Development of IoT Based Mobile Robot for Automated Guided Vehicle Application

Mobile robot has been one of the researches focuses in this era due to the demands in automation. Many industry players have been using mobile robot in their industrial plant for the purpose of reducing manual labour as well as ensuring more efficient and systematic process. The mobile robot for industrial usage is typically called as Automated Guided Vehicle (AGV). The advances in the navigation technology allows the AGV to be used for many tasks such as for carrying load to pre-determined locations sent from mobile app, stock management and pallet handling. More recently, the concept of Industry 4.0 has been widely practiced in the industries, where important process data are exchange over the internet for an improved management. This paper will therefore discuss the development of Internet of Things (IoT) bases mobile robot for AGV application. In this project a mobile robot platform is designed and fabricated. The robot is controlled to navigate from one location to another using line following mechanism. Mobile App is designed to communicate with the robot through the Internet of Things (IoT). RFID tags are used to identify the locations predetermined by user. The results show that the prototype is able to follow line and go to any location that was preregistered from the App through the IoT. The mobile robot is also able to avoid collision and any obstacles that exist on its way to perform any task inside the workplace

Annealing effect on structural and electrochemical performance of Ti-doped LiNi1/3Mn1/3Co1/3O2 cathode materials

NMC 111 cathode materials exhibit engaging properties in high energy density and low cost, making it great potential for the next generation of high-energy lithium-ion batteries. However, it still faces challenges such as fast capacity fade, especially at high C rates. Herein, we implement the novel Ti-doped cathode material, LiNi0.3Mn0.3Co0.3Ti0.1O2 (NMCT) synthesized via the combustion method. It was discovered that NMCT can effectively improve capacity delivery at high C rates. The T80 material demonstrated superior electrochemical annealed at 800 ˚C for 72 h, with an exceptional specific discharge capacity of 148.6 mAh g-1 and excellent cycle stability (capacity retention 96.8 %) after 30th cycles at 3 C. The results demonstrated that Ti-doped NMC had superior advantages for LiNi1/3Mn1/3Co1/3O2 (NMC 111) material at the optimum temperature of 800 °C for 72 h. It is one of the potential cathode materials for Li-ion batteries

Role conflict, role ambiguity and job stress in police officers

Purpose - The general aims of this study were to identify the relationship between role conflict and job stress in police officers; (ii) to identify the relationship between role ambiguity and job stress in police officers; (iii) to validate the positive relationship between role conflict and role ambiguity on the job stress in police officers.Judeh (2011) argued that no existence of consensus related to organizational factors that lead to job stress in police settings. A little studies to the job stress issues in police officers in Southern Asia and Malaysia area (Masilamani et. al., 2013). According to Wong (2003), political factors, differences in practice and theory, conservative organizational settings and the need to protect confidential information are the constraints that researchers encounter in conducting study in police settings.Role conflict and job stress has a significant positive relationship (Quarat-ul-ain, Khattak & Iqbal, 2013). Role conflict and role ambiguity lead to job stress (Idris, 2011; Muchinsky, 2000).Role conflict contributed to 41% to job stress (Hsu et al., 2010).Role ambiguity plays a significant role in reducing job stress (Ramadan; 2013).The evaluation of threats, barriers and challenge were different and closely related to role conflict and job stress (Tuckey, Searle, Boyd, Winefeld & Winefeld, 2015). The clash of role conflict and ambiguity caused wrong doing in police officers (Yesiltas; 2014; Cooper, 2012). The main result of this study summarized that there is a significant relationship between role conflict and ambiguity to job stress in police officers Methodology - The study sample was comprised of 280 police officers from several contingents using purposive sampling process due to the constraint to access the particular sample and private confidential that practiced by police. A questionnaire was used to collect the data consisted of role conflict and ambiguity issues developed by Rizzo et al. (1970). The second part was to measure job stress variable using Depression Anxiety Stress Scale (DASS 21) presented by Lovibond and Lovibond (1995). Analysis data was used Partial Least Square – Structural Equation Modelling (PLS-SEM)

Impact of Differentiated Instruction on the Mathematical Thinking Processes of Gifted and Talented Students

The aim of this study is to examine if differentiated instruction benefits the mathematical thinking process of gifted and talented students in Malaysia. Differentiated instruction is a student-centered technique in which instructors act as facilitators. It is doubtful, however, if differentiated instruction has a beneficial effect on the overall process of mathematical thinking. A disciplined approach to learning mathematics is deemed essential. In this study, a questionnaire was designed to assess students' motivation towards learning using differentiated instruction in mathematics, and a mathematics test was devised to assess students' mathematical thinking process. The study included 400 students who were identified as gifted and talented students; the data were analyzed using the SPSS software. The results suggest that statistically differentiated instruction has a significant effect on gifted and talented students' mathematical thinking processes. However, additional research is needed to discover which activities directly impact students' mathematical thinking processes positively and which should be avoided

Ganoderma boninense classification based on near-infrared spectral data using machine learning techniques

Ganoderma boninense (G. boninense) infection reduces the productivity of oil palms and causes a severe threat to the palm oil industry. Early detection of G. boninense is vital since there is no effective treatment to stop the continuing spread of the disease unless ergosterol, a biomarker of G. boninense can be detected. There is yet a non-destructive and in-situ technique explored to detect ergosterol. Capability of NIR to detect few biomarkers such as mycotoxin and zearalenone (ZEN) has been proven to pave the way an effort to explore NIR’s sensitivity towards detecting ergosterol, as discussed in this paper. A compact hand-held NIR with a measurement range of 900–1700 nm is utilized by scanning the leaves of three oil palm seedlings inoculated with G. boninense while the other three were non-inoculated from 16-weeks-old to 32-weeks-old. Significant changes of spectral reflectance have been notified occur at the wavelength of ~1450 nm which reflectance of infected sample is higher 0.2–0.4 than healthy sample which 0.1–0.19. The diminishing of the spectral curve at approximately 1450 nm is strongly suspected to happened due to the loss of water content from the leaves since G. boninense attacks the roots and causes the disruption of water supply to the other part of plant. However, a few overlapped NIRs’ spectral data between healthy and infected samples require for further validation which chemometric and machine learning (ML) classification technique are chosen. It is found the spectra of healthy samples are scattered on the negative sides of PC-1 while infected samples tend to be on a positive side with large loading coefficients marked significant discriminatory effect on healthy and infected samples at the wavelength of 1310 and 1452 nm. A PLS regression is used on NIR spectra to implement the prediction of ergosterol concentration which shows good corelation of R = 0.861 between the ergosterol concentration and oil palm NIR spectra. Four different ML algorithms are tested for prediction of G. boninense infection: K-Nearest Neighbour (kNN), Naïve Bayes (NB), Support Vector Machine (SVM) and Decision Tree (DT) are tested which depicted DT algorithm achieves a satisfactory overall performance with high accuracy up to 93.1% and F1-score of 92.6% compared to other algorithms. High accuracy shows the capability of the classification model to correctly predict the G. boninense detection while high F1-score indicates that the classification is able to validate the detection of G. boninense correctly with low misclassification rate. The result represents a significant step in the development of a nondestructive and in-situ detection system which validated by both chemometric and machine learning (ML) classification technique

3D Reconstruction of Fruit Shape based on Vision and Edge Sections

The fruit industry has been known as one of the largest businesses in Malaysia, where most of the fruits pass through the peeling process well in advance before the final product as juice in a bottle or slices in a can. The current industrial fruit peeling techniques are passive and inefficient by cutting parts of the pulp of the fruit with peels leading to losses. To avoid this issue, a multi-axis CNC fruit peeler can be used to precisely peel the outer layer with the guidance of a 3D virtual model of fruit. In this work, a new cost-effective method of 3D image reconstruction was developed to convert 36 fruit images captured by a normal RGB camera to a 3D model by capturing a single image every 10 degrees of fruit rotation along a fixed axis. The point cloud data extracted with edge detection were passed to Blender 3D software for meshing in different approaches. The vertical link frame meshing method developed in this research proved a qualitative similarity between the output result and the scanned fruit in a processing time of less than 50 seconds

Whole genome sequence analysis showing unique SARS-CoV-2 lineages of B.1.524 and AU.2 in Malaysia

SARS-CoV-2 has spread throughout the world since its discovery in China, and Malaysia is no exception. WGS has been a crucial approach in studying the evolution and genetic diversity of SARS-CoV-2 in the ongoing pandemic. Despite considerable number of SARS-CoV-2 genome sequences have been submitted to GISAID and NCBI databases, there is still scarcity of data from Malaysia. This study aims to report new Malaysian lineages of the virus, responsible for the sustained spikes in COVID-19 cases during the third wave of the pandemic. Patients with nasopharyngeal and/or oropharyngeal swabs confirmed COVID-19 positive by real-time RT-PCR with CT value < 25 were chosen for WGS. The selected SARS-CoV-2 isolates were then sequenced, characterized and analyzed along with 986 sequences of the dominant lineages of D614G variants currently circulating throughout Malaysia. The prevalence of clade GH and G formed strong ground for the presence of two Malaysian lineages of AU.2 and B.1.524 that has caused sustained spikes of cases in the country. Statistical analysis on the association of gender and age group with Malaysian lineages revealed a significant association (p <0.05). Phylogenetic analysis revealed dispersion of 41 lineages, of these, 22 lineages are still active. Mutational analysis showed presence of unique G1223C missense mutation in transmembrane domain of the spike protein. For better understanding of the SARS-CoV-2 evolution in Malaysia especially with reference to the reported lineages, large scale studies based on WGS are warranted

Whole Genome Sequencing Analysis of Spike D614G Mutation Reveals Unique SARS-CoV-2 Lineages of B.1.524 and AU.2 in Malaysia

The SARS-CoV-2 has spread throughout the world since its discovery in China, and Malaysia is no exception. WGS has been a crucial approach in studying the evolution and genetic diversity of SARS-CoV-2 in the ongoing pandemic, and while an exceptional number of SARS-CoV-2 complete genomes have since been submitted to GISAID and NCBI, there is a scarcity of data from Malaysia. This study aims to report new Malaysian lineages responsible for the sustained spikes in COVID-19 cases during the third wave of the pandemic. Patients whose nasopharyngeal and oropharyngeal swabs were confirmed positive by real-time RT-PCR with Ct-value < 25 were chosen for WGS. The 10 SARS-CoV-2 isolates obtained were then sequenced, characterized and analyzed, including 1356 sequences of the dominant lineages of D614G variant currently circulating throughout Malaysia. The prevalence of clade GH and G formed strong ground of the discovery of two Malaysian lineages that caused sustained spikes of cases locally. Statistical analysis on the association of gender and age group with Malaysian lineages revealed a significant association (p < 0.05). Phylogenetic analysis revealed dispersion of 41 lineages, for which 22 lineages are still active. Mutational analysis observed unique G1223C missense mutation in Transmembrane Domain of Spike protein. Thus, calls for the large-scale WGS analysis of strains found around the world for greater understanding of viral evolution and genetic diversity especially in addressing the question of the effect of deleterious substitution mutation in transmembrane region of Spike protein