Search

Background: Self-harm is an important public health problem with increasing incidences among adolescents for over a decade. This study aimed to identify factors associated with self-harm in adolescent psychiatric inpatients in Korea. We also investigated clinical characteristics associated with nonsuicidal self-injury disorder (NSSI-D) and suicidal behavior disorder (SBD) according to the Diagnostic and Statistical Manual of Mental Disorders 5 criteria.Methods: This retrospective study included 215 adolescents aged between 12 to 18 years, who were admitted to the psychiatric ward of Samsung Medical Center, Seoul, Korea, from January 2014 to February 2021. Participants were divided into three groups according to NSSI-D and SBD diagnostic criteria. Collected data were analyzed using chi-square test and analysis of variance test.Results: Overall, 57.7% of the participants (n=124) reported self-harm at least once in their lives. Patients with self-harm were more likely to have experienced parental conflict, parent-child relational problems, and verbal, physical, or sexual abuse. Among the three groups divided according to NSSI-D and SBD diagnostic criteria, patients with both NSSI-D and SBD were more likely to have experienced parent-child relational problems than those with either NSSI-D only or SBD only.Conclusion: This study shows that parent-child relational problem is associated with high incidences of selfharm. Especially, the parent-child relational problem seemed to be a risk factor for SBD among patients with NSSI-D. These are important findings that provide an understanding of the self-harm characteristics and therefore improve prevention and treatment strategies in psychiatric adolescent patients.

Background Attention problems and decreased quality of life are frequently accompanied in Cerebral Palsy (CP), which can negatively affect rehabilitation of physical disability. However, the majority of affected children remain untreated in the aspects of attention or psychosocial factors. Equine-Assisted Activities and Therapies (EAAT) use horse as a therapeutic modality including grooming as well as mounted riding activities in which patients exercise and experience mounted stimulation. It is known to help improve attention in children with ADHD, so that it can be an exercise therapy that is expected to improvement of attention as well as rehabilitating effects in CP patients. EAA may be a promising strategy to address the unmet need for CP patients. This study aims to investigate the efficacy of EAA for children with CP, those with both CP and ADHD and confirm the comorbidity between CP and ADHD. Methods Forty-six children with cerebral palsy participated in this study. For the exercise group, they participated in a 40-min session twice a week for a 16-week period, while the control group engaged in daily life without any special treatments. Each children individually were assessed on attention and psychological wellbeing at baseline and post-treatment. Comorbidity were identified based on the Diagnostic and Statistical Manual of Mental Disorder 5th edition (DSM-5) and confirmed by Korean Kiddie-Schedule for Affective Disorders and Schizophrenia Present and Lifetime Version (K-SADS-PL). Results Perseveration rated using the Conner’s Performance Test (CPT) showed a significant decrease only in the exercise group (p . However, no significant improvement in children’s quality of life was observed after EAA program compared with control group. Among the total participants, fifteen children (31.91%) were diagnosed with ADHD. When conducting an additional analysis with the subsample of CP patients diagnosed with ADHD, the d’, commission error and perseveration showed a significant decrease only in the exercise group. Children with CP and ADHD reported an improvement in quality of life both in exercise and control group, but only in the exercise group social functioning exhibited a significant difference. Conclusion The positive effects of the EAA on attention and quality of life were confirmed. Children with CP in the exercise group were more capable to sustain their attention longer. Those with CP and ADHD showed an increase in attention and perceived to have better social skills after receiving 16 weeks of EAA compared to those in the control group. Considering high comorbidity of CP and ADHD, it seems that the EAA program could be the better alternative treatment for CP with attentional problem. The results of this study will contribute to growing evidence for the efficacy of EAA in children especially with CP and ADHD. Trial registration This trial was registered on ClinicalTrials.gov (NCT03870893). Registered 26 July 2017.

한국 교육학에서 평등 개념은 명확히 정의되는 대신 그 반대개념인 불평등을 통하여 그리고 정의(正義)란 개념으로 대체되어 서술․논의되어 왔다. 그렇지만 불평등과 정의란 개념 역시 여전히 명확히 규정되지 않은 채 평등과 혼용되고 있다. 평등과 정의란 개념을 한 두 마디로 규정하기는 어렵다는 것이 학계의 주된 흐름이지만, 이들을 위한 교육정책을 구체적으로 추진하기 위해서는 우회적 접근을 통해서라도 그 돌파구를 찾을 필요가 있다. 이를 위해, 이 연구는 평등과 정의에 관한 관점이 어떻게 전개되어 왔으며 이 둘의 관계는 어떠하고, 평등과 정의가 교육에서 왜 중요하며 이들을 실현하기 위한 구체적인 작업은 무엇인가를 탐색하되, 특히 경제적 재분배와 같은 물질적 차원의 평등이나 정의에 초점을 맞춰 온 지금까지의 정책을 보완할 새로운 관점은 무엇인가를 탐색코자 한 것이다. 결론으로, 지역아동센터 등의 보호시설이나 기초학력 프로그램 등에 대한 지금까지의 단순한 재정적, 물질적 지원정책보다는 존엄성, 인정, 특히 애정을 기반으로 한 돌봄에 역점을 둔 평등정책의 보완과 이를 위한 의식 전환 및 개혁 등이 필요함을 제안하고자 하였다. 이러한 평등정책이야말로 소외계층 자녀들의 진정한 인간 존엄성과 인정 및 보편적 가치인 교육의 내재적 가치를 위한 것이라고 보기 때문이다.

Few empirical studies of adult distance learners’ decisions to drop out of degree programs have used large enough sample sizes to generalize the findings or data sets drawn from multiple online pro...

Reliability modeling and troubleshooting reasoning involving complex component interactions in complex systems are an active research topic and a critical challenge to be overcome in decision support. In this paper, we propose an innovative concept of decision support methodology for system failure diagnosis and prognosis in complex systems. Advanced causal structure, incorporating domain and engineering knowledge, and a new Bayesian network (BN) representation of system structure and component interaction are proposed. Based on the BN representation, a Bayesian framework is developed to analyze and fuse the multisource information from different hierarchical levels of a system. This capability supports higher-fidelity modeling and assessing of the reliability of the components, the subsystems, and the system as a whole. The feasibility of our advanced causal structure approach has been proven with implementation using test data acquired from electromechanical actuator systems. A case study is successfully conducted to demonstrate the effectiveness of the proposed methodology. The proposed decision support process in integrated system health management will enable enhancements in flight safety and condition-based maintenance by increasing availability and mission effectiveness while reducing maintenance costs.

Current technologies do not address the need for co mprehensive robustness assessment including system performance and energy consumption. The current technology solutions are isolated point tools that provide an indication of projected lifetimes such as MTBF, but they are not updated with real data from real components and subsystems, and also don’t provide comprehensive system robustness to maximize system effectiveness. Due to the criticality of aircraft electrical power systems (E PS) functionality, there is a need to go well beyond conventional reliability metrics, and system robustness assessment has remained a critical challenge to be overcome. We developed an innovative approach of calculating system-level robustness for EPS to meet system safe ty, reliability, maintainability, and energy optimization requirements in each design sta ge. The feasibility of the robustness assessment approach has been proved with several testing cases considering degradation factors including current, voltage, temperature and empirical data. This robustness assessment processing in EPS will enable enhancements in lifecycle improvement and continuing a high level of performance during envir onment variations while optimizing energy.

Modeling the reliability of multi-level engineering systems is critically important yet technically challenging. Existing methods have limited consideration of the failure interdependency caused by the interactions among subsystems and components. In this paper, a new Bayesian Network (BN) representation of system structure and component interactions is proposed. Based on the BN representation, a Bayesian framework is developed to fuse the information from different levels of a system for modeling the reliability of the components, subsystems and the system as a whole. A case study is conducted to demonstrate the effectiveness of the proposed methodology.

Troubleshooting and decision support system with reasoning is an active research topic, and causal analysis for complex component interactions in complex systems has remained a critical challenge to be overcome. We developed an innovative, constraint-based causal analysis to better detect, isolate, and troubleshoot complex systems. The feasibility of the causal Bayesian network (CBN) approach has been proven with implementation using test data acquired from electromechanical actuator (EMA) systems. The validation step is facilitated by comparing the trained CBN with original structure and shows the flexibility and extensibility of our solutions. This causal analysis processing in integrated system health management (ISHM) will enable enhancements in flight safety and condition-based maintenance (CBM) by increasing availability and mission-effectiveness while reducing maintenance costs.

Anomaly diagnostics and fault classification with prognostics is an active research topic, and real-time detection of anomalies and their classification has remained a critical challenge to be overcome. We developed an innovative, model-driven anomaly diagnostic and fault characterization system for electromechanical actuator (EMA) systems to mitigate catastrophic failures. The efficacy of the Model-based Avionic Prognostic Reasoner (MAPR) approach has been proven in real time using test data acquired from a MIL-STD-1553 testbed. Receiver operating characteristic (ROC) curves are generated as a result of this study to show the tradeoff between sensitivity and specificity. Results of model optimization and fault classification are also presented. This real-time processing will enable enhancements in flight safety and condition-based maintenance (CBM). Once this system is completely mature, flight safety will be improved by allowing the on-board flight computers to read from the MAPR and update their control envelope based on its evaluations of the hardware health, reducing damage propagation, decreasing maintenance time, and increasing operational safety.

This paper presents a Toolset that can be used to evaluate the performance and effectiveness of wireless intrusion detection techniques. Recently, autonomic computing methods have been widely used for intrusion detection [1]. Although some wireless intrusion detection systems (WIDSs) exist on the market, recent studies show that wireless networks are still vulnerable to complex, dynamic, and knowledgeable attacks. In this paper, we review current wireless intrusion detection systems and evaluate their performance to detect a wide range of wireless network attacks. To evaluate the performance of WIDSs, we use several performance metrics to quantify the accuracy, extendibility, adaptability, scalability, overhead, and latency of the examined WIDSs. Our experimental results show that this toolset can reveal different weaknesses in the examined WIDSs.

The current state of the art in electronic prognostic health management systems does not fully support detection, collection, and remediation of real-time faults. As a result, knowledge has not been captured from an actual platform failure mechanism. Thus, point-of-failure feedback cannot be applied by system designers or operators to improve lifecycle weak links in replacement platforms, or to strengthen effectiveness of mission-critical platforms. Our innovation makes it possible to extract and analyze the power system's eigenvalues, which are related to the intrinsic frequencies of the power system that determine correlations between extracted features and state of health (SoH). In-situ electronic prognostics for power systems are crucial for attaining a sound theoretical basis of health status. To provide correlation information such as state of health (SOH) using pattern analysis with real-time data from a non-intrusive smart power sensor, Ridgetop researched using data-driven modeling with a proposed health distance and Support Vector Machines (SVMs) with signatures in a standard IEEE 1451-enabled smart power sensor. Results of this study indicate that a fault pattern analysis methodology overcomes certain disadvantages of the standard failure modes and effects analysis (FMEA) approach, which does not account for the contribution of unobserved failure to a degradation trajectory. The efficacy of the proposed pattern analysis approach is illustrated with test results showing critical distinction in pattern analysis and test data acquired from a real-time IEEE 1451-enabled smart power sensor testbed, and monitored via a testbed with appropriate instrumentation. 1 2

One of the important design criteria for distributed systems and their applications is their reliability and robustness to hardware and software failures. The increase in complexity, inter connectedness, dependency and the asynchronous interactions between the components that include hardware resources (computers, servers, network devices), and software (application services, middleware, web services, etc.) makes the fault detection and tolerance a challenging research problem. In this paper, we present an innovative approach based on statistical and data mining techniques to detect faults (hardware or software) and also identify the source of the fault. In our approach, we monitor and analyze in realtime all the interactions between all the components of a distributed system. We used data mining and supervised learning techniques to obtain the rules that can accurately model the normal interactions among these components. Our anomaly analysis engine will immediately produce an alert whenever one or more of the interaction rules that capture normal operations is violated due to a software or hardware failure. We evaluate the effectiveness of our approach and its performance to detect software faults that we inject asynchronously, and compare the results for different noise level.

With rapid technological advances in networkinfrastructure, programming languages, compatiblecomponent interfaces and so many more areas, today thecomputational Grid has evolved with the potential ofseamless aggregation, integration and interactions. Thishas made it possible to conceive a new generation ofrealistic, scientific and engineering simulations ofcomplex physical phenomenon. These applications willsymbiotically and opportunistically combinecomputations, experiments, observations, and real-timedata, and will provide important insights into complexphenomena. However, the phenomena being modeled areinherently complex, multi-phased, multi-scaled, dynamicand heterogeneous (in time space and state).Furthermore, their implementations involve multipleresearchers with scores of models, hundreds ofcomponents and dynamic compositions and interactionsbetween these components. The underlying Gridinfrastructure is similarly heterogeneous and dynamic,globally aggregating large numbers of independentcomputing and communication resources, data stores andsensor networks. The combination of the two results inapplication development, configuration and managementcomplexities that break current paradigms that are basedon passive components and static compositions. In fact,we have reached a level of complexity, heterogeneity, anddynamism that our programming environments andinfrastructure are becoming unmanageable/insecure [3].In this paper we attempt to explore an alternativeprogramming paradigm and management technique thatis based on strategies used by biological systems to dealwith complexity, heterogeneity and uncertainty. Thisapproach is referred to as autonomic computing [1]. Wediscuss key technologies to enable the development ofautonomic Grid applications. We also present amiddleware architecture that sits on top of the existingGrid middleware, intelligently managing and executingautonomic applications with huge computationalrequirements over limited Grid resources. We discuss indetail how the proposed vGrid middleware can be used todynamically control and manage large-scale forest firesimulation.

Actual maritime exercise for improving the capability of submarine sonar operator leads to a lot of cost and constraints. Sonar simulator maximizes the capability of sonar operator and training effect by solving these problems and simulating a realistic battlefield environment. In this study, a passive sonar simulator algorithm is suggested, where the simulator is divided into three modules: maneuvering module, noise source module, and sound propagation module. Maneuvering module is implemented in three-dimensional coordinate system and time interval is set as the rate of vessel changing course. Noise source module consists of target noise, ocean ambient noise, and self noise. Target noise is divided into modulated/unmodulated and narrowband/broadband signals as their frequency characteristics, and they are applied to ship radiated noise level depending on the vessel tonnage and velocity. Ocean ambient noise is simulated depending on the wind noise considering the waveguide effect and other ambient noise. Self noise is also simulated for flow noise and insertion loss of sonar-dome. The sound propagation module is based on ray propagation, where summation of amplitude, phase, and time delay for each eigen-ray is multiplied by target noise in the frequency domain. Finally, simulated results based on various scenarios are in good agreement with generated noise in the real ocean.

Purpose: Laparoscopic surgery has been considered to be contraindicated for obstructive colorectal cancer. However, endoscopic stent insertion for obstructive colorectal cancer has recently allowed elective laparoscopic surgery. The aim of this study is to evaluate the feasibility and the short-term clinical outcomes of laparoscopic surgery following endoscopic stent insertion for management of malignant colorectal obstruction at a single center. Methods: The medical records of patients who had undergone endoscopic stent insertion for colorectal cancer obstruction, followed by laparoscopic colorectal resections, from August 2004 to August 2008 were reviewed. To evaluate the surgical and clinical outcomes, we analyzed the clinical and pathologic data. Results: Thirty-six endoscopic stent insertions were successfully performed during the study period. Of those 36, the 28 treated by laparoscopic surgery were enrolled in this study. The mean interval between stent insertion and surgery was 7.4 ±2.3 days. Two cases were converted to open surgery. The mean operative time was 185.5±53.1 min, and the mean blood loss was 77.0±72.9 mL. Flatus was passed on the mean 2nd postoperative day, and patients started to eat on the mean 4th postoperative day. The mean postoperative hospital stay was 11.2±4.4 days. Anastomosis leakages occurred in two cases and were treated by a secondary operation with a transient ileostomy. There were morbidities in five cases, but no mortalities. Conclusion: A combined endoscopic stent insertion and laparoscopic surgery is an effective and safe, minimally invasive operation for malignant colorectal obstruction.

The Model-based Avionic Prognostic Reasoner (MAPR) presented in this paper is an innovative solution for non-intrusively monitoring the state of health (SoH) and predicting the remaining useful life (RUL) of electronic and electromechanical assets by accessing and processing data obtained from a standard avionics data bus. To support Integrated Vehicle Health Monitoring (IVHM) initiatives, the solution being described here has been designed to be as non-intrusive as possible. An innovative, model-driven anomaly diagnostic and fault characterization system for electromechanical actuator (EMA) systems was developed to mitigate potentially catastrophic faults. EMA systems are used in a wide variety of aircraft applications to control critical components such as control surfaces, landing gear and thrust vector control. Failure in any one of these systems can compromise passenger safety, as well as mission success. A MIL-STD-1553 bus interface and monitor were designed to extract environmental (e.g., altitude, air speed, air density) and operational (i.e., response of system to a commanded change) data of a representative EMA system and to determine whether an anomaly is detected, and the corresponding severity. The MIL-STD-1553 bus was chosen as the test bed to develop this approach, due to its large installed base and availability of compatible development tools. Advanced and unique reasoning methodologies are applied to the extracted data sets to provide anomaly detection and fault classification on various fault modes and eventually yield SoH and RUL. In this paper we describe a data monitoring unit that will, in real time, identify, isolate, and characterize faults and establish their severity so that major performance problems can be alleviated. When built, this system will consist of a laptop with a Peripheral Component Interconnect (PCI) card slot that can accept multiple interfaces to the MAPR software package. The MAPR package will be designed to be adaptable for a large number of different platforms, for portability and for maximum input data type flexibility. This paper describes a ground-based prototype of the technology to show the efficacy of the method.