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The objectives of the study are: 1) to describe the design of the Cooperative Translation Based Learning model to improve students’ achievement in class XII; 2) to assess the feasibility of the Cooperative Translation Based Learning model to improve students’ achievement in class XII; and 3) to measure the effectiveness of the Cooperative Translation Based Learning model to improve students’ achievement in class XII. The type of research is Research and Development (R&D). The data of this research are English learning activities in class XII of SMKIT Nur Hasan Boarding School; data on the achievement of learning English in class XII of SMKIT Nur Hasan Boarding School. Data were collected through tests, observations, and questionnaires. Data were analyzed using inferential analysis and qualitative analysis. The feasibility test for developing the Cooperative Translation Based Learning model shows 96.7% with very practical criteria. Under the practicality criteria in the large group trial, the learning module “Cooperative Translation Based Learning for Writing Skill Grade XII” in learning to write English Business letters is stated to be very practical and feasible. The effectiveness test of the Cooperative Translation Based Learning model shows that the output of the Paired Samples Test has a Sig. (2-tailed) is 0.000 < 0.05; therefore, H0 is rejected and H1 is accepted. It can be concluded that there is a difference in the average value of learning outcomes between pre-test and post-test results. Therefore, developing the learning module “Cooperative Translation Based Learning for Writing Skill Grade XII” can improve writing learning skills.

Recurrent laryngeal nerve (RLN) injury is one of the main complications of total thyroidectomy. If the injury is bilateral, total airway obstruction, aphonia and hoarseness of voice could be precipitated. Hence, it is wise for the operating surgeon to be guided by neural monitoring during thyroidectomy. We present a valuable experience handling a middle-aged man with a huge papillary thyroid carcinoma . He needed an urgent thyroidectomy due to obstructive symptoms. We highlight our intraoperative dexterity in handling his surgery in the context of continuous monitoring of RLN using electromyography.

The construction sector plays an important role in a country's economy. This is because construction projects such as the construction of buildings, roads, bridges and other infrastructure are one of the benchmarks of economic progress and civilization of a country. Work accidents on construction projects can cause work to stop and result in financial losses and decreased work productivity. According to the Minister of Manpower in 2018 the number of work accidents has increased from the previous year even from the data of the Central Statistics Agency stating that the majority of construction workers are junior high school graduates and below. This is one of the factors causing the increase in occupational accidents in the construction sector. Losses from work accidents are also included in workers' losses, damage to equipment and materials wasted due to work accidents. Occupational health and safety (K3) risk control is very important as a preventive effort to prevent a bigger event. One such control is the use of Personal Protective Equipment (PPE) or more commonly called personal protective equipment (PPE). The existence of PPE is important for workers to minimize the impact of accidents so that each company is obliged to use PPE. This study aims to minimize the number of work accidents in Indonesia, especially in the construction sector. The method used in making the body vest is the addition of an airbag by applying the fall detection algorithm to the K-Nearest Neighbor (KNN), which is to calculate the Euclidean distance which is the distance between the sample and training data and then determine the k nearest data from the sample so that the sample can be classified on the sensor and microcontroller. The way it works is when a collision or a hard collision occurs, workers will generally be thrown or dropped then there will be a change in the acceleration of the position of the body wearing a body vest. The change in acceleration triggers the development of airbags on the body vest. This is expected to reduce injuries to vital organs in the worker's body.

Power developed by the wind turbines, at different wind velocities, is a key information required for the successful design and efficient management of wind energy projects. Conventionally, for these applications, manufacturer’s power curves are used in estimating the velocity–power characteristics of the turbines. However, performance of the turbines under actual field environments may significantly differ from the manufacturer’s power curves, which are derived under ‘standard’ conditions. In case of existing wind projects with sufficient performance data, the velocity–power variations can better be defined using artificially intelligent models. In this paper, we compare the performance of four such models by applying them to a 2-MW onshore wind turbine. Models based on ANN, KNN, SVM and MARS were developed and tested using the SCADA data collected from the turbine. All the AI models performed significantly better than the manufacturer’s power curve. Among the AI methods, SVM-based predictions showed the highest accuracy. A site-specific performance curve for the turbine, based on the SVM model, is presented. Wider adaptability of this approach has been demonstrated by successfully implementing the model for a 3.6-MW wind turbine, working under offshore environment. Being “site-specific data” driven, the proposed models are more accurate and hence better choice for applications like short-term wind power forecasting and pro-diagnostics of wind turbines.

Purpose of the study: The purpose of this research is to unearth English teaching, delivering and translating the holy book of instructional design in Pesantren Budi Utomo (PBU) which is the Indonesian moderate religious-based school of East Java, Indonesia in terms of kind of syllabus, learning objectives, materials instruction, evaluation, teaching designs, vigor and its debility of instructional design in English teaching. Methodology: It is qualitative research with ethnographic design. The data conducted in this research are the intended activities in the teaching-learning process, informants, and essential documents relied on the teaching-learning process. The data is collected through observation, interviews, and documentation. The collected data are analyzed through data reduction, categorization of data, and synthesis and arrange work hypothesis. Main Findings: The syllabus that applies as a reference for preparing lesson plans is based on the National Curriculum and the Pesantren (religious-based school) Curriculum. The learning objective includes general learning and specific learning purpose. The learning objective of English learning at PBU is in line with English for Specific Purposes (ESP). The application of curriculum is 50% for the religious-based school curriculum and 50% for the government curriculum. There are many instructional teaching materials such as print, audio, and visual and non-printed. Class activities are another sketch to make students spend their full attraction and attention. Evaluations are conducted periodically. Applications of this study: The syllabus produced from this study is used by the religion-based Islamic school at the secondary level. It has a function to become a bridge for language learning to ease the students for English acquisition. Novelty/Originality of this study: Some similar researches were done to find out more about translation but it has not found yet research that focused on creating a syllabus to facilitate the student in learning the holy book of Islam through translation.

Anaesthesia for patients with severe lung fibrosis post COVID-19 infection requires special consideration. This is due to its propensity to cause perioperative anaesthetic catastrophe and possibility of cross infection among healthcare workers if not properly managed. This interesting article elaborates in detail the anaesthetic and surgical challenges in a morbidly obese patient who had a severe COVID-19 infection presenting for an elective spine surgery.

A novel liquid-infiltrated photonic crystal fiber model applicable in liquid sensing for different test liquids—water, ethanol and benzene—has been proposed. One core hole and three air hole rings have been designed and a full vector finite element method has been used for numerical investigation to give the best results in terms of relative sensitivity, confinement loss, power fraction, dispersion, effective area, nonlinear coefficient, numerical aperture and V-Parameter. Specially, the assessed relative sensitivities of the proposed fiber with water, ethanol and benzene are 94.26%, 95.82% and 99.58%, respectively, and low confinement losses of 1.52 × 10−11 dB/m with water, 1.21 × 10−12 dB/m with ethanol and 6.01 × 10−16 dB/m with benzene, at 1.0 μm operating wavelength. This novel PCF design is considered simple and can be easily fabricated for practical use, and the assessed waveguide properties has determined the potential applicability in real liquid sensing applications.

Due to the recent environmental concerns and long-term challenges in energy security, the Global energy scenarios are shifting more towards sustainable and renewable energy resources. Brunei has planned to increase the use of cleaner energy technologies by contributing 10 percent or 954 GWh of renewable energy in its power generation mix by 2035. Out of the available renewable options, solar is the most promising one for Brunei, for example, the daily average solar installation is around 5kWh per day [1]. Though solar energy is an abundant resource, for optimally designing and successfully managing solar power projects, its availability in different time scales are to be analyzed and understood in a local context. In this paper, we present models for estimating the output of six different solar PV technologies using machine learning methods Performance data from the solar PV systems installed at the Tenaga Suria PV plant in Brunei are used to develop the models. Influence of relevant environmental parameters, such as irradiance, relative humidity, ambient temperature and wind speed on the power output has been analyzed for optimal feature selection. Performance models based on Support Vector Machine (SVM) and K-Nearest Neighbour (KNN) were developed and tested for predicting the PV system performance. Both the models could estimate the system performance with reasonably high level of accuracy.

Agriculture remains as one of the important economic sectors in Malaysia which provides an employment for more than 1.6 million people. However, the growth of this sector may be hampered by a small-scale production, limited technological application, declining number of arable lands, environmental degradation due to climate change, rapid urbanization and aging farmers. In order to improve the competitiveness of the agricultural sector, farmers are encouraged to fully utilise modern technologies in their farms. In this context, adoption of industrial revolution 4.0 (IR4.0) in agricultural sector could bring many benefits, especially in minimizing the production costs and improving the quality of the products. Thus, this review focuses on the adoption strategies of IR4.0 into agricultural sector in Malaysia. A suitability of enabling technologies such as IoT, autonomous robot, big data analytics and artificial intelligent which are pillars for IR4.0 are individually evaluated. The readiness of agricultural industry in Malaysia to embrace this new concept is also discussed. The review also investigates the potentials and possible challenges would be faced by the industry in embracing IR4.0. The recommendations are also provided for farmers, industrial players and policy makes to makes sure a smooth adoption of IR4.0 into agricultural sector in Malaysia.

Frequent haze episodes commonly caused by biomass burning has been a concern in South East Asia. One of such events was the June 2013 severe haze in the region. This study assessed the ability of WRF-Chem in capturing the spatial variability and concentrations of particulate emissions during this period. It analyzed the regional biomass burning emissions and its transport leading to higher particulate matter levels in the region. In order to analyze the effect of grid-scale, the horizontal resolution of the simulation was varied between low-resolution (100 km) and high-resolution (20 km). Evaluations of the simulations were made against meteorological observations pertinent to emission and transport of particulate matter, including surface and vertical air profile variables such as temperature, relative humidity, and wind speed and direction. Particulate matter (PM10 and PM2.5) levels were evaluated using ground measurements in Brunei and Singapore respectively. The meteorological parameters were adequately represented across the model simulations. Increasing the horizontal resolution of the simulations generally improved the representation of meteorology and air quality but some prognostic variables maintained similar or better performance with coarse resolution simulation. With the high-resolution simulation, PM10 concentration in Brunei had a correlation coefficient around 0.4, and the simulated PM2.5 level in Singapore had correlation coefficient around 0.9. Whereas, the low-resolution simulation had correlation coefficients around 0.2 and 0.8 for PM10 and PM2.5 levels at Brunei and Singapore, respectively. Both simulations could not repeat aerosol optical depth (AOD) from reanalysis unless the biomass burning emissions were enhanced. An enhancement factor of 6 with high-resolution simulation gave PM10 and PM2.5 correlations around 0.6 and 0.9 in Brunei and Singapore respectively.

Leptospirosis is a zoonotic disease caused by the pathogenic species of Leptospira. The initial symptoms include fever, myalgia, nausea, skin rash, chills, and headache, which can be misdiagnosed. LipL32 is the highly conserved and abundant outer membrane protein (OMP) of Leptospira, which is used as an antigen in serodiagnostic assays. We used three in silico methods to predict the immunodominant regions in the full-length LipL32 protein. We identified three regions, namely the N-terminus (NrLipL32, amino acid sequence 20th-120th), intermediate (amino acid sequence 120th-150th), and C-terminus (CrLipL32, amino acid sequence 160th-260th) regions. The full-length protein and two larger fragments were cloned into the pET22b plasmid and expressed in Escherichia coli BL21 (DE3). The purified proteins were used as antigens in an ELISA to detect Leptospira-specific antibodies. The CrLipL32 ELISA showed the highest sensitivity for IgM (73.3%) and IgG (65%), followed by the full-length rLipL32 ELISA (IgM 68% and IgG 60%). The full-length rLipL32 ELISA showed high specificity (IgM 85% and IgG 75%), followed by the NrLipL32 ELISA (IgM 75% and IgG 60%). The intermediate fragment showed very low sensitivity (IgM 17% and IgG 2%). The sensitivity of the rLipL32 ELISA could be enhanced by adding other OMPs of Leptospira.

Particulate matter (PM10) is the pollutant causing exceedances of ambient air quality thresholds, and the key indicator of air quality index in Brunei Darussalam for haze related episodes caused by the recurrent biomass fires in Southeast Asia. The present study aims at providing suitable forecasts for PM10 exceedances to aid in health advisory during haze episodes at the four administrative districts of the country. A framework based on random forests (RFs), genetic algorithm (GA) and back propagation neural networks (BPNN) computational intelligence techniques has been proposed in which the final prediction is made by the BPNN model. A hybrid combination of GA and RFs is initially applied to determine optimal set of inputs from the initial data sets of largely available meteorological, persistency of high pollution levels, short and long term variations of emissions rates parameters. The inputs selection procedure does not depend on the back propagation training algorithm. The numerical results presented in this paper show that the proposed model not only produced satisfactory forecasts but also consistently performed better via several statistical performance indicators when compared with the standard BPNN and GA optimisation based on back propagation training algorithm. The model also showed satisfactory threshold exceedances forecasts achieving for instance best true predicted rate of 0.800, false positive rate of 0.014, false alarm rate of 0.333 and success index of 0.786 at Brunei-Muara district monitoring station. Overall, the current study has profound implications on future studies to develop a real-time air quality forecasting system to support haze management.

The novel perovskite oxide series of Sm1-xZrxFe1-yMgyO3 (x,y = 0.5, 0.7, 0.9) were synthesized by solid state reaction method. X-ray diffraction (XRD), Rietveld refinement, scanning electron microscopy (SEM), electrochemical impedance spectroscopy (EIS) and conductivity analysis were carried out. XRD patterns of sintered materials revealed the shifted Bragg reflection to higher angle for the higher content of Zr and Mg. This is related to the ionic size of the dopant elements. Rietveld refinement showed that all compounds crystallized in cubic space group of Fm-3m. SEM images showed that the grains were well defined with highly dense surfaces makes it potential as an electrolyte material in solid oxide fuel cells (SOFCs) or gases sensors. Impedance spectroscopy at 550-800 °C shows that conductivity is higher at higher temperature. Sm0.5Zr0.5Fe0.5Mg0.5O3 shows the highest conductivity of 5.451 × 10-3 S cm-1 at 800 °C. It was observed that 50% molar ratio of Mg and Zr doping performed highest conductivity.