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As the educational landscape evolves, understanding and fostering student adaptability has become increasingly critical. This study presents a comparative analysis of XAI techniques to interpret machine learning models aimed at classifying student adaptability levels. Leveraging a robust dataset of 1205 instances, we employed several machine learning algorithms with a particular focus on Random Forest, which demonstrated highest accuracy at 91%. The models' precision, recall and F1-score were also evaluated, with Random Forest achieving a precision of 0.93, a recall of 0.94, and an F1-score of 0.94. Our study utilizes SHAP, LIME, Anchors, ALE, and Counterfactual explanations to reveal the specific contributions of various features impacting adaptability predictions. SHAP values highlighted 'Class Duration' significance (mean SHAP value: 0.175); LIME explained socio-economic and institutional factors' intricate influence. Anchors provided high-confidence rule-based explanations (confidence: 97.32%), emphasizing demographic characteristics. ALE analysis underscored the importance of 'Financial Condition' with a positive slope, while Counterfactual scenarios highlighted the impact of slight feature variations of 0.5 change in 'Class Duration'. Consistently, 'Class Duration' and 'Financial Condition' emerge as key factors, while the study also underscores the subtle effects of 'Institution Type' and 'Load-shedding'. This multi-faceted interpretability approach bridges the gap between machine learning performance and educational relevance, presenting a model that not only predicts but also explains the dynamic factors influencing student adaptability. The synthesized insights advocate for educational policies accommodating socioeconomic factors, instructional time, and infrastructure stability to enhance student adaptability. The implications extend to informed and personalized educational interventions, fostering an adaptable learning environment. This methodical research contributes to responsible AI application in education, promoting predictive and interpretable models for equitable and effective educational strategies. [ABSTRACT FROM AUTHOR]

The present study examines the antibacterial effects of edible plants (Neem, Garlic, and Ginger) extracts against Salmonella serovars isolated from layer flocks using phenotypic antimicrobial resistance techniques. The incidence of positive isolates was higher in Dinajpur (13.89%), followed by Thakurgoan (11.91%), Nilphamari (10%), and Panchagarh (8.33%). Organoleptic analysis showed a higher prevalence in the liver (17.86%) compared to the heart (7.14%) and lung (10.72%). Ethanolic extractions were performed from the plants, and the antibacterial activity of these extracts was assessed using the disc diffusion method at concentrations of 80, 100, and 120 mg/mL against multidrug-resistant (MDR) bacteria, which were identified from selected layer flocks using conventional laboratory techniques. These bacteria are resistant to at least two antibiotics. The maximum inhibition zones for Neem, Garlic, and Ginger extracts at 120 mg/mL were 14 mm, 10 mm, and 2 mm, respectively, while the minimum inhibition zones at 80 mg/mL were 10 mm, 3 mm, and none, respectively. Neem leaf extract showed the highest effectiveness against MDR bacteria compared to Garlic and Ginger extracts. Although the organisms were sensitive to ciprofloxacin and colistin, they showed intermediate resistance to levofloxacin and chloramphenicol and resistance to neomycin and kanamycin. When comparing the antibacterial efficacy of resistant antibiotics and plant extracts in vitro, plant extracts showed larger inhibition zones. Therefore, it was concluded that ethanolic Neem leaf extract could serve as an alternative to conventional antibiotics against field isolates of Salmonella spp. in poultry. [ABSTRACT FROM AUTHOR]

In recent times, e-learning has become indispensable for both technical and general education. Among all the subjects, programming education has drawn attention because of its importance for continuous development in the ICT sector. Finding errors in a solution code is a laborious task for novice programmers, teachers and instructors. Novice programmers are spending a lot of valuable time to search errors in the solution codes. In this paper, a method for the categorization of frequent errors in solution codes is presented. In the proposed method, the differences between wrong solutions and accepted solutions are used to define feature vectors for a clustering algorithm. A longest common subsequence (LCS) algorithm is leveraged to find the differences between wrong and accepted codes, then all the inequalities are converted into feature vectors. The k-mean clustering algorithm is applied to cluster the elements of the feature vector to find the most common errors in solution codes. In our experiment, the method was applied to a set of program solution codes accumulated in an e-learning system. Experimental results show that the proposed method is efficient and capable to detect the most common errors occurred in solution codes that can be helpful for novice programmers to resolve errors quickly as well as useful for teachers to prepare lesson plan.

Objective: This research work was conducted for isolation of Salmonella spp. from duck and pigeon and assessment of antibiotic sensitivity patterns of the isolated Salmonella spp. Materials and methods: A total of 48 duck samples comprising of liver (n=16) spleen (n=16) and intestinal content (n=16) were collected from 16 ducks (8 sick and 8 dead). Similarly, 42 pigeon samples comprising of liver (n=14), spleen (n=14) and intestinal contents (n=14) were collected from 14 pigeons (7 sick and 7 dead). The samples were collected from the selected Duck and Pigeon farms at three Upazillas (sub-districts) in Dinajpur district, Bangladesh. The samples were subjected for isolation and identification of Salmonella spp. following standard bacteriological examinations such as cultural and morphological characteristics, biochemical properties and motility test. Commercially available antibiotic discs were used for the assesment of antibiotic resistant patterns by disc diffusion method. Results: Salmonella spp. could be isolated from 39.58% (n=19/48) duck and 28.57% (n=12/42) pigeon samples. The growth of colony with different characteristics in Brilliant Green agar, Nutrient agar, Salmonella-Shigella agar and Mac Conkey agar indicated positive results. Dextrose and mannitol were fermented and in Triple Sugar Iron (TSI) slant media, the isolates produced red color, yellow in butt with slightly black color. Positive result was found in Indole and Methyl Red media. The isolated Salmonella organisms were found to be highly sensitive to Azithromycin, Ciprofloxacin and Levofloxacin. Conclusion: Pigeons and ducks found in the study area may be the carrier of Salmonella spp. The farm should be checked periodically to know the status of Salmonella infection. Further study might be isolation and identification of Salmonella organism with detail genomic analysis and drug resistance. [ABSTRACT FROM AUTHOR]