Earticle

목적: 국내에서 사용되고 있는 작업치료 평가 도구를 살펴본 결과, 과제 수행을 평가하는 도구가 부족하였다. 본 연구는 중추신경계(central nervous system; CNS) 질환 환자의 기능적 이동, 자기관리, 일상생활활동수행 수준을 평가할 수 있는 한국형 자기관리 수행기술 평가(Korean Performance Assessment of Self-Care Skills; K-PASS)를 개발하고자 하였다. 연구방법: K-PASS는 두 단계에 걸쳐 개발되었다. 1단계에서는 원척도를 한국어로 번역하고, 전문가 위원회를 통해 적합성을 검증하였으며, 번역본의 이해도를 평가하고, 역번역을 수행한 후 역번역본을 검토하였다. 2단계에서는 K-PASS의 타당도와 신뢰도를 검증하였다. 결과: 여러 과제가 한국 문화에 맞게 수정되었다. K-PASS의 내용 타당도 검증 결과 척도-수준 내용 타당도 지수(content validity index; CVI)는 0.92였으며, 문항-수준 CVI는 0.80에서 1.00의 분포를 보였다. 집단타당도 검증에서는 CNS 질환 환자군과 건강한 성인군 간에 통계학적으로 유의한 차이가 나타났다(p < 0.001). 내적 일관성 신뢰도는 높은 수준으로, Cronbach's alpha 값이 0.988이었다. 결론: K-PASS는 문화적으로 적합한 도구로 개발되었으며, 임상 환경에서 CNS 질환 환자의 일상 내 자기관리과제 수행을 평가하는 데 있어 높은 타당도와 신뢰도를 보였다.

Objective : This review of occupational therapy evaluation tools used in Korea highlights the lack of task performance assessments. This study aimed to develop the Korean Performance Assessment of Self-Care Skills (K-PASS) as a tool to evaluate functional mobility, self-care, and activities of daily living in patients with central nervous system (CNS) diseases. Methods : The K-PASS was developed in two phases. Phase 1 involved translating the original scale into Korean, validating its appropriateness through an expert panel, assessing the comprehensibility of the translation, performing back-translation, and reviewing the back-translation for accuracy. Phase 2 focused on testing the validity and reliability of K-PASS. Results : Several tasks were adapted for the Korean context. Content validity assessment yielded a scale-level content validity index (CVI) of 0.92, with item-level CVIs ranging from 0.80 to 1.00. Known-groups validity testing showed statistically significant differences between CNS disease patients and healthy adults (p < 0.001). Internal consistency reliability was excellent, with a Cronbach’s alpha of 0.988. Conclusion : The K-PASS was developed as a culturally adapted version, demonstrating strong validity and reliability for evaluating the performance of daily self-care tasks in patients with CNS diseases in clinical settings.

This paper investigates the performance of millimeter-wave (mmWave) multicell networks with repulsively deployed base stations (BSs), via the β-Ginibre point process (β-GPP) where β adjusts the BS repulsion. In particular, this study analytically expresses the SINR distributions and validates the results via extensive simulations. Further, this paper empirically demonstrates that the effects of BS repulsion can be well modeled via linear regression (LR) and this LR-based approach can be also quite simply approximated as the convex combination (CC) of the two extreme cases of β→0 and β=1. Therefore, the proposed CC approximation can significantly reduce the time for calculating the SINR distributions in β-GPP mmWave multicell networks.

Effective management of catastrophic events in high-risk zones necessitates a holistic technological approach to protect ecosystems, biodiversity, and native populations. Limitations in sensor range and connectivity hamper real-time data gathering in secluded areas, while financial and technical hurdles hinder the creation of cost-effective, automated systems. This study presents stochastic models, the LoRaW protocol, and cloud technology to enhance sensor deployment simulations. Wireless Sensor Networks and LoRa technology are crucial for extensive monitoring and communication infrastructures. Stochastic Petri Net models optimize system components by assessing crucial performance indicators, such as average response time and system utilization, thus improving disaster response and supporting research hypotheses.

The physical security layer of industrial wireless sensor networks in the event of an eavesdropping attack has been investigated in this paper. An optimal sensor selection scheme based on the maximum channel capacity is proposed for transmission environments that experience Nakagami fading. Comparing the intercept probabilities of the traditional round robin (TRR) and optimal sensor selection schemes, the system secure performance is analyzed. Simulation results show that the change in the number of sensors and the eavesdropping ratio affect the convergence rate of the intercept probability. Additionally, the proposed optimal selection scheme has a faster convergence rate compared to the TRR scheduling scheme for the same eavesdropping ratio and number of sensors. This observation is also valid when the Nakagami channel is simplified to a Rayleigh channel.

This study investigates energy efficiency issues of device-to-device (D2D) communications in heterogeneous networks. To minimize the total transmitted power, an approach based on Q-learning together with adaptive ? -greedy is proposed to optimize the connection of user equipment (UE) with base station (BS) or access point (AP). The proposed adaptive ? -greedy can conduct the adequate exploration and exploitation operations for effective optimization. Simulation results indicate that in the single-cell scenario, the proposed method can attain performance close to the best solution.

Providing web services to users has become expensive in recent times. For better web services, a web server is provided with high-performance technology. To achieve great web service experiences, tools such as general-purpose graphics processing units (GPGPUs), artificial intelligence, high-performance computing, and three-dimensional simulation are widely used. However, graphics processing units (GPUs) are used in high-speed operations and have limited general applications. In this study, we developed a task queue in a GPU to improve the performance of a web service using a multiprocessor and studied how to receive and process user requests in bulk. We propose the use of a GPGPU-based task queue to process user requests more than GPGPU based a central processing unit thread, and to process more GPU threads on task queue at about 136% to 233%, and proved that the proposed method is effective for web service.

Recently high-speed memory array based on RAM, which is a type of solid-state drive (SSD), has been introduced to handle the input/output (I/O) bottleneck. But there are only a few performance studies on RAM based SSD storage with regard to diverse workloads. In this paper, we focus on the file system for RAM based memory array based NGS (Next Generation Storage) system which is running on Linux operating system. Then we perform benchmark tests on practical file systems including Ext3, ReiserFS, XFS. The result shows XFS significantly outperforms other file systems in tests that represent the storage and data requests typically made by enterprise applications in many aspects. The experiment is used to design the dedicated file system for NGS system. The results presented here can help enterprises improve their performance significantly.

Various machine-learning models may yield high predictive power for massive time series for time series prediction. However, these models are prone to instability in terms of computational cost because of the high dimensionality of the feature space and nonoptimized hyperparameter settings. Considering the potential risk that model training with a high-dimensional feature set can be time-consuming, we evaluate a feature-importance-based feature selection method to derive a tradeoff between predictive power and computational cost for time series prediction. We used two machine learning techniques for performance evaluation to generate prediction models from a retail sales dataset. First, we ranked the features using impurity- and Local Interpretable Model-agnostic Explanations (LIME) -based feature importance measures in the prediction models. Then, the recursive feature elimination method was applied to eliminate unimportant features sequentially. Consequently, we obtained a subset of features that could lead to reduced model training time while preserving acceptable model performance.

Cells in a culture process transform with time and produce many overlapping cells in their vicinity. We are interested in a separation algorithm for images of overlapping cells taken using a fluorescence optical microscope system during a cell culture process. In this study, all cells are assumed to have an ellipse-like shape. For an ellipse fitting-based method, an improved least squares method is used by decomposing the design matrix into quadratic and linear parts for the separation of overlapping cells. Through various experiments, the improved least squares method (numerically stable direct least squares fitting [NSDLSF]) is compared with the conventional least squares method (direct least squares fitting [DLSF]). The results reveal that NSDLSF has a successful separation ratio with an average accuracy of 95% for two overlapping cells, an average accuracy of 91% for three overlapping cells, and about 82% accuracy for four overlapping cells.

For disaster exploration and surveillance application, this paper aims to present a novel application of a multi-robot agent based on WSN and to evaluate a multi-hop communication caused by the robotics correspondingly, which are used in the uncertain and unknown subterranean tunnel. A Primary-Scout Multi-Robot System (PS-MRS) was proposed. A chain topology in a subterranean environment was implemented using a trimmed ZigBee2006 protocol stack to build the multi-hop communication network. The ZigBee IC-CC2530 modular circuit was adapted by mounting it on the PS-MRS. A physical experiment based on the strategy of PS-MRS was used in this paper to evaluate the efficiency of multi-hop communication and to realize the delivery of data packets in an unknown and uncertain underground laboratory environment.

WiMAX is intended for fourth generation wireless mobile communications where a group of users are provided with a connection and a fixed length queue. In present literature traffic of such network is analyzed based on the generator matrix of the Markov Arrival Process (MAP). In this paper a simple analytical technique of the two dimensional Markov chain is used to obtain the trajectory of the congestion of the network as a function of a traffic parameter. Finally, a two state phase dependent arrival process is considered to evaluate probability states. The entire analysis is kept independent of modulation and coding schemes.

DVC (Distributed Video Coding) and FRUC (Frame Rate Up Conversion) techniques need to have an efficient motion compensated frame interpolation algorithms. Conventional works of these applications have mainly focused on the performance improvement of overall system. But, in some applications, it is necessary to evaluate how well the MCI (Motion Compensated Interpolation) frame matches the original frame. For this aim, this paper deals with the modeling methods for evaluating the block-based matching cost. First, several matching criteria, which have already been dealt with the motion compensated frame interpolation, are introduced and then combined to make estimate models for the size of MSE (Mean Square Error) noise of the MCI frame to original one. Through computer simulations, it is shown that the block-based matching criteria are evaluated and the proposed model can be effectively used for estimating the MSE noise.

This paper applies transmit antenna selection algorithms to spatial-temporal combining-based spatial multiplexing (SM) ultra-wideband (UWB) systems. The employed criterion is based on the largest minimum output signal-to-noise ratio of the multiplexed streams. It is shown via simulations that the bit error rate (BER) performance of the SM UWB systems based on the two-dimensional Rake receiver is significantly improved by antenna diversity through transmit antenna selection on a log-normal multipath fading channel. When the transmit antenna diversity through antenna selection is exploited in the SM UWB systems, the BER performance of the spatial-temporal combining-based zero-forcing (ZF) receiver is also compared with that of the ZF detector followed by the Rake receiver.

This paper presents a queuing analysis model of a PC-based software router supporting IPv6-IPv4 translation for residential gateway. The proposed models are M/G/1/K or MMPP-2/G/1/K by arrival process of the software PC router. M/G/1/K is a model of normal traffic and MMPP-2/G/1/K is a model of burst traffic. In M/G/1/K, the arriving process is assumed to be a Poisson process, which is independent and identically distributed. In MMPP-2/G/1/K, the arriving process is assumed to be two-state Markov Modulated Poisson Process (MMPP) which is changed from one state to another state with intensity. The service time distribution is general distribution and the service discipline of the server is processor sharing. Also, the total number of packets that can be processed at one time is limited to K. We obtain performance metrics of PC-based software router for residential gateway such as system sojourn time blocking probability and throughput based on the proposed model. Compared to other models, our model is simpler and it is easier to estimate model parameters. Validation results show that the model estimates the performance of the target system.

Purpose: This study aimed to evaluate the perceived importance and performance of evidence-basedclinical practice guidelines (EBCPG) for venous thromboembolism (VTE) prevention among nurses inorthopedic hospitals. Methods: A cross-sectional descriptive study was conducted among 188 nurses in three orthopedichospitals in South Korea. Participants rated the importance and performance of 110 EBCPG for VTEprevention items across 10 domains using a 5-point Likert scale. Importance-performance analysis wasutilized to identify gaps and prioritize areas for improvement. Results: The mean importance and performance scores for EBCPG for VTE prevention were 4.02 and 3.67,respectively, revealing a statistically significant gap (t ¼ 10.67, p < .001). Differences were observedacross domains, with “General interventions for VTE prevention” showing the highest scores for bothimportance and performance, while “VTE prevention for cancer patients” and “VTE prevention for surgicalpatients” exhibited the largest gaps. Conclusion: The findings highlight the need for targeted strategies to bridge the gap between theperceived importance and performance of EBCPG for VTE prevention, particularly in domains withsignificant performance deficiencies. Tailored education and institutional support are recommended toenhance guideline utilization and improve patient outcomes in orthopedic hospitals.

This paper proposes a novel method to provide secrecy outage performance in various fading environments using a mixture gamma (MG) distribution, which is well known to approximate numerous fading channel models. First, we derive the generalized moment generating function (G-MGF) and incomplete G-MGF (IG-MGF) of the MG distribution. Then, by applying those formulas, we obtain the exact and asymptotic expressions of the secrecy outage probability along with the corresponding lower bound and probability of the strictly positive secrecy capacity for various fading scenarios. Finally, the accuracy and effectiveness of all derived formulas are verified through extensive numerical experiments.

As urban networks integrate heterogeneous radio access technologies (RATs), such as Wi-Fi and 5G/B5G, modeling performance becomes challenging due to interference, spatial variability, and propagation conditions. This paper proposes a graph neural network (GNN)-based framework for predicting throughput, delay, and jitter in multi-RAT environments, considering RAT type. The model encodes network topology and channel characteristics using node and edge features, capturing spatial configuration, congestion, and line-of-sight (LoS) versus non-line-of-sight (NLoS) conditions. The results show that GNNs exhibit robustness across station densities and spatial conditions. The message-passing GNN method performs well for throughput and delay, while non-graph methods better estimate jitter.

Conventional mining enterprises, particularly coal-related ones, exhibit substantial environmental pollution and high energy consumption, while those involved in new energy resources, such as lithium and cobalt, face severe resource shortages. Consequently, the economic efficiency of China's mining enterprises is significantly constrained. This study examines data from nine representative listed enterprises in China spanning 2016 to 2021. Employing the DEA model-i.e., BCC (VRS) model, we analyze the economic efficiency of mining enterprises with a focus on stakeholders. The paper provides static and dynamic analyses, offering insights and recommendations for enhancing technology, reducing costs, and fortifying social relationships.

High-performance computing (HPC) provides to researchers a powerful ability to resolve problems with intensive computations, such as those in the math and medical fields. When an HPC platform is provided as a service, users may suffer from unexpected obstacles in developing and running applications due to restricted development environments and dependencies. In this context, operating system level virtualization can be a solution for HPC service to ensure lightweight virtualization and consistency in Dev-Ops environments. Therefore, this paper proposes three types of typical HPC structure for container environments built with HPC container and Docker. The three structures focus on smooth integration with existing HPC job framework, message passing interface (MPI). Lastly, the performance of the structures is analyzed with High Performance Linpack benchmark from the aspect of performance degradation in network communications under Docker.

A comprehensive understanding of functions and performances enables a selection of appropriate alternatives to the existing design and can prevent defective design. A performance-based design quality management can ensure successful project completion. This study proposes a new model for design quality management in order to prevent defective design and to minimize design change. The new quality management model defines the requirement about function and performance based on technical characteristic, and assesses suitability for design alternatives. This study attempts to propose a quality matrix assessment method that can compare the alternative design and requirements defined with the new quality management model. This method can judge conformity and suitability of design quality in accordance with the requirements configured.