Abstract This study assesses the impact of physicochemical pretreatment, enzymatic hydrolysis, and co-culture fermentation strategies on bioethanol production from corn husk biomass (CHB). Under optimal alkali pretreatment conditions (1.75% alkali, 4.0 g substrate concentration, 120 °C, 10 h), 35% lignin removal was achieved, with 48% cellulose and 39% hemicellulose recovery. In contrast, acid pretreatment resulted in 30% lignin removal, 45% cellulose recovery, and 34% hemicellulose recovery, showing lower efficiency than alkali pretreatment. During ultrasonication alkali pretreatment enhanced cellulose and hemicellulose exposure up to 51 and 46% and delignification up to 49%. Enzymatic hydrolysis of pretreated corn husk biomass was performed using commercial enzymes [Celluclast 1.5 L (700 EGU or 854 U mL−1) and Viscozyme (13.4 FBG/mL)] and isolated bacterial enzymes, including cellulase from Bacillus licheniformis (9.3 ± 0.3 U mL−1) and xylanase from Enterobacter asburiae PQ396173 (7.0 ± 0.4 U mL−1). The developed enzyme cocktail in ratio 3:2:3:1 (v/v; U mL−1) (Celluclast: Viscozyme: native cellulase: native xylanse) using a cocktail of native and commercial enzymes, yielded total reducing sugar of 740 mg g−1 glucose and 54.6 mg g−1 xylose. Fermentation of hydrolysate prepared with commercial enzymes using monoculture of Saccharomyces cerevisiae and Pichia pastoris yielded 17.6 g L−1 and 12.2 g L−1 bioethanol separately. Co-cultured yeasts produced 26.8 g L−1 ethanol at 96 h of incubation, exceeding monoculture yields. The fermentation with integration of commercial and isolated bacterial enzyme cocktails yielded the highest bioethanol output of 37.3 g L−1 at 96 h incubation, indicating that enzymatic saccharification with a combination of commercial and native enzyme cocktails results in maximum bioethanol production. Graphical abstract
The demand for accurate stock market trend prediction models has surged among financial traders, prompting the exploration of machine learning techniques to enhance predictive performance and reduce computational complexity. Traditional models often rely solely on historical stock data, which may not fully capture the intricate dynamics of financial markets. This research addresses this limitation by developing a model that utilizes Open, High, Low, and Close (OHLC) prices, integrating both classification and regression machine learning models to predict stock market trends. However, not all models are favorable for trend prediction and therefore, we compare using different models to find the best performing model. The regression models tested include: Long Short-Term Memory (LSTM): Achieved 99.31% accuracy, Linear Regression: Achieved 98.85% accuracy, Decision Tree: Achieved 98.22% accuracy, Stochastic Gradient Descent (SGD): Achieved 97.63% accuracy, Temporal Convolutional Networks (TCN): Achieved 96.95% accuracy, K-Nearest Neighbors (KNN): Achieved 80.35% accuracy, Random Forest: Achieved 57.12% accuracy. The classification models evaluated include: Artificial Neural Networks (ANN): Achieved 97.28% accuracy, Stochastic Gradient Descent (SGD): Achieved 93.65% accuracy, K-Nearest Neighbors (KNN): Achieved 89.53% accuracy, XGBoost: Achieved 87.01% accuracy, Decision Tree: Achieved 86.1% accuracy, Random Forest: Achieved 85.06% accuracy, Support Vector Machine (SVM): Achieved 61.94% accuracy, AdaBoost: Achieved 58.38% accuracy, Na & iuml;ve Bayes: Achieved 51.4% accuracy. Here, it is observed that the regression models are performing better in trend prediction when compared with classification models. However, it has also been noticed that ANN in classification; LSTM, and Linear regression in regression are performing better than other models in their categories.
This study examines the willingness to adopt AI-based wearable devices for mental health diagnosis, with a focus on the importance of psychological safety and algorithmic trust. It highlights the need for early and ongoing diagnosis of mental health issues, as AI-enabled wearable devices with advanced digital biomarkers and machine learning can help achieve this goal. Unlike studies on trust and TAM in digital health that focus on the perceived usefulness of the system, this study is the first to show that psychological safety is the dominant factor with stronger effects on adoption, illustrating a unique trust-emotion mechanism in AI-assisted wearables for mental health. Developing and validating a multidimensional adoption model, this study is grounded within the Technology Acceptance Model (TAM), extended with trust-based constructs and the Information Systems Success Model. Data were collected through a cross-sectional online survey of 763 respondents from urban and semi-urban areas in the Indian states of Odisha, Maharashtra, Delhi, and Tamil Nadu, representing a range of educational qualifications and occupations. Using SmartPLS 4, the findings suggest that psychological assurance is the dominant predictor of behavioral intention (β = 0.659, p < 0.001, 95% CI [0.608, 0.709]). While there was no direct effect on behavioral intention (β = −0.023, p = 0.452), a significant pathway trust was observed. Algorithmic credibility positively influenced psychological assurance (β = 0.276, p < 0.001) and perceived diagnostic accuracy (β = 0.555, p < 0.001), suggesting an indirect effect. Psychological assurance was positively influenced by perceived usefulness (β = 0.301, p < 0.001), and a positive effect was observed on behavioural intention (β = 0.062, p = 0.029) in relation to perceived diagnostic accuracy. Finally, the authors concluded that a range of influencing factors affects perceived diagnostic accuracy, with a moderate effect (β = 0.102, p = 0.003) also being found. These results underscore the importance of examining both the emotional trust aspect and the technical accuracy aspect of AI-enabled mental health wearables. For practitioners and policymakers, the findings underscore the importance of focusing on explainable AI, clinician endorsement, and reassurance feedback loops to enhance the adoption of mental health technologies and improve mental health care outcomes.
The constitutional mandate of reservations in Panchayati Raj Institutions has significantly increased the political participation of Dalit women at the grassroots level in India. However, their numerical presence does not necessarily translate into substantive empowerment. This study examines the gendered perspectives of Dalit women representatives in Gram panchayats. Focusing on their lived experiences, roles, challenges, and agency within local governance structures. Using a gender and intersectionality framework, the research explores how caste and gender jointly shape Dalit women’s political participation and decision-making processes. This paper adopts a gender-sensitive framework to analyse how patriarchal norms, caste-hierarchies, and socio-economic marginalisation limit Dalit women’s effective participation. The experience of Dalit women in local governance is characterized by a constant struggle against a system designed to keep them on the periphery of decision-making, where they must navigate both patriarchy within their community and upper-caste domination in the political sphere. Dalit women face a unique compounded oppression being Women, Dalits and Generally poor. Although, Dalit women are elected as placeholders for their husbands or dominant caste members with roughly 85% of elected positions being effectively controlled by others. In meetings they are often not taken seriously with their issues rarely discussed approved or implemented particularly when challenging existing power structures. Elected Dalit women frequently experience verbal, physical, and caste-based violence including threats, harassment, and even assassination attempts when asserting their authority. While, the 73rd Amendment provided a reservation 33.3% but it has not automatically translated into empowerment due to deeply entrenched patriarchal and caste-based norms that marginalize them. Despite these some Dalit women are overcoming barriers through previous social activism or support although they remain a minority.
