XGBoost (overall accuracy of 92.3% and AUC of 87.1%) models. The study revealed that the FDEMATEL-ANP outweighed the NBC and XGBoost machine learning models, which suggests that heuristic methods should be tested out before directly applying machine learning models.

This paper discusses doubly-fed induction motors (DFIM) integration possibilities in electric vehicle (EV) propulsion systems. The motivation behind this work is to develop an appropriate control law for DFIM to be suitable for EV applications. From this point of view, the main contributions of this study are: (i) a mixed control approach of an electric vehicle propulsion system based on a DFIM which is composed of a fuzzy-PI controller for the mechanical mode and a sliding mode controller for the electrical one; (ii) an active power distribution law of both stator and rotor circuits connected to space vector pulse width modulation voltage source inverter; and (iii) a new method to estimate speed by associating a model reference adaptive system (MRAS) estimator with an extended Luenberger observer in order to ensure the sensorless command and to compensate the load torque impacts. The effectiveness of the developed sensorless mixed control algorithm is proved by the obtained simulation results for different DFIM operating ranges.

River sedimentation is an important indicator for ecological and geomorphological assessments of soil erosion within any watershed region. Sediment transport in a river basin is therefore a multifaceted field yet being a dynamic task in nature. It is characterized by high stochasticity, non-linearity, non-stationarity, and feature redundancy. Various artificial intelligence (AI) modeling frameworks have been introduced to solve river sediment problems. The present survey is designed to provide an updated account of the latest and most relevant AI-based applications for modeling the sediment transport in river basin systems. The review is established to capture the subsequent developments in the advanced AI models applied for river sediment transport prediction. Also, several hydrological and environmental aspects are identified and analyzed according to the results produced in those studies. The merits and constraints of the well-established AI models are further discussed in much detail, particularly considering state-of-the art, modeling frameworks and their application-specific appraisal, and some of the key proposed future research directions. Together with the synthesis of such information to drive a new understanding of models and methodologies related to suspended river sediment prediction, this review provides a future research vision for hydrologists, water scientists, water resource engineers, oceanography and environmental planners.

Among several indicators for river engineering sustainability, the longitudinal dispersion coefficient () is the main parameter that defines the transport of pollutants in natural streams. Accurate estimation of has been challenging for hydrologists due to the high stochasticity and non-linearity of this hydraulic-environmental parameter. This study presents a new hybrid machine learning (ML) model integrating a Gaussian Process Regression (GPR) and an evolutionary feature selection (FS) approach (i.e. Covariance Matrix Adaptation Evolution Strategy (CMAES)) to estimate in natural streams. The dataset consists of geometric and hydraulic river system parameters from 29 streams in the United States. The modeling results showed that the proposed model outperformed other models in the literature, producing more stable and accurate estimations. The FS approach evidenced the significance of the cross-sectional average flow velocity (U), channel width (B), and channel sinuosity σ to estimate the dispersion coefficient. In quantitative terms, the integrated GPR model with feature selection approach attained the minimum root mean square error () and maximum coefficient of determination (). The proposed hybrid evolutionary ML model arises as robust, flexible and reliable alternative computer aid technology for predicting the longitudinal dispersion coefficient in natural streams.

This paper aims to provide rapid and precise methods to allow industrials to predict the amount of sewing thread needed to sew a garment using different lockstitches of class 300 (301, 301/301, 304, 308, 309, 310, 311, 312, and 315). To avoid unused stocks for each stitch type, a sewing consumption value was determined using a geometrical method of different lockstitch shapes. Furthermore, the relationships between overall geometrical models of the studied lockstitches of class 300 were developed. Indeed, based on the geometrical model of lockstitch type 301, all theoretical models proposed were investigated and proved to be accurate. Moreover, referring to the findings, the prediction of the sewing thread consumption relative to each investigated lockstitch was proposed as a function of the studied input parameters. To improve the established models using geometrical technique, a statistical method was conducted. In addition, based on multi-linear regression, compared geometrical and statistical results were discussed and the coefficient R2 value was determined to evaluate the accuracy of the tested methods. By comparing the estimated thread consumption with the experimental ones, we concluded that the accuracy of the models is significant (R2 ranged from 93.91% to 99.10%), which encourages industrialists to use geometrical models to predict thread consumption. Therefore, the accuracy of prediction using the geometrical method is more accurate than the statistical method regarding the range of R2 (from 92.84% to 97.87%). To classify the significance of all studied parameters, their contributions to the sewing thread consumption behavior were analyzed in the experimental design of interest. It was concluded that the most important parameters affecting thread consumption are stitch width, stitch density, and the gap between two needles. The thickness of fabric has a low contribution to the thread consumption value, whereas the effect of yarn count can be neglected.

The article evaluates the amount of the consumed sewing thread for women's underwear (underwear bras and panties). Based on the obtained findings, it was concluded that sewing thread amount depends enormously on the studied influential parameters. The present paper reports a contribution that allows industries and researchers to decrease the consumed amounts of sewing thread in case of women's underwear and panties The study takes into account the different stitch structures and fabric characteristics that are usually used. The effects of influential input parameters, such as fabric thickness, number of assembled layers, stitch density, and tension of the thread, are investigated. Useful models have been found and can be used by industries to accuracy predict the thread consumption for women's underwear and panties to launch the needed thread commands. The developed models use multiregressive method. In this study, the fabrics that have been considered are knitted fabrics because they are those used in women's underwear. We found that women's underwear bras consume more sewing threads than panties. Using linear regression method, good relationships (coefficients of correlation close to 1) between consumption behaviors and the investigated parameters such as fabric thickness, number of assembled layers, stitch number per centimeter, sizes and tension of threads, were found. Although, the increase of threads tension to sew female underwear decreases the consumed amount of threads, the increase of other studied parameters widely encourages the consumption values, especially for seams based on chain-stitch types.

The present paper presents numerical and experimental comparative studies between the conventional bending process (bending using punch and V-die) and bending with a rubber-pad cushion. In fact, a finite-element method is introduced to compare these two procedures used to bend AA1050-H14 Aluminum thin sheet. An elastoplastic constitutive model with quadratic yield criterion of Hill 48 and isotropic hardening behavior has been adopted in FE simulations performed using ABAQUS explicit. To model rubber material, a Mooney–Rivlin theory is used in the finite-element simulation. The purpose of this study is to see the difference between the two methods in terms of effort evolution, springback, stress distribution, thinning. Analyze the effect of the rubber material; in fact, natural rubber, and polyurethane with hardness 50, 70, and 90 shore A are investigated. This numerical study was validated by an experimental investigation that proved the efficiency of the elastic cushion to bend in a better way this thin aluminum sheet.

•Avicennia marinaand Rhizophora mucronatalocations showed significant differences in SBD.•Avicennia marinaand Rhizophora mucronatalocations showed significant differences in SOC concentration.•SOC concentration and SBD had a significant and inverse relationship.•SOC stock of Avicennia marinawas greater than that of Rhizophora mucronataby 114.3%.•Greater mean CSR was noted for Avicennia marinathan for Rhizophora mucronatalocations.

ABSTRACTIn this paper, we design a diagnostic technique for a partially observed labelled Petri net where the faults of the system are modelled by unobservable transitions. The fault detection and isolation uses an on-line count vector estimation associated with the firing of unobservable transitions exploiting the observation of firing occurrences of some observable transitions. The support of the approach is an algebraic description of the process under the form of a polyhedron developed on a receding horizon. We show that a diagnostic can be made despite that different transitions can share the same label and that the unobservable part of the Petri net can contain circuits.

This paper addresses the “curse of dimensionality” in the loss valuation of credit risk models. A dimension reduction methodology based on the Bayesian filter and smoother is proposed. This methodology is designed to achieve a fast and accurate loss valuation algorithm in credit risk modeling, but it can also be extended to valuation models of other risk types. The proposed methodology is generic, robust and can easily be implemented. Moreover, the accuracy of the proposed methodology in the estimation of expected loss and value-at-risk (VaR) is illustrated by numerical experiments. The results suggest that, compared to the currently most used Principal Component Analysis (PCA) approach, the proposed methodology provides more accurate estimation of expected loss and VaR of a loss distribution.

Information-centric networking (ICN) is a content-oriented network that uses content instead of traditional IP addresses, it facilitates the content retrieval and calculates downstream paths without the need of an optimal topology. ICN inherits mobility support features in its baseline design. Since the Internet of Things (IoT) faces major problems in supporting mobility management over IP, we believe that ICN would represent a major element in scalable IoT networks. This article discusses the use of ICN as a communication model in IoT environments, focusing on solving the mobility issue. Thus, we survey the relevant work that studies the merger between ICN and IoT, and describe the emergence, evolution, and state-of-the-art mobility approach. This article also presents discussions on major scalability challenges when using ICN as a communication enabler for mobile IoT and highlights key designs and guidelines.

The increasing penetration of wind farms in the energy sector directly affects the dynamic behavior of the power system. The increasing use of wind energy in the power system worsens its stability and inherently influences the firmness of a small signal. To investigate these effects, one of the synchronous generators (SGs) of the grid is considered defective and is replaced by a doubly fed induction generator (DFIG)-based wind farm of the same rating. The small-signal stability of a power system is usually evaluated via eigenvalue analysis where local-area and inter-area oscillatory modes for the New England test system are identified. SG controls, such as automatic voltage regulator (AVR) and power system stabilizer (PSS), are added to attenuate the generated disturbances. In this study, the impact of wind energy on the small-signal stability of the power system is investigated. Different combinations of AVR and PSS types are considered to mitigate the undesirable alterations. A comparative study is performed based on numerical simulations to choose the best combination of AVR and PSS types. The obtained results prove that the proposed combination yields good results in terms of stability enhancement both under normal operating conditions and in DFIG-based wind farms.

With the commercial deployment of 5G mobile communication systems, 6G is proposed to meet the needs of new use cases including connected and autonomous vehicles (CAVs). In this article, we surveyed different 6G embracing technologies such as mobile edge computing, unmanned aerial vehicles, network function virtualization, software defined networking, and artificial intelligence that can enhance the Internet of Autonomous Vehicles (IoAV) network performance comparing to existing 5G enabled solutions. Then, we highlight the convergence path of 6G and IoAV to satisfy the next generation of CAV applications. Further, we propose 6G cellular communication to support the quality of next IoAV applications. Furthermore, the article attempts to study the serious challenges in the 6G‐IoAV research field and propose potential future directions. With the commercial deployment of 5G mobile communication systems, research discussions on next generation mobile systems (i.e., 6G) is topical. On the other hand, vehicular technologies are rapidly increasing from connected vehicles as invented by vehicle to everything to the networks of connected autonomous vehicles (CAVs). The convergence path of 6G and CAVs areas is of great importance, which is the focus of this article.

Due to their amazing energy savings, the uniform nature of their operating temperature, and their fairly low cost, phase-change materials (PCMs) offer great potential to be employed for managing the mismatch between the energy supply and demand. The crucial challenge in the design of functional PCM systems remains the proper selection of thermal enhancers to overcome the limitations imposed by their low thermal response rates. This study quantifies the advantage of utilizing arc-shaped fins as an efficient thermal enhancer to modify the thermal functionality of PCM in a shell-and-tube storage system. As a direct consequence of their curved shapes, this fin layout would affect to a small degree the functionality of natural convection but significantly aid the enhancement of the overall heat transfer during the melting mode. Therefore, a number of parametric studies were conducted to compare the effectiveness of the different arc-shaped fin arrays while utilizing the same heat contact area under the same operating conditions. The goal was to modify the dimensions, orientation, and position of this fin configuration for different thermofluidic conditions to achieve the best enhancement results. The results reveal that increasing the length of the fin base from 5 mm to 15 mm results in a melting time saving of 58.3 % and an improved heat storage rate of 127.5 %. Further improvement in the heat storage rate by 15.4 % and 14.0 % would be achieved by increasing the joining angle between the top fins from 0° to 30° and the joining angle between the bottom fins from 0° to 60°, respectively.

This study aims to modify the melting performance of a vertical latent heat double-pipe heat exchanger by geometry modification of the phase change material (PCM) container. To modify the configuration of the PCM container placed in the outer tube, a framed structure is embedded in the inner surface of the outer tube. Different framed structures i.e. smooth, arc-shaped, reverse arc-shaped, and zigzag-shaped structures are examined and compared with that of the unframed enclosure. Moreover, different numbers of pitches are also examined. The results show that the melting duration drops by almost 55 % and the thermal energy storage rate improves by 115 % using a smooth framed structure for the PCM container compared with that of using an unframed structure. The thermal energy storage rate and melting time are ∼9.5 W and 244.4 min, respectively, for the system using an unframed structure. The analysis of the pitch number for the zigzag-shaped structure reveals that increasing the number of pitches from 0 to 11 enhances the thermal energy storage rate by 36.5 %. The evaluation of different shapes of the framed structure shows that the case with reverse arc-shaped structure is the most enhanced unit which has the thermal energy storage rate of ∼34 W and melting time of 63.15 min heated by water at the Reynolds number 1000 and inlet water temperature of 50 °C. The sensitivity analysis of the HTF characteristics reveals that by increasing the Refrom 1000 to 2000, the thermal energy storage rate increases by around 6 W. Furthermore, by increasing the inlet temperature of HTF from 50 °C to 55 °C, the thermal energy storage rate enhances by around 10 W. This paper shows the capability of geometry modification in enhancing the thermal energy storage rate of the systems.

In spite of the low specific capacity of Bi2O3(∼386 mA h g−1), it lies among auspicious contenders to be utilized as anode for future generation lithium-ion batteries (LIBs). Conversely, the persistently low valued capacity is causing the restrictions on its superb anticipated electro-chemical properties and obscuring their useful applications/devices. Similarly bimetallic transition metal sulfides with superior electro-chemical performances have gained the attention due to the existence of synergistic effect among bimetallic cations. Herein, a novel template-assisted, two-step hydrothermal method followed by electrodeposition technique is employed to fabricate hybrid 3D core-shell NiCo2S4@Bi2O3nanorod arrays (NRAs). The fabricated hybrid-structure comprises the qualities of both materials; NiCo2S4presents the backbone for structural stability, while Bi2O3provides the high specific capacity. Hybrid core-shell structure has provided the integrating facile strain relaxation, along with the fast electron and ion transport. Hybrid 3D core-shell NiCo2S4@Bi2O3NRAs with the possession of high Bi2O3mass loading has exhibited the excellent capacity of ∼2344 mA h g−1with a columbic efficiency of 96 %, which is much improved compare to pristine NiCo2S4NRAs (∼1865 mA h g−1) with columbic efficiency of 89 % and Bi2O3nanosheet arrays (∼1160 mA h g−1) with columbic efficiency of 54 % respectively, demonstrating that hybrid 3D core-shell NiCo2S4@Bi2O3NRAs have presented decent rate capability at current density 0.4 A g−1and particularly significant cycle stability (∼1930 mA h g−1) even after 100 successive cycles. Achieved outcomes not only highlight the innovative opportunity for novel core-shell formation, but also provide the vision of rational design for innovative electrode materials for LIBs.

L’objectif de notre étude était d’évaluer l’impact de deux types d’éducation thérapeutique sur les paramètres cliniques et biologiques des patients diabétiques type 2 au cours du mois de Ramadan.

De nombreux auteurs se sont intéressés au perfectionnement des brackets dans un souci d’améliorer la qualité, la stabilité, la rapidité et l’efficacité du traitement orthodontique. Dans le but de diminuer la friction entre le bracket et l’arc, de nouvelles approches thérapeutiques, basées sur de nouveaux moyens technologiques, ont été créées. Parmi ces nouvelles techniques, les brackets auto-ligaturants sont en plein essor. Ils peuvent être classés en plusieurs groupes en fonction de leur mode d’action et de leurs constituants. À travers une étude expérimentale, nous avons comparé les forces de frottement générées lors du glissement des arcs orthodontiques de divers alliages dans des brackets conventionnels et auto-ligaturants. Les résultats mettent en évidence une influence favorable de ces derniers, par rapport aux systèmes classiques associés à des ligatures élastomériques ou métalliques, sur le niveau des forces de frottement et en particulier en cas d’utilisation des arcs à mémoire de forme en Ni-Ti.

This work deals with the problem of state and fault estimation for systems described by Takagi-Sugeno fuzzy systems. The state and fault estimation is made using a proportional integral observer based on the sliding mode principle. Only sensor faults are considered in this work. In order to estimate this kind of fault, a particulate mathematical transformation is used. The application of this mathematical transformation to the initial system output allows to conceive an augmented system where the initial sensor fault appears as an unknown input. An adaptive mathematical form is used for the sign function to facilitate the determination of the proportional gains of the conceived observer. The observer convergence conditions are formulated in the form of linear matrix inequalities (LMI) allowing computing the observer gains and the Lyapunov theory is used to guarantee the system stability with faults. The proposed proportional integral sliding mode observer is applied to turbo-reactor showing the efficiency of the fault and state estimation.