International Journal of Engineering and Science Research
Distribution of Prime Numbers and Recent Advances Toward the Riemann Hypothesis
The distribution of prime numbers lies at the heart of analytic number theory. The Prime Number Theorem (PNT) gives the first-order asymptotic behaviour of the prime-counting function (\pi(x)), while the finer structure of the primes appears to be governed by the zeros of the Riemann zeta function (\zeta(s)). The Riemann Hypothesis (RH), asserting that all nontrivial zeros of (\zeta(s)) lie on the critical line (\Re(s)=\tfrac12), would yield near–optimal error terms in the PNT and far-reaching consequences for the distribution of primes in short intervals, arithmetic progressions, and many other problems. This paper reviews the classical framework connecting primes and zeta zeros via explicit formulas and discusses the role of zero-free regions and zero-density estimates. It then surveys selected recent advances connected—directly or indirectly—to RH: improved explicit zero-free regions, refinements in the proportion of zeros known to lie on the critical line, results on the pair correlation of zeros and its implications, developments around the de Bruijn–Newman constant, and progress on bounded gaps between primes. While a proof of RH remains elusive, these developments significantly sharpen our understanding of both prime distribution and the landscape of zeta zeros, suggesting promising directions for future research.
prime number theorem, Riemann zeta function, Riemann Hypothesis, zeros of zeta, zero-free region, pair correlation, bounded gaps between primes
Non-Governmental Organizations have emerged as vital partners in addressing India's community health challenges through innovative service delivery models and grassroots interventions. This study examines the multifaceted role of NGOs in strengthening community health systems across India through primary healthcare delivery, maternal and child health services, disease prevention programs, and health education initiatives. The research employed a descriptive cross-sectional design analyzing secondary data from government health reports, WHO databases, and published literature spanning 2020-2025. Analysis of 33 lakh registered NGOs in India, representing one NGO for every 600 people, reveals that NGOs contribute significantly to bridging healthcare gaps, particularly in underserved rural and tribal areas. Results demonstrate that NGO interventions have led to improved immunization coverage, reduced maternal mortality, enhanced community participation in health programs, and strengthened primary healthcare infrastructure. The study found that states with robust NGO partnerships achieved 15-20% higher health outcome indicators compared to states with limited NGO engagement. Discussion highlights NGO strengths including community trust, flexible programming, and innovative service models, while acknowledging challenges of funding constraints and coordination gaps. The research concludes that strategic NGO-government partnerships are essential for achieving Universal Health Coverage and sustainable community health improvements in India's diverse healthcare landscape. DOI: https://doi-ds.org/doilink/01.2026-62318779
Non-Governmental Organizations, Community Health, Primary Healthcare, Maternal Child Health, Health Service Delivery.
Oxygen-Bridged Transition Metal Chelates with Alkali & Alkaline Earth Metal Salts
Oxygen-bridged transition metal chelates are an important category of coordination compounds that are used in a variety of applications in catalysis, materials science and biological systems. This paper examines the synthesis, characterization and properties of oxygen bridged transition metal complexes in combination with alkali and alkaline earth metal salts. The study will focus on the structural changes, stability values, and possible uses of these heterometallic systems. To characterize compounds synthesized, a systematic approach by using spectroscopic techniques such as UV-Visible, FTIR, NMR, and thermal analysis were used. The hypothesis was that on incorporation of the alkali and alkaline earth metals the stability would increase and the electronic property of the transition metal chelates would be altered due to oxygen bridging. The findings indicated that there were substantial differences in stability constants, calcium and magnesium salts exhibited a higher level of coordination than sodium and potassium counterparts. The resulting complexes that were produced had changed spectroscopic characteristics and had higher thermal stability. It was discussed that the character of alkali and alkaline earth metals has a considerable impact on the bridging geometry and the stability as a whole of complexes. The paper finds that oxygen-bridged heterometallic chelates have a potential of providing a future development of high-dual-function materials.
Oxygen-bridged complexes, Transition metal chelates, Alkali metals, Alkaline earth metals, Heterometallic coordination
Growth, Yield, and Economic Analysis of Commercial Floriculture Crops under Controlled Environmental Conditions
Controlled environmental agriculture has revolutionized commercial floriculture by enabling year-round production and superior quality flowers. This study investigates the growth, yield, and economic viability of major commercial floriculture crops (rose, gerbera, carnation, and chrysanthemum) cultivated under polyhouse conditions in India. The primary objective was to evaluate agronomic performance and conduct comprehensive economic analysis comparing controlled environment cultivation with open-field systems. A factorial randomized block design was employed across four commercial floriculture farms in Maharashtra during 2023-2025, monitoring growth parameters, yield metrics, and input-output economics. Results demonstrated that controlled environmental conditions significantly enhanced flower yield (45-68% increase across crops), stem length (40-41% improvement), and vase life (52-53% extension) compared to open cultivation. Economic analysis revealed benefit-cost ratios ranging from 2.89 to 3.23 across different crops, with gerbera exhibiting maximum profitability. Gerbera cultivation generated highest net returns of ₹1,023,000 per hectare annually with benefit-cost ratio of 3.23, while rose yielded net returns of ₹847,000 per hectare with benefit-cost ratio of 2.89. The study concludes that controlled environmental floriculture presents substantial economic opportunities for Indian farmers despite higher initial investment requirements, with gerbera emerging as the most economically attractive crop followed by carnation and chrysanthemum. DOI: https://doi-ds.org/doilink/01.2026-77971983
Controlled environment agriculture, floriculture economics, polyhouse cultivation, yield optimization, commercial flower production
Nanotechnology Applications In Water Purification: Metal Oxide Nanoparticles And Membrane Nanofiltration
Water scarcity and contamination pose significant global challenges, necessitating innovative purification technologies. This study examines nanotechnology applications in water purification, specifically focusing on metal oxide nanoparticles and membrane nanofiltration systems. The primary objective is to evaluate the effectiveness of various nanomaterials in removing contaminants from water and to assess their practical implementation potential. A systematic methodology was employed, reviewing empirical data from published studies on nanoparticle efficiency, membrane performance, and removal rates of different pollutants. The hypothesis posited that nanotechnology-based systems demonstrate superior removal efficiency compared to conventional methods. Results indicate that metal oxide nanoparticles, particularly TiO₂, ZnO, and Fe₃O₄, achieve removal efficiencies exceeding 95% for heavy metals and organic contaminants. Membrane nanofiltration systems demonstrate 85-99% rejection rates for various pollutants. Discussion reveals that while nanotechnology offers exceptional purification capabilities, challenges including cost, scalability, and potential environmental impacts require attention. The study concludes that nanotechnology represents a promising solution for water purification, with metal oxide nanoparticles and nanofiltration membranes showing significant potential for addressing global water quality issues, though further research on long-term sustainability and economic viability is essential.
Nanotechnology, Water Purification, Metal Oxide Nanoparticles, Nanofiltration, Heavy Metal Removal
Navigating the Digital Bazaar: A Comprehensive Analysis of Social Media Marketing's Impact on Subscription-Based Services in the Indian Market
This study investigates the unique dynamics of social media marketing's impact on subscription-based services (SBS) in India, the world's fastest-growing digital economy. Through a mixed-methods approach analysing 150 Indian SBS companies (2021-2024), platform-specific user behaviour data, and cultural sentiment analysis, we identify distinct patterns shaped by India's demographic diversity, platform preferences, and value consciousness. Key findings reveal: (1) Regional language content on social media drives 3.2x higher conversion rates for SBS in tier 2/3 cities compared to English content; (2) WhatsApp-based marketing channels achieve 47% lower Customer Acquisition Cost (CAC) but 18% higher churn rates than platform-native advertising; (3) "Sachet pricing" previews on social media increase conversion by 68% for price-sensitive segments; (4) Influencer marketing ROI varies dramatically by platform, with YouTube influencers delivering 5.1x higher lifetime value than Instagram influencers for educational subscriptions. The study introduces the "India Social Media Adaptation Index" (ISMAI) to measure cultural and linguistic alignment, finding that services scoring above 7.0/10 achieve 42% better revenue retention. We conclude that success in India's SBS market requires hyper-localized, platform-diverse, and value-transparent social media strategies that account for the country's unique digital ecosystem.
Digital India, Subscription Economy, Social Media Marketing, Regional Content, WhatsApp Commerce, Influencer Marketing, Price Sensitivity, Tier 2/3 Markets
Affordable Sanitation Systems and Their Role in Inclusive Public Health
Affordable sanitation systems are foundational to inclusive public health because they reduce exposure to fecal pathogens, protect dignity and safety, and narrow health inequities that disproportionately affect low-income households, women and girls, persons with disabilities, and informal-settlement residents. Despite progress, sanitation gaps remain vast: in 2022, 57% of the world (≈4.6 billion people) used safely managed sanitation, while about 3.5 billion still lacked it; 419 million people continued to practice open defecation. These deficits translate into preventable illness and lost productivity; diarrhoeal disease alone kills ~443,832 children under five annually, and a significant proportion is preventable through safe water, sanitation, and hygiene (WASH). This paper synthesizes evidence on “affordable sanitation” (low-capex and low-opex options appropriate to local context) and explains how well-designed, inclusive sanitation services—paired with behavior change, fecal sludge management, and governance—advance public health, equity, and progress toward SDG 6. We propose a practical framework for selecting affordable systems (technical feasibility, life-cycle cost, inclusion/safety, climate resilience, and service-chain performance) and outline policy and program actions to accelerate equitable sanitation outcomes.
affordable sanitation, inclusive public health, fecal sludge management, SDG 6, equity, WASH, low-cost toilets, safely managed sanitation
Exploring Lexical Features of Indian English Newspapers for English Language Pedagogy
Indian English newspapers represent a distinctive variety of English that reflects the sociolinguistic landscape of India. This research explores the lexical features present in Indian English newspapers and examines their pedagogical implications for English language teaching in Indian contexts. The study aims to identify characteristic lexical patterns, borrowings, code-mixing practices, and semantic variations that distinguish Indian English print media from other English varieties. Using corpus linguistic methodology, a systematic analysis of five major Indian English newspapers was conducted over a six-month period, collecting approximately 500,000 words. The research employed quantitative and qualitative analytical techniques to identify frequency patterns, collocation structures, and context-specific lexical choices. Results reveal significant presence of Indianisms, hybrid constructions, cultural borrowings, and localized semantic extensions. The findings demonstrate that Indian English newspapers employ a unique lexical repertoire that bridges formal Standard English with indigenous linguistic elements. These lexical features provide authentic, culturally relevant materials for English language pedagogy in India. The study concludes that incorporating newspaper-based lexical features into curriculum design can enhance learner engagement, cultural awareness, and communicative competence in context-appropriate English usage.
Indian English, lexical features, newspapers, English language pedagogy, corpus linguistics
Electrical and Luminescent Behavior of Calcium Silicate Doped with Erbium and Terbium Ions
Calcium silicate phosphors doped with rare-earth ions exhibit remarkable luminescent and electrical properties for optoelectronic applications. This study investigates the electrical conductivity and photoluminescence characteristics of calcium silicate (Ca₂SiO₄) doped with erbium (Er³⁺) and terbium (Tb³⁺) ions at various concentrations. The synthesis was performed using solid-state reaction method at 1200°C. We hypothesized that dual doping would enhance both electrical conductivity and luminescence intensity compared to single-doped samples. Structural characterization was conducted using X-ray diffraction, while luminescent properties were analyzed through photoluminescence spectroscopy under UV excitation. Electrical measurements revealed that 2 mol% Er³⁺ and 1 mol% Tb³⁺ co-doped samples exhibited optimal conductivity of 3.8 × 10⁻⁴ S/cm at room temperature. Photoluminescence spectra showed characteristic green emission from Tb³⁺ at 544 nm and red emission from Er³⁺ at 660 nm. The energy transfer efficiency between Tb³⁺ and Er³⁺ was calculated as 42%. Results demonstrate that co-doping significantly improves material performance, suggesting potential applications in solid-state lighting, display devices, and optical sensors.
Calcium silicate, Erbium doping, Terbium doping, Photoluminescence, Electrical conductivity
Advanced AI Techniques For Cyber Hacking Breach Forecasting
Examining cyber event data sets is a crucial approach for enhancing our comprehension of the evolving threat landscape. This is a somewhat novel research area, and numerous investigations are yet to be conducted. This study presents a statistical analysis of a dataset pertaining to breach incidents during a 12-year period (2005–2017) including cyber hacking operations, including malware attacks. We demonstrate that, contrary to existing literature, both the inter-arrival periods of hacking breach incidents and the sizes of breaches should be described using stochastic processes rather than distributions, as they display autocorrelations. We offer specific stochastic process models to fit the inter-arrival periods and breach sizes, respectively. We additionally demonstrate that these models can forecast the inter-arrival intervals and the breach magnitudes. To gain deeper insights into the evolution of hacker breach incidents, we do both qualitative and quantitative trend studies on the dataset. We derive a collection of cyber security insights, indicating that the frequency of cyber intrusions is indeed growing, yet the severity of their damage remains unchanged. DOI: https://doi-ds.org/doilink/12.2025-65663568
An Improved Framework for Detecting Thyroid Disease Using Filter-Based Feature Selection and Stacking Ensemble
Machine learning (ML) had become quite significant in determining what type of thyroid disease an individual has and in informing individuals that they may possess one in the last several years. Single ML models have been examined but their capability to produce accurate forecasts is usually impaired by information imbalance and model-specific constraints. These problems were addressed using a number of ML techniques applied to a thyroid dataset primarily to increase the accuracy of the predictions. The most useful features that could make a correct evaluation were found by means of feature selection methods. Some of the models used to determine thyroid disease were support vector machines (SVM), decision trees (DT), k-nearest neighbors (KNN), logistic regression (LR), and artificial neural networks (ANN). There was also the ensemble approach of stacking and voting a classifier to combine multiple models to improve performance. There were significant improvements in the stacking ensemble including LightGBM, SVM, DT, KNN, LR, ANN and vote classifier including Boosted Decision Tree and ExtraTree. The best of these was the voting predictor whose accuracy was 98% reliable. This demonstrates that ensemble techniques may be useful in addressing imbalance of data and achieve improved outcomes in the prediction of what thyroid disease a patient has.
Artificial intelligence, healthcare, machine learning, filter-based stacking ensemble learning, thyroid disease”.
Interplay Of Aquatic Flora And Wetland Health: A Water Quality Study In Assam's Dhemaji And Jorhat Districts
Wetlands serve as critical transitional ecosystems between terrestrial and aquatic environments, providing indispensable ecological services including water purification, biodiversity conservation, and carbon sequestration. This research investigates the intricate relationship between aquatic macrophytes and water quality parameters in selected wetlands of Dhemaji and Jorhat districts of Assam, India. The primary objectives encompass documenting macrophyte diversity, assessing physicochemical water quality parameters, examining correlations between aquatic vegetation and water quality indicators, and evaluating overall wetland health status. A mixed-method approach integrating field sampling, laboratory analysis, and statistical correlation was employed across six wetland sites during 2019-2021. The hypothesis posited that macrophyte abundance significantly influences water quality parameters, thereby determining wetland health. Results revealed significant variations in dissolved oxygen (4.5-9.4 mg/L), pH (6.8-7.6), and nutrient concentrations across sites, with macrophyte-dominated areas exhibiting distinct water quality profiles. Statistical analysis confirmed significant negative correlations between free-floating macrophyte density and dissolved oxygen levels (r=-0.72, p<0.05). The study concludes that aquatic flora composition serves as a reliable bioindicator of wetland health, necessitating integrated conservation strategies for sustainable wetland management in northeastern India.
Aquatic Macrophytes, Water Quality Index, Wetland Health, Brahmaputra Floodplain, Bioindicators
Assessing Freshwater Integrity Using Planktonic Biological Monitoring Methods
The dire state of freshwater ecosystems in an era of worsening anthropogenic threats necessitates a timely development of effective water quality monitoring approaches based on biology. The objective of this study was to test the role of plankton as bioindicators to quantify integrity of freshwater systems in Indian water bodies. General aims included analysis of diversity patterns of phytoplankton and zooplankton, relating planktonic abundance to physicochemical parameters and assessment of the degree of usefulness of some Bio-indices to assess pollution. Description: A mixed-method approach combining field sampling and laboratory analysis was employed over a number of freshwater lakes and rivers. First, that the planktonic diversity indices are significantly related to water quality variables so that they can reflect the health of the ecosystem. Results showed Shannon-Weiner diversity index values between 1.2 and 3.4 within sites indicative of trophic conditions. Palmer's Pollution Index also indicated that organic pollution in water bodies under anthropogenic influence was moderate to high The dominance of Cyanophyceae and Chlorophyceae groups implied the eutrophic levels of the phytoplankton, whereas the composition of zooplankton was associated with the pollution stress. The dissolved oxygen, pH and planktonic density all correlated statistically significantly with these design parameters. It was discussed that biological monitoring, particularly in freshwater ecosystems, is a known and cost-effective reliable assessment of integrity, as opposed to or chemical methods alone (Suter, 1993). Planktonic biological monitoring constitutes a tool potentially suited to provide a more holistic assessment of freshwater quality that should be included in frameworks for environmental management.
Bioindicators, Freshwater integrity, Phytoplankton, Zooplankton, Water quality index
A Study on the Design and Analytical Evaluation of Liquid PVT Systems for Building-Integrated Renewable Energy
The integration of photovoltaic thermal (PVT) systems in building structures represents a significant advancement in sustainable energy generation, enabling simultaneous production of electricity and thermal energy from a single collector area. This study aims to evaluate the design parameters and analytical performance of liquid-based PVT systems for building-integrated renewable energy applications. The research objectives include assessing thermal and electrical efficiency variations across different absorber configurations, examining the influence of operational parameters such as mass flow rate and solar radiation intensity, and evaluating the overall energy performance of building-integrated photovoltaic thermal (BIPVT) systems. The methodology adopted a mixed-method research design incorporating both quantitative analysis of experimental data from existing literature and comparative evaluation of various liquid PVT collector configurations. The hypothesis proposed that liquid-based PVT collectors with optimized absorber designs demonstrate superior combined thermal-electrical efficiency compared to conventional standalone photovoltaic systems. The results indicate that spiral flow absorber configurations achieve the highest PVT efficiency of 68.4%, comprising 13.8% electrical and 54.6% thermal efficiency at optimal operating conditions. The discussion reveals that mass flow rate optimization and absorber design significantly influence system performance. In conclusion, liquid PVT systems present viable solutions for building integration, contributing substantially toward renewable energy targets and sustainable building development.
Photovoltaic Thermal Collector, Building-Integrated Renewable Energy, Liquid PVT, Thermal Efficiency, Sustainable Buildings
Design And Optimization Of A Robotic Arm Using Advanced Materials
This Project highlights the importance of digital prototyping over traditional prototyping in manufacturing engineering. In today’s fast paced industrial era, every company strives for speed and efficiency in manufacturing to meet client demands, with the agricultural sector being a notable exception. Robots now perform tasks more quickly, cost effectively, and accurately than humans ever could. The moving parts of a robotic arm experience significant elastic deformations and stresses, often operating at ve locities comparable to the tool itself. This project focuses on the shearing operation, with the primary challenge being the design and optimization of a lightweight robotic arm component that is both durable and reliable. Through extensive study, we have developed and analysed a jointed robotic arm where the base remains fixed, and the remaining joints can move vertically and horizontally. The design and analysis were conducted using three different materials: Epoxy Carbon Woven, Steel Alloys , and AL NI . A force of 4000N was applied to the robotic arm to evaluate its structural and thermal performance. Ultimately, the study identifies the most suitable material among these three options for use in the robotic arm.
Thermal Analysis Of A Nuclear Power Plant Heat Pipe Exchanger Using Ansys
Heat pipes are a unique type of heat transfer device that can transport large amounts of heat through a small cross sectional area, with minimal temperature differences. A heat pipe is made up of a pipe or tube and a base fluid. In practice, the heat pipe is poured into a mould that is compatible with the cooling media. These devices have found uses in a variety of fields, including space apparatus, solar energy systems, electronic equipment, and air conditioning systems, due to their simplicity of design and ease of manufacture and maintenance. Using heat pipe-cooled micro-reactors in power plants is one of the newest technologies in small power plants. In these power plants, heat is taken from the reactor by several heat pipes and transferred to the working fluid in the main heat exchanger of the power plant. The present work focused on the design and thermal analysis of heat pipe used in nuclear power plant. Three models namely Model-1, Model-2 and Model-3 were modeled in CATIA software. Thermal analysis was carried out on these models with steel and copper material properties in ANSYS software. Finally optimum model with suitable material was suggested for the heat pipe.
Modeling And Analysis Of A Cnc Milling Machine Bed With Composite Materials
The materials utilized as a part of a machine device have an unequivocal part in deciding the efficiency and exactness of the part made in it. The regular basic materials utilized as a part of exactness machine apparatuses, for example, cast iron and steel at high working velocities create positional mistakes because of the vibrations moved into the structure. Quicker cutting rates can be procured just by structure which has high firmness and great damping attributes. We know that by experiences life of a machine is inversely proportional to the levels of vibration that the machine is subjected. The further procedure is completed to experience the distortion, normal recurrence and removal utilizing Static examination, Modal investigation and Harmonic individually. Since the bed in machine apparatus assumes a basic part in guaranteeing the exactness and precision in segments. Is a standout amongst the most critical apparatus structures which have a tendency to retain the vibrations coming about because of the cutting operation. To break down the bed for conceivable material changes that could build solidness, diminish weight, improve damping characteristics. In this paper constant load is applied on a bed with and without Nanocoating on the material and with composite materials. Graphene is used for coating basing on strength and thermal properties. Modeling of the bed was carried out in CATIA V5 R24 and ANSYS is used for structural analysis. By comparing the stress distribution and deformation in the bed by changing its material with the previous. Finally this thesis summarizes the suitable materials can be used as machine bed material.
Machine Tool, Machine Bed, Stiffness, Damping, CATIA V5 R24, ANSYS
Design And Structural Analysis On A Frameless Chassis Construction Of Bus For Different Loads
Automotive chassis is a skeletal frame on which various mechanical parts like engine, tires, axle assemblies, brakes, steering etc. are bolted. The chassis is considered to be the most significant component of an automobile. It is the most crucial element that gives strength and stability to the vehicle under different conditions. Bus chassis is the design and quality of bus chassis depends on the capacity of bus. It can be tailor made according to the needs and can be availed with features like transverse mounted engine, air suspension as well as anti-roll bars. A well manufactured bus chassis offers various benefits like high torque from low revs, superior brake performance and more. Bus chassis designed for urban routes differs from the one manufactured for suburban routes. For bus frameless chassis construction is used. In this frame less chassis type all the components is attached to the body. All the functions of the frame carried out by the body itself. Due to elimination of long frame it is cheaper and due to less weight most economical also. Only Disadvantage is repairing is difficult. This type of frames will affect more in collision of vehicle. The aim of the project is to analyze the frameless chassis with existing material at different load conditions. Presently steel is used for chassis construction. Modeling of the frameless chassis will be done by using modeling software CATIA and structural analysis was performed in ANSYS software to determine deformations and stresses.
Design And Analysis To Analyse The Heat Transfer Of Space Heater For Boat
As fossil fuels are depleting gradually with the course of time there is a need in developing innovative products that can extract maximum potential that the system can deliver. One of the key areas that still make use of diesel fuels for experiencing thermal comfort is the boat heating system. Due to its user-friendly characteristics with low maintenance requirements has still been the benchmark for all other types of heaters. Boat cabin heater is an essential setup for all kinds of boats and ship not just for the warmth but also to eliminate the dampness that can spread throughout the boat in cold weather. The project presents a detailed analysis of thermal behavior of a space heater using steady state thermal analysis technique to study the effect of fin numbers and fin location inside the space heater. Initially base model of space heater with two numbers of fins was prepared and analyzed with steel alloy and copper alloy material properties. In the optimized model an increase in the number of fins inside the space heater was made and analyzed with same material properties. Steady state thermal analysis with same boundary conditions was made in both cases. Modeling of space heater was done in CATIA software and the analysis was carried out in ANSYS software.
Harnessing the Pyrazoline Scaffold: Rational Design, Synthesis, and Mechanistic Evaluation of Novel Derivatives as Potent and Selective Antitumor Agents
The clinical management of cancer remains profoundly constrained by the off-target toxicity and innate or acquired resistance that characterize conventional chemotherapeutic agents. This therapeutic impasse necessitates the urgent development of novel molecular entities with precise mechanisms and enhanced selectivity for malignant cells. In this pursuit, the pyrazoline scaffold stands out as a privileged and versatile pharmacophore, demonstrating a rich yet underexploited potential in oncological drug discovery. We posited that systematic, rational modification of this core structure with distinct electronic and steric substituents would serve as a powerful strategy to amplify its antitumor efficacy while concurrently optimizing its therapeutic index. To test this hypothesis, we deployed a multidisciplinary strategy centered on computer-aided molecular design. This involved the strategic incorporation of robust electron-withdrawing groups and the implementation of pharmacophore fusion techniques to engender synergistic bioactivity. Our integrated discovery pipeline seamlessly wove together precision synthetic chemistry, rigorous in vitro phenotypic screening, predictive in silico modeling, and deep-drive mechanistic dissection. This comprehensive approach yielded a seminal achievement: the discovery of our lead compound, PYR-12, which manifests exceptional, nanomolar-range cytotoxic potency across a diverse spectrum of human carcinoma cell lines. Notably, PYR-12 exhibits a remarkably high selectivity index, demonstrating minimal cytotoxicity towards non-tumorigenic cells, thereby forecasting a wide therapeutic window. Elucidation of its mode of action uncovered a compelling dual-mechanistic profile: it instigates mitochondrial-dependent apoptosis, evidenced by caspase-3/7 activation and PARP cleavage, while concurrently functioning as a potent inhibitor of tubulin polymerization, inducing a pronounced G2/M cell cycle arrest. Beyond introducing a highly promising lead candidate, this study delivers a decisive structural and mechanistic roadmap. It decrypts the fundamental structure-activity relationships governing pyrazoline efficacy and delineates a clear path for the rational optimization and advanced preclinical development of this compelling class of targeted anticancer therapeutics.
Pyrazoline, Targeted Cancer Therapy, Rational Drug Design, Structure-Activity Relationship (SAR), Mitochondrial Apoptosis, Tubulin Polymerization Inhibitor, Lead Compound, Molecular Docking, Pharmacophore Fusion, Selective Cytotoxicity, G2/M Arrest.
Theoretical and Mathematical Examination of Black Hole Spacetime Dynamics
Black holes represent one of the most profound predictions of Einstein's general theory of relativity, constituting regions of spacetime where gravitational effects become so extreme that nothing, including light, can escape. This review paper examines the theoretical and mathematical frameworks underlying black hole spacetime dynamics, synthesizing contemporary research on metric solutions, singularity theorems, and horizon properties. The primary objectives include analyzing Schwarzschild and Kerr metric formulations, evaluating Penrose-Hawking singularity theorems, and examining thermodynamic properties of black hole horizons. The methodology employs systematic literature review of peer-reviewed publications from Google Scholar, focusing on mathematical physics approaches to spacetime geometry. The hypothesis posits that unified mathematical treatment of black hole dynamics reveals fundamental connections between general relativity, quantum mechanics, and thermodynamics. Results demonstrate significant advances in understanding event horizon mathematics, geodesic equations, and Hawking radiation mechanisms. Discussion reveals that black hole spacetime dynamics remain central to theoretical physics, with implications for quantum gravity research. The conclusion emphasizes the necessity of continued mathematical investigation for resolving information paradox and singularity problems in black hole physics.
Black Hole, Spacetime Dynamics, General Relativity, Event Horizon, Singularity Theorems
DESIGN AND DEVELOPMENT OF A WIND TURBINE GENERATOR
This project focuses on the design and development of a small-scale wind turbine generator aimed at providing sustainable, off-grid power solutions. The generator is designed to efficiently harness wind energy and store it in a power bank for charging small electronic devices such as smart phones, portable lamps, and other off-grid appliances. By utilizing the waterfall methodology, the project ensures a structured approach to design, development, testing, and deployment. Key components of the system include anemometers, digital voltmeters, and a voltage regulator to monitor wind conditions, optimize energy generation, and maintain safety. The research emphasizes the integration of blade design optimization and performance monitoring to enhance the efficiency of the wind turbine, while also considering portability and ease of use. The study demonstrates the feasibility of using small-scale wind turbines as reliable energy sources for remote and off-grid areas, showcasing the potential for wind power in addressing the global demand for renewable energy solutions.
Small-scale wind turbines, Off-grid power systems, Portable wind turbines, Blade design optimization, Wind turbine efficiency.
LEGAL CHALLENGES IN REGULATING AI-POWERED CYBERSECURITY TOOLS
AI-powered cybersecurity tools can improve the threat detection process by analyzing the attack patterns. Biased datasets in implementing the AI model can increase the data privacy issue and enhance risks in business practices. Algorithmic bias, data privacy, and data protection are the key factors that can increase the legal challenges in AI-powered cybersecurity tools. Data governance and ethical practices can solve the legal challenges in AI-driven cybersecurity tools.
Cybersecurity, legal challenges, AI implementation, Data Privacy, Compliance issue
ADVANCED IMAGE PROCESSING AND DEEP LEARNING FOR SKIN CANCER SCREENING
Due to the limited availability of resources, skin cancer is one of the most quickly spreading diseases in the globe. Identification of skin cancer through an accurate diagnosis is essential for a preventative approach in general. Dermatologists struggle to detect skin cancer at an early stage, and in recent years, both supervised and unsupervised learning tasks have made extensive use of deep learning. One of these models, Convolutional Neural Networks (CNN), has surpassed all others in object detection and classification tests. The dataset is screened from MNIST: HAM10000 which consists of seven different types of skin lesions with the sample size of 10015 is used for the experimentation. The data pre-processing techniques like sampling, dull razor and segmentation using autoencoder and decoder is employed. Utilizing the HAM10000 dataset and an optimized CNN, the study identify seven types of skin cancer. Two optimization functions (Adam and RMSprop) and three activation functions (Relu, Swish, and Tanh) were used to train the model. In addition, an XAI-based skin lesion classification system with Grad-CAM and Grad-CAM++ was developed to explain the model's decisions. This system can assist physicians in making accurate early skin cancer diagnoses in their early stages.The future extension of this study includes increasing forecast accuracy through parameter tuning.
Skin Cancer Screening, Disease Detection, CNN, HAM10000 dataset, Adam RMSprop, XAI, Image Processing, Deep Learning.
G. Tejaswini, K. Gayathri, I. Dharani, G. Lokesh
A STUDY OF CHETAN BHAGAT'S PERSPECTIVE ON MODERN URBAN YOUTH LIFE
Chetan Bhagat is one of India's most prominent contemporary authors, known for his best-selling novels that focus on the lives, struggles, and aspirations of modern urban youth. His works delve into the complexities of love, relationships, career, and societal pressures, making them relatable to a vast demographic. This research paper aims to explore Chetan Bhagat's perspective on the lives of urban youth in India, analyzing his portrayal of the youth's challenges and dreams, as well as the socio-cultural context that influences them. Through a detailed study of his most notable works, such as Five Point Someone, 2 States, The 3 Mistakes of My Life, and Half Girlfriend, this paper highlights the ways Bhagat's narrative resonates with the aspirations, struggles, and contradictions inherent in the lives of young people in modern urban India.
Chetan Bhagat, Urban Youth, Modern Life, Youth Culture, India, Relationships, Society, Career, Social Issues.
MYTH AND REALITY IN AMISH TRIPATHI'S SHIVA TRILOGY
Amish Tripathi's Shiva Trilogy – consisting of The Immortals of Meluha, The Secret of the Nagas, and The Oath of the Vayuputras – has captivated readers worldwide through its unique blend of mythology and fiction. The series reimagines Hindu mythological figures and events in a fictional world, bringing together elements of myth, historical narratives, and modern interpretations. This research paper explores the role of myth and reality in the Shiva Trilogy, analyzing how the author blurs the line between the two. Through the lens of mythological retelling, this paper examines how Tripathi adapts and reinterprets ancient Indian mythology and reconfigures it for a contemporary audience. Additionally, the paper investigates how Tripathi’s narrative navigates the real and the imaginary, raising questions about the influence of myth in shaping modern perceptions of history, culture, and identity.
Mythology, Reality, Historical Fiction, Indian Literature, Deity ,Moral Dilemmas, Mythic Revival.
Traffic congestion is increasingly becoming a critical issue in urban areas due to the rapid growth of population and automobiles. This problem, particularly pronounced in megacities, leads to significant delays, stress for drivers, and increased fuel consumption and air pollution. The growing nature of traffic congestion makes it imperative to calculate real-time road traffic density for optimized signal control and more effective traffic management. The efficiency of the traffic controller plays a pivotal role in ensuring smooth traffic flow, thus highlighting the need for innovative solutions to accommodate this growing demand. Our proposed system leverages live images from traffic cameras to calculate traffic density using image processing techniques and artificial intelligence (AI) algorithms, which can help optimize traffic signal management and improve overall traffic control systems [1][2][5][6][7]. This system integrates advanced technologies such as intelligent traffic systems, image processing, and AI for real-time monitoring and management of traffic flow
Traffic congestion, Urban mobility, Image processing, Traffic density, Artificial intelligence, Signal optimization, Real-time monitoring, Smart traffic management, Traffic flow Megacities.
K. Akhila, K. Mounika, K. Nagarjuna Reddy
IMPROVING AUTONOMOUS VEHICLES NAVIGATION THROUGH TRAFFIC SIGN RECOGNITION
Traffic sign recognition is an essential component of intelligent transportation systems (ITS), which aims to improve road safety and assist drivers in navigating through road networks efficiently. This paper presents a system designed to recognize traffic sign boards using computer vision and machine learning techniques. The system processes images of road signs, classifies them into various categories, and provides relevant information to drivers, contributing to safer driving. This approach integrates image processing algorithms, deep learning models, and realtime data analytics to ensure high accuracy and fast processing. The proposed system improves upon traditional methods by reducing false positives and enhancing recognition speed. To ensure a smooth and secure flow of traffic, road signs are essential. A major cause of road accidents is negligence in viewing the Traffic signboards and interpreting them incorrectly. The proposed system helps in recognizing the Traffic sign and sending a voice alert through the speaker to the driver so that he/ she may take necessary decisions. The proposed system is trained using Convolutional Neural Network (CNN) which helps in traffic sign image recognition and classification. A set of classes are defined and trained on a particular dataset to make it more accurate.
Traffic Sign Recognition, Convolutional Neural Network (CNN), Intelligent Transportation Systems (ITS), Deep Learning, LeNet.
I. Rakesh Reddy, J. Renuka, G. Jagadish Goud
AN INTELLIGENT DATA DRIVEN MODEL TO SECURE INTRA VEHICLE COMMUNICATIONS BASED ON MACHINE LEARNING
The high relying of electric vehicles on either in vehicle or between-vehicle communications can cause big issues in the system. This paper is going to mainly address the cyber-attack in electric vehicles and propose a secured and reliable intelligent framework to avoid hackers from penetration into the vehicles. The proposed model is constructed based on an improved support vector machine model for anomaly detection based on the controller area network (CAN) bus protocol. In order to improve the capabilities of the model for fast malicious attack detection and avoidance, a new optimization algorithm based on social spider (SSO) algorithm is developed which will reinforce the training process offline. Also, a two-stage modification method is proposed to increase the search ability of the algorithm and avoid premature convergence. Last but not least, the simulation results on the real data sets reveal the high performance, reliability and security of the proposed model against denialof-service (DoS) hacking in the electric vehicle
Electric Vehicles, Cyber-Attack, Anomaly Detection, Support Vector Machine (SVM), Controller Area Network (CAN) Protocol, Social Spider Optimization (SSO) Algorithm, Malicious Attack Detection, Denial-of-Service (DoS) Hacking, Security Framework, Simulation Results.
G. Manikanta, G. Baswa Raj, K. Bhanu Prakash
The rapid growth of technology has significantly improved daily life; however, this advancement has also led to an increase in traffic hazards, resulting in a higher ratio of road accidents. Often, the loss of life occurs due to delayed emergency response and inadequate facilities. To address this issue, various systems have been proposed to detect accidents and enhance human safety. This study introduces an Android-based Automatic Vehicle Accident Detection system that incorporates GPS technology for location tracking, offering a comprehensive solution for accident detection and prevention. The system is divided into four modules, each addressing specific functionalities to ensure a robust and integrated response. The proposed application builds on existing research, such as smartphone-based accident detection systems, intelligent traffic monitoring solutions, and mobile edge computing for real-time responses. Additionally, it leverages advancements in participatory sensing and middleware technologies for social and emergency applications. By utilizing these technologies, this system aims to reduce response times and improve overall road safety.
Accident Detection, Road Safety, Emergency Response, GPS Technology, Android Application, Traffic Hazard Prevention, Real-Time Monitoring, Mobile Edge Computing, Participatory Sensing, Vehicle Safety System.
J. Sairohith, 3 M. Sushank, G. Sainath Reddy, K. Phanindra Kumar