Critical System Components Research Articles

METHODS OF ENSURING CYBER SECURITY OF CRITICAL COMPONENTSNETWORKS OF INFORMATION AND TELECOMMUNICATION SYSTEM

It is established that the efficiency and reliability of information and telecommunication systems, in particular the networks that are part of them, significantly depends on the high level of protection of critical components. However, the constant improvement of the technical equipment of these systems requires the creation of new and improvement of existing methodological support for cyber security. One of the promising approaches is the development of a universal method of cybersecurity in the context of cyberattacks (influences, threats, etc.) and unauthorized access by unauthorized users to critical nodes (components) of information and telecommunications systems. Timely detection, prompt counteraction to cyber threats and unauthorized access to critical network components is a necessary component of ensuring a high level of cybersecurity of the information and telecommunications system as a whole, especially in the context of hybrid warfare and armed aggression by the Russian Federation. To this end, the article proposes a method of cybersecurity of critical components of information and telecommunications systems, which is based on the integrated application of monitoring the state of network nodes and user access to them, recording the facts of cyberattacks based on analysis of incoming (outgoing) traffic, timely detection of unauthorized access to and commission of cyber threats, as well as operational response to these attempts. The paper presents the results of verification of the proposed methods. To this end, the article proposes a method of cybersecurity of critical components of information and telecommunications systems, which is based on the integrated application of monitoring the state of network nodes and user access to them, recording the facts of cyberattacks based on analysis of incoming (outgoing) traffic, timely detection of unauthorized access to and commission of cyber threats, as well as operational response to these attempts. The paper presents the results of verification of the proposed method. It is shown that its application allows to quickly detect the facts of cyber threats and unauthorized access to critical components of information and telecommunication systems networks and effectively counteract these attempts.

Philippine Performance Evaluation and Assessment Scheme (PPEAS): Experiences in Newborn Screening System Quality Improvement.

Newborn Bloodspot Screening (NBS) has existed for over 60 years, having been initiated by Guthrie in the U.S. In the Philippines, NBS was introduced in 1996 and later was supported by legislation. The NBS program now includes 29 conditions, covering 91.6% of the newborn population in 2019. Program growth and expansion necessitated development of a formal performance evaluation and assessment scheme (PEAS) for monitoring performance and for continuously improving quality. This study’s objective was to present the development, implementation, and results to date of the Philippine Performance PEAS (PPEAS). Using the comprehensive listing of laboratory and non-laboratory elements in the model PEAS system in the U.S., PPEAS tools were developed for critical Philippine NBS system components: regional Department of Health (national health agency, Philippines) (DOH) offices (CHDs), NBS laboratories (NSCs), NBS specimen submitters (NSFs), and long-term case management centers (NBSCCs). Data generated from the various PPEAS have been periodically reviewed and analyzed for NBS system impact. PPEAS were developed to facilitate quality improvement at various levels of the Philippine NBS system. PPEAS identified successes, gaps, and challenges to be addressed by NSCs, NSFs, CHDs, and NBSCCs with the assistance of the Newborn Screening Reference Center and the Department of Health.

A review of methods on Condition Monitoring and Fault Diagnosis using IR Thermography–An Expert System approach

Condition Monitoring is a process of checking faults in machinery. The main goal of Condition Monitoring is to check the health of critical components of machines and systems. In real situations during operation of machines decisions to be taken on maintenance are considered based on the condition. It has the benefits such as decreasing catastrophic failures, minimize the maintenance, operational and logistical cost, maximize the system security, availability and increasing the reliability. Different researchers use different methods for fault diagnosis. Every method has its own shortcomings. In this paper a study has been done on the Expert system to do condition monitoring and fault diagnosis using IR Thermography.

Uses of Telehealth to Support Identification and Treatment of Health Disorders in the Criminal Justice System

Telehealth, the interactive, electronic exchange of information for diagnosis, treatment, support, or care management, is a critical component of many systems of care.1 The variety of telehealth uses, technologies, and approaches, when implemented successfully, can improve access to care and overcome typical geographic and workforce barriers.1 The opioid epidemic and recent social distancing requirements due to coronavirus disease (COVID-19) have prompted public safety, behavioral health, and other community partners to explore how telehealth can be used for identification and treatment of disorders in the criminal justice system. Telehealth offers an innovative strategy for intervention and treatment within the criminal justice system, which is greatly needed given the prevalence of justice-involved populations affected by opioid-use disorder. For one, it is costly to provide treatment and care to people who are incarcerated, and telehealth can help reduce that cost.2 Using telehealth to connect these people to providers can also help avoid resource-heavy and potentially dangerous transfers to care settings. In addition, a telehealth strategy can minimize the burden associated with recruiting and retaining healthcare providers on site, particularly behavioral health providers, who are often in short supply. Offering options virtually is one way to ameliorate organizational challenges. Services that can be provided via telehealth include inpatient and outpatient medical services, behavioral health, physical and occupational therapy, disaster management, health education,1 and coordination of care support for inmates, providers, and officials. Telehealth can also be used to facilitate release of inmates by connecting people who are in prison or jail with community-based services before release.

Full Spectrum Phosphors for White LEDs and Virtual Windows for Light and Health Applications

Light and health is a topic of significant importance and requires fundamental studies of human interactions with light. This talk will present a review of some of the on-going studies on light and health by holistically exposing the humans to the outdoor conditions, while they are confined in indoor spaces. The designs of a new lighting-display fusion system along with examples of emerging commercial products will be discussed. Full spectrum lighting is a critical component of such systems. Results of phosphor blends based on well known thiogallates and sulfide phosphors for designing full spectrum lighting that could replicate sunlight quality will be presented. The synchronous modulation of phosphor based full spectrum light sources with electronic display playing real time outdoor videos creates a new tool for systematically studying the cognitive psycho-physiological health of human beings under different environmental conditions.

Scale‐aware limited deformable convolutional neural networks for traffic sign detection and classification

Traffic sign detection and classification is a critical component of intelligent transportation systems, which is applied to inform automatic unmanned driving systems and driving assistance systems about conditions and limits of roads. Although computer vision is widely utilised in traffic sign detection, detection and recognising traffic signs globally remains a great challenge due to the variety of sign types, scale-variance and geometric variations. To address these problems, this study proposes a region-based deep convolutional neural network (CNN) framework for traffic sign detection and classification. Specifically, a multi-branch sample pyramid module is proposed, which is based on multi-branch CNNs for multi-scaled feature exaction. A limited deformable convolutional module is then embedded into the CNN layers to learn the distorted information representation for deformation handing. Moreover, a scale-aware multi-task region proposal network module is applied to detect traffic signs with various scales. The whole network is trained in an end-to-end manner. Finally, experiments are conducted on two public detection data sets to demonstrate the effectiveness of the proposed method.

Emotional Preferences Towards E-Learning Based on Analytic Hierarchy Process and Kansei for Decision Making

Users in software world play key role in determining whether a software, including e-Learning system, has a long time of use or not. Past literatures have highlighted the importance of incorporating emotional requirement into software systems. It is important to consider what users need related to software. One of the critical component of a software systems that directly interacts with users is the interface. Users interface design (UID) could induce critical emotional experience and impression to users the first time they execute a software system. Kansei Engineering is adopted as a methodology to analyze users emotional experience towards the software UID. This research implemented a combination approach of Kansei Engineering and Analytic Hierarchy Process in order to analyze students’ emotional experience as users of e-Learning in higher learning institution, and then determines which of an open source e-Learning system that suits their positive emotional experience. This paper reports an attempt to discover the relationship between UID and users’ emotional experience in e-Learning systems. The research found that there were two critical students’ emotional factors, which are “clear” and “pleasant”. These two factors had a big impact in the selection of an e-Learning system, with factor of clear has larger impact. The research result then suggests the preferred e-Learning system for students based on those that evoked positive emotional experience to students. The result will benefit higher learning institution in promoting e-Learning, to extend the outreach potential of e-Learning among students.

Emotional Preferences Towards E-Learning Based on Analytic Hierarchy Process and Kansei for Decision Making

Users in software world play key role in determining whether a software, including e-Learning system, has a long time of use or not. Past literatures have highlighted the importance of incorporating emotional requirement into software systems. It is important to consider what users need related to software. One of the critical component of a software systems that directly interacts with users is the interface. Users interface design (UID) could induce critical emotional experience and impression to users the first time they execute a software system. Kansei Engineering is adopted as a methodology to analyze users emotional experience towards the software UID. This research implemented a combination approach of Kansei Engineering and Analytic Hierarchy Process in order to analyze students’ emotional experience as users of e-Learning in higher learning institution, and then determines which of an open source e-Learning system that suits their positive emotional experience. This paper reports an attempt to discover the relationship between UID and users’ emotional experience in e-Learning systems. The research found that there were two critical students’ emotional factors, which are “clear” and “pleasant”. These two factors had a big impact in the selection of an e-Learning system, with factor of clear has larger impact. The research result then suggests the preferred e-Learning system for students based on those that evoked positive emotional experience to students. The result will benefit higher learning institution in promoting e-Learning, to extend the outreach potential of e-Learning among students.

Supporting Healthcare-Associated Infection (HAI) Surveillance in Resource-Limited Settings: Lessons Learned, 2015–2019

Background: Since 2015, the CDC has supported the development and implementation of healthcare-associated infection (HAI) surveillance in resource-limited settings through technical support of case definitions and methods that are feasible with existing surveillance capacity and integration with clinical care to maximize sustainability and data use for action. Methods: Surveillance initiatives included facility-level implementation programs in Kenya, Sierra Leone, Thailand, and Georgia; larger national or regional network-level projects in India and Vietnam were also supported. For assessment and planning, surveillance capacities were grouped into 3 domains: staff, informatics, and diagnostic capacities. Based on these capacities, simplified case definitions surveillance methodologies were devised to balance resources and effort with the anticipated value and use of findings. Results: There was broad understanding of the importance of HAI surveillance; however, the required resources and other challenges (eg, training, staffing, quality of available data) were underappreciated. Staff capacities were often influenced by a lack of dedicated surveillance staff and limited experience in systematic data collection and analysis. Informatics capacities were generally limited by the lack of digital data management, nonstandardized clinical data collection and storage, and the inability to assign and maintain unique patient identifiers. We found that capacity for diagnostics, a critical component of traditional HAI surveillance systems, was limited by its availability, frequency of use, and inconsistent rationale in clinical care. We found that successful surveillance strategies were generally simple, matched existing capacities, and targeted specific HAI priorities identified by clinical teams. For example, in Kenya and Sierra Leone, participating facilities established, with minimal external support, simplified SSI surveillance among post–caesarean-delivery patients. These initiatives improved integration of surveillance with clinical care through encouraging participation of the clinical team in surveillance and planning. Furthermore, these models directly linked surveillance activities to improved patient care (eg, combined clinical checklists with surveillance data collection forms). Discussion: In resource-limited settings, the local cost and effort required to establish and sustain the necessary infrastructure for HAI surveillance can be substantial. Establishing actionable and sustainable HAI surveillance can be achieved through simplifying HAI surveillance to match existing capacities and can result in valuable surveillance programs, even in very resource-limited settings.Funding: NoneDisclosures: None

COVID-19's multipronged attack on mental health: Months of uncertainty and threats to health, social, and financial security have contributed to intense anxiety, depression, posttraumatic stress disorder, and even suicide. Here, in part 2 of a 2-part series, we investigate the pandemic's growing mental health toll on the general public and efforts to address it.

COVID-19's multipronged attack on mental health: Months of uncertainty and threats to health, social, and financial security have contributed to intense anxiety, depression, posttraumatic stress disorder, and even suicide. Here, in part 2 of a 2-part series, we investigate the pandemic's growing mental health toll on the general public and efforts to address it.

Direct ink writing combined with metal-assisted chemical etching of microchannels for the microfluidic system applications

Microchannels are critical components of the microfluidic systems. But the low efficiency and high cost of conventional machining methods severely hinder the wide applications of microchannels. Here, we have reported a facile and low-cost strategy for the fabrication of microchannels on silicon substrate based on direct marker pen ink writing (DIW) and metal-assisted chemical etching (MaCE). Firstly, ink patterns were directly written on the bare silicon substrate by DIW without any polymer resist. They were utilized as stable mask in subsequent step, and can be easily removed by ethanol when not needed. Then, Ag nanofilms were deposited on the spaces between ink patterns by electroless deposition. Finally, silicon microchannels were fabricated by MaCE. During MaCE process, the Ag nanofilm was acted as catalyst and hydrogen peroxide-hydrofluoric acid solution was used as etching solution. The mask effect of ink patterns in the DIW-MaCE fabrication strategy was studied, and the effects of etching time, Ag nanofilm thickness, and etchant concentration on the formation of microchannels structures were also investigated. The microchannels structures were also fabricated to demonstrate the applications in microfluidics. We have introduced diamond tips of multi-probe microfabrication equipment to control the geometry and size of ink patterns in this study, and thus the final microchannels structures can be optimized during MaCE. The proposed DIW-MaCE strategy is facile and cost-effective for the fabrication of microchannels, and the microchannels structures are promising for extending the microfluidics and microsensors devices applications.

Database Repair Meets Algorithmic Fairness

Fairness is increasingly recognized as a critical component of machine learning systems. However, it is the underlying data on which these systems are trained that often reflect discrimination, suggesting a database repair problem. Existing treatments of fairness rely on statistical correlations that can be fooled by anomalies, such as Simpson's paradox. Proposals for causality-based definitions of fairness can correctly model some of these situations, but they rely on background knowledge of the underlying causal models. In this paper, we formalize the situation as a database repair problem, proving sufficient conditions for fair classifiers in terms of admissible variables as opposed to a complete causal model. We show that these conditions correctly capture subtle fairness violations. We then use these conditions as the basis for database repair algorithms that provide provable fairness guarantees about classifiers trained on their training labels. We demonstrate the effectiveness of our proposed techniques with experimental results.

6.E. Workshop: Health promotion: An integrative paradigm for sustainable health, wellbeing and development

Abstract This workshop addresses the importance of health promotion for enabling all human beings to have healthy lives, to maximise their health potential and ensure that no one is left behind. Health promotion is a critical component of modern health systems and a vital strategy for sustainable health development and human flourishing. Effective health promotion strategies are essential in addressing the broader determinants of health, reducing health inequities, and tackling current and emerging health challenges to human development. Achieving the UN Sustainable Development Goals (SDGs) and universal health coverage calls for concerted action on health promotion as an integral component of modern health systems and future development. While successive global health policies have endorsed the need to invest in health promotion, political commitment to implementing health promotion is lacking and health policies and budgets remain focused on curative approaches. Health promotion is often poorly understood, both within the health sector and society more widely, and there is a limited appreciation of what is required to translate health promotion into effective action. Transformative health promotion actions are needed to deliver on improved population health and wellbeing and the creation of a healthy society where human wellbeing can flourish. Objectives This workshop will engage participants in an interactive discussion on how health promotion policy, research and practice can be strengthened in order to advance human health and wellbeing. Critical insights will be exchanged on how to bring a renewed focus on health promotion as a key transformative and disruptive strategy for advancing human wellbeing development. The workshop will critically consider what mechanisms are needed for strengthening health promoting policies, implementation processes and structures, capacities and research, to advance population health and wellbeing and a healthier future for all. Key messages Health promotion actions are critical to the delivery of improved population health and health equity, transforming health systems and enhancing human wellbeing and sustainable development. Strengthening health promoting polices, practices and research is essential to achieve the UN Sustainable Development Goals and enable the conditions for a healthier world.

Review of Health Monitoring Techniques for Capacitors Used in Power Electronics Converters.

Capacitors are critical components of power converter systems as they influence the cost, size, performance, and scale of such systems. However, capacitors exhibit the highest degeneration and breakdown rates among all power converter components due to their wear-out failures and short lifespans. Therefore, condition monitoring is a vital process to estimate the health status of capacitors and to provide predictive maintenance for ensuring stability in the operation of power converter systems. The equivalent series resistance (ESR) and the capacitance of the capacitor are two widely used parameters for evaluating the health status of capacitors. Unlike the ESR, the capacitance of a capacitor is suitable for the health monitoring of various types of capacitors; therefore, it is more preferable for large-scale systems. This paper presents an overview of previous research addressing this aspect of capacitors and provides a better understanding of the capacitance monitoring of capacitors utilized in power converter systems.

Effects of metallic system components on marine electromagnetic loop data

ABSTRACTElectromagnetic loop systems rely on the use of non‐conductive materials near the sensor to minimize bias effects superimposed on measured data. For marine sensors, rigidity, compactness and ease of platform handling are essential. Thus, commonly a compromise between rigid, cost‐effective and non‐conductive materials (e.g. stainless steel versus fibreglass composites) needs to be found. For systems dedicated to controlled‐source electromagnetic measurements, a spatial separation between critical system components and sensors may be feasible, whereas compact multi‐sensor platforms, remotely operated vehicles and autonomous unmanned vehicles require the use of electrically conductive components near the sensor. While data analysis and geological interpretations benefit vastly from each added instrument and multidisciplinary approaches, this introduces a systematic and platform‐immanent bias in the measured electromagnetic data. In this scope, we present two comparable case studies targeting loop‐source electromagnetic applications in both time and frequency domains: the time‐domain system trades the compact design for a clear separation of 15 m between an upper fibreglass frame, holding most critical titanium system components, and a lower frame with its coil and receivers. In case of the frequency‐domain profiler, the compact and rigid design is achieved by a circular fibreglass platform, carrying the transmitting and receiving coils, as well as several titanium housings and instruments. In this study, we analyse and quantify the quasi‐static influence of conductive objects on time‐ and frequency‐domain coil systems by applying an analytically and experimentally verified 3D finite element model. Moreover, we present calibration and optimization procedures to minimize bias inherent in the measured data. The numerical experiments do not only show the significance of the bias on the inversion results, but also the efficiency of a system calibration against the analytically calculated response of a known environment. The remaining bias after calibration is a time/frequency‐dependent function of seafloor conductivity, which doubles the commonly estimated noise floor from 1% to 2%, decreasing the sensitivity and resolution of the devices. By optimizing size and position of critical conductive system components (e.g. titanium housings) and/or modifying the transmitter/receiver geometry, we significantly reduce the effect of this residual bias on the inversion results as demonstrated by 3D modelling. These procedures motivate the opportunity to design dedicated, compact, low‐bias platforms and provide a solution for autonomous and remotely steered designs by minimizing their effect on the sensitivity of the controlled‐source electromagnetic sensor.

Development of Evaluation System for Iranian Health Research Networks: Challenges and Lessons Learned

Health research networks (HRNs) are critical components of large-scale systems of production and validation of scientific evidence. As evaluation of research systems is a reliable process to measure efficiency and effectiveness of their activities, we aimed to report the processes of development of evaluation indicators' for Iranian health research networks and the results of conducted assessment. In 2017, for the first time, aim to develop the evaluation framework for national HRNs, following the qualitative approach to assess the quality of research we designed the peer review method as one of the most important tools. This qualitative method was conducted according to experts' views in specific fields. Key policy makers and stakeholders collaboratively developed a number of criteria for evaluation of research performance of Iranian HRNs. Following the review of conducted studies, benefitting from published guide line, these indicators were defined under 4 main axes of governance and leadership; infrastructures; research products and research impact. Based on requirements of developed protocol for evaluation of HRNs in Iran, 18 HRNs completed the processes of evaluation. Results show a progressive need for more attention to precise planning of HRNs for achieving to goals. Another point to consider is the attention to documenting processes. The observational system for researches for detection of latest research priority was the most important issues that need to be more addressed by all of networks. Research evaluation of Iranian HRNs more over creating of constructive positive competition provide an overview of the shortcomings and research challenges could be used for better planning and promotion of the health research system.

Introduction to Special Issue: New Developments in Public Defense Research

This special issue focuses on interdisciplinary research in public defense. Seven papers represent a diverse group of scholars in an understudied field. Two overarching themes emerge. The first theme, “System Interventions: Evaluating Programs and Identifying Opportunities,” includes three studies of innovative policies and practices. Two evaluate new resource injections that support, respectively, social work-initiated holistic defense and counsel at first appearance. The third examines state sentencing schemes to identify opportunities for emphasizing defendant assets instead of deficits. The second theme, “Understanding Decision Makers,” includes four papers drawing on qualitative data regarding juvenile resentencing and reentry, defendant views of attorney–client communication, defender motivations for remaining in the profession, and manager perspectives on likely effects of caseload reductions. As a collection, these papers bridge gaps between theory and practice, offer new insight into public defense as a critical component of criminal legal systems, and identify new avenues for future research.

Assessments used to measure participation in life activities in individuals with cancer: a scoping review.

Individuals with cancer are at high risk for restricted participation in meaningful life activities such as work, school, and social activities. Evaluating participation in life activities is identified as a critical component of rehabilitation referral and triage systems. This scoping review investigates what assessments are used to measure participation in life activities in individuals with cancer. Six databases were systematically searched using keywords and controlled vocabulary through February 2020. Eligible studies used the term participation in the context of life situations and had an assessment with at least 5 participation-specific questions. A total of 4604 unique articles were identified and screened for eligibility. Thirty-two studies were included; 20 unique assessments of participation were identified. Assessments were developed with the primary purpose of measuring participation (n = 8); global or physical function (n = 9); community integration (n = 1); social activity (n = 1); or occupational gaps (n = 1). Most assessments (12/20) were not developed with the purpose of measuring participation and only 4 assessments measured key components of participation other than frequency. Measuring participation in life activities is a developing area in oncology research. Most studies do not accurately or comprehensively measure this construct. The heterogeneity in assessments used indicates no clear consensus on a gold-standard participation measure for use among cancer survivors. Comprehensively measuring participation will help identify individuals in need of rehabilitation services and is an essential first step to inform the development of targeted interventions to enhance participation.

Applicability of control algorithms for variable water-flow control practice of central air-conditioning cooling water system

Cooling water (CW) system with strong nonlinear characteristics is a critical component of central air conditioning systems. Further, variable flow control of CW pumps is a critical measure for achieving energy-saving operation of CW systems. Proportional–integral (PI) control algorithm is widely used in the variable flow control of CW systems, and fuzzy control algorithm also plays a significant role in these systems. In this study, the application of fuzzy control and PI control algorithms is experimentally studied in a CW constant temperature difference system. The results indicate that the PI control algorithm of the CW system is simple in structure, and it only requires a reasonable PI parameter setting to achieve better control effect with high accuracy, good steady-state performance, and low energy consumption. However, the PI control algorithm is not adaptive for different working conditions. The fuzzy control algorithm has better dynamic performance and the adjustment curve is smooth with small fluctuations, although its control accuracy is not high. Over the same test period, the regulation frequency of the water pump under PI control algorithm is higher than that of the fuzzy control algorithm, and the CW system exhibits better stability under the fuzzy control algorithm.

Learning traffic as a graph: A gated graph wavelet recurrent neural network for network-scale traffic prediction

Network-wide traffic forecasting is a critical component of modern intelligent transportation systems for urban traffic management and control. With the rise of artificial intelligence, many recent studies attempted to use deep neural networks to extract comprehensive features from traffic networks to enhance prediction performance, given the volume and variety of traffic data has been greatly increased. Considering that traffic status on a road segment is highly influenced by the upstream/downstream segments and nearby bottlenecks in the traffic network, extracting well-localized features from these neighboring segments is essential for a traffic prediction model. Although the convolution neural network or graph convolution neural network has been adopted to learn localized features from the complex geometric or topological structure of traffic networks, the lack of flexibility in the local-feature extraction process is still a big issue. Classical wavelet transform can detect sudden changes and peaks in temporal signals. Analogously, when extending to the graph/spectral domain, graph wavelet can concentrate more on key vertices in the graph and discriminatively extract localized features. In this study, to capture the complex spatial-temporal dependencies in network-wide traffic data, we learn the traffic network as a graph and propose a graph wavelet gated recurrent (GWGR) neural network. The graph wavelet is incorporated as a key component for extracting spatial features in the proposed model. A gated recurrent structure is employed to learn temporal dependencies in the sequence data. Comparing to baseline models, the proposed model can achieve state-of-the-art prediction performance and training efficiency on two real-world datasets. In addition, experiments show that the sparsity of graph wavelet weight matrices greatly increases the interpretability of GWGR.