Girls Incorporated

Plant essential oils extracted from various parts are known for their potential antimicrobial properties. The gas chromatography and mass spectrometry (GC-MS) of Ocimum tenuiflorum essential oil (TEO) revealed estragole and eugenol as major compounds. The FTIR profiling showed the presence of functional groups like O─H, C─H, C═O, C─C, which is in correspondence with the results of GC-MS analysis. In a similar way, UV-visible spectroscopy identified peaks within 315nm that showed the presence of C═C and aromatic compounds. The disc diffusion results revealed Aeromonas hydrophila to be more sensitive, with 29.57±0.32mm zone of inhibition (ZoI) and Aeromonas jandaei with 28.07±0.83mm. The MIC and MBC values of A. hydrophila were 0.125% and 0.25%, while 0.25% and 0.5% for A. jandaei. DPPH and ABTS assay for antioxidant activity determined the IC50 value to be 53.67±0.16 µg/mL and 53.7±0.2µg/mL. Strong antibiofilm activity (%) was observed against both A. hydrophila and A. jandaei, with 94.3% and 90% respectively. The present study provides insight into the sustainable use of TEO against bacterial infections by Aeromonas spp. isolated from moribund Labeo rohita.

Mechanical stirring (MS) and ultrasound (US) frequency are two key factors commonly used to reduce mass-transfer resistance during the reaction between methanol (MeOH) and triglycerides (TG), facilitating the efficient production of biodiesel (BD) from various feedstocks such as rapeseed oil and Jatropha curcas oil. In this study, a comparative model analysis has been conducted to evaluate the performance efficacy of optimum mechanical stirring (MS) and optimum ultrasound (US) frequency using rapeseed oil biodiesel production. Sensitivity and uncertainty analyses have been conducted using Latin hypercube sampling (LHS) and Partial Rank Correlation Coefficients (PRCCs) to identify the key kinetic and transport parameters influencing biodiesel yield. An optimal control framework has also been applied to regulate MS speed and US frequency over time to overcome initial mass-transfer resistance between oil and methanol. The influence of mixing intensity on biodiesel conversion has been examined at different temperatures and MeOH:oil molar ratios using mass-transfer correlations for both MS and US frequency. Numerical results show that, at a MeOH:TG molar ratio of 6:1 and temperature of 50 °C, optimal US frequency achieves a maximum biodiesel conversion of 97.67% within 40 min, whereas optimal MS attains 95.32% conversion after 60 min. The results further indicate that ultrasound provides faster mass-transfer enhancement and a superior production profile compared to mechanical stirring. This study addresses two key questions: whether rapeseed oil is a suitable feedstock for biodiesel production, and which mixing strategy, MS or US frequency, more effectively minimizes the mass-transfer resistance over time.

India's agriculture, vital for 50% of its workforce and food security for 1.4 billion people, Agrapples with intensifying climate stress. Erratic monsoons, frequent droughts in rainfed areas covering 60% of arable land, and extreme events like cyclones in coastal states have slashed yields by 20-40% in states such as Maharashtra, Rajasthan, and Bihar. Soil degradation—erosion, nutrient depletion, and salinization—affects 120 million hectares, inflating input costs and eroding farm incomes, with smallholders losing up to 30% of net returns annually. Soil conservation practices offer resilience. Contour bunding and terracing in the Deccan Plateau reduce runoff by 50%, enhancing water retention and boosting sorghum and pearl millet yields by 15-25%. No-till farming with residue retention, promoted under the National Mission for Sustainable Agriculture, preserves soil organic carbon, cutting fertilizer needs by 20% and increasing wheat productivity in Indo-Gangetic plains. Cover cropping with legumes like cowpea fixes nitrogen, suppresses weeds, and curbs erosion in black soils of Vidarbha. Agroforestry integrates trees like neem and gliricidia, providing fodder, fuelwood, and microclimate moderation, as seen in watershed projects doubling incomes in Madhya Pradesh. Integrated strategies—rainwater harvesting via farm ponds and check dams, alongside balanced nutrient management—yield synergies. In Gujarat's Kachchh district, such interventions raised cotton yields by 30% and farm incomes by 40%. Economic analyses from ICRISAT trials show 20-35% ROI through reduced inputs and premium prices for sustainable produce.

The fast development of mobile learning has revolutionized the field of language education as we know it today, but conventional formative assessment practices are still rigid and not adaptive to individual learning trajectories. This research put forth a new GPT-based formative assessment model for mobile-based English language studies that is heir to static evaluation paradigms due to incorporating a real-time, intuitive feedback feature. Different from the rigid classes of rule-based and ML approaches that are dependent on static assessment structures, the suggested GPT model adapts dynamically to inputs from students and gives personalized results based on ongoing language growth. With the incorporation of GPT, mobile learning applications can provide instant assessment and situational recommendations thereby increasing learner involvement and the feat of a language. The model attunement was performed on the English Language Learning Dataset from Kaggle and there showed the impressive precision and effectiveness in personalized evaluations compared to such traditional models as rule-based systems and SVMs. This real-time adaptability does not only create more interactive learning scenarios but also timely Addresses challenges, ensures perpetual growth and long-term retention. Comparative assessment from reference models emphasizes on the advantages of the proposed solution over the comparative precision, recall, and F1-score, which creates a new paradigm for mobile-assisted language learning. This research represents a major step forward in the mobile learning applications with its demonstration of the revolutionizing potential of AI-based personalized assessment in language education. Future studies will try to expand this model into different linguistic scenarios, proving its efficiency in unmodified real-life scenarios.

In this chapter, an in-depth study on colloids, the nature of colloids, types and their uses in the industry will be presented. Colloids are heterogeneous in the sense that a given substance is suspended in another substance, which is distinctly different in terms of physical and chemical characteristics. The chapter divides the colloids into the lyophilic (solvent-attracting) and the lyophobic (solvent-repelling) systems, and remarks on the principal features of the Brownian motion, the Tyndall effect, the electrophoresis, and the stability mechanisms. Its use is emphasized in paints, inks, foodstuffs, pharmaceuticals, cosmetics and wastewater treatment, with the texture, stability and performance being mainly dictated by the colloidal behavior. The methods of the colloid preparation, coagulation and protective colloid, have also been discussed in the chapter, and the theoretical models related to the real-life processes have been correlated. Concerns regarding colloidal stability, such as electrolyte concentration, pH and temperature, are brought up so that the challenges in the industrial formulations are highlighted. The theory, experiments and applications combined enable students to possess a consistent concept of colloidal chemistry, since it permits them to build and create stable colloidal systems and optimise business procedures. Any application of colloidal systems is pervasive in nature and the empirical world of mankind, whether in the field of material science, chemical engineering, pharmaceuticals, food technology and nanotechnology, all require colloidal science.

Urban Water Systems (UWSs) across the world are under mounting pressure as cities continue to expand, infrastructures age, and climate change introduces new levels of uncertainty into water availability and distribution. The traditional tools and hydraulic models that once guided urban water management are increasingly unable to cope with the highly dynamic, non-linear behavior of today's networks. These limitations have prompted a shift toward more adaptive and intelligent approaches capable of handling complex data environments. This review explores how Machine Learning (ML) is emerging as a powerful instrument for addressing these modern challenges. By examining research published largely in the past five years, the paper provides a structured overview of how ML has been applied to major UWS functions— such as forecasting future water demand, identifying leaks and pipe failures, monitoring water quality, and optimizing day-to-day system operations. A diverse range of techniques is discussed, from established learning models like Random Forests (RF) and Support Vector Machines (SVM), to more sophisticated deep learning methods including Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs). Across multiple studies, ML consistently surpasses traditional modelling approaches by learning intricate spatial and temporal relationships that conventional tools fail to capture. Certain algorithms have demonstrated notable advantages—for example, LSTM networks excel in predicting time-dependent water usage, while CNNs show strong performance in analyzing acoustic signals for leak detection. Beyond summarizing existing applications, the review highlights emerging themes and persistent gaps. Key concerns include the uneven availability and reliability of operational datasets, growing demands for data privacy and cybersecurity, and the ongoing challenge of interpreting decisions made by complex deep learning models. Additionally, hybrid frameworks, which combine the strengths of data-driven ML models with physically-based hydraulic models, are gaining interest as a promising direction for future research. Ultimately, the paper emphasizes the need for UWSs to evolve towards intelligent, autonomous, and sustainable systems. By integrating ML into standard practice—supported by robust data collection, careful preprocessing, and informed model selection—urban areas can significantly improve resource allocation, reduce water loss, and enhance overall resilience. This study demonstrates how data-driven strategies, when aligned with the realities of urban infrastructure, can play a pivotal role in shaping the next generation of efficient and sustainable urban water management.