Quantum Journal of Engineering, Science and Technology
Quantum Journal of Engineering, Science and Technology (QJOEST) is a multi-disciplinary with double-blind peer-reviewed journal publishes across the entire field, to advance our knowledge of our nature world and its various intersections with society. The journal publishes research articles, review articles, short communications, case studies and reports in Engineering, Science and Technology includes engineering, computer sciences, architecture and design, agricultural sciences, biological sciences, chemistry, physics, earth sciences, environmental science, life sciences, mathematics and statistics, as well as sport sciences. Abstract and Indexing: Quantum Journal of Engineering, Science and Technology (QJOEST) are abstracted and indexing in Bielefeld Academic Search Engine (BASE); Google Scholar; Malaysian Journal (MyJurnal); Malaysian Citation Index (MyCite); Neliti; and many more.
SILICONE-COATED ALUMINA HOLLOW FIBER MEMBRANES FOR SUSTAINABLE MICROPLASTIC SEPERATION IN WATER SYSTEM
Microplastic (MP) pollution represents a new wave of some of the most devastating environmental stressors, with such an impact on aquatic environment degradation, food chain contamination, and human health. Conventional filtration techniques are ineffective toward smaller MPs; hence there is a dire need for advanced methods of filtration. The present study fabricates and evaluates silicone-coated alumina hollow fiber membranes (AHFMs) by curing temperature variation, membrane configuration variation, and flow rate conditions variation. Contact angle analyses found that the curing at 60°C optimized rapid water interaction and at 70°C gave stable hydrophilicity which can be used for long-term applications. Multi-membrane configurations greatly improve water throughput validating surface area expansion that does not compromise MP separation. Flow rate variations influenced how much volume was collected but did not influence separation efficiency since all tested modules demonstrated complete removal of MPs. This puts a seal on the noticeable increase in durability and antifouling performance of AHFMs with silicone coatings, hence their ability to work perfectly well under diverse hydraulic conditions. The results prove that productivity is actually a function of operative conditions while selectivity lies within the material property as engineered. That places silicone-coated AHFMs as an accessible technology toward community-based purification systems up to industrial wastewater treatment plants. Apart from bringing membrane science one step closer, this study also plays its share in the Sustainable Development Goal 6 by offering an upbeat, resilient, and sustainable technology for mitigating global water contamination by microplastics.
Copyright (c) 2025 NORFAZLIANA ABDULLAH, MUHAMMAD AMMAR FARHAN HASBOLLAH, ANG KEAN HUA https://creativecommons.org/licenses/by/4.0
DEVELOPMENT OF ALUMINA HOLLOW FIBER MEMBRANE MODULES FOR EFFECTIVE OIL/WATER SEPARATION
This study critically engages with the context of increasing industrial and urban wastewater challenges where an effective oil-water separation process is considered. In most cases, the traditional gravity separator and centrifuge fail to isolate stable small droplets of oil from water in emulsified form. Alumina hollow fiber membrane modules that this study has undertaken fabrication efforts toward are proposed as one more step further to solve this problem. The hydrophobicity enhanced system shows all promises towards effectively carrying out the separation of oil from water in emulsions. Different membrane configurations tried possible relationships that this work presents between changing the number of membranes and efficiency in separation. Increasing surface area, i.e., an increasing number of fibers, allows more oil recovery but at a cost since fouling will tremendously take place. Even though the membrane could achieve very high separation efficiency, challenges related to operation, especially fouling at high concentrations of oil and the effects due to different flow rates have to be solved when applied on an industrial scale. This brings out the study's view that getting sustainable, cheap, and efficient ways of carrying out the oil-water separation process necessitates the optimization of membrane configurations and operating conditions.
Copyright (c) 2025 NORFAZLIANA ABDULLAH, HASHVININISHA PARAMASIVAM, ANG KEAN HUA https://creativecommons.org/licenses/by/4.0
SENSITIVITY OF SEABED MORPHOLOGY TO HYDRODYNAMIC AND SEDIMENT TRANSPORT PARAMETERS IN TIDAL SYSTEMS
Sediment transport and seabed evolution are key processes influencing the morphology and stability of coastal and estuarine systems. This study uses a one-dimensional (1D) coupled hydrodynamic–sediment transport model to simulate shallow water flow and sediment dynamics in the Kuala Terengganu region. The model integrates the Saint-Venant shallow water equations with the Exner equation for bed evolution and the advection–diffusion equation for suspended sediment concentration. Numerical implementation using the finite volume method ensures mass conservation and numerical stability, allowing accurate representation of flow–sediment feedback. The objectives are: (1) to simulate the temporal evolution of seabed morphology and quantify long-term bed elevation changes; (2) to evaluate the effects of hydrodynamic parameters, particularly tidal range and bed friction coefficient, on sediment entrainment, deposition, and erosion; and (3) to assess the influence of sediment transport parameters, especially the Grass coefficient, on seabed stability and erosion–deposition dynamics. Results show a non-linear decline in bed elevation with a 38% reduction over three years, indicating rapid early sediment mobility followed by stabilization. Increasing tidal range enhances sediment removal, while friction and Grass coefficients govern the transition between erosion and deposition. The findings provide insights for predicting coastal morphological change and improving sediment management.
Copyright (c) 2025 MOHD AZLAN MUSA, MOHAMMAD FADHLI AHMAD, SUNNY GOH ENG GIAP https://creativecommons.org/licenses/by/4.0
CFD STUDY ON WAVE TRANSMISSION OF SUBMERGED HEMISPHERICAL SHAPED ARTIFICIAL REEF (HASR) BREAKWATER
Coastal erosion is the recession of shorelines and loss of land caused by waves, currents, and wind. Hemispherical Shaped Artificial Reefs (HSAR) can act as submerged breakwaters to mitigate erosion, offering structural protection while enhancing aquatic habitats and biodiversity. Designed to provide shelter for marine life, HSARs combine engineering with ecological benefits. Previous experimental studies on wave transmission over submerged breakwaters were conducted at the Hydraulic and River Engineering Laboratory of Bangladesh University of Engineering and Technology. This study demonstrates wave transmission across HSAR breakwaters using the Volume of Fluid (VOF) method with the Reynolds-Averaged Navier–Stokes (RANS) equations in the FLOW-3D numerical suite. The reefs were modeled using RHINOCEROS 5.0 software. The main objective is to determine wave transmission through numerical simulation. First, the model was validated against experimental results for submerged breakwaters. Further simulations assessed transmission with varying HSAR structural heights and designs. The findings showed close agreement between simulations and experiments, with percentage errors ranging from 4.1% to 16.7%. Results emphasize the significance of structural height in reducing the wave transmission coefficient, K_t. HSARs outperformed thalame breakwaters in transmission reduction, proving beneficial for coastal protection, environmental sustainability, and aquatic life.
Copyright (c) 2025 MOHAMMAD FADHLI AHMAD, MOHD AZLAN MUSA, MUHAMMAD FARIS ROSLAN, MUHAMMAD YUSUF RUSLI, SUNNY GOH ENG GIAP, HANHAN MAULANA, LAMECK FIWA https://creativecommons.org/licenses/by/4.0
INVESTIGATION ON LEACHATE TRANSPORT ON GROUNDWATER CONTAMINATION USING VERTICAL ELECTRICAL SOUNDING (VES)
The rapid increase in population has heightened concerns regarding waste disposal, with open dumpsites serving as the primary method of waste management in many areas. This study investigates the geo-electric characteristics and groundwater quality of boreholes situated near an open dumpsite in Ogale Community, Eleme Local Government, Rivers State, Nigeria. Vertical Electrical Sounding (VES) using the Schlumberger configuration was employed to examine the effects of leachate on groundwater contamination, subsurface aquifer protective capacity, and lithological corrosivity. Borehole water samples collected from varying depths (50m, 100m, 150m) and a control location (2000m) revealed that pH, temperature, and dissolved oxygen (DO) exceeded the permissible limits set by the World Health Organization (WHO) and the Nigerian Standard for Drinking Water Quality (NSDWQ). Results indicated high chloride concentrations and microbial contamination, particularly in boreholes closest to the dumpsite. The regression model (R² values of 0.9743, 0.9962, and 0.9981) demonstrated significant metal corrosivity at varying depths. The Water Quality Index (WQI) values (67.7, 67.6, and 68.1) classified the groundwater as poor and unsuitable for drinking. VES analysis confirmed weak to moderate aquifer protective capacity, correlating with increased acidity and microbial presence. Consequently, metal pipes prone to corrosion are unsuitable for water conveyance, and plastic pipes are recommended. The study underscores the necessity for improved waste management practices, including landfill engineering, solid waste recycling, and enhanced environmental sanitation to mitigate groundwater contamination risks.
Copyright (c) 2025 CYPRIAN NDAMZI TOM, JOSEPH EDET, EUNICE NNETE GAM https://creativecommons.org/licenses/by/4.0
Electricity conversion monitoring machine is an electronic device that video display units load voltage and cutting-edge or packs with numerous parameters. But, on the market could be very steeply-priced and now not appropriate for low-price embedded structures. Because the Arduino Uno is widely used for low-price microcontroller boards, smooth programming environment, and open-supply systems for building digital tasks, consequently, this take a look at makes a speciality of Arduino Uno primarily based power conversion monitoring machine with clever automation. This system consists of modern and voltage sensors to reveal and use the PIR sensor to gain the automation, an Arduino Uno microcontroller with wifi module and a liquid crystal show (liquid crystal display) to view the monitored analyzing.To expand this gadget, there are 3 targets to be completed.the connection between enter and output of the sensors must be derived mathematically. The mathematical expression acquired may be demonstrated by means of connecting and disconnecting the circuit with load and tracking the value of output sensors. Then, a whole prototype of the energy conversion monitoring system changed into evolved via connecting the lcd, current and voltage sensors to the Arduino Uno microcontroller and reference to PIR sensor to automate the unit with relay. Think view application is used to tracking the power consumption of the burden this app is open source and by means of soliciting for the personal channel the statistics can included and best seen to legal personal most effective.
Copyright (c) 2025 RADHIKA, IMMANUVEL, DEVARAJ SOMASUNDAR https://creativecommons.org/licenses/by/4.0
PROSPECT IDENTIFICATION USING SEISMIC ATTRIBUTES FOR AN OFFSHORE NIGER DELTA BASIN X FIELD
The offshore Niger Delta Basin "X" field presents significant opportunities for hydrocarbon prospecting; however, effective reservoir characterization remains challenging. This study addresses this challenge by employing a robust combination of seismic attributes, including amplitude, Root Mean Square (RMS) amplitude, variance edge, phase, frequency, and reflector continuity. Utilizing advanced seismic data processing and visualization techniques, the research aims to accurately map structural features, faults, horizons, and potential hydrocarbon traps within the "X" field. The integrated seismic attribute approach enables precise identification of prospective zones by enhancing fault delineation and improving reservoir characterization. The application of the structural smooth attribute significantly increases seismic resolution, allowing for the clear visualization of fault lines and reservoir boundaries, while the variance edge attribute proves instrumental in delineating fault trends and identifying potential trapping mechanisms. Additionally, the construction of 3D fault models using amplitude attributes enhances the interpretation of subsurface features, offering critical insights for targeted well placement. The results reveal two principal faults that align with the reservoir zones, indicating promising areas for detailed hydrocarbon exploration. The findings demonstrate that deploying a multi-attribute seismic analysis provides a cost-effective and reliable method for improving exploration accuracy, optimizing resource allocation, and supporting decision-making in offshore hydrocarbon development.
Copyright (c) 2025 OKECHUKWU FRANK ADIZUA, NNABUIKE GODSPOWER UGWU https://creativecommons.org/licenses/by/4.0
COWPEA HAY AS A PARTIAL SUBSTITUTE FOR CONCENTRATE: EFFECTS ON MILK YIELD AND COMPOSITION
The study was carried out in Yabello Pastoral and Dry Land Agricultural Research Center to investigate the effect of replacement of cowpea (Vigna Unguiculata) for concentrate mix on milk yield and composition in lactating Borana cows fed Rhodes grass hay as a basal diet. Four lactating Borana cows of similar milk yield, body weight and stage of lactation were arranged in 4 x 4 Latin square design. The treatments included offering Rhodes grass hay a basal diet ad libitum with different proportions of concentrate mix and cowpea hay. T1= 100% concentrate mix and 0% cowpea hay (control), T2= 65 % concentrate mix and 20% cowpea hay, T3= 65% concentrate mix and 35% cowpea hay and T4= 50% concentrate mix and 50% cowpea hay. There were no significant differences (P>0.05) in dry matter and nutrient intakes among treatment groups, while milk yield was significantly different (P<0.05) among treatments. The overall mean of daily milk yield was 4.05 kg/day. Cows that were receiving 100% concentrate mix (T1) produced the highest milk yield (4.18 kg/day) followed by cows receiving the diet in which 50% of the concentrate mix was replaced by cowpea hay (T4).Except milk yield, cowpea hay can substitute up to 50% of the concentrate mix without adversely affecting feed intake, nutrient intake and milk composition of lactating Borana cows fed Rhodes grass hay as basal diet. Therefore, to improve their milk production, smallholder farmers can use cowpea hay 50% to replace the concentrate mix in feeding their cows on poor pasture, especially during dry seasons.
Copyright (c) 2025 BESHIR HUSSIEN, BIRHANU BEKELE, ADEM KUMBE, ANAF ONETA https://creativecommons.org/licenses/by/4.0
ENHANCING MICROSTRIP PATCH ANTENNA PERFORMANCE THROUGH GENETIC ALGORITHM AND ANFIS OPTIMIZATION
Microstrip patch antennas play an important role in modern wireless communication systems due to their small size, compactness, lightweight, and easy integration into electronic circuits. These types of antennas are widely used in various applications, such as mobile phones, Wi-Fi routers, satellite communications internal systems and radar systems. This paper provides a detailed research study on the improvement and optimization of the measurement of the Aperture Coupled Microstrip Patch Antenna (ACMPA) utilizing a Genetic Algorithm (GA) as well as an Adaptive Neuro-Fuzzy Inference System (ANFIS). The goal is to enhance the total efficiency of the antenna by achieving far better return loss and high directivity coupled with a reduced Voltage Standing Wave Ratio (VSWR). The recommended method includes the usage of GA within the CST software application together with ANFIS within the MATLAB software application to enhance the ACMPA measurements for the procedure at 7.5 GHz. The outcomes show the performance of the recommended antenna measurements in contrast with substitute information in the CST program, verifying the precision as well as the effectiveness of the optimization procedure. This research study offers beneficial insights into the application of innovative optimization methods for the style coupled with the improvement of microstrip patch antennas in contemporary cordless interaction systems.
Copyright (c) 2025 RANA RAAD SHAKER, NOOR RAAD SAADALLAH, SALAH ABDULGHANI ALABADY https://creativecommons.org/licenses/by/4.0
COPPER AND SULFUR CO-DOPED ZNO NANOPARTICLES FOR EFFICIENT SOLAR PHOTOCATALYTIC DEGRADATION OF METHYL ORANGE
In the present study pure ZnO nanoparticles were prepared using a co-precipitation technique. As-prepared ZnO, was used to prepare Cu-doped ZnO, S-doped ZnO, and Cu & S co-doped ZnO nanostructures by simple hydrothermal method. The synthesized materials were characterized employing XRD, FTIR, and UV-visible spectroscopic techniques. The crystallite particle size of pure ZnO (45.7 nm), was highest among the synthesized powders followed by Cu-doped ZnO (43.29 nm), S-doped ZnO (43.4 nm), and Cu & S co-co-doped ZnO (43.19 nm). The UV-visible absorption spectra of aqueous dispersion of synthesized powders revealed that doping Cu and S in ZnO, both contributed synergistically to lowering of band gap energy from 3.27 eV to 2.35 eV. The photocatalytic efficacies of as-synthesized powders under solar irradiation were determined in terms of methyl orange degradation, as a probe. The observed highest photocatalytic degradation (85 %) of 25 mg L-1 dye in aqueous solution within 60 minutes using Cu & S co-doped ZnO load 1.5 gL-1 can be attributed to the lower rate of photo-generated charge carriers’ recombination as well as lowering of band gap energy enabling absorption of more photons in the visible range of solar radiation. Considering the high photocatalytic efficiency of newly synthesized Cu and S co-doped ZnO powder, the same may be recommended for an effective treatment of methyl orange-contaminated wastewater.
Copyright (c) 2025 MENBERU GIDEY, TESFAY WELDERFAEL, OM PRAKASH YADAV https://creativecommons.org/licenses/by/4.0
FORECASTING INDUSTRIAL PH LEVELS: COMPARATIVE STUDY OF SARIMA, REGRESSION TREES AND CONTROL CHART DIAGNOSTICS
Implementation of Statistical Process Control (SPC) techniques in food and beverage industry are crucial to deliver consumable product that meets customer expectations. This study investigated industrial pH forecasting and process stability in a syrup manufacturing facility. We analyzed 1,247 pH observations with three objectives: (1) Quantify instability via control charts, (2) Model pH dynamics using Seasonal Autoregressive Integrated Moving Average (SARIMA) and Classification And Regression Trees (CART), and (3) Develop diagnostic frameworks for unstable processes. Methodologically, Exponentially Weighted Moving Average (EWMA) charts assessed stability; Box-Cox transformed SARIMA (λ=2) with seasonal differencing was used for forecasting; CART identified variable importance. Control charts revealed profound instability: 83.3% of points violated 3σ limits; run tests significant (p<0.001). For SARIMA, (1,0,1)(0,1,1)₁₂ achieved significant parameters (p<0.0001) with improved residual diagnostics versus non-seasonal ARIMA, though minor autocorrelation remained at lag 12 (p=0.003). CART explained training R²=18.86% and test R²=17.93% of pH variation, identifying filling weight and sodium benzoate as key predictors. Crucially, this study demonstrates that forecasting retains diagnostic utility even in unstable environments: SARIMA residuals provide seasonal fingerprints of assignable causes, while CART thresholds guide intervention priorities. SARIMA(1,0,1)(0,1,1)₁₂ demonstrated superior residual properties: eliminated back forecast warnings (present in ARIMA), reduced autocorrelation at lag 24 (p=0.017 vs 0.040), and explicitly modeled 12-period seasonality. While process instability persists, SARIMA provides diagnostic fingerprints of assignable causes through seasonal parameters (SMA₁₂=0.9846, T=513.12) and residual patterns. We conclude that SARIMA offers enhanced short-term forecasting capability, but process intervention remains essential for reliability. The study advocates for integrated instability-informed forecasting combining SARIMA diagnostics, real-time control charts, and expanded sensor deployment.
Copyright (c) 2025 MOSTAFA ESSAM EISSA https://creativecommons.org/licenses/by/4.0
ENHANCING CYBERSECURITY OF SMART CITY WATER DISTRIBUTION SYSTEMS USING BLOCKCHAIN AND PBFT CONSENSUS MECHANISM
This paper investigates cybersecurity attack models and defense strategies for water distribution systems (WDS) in smart city environments. As water infrastructure becomes increasingly digitized, it faces sophisticated cyber threats that could compromise system integrity and operational reliability. This study presents a systematic analysis of two prevalent attack vectors Denial of Service (DoS) and Man in the Middle (MITM) attacks and evaluates blockchain based defense mechanisms against these threats. Through experimental simulation using the C-town WDS model with 953 timestamp data points, we assess the vulnerability of water systems and the effectiveness of six consensus mechanisms: Proof of Work (PoW), Proof of Trust (PoT), Proof of Authority (PoA), Proof of Vote (PoV), Proof of Authentication (PoAuth), and Practical Byzantine Fault Tolerance (PBFT). The research quantifies attack impacts and demonstrates the superior resilience of PBFT, which achieved an 82.5% defense rate against DoS attacks and an 82.7% defense rate against MITM attacks, significantly outperforming alternative approaches. Furthermore, PBFT exhibited exceptional recovery capabilities with 71.1% recovery after DoS attacks and 89.3% recovery following MITM attacks. These findings provide valuable insights for implementing robust security frameworks that can maintain water system integrity even under sophisticated attack conditions.
Copyright (c) 2025 AMINA ALAJWARY, FIRAS ALSHARBATY https://creativecommons.org/licenses/by/4.0
ADVANCING QUANTUM DOT-SENSITIZED SOLAR CELLS: TRANSITION METAL-SULFIDE-BASED COUNTER ELECTRODES FOR HIGH-EFFICIENCY AND STABLE PHOTOVOLTAICS
Quantum dot-sensitized solar cells (QDSSCs) represent a leading-edge innovation within third-generation photovoltaic systems, owing to the unique light-harvesting capabilities of quantum dots (QDs), including multiple exciton generation, tunable band gaps, and high absorption coefficients. Despite their potential for achieving power conversion efficiencies (PCE) beyond those of conventional solar technologies, challenges remain; particularly in the stability and performance of counter electrodes (CEs). Platinum (Pt), while efficient, suffers from catalytic degradation in polysulfide electrolytes, limiting its suitability for QDSSC applications. This review explores the use of transition metal sulfides (TMS), such as NiS, CoS, Cu₂S, and PbS, as alternative CE materials due to their abundance, low cost, and promising electrocatalytic properties. Emphasis is placed on their compatibility with polysulfide electrolytes, structural stability, and ability to enhance interfacial charge transfer, which directly impacts overall device performance. The paper also discusses the synthesis techniques employed, chemical bath deposition and successive ionic layer adsorption and reaction, and how they influence the morphology, adhesion, and efficiency of the CEs. Among the materials reviewed, nickel sulfide-based electrodes stand out due to their low charge transfer resistance, high Jsc values, and excellent long-term operational stability. Composite materials such as NiS/PbS and Cu₂S/graphene hybrids show synergistic effects, enhancing both catalytic performance and mechanical durability. By identifying and evaluating viable alternatives to noble metals, this study underscores the critical role of CE materials in advancing QDSSC technology toward scalable, efficient, and cost-effective solar energy solutions.
Quantum Dot-Sensitized Solar Cells (QDSSCs)
Copyright (c) 2025 LAYLA HAYTHOOR KHARBOOT, KOLEOLA EBENEZER OJAOMO https://creativecommons.org/licenses/by/4.0
MECHANICAL BEHAVIOUR OF ANISOTROPIC FOLIATED ROCKS: IMPLICATIONS FOR TUNNEL STABILITY AND DESIGN
Understanding the mechanical behavior of anisotropic rocks is critical for the design and stability of underground structures, particularly in tunneling and deep excavation projects. This study investigates the effects of anisotropy on the strength, deformation, and failure mechanisms of foliated metamorphic rocks such as phyllite, schist, and gneiss using a combination of laboratory techniques, including triaxial compression tests, Unconfined Compressive Strength (UCS) tests, point load tests, and petrographic analysis. The results reveal a strong dependency of rock strength on bedding orientation. At 0° and 90° to foliation, rocks exhibited higher compressive strength, whereas at intermediate angles (particularly β=30°–45°), strength significantly decreased and failure became more pronounced. Triaxial testing showed that the influence of confining pressure was more effective at lower bedding angles, diminishing at steeper ones, indicating dominant anisotropic behavior. Petrographic analysis confirmed that mineral composition, particularly the presence of mica, along with microcracks and voids, plays a substantial role in weakening rock structure at certain orientations. Point load tests supported UCS findings and offered a practical means of estimating strength anisotropy. Crack propagation patterns also varied with loading angles, with brittle failure dominating at higher angles and shear failure observed along foliation planes at lower angles. This study underscores the necessity of accounting for anisotropy in geomechanical modeling and engineering design to ensure the structural integrity of subsurface constructions.
Copyright (c) 2025 GAUTAM HARKHANI, MANISH SHAH, ALPA SHAH, SHALINI SINGH https://creativecommons.org/licenses/by/4.0
COMPARATIVE STUDY ON BEHAVIOUR OF GEOCELL REINFORCED PAVEMENT
Infrastructure has become a major force for national development. Therefore, the development of ground improvement technology for weak subgrade composition has become an important goal in geotechnical engineering applications. Geosynthetics is one of the ground improvement techniques to improve bearing capacity of problematic subgrade soil. Geocell, the cell matrix expands to create radial stress and provide stability to the subgrade composition soil. In the past few years, the use of geocells and other methods to improve the properties of problematic soil, especially in pavement and foundation engineering, has been investigated. This paper focuses on presenting parameters such as increased bearing capacity, decreased settlement and increased stiffness when reinforcing various types of collapsible soils by addressing laboratory tests and field investigations for geocell configuration and highlights their advantages in various applications and future research. Research shows that geocells can improve the properties of problematic subgrade soil, making it suitable for many engineering applications.
Copyright (c) 2025 ANJALI GUNDALIYA, ALPA SHAH, MANISH SHAH https://creativecommons.org/licenses/by/4.0
INNOVATIVE MODELLING OF SOIL PHYSICOCHEMICAL PROPERTIES FOR PREDICTING HYDRAULIC CONDUCTIVITY IN ABIA STATE
Understanding the relationship between soil physicochemical properties and water movement is essential for optimizing agricultural productivity, resource management, and environmental sustainability. This study models the physicochemical properties and hydraulic conductivity (K) of soils in Abia State, Nigeria, using Response Surface Methodology (RSM). Focusing on three senatorial districts: Abia North, Abia Central, and Abia South; the research examines how soil texture, physical structure, and chemical composition influence hydraulic conductivity at varying depths (1-15 cm, 16-25 cm, and 26-35 cm). The study covers different soil types, including Ferralitic, Hydromorphic, and Alluvial soils, with a focus on improving irrigation, fertilizer application, and erosion control. Field sampling was conducted systematically across selected agricultural zones, resulting in 27 composite soil samples. These samples were analyzed for physical properties such as sand, silt, clay, bulk density, porosity, particle density, and hydraulic conductivity. Chemical properties examined included nitrogen (N₂), organic carbon (OC), calcium (Ca), magnesium (Mg), potassium (K), sodium (Na), and phosphorus (P). Laboratory analyses adhered to standardized methods, and data were processed using RSM to understand variable interactions and predict hydraulic conductivity. The results revealed significant spatial and vertical variations in soil characteristics. Sand was dominant in the topsoil, averaging 61.24%, while clay content increased with depth, particularly in Ferralitic soils of Abia South. Silt content remained relatively constant. Bulk density increased with depth, ranging from 1.12 to 1.82 g/cm³, while porosity decreased, indicating soil compaction. The models demonstrated high predictive accuracy (R²>0.85), confirming that soil texture, structure, and organic content significantly influence hydraulic conductivity. High organic carbon improved K in sandy soils but was less effective in clay-rich layers unless supplemented by calcium or porosity.
Copyright (c) 2025 JOSEPH EDET, CYPRIAN NDAMZI TOM, ISIGUZO EDWIN AHANEKU, UKACHI ETOAMAIHE, CHARLES UGOCHUKWU ORJI, EUNICE NNETE GAM https://creativecommons.org/licenses/by/4.0
SUSTAINABILITY OF WATER SUPPLY SYSTEMS UNDER CONDITIONS OF NEGATIVE ACTION NATURAL AND MAN-MADE PHENOMENA
This analyzes the sustainability of water supply systems under the effects of natural and anthropogenic factors. In theoretical terms, probability-based quantitative models will here be developed to serve stability assessment needs for water supply systems at different levels. This paper proposes a general scheme for assessing the stability of centralized water supply systems in its physical, technological, technical, and institutional aspects. The results indicate that continuous availability of water should be achieved to meet increasing demand together with environmental degradation and climate change. The findings provide an imperative test on how far the present system can go toward sustainability in supplying water in regions characterized by scarcity and improving it. An integrated approach to resource management is designed here after destabilizing factors such as pollution and infrastructure inadequacy are assessed. Results shall guide the formulation of programs on how to better manage water and ensure sustainability in the long run of a water supply system. Findings from this study are imperative for policymakers, urban planners, and environmental managers who plan an appropriate intervention concerning issues pertaining to global challenges where scarcity of water resources exists.
Copyright (c) 2025 NIKOLENKO ILYA VIKTOROVICH, MELNIKOVA NATALIA SERGEEVNA https://creativecommons.org/licenses/by/4.0
ENERGY FORECASTING MODELS FOR POWER AND TELECOMMUNICATION INFRASTRUCTURES: A SYSTEMATIC REVIEW
Energy demand for power and telecommunication infrastructures has risen in recent years owing to technological advancements. The downtimes caused by an inadequate energy supply to critical infrastructure pose a great risk to daily activities. Hence, knowledge of future energy demands is pertinent to minimizing losses and operational costs, while ensuring consistent and reliable services. This article comprehensively reviews different models for predicting energy requirements by power and telecommunication infrastructure. The findings reveal that, while traditional models such as linear regression are simple to implement, models utilizing machine learning (ML) and deep learning (DL) techniques demonstrate superior performance in predicting energy consumption, yielding more precise outcomes. It has also shown that ML and DL models, including long short-term memory (LSTM), convolutional neural networks (CNN), Gated Recurrent Units (GRU), and hybrid architectures, are particularly effective for handling the complexities of long-term forecasting and adaptive systems. Thus, this current study offers valuable insights for academia, researchers, and energy personnel in network planning of the power and telecommunication industries to improve energy efficiency and cost management by analyzing historical data, identifying complex patterns, enabling real-time adaptations, and accurately forecasting their energy requirements. Researchers can also build upon the identified gaps to enhance the existing models and improve productivity.
Copyright (c) 2025 TOLULOPE ERINOSHO, KAMOLI AMUSA, ABDULTAOFEEK ABAYOMI, FATAI KAZEEM, IMHADE OKOKPUJIE https://creativecommons.org/licenses/by/4.0
CIRCULAR HORIZONS: NAVIGATING THE PATH TO SUSTAINABLE PROSPERITY
The circular economy (CE) is emerging as a transformative framework for sustainable development, challenging the conventional linear model of "take-make-waste." This study critically examines the evolution and practical implementation of CE, focusing on three core areas: Circular Design, Industrial Symbiosis, and Grassroots Empowerment. By leveraging case studies from diverse regions, including Latin America, Europe, and Asia, the research highlights how innovative practices and strategic business models are reshaping industries towards sustainability. CE principles such as designing for longevity, maximizing resource efficiency, and fostering community-driven initiatives demonstrate their potential to minimize environmental impacts while enhancing economic resilience. This investigation also addresses critical challenges, including technological limitations, policy fragmentation, and socio-economic disparities, which hinder widespread adoption. The research underscores the importance of cross-sector collaboration and policy alignment to effectively embed CE practices into mainstream economic activities. By integrating sustainable design, collaborative industrial ecosystems, and community empowerment, CE offers a viable pathway toward a resilient and sustainable future. This study contributes to the ongoing discourse by providing actionable insights and strategic recommendations for stakeholders seeking to transition from traditional economic models to more circular and regenerative systems.
Copyright (c) 2025 SHARIEF UDDIN KHAN, SUHAIL ABDULLAH MALIK, BASHIR AHMAD DAR https://creativecommons.org/licenses/by/4.0
AN INSIGHT INTO THE ANTIBACTERIAL ACTIVITY OF COPPER DOPED GRAPHITIC CARBON NITRIDE COMPOSITE
The synthesis and doping of graphitic carbon nitride (g-C3N4) with copper (ii) chloride dihydride (CuCl2.2H2O) to the corresponding copper doped graphitic carbon nitride (Cu-g-C3N4) composite and also the application of the as-synthesized composite as an antibacterial agent is described. The graphitic carbon nitride (g-C3N4) was synthesized from melamine via condensation and then converted to copper doped graphitic carbon nitride (Cu-g-C3N4) by treatment with different percentage (5%, 10% and 20%) of the metal precursor to obtained Cu-g-C3N4. The resultant composite was characterized using XRD, FTIR, and FESEM. The antibacterial assay of the composite was assessed against the standard laboratory strains of Staphylococcus aureus (gram +ve), Salmonella typhi (gram –ve), Escherichia Coli (gram –ve) and Streptococcus pyogenes (gram +ve) using well diffusion method. The as-synthesized composite shows a good antibacterial activity against the test organisms (gram +ve and gram –ve) with zone of inhibition ranging between 2-21 mm. The results were found to be appreciable when compared with that of the standard antibiotic (Ampicillin). These indicate that the as-synthesized composites have good potentials for developing new antibiotics which will help in reducing healthcare costs and mortality rate due to resistance of bacteria to already manufactured drugs.
Copyright (c) 2025 IBRAHIM MUHAMMAD, ABUBAKAR ISAH, JAMILU USMAN https://creativecommons.org/licenses/by/4.0
GROWTH OF CANDIDA ALBICANS ON MODIFIED MEDIA BASED ON COOWBEANS (VIGNA UNGUICULATA L. WALP)
One fungus that can cause infection is Candida albicans. Supporting examinations to confirm the diagnosis can be carried out by growing the fungus in fungal growth media. The abundance of natural resources can be used as substitute raw materials for growth media for microorganisms. Cowpea is a natural ingredient that contains quite high nutrients, is widely available, easy to cultivate, has a fast harvest time, and is relatively affordable. This research aimed to determine the growth and morphology of Candida albicans on modified cowpea media. The method used was laboratory experimental and five variations were made to differ in the amount of cowpea added, namely 5 g (F1), 10 g (F2), 15 g (F3), 20 g (F4), 25 g (F5). The research showed that Candida albicans fungus grew on the modified cowpea media. The morphology of Candida albicans colonies in all cowpea-modified media formulations is characterized by smooth round colonies, yellowish white with a characteristic yeast odor. Candida albicans fungi in F1, F2 and F3, produce blastospores. Candida albicans fungi in F4 and F5 produce blastospores, pseudohyphae, and chlamydospores. Cowpea-modified media can be used as an alternative growth medium for Candida albicans, the best seen from its morphology is F5 with the addition of 25 g of cowpea.
Copyright (c) 2025 MAKHABBAH JAMILATUN, SEPTI DEA MAWARNI, WAWAN SOFWAN ZAINI https://creativecommons.org/licenses/by/4.0
ASSESSMENT OF SIX EVAPOTRANSPIRATION MODELS FOR UMUAHIA LOCAL GOVERNMENT AREA, ABIA STATE
Penman-Monteith (PM), Priestly Taylor (PT), Blaney-Morin Nigeria (BMN), Jensen-Haise (JH), Hargreaves-Samani (HS) and Thornthwaite (TH) models were used to estimate Reference Evapotranspiration (RET) for Umuahia, in South-Eastern Nigeria from 1997to 2017. The Penman-Monteithmodel was chosen as a standard for evaluating the other five empirical models. Good correlation was found between the RET values, estimated by each of the five radiation and temperature based models and the Penman-Monteith model, although there were some discrepancies. The mean annual RET estimated by the Penman-Monteith model as the standard tool for Umuahia was found to be 1041.0mm. While the mean annual RET estimated by other five models were found to be 579.3mm, 1487.7mm, 513.7mm, 1543.3mm and 1367.7mm, respectively. Conversely, the weather parameters influencing this station were found to be high due to the flatness of the terrain. In addition, from the statistical regression analysis, Priestly Taylor (PT) had the highest T-scores and lowest Root Mean Square Error (RMSE), in the ranking the Thornthwaite, Blaney-Morin and Hargreaves-Samani predicted best among the five models in the station. Good correlation was found by the temperature based models when evaluated RET with data for Umuahia Station. The Penman-Monteith estimates was used to develop correction factors for the three models that predicted best in the station for their potential use in the areawithout sensitive error. This was done in order to achieve accurate and reliable evapotranspiration estimate.
Copyright (c) 2025 CYPRIAN NDAMZI TOM, JOSEPH ANTIGHA EDET, EMMANUEL TEGA EROKARE, NGOZI FAITH UDENZE https://creativecommons.org/licenses/by/4.0
TURKEY’S ROLE IN ADVANCING ENVIRONMENTAL SUSTAINABILITY: THE SUSTAINABLE DEVELOPMENT GOALS (SDGS) FRAMEWORK REVIEW
In the last 15 years, Türkiye has attained all the Millennium Development Goals (MDGs) with remarkable success and is among the best in the reduction of poverty, child goals. mortality, This and sustainable success development environmental has strategies. Since paved sustainable the the 7th development way Development concepts SDG for Plan with implementation Sustainable the in the will Development sustainable 1996, 11th be Commission development the NDP private headed will goals NDPs integrating sector, facilitate which by have the non-governmental SDGs. form the the The embraced SDGs organizations the Ministry coordination. Turkish as and backbone of Awareness Statistical the academic of Development among Institute main institutions the with the (TurkStat) goals. are current a public, will The considered focus political be important for on support, responsible the policy and for achievement coherence involvement tracking of while of the the the the progress enhanced and National this will entail ensuring that the national indicators are in conformity with the UN framework and also addressing the data gaps. Türkiye has progressed in the environmental SDGs by paying attention to biodiversity, sustainable agriculture, climate adaptation, water management, energy efficiency, and ‘zero waste’ strategies. The Turkish Co-operation and Coordination Agency (TIKA) has been implementing bilateral projects in the country as Turkey’s development cooperation has evolved over the years. However, there are issues that include the challenge of breaking policy silos, enhancing data quality and ensuring proper stakeholder engagement. The country’s experience underlines the importance of the mutual learning and international cooperation in addressing these issues. However, the current research is designed using a qualitative research approach to assess Türkiye’s contribution to the environmental sustainability through the SDG framework based on literature reviews, project evaluation, and comparison.
Copyright (c) 2025 ABDUL WAHED AHMADI, NİLGÜN BALKAYA https://creativecommons.org/licenses/by/4.0
ASSESSMENT OF SOIL QUALITY INDEX FOR TEAK FOREST IN DRY LAND, ACEH BESAR REGENCY
This research aims to examine the analysis of the soil quality index on land use type specifically on teak forest on dry land of Aceh Besar Regency, Aceh Province. This investigation targeted multiple specific sites where teak forests are established on dry land. Descriptive techniques based on survey data, field observations, and laboratory analysis were used to conduct this study. Field surveys were conducted to gather primary data, including the overall biophysical conditions of the region, the physical and chemical properties of the soil from observations, and indicators of soil quality through laboratory analysis. The findings indicate that the SQI predominantly falls within the medium range. Factors such as low organic carbon levels, elevated bulk density, pH (H2O), exchangeable potassium (K-exchangeable), phosphorus availability, and reduced total nitrogen content significantly affect these results. To mitigate nutrient leaching and enhance soil organic matter, specific management strategies involving fertilization, pine reforestation, and improved drainage are recommended for these teak forest lands.
Copyright (c) 2025 UMAR HUSEIN ABDULLAH, YUSRAN AKBAR, SOFIA KEUMALASARI, LUKMAN MARTUNIS, ANDRIY ANTA KACARIBU, RUHALENA WILIS https://creativecommons.org/licenses/by/4.0
ENHANCING SMART EDUCATION: COMPARATIVE ANALYSIS OF HYBRID RECOMMENDATION ALGORITHMS FOR PERSONALIZED LEARNING
Smart education systems have emerged as pivotal tools in modern education, yet the effectiveness of different recommendation algorithms in personalizing learning experiences remains understudied, particularly in higher education contexts. This study evaluates and compares three recommendation algorithms collaborative filtering, content-based filtering, and a hybrid approach within a web-based smart education system designed for computer science education, focusing on their ability to enhance personalized learning experiences. The author developed a web-based smart education system incorporating these three algorithms and tested it using a dataset of 100 users across 20 courses. The system's performance was evaluated using precision, recall, accuracy, and F1-score metrics. A four-week case study with 20 users was conducted to assess practical implementation outcomes. The hybrid algorithm demonstrated superior performance with 85.96% precision, 98.99% recall, 94.33% accuracy, and a 91.98% F1-score, significantly outperforming both collaborative filtering (precision: 68.09%, recall: 94.12%) and content-based filtering (precision: 72.73%, recall: 90.32%). Case study results showed consistent improvement in user performance, with score improvements ranging from 15% to 23%.The hybrid algorithm proves most effective for personalizing educational content delivery, though with higher computational overhead. These findings suggest that hybrid recommendation approaches can significantly enhance smart education systems' ability to provide personalized learning experiences, despite computational challenges in large-scale deployments.
Copyright (c) 2025 EMMANUEL NWELIH, VICTOR OSASU EGUAVOEN https://creativecommons.org/licenses/by/4.0
HSML-ITD: HYBRID SUPERVISED MACHINE LEARNING FRAMEWORK FOR INSIDER THREAT DETECTION
The digital transformation driven by Information and Communication Technology (ICT) has amplified data accessibility and operational efficiency across organizations. However, it has also escalated cybersecurity vulnerabilities, with insider threats emerging as a critical concern. Traditional detection methods often fail to address the sophisticated and evolving nature of these threats, necessitating advanced solutions. This study proposes the Hybrid Supervised Machine Learning Framework for Insider Threat Detection (HSML-ITD), integrating Support Vector Machines (SVM) for initial data classification and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for predictive learning. The model aims to enhance detection accuracy, reduce false positives, and provide a robust mechanism for insider threat mitigation. The framework was developed using the CERT insider threat dataset, with a structured methodology comprising data preprocessing, SVM-based classification, and ANFIS-based predictive learning. Performance was evaluated using a 5-fold cross-validation technique and comparative analyses with conventional models were conducted to validate the hybrid approach. The HSML-ITD demonstrated superior performance, achieving an accuracy of 92%, precision of 93%, recall of 89%, and F1-Score of 91%. Comparative analysis revealed significant improvements over standalone models, particularly in handling noisy data and high-dimensional input spaces. The hybrid model effectively balanced prediction accuracy and robustness, addressing limitations of conventional methods. The HSML-ITD offers a scalable and accurate solution for insider threat detection, significantly enhancing organizational cybersecurity. Future research will focus on incorporating real-time detection capabilities, optimizing computational efficiency, and expanding validation across diverse datasets. By addressing these aspects, the model can further solidify its applicability in dynamic threat environments.
Adaptive Neuro-Fuzzy Inference System (ANFIS)
Copyright (c) 2025 VICTOR OSASU EGUAVOEN, EMMANUEL NWELIH https://creativecommons.org/licenses/by/4.0
SECURING UAV SWARM-BASED SMART METERING INFRASTRUCTURE: A MULTI-PHASED APPROACH TO THREAT MITIGATION
Unmanned Aerial Vehicle (UAV) swarms offer a promising solution for revolutionizing Smart Metering Infrastructure (SMI) by enabling efficient, scalable, and cost-effective data collection. However, the deployment of UAV swarms in critical infrastructure applications raises significant security concerns. This paper presents a comprehensive security model designed to protect a UAV swarm-based SMI against various threats throughout its operational phases. We identify key vulnerabilities and propose a multi-layered security framework that incorporates bidirectional entity authentication, secure communication channels using IPSec (Internet Protocol Security), and proactive measures to mitigate specific attacks, such as denial-of-service and man-in-the-middle attacks. We analyze the effectiveness of our proposed solutions in addressing potential threats during different operational phases: DMC (Data Management Center) interaction, in-flight operations, and data collection. We present a comparative analysis highlighting the advantages of our approach over existing security schemes for UAV swarms. Our findings provide valuable insights into securing UAV swarm-based critical infrastructure and contribute towards building more resilient and trustworthy smart city applications.
Copyright (c) 2025 QUTAIBA IBRAHIM ALI, MUSTAFA QASSAB https://creativecommons.org/licenses/by/4.0
SMART AQUAPONIC SYSTEM USING FUZZY LOGIC AND CONVOLUTIONAL NEURAL NETWORK (CNN)
Aquaponics is an innovative and sustainable farming method that combines aquaculture (fish farming) and hydroponics (soilless plant cultivation) in a symbiotic environment. While aquaponics offers significant advantages, such as reduced water usage and efficient nutrient recycling, it faces challenges, including the need for precise monitoring, early disease detection, and optimal resource management. Traditional systems often lack real-time data analysis and predictive capabilities, leading to inefficiencies, crop losses, and increased operational risks. To address these limitations, this study introduces a smart aquaponic system that integrates fuzzy logic, convolutional neural networks (CNNs), and IoT technologies to enhance yield and resource management. The proposed system employs sensor-based monitoring to collect real-time data on critical parameters such as water quality, temperature, pH levels, and nutrient concentrations. A fuzzy logic model evaluates the health of fish and plants using predefined rules, effectively managing uncertainties in biological systems. Additionally, a CNN-based plant disease detection module achieves exceptional performance, with a precision of 93%, recall of 91%, and an accuracy of 92%, enabling early identification and mitigation of diseases. The system is accessible via a mobile application, providing farmers with user-friendly tools for remote monitoring and management. By enhancing real-time monitoring, predictive analytics, and disease detection, this research supports global sustainability goals, offering a scalable solution for modernizing aquaponics and advancing food security.
Copyright (c) 2025 CHENG LUE XUE, ANAS MOHD NOOR https://creativecommons.org/licenses/by/4.0
OPTIMIZATION OF LAST-MILE DELIVERY IN KSA
The rapid growth of e-commerce in Saudi Arabia has intensified the need for efficient last-mile delivery solutions. This study investigates the optimization of last-mile delivery using a mathematical model and a Python-based approach to enhance efficiency, reduce costs, and improve customer satisfaction. A survey conducted among Saudi residents highlighted significant challenges, including delivery delays, high costs, and inefficient routing. The study developed a capacitated vehicle routing problem (CVRP) model, which aimed to minimize total travel distance while meeting customer demands within vehicle capacity constraints. The model, implemented using LINGO software, demonstrated a 17.21% reduction in travel distance, translating into cost and time savings. However, due to computational complexity, a Python-based optimization program was developed as an alternative, reducing the total distance traveled by 31.15% and cutting delivery time by 33.82%. The results indicate that automated route optimization significantly enhances last-mile logistics, ensuring timely deliveries and reducing operational expenses. The findings highlight the importance of integrating mathematical optimization with programming solutions to streamline logistics operations. This study offers practical insights for logistics companies in Saudi Arabia to adopt advanced route-planning technologies, ultimately improving service efficiency and customer experience. Future research should explore integrating real-time traffic data and customer preferences to further optimize last-mile delivery processes.
Copyright (c) 2025 DALIA SABER, NAJAH YOUSFI, SARRA SELMI, MANAL ALOTAIBI, NORA ALOTAIBI, ROAA ALHARTHI, HANEEN ALWGDANI, REEM ALOTAIBI https://creativecommons.org/licenses/by/4.0
METHODS FOR PREDICTING HYDRAULIC CONDUCTIVITY OF NIGERIAN AGRICULTURAL SOILS
Accurate prediction of hydraulic conductivity is very crucial for infiltration and runoff estimation. Factors which affect water infiltration in the soil include hydraulic conductivity, wetting front and soil sorptivity, ponding depth, depth to water table and impact of rain drops. Amongst these factors, the importance of hydraulic conductivity cannot be overemphasized. Hydraulic conductivity is one of the most important parameter for flow and transport related phenomena in soil. There is concern arising from the suitability, efficiency and ease of the different measuring methods under different conditions. The various methods of predicting hydraulic conductivity include field; laboratory and correlation methods each with individual merits and demerits. Although selection of a specific method for a particular application will depend on the objectives to be achieved, most researchers prefer direct measurement of soil hydraulic conductivity. The estimation of soil hydraulic conductivity using correlation method depends on the local soil maps, soil particle size distribution, organic matter content and bulk density. Field methods are usually more expensive than laboratory methods. Consequently, when the question of cost becomes decisive, or when actual representation of field conditions is not of fundamental importance and in-situ hydraulic conductivity is not available, laboratory methods may be used to predict the saturated hydraulic conductivity K. This study explains the various methods that can be used to predict hydraulic conductivity in-situ or within the laboratory.
Copyright (c) 2025 JOSEPH ANTIGHA EDET, CYPRIAN NDAMZI TOM, ISIGUZO EDWIN AHANEKU, UKACHI ETOAMAIHE, CHARLES ORJI, CHIDOBERE DAVID NWA-DAVID https://creativecommons.org/licenses/by/4.0