New Horizons of Science, Technology and Culture Vol. 3

Electrical Conductivity Analysis of Ground Water at Surrounding Areas of Dildar Nagar of U.P, India

Salahuddin — 2025-07-22

One of the essential requirements of human beings is water. It is very crucial for the survival of all human beings and animals. It is a single worldwide natural resource distributed in all land, sea and atmosphere and unified by the hydrological cycle. It is the most important factor in the life of an organism as it is the major constituent of the protoplasm, plants, animals, microorganisms and aquatic life, all need water for their existence. In plants, all physiological processes like respiration, photosynthesis, absorption of nutrients and other metabolic processes are influenced by the amount of water available. This study was conducted to measure the Electrical Conductivity of Groundwater in the surrounding areas of Dildar Nagar of U.P., India. The groundwater samples were collected from different locations in Dildar Nagar village, and their electrical conductivity was measured in the laboratory. The data was presented graphically and interpreted using the method called analysis of variance. From the findings and analysis, it is concluded that electric conductivity depends on areas as well as months.

Machine Learning Approaches for Gender Identification Using Fingerprint Biometrics: Role of Ridge Flow, Minutiae, and Multi-resolution DWT Features

Sayed Abulhasan Quadri — 2025-07-22

Fingerprints serve as an extraordinary biological signature that encapsulates both identity and subtle gender-specific characteristics through their complex structural and spectral properties. This study introduces an advanced computational framework for gender classification by synergistically combining three distinct fingerprint feature domains: ridge geometry for macroscopic pattern analysis, minutiae distribution for microscopic feature examination, and frequency decomposition through sophisticated wavelet transformation. The methodology processes high-resolution fingerprint images through a multi-stage analytical pipeline that precisely quantifies ridge length variations (capturing minimum, maximum, and average measurements), systematically enumerates minutiae points (including ridge terminations and bifurcations), and performs multi-resolution spectral analysis using a six-level discrete wavelet transform to isolate discriminative frequency components. Validated on a carefully balanced dataset of 100 subjects (50 males and 50 females), the extracted features are intelligently organised into gender-specific clusters through an optimised stratification process, yielding an impressive overall classification accuracy of 88.28%. Notably, the right ring finger demonstrated exceptional diagnostic performance with 95.46% accuracy, a finding consistent with established embryological research on androgen-influenced ridge formation patterns. The technical sophistication of this approach lies in its ability to achieve high accuracy without relying on computationally intensive deep learning architectures, making it particularly suitable for real-world applications where efficiency is crucial. Beyond its immediate results, this research opens promising avenues for future investigation, including the incorporation of additional discriminative features such as sweat pore distribution and three-dimensional ridge curvature analysis, as well as expansion to larger, more diverse demographic datasets to enhance generalizability. By demonstrating that fingerprints contain a wealth of underutilised gender information, this work makes a significant contribution to the field of soft biometrics, with important implications for forensic science, security systems, and demographic research, while simultaneously establishing a foundation for future exploration of ancillary biometric markers embedded within fingerprint patterns. The balanced integration of robust methodology, empirical validation, and practical applicability positions this research as a valuable reference point for both academic investigation and applied biometric solutions, bridging the gap between theoretical innovation and real-world implementation in the evolving landscape of gender classification technologies.

Exploring ChatGPT’s Role in Learning Integrating Factors in Differential Equations: A Case Study of Engineering Students at the University of Antofagasta, Chile

Jorge Olivares Funes — 2025-07-22

At the University of Antofagasta, Chile, during the second semester of 2024, a qualitative case study was conducted with three engineering students studying differential equations. The main objective was to explore the perception of ChatGPT in the learning of the integrating factor of the differential equation (y³+ x) dx + xy²dy = 0, in engineering students. To do this, the study sought to investigate and analyse the interaction that students have when using this tool and to know the answers they obtain while solving the differential equation. The preliminary findings suggest that ChatGPT can serve as a complementary resource for understanding concepts, offering detailed explanations and practical calculations. This study provides valuable information on the integration of ChatGPT artificial intelligence in higher education, highlighting the need for adequate academic supervision to maximise its benefits and mitigate potential disadvantages in the learning process.

Predicting Diabetes Using Machine Learning: A Comprehensive Framework with Model Interpretability

Mounika Panjala — 2025-07-22

This chapter explores the construction of a detailed machine learning (ML) framework for predicting diabetes using diverse real-world datasets. The alarming rise in diabetes prevalence globally and particularly in developing nations such as India necessitates innovative approaches for early detection and intervention. Traditional diagnostic techniques, though clinically established, often fall short in scalability and adaptability. This study focuses on bridging this gap by integrating ML methodologies that not only offer superior prediction accuracy but also provide transparency through interpretability tools.

Key contributions of this work include the comprehensive data preprocessing steps (missing value treatment, normalisation, encoding, and SMOTE-based class balancing), the comparative evaluation of three widely used classifiers (Logistic Regression, Random Forest, and XGBoost), and the use of SHAP values for enhancing model interpretability. Among the models tested, XGBoost achieved the highest performance with an accuracy of 97.93%, AUC of 0.9974, and excellent sensitivity and specificity values, confirming its suitability for real-world healthcare applications. The chapter concludes with discussions on model performance, interpretability, clinical relevance, limitations, and avenues for future research.

A Narrative Review on Various Methods to Assess Forward Head Posture

Noel Samuel Macwan — 2025-07-22

Background: With the rising demands of computers in the fast-growing Information Technology Industry, the number of hours spent working on computers has also been drastically increased. Individuals spend more time on the computer in a day in recent times, which results in poor posture, causing neck pain. This in turn have lead to increase in the number of work-related musculoskeletal problems related to neck and upper limbs as sitting with the forward head posture in front of computers lead to changes in cervical spine curvature, which if continued for longer duration can lead to Cervical spine degeneration which can thereby lead to forward head posture, during which the head remains forward to the body’s line of gravity. It may lead to degenerative changes in the joints of the cervical spine and may cause forward head posture. Forward-headed posture is common in all age groups, more prominently found in 25-50 years of age, and can be measured by assessing the Craniovertebral angle (CVA).

Aim: The purpose of this article is to focus on different methods used to measure the CVA, thereby measuring the forward head posture.

Methodology: The PUBMED and the other search engines/databases (Cochrane database / EMBASE / PEDro / CINAHL) were searched. The keywords used were – Craniovertebral angle, forward head posture, Computer workers, Neck pain, neck posture. Studies including CVA & forward head posture assessment were included. Studies done before 2003 were excluded.

Results: A Total of 22 relevant studies were found. After removing the duplicates, articles with abstract only and articles published in a language other than English, 12 relevant studies meeting the Inclusion criteria were reviewed in detail as they measured CVA for assessment of Forward head posture & neck posture.A study concluded that the photographic method used in the study showed a high intra-rater and inter-rater reliability in measuring the sagittal postures of the thoracic and cervical spine (ICCs ranged from 0.80 to 0.87). A study concluded that the intra-rater reliability (ICC) of the Modified Head Posture Spinal Curvature Instrument (MHPSCI) is 0.87 (CI range from 0.82–0.91) and the inter-rater reliability between the two raters is 0.76 (CI range from 0.65–0.84) which is graded as “good” in the reliability criteria.

Conclusion: With this review it is found that the FHP assessment for CVA is valid and reliable outcome measure. There are different methodologies that are used to assess the CVA which are reliable and valid.

Student Result Analysis System Using Python Libraries

Mudassir Ashrafi — 2025-07-22

In the domain of educational data analysis, the extraction and interpretation of student records from PDF documents present significant challenges. This paper introduces a Python-based PDF Analysis Tool designed to streamline the extraction, analysis, and visualisation of academic data embedded within PDF files. Featuring a user-friendly graphical user interface (GUI), the tool enables users to select specific academic years and semesters for targeted analysis. By leveraging the PyPDF2 library, the tool efficiently extracts text from PDF files, while dynamically configured regular expressions ensure accurate data parsing across diverse academic formats. The analysed data is visualised using the Matplotlib library, producing bar charts for gender GPA distributions. Additionally, the tool highlights top-performing students based on GPA and supports exporting analysed data to Excel files for further exploration and collaboration. This paper discusses the tool's architecture, implementation details, and the significant role of Python libraries such as PyPDF2, OpenPyxl, Tkinter, and Matplotlib in enhancing the tool’s functionality. The PDF Analysis Tool exemplifies a robust, adaptable solution for academic data analysis, providing educators and researchers with actionable insights through streamlined data extraction and comprehensive visualisations.

Present Energy Scenario and Solar Energy as an Alternative Option

Dilip Mishra — 2025-07-22

With the global energy demand surging and fossil fuel reserves depleting, solar energy has emerged as a pivotal alternative for clean and sustainable energy generation. Among solar thermal technologies, Flat Plate Collectors (FPCs) and Evacuated Tube Collectors (ETCs) are widely employed for domestic and industrial heating applications. This paper investigates the current energy scenario with a focus on solar energy as a replacement for conventional sources, and presents a comparative evaluation of FPCs and ETCs. The study highlights the distinct advantages of ETCs—such as higher thermal efficiency, better insulation, and improved performance under diffused radiation and colder climates. Through theoretical analysis and technical comparison, the findings reveal that ETCs consistently outperform FPCs, particularly in applications requiring high operating temperatures and year-round efficiency. As the need for reliable, low-emission energy systems grows, ETC-based solar heating systems offer a compelling solution for energy security and environmental protection.

Enhancing Green Employability Using ICT: A Framework Based on Skill Gap Analysis and Reputation Mechanisms

G Ponni — 2025-07-22

The rate of unemployment has shown a gradual increase over the past few years. Several factors contribute to unemployment, including structural changes in the local economy, limited availability of job opportunities in specific sectors, skills mismatch between the education system and industry requirements, and demographic factors such as population growth and youth unemployment. This paper proposes a structured framework for skill building and sustainable employment generation by incorporating Trust and Reputation as core decision variables, mediated through Information and Communication Technology (ICT) tools. The aim is to address persistent issues of high unemployment, skill-demand mismatch, and the lack of a responsive workforce ecosystem — particularly in the context of emerging green jobs and eco-oriented industries. Several cities and districts in India possess economically diverse sectors — including agriculture, agro-processing, manufacturing, and services — yet face chronic challenges in aligning available human capital with evolving, often sustainability-driven industry requirements. The shift toward climate-resilient development and circular economy models demands not only traditional technical skills but also eco-competencies relevant to environmental stewardship, resource efficiency, and green innovation. This paper conducts an in-depth analysis of the structural and algorithmic aspects contributing to unemployment, with a specific focus on computer science methods. It introduces a robust framework integrating capability profiling, digital learning pathways, and trust-reputation metrics to facilitate dynamic, ICT-enabled matching between individuals and demand nodes — including those in green economy sectors. Beyond mere technical qualification, the model emphasises behavioural fitness, reputational credibility, and social responsibility, enabling a more holistic, environmentally aligned employment strategy. The framework also outlines mechanisms for real-time assessment, domain-specific skill evolution, and scalable implementation — promoting not only economic inclusion but also long-term environmental sustainability in workforce planning.

Enhanced Simulation Technique for Hot Spot Temperature Modelling under Dynamic Climate Conditions

Srinivasan. M — 2025-07-22

The Hot-Spot Temperature (HST) of transformer windings is a vital diagnostic parameter used to assess the thermal ageing and overall health condition of power transformers. As the hottest point in the winding insulation system, the HST largely determines the rate at which insulation deteriorates over time. Prolonged exposure to elevated HST levels can lead to significant reductions in insulation lifespan, increasing the risk of transformer malfunction or failure. Therefore, accurately modelling and monitoring the HST is crucial for asset management, preventive maintenance, and ensuring long-term transformer reliability. Traditionally, thermal models used to estimate HST rely on simplified assumptions and steady-state conditions, typically neglecting the real-time influence of environmental factors. These include short-term variations in ambient temperature, fluctuations in wind speed, and changes in solar radiation levels, all of which significantly impact a transformer's cooling capacity and internal temperature distribution. The absence of such dynamic inputs in conventional models results in the underestimation of the actual thermal stress experienced by transformers during transient or extreme climate events, such as heatwaves or rapid weather shifts. To overcome these limitations, this study proposes an enhanced simulation framework that integrates both physical transformer behaviour and statistical modelling techniques to more accurately predict the HST under dynamic operating conditions. The framework considers essential external parameters—ambient temperature, solar irradiance, and wind velocity—that affect the transformer’s heat dissipation characteristics. Internally, it processes real-time transformer loading data, thermal time constants, and historical operating profiles to derive more realistic estimates of both the Top-Oil Temperature (TOT) and the resulting HST rise. One of the key innovations in the proposed method is the development of a hybrid physical–statistical model. While the physical model applies established transformer thermal equations to simulate heat generation and transfer, the statistical component corrects for deviations due to environmental variability using regression-based estimation or adaptive response curves. This dual-layered approach compensates for the superposed inaccuracies found in traditional models, especially under non-steady climatic conditions. As a result, the model delivers improved accuracy and responsiveness when simulating transformer temperature dynamics in real-world scenarios.

The entire simulation framework is developed and executed in MATLAB/Simulink, which allows for modular implementation and high configurability. The model’s architecture comprises subsystems that handle specific functionalities, such as real-time environmental input acquisition, transformer loss calculations, TOT estimation, and HST prediction. A feedback mechanism is embedded into the system to utilise delayed TOT values, providing better stability and reflecting the thermal inertia of the transformer components. This structure ensures that the simulation remains responsive to real-time changes in input data while maintaining computational efficiency and scalability for utility-scale applications. Furthermore, the framework supports the integration of sensor-based and IoT-enabled data streams, enabling continuous monitoring and live simulation of transformer performance. This feature is particularly beneficial for utilities aiming to implement predictive maintenance strategies and reduce unplanned outages. The model can be calibrated using field data from operating transformers, ensuring its adaptability across various transformer types and geographic locations. In summary, this work presents a comprehensive and practical solution for transformer hot-spot temperature modelling under dynamic climate conditions. By merging physical and statistical modelling within a modular simulation platform, the approach bridges the gap between theoretical prediction and real-world performance. The model not only enhances the accuracy of HST estimation but also empowers asset managers with reliable thermal indicators that support informed operational and maintenance decisions. This makes it a valuable tool in modern smart grid environments, where climate variability and load diversity are critical challenges in transformer lifecycle management.

Synergistic Effects of Herbs and Probiotics in Traditional Fermented Foods: Efficacy, Safety, and Clinical Implications

ABDULKADIR, MUSLIU — 2025-07-22

Traditional fermented foods in West Africa serve not only as dietary staples but also as reservoirs of health-promoting microbes and bioactive herbal components. This review explores the synergistic potential between probiotic lactic acid bacteria (LAB) and indigenous herbs in Nigerian fermented foods, such as Fura da Nono and soy Iru. The chapter provides current evidence on the antimicrobial, immunomodulatory, and nutraceutical effects of these interactions, while also addressing safety, toxicological considerations, with emphasis on clinical relevance—such as improved glycemic control, enhanced gut barrier function, and support in combating malnutrition and infectious diseases. Safety concerns, challenges in standardisation, and cultural acceptability are also reviewed. Future research should prioritize well-designed clinical trials and scalable production frameworks to advance the use of herb-probiotic systems in public health. It underscores how the combination of herbs and probiotics can be optimized through starter cultures and formulation technologies to combat enteric infections, improve nutrition, and support functional food development. By integrating ethnobotanical traditions with modern microbiological and pharmacological insights, this work aims to advance sustainable, culturally relevant health interventions in Africa.

Legal Ease (Privacy Policy Simplification Web Extension): Implications of Disclosing Personal Information across Various Platforms

P. V. Siva Kumar — 2025-07-22

Legal Ease is a privacy-first web extension that demystifies the dense, often confusing language of online privacy policies. Designed for users who lack the time or legal expertise to decipher these documents, Legal Ease uses cutting-edge Natural Language Processing (NLP) and Machine Learning (ML) models—including Random Forest, AdaBoost, and XGBoost—to automatically extract, classify, and translate complex legal content into clear, concise, and accessible summaries. The extension highlights essential information such as the types of personal data collected, how that data is used, shared, or stored, and any associated risks or privacy concerns. Unlike conventional solutions, Legal Ease delivers real-time, context-aware summaries tailored to the specific website being visited—all without storing or retaining the original legal documents. This ensures maximum transparency while preserving user privacy at every step.

Exploring Student Perceptions of GeoGebra Software for Learning the Parabola on the Cartesian Plane: A Study in Antofagasta, Chile, 2024

Jorge Olivares Funes — 2025-07-22

The use of GeoGebra software is a form of technological integration for learning mathematics. Its use provides students with more opportunities to visualize geometric concepts—particularly benefiting those who are below average in traditional learning environments. This study presents in a structured manner the core elements of qualitative research within a case study. This study is based on a general objective accompanied by two specific objectives, which guide both the design and data collection. Based on these objectives, three semi-structured interview questions were formulated, one for each objective, addressed to five secondary school students at an educational institution in the city of Antofagasta during the year 2024. The responses provided by the participants allowed us to draw three significant conclusions that reflect their perceptions, experiences, and learning related to the use of digital tools in mathematics teaching.

The findings indicate that the participants consider GeoGebra to be a very useful tool for learning Cartesian plane parabolas due to its creative and interactive approach, which facilitates understanding and increases engagement.

One of the most notable contributions of this research is the incorporation of a concrete and motivating example of the use of ChatGPT to support the learning of the parabola, an innovative content in mathematics education. This approach seeks to promote active student participation through dialogue with artificial intelligence. The study also explores how the integration of ChatGPT with GeoGebra not only fosters the conceptual understanding of mathematical content but also opens up new pedagogical possibilities. This study contributes a renewed perspective on the role of emerging technologies in mathematics instruction and promotes new directions for research and innovative classroom practice.

Community Awareness on Locating Building Construction Proximity to Highways: Evidence from Bonga and Kibaigwa Wards

Helene Stephene Francis — 2025-07-22

Although construction of houses in developing countries depends solely on a self-help approach, little has been done to understand the extent to which community members are aware of the construction standards and factors pushing them to construct houses in proximity to highways. This study sought to examine the community members’ awareness of the conditions required for housing construction and factors pushing community members to construct houses close to highways.

Both qualitative and quantitative data were analysed, focusing on access to land for housing construction, community awareness of the conditions to be adhered to before constructing a building, distance of the existing buildings from the centre of the highway and reasons for building houses in proximity to highways in Bonga and Kibaigwa wards. Quantitative data were analysed using Statistical Package for Social Sciences (SPSS) version 26, while qualitative data were subjected to content analysis.

The study revealed that 37.2% of the respondents accessed land through purchasing, and a few were given by their friends and relatives. About 88.4% and 85.7% in Bonga and Kibaigwa, respectively, were found within 50m distance from the centre of the highway. The study demonstrated that the majority of the respondents, 81.4% and 91.8% in Bonga and Kibaigwa wards, respectively, were not aware of the conditions to be adhered to before constructing a building. However, only a small fraction, that is, 7 % and 6.1 % in Bonga and Kibaigwa, respectively, showed to have knowledge of the conditions required for house construction. Several reasons were noted for constructing houses in proximity to highways. These included eviction during road construction, social networking, allocated by the district council, easy access to services and land market forces. The study concludes that conditions necessary for housing construction are not explicit to the majority of community members, thus the study underscores the need for the local government authority to create awareness and enforce conditions necessary for building construction.