Recent Articles

Praveen  ·  International Journal of Communication, Media and Linguistics  ·  16 Aug 2026

Despite decades of expansion in Indian journalism, women remain structurally underrepresented as news subjects, sources, bylined writers, television panellists, and newsroom decision-makers, and this gap is increasingly compounded by unequal digital access and rising online violence that make sustained participation in the profession harder rather than easier. This paper synthesises evidence from major cross-national and India-specific monitoring studies published between 2019 and 2025 to assess whether this disparity has narrowed and whether these compounding barriers have eased. Drawing on the Global Media Monitoring Project, UN Women-Newslaundry newsroom audits, the GSMA Mobile Gender Gap Report, and UNESCO-ICFJ studies on online violence, the review finds that women accounted for just 14% of news subjects and sources in India in the most recent nationally coordinated count, down from 22% in 2010; that women wrote roughly one in four print bylines and held almost no top editorial positions at sampled newspapers; that the South Asian mobile internet gender gap stood at 32% in 2024, more than double the global low- and middle-income country average; and that 75% of women journalists surveyed globally in 2025 reported online violence, up from 73% in 2020, with reports of offline spillover more than doubling. The paper concludes that gender disparity in Indian journalism has not meaningfully narrowed and, on several indicators, is worsening, and argues that closing this gap sustainably requires media organisations to combine leadership reform with digital-safety and connectivity measures, an approach for which women-led outlets such as Khabar Lahariya offer a working model.

Maidhili Mohan, Aysha M. K.  ·  International Journal of Technology and Emerging Research  ·  16 Aug 2026

Industrial control systems (ICS) are fundamental to critical infrastructures such as electrical grids, water processing plants, and manufacturing plant, where Programmable Logic Controllers (PLCs) and Supervisory Control and Data Acquisition (SCADA) systems regulate physical processes that cannot stop operating at ease. These formerly isolated systems acquire a greater variety of challenges as they become interconnected with IT networks, including command-injection, replay, and false-data injection attacks that may actually result in serious physical harm in addition to some data loss. Two defense methods that have been evolved are discussed in this review. By integrating a private random signal on the control command, dynamic watermarking (DW) takes on an active, physics-based approach resulting in any anomaly in the sensor-actuator feedback loop becoming statistically visible. In contrast, machine learning (ML) approaches try to learn what malicious behavior appears from data. We keep track of the innovations in both directions to distinguish their supplementing powers and blind spots, as well as look at the simple but growing set of work that attempts to bring them together. Also we employed the case studies from PLC-controlled water-tank testbeds, networked control systems, and power-system automatic generation control. The paper ends by discussing the barriers that prevent these research findings from being put into practice, including the ability to scale, adversarial robustness, and real-time deployment on legacy PLC hardware.

Dr. Rakesh Kumar Yadav, Dr. Nabin Kumar Yadav, Dr. Bibha Yadav  ·  International Journal of Medical and Health Sciences  ·  16 Aug 2026

Surgical site infections (SSIs) remain one of the most common and serious postoperative complications, contributing to increased patient morbidity, prolonged hospital stays, and elevated healthcare costs. Early and accurate diagnosis, combined with timely intervention, is essential to improve outcomes. Radiology plays a pivotal role in both diagnosing and managing SSIs through various imaging modalities and interventional procedures. This review highlights the comprehensive role of radiology in detecting and treating SSIs, focusing on the use of ultrasound, computed tomography (CT), and magnetic resonance imaging (MRI) across general surgery, orthopedics, gynecology, and cardiovascular specialties. Ultrasound is valuable for assessing superficial wounds and guiding fluid aspiration; CT offers high-resolution visualization of deep collections and surgical complications; MRI excels in evaluating soft tissue and bone infections, especially around prostheses or in complex regions like the pelvis or spine. Interventional radiology enables percutaneous drainage, abscess aspiration, and catheter placement, offering a minimally invasive alternative to surgical re-intervention. Proper imaging interpretation helps distinguish normal postoperative findings from infection, aiding in accurate diagnosis and targeted management. Overall, radiology significantly enhances postoperative infection control, and its integration into multidisciplinary care is critical for improving surgical outcomes.

Nandini Mishra  ·  International Journal of Philosophy, Ethics and Humanities  ·  16 Aug 2026

The question of consciousness remains one of the most challenging issues in contemporary philosophy. David Chalmers has transformed the issue through his formulation of the "Hard Problem of Consciousness," which distinguishes subjective experience from cognitive and behavioral functions. Chalmers's philosophy of consciousness, focusing on the explanatory gap between physical processes and phenomenal experience. It analyzes reductive materialism, explores his concept of naturalistic dualism, and evaluates major criticisms from functionalist perspectives. The study continues to provide an indispensable framework for understanding consciousness and the limitations of contemporary scientific explanations. Keywords: Consciousness, Naturalistic Dualism, Philosophy of Mind, Phenomenal Experience

Anas Yasir Khan .N  ·  International Journal of Technology and Emerging Research  ·  16 Aug 2026

Abstract Governments use "sin taxes" on things like tobacco and alcohol to help fix the health problems and social costs they cause. In India, we've seen these taxes go up quite a bit, especially after the GST was introduced in 2017. This paper looks into whether these higher prices actually made people across different states stop using these products. We used data from the National Family Health Surveys (NFHS-4 and 5) and the Global Adult Tobacco Survey to test this. Using OLS regression and price elasticity models, we checked how much the post-GST changes really mattered. The data shows that tobacco use dropped from 48.2% in 2010 to 32.6% in 2021, and alcohol use fell from 32.1% to 17.5%. However, the numbers show that both products have "inelastic demand".The Price Elasticity of Demand for tobaccowas $-0.313 and alcohol was $-0.682. Basically, this means people don't change their habits very much just because the price goes up. Because of this, the study finds that while taxes are a good start, they aren't enough on their own. To really see a long-term change, India needs to combine these taxes with better regulations and programs that actually change how people behave.

Manash Pratim Sarma  ·  International Journal of Technology and Emerging Research  ·  16 Aug 2026

Labour force participation and wage determination are critical indicators of labour market performance and inclusive economic development, particularly in rural regions where employment opportunities remain constrained. This study examines the socio-economic determinants of labour force participation and wage earnings in Darrang district, Assam. Specifically, it aims to analyse the socio-economic profile of the labour force, identify the factors influencing labour force participation, and examine the determinants of wage earnings among employed individuals. The study is based on primary data collected from 300 working-age respondents selected through a multistage random sampling technique. Descriptive statistics were employed to analyse the socio-economic characteristics of respondents, while Binary Logistic Regression and Multiple Linear Regression models were used to estimate the determinants of labour force participation and wage earnings, respectively. The findings indicate that education, work experience, skill training, and gender significantly influence both labour force participation and wage earnings. Higher educational attainment and work experience increase the likelihood of labour market participation and contribute to higher monthly earnings, whereas larger household size negatively affects labour force participation. The results further reveal that rural residence is associated with lower wage earnings, reflecting limited access to productive employment opportunities in rural areas. The study also identifies a significant gender disparity, with male respondents exhibiting higher labour force participation and wage earnings than female respondents. These findings underscore the importance of strengthening education, expanding vocational skill development, promoting rural non-farm employment, and implementing gender-inclusive labour market policies to improve employment opportunities and wage outcomes. The study provides empirical evidence that may assist policymakers in designing targeted labour market interventions aimed at promoting productive employment, reducing wage disparities, and fostering inclusive economic development in Assam.

Mrs.Sarika Suresh Gaikwad, Mrs. D. N. Dhang.  ·  International Journal of Technology and Emerging Research  ·  16 Aug 2026

The detection of color and synthesis of colors play a crucial role in computer vision, textile technology, and digital imaging. Traditional methods involve the use of either physical sensors or thresholding methods that depend completely on the environment. The problems associated with the traditional methods are variability of illumination, dependence on physical sensors, and high costs due to hardware usage. In the proposed work, the method uses an intelligent software-based simulation approach to detect and synthesize red, blue, and green colors. The proposed system has been developed using Python along with libraries like OpenCV, NumPy, Matplotlib, and Streamlit to support real-time color detection, visualization, and RGB blending. Through experimental results, it is found that the time delay is less than 100 milliseconds with more than 25 frames per second. By eliminating the need for physical sensors, the method enhances accuracy and can be used for industrial purposes and textile color detection.

Abdullah Khan  ·  International Journal of Technology and Emerging Research  ·  16 Aug 2026

Abstract Dal Lake, located in Srinagar, Jammu and Kashmir, is one of India's most iconic freshwater bodies. However, it has undergone severe environmental degradation due to anthropogenic activities, particularly sewage discharge, houseboat waste, and agricultural runoff. This research paper examines how pollutants in Dal Lake water alter its freezing point compared to pure water. Through analysis of existing water quality data, freezing point depression principles, and field observations, we demonstrate that dissolved impurities—primarily nitrogen compounds, phosphorus, calcium, magnesium, and dissolved salts—lower the freezing point of Dal Lake water to approximately −11°C, significantly below the standard 0°C freezing point of pure water (Atkins, 2010; Castellan, 1983; Organic Biotech, 2025). This phenomenon has profound implications for the lake's winter ecology, aquatic life cycles, and regional climate patterns. The paper integrates colligative property theory with empirical data from Dal Lake water quality assessments conducted between 2005 and 2024 (GeoJournal, 2005; IWA Publishing, 2024).

Dr. Ashok Kumar, Anmol Thakur  ·  International Journal of Technology and Emerging Research  ·  16 Aug 2026

The Differential equations play a very important role in the mathematical modelling of physical phenomena by explaining how physical quantities differ with respect to space and time. These applications like, heat conduction is one of the most important process modeled using partial differential equations, particularly the heat equation. This study throws light on the analysis of differential equations arising in heat conduction and their numerical treatment using the matrix method. The continuous heat conduction model is discretized into a system of algebraic equations through finite difference approximation, which is then represented in matrix form for accomplished calculation. The matrix method presents an orderly approach for solving large systems of equations and analyzing temperature distribution within conductive media under various boundary and initial conditions. This approach shows that the physical importance of differential equations in engineering and applied sciences while throw light on the effectiveness of matrix techniques in obtaining approximate solutions to heat transfer problems.In this problem we find general solution of heat conduction equation by using matrix method

Dr. Ashok Kumar, Aditi Singh, Taniya  ·  International Journal of Technology and Emerging Research  ·  16 Aug 2026

In this paper we compare the inapplicable cases of the Method of Undetermined Coefficients and Method of Variation of Parameters in solving second order differential equations with constant coefficients. We first analyse the cases of in applicability for each method to identify the in applicability, investigate their respective limitations and the underlying reasons responsible for it .The applicability of Method of undetermined coefficients clearly depends upon the forcing functions on the other hand the method of variation of parameter limits itself over computational complexities over non elementary integrals. Then finally we carried out comparative analysis to highlight the differences and sameness between the limitations of these methods.

Ayush Tripathi , Avnish Sharma , Aashi , Apeksha Yadav , Deepa Solanki  ·  International Journal of Technology and Emerging Research  ·  16 Aug 2026

The increasing prevalence of skin diseases and the shortage of dermatologists in many regions have created a need for automated and efficient diagnostic systems. Traditional diagnosis relies heavily on expert analysis, which may be time- consuming and inaccessible in remote areas. This paper proposes a Skin Disease Prediction System that utilizes machine learning and deep learning techniques to detect and classify skin diseases from images. The system employs a convolutional neural network (CNN) model trained on a large dataset of skin disease images. It integrates a full-stack architecture consisting of a frontend interface for user interaction, a backend server for processing, and a database for storing patient records and predictions. The system ensures accurate classification, fast processing, and scalability. Experimental results demonstrate that the proposed system achieves high accuracy and reliability in predicting multiple skin diseases. This solution can assist dermatologists, improve early diagnosis, and enhance healthcare accessibility.

DIPAK RAMDAS KALASKAR , Dr. Satya M. Mishra  ·  International Journal of Technology and Emerging Research  ·  16 Aug 2026

ABSTRACT Digital marketing has become an important tool for communication, product promotion, customer engagement, and business growth. In India, digital technology is expanding rapidly through smartphones, internet services, and digital payment systems. However, many rural areas are still unable to use digital tools effectively for business purposes. Rural Akola is also facing a similar situation. Although smartphones and internet access are increasing in villages, many people still use mobile phones mainly for entertainment, chatting, and social media browsing. Farmers, shopkeepers, women entrepreneurs, and youth have good opportunities to use digital marketing, but low awareness, poor digital skills, trust issues, and weak network connectivity are slowing adoption. This study examines the diffusion of digital marketing in Rural Akola. The study is based on descriptive research design. Primary data was collected through a structured questionnaire using Google Forms from respondents of Rural Akola. Secondary data was collected from journals, reports, books, and previous research studies. The findings show that smartphone ownership is high, internet usage is increasing, and awareness of digital marketing is growing. However, business use of digital platforms is still limited. Most respondents are interested in learning digital marketing. The study concludes that Rural Akola has strong growth potential if proper training, awareness programs, and infrastructure support are provided.

DR. SHANKAR PRASAD BHATTACHARYA  ·  International Journal of Technology and Emerging Research  ·  16 Aug 2026

Adolescence is a decisive life stage characterized by rapid biological, psychological, emotional, and social transitions. Adolescent-friendly health care systems are intended to provide equitable, accessible, acceptable, confidential, and effective services. However, many such systems underperform when family health care is insufficiently integrated. Families influence nutrition, mental well-being, health-seeking behaviour, adherence to treatment, sexuality education, prevention of substance abuse, and continuity of care. Evidence from global and Indian settings suggests that positive family engagement improves adolescent outcomes, whereas family conflict, neglect, and poor communication increase vulnerability to risky behaviours and delayed care. This paper argues that family health care is an essential pillar of adolescent-friendly health systems. Using a narrative review approach, literature from international agencies and peer-reviewed studies was synthesized. Findings indicate that combining adolescent autonomy with family-centred support strengthens preventive, promotive, curative, and rehabilitative services. Policies should therefore shift from adolescent-only service models toward adolescent-with-family frameworks that are rights-based, culturally responsive, and confidentiality-sensitive.

Gaurav Sharma, Mandeep Chaudhary  ·  International Journal of Technology and Emerging Research  ·  16 Aug 2026

How will the occupation feature changed from present? While no one doubts web will be platform for publication any longer as scholars shift from paper to digital content there is an increasing need to provide means supporting production of units self-referential knowledge and helping their release, sharing with others future re-use. These systems should support such functions as purposeful input, suitable forms for publication and management of publicly available resources while at the same time leaving no room for non-disclosure of interests. It is a fact that academic research can now become both scalable and reproducible. Confidence in results will derive from not only knowledge Code itself but also knowing who has used Codes and what they do with them--a transparency unlike any other before Perceived performance has attracted number of influential scientists to this approach. By providing them with secure static URLs for their data (which can then link into any newer version gallery on Web as a live retrieve file from database) we have already begun dealing. It is needed both amateur collector and experienced professional to verify documents--otherwise false data will be spread all around at once. Furthermore one can never know when going about searching through the network just which WYSIWYG editor ought best match your processing needs exactly or whether XML stands up better to future validation than does plain unmarked text for input purposes later. I in this paper we introduce the notion of research objects, semantically rich aggregations of resources which can have some scientific intent or support some research objective. We give several principles that we expect such objects and their associated services to follow.

Mrs. Komal. S, Dr. Deeksha S  ·  International Journal of Technology and Emerging Research  ·  15 Aug 2026

Social entrepreneurship is becoming a vital means of responding to complex problems related to both socio-economic and environmental conditions globally. Despite the potential for social entrepreneurship to transform society and achieve the Sustainable Development Goals (SDGs), social enterprises experience numerous barriers to scale-up, resource mobilization, impact measurement and operational efficiencies. The use of Artificial Intelligence (AI) offers social enterprises potential solutions to enhance decision making; optimize resource allocation; strengthen their ability to innovate and deliver measurable social impacts. Therefore, the purpose of the current study is to investigate whether social entrepreneurs' awareness of AI capabilities influences their ability to develop transformative social entrepreneurship that achieves SDGs. The research study uses a mixed-methods design and collects primary data from registered social enterprises. A quantitative survey instrument will be used to collect data from social entrepreneurs. Quantitative data analysis will be conducted via a factor analysis and structural equation model. Qualitative data collection methods, specifically interviews with social enterprises utilizing AI technology, will be used to gather deeper insight into transformative processes. It is expected that the ability of AI to increase the agility and innovation capacity of organizations, along with measured SDG outcomes, will be significantly enhanced. However, there are anticipated financial constraints, digital skills deficits and concerns about ethics of data usage that could impede the adoption of AI technology. Findings from the research study should be useful for policymakers, social enterprise leaders and development agencies seeking ways to leverage AI technology as a catalyst for sustainable transformation.

Binji Seidu Zakaria  ·  International Journal of Arts, Culture and Creative Studies  ·  15 Aug 2026

This thesis conducted a synchronic evaluation of the TfD studies carried out by Ansah and Adamah (2015) and Borzah, Mensah, Bediako, and Aryeetey (2019), and provided two (2) separate illuminative reports on them as a single dissertation. The former highlighted the pivotal role of parents in advancing their wards’ education, while the latter addressed proper waste disposal practices in Osubon City. This study did a rigorous analysis and gave a clear picture of the goals, objectives, and activities of these previous projects and brought to light the results produced (the impacts), the expected benefits delivered, and their associated issues. The study concludes that the projects have ably made some significant improvements in the community, but there are/were some challenges associated with the projects that both Osubon City and the DTA must take notice of and then take corrective actions on them. This study, therefore, only provided the ingredients needed for quality assurance and improvement of TfD projects carried out by facilitators under auspices of the Department of Theatre Arts (DTA) at the Osubon City and the TfD programme as a special area of study, unless future facilitators, the DTA, and the Osubon City itself pay heed to the lessons of this evaluation study and then take the needed corrective actions. The stakeholders in these projects, especially Osubon City, must obtain and act responsibly on or make sound use of these evaluation findings and recommendations on the projects, especially the sanitation issue, as a matter of urgency. Because the filth is vehemently regaining its root in the community subtly, and that is a threat to the community and should be relooked at. The study further concluded that education appeared as an individual responsibility because its sustenance at the individual level was/is very high, while sanitation seemed like a collective/community responsibility, which witnessed a very impressive effort at the community level in terms of sustenance of the sanitation project. This is a qualitative study that relied extensively on interviews, observations, Focus Group Discussions (FGDs) and secondary materials, including previous Project Reports (PRs) submitted to DTA, books, and journal articles, that were used to corroborate the information obtained from the primary data. This study will be a valuable document for the DTA, Osubon City, TfD Lecturers and Practitioners because it will aid in teaching and learning of TfD as a special field of study. M Phil. Thesis Submitted to the University of Education, Winneba in 2021.

Rekha Agarwal, Dr. RAJESH KUMAR MISHRA, Divyansh Mishra  ·  International Journal of Technology and Emerging Research  ·  15 Aug 2026

Galactic cosmic rays (GCRs) entering the heliosphere are modulated by the time-varying solar wind and heliospheric magnetic field, producing the well-documented 11-year (Schwabe) and 22-year (Hale) cyclic variation in cosmic ray intensity observed at Earth, together with transient, CME-driven depressions known as Forbush decreases (FDs). This paper (i) reviews the physical basis of cosmic ray modulation, centered on the force-field approximation of Gleeson and Axford and its use in reconstructing the solar modulation potential; (ii) reviews the growing literature applying machine learning and deep learning to cosmic ray intensity and Forbush-decrease forecasting; and (iii) reports two complementary, fully reproducible simulation-based experiments. First, a multi-solar-cycle (62-year) force-field simulator reproduces the characteristic anti-correlation between sunspot number and cosmic ray intensity (correlation coefficient r = -0.904) and the drift-related hysteresis loop between cosmic ray intensity and solar activity for opposite heliospheric magnetic polarity states. Second, an hourly-resolution, 3-year Forbush-decrease simulator, driven by the same class of solar wind turbulence proxies used in the companion geomagnetic-storm study, was used to train and evaluate Random Forest, Gradient Boosting, Multilayer Perceptron, and linear baseline models at 6-, 24-, and 72-hour forecast horizons under a chronologically leakage-aware protocol. The best model (MLP) achieved RMSE = 0.41% and R² = 0.965 at 6 hours, degrading to RMSE = 1.41% and R² = 0.592 at 24 hours, and to marginal skill by 72 hours, closely mirroring the multi-horizon skill-decay pattern reported for geomagnetic Dst forecasting and for real Forbush-decrease nowcasting studies in the literature. Feature-importance and ablation analysis confirm that recent cosmic-ray history and the local interplanetary magnetic field magnitude dominate short-horizon predictability. Because live access to real neutron monitor and OMNI archives was not available in this environment, both experiments are explicitly disclosed as physically-grounded simulations rather than analyses of observational data, and the paper concludes with a discussion of the sim-to-real gap, operational relevance (radiation dose forecasting, single-event upsets, atmospheric ionization), and directions for future work.

Prof. Adekunle Odunayo Adejuwon, Tega Grace Duruji, Olaleke David Odeleye, Okikioluwa Ayoade Odeleye, Barakat Yusuf, Dr. Abdul-hafiz Kolade Ashiru, Abiola Sijuade Dirisu, Dr. Victoria Anatolyivna Tsygankova  ·  International Journal of Oncology Research  ·  15 Aug 2026

Antenatal care (ANC) is a critical component of maternal and child health. This study examined the relationship between antenatal care attendance and maternal and fetal outcomes among pregnant women attending ALGON Health Centre, Eyin Grammar, Ibadan, between January 2023 and December 2024. Data were obtained from the hospital records. The findings revealed that Islam was the dominant religion (56.1%). The Yoruba ethnic group constituted the majority (81.9%). The study showed a high level of ANC utilization, with 345 (92.7%) of respondents attending at least four ANC visits. Specifically, 91.5% attended at least four visits in 2023, increasing to 94.1% in 2024, indicating improved compliance. Maternal health outcomes demonstrated that complications such as anemia, pre-eclampsia, postpartum hemorrhage, and maternal mortality were more prevalent among women who attended fewer than four ANC visits. Notably, postpartum hemorrhage and maternal mortality occurred only among women with inadequate ANC attendance. Fetal outcomes followed similar trend, as respondents with adequate ANC attendance recorded better birth outcomes, including higher proportions of normal birth weight and reduced cases of preterm delivery, stillbirth, and neonatal mortality. Conversely, poor fetal outcomes were more common among women with fewer ANC visits. Furthermore, pregnancy-related complications varied across trimesters, with anemia and pregnancy-induced hypertension more common in the first trimester, preterm delivery in the second trimester, and postpartum hemorrhage and hypertension more prominent in the third trimester. The study concluded that adequate and consistent antenatal care attendance plays a significant role in improving maternal and fetal health outcomes and reducing pregnancy-related complications.

Narendra Pramanik, Dr. Devaprasad Dev, Dr N K Gour, Dr Bhupesh Kumar Mishra  ·  International Journal of Physical and Chemical Sciences  ·  15 Aug 2026

In recent pasts, HFE-7100 finds its industrial applications like cleaning electronic equipment, secondary refrigerant and carrier fluids for lubricant. Detailed theoretical investigations have been carried out on the mechanism, kinetics and thermochemistry of the gas-phase reactions between CF3CF2CF2CF2OCH3 (HFE-7100) and HO2 and NH2 radicals using DFT-based M06-2X/6-311++G(d,p) level of theory. Two important H abstraction channels have been identified and one transition state has been located for each reaction channel. The pre-reactive and post-reactive complexes are validated at entrance and exit channels, respectively. The potential energy surface of the HFE-7100 with HO2 and NH2 radicals has been investigated using DFT method. The rate constants of the two reactions are computed over the temperature range of 250–450 K for the first time. Our results suggest that hydrogen abstraction by NH2 radical is likely the dominant route for the atmospheric oxidation of HFE-7100 under reaction conditions.

Dr.T.S.Kavitha  ·  International Journal of Technology and Emerging Research  ·  15 Aug 2026

The rapid integration of digital technologies into higher education has transformed the learning environment, making digital tools and online learning platforms essential components of academic engagement. This study aims to explore the use of digital tools and online learning platforms among higher education students and examine their role in supporting academic activities. The study employs a convenience sampling technique to select respondents from various higher education institutions. The findings reveal that students extensively utilize a wide range of digital tools and online learning platforms for academic communication, content access, assignment submission, collaborative learning, and examination preparation. The study further indicates that the increasing reliance on digital technologies has enhanced students' accessibility to educational resources, learning flexibility, and overall academic engagement.