Background: It is critical to understand the factors that could affect the acceptance of the coronavirus disease 2019 (COVID-19) vaccine in the community. The aim of this study was to determine factors that could possibly affect the acceptance of Indonesian citizens of COVID-19 vaccination using a Technology Acceptance Model (TAM), a model how users come to accept and use a technology. Methods: An online survey was conducted between the first and fifth of November, 2020. Participants were asked to respond to questions on acceptance, perceived usefulness, perceived ease of use, perceived religiosity towards, and amount of information about COVID-19. This study used the Technology Acceptance Model (TAM) as the framework to decide factors that affect vaccine acceptance. Structural Equation Model was employed to assess the correlation between all explanatory variables and vaccine acceptance. Mann-Whitney test and Kruskal-Wallis rank were employed to assess demographic factors associated with acceptance. Results: In total, 311 responses were included for analysis. Our TAM model suggested that high perceived usefulness significantly increased COVID-19 vaccine acceptance and high perceived ease of use significantly increased the perceived usefulness. Perceived religiosity did not substantially affect vaccine acceptance. The amount of information on COVID-19 also did not significantly affect vaccine acceptance. Our data suggested that vaccine acceptance was associated with age, type of occupation, marital status and monthly income to some degree. Conclusion: Since perceived usefulness affects vaccine acceptance, the government should focus on the usefulness of the vaccine when promoting the COVID-19 vaccine to Indonesian citizens. In addition, since perceived ease of use significantly affects users' acceptance to COVID-19 vaccine, the easier to acquire the vaccine in the community, the higher chance that the citizens are willing to be vaccinated.
Background Digital transformation is changing the structure and landscape of future banking needs with much emphasis on value creation. Autonomous banking solutions must incorporate on-the-fly processing for risky transactions to create this value. In an autonomous environment, access control with role and trust delegation has been said to be highly relevant. The aim of this research is to provide an end to end working solution that will enable autonomous transaction and task processing for banking. Method We illustrate the use case for task delegation with the aid of risk graphs, risk bands and finite state machines. This paper also highlights a step by step task delegation process using a risk ordering relation methodology that can be embedded into smart contracts. Results Task delegation with risk ordering relation is illustrated with six process owners that share immutable ledgers. Task delegation properties using Multi Agent Systems (MAS) is used to eliminate barriers for autonomous transaction processing. Secondly, the application of risk graph and risk ordering relation with reference to delegation of tasks is a novel approach that is nonexistent in RBAC. Conclusion The novelty of this study is the logic for task delegation and task policies for autonomous execution on autonomous banking platforms akin to the idea of federated ID (Liberty Alliance).
Background - Recently, there have been attempts to develop mHealth applications for asthma self-management. However, there is a lack of applications that can offer accurate predictions of asthma exacerbation using the weather triggers and demographic characteristics to give tailored response to users. This paper proposes an optimised Deep Neural Network Regression (DNNR) model to predict asthma exacerbation based on personalised weather triggers. Methods - With the aim of integrating weather, demography, and asthma tracking, an mHealth application was developed where users conduct the Asthma Control Test (ACT) to identify the chances of their asthma exacerbation. The asthma dataset consists of panel data from 10 users that includes 1010 ACT scores as the target output. Moreover, the dataset contains 10 input features which include five weather features (temperature, humidity, air-pressure, UV-index, wind-speed) and five demography features (age, gender, outdoor-job, outdoor-activities, location). Results - Using the DNNR model on the asthma dataset, a score of 0.83 was achieved with Mean Absolute Error (MAE)=1.44 and Mean Squared Error (MSE)=3.62. It was recognised that, for effective asthma self-management, the prediction errors must be in the acceptable loss range (error<0.5). Therefore, an optimisation process was proposed to reduce the error rates and increase the accuracy by applying standardisation and fragmented-grid-search. Consequently, the optimised-DNNR model (with 2 hidden-layers and 50 hidden-nodes) using the Adam optimiser achieved a 94% accuracy with MAE=0.20 and MSE=0.09. Conclusions - This study is the first of its kind that recognises the potentials of DNNR to identify the correlation patterns among asthma, weather, and demographic variables. The optimised-DNNR model provides predictions with a significantly higher accuracy rate than the existing predictive models and using less computing time. Thus, the optimisation process is useful to build an enhanced model that can be integrated into the asthma self-management for mHealth application.
Background - 3D printing is a dynamic process with many process parameters influencing the product, including the type of the material; it is often difficult to understand the combined influence of these parameters. Purpose - The tensile strength of 3D printed parts is important for the functionality of components. The effects of process parameters on tensile strength must therefore be examined. The objective of this study is to develop a response surface model (RSM) to predict the final quality of a 3D printed bronze part from a different set of input parameters. Methods - The tensile test specimen was built in a Makerbot 3D printer with bronze polylactic acid (PLA) material. The three controllable input parameters were; thickness of layers, number of shells, and infill density. The three levels of layer thickness were 0.1mm, 0.2mm and 0.3mm. The number of shells was 2, 3 and 4. The infill densities were 20%, 30% and 40%. A tensile experiment tested the strength of the specimens. RSM is a statistical approach for modelling and analyzing how different variables affect the response of interest, and for optimizing it. Results - The result obtained shows that the specimen with a high layer thickness of 0.3mm and infill density of 40% is the best among all the other parameters. Finally, the regression equation produced was used for random values of layer thickness, the number of shells, and infill density, to see whether the values obtained from the tests fall into the range of experimental data. Conclusion - Infill density and layer thickness are the two criteria that significantly influence the tensile property. The number of shells has the least influence on the tensile property. However, the best tensile strength is the part printed with higher infill density, a greater number of shells, and higher layer thickness.
Background: The Malaysian government reacted to the pandemic's economic effect with the Prihatin Rakyat Economic Stimulus Package (ESP) to cushion the novel coronavirus 2019 (COVID-19) impact on households. The ESP consists of cash assistance, utility discount, moratorium, Employee Provident Fund (EPF) cash withdrawals, credit guarantee scheme and wage subsidies. A survey carried out by the Department of Statistics Malaysia (DOSM) shows that households prefer different types of financial assistance. These preferences forge the need to effectively customise ESPs to manage the economic burden among low-income households. In this study, a recommender system for such ESPs was designed by leveraging data analytics and machine learning techniques. Methods: This study used a dataset from DOSM titled "Effects of COVID-19 on the Economy and Individual - Round 2," collected from April 10 to April 24, 2020. Cross-Industry Standard Process for Data Mining was followed to develop machine learning models to classify ESP receivers according to their preferred subsidies types. Four machine learning techniques-Decision Tree, Gradient Boosted Tree, Random Forest and Naïve Bayes-were used to build the predictive models for each moratorium, utility discount and EPF and Private Remuneration Scheme (PRS) cash withdrawals subsidies. The best predictive model was selected based on F-score metrics. Results: Among the four machine learning techniques, Gradient Boosted Tree outperformed the rest. This technique predicted the following: moratorium preferences with 93.8% sensitivity, 82.1% precision and 87.6% F-score; utilities discount with 86% sensitivity, 82.1% precision and 84% F-score; and EPF and PRS with 83.6% sensitivity, 81.2% precision and 82.4% F-score. Households that prefer moratorium subsidies did not favour other financial aids except for cash assistance. Conclusion: Findings present machine learning models that can predict individual household preferences from ESP. These models can be used to design customised ESPs that can effectively manage the financial burden of low-income households.
Knowledge graph (KG) publishes machine-readable representation of knowledge on the Web. Structured data in the knowledge graph is published using Resource Description Framework (RDF) where knowledge is represented as a triple (subject, predicate, object). Due to the presence of erroneous, outdated or conflicting data in the knowledge graph, the quality of facts cannot be guaranteed. Trustworthiness of facts in knowledge graph can be enhanced by the addition of metadata like the source of information, location and time of the fact occurrence. Since RDF does not support metadata for providing provenance and contextualization, an alternate method, RDF reification is employed by most of the knowledge graphs. RDF reification increases the magnitude of data as several statements are required to represent a single fact. Another limitation for applications that uses provenance data like in the medical domain and in cyber security is that not all facts in these knowledge graphs are annotated with provenance data. In this paper, we have provided an overview of prominent reification approaches together with the analysis of popular, general knowledge graphs Wikidata and YAGO4 with regard to the representation of provenance and context data. Wikidata employs qualifiers to include metadata to facts, while YAGO4 collects metadata from Wikidata qualifiers. However, facts in Wikidata and YAGO4 can be fetched without using reification to cater for applications that do not require metadata. To the best of our knowledge, this is the first paper that investigates the method and the extent of metadata covered by two prominent KGs, Wikidata and YAGO4.
Background: A recommender system captures the user preferences and behaviour to provide a relevant recommendation to the user. In a hybrid model-based recommender system, it requires a pre-trained data model to generate recommendations for a user. Ontology helps to represent the semantic information and relationships to model the expressivity and linkage among the data. Methods: We enhanced the matrix factorization model accuracy by utilizing ontology to enrich the information of the user-item matrix by integrating the item-based and user-based collaborative filtering techniques. In particular, the combination of enriched data, which consists of semantic similarity together with rating pattern, will help to reduce the cold start problem in the model-based recommender system. When the new user or item first coming into the system, we have the user demographic or item profile that linked to our ontology. Thus, semantic similarity can be calculated during the item-based and user-based collaborating filtering process. The item-based and user-based filtering process are used to predict the unknown rating of the original matrix. Results: Experimental evaluations have been carried out on the MovieLens 100k dataset to demonstrate the accuracy rate of our proposed approach as compared to the baseline method using (i) Singular Value Decomposition (SVD) and (ii) combination of item-based collaborative filtering technique with SVD. Experimental results demonstrated that our proposed method has reduced the data sparsity from 0.9542% to 0.8435%. In addition, it also indicated that our proposed method has achieved better accuracy with Root Mean Square Error (RMSE) of 0.9298, as compared to the baseline method (RMSE: 0.9642) and the existing method (RMSE: 0.9492). Conclusions: Our proposed method enhanced the dataset information by integrating user-based and item-based collaborative filtering techniques. The experiment results shows that our system has reduced the data sparsity and has better accuracy as compared to baseline method and existing method.
Introduction: Malaysia is one of the countries with the highest lightning flash density globally. While sufficiency of lightning protection system is crucial to ensure human safety against lightning strikes, the public awareness towards lightning safety is also equally important in Malaysia. Hence, this study was conducted to understand the current lightning safety awareness level of the Malaysian population. Methods: An online questionnaire survey which consists of 22 scientific statements of lightning was first developed in Malay and English. The questionnaire allows the respondent to also check their own score upon completion of the questionnaire. It was then distributed to the public for data collection. The sample size comprised of both genders, all layers of society from various educational level and social background. Results: Overall, the awareness on lightning safety amongst Malaysian is at moderate level with an average score of slightly above 50%. Urbanites scored marginally better than their rural counterparts. One's education level does not dictate their awareness level of lightning safety. Discussion: In conclusion, the public in Malaysia needs to be better educated on lightning safety. Similar studies should be replicated in other countries experiencing similar levels of lightning activity to better understand the public's perception on lightning.
Background: Little is known on how time spent on touch-screen technology affects the hand skills development of preschool children. This study aimed to investigate the effects of touch-screen technology usage on hand skills among preschool children. Methods: Case-control design was employed to compare the hand skills of children who were engaged in touch-screen technology. A total of 128 participants aged between five and six years old who attended preschool were recruited and divided into two groups: high usage touch-screen technology (HUTSTG) and, low usage touch-screen technology (LUTSTG). Children's Hand Skills ability Questionnaire (CHSQ) and Assessment of Children's Hand Skills (ACHS) were used to evaluate the children's hand skills. Results: There were significant differences in the hand skills of preschool children between HUTSTG and LUTSTG. Results showed that preschool children in LUTSTG had better hand skills in all domains of CHSQ (p≤0.001) and ACHS (p<0.001) as compared to HUTSTG. Conclusion: Frequent use of touch-screen technology might cause disadvantages to the development of hand skills among preschool children.
The interactivity and ubiquity of digital technologies are exerting a significant impact on the knowledge creation in information technology (KC-IT) projects. According to the literature, the critical relevance of KC-IT is highly associated with digital innovation (DI) for organisational success. However, DI is not yet a fully-fledged research subject but is an evolving corpus of theory and practise that draws from a variety of social science fields. Given the preceding setting, this study explores the interaction of KC-IT with DI. This work provides a systemic literature review (SLR) to examine the literature in KC-IT and its connection to DI. A SLR of 527 papers from 2001 to 2021 was performed across six online databases. The review encompasses quantitative and qualitative studies on KC-IT factors, processes and methods. Three major gaps were found in the SLR. Firstly, only 57 (0.23%) papers were found to examine the association between KC and IT projects. These works were analysed for theories, type of papers, KC-IT factors, processes and methods. Secondly, the convergence reviews indicate that scarce research has examined TMS and trust in KC-IT as factors. Thirdly, only 0.02% (5) core papers appeared in the search relevant to KC in IT projects to accelerate DI. The majority of the papers examined were not linked to DI. A significant gap also exists in these areas. These findings warrant the attention of the research community.
Background: Spirulina platensis contains several bioactive molecules such as phenol, flavonoid and phycocyanin pigments. This study unveils total phenol, flavonoid, antioxidant activity, phycocyanin content and evaluated encapsulation efficiency from Ocimum basilicum intervention on S. platensis. O. basilicum intervention aims to reduce unpleasant odors from S. platensis that will increase consumption and increase bioactive compounds. Methods: The intervention was carried out by soaking a S. platensis control sample (SP) in O. basilicum with a ratio of 1:4 (w/v) and it was then dried (DSB) and microencapsulated by freeze drying methods (MSB) using a combination of maltodextrin and gelatin. Total flavonoid and phenolic analysis with curve fitting analysis used a linear regression approach. Antioxidant activity of samples was analysed with the 2,2'-azino-bis-3-3thylbenzthiazoline-6-sulphonic acid (ABTS) method. Data were analysed using ANOVA at significance level (p < 0.05) followed by Tukey test models using SPSS v.22. Results: The result of this study indicated that O. basilicum intervention treatment (DSB) has the potential to increase bioactive compounds such as total phenol, antioxidant activity and phycocyanin, and flavonoid content. Intervention of O. basilicum on S. platensis (DSB) significantly increases total phenol by 49.5% and phycocyanin by 40.7%. This is due to the phenol and azulene compounds in O. basilicum which have a synergistic effect on phenol and phycocyanin in S. platensis. Microencapsulation using a maltodexrin and gelatin coating is effective in phycocyanin protection and antioxidant activity with an encapsulation efficiency value of 71.58% and 80.5%. Conclusion: The intervention of O. basilicum on S. platensis improved the total phenol and phycocyanin content and there is potential for a pharmaceutical product for a functional food and pharmaceutical product.
Background: The increase in aged populations in Malaysia has brought unprecedented challenges to families, policy makers, scholars, and business organisations. This paper adapted the WHO 2007 framework of features of age-friendly cities to examine age-friendly environment constructs and their linkages with social connectedness from the perspective of Malaysian middle-aged and older adults caring for themselves. Methods: A face-to-face cross-sectional survey was conducted on 402 middle-aged and older adults caring for themselves across selected states in west Malaysia, selected via purposive sampling. Firstly, features of age-friendly cities were explored through exploratory factor analysis involving 82 respondents. Subsequently, structural equation modelling was performed, involving 320 respondents. Results: The results indicated that the constructs of an age-friendly environment were built environment, community support and health services, civic participation, and employment as well as communication and information. The structural model provided evidence that implementing age-friendly initiatives relating to built environment, community support and health services, civic participation and employment as well as communication and information enables the ageing population to improve their connectedness with society. These findings supported the ecological theories, agreeing that age-friendly environments help middle-aged and older adults caring for themselves to increase their adaptability and reduce perceived pressure from the environment. This result was in line with the current literature in which an age-friendly environment is a form of support and an enabling environment to cultivate positive social relationships and connectivity. Conclusions: Creating an age-friendly environment that supports active and healthy living for middle-aged and older adults caring for themselves allows them to continue to share their experiences, knowledge, and wisdom. This is helpful and beneficial for societal well-being and economic development as well as for the future generations.
Background: Changing trends in the use of technology have become an impelling force to be reckoned with for the accounting and finance profession. The curriculum offered in higher learning institutions must be quickly revamped so that students who complete a bachelor's degree are digitally competent upon graduation. With US$55.3 billion invested in FinTech in 2019 alone and more than 72% of accounting jobs being automated, graduates must be trained on digital skills to be future proof. Accounting and finance graduates must be made competent in skills that are related to digital content such as blockchain technology, information assets and autonomous peer to peer systems, to name a few. Methods: We used a three-phase approach: 1) careful mapping of digital topics taught within the course structure offered at these institutions; 2) review of current best practices and digital learning tools for digital inclusion which was ascertained from literature; and 3) 80 experts in a think tank group were interviewed on antecedents, awareness and problems in relation to digital inclusion within the curriculum to validate our research objective. Results: Eleven key tools for inclusion in the curriculum were discussed with experts and then mapped to current curriculum offered at institutions. We discovered that less than 5% of these were being taught. In total, 78% of experts agreed that digital content is inevitable, 90% agreed that digital inclusion based on tools that were discussed will yield great benefits for students, and lastly 75% agreed that giving digital exposure to students must be standard practice. Conclusions: The response from experts confirms that digital inclusion is imperative, but instructors themselves lacked the know-how of emerging technologies. Only the curriculum of institutions with approved bachelor's programs were included in this research. In our future work we hope to include all institutions and professional bodies as well.
Background: Studies have acknowledged that social media enables students to connect with and learn from experts from different ties available in the students' personal learning environment (PLE). Incorporating experts into formal learning activities such as scaffolding problem-solving tasks through social media, allows students to understand how experts solve real-world problems. However, studies that evaluate experts' problem-solving styles on social media in relation to the tie strength of the experts with the students are scarce in the extant literature. This study aimed to explore the problem-solving styles that the experts portrayed based on their ties with the students in problem-based learning (PBL) on Facebook. Methods: This study employed a simultaneous within-subject experimental design which was conducted in three closed Facebook groups with 12 final year management students, six business experts, and one instructor as the participants. The experts were invited by the students from the weak and strong ties in their PLE. Hinging on the Strength of Weak Ties Theory (Granovetter, 1973) and problem-solving styles (Selby et al., 2004), this study employed thematic analysis using the ATLAS.ti qualitative data analysis software to map the experts' comments on Facebook. Results: The experts from strong and weak ties who had a prior relationship with the students showed people preference style by being more sensitive to the students' learning needs and demonstrating firmer scaffolding compared to the weak ties' experts who had no prior relationship with the students. Regardless of the types of ties, all experts applied all manner of processing information and orientation to change but the degree of its applications are correlated with the working experience of the experts. Conclusion: The use of weak or strong ties benefited the students as it expedited their problem-solving tasks since the experts have unique expertise to offer depending on the problem-solving styles that they exhibited.
Background : As the standard for the exchange of data over the World Wide Web, it is important to ensure that the eXtensible Markup Language (XML) database is capable of supporting not only efficient query processing but also capable of enduring frequent data update operations over the dynamic changes of Web content. Most of the existing XML annotation is based on a labeling scheme to identify each hierarchical position of the XML nodes. This computation is costly as any updates will cause the whole XML tree to be re-labelled. This impact can be observed on large datasets. Therefore, a robust labeling scheme that avoids re-labeling is crucial. Method: Here, we present ORD-GAP (named after Order Gap), a robust and persistent XML labeling scheme that supports dynamic updates. ORD-GAP assigns unique identifiers with gaps in-between XML nodes, which could easily identify the level, Parent-Child (P-C), Ancestor-Descendant (A-D) and sibling relationship. ORD-GAP adopts the OrdPath labeling scheme for any future insertion. Results: We demonstrate that ORD-GAP is robust enough for dynamic updates, and have implemented it in three use cases: (i) left-most, (ii) in-between and (iii) right-most insertion. Experimental evaluations on DBLP dataset demonstrated that ORD-GAP outperformed existing approaches such as ORDPath and ME Labeling concerning database storage size, data loading time and query retrieval. On average, ORD-GAP has the best storing and query retrieval time. Conclusion: The main contributions of this paper are: (i) A robust labeling scheme named ORD-GAP that assigns certain gap between each node to support future insertion, and (ii) An efficient mapping scheme, which built upon ORD-GAP labeling scheme to transform XML into RDB effectively.
Background: Gamification is an innovative approach to engaging in activities that people believe as less interesting. Recycling has been an issue not taken aware by the people in environmental sustainability. There are substantial studies on recycling intention due to the continual growth of unethical and unsustainable waste disposal. Creative approaches to recycling awareness activities should be made to fulfil youths' increasing interest in and demand for recycling. The main objective of this study is to explore the factors related to youths' recycling intentions after experiencing a gamified online recycling learning activity, Edcraft Gamified Learning (EGL). Gamified recycling education is believed to be a practical and engaging approach for youths. Methods: 100 students participated in EGL, consisting of two levels of plastic crafting and recycling activities. They experienced online EGL at home between May and September in 2020, during the COVID-19 pandemic total lockdown in Malaysia, namely, Movement Control Order (MCO). 29 participants were selected to participate in five focus group discussions (FGDs) with five to eight participants per session to explore their opinions towards gamified learning, motivation and recycling intention. Results: This paper reports the findings of the FGDs. A codebook was developed based on the codes from the FGD feedback. The codes were rated by two raters, followed by an assessment of inter-rater reliability and thematic analysis. The findings emerged and were confirmed with four themes as factors that influence recycling intention. They are gameful experience, social influence, intrinsic motivation, and extrinsic motivation. Conclusion: The dependent variable, recycling intention, was connected to the four themes to verify the conceptual framework. One limitation of the study was the design of the EGL activity, which was only carried out over two days with two levels of gamified recycling education, as students had concurrent academic online classes during that period.
Background Citations of papers are positively influenced by the journal's impact factor (IF). For non-open access (non-OA) journals, this influence may be due to the fact that high-IF journals are more often purchased by libraries, and are therefore more often available to researchers, than low-IF journals. This positive influence has not, however, been shown specifically for papers published in open access (OA) journals, which are universally accessible, and do not need library purchase. It is therefore important to ascertain if the IF influences citations in OA journals too. Methods 203 randomized controlled trials (102 OA and 101 non-OA) published in January 2011 were included in the study. Five-year citations for papers published in OA journals were compared to those for non-OA journals. Source papers were derived from PubMed. Citations were retrieved from Web of Science, Scopus, and Google Scholar databases. The Thompson-Reuter's IF was used. Results OA journals were found to have significantly more citations overall compared to non-OA journals (median 15.5 vs 12, p=0.039). The IF did not correlate with citations for OA journals (Spearman's rho =0.187, p=0.60). The increase in the citations with increasing IF was minimal for OA journals (beta coefficient = 3.346, 95% CI -0.464, 7.156, p=0.084). In contrast, the IF did show moderate correlation with citations for articles published in non-OA journals (Spearman's rho=0.514, p<0.001). The increase in the number of citations was also significant (beta coefficient = 4.347, 95% CI 2.42, 6.274, p<0.001). Conclusion It is better to publish in an OA journal for more citations. It may not be worth paying high publishing fees for higher IF journals, because there is minimal gain in terms of increased number of citations. On the other hand, if one wishes to publish in a non-OA journal, it is better to choose one with a high IF.
Background - Handling non-observed activities pose major challenges to the governments and other stakeholders. Non-observed activities refer to underground activities, illegal activities, informal sector and any other activities that result in goods or services consumed by the household. The impact of these non-observed activities shows that the volume of people involved in the informal sector will rapidly increase. Informal economic activities are technically illegal yet are not intended as antisocial, thereby remaining acceptable to many individuals within the society. This research aimed to identify the factors that lead to entrepreneurial necessity and opportunity. Methods - The data of 51 respondents who were employed as informal entrepreneurs in Klang Valley areas in Malaysia was collected with the use of a questionnaire and convenient and proportionate sampling techniques. The data were analysed using SPSS software. Results - The two primary drivers of informal entrepreneurial activity were necessity and opportunity. The inability to find a formal job was an example of being driven by necessity. Meanwhile, individuals that are driven by opportunity chose to work independently in these informal sectors. Between necessity and engagement, refinement acted as a mediator. Often, necessity and opportunity do not automatically translate into successful entrepreneurship; further refinement is required in terms of market potential, technology usage, location preferences, and capital requirements. Improved refinement results in increased entrepreneurial engagement. Conclusions - The role and contribution of the informal sector entrepreneurship in economic development need to be evaluated and not just observed as an opportunity for individuals who choose this type of career. Therefore, further research is required in a wider variety of contexts to evaluate whether the same remains true in different populations. The results of this study can be useful for the government to set policies to encourage the transition of informal to formal entrepreneurships in Malaysia.
Background: Banks and financial institutions are vulnerable to money laundering (ML) as a result of crime proceeds infiltrating banks in the form of significant cash deposits. Improved financial crime compliance processes and systems enable anti-ML (AML) analysts to devote considerable time and effort to case investigation and process quality work, thereby lowering financial risks by reporting suspicious activity in a timely and effective manner. This study uses Job Characteristics Theory (JCT) to evaluate the AML system through the job satisfaction and motivation of its users. The purpose of this study is to determine how satisfied AML personnel are with their jobs and how motivated they are to work with the system. Methods: This cross-sectional study used JCT to investigate the important elements impacting employee satisfaction with the AML system. The five core dimensions of the job characteristics were measured using a job diagnostic survey. The respondents were employees working in the AML department of a Malaysian bank, and the sample group was chosen using a purposive sampling approach. A total of 100 acceptable replies were gathered and analysed using various statistical approaches. A motivating potential score was generated for each employee based on five main job characteristics. Results: Findings revealed that five core job characteristics, namely, skill diversity, task identity, task importance, autonomy and feedback, positively influence the AML system employees' job satisfaction. However, skill variety and autonomy are found to be low, which are reflected in the poor motivating potential score. Conclusion: This study examined the characteristics of the AML system and its users' job satisfaction. Findings revealed that task significance is the most widely recognised characteristic, followed by feedback and task identity. However, there is a lack of skill variety and autonomy, which must be addressed to improve employee satisfaction with the AML system.
Background: A major player in industry is the induction motor. The constant motion and mechanical nature of motors causes much wear and tear, creating a need for frequent maintenance such as changing contact brushes. Unmannered and infrequent monitoring of motors, as is common in the industry, can lead to overexertion and cause major faults. If a motor fault is detected earlier through the use of automated fault monitoring, it could prevent minor faults from developing into major faults, reducing the cost and down-time of production due the motor repairs. There are few available methods to detect three-phase motor faults. One method is to analyze average vibration signals values of V, I, pf, P, Q, S, THD and frequency. Others are to analyze instantaneous signal signatures of V and I frequencies, or V and I trajectory plotting a Lissajous curve. These methods need at least three sensors for current and three for voltage for a three-phase motor detection. Methods: Our proposed method of monitoring faults in three-phase industrial motors uses Hilbert Transform (HT) instantaneous current signature curve only, reducing the number of sensors required. Our system detects fault signatures accurately at any voltage or current levels, whether it is delta or star connected motors. This is due to our system design, which incorporates normalized curves of HT in the fault analysis database. We have conducted this experiment in our campus laboratory for two different three-phase motors with four different fault experiments. Results: The results shown in this paper are a comparison of two methods, the V and I Lissajous trajectory curve and our HT instantaneous current signature curve. Conclusion: We have chosen them as our benchmark as their fault results closely resemble our system results, but our system benefits such as universality and a cost reduction in sensors of 50%.