A vulnerability-driven cyber security maturity model for measuring national critical infrastructure protection preparedness

Critical infrastructures are vital assets for the public safety, economic welfare and national security of countries. Cyber systems are used extensively to monitor and control critical infrastructures. A number of infrastructures are connected to the Internet via corporate networks. Cyber security is, therefore, an important item of the national security agenda of a country. The intense interest in cyber security has initiated research focusing on national cyber security maturity assessments. However, little, if any, research is dedicated to maturity assessments of national critical infrastructure protection efforts. Instead, the vast majority of studies merely examine diverse national-level security best practices ranging from cyber crime response to privacy protection. This paper proposes a maturity model for measuring the readiness levels of national critical infrastructure protection efforts. The development of the model involves two steps. The first step analyzes data pertaining to national cyber security projects using grounded theory to extract the root causes of the susceptibility of critical infrastructures to cyber threats. The second step determines the maturity criteria by introducing the root causes to subject-matter experts polled in a Delphi survey. The resulting survey-based maturity model is applied to assess the critical infrastructure protection efforts in Turkey. The results are realistic and intuitively appealing, demonstrating that the maturity model is useful for evaluating the national critical infrastructure protection preparedness of developing countries such as Turkey

Regulatory approaches for cyber security of critical infrastructures: The case of Turkey

Critical infrastructures are vital assets for public safety, economic welfare and/or national security of countries. Today, cyber systems are extensively used to control and monitor critical infrastructures. A considerable amount of the infrastructures are connected to the Internet over corporate networks. Therefore, cyber security is an important item for the national security agendas of several countries. The enforcement of security principles on the critical infrastructure operators through the regulations is a still-debated topic. There are several academic and governmental studies that analyze the possible regulatory approaches for the security of the critical infrastructures. Although most of them favor the market-oriented approaches, some argue the necessity of government interventions. This paper presents a three phased-research to identify the suitable regulatory approach for the critical infrastructures of Turkey. First of all, the data of the critical infrastructures of Turkey are qualitatively analyzed, by using grounded theory method, to extract the vulnerabilities associated with the critical infrastructures. Secondly, a Delphi survey is conducted with six experts to extract the required regulations to mitigate the vulnerabilities. Finally, a focus group interview is conducted with the employees of the critical infrastructures to specify the suitable regulatory approaches for the critical infrastructures of Turkey. The results of the research show that the critical infrastructure operators of Turkey, including privately held operators, are mainly in favor of regulations

Identifying critical success factors for wearable medical devices: a comprehensive exploration

For healthy living, the successful use of wearable medical devices such as smartwatches, smart clothes, smart glasses, sports/activity trackers, and various sensors placed on a body is getting more important as benefits of these devices become apparent. Yet, the existing knowledge about the critical success factors for wearable medical devices needs to evolve and develop further. The main objective of this research is to distill salient constructs to enhance the successful use of wearable medical devices. Specifically, the study aims to identify factors, associated items, and interactions of the relevant factors. A questionnaire has been developed and deployed. The data were collected from 1057 people specifically chosen to represent a wide range of the population. Comprehensive and meaningful inferences have been drawn. Principally, as a fusion of factor analysis and path analysis, a partial least squares structural equation modeling approach consisting of exploratory and confirmatory factor analyses has been applied. In order to assess internal generalization and to precisely identify additional constructs, quasi-statistics have been used. The analyses of data collected revealed 11 salient constructs with 39 items and 18 statistically significant relationships among these constructs. Consequently, composed of distilled constructs and their associations, a novel model with an explanatory power of 73.884% has been approved. Moreover, 13 additional factors were identified as a result of the applied quasi-statistics. This research is the first of its kind on account of its sample characteristics with applied comprehensive methodology and distilled results. This research contributes to the pertinent body of knowledge concerning the critical success factors for wearable medical devices with distilled results. These contributions notably advance the relevant understanding and will be beneficial for researchers and for developers in the field of wearable medical devices

Prediction of Water Level in Lakes by RNN-Based Deep Learning Algorithms to Preserve Sustainability in Changing Climate and Relationship to Microcystin

In recent years, intensive water use combined with global climate change has increased fluctuations in freshwater lake levels, hydrological characteristics, water quality, and water ecosystem balance. To provide a sustainable management plan in the long term, deep learning models (DL) can provide fast and reliable predictions of lake water levels (LWLs) in challenging future scenarios. In this study, artificial neural networks (ANNs) and four recurrent neural network (RNN) algorithms were investigated to predict LWLs that were applied in time series such as one day, five days, ten days, twenty days, one month, two months, and four months ahead. The results show that the performance of the Long Short-Term Memory (LSTM) model with a prediction of 60 days is in the very good range and outperforms the benchmark, the Naïve Method, by 78% and the ANN at the significance level (p < 0.05) with an RMSE = 0.1762 compared to other DL algorithms. The RNN-based DL algorithms show better prediction performance, specifically, for long time horizons, 57.98% for 45 days, 78.55% for 60 days, and 58% for 120 days, and it is better to use a prediction period of at least 20 days with an 18.45% performance increase to take advantage of the gated RNN algorithms for predicting future water levels. Additionally, microcystin concentration was tightly correlated with temperature and was most elevated between 15 and 20 m water depths during the summer months. Evidence on LWL forecasting and microcystin concentrations in the context of climate change could help develop a sustainable water management plan and long-term policy for drinking water lakes

A Structural Model for Students' Adoption of Learning Management Systems: An Empirical Investigation in the Higher Education Context

With the recent advances in information technologies, Learning Management Systems have taken on a significant role in providing educational resources. The successful use of these systems in higher education is important for the implementation, management and continuous improvement of e-learning services to increase the quality of learning. This study aimed to identify the factors affecting higher education students' behavioral intention towards Learning Management Systems. A research model was proposed based on the belief factors of the technology acceptance model; namely perceived usefulness, perceived ease-of-use and external factors including self-efficacy, enjoyment, subjective norm, satisfaction, and interactivity and control. Then, a self-reported questionnaire was distributed online. A total of 470 higher education students participated in the survey. The proposed structural model was assessed and validated using structural equation modeling, in particular the partial least square method. The predictors of behavioral intention were identified as perceived usefulness, perceived ease of use, enjoyment, subjective norm, satisfaction, and interactivity and control with the validated structural model. The relationships between the influencing factors provided an insight about the students' behavioral intention towards the use of Learning Management Systems. It is expected that the academicians and practitioners will benefit from the design and findings of the current study in their future research

Prevalence of Childhood Affective disorders in Turkey: An epidemiological study

Aim: To determine the prevalence of affective disorders in Turkey among a representative sample of Turkish population. Methods: This study was conducted as a part of the "The Epidemiology of Childhood Psychopathology in Turkey" (EPICPAT-T) Study, which was designed by the Turkish Association of Child and Adolescent Mental Health. The inclusion criterion was being a student between the second and fourth grades in the schools assigned as study centers. The assessment tools used were the K-SADS-PL, and a sociodemographic form that was designed by the authors. Impairment was assessed via a 3 point-Likert type scale independently rated by a parent and a teacher. Results: A total of 5842 participants were included in the analyses. The prevalence of affective disorders was 2.5 % without considering impairment and 1.6 % when impairment was taken into account. In our sample, the diagnosis of bipolar disorder was lacking, thus depressive disorders constituted all the cases. Among depressive disorders with impairment, major depressive disorder (MDD) (prevalence of 1.06%) was the most common, followed by dysthymia (prevalence of 0.2%), adjustment disorder with depressive features (prevalence of 0.17%), and depressive disorder-NOS (prevalence of 0.14%). There were no statistically significant gender differences for depression. Maternal psychopathology and paternal physical illness were predictors of affective disorders with pervasive impairment. Conclusion: MDD was the most common depressive disorder among Turkish children in this nationwide epidemiological study. This highlights the severe nature of depression and the importance of early interventions. Populations with maternal psychopathology and paternal physical illness may be the most appropriate targets for interventions to prevent and treat depression in children and adolescents