Cost effectiveness analysis of using different monitoring modalities in treating severe traumatic brain injury (CESTBI) in neuro-ICU, HUSM, Kelantan

Introduction: There are two schools of thought in practicing neurotrauma monitoring for patients with severe traumatic brain injury (TBI); the application of the baseline neuro-monitoring (BNM) and the use of multiple modalities neurotrauma monitoring (M3) which is very expensive. The answer of which of the two monitoring systems is more eflicient and worth doing should be sought. Objective: To determine the cost effectiveness analysis between BNM and M3 monitoring modalities in the management of severe TBI. Methodology: Sixty-two patients with severe TBI admitted to Neuro-ICU, USM who fulfilled the predetermined criteria were selected using systematic random sampling. The macro and micro costing were performed on each of patient. Barthel Index was used to measure physical performance as an outcome six months after discharge. The analyses used were the Independent t- test, ANCOVA, and Repeated Measure ANOVA. Results: The mean total equipment cost of M3 was significantly higher at p = 0.049 (mean difference of RM23.74) after controlling other variables. The mean difference in Barthel Index after six months was significance between the two groups (p = 0.031), patients that were treated with M3 had higher score 163.7 (SD 30.03)J compared to those who were treated with BNM 146.83 (SD 30.36)]. However, the cost-effectiveness ratio of using M3 was significantly lowered (p=O.031) with a mean of RM476.29 was needed to increase a unit improvement in mean Barthel Index compared to RM629.12 if we used BNM. Conclusion: Although M3 is more costly, the outcome of patients treated with M3 was better than that of BNM. Therefore we can conclude that the used of multiple neuro-monitoring was more cost effective than the use of only baseline neuro-monitoring in treating severe traumatic brain injury

Preparation and characterization of magnetite (Fe3O4) nanoparticles By Sol-Gel method

The magnetite (Fe3O4) nanoparticles were successfully synthesized and annealed under vacuum at different temperature. The Fe3O4 nanoparticles prepared via sol-gel assisted method and annealed at 200-400ºC were characterized by Fourier Transformation Infrared Spectroscopy (FTIR), X-ray Diffraction spectra (XRD), Field Emission Scanning Electron Microscope (FESEM) and Atomic Force Microscopy (AFM). The XRD result indicate the presence of Fe3O4 nanoparticles, and the Scherer`s Formula calculated the mean particles size in range of 2-25 nm. The FESEM result shows that the morphologies of the particles annealed at 400ºC are more spherical and partially agglomerated, while the EDS result indicates the presence of Fe3O4 by showing Fe-O group of elements. AFM analyzed the 3D and roughness of the sample; the Fe3O4 nanoparticles have a minimum diameter of 79.04 nm, which is in agreement with FESEM result. In many cases, the synthesis of Fe3O4 nanoparticles using FeCl3 and FeCl2 has not been achieved, according to some literatures, but this research was able to obtained Fe3O4 nanoparticles base on the characterization results

Reclassifying forest type to a new forest class based on vegetation and lithology characteristics using geographic information system at southern Johore, Malaysia

Recently forest resources management with regard to precision forestry concept has been highlighted by forest managers, in order to fulfill the demand on quality and reliable information about forest area. According to the Malaysian National Forestry Act 1984, forest is classified into several types by general classification which is based on vegetation types broadly into dipterocarp forest, peat swamp forest and mangrove forest. In applying precision forestry approach, details classification and information are required to render more accurate about managed forest. Therefore, this study was carried out to reclassify forest type to a new forest class based on vegetation and lithology characteristic using GIS technique. Ten new classes were successfully generated and mapped by fusing layer of forest vegetation types and lithology layer in Southern Johore, namely Dipterocarp-Igneous, Dipterocarp-Sediment, Dipterocarp-Alluvial, Peat-Igneous, Peat-Sediment, Peat-Alluvial, Mangrove-Igneous, Mangrove-Sediment, Mangrove-Alluvial and Limestone forest. In this study, Syzygium spp. (19.83 %) was found in abundance in two new forest classes; Dipterocarp-Igneous and Dipterocarp-Sediment forest in Hulu Sedili Permanent Forest Reserve (PFR). Beside that, Elateriospermum tapos (9.92 %) and family of Lauraceae (7.22 %) were found to be the most dominant species in the Dipterocarp-Sediment forest, while Macaranga spp. (11.21 %) and Elateriospermum tapos (11.02 %) found dominant in Dipterocarp-Igneous forest. From the sample plot, Dipterocarpaceae family constituted only 3.09 % whereas the non-Dipterocarpaceae family was 96.91 %. Hence, this study indicated that there is variation in species dominancy at different lithology of the same forest vegetation site

Mangrove mapping using Landsat imagery and aerial photographs: Kemaman District, Terengganu, Malaysia

Classification and distribution of mangrove vegetation are vital information for the proper development of a mangrove management plan. In this study, classification for the mangroves of the district of Kemaman were done using both 1 : 5000 aerial photographs and Landsat TM imageries. The coverage by aerial photographs is limited to the coastal and estuarine areas only. Thus, for areas further upstream of the aerial photo coverage, Landsat TM imageries were used. Analysis of aerial photographs and remote sensing images revealed that the mangroves of Kemaman could be classified into 14 different classes of vegetation. All the 14 classes were identified from areas covered by the aerial photographs. For areas covered by the Landsat images only 7 classes of vegetation were identified. The accuracy for aerial photograph and Landsat images are 91.2% and 87.8%, respectively. It can be concluded that although both techniques are useful in determining the mangrove vegetation classes, the large 1 : 5000 aerial photographs are more accurate and provided more detailed information comparatively

Rice Yield Estimation Using Below Cloud Remote Sensing Images Acquired by Unmanned Airborne Vehicle System

A method using unmanned airborne vehicle system (UAVS) and image processing technique to enable estimation of rice yield was developed. A digital Tetracam camera was mounted on a CropCam unmanned airborne vehicle (UAV) to acquire red (R), green (G) and near infrared (NIR) images of rice crops at the height of 300 m above ground.  NIR and R values were used to calculate normalised difference vegetation index (NDVI) value. Relationships between yield versus R, G, NIR and NDVI values were analysed. Results showed that the highest relationship was found in NDVI followed by R, G and NIR with coefficient of determination (r2) values of 0.748, 0.727, 0.395 and 0.014 respectively. Therefore, a yield estimation model using NDVI value was developed from the linear regression analysis. The results showed that the model was capable of estimating rice yield with an average accuracy value of 80.3%

The preparation, delivery and outcome of COVID-19 pandemic training program among the Emergency Healthcare Frontliners (EHFs): the Malaysian teaching hospital experience

One of the strategies in strengthening the healthcare providers in mitigating the impact of COVID-19 pandemic is through training. Safety and disease unfamiliarity with COVID-19 was the main reason for developing this dedicated specialized training modules in order to address the issue. The training modules were developed based on three strategies that are learning from experience, design suitable dedicated module and identify weakness and vulnerability. The training modules created were donning-doffing of Personal Protective Equipment (PPE), airway management and cardiopulmonary resuscitation of suspected COVID-19 patients which were delivered through immersive life simulation technique. A total of 178 Emergency Healthcare Frontliners (EHFs) were trained. Each module was guided with a checklist that the participants found to be very useful. None of the participants reported developing symptoms of infection after undergoing the face-to-face simulation training even after two weeks of post-training periods. Seven important steps were found to be crucial that contributed to these findings which included room space, participants number per group, COVID-19 screening, taking of temperature, hand sanitization, PPE, and equipment sanitization before and after training. Hands-on training with guided-checklist was found to be very useful to the EHFs in managing an unfamiliar situation of COVID-19. In time-constraint-resource-limited conditions, training modules should be focused on addressing the pressing problem at hand. In conducting a face-to-face training, precautionary safety measures should be strictly adhered to prevent the spread of the disease

Wasatiyyah Values Appreciation in the Syariah Governance as Consumer Understanding Education Mechanisme Towards Islamic Banking System

The concept and values of wasatiyyah are fundamental in Islamic banking practice as the syariahlaw has delineated basic muamalat principles as an operation guideline for the banking industry.So that, all kind of extreme characteristics may be prevented. The principles of wasatiyyah prohibitany element which overlooks the permitted limit namely gambling, uncertainty (gharar), usury,fraud and so on. These elements have to be avoided in life because they were clearly prohibited byAllah SWT due to their oppressive nature especially towards the unfortunate such as the poor orlow-income group. This prohibition is also attached with the heavy intimidation in the hereafter.The Islamic banking system upholds the concept of wasatiyyah through the values of fairness,trust, transparency and accountability which are instilled through the syariah governance system.The appreciation of these values is proposed to be a part of the new curriculum for the financialawareness subject has been presented by the Malaysia government in the 13th Parliamentarysession. It is important to educate and enhance the understanding of the public especially the youngabout the Islamic banking system as an alternative to avoid the prohibited conventional bankingsystem

Performance Comparison of Downlink Packet Scheduling Algorithms in LTE Network

Long Term Evolution (LTE) was introduced by the Third-Generation Partnership Project (3GPP) and is considered as the latest step towards the fourth generation of radio technology. This paper investigates the performance of well-known packet scheduling algorithms such as Proportion Fair (PF), Maximum- Largest Weighted Delay First (M-LWDF), Exponential Proportion Fair (EXP/PF), Frame Level Scheduler (FLS), Exponential rule (EXP rule), and Logarithmic rule (LOG Rule) in terms of delay, throughput, and packet loss ratio (PLR) by using the LTE-Sim open source simulator. Different traffic types are used, and Simulation results show that in video traffic, FLS and EXP algorithms provide a higher system throughput compared to other algorithms while keeping the delay and packet loss ratio small. However, in the case of best-effort traffic, results show a high delay and PLR with low throughput. The main contribution of this paper is to determine the appropriate downlink scheduling algorithm for VOIP, video, and best-effort traffics in 3GPP LTE

Sensoring Leakage Current to Predict Pollution Levels to Improve Transmission Line Model via ANN

Pollution insulator is a serious threat to the safety operations of electric power systems. Leakage current detection is widely employed in transmission line insulators to assess pollution levels. This paper presents the prediction of pollution levels on insulators based on simulated leakage current and voltage in a transmission tower.The simulation parameters are based on improved transmission line model with leakage current resistance insertion between buses. Artificial neural network (ANN) is employed to predict the level of pollution with different locations of simulated leakage current and voltage between two buses. With a sufficient number of training, the test results showed a significant potential for pollution level prediction with more than 95% Correct Classification Rate (CCR) and output of the ANN showed high agreement with Simulink results