Academy Journal of Science and Engineering

HOW FIRM’S DIGITAL TRANSFORMATION INFLUENCE ITS INNOVATION PERFORMANCE: EMPIRICAL EVIDENCE FROM CHINA

2025-05-06T07:35:22+00:00

In recent years, firms across industries have increasingly adopted digital technologies to boost their competitive edge and innovation. Despite this growing focus, there is a lack of empirical studies examining the impact of digital transformation on innovation performance in Chinese manufacturing firms. This study uses a fixed effect model investigates the impact of digital transformation on the innovation performance of 3,678 Chinese manufacturing firms from 2016 to 2023. Our findings indicate that digital transformation significantly enhances innovation performance, with a regression coefficient of 4.146 (p < 0.01), suggesting a 41.5% improvement in innovation output for firms adopting digital technologies. Consistent results were observed across control variables such as firm size, total assets, and firm age. Furthermore, the research explores the role of government subsidies, indicating that while these subsidies may hinder digital transformation efforts, they significantly affect innovation performance when controlling for other variables. This dual focus on digital transformation and government subsidies is novel, as it provides a comprehensive understanding of how both factors interact and affect innovation in the context of Chinese manufacturing. Overall, this research highlights the crucial role of digital transformation in fostering innovation and calls for policymakers to strategically refine subsidy programs to better support firms’ innovation objectives. For firms undergoing digital transformation, it is recommended that they strategically invest in digital
technologies that align with their innovation goals to achieve a competitive edge in an increasingly dynamic technological landscape. However, this study is limited by its focus on Chinese ...

PREDICTION OF CONCRETE STRENGTH WITH MODIFIED PLASTIC WASTE AGGREGATE AS PARTIAL REPLACEMENT FOR COARSE AGGREGATE

2025-05-06T07:41:08+00:00

The disposal of plastic waste presents significant environmental challenges, including degradation of landfills and water bodies, greenhouse gas emissions, and soil contamination. Utilizing plastic waste in concrete production offers a solution to illegal dumping and reduces the reliance on mined aggregates, promoting sustainable construction practices. Polyethylene Terephthalate (PET), commonly found in plastic bottles and food containers, is a readily available source of plastic waste. This study investigates the effects of treating PET waste with calcium hypochlorite solution (Ca(ClO)2) before incorporating it into concrete as a partial replacement for coarse aggregate. Various compressive strength, ultrasonic pulse velocity (UPV), and density tests were conducted for three replacement percentages: 15 %, 30 %, and 45 % of conventional coarse aggregate with modified plastic aggregates (MPA). The findings show that chemically treated plastic aggregates maintained fresh density while reducing slump value at 30% and 45% replacement levels, even with the addition of polycarboxylate acid (superplasticizer), possibly due to surface roughness and irregular shapes of the MPA. However, concrete with 30% MPA achieved a 28-day compressive strength, UPV, and density of 23.13 N/mm², 3643 m/s, and 1996 kg/m³, respectively, which conforms with BS EN 206-1 (2013) standards for the minimum requirement of structural lightweight concrete. Additionally, three machine learning models which include Artificial Neural Network (ANN), K-Nearest Neighbor (KNN) and Random Forest (RF) were developed to predict water absorption and sorptivity. Pre-processing, statistical methods and data visualization techniques were employed for data understanding. Experimental results were used to generate a dataset, and the models ...

PREVALENCE OF GATRO-INTESTINAL PARASITES IN Clarias gariepinus SOLD IN IBADAN CENTRAL MARKET, IBADAN, NIGERIA

2025-05-06T07:47:11+00:00

Gastrointestinal parasitic infections in fish pose significant public health and economic challenges, especially in urban markets with suboptimal handling conditions. This study examined the prevalence of gastrointestinal parasites in Clarias gariepinus (African catfish) sold at Ibadan Central Market, Nigeria, to assess associated health risks. Due to increasing catfish demand and limited research on
market-sold fish, forty (40) C. gariepinus samples of varying sizes were randomly purchased and transported alive to the Fish Biology Laboratory, University of Ibadan. The gastrointestinal tracts were dissected, and parasites were identified using morphological analysis under a dissecting microscope. Statistical analysis were done using Microsoft excel to calculate prevalence in frequencies and percentages. Results showed a high prevalence of gastrointestinal parasites: tapeworms (37%), Entamoeba histolytica (25%), Taenia spp. (13%), and Ascaris eggs (25%), mainly in the intestines. Larger fish had higher parasite loads and female fish showed a greater prevalence of Ascaris eggs. Findings suggest contamination from environmental sources and poor fish-handling practices. These
results highlight the public health risks of consuming inadequately cooked or improperly handled C. gariepinus. Given the zoonotic potential of some parasites, urgent interventions are needed to improve fish handling, storage, and market sanitation.

DYNAMIC RESPONSE OF A RAYLEIGH BEAM WITH TIMEDEPENDENT STIFFNESS UNDER MOVING LOADS WITH VARIABLE VELOCITY

2025-05-06T07:56:38+00:00

This study examines the dynamic response of uniform beam under the action of partially distributed moving masses with variable velocity. Utilizing an analytical approach, the research investigates the impact of variable foundation stiffness, velocity variation, and structural parameters on dynamic behaviour. The results reveal significant effects of variable foundation stiffness and velocity variation
on structural vibration and stability. These findings contribute to the development of more accurate design guidelines and effective vibration control strategies for elastic structures under dynamic loads.

CHARACTERIZATION OF SUBSURFACE LITHOLOGICAL UNITS AND AQUIFEROUS FORMATIONS USING INTEGRATED VLF AND VES TECHNIQUES IN OWO, ONDO STATE

2025-05-06T08:01:00+00:00

This study applies integrated Very Low Frequency (VLF) electromagnetic profiling and Vertical Electrical Sounding (VES) to delineate groundwater potential zones in Owo, Ondo State, Nigeria. Utilizing the Schlumberger array configuration, subsurface resistivity variations revealed lithological units and aquiferous formations. VES results indicate an overburden depth of 24 metres – 27 metres with resistivity values between 43 Ωm and 50 Ωm, suggesting moderate hydrogeologic significance for groundwater development. The VLF pseudo section identifies conductive zones associated with groundwater-bearing structures, with isoline conductivity values ranging from 0.02 S/m in fractured zones to 0.26 S/m in fresh basement rock. The survey highlights a thick overburden (0–25 metres), underlain by a weathered basement (25 metres – 50 metres), a wet basement at 50 metres, and a thin fractured basement (80 metres – 90 metres). This research aims to optimise drilling site selection and enhance sustainable groundwater resource management in erosion-prone terrains. Aligned with Sustainable Development Goal (SDG) 6.2, which targets universal access to safe water, sanitation, and hygiene, the study underscores the critical role of geophysical methods in advancing water security, resilience, and equitable resource distribution in regions vulnerable to water scarcity.

ARTIFICIAL INTELLIGENCE-BASED EXPERT SYSTEM FOR DIPHTHERIA DIAGNOSIS

2025-05-06T08:05:39+00:00

Conventional diagnostic techniques frequently depend on the observational conclusion of lab results and clinical indicators, which can cause delays. Consequently, there is a need for a fast, precise and cost-effective technology to diagnose infectious diseases and thus reduce morbidity and mortality in the under-developed and developing countries. Although numerous research in the medical domain have been conducted by different researchers utilizing diverse diagnosis approach, In this study, A V-P Expert System shell was utilized in the creation of a diphtheria diagnosis system (DDS). This is a rulebased system that employs forward-chaining approach for diagnosis. It consists of three modules, the User Interface to facilitate user interaction, allowing input of queries and displaying result, the Inference Engine to process queries and applies rules to derive conclusions and lastly, the Knowledge Base to store facts, rules, and relationships about the domain. The knowledge base was created by compiling accurate knowledge from medical experts in diphtheria. The system offers a simple, interactive user interface, where diagnosis of patient is achieved based on microbiological and clinical examinations. Preliminary testing shows that the system provides consistent and reliable diagnostic support, making it beneficial for remote areas with limited access to medical experts. However, its accuracy depends on the completeness of the knowledge base, and it may be less effective in cases with atypical symptoms. Future improvements could include expanding the knowledge base and integrating adaptive learning techniques. This automated approach enhances diphtheria detection in underdeveloped regions, improving diagnosis and treatment outcomes.

PROACTIVE MITIGATION OF DDoS IoT-RELATED ATTACK USING MACHINE LEARNING AND SOFTWARE DEFINED NETWORKING TECHNIQUES

2025-05-06T08:11:37+00:00

The number of Internet of Things (IoT) connected to the Internet have increased globally. The insecure nature of IoT have made attackers to capitalize on the devices to launch Distributed Denial of Service (DDoS) attacks on networks, thus causing massive destruction to network resources. The setting of the research work is an enterprise organization wide area network (WAN) that is structured into 3 LANs topology in Software Defined Networking (SDN) environment. The WAN is emulated, and includes a single RYU SDN controller, three routers, three OpenFlow switches with three simulated IoT devices connected to each switch, to form the 3 LANs topology. Both normal and DDoS IoT-related attack data traffics are generated every 5 seconds, from Transport Control Protocol (TCP), User Datagram Protocol (UDP), Internet Control Message Protocol (ICMP) and Hypertext Transfer Protocol (HTTP). The packets capture (pcap) files from Wireshark are exported as comma-separated values (csv) files. The datasets are preprocessed to extract relevant features using Python libraries. The large dataset was scaled down using Min Max Scaler before the Machine Learning (ML) classification stage. Four (4) ML algorithms namely, Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT) and
Random Forest (RF) were used to classify the models. The performances of SVM and LR recorded higher percent accuracy of 99.474 each while the DT and RF recorded 99.123 percent accuracy each in detecting the DDoS-IoT data traffic from the normal data. The flow table entries (FTE) rules of the OpenFlow switches together with the RYU controller..

MODELLING FLUCTUATIONS OF GROUNDWATER LEVEL USING MACHINE LEARNING ALGORITHMS IN THE SOKOTO BASIN

2024-09-11T19:16:18+00:00

Groundwater level forecasting is essential for the sustainable management of water resources, especially water scarce regions such as the Sokoto Basin. This study investigates the application of machine learning models, specifically Long Short-Term Memory (LSTM), eXtreme Gradient Boosting (XGBoost)and Random Forest (RF) algorithms to predict groundwater levels across six boreholes within the Sokoto Basin. A thorough preprocessing procedure was applied to the daily groundwater data spanning a range of three to four years. This included removing null values, interpolating missing data and downsampling to weekly intervals engineering to improve model performance. Time series decomposition and the creation of lag features were also utilized to capture temporal dependencies effectively. Among the models, the XGBoost algorithm demonstrated the highest performance, providing precise predictions that closely aligned with the actual groundwater levels. Hyperparameters for the XGBoost model were fine-tuned using grid search techniques, resulting in optimal settings that significantly enhanced predictive accuracy with Mean Absolute Error (MAE) ranging from 0.016 – 0.757m and Root Mean Square Error (RMSE) ranging from 0.051 - 2.859m. The LSTM model also showed strong performance, particularly in capturing the peaks and valleys of the groundwater level time series, with MAE and RMSE values ranging from 0.016 – 0.757m and 0.051 – 2.859m, respectively. The RF model exhibited reliable performance across most locations. The research findings offer a practical method for forecasting groundwater levels. The frameworks could be used to manage water resources, particularly in dry years, where water restrictions and drought alerts can also be rapidly issued.