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In this paper, the influence of the Coefficient of Thermal Expansion (CTE) on the thermal stress analysis of laminated composite plates is explored. By introducing the undetermined integral terms in the displacement field, a new simple and efficient higher-order shear deformation theory is formulated for the thermo-mechanical behavior of thick laminated composite plates. This formulation aims to reduce the number of generated unknowns. Typically, a reduced order of the governing partial differential equations is expressed using the principle of virtual displacements. By using Navier’s technique, closed-form solutions are derived for laminated composite plates under thermal and/or mechanical loading. Unfortunately, several traditional research investigations significantly depend on the rule of the mixture to determine reliable CTE for composites. This paper offers and examines a variety of analytical micromechanics-based models for estimating CTE in laminated composite materials, incorporating into consideration different considerations. The obtained results are compared to those given by other alternative plate theories, and the efficiency and accuracy of the present theory are demonstrated for the thermomechanical behavior of laminated composite plates. This study reviews and applies several micromechanics-based models, contrary to previous investigations. Laminated composite plates could delaminate or crack due to the matrix material's longitudinal CTE, affecting fiber volume fraction and stacking sequence. Micromechanics-based approaches are important when arbitrary thermo-mechanical characteristics can generate inaccuracies. Interestingly, micromechanics-based models can estimate effective CTE. Schapery, Chamberlain, and Chamis provide models with identical longitudinal CTE. For increasing fiber volume fractions, Chamberlain's model is more sensitive to increasing fiber volume fractions. Mechanical stress changes laminated plate behavior more than thermal loading. Although all presented micromechanical-based models have simplified representations, this research attempts to provide a standard for future investigations. The use of detailed micromechanical-based models stimulates further progress in understanding and utilizing complex composite plates.

To effectively tackle the challenges posed by climate change, it is crucial to enhance the accuracy of rainfall-runoff models to ensure reliability amidst changing climatic conditions. Neural networks, renowned for their ability to capture complex patterns and relationships within uncertain input and output data, offer valuable tools in this pursuit. This study aims to evaluate the efficacy of two neural network (NN) models: the Radial Basis Function Neural Network (RBFNN) and the Model Tree M5 Neural Network (MTM5NN). These models are assessed both individually and in combination with the Wavelet (WT) data processing technique for rainfall-runoff modeling in the Kolar River watershed located in Madhya Pradesh, India. Fifteen models were developed employing four algorithms: RBFNN models, WRBFNN (RBF model integrating wavelet components of rainfall as inputs), MTM5NN, and WMTM5NN (MT model incorporating wavelet components of rainfall as inputs). Initially, rainfall and runoff data underwent normalization and were applied to RBFNN and MTM5NN networks. Subsequently, time series data for both rainfall and runoff were decomposed using wavelet transforms, resulting in various sub-time series signals such as approximations and decompositions. These derived signals were then utilized as input data for RBFNN and MTM5NN, specifically designated as WRBFNN and WMTM5NN. The most effective model identified was Model 8 of WMTM5NN, which demonstrated R2 values close to 0.97, outperforming the other models. These results underscore the superior performance of the WMTM5NN model, highlighting its effectiveness in achieving heightened accuracy in predicting runoff for this specific watershed.

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.

The Narmada River basin, a significant water resource in central India, plays a crucial role in supporting agricultural, industrial, and domestic water supply. Effective management of this basin requires accurate streamflow forecasting, which has become increasingly important. This study delves into streamflow forecasting using historical data from five major river stations, covering the upper reaches in the East and middle sections. The dataset spans from 1978 to 2020 and undergoes rigorous screening and preparation, including normalization using StandardScaler. The research adopts a comprehensive approach, developing models for training on 70 % of historical data, validation on the most current 15 %, and testing against future targets with another 15 % of the data. To achieve precise predictions, a suite of machine learning models is employed, including CatBoost, LGBM (Light Gradient Boosting Machine), Random Forest, and XGBoost. Performance evaluation of these models relies on key indices such as mean squared error (MSE), mean absolute error (MAE), root mean square error (RMSE), root mean square percent error (RMSPE), normalized root mean squared error (NRMSE), and R-squared (R2). Notably, among these models, Random Forest emerges as the most robust for streamflow prediction, showcasing its effectiveness in handling the complexities of hydrological forecasting. This research contributes significantly to the field of streamflow forecasting in the Narmada River basin by providing insights into the performance of various machine learning models. The findings not only enhance our understanding of watershed dynamics but also highlight the pivotal role that machine learning can play in improving hydrological forecasting for sustainable watershed management.

Despite the many articles about activated carbon with different precursors in adsorption process, no in-depth research has been carried out to understand the causes of the difference in surface adsorption characteristics of activated carbon with different precursors and different activation processes. In this work, the ability of two active carbon adsorbents made of walnut shell and peach kernel by two chemical and physical methods (totally 4 different types of activated carbon) in treatment of oily wastewater including diesel, gasoline, used oil or engine lubricant has been compared. The results show that the chemical activated peach carbon active with 97% hardness has provided the highest hardness and physical activated walnut carbon active has obtained the lowest hardness value (87%). It is also found that peach activated carbon has a higher iodine number than walnut activated carbon, and this amount can be increased using chemical methods; Therefore, the highest amount of Iodine Number is related to Peach activated carbon that is made by chemical method (1230 mg/g), and the lowest amount of iodine number is seen in walnut activated carbon that is made by physical method (1020 mg/g). moreover, the pore diameter of physical activated carbon is lower than chemical activated carbon in all cases. So that the pore diameter of chemical activated peach carbon active is equal to 22.08 μm and the measured pore diameter of physical activated peach carbon active is equal to 20.42 μm. These values for walnut are obtained as 22.74 μm and 21.86 μm, respectively. Furthermore, the temperature and pH effects on the adsorption of different synthesized oily wastewater was studied and it was found that a decrease in adsorption can be seen with an increase in temperature or decreasing the pH value, which can be referred to this fact that the process of adsorption is an exothermic process. Finally, to analyze the compatibility of adsorption isotherms with experimental data and to predict the adsorption process, three different isotherms named Langmuir, Temkin, and Freundlich isotherms were applied and their parameters were correlated. The correlation results show that the Langmuir isotherm had the best correlation in all cases compared to the Freundlich and Temkin isotherms, based on the correlation coefficient, and the calculated R2 values which was greater than 0.99 in all the studied cases.

Rapid urbanization is a global trend that significantly alters Land Use and Land Cover Change (LULCC) patterns. Dhaka, a megacity, and Chittagong, the country’s second-largest city, have experienced substantial shifts in LULCC patterns over the past three decades. This study aims to examine the LULCC in Dhaka and Chittagong districts of Bangladesh from 1991 to 2021, focusing on identifying patterns and quantifying the effects of population growth and urbanization. Changes in LULCC patterns were identified by analyzing Landsat satellite imagery using remote sensing and geospatial techniques. The LULCC classes were classified using the Random Forest model and an inconsistency diagram, demonstrating high precision with an overall accuracy rate exceeding 85 %. The study’s findings reveal significant transformations in LULCC patterns across Dhaka and Chittagong districts from 1991 to 2021. A notable increase in built-up areas was observed in both districts, with Dhaka division witnessing a 129 % increase in built-up areas while Chittagong district saw a 515 % increase. This was due to transforming water bodies and barren land into built-up areas. However, the study also highlighted a promising increase in vegetation cover in both districts, indicating successful ecological restoration efforts. Chittagong and Dhaka saw increments of 597.95 km2 and 252.15 km2 in vegetation cover, respectively. Despite these positive changes, the study identified decreased surface water bodies in both regions. The results show a significant positive correlation between population growth/density and the built-up class, while a significant negative correlation with the barren land was observed in both districts. The findings could potentially guide policymakers in making informed decisions regarding land use, land cover, and urban sprawl management from an ecological restoration perspective. This could contribute to achieving sustainable development goals by promoting resilience ecological restoration practices.

The temporal and geographical variety of population dynamics in coral reef fish profoundly affects environmental resources.Parrotfishes are a group of about 90 fish species regarded as a family (Scaridae) or a subfamily (Scarinae) of the wrasses. The Indo-Pacific has the highest species richness in this category, with around 95 species. They are found in coral reefs, rocky coasts, and seagrass beds and can play a significant role in bioerosion. Most parrotfish species are herbivores, feeding mainly on epilithic algae. The development of parrotfishes is complex and accompanied by a series of changes in sex and color (polychromatism). In this review, the biological and ecological studies, the search for seasonal food, and finally, the breeding of parrotfish were shed light and clarified. Also modern tracking methods have been highlighted to monitor the migration of parrot fish. Finally, highlighting the sight of the annual seasonal stampede of parrotfish migration to Farasan Island.

This 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).

Abstract Lakes help increase the sustainability of the natural environment and decrease food chain risk, agriculture, ecosystem services, and leisure recreational activities locally and globally. Reliable simulation of monthly lake water levels is still an ongoing demand for multiple environmental and hydro-informatics engineering applications. The current research aims to utilize newly developed hybrid data-intelligence models based on the ensemble adaptive neuro-fuzzy inference system (ANFIS) coupled with metaheuristics algorithms for lake water-level simulation by considering the effect of seasonality on Titicaca Lake water-level fluctuations. The classical ANFIS model was trained using three metaheuristics nature-inspired optimization algorithms, including the genetic algorithm (ANFIS-GA), particle swarm optimizer (ANFIS-PSO), and whale optimization algorithm (ANFIS-WOA). For determining the best set of the input variables, an evolutionary approach based on several lag months has been utilized prior to the lake water-level simulation process using the hybrid models. The proposed hybrid models were investigated for accurately simulating the monthly water levels at Titicaca Lake. The ANFIS-WOA model exhibited the best prediction performance for lake water-level pattern measurement in this study. For the best scenario (the inputs were $${X}_{t-1},\; {X}_{t-2}, \;{X}_{t-3}, \;{X}_{t-4}, \; {X}_{t-12}$$ X t - 1 , X t - 2 , X t - 3 , X t - 4 , X t - 12 ) the ANFIS-WOA model attained root mean square error (RMSE $$\approx$$ ≈ 0.08 m), mean absolute error (MAE $$\approx$$ ≈ 0.06 m), and coefficient of determination (R 2 $$\approx$$ ≈ 0.96). Also, the results showed that long-term seasonal memory for this lake is suitable input for lake water-level models so that the long-term dynamic memory of 1-year time series for lake water-level data is the best input for estimating the water level of Titicaca Lake.

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.

Abstract Coastal vulnerability assessment is the key to coastal management and sustainable development. Sea level rise (SLR) and anthropogenic activities have triggered more extreme climatic events and made the coastal region vulnerable in recent decades. Many parts of the world also noticed increased sediment deposition, tidal effects, and changes in the shoreline. Farasan Island, located in the south-eastern part of Saudi Arabia, experienced changes in sediment deposition from the Red Sea in recent years. This study used Digital Shoreline Analysis System (DSAS) to delineate the shoreline changes of Farasan Island during 1975–2020. Multi-temporal Landsat data and DSAS were used for shoreline calculation based on endpoint rate (EPR) and linear regression. Results revealed an increase in vegetation area on the island by 17.18 km2 during 1975–1989 and then a decrease by 69.85 km2 during 1990–2020. The built-up land increased by 5.69 km2 over the study period to accommodate the population growth. The annual temperature showed an increase at a rate of 0.196 °C/year. The sea-level rise caused a shift in the island's shoreline and caused a reduction of land by 80.86 km2 during 1975–2020. The highly influenced areas by the environmental changes were the north, central, northwest, southwest, and northeast parts of the island. Urban expansion and sea-level rise gradually influence the island ecosystem, which needs proper attention, management, policies, and awareness planning to protect the environment of Farasan Island. Also, the study’s findings could help develop new strategies and plan climate change adaptation.

Abstract This study appraised and compared the performance of process-based hydrological SWAT (soil and water assessment tool) with a machine learning-based multi-layer perceptron (MLP) models for simulating streamflow in the Upper Indus Basin. The study period ranges from 1998 to 2013, where SWAT and MLP models were calibrated/trained and validated/tested for multiple sites during 1998–2005 and 2006–2013, respectively. The performance of both models was evaluated using nash–sutcliffe efficiency (NSE), coefficient of determination (R 2), Percent BIAS (PBIAS), and mean absolute percentage error (MAPE). Results illustrated the relatively poor performance of the SWAT model as compared with the MLP model. NSE, PBIAS, R 2, and MAPE for SWAT (MLP) models during calibration ranged from the minimum of 0.81 (0.90), 3.49 (0.02), 0.80 (0.25) and 7.61 (0.01) to the maximum of 0.86 (0.99), 9.84 (0.12), 0.87 (0.99), and 15.71 (0.267), respectively. The poor performance of SWAT compared with MLP might be influenced by several factors, including the selection of sensitive parameters, selection of snow specific sensitive parameters that might not represent actual snow conditions, potential limitations of the SCS-CN method used to simulate streamflow, and lack of SWAT ability to capture the hydropeaking in Indus River sub-basins (at Shatial bridge and Bisham Qila). Based on the robust performance of the MLP model, the current study recommends to develop and assess machine learning models and merging the SWAT model with machine learning models.

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 Cd was 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 Cd under 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 Cd under the submerged flow condition.

It's undeniably true that fractional calculus has been the focus point for numerous researchers in recent couple of years. The writing of the Caputo-Fabrizio fractional operator has been on many demonstrating and real-life issues. The main objective of our article is to improve integral inequalities of Hermite-Hadamard and Pachpatte type incorporating the concept of preinvexity with the Caputo-Fabrizio fractional integral operator. To further enhance the recently presented notion, we establish a new fractional equality for differentiable preinvex functions. Then employing this as an auxiliary result, some refinements of the Hermite-Hadamard type inequality are presented. Also, some applications to special means of our main findings are presented.

The exposure of concrete to elevated temperatures is known to cause diverse severe damages in concrete composites. Hence, measures to improve the performance of concrete during exposure to fire are continually proposed. The present study investigated the postfire residual strength and morphology of concrete incorporating natural rubber latex exposed to elevated temperature. Four different concrete mixes were considered for the investigation, namely, a control sample made without natural rubber latex, the second sample containing 1% natural rubber latex, the third sample containing 1.5% natural rubber latex, and the fourth sample containing 3% of natural rubber latex. The concrete samples (150 mm cubes and 100 × 200 mm cylinders) were exposed to varying temperatures 300°C, 800°C, and 1000°C, after the curing process. Nondestructive tests using Schmidt rebound hammer and ultrasonic pulse tester were carried out on samples. The compressive strength and split-tensile strength of concrete cubes and cylinders, respectively, were determined. Micrographs and elemental distribution in the sample were studied using the scanning electron microscopy (SEM-EDX) apparatus. It could be seen from the results that the concrete strength properties reduced as the exposure temperature increased. The results also showed that NRL could be sparingly utilized as a concrete admixture, at 1% content. The performance of concrete was not stable at over 300°C when NRL addition was above 1%.

The disposal of biological waste into water bodies is a major global concern as it leads to water pollution resulting in the loss of plenty of revenue in the cleaning of water bodies. Here, in the present research work, sacred flowers were collected, segregated, sun-dried, and powdered. The dried floral powders (marigold and rose) were characterized by field emission scanning electron microscopy (FESEM), electron diffraction spectroscopy (EDS), Fourier transforms infrared spectroscopy (FT-IR), and X-ray diffraction (XRD). The microscopy revealed the irregular spherical shape of the sheet-like structure whose size varies in microns. The EDS revealed the elemental composition which was dominated by mainly carbon and oxygen. The XRD shows the presence of carbon (10-25ɵ) in the amorphous form and the absence of any crystalline phase in the biosorbents. The FT-IR showed peaks that conformed to the presence of functional groups like -OH and a carbonyl group. The dried powders were used as an economical and eco-friendly biosorbent for the removal of methyl red (MR) dye from the aqueous solutions by batch adsorption study. After 60 minutes of contact time, the marigold powder (MGP) and rose petal powder (RPP) showed decolorization of 61.16% and 56.08% for 2 ppm of MR dye. The kinetic revealed that the dye removal reaction does not follow the pseudo-first-order as well as the pseudo-second-order. The utilization of such waste-based biosorbents will minimize solid waste and also will provide an economical biosorbent for the removal of environmental pollutants.

Abstract Morphometric analysis is a pertinent scientific approach in any hydrological analysis, and it is necessary in the progress and management of drainage basin. Identification of areas at risk of erosion, and the prioritization of 48 sub-watersheds of Inaouene basin, was done by using linear, relief and areal aspects of watershed. The research carried out the use of geographic information system spatial data. The linear aspects include stream number, stream sequence, stream length, and bifurcation ratio, mean length of stream order, stream length ratio, mean stream length ratio, and form factor. The areal aspect includes frequency of stream, drainage density, texture ratio, channel length constant, and overland flow maintenance length. Ultimately, the relief dimensions included relief proportion, relief and ruggedness number. The array of compound (Cp) values computed allow us to set the priority ranks and classify the sub-watershed into three priority ranks groups: low, moderate, and high priority. Such morphometric analyses can be used therefore as a watershed erosion status estimator to prioritize land and water conservation initiatives and natural resources management.

The frequency and intensity of typhoons have increased due to climate change. These climate change-induced disasters have caused widespread damage to forests. Evaluation of the effects of typhoons on forest ecosystems is often complex and challenging, mainly because of their sporadic nature. In this paper, we compared existing forest damage estimation techniques with the goal of identifying their respective advantages and suitable use cases. We considered Hokkaido in northern Japan as a case study, where three typhoons successively struck in 2016 and led to forest destruction. Forest damage was estimated from Landsat 8 imagery by three approaches, namely using vegetation damage indices (DVDI, DNDVI and ΔEVI), using supervised classification with Random Forest (RF) and Support Vector Machines (SVM) and finally by using the commercial CLASlite software with built-in methods to detect forest disturbance. Machine learning classifiers obtained the highest damage assessment accuracy, but intensive computation and complex processing steps were required. The RF and SVM classifiers gave the highest accuracies when using Fractional Cover as a predictor variable (Overall Accuracy = 80.36% in both cases, and ROC AUC values of 0.89 and 0.88, respectively.) Among the vegetation damage indices, DNDVI produced the highest accuracy (AUC = 0.85, OA = 77.68%).The most damaged areas were on the windward slopes, where forest patches were exposed to the brunt of the typhoon winds. Forest damage also peaked at the highest elevations in the study area, possibly representing exposed hilltops. Methods and findings presented in this study can help stakeholders to implement more effective forest damage monitoring after typhoons and other extreme weather events in the future.

Abstract In this work, we present new necessary and sufficient conditions for the oscillation of a class of second-order neutral delay impulsive differential equations. Our oscillation results complement, simplify and improve recent results on oscillation theory of this type of nonlinear neutral impulsive differential equations that appear in the literature. An example is provided to illustrate the value of the main results.

Abstract In this paper, we study the oscillatory and asymptotic behavior of a class of first-order neutral delay impulsive differential systems and establish some new sufficient conditions for oscillation and sufficient and necessary conditions for the asymptotic behavior of the same impulsive differential system. To prove the necessary part of the theorem for asymptotic behavior, we use the Banach fixed point theorem and the Knaster–Tarski fixed point theorem. In the conclusion section, we mention the future scope of this study. Finally, two examples are provided to show the defectiveness and feasibility of the main results.

Abstract Today, it’s getting harder to find natural resources for concrete production. Utilization of the waste materials not just helps in getting them used in concrete, cement, and other construction materials, but also has various secondary advantages, for example, saving in energy, decrease in landfill cost, and protecting climate from pollution. Considering this in the development of modern structural design, utilizing waste materials instead of natural aggregate is a good option to make concrete that is sustainable and eco-friendly. The present research aims to find the impact of adding glass fiber into sustainable concrete made with silica fume, as a partial replacement of cement, and coconut shell added with different ratios as a replacement of coarse aggregate, on concrete mechanical and durability aspects. Various blends were made, with coconut shell as a substitution of coarse aggregates with different ratios. Portland cement was substituted with silica fume at 5%, 10%, 15%, and 20% by cement weight in all concrete blends. The volume ratios of glass fibers utilized in this study were 0.5%, 1.0%, 1.5% and 2.0%. Adding glass fibers increases concrete density to some extent and then marginally reduces the density of coconut shell concrete. When the percentage of glass fibers increases, the compressive, flexural and split tensile strength of coconut shell concrete also increases. From the lab results and SEM images of the present research display that glass fibers might be utilized in coconut shell concrete to enhance its mechanical and durability attributes, to accomplish sustainable concrete with acceptable strength with ease.

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 ( $ K_x $ ) is the main parameter that defines the transport of pollutants in natural streams. Accurate estimation of $ K_x $ 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 $ K_x $ 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 ( $ {\rm RMSE} = 48.67 $ ) and maximum coefficient of determination ( $ R^2 = 0.95 $ ). 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.

Landslides 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.

The 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.

The 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:

In this paper, first, we find the integrability theorems for the invariant and slant distributions which appeared in the concept of semi-slant submanifolds. Utilizing these theorems, we prove that a semi-slant submanifold reduces to be a warped product semi-slant submanifold, provided some necessary and sufficient conditions concerning the shape operators. Also, it is shown that a few earlier results are exceptional cases of this paper results.

Precise knowledge of the soil organic carbon (SOC) stocks under various land uses is needed to meet the Kyoto Protocol and for the sustainability of natural resources. The purpose of the present study was to (1) gauge the depth and spatial distribution of the soil bulk density (BD), soil organic carbon (OC) stocks, and soil organic matter (OM) among the various land uses in the northern Nile Delta in Egypt; (2) estimate the soil carbon sequestration rate (CSR) under different land uses in the region; and (3) establish baseline data for SOC stocks in future studies on the dynamics of SOC. The study area was divided into ten sampling zones to represent each land use in the northern Nile Delta. Each sampling zone was further divided into four sampling sites to represent virgin lands and fish farms, and twelve sites were used to represent three crop types and four cultivation histories. The crops included clover, (Trifolium alexandrinum L.), sugar beet (Beta vulgaris L.), and rice, (Oryza sativa L.) and the years spanned were 5, 15, 30 and 50. The effects of the crop type on the SBD, SOC content, and SOC stocks were significant. In general, the SOC stocks increase as the number of years of cultivation increased. Thematic maps were produced using Geographical Information Systems (GIS) mapping. The Inverse Distance Weighted (IDW) technique in ArcGIS10.4 software revealed that the spatial pattern of the SBD, OC content, and stocks conformed to the soil analysis results. The SOC stocks of the croplands and fish farms were 1.6 and 1.5 times higher, respectively, compared to those of virgin land. Rice cropland had the lowest SBD (1.34 g cm−3) and the highest OC stocks (7.46 g C kg−1). The conversion of virgin land into croplands or fish farms actively contributed to the carbon storage rate (CSR).

The problem of water scarcity and clean water in sub-Saharan Africa is a growing concern. This study aims to quantify the water quality on a temporal scale in the Doorndraai dam site in sub-Saharan Africa to design possible management options. Here, an integrated approach using both in-situ measurements of water quality parameters and remote sensing data was used to derive the water quality index (WQI) and inherent optical properties of water to deduce the factors governing seasonal and annual variability. The results show that all the water quality parameters analyzed fall under the permissible limit of the World Health Organization (WHO) for drinking water, except turbidity. The average value of turbidity for the dry and wet periods was 12.52 and 3.39 NTU, respectively. WQI value ranges from good to excellent during the wet season, and poor in the dry season owing to the high values of turbidity in the water samples. Both in-situ and remote sensing-based analysis shows that during the last five years, the value of suspended particulate matter (SPM) based on Landsat-8 increased gradually in the study area. The Sentinel-2 derived modified normalized difference water index (MNDWI) shows a decreasing trend in the water area due to encroachment. The strong correlation between in-situ and remote sensing data supports the usefulness of remote sensing techniques for water resource management, especially in data-scarce regions. Looking at the spatio-temporal trend of water quality evolution, the findings of this study will help local decision-makers design sustainable plans for water resource management of Doorndraai dam.

In this research article, a novel analytical and numerical solutions of the Caudrey–Dodd–Gibbon–Sawada–Kotera (CDGDK) equation are established by means of Tanh method and 4thorder residual power series method (RPSM). Dynamics and shapes of the extracted solutions are shown by constructing 3D surface plots. To better understand the mechanism of the CDGDK equation, 3D surface plots are developed to view the physical illustration of the extracted solutions with the selection of suitable values of the parameter. Comparisons of analytical and approximate solutions indicate that RPSM is very efficacious and reliable approach for exploring nonlinear models appearing in different areas of science and engineering.

Urban areas are mostly heterogeneous due to settlements and vegetation including forests, water bodies and many other land use and land cover (LULC) classes. Due to the overwhelming population pressure, urbanization, industrial works and transportation systems, urban areas have been suffering from a deficiency of green spaces, which leads to an increase in the variation of temperature in urban areas. This study investigates the conceptual framework design towards urban green space (UGS) and thermal variability over Kolkata and Howrah city using advanced remote sensing (RS) and geospatial methods. The low green space is located in the highly built-up area, which is influenced by thermal variations. Therefore, the heat stress index showed a high area located within the central, north, northwestern and some parts of the southern areas. The vegetated areas decreased by 8.62% during the ten years studied and the other land uses increased by 11.23%. The relationship between land surface temperature (LST) and the normalized difference vegetation index (NDVI) showed significant changes with R2 values between 0.48 (2010) and 0.23 (2020), respectively. The correlation among the LST and the normalized difference built-up index (NDBI) showed a notable level of change with R2 values between 0.38 (2010) and 0.61 (2020), respectively. The results are expected to contribute significantly towards urban development and planning, policymaking and support for key stakeholders responsible for the sustainable urban planning procedures and processes.

The development of aluminium composite with the inclusion of advanced materials is a continuous research process due to the increasing industrial demand for advanced hybrid materials. To cater for this need, this research work focuses on the development of Al 7075 alloy reinforced with TiB2 and graphene and on the evaluation of its strengthening mechanism. Two different modes of improving the strength of the hybrid composite have been followed; one is by the inclusion of graphene at three levels of 0.1, 0.2 and 0.3%, and another by the processing route, squeeze casting technique by compression of the molten hybrid composite slurry before casting. The microstructure and characterisation of the composite material are examined and analysed with the help of XRD, SEM, EDAX and chemical spectroscopy. A microstructure evaluation is employed to justify the homogenous dispersal and the existence of reinforced particles. A tensile test is conducted at room temperature and high temperature environments to assess the tensile strength. The research outcome affirms that a significant improvement in tensile and hardness has been noted in comparison with base alloy. The fracture-morphology results affirm the change in fracture mode from brittle to ductile when the tensile testing environment changes from room temperature to high temperature. Finally, the dispersion strengthening mechanism is validated with an empirical approach.

This study examines land use changes and evaluates the past and projected forest carbon sequestration and its valuation in Viti Levu Island, Fiji, through a combination of remote sensing with a geospatial-based modeling approach. Land use classification was performed using Landsat 7 and Landsat 8 imageries of the years 2000 and 2020; then, cellular automata and artificial neural network (CA-ANN) modeling was conducted to predict the land use map of 2040. Carbon sequestration and the economic valuation were estimated using the land use maps of the past, present, and future (2000, 2020, and 2040) within the Integrated Valuation of Ecosystems Trade-off (InVEST) model. The results showed that deforestation occurred during the past two decades, and the forest area was predicted to keep decreasing in 2040, with the major contribution from the conversion to the agricultural area. Local communities’ perceptions confirmed that the forest conversion to croplands would persist due to the demand for fertile lands. This study estimated a loss of −7.337 megatonnes of forest carbon (Mt C) with an economic loss of USD −1369.38 million during 2000–2020 due to deforestation. If the business-as-usual scenario does not change in the near future, a potential carbon loss of −7.959 Mt C is predicted in the upcoming 20 years. The predicted results can be used to assist as a reference in establishing a national baseline and reference level for implementing the REDD+ mechanism in Fiji and sustainably managing the limited pristine forest by implementing forest-related programs.

In recent years, steel-concrete composite shear walls have been widely used in enormous high-rise buildings. Due to their high strength and ductility, enhanced stiffness, stable cycle characteristics and large energy absorption, such walls can be adopted in auxiliary buildings, surrounding the reactor containment structure of nuclear power plants to resist lateral forces induced by heavy winds and severe earthquakes. The current study aims to investigate the seismic behaviour of composite shear walls and evaluate their performance in comparison with traditional reinforced concrete (RC) walls when subjected to cyclic loading. A three-dimensional finite element model is developed using ANSYS by emphasising constitutive material modelling and element type to represent the real physical behaviour of complex shear wall structures. The analysis escalates with parametric variation in reinforcement ratio, compressive strength of the concrete wall, layout of shear stud and yield stress of infill steel plate. The modelling details of structural components, contact conditions between steel and concrete, associated boundary conditions and constitutive relationships for the cyclic loading are explained. The findings of this study showed that an up to 3.5% increase in the reinforcement ratio enhanced the ductility and energy absorption with a ratio of 37% and 38%, respectively. Moreover, increasing the concrete strength up to 55 MPa enhanced the ductility and energy absorption with ratios of 51% and 38%, respectively. Thus, this improves the contribution of concrete strength, while increasing the yield stress of steel plate (to 380 MPa) enhanced the ductility (by a ratio of 66%) compared with the reference model. The present numerical research shows that the compressive strength of the concrete wall, reinforcement ratio, layout of shear stud and yield stress of infill steel plate significantly affect ductility and energy absorption. Moreover, this offers a possibility for improving the shear wall’s capacity, which is more important.

Extreme climatic conditions and natural hazard-related phenomenon have been affecting coastal regions and riverine areas. Floods, cyclones, and climate change phenomena have hammered the natural environment and increased the land dynamic, socio-economic vulnerability, and food scarcity. Saltwater intrusion has also triggered cropland vulnerability and, therefore, increased the area of inland brackish water fishery. The cropland area has decreased due to low soil fertility; around 252.06 km2 of cropland area has been lost, and 326.58 km2 of water bodies or inland fishery area has been added in just thirty years in the selected blocks of the North 24 Parganas district, West Bengal, India. After saltwater intrusion, soil fertility appears to have been decreased and crop production has been greatly reduced. The cropland areas were 586.52 km2 (1990), 419.92 km2 (2000), 361.67 km2 (2010) and 334.46 km2 (2020). Gradually the water body areas were increased 156.21 km2 (1990), 328.15 km2 (2000), 397.77 km2 (2010) and 482.78 km2 (2020). The vegetated land area also decreased due to it being converted into inland fishery areas, and around 79.15 km2 were degraded during the last thirty years. The super cyclone Aila, along with other super cyclones and other environmental stresses, like water-logging, soil salinity, and irrigation water scarcity were the reasons for the development of the new fishery areas in the selected blocks. There is a need for proper planning for sustainable development of this area.

The optical characteristics of vertically distributed aerosols over Saudi Arabia were investigated using the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data from 2007 to 2019. The study region was divided into three parts (Region I: Tabuk, Makkah, Al Madinah, Asir, Al Bahah, Jizan, Riyadh, Mecca, Medina, the eastern region, Kassim, Hale, Asir, Baha, Tabuk, the northern border region, Jizan, Najilan, and Jufu. Region II: Ar, Al, Ha, Al, and Najran. Region III Al Hudud ash Shamaliyah and Ash Sharqiyah) to understand regional aerosol characteristics by performing interannual and seasonal analysis for nine aerosol types during the day and nighttime. We found that the aerosol optical depth (AOD) estimates were the highest over eastern Saudi Arabia (region III) and were seemingly driven by the presence of an expansive desert in the region. As anticipated, the AOD observations were substantially higher in spring and summer than in autumn and winter owing to the frequent occurrence of dust events during the former. Daytime observations exhibited higher AOD values than those at nighttime, which might be related to higher daytime anthropogenic activities. The estimates of the base height of the lowest aerosol layer (HB1) and the top altitude of the highest aerosol layer (TAH) were altered depending on the topography (the higher the altitude, the higher the annual mean value of HB1 and TAH). The aerosol layers (N) were relatively abundant over region III, seemingly due to the relatively stronger atmospheric convection over this region. The volume depolarization ratio of the lowest aerosol layer (VDR1) was considerable during the night due to deposition at nighttime, and VDR1 was relatively substantial in spring and summer. The color ratio of the lowest aerosol layer (CR1) estimates over regions II and III was higher at night. We report a weak positive correlation between the thickness of the lowest aerosol layer (HTH1) and the AOD of the lowest aerosol layer (AOD1) in the three regions, a strong positive correlation between TAH and N, and a negative correlation between the AOD proportion of the lowest aerosol layer (PAOD1) and N in Saudi Arabia. In this paper, the optical and physical properties of aerosols in Saudi Arabia have been studied for 13 years. Our results could provide references for researchers and the government, and relevant departments with data support on the aerosol layer to help control air pollution in Saudi Arabia.

In this paper, we study the general solution of the functional equation, which is derived from additive–quartic mappings. In addition, we establish the generalized Hyers–Ulam stability of the additive–quartic functional equation in Banach spaces by using direct and fixed point methods.

The mass accumulation of population in the larger cities of India has led to accelerated and unprecedented peripheral urban expansion over the last few decades. This rapid peripheral growth is characterized by an uncontrolled, low density, fragmented and haphazard patchwork of development popularly known as urban sprawl. The Kolkata Metropolitan Area (KMA) has been one of the fastest-growing metropolitan areas in India and is experiencing rampant suburbanization and peripheral expansion. Hence, understanding urban growth and its dynamics in these rapidly changing environments is critical for city planners and resource managers. Furthermore, understanding urban expansion and urban growth patterns are essential for achieving inclusive and sustainable urbanization as defined by the United Nations in the Sustainable Development Goals (e.g., SDGs, 11.3). The present research attempts to quantify and model the urban growth dynamics of large and diverse metropolitan areas with a distinct methodology considering the case of KMA. In the study, land use and land cover (LULC) maps of KMA were prepared for three different years (i.e., for 1996, 2006, and 2016) through the classification of Landsat imagery using a support vector machine (SVM) classification approach. Then, change detection analysis, landscape metrics, a concentric zone approach, and Shannon’s entropy approach were applied for spatiotemporal assessment and quantification of urban growth in KMA. The achieved classification accuracies were found to be 89.75%, 92.00%, and 92.75%, with corresponding Kappa values of 0.879, 0.904, and 0.912 for 1996, 2006, and 2016, respectively. It is concluded that KMA has been experiencing typical urban sprawl. The peri-urban areas (i.e., KMA-rural) are growing rapidly, and are characterized by leapfrogging and fragmented built-up area development, compared to the central KMA (i.e., KMA-urban), which has become more compact in recent years.