RESUMEN
Acanthamoeba, a free-living amoeba (FLA) found in diverse ecosystems, poses significant health risks globally, particularly in Malaysia. It causes severe infectious diseases, e.g., Acanthamoeba keratitis (AK), primarily affecting individuals who wear contact lenses, along with granulomatous amoebic encephalitis (GAE), a rare but often life-threatening condition among immunocompromised individuals. AK has become increasingly prevalent in Malaysia and is linked to widespread environmental contamination and improper contact lens hygiene. Recent studies highlight Acanthamoeba's capacity to serve as a "Trojan horse" for amoeba-resistant bacteria (ARBs), contributing to hospital-associated infections (HAIs). These symbiotic relationships and the resilience of Acanthamoeba cysts make treatment challenging. Current diagnostic methods in Malaysia rely on microscopy and culture, though molecular procedures like polymerase chain reaction (PCR) are employed for more precise detection. Treatment options remain limited due to the amoeba's cyst resistance to conventional therapies. However, recent advancements in natural therapeutics, including using plant extracts such as betulinic acid from Pericampylus glaucus and chlorogenic acid from Lonicera japonica, have shown promising in vitro results. Additionally, nanotechnology applications, mainly using gold and silver nanoparticles to enhance drug efficacy, are emerging as potential solutions. Further, in vivo studies and clinical trials must validate these findings. This review highlights the requirement for continuous research, public health strategies, and interdisciplinary collaboration to address the growing threat of Acanthamoeba infections in Malaysia while exploring the country's rich biodiversity for innovative therapeutic solutions.
RESUMEN
The climate, geomorphological changes, and hydrological elements that have occurred have all influenced future flood episodes by increasing the likelihood and intensity of extreme weather occurrences like extreme precipitation events. River bank erosion is a natural geomorphic process that occurs in all channels. As modifications of sizes and channel shapes are made to transport the discharge, sediment abounds from the stream catchment, and floods are triggered dramatically. The aim of this study is to analyze the flood-sensitive regions along the Pahang River Basin and determine how climate and river changes would have an impact on flooding based on hydrometeorological data and information on river characteristics. The study is divided into three stages, namely the upstream, middle stream, and downstream of the Pahang River. The main primary hydrometeorological data and river characteristics, such as Sinuosity Index, Dominant Slope Range and Entrenchment Ratio collected as important inputs in the statistical analysis process. The statistical analyses, namely HACA, PCA, and Linear Regression applied in river classification. The result showed that the middle stream and downstream areas demonstrated the worst flooding affected by anthropogenic and hydrological factors. Rainfall distribution is one of the factors that contributed to the flood disaster. There are strong correlations between the Sinuosity Index (SI) and water level, which indicates that changes occurred at both planform and stream classification. The best management practices towards sustainability are based on the application of the outcomes that have been obtained after the analysis of Pahang River planform changes, Pahang River geometry, and the local rainfall pattern in the Pahang River Basin.
RESUMEN
Iraq is facing a dire water crisis due to the decrease in water quantities flow in Tigris and Euphrates Rivers. Due to population growth, several studies estimated the water shortage in 2035 to be 44 Billion Cubic Meter (BCM). Thus, Water Budget-Salt Balance Model (WBSBM) has been developed, applied and examined for the Euphrates River basin to compute the net water saving from Non-Conventional Water Resources (NCWRs). WBSBM includes 4-stages; the first is to identify the required data correspond to the conventional water resources in the study-area. The second stage is demonstrating the water-users activities. Thirdly, develop model through the proposed NCWR projects that reflect the required data. The final stage involves net water saving computation while applying all the NCWR projects simultaneously. The results obtained the optimal potential net water saving amount, which are 6.823 and 6.626 BCM/year in 2025 and 2035, respectively. In conclusion, the proposed WBSBM model has comprehensively examined different scenarios of utilizing NCWRs and has determined the optimal potential the net water saving amounts.
RESUMEN
Efficacious operation for dam and reservoir system could guarantee not only a defenselessness policy against natural hazard but also identify rule to meet the water demand. Successful operation of dam and reservoir systems to ensure optimal use of water resources could be unattainable without accurate and reliable simulation models. According to the highly stochastic nature of hydrologic parameters, developing accurate predictive model that efficiently mimic such a complex pattern is an increasing domain of research. During the last two decades, artificial intelligence (AI) techniques have been significantly utilized for attaining a robust modeling to handle different stochastic hydrological parameters. AI techniques have also shown considerable progress in finding optimal rules for reservoir operation. This review research explores the history of developing AI in reservoir inflow forecasting and prediction of evaporation from a reservoir as the major components of the reservoir simulation. In addition, critical assessment of the advantages and disadvantages of integrated AI simulation methods with optimization methods has been reported. Future research on the potential of utilizing new innovative methods based AI techniques for reservoir simulation and optimization models have also been discussed. Finally, proposal for the new mathematical procedure to accomplish the realistic evaluation of the whole optimization model performance (reliability, resilience, and vulnerability indices) has been recommended.