Present data article based on the investigation which enumerates the influence of CNG (compressed natural gas) and HCNG (hydrogen enriched compressed natural gas) on performance and emission parameters of direct injection diesel engine at 200, 220, and 240 bar injection pressures. The CNG and HCNG gaseous alternative fuels were injected in amount (by mass) of 10%, 20% and 30% as secondary fuels to enrich the pilot fuel (pure diesel) during the operation. The performance and emission data of dual fuel (CNG + pure diesel, HCNG + pure diesel) operation was analysed to compare with the pure diesel data. The data for present investigational work were assessed at 25%, 50%, 75% and 100% diverse engine loads for all trials under diverse injection pressures. Eddy current dynamometer was employed to raise the engine load from quartile to maximum. AVL DiGAS 444 N multi gas analyser was used to measure the values of carbon monoxide (CO), unburned hydrocarbon (UHC), and oxides of nitrogen (NOx) detrimental emissions in engine exhaust.
The skin of a fast swimming shark reveals riblet structures that help reduce the shark's skin friction drag, enhancing its efficiency and speed while moving in the water. Inspired by the structure of the shark skin denticles, our team has carried out a study as an effort in improving the hydrodynamic design of marine vessels through hull design modification which was inspired by this riblet structure of shark skin denticle. Our study covers on macroscaled design modification. This is an attempt to propose an alternative for a better economical and practical modification to obtain a more optimum cruising characteristics for marine vessels. The models used for this study are constructed using computer-aided design (CAD) software, and computational fluid dynamic (CFD) simulations are then carried out to predict the effectiveness of the hydrodynamic effects of the biomimetic shark skins on those models. Interestingly, the numerical calculated results obtained show that the presence of biomimetic shark skin implemented on the vessels give about 3.75% reduction of drag coefficient as well as reducing up to 3.89% in drag force experienced by the vessels. Theoretically, as force drag can be reduced, it can lead to a more efficient vessel with a better cruising speed. This will give better impact to shipping or marine industries around the world. However, it can be suggested that an experimental procedure is best to be conducted to verify the numerical result that has been obtained for further improvement on this research.
Polycystic ovarian syndrome (PCOS) is one of the main causes of infertility and affects 5-20% women of reproductive age. Despite the increased prevalence of PCOS, the mechanisms involved in its pathogenesis and pathophysiology remains unclear. The expansion of omics on studying the mechanisms of PCOS has lead into vast amounts of proteins related to PCOS resulting to a challenge in collating and depositing this deluge of data into one place. A knowledge-based repository named as PCOSBase was developed to systematically store all proteins related to PCOS. These proteins were compiled from various online databases and published expression studies. Rigorous criteria were developed to identify those that were highly related to PCOS. They were manually curated and analysed to provide additional information on gene ontologies, pathways, domains, tissue localizations and diseases that associate with PCOS. Other proteins that might interact with PCOS-related proteins identified from this study were also included. Currently, 8185 PCOS-related proteins were identified and assigned to 13 237 gene ontology vocabulary, 1004 pathways, 7936 domains, 29 disease classes, 1928 diseases, 91 tissues and 320 472 interactions. All publications related to PCOS are also indexed in PCOSBase. Data entries are searchable in the main page, search, browse and datasets tabs. Protein advanced search is provided to search for specific proteins. To date, PCOSBase has the largest collection of PCOS-related proteins. PCOSBase aims to become a self-contained database that can be used to further understand the PCOS pathogenesis and towards the identification of potential PCOS biomarkers. Database URL: http://pcosbase.org.
Plants produce a wide range of secondary metabolites that play important roles in plant defense and immunity, their interaction with the environment and symbiotic associations. Sulfur-containing compounds (SCCs) are a group of important secondary metabolites produced in members of the Brassicales order. SCCs constitute various groups of phytochemicals, but not much is known about them. Findings from previous studies on SCCs were scattered in published literatures, hence SuCComBase was developed to store all molecular information related to the biosynthesis of SCCs. Information that includes genes, proteins and compounds that are involved in the SCC biosynthetic pathway was manually identified from databases and published scientific literatures. Sets of co-expression data was analyzed to search for other possible (previously unknown) genes that might be involved in the biosynthesis of SCC. These genes were named as potential SCC-related encoding genes. A total of 147 known and 92 putative Arabidopsis thaliana SCC-related genes from literatures were used to identify other potential SCC-related encoding genes. We identified 778 potential SCC-related encoding genes, 4026 homologs to the SCC-related encoding genes and 116 SCCs as shown on SuCComBase homepage. Data entries are searchable from the Main page, Search, Browse and Datasets tabs. Users can easily download all data stored in SuCComBase. All publications related to SCCs are also indexed in SuCComBase, which is currently the first and only database dedicated to plant SCCs. SuCComBase aims to become a manually curated and au fait knowledge-based repository for plant SCCs.
Mental health conditions are a major part of workers' health that predisposes to poor self-motivation for sustaining productivity. This study was aimed to determine the prevalence of depression, anxiety, and stress among staff in a Malaysian public university and its associated factors. A cross-sectional study was conducted among 459 staff from the Universiti Kebangsaan Malaysia (UKM) between April and June 2019. A questionnaire that consisted of items on socio-demographic and socioeconomic characteristics, employment description, lifestyle risk behaviors, personal medical history, and symptoms of depression, anxiety, and stress was administered to participants. Descriptive and inferential statistics were conducted using SPSS version 22.0. The prevalence of perceived symptoms of depression, anxiety, and stress among the respondents was 28.7%, 50.1%, and 14.8%, respectively. Over one-quarter (26.5%) of the participants presented symptoms of two or more mental disorders. Women, those aged less than 40 years old, and non-academic professionals were more likely to exhibit depressive symptoms, while those with medical conditions that required hospitalizations sustained anxiety symptoms. Perceived stress was more likely to be prevalent among staff with secondary education or less and smokers. Proactive support for staff needs to be offered in sustaining their emotional well-being.
Public health systems are concerned with the commensurate rise of metabolic syndrome (MetS) incidence across populations worldwide, due to its tendency to amplify greater risk of diabetes and cardiovascular diseases within communities. This study aimed to determine the prevalence of MetS and its associated risk factors among staffs in a Malaysian public university. A cross-sectional study was conducted among 538 staffs from the Universiti Kebangsaan Malaysia (UKM) between April and June 2019. MetS was defined according to JIS "Harmonized" criteria. A questionnaire that consisted of items on socio-demographics, lifestyle risk behaviors and personal medical history information was administered to participants. Subsequently, a series of physical examination and biochemical assessment was conducted at the hall or foyer of selected faculties in the university. Descriptive and inferential statistics were conducted using SPSS version 22.0. Multivariate models were yielded to determine the risk factors associated with MetS. Statistical significance was set at P