Excess fluoride (F) ion of drinking water is a major problem in many areas of India and causes harmful effects such as dental and skeletal fluorosis. The World Health Organization (WHO 2004) ...recommends an upper limit of 1.5 mg/L fluoride in drinking water, and the concentration of fluoride in groundwater has been found 10–20 times higher in many of the States in India. In this study, the performance of inorganic polymeric coagulant (IPC) named as IPC-23, IPC-13, IPC-17, and alum for fluoride removal from drinking water was investigated. The amount of IPC was decided according to the Al
2
O
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amount present in the alum dose recommended in the batch Nalgonda defluoridation technique. The effects of coagulant dosage (IPC) at different pH and initial concentrations of fluoride on fluoride removal have been studied. The synthetic sample having a fluoride concentration of 2 to 6 mg/L was treated at the optimized dosage and residual fluoride was reduced to 1.0 to 1.2 ppm with IPC-17. Residual aluminum in treated water was well within WHO norms (< 200 μg/L) for drinking water. Optimum pH for fluoride removal was 6.5, and there was deterioration in the performance of IPC at both lower and higher pH.
Batchwise adsorption of Cu(II), Pb(II), Ni(II) and Fe(II) ions on coconut coir was studied. Coir fiber was subjected to alkali treatment (18% (w/v) NaOH) in order to enhance its metal adsorption ...capacity and rate of uptake. The maximum metal ion uptake capacity of Cu(II), Pb(II), Ni(II), and Fe(II) on alkali treated coir increased by nearly 3 times to 9.43, 29.41, 8.84, and 11.11 mg g
−1
, respectively. The solution pH played a crucial role in metal adsorption and the optimum pH for maximum adsorption was found to be different for each metal ion. The adsorption followed pseudo second-order kinetics and the isotherm data fits to the Langmuir adsorption isotherm. Repeated desorption-adsorption cycles explored the potential of reuse of alkali treated coir fibers upto 3 times with negligible loss of adsorption capacity.
Fluoride contamination has become a considerable threat to our society worldwide. Fluoride in drinking water is primarily due to rich fluoride soil, volcanic activity, forage, grasses and grains, and ...anthropogenic reasons. World Health Organization has regulated the upper limit for fluoride in drinking water to be 1.5 mg/L while different countries have set their standards according to their circumstances. Excess amounts of fluoride ions in drinking water can cause dental fluorosis, skeletal fluorosis, arthritis, bone damage, osteoporosis, muscular damage, fatigue, joint-related problems, and chronicle issues. In extreme conditions, it could adversely damage the heart, arteries, kidney, liver, endocrine glands, neuron system, and several other delicate parts of a living organism, briefed in the present article. Moreover, a comprehensive scenario for the situations in countries like, China, Canada, Mexico, United States, Yemen, Pakistan, Saudi Arabia, South Korea, Sri Lanka, Indonesia, Iran, Turkey, Australia, and India affected with high fluoride levels in ground water has been described. To analyze the presence of fluoride molecule, out of different detections methods, ion selective and colorimetric method has been adopted for real situation in the field of water application. Also, different methods to remove fluoride from water like reverse osmosis, nano filtration, adsorption, ion-exchange, and precipitation/coagulation with their removal mechanism were highlighted in the review. Moreover, the applicability of the approach with the prospect of country's economic status has been discussed, due to high cost and maintenance the membrane technology is not popular in developing countries like India, Senegal, Tanzania, and Kenya which employ adsorption and coagulation-precipitation for fluoride removal. It is noticeable from literature study that different approaches show unique potential for defluoridation. Some key parameters and mechanistic adaptations which could pave the defluoridation methods to newer horizons have been put forward.
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•In this review, occurrence of fluoride ion has been highlighted with its worldwide and national scenario.•The effect of fluoride ions in the environment, as well as the human body, was elaborated.•Fluoride detection technologies with special reference to Sensor based technologies•Removal techniques were discussed including process mechanism and economics.•Future recommendations were put forward in the field of fluoride removal.
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•Synthesis and characterization of coumarin based receptor were carried out.•Application of receptor C13H13N3O2S (m/z = 276.0732 R+H+) towards the detection of fluoride.•Color shift ...(pale yellow to pink) was observed when fluoride was applied with the receptor distinguished by naked eye.•Preliminary investigation was done with synthetic water as well as groundwater.•Reliability of receptor was checked with ion selective electrode for fluoride measurement.
In this communication, a novel receptor (E)-N-methyl-2-(1-(2-oxo-2H-chromen-3 yl)ethylidene)hydrazinecarbothioamide), R was synthesized using microwave irradiation that acts as a highly selective and sensitive receptor towards fluoride detection in aqueous media. Receptor (R) shows naked eye color change from pale yellow to pink towards fluoride ions. Anion binding studies were performed by UV–vis, Mass (ESI-MS) and 1H NMR spectroscopy. Using the Benesi–Hildebrand equation the binding constant of the receptor for fluoride was found to be 1.565 × 104 M−1 and lowest detection limit was 0.18 mg/L (9.024 × 10-6 M) lower than WHO guidelines. To check the applicability of receptor for the detection of fluoride in groundwater samples, preliminary investigations were carried out with synthetic water and groundwater collected from high fluoride content regions in Rajasthan (India). Studies were also carried out using Ion-selective electrode and the results were found to be in good agreement with the results from UV–vis spectroscopy using synthesized receptor.
Fluoride, an anionic pollutant, existing in concentrations exceeding the allowed limit of 1.5 mg/L in drinking water, has been reported to cause detrimental impact on human health. The traditionally ...employed methods for water defluoridation mostly involve Al-based coagulants, which however face some limitations, such as requirement of relatively high dosage and production of excessive amounts of chemical sludge posing a problem of its safe disposal. In this study, two inorganic polymeric coagulants of medium (IPC-M) and ultrahigh basicity (IPC-UH) were synthesized using polymerization of aluminum trihydrate (Al2O3·3H2O) with an aqueous solution of 32% hydrochloric acid. The basicity of coagulants was increased by manipulating the redox reaction of the product with the aluminum metal. The synthetic coagulants were analyzed using various characterization techniques, viz., Fourier transform infrared spectroscopy, electrospray ionization–mass spectrometry, and field emission scanning electron microscopy with electron-dispersive X-ray spectroscopy, and the main physicochemical properties such as % Al2O3, relative basicity, and % chloride. The aluminum species distribution was assessed by the ferron assay, and their electrochemical properties such as dissolved charge, conductivity, acidity, and pH were also measured. The application of IPCs was explored for their fluoride removal efficacy using jar tests. The outcome showed that IPC-M was the most efficient when applied in a pH range relevant to fluoride-containing water as it was the only coagulant that showed increasing efficiency at pH values > 7. The uptake capacity of coagulants for using synthetic samples prepared in Milli-Q water containing 9 mg/L of raw fluoride concentrations to achieve residual concentration of less than 1.5 mg F/L at the pH value 6.5 ± 0.1 was calculated as 87.68 and 68.48 mg F/g Al2O3 for IPC-M and IPC-UH, respectively, which were higher than the reported values of 37.42 and 37.75 mg F/g Al2O3 for alum and poly-aluminum chloride in an earlier published paper. The residual aluminum concentration in these experiments ranged at 30 ± 5 and 20 ± 5 μg Al/L, respectively, for IPL-M and IPL-UH, which were well within the WHO norm for drinking water (<200 μg/L), indicating their immense application in the field.
Producing sports highlights is a labor-intensive work that requires some degree of specialization. We propose a model capable of automatically generating sports highlights with a focus on cricket. ...Cricket is a sport with a complex set of rules and is played for a longer time than most other sports. In this paper we propose a model that considers both event-based and excitement-based features to recognize and clip important events in a cricket match. Replays, audio intensity, player celebration, and playfield scenarios are examples of cues used to capture such events. To evaluate our framework, we conducted a set of experiments ranging from user studies to a comparison analysis between our highlights and the ones provided by the official broadcasters. The general approval by users and the significant overlap between both kinds of highlights indicate the usefulness of our model in real-life scenarios.
A sign language recognition system is an attempt to bring the speech and the hearing impaired community closer to more regular and convenient forms of communication. Thus, this system requires to ...recognize the gestures from a sign language and convert them to a form easily understood by the hearing. The model that has been proposed in this paper recognizes static images of the signed alphabets in the Indian Sign Language. Unlike the alphabets in other sign languages like the American Sign Language and the Chinese Sign language, the ISL alphabet are both single-handed and double-handed. Hence, to make recognition easier the model first categorizes them as single-handed or double-handed. For both categories two kinds of features, namely HOG and SIFT, are extracted for a set of training images and are combined in a single matrix. After which, HOG and SIFT features for the input test image are combined with the HOG and SIFT feature matrices of the training set. Correlation is computed for these matrices and is fed to a K-Nearest Neighbor Classifier to obtain the resultant classification of the test image.
While explainability is becoming increasingly crucial in computer vision and machine learning, producing explanations that can link decisions made by deep neural networks to concepts that are easily ...understood by humans still remains a challenge. To address this challenge, we propose a framework that produces local concept-based explanations for the classification decisions made by a deep neural network. Our framework is based on the intuition that if there is a high overlap between the regions of the image that are associated with a human-defined concept and regions of the image that are useful for decision-making, then the decision is highly dependent on the concept. Our proposed CAVLI framework combines a global approach (TCAV) with a local approach (LIME). To test the effectiveness of the approach, we conducted experiments on both the ImageNet and CelebA datasets. These experiments validate the ability of our framework to quantify the dependence of individual decisions on predefined concepts. By providing local concept-based explanations, our framework has the potential to improve the transparency and interpretability of deep neural networks in a variety of applications.
We propose a novel approach to mitigate biases in computer vision models by utilizing counterfactual generation and fine-tuning. While counterfactuals have been used to analyze and address biases in ...DNN models, the counterfactuals themselves are often generated from biased generative models, which can introduce additional biases or spurious correlations. To address this issue, we propose using adversarial images, that is images that deceive a deep neural network but not humans, as counterfactuals for fair model training. Our approach leverages a curriculum learning framework combined with a fine-grained adversarial loss to fine-tune the model using adversarial examples. By incorporating adversarial images into the training data, we aim to prevent biases from propagating through the pipeline. We validate our approach through both qualitative and quantitative assessments, demonstrating improved bias mitigation and accuracy compared to existing methods. Qualitatively, our results indicate that post-training, the decisions made by the model are less dependent on the sensitive attribute and our model better disentangles the relationship between sensitive attributes and classification variables.
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