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Detecting counterfeit currency is a critical issue that has gained increasing attention in recent years. Deep learning techniques have shown significant promise in a variety of image processing tasks, including counterfeit currency detection. In this project, we propose a deep learning-based counterfeit currency detection system that uses convolutional neural networks (CNNs) for feature extraction and classification. The proposed system consists of two main phases: training and testing. In the training phase, a dataset of genuine and counterfeit banknotes is used to train the CNN model to distinguish between genuine and counterfeit banknotes. A CNN model extracts features from an input image using convolutional layers and then applies fully connected layers for classification. Experimental results show that the proposed deep learning-based counterfeit currency detection system achieves high accuracy. The system outperforms existing state-of-the-art techniques in terms of detection accuracy, robustness, and real-time performance. In conclusion, it can be said that the proposed system could be a useful tool for preventing the circulation of counterfeit currency and minimizing economic losses. The system can be integrated into various automated teller machines (ATMs) and vending machines to detect counterfeit money in real time

Artificial Intelligence (AI) has emerged as a disruptive force in various domains, including education. This article explores the evolution of AI in education, highlighting its transformative impact on teaching and learning processes. It provides an overview of the key milestones and advancements in AI technology, discusses the benefits and challenges associated with its implementation, and examines the potential future developments. By analysing the current state of AI in education, this article aims to shed light on the promising opportunities and considerations for harnessing AI's potential to enhance educational outcomes.

Despite the presence of E-trade humans have a tendency to shop for many merchandise handiest in supermarkets and department stores for the sake in their personal satisfaction. Among the problems confronted through the clients one trouble is to observe queue thru the billing manner. Though their cause is simply to shop for one or merchandise, ready to invoice merchandise consumes time and additionally inconvenient in recent times as humans stay in a hectic environment. As in line with our survey cash and common time spent on every patron is excessive mainly in over-crowded supermarkets. The shopkeepers are geared up to welcome any clever machines that automate the billing manner to lessen manpower and time fed on for that manner. The most important goal is to fulfill the patron and additionally lessen the time spent at the billing manner that is to finish the billing manner with inside the trolley in place of ready in a queue even for one or merchandise. The clients need to upload the goods after a brief test in trolley and whilst finished the finalized quantity can be displayed with inside the trolley. Customer ought to both pay their invoice through their ATM playing cards or thru pre-recharged patron card furnished through the shop. We have ensured protection for stopping robbery and additionally facilitated for customers who unknowingly drop their tasks into trolley through cautioning them. Our remaining motto is to mitigate the time intake in buy through casting off queue making sure patron`s consolation and shrinking the tediousness of barcode scanning and putting off waging of billers.

COVID-19 (SARS-CoV-2) was the reason for causing a pandemic all over the world which created many problems and challenges for all countries. Increasing number of cases in every nation led the authorities of each country to put lockdown measures all over the nation. Although the lockdown implemented was to prevent the community from the spread of infection, but it had its own effect on mental as well as physical health and lifestyle of people. We devised a survey to focus on the physiological, psychological, and behavioural changes witnessed during the lockdown and to establish a correlation between the lockdown attributing to the negative changes developed during this period such as weight gain, increase in sleeping hours and screen time. All the data was gathered through a questionnaire via Google forms which was distributed to a substantial amount of people through social networking platforms. The responses received were 421 in total, which on analysis and comparison of the pre-lockdown and post-lockdown periods, showed trends like 58.5% people had increase in BMI, 33.9% people had increased more than 7 hours of daily screen time whereas 46.5% had a reduction of working hours down to less than 6 hours. Keywords: COVID-19, lockdown, BMI, working hours, screen time, sleeping time

In the present study, we have evaluated the extreme precipitation and temperature indices over the 10 agro-climatic zones falling under Ganga River basin (GRB)utilizing a high resolution daily gridded temperature and precipitation multi-model ensembled CMIP6 dataset (0.25°x0.25°) under global warming levels of 1.5°C, 2°C, and 3°C.We found that annual daily minimum temperature(TNN) showing higher rise of about 67% than maximum temperature (TXX)of 48%in GRB. The basin also experiences a greater increase in the frequency of warm nights (TN90P) of about 67.71% compared to warm days (TX90P) of 29.1% for 3°C global warming level.Along with extreme indices, population exposed due to the impact of the extreme maximum temperature has also been analysed for progressive warming levels. Population exposure to extreme temperature event (TXX) has been analysed with 20-year return period using GEV distribution method. The study concludes that exposed population to extreme temperature event experienced an increase from 46.99–52.16% for whole Ganga Basin. Consecutive Dry Days (CDD) and Consecutive Wet Days (CWD) both show a significant increasing trend, but CWD has significant increase in majority of the zones, while CDD show significant decreasing trend for some of the zones for three warming levels periods.Extreme climate indices help to understand the frequency and intensity of the extreme weather events such as heavy rainfall, droughts, heatwaves etc. to develop early warning systems and adaptation strategies to mitigate such events.

Post-translational modifications (PTMs) provide a critical means of calibrating the functional proteome and, thus, are extensively utilized by the eukaryotes to exert spatio-temporal regulation on the cellular machinery rapidly. Ubiquitination and phosphorylation are examples of the well-documented PTMs. SUMOylation, the reversible conjugation of the Small Ubiquitin-related MOdifier (SUMO) at a specific lysine residue on a target protein, bears striking similarity with ubiquitination and follows an enzymatic cascade for the attachment of SUMO to the target protein. Unlike Ubiquitination, SUMOylation can modulate the target protein's structure, stability, activity, localization, and interaction. Thus, SUMOylation regulates cellular events such as signal transduction, cell-cycle progression, transcription, nucleocytoplasmic transport, and stress responses. Accordingly, deregulation of SUMOylation is an avenue for diseases, which makes the investigation of SUMO and its substrates within the cell essential. However, the low extent of SUMOylation has posed a significant challenge in detecting SUMO modification within the cell. Bioinformatics tools can help predict SUMOylation, and mass-spectrometric analysis can identify a pool of cellular protein SUMOylome. Nevertheless, the biochemical methods for observing the enhanced level of in vitro SUMOylation help validate protein SUMOylation, critical lysine(s) utilized in the process, and its effect on substrate protein function. This chapter provides a detailed account of biochemical methods commonly utilized to detect SUMOylated proteins that are central for understanding the biological functions and mechanism of regulation of SUMO targets.

Recent advances in the area of artificial intelligence and deep learning have motivated researchers to apply this knowledge to solve multipurpose applications in the area of computer vision and image processing. Super-resolution (SR), in the past few years, has produced remarkable results using deep learning methods. The ability of deep learning methods to learn the nonlinear mapping from low-resolution (LR) images to their corresponding high-resolution (HR) images leads to compelling results for SR in diverse areas of research. In this article, we propose a deep learning-based image SR architecture in the Tchebichef transform domain. This is achieved by integrating a transform layer into the proposed architecture through a customized Tchebichef convolutional layer (TCL). The role of TCL is to convert the LR image from the spatial domain to the orthogonal transform domain using Tchebichef basis functions. The inversion of the transform mentioned earlier is achieved using another layer known as the inverse TCL (ITCL), which converts back the LR images from the transform domain to the spatial domain. It has been observed that using the Tchebichef transform domain for the task of SR takes the advantage of high and low-frequency representation of images that makes the task of SR simplified. Furthermore, a transfer learning-based approach is adopted to enhance the quality of images by considering Covid19 medical images as an additional experiment. It is shown that our architecture enhances the quality of X-ray and CT images of COVID-19, providing a better image quality that may help in clinical diagnosis. Experimental results obtained using the proposed Tchebichef transform domain SR (TTDSR) architecture provides competitive results when compared with most of the deep learning methods employed using a fewer number of trainable parameters.

Lean management is a work philosophy of continuous improvement to achieve a smooth work flow by minimizing all kinds of waste including time, cost and manpower. The concept of lean management started as early as 1980’s and was soon adopted by production industry. However, incorporation of this concept in healthcare has been very gradual and hesitant, though lean heath care has immense potential to improve operational performance. A clinical laboratory, which is essential in practice of evidence-based medicine, can be efficiently managed using lean management concept. Only a few publications in English literature have studied the effect of implementation of lean management in a clinical laboratory. There are certain challenges that a health care manager is likely to face in the implementation of lean management in clinical laboratory especially in developing nations. The present review discusses the implementation of lean management in a clinical laboratory with identification of various steps which require lean thinking.

Chronic Kidney Disease (CKD) is a disease which doesn't shows symptoms at all or in some cases it doesn't show any disease specific symptoms it is hard to predict, detect and prevent such a disease and this could be lead to permanently health damage, but machine learning can be hope in this problem it is best in prediction and analysis. The objective of paper is to build the model for predicting the Chronic Kidney Disease using various machine learning classification algorithm. Classification is a powerful machine learning technique that is commonly used for prediction. Some of the classification algorithm are Logistic Regression, Support Vector Machine, Naïve Bayes, Random Forest Classifier, KNN. This paper investigate which algorithm is used for the improving the accuracy in the prediction of Chronic Kidney Disease. And, a comparative analysis on the accuracy and mean squared error is to done for predicting the best model.

Background: Quality control is an essential part of quality management in medical laboratory. Various methods are available for the analysis of laboratory quality control data, common ones are Levey Jennings chart and Westgard rules. Westgard multi-rules are a set of rules based on combination of criteria to decide whether an analytical run is acceptable or unacceptable. CUSUM uses the cumulative sum of deviations from a target, however, it is rarely used in current medical laboratory practice. We share our experience of using Westgard rules and CUSUM in the analysis of laboratory quality control data. Material and methods: Internal quality control values of 2-year period was included in the present study. Data for platelet count values of normal level control material run on a fully automated haematology analyser (Sysmex XT-2000i) were analysed. A total of 1825 data points were obtained. The data was interpreted by Westgard rules as well as cumulative sum method. The out of control events was analysed. Results: There was 9 incidence of control value outside 3SD (Westgard Rule 13s) which was picked on Levey Jennings chart but missed by CUSUM method. There were 22 instances of shift in mean (bias) which were only picked by CUSUM method. Conclusion: CUSUM was more sensitive for detection of bias whereas random error was picked-up early by Westgard rules. In conclusion we recommend the use of more than one method for analysis of quality control data.

Aim: To study the prevalence of various types of Uncorrected Refractive errors in a paediatric age group (7-15 years) attending a tertiary care centre. Materials and Methods: A hospital based cross sectional study was carried out to evaluate proportion of various types of uncorrected refractive errors in age group of 7–15yrs. in a tertiary eye care centre for a period of 3 months. Total of 4000 children were screened for presence of refractive error after taking informed consent from their parents. Children were then grouped into different types of refractive errors based on the results of cycloplegic refraction. Results: Out 4000 children, the most common age with refractive error was 10-12 years followed by 13-15 years and the commonest refractive error was astigmatism (55%) followed by myopia (34%). The most common presenting symptom was headache followed by visual disturbance. Significant family history was noted among group with prevalence of positive family history in 17% of study group. Discussion & amp, Conclusion: Astigmatism & Myopia is the commonest types of refractive error in children that was consistent with other Indian studies. Study suggests the significance of early school based screen to reduce the burden of childhood ocular morbidity because of uncorrected refractive error. Keywords: Uncorrected refractive error, Childhood blindness, Assam, Scenario.

The spread of severe acute respiratory syndrome-coronavirus-2 has already taken on pandemic proportions, affecting over 100 countries in a matter of weeks. A global response to prepare health systems worldwide is imperative. Specific containment measures are needed to reduce the risk of spread while simultaneously maintaining the emergency health services. The purpose of this article is to propose the simple and effective measures implemented in our institution to ensure the minimum risk of COVID spread while taking care of the emergency patients. Effectiveness of these measures is still in the early phase of testing, but in view of lack of personal protective equipment for health caregivers, these measures could be implemented with ease and will be helpful in reducing the risk of exposure in both ophthalmic and nonophthalmic institutions.

Soil organic carbon (SOC) is a key component for sustaining crop production. A field experiment was conducted during 2004–2018 to assess the changes in soil carbon fractions under different fertili...

Trichoderma is an important biocontrol agent for managing plant diseases. Trichoderma species are members of the fungal genus hyphomycetes, which is widely distributed in soil. It can function as a biocontrol agent as well as a growth promoter. Trichoderma species are now frequently used as biological control agents (BCAs) to combat a wide range of plant diseases. Major plant diseases have been successfully managed due to their application. Trichoderma spp. is being extensively researched in order to enhance its effectiveness as a top biocontrol agent. The activation of numerous regulatory mechanisms is the major factor in Trichoderma ability to manage plant diseases. Trichoderma-based biocontrol methods include nutrient competition, mycoparasitism, the synthesis of antibiotic and hydrolytic enzymes, and induced plant resistance. Trichoderma species may synthesize a variety of secondary metabolites that can successfully inhibit the activity of numerous plant diseases. GPCRs (G protein-coupled receptors) are membrane-bound receptors that sense and transmit environmental inputs that affect fungal secondary metabolism. Related intracellular signalling pathways also play a role in this process. Secondary metabolites produced by Trichoderma can activate disease-fighting mechanisms within plants and protect against pathogens. β- Glucuronidase (GUS), green fluorescent protein (gfp), hygromycin B phosphotransferase (hygB), and producing genes are examples of exogenous markers that could be used to identify and track specific Trichoderma isolates in agro-ecosystems. More than sixty percent of the biofungicides now on the market are derived from Trichoderma species. These fungi protect plants from harmful plant diseases by developing resistance. Additionally, they can solubilize plant nutrients to boost plant growth and bioremediate environmental contaminants through mechanisms, including mycoparasitism and antibiosis. Enzymes produced by the genus Trichoderma are frequently used in industry. This review article intends to provide an overview update (from 1975 to 2022) of the Trichoderma biocontrol fungi, as well as information on key secondary metabolites, genes, and interactions with plant diseases.

The present study was carried out to determine wetting front dimensions in drip irrigation and to establish a relationship between emitter discharge and wetting front movement in vertical and horizontal directions. The dimensions of wetting front in horizontal and vertical directions for three different emitters with discharge rate of 2, 4 and 8 L per hour (lph), respectively, were observed in medium loam soil. Results revealed that vertical distance of wetting front was increasing rapidly in the beginning but slowly at later stages, and variation of moisture content varied between 16 and 33%. Mathematical equations were developed for estimating the dimension of wetting front in horizontal and vertical directions. The developed equations for 2, 4 and 8 lph dripper showed strong correlation (coefficient of determination i.e. R2 is 0.98, 0.982, 0.922 and 0.921, 0.985, 0.958 for horizontal and vertical directions, respectively). Further, it was observed that for same volume discharge, wetting front movement in both directions can vary with variation in water application duration. These mathematical models will be useful to estimate the horizontal and vertical movement of water for the design of drip irrigation system and can also be used in determining irrigation periods for various crops to match their varying root zone temporally.

Restoration and reconnection of floodplain systems provide multiple societal and ecosystem benefits, while providing municipalities the opportunity to attempt alternative approaches to maintain infrastructure protection and function. In some restored floodplains, treated wastewater effluent discharge is redirected over land instead of directly into rivers to allow natural flow and infiltration, to facilitate restoration designs such as levee setback, and to provide additional freshwater to floodplain ecosystems. However, indirect discharge of treated effluent over land may pose risks to surface and groundwater when pollutants like excess nutrients enter the floodplain and undergo transformation. We investigated the consequences for groundwater and surface water quality when effluent was redirected as open water channels over a floodplain surface. In this study, seasonal floodplain nutrient concentrations in groundwater and surface water were observed for more than 5 years as a floodplain and wastewater treatment plant underwent a major restoration project that included river-floodplain reconnection with levee setback and redirection of effluent discharge from a river channel to open flow across the restored floodplain. Nutrient loading to the surrounding floodplain groundwater and surface water was observed, but based on measures of hydrological connectivity, groundwater flow paths, and biogeochemistry, nutrients from the effluent moved within the floodplain with minimal effect to the surrounding floodplain water quality. We did not find evidence of substantial additional processing that could replace advanced nutrient treatment in this system, however we did observe evidence of diverse nutrient processes that may support enhanced retention if treatment channels were designed to enhance these processes. We suggest that indirect discharge of high quality treated effluent in a restored floodplain is a viable alternative to direct discharge into a river when groundwater flow directs that discharge to habitats where minimal nutrient sensitivity is expected.

Background: Platelet volume indices (PVI) such as mean platelet volume (MPV), platelet distribution width (PDW) and platelet large cell ratio (P-LCR), are the indicators of increased platelet activity which may play a role in development of vascular complications in diabetic patients. This study was performed to evaluate and compare the platelet volume indices such as MPV, PDW, P-LCR in patients of type 2 diabetes mellitus (DM) with and without manifested coronary artery disease in order to identify their usefulness in determining the risk for development of coronary complications.Methods: Analytical cross - sectional study included 150 patients of which 100 patients were diagnosed as type 2 DM and 50 apparently healthy controls. The study cases were divided into two groups based on presence or absence of coronary artery disease. Group A included 50 cases of type 2 DM without manifested coronary artery disease and group B included 50 cases of type 2 DM with manifested coronary artery disease. PVI was obtained using automated cell counter.Results: MPV, PDW, P-LCR were significantly higher in diabetics as compared to controls subjects (P < 0.001 for all). However, no statistically significant difference was found between diabetics with and without manifested coronary artery disease.Conclusions: The study showed higher PVI in diabetic subjects when compared to control subjects, but no difference between patients with and without manifested coronary artery disease suggesting that various other factors might be associated with the pathogenesis of CAD in patients of DM.

Hyporheic exchange between a river channel and its floodplain region assists in mediating processes such as nutrient removal and temperature regulation. Floodplain restoration in the form of levee setbacks are often carried out to improve the hyporheic exchange. In this study Light Detection and Ranging (LiDAR) data were used along with the head data from observation wells and stage data from rivers to setup and calibrate a groundwater model for 458 km 2 of area within Gap to Gap reach of the Yakima River, WA. This area has witnessed several efforts of floodplain restoration in the form of levee setbacks. The groundwater model was used to quantify hyporheic flow emerging from the Yakima River in steady and transient states during pre-restoration (using LiDAR data of 2008) and post-restoration period (after levee setback using LiDAR data of 2013). The comparison of results from the model runs during pre and post-restoration periods showed that the length of the pathlines increased after levee setback for both steady and transient state model simulations. The largest increase of about 62 m was noticed in the month of September 2014 (pre: 398 m and post: 460 m). The study also showed that the direction of the flow changed following levee setback, expanding the area for hyporheic flux exchange between surface and groundwater. The model run during transient state also suggested that pathlines were longer during drier months compared to wet months. Overall, the study showed that levee setbacks improved the hyporheic connection between surface and groundwater in the Yakima floodplain which demonstrates that levee setback can provide a valuable hydrologic tool to restore ecosystem processes in previously leveed rivers.

Shallow aquifers typically have greater hydrologic connectivity and response to recharge and changes in surface water management practices than deeper aquifers and are therefore often managed to reduce the risk of flooding. Quantification of the water table elevation response under different management scenarios provides valuable information in shallow aquifer systems to assess indirect influences of such modifications. The episodic master recession method was applied to the 15-min water table elevation and NEXRAD rainfall data for 6 wells to identify water table response and individual rainfall events. The objectives of this study were to evaluate the effects of rainfall, water table elevation, canal stage, site-specific characteristics, and canal structure modification/water management practice on the fluctuations in water table elevations using multiple/stepwise multiple linear regression techniques. With the modification of canal structure and operation adjustment, significant difference existed in water table response in the southern wells due to its relative downstream position regarding the general groundwater flow direction and the structural modification locations. On average, water table response height and flood risk were lower after than before the structure modification to canals. The effect of rainfall event size on the height of water table response was greater than the effect of antecedent water table elevation and canal stage on the height of water table response. Other factors including leakance of the canal bed sediment, specific yield, and rainfall on i − 1 day had significant effects on the height of water table response as well. Antecedent water table elevation and canal stage had greater and more linear effects on the height of water table response after the management changes to canals. Variation in water table response height/rainfall event size ratio was attributed to difference in Sy, antecedent soil water content, hydraulic gradient, rainfall size, and run-off ratio. After the structure modification, water table response height/rainfall event size ratio demonstrated more linear and proportional relationship with antecedent water table elevation and canal stage.

Grassed waterways (GWWs) transport sediment and nutrients from upland source areas to receiving waters. Watershed planners have a critical need to understand GWW sediment delivery to optimally target source area management practices. Better physically based tools are needed to estimate sediment delivery by GWWs. This study developed several distributed sediment delivery ratio (SDR) regressions for GWWs using the process-based Water Erosion Prediction Project (WEPP) model to provide simple equations to estimate sediment delivery for planning applications. Water Erosion Prediction Project was calibrated and validated for runoff and sediment yield for large 30.2-ha and smaller 5·7-ha nested watersheds with terraces and a common GWW outlet. A crop rotation of corn, oat and alfalfa and fall tillage using chisel plow were used in the nested watersheds. A hypothetical management case without terraces using corn, oat and alfalfa rotation with chisel plow as fall tillage was also evaluated for the 5·7-ha watershed and the GWW. The length, slope, Manning's roughness coefficient and infiltration rate for the GWW were varied and SDRs calculated for 30 representative (in terms of daily rainfall) days over a 20-year period of simulated climate. Regressions were developed for the existing (terraced) and hypothetical (non-terraced) management scenarios for early (April–July), late (August–October) and full (April–October) growing seasons. Equations developed for the non-terrace watershed had higher R2 values compared to the terraced watershed suggesting that channel and rainfall parameters were better able to explain the variation in SDR for the non-terraced watershed. Manning's roughness coefficient was the most significant parameter for predicting SDR for both the terraced and non-terraced watersheds. The equations developed here can be used to estimate SDRs for watersheds that are drained via GWWs having similar physical characteristics: slope (1–5%), Mannings's roughness coefficient (0·1–0·3), length (0·15–1 km) and infiltration rate (0·025–25 mm h−1). The SDRs can be used to estimate sediment yield, which is an essential element for making land management decisions but is rarely measured. Copyright © 2017 John Wiley & Sons, Ltd.