VFAST - Virtual Foundation for Advancement of Science and Technology (Pakistan)
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Study of the Goodness and Evil of Actions in Light of Allama Ayub Dehlavi\u27s Teachings
Full text linkThis article examines Allama Ayub Dehlavi’s perspective on Husn (goodness) and Qubh (evil) of action an essential discourse in Islamic theology (ʿIlm al-Kalām). Central to the discussion is the question of whether the moral value of human actions is determined by reason (ʿaql) or divine command (sharīʿah). Allama Ayub Dehlavi (1888–1969), a distinguished scholar of logic, theology, and philosophy, diverged from major theological schools such as the Māturīdīs, Ashʿarites, and Muʿtazilites. He argued that moral value is not intrinsic to actions themselves but is contingent upon their consequences. Dehlavi proposed a dual framework: actions are evaluated as good or evil based on their worldly consequences (ʿaql-based) and their implications for the hereafter (sharīʿah-based). The article explores Dehlavi’s critical engagement with classical theologians. He opposed the Muʿtazilite and Māturīdī view that reason alone can discern moral values, as well as the Ashʿarite notion that moral value is solely dependent on divine command. Dehlavi maintained that while reason plays a role, it is insufficient to grasp the full moral weight of actions without divine guidance. He further challenged the long-standing philosophical axiom that averting harm (dafʿ al-maḍarrah) is superior to securing benefit (jalb al-manfaʿah). Contrary to this view, Dehlavi asserted that the divine order prioritizes the pursuit of benefit, as creation itself is an act of bringing about good, not merely the prevention of harm. In conclusion, Allama Ayub Dehlavi’s thought presents a critical reappraisal of traditional theological positions, offering a balanced synthesis of reason and revelation while contributing a fresh moral framework within Islamic philosophical discourse
Next-Gen Railway Crossings with IoT Solutions for Enhanced Safety and Control
Full text linkUnmanned level crossings particularly at the railway crossing are very dangerous, and cause accidents, which are normally caused by human oversights. This study provides an IoT-based railroad crossing system, which can improve safety using a combination of the NodeMCU, vibration sensors, and Firebase to control the gates in real time. In contrast to the manual or semi-automated systems used in the past, our solution exploits the use of wireless data transmission to monitor a train that is heading towards or leaving as well as automate the operation of the gates. The system will comprise three interconnected nodes namely two vibration sensor nodes that will be located at strategic points along the track and a gate node that has the leadership of a servo motor, a buzzer and LED indicators. Sensor data of the presence of a train is sent to the gate node when it has been recognized by the firebase, which closes the gate automatically and sends alert notifications. When the train takes off, the second sensor is utilized to open the system again. Our scalable, low-cost and real-time solution is expected to dramatically decrease the number of accidents in on coming trains, as well as offer a very efficient and IoT-based alternative to the traditional gate control systems
An ACID-BASE Analysis of NoSQL Database Structuring Models for Efficient Data Management
Full text linkDue to the massive growth of data in the modern era, NoSQL databases have achieved significant popularity for their ability to scale and adapt, making them a favored option for managing extensive data in distributed systems or cloud databases. The primary goal of this research is to explore the ACID (Atomicity, Consistency, Isolation, Durability), BASE (Basically available, soft state, Eventual Consistency), and CAP (Consistency, Availability, and Partition tolerance) database structuring models to conduct a comparative analysis of ACID and BASE of twelve mostly used NoSQL databases. We also categorize each database with Brewer’s CAP theorem. This research also compares the functional and non-functional features of databases adhering to the discussed models and explores the variety of data stores from each of the four NoSQL categories (Document, Key-value, Column, and Graph). The research summarizes the suggestions, benefits, and challenges of using NoSQL databases based on their applicability to cloud-based environments
Modern Search Engines: A Journey of Technological Advancements, Privacy, and User-Centered Improvements for Information Need
Full text linkSearch Engines (SEs) become increasingly important for locating, organizing, and retrieving relevant information in a world of massive and complex digital information. However, contemporary SEs are poor at striking a balance between precision and comprehensiveness, particularly when conducting exploratory or specific information queries. Therefore, the study aims to find out which search engine most effectively satisfied the needs of various users with a lower amount of time and effort. In order to solve this issue, ten such as SEs consisting of Google, Bing, Yahoo, DuckDuckGo, Swisscows, Qwant, Ecosia, Ask.com, WolframAlpha, and StartPage, were tested using two user-effort-sensitive measures: time to relevant result and number of relevant results. The study employed a lot of standard search queries and placed emphasis on the performance of each engine. Ultimately, we established that Google (83%) and StartPage (80%) delivered the most suitable results, while Ask.com (26%) and Swisscows (43%) performed less. In conclusion, contemporary SEs are getting better, but current ones still contain privacy problems, omitted results, and un balanced performance. The future research ought to be dedicated to enhancing ranking techniques, better search systems, and functionality which will benefit even more people, such as non-English users
A Comparative Study of Object-Oriented, Procedural, and Functional Programming Paradigms in Microservice Architecture
Full text linkIt is noted here that the microservices architecture has changed the whole paradigm of software engineering so that it permits building systems that could be distributed, scalable, and maintainable. Object-Oriented Programming (OOP) still dominates the design paradigms in industry. However, there has been revived interest in evaluating it in light of Procedural Programming (PP) and Functional Programming (FP) paradigms with respect to the evolving nature of software architecture. This paper covers a comprehensive comparative analysis of these three paradigm types in relation to microservice-based system design. In this case, each of the paradigms is applied in almost the exact architectural requirements upon a real-world e-commerce domain model, and finally, the evaluation was made on the basis of modularity, scalability, maintainability, and operational efficiency. Our finding is that, although OOP provides a balanced mix between abstraction and modularity beneficial for service-based architecture, FP is about minimizing mutable state through immutability and pure functions; hence the reduction of race conditions and making concurrency safer in distributed environments. Procedural programming is quite efficient for small-scale operations, but it faces serious hurdles in establishing service modularization and maintainability. In terms of such an ideal model supported by the current literature (2020-2025). This paper argues that OOP will be the most relevant paradigm for microservices, with Functional Programming reaping its benefits and promising alternatives for cloud-native or event-driven scenarios. In conclusion, this study narrates the call for hybrid approaches-the approaches that will utilize the strength of all paradigms to fulfill the ever-evolving software needs
Forgotten Footprints of Faith a Systematic Literature Review of Female Sufis in South Asia
Full text linkDespite their significant contribution, the role of women in Sufi tradition of South Asia has largely been ignored in the extant literature. This paper which conducted systematic literature review of the study is composed of about 25 articles, books and other materials illuminates the subject on the role of women in Sufism in South Asia. The results indicate that the female Sufis played their role in numerous fields such as gender-free spirituality, shrine culture and religious consciousness. The results also indicate that most of the work done by female Sufis are unrecorded in a way that limits a proper judgment on how the Sufi culture in South Asia owed itself to the contribution of female Sufis.The female figure has been either demarginalized or unrepresented in South Asian religious traditions, particularly, in traditions formed on the basis of Islamic mysticism. This study sheds more light on the significance of the sacred and cultural roles that women have been playing to perpetuate and model Sufi practices. By reviewing a significant amount of an academic literature, one can see that their impact is very wide and significant. However, due to less documentation and identifications in history, a lot of that contribution is shadowed or ignored. Thus, this review underlines the necessity of additional studies, preservation, and representation of women and their position in Sufi customs, especially in the situation of the country of the South Asian region with its multicultural background and rich background
Critical Discourse Analysis of Different Company Logos in constructing the Brands\u27 Identity
No full textLogos, as visual representations, express a company’s image, philosophy, and strategic direction in marketing and business. As a result, companies aim to design a ‘gorgeous’ brand logo that effectively communicates their identity. This study explores the fundamental aspects of logo design—visual, psychological, and cultural—and how these elements contribute to brand identity. It also examines the connection between logos and corporate heritage, focusing on how logos shape identity for business and marketing success. The research adopts a Critical Discourse Analysis (CDA) framework, using Norman Fairclough’s (1995) multimodal approach to analyze selected company logos. Consumer perception is a key focus of the study, with data gathered through purposive sampling. Sources include visual representations of logos, the narratives behind their creation, official company websites, marketing literature, and business-related articles. Additionally, social media posts were analyzed to understand consumer reviews and reactions. This research highlights how logos function beyond aesthetics, playing a crucial role in communicating values, building trust, and establishing long-term recognition in competitive markets
A Supervised Intrusion Detection System Leveraging Machine Learning for Secure Smart Education in the Internet of Education (IoEd)
Full text linkThe Internet of Education (IoEd) is an emerging field of IoT that combines Internet of Things (IoT) technologies with various learning environments such as smart classrooms, LMS, and web applications to improve educational productivity more efficiently and effectively. However, the integration of IoT Technologies within the educational environment, led to significant security risks, like different cyber-attacks, especially due to their limited processing capacity, which makes traditional defenses like firewalls and VPNs less effective. A single vulnerability within an IoT device can compromise the integrity of the entire network. Thus, this study highlights the urgent need to secure IoT-enabled academic systems to support sustainable educational advancement. This study proposes an Intrusion Detection System (IDS) based on Machine Learning, that identifies abnormal activities and potential cyber threats in an academic network, using a recently curated dataset, IoEd-Net. Leveraging Python packages like scikit-learn, TensorFlow, pandas, and NumPy. Six ML classifiers are used; Logistic Regression, Decision Tree, AdaBoost, Random Forest, ANN, and KNN. The experiment was done into two ways, 70/30 train-test split had achieved the highest accuracy of 98\% for Random Forest. The same highest accuracy was obtained in a second run with 5-fold cross-validation, by contributing to the IoEd security by developing an Intrusion Detection system (IDS) that can predict unseen network activities. It gives valuable analysis into network behavior, also enhancing privacy and security in an IoEd networks
AI-driven Physics Informed Neural Network for Daily Temperature Forecasting with Constraint Aware Learning and Explainable Feature Attribution
Full text linkThe daily mean temperature prediction is essential to implement agricultural adaptation and disaster risk reduction strategies in countries with varied climatic regions like Pakistan. Traditional machine learning methods often had difficulty complying with thermodynamic constraints, limiting their practicality for temperature estimation. The study proposed a Physics-Informed Neural Network (PINN), which integrates data and governing thermal principles to overcome these limitations. Integrating thermal equilibrium conditions within the loss function inherently consolidates the thermodynamic coherence of the construction. The high-resolution meteorological data from the Badin, Dadu and Rohri observation networks are used to build domain-specific features. These include diurnal excursions of temperature and humidity, a cyclic year encoding and wind-humidity ratios that capture the nonlinear mesoscale thermodynamics of the system. The PINN shows strong predictive ability as compared to a benchmark linear regression, some ensemble algorithms, and feedforward networks (R2 = 0.9975 Badin, 0.9974 Dadu, 0.9949 Rohri). SHAP and LIME, used in feature importance quantification, help to identify temperature drivers. In Badin, wind regimes have the most influence, whereas in Dadu and Rohri, lingering time trends have the most impact. With a focus on physical plausibility and explainable AI, the proposed methodology combines the probabilistic advantages of statistical learning with the constraint-based approach of atmospheric physics. This leads to resilient and spatially flexible predictions of temperature in data-scarce regions. As the study shows, PINNs could become a game-changing operational meteorological forecast technology when observational networks are weak or lacking altogether
Breast Cancer Classification Using Gene Expression Profile Data with a Deep Learning Framework
Full text linkBreast cancer is a harmful disease that is dangerous to human life. It causes a continuous increase in the overall death rate. It is a challenging task for machine learning models to classify gene expression profile data because of its complex nature. These machine-learning models took a lot of time and consumed larger data during their training, which resulted in inconsistent accuracy. The main purpose of this research is to build a deep neural network model that can accurately classify cancerous and non-cancerous patients by using gene expression profile datasets of breast cancer. This study presents a novel deep learning framework specifically designed for the classification of breast cancer using gene expression profile data. Unlike conventional machine learning approaches that require extensive preprocessing and manual feature extraction, the proposed model automatically learns discriminative features from high-dimensional genomic data. The proposed research implies a highly accurate model having precision (0.91), recall (1.00), and F1 score (0.95) with 95% accuracy. The macro average and weighted average both indicate that the model performs well on average also which is helpful for medical practitioners for early breast cancer prognosis