Current head imaging technologies, such as computed tomography (CT) and magnetic resonance imaging (MRI), can accurately diagnose brain injuries like stroke and brain tumors. However, these technologies have several limitations, notably their bulkiness, high cost, lengthy scanning times, and stationary nature. In addition, most of the antennas used for microwave imaging suffer high signal loss due to strong reflections from the body surface. This issue would lead to inaccurate diagnosis of diseases such as false negative results for cancer detection. Typically, standard antennas release electromagnetic (EM) energy into free space with a dielectric constant (or relative permittivity), 𝜀𝜀𝑟𝑟=1. However, the dielectric properties of the human body are approximately 20 to 30. Due to this impedance mismatch, almost ¾ of the energy transmitted into the body is reflected back at the body-air boundary. Considering the very low limit set upon the level of electromagnetic energy that the human body should be exposed to, simply increasing the transmitted power to probe inside the body is not an option. Moreover, the existing antennas are rigid to be used for wearable head imaging applications. This thesis proposes a flexible body-coupled (co-planar waveguide) CPW-fed triangular planar monopole antenna (PMA) for wide-band microwave sensing and imaging, particularly for head imaging applications. The proposed antenna has been made with advanced ceramic material barium titanate (BaTiO3) incorporated with silicone-based hyperplastic elastomer to synthesize a flexible body-matched impedance substrate with low loss. The proposed barium titanate silicon-elastomer composite layer is designed with the dielectric property of 20 which acts as an impedance-matching layer for the CPW-fed triangular PMA. The dimension of the proposed antenna is 70mm × 30mm × 6mm. The mixed ratio by weight of barium titanate powder and silicon used to synthesize the impedance matching layer is 1:1. The measured dielectric constant of the impedance matching layer is 6.9. It has been shown that the power radiated into an artificial head phantom improved by almost 160% as compared to an antenna without an impedance-matching layer. Moreover, the SAR level is 0.0286 W/kg when 1 mW of power is transmitted, which is well below the limit set by the FCC (<1.6 w/kg for mass) regulation for the public at microwave frequencies. The proposed antenna has a bandwidth of almost 900 MHz (0.6 GHz - 1.5GHz) when measured directly on the actual human head. This makes the antenna suitable for wearable head imaging applications due to its wideband characteristic and improved power penetration into the human head.

The global COVID-19 pandemic highlighted the need for rapid, point-of-care (POC) virus detection, particularly in remote and resource-limited settings. Conventional polymerase chain reaction (PCR) methods, while effective, have some limitations like requiring bulky equipment, expensive infrastructure, and complex procedures. This research addresses the PCR methods’ limitations by proposing a novel, portable biosensor system for POC DNA amplification detection using loop-mediated isothermal amplification (LAMP). This research focuses on developing three key components of the system which are a heating and temperature control system, device’s packaging, and its integrated fluidic device. A heating system that can maintain a stable temperature of 65°C is important for LAMP. Two types of heater controller systems were compared, the proportional-integral-derivative (PID) controller and the on-off controller. The device packaging was aimed to integrate all components in the system into a user-friendly compact and portable package. SolidWorks 2023 was used to design the entire packaging, housing for the biosensor, heater chamber, and heating circuit. The package was 3D printed using fused deposition modelling (FDM) technique. The packaging went through several iterations based on user feedback, leading to improvements in ergonomics and functionality. The fluidic device was fabricated using masked stereolithography apparatus (MSLA) 3D printing. This is to ensure precise control of sample flow and optimal interaction with the biosensor’s surface. The results demonstrate that the device reached a temperature stability of 65°C through the PID controller. The PID controller shows significantly better performance than the on-off controller, achieving a lower overshoot (1.9%) and steady-state error (0.3%) in maintaining the target temperature, ensuring optimal LAMP efficiency. The biosensor, heater chamber and heating circuit were successfully integrated into a compact and portable package. The fluidic channel with a diameter of 1.7mm was designed based on the reliable minimum printing resolution of MSLA. A flow rate of 3.2 μl/min was determined to achieve a 35-minute flow time over the heating area, achieving the LAMP reaction requirements. This work has contributed to the development of a portable biosensor system for POC virus detection particularly COVID-19 by developing a robust heating system with a precise temperature control, developing a user-friendly and portable device packaging, and optimizing a fluidic device for efficient LAMP amplification. In conclusion, this work holds considerable promise for revolutionizing POC virus detection, particularly in remote or resource-limited settings where access to laboratory facilities may be limited, and timely diagnosis is crucial for disease management and outbreak control.

Organic solar cells are undergoing a considerable interest in the field of photovoltaics because of its solution processability, flexibility, low temperature fabrication, easily integrable and environmentally friendly. Despite these characteristics, its power conversion efficiency is still lower than other solar cells. Therefore, it is inevitable to focus on the efficiency enhancement of organic solar cell. With this in mind, we have developed a nanocomposite film based on zinc oxide (ZnO) and polyvinyl alcohol (PVA) using a solution casting method with varying concentrations of ZnO nano powder in the PV A matrix. The ZnO / PV A film surface morphology was observed by the scanning electron microscope (SEM). The micrographs indicate that ZnO nanoparticles in the PV A matrix are homogeneously distributed. XRD results indicated that the crystallinity of film was influenced by the interaction of ZnO nanoparticles and PV A main chain. Crystallinity also affected by the doping of ZnO nanoparticle in PV A matrix and it increases when the concentration of ZnO is low and then decreases when the excess concentration of ZnO is present in the PV A matrix. The FTIR transmission spectra confirmed that the significant interaction took place between the ZnO nanoparticles and PV A main chain over the wave number range of 400-4000 cm-1. The UV-Vis spectra reveal that the increase in concentration of ZnO nanoparticles in polymer matrix results in the movement of absorption edge in the direction of higher wavelength or lower energy associated with blue/green portion of the visible spectrum. Decrease in optical energy bandgap is observed with the increase of nano ZnO concentration into the matrix. Optimum weight percentage of 16.6% (ZnO) and 83.3% (PVA matrix) was observed. The film with 1 :5 ratio showed the optimum results and was applied on organic solar cell having architecture of [Carbon Fiber)/(ZnO/Epoxy Resin)/(CuO/Epoxy Resin)/Carbon Fiber]. Significant changes are observed in the efficiency of organic solar cell before and after applying the ZnO/PV A nanocomposite film. The optimum efficiency of solar cell before and after applying the nanocomposite film was found to be 9.01 % and 14.65% respectively. The increase in efficiency of organic solar cell showed that the ZnO/PV A nanocomposite film possess significant potential to be applied on organic solar cell for its efficiency enhancement. Solution casting method is found to be cost-effective and hazzle free method to develop the nanocomposite films. Low temperature processing and good conductivity of ZnO/PVA nanocomposite film makes it a suitable candidate for potential application in organic solar cells.

Wastewater from textile industry resulted in organic pollutants, mainly dyes which could impair the quality of water and give negative effects to human and aquatic life, especially after a prolonged exposure to the pollutants. Thus, sustainable, and efficient technology to remove them is needed. Catalytic removal of dyes is one of the solutions that have been extensively studied. Silver nanoparticles (AgNPs) are among metal nanocatalysts that have been studied for the removal of dyes in wastewater. The most common dyes being tested is methylene blue (MB). However, using the catalysts alone are not convenient as the catalysts cannot be recycled. Therefore, one of the common solutions is to use support materials such as activated carbon. In this study, we use activated carbon synthesized from durian skins (abbreviated as AC-DS) to immobilize AgNPs. Nevertheless, AgNPs can easily leach from the surface of AC-DS if not properly immobilized. Therefore, research was conducted to investigate the role of stabilizers in the formation of AgNPs and how these stabilizers affect the efficiency of immobilized AgNPs on AC-DS in removing methylene blue. In this study, two types of stabilizers were employed: polyvinylpyrrolidone (PVP) and citrate (Cit). The results were compared to those of the AgNPs synthesized directly on AC-DS without any stabilizers. The effects of different activating agents, temperature and with and without acid washing were also determined. Then, the AgNPs were immobilized on AC-DS by precipitate-deposition method. The efficiency of AC-DS was evaluated based on percentage of MB removal. The faster change from blue MB solution to colorless solution indicate fast and more of MB being absorbed by the AC-DS. UV-Vis’s absorption was used to indicate these results quantitatively. Meanwhile, the performance of AgNPs immobilized on AC-DS was evaluated by catalytic removal of methylene blue using oxidation reaction in which hydrogen peroxide (H2O2) was used as oxidizing agent. Results obtained from this study show that the shape of AgNPs/Citrate were mainly round-like shape while for AgNPs/PVP, about half of the nanoparticles were rod-like or oval-like shape. 1:3 molar ratio of silver precursor-to-stabilizer was the best condition for the synthesis of AgNPs based on the highest AgNPs formation and longer stability. SEM analysis revealed that AC-DS activated by KOH shows more pores. Second, MB adsorption analyses resulted in the best MB removal by AC-DS synthesized using KOH, at 400oC and with acid-washing The AgNP/Citrate@AC-DS resulted in the highest percentage of MB removal (77.44±13%)) as compared to AgNPs/PVP@AC-DS (56.19±18%). In addition, AC-DS immobilised with both AgNPs had better recyclability than the AC-DS alone (only 42.33 ±33.34% of MB removal) showing about 50% improvement to the percentage of MB removal after being used once when the AC-DS was immobilised with AgNPs/Citrate, as compared to the AC-DS alone. These results indicate that the AgNPs that catalyzed the degradation of adsorbed MB by oxidation reaction had facilitated the regeneration of the adsorption sites on the AC-DS surface, thus improving its recyclability. The AgNPs (AgNPs /Citrate) immobilised with AC-DS yielded the highest MB removal percentage (77.4 ± 13%) compared with AgNP/PVP (AgNPs /PVP) immobilised (56.9 ± 18%). The AgNPs also improved the recyclability of AC-DS compared to AC-DS on its own (47.7 ± 4.7%). The AgNPs / Citrate immobilised AC-DS showed the best recyclability (47.3 ± 4.7%) compared with AC-DS (47.9 ± 4.7) on its own (45.3 ± 3.6%). The percentage of MB removed after use once when AC-DS is AgNP immobilised (47.33 ± 3.34%) showed approximately 50% improvement compared to the percentage after use alone. These results suggest that AgNPs, which catalyze the degradation of adorbed MB through oxidation reaction, have enabled the regeneration of adhesion sites on the surface of AC-DS, resulting in improved recyclability.

The research on evaluating and improving the performance of hybrid rocket propulsion has been initiated. Since the Hybrid Rocket Motor's (HRM) deployment in the propulsion system is limited by its poor combustion efficiency and low regression rate. As a result, a thorough examination into the use of high-energy additives to improve the performance of the Hybrid Rocket Motor (HRM) in terms of regression rate and thrust is underway. Experimental studies are conducted to see the potential of High Energetic Additives as regression rate enhancer alongside with other different energetic additives. The parameters to be found in the experiment are the regression rate, thrust, and specific impulse. A lab-scale motor is developed to conduct the experimental studies. This is done by doping Boron, Aluminium and High Entropy Alloy (HEA) into the Paraffin-wax as a regression rate enhancer. Different composition of additives is prepared to find the optimum performance for the hybrid rocket. High entropy alloys are among the most advanced materials for high performance applications. Characterizations and thermal analysis of the solid fuel using x-ray diffraction (XRD), Scanning Electron Microscope (SEM), Energy Dispersive X-Ray Analysis (EDX), differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA) is carried out to determine the structure, thermal decomposition, particle distribution and energy characteristics of the solid fuel with the energetic additives. This is done to further study and identify the effect of elemental composition of the materials use as energetic additives. From the experimental study, the sample fuel doped with 5 wt% of boron had the highest regression rate, followed by 7 wt% of aluminium and 5 wt% of high entropy alloy, with regression rates of 1.45, 1.37, and 1.2, respectively. It is determined that High Entropy Alloy does enhance the hybrid rocket's performance in terms of regression rate.

The COVID-19 pandemic, a global health crisis, has significantly disrupted waste management systems worldwide, including in Malaysia, leading to an unexpected surge in waste generation and potential environmental threats. This study aims to address four key objectives: Firstly, it to identify the primary waste sources in Selangor during the pandemic through data visualisation using Microsoft Excel, involving data preparation, selection, graph insertion, subtype selection and validation. Secondly, it is to validate the Life Cycle Assessment (LCA) method, assessing its conclusion, internal, construct and external validity. Thirdly, it is to evaluate the environmental impacts of waste management systems in Selangor during the pandemic using LCA with OpenLCA software, involving goal and scope definition, inventory analysis, impact assessment and result interpretation. Finally, to implement strategies for reducing these environmental impacts, focusing on collection, transportation and disposal processes. The study identified municipal solid waste (MSW) and clinical solid waste (CSW) as the primary sources, with MSW showing a 4% average annual growth rate and CSW a significant 33% growth rate from 2019 to 2021. CSW was thus selected for further impact evaluation. The LCA method validation demonstrated a clear relationship between CSW generation and environmental impact, though confounding variables like fuel consumption affected internal validity. The indicators used (CCE, TA, CCH, HT, PMF, POF) effectively represented environmental impacts, and the ReCiPe method and OpenLCA tool adhered to ISO 14040/14044 standards. Evaluation of four hospitals in Central Selangor revealed that Tengku Ampuan Rahimah Hospital had the highest environmental impact score of 78,315 points, with the peak impact observed in 2021 and the particulate matter formation category showing the highest scores due to emissions from CSW disposal. The study recommends strategies such as establishing storage rooms, optimising transportation routes and incorporating emission control technologies to mitigate these impacts. Overall, the research provides a comprehensive analysis of the environmental impacts of solid waste management during the pandemic and emphasises the need for improved practices. It also demonstrates the suitability of LCA for evaluating waste management processes, highlighting its effectiveness in assessing the environmental impacts and guiding the development of strategies to mitigate adverse effects. This study provides actionable strategies for waste management organisations to mitigate environmental impacts, especially during crises like COVID-19. It supports improved practices aligned with the National Solid Waste Management Policy and contributes to SDGs, particularly Goal 12 (Responsible Consumption and Production) and Goal 13 (Climate Action), by promoting sustainability and reducing greenhouse gas emissions.

Driver drowsiness is one of the major factors which lead to very severe and fatal accidents in day to day life. Globally millions of people have died in road accidents annually, on average, globally 3 700 people lose their lives everyday on the road. Young adults aged between 15-30 account for more than half of all road accident deaths. Many more lose their lives due to accidents every day in other forms of travel. Drowsiness and distractions are the main causes of these fatalities. Behavioral or facial features like eyes closing or too much blinking, yawning and nodding are considered important features for recognizing one's drowsiness state. It has been found that the available Deep Learning models are not able to perform well for facial features detection like eyes detection (blinking and closing) and mouth detection (yawning) and also for pose variations and occlusions (wearing sunglasses). Therefore, a specialized Ensemble Convolutional Neural Network (CNN) configured with three CNN models which are better than one single CNN has been developed in this work that is invariant to pose and occlusion. The systematic methodology of drowsiness detection has been described in detail with their results in this study. The experiment has been carried out on Yawning Detection Dataset (Yaw DD) for yawning detection and the University of Texas at Arlington Real-Life Drowsiness Dataset (UT A-RLDD) for blink feature detection, to examine the extent of drowsiness depending on the frequency of yawning and blinking with certain pose and occlusion variation from these datasets. Results of the FI Score that were computed from the Confusion Matrix of CNN 1, CNN 2 and CNN 3 of YawDD are 0.92, 0.90 and 0.912 and that from UTA-RLDD are 0.901, 0.921 and 0.942 respectively and our proposed Ensemble CNN configuration of Yaw DD and UTA-RLDD yielded Fl score of 0.935 and 0.954 respectively. The proposed Ensemble CNN configuration showed more promising results than individual CNN's.

Landmine detection stands as a critical yet formidable challenge in numerous countries plagued by longstanding landmine issues. This issue transcends mere military concern where it embodies a significant humanitarian dilemma. The landmines can survives for decade depend on the land area condition where it was buried. The cost to clear the land mines and risk is high to bear with. Therefore, to confront this challenge, this project proposed a straightforward robot designed to detect landmines and mark their location by combining metal detector sensor and camera sensor for detection while GPS module was used for location marking. The robot is a simple, lightweight, autonomously controlled robot capable of detecting landmines that were buried under the surface (2cm – 4 cm). This robot can help to reduce the risk to human operator by putting the operator far from the landmines search location area. Beside that, this robot also can reduce the cost by put the operation under automation. To reduce the false indication, the robot will send the photograph of the suspected object for humans to evaluate the object. From the analysis, the robot can carry out the task as required. This report outlines the design and implementation of the landmine detection mechanism. The findings exhibit promising results, indicating that this robot holds considerable potential for practical deployment in field settings for the identification and localization of landmines.

The Internet continues to grow and spread across the globe, resulting in an enormous amount of data. Consequently, the number of cyberattacks grows daily. Intrusion Detection Systems (IDS) are indispensable security tools for detecting cyberattacks and malicious network traffic. Machine Learning and deep learning techniques are proposed to classify and analyse the network traffic content to identify abnormal activities and impending cyberattacks. Current intrusion detection techniques still face many obstacles, including a low detection rate, a high prediction latency, a high false alarm rate, and using an outdated dataset. This research aims to develop a fast and accurate algorithm for detecting cyberattacks. Dimensionality reduction is vital in the development of an intrusion detection system to reduce the complexity of the dataset and the inference time for faster detection of cyberattacks. The proposed feature selection is based on maximizing relevance and minimizing redundancy using Correlation Feature Selection (CFS), Mutual Information (MI), and Recursive Feature Elimination (RFE). The proposed feature selection algorithm has reduced the dataset's features from 78 to 25 features with remarkable results: an accuracy of 99.718% and 99.915% and FPR of 0.0929% and 0.0281% using CNN and XGBoost, respectively, and an inference time of 50.79μs and 4.78μs, respectively. Experiments were conducted using the CICIDS2017 dataset to evaluate the efficacy and efficiency of our developed IDS and the need to secure E-healthcare applications. Due to its low prediction latency, high accuracy, and high detection rate, the proposed IDS could be used as a server-side security layer for the server of NACOTS (Nanosystem for COVID-19 DNA/Antibodies On The Spot Test) application.

Weak soil, such as clayey sand and peat soil portray the characteristics of low bearing capacity, shear strength and permeability, high water content and compressibility, which are unsuitable to be the foundation of structure and pavement subgrade. Differential settlement and structural cracks will be most likely to occur when construct a structure on these types of soil. Thus, soil stabilisation introduces mechanical treatment to improve the properties and performance of the weak soils and achieve the desired soil stability by mixing with stabiliser. On the other hand, the need of treating the ground which is peat and clayey sand for construction as well as using scrap materials for stabiliser which can reduce environmental pollution and economic shall be considered. Therefore, this study performs to characterise the strength and performance of clayey sand and peat soil incorporate with waste rubber tyres and identify the optimum percentage of rubber tyres for soil enhancement. Clayey sand and peat soil were mixed with 0%, 5%, 10% and 15% of rubber tyre by the weight of the soil sample in each testing standard prior to conduct several laboratory experiments, such as physical properties test, for example, sieve analysis, hydrometer, pycnometer and Atterberg limit test. Also, several tests were conducted to determine the mechanical properties of modified soils, like moisture content test, consolidation test, standard Proctor compaction test and California bearing ratio test. The results showed that addition of stabiliser reduced the liquid limit, plasticity index and specific gravity of both soils. Subsequently, the moisture content of peat soil was 70.9% higher than clayey sand. Increased in the percentage of rubber tyre up to 10% and 15% revealed an improvement in the CBR value and settlement value of clayey sand, respectively. The CBR value was improved by 65% and addition of 50kN/m2 load to the CRT15 sample showed a lower settlement occurred than the parent soil. In contrast, the alteration of strength and settlement of peat-rubber tyre mixture did not give satisfactory results where it reduced the CBR value and increased the settlement thickness when more rubber tyre were included. Therefore, this research anticipated that rubber tyre can alter the strength and performance of clayey sand to meet the desired requirements of civil engineering construction, such as for ground foundation and pavement subgrade.

Polyamide 11 (PA 11) is a semi-crystalline engineering polymer with excellent impact strength and the ability to accept high filler loading, making it a promising alternative in a variety of applications. However, using PA 11 alone results in lower stiffness and heat resistance, as well as more expensive materials; therefore, reinforcement with halloysite nanotube (HNTs) filler, magnesium hydroxide (MH) flame retardant additives and styrene-ethylene/butylene-styrene grafted maleic anhydride (SEBS-g-MA) elastomers has been considered. The goal of this research is to investigate the effects of HNTs as fillers, MH as flame retardants and SEBS-g-MA as an impact modifier contents on the thermal, mechanical and flammability properties of PA 11. This study used counter-rotating twin-screw extrusion and injection moulding to create nanocomposite samples, which were then characterized by using Scanning electron microscopy (SEM), Thermogravimetric analysis (TGA), Differential Scanning Calorimetry (DSC), Fourier Transform Infrared Spectroscopy (FTIR), Dynamic Mechanical Analysis (DMA), tensile, flexural, impact, UL-94 standard flammability and limiting oxygen index (LOI). The addition of 4 phr HNTs improved the PA 11 matrix’s thermal, mechanical and flammability properties of the PA 11 matrix. The addition of 30 phr MH flame-retardant additives increased the young’s and flexural modulus by up to 34% and 31%, respectively, but decreased the elongation at break for all formulations. All the nanocomposite samples passed the UL 94 V-2 rating. The addition of 15 phr of SEBS-g-MA increased the elongation at break by 40% and impact strength up to 57%. However, the addition of elastomers reduced tensile and flexural strength. The study discovered that the adding nanofillers, flame retardant additives and elastomer to PA 11 can effectively improve these properties to a desired level, potentially opening new market opportunities for PA 11 manufacturers.

In high-speed fluid dynamics, base pressure controls find many engineering applications such as automobile industry and defense applications, and sudden expansion is a common problem for numerous fields. Several studies have been reported on the control of the flow field with sudden expansion. The passive control method controls the high-speed supersonic flows with geometrical change in a sudden expansion duct-like with cavities, ribs, cylinders, aerospikes, step bodies, etc. Based on the passive control method, it is found that the passive control does not need energy and resulting cost-effectively. Hence, the researchers introduce the active control method using a microjet controller. Therefore, the present study focuses on dynamic control with the microjets in an orifice of a 1 mm diameter to inject the air in the base recirculation region. The microjets with a sonic Mach number of 1 mm diameter were in the base area as a control mechanism at 90° intervals. Since the air is drawn from the main settling chamber, the NPR will be the same as the respective NPR's used for tests. Experiments were conducted in the presence and absence of the microjets for area ratio 3.24 and LID ratio from 10 to I. Mach numbers of the study were 1.87, 2.2, and 2.58. The parameters were optimized using the design of experiments (DOE) approach. Three parameters have been selected for the flow and the DOE. In this study, an Ln orthogonal array of Taguchi design is used. The variance analysis is used to examine the contribution in terms of percentage. A multiple linear regression equation is used to find the correlations between the numerous factors that affect the base pressure. It is found that the developed models are statistically suitable and capable of producing accurate predictions for both the cases in the presence and absence of control. According to the present findings, it is evident that the LID ratio is the most critical parameter that affects the maximum increase or decrease of the base pressure for a given parameter.

Minimizing friction and wear between contacting surfaces is a central focus in tribology, especially in improving lubricants' performance for engine durability and reliability. Low-viscosity lubricants, while beneficial for fuel efficiency, present challenges in reducing friction and wear. The shift to low-viscosity lubricants, such as SAE 5W30 and SAE 0W20, raises concerns about increased surface contact and potential durability challenges, highlighting the need for advanced additives like graphene and fullerene, as well as coatings, to mitigate these issues, while leveraging machine learning to predict tribological performance and reduce resource-intensive experimental trials. Graphene and fullerene have gained attention as potential additives to enhance the tribological properties of lubricants This study investigates the effects of 0.005 wt.% graphene and fullerene in SAE 0W20 lubricant, along with six different coatings (TiN, AlTiN, CrN, TiAlSN, TiCN, and DLC-AlTiN) on steel block. The primary aim is to develop a dataset that explores the relationships between these additives and coatings with critical tribological parameters—coefficient of friction, wear area, and temperature. Using a rod and ring tribometer, this study measures the tribological performance, with SEM and EDX analyses for surface characterization. The findings indicate that graphene and fullerene additives significantly reduce friction and wear, with graphene showing better heat dissipation. The coatings, particularly CrN, reduce wear and friction, although CrN showed higher wear compared to others. Machine learning models, including Support Vector Regression (SVR), Random Forest (RF), and Decision Tree (DT), were used to predict the tribological parameters. RF outperforms SVR and DT in predicting both friction and wear area, while DT excels in predicting temperature. This research contributes to improving lubrication strategies and predictive modeling, offering valuable insights for enhancing engine performance and durability in automotive engineering.

Electric cars play a clear and important role in solving issues related to the phenomenon of global warming, because when they operate, they do not emit any emissions that pollute the environment, and the electrical network can also be used to organize its work. However, there are still significant problems with electric cars that need to be fixed. The main challenges are all related to the battery package of the car. The battery package should contain enough energy in order to have a certain driving range and power capability. The first step in creating a decent electric vehicle model is choosing the right parameters and comprehending their properties. In this research, the electric vehicles are modelled. Three vehicle model is simulated with three different drive cycles. The simulation result demonstrates the significance of each segment parameter to the performance and fuel economy of electric vehicles. All works are performed in MATLAB/Simulink environment.

A vast investigation has deduced the drawbacks of stress that have caused an impact on society. The observed adverse consequences have been found to contribute to a negative quality of life for individuals. If prolonged without seeking help, it results in severe physical and mental health problems such as post-traumatic stress disorder (PTSD), depression, stroke, insomnia, and others. The present way of detecting stress using the bio-signals method is considered an invasive and ineffective method of detecting patient stress. The local newspaper also reports on the rise in this issue, citing an increase in Malaysian suicide attempts as a result of various issues like the financial crisis, unemployment, and environmental issues. The Malaysian Ministry of Health (KKM) reported that 91.2% of over one hundred thousand inquiries to the psychosocial helpline required emotional support and counselling more than other help. As seen in previous studies, stress speech features provide better accuracy compared to stress images or video-based features. It is also more convenient since it is a non-invasive and contactless approach. It will not generate any unnecessary stress when measuring it since the patients are not required to wear any equipment during the stress measurement. This research study used speech features as the input data to classify stress into three levels (low stress, medium stress, and high stress). The use of three levels of stress classes assists health practitioners in properly producing a suitable remedy for patients. Mel-frequency cepstral coefficients (MFCCs) and Teager Energy Operator-MFCCs (TEO-MFCCs), which are made by combining MFCCs and TEO, are compared in this study. The classification is done using convolutional neural networks (CNN), a deep learning approach. The dataset was taken from an experimental study that was conducted on 50 students in tertiary education. This study’s combination of MFCCs and CNN has produced exceptional performance metrics with an overall accuracy of 95.67%, which beat the previous research that used an unscripted dataset (an experimental study) with 81.86%. Furthermore, our study demonstrated the highest performance outcomes, achieving 99.58% accuracy when utilizing the same proposed system in a scripted dataset. The contribution of this study includes: discovering suitable speech features to detect the presence of stress in speech; designing deep learning algorithms that offer higher accuracy in stress predictions; designing the stress experiment that can elicit the participants’ stress; and lastly, the unscripted dataset consisting of the speech produced by the participants during the stress experiment. Finally, it contributes to the current research field, and in further development, it will be an early stress detection tool using voice that will assist responsible parties in taking prompt action to provide further care to patients.

Porous NiTi alloy has been developed especially in the biomedical sector due to its shape memory ability and mechanical properties which is similar to human bones. However, NiTi alloy carries the risk of Ni ions leaching when implanted in the human body making it less favorable for biomedical use for long term. Therefore, this project aimed to passivate porous NiTi by oxidizing its surface through annealing treatment. The porous NiTi alloy was initially fabricated using the powder metallurgy method, incorporating CaH₂ as a pore-forming agent in varying amounts from 0.25 to 15.00 wt%. Meanwhile, the annealing process was conducted at temperatures ranging from 300°C to 700°C for 15 minutes to achieve surface oxidation. Here, the morphology, porosity level, phase identification, transformation behavior, and mechanical properties were evaluated using various characterization techniques such as densitometer, scanning electron microscopy (SEM), X-ray diffraction (XRD), differential scanning calorimetry (DSC), and universal testing machine (UTM) in compression mode, respectively. The results reveal that when the percentage of CaH2 is increased to 15wt.%, the porosity of sintered sample can reach up to 42%. The shape of the pores become irregular and partially interconnected. Higher percentage of CaH2 tend to create more interconnected pore structure that beneficial for the transport of bodily fluids and bone healing process. In terms of phase identification, during phase formation, the undesired phases like NiTi2, Ni3Ti and Ni-rich phases co-exist with NiTi. For the transformation behavior, martensitic transformation peaks were observed for samples with ≤3wt% CaH2 with an enthalpy value of approximately 4 J/g. However, when the concentration of the CaH2 increased to ≥6 wt. %, the enthalpy change (ΔH) values decrease significantly, and the samples no longer exhibit martensitic transformation. This is attributed to the pores structure and the presence of undesirable phases alongside NiTi formation, both of which hinder the transformation enthalpy for porous NiTi. For mechanical properties, as the CaH2 content increases to 15 wt. %, both the strength and stiffness decrease substantially, dropping from 78 MPa to 4 MPa and from 3 GPa to 0.31 GPa, respectively. Notably, a stiffness of 2 GPa was recorded with 3 wt. % of CaH2, which is comparable to the stiffness of cancellous human bones (typically in the range of 2-4 GPa). This similarity minimizes the stress shielding effect. Consequently, porous NiTi alloy, with its lower stiffness and unique shape memory properties, emerges as an excellent choice for surgical implants. Based on these findings, the sample with 3 wt. % of CaH2 was identified as the optimal sample. The subsequent phase of the study focused on surface passivation via thermal oxidation using annealing treatment. The sample was annealed at various temperatures ranging from 300°C to 700°C. It is evident that higher annealing temperatures promote the formation of thicker oxide layers due to thermodynamic principles. The evaluation of biocompatibility revealed that raising the annealing temperature to 500°C significantly lowered the measurement of Ni ion release. However, further temperature increments led to the development of an unstable and brittle oxide layer, resulting in an increased release of Ni ions. Overall, the development of a porous NiTi alloy with a passivated surface through thermal oxidation treatment has exhibited promising outcomes for applications in the field of biomedical.

The community reporting system is a system that allows the user to report any misconduct to the relevant authority, easily and hustle free. In this thesis, the focus is on a road violation reporting system android app. In Malaysia, there are two types of reporting system, a manual reporting system and a website-based reporting system. These systems are fully authorized by the government body called JP J ( Jabatan Pengangkutan Jalanraya) or RTD (Road Transport Department). The problem with the existing manual system is time-consuming and to many platforms, thus, the report database will not be organized and well-managed. The system makes the users unwilling to make reports to the authority due to any road violation that they have seen. The objective of this project is to design a road violation reporting system using an android system. It is cost-effective, easy to use and more portable than the current reporting system. Also, to provide more services and implement the reporting system app in real time. The scope of this project is on application layer based on the design and the implementation of the reporting system. As the outcome, this community reporting app provides a user with a secure reporting system app and also the authority can manage the report quickly. The reporting app also provides more services than the current system and able to work on real-time. The development of the system is expected to enhance the reporting system and betterment for the community as well as the authority as a whole.