Volume 13 | Issue 12

 Abstract

Tetra-amide macrocyclic ligands have emerged as versatile scaffolds in coordination chemistry owing to their robust donor frameworks, structural rigidity, and metal-binding adaptability. Their complexes with transition metals exhibit diverse physicochemical behaviors, enabling applications spanning catalysis, sensing, environmental remediation, and biological therapeutics. Recent advances in green and template-assisted synthetic strategies have facilitated the preparation of highly stable macrocycles with tunable cavity sizes and donor zatom arrangements. Spectroscopic, thermal, and computational studies have provided deeper insights into their coordination geometries, electronic transitions, and reactive properties. These complexes demonstrate remarkable catalytic efficiency in oxidation, coupling, and CO? fixation reactions, along with significant antimicrobial, antioxidant, and anticancer activities. Furthermore, their ability to act as molecular sensors and peroxidase mimics underscores their functional diversity. The integration of theoretical modeling with experimental validation continues to enhance the understanding of structure-activity relationships, guiding the design of next-generation macrocyclic systems for sustainable and biomedical applications.

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Transition metal complexes; Coordination chemistry; Catalysis; Antimicrobial activity; Molecular docking; DFT studies; Environmental applications

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Herbal medicine is undergoing a profound transformation, guided by rapid advancements in nanotechnology and supported by emerging fields such as artificial intelligence, synthetic biology, and personalized medicine. This paper explores the future trajectory of herbal therapeutics, emphasizing the integration of nano-herbal systems, intelligent drug delivery mechanisms, green synthesis methods, and progressive regulatory frameworks. Nanotechnology enables enhanced bioavailability, stability, and targeted delivery of phytoconstituents, while AI-driven tools aid in compound identification, formulation optimization, and predictive modeling of therapeutic outcomes. Additionally, innovations in synthetic biology are paving the way for sustainable production of bioactive compounds, reducing dependence on endangered plant resources. As traditional botanical knowledge harmonizes with scientific and technological progress, herbal medicine is evolving into a more evidence-based, efficient, and patient-centered discipline. This convergence marks the dawn of a new era in global healthcare--where nature's wisdom meets modern science to deliver safe, effective, and personalized phytotherapeutic solutions.

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Herbal medicine; Nanotechnology; Green synthesis; Phytoconstituents.

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The rise of quantum computing brings powerful capabilities but also poses risks to current cryptographic schemes like RSA and ECC, which depend upon problems that quantum procedures like Shor's and Grover's can solve. This creates an urgent need for new methods that can withstand both classical and quantum attacks. Post-Quantum Cryptography (PQC) addresses this need by emerging secure algorithms for classical computers that can resist quantum threats. This paper reviews major families of PQC algorithms, including lattice-based, code-based, multivariate polynomial, hash- based, and isogeny-based. It analyses their security, performance, and implementation challenges. It also looks at global standardization efforts, particularly by NIST, and emphasizes the importance of early adoption, adaptable cryptographic systems, and planned migration to ensure secure communication in the quantum era.

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Post-Quantum Cryptography (PQC), Quantum Computing threats, Shor's Algorithm, Lattice-Based Cryptography, Quantum-Resistant Algorithms, Public Key Cryptography, Cryptographic Standardization, Digital Security, Quantum Security, NIST PQC project, Key Exchange, Multivariate Cryptography.

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A key component of contemporary artificial intelligence is machine learning (ML), which allows systems to learn from data, see patterns, and make judgements or predictions with little assistance from humans. This review paper presents a comprehensive overview of ML, covering its evolution, theoretical foundation and the primary learning paradigms like supervised, unsupervised, and reinforcement learning. It analyzes widely used algorithms including decision trees, support vector machines, k-means clustering , neural networks and ensemble techniques along with developments in deep learning, transfer learning and self supervised learning. The study demonstrates the revolutionary power of machine learning (ML) across industries by highlighting real-world applications in fields like healthcare, banking, manufacturing, autonomous systems, and natural language processing. In addition , it critically examines the challenges faced by ML systems, including data scarcity, model interpretability, overfitting, equity, and security flaws. Ethical and regulatory concerns such as bias mitigation, transparency, and privacy preservation and responsible deployment, are also addressed. By synthesizing current research trends and identifying open problems, this review aims to guide future advancements in ML, fostering innovations that are accurate, efficient, and aligned with societal values.

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Machine Learning, Supervised Learning, Data Privacy, Deep Learning, Reinforcement Learning

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The rapid growth of cloud computing has led to an unprecedented demand for large-scale data centers, which consume significant amounts of energy and contribute substantially to global carbon emissions. Achieving sustainability in cloud infrastructures has therefore become a critical challenge for researchers, industry, and policymakers. This paper investigates the environmental impact of modern cloud data centers and explores optimization approaches to enhance their energy efficiency while minimizing carbon footprint. A systematic assessment of energy consumption patterns, resource utilization, and cooling mechanisms is presented to identify the primary contributors to environmental degradation. The study reviews state-of-the-art green cloud computing techniques, including virtualization, dynamic workload consolidation, renewable energy integration, and intelligent scheduling algorithms. Furthermore, optimization models and energy-aware frameworks are evaluated for their effectiveness in balancing performance with sustainability goals. The findings highlight that multi-dimensional strategies--combining hardware innovations, software-level optimizations, and renewable integration--are essential to achieve sustainable cloud ecosystems. This research provides insights into practical approaches and future directions for developing environmentally responsible cloud data centers that align with global sustainability objectives.

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Carbon emission, environmental sustainability, dynamic VM, optimizing resource, renewalable energy, simulation-based framework, SLA constraints.

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The role of conversational agents in delivering mental health support has grown substantially. Yet, many existing systems struggle to respond in emotionally nuanced ways. This research introduces an innovative approach to equip mental health chatbots with emotionally adaptive reinforcement learning capabilities. By analyzing user inputs for emotional cues and context, the chatbot dynamically adjusts its conversational strategies using reinforcement learning techniques. The study presents the system's architecture, learning algorithms, and evaluation metrics. Results indicate significant improvements in user satisfaction and emotional well-being.

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Mental Health, Chatbots, Reinforcement Learning, Emotion Recognition, Affective Computing, Adaptive Dialogue Systems

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Mangrove ecosystems play an important role in providing ecological and economic benefits to coastal communities. The present study was conducted to assess the species diversity and distribution of mangroves in Dahanu, Maharashtra, India. Field surveys were conducted to identify and map the different mangrove species in the area. A total of 8 species of mangroves were identified, including Avicennia marina, Rhizophora mucronata, A. marina var. acutissima, A. officinalis, Sonneratia apetala, Brugiera cylindrical and Ceriops tagal. Among these, Avicennia marina was found to be the most dominant species in the area, followed by Rhizophora mucronata. The study also revealed that the mangroves were distributed unevenly in the study area, with the highest density of mangroves observed in the southern part of the estuary. The results of the study suggest that Dahanu has a diverse range of mangrove species, and conservation efforts should be undertaken to protect and restore these ecosystems

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Mangrove, Dahanu

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Digital entrepreneurship has emerged as a powerful tool for empowering women and driving sustainable development, particularly in regions like India and Africa. Despite significant advancements in digital technologies, women in these regions continue to face systemic barriers such as limited access to digital tools, financial resources, and socio-cultural constraints. This paper examines how digital entrepreneurship can bridge the gender gap in these regions, fostering inclusive growth and contributing to sustainable development. The study employs a mixed-methods approach, combining quantitative analysis of existing government and industry reports with qualitative interviews of women entrepreneurs, policymakers, and stakeholders in India and Africa. The findings highlight several key enablers for women in digital entrepreneurship, including the expansion of mobile technology, financial technologies (FinTech), and targeted skill development initiatives. However, persistent barriers such as the digital divide, gendered socio-cultural norms, and inconsistent policy implementation continue to hinder progress. In India, the widespread adoption of mobile technology has facilitated access to markets and financial inclusion, yet deep-rooted gender norms and familial responsibilities remain significant obstacles. In Africa, while digital inclusion initiatives have expanded, infrastructural challenges like unreliable electricity and internet connectivity, coupled with high levels of illiteracy, impede women's entrepreneurial potential. The paper proposes several policy implications to address these challenges, including enhancing digital infrastructure, creating gender-focused training programs, facilitating access to finance, promoting awareness to challenge gender norms, and fostering cross-regional collaboration. The study concludes that while both India and Africa have made strides in promoting women's digital entrepreneurship, further investments in infrastructure, policies, and education are crucial for achieving long-term sustainability and bridging the gender gap. A comparative, cross-regional approach is key to scaling successful initiatives and ensuring the empowerment of women in the digital economy.

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Digital entrepreneurship, gender gap, women empowerment, sustainable development, India, Africa, policy recommendations.

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WS2, Hydrothermal synthesis, PECVD, Photocatalysis, HER, Photodetector, Supercapacitor, Nanostructure morphology.

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Fish is protein rich food that needs skilful handling (Sujatha et al,2013). Main reason behind this is fish spoilage after capture due to high tropical heat which enhances activities of bacteria, enzyme and chemical oxidation of fat in fish. Due to improper handling near about 20-35% of fish harvest are wasted in India. These major losses could be minimized by application of skilful handling, processing and preservation techniques. The utilization of plant extract as reducing and stabilizing agents for nanoparticle synthesis offers numerous advantages over traditional chemical method including cost effectiveness, scalability and absence of toxic contaminant thus making plant mediated synthesis a sustainable alternative to its counterparts. (Singh et al,2023). Green synthesis uses plant extract rich in bioactive compound like flavonoids, alkaloid and phenolics as natural reducing and stabilizing agents instead of conventional method using harmful reducing agents. Green synthesis adheres to biodegradability, energy efficiency, waste reduction and uses of renewable resources as plants. (Abuzeid et al,2023). Use of indigenous plant knowledge for bioactive extraction connect modern nanotechnology with ancient ecological wisdom.

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1.Deterioration 2. Whatman 3. crystallinity 4. minced 5. capping

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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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 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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