INFLUENCE OF ANTHROPOGENIC POLLUTANTS ON MUD CRAB ECOSYSTEMS TO ENSURE SUSTAINABLE AQUACULTURE AND SAFE CONSUMPTION TO HUMAN HEALTH

Heavy metal pollution from anthropogenic activities can harm aquatic ecosystems. This study aims to determine the concentration of heavy metals (Pb and Cu) in waters, sediments, and mud crabs (Scylla serrata), and to analyze the relationship between environmental parameters and S. serrata which is consumed by humans. Samples were taken in the mangrove ecosystem around the Tanjung Api-Api port area in South Sumatra, Indonesia. Pb and Cu analysis used the Atomic Absorption Spectrophotometer (AAS). Pb and Cu linkages in waters, sediments, and S. serrata analyzed by SigmaPlot V12.5 and Principal Component Analysis (PCA) analyzed by XLSTAT 2022. The limit consumption of S. serrata was calculated using MWI (MaximumWeekly Intake) and MIT (Maximum Intake Tolerance). Based on the results, the heavy metal Pb in water was 0.1055 – 0.1322 mg.L−1, and Cu was not detected. Furthermore, Pb in sediments ranged from 7.0104 - 11.8186 mg.kg−1, Cu 3.7127 - 4.5347 mg.kg−1, and Pb in S. serrata ranged from 0.0001 - 0.0021 mg.kg−1, and Cu ranged from 0.03 – 0.0791 mg.kg−1. The concentration of heavy metals in water, sediment, and S. serrata had not exceeded the specified quality standard, except for Pb in water. The principal component analysis obtained F1 (44.35%), F2 (27.53%) and F3 (17.83%) groups. Based on MWI and MIT values that S. serrata was still safe for human consumption

GENETIC ALGORITHM-ARTIFICIAL NEURAL NETWORK (GA-ANN) AND GIS-BASED WIND MAPPING FOR WIND ENERGY EXPLOITATION: CASE STUDY IN MALAYSIA

Wind maps are required to determine wind resource over a given areas and they are an important component of wind energy exploration and exploitation. The intermittency of wind, geographical, and temporal variability, as well as the complex relationship between wind and their nature, have made accurate spatial wind speed modelling more difficult. The aim of this study was to contribute a novel and original solution to the problem of developing wind maps for wind energy exploitation in Malaysia. The main inputs of this study were 37 Malaysian Meteorological Department stations’ wind data and 3 installed wind masts’ data. The Genetic Algorithm-Artificial Neural Network model was applied in the Measure-Correlate- Predict method to substitute and fill missing data. Spatial modelling was conducted to establish wind maps by interpolating point sources of wind data and extrapolating the wind flow at 10-m and 50-m heights. The Genetic Algorithm-Artificial Neural Network model was also applied to training spatial modelling and to generate a nonlinear wind map. The results revealed that nonlinear wind map had addressed the overprediction issue of the wind maps in mountainous areas at the Cameron Highlands site, where the root mean squared error, and the mean absolute error decreased by 60.39% and 64.01% respectively. Overall, the nonlinear wind map improved simulated wind data by increasing accuracy and decreasing errors, up to 18.39% and 31.42% respectively. In conclusion, the results clearly prove that addressing the complex nonlinear relationship between the input parameters and output wind map decrease errors in the simulation of wind speed

MODELLING OF SICK BUILDING SYNDROME (SBS) SYMPTOMS AND INDOOR AIR QUALITY (IAQ) ACROSS DOMINANT SUB-ECONOMIES IN TERENGGANU: A STUDY OF MONSOONAL VARIATIONS

Optimum indoor air quality (IAQ) is crucial for maintaining a healthy work environment. This study examines the effects of IAQ on Sick Building Syndrome (SBS) symptoms across various economic subsectors during the monsoonal seasons in Terengganu, Malaysia. Four locations representing the education (S1), wholesale or retail trade (S2), manufacturing (S3), and services (S4) subsectors were assessed. IAQ was measured using ventilation indicators (carbon dioxide, CO2), chemical parameters (formaldehyde (HCHO), total volatile organic compounds (TVOC), and carbon monoxide (CO)), and physical parameters (temperature, relative humidity, air movement) during the Southwest Monsoon (SWM) and Northeast Monsoon (NEM). The objectives included evaluating IAQ compliance, simulating 3D distributions using Computational Fluid Dynamics (CFD), identifying IAQ factors through Principal Component Analysis (PCA), and developing predictive Generalized Linear Models (GLM). Data included SBS symptom feedback and IAQ metrics, analysed using GLM with SBS syptoms as the dependent variable. Results showed seasonal IAQ variations, with temperatures ranging from 23.50°C to 32.91°C and relative humidity from 57.77% to 90.68%. CO2 levels were higher in enclosed spaces, particularly in manufacturing and retail sectors during the SWM. CFD simulations revealed increased turbulence near ventilation systems, with accuracies of up to 91.90% (SWM, S1) and 91.17% (NEM, S4). PCA identified three main IAQ contributors: physical conditions, chemical exposure, and human activities, accounting for up to 45.58% (NEM, S3), 24.17% (SWM, S3), and 31.42% (SWM, S4) of variance. The GLM demonstrated higher predictive accuracy during the NEM, with an R2 of up to 0.9949. Seasonal variations in IAQ significantly impacted SBS symptoms across different economic sectors in Terengganu, Malaysia. Poor IAQ, driven by physical conditions, chemical exposures, and human activities, was found to be worse during the SWM. The study recommends improving ventilation in enclosed spaces, regularly monitoring IAQ to address seasonal changes, reducing chemical emissions, controlling indoor activities, and enforcing IAQ compliance to create healthier work environments

EFFECT OF LACTIC ACID BACTERIA AS BIO-PRESERVATIONFFECT OF LACTIC ACID BACTERIA AS BIO-PRESERVATION AAGAINST SPOILAGE FUNGI FROM PAPAYA FRUITGAINST SPOILAGE FUNGI FROM PAPAYA FRUIT

Lactic acid bacteria (LAB) produce several antibacterial compounds, including organic acids that inhibit many types of pathogenic bacteria. The antibacterial activity of LAB with the ability to inhibit growth of pathogenic bacteria associated with foodborne illness is seen as a natural way to improve food safety. This study was carried out to isolate and identify LAB from local pickled guava (Psidium guajava) and papaya (Carica papaya) and to evaluate their antibacterial activity against selected foodborne pathogens. Standard method was used for the isolation of LAB, while identificationwas done based on their morphological characteristics, biochemical reaction and polymerase chain reaction (PCR) amplificationof 16S rRNA gene and sequencing. This study evaluated the ability of cell free supernatant (CFS) of the identifiedLAB to inhibit the growth of selected Gram-positive and Gram-negative foodborne pathogens through microtiter plate method. Determination of the organic acids formation in the CFS that are responsible for the antibacterial activity of the LAB was also conducted using high-performance liquid chromatography (HPLC). The results showed that three LAB from the genus Lactobacillus have been successfully isolated and identifiedas Lactobacillus plantarum (LABP), Lactobacillus reuteri (LABR) and Lactobacillus paracasei (LABC). All three Lactobacillus sp. were able to demonstrate antibacterial activity against foodborne bacterial pathogens used in this study. The results also suggested that the antibacterial activity of CFS of all three Lactobacillus sp. was due to organic acids production

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THE ROLE OF ORGANIZATIONAL COMMITMENT ON TRAINING AND DEVELOPMENT AND ITS IMPACT ON PRODUCTIVITY OF JORDANIAN SHIPPING COMPANIES

Shipping companies in Jordan have recently faced a decline in productivity, yet the specific reasons behind this trend remain unclear. While various studies have examined productivity challenges in Jordanian companies, little research has focused on the shipping sector. This study investigates the role of training and development (TD) in enhancing productivity by examining four key dimensions: training and development needs (TDN), training and development design (TDD), training and development program implementation (TDPI), and training and development program evaluation (TDPE). Additionally, it explores organizational commitment (OC) as a moderating factor. A quantitative research approach was adopted, utilizing a self- administered survey through simple random sampling. Based on Social Security Corporation (SSC) data, the total number of employees in Jordanian shipping companies is 250, from which a sample of 152 employees was selected. After excluding five incomplete responses, 147 valid questionnaires were analysed. The collected data were processed using Partial Least Squares-Structural Equation Modelling (PLS-SEM) to assess the relationships between TD and productivity. The findings reveal significant positive relationships between TDN, TDD, TDPI, TDPE, and productivity. Moreover, OC was found to moderate the relationship between TDD and productivity, suggesting that committed employees respond better to well- structured programs. However, OC did not significantly moderate the relationships between productivity and the other TD dimensions. These findings underscore the importance of well-designed TD programs and fostering OC to enhance productivity in Jordanian shipping companies

COMPARATIVE ANALYSIS OF WASTE COOKING OIL BIODIESEL MIXED WITH NANOPARTICLE ADDITIVES ON PHYSICOCHEMICAL PROPERTIES AND DIESEL ENGINE PERFORMANCE

The hazardous effect of the pollution of fossil fuels has brought the necessity of shifting conventional energy sources to renewable and clean ones. In this study, the effect of hydrogen addition and CeO2 nanoparticle addition in waste cooking palm biodiesel on a CRDI engine is evaluated. The dosage of the nanoparticle is fixed at 75 ppm and a hydrogen flow rate of 10 L/min is selected for the engine operations. The crystalline structure of the nanoparticles is determined by XRD analysis. Results showed that on the addition of both H2 and CeO2 in a B20 biodiesel blend (80% diesel and 20% biodiesel) the performance, emission, and combustion parameters of the diesel engine improved compared to neat diesel. The brake thermal efficiency was improved by 3.53% and brake fuel consumption was reduced by 16.12% in comparison to diesel at 90% loading condition. The addition of both nanoparticles and hydrogen in the biodiesel blend lowered the emissions of CO by 30%, and HC and smoke by 50% and 42% respectively. However, NOx increased by 11% as compared to diesel. A 6% higher HRR values and 8% higher in-cylinder pressure were obtained while using hydrogen and CeO2 nanoparticle blended biodiesel. This blend also shows the lowest ignition delay period at full load condition which results in more engine power and efficiency. This experimental study has helped pave the way for the use of hydrogen-enriched and nanoparticle-blended biodiesel in place of fossil fuel for the applications of diesel engines