BENTHIC MACROINVERTEBRATE COMMUNITIES AND ITS DIVERSITY IN TAMASI LAKE, TEHSIL- BHADRAVATI, DISTRICT-CHANDRAPUR(M.S.)

Benthic macroinvertebrates are a bioindicator forevaluating overall health of aquatic ecosystem and the quality of water. Change in environmental factors reflected in the structure of benthic macroinvertebrate community. Study was carried out during May 2022 to April 2023 at Tamsi Lake Tahsil- Bhadravati District – Chandrapur. In presentinvestigation total of 23 macroinvertebrates species were recorded and identified, belonging to 11Order from all the study sites. Benthic population size and distribution were showing spatial effect. Climatic effect has shown a considerable difference in the number of benthic communities. In comparison to the dry season, more individuals were seen during the wet season. The Hemiptera family species of macroinvertebrates showed their maximum appearance, followed by Diptera, Coleoptera and Gastropoda. Less number of animals representing the order Ephemeroptera, Tubificida and Glossiphoniiformes were identified. When compared the presence of bioindicator species at the majority of sampling sites, the Benthic community of Tamasi lake shows tolerant species. This indicates the existence of pollution and hence is likely to undergo environmental stress

The Theme of ‘Reinventing America’ in Jhumpa Lahiri’s “Unaccustomed Earth”

Identity is a common theme in fiction, but it takes on a special charge in the stories in Unaccustomed Earth, because, in each story, a character or family is caught between cultures, and often between generations. The result is an active and ongoing questioning as to whom each person is. What is more, a change in one person, or even in one person's understanding of another, changes the other characters here. This can be seen in Unaccustomed Earth. The eight stories in Unaccustomed Earth fall into two groups. The first five share only themes; the characters and settings are self-sufficient of one another. The last three can be read independently, but work well as they are designed: as a triptych telling the story of Hema and Kaushik. The first story focuses on their meeting as children; the second follows Kaushik when his father remarries; the third focuses on their gathering as adults

An Evaluation of Multi-Label Classification Approaches for Method-Level Code Smells Detection

(1) Background: Code smell is the most popular and reliable method for detecting potential errors in code. In real-world circumstances, a single source code may have multiple code smells. Multi-label code smell detection is a popular research study. However, limited studies are available on it, and there is a need for a standardized classifier for reliably identifying various multi-label code smells that belong to the method-level code smell category. The primary goal of this study is to develop a rule-based method for detecting multi-label code smells. (2) Methods: Binary Relevance, Label Powerset, and Classifier Chain methods are utilized with tree based single-label algorithms, including some ensemble algorithms in this research paper. The chi-square feature selection technique is applied to select relevant features. The proposed model is trained using 10-fold cross-validation, Random Search cross-validation parameter tuning, and different performance measures are used to evaluate the model. (3) Results: The proposed model achieves 99.54% of the best jaccard accuracy for detecting method-level code smells using the Classifier Chain method with the Decision Tree. The Decision Tree model incorporating a multi-label classifier outperforms alternative approaches to multi-label classification. Single-label classifiers produced better results after considering the correlation factor. (4) Conclusion: This study will facilitate scientists and programmers by providing a systematic method for detecting various code smells in software projects and saving time and effort during code reviews by detecting multiple problems simultaneously. After detecting multi-label code smell, programmers can create more organized, easier-to-understand, and trustworthy programs.publishedVersio

A prospective study on the predictors of mechanical ventilation in organophosphate poisoning

Background: Organophosphorus poisoning is one of the most common poisonings often requiring ICU care and ventilatory support. The objective and aim of this study are to identify the factors which predict the need for ventilation in these patients.Methods: 50 patients who were diagnosed to have consumed organophosphorus compound poison admitted in Konaseema Institute of Medical Sciences and Research Foundation who presented within 24 hours of consumption are included in the study. Patients with double poisonings, concomitant illnesses, chronic lung diseases and those treated outside are excluded from the study.Results: A total number of 50 patients were studied. 18(36%) patients required ventilation. Generalized fasciculations was a discernible feature in 66% of cases in this study. 69.2% of patients with a fasciculation score of ≥4 required ventilation. Ventilation was needed by 55% of patients who had a Glasgow Coma Scale score of ≤10.Conclusions: Patients who presented with higher fasciculation scores and/or lower GCS scores were more likely to require ventilation. Using GCS scores as a predictor for the requirement of ventilatory support in organophosphate poisoning, a GCS score ten or less was significantly associated with an increased need for ventilatory support

Ensemble methods with feature selection and data balancing for improved code smells classification performance

Code smells are software flaws that make it challenging to comprehend, develop, and maintain the software. Identifying and removing code smells is crucial for software quality. This study examines the effectiveness of several machine-learning models before and after applying feature selection and data balancing on code smell datasets. Extreme Gradient Boosting, Gradient Boosting, Adaptive Boosting, Random Forest, Artificial Neural Network (ANN), and Ensemble model of Bagging, and the two best-performing Boosting techniques are used to predict code smell. This study proposes an enhanced approach, which is an ensemble model of the Bagging and Boosting classifier (EMBBC) that incorporates feature selection and data balancing techniques to predict code smells. Four publicly available code smell datasets, Blob Class, Data Class, Long Parameter List, and Switch Statement, were considered for the experimental work. Classes of datasets are balanced using the Synthetic Minority Over-Sampling Technique (SMOTE). A feature selection method called Recursive Feature Elimination with Cross-Validation (RFECV) is used. This study shows that the ensemble model of Bagging and the two best-performing Boosting techniques performs better in Blob Class, Data Class, and Long Parameter List datasets with the highest accuracy of 99.21%, 99.21%, and 97.62%, respectively. In the Switch Statement dataset, the ANN model provides a higher accuracy of 92.86%. Since the proposed model uses only seven features and still provides better results than others, it could be helpful to detect code smells for software engineers and practitioners in less computational time, improving the system's overall performance.publishedVersio

Demonstration of Software for Optimizing Machine Critical Programs by Call Graph Generator

While working with software that are complex, representation in visual forms improves the understanding and also enhances the programmers ability to analyze the relationships between the components of a code. Placing all tools together which performs cyclomatic complexity on mission critical codes to optimize the solution is thr real motto of this work. These include generation of call graphs, which are visually represented with different metrics and to assist in software coding to the programmers. The different metrics include total number of lines in the function, total number of executable lines, and number of unreachable lines. This tool accepts only C program and generates a function call graph along with function metrics providing both static and dynamic view. This software helps the developer to take decision on optimality of the program and to know the program flow, thus optimizing the program. This paper depicts the working of call graph Generator to assess the reachability and exactness of the programs.  

Modified fracture properties of cement composites with nano/micro carbonized bagasse fibers

A novel cost-effective alternative in the form of nano/micro carbonized particles produced from waste bagasse fibers has been explored to modify the mechanical properties and fracture pattern of the resulting cementitious composites. Carbonized bagasse particles were produced at Politecnico di Torino and characterized by Raman spectroscopy and scanning electron microscopy. When added with cement paste up to 1 wt% in six different proportions, the carbonized bagasse particles were found effective in significant enhancement of mechanical strength as well as fracture toughness. From micro-graphical observations it is evident that these heterogenic inclusions either block the propagation of micro cracks which has to deviate from its straight trajectory and has to follow the carbon nano/micro particles contour or distribute it into multiple finer cracks. Crack contouring along the carbonized particle, crack pinning, crack diversions and crack branching are the mechanisms which can explain the increase of toughness in the composite samples

Modified fracture properties of cement composites with nano/micro carbonized bagasse fibers

A novel cost-effective alternative in the form of nano/micro carbonized particles produced from waste bagasse fibers has been explored to modify the mechanical properties and fracture pattern of the resulting cementitious composites. Carbonized bagasse particles were produced at Politecnico di Torino and characterized by Raman spectroscopy and scanning electron microscopy. When added with cement paste up to 1 wt% in six different proportions, the carbonized bagasse particles were found effective in significant enhancement of mechanical strength as well as fracture toughness. From micro-graphical observations it is evident that these heterogenic inclusions either block the propagation of micro cracks which has to deviate from its straight trajectory and has to follow the carbon nano/micro particles contour or distribute it into multiple finer cracks. Crack contouring along the carbonized particle, crack pinning, crack diversions and crack branching are the mechanisms which can explain the increase of toughness in the composite samples

Role of clobetasol propionate 0.025% topical therapy in various dermatoses

The anti-inflammatory and vasoconstrictive properties of topical corticosteroids (TCs) contribute in providing therapeutic benefits in several skin conditions, including atopic eczema, localized vitiligo, psoriasis, and chronic hand eczema. Clobetasol propionate (CP) is the most common topical agent used for psoriasis management and demonstrates an efficacy superior to other TCs. A new CP 0.025% cream formulation has demonstrated hypoallergenic effects due to the absence of known contact allergens, such as propylene glycol, short-chain alcohols, and sorbitol-based emulsifiers. Lower CP serum levels and less hypothalamic–pituitary–adrenal axis suppression with CP 0.025% cream formulation than with CP 0.05% ensure better safety. The present case series discusses the clinical experience of using CP 0.025% cream in various dermatological conditions