A Unique Approach for Functionalization of Cotton Substrate with Sustainable Bio-based Vanillin for Multifunctional Effects

Exploration of novel bio-based materials for value addition of textile substrates is a desirable research trend. In this context, vanillin (4-hydroxy-3-methoxybenzaldehyde), a bio-based material (obtained from vanilla pod) having multifunctional properties, can be a well-suited candidate. In the present article, both the vanillin alone and vanillin–copper (vanillin-Cu) complexes have been incorporated into cotton textiles separately using a high temperature-high pressure (HT-HP) dyeing machine and the quantity of copper has successfully been optimized to demonstrate enhanced properties. The vanillin-treated fabric has shown very good all-round properties like ultraviolet protection factor (UPF), color value (K/S value), and antibacterial activity (AM) in the presence of copper salt. However, only vanillin itself has rendered as such no UPF value but demonstrated 70% antimicrobial activity against E. coli (Gram-negative) bacteria and during washing operation small molecules of vanillin get leached out of fabric (as represented by significant loss in properties). This is the first time that the functional properties of vanillin and vanillin-Cu complex have directly been explored on cotton textile wherein significantly enhanced properties have been achieved on vanillin-Cu complex-treated fabric (i.e. 99% AM, a good UPF factor of 78, K/S value of 11.55, and even 25% antioxidant activity)

Simplified product value measurement framework for small and medium sized enterprises

Background: The emergences of global markets have increased competition worldwide. For Small Medium Sized Enterprises with limited resources to sustain in what is already a very competitive market there is a need for strong and continuously increasing Product Value to reduce business risks and revenue losses and to increase market share and customer satisfaction. To fulfill this need, Product Value Measurement is necessary to characterize the current status and further improvement. It is not easy to obtain the measures about the Product Value because its many features have qualitative characteristics. We need simplified but result oriented systematic framework to measure it while considering the measurement purpose and how to measure and why do it. Methods: How to design this is the main aim of this research paper. In this paper, GQM (Goal-Question-Metric) method as a measurement framework is introduced to measure the Product Value for Small Medium Sized Enterprises along with case study to represent that this framework was effective. Results and conclusions: The proposed Model was effective for focusing on the essence of measurement and for avoiding extra excessive data not necessary for doing the effective measurement

Genetic diversity of sulphur oxidizing bacteria from different ecosystems

72-80Sulphur is one of the fourth major nutrients after N, P, and K for oil crops and pulses. To explore the possibility of meeting the sulphur nutrition of groundnut crop through bioinoculant, in the present study, a total of 24 isolates of sulphur oxidizing bacteria (SOB) obtained from different ecosystems were screened. Among the screened isolates, VSS4 isolate produced 72 mg 100 mL-1 sulphate from elemental sulphur. Species diversity, phylogenetic affiliations and environmental occurrence patterns of SOB were investigated by amplified ribosomal DNA restriction analysis (ARDRA). Finally, based upon the suphate production, pH reduction and other biochemical studies, four isolates, viz.,VSS4, VSM8, VSB3 and VST3, were selected for the study of genetic diversity between different ecosystems by using 16S ribosomal RNA (rRNA) gene sequences and phylogenetic analysis. SOB isolates VSS4, VSM8, VSB3 and VST3 were identified as Pseudomonas sp., Burkholderia sp., <i style="mso-bidi-font-style: normal">Exiguobacterium sp., and Bacillus sp., respectively. For the conformation of their chemoautotrophic nature, the selected isolates were screened with the primers of cbbL (form I), cbbM (form II) genes of ribulose 1,5-bisphosphate carboxylase/oxygenase (RuBisCO). All the four selected isolates were chemoautotrophic in nature as they showed the presence of cbbL (form I) gene. </span

A simplified, result oriented supplier performance management system testing framework for SME

Background: Supplier performance management continues to be a significant concern for small &amp; medium enterprises (SME). How can small &amp; medium enterprises better position themselves to check and sustain actual supplier performance improvement? A key framework is the establishment of a value-added supplier performance audit program that places significant emphasis on supplier performance controls. A value-added supplier audit program can help SME mitigate business and regulatory risk while reducing the cost of poor quality (COPQ). Thus a good supplier performance audit program is the cornerstone of supplier performance management integrity. Methods: By acknowledging and addressing the challenges to an effective supplier Performance Audit program, this paper proposes an objective framework of supplier performance audit program, built on a strong, yet versatile statistical methodology - Analysis of variance (ANOVA). This performance audit framework considers process definition, standardization, review of the contemporary literature on ANOVA &amp; its practical application in supplier performance scorecard of one of the reputed Sports Goods Industry in India. Results and conclusions: The advantages of this framework are that: it simultaneously considers multiple supplier performance in multiple time frames and effectively identifies the differences across the suppliers in terms of their performance. Through this framework the organization will be able to increase the odds of performing a predictable and successful implementation of a value-added supplier performance audit

A Systematic Evaluation of Recurrent Neural Network Models for Edge Intelligence and Human Activity Recognition Applications

The Recurrent Neural Networks (RNNs) are an essential class of supervised learning algorithms. Complex tasks like speech recognition, machine translation, sentiment classification, weather prediction, etc., are now performed by well-trained RNNs. Local or cloud-based GPU machines are used to train them. However, inference is now shifting to miniature, mobile, IoT devices and even micro-controllers. Due to their colossal memory and computing requirements, mapping RNNs directly onto resource-constrained platforms is arcane and challenging. The efficacy of edge-intelligent RNNs (EI-RNNs) must satisfy both performance and memory-fitting requirements at the same time without compromising one for the other. This study’s aim was to provide an empirical evaluation and optimization of historic as well as recent RNN architectures for high-performance and low-memory footprint goals. We focused on Human Activity Recognition (HAR) tasks based on wearable sensor data for embedded healthcare applications. We evaluated and optimized six different recurrent units, namely Vanilla RNNs, Long Short-Term Memory (LSTM) units, Gated Recurrent Units (GRUs), Fast Gated Recurrent Neural Networks (FGRNNs), Fast Recurrent Neural Networks (FRNNs), and Unitary Gated Recurrent Neural Networks (UGRNNs) on eight publicly available time-series HAR datasets. We used the hold-out and cross-validation protocols for training the RNNs. We used low-rank parameterization, iterative hard thresholding, and spare retraining compression for RNNs. We found that efficient training (i.e., dataset handling and preprocessing procedures, hyperparameter tuning, and so on, and suitable compression methods (like low-rank parameterization and iterative pruning) are critical in optimizing RNNs for performance and memory efficiency. We implemented the inference of the optimized models on Raspberry Pi

Techniques of Machine Learning for the Purpose of Predicting Diabetes Risk in PIMA Indians

Chronic Metabolic Syndrome Diabetes is often called a “silent killer” due to how little symptoms appear early on. High blood sugar occurs in people with diabetes because their bodies have a hard time maintaining normal glucose levels. Care for a recurrent sickness would be permanent. The two most common forms of diabetes are type 1 and type 2. A better prognosis can help reduce the high risk of developing diabetes. In order to better predict the likelihood that a PIMA Indian may develop diabetes, this study will use a machine learning-based algorithm. The demographic and health records of 768 PIMA Indians were used in the analysis. Standardisation, feature selection, missing value filling, and outlier rejection were all parts of the data preparation process. Machine learning techniques such as logistic regression, decision trees, random forests, the KNN model, the AdaBoost classifier, the Naive Bayes model, and the XGBoost model were used in the study. Accuracy, precision, recall, and F1 score were the only metrics utilised to assess the models' efficacy. The results demonstrate that. The results of this study reveal that diabetes risk may be reliably predicted using machine learning-based models, which has important implications for the early detection and prevention of this illness among PIMA Indians

Predicting the Spread of the Corona Virus Disease Requires Analyzing Data from Cases across Multiple States in India

Data analysis is very sophisticated tool in recent corona virus pandemic to find the trend of spreading pattern for controlling the infection. In this perspective, predictive analytics can be useful for data analysis to forecast the corona virus pandemic. This paper presents the infection pattern of corona virus disease, termed as COVID-19 in top seven states in India. Prophet Algorithm forecasting model was used to analyze state-wise spreading pattern of corona virus disease with respect to confirmed, deaths and cured cases. This predictive model can be very helpful to government and health care communities to combat this deadly virus by initiating suitable actions to control its spread

Human Action Recognition by Learning Spatio-Temporal Features with Deep Neural Networks

Human action recognition plays a crucial role in various applications, including video surveillance, human-computer interaction, and activity analysis. This paper presents a study on human action recognition by leveraging CNN-LSTM architecture with an attention model. The proposed approach aims to capture both spatial and temporal information from videos in order to recognize human actions. We utilize the UCF-101 and UCF-50 datasets, which are widely used benchmark datasets for action recognition. The UCF-101 dataset consists of 101 action classes, while the UCF-50 dataset comprises 50 action classes, both encompassing diverse human activities. Our CNN-LSTM model integrates a CNN as the feature extractor to capture spatial information from video frames. Subsequently, the extracted features are fed into an LSTM network to capture temporal dependencies and sequence information. To enhance the discriminative power of the model, an attention model is incorporated to improve the activation patterns and highlight relevant features. Furthermore, the study provides insights into the importance of leveraging both spatial and temporal information for accurate action recognition. The findings highlight the efficacy of the CNN-LSTM architecture with an attention model in capturing meaningful patterns in video sequences and improving action recognition accuracy. You should leave 8 mm of space above the abstract and 10 mm after the abstract. The heading Abstract should be typed in bold 9-point Arial. The body of the abstract should be typed in normal 9-point Times in a single paragraph, immediately following the heading. The text should be set to 1 line spacing. The abstract should be centred across the page, indented 17 mm from the left and right page margins and justified. It should not normally exceed 200 words