Vol 14 Issue 1 January 2026-March 2026

Abstract: The rapid industrial advancements of Industry 4.0 make it necessary for companies to adopt maintenance systems which use intelligent and adaptive technology together with data analysis methods that exceed basic condition monitoring capabilities. Structured sensor data dominates predictive maintenance models, maintenance logs, operator reports, and technical documentation are underutilised. This paper presents a conceptual approach to integrate the NLP and hybrid intelligence frameworks into industrial condition monitoring and predictive maintenance systems. The study uses structured machine data with unstructured textual insights to improve defect identification, prognostics, and decision-making. It is based on interdisciplinary research in Machine Learning, AI, Computer Science, Statistics, and Automation Engineering. This study employs qualitative research approaches, including systematic literature analysis, thematic synthesis of AI-driven maintenance frameworks, and conceptual modelling to construct an integrated architecture for smart manufacturing. The proposed framework demonstrates that machine learning models which use sensors together with natural language processing-based information extraction systems, enhance the accuracy of detecting anomalies and their underlying causes while improving maintenance schedule establishment. The hybrid intelligence framework enables human-AI collaboration for decision-making processes, which improves situational understanding and process transparency. The research discovered that system resilience and operational downtime improvements result from using NLP together with predictive analytics, which help organizations develop sustainable automation strategies. The research presents an adaptable framework which enables smart factories to implement data-driven transformations within Industry 4.0 ecosystems. The study develops manufacturing analytics through conceptual advancements, which enable future empirical testing within industrial environments.

Keywords: Predictive Maintenance; Natural Language Processing (NLP); Hybrid Intelligence; Industry 4.0; Smart Manufacturing.

Title: A Conceptual Model for Integrating NLP and Hybrid Intelligence in Industrial Condition Monitoring and Predictive Maintenance

Author: Suraj Shrestha

International Journal of Engineering Research and Reviews

ISSN 2348-697X (Online)

Vol. 14, Issue 1, January 2026 - March 2026

Page No: 53-60

Research Publish Journals

Website: www.researchpublish.com

Published Date: 27-March-2026

Abstract: This study evaluates the geotechnical properties of subsurface soils for shallow foundation design in Port Harcourt, Rivers State, Nigeria. Soil samples obtained from five locations—RSU Campus, Iwofe, Eneka, Rukpokwu, and Egbelu—were subjected to laboratory tests including natural moisture content, specific gravity, Atterberg limits, triaxial compression, and one-dimensional consolidation. Results indicate that the soils are predominantly fine-grained and classified as A-7-5 clayey soils under the AASHTO system. Natural moisture content ranged from 22.41% to 25.36%, while specific gravity varied between 2.54 and 2.62. Plasticity characteristics show mostly medium plastic clays, with relatively higher plasticity at Iwofe. Shear strength parameters yielded cohesion values of 37–118 kPa and friction angles of 11.55°–20.55°. The estimated ultimate bearing capacity ranged from 169–214 kN/m², indicating moderate load-bearing capacity. Consolidation analysis predicted settlements between 0.44 and 3.61 mm, suggesting that the soils are generally suitable for shallow foundations for low- to medium-rise structures with proper design considerations.

Keywords: geotechnical characterization, shallow foundation, soil properties, bearing capacity, consolidation, Port Harcourt.

Title: Assessment of Geotechnical Properties and Bearing Capacity of Subsurface Soils for Shallow Foundation Design in Port Harcourt, Nigeria

Author: Tamunoemi Alu LongJohn, Godfrey Waribo Tom Jaja

International Journal of Engineering Research and Reviews

ISSN 2348-697X (Online)

Vol. 14, Issue 1, January 2026 - March 2026

Page No: 49-52

Research Publish Journals

Website: www.researchpublish.com

Published Date: 09-March-2026

Abstract: Sequel to limited data on soil properties in southwestern Nigeria hinders sustainable construction practices. This study addresses this gap by investigating the shear strength and index properties of soils in Ibadan, Oyo State. The research aimed to gain a deeper understanding of local soil behavior, focusing on shear strength and index properties. Soil samples were collected from four locations using core cutters and analyzed in the laboratory. Testing revealed that over 70% of the soil particles are fine-grained, passing a 0.075mm sieve. Shear strength varied significantly, ranging from 135 kN/m² to 285 kN/m² with an average of 243 kN/m². Index properties like liquid limit (23.57% - 43.57%), plastic limit (15.74% - 25.85%), and plasticity index (4.2% - 17.7%) also exhibited variations. Specific gravity ranged from 1.94g to 3.02g. Based on these results, the soil is classified as A-6 in the AASHTO system, indicating high clay content. The triaxial tests suggest relatively high cohesive strength. This comprehensive study provides valuable data on the shear strength and index properties of soils in Ibadan. This information will be instrumental for geotechnical and structural engineers when designing and planning construction projects in the region.

Keywords: Shear strength, geotechnical properties, foundation, CBR, Atterberg limit.

Title: Investigation of Shear Strength and Index Properties of disturbed Soil in South-West Nigeria

Author: LongJohn, T. A

International Journal of Engineering Research and Reviews

ISSN 2348-697X (Online)

Vol. 14, Issue 1, January 2026 - March 2026

Page No: 44-48

Research Publish Journals

Website: www.researchpublish.com

Published Date: 04-March-2026

Abstract: The use of Solar Panel for Auckland’s Schools was a point of discussion between the Capital Works team and their higher management. A detailed study explored the feasibility of using Renewable Energy Sources in Auckland’s School and weather other methods could be presented to reduce the cost and improve the Environmental parameters, Solar and Wind Energy were examined in addition to some other smart controlling system. Utilizing the latest meteorological parameter with Solar and Wind Energy applicable practices supported by technical and commercial product to estimate costs and performances. It was found that due to the operational conditions of Schools, the average radiation rate and wind speed of Auckland city, using a Solar System will provide only 23% of the schools needs combining wind Turbines with Solar Panels would improve the rate to maximum of 40%. The study also examined the saving could be achieved using Smart Monitoring and Controlling System and found that there is no remarkable impact on Environment. The research suggests that other Renewable Energy Sources available in New Zealand such as Geothermal applications and Green Hydrogen might be more environmentally and economically effective. The research provides an insight for the use of Solar/Wind Energy applications in managing Auckland School Energy Consumption considering real time weather conditions and schools’ demands.

Keywords: Renewable Energy Sources, Solar and Wind Energy, Solar Panels.

Title: Study of Suitable Renewable Energy Sources for Auckland’s Schools, New Zealand

Author: Ahmed Gomaa

International Journal of Engineering Research and Reviews

ISSN 2348-697X (Online)

Vol. 14, Issue 1, January 2026 - March 2026

Page No: 32-43

Research Publish Journals

Website: www.researchpublish.com

Published Date: 02-March-2026

Abstract: Clinical Decision Support Systems (CDSS) assist healthcare professionals in making timely, accurate, and evidence-based clinical decisions. The increasing volume, velocity, and heterogeneity of healthcare data have exposed the limitations of traditional rule-based CDSS, particularly in managing multimorbidity and personalized care. Intelligent CDSS capable of adaptive learning, predictive modeling, and patient stratification has been enabled by the recent advances in Artificial intelligence which include machine learning, deep learning, and natural language processing (NLP),

This paper presents a structured and system-level review of AI-powered CDSS, focusing on their historical evolution, enabling technologies, architectural design, and clinical applications. The surveys conducted earlier emphasized isolated algorithms, whereas this review integrates AI techniques with system architecture, workflow design, and ethical considerations. Key AI approaches for patient stratification, deep learning models for diagnosis and prognosis, and NLP-based early warning systems are examined. Ethical, legal, and explainability challenges are critically discussed, and emerging research directions such as federated learning, digital twins, and genomic CDSS are highlighted. The paper aims to provide researchers and clinicians with a comprehensive understanding of AI-CDSS design principles and future potential.

Keywords: Clinical Decision Support Systems, Artificial Intelligence, Deep Learning, Patient Stratification, Explainable AI.

Title: Artificial Intelligence in Clinical Decision Support Systems: Architectures, Applications, and Ethical Challenges

Author: Deepika D Pai, Ramya R, Inchara M, H Sunil

International Journal of Engineering Research and Reviews

ISSN 2348-697X (Online)

Vol. 14, Issue 1, January 2026 - March 2026

Page No: 26-31

Research Publish Journals

Website: www.researchpublish.com

Published Date: 25-February-2026

Abstract: The subject of fluid mechanics, which deals with both static and dynamics fluids, is the pillar that supports the planetary system, all living things, and also the inanimate global economy as it covers not only the planetary movements, but also handles nearly every facet of life on the planet, such as in breathing, blood flow, swimming, pumps, fans, turbines, airplanes, ships, rivers, windmills, pipes flow, missiles, icebergs, engines, filters, jets, sprinklers, plants, and other living organisms.  This paper therefore x-rayed the critical role fluid mechanics plays in supporting human life and the planetary systems from medicine, meteorology, oceanography, hydrology, biofluids, and environmental fluid mechanics all of which deals with naturally occurring flows in human and in the environment. It is also the pivot upon which all forms of transportations are anchored from aerodynamics in aircraft and rockets to hydrodynamics in naval ships and submarines and therefore employs different models of fluid flow patterns. Fluid mechanics is also behind all forms of combustion systems, so also in agriculture and food production that involves irrigation, flood control, water supply, sewage disposal. It is also responsible for global energy and oil and gas operations. Recognizing its vast applications in all aspects of human life, this paper recommends continuous investment in research and development in the study of the various disciplines of fluid mechanics, especially biofluid, environmental and geophysical fluid mechanics. More research is also needed in computational fluid dynamics that can provide concise solutions to the problems of turbulence, since most fluids in life and at industrial scales occur under turbulence regimes.

Keywords: Static and Dynamics fluid mechanics, Computational Fluid Dynamics, Flow Patterns, Environmental Fluid Mechanics.

Title: Fundamentals of fluid mechanics and its contributions to sustainability of the planet, humans and the economy

Author: Dr Amieyeofori Valentine Felix

International Journal of Engineering Research and Reviews

ISSN 2348-697X (Online)

Vol. 14, Issue 1, January 2026 - March 2026

Page No: 1-25

Research Publish Journals

Website: www.researchpublish.com

Published Date: 07-February-2026