Volume 14 | Issue 7 |

 Abstract

The Arab world, a vast geographical expanse often characterized by its arid deserts, holds a subsurface wealth of archaeological and historical treasures that span the entirety of human civilization. This review article synthesizes current archaeological understanding and recent discoveries to trace the arc of human history across this vital region. It begins with the monumental legacy of the Nile, exploring the engineering evolution of Egypt's pyramid fields at Dahshur and Saqqara, before examining the less-traveled Kushite pyramids of Sudan, which challenge the narrative of Egyptian exclusivity in monumental tomb construction. The article then shifts focus to the Arabian Peninsula, where recent excavations are radically reshaping historical narratives. Sites like the AlUla oasis in Saudi Arabia, with its Nabataean capital of Hegra and the open-air library of Jabal Ikmah, reveal the peninsula as a vibrant crossroads of ancient trade and culture rather than a peripheral hinterland. Complementary discoveries in Oman and the UAE provide evidence of prehistoric coastal settlements and Iron Age industrial hubs, further illustrating the region's deep and complex history. Finally, the article reviews contemporary preservation efforts, including new UNESCO designations and the launch of the Arab Architectural and Urban Heritage Register by ALECSO. By connecting the Pyramids to the desert, this review demonstrates that the treasures of the Arab world are not static relics but dynamic sites of ongoing discovery that are essential to understanding our shared global heritage.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Arab world archaeology; cultural heritage; pyramids; Nabataean; UNESCO

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Edge Artificial Intelligence (Edge AI) and the Internet of Things (IoT) are revolutionizing the smart food manufacturing landscape, offering enhanced adaptive process control, real-time quality monitoring, and more sustainable production processes. Food manufacturers must make quick and accurate decisions to ensure product consistency, safety, and operational efficiency in their food manufacturing processes. Smart use of IoT-based infrastructure, with its sensors and edge-based analytics, can process data near its source, thereby reducing latency and enabling instant responses to process changes. This paper reviews the system architecture for smart food manufacturing enabled by Edge AI, focusing on physical sensing, local data collection, Edge AI intelligence, and cloud connectivity, and highlighting communication protocols and deployment considerations. It also focuses on the use of artificial intelligence (AI) for developing adaptive control strategies, including predictive modeling, anomaly detection, and closed-loop feedback for process optimization and quality assurance. Furthermore, the article discusses the benefits of minimizing waste, conserving energy, and predicting maintenance needs. Lastly, the following key challenges are addressed: security, interoperability, scalability, and integration complexity, as well as future opportunities such as 5G, blockchain, and digital twins for smart and resilient food systems.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

AI for the Edge, Internet of Things (IoT), smart food manufacturing, adaptive process control, real-time quality monitoring, sustainable production, food safety.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The rapid adoption of artificial intelligence (AI) tools has transformed the way students and working professionals perform academic and workplace tasks. However, empirical evidence on the relationship between screen time, AI engagement, and perceived performance remains limited, particularly in exploratory settings. This study investigates the influence of screen time and AI tool usage on perceived academic and professional performance. Data were collected from 45 respondents, comprising college students and working professionals, through a structured online questionnaire. The proposed model examined the relationships among screen time, device usage breadth, AI engagement, AI usage purposes, perceived performance improvement, and screen time growth. The findings reveal that AI has become an integral part of respondents' daily routines, with ChatGPT emerging as the most frequently used AI application. Working professionals reported significantly higher AI usage than full-time students. Most respondents perceived AI tools as enhancing their productivity and academic or professional performance. Furthermore, respondents reported a noticeable increase in overall screen time compared with the previous year, reflecting the growing integration of AI into everyday digital activities. Although several hypothesized relationships were not statistically significant because of the limited sample size, the study offers valuable exploratory evidence on emerging AI usage patterns. The findings contribute to the growing literature on AI adoption by providing preliminary empirical insights while emphasizing the need for larger, more representative samples to validate the proposed relationships.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Academic Performance, Artificial Intelligence, ChatGPT, Digital Behaviour, Professional Performance, Screen Time and Technology Adoption

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Cricket, especially in shorter formats like T20 and One-Day matches, requires batsmen to perform repeated high-speed sprints while running between the wickets. Sprint performance plays a crucial role in improving scoring rate and reducing the risk of run-outs. Core muscles are believed to contribute significantly to athletic performance by providing stability and efficient transfer of force between the upper and lower body. However, there is limited research exploring how core endurance and core strength relate to sprint speed in amateur cricket players. The present study aims to investigate the correlation between core endurance, core strength, and sprint speed in amateur cricket batsmen. A total of 80 male participants aged between 18-25 years will be selected using convenient sampling from cricket clubs. Core endurance will be assessed using the McGill torso endurance tests, core strength will be measured using the plank test, and sprint performance will be evaluated using the Repeated Sprint Ability (RSA) test. Data will be analyzed using statistical software to determine the relationship between these variables. It is expected that core strength will show a stronger correlation with sprint performance compared to core endurance. The findings of this study may help in designing better training programs focusing on core strengthening to enhance sprint ability and overall performance in amateur cricketers.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Amateur cricket batsmen, Core strength, Core endurance, Sprint speed, Repeated Sprint Ability (RSA), McGill torso endurance test, Plank test, Athletic performance, Running between the wickets, Sprint performance

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Purpose The Indian IT sector is a global leader in digital transformation, yet empirical evidence on how an integrated HR tech ecosystem affects employee outcomes remains limited. This study examines the impact of Cloud-based Human Resource Information Systems (HRIS), Robotic Process Automation (RPA), and AI-powered HR Chatbots on HR service delivery, employee experience, engagement, and retention in Indian IT firms. Design/Methodology/Approach Grounded in the UTAUT2 and AMO frameworks, the study uses a cross-sectional survey of 428 HR professionals and IT employees across 9 leading Indian IT companies. Data were analyzed using Confirmatory Factor Analysis and Structural Equation Modeling in AMOS 26. Findings Cloud HRIS and RPA significantly enhance HR service delivery efficiency. AI Chatbots and HR service efficiency positively influence employee experience and engagement. Organizational change management and employee digital literacy significantly moderate these relationships. Employee experience mediates the path from HR tech to engagement. Originality/Value This is among the first studies in India to test the HR tech ecosystem as an integrated construct and link it to both organizational and employee-level outcomes. The findings provide actionable insights for CHROs on designing tech that delivers both efficiency and human value. Practical Implications HR leaders must invest not only in platforms but also in change management and digital literacy. Technology should be designed for inclusion, with multilingual chatbots and self-service for distributed teams.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

HR Tech Ecosystem, Cloud HRIS, RPA, HR Chatbot, Employee Experience, Employee Engagement, Indian IT Sector, UTAUT2, AMO

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Social media has quietly but substantially changed how people discover and decide to buy things. What was once driven by television commercials and newspaper ads is now increasingly shaped by a person with a ring light talking directly to their audience about a product they may or may not genuinely use. This shift from broadcast advertising to influencer-driven recommendation is the starting point of this research. This study explores how social media influencers shape consumer purchase intention, with particular interest in the human side of this process: the trust consumers place in influencers, the emotional connections they form, and the mental calculations they make before buying something. A qualitative approach was used, drawing on semi-structured interviews with six purposively selected consumers. Three theoretical frameworks guide the analysis: the Elaboration Likelihood Model, Parasocial Interaction Theory, and the Source Credibility Model. Five themes emerged from the data: how and why consumers follow influencers, what builds or breaks trust in a recommendation, how authenticity and emotional connection factor into purchase decisions, how the decision-making process actually unfolds across multiple stages, and how platform and content format shape the whole process. The findings suggest that effective influencer marketing is less about reach and more about trust built over time through credible, experience-based content.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

influencer marketing, purchase intention, social media, consumer behavior, qualitative research

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Psychological stress is a major, often invisible, contributor to cardiovascular disease, anxiety disorders, burnout and reduced workplace productivity. Conventional stress assessment relies either on subjective self-report questionnaires or on contact-based physiological sensors (electro dermal activity, ECG, respiration belts) that are intrusive, uncomfortable over long durations, and impractical for continuous, unobtrusive monitoring. Thermal infrared imaging offers a non-contact alternative because psychophysiological arousal produces measurable, involuntary changes in facial skin temperature distribution, most notably nasal-tip cooling due to peripheral vasoconstriction and forehead/periorbital warming due to sympathetic activation. However, existing thermal-based stress detection systems are largely opaque "black-box" deep networks, which limits clinical trust, auditability, and adoption in healthcare and human-factors settings. This paper proposes an Explainable Multimodal Deep Learning Framework that fuses spatial thermal facial features with thermally-derived respiratory dynamics through an attention-based fusion layer, and augments the resulting classifier with Gradient-weighted Class Activation Mapping (Grad-CAM) for spatial saliency and Shapley Additive explanations (SHAP) for global and local feature attribution. Because no proprietary clinical thermal-stress corpus was accessible for this study, we validate the framework as a reproducible proof-of-concept using a literature-parameterized synthetic multimodal dataset whose class-conditional statistics reproduce documented directional thermal and respiratory stress effects. A compact feed-forward fusion network (MLP), trained and evaluated with stratified 5-fold cross-validation, is compared against Random Forest and Support Vector Machine baselines. The proposed fusion model achieves 89.5% accuracy, 89.6% F1-score and an AUC of 0.953, comparable to the 74-98% accuracy range reported by related thermal- and physiological-signal stress-detection studies in the literature. Permutation-based feature importance and Grad-CAM-style saliency consistently highlight facial-temperature variability, nasal-tip temperature and periorbital temperature as the dominant, physiologically plausible decision drivers, corroborating the model's internal reasoning against established psychophysiology. The results support the feasibility and design rationale of an explainable, non-contact, multimodal thermal stress-detection pipeline and outline the steps required to move from this proof-of-concept to validation on real clinical thermal video datasets.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Thermal facial imaging, non-contact stress detection, explainable artificial intelligence, multimodal deep learning, Grad-CAM, SHAP, affective computing, physiological signal fusion, infrared thermography.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Forensic entomology relies on the accurate identification of insects and other arthropods recovered from crime scenes, decomposing remains, and skeletal recoveries to estimate the post-mortem interval (PMI), reconstruct events surrounding death, and, in some cases, associate suspects or victims with a scene. For decades, this identification depended almost entirely on morphological taxonomy, a method that performs poorly on damaged specimens, immature life stages such as eggs and early-instar larvae, and cryptic or sibling species that are morphologically indistinguishable. Over the past two decades, DNA barcoding, most commonly built around a standardized fragment of the mitochondrial cytochrome c oxidase subunit I (COI) gene, has emerged as a robust molecular complement and, in many casework scenarios, a superior alternative to morphology-based identification. This review synthesizes the theoretical foundation of DNA barcoding, the practical workflow from sample collection through sequence-based species assignment, and its documented applications in forensic entomology, drawing on peer-reviewed research largely published between 2016 and 2026. Particular attention is given to regional case studies from India; emerging technologies including mini-barcoding, next-generation sequencing (NGS)-based metabarcoding, multi-locus approaches, and machine-learning-assisted identification; and persistent challenges such as reference database gaps, the barcode gap problem, nuclear mitochondrial pseudogenes (NUMTs), contamination risk, and courtroom admissibility. The review concludes that DNA barcoding has matured into an indispensable, though not standalone, pillar of modern forensic entomology, and it outlines priority areas -- regional reference libraries, standardized protocols, and integration with artificial intelligence -- for strengthening its evidentiary reliability going forward.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

DNA barcoding; forensic entomology; cytochrome c oxidase I (COI); post-mortem interval; Calliphoridae; metabarcoding; BOLD; GenBank; species identification

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Keywords: Coronary artery disease, structured teaching programme, knowledge, practice, staff nurses, cardiovascular disease prevention.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

cashless society is the future today . are we prepared for this ? what are the challenges with us today and the methods to overcome them

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

becoming fearless cashless society

  License

Creative Commons Attribution 4.0 and The Open Definition