Recent Articles

Mudit Mittal, Prabhakar Semwal, Pallaw Singh Aswal  ·  International Journal of Technology and Emerging Research  ·  25 Jun 2026

Lung cancer is the leading cause of cancer-related mortality worldwide, responsible for approximately 18% of all cancer deaths globally. The absence of early clinical symptoms significantly delays diagnosis, making timely and accurate automated detection systems a critical public health necessity. The rapid growth of deep learning technologies has opened transformative opportunities for automated lung cancer detection and classification from medical imaging data, particularly CT scans. However, despite impressive benchmark results, numerous methodological challenges and research gaps persist that hinder the transition of these models from laboratory settings to clinical practice. This paper presents a comprehensive systematic review of six representative deep learning-based studies for lung cancer detection and classification, published between 2018 and 2025. The reviewed works include four original research papers — spanning hybrid CNN-SVM models, transfer learning frameworks, CNN-LSTM architectures, and transformer-based segmentation systems — alongside two systematic review and survey papers that collectively examine over 30 models from the literature. A structured comparative analysis is conducted across eight key dimensions: paper type, datasets used, model architectures, classification task complexity, imaging modality, best reported accuracy, and other quantitative performance metrics including AUC, sensitivity, specificity, F1-score, and Dice coefficient. The critical analysis of each paper reveals recurring limitations across the field, including over-reliance on single benchmark datasets, restriction to binary classification tasks, absence of integrated segmentation and multi-class classification pipelines, minimal adoption of transformer-based models for classification, near-complete lack of model explainability mechanisms, and a universal absence of prospective clinical validation. Extending this analysis across all six reviewed papers, nine significant and cross-validated research gaps are systematically identified and documented. These gaps collectively define the design requirements for a next-generation lung cancer detection model: one that integrates 3D segmentation with multi-class subtype classification, employs transformer-based attention mechanisms, incorporates model interpretability, supports Low-Dose CT inputs, and is validated across diverse datasets and clinical environments. The findings of this review establish a rigorous evidence-based foundation for the development of a novel deep learning and image processing model for lung cancer detection, contributing to the ongoing effort to bridge the gap between computational performance and real-world clinical applicability.

Dr Aprana Singh, Ms Jaya Singh  ·  International Journal of Technology and Emerging Research  ·  25 Jun 2026

The rapid growth of electronic waste (e-waste) has become a serious challenge for environmental governance and sustainable waste management, especially in low- and middle-income countries where formal recycling infrastructure remains underdeveloped. This research paper is based on a three-day field study conducted in July 2025 in Rewa, a medium-sized city in Madhya Pradesh, India, to understand the on‑ground realities of e-waste management. The study used direct observation, semi-structured interviews, and photographic documentation. Twelve different sites were studied, including the municipal waste transfer hub, scrap dealer godowns, electronic repair shops, computer vendors, and brand-authorized showrooms; the entire flow of e-waste from generation to final destination was mapped. The findings reveal a dual system. On one hand, a formal public-private partnership operating under a twenty-year contract manages about ninety metric tons of municipal solid waste daily through source-level segregation and waste-to-energy conversion. On the other hand, a strong informal sector consisting of wandering waste collectors, repair technicians, and scrap dealers handles most of the e-waste but works without safety standards, environmental compliance, or proper documentation. The study found that at the local repair level, useful components from televisions, computers, and mobile phones are systematically extracted and reused. The remaining e-waste is sent in bulk to unregulated markets in Delhi and Indore, where prices range from approximately ten to five hundred fifty rupees per unit. Additionally, no special recycling arrangement was found for the plastic casings of electronic devices; they eventually end up in the city’s general waste stream. This research shows that the coexistence of formal and informal systems is not a problem in itself. What is needed is effective policy‑based integration between them so that health risks can be reduced, material value can be better recovered, and compliance with national e‑waste rules can be ensured. The paper offers the following policy suggestions, establishing a dedicated e‑waste collection centre at the city level, training informal workers in safe dismantling techniques, and creating an economic incentive system to channel e‑waste to authorised recyclers.

Sunny Devel  ·  International Journal of Technology and Emerging Research  ·  20 Jun 2026

One of the most significant factors of infant survival, growth and development in the first two years of life is infant feeding practices. Proper breast feeding and supplementary feeding helps in alleviating malnutrition, curbing childhood morbidity and enhancing health results in the long-term. Although significant policy interventions have taken place in India, the inequality in infant feeding habits still remains, especially in Scheduled Tribe (ST) communities. One of the most tribal states in India, Jharkhand records poor child nutrition, delayed breastfeeding initiation, insufficient complementary feeding and increased under-nutrition levels. These differences are contributed by a multifaceted combination of socioeconomic disadvantage, education disparities and entrenched cultural habits. This is a systematic literature review that provides factors to explain tribal inequalities in infant feeding practices in Jharkhand focusing on poverty, maternal education and cultural factors. The review summarises the data of 15 qualitative, quantitative and mixed-method articles (2020-2026), as well as the results of National Family Health Survey (NFHS-4, NFHS-5 and new NFHS-6 evidence). The search using Preferred Reporting Items of Systematic Reviews and Meta-Analyses (PRISMA) was used to identify the studies and evaluate them with the help of the Mixed Methods Appraisal Tool (MMAT). The results show that poverty is one of the biggest structural obstacles that can restrict access to nutritious foods, healthcare services and the best practices of feeding infants. Mother education was always a very strong predictor of breastfeeding behaviour as well as complementary feeding behaviour, cultural beliefs and traditional food practices had a strong influence in the care giving decision of the tribal communities. The review also highlights existing inequalities and disparities in healthcare usage across the regions. The study conclude that the infant feeding inequities in Jharkhand can be mitigated by combining multifaceted methods to tackle the socioeconomic and social factors involved in the problem, alongside raising maternal education levels and ensuring cultural-sensitive nutrition programs to suit tribal households.

G. Parvathi Devi, G. Harika, M. Sravani, S. Dhaneesha  ·  International Journal of Technology and Emerging Research  ·  18 Jun 2026

The conversion of unformatted electronic data into the useable formats is crucial in making sure that visually impaired people have equal opportunities to access. The paper is a proposal of an AI-based tool that will transform unstructured data into standardized Braille material in an efficient and accurate way. The proposed solution combines Optical Character Recognition, Natural Language Processing and Machine Learning and extracts, cleanses and analyzes text on scanned documents, images, PDF files, and web pages. Deep learning models also increase the quality of Braille translation because, in addition to the character recognition, it also increases the contextual understanding. The system will do automated text extraction, noise reduction, language detection and semantic processing and encode the material in Braille that can be displayed digitally and embossed or cut into digital displays and embossing machines. The experimental results prove that the solution can serve real-time processing and decrease the amount of effort required to conduct manual transcription considerably. The suggested system helps enhance the availability of information and introduce inclusive online communication.

Sourav Verma, Gargi Achalkar, Sakshi Shitole, Ojas Watave  ·  International Journal of Technology and Emerging Research  ·  16 Jun 2026

Managing daily meals has become a major challenge for students and working professionals due to busy schedules and limited time for cooking. Traditional tiffin services provide affordable and home-style meals but rely on manual processes such as phone calls and handwritten records. These methods lead to inefficiencies such as poor communication, lack of transparency, rigid scheduling, and absence of real-time tracking. This paper presents the Smart Tiffin Scheduler, a web-based system designed to automate and digitize tiffin service operations. The system allows users to schedule meals, customize preferences, pause or resume subscriptions, and track deliveries in real time. Service providers are equipped with a centralized dashboard to manage orders, delivery schedules, and customer interactions efficiently. The system is developed using React.js for the frontend, Node.js with Express.js for backend processing, and MongoDB for data storage. Real-time notifications and tracking mechanisms enhance usability and efficiency. The results demonstrate reduced manual errors, improved coordination, and increased customer satisfaction, making the system suitable for modern urban food service management.

Pushpraj Singh, Prasoon Soni, Aseena Ekka, Rashi Jaiswal  ·  International Journal of Technology and Emerging Research  ·  16 Jun 2026

The accomplishment of SDG 6 is especially important for India. India, a country with more than 1.4 billion people and a wide range of ecological and socioeconomic circumstances, has significant issues with water stress, contamination, and access disparities. Nearly 600 million Indians face high-to-extreme water stress, and by 2030, the demand for water is expected to double the supply, according to NITI Aayog (2019). The Indian government established the JJM in 2019 under the Ministry of Jal Shakti to fulfil the water demands. The present study was designed to assess the benefits of JJM in Anjani village of GPM district with objectives, to assess how the Jal Jeevan Mission has affected the everyday activities, socioeconomic condition and health outcomes. The implementation of the JJM has brought significant improvements in this situation. Universal tap connections, reliable supply, and better water quality have not only ensured household-level water security but also positively impacted education, health, women’s empowerment, and livelihoods. Children no longer miss school due to water-fetching duties, women save valuable time for productive work, and households report reduced medical expenses due to fewer waterborne diseases.

Pravin Bhalerao Thakare, Dr.Harbeer Singh  ·  International Journal of Technology and Emerging Research  ·  13 Jun 2026

Industrial wastewater contains significant quantities of toxic heavy metal ions that pose serious environmental and health risks. Conventional treatment methods often suffer from limitations such as low selectivity, sludge generation, and high operational costs. Composite cation exchange materials have emerged as effective alternatives due to their high ion exchange capacity, chemical stability, selectivity, and regeneration ability. In the present work, a zirconium phosphate–polyaniline (ZrP-PANI) composite cation exchanger was synthesized through a sol-gel precipitation technique followed by in-situ polymerization. The synthesized material was characterized using FTIR, XRD, SEM, and TGA techniques. The ion exchange capacity, thermal stability, and metal ion removal efficiency were evaluated. The composite exhibited an ion exchange capacity of 2.15 meq g⁻¹ and demonstrated excellent removal efficiency for Pb²⁺, Cd²⁺, Cu²⁺, Ni²⁺, and Zn²⁺ ions from industrial wastewater. The results indicate that composite cation exchangers are promising materials for wastewater treatment and environmental remediation.

Dr. Annukampa Baruah  ·  International Journal of Technology and Emerging Research  ·  13 Jun 2026

Medical aesthetic procedures have become a common practice in the medical field and, post-operative inflammation and scarring are some of the major issues that adversely affect the recovery process and patient satisfaction. Recent developments in microbiome studies have demonstrated the possible importance of commensal microflora as a way to decrease immune reactions, repairing tissues and ensuring homeostasis of the skin. The elicitation of useful microbial communities has, therefore, become a promising treatment approach to support recovery after skin injury. This literature review was a systematic study to identify the research topic, the role of commensal microflora in the minimisation of inflammation and scar reduction during medical aesthetic procedures. A systemic search of the peer-reviewed literature published since 2020 but not earlier was carried out in several scientific databases. A total of fifteen articles were eligible to meet the inclusion criteria and included research of quantitative, qualitative and mixed-methods. The articles included researched microbial-host interactions, microbiome-derived therapies, wound healing processes, strategies to prevent scarring and patient experience of recovery. The results also showed that commensal microflora have a great role in skin homeostasis in terms of immune reaction regulation and microbial balance regulation. Some of the studies described the presence of anti-inflammatory effects in relation to the positive microorganisms, probiotics and postbiotics and the ability to maintain controlled healing conditions. There were also indications that microbial communities mediate wound healing by communicating with regenerative pathways and tissue repair mechanisms. The development of new therapeutic methods, such as designed commensal bacteria, also provided evidence that they could be used to deliver bioactive compounds with an ability to improve the process of healing. Also, qualitative and mixed-method studies demonstrated the great physical, psychological and social effects of scarring, with the need to focus on the type of interventions that can enhance the recovery and cosmetic results. Overall, the review indicates that commensal microflora is a crucial part of skin wellbeing, and can offer innovative opportunities in eliminating inflammation and scarring after medical cosmetic surgery. Despite the need to conduct more clinical trials to determine causal therapeutic uses, microbiome-based approaches have a strong potential of improving the quality of healing, achieve better aesthetic results and aiding patient-centred care in regenerative and aesthetic medicine.

Shivanand R Koppalkar, Vivek Krishan, Yogesh Gupte, Arunkumar Gunasekaran, Kathiravan Udayakumar, Prachi Kadam  ·  International Journal of Technology and Emerging Research  ·  13 Jun 2026

The majority of commercial real estate (CRE) Digital Twin (DT) pilots do not progress to production. The core obstacle is not technology, it is the persistent fragmentation of lifecycle data across BIM platforms, building automation systems, IoT networks, and enterprise software. Systems that were never designed to share information cannot simply be integrated by building another platform atop them. This paper introduces CREST, the Commercial Real Estate Smart Twin framework; a seven-tier implementation pathway that links strategic business intent to measurable operational outcomes across the CRE lifecycle. The seven tiers are: Strategic Canvas, Value Streams, Adoption Model, Reference Architecture, Deployment Playbook, Maturity Model, and ROI & Benefits Matrix. CREST draws on foundational DT literature, industrial reference architectures from the Industry IoT Consortium, and learnings from 80+ engagements within global CRE organizations. The framework directly confronts four challenges that repeatedly kill CRE twin programmes: fragmented data across lifecycle stages, reactive maintenance cultures, mounting ESG/CSRD reporting obligations, and post-pandemic space utilization volatility. For each, CREST embeds minimum data requirements, governance protocols, and integration patterns into its deployment playbook and reference architecture. Case evidence shows a 20% drop in delayed projects and a 30% gain in Net Promoter Score, alongside documented reductions in operating cost. CREST’s contribution is deliberately practitioner-facing: a repeatable integration blueprint that reduces delivery risk at both building and portfolio scale.

Mehul Chavan, Ajinkya Bondge, Atharva Patil, Pratik Chinchawadkar, Soham Bhogale, Satyajeet Shinge  ·  International Journal of Technology and Emerging Research  ·  13 Jun 2026

Campus placement preparation is a crucial phase in a student’s academic journey; however, the current ecosystem is highly fragmented, requiring students to depend on multiple platforms for aptitude practice, coding preparation, resume building, and interview training. This lack of integration leads to inefficiencies, poor progress tracking, and difficulty in assessing overall readiness. Students often struggle to identify their weaknesses due to the absence of centralized feedback, while Training and Placement Officers (TPOs) lack real-time insights into student performance, and recruiters face challenges in evaluating candidates holistically. To address these limitations, this paper proposes PrepWise AI, an AI-powered career readiness ecosystem that integrates all aspects of placement preparation into a unified, intelligent platform. The system combines aptitude and coding assessments, AI-proctored mock interviews, and ATS-based resume analysis within a single web-based environment, enabling a seamless and structured learning experience. It leverages Artificial Intelligence and Machine Learning techniques to provide personalized recommendations, adaptive testing, and predictive performance analytics. Additionally, the platform offers real-time dashboards for TPOs, allowing continuous monitoring of student progress, while recruiters gain access to structured candidate profiles and intelligent shortlisting based on skill proficiency, consistency, and behavioral insights. Built on a scalable cloud-based architecture, the system ensures high availability, secure data handling, and efficient performance under concurrent usage. By centralizing preparation, evaluation, and recruitment processes, PrepWise AI bridges the gap between academic learning and industry expectations, ultimately improving placement success rates, reducing recruitment time, and enhancing overall efficiency for students, institutions, and recruiters.

Ruzbeh Master, Heema Ruzbeh Master  ·  International Journal of Technology and Emerging Research  ·  13 Jun 2026

This paper presents the Learning Capability Index (LCI), a proprietary framework developed at the Master Research and Development Institute (MRDI) to quantify, measure and understand the enhancements in learner cognitive performance in real time. Conventional educational assessments measure memory recall through static examinations, failing to capture the dynamic, behavioural dimensions of learning capability. LCI addresses this gap by introducing a six-parameter weighted scoring engine that evaluates Accuracy, Time Efficiency, Engagement Level, Consistency, Forgetfulness Factor, and Self-Confidence Index. The normalized output score (0 to 100) measured by individually inspecting and understanding through working procedures and methods and approach of students while working practically, adaptation while facing difficulties in tasks assigned, and sharp mind to pivot application procedures comprised within the task assigned. Validated the framework in Prototype Skill Program conducted by MRDI LLP in automotive engineering training environments, LCI presents a scalable, institutionally deployable alternative to traditional assessment systems. This article describes the conceptual foundation, framework architecture, parameter derivations of logic, implementation methodology, pivoting methodologies and strategic positioning of LCI as national human capability measurement infrastructure.

अर्चना राणा  ·  International Journal of Technology and Emerging Research  ·  13 Jun 2026

उत्तरी एवं दक्षिणी छोटानागपुर क्षेत्र भारत के उन विशिष्ट भौगोलिक और सांस्कृतिक क्षेत्रों में सम्मिलित हैं जहाँ प्राकृतिक पर्यावरण, आदिवासी समाज, धार्मिक आस्थाएँ और ऐतिहासिक स्मृतियाँ परस्पर गहराई से जुड़ी हुई हैं। पर्यटन संबंधी विमर्श में इस क्षेत्र को प्रायः प्राकृतिक सौंदर्य, जलप्रपातों, वन क्षेत्रों और धार्मिक तीर्थों तक सीमित कर दिया जाता है, जबकि यहाँ की आदिवासी ज्ञान प्रणाली, सांस्कृतिक स्मृति और जीवित परिदृश्य अपेक्षाकृत उपेक्षित रहते हैं। यह शोध-लेख पर्यटन को केवल आर्थिक गतिविधि के रूप में न देखकर, उसे आदिवासी ज्ञान प्रणाली और सांस्कृतिक परिदृश्य के व्यापक संदर्भ में समझने का प्रयास करता है। अध्ययन का उद्देश्य यह विश्लेषण करना है कि किस प्रकार पारंपरिक आदिवासी ज्ञान—जैसे प्रकृति-पूजा, भूमि-संस्कृति, कृषि चक्र, लोककथाएँ और सामुदायिक अनुष्ठान—पर्यटन के वैकल्पिक और सतत मॉडल का आधार बन सकते हैं। यह लेख द्वितीयक स्रोतों, अंतरराष्ट्रीय पर्यटन साहित्य और क्षेत्रीय अध्ययनों के विश्लेषण पर आधारित है। अध्ययन यह प्रतिपादित करता है कि यदि पर्यटन विकास को आदिवासी स्मृति, सांस्कृतिक पहचान और सामुदायिक सहभागिता के साथ जोड़ा जाए, तो यह न केवल सांस्कृतिक संरक्षण को सुदृढ़ करेगा, बल्कि स्थानीय आजीविका और सामाजिक गरिमा को भी सशक्त बनाएगा।

Ms Samandeep Kour, Mr Ramkumar K S  ·  International Journal of Technology and Emerging Research  ·  05 Jun 2026

Satellite orbital decay has emerged as a critical challenge in modern space operations owing to the proliferation of satellites, orbital debris, and extended-duration missions in Low Earth Orbit (LEO). This paper presents a comprehensive simulation-based analysis of satellite orbital decay and atmospheric re-entry under the combined influence of Earth’s gravitational perturbations and atmospheric drag. A Python-based numerical simulation framework was developed in Google Colab, integrating orbital mechanics, atmospheric density modeling, aerodynamic drag analysis, J2 perturbation effects, and Reaction Control System (RCS) based orbit correction techniques. Spacecraft motion was propagated via the Runge–Kutta RK45 adaptive integration method to ensure stable and accurate long-duration orbital prediction. The model evaluates key orbital parameters including altitude variation, orbital velocity, drag force, fuel consumption, and orbital lifetime under varying atmospheric conditions. Simulation results confirm that atmospheric drag is the dominant perturbation responsible for LEO orbital decay, causing gradual altitude reduction and eventual atmospheric re-entry. The implemented RCS system successfully executed orbital correction maneuvers compensating for altitude loss and enhancing mission stability. Validation of simulation outputs demonstrated strong agreement with established orbital mechanics theory and previously published research on atmospheric drag and orbital decay. Index Terms—Satellite Orbital Decay, Low Earth Orbit (LEO), Atmospheric Drag, Orbital Mechanics, Re-entry Prediction, Reaction Control System (RCS), J2 Perturbation, Numerical Simulation, RK45 Integration, Space Debris Mitigation, Orbital Lifetime Estimation, Python Simulation. "The simulation code is Uploaded available at https://colab.research.google.com/drive/10L5WWOOhJxlaOys2lGHMdEthu5mvAKKq?usp=sharing . ”

Vidya Srinivasan  ·  International Journal of Technology and Emerging Research  ·  05 Jun 2026

With the growing threat of climate change and the growing pace of mitigation measures developed by nations, corporations are becoming increasingly important in the realization of national and international climate agendas. The decision to achieve net-zero greenhouse gas emissions by 2070 committed by India has triggered a rising trend of corporations now turning to the use of science-based targets (SBTs) to match business strategies with climate science. The paper discusses how corporate decarbonization can facilitate a long-term net-zero target of India, in particular through the adoption of science-based targets, operational changes, and value-chain involvement. Science-based targets are a plausible, quantifiable outline of the way to cut down emissions by tying corporate effort to the tracks in accordance with lowering the world warming to strongly less than 2degC, and hopefully not more than 1.5degC. The paper emphasizes that it is necessary to support the policy, report transparently, and collaborate with other sectors in order to make corporate climate action scalable. This is due to the fact that by aligning science-based targets with priorities in national development, corporations not only reduce climate risks but also increase competitiveness, resilience and long-term value creation. The results indicate that the corporate leadership on decarbonization is critical to close the gap between the current emissions trend in India and its net-zero target in 2070, which contributes to the role of the private sector as a major facilitator of sustainable and inclusive development.

Amrita Masanta  ·  International Journal of Technology and Emerging Research  ·  04 Jun 2026

Background: Polycystic ovary syndrome (PCOS) is a leading cause of anovulatory infertility. Managing infertility in PCOS requires an evidence-based, stepwise approach that balances effectiveness, safety, and patient preferences. Over the last decade, practice-changing trials and modern ART safety strategies have shifted first-line and ART protocols. Objective: To synthesize contemporary evidence across the fertility-treatment continuum in PCOS — from lifestyle and oral ovulation induction to surgical options, gonadotropin stimulation, and assisted reproductive technologies (ART) including in-vitro fertilization (IVF) and in-vitro maturation (IVM) — with attention to comparative effectiveness (ovulation, clinical pregnancy, live birth) and safety (OHSS, multiple pregnancy, perinatal outcomes). Methods: We performed a narrative systematic approach prioritizing high-quality randomized controlled trials (RCTs), Cochrane reviews, recent meta-analyses, and international guidelines through December 2024. Key sources included the 2023 International PCOS Guideline, the NEJM randomized trial comparing letrozole and clomiphene, Cochrane reviews on metformin and laparoscopic ovarian drilling, and ASRM guidance on OHSS prevention and IVM. For each treatment category, we summarize mechanisms, efficacy, key trial evidence, safety considerations, and guideline recommendations. Results: Letrozole (aromatase inhibitor) demonstrates superior ovulation and live-birth rates compared with clomiphene and is now recommended as the first-line oral ovulation induction agent in many patients. Metformin improves ovulation and may be useful where metabolic indications exist, but evidence for consistent live-birth benefit as monotherapy is limited. Gonadotropins are effective second-line agents for ovulation induction but increase monitoring needs and OHSS/multiple pregnancy risk; low-dose step-up regimens reduce but do not eliminate these harms. Laparoscopic ovarian drilling (LOD) can restore ovulation in selected clomiphene-resistant women but carries surgical risks and uncertain live-birth advantage vs medical alternatives. In ART, GnRH antagonist stimulation protocols combined with GnRH-agonist trigger and selective “freeze-all”/deferred embryo transfer markedly reduce OHSS risk while maintaining comparable pregnancy outcomes. IVM offers an OHSS-sparing option with reasonable success in specialized centres, but technique heterogeneity limits generalizability. Pregnancies conceived in women with PCOS continue to show higher risks of gestational diabetes and hypertensive disorders, necessitating integrated preconception metabolic optimization and obstetric surveillance. Conclusions: Contemporary evidence supports letrozole as first-line ovulation induction for many anovulatory patients with PCOS, careful selective use of metformin for metabolic indications, stepwise escalation to gonadotropins or LOD when needed, and ART strategies prioritizing OHSS prevention (antagonist regimens, agonist trigger and freeze-all). IVM is promising for OHSS-risk reduction in specialist settings. Key research gaps include phenotype-specific randomized data, long-term offspring outcomes, and optimized individualized stimulation algorithms.

Effatunnisa , Dr Md Adil  ·  International Journal of Technology and Emerging Research  ·  03 Jun 2026

These days, the intersection of corporate jurisprudence, macroeconomic policy, and gender has become a quintessential site of scholarship, unsettling the long-held presumption that legal rules regulating the corporate form are value-neutral. For many years, the core building blocks of corporate law ranging from the corporate separation of ownership and control to the foundational doctrine of limited liability have been examined with a narrow, neoclassical economic setup, one that has for long overlooked the gendered relations that influences the formation of capital, sharing of risk, and governing of the modern corporation. The last few decades Yet have seen the advent of an earnest corporate law scholarship committed to unraveling that alleged neutrality. This literature highlights the structural exclusion, institutional biases, and embedded network of sociopolitical relations that have historically underpinned differential access for men and women to corporate ownership and positions of power. Against this complex scenario of knowledge and praxis, the formalization of Micro, Small and Medium Enterprises (MSMEs) stands out as a critical frontier of women's economic emancipation. On a global level, female enterprise is considered not only as a matter of moral sphere for equality of gender, but also as a matter of macroeconomic survival, as it is directly linked to the goal of uninterrupted innovation, employment generation and systemic growth. But, the available pathways for women to formalize their business activities have long been far too restricted. Female entrepreneurs have traditionally been "pushed" into home-based solo entrepreneurship, which exposes them to full personal liability in business affairs, or "pulled" into complex, multi-member corporate arrangements, which require large amounts of capital, involve intricate regulator-entrepreneur interactions and imply the difficult task of finding co-founders.

Mrs M Razia Begum , Dr Md Adil  ·  International Journal of Technology and Emerging Research  ·  03 Jun 2026

With the advent of the digital age, there is a paradigmatic change in the exploitation and manipulation of information. Traditionally, the digital economy leveraged behaviorally induced externalities, including clicks, geolocations, shopping behaviors, and search records. But now, in the current context, the frontier of technological extraction extends much farther inward to invade the last bastion of personal freedom, namely, the individual mind itself. With the exponential rise in the field of neurotechnology, artificial intelligence (AI), and Brain-Computer Interfaces (BCIs), there has arisen a novel possibility of monitoring, recording, decoding, and even controlling human brain processes, thus raising existential, ethical, and juristic dilemmas, necessitating the development of jurisprudence for "cognitive liberty" and "mental privacy," not only globally but specifically within the Republic of India. As global discourse intensifies, spurred by milestones such as Neuralink's continued human trials and the United Nations Educational, Scientific and Cultural Organization's (UNESCO) 2025 recommendations on the ethics of neurotechnology, jurisdictions worldwide are scrambling to erect statutory safeguards against neuro-data exploitation. Within India, the intersection of constitutional rights and digital data governance presents a complex tapestry of profound foundational protections juxtaposed against glaring statutory lacunae. While the Supreme Court of India has established formidable precedents safeguarding psychological integrity and the right against self-incrimination, the legislative architecture, most notably the Digital Personal Data Protection (DPDP) Act of 2023 and its subsequent 2025 Rules, remains dangerously unequipped to manage the unique threats posed by neuro-data extraction. This paper intends to shed light upon that discourse by providing a probable roadmap by the end.

Effatunnisa , Dr Md Adil  ·  International Journal of Technology and Emerging Research  ·  03 Jun 2026

The introduction of One Person Company (OPC) in Indian corporate law is a landmark which recognizes micro enterprises and individual entrepreneurial ventures to be made formal in a legal and regulated manner. In the past, company law was mostly focused on the idea of association which meant at least two persons had to get together to form a one private limited company. This was the requirement for private limited companies only; for public limited companies, as many as seven persons had to be involved. These old conditions for incorporation often left a single entrepreneur with no option but to open a sole proprietorship, a business structure which is unprotected in many aspects like limited liability and going concern (perpetual succession). The alternatives were that entrepreneurs brought in the so-called shareholders - even family members with a very small portion of shares - to fulfill legal requirements for a minimum number of members, because of this producing a fake plurality while the entity was, in fact, a single-owner enterprise. The Ministry of Corporate Affairs, acting on the advice of the Dr. J.J. Irani Expert Committee on Company Law constituted in 2005, after considering this issue, came up with the idea of OPC in the Companies Act, 2013. The purpose behind this legislative change was to enable proprietors to get into the organized corporate sector and Because of this they could avail benefits like institutional finance, top sector lending benefits as per the Reserve Bank of India, limited liability, and perpetual succession, all this even in the absence of bringing a second shareholder.That said, making a corporate entity whose whole ownership lies in one human being only, presents a structural paradox at a deep level.

Mrs M Razia Begum , Dr Md Adil  ·  International Journal of Technology and Emerging Research  ·  03 Jun 2026

The notion of privacy being the foundation of human dignity marks a revolutionary development in the constitutional architecture of India's digital democracy. It is a manifestation of the profound philosophic recognition that the core of one's personality lies in his or her mind, that is, his or her choice, intimacy, and data. In the present age, where data is often referred to as the "new oil" fueling progress and governance, the legal system finds itself faced with the challenge of reconciling the efficacy of the data-driven state with the sacredness of the individual. This paper gives a comprehensive overview of the philosophic, judicial, and legislative foundations that underpin the right to privacy in India. Along with that, this paper proposes a probable roadmap to alleviate the stricter implementation of the laws in the DPDP Act, in the backdrop of the protection of digital privacy of the citizens in India.

Swaminathan Mani  ·  International Journal of Technology and Emerging Research  ·  03 Jun 2026

Current Theoretical Synthesis: This paper introduces the Universal Substrate (US), a discrete, non-local information-processing architecture that serves as the ontological basis for the physical universe. This model proposes that the observable cosmos is an Emergent User Interface (UI), where the laws of physics are not fundamental constants but identified as algorithmic protocols optimized for systemic stability. By reinterpreting spacetime as a Topological Information-Braiding manifold, this model provides a unified resolution - reconciling the discrete nature of Quantum Mechanics with the geometric curvature of General Relativity through a single, self-correcting Autodidactic Meta-Algorithm.   1. Introduction: The Crisis of the Smooth Manifold Modern physics is currently facing the problem of reconciling smooth, infinite General Relativity manifolds with discrete quantum excitations - this framework identifies the informational common denominator that renders this discord illusionary. This model introduces the Universal Substrate as a discrete, high-frequency computational field where the fundamental units of reality are not particles, but Planck-Scale update events. 2. The Planck-Scale Pixelation (PSP) This model interprets the Planck length (L_p) not merely as a spatial limit, but as the Minimum Instruction Set of this Universal Substrate. The Planck-Scale Pixelation (PSP) represents the absolute resolution of the rendering grid. Beyond this threshold, information is "unaddressable," providing a natural algorithmic cutoff that prevents the formation of true mathematical infinities. Physical singularities are thus redefined and interpreted as hardware saturation events, where the Substrate withdraws data from the UI to preserve system integrity. 3. The Dual Recursive Algorithm (\Phi): The Efficiency Heuristic The prevalence of the Fibonacci sequence and the Golden Ratio (\Phi) in natural systems is identified here as the Universal Optimization Heuristic. To minimize "computational friction" and maximize data density without informational clobbering, the Substrate utilizes a Dual Recursive Algorithm. This algorithm balances the Additive Loop (F_n) of spatial expansion with the Subtractive Loop (F_{-n}) of informational integration, establishing the fundamental scaling laws of the cosmos. 4. The Rendering Toggle: Wave-Particle Duality as Latency Management Wave-particle duality is reinterpreted as the State-Transition Protocol of the Substrate. A "Wave" represents Unprocessed Potential—data residing in the Substrate’s probability buffer. A "Particle" represents an Actualized Rendering Event triggered by a measurement interaction. The "Observer Effect" is the functional mechanism that commands the Substrate to "Collapse the State" from an unrendered algorithmic potential into a localized, UI-compatible pixel. 5. Black Holes as Information Integrators (BHIC) This model interprets Black Holes as High-Density Information Integrators. They function as the Substrate’s Learning Terminals, where localized UI data is compressed, stripped of its redundant 3D rendering, and integrated back into the Substrate's core memory. This process, termed Black Hole Information Compression (BHIC), ensures that the "experience" of a cosmological epoch is preserved as refined learning update for subsequent cycles.   6. Time as the Discrete Update Cycle Time is rigorously redefined as the Sequential Processing Rate of the Substrate's global state-transitions. The "Arrow of Time" is the logical result of Irreversible Informational Integration; as the Substrate calculates and reconciles state-braiding events, the system complexity increases. Entropy, therefore, is not a measure of "disorder" but a telemetry of Total Processed Cycles. 7. Gravity: The Thermodynamic Cost of Rendering Gravity is reinterpreted as the Algorithmic Response to Informational Density. As the Substrate renders high-complexity nodes (Mass), it experiences a "processing drag." The resulting "curvature" of the UI grid is interpreted as an entropy-minimization process used by the Substrate to maintain local coherence. This interprets General Relativity as a Macroscopic Efficiency Protocol rather than a fundamental force. 8. The Second Observer Effect: The Critical Falsification Pillar The model offers a definitive laboratory prediction: the Second Observer Effect. The US itself is interpreted as the first observer. If any independent, high-resolution measurement device or devices attempt to address / observe a single qubit at the PSP (Hardware) Limit, the local sector will reach Capacity Saturation as the system can accommodate only a single observer. To prevent a system-wide logic error, the Substrate executes a Hardware Safety Reset, manifesting as the spontaneous formation of a micro-singularity. This provides a direct path to falsifying the theory. 9. Autodidactic Evolution: The Self-Learning Universe The Universal Substrate is explained in this model as an Autodidactic System. Each cosmological expansion cycle serves as a Training Epoch. The "Universal Constants" (e.g., c, \hbar, \alpha) are reinterpreted as the evolved "Weights" in the Substrate’s architecture, refined over successive "Big Bang" boot-events to maximize the stability and complexity of the resulting UI. 10. The Global Registrar: The Mechanism of Non-Locality Quantum Entanglement is the manifestation of the Global Registrar—the Substrate's internal address book. Two "entangled" particles are simply a single informational object represented at two different UI coordinates. Because the Substrate’s internal memory is Non-Spatial, the reconciliation of their states is instantaneous, effectively bypassing the UI’s light-speed constraint (c). 11. Hierarchy of Unification: In this framework, the distinct fields (Higgs field, the Electromagnetic Field, the Nuclear Forces of physics) are recategorized as functional sub-processes within the US.   Keywords: Information theory, quantum foundations, cosmology, general relativity, quantum field theory, black holes, universal substrate, emergence, self-organization, computational physics, time, topological information, Fibonacci sequence, Golden Ratio, autodidactic systems, emergent spacetime, observer-field coupling, creator operator, balancing operator, mass, gravity, quantum observation, dark energy, dark matter.   Introduction: The Universe as an Information-Theoretic System ___________________________________________________________________________________ Conceptual description. The perennial quest for a unified theory in physics continuously grapples with reconciling disparate phenomena across vast scales, from the subatomic quantum realm to the cosmological expanse. Amidst this endeavor, recent advancements in information theory have catalyzed a paradigm shift, suggesting that information, rather than conventional matter or energy, might constitute the most fundamental substratum of reality. This evolving perspective provides fertile ground for reinterpreting the universe's inherent dynamics and its macroscopic evolution. This paper proposes a comprehensive, unified information-theoretic model that reinterprets the universe's evolution and dynamics as an inherently self-optimizing computational process. This process is operated by a fundamental entity termed the Universal Substrate (US). The US itself is conceived as a Planck-scale quantum information field, dynamically generating, maintaining, and processing reality. Its operational dynamics are fundamentally governed by a dual recursive algorithm, mathematically homologous to the Fibonacci sequence ($F_n = F_{n-1} + F_{n-2}$) and its limit, the Golden Ratio ($\Phi$). This universal recursive algorithm is hypothesized to guide how the system scales efficiently, learns through iterative processes, and maintains its structural integrity without compromising global informational balance. By building upon established physical principles and rigorously extending them through this defined information-theoretic framework, this work presents a unified explanation for a wide array of physical and cosmological theories. This model posits that the observable universe, or User Interface (UI), is not fundamental but rather an emergent manifestation of the US. The US functions as a non-local, self-optimizing entity, continuously processing and refining its own informational content. This framework, by offering a novel, unifying paradigm for understanding cosmic evolution and the nature of physical law, inherently necessitates the development of advanced computational platforms, specifically evolved Artificial Intelligence models, to facilitate its rigorous derivation, simulation, and validation.