The Impact of Supervisory Inputs on Postgraduate Students

Pengawasan telah menjadi isu utama dalam studi di sekolah pascasarjana. Pengawasan dapat didefinisikan sebagai hubungan orang per orang secara intensif dan interpersonal. Pengawas dirancang untuk dapat memfasilitasi perkembangan akademik mahasiswa baik terkait dengan tugas maupun penelitian mereka. Paper ini menunjukkan betapa kompleksnya bidang pengawasan terhadap mahasiswa, yang dipengaruhi oleh banyak faktor, di antaranya latar belakang sosial, kepribadian pengawas dan mahasiswa, hubungan yang berkembang di antara mereka, keahlian pengawas, dan masalah-masalah yang dihadapi oleh para mahasiswa. Paper ini mendiskusikan pentingnya input-input kepengawasan dalam proses pengawasan, dan juga meneropong hakikat interaksi sosial antara pengawas dengan mahasiswa. Sasaran yang dituju dalam paper ini adalah untuk mengembangkan pengawasan yang efektif terhadap mahasiswa sekolah pascasarjana guna menghasilkan modal sumber daya manusia yang unggul. Kata kunci : pengawasan, mahasiswa pascasarjana, input kepengawasan, dan pengawasan efekti

LSTM-based Electroencephalogram Classification on Autism Spectrum Disorder

Autism Spectrum Disorder (ASD) is categorized as a neurodevelopmental disability. Having an automated technology system to classify the ASD trait would have a huge influence on paediatricians, which can aid them in diagnosing ASD in children using a quantifiable method. A novel autism diagnosis method based on a bidirectional long-short-term-memory (LSTM) network's deep learning algorithm is proposed. This multi-layered architecture merges two LSTM blocks with the other direction of propagation to classify the output state on the brain signal data from an electroencephalogram (EEG) on individuals; normal and autism obtained from the Simon Foundation Autism Research Initiative (SFARI) database. The accuracy of 99.6% obtained for 90:10 train:test data distribution, while the accuracy of 97.3% was achieved for 70:30 distribution. The result shows that the proposed approach had better autism classification with upgraded efficiency compared to single LSTM network method and potentially giving a significant contribution in neuroscience research

Biofertilizer And Bioenhancer Concepts For Sustainable Oil Palm Seedling Production.

In oil palm production, nitrogen fertilizer is the most expensive nutrient input required

Educational data mining: enhancement of student performance model using ensemble methods

Nowadays, Educational Data Mining (EDM), begun as a new research area due to the broadening of numerous statistical approaches that are used to perform data exploration in educational settings. One of the applications of EDM is the prediction of performance of students. In a web based education system, the behavioural features of learners are very significant in showing the interaction between students and the LMS. In this paper, our aim is to propose a new performance prediction model for students which is based on data mining methods which includes new features known as behavioural features of students and based on sequential feature selection which is used to identify most important features. The proposed performance model is evaluated using classifiers such as Support Vector Machine (SVM), K-Nearest Neighbour (KNN) and Decision Tree (DT). Furthermore, so as to enhance the classifiers performance, the ensemble methods such as Bagging, Boosting and Random Forest were applied. The achieved results show that there exists a strong relationship between behaviour of students and their academic performance. An accuracy of 91.5% was gotten when the ensemble methods were applied to the classifiers to improve the academic performance. Thus, the result gotten shows the reliability of the proposed model

Gastroprotective activity of Spirulina platensis in acetic acid and ethanol induced ulcers in rats

Objective: The effects of gastroprotective properties of Spirulina platensis was investigated in acetic acid and ethanol induced ulcers in rats. Methods: Administration of 2 and 4mg/kg Spirulina platensis extract for 7 days. After day 7, oral administration of either 80% (v/v) ethanol or 6% (v/v) acetic acid. Control rats received saline or anti-ulcer drug omeprazole (20 mg/kg) prior to ulcer induction. Results: The extract inhibited the mean lesion score of acetic acid, 4.333 to 3.000. Whereas, for ethanol induced ulcers, the extract reduced the lesion scoring from 2.833 to 1.677. However, this activity was statistically less potent than the anti-ulcer drug, omeprazole. Spirulina platensis alone did not induce any ulcers in rats. Conclusions: These results suggested that Spirulina platensis has gastroprotective activity against ulcers induced by acetic acid and ethanol

Comparison of global and local features for author's identification by using geometrical and zoning methods

Identification analysis for author's handwriting image in forensic investigation is still an important research area in this current big data era. Images feature extraction can lead to an issue of high dimensionality of data. The process of feature extraction is the most crucial process in author's identification. It is important to choose the best method to represent the image. This study compared two feature extraction methods, namely Higher-Order United Moment Invariant (HUMI) and the Edge-based Directional (ED) method that construct the Global and Local Features respectively. The additional process of discretization was implemented before the training and testing phase to represent the generalized features for the classifier models. This process induced a better performance accuracy for both methods where the discretized Local Features achieved 99.95% accuracy rate that slightly outperforms the discretized Global Features with only 99.91%

Retinal Muller Glia Initiate Innate Response to Infectious Stimuli via Toll-Like Receptor Signaling

Ocular surgeries and trauma predispose the eye to develop infectious endophthalmitis, which often leads to vision loss. The mechanisms of initiation of innate defense in this disease are not well understood but are presumed to involve retinal glial cells. We hypothesize that retinal Muller glia can recognize and respond to invading pathogens via TLRs, which are key regulators of the innate immune system. Using the mouse retinal sections, human retinal Muller cell line (MIO-M1), and primary mouse retinal Muller cells, we show that they express known human TLR1-10, adaptor molecules MyD88, TRIF, TRAM, and TRAF6, and co-receptors MD2 and CD14. Consistent with the gene expression, protein levels were also detected for the TLRs. Moreover, stimulation of the Muller glia with TLR 2, 3, 4, 5, 7 and 9 agonists resulted in an increased TLR expression as assayed by Western blot and flow cytometry. Furthermore, TLR agonists or live pathogen (S. aureus, P. aeruginosa, & C. albicans)-challenged Muller glia produced significantly higher levels of inflammatory mediators (TNF-α, IL-1β, IL-6 and IL-8), concomitantly with the activation of NF-κB, p38 and Erk signaling. This data suggests that Muller glia directly contributes to retinal innate defense by recognizing microbial patterns under infectious conditions; such as those in endophthalmitis

An heuristic feature selection algorithm to evaluate academic performance of students

The value of schooling and academic performance of student is the topmost priority of all academic institutions. Educational Data Mining (EDM) is an evolving area of research which aids academic institutions to enhance their student's performances. Feature Selection algorithms eradicates inapt and unrelated data from the dataset, thereby increasing the classifiers performances that are utilized in EDM. This aim of this paper is to evaluate the performance of students utilizing a heuristic technique known as Differential Evolution for feature selection algorithms on the dataset of students and some other feature selection algorithms have also been used which have never been used before on the dataset. Also, classification techniques such as Naïve Bayes (NB), Decision Tree (DT), K-Nearest Neighbor (KNN) and Discriminant Analysis (DISC) were used to evaluate. The Differential Evolution (DE) algorithm is proposed as a better feature selection algorithm for evaluating the academic performance of students and this gave a better accuracy than other feature selection algorithm that were used. The outcome of the different feature selection algorithms and classification techniques will help researchers to find the finest combinations of the classifiers and feature selection algorithms. This paper is a step towards playing an important role in enhancing the standard of education in academic institutions and also to carefully guide researchers in strategically interfering in academic issues

Decision making of green technology retrofitting in higher learning institution

The increases in energy consumption in both new and existing buildings are currently a major concern in the country. Energy consumption in existing buildings has to be reduced since it contributes to the largest portion of the global end-use of energy at approximately 40%. The consumption of energy in an existing building at a Higher Learning Institution (HLI) is influenced by activities in the building, time of use, length of academic term, and the number of academic staff, students and visitors in the building. Thus, the retrofitting approach has become a priority in achieving energy efficiency. This approach can be divided into three steps, namely lean energy, green technology and clean energy initiatives. However, selecting the retrofitting initiatives is a complex process. It is important to initially evaluate several factors to minimise the risk and to ensure the success of the project. These factors include design, economics, occupants’ comfortability criteria, and others. This paper highlights the criteria affecting the retrofitting of an existing building in a HLI with green technology which consists of lighting and occupancy sensors towards achieving energy usage reduction. Factor Analysis with Principal Component Analysis Varimax Rotation was applied as a method of analysis where inputs were generated from the questionnaire surveys distributed to electrical and mechanical engineers who have expertise in retrofitting projects. The priority of the sub-criteria was summarised based on the significant threshold factor loading of 0.50 and above, where the results revealed that design, economics, technical, occupants’ criteria and physical criteria achieved the significant factor loading value. This paper contributes to providing information about the significant criteria which must be assessed during the decision to retrofit a building with green technology to maximise the retrofitting benefits and to achieve optimal retrofitting strategy

Design and Development of Ion Probe Testing on Four Stroke Engine Model

This paper carried out the study on an ion probe testing on four stroke engine model. The testing probe was installed inside the engine combustion chamber with specific distance, when the fuel is supply to the engine and the spark plug light up the fuel, it will produce an ionization flame, at the same time, when the flame touch the probe, it will generate a signal. The probe is connected to a circuit, when the signal is generated, the circuit will process the signal. The coding for the process is written inside a chip that installed in the circuit. A user interface was created to display the result. Besides that, a camera will use to catch the flame to compare with the result