Volume 13 | Issue 12 |

 Abstract

The growing pressures of climate change, water scarcity, and the need for sustainable agricultural practices have driven significant research into smart irrigation and machine learning-based solutions. Agriculture, as one of the most water-intensive sectors, faces the dual challenge of maximizing crop yields while minimizing resource consumption. Recent advancements in sensor technologies, Internet of Things (IoT) systems, predictive analytics, and atmospheric water harvesting have enabled data-driven approaches to irrigation management, offering precise control over water delivery based on real-time environmental and crop conditions. Machine learning, in particular, has emerged as a transformative tool, enabling accurate predictions of soil moisture, evapotranspiration, and rainfall patterns, as well as adaptive decision-making for diverse agricultural contexts.

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Machine Learning, IoT, Agriculture, Smart Irrigation.

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The invasive aquatic plant Eichhornia crassipes (water hyacinth) is a significant ecological challenge affecting freshwater ecosystems globally. This study investigates the impact of water hyacinth infestation on water quality in four lakes within the Dahanu Municipal Area, Maharashtra. Using seasonal water samples collected from December 2024 to March 2025, parameters such as pH, conductivity, and biological oxygen demand (BOD) were analyzed. Results revealed marked differences in water quality correlated with the presence and density of hyacinth cover, with the most heavily infested site showing acidic pH and reduced water quality. The study emphasizes the urgent need for site-specific management strategies, underlining the importance of biological control and integrated environmental planning to mitigate the negative ecological, economic, and social impacts of water hyacinth.

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Eichhornia crassipes. pH, conductivity, and biological oxygen demand

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Decision Tree is the most prominent classification algorithm for supervised machine learning. Various decision tree algorithms, such as J48, Random Tree, LMT, and Random Forest, are applied to the datasets depending on the types of data and the application requirements. This paper presents a comparison analysis of J48, Random Forest, and LMT algorithms based on multiple evaluation parameters. The results demonstrate that J48 consistently outperforms other algorithms in terms of accuracy and robustness, compared to algorithms such as Random Forest and LMT.

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Supervised machine learning, J48, Random Forest, LMT, Classification Algorithm Comparison

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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???? ??? ?? ???, ??????? ???? ?? ????????????

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Political discourse in contemporary poets and their poetry: a consciousness

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Political discourse in contemporary poets and their poetry: a consciousness

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Adhunik Bihar Ke Unnayak: Dr. Anugrah Narayan Singh

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Adhunik Bihar Ke Unnayak: Dr. Anugrah Narayan Singh

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Joint cosmological analysis to constrain a time-varying dark energy equation of state using cosmic microwave background (CMB) anisotropy data and galaxy clustering observables from large-scale structure surveys. We extend the standard LambdaCDM model by adopting the Chevallier-Polarski-Linder (CPL) parameterization for the dark energy equation of state, w(z) = w0 + wa z/(1+z), and study its impact on both the background expansion H(z) and the linear growth of matter perturbations. The CMB temperature and polarization spectra, together with CMB lensing, tightly constrain early-universe parameters and the distance to last scattering, while baryon acoustic oscillation (BAO) and redshift-space distortion (RSD) measurements constrain late-time geometry and growth through distance indicators and f sigma8(z). We build a consistent joint likelihood using Planck 2018 TT,TE,EE plus lowE and lensing, combined with BOSS DR12 consensus BAO+RSD constraints at z_eff = 0.38, 0.51, and 0.61. Posterior distributions are sampled using Markov Chain Monte Carlo, and we quantify improvements in constraints on (w0, wa) when adding low-redshift information to the CMB. Model comparison with LambdaCDM is reported using information criteria, and robustness is assessed under alternative assumptions on priors, scale cuts, and neutrino mass treatment. This work provides a complete observationally anchored framework for testing dark energy evolution with current precision cosmology data.

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dark energy, evolving equation of state, CMB anisotropies, CMB lensing, BAO, RSD, galaxy clustering, parameter estimation

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The hotel and tourism industry is striving to develop and augment the guest experience. The hotel front desk is an imperative part of the front office operations because it creates the guest's first impression. In this era of competition, the hotels have adopted new and innovative practices to improve their level of performance and at the same time delight the guest. It became essential for the hotel to offer the excellent services and to do something innovative for their guest to keep them satisfied and fulfill their request. The guests had more options to choose from and it was important to retain the guest and give them life experiences so that they became loyal guests. The guest's feedback and suggestions helped the hotel to change a few of its practices and implement innovative and effective in the industry. This Research paper has stated guest satisfaction is a differentiator and vital success factor for the hotel. Research has shown that front office management and guest satisfaction have a relationship and that hotel performance is impacted because of the attitude and motivational levels of the employees.

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Hotel Industry, Front office, Guest Satisfaction

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This study delves into the timeless wisdom of Saatvik Aahar, an ancient dietary philosophy deeply rooted in Indian traditions. The research explores the foundational principles guiding Saatvik Aahar, emphasizing the consumption of unprocessed, locally sourced, and seasonally appropriate foods. It investigates the holistic approach of Saatvik Aahar, addressing its impact on physical health, mental well-being, and spiritual harmony. At its core, Saatvik Aahar operates on the principle of balance, aiming to harmonize the three gunas - Sattva, Rajas, and Tamas. Sattva, representing purity and balance, is encouraged through the intake of fresh fruits, vegetables, grains, and dairy products. This dietary approach fosters physical health by providing essential nutrients while aligning with the body's natural rhythms. The study investigates the holistic nature of Saatvik Aahar, recognizing its impact on mental well-being. By promoting Sattva, individuals engaging in this dietary practice may experience heightened mental clarity, improved focus, and a sense of inner peace. The avoidance of Rajasic (stimulating) and Tamasic (dull, inertia-inducing) foods contributes to emotional equilibrium and a tranquil mind. The study examines the potential benefits associated with Saatvik Aahar, such as enhanced digestion, improved energy levels, and a sense of inner peace. Through a combination of literature review, nutritional analysis, and insights from traditional practices, the research aims to provide a comprehensive understanding of how Saatvik Aahar contributes to overall wellness.

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Saatvik Aahar, Indian diet, holistic health, nutrition, mental well-being, Ayurveda

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Social Emotional Learning (SEL) is very essential for holistic education which emphasizes emotional intelligence, empathy, social skills and responsible decision making. English Language Teaching (ELT) provides an ideal context for integration on account of its emphasis on communication, expression, collaboration and critical thinking. This conceptual paper explores the concept, theoretical framework, strategies, benefits and challenges of implementing SEL in the Indian English language classrooms. It highlights the need for promoting social emotional learning in English Language Learning for the holistic development of students in the Indian context.

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Social-Emotional Learning, Socio-emotional Learning, Social -Emotional Literacy, English Language Learning, Language learning, English pedagogy

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