The emergence of Education 5.0 marks a paradigm shift towards more human-centric, technology-enhanced, and experiential learning models that prepare learners for a rapidly evolving digital society. Among the key enablers of this transformation is Augmented Reality (AR), which offers immersive and interactive learning experiences that bridge physical and digital environments. This article explores the integration of AR within Education 5.0 frameworks, and focused on its potential to design immersive learning environments that support personalized, collaborative, and skill-oriented education........

[1]. Akçayır, M., andAkçayır, G. (2017). Advantages and challenges associated with augmented reality for education: A systematic review of the literature. Educational Research Review, 20, 1–11. https://doi.org/10.1016/j.edurev.2016.11.002
[2]. Bacca, J., Baldiris, S., Fabregat, R., Graf, S., and Kinshuk. (2014). Augmented reality trends in education: A systematic review of research and applications. Educational Technology and Society, 17(4), 133–149.
[3]. Billinghurst, M., andDuenser, A. (2012). Augmented reality in the classroom. Computer, 45(7), 56–63. https://doi.org/10.1109/MC.2012.111
[4]. Bower, M., Howe, C., McCredie, N., Robinson, A., and Grover, D. (2014). Augmented Reality in education – cases, places and potentials. Educational Media International, 51(1), 1–15. https://doi.org/10.1080/09523987.2014.889400
[5]. Chen, C. M., Ho, C. H., and Huang, Y. M. (2020). Emotion recognition and support system for children with autism using augmented reality and facial expression analysis. Computers and Education, 145, 103728. https://doi.org/10.1016/j.compedu.2019.103728

As digital transformation accelerates across every sector, the cybersecurity landscape is undergoing profound and complex changes. This article explores the projected trajectory of cybersecurity through 2030, focusing on emerging trends, disruptive technologies, and evolving threat horizons. It analyzes the implications of advancements such as artificial intelligence, quantum computing, 5G, and the Internet of Things (IoT), while also examining the growing sophistication of cyber adversaries. Key challenges addressed include the rise of AI-enabled attacks, the urgent need for post-quantum cryptography, and the increasing importance of zero trust security models...........

[1]. Aguh, P. S., Udu, C. E., Chukwumuanya, E. O., and Okpala, C. C. (2025). Machine learning applications for production scheduling optimization. Journal of Exploratory Dynamic Problems, 2(4). https://edp.web.id/index.php/edp/article/view/137
[2]. Alrawais, A., Alhothaily, A., Hu, C., and Cheng, X. (2017). Fog computing for the Internet of Things: Security and privacy issues. IEEE Internet Computing, 21(2), 34–42. https://doi.org/10.1109/MIC.2017.37
[3]. Biggio, B., and Roli, F. (2018). Wild patterns: Ten years after the rise of adversarial machine learning. Pattern Recognition, 84, 317–331. https://doi.org/10.1016/j.patcog.2018.07.023
[4]. Binns, R. (2018). Fairness in machine learning: Lessons from political philosophy. Proceedings of the 2018 Conference on Fairness, Accountability and Transparency, 149–159. https://doi.org/10.1145/3287560.3287598
[5]. Bradford, A. (2020). The Brussels effect: How the European Union rules the world. Oxford University Press.

As cybersecurity threats become increasingly sophisticated, the role of the human factor is gaining critical importance in shaping effective and ethical defense strategies. This article explores the evolving relationship between individuals and cybersecurity systems, emphasizing how trust, privacy, and responsibility are central to future resilience. It argues that technological solutions alone are insufficient without an integrated understanding of human behavior, motivation, and interaction. Through a multidisciplinary lens, the article examines how user awareness, design thinking, ethical leadership, and organizational culture can reduce vulnerabilities, while enhancing trust and accountability........

[1]. Acquisti, A., Brandimarte, L., and Loewenstein, G. (2015). Privacy and human behavior in the age of information. Science, 347(6221), 509–514. https://doi.org/10.1126/science.aaa1465
[2]. Acquisti, A., Adjerid, I., and Loewenstein, G. (2017). Nudges for privacy and security: Understanding and assisting users' choices online. ACM Computing Surveys (CSUR), 50(3), 1–41. https://doi.org/10.1145/3054926
[3]. Ashenden, D., and Sasse, M. A. (2013). CISOs and organisational culture: Their own worst enemy? Computers and Security, 39, 396–405. https://doi.org/10.1016/j.cose.2013.09.001
[4]. Beldad, A., De Jong, M., and Steehouder, M. (2010). How shall I trust the faceless and the intangible? A literature review on the antecedents of online trust. Computers in Human Behavior, 26(5), 857–869. https://doi.org/10.1016/j.chb.2010.03.013
[5]. Brundage, M., Avin, S., Clark, J., Toner, H., Eckersley, P., Garfinkel, B., ... and Amodei, D. (2018). The malicious use of artificial intelligence: Forecasting, prevention, and mitigation. Future of Humanity Institute. https://arxiv.org/abs/1802.07228

As the digital threat landscape becomes increasingly complex, the evolution of cybersecurity technologies has shifted from traditional, perimeter-based defenses to intelligent, autonomous systems. This article examines the historical trajectory and future direction of cybersecurity tools, highlighting key developments such as firewalls, Intrusion Detection and Prevention Systems (IDS/IPS), and the integration of artificial intelligence and machine learning in modern defense strategies. Emerging paradigms including Zero Trust Architecture (ZTA), Extended Detection and Response (XDR), Security Orchestration, Automationcteria.......

[1]. Aguh, P. S., Udu, C. E., Chukwumuanya, E. O., and Okpala, C. C. (2025). Machine learning applications for production scheduling optimization. Journal of Exploratory Dynamic Problems, 2(4). https://edp.web.id/index.php/edp/article/view/137
[2]. Anderson, R., and Moore, T. (2019). Security economics and the internal market. European Network and Information Security Agency (ENISA).
[3]. Axelsson, S. (2000). The base-rate fallacy and its implications for the difficulty of intrusion detection. ACM Transactions on Information and System Security (TISSEC), 3(3), 186–205. https://doi.org/10.1145/357830.357849
[4]. Biggio, B., and Roli, F. (2018). Wild patterns: Ten years after the rise of adversarial machine learning. Pattern Recognition, 84, 317–331. https://doi.org/10.1016/j.patcog.2018.07.023
[5]. Brundage, M., Avin, S., Clark, J., Toner, H., Eckersley, P., Garfinkel, B., ... and Amodei, D. (2018). The malicious use of artificial intelligence: Forecasting, prevention, and mitigation. arXiv preprint arXiv:1802.07228.

In order to meet water needs, especially for water needs in rice fields, it is necessary to establish an irrigation system and build a weir. The need for water in rice fields is then called the need for irrigation water. This research aims to determine and analyze irrigation water needs in the Air Kelingi Tugu Mulyo irrigation service area. The results of the research produced irrigation water requirements in the Kelingi irrigation service area of 0.2847 – 11.5184 m3/sec. The need for irrigation water during each planting period I. The maximum water requirement occurs in the third week of November at 11,038 m3/s. This occurs because that month is the month for preparing the land for the planting period I, so the need for irrigation water increases........

[1]. Andikha, F., 2017, Penerapan Sistem Ecodrainage Dalam Mengurangi Potensi Banjir (Studi Kasus di Kabupaten Sampang)
(Doctoral dissertation, Institut Teknologi Sepuluh Nopember)
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ISSN 2477- 4960, EISSN 2621-7929
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This research examines the potential of LIQUID 3, an urban photobioreactor with microalgae, as an innovative tool for mitigating air pollution and complementing traditional greenery in cities. Through photosynthesis, LIQUID 3 captures carbon dioxide and produces oxygen, supported by solar panels, built-in lighting, and a water-based system that enables year-round operation. Developed in response to Serbia's high pollution rates in 2020, LIQUID 3 serves as a case study for densely populated areas where green spaces are limited.......

[1] S. D. Koolen and G. Rothenberg, Air Pollution in Europe. 2018.
[2] A. Podic, Pollution in Sarajevo. 2019.
[3] I. Spasojevic, University of Belgrade – Liquid Tree Photobioreactor Research. 2020.
[4] Castim, Liquid 3 Bioreactor Design. 2021.
[5] M. Krieger, Algae-based Urban Photobioreactors. 2022.

In Ayurveda, Varnya Dravyas are herbs that support the enhancement of natural complexion and skin health. These agents function by maintaining the balance of Bhrajaka Pitta, Rakta Dhatu, and Ojas, which are considered key to achieving a radiant appearance. This review critically examines the Varnya herbs described in Kaiyadeva Nighantu, a significant 15th-century Ayurvedic lexicon known for its structured pharmacognostic classification. A total of 27 Varnya herbs have been identified from the Aushadhi Varga, with an emphasis on their Rasa (taste), Veerya (potency), and traditional indications.......

[1]. Pallavi G. A study on the concept of varnya vis-a-vis clinical evaluation of efficacy of varnya gana lepa in vyanga. Dissertation. Department of Postgraduate Studies in Ayurveda Siddhanta, Government Ayurved Medical College, Mysore; p. 18

Accurate pore pressure prediction is a cornerstone of safe and efficient petroleum exploration, especially in geologically complex deepwater basins like the Niger Delta. This study presents an integrated framework that combines traditional wellbore-based pore pressure estimation with advanced geostatistical modeling to produce a comprehensive, field-wide pressure surface. We utilized well log data from a deepwater field, applying Eaton's method to derive pore pressure at discrete well locations. The derived pressures were rigorously validated against direct formation pressure measurements, showing a high degree of correlation with an average deviation of less than ±1.5 bar, confirming the method's applicability.......

[1]. Abbey, C. P., Meludu, O. C., &Oniku, A. S. (2021). 3D modeling of abnormal pore pressure in shallow offshore Niger Delta: An application of seismic inversion. Petroleum Research, 6(2), 158–171. https://doi.org/10.1016/j.ptlrs.2020.12.001
[2]. Ahmed, A. A. S., Mahmoud, A. A., Elkatatny, S., Mahmoud, M., & Abdulraheem, A. (2019, March). Prediction of pore and fracture pressures using support vector machine. Paper presented at the International Petroleum Technology Conference, Beijing, China. https://doi.org/10.2523/IPTC-19523-MS
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[4]. Carpenter, C. (2023). Pore-pressure-prediction model investigates hydrocarbon generation. Journal of Petroleum Technology, 75, 81–83. https://doi.org/10.2118/0223-0081-JPT
[5]. Civan, F. (2019). Stress-dependent porosity and permeability of porous rocks represented by a mechanistic elastic cylindrical pore-shell model. Transport in Porous Media, 129, 1–25. https://doi.org/10.1007/s11242-019-01311-0

Unmanned Aerial Vehicles (UAVs) have undergone a remarkable transformation from simple radio-controlled aircraft to sophisticated autonomous systems capable of complex decision-making and independent operation. This paper examines the evolutionary trajectory of UAV technology, exploring the fundamental principles governing their operation, the historical milestones that shaped their development, and the technological breakthroughs that enabled the transition to autonomous flight.......

[1]. Austin, R. (2010). Unmanned aircraft systems: UAVS design, development and deployment. John Wiley & Sons.
[2]. Blom, H. A., & Barker, G. J. (2009). Air traffic management and systems. IEEE Transactions on Aerospace and Electronic Systems, 45(4), 1295-1308.
[3]. Boucher, P. (2015). Domesticating the drone: The demilitarisation of unmanned aircraft for civil markets. European Parliamentary Research Service.
[4]. Colomina, I., & Molina, P. (2014). Unmanned aerial systems for photogrammetry and remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 92, 79-97.
[5]. Fahlstrom, P., & Gleason, T. (2012). Introduction to UAV systems. John Wiley & Sons.

After seeing immense sufferings and unbelievable losses incurred from covid-19, I dreamt of a typical concept which I coined as "anviunpro-2040RC". Its expanded form is given below: "AntiViralUniversalProtein2040RC" [RC: Rama Chandran]

[1] A simple dipeptide has two possible structures based on the amino acid which contributed OH part [from COOH] (or) H part [from NH2]. eg: Glycine + Alanine → Glycylalanine [1] + Alanylglycine [2] where in [1], NH2 of Gly is free while in [2], NH2 of Ala is free. So, [1] has structural formula as : H2N-CH2-CO-NH-*CH(Me)-COOH [N to C terminal] [2] has structural formula as: H2N-*CH(Me)-CO-NH-CH2-COOH [N to C terminal] In both [1] and [2], C* = stereogenic centre (or chiral carbon) and both are optically active.

Pandanus tectorius is widely marketed in Vietnam as crude herbal material and tea, yet comparative data on its phytochemical quality and α-amylase inhibition remain limited. In this study, thirteen fruit and nine leaf samples were purchased from local markets and e-commerce sources and extracted with 90% ethanol. Total phenolic content (TPC) and total saponin content (TSC) were determined by Folin–Ciocalteu and vanillin–sulfuric acid assays, while α-amylase inhibition was evaluated using a modified DNSA method. Results were expressed on both a sample basis (mg/g dry material) and an extract basis........

[1]. Gallaher, T., Callmander, M. W., Buerki, S., Keeley, S. C. (2015). A long-distance dispersal hypothesis for the Pandanaceae and the origins of the Pandanus tectorius complex. Molecular Phylogenetics and Evolution, 83, 20-32.
[2]. Tan, M. A., Nonato, M. G., Kogure, N., Kitajima, M., Takayama, H. (2012). Secondary metabolites from Pandanus simplex. Biochemical Systematics and Ecology, 40, 4-5.
[3]. Lim, T. K., Pandanus tectorius. In Edible Medicinal And Non-Medicinal Plants: Volume 4, Fruits, Springer Netherlands: Dordrecht, 2012; pp 136-146.
[4]. Do Huy Bich, Dam Quang Trung., Bui Xuan Chuong, Nguyen Thuong Dong, Do Trung Dam, Pham Van Hien,Vu Ngoc Lo, Pham Duy Mai, Pham Kim Man, Doan Thi Nhu, Nguyen Tap, Tran Toan, Medicinal plants and animals in Vietnam. NXB Khoa học và Kỹ thuật: 2006.
[5]. Pham Hoang Ho, Plants in Vietnam. Tre Publisher: Ha Noi, 2003.

The hydraulic modeling of public water supply systems has undergone significant technological and methodological advancements over the past decades. The integration of computer simulations, geographic information systems (GIS), and machine learning-based predictive algorithms has opened new possibilities for understanding network behavior, optimizing operations, and forecasting water quality. This study reviews the historical development of hydraulic modeling, highlights the role of modern data-driven approaches, and provides a detailed analysis of methodologies for spatial distribution of consumption data.......

[1] L. A. Rossman, Users manual. National Risk Management Research Laboratory, Office of Research and Development: U.S. Environmental Protection Agency, 2000.
[2] L. Ormsbee, "The History of Water Distribution Network Analysis: The Computer Age," 8th Annual Water Distribution Systems Analysis Symposium, Cincinnati, Ohio, USA, August 27-30, 2006.
[3] E. Creaco, A. Campisano, N. Fontana, G. Marini, P. Page és T. Walski, "Real time control of water distribution networks: A state-of-the-art review," Water Research, %1. kötet161, pp. 517-530, 2019.
[4] D. Sarisen, V. Koukoravas, R. F. Farmani és Z. Kapelan, "Review of hydraulic modelling approaches for intermittent water supply systems," AQUA - Water Infrastructure, Ecosystems and Society, %1. kötet71 (12), p. 1291–1310, 2022.

Biometric authentication has gained significant attention due to its ability to provide secure and user-specific identification. However, the performance of convolutional neural networks (CNNs) in biometric recognition is strongly dependent on the preprocessing strategies applied to the dataset. This paper investigates dataset-specific preprocessing for two benchmark databases: ORL for face recognition and CASIA-IrisV4 for iris recognition. The study proposes tailored preprocessing methods including normalization, resizing, histogram equalization, and iris boundary refinement to improve CNN feature extraction. Results show that optimizing preprocessing for each dataset significantly enhances recognition accuracy and robustness compared to uniform preprocessing strategies.

[1]. Abdo, M., Mekdad, Y., & Ahmad, A. (2022). Multimodal biometric fusion based on convolutional neural networks. Sensors, 22(5), 2451.
[2]. Abdo, M., Mekdad, Y., Ahmad, A., & Saeed, M. (2020). Feature-level fusion of face and iris using CNN. International Journal of Computer Vision, 128(9), 2405–2420.
[3]. All About Vision. (2024, March 20). How the iris works. Retrieved from https://www.allaboutvision.com/
[4]. Asha, M. J., Deepa, P. S., &Madhusoodanan, N. (2023). Face and iris verification using Siamese network. Computers & Electrical Engineering, 105, 108534.

This study explores the application of green building technologies and the development of a tailored evaluation system for student accommodations, using Xinzheng City as a case study. It aims to support the green transformation of such buildings by promoting energy efficiency, environmental protection, and optimal resource use.A five-dimensional evaluation framework—covering energy efficiency, water management, material use, indoor environmental quality, and innovation—was established through literature review and fieldwork. A mixed-methods approach combined qualitative (expert consultation) and quantitative (surveys and IoT data analysis) methods to assess technology effectiveness........

[1] Lai, F., Zhou, J., Lu, L., Hasanuzzaman, M., & Yuan, Y. (2023). Green building technologies in Southeast Asia: A review. Sustainable Energy Technologies and Assessments, 55, 102946.
[2] Azis, S. S. A. (2021). Improving present-day energy savings among green building sector in Malaysia using benefit transfer approach: Cooling and lighting loads. Renewable and Sustainable Energy Reviews, 137, 110570.
[3] Olanrewaju, O. I., Enegbuma, W. I., Donn, M., & Chileshe, N. (2022). Building information modelling and green building certification systems: A systematic literature review and gap spotting. Sustainable Cities and Society, 81, 103865.
[4] Li, Z., Chow, D., Ding, D., Ying, J., Hu, Y., Chen, H., & Zhao, W. (2020). The development and realisation of a multi-faceted system for green building planning: A case in Ningbo using the fuzzy analytical hierarchy process. Energy and Buildings, 226, 110371.
[5] Guo, K., Li, Q., Zhang, L., & Wu, X. (2021). BIM-based green building evaluation and optimization: A case study. Journal of Cleaner Production, 320, 128824.

Coconut plantations play a vital role in supporting rural economies, yet their management often faces challenges such as unpredictable weather, pest outbreaks, and difficulties in yield estimation. In recent years, machine learning has emerged as a valuable tool for addressing these issues by offering intelligent, data-driven solutions. This survey provides analytical insights into how machine learning techniques are being applied in coconut plantation management. From maturity detection and automated harvesting to yield forecasting and disease diagnosis, machine learning models such as Support Vector Machines, Random Forests, and Convolutional Neural Networks are demonstrating great promise.......

[1] Goswami, A., & Dhablia Kirit, D. (2025). Predictive Analytics in Agriculture: Machine Learning Models for Coconut Tree Health. In
SHS Web of Conferences, 216, 01051. EDP Sciences.
[2] Shokri, R., Samiei, S., & Møller, S. (2025). Smart sensing technologies in agriculture: A review of machine learning applications for
crop monitoring. Sensors, 25(12), 3583.[3] Caladcad, J. A., & Piedad, E. J. J. (2024). Deep learning classification system for coconut maturity levels based on acoustic signals
[Preprint]. arXiv.
[4] Singh, A., Sharma, R., Behera, S. K., & Mishra, B. (2020). Pest damage identification of coconut plant using damage texture and color
analysis. Frontiers in Plant Science, 11, 612958.
[5] Nayak, S. K. (2020, December 1). Analysis and high accuracy prediction of coconut crop yield production based on principal component
analysis with machine learning models. International Journal of Modern Agriculture, 9(4), 439–446.

The freight forwarding industry, as the primary orchestrator of global logistics, is uniquely vulnerable to disruptions that propagate as costly ripple effects. This study develops a hybrid simulation model within the AnyLogic environment to equip freight forwarders with a quantitative tool for analyzing port disruptions and evaluating mitigation strategies. Combining Discrete-Event Simulation (DES) for process flow and Agent-Based Modeling (ABM) to simulate forwarder decision-making, the model replicates a major Asia-Europe trade lane. Scenarios simulate a 21-day closure of the Port of Rotterdam.......

[1]. Ivanov, D. (2021). Supply chain resilience: the ripple effect and COVID-19. International Journal of Production Research, 59(7),
1999-2012.
[2]. Global Freight Forwarding 2023: Market Size, Growth & Forecasts. (2023). Armstrong & Associates, Inc.
[3]. Wagner, S. M., & Bode, C. (2008). An empirical examination of supply chain performance along several dimensions of risk. Journal
of Business Logistics, 29(1), 307-325.
[4]. Notteboom, T. E., & Rodrigue, J. P. (2008). Containerisation, box logistics and global supply chains: The integration of ports and liner shipping networks. Maritime Economics & Logistics, 10(1-2), 152-174.
[5]. Hausman, W. H., Lee, H. L., & Subramanian, U. (2013). The impact of logistics performance on trade. Production and Operations
Management, 22(2), 236-252.

Reservoir characterization plays a vital role in ensuring efficient hydrocarbon exploration, development, and production. A precise understanding of subsurface reservoir properties such as porosity, permeability, lithology, and fluid saturation enables geoscientists and engineers to design better strategies for field development and production optimization. However, traditional reservoir characterization techniques often rely on either well logging or seismic reflection data independently, which results in incomplete, inconsistent, or even contradictory interpretations......

[1]. Al Ibrahim, R. N., Al-Owaidi, M. R., Alnajen, F. M., & Al-Khafaji, A. J. (2022). Investigation of the Geochemical Properties and Origin of the Crude Oils Accumulated in the Mishrif Reservoirs in the Zubair, Halfaya, and Buzurgan Oilfields, Southern Iraq. The Iraqi Geological Journal, 98-110.
[2]. Abbas, M. A., & Al-Mudhafar, W. J. (2021, August). Lithofacies classification of carbonate reservoirs using advanced machine learning: a case study from a Southern Iraqi oil field. In Offshore Technology Conference (p. D012S059R012). OTC.
[3]. Zhang, L., Zhang, W., Li, Y., Song, B., Liu, D., Deng, Y., ... & Wang, Y. (2023). Sequence stratigraphy, sedimentology, and reservoir characteristics of the middle cretaceous Mishrif Formation, South Iraq. Journal of Marine Science and Engineering, 11(6), 1255.
[4]. Chafeet, H. A., &alhussein, R. B. A. (2025). Characterization of tertiary carbonate reservoir in the ABG oilfield using advanced magnetic resonance imaging logging (MRIL-P): a case study from southeastern Iraq. Modeling Earth Systems and Environment, 11(3), 194.
[5]. Radwan, A. E. (2023). Western desert petroleum system: New exploration opportunities and challenges. The Phanerozoic Geology and Natural Resources of Egypt, 691-717.

When companies from different countries try to make business deals, language differences can cause confusion and slow things down. Artificial Intelligence (AI) solves this with real-time translation tools that instantly convert what one person says into another language during talks. For example, a U.S. company can negotiate with a Japanese partner over a video call, and AI will translate both sides' words on the spot, making conversations clear and smooth. This saves time and money compared to hiring human translators and can increase deal success by 15% by reducing misunderstandings. By fostering clear communication, AI builds trust, a key psychological factor in sales persuasion. While AI might miss some cultural details, it's a huge help for international business.......

[1]. Liu, Z. (2024). New Dynamics in Cross-Cultural Exchange: A Study on the Role and Impact of Artificial Intelligence Technology in the Global Cultural Market. Academic Journal of Business & Management, 6(12), 137-143.
[2]. Lv, L. (2023, November). The investigation focuses on investigating the use of artificial intelligence technology in the context of intercultural interaction pertaining to intangible cultural assets. In IEEE International Conference on Advanced Infocomm Technology (pp. 423-433). Singapore: Springer Nature Singapore.
[3]. Murshed, A. A., alrahamneh, A., Al-Tarawneh, A., Al-Badawi, M., & Alhalalmeh, A. H. (2024). The Influence of Artificial Intelligence and Language in the International Economic Landscape. In Frontiers of Human Centricity in the Artificial Intelligence-Driven Society 5.0 (pp. 999-1009). Cham: Springer Nature Switzerland.
[4]. Kromer, S. (2024, August). A Transnational Perspective on Artificial Intelligence Cultural Challenges. In IFIP International Conference on Human Choice and Computers (pp. 1-8). Cham: Springer Nature Switzerland.
[5]. BOUKHOBZA, B., DJEBILOU, M., & BENFODDA, A. (2024). Cultural Identity and Artificial Intelligence: Challenges and Perspectives (Doctoral dissertation, UNIVERSITY OF AIN TEMOUCHENT).

Factories in developing countries often face big problems when machines break down unexpectedly, causing delays and expensive repairs. Artificial Intelligence (AI) helps by using predictive maintenance to spot issues before they happen. Sensors on machines collect data, like temperature or vibration, and AI analyzes it to predict when a machine might fail. For example, if a motor is running too hot, AI can warn technicians to fix it early, preventing a shutdown. This can save factories up to 20% in maintenance costs and keep production running smoothly, which is crucial in emerging markets where money is tight. By showing clear cost savings, AI persuades factory owners to adopt it, tapping into their desire for efficiency......

[1]. SARDJONO, W., & PRATAMA, D. A. (2025). UTILIZING ARTIFICIAL INTELLIGENCE PREDICTIVE MAINTENANCE IN LEAN MANUFACTURING TO BOOST INDUSTRIAL SUSTAINABILITY AND ENERGY EFFICIENCY. Journal of Theoretical and Applied Information Technology, 103(13).
[2]. Ekwomadu, C. C., Adeniyi, S. A., Kelvin, O. E., & Akpan, E. D. U. Integrating AI-Driven Predictive Maintenance and Process Control in Industrial Automation for Sustainable FMCG Manufacturing.[3]. Sekar, K., Subbiiah Nattar, M., Muthukamatchi, P. K., Ranganathan, N., Srithar, S., & Gurunathan, N. (2025). Integrating machine learning and IoT for real-time predictive maintenance in industrial ecosystems: A case study analysis. International Journal of Research in Industrial Engineering, 14(2), 385-409.
[4]. Nagalalli, G. Integrating Machine Learning and IoT for Real-Time Predictive Maintenance in Industrial Ecosystems: A Case Study Analysis.
[5]. Sekar, K., Nattar, M. S., Muthukamatchi, P. K., Ranganathan, N., Srithar, S., & Gurunathan, N. (2025). Integrating Machine Learning and IoT for Real-Time PdM in Industrial Ecosystems: A Case Study Analysis. International Journal of Research in Industrial Engineering (2783-1337), 14(2).

Emotional intelligence (EQ) is a key skill for tech sales and business development, helping professionals understand and manage customer emotions. Unlike regular intelligence, EQ focuses on empathy, self-awareness, and social skills, making it easier to build trust with clients. In tech sales, EQ helps salespeople handle rejections, address customer concerns, and close deals, with studies showing a 12% sales increase for high-EQ teams. For business development, EQ supports forming partnerships and entering new markets by creating strong relationships. This article explains how tech companies can train their teams in EQ to improve sales performance and grow their business, using real-world examples and simple strategies.

[1]. Reid, M. (2015). Sales performance and emotional intelligence of technology sales professionals. Walden University.
[2]. Chipain, G. C. (2003). Emotional intelligence and its relationship with sales success. DePaul University.
[3]. Sojka, J. Z., & Deeter‐Schmelz, D. R. (2002). Enhancing the emotional intelligence of salespeople. American Journal of Business, 17(1), 43-50.
[4]. Bryant, D. (2005). The components of emotional intelligence and the relationship to sales performance. The George Washington University.
[5]. Bryant, D. (2005). The components of emotional intelligence and the relationship to sales performance. The George Washington University.

Groundwater remains the most reliable water source in the Niger Delta, where surface water is vulnerable to anthropogenic contamination and saline intrusion. This study characterizes aquifer units within the Benin Formation at the Abuja Campus of the University of Port Harcourt using Vertical Electrical Sounding (VES) and Electrical Resistivity Tomography (ERT). VES data from analog terrameters revealed a five-layer subsurface sequence with a QH-type curve, dominated by alternating clay and sand units. A thick sandy aquifer with resistivity of ~575.57 Ωm at ~25 m depth, and a deeper sandy aquifer with ~718.30 Ωm resistivity, were delineated........

[1]. Aizebeokhai, A. P., Oyeyemi, K. D., & Odukoya, A. M. (2022). Vertical electrical sounding for groundwater exploration in sedimentary basins: A case study from Nigeria. Environmental Earth Sciences, 81(3), 102–115. https://doi.org/10.1007/s12665-022-10123-4
[2]. Aizebeokhai, P. A., Oyeyemi, K. D., Noiki, F. R., Etete, B. I., Arere, U. E., Eyo, U. J., & Ogbuehi, V. C. (2017). Geoelectrical resistivity data sets for subsurface characterisation and aquifer delineation in Iyesi, southwestern Nigeria. Data in Brief, 15, 828–832. https://doi.org/10.1016/j.dib.2017.10.057

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The fashion industry ranks as the third-largest global manufacturing sector, following technology and automotive industries. With the increasing adoption of social media in Nigeria, a substantial digital market has emerged, offering significant opportunities for business promotion and product sales. This study investigates the impact of social media marketing on the sales performance of fast fashion brands, using Ziva Fashion in Lagos, Nigeria. It examines the extent to which social media marketing influences sales, explores the challenges encountered, and evaluates strategies employed by Ziva Fashion in its digital marketing efforts........

[1]. Bell, J. and Waters, S. (2018) Doing Your Research Project: A Guide for First-Time Researchers. 7th edn. Maidenhead: Open University Press.
[2]. Bonilla, M.D.R., delOlmo Arriaga, J.L. and Andreu, D., 2019. The interaction of Instagram followers in the fast fashion sector: The case of Hennes and Mauritz (HandM). Journal of Global Fashion Marketing, 10(4), pp.342–357.
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A hydraulic ram pump is a simple mechanical device that can pump water to higher elevations without using external energy such as electricity or fuel. This technology utilizes the kinetic energy of gravitational water flow to generate pressure through the water hammer effect. This article aims to examine the potential application of hydraulic ram pumps in water supply systems in remote areas and evaluate their efficiency and performance through analytical approaches and literature simulations. The analysis results indicate that hydraulic ram pump efficiency can reach 60–80% depending on the configuration and inlet flow rate. This pump is ideal for areas with a continuous flowing water source and a minimum elevation difference of 1 meter. This study is expected to provide a basis for the development of sustainable and energy-efficient water supply systems in areas with limited infrastructure.

[1]. Jafri, M., Bale, J. S., and Thei, A. R. 2021. Studi Eksperimental Pengaruh Diameter Katup Limbah dan Diameter Katup Pengantar terhadap Efisiensi Pompa Hidram 3 Inchi. LONTAR Jurnal Teknik Mesin Undana, 3(1). https://ejurnal.undana.ac.id/LJTMU/article/view/465
[2]. Amanda, T., and Fitria. 2023. Efisiensi Pompa Hidram Berdasarkan Perbedaan Diameter Pipa Masukan dan Volume Tabung. SINERGI POLMED: Jurnal Ilmiah Teknik Mesin, 4(1). https://ojs.polmed.ac.id/index.php/Sinergi/article/view/1003
[3]. Nasution, M. 2023. Pengaruh Variasi Panjang Pipa Masuk Terhadap Efisiensi Pompa Hidram. PISTON: Jurnal Ilmiah Teknik Mesin Fakultas Teknik UISU. https://www.jurnal.uisu.ac.id/index.php/piston/article/view/7746
[4]. Jafri, M., Gusnawati., and Adoe, D. G. H. 2021. Analisa Beda Tinggi Katup dan Variasi Diameter Pipa Inlet Terhadap Unjuk Kerja Pompa Hidram Ukuran Dua Inchi. LONTAR Jurnal Teknik Mesin Undana, 3(1). https://ejurnal.undana.ac.id/LJTMU/article/view/467
[5]. Puspawan, A., Suandi, A., Witanto, Y., and Putra, S. T. 2024. Analisa Pengaruh Variasi Diameter Tabung dan Jarak Katup Buang Limbah terhadap Unjuk Kerja Sistem Pompa Hidrolik Ram. Rekayasa Mekanika: Jurnal Ilmiah Teknik Mesin, 4(2). https://ejournal.unib.ac.id/index.php/rekayasamekanika/article/view/18395

Combination products (CPs), particularly drug–device combinations (DDCs), are increasingly important in healthcare, as they integrate diverse technologies into a single product. These innovations show strong potential to improve treatment outcomes and address unmet needs, especially in rare diseases. However, development and approval remain complicated due to classification uncertainties, inconsistent regulatory oversight, and complex operational requirements. Global trends highlight three key directions: the move toward harmonized frameworks, greater use of real-world evidence, and collaborative evaluations among agencies........

[1]. Kim, Joo & Kwon, Sera & Seol, Ju & Kim, Mi & Kim, Su. (2024). Regulatory Framework for Drug-Device Combination Products in the United States, Europe, and Korea. Therapeutic innovation & regulatory science. 58. 10.1007/s43441-024-00661-2.
[2]. Tsourounis M, Stuart J, Smith M, Toscani M, Barone J. Challenges in the development of drug/device and biologic/device combination products in the United States and European union: a summary from the 2013 DIA meeting on combination products. TherInnovRegul Sci. (2015) 49(2):239–48. doi: 10.1177/ 2168479014553900
[3]. Sun G, Zhou Y-H. AI In healthcare: navigating opportunities and challenges in digital communication. Front Digit Health. (2023) 5:1–5. doi: 1291132.10.3389/ fdgth.2023.1291132
[4]. Sachdeva P, Kaur K, Fatima S, Mahak FNU, Noman M, Siddenthi SM, et al. Advancements in myocardial infarction management: exploring novel approaches and strategies. Cureus. (2023) 15:9. doi: 10.7759/cureus.18617
[5]. Avula MN, Grainger DW. Addressing medical device challenges with drug–device combinations. Drug-Device Combinations Chronic Dis. (2015) 11(1):1–38. doi: 10. 1002/9781119002956.ch01