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Pistachio shell powder supported nano-zerovalent copper (ZVC@PS) material prepared by borohydride reduction was characterized using SEM, FTIR, XRD, TGA/DTA, BET, and XPS. SEM, XRD, and XPS revealed the nano-zerovalent copper to consist of a core-shell structure with CuO shell and Cu(0) core with a particle size of 40–100 nm and spherical morphology aggregated on PS biomass. ZVC@PS was found to contain 39% (w/w %) Cu onto the pistachio shell biomass. Batch sorption of Cr(VI) from the aqueous using ZVC@PS was studied and was optimized for dose (0.1–0.5 g/L), initial Cr(VI) concentration(1–20 mg/L), and pH (2–12). Optimized conditions were 0.1 g/L doses of sorbent and pH=3 for Cr(VI) adsorption. Langmuir and Freundlich adsorption isotherm models fitted well to the adsorption behavior of ZVC@PS for Cr(VI) with a pseudo-second-order kinetic behavior. ZVC@PS (0.1g/L) exhibits q max for Cr(VI) removal up to 110.9 mg/g. XPS and other spectroscopic evidence suggest the adsorption of Cr(VI) by pistachio shell powder, coupled with reductive conversion of Cr(VI) to Cr(III) by ZVC particles to produce a synergistic effect for the efficient remediation of Cr(VI) from aqueous medium. Graphical abstract

Laser welding of dissimilar materials, such as steel and copper, is highly susceptible to crack formation, which compromises joint integrity and service life. Traditional inspection techniques are slow, labor-intensive, and error-prone, underscoring the need for intelligent defect-prediction systems. This study utilizes a publicly available dataset of 360 weld cross-sections, generated using a definitive screening design (DSD) that encompasses six key process parameters: laser power, welding speed, angular orientation, focal position, gas flow rate, and sheet thickness. Multiple machine learning classifiers, including Decision Trees, Random Forests, Gradient Boosting, Support Vector Machines, and Neural Networks, were systematically evaluated using Orange data mining software with imbalance handling strategies. The novelty of this study lies in the application of the Orange data mining tool to address data imbalance in welding defect prediction and its optimization through a neural network framework, thereby enhancing both model reliability and predictive performance. Among them, a Multilayer Perceptron (MLP) neural network achieved the best performance, attaining 94.9% accuracy, 86.3% sensitivity, and 96.8% specificity, with an AUC of 0.961. The results establish neural networks as a robust and scalable tool for defect classification in steel-copper welding, offering a practical pathway for intelligent process monitoring and predictive quality assurance in Industry 4.0 manufacturing.

The study proposes the machine learning based algorithm for autonomous vehicle. The dynamic characteristic of unmanned aerial vehicle and real time disturbances such as wind current, obstacles are considered. The novelty of the work lies in the introduction of reinforcement learning for achieving optimized path which can be followed by the unmanned aerial vehicles to complete the tour from initial to destination point. The feasible optimal route may be found by incorporating the L algorithm with a reasonable optimality and computational cost when map and current field data are given.

Postharvest fungal pathogenic invasions are the major root cause of reduced shelf life of kinnow fruit, thereby contributing to the postharvest losses. Development of eco-friendly alternates are the need of the hour owing to health safety concerns for replacing the ongoing synthetic fungicide use. Essential oils with promising antimicrobial activities offer a promising solution but their hydrophobicity poses a big hindrance for exploiting the same. Present work was planned to explore their antimicrobial potential by developing their hydrophilic formulation with the use of nanotechnology. An in vitro study was conducted to assess the efficacy Monarda citriodora essential oil (MCEO) and its emulsions against major postharvest fungal pathogen of Kinnow; Penicillium digitatum . Both micro and nano formulations were prepared for different ratios of MCEO (0.5 to 3%) with different surfactant combinations and oil-surfactant-ratios (OSR) of 1:1 to 1:3. The influence of several process factors such as surfactant and oil phase concentrations, as well as sonication time intervals on emulsion stability was investigated by assessing attributes such as droplet diameter, Polydispersity index (PDI), zeta (ζ) potential and rheology. An emulsion formulated with 1% oil and 1:1 OSR treated with ultrasonic waves for 15 min was optimized with droplet diameter of 52.2 nm, 0.245 PDI and − 21 mV of ζ potential with consistent stability till 1 month. Further, in vitro antifungal activity of the optimized MCEO nanoemulsion exhibited the best efficacy with 100% inhibition at 200 mg L −1 . Graphical Abstract

Computer aided detection systems are used for the provision of second opinion during lung cancer diagnosis. For early-stage detection and treatment false positive reduction stage also plays a vital role. The main motive of this research is to propose a method for lung cancer segmentation. In recent years, lung cancer detection and segmentation of tumors is considered one of the most important steps in the surgical planning and medication preparations. It is very difficult for the researchers to detect the tumor area from the CT (computed tomography) images. The proposed system segments lungs and classify the images into normal and abnormal and consists of two phases, The first phase will be made up of various stages like pre-processing, feature extraction, feature selection, classification and finally, segmentation of the tumor. Input CT image is sent through the pre-processing phase where noise removal will be taken care of and then texture features are extracted from the pre-processed image, and in the next stage features will be selected by making use of crow search optimization algorithm, later artificial neural network is used for the classification of the normal lung images from abnormal images. Finally, abnormal images will be processed through the fuzzy K-means algorithm for segmenting the tumors separately. In the second phase, SVM classifier is used for the reduction of false positives. The proposed system delivers accuracy of 96%, 100% specificity and sensitivity of 99% and it reduces false positives. Experimental results shows that the system outperforms many other systems in the literature in terms of sensitivity, specificity, and accuracy. There is a great tradeoff between effectiveness and efficiency and the proposed system also saves computation time. The work shows that the proposed system which is formed by the integration of fuzzy K-means clustering and deep learning technique is simple yet powerful and was effective in reducing false positives and segments tumors and perform classification and delivers better performance when compared to other strategies in the literature, and this system is giving accurate decision when compared to human doctor’s decision.

Composites play a significant role in societal development. Therefore, the machining of composites is a significant topic of interest among the research community. In this context, this work uses stir-casted composite (Al-6061 alloy with graphene powder (5%), and nano-TiO2 (10%)) as a workpiece. Depth of cut, cutting speed, and feed rate were considered significant factors at three levels. The experimental design was formulated based on Taguchi's design of experiment (DOE) and used an L 9 orthogonal array. The process’s output characteristic was measured in terms of surface roughness (R a ) using a Surface Roughness Tester. The regression analysis has been applied to determine the best process parameters with little trial and error. The likelihood estimator (lambda) was calculated using the Box-Cox transformation, yielding a powerful regression equation. The estimated values from the regression equation and the observed values were quite close to one another. A 0.687 R a value was achieved with a 1 mm depth of cut, 1000 rpm spindle speed, and a 50 mm/min feed rate. To produce the smallest possible discrepancy between observed and anticipated values, the 'hyperparameter' of the regression equation was fine-tuned. The maximum likelihood estimator value of lambda was found to be 2, with a mean error of 0.03%. The variance inflation factor was also found to be 1.00, which justifies the correctness of the equation.

A compression ignition (CI) engine can produce significant pressures and temperatures in its combustion chamber. The exhaust system’s outlet is the atmosphere so that a conventional silencer can investigate pressure wave attenuation. As a result of an adverse pressure gradient, the exhaust system may not function properly. For computational fluid dynamics simulations, hot exhaust gas can be selected from muffler pipes exiting engine exhaust systems. This study examines a Mahindra Maximo C.I. engine’s rectangular chambered muffler model. In the design of one of the two mufflers considered, the inlet, outlet, and center pipes are perforated, which may result in better, more efficient noise reduction. CFD analysis results are used to establish the pressure distribution used in both muffler models to compute transmission loss. Ansys Fluent 2022 R2 is used in this study to compare two reactive-type muffler designs, one without perforations and one with perforations, and Fusion 360 (V.2.0.13168) software is used to design reactive-type mufflers. It was found that the perforated muffler model exhibits a higher transmission loss than the non-perforated muffler model, thereby establishing its superiority over the non-perforated muffler model. Static pressure and transmission loss are proportional in the rectangular chamber muffler model, with a 1.56% increase in static pressure corresponding to a 25% increase in transmission loss.

Microcrystalline cellulose immobilized zerovalent iron nanoparticles (CI-1-3) with different loading of 6, 12 and 24% w/w Fe 0 were synthesized by NaBH 4 reduction under simultaneous co-precipitation of cellulose from ionic liquid ([BMIM]Cl)-water binary mixture. SEM, TEM, FTIR, VSM, XRD and XPS analysis were carried out to characterize the material. The electron microscopy studies revealed the immobilization of iron nanoparticle in the bulk and surface of microcrystalline cellulose with a size range of 20–100 nm. CI-1-3 showed strong interaction between cellulose hydroxyl moiety and nZVI, immobilized on the polymer and saturation magnetization of 3 emu/g for CI-2. The materials were studied for Cr(VI) adsorption which revealed the q max value of 28.57, 58.82 and 38.48 mg Cr(VI)/g of CI-1-3, respectively. Graphical abstract

The Publisher has been made aware of ethical breaches affecting this proceeding published in E3S Web of Conferences, Volume 430 (2023) . These instances involve a specific author, K.K. Saxena who used citation manipulation and inappropriate references in 47 articles, for a total of 310 citations. We are extremely concerned by such malpractice which considerably impacts the image of our title and our Publisher’s reputation. See our publishing ethics policies . The Guest Editor of the proceedings volume endorsed the Publisher's decision to retract these articles. Web of Conferences is extremely grateful to the whistleblower for bringing this case to our attention. See the retraction notice E3S Web of Conferences 430 , 00002 (2023), https://doi.org/10.1051/e3sconf/202443000002

PURPOSE: Arsenic (As) contamination of groundwater has received significant attention recently in district Bathinda, due to consequent health risk in this region. Soil is the one of the primary medium for arsenic transport to groundwater. Thus, there is an essential requirement for understanding the retention capacity and mobility of arsenic in the soils to ensure sustainability of the groundwater in the locality. Arsenic interaction with various physicochemical properties of soil would provide a better understanding of its leaching from the soil. MATERIALS AND METHODS: Fifty-one soil samples were collected from two regions of Bathinda district with extensive agricultural practices, namely, Talwandi Sabo and Goniana. The soils were analyzed for arsenic content and related physicochemical characteristic of the soil which influence arsenic mobility in soil. Adsorption studies were carried out to identify the arsenic mobilization characteristic of the soil. SEM-EDX and sequential extraction of arsenic adsorbed soil samples affirmed the arsenic adsorption and its mobility in soil, respectively. Multiple regression models have been formulated for meaningful soil models for the prediction of arsenic transport behavior and understand the adsorption and mobilization of arsenic in the soil matrices. RESULTS AND DISCUSSION: Region-wise analysis showed elevated levels of arsenic in the soil samples from Goniana region (mean 9.58 mg kg⁻¹) as compared to Talwandi Sabo block (mean 3.38 mg kg⁻¹). Selected soil samples were studied for As(V) and As(III) adsorption behavior. The characteristic arsenic adsorption by these soil samples fitted well with Langmuir, Freundlich, Temkin, and D-R isotherm with a q ₘₐₓ in the range of 45 to 254 mg kg⁻¹ and 116 to 250 mg kg⁻¹ for As(III) and As(V), respectively. Adsorption isotherms indicate weak arsenic retention capacity of the soil, which is attributed to the sandy loam textured soil and excessive fertilizer usage in this region. PCM and MLR analysis of the soil arsenic content and its adsorption strongly correlated with soil physicochemical parameters, namely, Mn, Fe, total/available phosphorus, and organic matter. CONCLUSIONS: Manganese and iron content were firmly established for retention of arsenic in soil, whereas its mobility was influenced by organic matter and total/available phosphorus. The poor adsorptive characteristic of these soils is the primary cause of higher arsenic concentration in groundwater of this region. A strong correlation between monitored arsenic and adsorbed As(III) with manganese suggests As(III) as the predominant species present in soil environment in this region.