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The effect of pH-increases due to Ca(OH)2 and KOH addition on the adsorption of cadmium (Cd) was examined in two soils which varied in their variable-charge components. The effect of Ca(OH)2 on immobilization and phytoavailability of Cd from one of the soils, treated with various levels of Cd (0–10 mg Cd kg-1 soil), was further evaluated using mustard (Brassica juncea L.) plants. Cadmium immobilization in soil was evaluated by a chemical fractionation scheme. The addition of Ca(OH)2 and KOH increased the soil pH, thereby increasing the adsorption of Cd, the effect being more pronounced in the soil dominated by variable charge components. There was a greater increase in Cd2+ adsorption in the KOH-treated than the Ca(OH)2-treated soil, which is attributed to the greater competition of Ca2+ for adsorption. Increasing addition of Cd enhanced Cd concentration in plants, resulting in decreased plant growth (i.e., phytotoxicity). Although addition of Ca(OH)2 effectively reduced Cd phytotoxicity, Cd uptake increased at the highest level, probably due to decreased Cd2+ adsorption resulting from increased Ca2+ competition. There was a significant inverse relationship between dry matter yield and Cd concentration in soil solution. Addition of Ca(OH)2 decreased the concentration of the soluble + exchangeable Cd fraction but increased the concentration of inorganic-bound Cd fractions in soil. Since there was no direct evidence for CdCO3 or Cd(OH)2 precipitation in the variable charge soil used for the plant growth experiment, alleviation of phytotoxicity can be attributed primarily to immobilization of Cd by enhanced pH-induced increases in negative charge.

Aim This study aimed to assess the impact of positive pressure, negative pressure (EndoVac), and sonic-activated irrigation (EndoActivator) on postoperative pain with symptomatic irreversible pulpitis and symptomatic apical periodontitis. The hypothesis tested the superiority of negative pressure irrigation in reducing pain and analgesic requirements. Methodology Forty-eight eligible patients meeting inclusion criteria were enrolled, ensuring comprehension through verbal and written patient information sheets. The sample size calculation, based on prior data, determined 14 teeth per group with consideration for potential dropouts, resulting in 16 teeth per group. Inclusion criteria included healthy individuals aged 16-65 years with single-rooted teeth diagnosed with symptomatic irreversible pulpitis and symptomatic apical periodontitis, while exclusion criteria comprised recent analgesic intake, pregnancy, lactation, and specific dental conditions. Participants were allocated to three groups using computer-generated block randomization with allocation concealment via sequentially numbered opaque sealed envelopes. While blinding of the operator was not feasible, patient and assessor blinding was ensured. Preoperative data collection included patient demographics, tooth details, and pain intensity assessed on a Visual Analogue Scale (VAS). Root canal therapy procedures, conducted in two visits, included instrumentation and irrigation using 3% NaOCl across three groups: positive pressure irrigation, negative pressure irrigation, and sonic activation. Postoperative pain and analgesic intake were evaluated using VAS at specific intervals. One assessor tabulated and analyzed all the information. Results Postoperative pain assessments revealed that the EV group experienced the lowest pain levels, followed by the EA and SVN groups, with significant differences observed at six and 24 hours postoperatively (p < 0.05). Analgesic requirements correlated with pain levels, with the SVN group requiring the most analgesics and the EV group the least, highlighting the efficacy of the interventions. Conclusions Negative pressure irrigation (EndoVac) significantly reduced postoperative pain compared to conventional side-vented needle irrigation. These findings enhance understanding and guide evidence-based recommendations for optimizing endodontic procedures and prioritizing patient comfort and outcomes.

Cross Site Scripting (XSS) Flaws are currently the most popular security problems in modern web applications. These Flaws make use of vulnerabilities in the code of web-applications, resulting in serious consequences, such as theft of cookies, passwords and other personal credentials. Cross-Site scripting Flaws occur when accessing information in intermediate trusted sites. Client side solution acts as a web proxy to mitigate Cross Site Scripting Flaws which manually generated rules to mitigate Cross Site Scripting attempts. Client side solution effectively protects against information leakage from the user's environment. Cross Site Scripting Flaws are easy to execute, but difficult to detect and prevent. This paper provides client-side solution to mitigate cross-site scripting Flaws. The existing client-side solutions degrade the performance of client's system resulting in a poor web surfing experience. In this project provides a client side solution that uses a step by step approach to protect cross site scripting, without degrading much the user's web browsing experience.

THE basic aim of corporal punishment, in the context of school discipline, is primarily to instil in students the awareness that committing or repeating undesirable actions, misbehaviour, disobedience and the like will bring them pain. It is also to serve as a deterrent. Inflicting temporary pain purely for correctional purposes can hardly be described as violence or physical abuse. Abuse is only when the punishment causes lingering trauma and adverse psychological effects. Judiciously exercised corporal punishment (carried out under strict guidelines, care, manner and, above all, with affection, compassion and the best interest of the recipient) is likely to serve as a deterrent against committing or repeating \"punishable wrongdoings\", thus paving the way for a healthy learning environment.

The main objective of the present study is to evaluate the hydrogeochemical characteristics of groundwater and its suitability for drinking water supply in Kangayam taluk, Tirupur district, Tamil Nadu, India. To achieve this objective, seventy-eight groundwater samples were collected from the wells spread over the study area during December 2016. The collected groundwater samples were tested in the laboratory for various hydrogeochemical parameters such as hydrogen ion concentration (pH), electrical conductivity, total dissolved solids, total hardness, calcium, magnesium, sodium, potassium, chloride, bicarbonate, carbonate, nitrate, sulphate and fluoride. The analytical results were compared with WHO drinking water standards to assess the suitability of groundwater for drinking purposes. To understand the spatial variation of hydrogeochemical parameters over the study area, choropleth (zonation) maps were prepared using geographical information system (GIS). Overall groundwater quality zones were demarcated by overlaying and integrating all the spatial plots using GIS. Three groundwater quality zones such as (1) most desirable, (2) maximum allowable and (3) not permissible were demarcated based on the limits prescribed by the WHO for drinking purposes. This study indicates that 49% of the study area does not possess potable groundwater. About 21% of the area represents “most desirable” category, and the remaining 30% area represents “maximum allowable” category for drinking purposes. The Piper’s trilinear diagram indicates that groundwater of this region is Mixed CaMgCl type. As the groundwater quality is poor nearly 49% of the total area, it is necessary to go for treatment before drinking water supply. It is also essential to recharge the aquifer artificially to improve the quantity and quality of groundwater.

An approximation model based on convolutional neural networks (CNNs) is proposed for flow field predictions. The CNN is used to predict the velocity and pressure field in unseen flow conditions and geometries given the pixelated shape of the object. In particular, we consider Reynolds Averaged Navier–Stokes (RANS) flow solutions over airfoil shapes as training data. The CNN can automatically detect essential features with minimal human supervision and is shown to effectively estimate the velocity and pressure field orders of magnitude faster than the RANS solver, making it possible to study the impact of the airfoil shape and operating conditions on the aerodynamic forces and the flow field in near-real time. The use of specific convolution operations, parameter sharing, and gradient sharpening are shown to enhance the predictive capabilities of the CNN. We explore the network architecture and its effectiveness in predicting the flow field for different airfoil shapes, angles of attack, and Reynolds numbers.

Water resources are crucial in developing any area as they serve as a major source of potable, agricultural, and industrial water. Water contamination, caused by natural and anthropogenic activities, poses a significant threat to public health globally. This review synthesizes data from various studies published in national and international journals, as well as reports from governmental and non-governmental organizations. Our primary objective is to understand and review previous research on water pollution, contamination types, and the effects of water contamination on public health. Water pollution studies generally involve a scientific understanding of the biological, chemical, and physical processes that control the movement of contaminants in the underground environment. The nature and severity of health consequences vary based on several factors, including the chemical composition, duration of exposure, and concentration of pollutants. This work highlights the human health risks associated with current research topics such as anthropogenic, geogenic, microplastics, pharmaceuticals, and heavy metals. A section on remedial measures and mitigation strategies is included to emphasize sustainable approaches to water conservation, replenishment, and sustainability. However, there is a lack of comprehensive knowledge regarding the distribution, toxic effects, and human health risks associated with different sources of contamination. This review thus establishes links between multiple sources of pollution, their toxicity to human health, and approaches to health risk assessment.

Vehicle manufacturers positioned electric vehicles (EVs) and hybrid electric vehicles (HEVs) as reliable, safe and environmental friendly alternative to traditional fuel based vehicles. Charging EVs using renewable energy resources reduce greenhouse emissions. The Lithium-ion (Li-ion) batteries used in EVs are susceptible to failure due to voltage imbalance when connected to form a pack. Hence, it requires a proper balancing system categorised into passive and active systems based on the working principle. It is the prerogative of a battery management system (BMS) designer to choose an appropriate system depending on the application. This study compares and evaluates passive balancing system against widely used inductor based active balancing system in order to select an appropriate balancing scheme addressing battery efficiency and balancing speed for E-vehicle segment (E-bike, E-car and E-truck). The balancing systems are implemented using “top-balancing” algorithm which balance the cells voltages near the end of charge for better accuracy and effective balancing. The most important characteristics of the balancing systems such as degree of imbalance, power loss and temperature variation are determined by their influence on battery performance and cost. To enhance the battery life, Matlab-Simscape simulation-based analysis is performed in order to fine tune the cell balancing system for the optimal usage of the battery pack. For the simulation requirements, the battery model parameters are obtained using least-square fitting algorithm on the data obtained through electro chemical impedance spectroscopy (EIS) test. The achieved balancing time of the passive and active cell balancer for fourteen cells were 48 and 20 min for the voltage deviation of 30 mV. Also, the recorded balancing time was 215 and 42 min for the voltage deviation of 200 mV.

In this investigation, the geochemical progression of a total of 31 groundwater samples of pre-monsoon season was assessed with categorization based on entropy weight water quality index and risk assessment on public health in the semi-arid area of Godavari basin, Maharashtra, Central India. Graphically, the major groundwater types identified were Ca–HCO 3 , mixed Ca–Mg–Cl, and mixed Ca–Na–HCO 3 . Based on [Mg 2+ /Na + ] with [Ca 2+ /Na + ] and [HCO 3 − /Na + ] with [Ca 2+ /Na + ] plots, carbonate and silicate weathering were identified as a major geochemical process governing groundwater chemistry. The presence of reverse ion exchange process was authenticated by (Ca 2+ + Mg 2+ ) vs. (HCO 3 − + SO 4 2− ) and Na + + K + –Cl − vs. (Ca 2+ + Mg 2+ )–(HCO 3 − + SO 4 2− ) plots. The saturation index values for calcite and dolomite showed that these minerals were in dissolution state. The dissolution of gypsum, dolomite, and anhydrite increased Ca 2+ load in groundwater which accelerated the precipitation of calcite. The high toxic level of NO 3 − (> 45 mg/L) was identified in 64.5% of the 31 groundwater samples, whereas F − concentration exceeded the threshold value in 12.9% of samples. Based on the entropy weight water quality index values, 70% of the samples were found to have moderate quality for drinking. In addition, health risk evaluation showed that the total hazard, due to fluoride and nitrate through oral pathways, was much higher than that through the dermal pathway. Children were found to be at high risk due to the consumption of NO 3 − and F − contaminated water. The calculated irrigation water quality index (IWQI) diverge from 7.4–89.2, expressing excellent to good quality for irrigation. Based on the irrigation water quality index, 90.3% of samples were found excellent for irrigation and 6.4% of good quality for irrigation. Authors recommend that continuous water quality monitoring programs along with effective management practices should be developed to avoid excessive extraction of groundwater.

Koopman decomposition is a nonlinear generalization of eigen-decomposition, and is being increasingly utilized in the analysis of spatio-temporal dynamics. Well-known techniques such as the dynamic mode decomposition (DMD) and its linear variants provide approximations to the Koopman operator, and have been applied extensively in many fluid dynamic problems. Despite being endowed with a richer dictionary of nonlinear observables, nonlinear variants of the DMD, such as extended/kernel dynamic mode decomposition (EDMD/KDMD) are seldom applied to large-scale problems primarily due to the difficulty of discerning the Koopman-invariant subspace from thousands of resulting Koopman eigenmodes. To address this issue, we propose a framework based on a multi-task feature learning to extract the most informative Koopman-invariant subspace by removing redundant and spurious Koopman triplets. In particular, we develop a pruning procedure that penalizes departure from linear evolution. These algorithms can be viewed as sparsity-promoting extensions of EDMD/KDMD. Furthermore, we extend KDMD to a continuous-time setting and show a relationship between the present algorithm, sparsity-promoting DMD and an empirical criterion from the viewpoint of non-convex optimization. The effectiveness of our algorithm is demonstrated on examples ranging from simple dynamical systems to two-dimensional cylinder wake flows at different Reynolds numbers and a three-dimensional turbulent ship-airwake flow. The latter two problems are designed such that very strong nonlinear transients are present, thus requiring an accurate approximation of the Koopman operator. Underlying physical mechanisms are analysed, with an emphasis on characterizing transient dynamics. The results are compared with existing theoretical expositions and numerical approximations.