Evaluation of Muscle Activities on Different Type of Exercises During Prolonged Sitting

Electromyography (EMG) signal is an analysis of electrical signals generated during muscular contractions that have been used to measure and record electrical muscle activity usually applied for medical tests. In this research EMG signal is used to; (1) evaluate muscle activations on different gender during prolonged sitting task; (2) investigate the influence of various types of exercise during prolonged sitting on muscle activation and (3) proposed the best exercise that can help to prevent low back discomfort. Twenty subjects (10 males and 10 females) were recruited from undergraduate engineering student’s education background in UniMAP with age ranging between 20 to 24 years old. The subject must be asymptomatic back pain, normal BMI and right-handed. . Three different types of sitting, one hour per sitting were done by each subject in order to reach the goals of this study. First sitting (sitting without exercise) was conducted while second and third sitting (sitting with exercise) were done in order the investigate the effectiveness of the exercises. Two different types of exercise were performed in second and third sitting, both of exercises were chosen from Mc Kenzie’s exercise. EEGOTM sport device were used to record the EMG signal from four types of muscles which are Erector Spinae, Latissimus Dorsi, Internal Oblique and External Oblique. By comparing the Root Mean Square (RMS) values from EMG signals muscle activation during prolonged sitting and the effectiveness of performing the exercises have been evaluated based on RMS values. As the results, muscle become deactivate during prolonged sitting. The best exercise to reduce back pain discomfort is exercise type 1 (Seated Lumbar Exercise) and it is recommended to perform this exercise regularly to reduce the risk of getting hazardous disease due to prolonged sitting.

Detection of topic on Health News in Twitter Data

Electromyography (EMG) signal is an analysis of electrical signals generated during muscular contractions that have been used to measure and record electrical muscle activity usually applied for medical tests. In this research EMG signal is used to; (1) evaluate muscle activations on different gender during prolonged sitting task; (2) investigate the influence of various types of exercise during prolonged sitting on muscle activation and (3) proposed the best exercise that can help to prevent low back discomfort. Twenty subjects (10 males and 10 females) were recruited from undergraduate engineering student’s education background in UniMAP with age ranging between 20 to 24 years old. The subject must be asymptomatic back pain, normal BMI and right-handed. . Three different types of sitting, one hour per sitting were done by each subject in order to reach the goals of this study. First sitting (sitting without exercise) was conducted while second and third sitting (sitting with exercise) were done in order the investigate the effectiveness of the exercises. Two different types of exercise were performed in second and third sitting, both of exercises were chosen from Mc Kenzie’s exercise. EEGOTM sport device were used to record the EMG signal from four types of muscles which are Erector Spinae, Latissimus Dorsi, Internal Oblique and External Oblique. By comparing the Root Mean Square (RMS) values from EMG signals muscle activation during prolonged sitting and the effectiveness of performing the exercises have been evaluated based on RMS values. As the results, muscle become deactivate during prolonged sitting. The best exercise to reduce back pain discomfort is exercise type 1 (Seated Lumbar Exercise) and it is recommended to perform this exercise regularly to reduce the risk of getting hazardous disease due to prolonged sitting.

Toughening of Ceramic Shell Mould with Rice Husk Fiber (CSm-RH) to Improve Strength Property and Mould Performance

For ages, ceramic shell mould (CSm) have been extensively applied in investment casting industry. The formation of CSm requires multiple steps of dipping, layering drying and firing stages. The later steps are very crucial as the solidification thin layer CSm that consist of loose ceramic particles easily cracks when exposed to the higher thermal effect. The inclusion of fiber or any reinforces phases is able to enhance fired ceramic body and also strengthen the green ceramic structure. Thus, the feasibility of rougher NaOH treated rice husk fiber (RHT) prior embedded into composited structure has shown a significant CSm improvement by induced a better adhesion properties and larger bonding area with brittle ceramic matrix, resulted in increased green strength (1.34 MPa) and fired body strength (4.32 MPa). Owing to the decomposed of lignin layer in CSm with untreated rice husk fiber (CSm-RHU) exhibited a higher porosity that provide a better permeation paths of air flow during molten metal pouring as increased 30 % from the standard CSm permeability, giving an enormous benefit for investment casting cooling process. Overall, the incorporation of RHT fiber in a CSm matrix of both green and fired body governed in toughening of brittle ceramic body, hence avoid failure to the casting mould.

Digital-Health Tourism Research-Methodology Coronavirus-Vaccination Trials: A Study Interpreting Geometa-Data Profiling to use Mobile-Health Technologies Nigeria

Digital-Health Tourism Innovation (DTI) worldwide is in its infancy due to the emergent of coronavirus (COVID-19) disease. With the growth of open geometa data, use of government electronic services including electronic health (e-health), electronic commerce (e-commerce) and mobile health (m-health), Artificial Intelligence (AI) and machine learning strategies. Health and primary healthcare sectors are currently adopting these innovations for socio-economic wellbeing. Digital-health (also termed as e-health) is part of digital tourism innovation. Adapting geometa data profiling to develop a digital-health tourism framework for Primary Healthcare Workers (PHWs) to use mobile health technologies in COVID-19 vaccination trials are the key challenges of this study. Nevertheless, digital health tourism skills have been launched in developing Nations that created thousands of jobs to protect digital tourism businesses from potential vulnerabilities. Despite the benefits of this novel innovation, its deployment and implementation have been treated by inadequate of ICT facilities, lack of geometa data pre-processing to remove noise, data integrity, insufficient of academic research fundings, and reliable research methodology beyond COVID-19 vaccination trials to highlight these aspects. Therefore, qualitative, and quantitative research methods using Precaution Adoption Model Process (PAMP) questionnaire are employed to enable new ways of pre-processing behavior intention factors items. Eight academic researchers who were conversant with digital health technology validated 28 behavior intention factors with average factor loading values of 50% to 75%. Pilot survey conducted among 700 respondents from March 18, 2020, to September 10, 2021, among them are undergraduate students that may use this technology for research purposes. Pre-processed geometa data have shown percentage frequency counts of internet access and other online services 8% to 95%, adapted training factors 49% to 92% and factor items 34% to 78.3% for hypothesis generation towards development of digital health tourism framework in finding explanation to COVID-19 economic challenges. Except behavior intention factors and factor items insights are known and mapped, mobile health technology design process may result in poor conclusions. Thus, patients recovered from COVID-19 infection can still be infected again.

Conceptual Paper: Sentience of Big Data towards User Privacy Concerns and Online Self-Disclosure Activities

Big data allows widespread use and exchange of user data, and this will lead to the possibility of privacy breaches. Governments and corporations will incorporate personal data from different sources and learn a great deal about people and in turn, raise concerns about privacy. This paper will provide a conceptual understanding on the antecedents towards user privacy concerns and online self-disclosure activities, which are the knowledge and perceived risks of big data. In this paper, big data knowledge is hypothesized to decrease privacy concerns, meanwhile perceived risks is suggested to increase the outcome. Based on the framework, propositions are formulated as a basis for the study that will follow.

Visual Decision Support System Model for Managing Unemployment by Hybrid Education

Hybrid eeducation is an approach that helps in conducting face-to-face and online ddistance learning especially for formal education. However, during COVID-19 pandemic, formal education institutions including schools and universities as well as informal education tthrough the media for communities and industries are practiced by the lawmakers to manage workplace distancing. Food supply chain are experiencing unexpected disruptions due to interrelated activities that will determine supply chain performance and efficiency. This article explores the challenges faced by food industry and related sectors due to emergence impact of COVID-19 in Malaysia. Food supply chain management (FSCM) is a complex task involving interconnected activities and numerous stakeholders for a specific food product from raw materials to final product before it reaches the end customers. An overview of COVID-19 scenarios, followed by the impact of pandemic towards the activities in food supply chain, and the management issues that need to be addressed are presented. Several challenges identified are including the worldwide implication of the pandemic scenarios to food supply chain management and food supply chain activities, and indirect impact of supply chain disruption to labour force; unemployment and retrenchment. Finally, a visual decision support system (VDSS) model is developed to illustrate the main impacts of the pandemic scenario on food supply chain as well as addressing the labour management and opportunities. It has potential to provide general guidelines to educators, policy-makers, and decision-makers to deal with uncertainties after the pandemic.

Decolourisation of Sewage using Commercial Tannin-Based Coagulant

In this work, the effectiveness of removing colour and total suspended solid (TSS) from complex organic particulate artificial sewage (COPAS) via the coagulation process was studied. Organo-floc (OF), a tannin-based coagulant derived from the plant was used as a coagulant. OF was applied in a jar test experiment at the various dosages at 57 rpm for 5 min, and 30 min sedimentation time to find out the optimum dosage for colour and TSS removal. Zeta potential, pH and conductivity were also analysed. Experiment with tannin dosage of 125 ppm resulted in the highest colour and TSS removal of 94% and 79%, respectively. Organo-floc showed promising results in decolourisation and TSS removal from artificial sewage.

The Optimization of RHS-polysulfone Membrane towards Operating Condition for Humic Acid Removal

This study investigates the effects of rice husk silica (RHS) as additive in the polysulfone membrane to enhance antifouling properties in membrane separation process. The performance (of what?) was evaluated in term of pure water flux (PWF), rejection and antifouling properties. The optimized of normalized flux (Jf /Jo) at different parameter in filtration (pH, ionic strength and tranmembrane-pressure) was carried out by using the response surface methodology (RSM). The results showed that the addition of 4 wt. % RHS give the highest flux at 300.50 L/m².hour (LMH). The highest rejection was found at 3 wt. % of RHS membrane with value 98% for UV254 and 96% for TOC. The optimal value of Jf/Jo was found at 0.62 with the condition of pH: 6.10, ionic strength: 0.05 mol/L and transmembrane-pressure: 2.67 bars. Optimize of RSM analysis from ANOVA also proved that the error of model is less than 0.05% which indicates that the model is significant.

Effect of Kaolin Particle Size Towards Preparation of Kaolin Ceramic Membrane

The purpose of this work is to study the effect of kaolin particle size for the preparation of low cost ceramic membrane suspension and ceramic membrane structure. Kaolin particle size is categorized into two categories; i) ≤ 1µm and ii) ≥ 1 µm. The suspension is prepared via stirring technique under 1000 rpm at 60°C. The particle size of kaolin is characterized using field emission scanning electron microscope (FESEM) and the prepared suspension is characterized in term of its viscosity. Results indicate that the particle size gave significant effect to the viscosity of ceramic membrane suspension. Preliminary data showed that kaolin with particle size ≤ 1µm resulted ceramic membrane with dense structure.

A Brief Review on Thermal Behaviour of PANI as Additive in Heat Transfer Fluid

Since a decade ago, investigation on nanofluids has grown significantly owing to its enhanced thermal properties compared to conventional heat transfer fluids. This engineered nanofluid has been widely used in the thermal engineering system to improve their energy consumption by improving the thermal efficiency of the system. The addition of nano-size particles as additives dispersed in the base fluids proved to significantly either improve or diminish the behaviour of the base fluids. The behaviour of the base fluid highly depends on the properties of the additives material, such as morphology, size, and volume fraction. Among the variety of nanoparticles studied, the conducting polymers have been subject of high interest due to its high environmental stability, good electrical conductivity, antimicrobial, anti-corrosion property and significantly cheap compared to other nanoparticles. As such, the main objective of the present review is to provide an overview of the work performed on thermal properties performance of conducting polymers based nanofluids.