University of Burdwan
  • Barddhamān, India
Recent publications
Groundwater from the coastal alluvial plain is the primary water source in coastal areas. Its contaminants, such as nitrate and fluoride, are significant concerns for freshwater supply and human health issues. Although spatiotemporal variability of nitrate and fluoride levels has been analyzed individually in the Indo-Bangladesh coastal region, their drivers and hydro-chemical analysis have scarcely been studied. Thus, to assess the groundwater quality, hydro-chemistry, and drivers in coastal alluvial aquifers, 123 groundwater samples were collected from the east (Bangladesh) and west coasts (India) for assessment of nitrate and fluoride levels and major physiochemical parameters. Multivariate statistical and hydro-chemical analysis and public health risk appraisal were carried out for this purpose. The results showed that 25% (East coast) and 22.39% (West coast) of groundwater samples surpassed the allowable limit of fluoride with a maximum concentration of up to 16.11 mg/L and the tolerable nitrate limit slightly exceeded 10 mg/L. Furthermore, we note that industrial waste, synthetic pesticides, and agricultural fertilizer triggered the leaching of nitrate into groundwater, while the release of fluoride into groundwater was possibly due to evaporite dissolution, carbonate mineral weathering, and ion exchange processes on both coasts. About 28.36% (26.79%) of groundwater samples possessed poor quality on the east (west) coasts. Considering the uncertainties of the variables, the mean hazard quotient ingestion values of fluoride faced by children (adults) were 10.5 (1.39) and 5.56 (6.9 ×10⁻¹), respectively, on the east (west) coasts, demonstrating a high non-carcinogenic risk to people, particularly, children, who cannot be neglected. Children had two times higher health risks than the adult inhabitants in both regions studied. Probabilistic models can reveal health risks more extensively than deterministic models. This study came up with strategies for improving sustainable groundwater quality and managing health risks in coastal regions.
NrfA is the molecular marker for dissimilatory nitrate reduction to ammonium (DNRA) activity, catalysing cytochrome c nitrite reductase enzyme. However, the limited study has been made so far to understand the structural homology modeling of NrfA protein in DNRA bacteria. Therefore, three model DNRA bacteria (Escherechia coli, Wolinella succinogenes and Shewanella oneidensis) were chosen in this study for in-silico protein modeling of NrfA which roughly consists of similar length of amino acids and molecular weight and they belong to two contrasting taxonomic families (γ-proteobacteria with nrfABCDEFG and ε-proteobacteria with nrfHAIJ operon). Multiple bioinformatic tools were used to examine the primary, secondary, and tertiary structure of NrfA protein using three distinct homology modeling pipelines viz., Phyre2, Swiss model and Modeller. The results indicated that NrfA protein in E. coli, W. succinogenes and S. oneidensis was mostly peri-plasmic and hydrophilic. Four conserved Cys-X1-X2-Cys-His motifs, one Cys-X1-X2-Cys-Lys haem-binding motif and Ca ligand were also identified in NrfA protein irrespective of three model bacteria. Moreover, 11 identical conserved amino acids sequence was observed for the first time between serine and proline in NrfA protein. Secondary structure of NrfA revealed that α-helices were observed in 77.9%, 73.4%, and 77.4% in E. coli, W. succinogenes and S. oneidensis, respectively. Ramachandran plot showed that number of residue in favored region in E. coli, W. succinogenes and S. oneidensis was 97.03%, 97.01% and 97.25%, respectively. Our findings also revealed that among three pipelines, Modeller was considered the best in-silico tool for prediction of NrfA protein. Overall, significant findings of this study may aid in the identification of future unexplored DNRA bacteria containing cytochrome c nitrite reductase. The NrfA system, which is linked to respiratory nitrite ammonification, provides an analogous target for monitoring less studied N-retention processes, particularly in agricultural ecosystems. Furthermore, one of the challenging research tasks for the future is to determine how the NrfA protein responds to redox status in the microbial cells.
Increasing groundwater pollution through toxic contamination is the primary concern for severe environmental and health hazards across the world. Groundwater resources are more vulnerable to toxic contamination compared to surface water because they require a long time to restore. Groundwater vulnerability and risk assessment is a challenging task among the researchers responsible for quality monitoring and management of groundwater. In this regard, predictive modeling for groundwater vulnerability is essential to defend the vulnerability of groundwater resources in current scenario. Contamination of groundwater with arsenic (As) and fluoride (F‾) poses a threat to human health in the Bengal delta region. This study developed a framework approach to assess groundwater vulnerability and related health hazard risks in the South 24 Parganas district of West Bengal, India. For modeling groundwater vulnerability, the Logistic regression (LR) method coupled with fifteen hydrogeochemical parameters was used for modeling. The health hazard risk of this study region was assessed using the human health hazard index. Furthermore, hydrogeochemical properties and groundwater quality were assessed through Piper, USSL, and Wilcox's diagram accordingly. The study revealed that F‾, depth, PO42− and SO42− are the highest controlling factors of groundwater vulnerability in this area. It is also found that because of the presence of alkaline organisms in groundwater, it is unfit for farming and drinking purposes as well. The evaluation matrices used in this study revealed that LR gives optimal prediction results (AUC-ROC is 0.891 and 0.861, kappa index is 0.810 and 0.720 in training and validation, respectively) in groundwater vulnerability. The applied approach in this study may be useful to other regions for predicting groundwater vulnerability and for the sustainable adoption of several management strategies. In the future, deep learning as well as other sophisticated learning algorithms should be useful for optimal prediction of groundwater resources.
Purulia is one of the most intense drought-prone districts of the western part of West Bengal, India. Acute form of water scarcity is a common phenomenon in this area during the hot-summer period. The water scarcity in this study region is due to the presence of monsoonal vagaries, unfavorable lithological condition and availability of poor groundwater. Therefore, watershed management is the primary concern for sustainable development of natural resources like water and land for optimal development of watershed and economic activities. Therefore, optimal measurement of rainfall-induced runoff is indeed necessary to understand the hydrological behavior. Several traditional statistical and advanced machine learning methods has been used previously to measure surface runoff among the researchers. It is difficult to simulate the required runoff with physical-based models due to the complexity and non-linear behavior of the runoff phenomena, as well as the absence of relevant historical data in all places. Thus, in the present research study of gravelly dominated drought-prone area of Upper Kangsabati Watershed (UKW) is considered to assess rainfall-induced surface runoff using most reliable method of Soil Conservation Service Curve Number (SCS-CN), and considering remote sensing and geographic information system platform. The SCS-CN method is very much reliable and till now has been frequently used among the global hydrological community to optimal assessment of surface runoff and adaptation of proper watershed management strategies. Henceforth, in this study SSC-CN method is used in the hard rock terrain landscape of extended plateau fringe of western West Bengal. The estimated result of runoff depth and runoff volume is 979.45 mm and 280.85 m3, respectively, and the rainfall–runoff is strongly positively correlated with (r) value being 0.98. Additionally, the applied statistical methods and the outcomes of this study will be helpful among the hydrological communities, different stakeholders and policy makers for sustainable watershed management in terms of optimal conserve of water resources and reduced threating of drought condition.
Soil is a outer most surface on earth which is a home for different microorganisms. Biodiversity of soil is a mixed population of different type biological organisms. It is one of the most biologically diverse upper most part of Earth. Soil structure and health depends on interaction of microbes and soil organic materials. Soil microbes also interact with plants and influence soil health and production of crop. The soil organic mater is a food for soil bacteria and other microorganism. Soil bacteria improve the soil quality by interaction with organic mater as a result increase the entry and storage of soil water, resistance to soil erosion. Different soil bacteria has different ability to react with soil organic mater and control soil health from season to season. Soil microbes play a wide range of essential role with sustainable function on all ecosystems. They are also help to maintain the soil nutrients, nitrogen, phosphorus, soil organic matter, carbon for plants. Some beneficial soil microbes helps to reduce soil-borne disease of plants. By the use of these beneficial microbes inoculums we increase the yield of crop and reduce of plant disease. These advance technology is essential and important resource for the development of sustainable agricultural systems.KeywordsSoil microbesSoil healthDisease suppression
The invention of CRISPR-Cas9 technology has opened a new era in which genome manipulation has become precise, faster, cheap and more accurate than previous genome editing strategies. Despite the intricacies of the genomes associated with several protozoan parasites, CRISPR-Cas9 has made a substantial contribution to parasitology. The study of functional genomics through CRISPR-Cas9 mediated gene knockout, insertion, deletion and mutation has helped in understanding intrinsic parasite biology. The invention of CRISPR-dCas9 has helped in the programmable control of protozoan gene expression and epigenetic engineering. CRISPR and CRISPR-based alternatives will continue to thrive and may aid in the development of novel anti-protozoan strategies to tame the protozoan parasites in the imminent future.
The study's specific objective is to identify prospective cashew nut (Anacardium occidentale) producing areas in the Bankura district based on soil availability and land suitability in red and laterite soil. The index of crop suitability (ICS) analysis was carried out utilising the Fuzzy-Analytical hierarchy process (F-AHP) approach and sixteen geo-environmental variables such as soil texture, soil organic carbon (OC), Soil electrical conductivity (EC), soil pH, slope, rainfall, etc. The criteria were also developed following FAO's crop suitability index for the distinction between S (suitable) and N (unsuitable) order. According to the cashew suitability class, 9.24% of cashew growth regions can be categorised as highly suitable, 34.57% as moderately suitable, and 19.59% as marginally suitable. The F-AHP weight shows the important factors of cashew cultivation that are soil OC and EC. The results demonstrate that cashew production is more beneficial in the red and laterite soil.
In cognitive radio (CR), spectrum sensing is the fundamental and basic component of wireless communication. Requirement of higher sampling is the essential need for the sensing of wideband ultra-wideband spectrum sensing. In this paper, a comprehensive survey on the compressive sensing is done, and a wideband (ultra) spectrum sensing model has been presented which utilizes the sub-Nyquist sampling (SNS) rate for sampling need. A matrix of correlation for desired number of samples is computed, and subspace estimation is utilized to sense vacant and unoccupied wireless channels of wideband spectrum. As compared to the previous techniques, the proposed technique does not require all signal parameters which are essential for transmission uncertainty issues. We have analyzed the performance of this technique by calculating the probability of detection of signal in terms of required number of samples and SNR for generated and received signals. The outcome of results proves that for low SNR, high detection rate is acheived even if the numbers of samples are low. Work done in this research is mainly discussing the cognitive radio spectrum with wideband spectrum compressive sensing techniques and their features with comparative analysis. The proposed techniques use the multi-coset sampler for reducing the sampling rates and achieve the better results for spectrum sensing parameters.KeywordsWideband spectrum sensingCognitive radioEnergy sensingWideband spectrum sensing techniquesProbability of detectionFalse alarm rateNyquist-based sensing
This work demonstrates the Laplace and inverse transforms of interval valued functions with exaggerating its necessary properties under interval flexibility. After proposing the formal definition interval Laplace transform (i.e., Laplace transform of interval valued functions), some of its important properties are derived. Thereafter, the sufficient condition for the existence of interval Laplace transforms is established. Then, some important results regarding switching points of interval Laplace transform are discussed and illustrated with some numerical examples. Finally, the definition of inverse transform of interval valued functions is proposed, and as an application, a production inventory model in interval uncertainty is studied using all of the proposed theoretical results.
Soil erosion-induced land degradation is susceptible to climate change, specifically in the sub-tropical third world countries. Simulations of 21st century climate change in India predict notable variation in rainfall that causes soil erosion-induced land degradation. Land degradation susceptibility modelling of the “red and lateritic agro-climatic zone” of Bengal (Eastern India) has been prepared using “random forest (RF)”, “support vector machine (SVM)”, and “extreme gradient boost (XGBoost)” algorithms. Assessment of models using validation data in AUC-ROC revealed that XGBoost (0.909 and r = 0.91) is the most optimal followed by SVM (0.881 and r = 0.87) and RF (0.879 and r = 0.85). Furthermore, future land degradation risk dynamics were assessed through “Coupled Model Intercomparison Project six (CMIP6)” down-scaled based ensembles of nine “global climate models (GCMs)” on four SSPs scenarios. The combination of deep learning along with climate modelling should be useful to enhance the result more precisely.
The second wave of the COVID-19 pandemic outburst triggered enormously all over India. This ill-fated and fatal brawl affected millions of Indian citizens, with many active and infected Indians struggling to recover from this deadly disease to date, leading to a grief situation. The present situation warrants developing a robust and sound forecasting model to evaluate the adversities of the epidemic with reasonable accuracy to assist officials in curbing this hazard. Consequently, we employed Auto-ARIMA, Auto-ETS, Auto-MLP, Auto-ELM, AM, MLP and proposed ELM methods for assessing accumulative infected COVID-19 individuals by the end of July 2021. We made 90 days of advanced forecasting, i.e., up to 24 July 2021, for the number of cumulative infected COVID-19 cases of India using all seven methods in 15 days’ intervals. We fine-tuned the hyper-parameters to enhance the prediction performance of these models and observed that the proposed ELM model offers satisfactory accuracy with MAPE of 5.01, and it rendered better accuracy than the other six models. To comprehend the dataset's nature, five features are extracted. The resulting feature values encouraged further investigation of the models for an updated dataset, where the proposed model provides encouraging results.
Geomorphological processes that occur on the surface structure of the region are included in badland geomorphology. Rapid soil erosion and the production of various geomorphic characteristics are related to badland development. The expansion of their main channels is inextricably tied to the formation of gullies. The “degree of badland formation” inside that catchment and the characteristics of the substrate may both be learned from an analysis of the longitudinal shape of these channels. In this work the mechanism and trend of erosion has been done with help of cross section analysis and prominent geomorphic process. The very high resolution DEM has been prepared in this perspective. Finding out how large-scale erosion occurs and how it contributes to the deterioration of the land is the main objective of the research. According to this viewpoint, the nature of erosion may be measured using the connectivity index and gullies. In this region, the early “rills and minor gullies emerge over the high duricrust escarpment”, where it is discovered that the ambient lateritic hardpan has a substantial impact on the gully channel character, with the majority of them having steeper portions in the middle of their course. Apart from this there is an increasing tendency of erosion from the previous period to present time.
In the present situation, environment is rapidly polluted by the manufacturing of non-eco-friendly products and carbon emission from production industries. So, to control this pollution as well as carbon emission, everyone should be aware to use eco-friendly products and reduce carbon emission. Motivating on this topic, a model on green manufacturing system has been formulated where the produced items are eco-friendly and have a fixed lifetime. Also, in this model, the green level of the produced items enhances the demand of the customers. The objective of this work is to determine the green level of the product and business period by maximizing the average profit of the system. To solve the corresponding maximization problems, a hybrid tournament differential evolution (TDE) algorithm is applied to obtain the best-found solutions along with the average profit of the system. To check the validity of the proposed model, a numerical example is considered and solved by the different variants of the said algorithm. Also, the simulated results obtained from the different variants of TDE algorithm are compared with the simulated results obtained from some of the existing algorithms reported in the literature. Then to test the performance and efficiency of the said hybrid algorithm, statistical comparisons, statistical tests are performed. Finally, sensitivity analyses are carried out in order to examine the effects of changes of parameters involved in the model on the average profit, green level of the product, business and production periods.
Eco-tourism is a form of nature based ecologically sustainable tourism, getting popularized in recent years among tourists due to the adverse impact of conventional tourism. The conventional way of tourism severely impacted the environment, loss of habitat and destruction of nature and natural landscape, Pressurizing local resources, and the Loss of cultural uniqueness of the local community. Purulia district in West Bengal (India), with its diversified picturesque landscape such as lush green Forest, mesmerizing riverscape, lakes, waterfall, hills, and uniqueness in the local community's culture gives ample potentiality of eco-tourism development. The aim of this paper to Explore potentiality of Ecotourism in Purulia district, West Bengal, India using analytical hierarchy process (AHP) and geographical information system (GIS). Initially ecotourism inventory dataset was developed based on following criteria: Elevation, Slope, Proximity River area, Distance from Road, Distance from Settlement patches, Distance from Ecological sites, Distance from railway Track, Distance from existing Tourism sites using ARC-GIS 10.6.1 software. Later, the suitability map of eco-tourism development has been developed by applying Weighted Linear Combination (WLC) with combination of the criteria with their respective weights and categorized into five suitability classes as Highly Suitable (S 1 ) , Moderately Suitable (S2) , suitable (S3) less Suitable (S4) , and unsuitable (S5) . Finally, After the identification of suitable zones, six alternatives eco-tourism destinations are identified. This proposed method may be helpful for the for local stakeholders and public administration in identifying potential ecotourism destination and planning for sustainable eco-tourism development.
The purpose of the study is to examine the relationship between corporate environmental sustainability through the lens of energy intensity and the financial performance of firms in the presence of credit constraints. Employing panel data on Indian manufacturing firms, the study found an inverted U-shaped relationship between energy intensity and financial performance. While, credit-constraints act as an impediment towards achieving reduced energy intensities, an improvement in financial performance lessens the inimical impact of credit constraints on corporate environmental sustainability. Our findings might help policy formulation in achieving the Mission 2070 Net Zero Emission goal as recently pledged by India. ARTICLE HISTORY
Vulnerability of groundwater is critical for the sustainable development of groundwater resources, especially in freshwater-limited coastal Indo-Gangetic plains. Here, we intend to develop an integrated novel approach for delineating groundwater vulnerability using hydro-chemical analysis and data-mining methods, i.e., Decision Tree (DT) and K-Nearest Neighbor (KNN) via k-fold cross-validation (CV) technique. A total of 110 of groundwater samples were obtained during the dry and wet seasons to generate an inventory map. Four K-fold CV approach was used to delineate the vulnerable region from sixteen vulnerability causal factors. The statistical error metrics i.e., receiver operating characteristic-area under the curve (AUC-ROC) and other advanced metrices were adopted to validate model outcomes. The results demonstrated the excellent ability of the proposed models to recognize the vulnerability of groundwater zones in the Indo-Gangetic plain. The DT model revealed higher performance (AUC = 0.97) followed by KNN model (AUC = 0.95). The north-central and north-eastern parts are more vulnerable due to high salinity, Nitrate (NO3−), Fluoride (F−) and Arsenic (As) concentrations. Policy-makers and groundwater managers can utilize the proposed integrated novel approach and the outcome of groundwater vulnerability maps to attain sustainable groundwater development and safeguard human-induced activities at the regional level.
Floods and river-bank erosion are the most frequent natural hazards in India, specifically in the deltaic regions. In West Bengal, floods and river-bank erosion predominantly affect Malda district as it is located in the moribund part of the Bengal delta. This article studies the recent trend of shifting course of the River Ganga and the effects of floods and consequent river-bank erosion on livelihoods of the residents of chars [The chars (called Diara in the upper reaches of the Gangetic plains) are virgin, low-lying river islands and sand bars occurring in the plains, particularly the deltaic parts of rivers (Lahiri-Dutt and Samanta, South Asia: J South Asia Stud 30:327–350, 2007).] and river-bank areas of Manikchak block in the Malda district. Around 300 sample households were selected by random stratified sampling technique from four gram panchayats of Manikchak block. Both primary and secondary data have been used. After analysing satellite images from the year 1973 to 2018, it has been observed that the River Ganga continues to shift eastwards and is eroding villages one after another. Inhabitants face multidimensional obstacles to run their households. Large numbers of people are displaced every year due to loss of land. Failure in facilitating the required assistance in the form of alternative spaces for resettlement and other disaster-mitigating public support systems against these hazards would make it impossible for the deplorable condition of the vulnerable people to improve.
Hysteria, a mysterious symptom registers a lot of experiments and discourses for centuries to ultimately render erroneous arguments and a lack of scientific exuberance. The connotation of hysteria had a very gendered perspective and the entire discourse was based on the physicality of the sufferers. The intervention of Freud completely changed the discourse of hysteria. Hence, I propose to study the trajectory of hysteria from the ancient time through the Middle Ages through the 16th and 17th centuries to the Freudian intervention to signify the importance of Freud in order to create a reference point, using which the later scientists could develop the scientific technique to treat hysteria. In this paper, I would also study the modern interpretation of hysteria and the great role played by Freud in the academic understanding of hysteria.
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876 members
Debashis Dey
  • Department of Mathematics
Swati Mukhopadhyay
  • Department of Mathematics
Debasis Das
  • Department of Chemistry
Bidyut Saha
  • Department of Chemistry
Barddhamān, India