Browsing by Type "Master Theses"

The goal of this project is to develop an object detection and classification system for road crossing areas as part of a monitoring system using 4D radar imaging with a deep-learning neural network approach. In this work, we utilised deep neural networks powered by Keras and Tensorflow to detect and classify multiple pedestrians, cars, buses, and trucks. This paper presents Retina-4F, which is a multi-chip radar imaging system with high range resolution for object detection and localization. Retina-4F, which was developed by Smart Radar System, allows the system to provide 4D real-time information about the target. Retina-4F utilises a multi-chip cascade onboard with three transmitters and four receivers in each chip. We demonstrated two road-crossing scenes to collect data for creating a point cloud dataset with a target class label to be used for training and testing a deep learning model. There are two main sensors implemented in this work: Retina-4F as a 4D radar imaging and a mono-camera. The data from both sensors is pre-processed using DBSCAN and YOLOv7. Retina-4F operates at 77 GHz, and the test was conducted in two different road areas. After conducting data measurement at two road crossing areas, the collected data is passed for preprocessing and data fusion processing. This results in a complete point cloud dataset with approximately 10,000 frames of point cloud images that can be used for neural network training and testing. The model evaluation showed satisfying performance of the deep neural network in classifying multiple targets with 97 percent of overall accuracy. The approach of sensor data fusion for multiple target classification shows good results where it manages to distinguish different types of targets: cars, pedestrians, buses, and trucks. The proposed radar point cloud classification using sensor fusion can be applied to a wide variety of complex monitoring applications.

This study assesses the life-cycle environmental impacts of magnesium (Mg) and aluminum (Alu) in automotive transmission systems, with emphasis on energy consumption, greenhouse gas (GHG) emissions, and trade-offs between production and use phases. The study employs Life Cycle Assessment (LCA) based on ISO 14040/14044 standards and iterative analysis through the GREET model covering material extraction, production processes, use phase and end-of-life processes. Results show that due to its lightweight property, Mg provides a significant benefit during the use phase, with total energy consumption for Mg transmissions being 16.6–17.4% lower than for Alu systems. The emissions in the use phase of Mg are also lower, with a decrease in CO₂ emissions of 23.6%, compensating for its higher production-phase emissions. Mg shows better performances in some emissions categories, such as a 74% reduction in SOₓ emissions compared to Alu. While the GHG emissions during production of Mg are higher, advancements in Mg manufacturing technologies such as cleaner electrolytic processes and alternative cover gases may reduce the environmental impact in the future. The results highlight Mg's potential to replace Alu as the primary material in automotive transmission systems, especially as production advances continue to improve. Based on Mg's higher use-phase performance and the continuous development of Mg technologies, the results of the study confirm that, compared to Alu, Mg can be considered as a promising and sustainable alternative, which may lead to Mg potential becoming one of the primary materials across multiple sectors within the next decade.

Muscle spasticity is a condition that occurs in patients with neurological disorders when their muscles are stiff, tight, and resistant to stretching. The current assessment method, relying on the subjective judgment of therapists using the Modified Ashworth Scale (MAS), introduces variability that may affect the rehabilitation process. In addition, many existing computational models are not aligned with clinical standards such as MAS, limiting their practical adoption in clinical settings. They also often overlook direction-specific movement phases and fail to distinguish muscle responses across different axes and muscle groups. To address the limitations, this research aimed to develop a quantitative assessment method by muscle spasticity characteristics based on mechanomyography (MMG) signals and MAS levels, utilising machine learning techniques. A Quantitative Spasticity Assessment Technology (QSAT) platform has been developed which consists of two sensors that were tri-axial accelerometer mechanomyography (ACC-MMG) functioning in measure acceleration of biceps and triceps muscle contraction and potentiometer to measure the angular position of forearm during flexion and extension movement. A comprehensive investigation was conducted to assess muscle spasticity level by recording ACC-MMG signals from patients' forearm musculature during flexion and extension movements using QSAT platform. A total of 30 patients with neurological disorders were classified into five MAS levels (0, 1, 1+, 2, and 3), along with 10 healthy subjects serving as a baseline group. The pre-processed data comprised 48 extracted features from ACC-MMG signals along the x, y, and z axes for both flexion and extension movements of the biceps and triceps. These features corresponded to the longitudinal, lateral, and transverse muscle orientations. For both flexion and extension movements, machine learning models were trained using the selected subset of 25 significant features and the full set of 48 features respectively, with performance comparisons made to identify the most effective approach. Various machine learning models algorithms, including Linear Discriminant Analysis (LDA), Decision Tree (DT), Support Vector Machine (SVM), and K-Nearest Neighbour (KNN), were tested. The KNN-based classifier demonstrated the highest performance using a 90% training and 10% testing data split surpassing the performance of other classifiers. Specifically, at k = 15 using Euclidean distance, the KNN achieved an accuracy of 91.29% for flexion using the significant features, with corresponding precision, recall, and F1-score of 91.64%, 91.25%, and 91.47%, respectively. For extension, the same configuration resulted with 96.30% for extension using the full feature set, with precision, recall, and F1-score of 96.53%, 96.30%, and 96.33%, respectively. These results indicate high classification performance, with minimal false positives or false negatives, particularly in distinguishing between different MAS levels. This research suggests that the muscle characteristic model embedded in the QSAT can serve as a standardised and objective assessment tool for measuring the spasticity level of the affected limb using computational method, leading to support clinical evaluations and enabling more effective rehabilitation strategies.

Solar energy has become an increasingly important component in the global shift toward renewable energy. However, conventional solar energy systems that depend only on chemical batteries often face limitations such as high cost, energy degradation, and short operational lifespan, particularly in elevation-based applications. The objective was to design and optimize a hybrid energy storage system by integrating solar PV, battery, and gravity-based storage, and to evaluate its performance under real operating conditions. The goal was to optimize the design using a 50-Watt Solar PV, an 18Ah SLA Battery, and a Water Gravity Energy Storage Tank. The method included three steps and used five data loggers: a flow meter, a pyranometer, and three-watt meters. First, a fully charged SLA Battery was tested at various tank heights (from 1.5m to 3.5m) every 15 minutes. Then, the 50-Watt Solar PV was tested directly at a height of 3 meters. Lastly, the Solar Hybrid Gravity System with Battery Energy Storage was monitored over seven days at a 3-meter height. Energy use was measured through the SLA Battery, Solar PV, and a 22-Watt Water Pump at different tank heights to see improvements in efficiency and battery life. The results showed a 600% improvement in battery performance at 80% Depth of Discharge (DOD), proving the battery's potential as a reliable backup power source and extending its lifespan. The SLA Battery had a 22.1% energy loss during charging and discharging at 5% DOD, while the 22-Watt Water Pump achieved a flow rate of 11.0 L/min at peak solar irradiance of 900 W/m², with a maximum motor power of 24.32 Watts. A minimum of 300 W/m² solar irradiance was needed for the pump to run efficiently. In conclusion, the energy efficiency of the solar hybrid gravity system was optimized, reducing reliance on the battery and extending its lifespan, making it a sustainable solution for elevation applications. This system can be applied in water pumping, agricultural irrigation, and elevator systems in off-grid or rural areas, offering a cost-effective and environmentally friendly energy storage alternative.

The demand for collagen from marine sources has risen significantly due to recent advancements in biomedical, pharmaceutical and cosmetic applications. Collagen derived from mammalian or synthetic sources poses several limitations, including potential allergic reactions, risks associated with prion diseases, and restricted acceptance due to halal status and religious prohibitions in Islam and Hindu communities. As such, marine-based alternatives offer a promising solution. Acid-soluble collagen (ASC) is typically extracted by using mild acidic solutions, such as acetic acid, followed by salting-out, dialysis and lyophilization. It can be obtained from various fish parts, including skin, bones, scales, viscera, head, trimmings and roe. In this study, collagen was extracted from the skin of red tilapia fish (Oreochromis niloticus) to produce ASC. The aim of this research was to extract ASC, characterise its physicochemical properties, and evaluate its wound healing potential. The ASC was extracted from red tilapia skin, and its yield was determined. Characterisation was conducted using Fourier Transform Infrared Spectroscopy FTIR spectra and Sodium Dodecyl Sulfate-polyacrylamide Gel Electrophoresis (SDS-PAGE). The wound healing activity was assessed through an in vitro scratch assay. The ASC yield was found to be 53.4% on a dry weight basis. FTIR analysis confirmed the presence of type I collagen, indicated by a characteristic triple helical structure comprising Amide A, I, II, and III. SDS-PAGE analysis revealed the presence of δ, β, α1, and α2 chains, also confirming type I collagen, with respective molecular weights of 170 kDa, 127 kDa, 120 kDa, and 90 kDa. The in vitro scratch assay demonstrated that ASC significantly enhanced wound closure compared to the control group, with all concentrations tested (25, 50, 100, and 200 μg/mL) showing marked improvement after 72 hours. Therefore, ASC extracted from red tilapia fish skin exhibits excellent potential as an alternative source of collagen for wound healing applications.

Rapid urbanisation in Kedah has led to an increase in eating out, raising concerns about dietary behaviours among families. Despite the health implications, little is known about how Malay parents adhere to healthy eating when eating out. This study aimed to explore Malay parents’ perceptions, practices, and perceived barriers to healthy eating when eating out, mapped under the lens of Social Cognitive Theory (SCT). An interpretative qualitative research design was adopted, using purposive sampling to recruit 37 Malay parents (27 females and 10 males) from Sungai Petani, Kedah. Data collection was held between November 2023 and March 2024. In-depth face-to-face interviews were conducted and the qualitative data analysed thematically. Aligned with SCT, the parents’ narratives highlighted personal, behavioural, and environmental influences on parental perception, practices, and barriers to healthy eating. Personal factors encompassed Malay cultural and social dimensions of food choices, perceived health benefits, and struggling with cravings and social influences. Behavioural factors were reflected in the parents’ nutritional preferences and mindful consumption practices. Environmental factors included sociocultural dynamics, restaurant quality and standards, and the practicality of health guidelines. Many parents demonstrated a positive understanding of healthy eating and actively practised it when eating out. However, parents faced difficulties navigating modern food environments dominated by “viral” and visually appealing foods. These findings highlight the reciprocal interaction of personal, behavioural, and environmental influences on food choices when eating out. This study offers culturally grounded insights for the field of health psychology, informing tailored nutrition education, behavioural interventions, and health promotion strategies. Such efforts can support interventions that consider the psychological, social, and environmental realities of eating out, enabling healthier decision-making among parents in diverse food settings.

Infections associated with carbapenem-resistant Enterobacterales (CRE) represent a significant public health concern due to their resistance to carbapenems and other commonly prescribed antibiotics. This study focused on assessing the in vitro susceptibility of CRE isolates to colistin, tigecycline and ceftazidime-avibactam (CZA), thus offering valuable insights into resistance mechanisms and potential treatment strategies. From Sultan Ahmad Shah Medical Center@IIUM, 19 clinical CRE isolates were analysed, primarily obtained from adult patients with an approximately equal gender distribution. Identification of the isolates was performed using MALDI -TOF MS, while carbapenemase production was determined using the mCIM method. Antimicrobial susceptibility testing (AST) was conducted using the VITEK 2 system and E-test, with resistance rates assessed following CLSI guidelines. Key risk factors for CRE infection included underlying diseases, recent hospitalizations and the presence of indwelling medical devices. Overall, carbapenemase-producing (CP) CRE demonstrated higher resistance rates compared to carbapenems and CZA compared to non-CP variants. Tigecycline however, remained effective against CP CRE, with low resistance observed among non-CP CRE. Meanwhile, uniform resistance rate was observed in both CP and non-CP isolates to colistin and other β-lactam antibiotics tested. Resistance rates varied significantly across species, emphasizing the complexity of CRE infections. This study highlights the urgent need for robust antimicrobial stewardship programs and routine carbapenemase detection to guide effective therapy. The high prevalence of CP CRE and the observed variability in species-specif ic resistance patterns underscore the importance of tailored treatment strategies to manage and mitigate the threat of CRE infections at SASMEC@IIUM.

This study aims to investigate the relationship between personality traits, test anxiety, and psychological well-being among undergraduate students at the Kulliyyah of Education (KOED), International Islamic University Malaysia (IIUM). The study has three main objectives: (1) To identify the five personality traits based on Lewis Goldberg's OCEAN theory among KOED undergraduate students, (2) To determine the levels of test anxiety among KOED undergraduate students, and (3) To examine how the relationship between personality traits and test anxiety impacts the psychological well-being of undergraduates. This research employed a quantitative method, collecting data from 275 undergraduate students at KOED, IIUM, using a random sampling technique. The data were analysed using descriptive statistics, correlation, multiple regression, independent sample t-tests, and ANOVA to address the research objectives and questions. The main findings revealed that neuroticism was strongly negatively correlated with test anxiety, while conscientiousness and agreeableness positively influenced psychological well-being. Additionally, test anxiety was found to significantly predict lower well-being. The study recommends that students actively engage in self-regulation, employ effective strategies to manage test anxiety and utilise their personality strengths to enhance their psychological well-being. Keywords: Personality traits, Test anxiety, psychological well-being, and Undergraduate students.

Smart valve automation systems are increasingly used in modern water networks to achieve precise, real-time flowrate control. However, commonly used valves such as butterfly and gate valves are not inherently designed for accurate throttling. Their operational limitations can lead to unstable flow and pressure fluctuations when integrated into automated systems. Additionally, the performance of such systems heavily relies on the accuracy of flowmeters, which can vary significantly based on the technology used. This raises the need to investigate how different valve and flowmeter combinations affect overall control performance. This study addresses these challenges by evaluating the performance of six valve–flowmeter pairings in a flowrate-based automation setup. Two valve types (butterfly and gate) are tested in combination with three flowmeter types (electromagnetic, ultrasonic clamp-on, and electrode/insertion type). A custom-built simulation model was developed in MATLAB to replicate a closed-loop control system where flowmeter feedback adjusts valve opening via a PID controller. The performance of each combination is assessed under ideal conditions using metrics such as overshoot, steady-state error, settling time, and valve opening percentage. The results show that the butterfly valve paired with an electromagnetic flowmeter delivers the most responsive and accurate control performance, achieving low overshoot (3.5%) and quick settling time (4.2 seconds). Meanwhile, the gate valve with an electromagnetic flowmeter produces the lowest steady-state error (0.02%), making it suitable for systems with more stable flow demands. This study concludes that valve and flowmeter selection significantly influences automation performance. The findings provide a useful reference for engineers and designers to make informed decisions when implementing smart valve automation systems, particularly in selecting components that suit their design goals and site-specific limitations.

Kelulut honey, a highly valued product in Malaysia’s agricultural sector, is prized for its unique nutritional properties and increasing consumer demand. However, its high moisture content makes it susceptible to fermentation and microbial growth if not stored and processed correctly. Local Small and Medium Enterprises (SMEs) face challenges in managing these risks, particularly often lack of the dehumidifier equipment needed to meet the Malaysian Standard MS2683:2017. This gap impacts the quality, taste, texture, and shelf life of kelulut honey. The financial constraints faced by many SMEs, particularly in procuring proper dehumidification and storage equipment, further exacerbate these challenges, especially since kelulut honey production tends to be lower during the final quarter of the year due to seasonal weather patterns, such as frequent rain. As a result, the long-term sustainability of Malaysia’s kelulut honey industry is at risk. This study addresses a critical gap in research related to the challenges SMEs faced in terms of the supply chain constraints, which impact production efficiency, product quality, and market competitiveness. Through a mixed-method approach, including in-depth interviews, physical site observations, focus group discussions (FGDs), and literature analysis, this research aims to provide a comprehensive understanding of the issues faced by local SMEs and compare these challenges with those encountered by their counterparts in other countries. Three primary challenges were identified: inadequate storage conditions, processing limitations, and inefficient inventory management. Many SMEs are unable to maintain proper storage conditions, which leads to premature spoilage and fermentation,reducing honey’s commercial viability. The inventory management system is largely manual, with no effective tracking mechanisms, leading to difficulties in maintaining stock and product movement. To mitigate these issues, this study proposes a cost-effective, easy to-implement strategy by adopting the First-In, First-Out (FIFO) inventory management approach. FIFO ensures older stock is used or sold first, minimizing spoilage and preserving product freshness. The FIFO method not only improves storage layout and addresses processing limitations but also enhances inventory management, reducing spoilage and ensuring consistent product quality. Importantly, the solution does not require monetary investment, making it accessible for SMEs operating under financial constraints. Rather than costly equipment or infrastructure, FIFO leverages existing resources and operational adjustments that can be implemented without additional financial burden. Furthermore, this research highlights the importance of aligning FIFO practices with Sustainable Development Goals (SDG) and Environmental, Social, and Governance (ESG) principles. By doing so, SMEs can improve operational efficiency, enhance product quality, and increase market competitiveness while promoting responsible business practices. Incorporating SDG and ESG frameworks also contributes to the long-term resilience of the kelulut honey industry, ensuring that it remains sustainable in the face of both local and global challenges. In conclusion, the study provides practical recommendations for SMEs to overcome their supply chain challenges. By adopting cost-effective strategies like FIFO, which require no monetary investment, and aligning with SDG and ESG principles, SMEs can improve their operational efficiency, reduce waste, enhance product quality, and contribute to the sustainable growth of Malaysia’s kelulut honey industry.

In recent years, global inflationary pressures have intensified, and the high costs of raw materials, energy, and logistics for production have made the automotive industry face severe challenges in both the supply chain and market demand. Against this backdrop, it is crucial for industry practitioners and policymakers to quantitatively assess the impact of inflation on the automotive industry and find strategies to deal with it. Hence, this study examines the impact of inflation on the U.S. automotive industry, focusing on the short-run and long-run impact of the dynamics of inflation and other macroeconomic variables such as the Producer Price Index (PPI), energy prices, and interest rates on the U.S. automotive industry. In examining the objectives, this study employs the Autoregressive Distributed Lag Model (ARDL) with quarterly data from the third quarter of 2004 to the third quarter of 2024. The findings of the study reveal that inflation has a significant negative impact on the U.S. automotive industry in the long run, indicating that when the aggregate price level continues to rise, consumers' disposable income and consumer confidence weaken, which in turn suppresses the demand for auto purchases. However, the study finds that inflation does not significantly impact the industry in the short run. In addition, changes in energy prices and interest rates also have different degrees of impact on car sales in different periods: in the short term, fuel price fluctuations can magnify the cost of car purchases or prompt consumers to switch to more energy-efficient models; while interest rate increases tend to increase financing costs, but to a certain extent, they also go hand in hand with economic expansion and job growth, which may have a double effect on car demand. Keywords: Inflation, U.S. Automotive Industry, ARDL, Cointegration

In February 1929, Benito Mussolini’s Fascist government and the Vatican signed a pact, officiating the reconciliation of Italy and the papacy. Known as the Lateran Accords, previous works have emphasised the pact’s role in enhancing Mussolini’s political image and restoring the sovereignty of the papacy; however, discussions on the overall relationship have been mostly overlooked. This research applies a dual framework of the church-state relations and instrumentalism to delve deeper into the partnership between the Italian Fascist government and the Vatican. Through the analysis of several archival resources such as autobiographies and encyclicals, it becomes apparent that both parties aimed to strengthen their authority over their respective domains, a goal they believed could only be achieved with each other’s support. This is further displayed through a survey of policies and reforms created and reinstated by either the Vatican or the Fascist government. Its biggest implication being on the Protestants and the Jews, which both sides aimed to marginalise; yet found themselves in a bigger conflict due to concurrent social tensions, such as the unresolved Fascist violence against Catholic institutions and the dispute over youth associations, as well as a dissimilar moral perspective on the treatment of these religious groups. Secondary sources were also utilised in this research. Hence, this research contributes new insights into the relationship’s centrality in the Italian political atmosphere and the treatment of religious minorities in Italy.

The recent developments in networking have led to the generation of massive volumes of network traffic, creating many obstacles in network traffic management. The traditional deep packet inspection (DPI) is used to inspect, identify, classify and manage Internet Protocol (IP) based network packets from massive volume of traffic flow within the connected network. However, with the integration of DPI at various inspection points at the network edges, it creates certain challenges for the connected networks. Also, there are security challenges posed with conventional deep packet inspection related to the identification of tampered, manipulated and exploited IP network packets within the network transmission classified for critical data having higher priority access using differentiated service code point (DSCP). The purpose of this research is to design and develop a blockchain-secured deep packet inspection system to secure the DSCP tagging and examine the impact of blockchain on DPI system as well as on real-time operations. Moreover, the study explores the use of deep learning models due to its efficient learning hierarchical representation of features through series of layers as well as with memory for its ability to extract features through time series data. Also, the use of blockchain in the spectrum of DPI to enhance the performance and security of network. The study has utilized four deep learning neural classifiers including Autoencoders, Convolutional Neural Network, Long-Short Term Memory and Recurrent Neural Network having accuracies of 92.03%, 96.88%, 99.64% and 99.72% respectively. It was discovered the RNN performed well around 99.72% than the rest of the adopted classifiers. There is an ensemble approach used based on soft-voting mechanism with an accuracy of 99.28% having all the features of deployed models into one single model. Additionally, the ethereum-based blockchain is used to develop and deploy blockchain into DPI ecosystems, storing critical IP packets information moving forward from the classification phase of the neural network. The IP packets data was also stored in NoSQL database systems for future analysis on different use-cases. The objective of this study contributes to the analysis of the security issues related to DSCP faced by DPI with the growing volume of network data.

Islamic legal maxims (al-qawa'id al-fiqhiyyah) constitute a crucial foundation in Islamic jurisprudence. They emerged as an independent discipline in the fourth century AH and have evolved through authorship and research until our present time. This study aims to identify the legal maxims related to combating and preventing infectious diseases, while clarifying the nature of these diseases and explaining how Islamic law addresses them. The study also seeks to examine the applications of these maxims in fatwas concerning infectious disease control in Malaysia. The researcher employed both inductive and analytical methodologies, given the multiplicity of legal maxims related to infectious disease control and the numerous fatwas issued by the Malaysian National Fatwa Council and the Federal States' Fatwa Councils on this matter. The researcher selected a curated collection of these fatwas for study and analysis. The study identified the fundamental legal maxims relevant to combating infectious diseases and their implementation mechanisms in specialized fatwas. Among the most prominent of these maxims is "There should be neither harm nor reciprocating harm" (lā ḍarar wa-la ḍirar), which has been applied in various contexts, including the suspension of activities in mosques and prayer halls during the COVID-19 pandemic and in fatwas concerning vaccination rulings. Another significant maxim is "The ruler's decisions regarding his subjects are governed by public interest" (tasarruf al-imam ʿala al-raʿiyyah manuṭ bil-maṣlaḥah), which has been applied in cases such as the fatwa on embalming bodies of those who died from infectious diseases for international transport purposes. The study concluded with a comprehensive presentation of the legal maxims and their applications in fatwas issued by the Malaysian National Fatwa Council and the Federal States' Fatwa Councils, highlighting the importance of these maxims in addressing contemporary issues related to infectious diseases

This research aims to study three main issues: First, to define the concept of the variation of expressions, its purpose, and outline its types. Second, to clarify the concept of contemplation (Tafakkur), review its examples, significance, fields, and objectives. Third, to analyse the areas of the variation of expressions in the verses of contemplation and highlight its impact on educating oneself. The significance of the study is found in reviving the term variation in the Quran and highlighting its eloquence, which opens new horizons for contemplating the Quran and understanding its meanings more deeply. Thus, it positively affects individuals, strengthens social cohesion, and deepens the connection with the Quran. This study was conducted using two methodologies: the inductive and analytical methods. The inductive method involved gathering materials related to the variation of expressions and collecting texts that deal with the concept of contemplation in the Quran. The analytical method was employed to study the collected information regarding the variation of expressions in the contemplation verses, analyse the variation aspects of these verses, and identify outcomes that help Muslims in self-education. The research reached several conclusions, including the understanding of variation in the Quran, which encompasses clarification, explanation, repetition, variety, and diversification. Its objectives include enhancing understanding, remembrance, devotion, and repentance. 2. The importance of contemplation lies in God’s honouring of humankind above all other creations, where contemplation is considered the light of faith and one of the best deeds. It is the cornerstone of building and advancing civilisation. 3. Understanding the various elements in the contemplation verses helps nurture preachers following a practical method for calling others to God. Through comprehending these elements in the Quran, multiple strategies can be developed to enhance the effectiveness of da’wah (inviting others to Islam). The study concluded with several recommendations.

This study aims to study and authenticate the Marasil – Cast of Isnad – of ‘Abdul Razaq’s book “Al-Musannaf” a book categorized topically by collecting all Marasil in both The Book of Divorce and The Book of Sales. The researcher used the inductive method to collect other scholars’ statements about the personality of Imam Abdur Razaq and his scientism rank, and to examine and discuss the scholars’ doctrine about Hadith Mursal and its authority, searching for the roots of the Marasil. She also used the critique method to find all appearances of the Marasil (Takhrij), then identifying its grade through its chain of narrators (Sanad) and its roots. The reseacher started her research by finding out about Imam Abdur Razaq, his study, his teachers and students, his rank and the scientific recommendations made about him, also criticisms about him. The next step was to examine his book Al-Musannaf, its circumstances, its history, its method, and its rank. Hadith Mursal, its source, the motives of the narrators, its authority and the multiways Hadith was used to strengthen Hadith Mursal were also assessed. Next was the applied study was used to find all appearances of the collected Marasil by mentioning their roots that are incompletely transmitted and connected, also their testimonies through The Books of Hadith. The scholars’ judging of its hadith, if any, or the narrators’ were also studied. The Marasil, after studying its narrators and its roots, was closely reviewed. At the end, the reseacher concludes that some Marasil have been upgraded “due to external factors” (hasan li ghairih), while others have not been upgraded, rather they remain weak (da’if) due to their incomplete and singular conditions. As a result, the researcher found 51 hadiths, of which 32 hadiths have been upgraded to Hasan li Ghairihi while 19 hadiths remain weak.