Sustainable crop planning in arid and semi-arid regions is constrained by soil degradation, water scarcity, and high environmental variability, while the adoption of data-driven decision methods remains limited by poor transparency and trust. This study investigates whether unsupervised soil-environment regimes can be discovered and interpreted in a scientifically transparent manner to support crop planning and soil management in data-scarce arid regions. To address this need, an integrated IoT-AI-XAI-blockchain framework is presented for interpretable and auditable soil-environment regime discovery. Using soil and climatic variables including nitrogen, phosphorus, potassium, pH, temperature, humidity, and rainfall, we evaluate multiple unsupervised clustering approaches K-Means, DBSCAN, and Agglomerative clustering to identify homogeneous agro-environmental regimes. Cluster quality and stability are assessed using repeated internal validation. K-Means produced four stable and interpretable regimes, achieving a mean Silhouette Score of 0.62 +/- 0.03 and a Davies-Bouldin Index of 0.71 +/- 0.05 showing stronger internal validation performance than the alternative methods considered in this study. Instance-level and global explanations are generated using SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) through a surrogate-based explainability strategy for better interpretability of soil monitoring parameters. Cryptographic hashes of analytical outputs are recorded for auditability. The results suggest that the integration of explainable unsupervised learning and lightweight provenance logging can provide a structured, interpretable, and auditable analytical basis for data-driven agronomic analysis.

With the goal of improving the performance of cooling systems of magnetic sensors, the effects of adding nanoparticles and employing ferrohydrodynamics (FHD) are studied with numerical simulations. To produce ferrohydrodynamics, a wire near the hot surface is used to produce a varying Kelvin force. To better describe the magnetic force, a ferrofluid consisting of iron oxide and water is used. In the velocity and temperature equations, new terms are added to represent ferrohydrodynamics and buoyancy. To apply such complex physics, the control volume finite element method (CVFEM) is applied, and the equations are written in vorticity form to help remove pressure terms. This modeling approach is validated against previously published work and the results show good agreement. An improvement of 11.65 % in the convection rate is achieved by adding nanoparticles. Considering a higher buoyancy force results in a 118.92 % increase in the Nusselt number Nu. As MnF increases up to 2 x 10(3), Nu increases by about 81.88 % at the lowest Rayleigh number Ra. The influence of ferrohydrodynamics on Nu declines as the gravity force increases. The hot surface becomes cooler by about 10 % and 37.5 % when MnF and Ra are increased.

This research focuses on enhancing the machinability and formability of tool steels by applying different heat treatment scenarios. Machinability is the process of cutting metals with the least energy effort, while formability is the ability of a metal to be shaped into different forms without severe damage. Enhancing machinability and formability improves dimensional tolerance and surface finish and increases the lifetime and reliability of tool steels. This research will use two tool steel materials, “D2” and “O1,” which are widely used in industry. They contain high percentages of carbon by weight (1.55% and 0.95%, respectively). The “O1” tool steel is used in blanking dies and room-temperature cutting tools, whereas the “D2” tool steel is used for carpentry cutting tools and gauges. The different heat treatment scenarios are based on the latest literature, which shows excellent results with other materials. The heat treatment plan is set to cover various scenarios to determine the most effective treatment for machinability and formability. The experimental work will include several mechanical tests: tensile tests, compression tests, hardness tests, and toughness tests. The conclusions regarding the different heat treatments will be based on the test results.

The existence of diverse microbes in unprocessed camel milk poses a significant threat to the well-being of a large population, especially infants and toddlers. The objective of this study was to ascertain the existence of microorganisms in unprocessed raw camel milk by employing a molecular-based technique in combination with a histological examination of bacteria. The identification of microbial species was achieved by employing PCR amplification and sequencing of 16s rRNA gene fragments. Various micorganisms found includes the probiotic Lactobacillus species, Staphylococcus succinic, Macrococcus casealyticus, Bacillus cohnii, and Salinicoccus kunmingensis. To prevent microbial contamination in raw milk, it is necessary to adequately heat or pasteurise the milk and to wash and sterilise the udder before milking the camel. This is because raw milk contains microbes that cause multiple diseases. Moreover, in the current era of the COVID-19 pandemics, ensuring proper sanitary conditions in milk and its derivatives might potentially mitigate the transmission of various diseases among consumers shortly. Keywords: camel, microbiota, 16s rRNA gene, PCR.

Experimental and numerical studies were conducted to study the plastic and fracture behaviors of AISI 4340 steel (with three different heat treatments) under multiaxial stress loading conditions, including axial symmetric and plane strain loadings. The classical Gurson-Tvergaard-Needleman (GTN) plasticity model was extended and calibrated to consider Lode angle dependence on the material's matrix plastic strength especially needed for plane strain loadings. The 3D fracture loci of AISI 4340 steels were previously calibrated by the Modified Mohr -Coulomb (MMC) model. Considering the microvoid growth and nucleation mechanisms, GTN model is combined with MMC model to derive a new analytical solution of microvoid volume fraction at fracture (ff) under general loading conditions. The ff should NOT be a constant, and it depends on different stress states. The modified GTN-MMC model was implemented using VMAT in Abaqus, and it gives a very good correlation between experimental data and finite element simulations.

The applications of fused disposition modeling (FDM) 3D printed components have dramatically increased due to their efficient production time, exceptional mechanical properties, and excellent finishing quality. The challenge of studying the behavior and predicting the mechanical properties of 3D PLA (polylactic acid) fabricated parts with varying sets of printing parameters has risen with innovative and complex component shapes. Therefore, this study highlights three main printing parameters that influence the durability and integrity of the parts produced by PLA. This study illustrates the effect of three printing process variables: raster angle (RSA), layer thickness (LYT), and infill density (IFD), on two outputs: strength at fracture and strain behavior of PLA-printed specimens. The study shows extensive experimental work of 27 sets of different combinations of printing processes (108 tested specimens). It also presents two quadratic mathematical models to predict: (1) the tensile strength and (2) strain at fracture of PLA-printed specimens, including the effects of the printing parameters. The prediction and experimental results are quite comparable to one another. The study concludes with an optimized set of printing parameters for higher durability and greater integrity.

Separation and capture of CO(2 )from gas mixtures is of great importance from environmental point of view which can be effectively achieved using amino acids as new class of chemical absorbents. However, screening the proper absorbent with desired separation properties using experimental measurements is tedious and costly. The predictive computational techniques can be employed to overcome this problem. In this study, for estimating and analyzing CO2 solubility in chemical solvents based on amino acid salt solutions, we created two regression models from different classes of machine learning methods. The main aim is to analyze the effect of physicochemical parameters on the CO2 dissolution in solvent which can be carried out in chemical reactors for separation/conversion of CO2 for environmental applications. A number of CO2 solubility data are collected from resources and used for training and validation of machine learning computations. Several inputs were considered for the developed machine learning models. Inputs in this regression task are T (temperature), weight% (overall mass percentage of solvent), P-CO2 (partial pressure of CO2 in the gas), MW-am (molecular weight of amino acid salt), MPC (melting point of amino acid salt), M-WC (molecular mass of cation). In this task, we must predict alpha (CO2 loading in the amino acid solution) as the only output of the developed models. The models studied in this research are the Gaussian process and the decision tree boosted with Gradient boosting. With the R-2 criterion, the scores of the two Gradient boosting and Gaussian process models were obtained 0.985 and 0.993, respectively. As the third efficiency metric of the models, the Gradient boosting and regression of the Gaussian process with the RMSE criterion is the error rates of 1.10E-01 and 1.44E-01. The models developed in this work indicated to be reliable and robust enough for screening the solvents for a particular application and to save time and cost of experimental measurements. (C)& nbsp;2022 The Author(s). Published by Elsevier B.V.& nbsp;

This researchreports the fabrication of silver nanoparticles (AgNPs) from endophytic fungus,Amesia atrobrunnea isolated from Ziziphus spina-christi (L.). Influencing factors for instance, thermal degree of incubation, media, pH, and silver nitrate (AgNO3) molarity were optimized. Then, the AgNPs were encapsulated with chitosan (Ch-AgNPs) under microwave heatingat 650 W for 90 s. Characterization of nanoparticles was performed via UV–visible (UV–vis) spectrophotometer, Fourier-transform infrared spectrophotometer (FTIR), zeta potential using dynamic-light scattering (DLS), and field-emission-scanning electron microscope (FE-SEM). Anti-fungal activity of Ch-AgNPs at (50, 25, 12.5, 6.25 mg/L) was tested againstFusarium oxysporum,Curvularia lunata, andAspergillus nigerusing the mycelial growth inhibition method (MGI).Resultsindicated that Czapek-dox broth (CDB) with 1 mM AgNO3, an acidic pH, and a temperature of 25–30 °C were the optimum for AgNPs synthesis. (UV–vis) showed the highest peak at 435 nm, whereas Ch-AgNPs showed one peak for AgNPs at 405 nm and another peak for chitosan at 230 nm. FTIR analysis confirmed that the capping agent chitosan was successfully incorporated and interacted with the AgNPs through amide functionalities. Z-potential was −19.7 mV for AgNPs and 38.9 mV for Ch-AgNPs, which confirmed the significant stability enhancement after capping. FES-SEM showed spherical AgNPs and a reduction in the nanoparticle size to 44.65 nm after capping with chitosan. The highest mycelial growth reduction using fabricated Ch-AgNPs was93%forC. lunatafollowed by 77% forA. nigerand 66%F. oxysporum at(50 mg/L). Biosynthesis of AgNPs using A. atrobrunnea cell-free extract was successful. Capping with chitosan exhibited antifungal activity against fungal pathogens.

Polyurethane (PU) paint with a hydrophobic surface can be easily fouled. In this study, hydrophilic silica nanoparticles and hydrophobic silane were used to modify the surface hydrophobicity that affects the fouling properties of PU paint. Blending silica nanoparticles followed by silane modification only resulted in a slight change in surface morphology and water contact angle. However, the fouling test using kaolinite slurry containing dye showed discouraging results when perfluorooctyltriethoxy silane was used to modify the PU coating blended with silica. The fouled area of this coating increased to 98.80%, compared to the unmodified PU coating, with a fouled area of 30.42%. Although the PU coating blended with silica nanoparticles did not show a significant change in surface morphology and water contact angle without silane modification, the fouled area was reduced to 3.37%. Surface chemistry could be the significant factor that affects the antifouling properties of PU coating. PU coatings were also coated with silica nanoparticles dispersed in different solvents using the dual-layer coating method. The surface roughness was significantly improved by spray-coated silica nanoparticles on PU coatings. The ethanol solvent increased the surface hydrophilicity significantly, and a water contact angle of 18.04° was attained. Both tetrahydrofuran (THF) and paint thinner allowed the adhesion of silica nanoparticles on PU coatings sufficiently, but the excellent solubility of PU in THF caused the embedment of silica nanoparticles. The surface roughness of the PU coating modified using silica nanoparticles in THF was lower than the PU coating modified using silica nanoparticles in paint thinner. The latter coating not only attained a superhydrophobic surface with a water contact angle of 152.71°, but also achieved an antifouling surface with a fouled area as low as 0.06%.

Solubility data for ANA (Anastrozole) drug in supercritical solvent was investigated in this study, and models were developed to estimate the solubility values. The main aim was to provide a predictive methodology for determination of drug solubility in wide range of operational parameters for advanced green pharmaceutical manufacture. The properties used are temperature and pressure which were considered as the models' inputs. Modeling has been done using three models based on the support vector regression. These models include support vector regression (with polynomial kernel), boosted support vector machine with AdaBoost, and improved support vector machine with bagging. These models were evaluated after optimization, and all three models have a coefficient of determination (R2) higher than 0.98. Also considering RMSE, AdaBoosted SVR, Bagging SVR, and SVR have error rates of 2.31E-01, 4.31E-01, and 5.01E-01.

This research aimed to myco-fabricate silver nanoparticles (AgNPs) from the endophytic fungus Curvularia kusanoi. The AgNPs were encapsulated with chitosan (Ch-AgNPs) under microwave heating at 650 W for 90s. Characterization of nanoparticles was performed using different UV-vis spectroscopy (UV-vis), Fourier-transform infrared spectroscopy (FTIR), field emission scanning electron microscopy (FE-SEM), Zeta potential, and dynamic light scattering (DLS). Antifungal ac-tivity of Ch-AgNPs at (50, 25, 12.5, and 6.25 mg/L) was tested against Aspergillus fumigatus, Curvularia lunata, and Cladosporium sp. using the mycelial growth inhibition method (MGI). After capping, UV-vis analysis showed one peak for AgNPs at 400 nm and another for chitosan at 230 nm. FTIR analysis confirmed the incorporation of chitosan as capping agent by interacting with the AgNPs through amide functionalities. The Z-potential revealed the change of nanoparti-cles charge from -15.7 mV to +59 mV. FES-SEM showed spherical Ch-AgNPs with a size of 33.82 nm. Ch-AgNPs showed a highly significant antifungal activity against all tested fungi. The highest mycelial growth reduction was 97% for C. lunata, followed by 89% for Cladosporium sp. and 83% for A. fumigates at (50 mg/L). The lower concentration (6.25 mg/L) inhibited mycelial growth by 62% of Cladosporium sp., 60% of C. lunata, and 42% of A. fumigates. Biosynthesized Ag-NPs using C. kusanoi cell-free extract were successfully capped with chitosan for enhanced stabil-ity while maintaining their antifungal activity against fungal pathogens.

The phenomenon of steam–water direct contact condensation has significance in a wide range of industrial applications. Superheated steam was injected upward into a cylindrical water vessel. Visual observations were conducted on a turbulent steam jet to determine the dimensionless steam jet length compared to the steam nozzle exit diameter and the steam maximum swelling ratio as a function of steam mass flux at the nozzle exit, with a gas steam flux ranging from 295–883 kg/m2s. The Reynolds number based on the steam jet’s maximum expansion ranged from 41,000 to 93,000. Farther above of the condensation region, the jet evolved as a single-phase heated plume, surrounded by ambient water. Mean axial central velocity profiles were determined against the steam mass flux ranging from 295–883 kg/m2s to observe the exponential drop in the mean axial velocity as the vertical distance increased. The radial velocity distribution within the spread of the jet was determined to be self-similar, and the radial distribution of the velocity profile followed the Gaussian function, after the proper scaling of the vertical distance and the axial mean velocity.

We developed two distinct regression models based on machine learning approach for estimating CO2 loading in solvents in this study. The methods of Adaptive boosted support vector (SVR) machines and the Adaptive boosted Gaussian process (GPR) are the models employed in this study for approximating carbon dioxide loading in the solvent for the purpose of environmental applications. Indeed, the case study is molecular separation for capture of CO2 from a mixture using amino acid salt solutions and understanding the influence of variables on variation of gas solubility. The target output parameter in the computations is the CO2 solubility in liquid phase (alpha). The scores of the two models of boosted SVR and boosted GPR were 0.830 and 0.991, respectively, using the R-2 criterion. Furthermore, the error rate with the MAPE standard is 2.21758E-01 and 1.06938E-01 for the two models, respectively. The boosted SVR and regression of the boosted GPR with the MAE criterion have error rates of 1.64937E01 and 7.28501E-02, respectively, as the models' third efficiency metric. The used machine learning models indicated to be robust for simulation of CO2 absorption in liquid phase for environmental applications and can be used to save time and costs of measurements. (C) 2022 Elsevier B.V. All rights reserved.

In order to optimize productin of biodiesel from waste cooking oil utilizing Fe-exchanged montmorillonite 12 K10 (Fe-MMT K10) heterogeneous catalyst was applied in this work. The data of batch reaction experiments were collected for optimization considering four inputs and one output. The input parameters included reaction temperature, reaction time, catalyst loading, and ratio of methanol to oil. The model was developed to predict the output which is the production yield of biodiesel (%). For optimization of the process, three ensemble models were utilized as a novel method for the first time in this study: Huber Regression, Decision Trees, and Gaussian process which were all boosted using AdaBoost technique. The R2-Scores for Boosted Huber Regression (ADABOOST-HBR), Boosted Decision Tree (ADABOOST-DT), and boosted Gaussian process (ADABOOST-GPR), respectively, were 0.814, 0.780, and 0.996. The calculated MAE parameter for the models illustrated that the error rates for Boosted Huber Regression ADABOOST-HBR, ADABOOST-DT, and ADABOOST-GPR were 3.84, 5.94, and 1.82, respectively. Indeed, the boosted GPR model has a better accuracy over the two models in optimization of the process. Moreover, applying the input values (X1=145, X2=5.625, X3=4.22, X4=11.73), the recommended methods produced an ideal output value of 96.75% which was considered to be the optimum yield for production of biodiesel.

In current article, absorber pipe of solar unit has been analyzed considering complex turbulator and hybrid nanomaterial. The average value of radiation flux has been considered as outer wall boundary condition. The base fluid is H 2 O and addition of CNT and iron oxide were selected as nano-powders. The turbulent regime was modeled via K − ɛ and features of nanofluid were derived according to single-phase technique. Two shapes of turbulator were employed and two set of Re were analyzed. Employ of turbulator generates the secondary flow and disrupt the boundary layer. Thus, stronger convective migration of hybrid nanoparticles can be observed with adding external device. With install of obstacle along the twisted tape, temperature decreases about 1% and velocity augments about 12%. With rise of Re, velocity enhances 276%, while temperature declines about 4%. Insert of obstacle makes pressure drop to augment 835% when Re = 5000. The outlet temperature declines about 0.81% with employing simple twisted tape.

In this study, machine learning (ML) computations were carried out for description of drug solubility in supercritical carbon dioxide. Supercritical solvent has been used in this work due to its superior properties and high solvation capacity for drug dissolution. The model has been developed and tested for salsalate as well as decitabine drugs, and their solubility at various pressures and temperatures were predicted using the developed machine learning model. The models were developed by taking into account the pressure between 120 and 400 bar, and temperature between 308 and 338 K for understanding the influence of pressure and temperature on salsalate and decitabine drug dissolution in the solvent. Moreover, the model's accuracy was compared with some empirical correlations from previous studies. It was indicated that the ML model had better accuracy compared to the semi-empirical correlations. The pressure was indicated to have considerable influence on the solubility variations for both drugs. The best thermodynamic model showed the least average absolute relative deviation percent of around 8 % for the whole data points for salsalate. For development of machine learning model, artificial neural network was trained using the measured data. The neural network was developed using one hidden layer, two inputs, and one output. Pressure and temperature were taken as inputs for the network, and the solubility of drug as the predicted output in the neural network. The training and validation of the neural network using salsalate and decitabine solubility indicated great accuracy with coefficient of determination higher than 0.99 for both steps. (C) 2022 Elsevier B.V. All rights reserved.

Multiple machine learning models were developed in this study to optimize biodiesel production from waste cooking oil in a heterogenous catalytic reaction mode. Several input parameters were considered for the model including reaction temperature, reaction time, catalyst loading, methanol/oil molar ratio, whereas the percent of biodiesel production yield was the only output. Three ensemble models were utilized in this study: Boosted Linear Regression, Boosted Multi-layer Perceptron, and Forest of Randomized Tree for optimization of the yield. We then found their optimized configurations for each model, namely hyper-parameters. This critical task is done by running more than 1000 combinations of hyper-parameters. Finally, The R2-Scores for Boosted Linear Regression, Boosted Multi-layer Perceptron, and Forest of Randomized Tree, respectively, were 0.926, 0.998, and 0.992. MAPE criterion revealed that the error rates for boosted linear regression, boosted multi-layer perceptron, and Forest of Randomized Tree was 5.68 × 10-2, 5.20 × 10-2, and 9.83 × 10-2, respectively. Furthermore, utilizing the input vector (X1 = 165, X2 = 5.72, X3 = 5.55, X4 = 13.0), the proposed technique produces an ideal output value of 96.7 % as the optimum yield in catalytic production of biodiesel from waste cooking oil.

In recent years, the emergence of disparate micro-contaminants in aquatic environments such as water/wastewater sources has eventuated in serious concerns about humans' health all over the world. Membrane bioreactor (MBR) is considered a noteworthy membrane-based technology, and has been recently of great interest for the removal micro-contaminants. The prominent objective of this review paper is to provide a state-of-the-art review on the potential utilization of MBRs in the field of wastewater treatment and micro-contaminant removal from aquatic/non-aquatic environments. Moreover, the operational advantages of MBRs compared to other traditional technologies in removing disparate sorts of micro-contaminants are discussed to study the ways to increase the sustainability of a clean water supplement. Additionally, common types of micro-contaminants in water/wastewater sources are introduced and their potential detriments on humans' well-being are presented to inform expert readers about the necessity of micro-contaminant removal. Eventually, operational challenges towards the industrial application of MBRs are presented and the authors discuss feasible future perspectives and suitable solutions to overcome these challenges.

Latent heat thermal energy storage (LHTES) systems are attractive for bridging the energy supply and de-mand gap. In such systems, reducing storage time is critical, especially for solar applications. Accordingly, this study mainly aims to employ various nano-additives, including metal (Ag and Cu) and metal-oxide (Al2O3, CuO, and TiO2) nanoparticles and carbon-based nanomaterials (GNP, MWCNT, SWCNT), to improve the thermophysical properties of pure phase change materials (PCM) to accelerate the melting process. For this purpose, the energy storage performance was numerically analyzed in a vertical shell and tube LHTES unit where D-mannitol was utilized as the PCM on the shell side. Dynaleneht was employed as a heat transfer fluid (HTF) in the tube. Using computational fluid dynamics (CFD) modeling, transient variations in liquid fraction, PCM temperature, and total melting time were investigated under the impact of the following parameters: the thermophysical properties and volume fraction of nanomaterials, Re and the inlet temperature of HTF. In addition, a methodology based on Bayesian inference was adopted by coding the Bayesian MCMC simulation to create proper models for predicting the melting time. The numerical results showed that adding carbon-based nanomaterials to pure PCM reduced the melting time by about 50%, while metal nanoparticles impaired the melting performance. It was also observed that adding metal oxide nanoparticles did not add any essential advantage to the LHTES system. This research will help design TES applications in the operating temperature range of 160-200 degrees C, especially in solar cooling systems. (C) 2022 Elsevier Ltd. All rights reserved.