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Background Neck of femur fractures result in impaired function for older people. Despite surgery, many patients experience a decrease in functional level and poorer health status after the injury. The objectives of this study were (1) to determine the short-term mobility and hip function of geriatric patients who underwent hip replacement surgery for a displaced neck of femur fracture in our local population and (2) to identify factors which affect the functional outcome of these patients. Methods Patients aged 60 years and above, who were admitted for neck of femur fracture from January 2017 to December 2020, and treated surgically with arthroplasty, were included. Information on patient demography, comorbidities, perioperative data, mobility, hip function and complications were retrospectively collected. Outcome measures used were independent ambulation and recovery of pre-fracture mobility at 1 year after surgery while hip function was assessed using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) total, pain, stiffness, and physical function scores. Factors associated with these outcomes were analyzed. Results 168 patients with a mean age of 75.2 (SD 8.4) years met the inclusion criteria. 32.1% of patients regained their pre-fracture mobility and 59.6% remained independent 1 year after surgery. Logistic regression analysis identified age, gender, surgical procedure, and time to surgery as significant contributors to recovery of pre-fracture mobility. Older age and increasing requirement for postoperative ambulatory aid resulted in worse WOMAC total and physical function scores. No significant differences were observed in patient-reported hip function between those who had a total hip arthroplasty and those who had a hemiarthroplasty. Conclusion Most geriatric patients with displaced neck of femur fractures did not regain pre-fracture mobility despite surgical treatment with arthroplasty.

This study aims to investigate the determinants that influence level of job satisfaction and the relationship on work environment, payment, promotion opportunities, supervision and nature of job towards job satisfaction among management and professional officers in DBKL. The population was selected due to the diversity of the officer’s group, particularly in terms of age, gender and race. The stratified sample size of 217 respondents were collected through questionnaires. Data were analyzed using descriptive and inferential statistics with the aid of Statistical Packages for Social Scientists (SPSS) version 26.0. The collected data was tested and analyzed using descriptive analysis, reliability testing, multiple linear regression, Pearson correlation coefficient analysis and one-way ANOVA. Findings have shown significant, positive and strong relationship between all the independent variables and the dependent variable. DBKL as a local authority for capital city of Malaysia may use this study as a reference in order to make a right decision towards job satisfaction among the employees. In addition, the study impacts upon the future performance of the organization by taking all the factors that influence job satisfaction more seriously within their organizations to increase the motivation and commitment level of their employees. As a conclusion, the top management may relook and further improve aspects that influence the job satisfaction, design better work policies and provide a conducive work environment to enhance job satisfaction as well as performance and productivity of the employees.

Abstract Piper sarmentosum is a herbaceous shrub with numerous pharmacological benefits. However, the presence of two toxic phenylpropanoids (α- and β-asarone) limits the medicinal usage of the plant. In this study, the extraction of three asarone isomers, namely α-, β-, and -asarone was optimised using supercritical carbon dioxide extraction (SC-CO2) combined with Box-Behnken experimental design. Comparison of asarone contents in different conventional solvent extracts of P. sarmentosum leaves prior to and after SC-CO2 extraction was performed. The SC-CO2 method successfully maximised the extraction of α-, β-, and ɣ-asarone at P = 81.16 bar, T = 50.11°C, and t = 80.90 min, yielding 13.91% α-asarone, 3.43% β-asarone, and 14.95% ɣ-asarone. The SC-CO2 residue of the leaves re-extracted with conventional solvents showed a significant decrease of asarone ranging from 45% to 100% (p

Abstract The rapid spread of the SARS-CoV-2 in the COVID-19 pandemic had raised questions on the route of transmission of this disease. Initial understanding was that transmission originated from respiratory droplets from an infected host to a susceptible host. However, indirect contact transmission of viable virus by fomites and through aerosols has also been suggested. Herein, we report the involvement of fine indoor air particulates with a diameter of ≤ 2.5 µm (PM2.5) as the virus’s transport agent. PM2.5 was collected over four weeks during 48-h measurement intervals in four separate hospital wards containing different infected clusters in a teaching hospital in Kuala Lumpur, Malaysia. Our results indicated the highest SARS-CoV-2 RNA on PM2.5 in the ward with number of occupants. We suggest a link between the virus-laden PM2.5 and the ward’s design. Patients’ symptoms and numbers influence the number of airborne SARS-CoV-2 RNA with PM2.5 in an enclosed environment.

Abstract Background Indoor air quality (IAQ) is a concern in kindergartens as children spend much of their time there. Yet, there is a shortage of biological indicators needed for assessing IAQ. Thus, this study evaluated IAQ using transplanted lichen Usnea misaminensis as a biological indicator. Methods Lichen samples, collected from Bukit Larut, Perak, Malaysia, were exposed to indoor and outdoor environments in an urban area (Ummi Aiman Kindergarten) and a rural area (Ummi Qaseh Pelangi Kindergarten) for 2 months during August 15 to October 14, 2019. The concentrations of 12 selected elements and the vitality of the lichens were then evaluated. Results Increased concentrations of eleven of the twelve elements deposited in the lichen samples in both urban and rural areas were observed. For both areas, the element concentrations in the samples from the indoor environment was lower than those from the outdoor environment, and those in the rural area were lower than those from in the urban area, suggesting the impacts of traffic emissions. The vitality of the lichens showed no significant change in indoor environment, compared to that in outdoor environment, indicating that even exposed to indoor environment, the lichens remained effective biological indicators as same as they were in the outdoor environment. Conclusions Lichens are effective biological indicators for both outdoor and indoor environments. Furthermore, outdoor emissions could influence IAQ, which could be problematic in densely populated areas such as kindergartens. Mitigation measures should be taken.

This paper presents a critical assessment analysis on mental health detection in Online Social Networks (OSNs) based on the data sources, machine learning techniques, and feature extraction method. The appropriateness of the mental health detection was also investigated by identifying its data analysis method, comparison, challenges, and limitations. This study reviewed articles published in major databases between 2007 and 2018 through keyword searches. The articles were screened base on their titles and abstracts before the full texts were reviewed. The articles were coded in accordance with data set (e.g., data sources, keywords, and geographical locations), method of data analysis, machine learning or deep learning technique, classifier performance, and feature extraction method. 22 articles were selected for review from the total of 2770. As OSNs exhibit high potential as a data source in early detection of mental health problems, most researchers used text analysis on a new data set extracted from different OSNs sources. The extracted data were examined using a statistical analysis or machine learning techniques. Several studies also applied multimethod techniques, which included distributing questionnaires while requesting for the respondents' consent to later access and extract information from his/her OSNs account. Big data in OSNs contribute on mental health problem detection. The presented method is an alternative approach to the early detection of mental health problems rather than using traditional strategies, such as collecting data through questionnaires or devices and sensors, which are time-consuming and costly. However, mental health problem detection through OSNs necessitates a comprehensive adoption, innovative algorithms, and computational linguistics to describe its limitations and challenges. Moreover, referrals from mental health specialists as subject matter experts are also required to help obtain accurate and effective information.

Abstract – Autonomous intelligent vehicles are keep expanding under a real development in this era after many researches have been made to implement a better safety features in a transportation sector. In this paper, an algorithm of recognition of the traffic light arrow in the daytime specifically for the operation of the video system has been proposed using the technology of the image processing and machine learning. In the machine learning technique, the Support Vector Machine, SVM classifier has been used for the learning process and conduct a classification process as well. Firstly, the images are converted to Hue Saturation Value (HSV) color space while the color thresholding has been used to obtain the region of interest for the traffic light arrow in the image. In the learning process, each of the traffic light arrows in the dataset will go through the pre-processing phase, detection phase before going through the hog descriptor for feature extraction purpose. Prior to send the image to the SVM, the hog descriptor process is to extract the features for each of the shape of the traffic light arrow. As a result, this algorithm has achieved 98.52% accuracy, 1.48% error, 98.52% precision and 98.68% of recall through the testing process.

An exploration task can be performed by a team of mobile robots more efficiently than human counterparts. They can access and give live updates for hard-to-reach areas such as a disaster site or a sewer. However, they face some issues hindering them from optimal path planning due to the symmetrical shape of the environments. Multiple robots are expected to explore more areas in less time while solving robot localization and collision-avoidance issues. When deploying a multi-robot system, it is ensured that the hardware parts do not collide with each other or the surroundings, especially in symmetric environments. Two types of methods are used for collision avoidance: centralized and decentralized. The decentralized approach has mainly been used in recent times, as it is computationally less expensive. This article aims to conduct a systematic literature review of different collision-avoidance strategies and analyze the performance of innovative collision-avoidance techniques. Different methods such as Reinforcement Learning (RL), Model Predictive Control (MPC), Altruistic Coordination, and other approaches followed by selected studies are also discussed. A total of 17 studies are included in this review, extracted from seven databases. Two experimental designs are studied: empty/open space and confined indoor space. Our analysis observed that most of the studies focused on empty/open space scenarios and verified the proposed model only through simulation. ORCA is the primary method, against which all the state-of-the-art techniques are evaluated. This article provides a comparison between different methods used for multi-robot collision avoidance. It discusses if the methods used are focused on safety or path planning. It also sheds light on the limitations of the studies included and possible future directions.

Air quality monitoring is important in the management of the environment and pollution. In this study, time series of PM10 from air quality monitoring stations in Malaysia were clustered based on similarity in terms of time series patterns. The identified clusters were analyzed to gain meaningful information regarding air quality patterns in Malaysia and to identify characterization for each cluster. PM10 time series data from 5 July 2017 to 31 January 2019, obtained from the Malaysian Department of Environment and Dynamic Time Warping as the dissimilarity measure were used in this study. At the same time, k-Means, Partitioning Around Medoid, agglomerative hierarchical clustering, and Fuzzy k-Means were the algorithms used for clustering. The results portray that the categories and activities of locations of the monitoring stations do not directly influence the pattern of the PM10 values, instead, the clusters formed are mainly influenced by the region and geographical area of the locations.

Air pollution has recently become a subject of increasing concern in many parts of the world. The World Health Organization (WHO) estimated that nearly 4.2 million early deaths are due to exposure to fine particles in polluted air, which causes multiple respiratory diseases. Algae, as a natural product, can be an alternative treatment due to potential biofunctional properties and advantages. This systematic review aims to summarize and evaluate the evidence of metabolites derived from algae as potential anti-inflammatory agents against respiratory disorders induced by atmospheric particulate matter (PM). Databases such as Scopus, Web of Science, and PubMed were systematically searched for relevant published full articles from 2016 to 2020. The main key search terms were limited to “algae”, “anti-inflammation”, and “air pollutant”. The search activity resulted in the retrieval of a total of 36 publications. Nine publications are eligible for inclusion in this systematic review. A total of four brown algae (Ecklonia cava, Ishige okamurae, Sargassum binderi and Sargassum horneri) with phytosterol, polysaccharides and polyphenols were reported in the nine studies. The review sheds light on the pathways of particulate matter travelling into respiratory systems and causing inflammation, and on the mechanisms of actions of algae in inhibiting inflammation. Limitations and future directions are also discussed. More research is needed to investigate the potential of algae as anti-inflammatory agents against PM in in vivo and in vitro experimental models, as well as clinically.

Agarwood has been used widely in various ways, including traditional medicine and art. The usage of agarwood has grown broader in modern times include in therapeutic medicines and perfumery. In this paper the agarwood waste has been explored to be used as a fluid loss control agent to control fluid loss without affecting the drilling fluid rheological properties which are density, pH, viscosity, yield point and gel strength. Agarwood waste was used as an additive in the drilling fluid system due to its unique characteristic. Rheological and filtration measurements were performed on the formulated water-based drilling fluid. Formulations of a base solution of fresh water, sodium hydroxide, bentonite, barite, and xanthan gum were presented. The performance of the agarwood waste as the fluid loss control agent was compared with based fluid formulation and water-based drilling fluid with treating with conventional fluid loss control agent (starch). The filtrate volume of drilling fluid with agarwood waste was about 13 ml while for drilling fluid with conventional fluid loss control agent, starch gave 12 ml of filtrate volume after undergoing filtration test by using LPLT filter press. The performance of drilling fluid with agarwood was efficient as drilling fluid with starch. Doi: 10.12777/ijse.5.2.101-105 How to cite this article: Azizi, A., Ibrahim, M.S.N., Hamid, K.H.K., Sauki, A., Ghazali, N.A., Mohd, T.A.T. (2013). Agarwood Waste as A New Fluid Loss Control Agent in Water-based Drilling Fluid. International Journal of Science and Engineering, 5(2),101-105. Doi: 10.12777/ijse.5.2.101-105]

Conventional air quality monitoring systems, such as gas analysers, are commonly used in many developed and developing countries to monitor air quality. However, these techniques have high costs associated with both installation and maintenance. One possible solution to complement these techniques is the application of low-cost air quality sensors (LAQSs), which have the potential to give higher spatial and temporal data of gas pollutants with high precision and accuracy. In this paper, we present DiracSense, a custom-made LAQS that monitors the gas pollutants ozone (O3), nitrogen dioxide (NO2), and carbon monoxide (CO). The aim of this study is to investigate its performance based on laboratory calibration and field experiments. Several model calibrations were developed to improve the accuracy and performance of the LAQS. Laboratory calibrations were carried out to determine the zero offset and sensitivities of each sensor. The results showed that the sensor performed with a highly linear correlation with the reference instrument with a response-time range from 0.5 to 1.7 min. The performance of several calibration models including a calibrated simple equation and supervised learning algorithms (adaptive neuro-fuzzy inference system or ANFIS and the multilayer feed-forward perceptron or MLP) were compared. The field calibration focused on O3 measurements due to the lack of a reference instrument for CO and NO2. Combinations of inputs were evaluated during the development of the supervised learning algorithm. The validation results demonstrated that the ANFIS model with four inputs (WE OX, AE OX, T, and NO2) had the lowest error in terms of statistical performance and the highest correlation coefficients with respect to the reference instrument (0.8 < r < 0.95). These results suggest that the ANFIS model is promising as a calibration tool since it has the capability to improve the accuracy and performance of the low-cost electrochemical sensor.

Rain rate influenced by atmospheric factors is related to rainfall patterns affected by climate change. Attenuation and signal losses due to rain are crucial constraints in communication systems such as 5G, microwave radio links, and communication satellite systems. These communication systems have been deteriorated due to signal fading and distortion when exposed to rain. Signal fading by hydrometeor particles in the atmosphere may occur during wireless signal transmissions at frequencies higher than 10 GHz. In this paper, Hydro-Estimator data for the Peninsular Malaysia, Sabah and Sarawak regions from 2011 to 2022 were extracted to determine the rain rate distribution, which is a crucial parameter for simulating and analyzing rain fade. Climate change affects rainfall distribution when signal transmission with higher frequency carries more data, thus resulting in a higher chance of signal loss and degradation. Long-term forecasts of the signal performance in 5G systems and high-frequency radio link frequencies can be developed by analyzing the effects of climate change based on the rainfall rate and the rain fade distribution. [ABSTRACT FROM AUTHOR]