UniMAP RIS :: Browsing Publications 2024 by Issue Date

Publication

Design and Simulation of a Customize Three-axis Gimbal Structure using Finite Element Analysis Method

( 2023-03-01)

Kamaruzzaman M.A.

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This paper presents a Finite Element Analysis (FEA) on a customized three-axis gimbal design application. Examples of applications of the gimbals such as drones, camera stabilizers, and spacecraft. The SolidWorks software checked the gimbal’s FEA characteristics with no existing load or normal conditions. Using the FEA method, a static simulation analysis where the material of this assembly design uses Polylactic Acid (PLA), used mainly by 3D printer machines. The force is given to the gimbal structure and obtains the results of the maximum value of stress in MPa, displacement in mm, and strain. Thus, based on the results obtained from SolidWorks, the structure will not fail. The maximum stress value between parts is 2.31 MPa for the support part and 3.09 MPa for the assembly model when the yield stress value of the PLA material properties is at 70 MPa. The new design structure for the gimbal hardware focuses on academic purposes based on PLA material and is easy to build using a 3D printer. In the summary, the customized three-axis gimbal design using SolidWorks will not fracture when the design is in normal condition which has a total force of 6.87 N, which is equal to 0.70 kg at 3.09 MPa where the weight of the base, O-ring, and servo motors at the U-shape part. In addition, the design can hold up to 230.87 N, which is equal to 23.54 kg at 69.90 MPa of the stress value before it will fail at 70 MPa.

1

Publication

An update on pathogenesis and clinical scenario for Parkinson’s disease: diagnosis and treatment

( 2023-05-01)

Adam H.

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Subash Chandra Bose Gopinath

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Nor Azizah Parmin

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Tijjani Adam

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Mohd Khairuddin Md Arshad

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Husein I.

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Uda Hashim

In severe cases, Parkinson’s disease causes uncontrolled movements known as motor symptoms such as dystonia, rigidity, bradykinesia, and tremors. Parkinson’s disease also causes non-motor symptoms such as insomnia, constipation, depression and hysteria. Disruption of dopaminergic and non-dopaminergic neural networks in the substantia nigra pars compacta is a major cause of motor symptoms in Parkinson’s disease. Furthermore, due to the difficulty of clinical diagnosis of Parkinson’s disease, it is often misdiagnosed, highlighting the need for better methods of detection. Treatment of Parkinson’s disease is also complicated due to the difficulties of medications passing across the blood–brain barrier. Moreover, the conventional methods fail to solve the aforementioned issues. As a result, new methods are needed to detect and treat Parkinson's disease. Improved diagnosis and treatment of Parkinson's disease can help avoid some of its devastating symptoms. This review explores how nanotechnology platforms, such as nanobiosensors and nanomedicine, have improved Parkinson’s disease detection and treatment. Nanobiosensors integrate science and engineering principles to detect Parkinson’s disease. The main advantages are their low cost, portability, and quick and precise analysis. Moreover, nanotechnology can transport medications in the form of nanoparticles across the blood–brain barrier. However, because nanobiosensors are a novel technology, their use in biological systems is limited. Nanobiosensors have the potential to disrupt cell metabolism and homeostasis, changing cellular molecular profiles and making it difficult to distinguish sensor-induced artifacts from fundamental biological phenomena. In the treatment of Parkinson’s disease, nanoparticles, on the other hand, produce neurotoxicity, which is a challenge in the treatment of Parkinson’s disease. Techniques must be developed to distinguish sensor-induced artifacts from fundamental biological phenomena and to reduce the neurotoxicity caused by nanoparticles.

5

Publication

Predicting student final examination result using regression model based on student activities in learning management system

( 2024-01-01)

Pa N.N.N.

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Aziz A.A.

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Safei S.

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Wan Azani Wan Mustafa

Background: Learning analytics (LA) is the measurement, collection, analysis, and reporting of data about learners and their contexts to understand and optimize learning and the environments in which it occurs. Most teaching and learning (T & L) data was obtained from learning management systems (LMS), such as Moodle platform. However, this data is not utilized for teaching purposes, for example, learning how students’ activities can influence the final exam marks. Methods: Therefore, this study aims to find a correlation and develop a prediction model between students’ activities, independent variables known as LMS factors, against dependence variables, which are students’ final results. Besides, four non-LMS factors (race, sponsorship, admission requirements and final coursework marks) were also included in the research to obtain the best model. The regression analysis, models are used to predict the outcomes by evaluating the accuracies of testing data. Results: The findings reveal that the best model utilizes Simple Linear Regression (SLR) with coursework as an independent variable, resulting in an average error difference (AED) of only 1.8. The remaining experiments produced AED results ranging from 2.74 to 7.58 using Multilinear Regression (MR). Conclusions: in summary, this study provides a significant finding that demonstrates the potential of utilizing LMS activities to predict final marks, enabling lecturers to enhance their students’ results.

Publication

Aerobic Co-composting of Spent Mushroom Medium Using Food Waste Fermented Liquid

( 2024-01-01)

Siti Nazrah Zailani

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Zaidnuddin A.S.

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Mokhtar N.F.

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Zulaika E.

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Shovitri M.

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Kuswytasari N.D.

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Hidayati D.

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Zabidi K.A.A.

Spent mushroom medium and dried sludge from bioethanol industry can be utilized into beneficial organic compost after completion the composting process. Food waste fermented liquid and commercially effective microorganisms (EM) from EMRO Sdn. Bhd. were used as the microbial inoculant for the aerobic composting. Three kg of composting pile made of spent mushroom and dried sludge are used to obtain a 23.07 C/N ratio at the initial composting process. In order to ascertain the impact of composting process in seven days, the sample from each of the composting beds for analysis of temperature, pH, moisture, and total organic matter was assessed. Food waste fermented liquid and commercial EM composts reached the thermophilic phase (40 °C) after a day of the composting process. Moisture content showed a significant result for both composting piles on day 1 due to the highest evaporation of water at the thermophilic phase. The composting process was not extended until reached stability and maturity phases; thus, the pH for both composting piles was recorded at 9, which is far from the stable compost normally at neutral pH (~7). The organic matter content of each composting pile showed gradually decreased during the composting process. The degradation rate for the composting pile using food waste fermented liquid showed slightly higher than commercial EM which was 0.0363 and 0.0232 day 1, respectively. In conclusion, food waste fermented liquid shall be used as a replacement for EM commercial as a microbial inoculant for the composting process based on the results shown in this study.

Publication

Design and Fabrication of Surface Acoustic Wave (SAW) Device for Cell Migration Control

( 2024-01-01)

Mazalan M.

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Anas Mohd Noor

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Zakaria N.F.

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Zakaria M.R.

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Ismail A.M.

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Zaman W.S.W.K.

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Mohammad Shahrazel Razalli

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Do T.D.

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Wahab Y.

Surface acoustic wave (SAW) devices are widely used for cell manipulation and cell separation in cellular biology research. Despite the fact that the SAW stimulation is biocompatible and noninvasive, it is still unknown how cells will respond to the stimulation in terms of migration. In this study, we developed two types of SAW device model with different frequency at 83 MHz and 124 MHz and investigated the effect of standing SAW stimulation to the cells model. We also fabricated the SAW devices to measure its electrical properties. As a result, different local stresses along cell-fluid boundaries was observed as different frequencies of standing SAW were applied to the cells. The standing SAW with higher frequency ascribed to a higher stress at the cell's apical and basal as compared to lower frequency stimulation. Moreover, the fabricated SAW shows a low insertion loss at −19.8 dB and −13.5 dB for the 83 MHz and 124 MHz of SAW, respectively. Based on these results, the standing SAW can be used as an alternative tool in regulating the aberrant cellular activities related with cell migration.

Publication

A Review on Graphene Analytical Sensors for Biomarker-based Detection of Cancer

( 2024-01-01)

Subash Chandra Bose Gopinath

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Ramanathan S.

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More M.

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Patil K.

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Patil S.J.

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Patil N.

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Mahajan M.

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Madhavi V.

The engineering of nanoscale materials has broadened the scope of nanotech-nology in a restricted functional system. Today, significant priority is given to immediate health diagnosis and monitoring tools for point-of-care testing and patient care. Graphene, as a one-atom carbon compound, has the potential to detect cancer biomarkers and its derivatives. The atom-wide graphene layer specialises in physicochemical characteristics, such as improved electrical and thermal conductivity, optical transparency, and increased chemical and mechanical strength, thus making it the best material for cancer biomarker detection. The outstanding mechanical, electrical, electrochemical, and optical properties of two-dimensional graphene can fulfil the scientific goal of any biosensor development, which is to develop a more compact and portable point-of-care device for quick and early cancer diagnosis. The bio-functionalisation of recognised biomarkers can be improved by oxygenated graphene layers and their composites. The significance of graphene that gleans its missing data for its high expertise to be evaluated, including the variety in surface modification and analytical reports. This review provides critical insights into graphene to inspire research that would address the current and remaining hurdles in cancer diagnosis.

Publication

FloodIntel: Advancing flood disaster forecasting through comprehensive intelligent system approach

( 2024-01-01)

Ayoub N.A.

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Aziz A.A.

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Wan Azani Wan Mustafa

Background: Every year, floods are the most devastating natural disaster that hits Malaysia, causing damage to people’s livelihoods, destroying property and infrastructure, and taking lives. Flood disasters are becoming more frequent and severe, necessitating the development of sophisticated forecasting and early warning systems to mitigate their effects. This study presents the design and implementation of a sophisticated flood forecasting and early warning system, utilizing intelligent technologies to enable timely prediction and proactive measures in high-risk areas. Methods: The proposed system incorporates Internet of Things (IoT) technology to collect real-time data on the river’s water level. The collected data are analyzed using an association rule technique to generate accurate forecasts of prospective flood occurrences. By using this intelligent flood disaster prediction system, users and authorities can receive early warnings and make informed decisions regarding evacuation, resource allocation, and infrastructure reinforcement. The system’s capability to provide early flood forecasts in high-risk areas can substantially enhance flood preparedness and response and save more life. Results: The findings of the study highlight the potential of the system to improve flood risk management strategies and reduce flood-related devastation and human suffering in vulnerable regions. Conclusions: In conclusion, it is important to implement IoT and AI technologies to improve flood prediction systems and reduce the negative effects of flood disasters.

Publication

Development of IoT-Enabled Smart Water Metering System

( 2024-01-01)

Wen S.D.

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Hazry Desa

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Muhammad Azizi Azizan

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Hussain A.S.T.

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Tanveer M.H.

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Patan R.

This paper introduces a smart water meter that utilizes the capabilities of the Internet of Things (IoT) to automate the collection of meter readings. The primary goal of this project is to create an IoT-based device for reading water meters, while simultaneously developing a compatible mobile application. Instead of relying on manual meter reading, which requires human effort, this project proposes the use of an IoT-enabled water meter to collect the data automatically. The device employs a camera and Convolutional Neural Network (CNN) for image processing, making it easy to detect the meter reading accurately. The IoT system architecture involves the use of an ESP32 CAM for data collection, a laptop as a gateway, and the Message Queuing Telemetry Transport (MQTT) protocol for data transfer. The collected data is stored in Firebase's real-time database, and the mobile application is designed to monitor and analyze the data. A functional prototype of the device is constructed and tested in a housing area. The collected data is then monitored through the developed mobile application. Lastly, the data is analyzed to assess the suitability of the proposed method, and recommendations for future improvements are provided.

Publication

Physicochemical Properties of Industrial Wood Waste-Derived Cellulose Nanofibrils

( 2024-01-01)

Hing M.H.

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Mohd Hanif Mohd Pisal

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Sezali N.A.A.

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Ong H.L.

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Doong R.A.

Wood is an important raw material, especially for construction and industrial scale activities which have resulted in a large amount of wood waste (WW). The accumulation of industrial WW has led to serious environmental issues; hence, the utilization of the industrial WW is being studied by researchers due to the rich content of cellulose. This study investigated the physicochemical properties of cellulose nanofibrils (CNFs) derived from industrial WW. The preparation of the CNFs involves the pretreatment of WW with an alkaline deep eutectic solvent (DES) and bleaching with peracetic acid, followed by 2,2,6,6-tetramethylpiperidine-1-oxyl (TEMPO)-mediated oxidation and mechanical post-treatment. Interestingly, the yield of the CNFs produced was 52%, which is half of the raw material used. Furthermore, the morphology of the WW-derived CNFs was analyzed from scanning electron microscopy (SEM) and transmission electron microscopy (TEM). The WW-derived CNFs showed a uniform size with a width of around 20–100 nm and a length of several micrometers. Moreover, the production of WW-derived CNFs was further verified by Fourier transform infrared spectroscopy (FTIR) for the surface functional groups, X-ray diffraction (XRD) for the crystallography, and thermal gravimetry analysis (TGA) for thermal stability. The results obtained from these characterization methods have proved the successful transformation of the industrial WW into a high-potential nanomaterial, which is the CNFs that can be used for further applications in paper making, composites, packaging, textiles, biomedicine, energy storage, and electronics.

Publication

SOSFloodFinder: A text-based priority classification system for enhanced decision-making in optimizing emergency flood response

( 2024-01-01)

Kamal S.H.

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Aziz A.A.

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Wan Azani Wan Mustafa

Flooding is a significant concern in nations with frequent precipitation because it can instantly affect multiple regions simultaneously. Due to the unpredictability of their occurrence caused by rapid water level rise, it is challenging to predict such natural disasters accurately. During flooding, prompt rescue efforts are crucial for the affected population. Due to flooded highways and residences, rescue teams may have difficulty locating victims. This hinders the potentially perilous and time-consuming rescue operation. To address this problem, we propose a web-based system that integrates natural language processing (NLP) with global positioning system (GPS) functionality. The SOSFloodFinder system provides automatic classification priorities for text messages sent by flood victims, as well as their most recent or current locations. The classification of text based on priority enables efficient resource allocation during rescue operations. In conclusion, this system has the potential to reduce future flood-related fatalities. Additional research and development are necessary to thoroughly investigate this method’s practical capabilities and effectiveness.

Publication

Enhancing Active Power Dispatchability of WTG-BESS Through Integrated Control Considering Wind Forecast Error

( 2024-01-01)

Suboh S.M.

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Sharma R.

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Tushar W.

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Ahamad N.B.

This paper proposes a new integrated control scheme for large-scale wind turbine generators (WTG) incorporated with a battery energy storage system (BESS). The primary goal is to improve the dispatchability of wind power over a wide range. The proposed controller consists of switching feedback controllers for WTG equipped with BESS management and is developed to systematically govern both systems for optimal regulation while ensuring safe operation within defined limits. A scenario-based switching logic is recommended to coordinate the hybrid systems, and a three-layer WTG controller is devised using a gain-scheduled control method. The suggested controller's performance is evaluated through simulation case studies, particularly in dealing with wind forecast errors (WFE) in the short-term dispatch scheme. To assess the proposed controller's effectiveness, a comparison is made against a baseline controller in three criteria: standard deviation (STD), mean ( $\bar {X}$ ) and an integral absolute error (IAE). These criteria help to evaluate the energy mismatch concerning the reference power command. The results are presented to elucidate the effectiveness of the proposed controller in regulating WTG-BESS throughout the entire operating region using a real wind profile from a wind farm in Australia. The findings show that the proposed approach has significant potential to enhance performance, leading to vastly improved wind power dispatch compared to the baseline approach.

Publication

Computational Fluid Dynamics Analysis of Varied Cross-Sectional Areas in Sleep Apnea Individuals across Diverse Situations

( 2024-01-01)

Wan Mohd Faizal Wan Ab Rahim

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Khor Chu Yee

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Mohamad Suhaimi Shahrin

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Muhamad Nur Misbah

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Mohd. Hazwan Mohd. Hanid

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Masniezam Ahmad

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Mohd Haidiezul Jamal Ab Hadi

Obstructive sleep apnea (OSA) is a common medical condition that impacts a significant portion of the population. To better understand this condition, research has been conducted on inhaling and exhaling breathing airflow parameters in patients with obstructive sleep apnea. A steady-state Reynolds-averaged Navier–Stokes (RANS) approach and an SST turbulence model have been utilized to simulate the upper airway airflow. A 3D airway model has been created using advanced software such as the Materialize Interactive Medical Image Control System (MIMICS) and ANSYS. The aim of the research was to fill this gap by conducting a detailed computational fluid dynamics (CFD) analysis to investigate the influence of cross-sectional areas on airflow characteristics during inhale and exhale breathing in OSA patients. The lack of detailed understanding of how the cross-sectional area of the airways affects OSA patients and the airflow dynamics in the upper airway is the primary problem addressed by this research. The simulations revealed that the cross-sectional area of the airway has a notable impact on velocity, Reynolds number, and turbulent kinetic energy (TKE). TKE, which measures turbulence flow in different breathing scenarios among patients, could potentially be utilized to assess the severity of obstructive sleep apnea (OSA). This research found a vital correlation between maximum pharyngeal turbulent kinetic energy (TKE) and cross-sectional areas in OSA patients, with a variance of 29.47%. Reduced cross-sectional area may result in a significant TKE rise of roughly 10.28% during inspiration and 10.18% during expiration.

Publication

Synthesis of ultrasonicated amine-functionalized MgO-deposited empty fruit bunch (EFB)-derived biochar for CO2 adsorption

( 2024-01-01)

Shafawi A.N.

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Mohd Amer N.

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Aghamiri A.R.

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Lahijani P.

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Mohammadi M.

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Alina Rahayu Mohamed

In this study, biochar derived from empty fruit bunch (EFB) was modified by sonication, amine functionalization, and MgO deposition to increase CO2 uptake capacity towards addressing the global warming problem. The optimal conditions for amine functionalization were a biochar to melamine mass ratio of 5:2, an activation temperature of 700 °C, and a heating rate of 15 °C/min. The sequential sonication, amine functionalization, and MgO deposition resulted in the ultrasonicated amine-functionalized MgO-deposited biochar (UAMB) with the highest CO2 uptake capacity of 84.95 mg/g, which is a 142% increase compared to the pristine biochar (35.10 mg/g). The results of XRD, SEM–EDX, FTIR, Raman, BET, Boehm titration, and XPS analysis showed that the sequential treatments improved the porosity, surface area, and surface chemistry of the modified biochar due to the presence of MgO, pyridine, pyrrole, and nitrile, resulting in a superior increase in CO2 uptake capacity. Advantageously, this modified biochar exhibited the highest affinity for CO2 compared to air, methane, and nitrogen and was stable up to 12 cycles of CO2 adsorption–desorption. Kinetic studies showed that the Avrami kinetic model best described CO2 adsorption on the biochar, with physisorption being the main adsorption mechanism and chemisorption making only a minor contribution to CO2 adsorption. CO2 capture tests in a fixed-bed adsorption system showed that the best adsorption conditions were at a gas flow rate of 30 mL/min, an initial CO2 concentration of 15%, and an adsorption temperature of 30 °C. The excellent performance of this modified biochar is promising for efficient CO2 capture to reduce CO2 emissions.

Publication

Exploring the potential of agricultural waste as natural resource-based adsorbents for methylene blue removal

( 2024-01-01)

Nur Liza Rahim

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Muhamad Farid Idham Sulaiman

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Ain Nihla Kamarudzaman

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Norlia Mohamad Ibrahim

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Roshazita Che Amat

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Syakirahafiza Mohammed

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Norazian Mohamed Noor

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Deák G.

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Liyana Ahmad Sofri

Excessive agricultural waste in the agricultural industry leads to various forms of pollution, including water pollution. To address this issue, there's a growing interest in finding alternative methods. One approach is to utilize agricultural waste as natural resource-based adsorbents to eliminate contaminants, such as the case of methylene blue (MB) in this study. The study specifically focuses on using rice husk (RH) from a local rice mill in Perlis, Malaysia, to absorb methylene blue. The structure of rice husk, characterized by scanning electron microscopy (SEM), reveals a coarser and more compact outer area, contributing to its absorption capacity for methylene blue. The study on rice husk involves three main aspects: contact time, adsorbent dosage, and dye concentration. The removal percentage of MB increased as the three studied adsorption parameters increased. The adsorption data were analyzed using Langmuir and Freundlich adsorption isotherms, with the the Freundlich Isotherms were found to be more suitable based on higher coefficient of correlation (R2) values compared to Langmuir. The pseudo-second-order kinetics model demonstrated a higher R2value (1.00) compared to the pseudo-first-order model (0.747). The results indicate promising potential for addressing pollution through sustainable means and provide insights into the adsorption process under varying conditions.

2

Publication

Tailoring alkyl ketene dimer on structural-properties relationship of cellulose-based materials: a short review

( 2024-01-01)

Aziz F.A.

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Mokhtar Mat Salleh

The development of cellulose-based materials is rapidly increasing due to their promising interest and being widely used in various applications such as food, industrial, pharmaceutical, paper, textile production, and wastewater treatment. Cellulose has several advantages including low cost, renewability, superior mechanical properties, and biodegradability. Nowadays, a novel cellulose-based material, which is known as all-cellulose composites (ACCs), has emerged, consisting of cellulose for both reinforcing and matrix phases. However, the hydrophilic characteristics of cellulose can lead to poor interfacial adhesion between reinforcing and matrix phases, affecting its mechanical properties, water absorption, water contact angle, and microstructural changes. Therefore, this review summarized the effect of alkyl ketene dimer (AKD) on the structural-properties relationship of cellulose-based materials. In this review, it was observed that the mechanical properties for cellulose-based materials varied with the use of AKD concentration in the range of 4–16 wt.%. A decrease in the water absorption was also identified in the range of 10–85%. The water contact angles were observed beyond 100° after AKD treatment. In addition, the possible reactions between AKD and cellulose structures are also discussed. It is envisaged that this review will help the development of potential hydrophobic ACCs in the future.

1

Publication

Risk Management Planning by Risk Register in Building Construction Project

( 2024-01-01)

Rani H.A.

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Syammaun T.

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Azzahra F.

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Afizah Ayob

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Mohammad Harith Amlus

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Zakirullah

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Aqsha M.S.

This research addresses the common issue of vulnerabilities emerging during construction projects due to inadequate risk identification and analysis. It aims to mitigate this by creating a comprehensive risk register, documenting potential risk events, their causes, and characteristics. The main goal is to identify prominent risk factors in building construction projects and develop a risk management strategy using this register. The study used purposive sampling and considered 22 indicators, including human resources, cost, materials, equipment, and environmental conditions. Questionnaires were distributed to 50 contractors in the Banda Aceh region. Validity and reliability were confirmed through statistical analysis. The analysis showed that human resources were the most prevalent risk factor. Using a probability impact matrix, five indicators had medium-risk levels, while seventeen indicated low risk. Human resources, materials and equipment, and environmental conditions were identified as dominant risk factors, all at a medium-risk level. The study underscores the importance of addressing human resources risks through a structured risk register, which can significantly improve project management and execution. By systematically assessing and mitigating potential risks, project stakeholders can make informed decisions and enhance project outcomes in terms of timelines, costs, and quality. In conclusion, this research highlights the critical role of risk management in construction projects and emphasizes the value of a comprehensive risk register. Addressing dominant risks proactively can positively impact building construction projects.

Publication

Influence of Financial Innovation on Business Performance: Evidence from the SME Food Industry in Malaysia

( 2024-01-01)

Buhary S.A.M.

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Nasir H.

The impact of financial innovation (FI) on the economic performance of small-and medium-sized food companies is the focus of this study. This study focuses on the effect of FI on business performance in the small-and medium-sized enterprise (SME) food industry. This chapter aims to provide new information about business performance in the food industry. SMEs often use innovative financial practices to boost the performance of their businesses and increase the value of their organizations. This study uses quantitative and multiple regression techniques to analyze the collected information. The results of this study lend credence to the conclusions of prior studies by demonstrating a substantial connection between FI and the commercial performance of SMEs in the food industry. The significance of this research lies in the fact that it demonstrates FI’s capacity to predict a company’s future growth potential. According to the findings of this study, financial institutions offer vital information and strategies that may assist business owners in improving their company’s performance and remaining competitive compared to other businesses in their industry.

Publication

Drones and Data: A Comprehensive Exploration of UAVs in Data Mining

( 2024-01-01)

Muhammad Azizi Azizan

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Nurfadzillah Ishak

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Hazry Desa

Encapsulates a comprehensive investigation into the symbiotic relationship between Unmanned Aerial Vehicles (UAVs) and data mining, as encapsulated in the discourse titled "Drones and Data." This exploration delves into the multifaceted applications and transformative impact of UAV technology within the data mining landscape. The examination begins by elucidating the pivotal role of UAVs, highlighting their mobility, accessibility, and capability to collect data from challenging or remote environments. As technology evolves, UAVs have emerged as versatile platforms that redefine the data is collected and analysed across diverse sectors. The narrative unfolds through distinct dimensions, encompassing precision agriculture, environmental monitoring, infrastructure inspection, mining and exploration, disaster response, and urban planning. The technological transitions facilitated by UAVs, emphasizing the integration of machine learning algorithms, cloud-based data processing, and the collaborative synergy between stakeholders. These advancements position UAVs as transformative tools that not only enhance the efficiency of information acquisition but also open avenues for innovative solutions and insights. Therefore, this study a glimpse into the intricate web of applications and technological advancements at the intersection of UAVs and data mining. It serves as a scholarly guide, navigating the reader through the evolving landscape of "Drones and Data," UAVs play a central role in unlocking unprecedented insights and efficiencies, reshaping the future of data mining.

1

Publication

Surface Modification of GO/TiO2 Thin Film by Sodium Dodecyl Sulphate for Photocatalytic Applications

( 2024-01-01)

Azliza Azani

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Dewi Suriyani Che Halin

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Mohd Arif Anuar Mohd Salleh

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Kamrosni Abdul Razak

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Mohd. Mustafa Al Bakri Abdullah

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Muhammad Mahyiddin Ramli

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Vizureanu P.

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Mohd Fairul Sharin Abdul Razak

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Sandu A.V.

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Mohamad N.

Photocatalyst material titanium dioxide (TiO2) and graphene oxide (GO) were used to improve the self-cleaning properties of thin films. The sol–gel spin-coating method was successfully used to synthesize GO/TiO2 thin films. Surface modification was applied to optimize the self-cleaning capabilities by adding several concentrations of sodium dodecyl sulfate (SDS) (0.1 w/v%, 0.2 w/v%, 0.3 w/v%, 0.4 w/v%, and 0.5 w/v%) to the parent solution. The synthesized thin films were investigated using scanning electron microscopy (SEM), atomic force microscopy (AFM), X-ray diffraction, UV-visible spectroscopy, water contact angle analysis, and photocatalytic analysis. The AFM and SEM images revealed that as the SDS amount increased, the particles became less agglomerated, and the roughness of the surface reduced from 2.54 × 102 nm to 0.83 × 102 nm. The wettability analysis showed that when SDS increased to 0.4 w/v%, the water contact angle dropped to 15.30°, implying that the thin film exhibits hydrophilic qualities. A decrease in the GO/TiO2 band gap was obtained linearly with the increase in SDS addition from 3.17 eV to 2.75 eV. Finally, the improvement of the surface coating and reduction of the band gap enhanced the photocatalytic activity, which degraded 78.97% of methylene blue, which was obtained by 0.4SDS.

2

Publication

Enhancing Predictive Models for Assessing 5G Exposure Effects on Human Health and Cognition through Supervised Machine Learning: A Multi-Stage Feature Selection Approach

( 2024-01-01)

Sofri T.

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Allan Melvin Andrew

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Hasliza A Rahim @ Samsuddin

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Nishizaki H.

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Latifah Munirah Kamarudin

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Wong P.W.

;

Soh P.J.

No prior reviews have focused on any comprehensively examine the effects of 5G exposure (700 MHz to 30 GHz) on human health and cognition using supervised Machine Learning (ML). This novel research combined the Multi-Stage Feature Selection (MSFS) and hybrid features for classification machine learning model. The approach which includes the use of MSFS, yielded better results in terms of accuracy, precision, F1-score, sensitivity, and specificity when contrasted with the approach that did not incorporate MSFS with accuracy more than 0.95 for both datasets.

1

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