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Engine performance and emission of biodiesel from waste cooking oil using jackfruit peel waste derived catalyst / Andi Mulkan
No full textNowadays, the amount of waste cooking oil (WCO) continues to increase. Unfortunately, much of this waste is improperly disposed of, harming the environment. Utilizing WCO as biodiesel feedstock offers a solution to reduce the impact of this waste on the environment, creating a more cost-effective and environmentally friendly fuel. Moreover, the catalyst plays a crucial role in facilitating the biodiesel production reaction. In order to decrease the costs associated with catalysts in biodiesel production, one potential approach is to utilize heterogeneous or solid catalysts derived from easily accessible waste biomass products, such as fruit peel waste. By utilizing waste-derived biomass materials for catalyst development, the process becomes more cost-effective and sustainable. The utilization of fruit peel wastes for catalyst development has gained popularity in recent times due to the abundance of waste resources and the desire to mitigate disposal challenges. In the present study, jackfruit (Artocarpus heterophyllus) peel waste (JPW) has been chosen and developed as a heterogeneous catalyst for biodiesel synthesis. The prepared catalyst has been characterized and it is showed the presence of significant components such as potassium (K), calcium (Ca), and magnesium (Mg) in the catalyst, which play a significant role in the synthesis of biodiesel. Furthermore, response surface methodology (RSM) with a centered composite design type has been applied to examine the best conditions of the transesterification process. The optimization process shows the optimum conditions were achieved for biodiesel synthesis with an oil-tomethanol molar ratio of 1:9, a catalyst weight of 12 % (w/w), a reaction time of 105 minutes, and a constant reaction temperature of 65 °C, yielding a methyl ester content of 98.88%. Additionally, reusability studies were conducted to assess the stability of the prepared catalysts. The results indicated that the JPW catalyst can be utilized for up to three cycles, with the highest yield observed at 93.33%. Moreover, the fuel properties of the waste cooking oil (WCO) biodiesel were investigated, and it was found that the physicochemical properties of the WCO biodiesel comply with the requirements outlined in ASTM D 6751. Moreover, an experimental study of tested fuels using a single-cylinder direct injection diesel engine has been conducted, to evaluate engine performance and exhaust emissions. The BSFC demonstrated an average decrease of 16.67-22.69% with the rise in engine speed. Conversely, BTE exhibited an average increase of 16.67% as engine speed increased. Moreover, engine torque experienced a slight decrease initially, followed by a significant decrease at high speeds. In contrast, BP exhibited a proportional increase with the rise in engine speed. Meanwhile, it was observed that CO emissions exhibited a significant reduction, averaging 6.11-48.63% across all speeds, when compared to pure diesel. Conversely, CO2 and NO emissions showed an overall increase, although some reductions were noted for certain fuel samples. Additionally, as engine speed increased, a downward trend in HC emissions was observed. While an increase in smoke opacity was generally observed in this study, a slight decrease was noted for certain fuel samples within the engine speed range of 1800 to 2400 rpm
Production and perception of zero and nasal initials in Malaysian Cantonese / Chan Huey Jien
No full textCantonese zero initial /Ø/ and nasal initial /ŋ/ are allophones in complementary distribution since the Middle Chinese period. However, the phonetic variation of these two initials has been found in contemporary Cantonese in China. The nasal initial /ŋ/ has been frequently replaced by the zero consonant /Ø/. This is considered a “lazy pronunciation” that commonly occurs among the younger generation and is presumed to be influenced by Mandarin, the national language of China. However, previous research pointed out that this “lazy pronunciation” does not seem to occur in Cantonese in Southeast Asia. In light of this, it is worth examining the development of these two initials in Malaysia, a country in Southeast Asia that is well known for its multilingualism. The speech production and perception of Cantonese speakers towards these two initials were investigated to achieve the aim of this study. A total of 20 Cantonese speakers participated in the production task, and 40 Cantonese speakers participated in the perception task. 20 of them participated in both the production task and the perception task. All of them are third-generation and onward Malaysian Chinese from the central region, aged between 20 and 31 years. A list of disyllabic words was used to collect production and perception data. The production of these two initials is analysed using phonetic analysis; the perception of the listeners towards these two initials is investigated using a discrimination task; and the effects of linguistic and nonlinguistic factors on production and perception are investigated using the variable rule analysis method. This study reveals that these two Cantonese initials may not be in complementary distribution but rather in free variation in the central region of Malaysia. An opposite situation to Cantonese in China emerged in the central region of Malaysia. The nasal consonant [ŋ] is the phoneme dominating among these two initials in the central region of Malaysia. Furthermore, the findings in both production and perception suggest that linguistic factors show a more significant effect on phonetic variation compared to non-linguistic factors. The vowel type is the most significant factor constraining the phonetic variation of these two initials
Intelligent tool wear condition prediction for CNC milling with modular neural network / Wong Shi Yuen
No full textCutting tool wear in Computer Numerical Control (CNC) milling is a gradual process which significantly affects product quality. Left unmonitored, risks of tool breakage would increase, leading to losses due to scrap and equipment damage. A Modular Neural Network (MNN), the Dissociation Artificial Neural Network (Dis-ANN), was proposed in this work for tool wear prediction. The Dis-ANN consists of a modular structure constructed out of parallel Artificial Neural Network (ANN) modules (referred to collectively as the Dissociation Unit), connected to an intermediary. The output of each ANN module is dependent on input feature vectors formed from the concatenation of both previous and current feature values, allowing each module to account for feature trends in a limited fashion. Three choices of complementary Dis-ANN optimization methods were also introduced – Selective Brute Force (SBF), Partial Correlation Evaluation (PCE), and Feature Selection with Partial Correlation Evaluation (FS+PCE). In addition to minimizing network redundancy in Dis-ANN, these optimization methods aim to optimize the number of time-steps between previous and current feature values in input feature vectors. The performance of Dis-ANN combined with each optimization method was investigated using the Universiti Malaya-Slot Milling Dataset (UM-SM Dataset), 2010 Prognostics and Health Management (PHM) Data Challenge Dataset, and NASA Ames Milling Dataset. The UM-SM Dataset contains data in the form of images of machined workpiece surfaces and acoustic signals during milling. In order to account for uneven lighting in each workpiece surface image, image features were extracted by processing texture descriptors based on the Grey-level Co-occurrence Matrix (GLCM) of different non-overlapping sections within the same image. For model validation using the 2010 PHM Data Challenge Dataset, Dis-ANN was tested using feature sequences extracted from the dataset under two conditions – with and without the addition of random noise in the feature sequences. Results showed Dis-ANN optimized with SBF or FS+PCE was better at learning complex non-linear relationships in tool wear trends compared with Linear Regression (LR), Support Vector Regression (SVR), and monolithic ANN. The Dis-ANN model optimized by FS+PCE had the possibility of achieving less accuracy than the Dis-ANN model optimized by SBF when the relationships in a dataset are highly complex or noisy. However, FS+PCE required much less computational time compared with SBF. Furthermore, FS+PCE optimized Dis-ANN to be more accurate when handling low-noise datasets. Dis-ANN optimized using PCE appeared to have low robustness to noisy datasets such as the UM-SM Dataset. In addition, further investigations on the performance of Dis-ANN optimized using FS+PCE with the UM-SM Dataset showed that features extracted from image data were beneficial for accurate tool Remaining Useful Life (RUL) predictions. This implied that images of machined surfaces, which can be considered a form of product quality data in industrial applications, have a certain level of utility in Tool Condition Monitoring (TCM), especially when used in conjunction with other sensor data. Moreover, results from model testing using the NASA Ames Milling Dataset showed that a properly optimized Dis-ANN had good generalization when handling machining data obtained from different experiments under different machining conditions
Legitimation strategies IN Donald Trump’s speeches on The Israeli-Palestinian conflict / Muath O. J. Seyam
No full textDonald Trump’s presidency was marked by controversial policies and speeches that distinguished him from his predecessors. One area where his rhetoric had a significant impact was US foreign policy towards the Israeli-Palestinian conflict (IPC). This study seeks to explore the linguistic strategies employed by Trump to influence this policy and how he legitimized it through his speeches. The study utilises discourse analysis approaches and Van Leeuwen’s (2007) legitimation in discourse and communication framework to analyze five of Trump’s political speeches. The NVivo 12 Pro software application was used to aid the analysis. By examining the discursive strategies used by Trump, the study aims to reveal how he legitimised his policies and created new realities in the IPC. The research is expected to shed light on the legitimation strategies used by Trump concerning the IPC and his representation of Israelis and Palestinians. This study is relevant as it contributes to a better understanding of how the US administration, represented by President Trump, dealt with the IPC and its players. It highlights the linguistic tactics that were employed to influence foreign policy, thereby providing insights that could inform future policymaking in this area. Through this study, it is anticipated that a clearer picture of the IPC will emerge, and the language used by political leaders will be recognised as a critical factor in shaping policy and public opinion. Moreover, the findings of the study could contribute to more nuanced discussions and debates on the IPC, as well as enhance the understanding of the complexity of the conflict and the challenges involved in resolving it. In conclusion, this study examines the linguistic strategies employed by President Trump in influencing US foreign policy towards the Israeli-Palestinian conflict. It provides insights into how Trump legitimised his policies and created new realities in the IPC through his speeches. The study contributes to a better understanding of the conflict and how political leaders use language to influence foreign policy, thus informing future policymaking in this area
Three-dimensional craniometrics identification model and cephalic index classification of Malaysian sub-adults: A multi-slice computed tomography study / Sharifah Nabilah Syed Mohd Hamdan
No full textHuman identification is the main goal in anthropological and forensic investigations such as examination of ancient skeletons, investigations at criminal related scenes, or due to mass disasters. The primary focus is to determine the biological profile of unknown individuals by estimating their sex and ethnicity. Sex and ethnicity estimation methods utilised in adult are less effective in sub-adults due to varied cranium patterns during growth. Therefore, this study aimed to develop three-dimensional (3D) craniometric models in Malaysian sub-adults for sex and ethnicity estimation, and to establish a cephalic index (CI) classification for Malaysian sub-adults. A total of 521 cranial multi-slice computed tomography (MSCT) dataset of sub-adult Malaysians aged 0 to 20 with Malay, Chinese, and Indian ethnicities were obtained. MIMICS software version 21.0 (Materialise, Leuven, Belgium) was used to construct 3D models and plane-to-plane (PTP) protocol was utilised to measure 14 selected craniometric parameters. Discriminant function analysis (DFA), binary logistic regression (BLR), and several machine learning (ML) algorithms (random forest (RF), support vector machines (SVM), and linear discriminant analysis (LDA)) were used to statistically analyse the data. Additionally, CI was calculated according to the following equation: cephalic width/cephalic length×100. This present study demonstrated a minimal degree of sexual dimorphism in the cranium of individuals below the age of six, and the level was then increased with age. All the age groups, except for 0–2 years and 3–6 years, exhibited reliable sex estimation with a high accuracy percentage (≥75%) when tested using DFA and BLR. As for the ethnicity estimation models, a high similarity of craniometric measurements between Chinese and Malays (as compared to Indians and Malays, and Chinese and Indians) was demonstrated. This resulted in the highest classification accuracy obtained by Indians, followed by Chinese and Malays in the age groups of 10–12 years and 16–20 years. Moreover, ML methods obtained slightly higher accuracy rates than classical methods for sex (RF: 73% vs BLR: 66.9% and DFA: 61.6%) and ethnicity estimation (LDA: 58% vs DFA: 57.5%) using sub-adults’ crania. In addition, the modified CI of Malaysian sub-adults were found to be as follows: dolichocephalic, 78.8 or less; mesocephalic, 78.9–89.0; brachycephalic, 89.1–94.0; and hyperbrachycephalic, 94.1 or higher. Hence, the proposed CI index indicated that the dominating type of head for Malaysian sub-adults was mesocephalic (66.4%), followed by dolichocephalic (18.4%), brachycephalic (12.3%), and hyperbrachycephalic (2.9%). The present study has demonstrated that sex and ethnicity estimation of sub-adults can be effectively performed by assessing the cranium via 3D virtual anthropometry. To the best of our knowledge, this was the preliminary study that described craniometric variations of multi-ethnic groups in Malaysian sub-adult population using MSCT data. Ultimately, this present study has bridged the gap of population-specific cranial data in Malaysian sub-adults
Knowledge, attitudes, practices and barriers of oral hygiene care among nurses caring for geriatric inpatients in Malaysia / Ghayathri Devi Manoharan
No full textBackground: The older adult population in Malaysia and around the world is increasing due to the advancement and improvement in health care delivery and awareness on healthcare needs of this population. An important area of need that is often neglected in older people is oral health, especially when they are frail and need hospitalisation. The hospital admission provides an excellent opportunity to address poor oral health in older people, a group rarely seen by dental professionals and for whom oral hygiene tasks performed in hospital is inconsistent and suboptimal. Nurses are in the best position to educate and provide daily oral hygiene care for these patients. Aim: To assess the level of knowledge, attitude, current practices, and barriers regarding oral hygiene care among nurses caring for older inpatients. Methods: This multi-centre study was conducted in ten hospitals with geriatric medicine units in Malaysia. Cross sectional study design and a purposive sampling were used to assess the knowledge, attitude, practices, and barriers (KAP+B) of oral hygiene care among nurses caring for older inpatients. This study was conducted using a newly adapted oral hygiene care questionnaire on the KAP+B of nurses caring for older inpatients, as the primary tool, which was distributed via the online platform (Google Forms). The adapted questionnaire comprised of five main areas: the respondent’s personal and work-related information (social demographic), assessment oral hygiene care knowledge, identification of current oral hygiene care practices, attitude to oral hygiene care, and the barriers that may impact their ability to conduct oral hygiene care for older inpatients. Results: A total of 141 nurses from the geriatric medicine wards of ten selected hospitals under the Ministry of Health Malaysia participated in the study giving an overall response rate of 94%. This study revealed that participants were at a good level of knowledge, had a good attitude level regarding oral hygiene care and exhibited high level of practices for oral hygiene care for older inpatients. Majority of the participants are in the low-level regarding barriers faced to provide oral hygiene care for older inpatients The results also suggest, there is a significant association between the level of highest qualification and the factors of knowledge, attitude, practices, and barriers during oral hygiene care for older inpatients among nurses. Conclusion: The findings of this study show that the nurses had a sound knowledge, good attitudes and practices and were able to identify the barriers faced in the provision of oral hygiene care for older inpatients under their care. It can also be concluded that knowledge, attitude, practice, and barriers among nurses providing oral hygiene care for older inpatients in the geriatric ward are not dependent on each other
Development of novel high damping rubber damper for dynamic energy dissipation under direct axial cyclic load / Teh Tzyy Wooi
No full textHigh Damping Rubber (HDR) has gained extensive utilization in structural bearings as seismic isolation devices. However, its application in dampers designed to effectively dissipate energy under direct axial loads, thereby mitigating structural responses induced by seismic excitations, remains unexplored. Presently, dampers that operate under direct axial conditions encompass cylinder-type viscous-fluid velocity-dependent devices and Viscoelastic (VE) dampers, commonly employed as isolators against seismic and wind-induced dynamics. Nevertheless, VE dampers exhibit reduced stiffness and energy dissipation capacity due to decreased storage and loss moduli triggered by cyclic loading temperatures. Conversely, viscous fluid dampers performance is significantly compromised upon liquid leakage, necessitating frequent maintenance and driving up overall life cycle costs. Integrating HDR dampers would overcome the limitations associated with existing dampers. Additionally, there is a pressing industry need for medium-sized dampers that provide improved durability and cost-effectiveness for small and medium-scale structures. This research aims to develop a novel damper utilizing Hyper Elastic Composite Material (HECM), an HDR material, and conduct experimental investigations to evaluate its damping ratio, compressibility, and elasticity behaviour under axial cyclic loads. The research project commences with a series of material tests on HECM employing the double shear method in EN 15129:2009 Clause 8.2.4.2.5 to identify the most suitable damping material for the damper under cyclic compression loading. The optimal HECM material is then employed to fabricate small scaled dampers with varying HECM thicknesses (6 mm, 8 mm, 10 mm) for examination under constant axial forces at different frequencies (0.01 Hz, 0.1 Hz, 0.25 Hz, 0.5 Hz). Results indicate the 10 mm-thick damper, with a damping ratio ranging from 10.05% to 13.7% across frequencies, demonstrating the remarkable potential of HECM for damper applications. Upon selecting the optimal damping material and thickness, a model response spectrum analysis (RSA) for building structures is conducted according to the National Annex (NA) to MS EN 1998-2017. This analysis predicts seismic base shear and displacement demand for full-scale damper testing parameters. Subsequently, full-scale dampers are developed using the three different HECM materials, which have achieved a damping ratio exceeding 10% using the shear method specified in EN 15129:2009 Clause 8.2.4.1.5. These dampers undergo damping tests and wind load tests in accordance with EN 15129 Clause 7.4.2.7 and Clause 7.4.2.8, respectively, incorporating parameters obtained from the model response spectrum analysis. Finite element models are then developed to simulate the hysteresis curves and damping properties of the HECM damper. After simulation, the HECM properties within the finite element model can be further developed and utilized for future damper designs to meet industry requirements. An empirical formula is derived to enable structural designers to estimate the mechanical properties of the tested damper. Based on the testing results, the developed damper achieves a damping ratio exceeding 10% and satisfies the requirements outlined in the response spectrum analysis for buildings. Consequently, the integration of HDR with HECM material, offering a novel damper solution, is expected to deliver compatible and competitive performance compared to viscoelastic and viscous-fluid dampers
Diabetic retinopathy detection using fusion of textural and optimized convolutional neural network features / Uzair Ishtiaq
No full textOne of the most prevalent chronic conditions that can result in permanent vision loss is Diabetic Retinopathy (DR). The diabetic retinopathy can broadly be categorized as Non-Proliferative DR (NPDR) and Proliferative DR (PDR) and it occurs in five stages: no DR, mild, moderate, severe, and proliferative DR. Early detection of DR is essential for the diabetic patients to prevent vision loss. DR can be detected either manually by an Ophthalmologist or using an automated system. Usually, DR can have mild signs which are negligible and very hard for an ophthalmologist to diagnose, making it difficult to be categorized in its particular class. However, an automated system is capable enough to distinguish even mild signs of DR by extracting salient and discriminative features from retinal images. In this study, a method for the detection and classification of DR stages is proposed to determine whether it is in any of the non-proliferative stage or the proliferative stage. The hybrid approach based on image preprocessing and fusion of features is the foundation of the proposed classification method. The preprocessing steps involved in this study include image resizing, data augmentation, applying median filter and image sharpening. A Convolutional Neural Network (CNN) model was created from scratch for this study. Combining Local Binary Patterns (LBP) based texture features and deep learning features resulted in the creation of the fused features vector which was then optimized using Binary Dragonfly Algorithm (BDA) and Sine Cosine Algorithm (SCA). Moreover, this optimized feature vector was fed as input to the machine learning classifiers including SVM (Linear, Quadratic, Fine Gaussian, Medium Gaussian, and Coarse Gaussian) and KNN (Fine, Medium, Coarse, Cosine and Weighted). SVM classifier achieved the highest classification accuracy of 98.85% on a publicly available dataset i.e., Kaggle EyePACS. Rigorous testing and comparisons with state-of-the-art approaches in the literature indicate the effectiveness of the proposed methodology and it can widely be applied to different DR datasets in future
Simulation and experimental study of multi nozzle arrangements on electrospun jets and their resultant nanofibrous properties / Hanna Sofia Saleh Hudin
No full textElectrospinning is a simple and versatile method to fabricate nanofibers with the application of high voltage. Owing to their distinctive properties, electrospun nanofibers could be useful in a variety of applications, including biomedical, filtration, protective textiles, sensors, and energy storage devices. Even though the setup is quite simple and straightforward, the process is complex and time-consuming, with low product yields. Multi-nozzle electrospinning is a direct approach to increasing nanofibre productivity. However, the addition of nozzles usually causes processing issues and deterioration of fibre quality due to electric field interference and low process homogeneity, restricting the prospect of mass production. This study aims to investigate the effects of multi-nozzle configurations and parameters on the electrospinning of poly(ethylene oxide) fibres. Simulations of the electric field were carried out using finite element analysis, and the jet paths were modelled using the discrete element method. The properties of jets and fibres measured from the experiments were analysed and compared between different setups. Different linear nozzle arrangements of equal and unequal spacings were tested, and the results indicate that the electric fields in the electrospinning process can be altered by varying the number of nozzles and spacing between nozzles. The change in electric field intensity and distribution consequently affects the processibility, jet behaviour, and resulting nanofibres. In multi-nozzle electrospinning, the process is notably improved by employing unequal nozzle arrangements with larger inner-to-outer nozzle distance ratios, resulting in increased electric field strength and uniformity, lower voltage requirement, and increased uniformity of the deposited mats and fibres. In terms of productivity, such arrangements also tend to maximise fibre output from a continuous and uninterrupted process, which can be beneficial in the efforts of mass production of nanomaterials. For the setup with four and five nozzles, the unequal nozzle distribution with a 3:1 inner-to-outer nozzle ratio was found to be the optimal design for improved performance. The prediction model developed was also found to reasonably predict the deflection of jets in the multi-nozzle electrospinning process
Pertimbangan moral sebagai pengantara dalam hubungan antara emosi moral dan identiti moral terhadap tingkah laku prososial pelajar sekolah menengah / Sathish Rao Appalanaidu
No full textPeningkatan penglibatan pelajar sekolah menengah dalam tingkah laku tidak bermoral telah memberikan cabaran yang tinggi kepada sistem pendidikan Malaysia yang komited melahirkan pelajar sebagai modal insan yang memiliki perkembangan moral yang seimbang. Masalah kematangan pertimbangan moral pelajar boleh menjadi punca tingkah laku pelajar keluar daripada landasan moral. Justeru, kajian ini bertujuan menganalisis kesan pengantaraan pertimbangan moral dalam hubungan antara emosi moral dan identiti moral terhadap tingkah laku prososial pelajar sekolah menengah. Kajian ini turut meninjau tahap, hubungan dan pengaruh emosi moral, identiti moral dan pertimbangan moral terhadap kecenderungan tingkah laku prososial pelajar. Selaras dengan itu, Model Pengantaraan Pertimbangan Moral Pelajar Sekolah Menengah (PPMPSM) dicadangkan dalam kajian ini. Kajian kuantitatif ini menggunakan reka bentuk deskriptif, korelasi dan juga komparatif sebab-akibat dalam proses penganalisisan. Seramai 393 pelajar tingkatan empat di negeri Selangor terpilih sebagai sampel kajian melalui persampelan rawak berkelompok pelbagai peringkat. Pengumpulan data dijalankan dengan menggunakan empat instrumen iaitu Skala Kecenderungan Rasa Bersalah dan Malu, Skala Identiti Moral, Pengukuran Refleksi Sosiomoral dan Pengukuran Kecenderungan Prososial. Kajian ini mendapati pelajar sekolah menengah mempunyai tahap kecenderungan emosi moral (M = 5.29, S.P. = 0.87) dan pembentukan identiti moral (M = 4.58, S.P. = 0.75) yang tinggi. Malah, skor kematangan pertimbangan moral pelajar secara keseluruhannya berada pada tahap matang (M= 67.51, S.P. = 13.73) dengan jumlah 69.9% di tahap matang dan selebihnya 30.1% masih berada di tahap tidak matang. Bagaimanapun, keseluruhan pelajar menunjukkan kecenderungan yang sederhana untuk bertingkah laku prososial (M = 3.67, S.P. = 0.63). Ujian korelasi Pearson mendapati kesemua pemboleh ubah mempunyai hubungan signifikan antara satu sama lain. Dapatan model berstruktur melaporkan identiti moral (β = 0.294, p = 0.001) dan pertimbangan moral (β = 0.291, p = 0.028) meramalkan tingkah laku prososial pelajar secara signifikan, manakala emosi moral (β = 0.135, p = 0.098) tidak meramalkan secara signifikan. Akan tetapi, emosi moral (β = 0.211, p = 0.001) dan identiti moral (β = 0.325, p = 0.000) berjaya meramalkan pertimbangan moral pelajar secara signifikan. Sumbangan model PPMPSM terhadap kecenderungan tingkah laku prososial pelajar adalah sebanyak 22% dengan kesan 0.88 keseluruhan. Model ini merumuskan bahawa identiti moral merupakan pengaruh terbesar dalam meramalkan kecenderungan tingkah laku prososial. Malah, pertimbangan moral didapati memainkan peranan penting dalam memberi kesan pengantaraan penuh dan separa masing-masing ke atas hubungan antara emosi moral dan identiti moral terhadap tingkah laku prososial pelajar. Justeru, Model PPMPSM ini diterima dan padan dengan data yang dicerap. Kesimpulannya, kajian ini secara teorinya menyumbang kepada ilmu bidang perkembangan moral menerusi pengintegrasian domain moral seperti identiti, emosi, kognitif dan tingkah laku dalam satu model kajian PPMPSM untuk memberikan penjelasan yang lebih holistik tentang keberhasilan tingkah laku prososial dalam kalangan pelajar sekolah menengah. Secara praktikalnya, kajian ini memberi maklumat kepada pemegang taruh yang berkait bahawa pertimbangan moral penting untuk diangkat sebagai kemahiran yang wajib dididik untuk mencapai kemenjadian pelajar