Leadership Styles and Organizational Knowledge Management Activities: A Systematic Review

Leaders play a critical role in the success or failure of their organizations. Leaders can be effective in implementing changes, building their organization's capabilities, and improving its performance, or the opposite, they could be ineffective. In this systematic review, the authors aim to summarize the findings of previous quantitative research, published between the period from 2000 to 2018, to identify the effect of various leadership styles on organizational Knowledge management (KM) capabilities and activities. The authors reviewed 50 articles found in well-known databases included Emerald, ScienceDirect, Taylor and Francis, Ebsco, Google Scholar, and others, concerning the impact of leadership when implementing KM in business organizations. The review revealed that transformational, transactional, knowledge-oriented leadership, top executives, and strategic leadership have evidence of their constant and positive effect on the KM process. The authors encourage organizations to use a combination of those styles to maximize the effect of leadership on KM. The authors also recommend conducting further studies on the effect of the remaining leadership styles, such as the ethical and servant leadership styles on KM and the other specific KM activities. 

Effective idea mining technique based on modeling lexical semantic

Automatic extraction of hidden ideas from texts is extremely important that would help decision makers to identify and retrieve significant information, which possibly used to solve current problems. However, adequate measurements need to be utilized to verify candidate ideas. In existing idea mining measurement research, a well-balanced measurement is used to measure the distribution of the number of known and unknown terms from the idea text and the context text to find useful ideas within a text pattern. The existing models do not take into consideration the relationships between these terms which may share one or more semantic component. This leads to a limited characterization of potential ideas. Therefore, this paper proposes an improvement to the idea mining model by considering the semantic relationships among terms based on synonyms by using the WordNet. The effectiveness of the proposed model is evaluated on a dataset consisting of 50 randomly selected abstracts of scientific articles. Based on the results, the proposed model showed an improvement in the performance of the idea mining model where an increase of 28.4% is achieved

An Insider Threat Categorization Framework for Automated Manufacturing Execution System

Insider threats become one of the most dangerous threats in the cyber world as compared to outsider as the insiders have knowledge of assets. In addition, the threats itself considered in-visible and no one can predict what, when and how exactly the threat launched. Based on conducting literature, threat in Automated Manufacturing Execution Systems (AMESs) can be divided into three principle factors. Moreover, there is no standard framework to be referring which exist nowadays to categorize such factors in order to identify insider threats possible features. Therefore, from the conducted literature a standard theoretical categorization of insider threats framework for AMESs has been proposed. Hence, three principle factors, i.e. Human, Systems and Machine have considered as major categorization of insider threats. Consequently, the possible features for each factor identified based on previous researcher recommendations. Therefore, via identifying possible features and categorize it into principle factors or groups, a standard framework could be derived. These frameworks will contribute more benefit specifically in the manufacturing field as a reference to mitigate an insider threat.   Keywords—automated manufacturing execution systems insider threats, factors and features, insider threat categorization framework

Cyber-Security Incidents: A Review Cases In Cyber-Physical Systems

Cyber-Physical Systems refer to systems that have an interaction between computers, communication channels and physical devices to solve a real-world problem. Towards industry 4.0 revolution, Cyber-Physical Systems currently become one of the main targets of hackers and any damage to them lead to high losses to a nation. According to valid resources, several cases reported involved security breaches on Cyber-Physical Systems. Understanding fundamental and theoretical concept of security in the digital world was discussed worldwide. Yet, security cases in regard to the cyber-physical system are still remaining less explored. In addition, limited tools were introduced to overcome security problems in Cyber-Physical System. To improve understanding and introduce a lot more security solutions for the cyber-physical system, the study on this matter is highly on demand. In this paper, we investigate the current threats on Cyber-Physical Systems and propose a classification and matrix for these threats, and conduct a simple statistical analysis of the collected data using a quantitative approach. We confirmed four components i.e., (the type of attack, impact, intention and incident categories) main contributor to threat taxonomy of Cyber-Physical System

Feasibility of using the position as feature for idea identification from text

Abstracts of research papers are meant to provide a brief condensed overview of respective research topics. This includes a glimpse of the new idea that the paper proposes. The aim of the research presented here is to investigate the feasibility of the effect of text position in the idea identification. The abstracts are structured in the form of introduction, body, and conclusion. It is hypothesized that research ideas tend to be phrased in conclusion section of paper abstracts. 25 abstracts of the scientific papers were used to automatically identify the position of ideas within abstract sections. The results support the notion that the conclusion of the abstracts significantly represents the ideas

New Insider Threat Detection Method Based On Recurrent Neural Networks

Insider threat is a significant challenge in cybersecurity. In comparison with outside attackers, inside attackers have more privileges and legitimate access to information and facilities that can cause considerable damage to an organization. Most organizations that implement traditional cybersecurity techniques, such as intrusion detection systems, fail to detect insider threats given the lack of extensive knowledge on insider behavior patterns. However, a sophisticated method is necessary for an in-depth understanding of insider activities that the insider performs in the organization. In this study, we propose a new conceptual method for insider threat detection on the basis of the behaviors of an insider. In addition, gated recurrent unit neural network will be explored further to enhance the insider threat detector. This method will identify the optimal behavioral pattern of insider actions

Public knowledge, attitude and practice towards antibiotics use and antimicrobial resistance in Saudi Arabia: A web-based cross-sectional survey

Background: Antimicrobial resistance is a global issue that causes significant morbidity and mortality. Therefore, this study aims to assess knowledge, attitudes, and practices (KAP) of the general Saudi populations toward antibiotics use. Design and methods: A cross-sectional, anonymous online survey was conducted from January 1 to May 11, 2020, across five major regions of Saudi Arabia. Participants (aged ≥18 years) were invited through social media to complete an online self-structured questionnaire. All data were analyzed by Statistical Package (SPSS v.25). Descriptive statistics, Pearson's Chi-squared, t-tests, one-way analysis of variance (ANOVA), and Pearson correlation analyses were conducted. Results: Out of 443 participants, the majority (n=309, 69.8%) were females, 294 (64.4%) were married, 176 (39.7%) were 25-34 years of age, 338 (76.3%) were living in the Eastern Province, 313 (70.7%) had college or higher education, 139 (31.4%) were not working, and 163 (36.8%) had a monthly income of USD 800-1330. Overall, most participants demonstrated good knowledge and practice (88% and 85.6%, respectively).  However, 76.8%had inadequate attitude score levels towards antibiotics use. Of all the respondents, 74.9% knew that not completing a full course of antibiotics may cause antibiotics resistance, 91.33% did not agree that antibiotics should be accessed without a prescription, and 94.04% will not hand over leftover antibiotics to family members. Factors associated with adequate knowledge were female, medical jobs, and higher income (p<0.05). Conclusions: Our findings revealed that while most participants were aware of antibiotics use and demonstrated good knowledge, good practices, they had negative attitudes towards antibiotics use

Description of the COVID-19 epidemiology in Malaysia

IntroductionSince the COVID-19 pandemic began, it has spread rapidly across the world and has resulted in recurrent outbreaks. This study aims to describe the COVID-19 epidemiology in terms of COVID-19 cases, deaths, ICU admissions, ventilator requirements, testing, incidence rate, death rate, case fatality rate (CFR) and test positivity rate for each outbreak from the beginning of the pandemic in 2020 till endemicity of COVID-19 in 2022 in Malaysia.MethodsData was sourced from the GitHub repository and the Ministry of Health’s official COVID-19 website. The study period was from the beginning of the outbreak in Malaysia, which began during Epidemiological Week (Ep Wk) 4 in 2020, to the last Ep Wk 18 in 2022. Data were aggregated by Ep Wk and analyzed in terms of COVID-19 cases, deaths, ICU admissions, ventilator requirements, testing, incidence rate, death rate, case fatality rate (CFR) and test positivity rate by years (2020 and 2022) and for each outbreak of COVID-19.ResultsA total of 4,456,736 cases, 35,579 deaths and 58,906,954 COVID-19 tests were reported for the period from 2020 to 2022. The COVID-19 incidence rate, death rate, CFR and test positivity rate were reported at 1.085 and 0.009 per 1,000 populations, 0.80 and 7.57%, respectively, for the period from 2020 to 2022. Higher cases, deaths, testing, incidence/death rate, CFR and test positivity rates were reported in 2021 and during the Delta outbreak. This is evident by the highest number of COVID-19 cases, ICU admissions, ventilatory requirements and deaths observed during the Delta outbreak.ConclusionThe Delta outbreak was the most severe compared to other outbreaks in Malaysia’s study period. In addition, this study provides evidence that outbreaks of COVID-19, which are caused by highly virulent and transmissible variants, tend to be more severe and devastating if these outbreaks are not controlled early on. Therefore, close monitoring of key epidemiological indicators, as reported in this study, is essential in the control and management of future COVID-19 outbreaks in Malaysia

The effects of the COVID-19 pandemic on dengue cases in Malaysia

BackgroundGlobally, the COVID-19 pandemic has affected the transmission dynamics and distribution of dengue. Therefore, this study aims to describe the impact of the COVID-19 pandemic on the geographic and demographic distribution of dengue incidence in Malaysia.MethodsThis study analyzed dengue cases from January 2014 to December 2021 and COVID-19 confirmed cases from January 2020 to December 2021 which was divided into the pre (2014 to 2019) and during COVID-19 pandemic (2020 to 2021) phases. The average annual dengue case incidence for geographical and demographic subgroups were calculated and compared between the pre and during the COVID-19 pandemic phases. In addition, Spearman rank correlation was performed to determine the correlation between weekly dengue and COVID-19 cases during the COVID-19 pandemic phase.ResultsDengue trends in Malaysia showed a 4-year cyclical trend with dengue case incidence peaking in 2015 and 2019 and subsequently decreasing in the following years. Reductions of 44.0% in average dengue cases during the COVID-19 pandemic compared to the pre-pandemic phase was observed at the national level. Higher dengue cases were reported among males, individuals aged 20–34 years, and Malaysians across both phases. Weekly dengue cases were significantly correlated (ρ = −0.901) with COVID-19 cases during the COVID-19 pandemic.ConclusionThere was a reduction in dengue incidence during the COVID-19 pandemic compared to the pre-pandemic phase. Significant reductions were observed across all demographic groups except for the older population (&gt;75 years) across the two phases

Optimization of an ammonia-cooled rectangular microchannel heat sink using multi-objective non-dominated sorting genetic algorithm (NSGA2)

The ever decreasing size of modern electronic packaging has induced researchers to search for an effective and efficient heat removal system to handle the continuously increasing power density. Investigations have involved different geometry, material and coolant to address the thermal management issues. This paper reports the potential improvement in the overall performance of a rectangular microchannel heat sink using a new gaseous coolant namely ammonia gas. Using a multi-objective general optimization scheme with the thermal resistance model as an analysis method in combination with a non-dominated sorting genetic algorithm as an optimization technique, it was found that significant reduction in the total thermal resistance up to 34 % for ammonia-cooled compared to air-cooled microchannel heat sink under the same operating conditions is achievable. In addition, a considerable decrease in the microchannel heat sink's mass up to 30 % was achieved due to the different heat sink's material used