Water quality of springs and lakes in the Kumaon Lesser Himalayan Region of Uttarakhand, India

The scarcity of drinking water has become a bitter reality in many countries. The gap between demand and supply of water has been increasing exponentially year by year. Deforestation, vigorous use of groundwater for agricultural practices, and pollution of our present water resources such as rivers, lakes, and wells are triggering the freshwater scarcity problem. Ninety percent of people in Uttarakhand depend on springs for their daily life activities. In such a case, the quality and quantity of spring water should be a prime topic to be focussed on. In the Kumaon region of Uttarakhand, spring water quality is good but there is an issue with its availability, especially in summer. This review paper details the studies that have been conducted on nutrient status, hardness, heavy metals, and the presence of microbiological diversity in spring water. It also uncovers information on some critical springs, geological settings of their aquifers, and the steps that have been adopted to rejuvenate the spring. Some other measures have been carried out by the government and local communities for springs’ revival and their improvement in discharge rate, including the construction of percolation pits, contour trenches, check dams, and improvement of water resources. It has been observed among the analyzed sample that the Kumaon region is dominated by arsenic, cadmium, chromium, and lead, whereas aluminum, barium, cobalt, and manganese are more in the Garhwal region. Apart from springs, this review paper also reveals the physicochemical characteristics of the spring-fed rivers and lakes of the Kumaon region

Therapeutic potential of active components of saffron in post-surgical adhesion band formation

Background Abdominal adhesions are common and often develop after abdominal surgery. There are currently no useful targeted pharmacotherapies for adhesive disease. Saffron and its active constituents, Crocin and Crocetin, are wildly used in traditional medicine for alleviating the severity of inflammatory or malignant disease. Purpose The aim of this study was to investigate the therapeutic potential of the pharmacological active component of saffron in attenuating the formation of post-operative adhesion bands using different administration methods in a murine model. Material method saffron extract (100 mg/kg), Crocin (100 mg/kg), and Crocetin (100 mg/kg) were administered intraperitoneally and by gavage in various groups of male Wistar rat post-surgery. Also three groups were first treated intra-peritoneally by saffron extract, Crocin, and Crocetin (100 mg/kg) for 10 days and then had surgery. At the end of the experiments, animals sacrificed for biological assessment. Result A hydro-alcoholic extract of saffron and crocin but not crocetin potently reduced the adhesion band frequency in treatment and pre-treatment groups in the mice given intra-peritoneal (i.p) injections. Following the saffron or crocin administration, histological evaluation and quantitative analysis represented less inflammatory cell infiltration and less collagen composition, compared to control group. Moreover, the oxidative stress was significantly reduced in treatment groups. Conclusion These findings suggest that a hydro-alcoholic extract of saffron or its active compound, crocin, is a potentially novel therapeutic strategy for the prevention of adhesions formation and might be used as beneficial anti-inflammatory or anti-fibrosis agents in clinical trials. Taxonomy Abdominal surgeries/post-surgical adhesions

Measuring Circular Supply Chain Risk: A Bayesian Network Methodology

The world is facing economic, as well as social, crisis due to the COVID-19 pandemic. Implementing sustainable practices is one of the possible ways to address these issues. Adopting circular oriented techniques throughout the supply chain not only guarantees economic profitability, but also provides an edge to the organization in the market of fierce global competition. The concept of implementing circularity in the supply chain is novel and dynamic in nature, and it involves certain risk. In this study, a Bayesian Network methodology is adopted to analyze how the risk propagation takes place in a circular supply chain network of an automobile organization. The circular supply chain network consists of a group of manufacturers, retailers and recyclers, located in the Delhi–NCR region. Economic, environmental, social, technological, waste management, agile vulnerability, and risk of cannibalization are the major risk categories that were identified through an extensive literature review. Further, the impact of risk on the performance of the circular supply chain is analyzed by considering performance parameters such as lost sales, impact on supply chain revenue, and inventory holding cost. Risk exposure index is incorporated into the study to analyze the vulnerability of each node. The findings of the study reveal that the reverse side of the circular supply chain can be a source of risk propagation during the implementation of the circularity concept. This work is carried out under a single industry domain. In the future, risk propagation analysis can be examined in the supply chain of other sectors. The findings of the study can assist the supply chain managers and the risk experts to focus on the areas that are more vulnerable to risk

Code Switched and Code Mixed Speech Recognition for Indic languages

Training multilingual automatic speech recognition (ASR) systems is challenging because acoustic and lexical information is typically language specific. Training multilingual system for Indic languages is even more tougher due to lack of open source datasets and results on different approaches. We compare the performance of end to end multilingual speech recognition system to the performance of monolingual models conditioned on language identification (LID). The decoding information from a multilingual model is used for language identification and then combined with monolingual models to get an improvement of 50% WER across languages. We also propose a similar technique to solve the Code Switched problem and achieve a WER of 21.77 and 28.27 over Hindi-English and Bengali-English respectively. Our work talks on how transformer based ASR especially wav2vec 2.0 can be applied in developing multilingual ASR and code switched ASR for Indic languages.Comment: This paper for submitted to Interspeech 202

Is Word Error Rate a good evaluation metric for Speech Recognition in Indic Languages?

We propose a new method for the calculation of error rates in Automatic Speech Recognition (ASR). This new metric is for languages that contain half characters and where the same character can be written in different forms. We implement our methodology in Hindi which is one of the main languages from Indic context and we think this approach is scalable to other similar languages containing a large character set. We call our metrics Alternate Word Error Rate (AWER) and Alternate Character Error Rate (ACER). We train our ASR models using wav2vec 2.0\cite{baevski2020wav2vec} for Indic languages. Additionally we use language models to improve our model performance. Our results show a significant improvement in analyzing the error rates at word and character level and the interpretability of the ASR system is improved upto $3$\% in AWER and $7$\% in ACER for Hindi. Our experiments suggest that in languages which have complex pronunciation, there are multiple ways of writing words without changing their meaning. In such cases AWER and ACER will be more useful rather than WER and CER as metrics. Further, we open source a new benchmarking dataset of 21 hours for Hindi with the new metric scripts.Comment: Need to upgrade the content completel

Improving Speech Recognition for Indic Languages using Language Model

We study the effect of applying a language model (LM) on the output of Automatic Speech Recognition (ASR) systems for Indic languages. We fine-tune wav2vec $2.0$ models for $18$ Indic languages and adjust the results with language models trained on text derived from a variety of sources. Our findings demonstrate that the average Character Error Rate (CER) decreases by over $28$ \% and the average Word Error Rate (WER) decreases by about $36$ \% after decoding with LM. We show that a large LM may not provide a substantial improvement as compared to a diverse one. We also demonstrate that high quality transcriptions can be obtained on domain-specific data without retraining the ASR model and show results on biomedical domain.Comment: Need to upgrade the content completel

Vakyansh: ASR Toolkit for Low Resource Indic languages

We present Vakyansh, an end to end toolkit for Speech Recognition in Indic languages. India is home to almost 121 languages and around 125 crore speakers. Yet most of the languages are low resource in terms of data and pretrained models. Through Vakyansh, we introduce automatic data pipelines for data creation, model training, model evaluation and deployment. We create 14,000 hours of speech data in 23 Indic languages and train wav2vec 2.0 based pretrained models. These pretrained models are then finetuned to create state of the art speech recognition models for 18 Indic languages which are followed by language models and punctuation restoration models. We open source all these resources with a mission that this will inspire the speech community to develop speech first applications using our ASR models in Indic languages