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This paper presents a critical assessment analysis on mental health detection in Online Social Networks (OSNs) based on the data sources, machine learning techniques, and feature extraction method. The appropriateness of the mental health detection was also investigated by identifying its data analysis method, comparison, challenges, and limitations. This study reviewed articles published in major databases between 2007 and 2018 through keyword searches. The articles were screened base on their titles and abstracts before the full texts were reviewed. The articles were coded in accordance with data set (e.g., data sources, keywords, and geographical locations), method of data analysis, machine learning or deep learning technique, classifier performance, and feature extraction method. 22 articles were selected for review from the total of 2770. As OSNs exhibit high potential as a data source in early detection of mental health problems, most researchers used text analysis on a new data set extracted from different OSNs sources. The extracted data were examined using a statistical analysis or machine learning techniques. Several studies also applied multimethod techniques, which included distributing questionnaires while requesting for the respondents’ consent to later access and extract information from his/her OSNs account. Big data in OSNs contribute on mental health problem detection. The presented method is an alternative approach to the early detection of mental health problems rather than using traditional strategies, such as collecting data through questionnaires or devices and sensors, which are time-consuming and costly. However, mental health problem detection through OSNs necessitates a comprehensive adoption, innovative algorithms, and computational linguistics to describe its limitations and challenges. Moreover, referrals from mental health specialists as subject matter experts are also required to help obtain accurate and effective information.

This paper introduces Dengue Active Surveillance System (DASS) framework for an early warning system of the outbreak. Dengue and dengue hemorrhagic fever are emerging as major public health problems in most Asian countries such as Malaysia. Effective prevention and control programs will depend on improved surveillance. A new approach to active surveillance outlined with emphasis on the inter-epidemic period. The objective is to develop an early warning surveillance system (framework) that can predict epidemic dengue to improve current passive surveillance system available in Malaysia. Basically, the framework introduced data harvesting process from multiple sources as input, data pre-processing using data aggregator and filtering engine, storing large data in repository, analytic engine for analysis and processing the large data, and presentation of the information to the users. The data harvested from two major sources such as weather or flood information, and social media such as build development and dengue symptom using system API, SOAP and others. The data aggregator will aggregate the data from three different types of data such as structured, semi-structured and unstructured data to be stored into the semi-structured database such as MongoDB and NoSQL. The data parse to the filtering engine for filtering and cleaning the data sources using suitable keywords prior to store it in the large data repository. After that, the large data will be processed and analyzed using algorithm or mathematical calculation to determine the expected dengue cases. Then, the processed information will be presented to the users in a form of web or mobile application and other method, for example, short message service (SMS). Finally, the system accuracy will be evaluated based on the comparison study with the traditional passive system.

Preliminary studies of Cabomba furcata from Chini Lake, Pahang, Malaysia has shown that this plant containing bioactive phytochemical such as saponin. Unfortunately no intensive research has been conducted since saponin are believed to have medicinal benefits to the humankind and they have the potential to be commercialized in pharmaceutical field. This study was done to identify the type of saponin that are present in C. furcata. Hot extraction was performed by using methanol and HPLC - Dad with C18 column had confirmed the presence of oleanolic acid and ursolic acid in the aquatic plant.

Single layer OLEDs by using polymer poly(9,9-di-n-hexylfluorenyl-2,7-diyl) (PHF) mixed with organic salt tetrabutylammonium hexafluorophosphate (TBAPF 6 ) as the electroluminescent (EL) layer were fabricated. This paper reports the effects of variation of that EL layer thickness on the OLED performance. OLEDs performance are determined in terms of their turn-on voltage and luminance. The PHF:TBAPF 6 EL layers with weight ratios of 100:20 were used and prepared by spin coating technique using chloroform as solvent. The thickness being incremented by adding layer-by-layer during the spin coating process. The thickness effects to the EL layer surface roughness were also studied. The best device performance was given by the thinnest layer which also has the lowest average surface roughness (0.557 nm), it gave the lowest turn-on voltage (7.0 V) and highest maximum luminance (9.12 cd/m2 at 10 V). It emitted greenish-yellow color at it maximum luminance with CIE coordinate (0.30, 0.55).

This research paper investigates about the readability of Amplitude Modulation Transmitter and Receiver Lab Manual. Recent research has indicated that the lack of procedure, the structure of the manual lab and the complexity of the words or phrase used in the manual are the main factor UiTM Engineering students are not capable of fully comprehending this lab manual. This research used three (3) readability software such as, Flesch Reading Ease, Flesch-Kincaid Grade Level and Gunning Fog Index. The Flesch Reading Ease factor was identified as 69.5 while the Flesch Kincaid Grade Level was determined at 5.8. In addition, the Gunning Fog Index test result yields a score of 8.1. These results indicate that the lab manual should be easily read and understood by 12 years old American student. During the research, changes were made to improve the readability factor. Further analysis of the lab manual, the readability test result indicates that the readability of the Flesch Reading Ease factor was identified as 63 while the Flesch Kincaid Grade Level was determined at 7.4 and the Gunning Fog Index test result yields a score of 10.4. Even though the readability factor has generally increased, the changes made were deemed necessary.

The effects of glycerol content in modified polyvinyl alcohol-tapioca starch were investigated. A series of plasticized polyvinyl alcohol (PPVA) and TS blends were prepared. Prior to compounding process, PVA was plasticized with 15–40 phr glycerol as well as addition of calcium stearate (CaS) and phosphoric acid. Blends of PPVA at composition of 50%, 60%, 70%, 80% and 90% TS with 20 phr glycerol were compounded using twin screw extruder. Blends of PPVA-TS were then processed via injection molding to produce test specimen for mechanical testing. It was found that the addition of glycerol in the blending is compulsory in order to have a better flowability during the processing process. In spite of that, the amount of glycerol inside the blending must be in the optimum composition to ensure that the mechanical properties of the blends are not affected.