Digital Twins (DTs) are transforming education by creating immersive, data-driven learning environments that integrate theoretical knowledge and practical application. In the current educational landscape, traditional teaching methods often struggle to engage diverse learners, adapt to individual needs, and provide hands-on experiences. While existing literature highlights DT potential in specific domains like engineering, comprehensive analyses of recent advancements and cross-disciplinary applications remain scarce. This systematic literature review addresses this gap by analyzing 25 peer-reviewed studies, identifying the current applications and implementation approaches of DTs in educational settings, their distribution across different educational disciplines, and emerging trends in educational DT development. Our analysis reveals four dominant trends: (1) AI-driven cognitive modeling for personalized learning, (2) immersive technology integration, (3) gamification mechanisms, and (4) cross-domain convergence. Researchers are leveraging these trends to address critical challenges, such as enhancing student engagement, enabling adaptive learning, and optimizing campus operations. However, critical gaps persist, including limited scalability in non-technical fields, insufficient longitudinal validation, and heterogeneous data integration challenges. Future research should prioritize empirical validation, scalability across diverse educational contexts, and ethical frameworks to ensure inclusive and sustainable adoption of DTs in education.

Considering the sensitivity of healthcare-related patient information processed in a Digital Twin (DT) environment, it is essential to have a well-defined cybersecurity framework and implementation strategy. Failure to successfully adopt a functional cybersecurity approach could result in a data breach, which would incur exorbitant fines for the responsible healthcare organization; resulting in negatively impacted stakeholder confidence and public opinion. Existing research focuses on specific security challenges when implementing DT, but does not adequately detail implementation of an environmentwide cybersecurity strategy, such as the National Institute of Standards and Technology (NIST) Risk Management Framework (RMF). Although a dedicated RMF specifically for Internet of Things (IoT) environments exists, one tailored for DT Healthcare does not. Existing research continually identifies a knowledge gap with regards to the incorporation of cybersecurity in the design, planning, and implementation stages of DT development. The paper queries several research databases for publications pertaining to DT healthcare cybersecurity; A total of $\mathbf{4 0}$ papers were found. Of these 40 publications, none of them detail the implementation of cybersecurity in DT healthcare at a substantiative level. The paper serves to highlight the importance implementing a cybersecurity RMF, present possible RMF implementation strategies, and identify existing research gaps relating to its implementation.

Social media platforms and online communities have become essential sources for sharing information on medical issues and expressing personal health experiences. Platforms like Reddit’s r/health, health boards, and Quora serve as vital spaces where researchers and health-interested individuals can gather and exchange information for various purposes.According to official web statistics from Quora and Reddit, Reddit hosts over 3 million niche communities and receives approximately 1.9 billion monthly visits, making it a central hub for diverse discussions, including health-related topics. Quora, with around 300 million monthly active users, is another significant platform, offering a wealth of Q&A discussions that provide valuable insights into health issues and personal experiences.In recent years, evaluating health literacy has become an important area of research. Researchers are increasingly focused on assessing users’ abilities to manage, express, and engage with health-related information.This paper aims to cover the most common definitions of health literacy, the opportunities and threats it presents, as well as the available datasets for these studies, without neglecting the challenges inherent in this field of research.

One of the most important factors influencing patient outcomes is health literacy (HL), which is the capacity to obtain, comprehend, and use health information. Disparities still exist despite the abundance of digital health resources because of complicated medical terminology, a lack of personalization, and a lack of multilingual support. By utilizing diverse data sources, such as electronic health records (EHRs), online health communities (like Reddit), and medical ontologies (like UMLS, SNOMED-CT), this study examines how artificial intelligence (AI) can close the HL gap. We examine cutting-edge methods like large language models (LLMs) for text simplification (e.g., grade-level adaptation in GPT-4) and natural language processing (NLP) for HL classification (e.g., linguistic profiling in the ECLIPPSE study). We draw attention to issues such as cultural biases in HL evaluation, oversimplification of medical information, and difficulties integrating data. To personalize the delivery of health information, our suggested framework integrates AIdriven methods such as automatic HL level identification, concept mapping, and semantic enrichment. This work attempts to improve accessibility while maintaining clinical accuracy by combining structured (EHRs) and unstructured (social media) data. To guarantee equitable health communication, future directions include multilingual adaptation and real-world validation.

As authors have investigated the use cases for Blockchain, they have gravitated to Hyperledger Fabric, a framework supported by the Linux Foundation to allow for its use in enterprise environments. While Hyperledger Fabric provides the framework, it still has unanswered gaps that become apparent as we review the application of the technology across multiple sectors. Identifying gaps will allow us to focus on those areas and make Hyperledger Fabric more useful. Research has shown similar challenges across sectors but no clear guidance that allows researchers to know what is permissible and what is not permissible in an enterprise environment when using Hyperledger Fabric. Compliance, scalability, and security are areas of study across sectors, and bringing these ideas together will allow further targeted research.

The increasing adoption of artificial intelligence (AI) in mobile health applications has introduced critical concerns regarding user privacy and data security. Privacy risks, particularly when health data is transmitted to cloud servers, present a major barrier to widespread adoption of AI-driven personal health monitoring systems. Regulatory frameworks such as HIPAA and GDPR outline strict requirements for securing patient data, yet achieving compliance in mobile environments remains challenging. Previous research has explored encryption techniques and secure data transmission methods to address privacy concerns. While effective to some extent, these methods still rely on cloud infrastructure, creating vulnerabilities and limiting offline functionality. Lightweight on-device AI models have emerged as a promising alternative, but ensuring privacy without compromising diagnostic accuracy presents a technical challenge. In this paper, we propose an on-device AI model that integrates TensorFlow Lite for lightweight inference and the Gemma 2B language model for personalized health insights. Our model addresses key privacy concerns by analyzing health data locally on the user’s device, ensuring secure, private assessments without relying on cloud connectivity. We implemented an on-device AI module for patient health monitoring as a proof of concept.

In 2024, 23% of U.S. adults experienced a mental health illness. Even though rates of mental health conditions are rising, help-seeking behaviors are remaining constant. This highlights a need for improved mental health literacy (MHL) education and training. In the ever-advancing technological landscape, various AI technologies have been used for detection, diagnosis, treatment, public health, and research of mental health conditions as touted as tools to improve education and literacy of mental health concerns. The purpose of this scoping review was to determine how AI has been used to improve patients’ MHL and identify gaps in the literature for future projects. The search strategy included articles from 2007-2025 with key terms pertaining to mental health, mental health literacy, AI, and the U.S. The initial search pulled 30 articles, which were funneled down to 8 for full text review. The results indicate that AI has been used in three main ways: roleplay simulations, Chatbots and large language models, and machine and deep learning. AI has been used to improve mental health literacy for depression, suicide prevention, ADHD, and PTSD, but there are tremendous opportunities for growth in these areas and across the spectrum of mental health conditions and services.

By far, the most effective knowledge assessment in college education is to give students exam and grade their answers then assess their level of understanding. However, exam grading can be time-consuming, tedious, cumbersome, and sometimes the grading results are not consistent with the rubric. Here, we propose an AI based exam grader that can not only ease educators’ burden but also produce accurate, consistent, and precise grading results. We have used GPT-3.5, GPT-4.0, and Gemini-pro, respectively, as our grading engine. To verify the correctness, precision, and accuracy of our proposed grader, the results were compared with the instructor’s grading result and also with human grader such as teaching assistants. In our experiment, GPT-4.0 showed the most reliable and consistent results.

Effective communication of patient clinical and care information between pre-hospital services and Emergency Departments (EDs) is crucial for the rapid and effective treatment of patients, potentially saving lives. Recent research indicates a frequent lack of patient data metrics, such as vital signs, which represents a potential limitation in the comprehensiveness of handoffs received by ED physicians from pre-hospital providers. To address this challenge, we propose a solution that utilizes a patient's digital twin and permissioned blockchain technology to ensure complete, real-time, secure, and shareable access to patients' vital signs and other data metrics for Emergency Departments. This solution seeks to answer the following questions: Which data model for the digital twin best represents the patient during transport, meeting the requirements of the receiving facility? How can patient digital twin data, including the history of clinical information and patient care, be shared in real-time with the receiving facility? How can EHR-compliant digital twin data be produced to satisfy the reconciliation between the Patient Digital Twin (PDT) and the Electronic Health Record (EHR)? Method: We conducted a comprehensive literature review and a series of interviews with both pre-hospital and hospital care providers to identify the problem and develop a model based on it. To address the second question, we designed an architecture that includes all parties in the care team, during and after the transport. For the third question, we explored NEMSIS data exchange standards and a Natural Language Processing (NLP) Module. Objective: Our goal is to improve the survivability of patients in emergency care through secure, effective, and real-time sharing of patient metric data between Emergency Medical Services (EMS) and Emergency Departments or Trauma Centers. This approach also aims to reduce the waiting time for patients upon arrival at the receiving facility.

In this paper, we explore the potential of utilizing Digital Twin (DT) technology for real-time data storage and processing in emergency healthcare. Focusing on Internet of Things (IoT) and cloud computing technologies, we investigate various enabling technologies, including cloud platforms, data transmission formats, and storage file formats, to develop a feasible DT storage solution for emergency healthcare. Through our analysis, we find Amazon AWS to be the most suitable cloud platform due to its sophisticated real-time data processing and analytical tools. Additionally, we determine that the MQTT protocol is suitable for real-time medical data transmission, and FHIR is the most appropriate medical file storage format for emergency healthcare situations.We propose a cloud-based DT storage solution, in which real-time medical data is transmitted to AWS IoT Core, processed by Kinesis Data Analytics, and stored securely in AWS HealthLake. Despite the feasibility of the proposed solution, challenges such as insufficient access control, lack of encryption, and vendor conformity must be addressed for successful practical implementation. Future work may involve incorporating Hyperledger Fabric technology and HTTPS protocol to enhance security, while the maturation of DT technology is expected to resolve vendor conformity issues. By addressing these challenges, our proposed DT storage solution has the potential to improve data accessibility and decision-making in emergency healthcare settings.

Emergency medical services universally get regarded as the essential part of the health care delivery system [1]. A relationship exists between the emergency patient death rate and factors such as the failure to access a patient's critical data and the time it takes to arrive at hospitals. Nearly thirty million Americans do not live within an hour of trauma care, so this poor access to trauma centers links to higher pre-hospital death rates in more than half of the United States [2]. So, we need to address the problem. In a patient care-cycle, loads of medical data items are born in different healthcare settings using a disparate system of records during patient visits. The ability for medical care providers to access a patient's complete picture of emergency-relevant medical data is critical and can significantly reduce the annual mortality rate. Today, the problem exists with a continuous recording system of the patient data between healthcare providers. In this paper, we've introduced a combination of secure file transfer methods/tools and blockchain technology as a solution to record patient Emergency relevant medical data as patient walk through from one clinic/medical facility to another, creating a continuous footprint of patient as a secure and scalable data source. So, ambulance crews can access and use it to provide high quality pre-hospital care. All concerns of medical record sharing and accessing like authentication, privacy, security, scalability and audibility, confidentiality has been considered in this approach.

Health Data Records Fragmentation across hospitals, labs, pharmacies, and general caregivers has been hurting medical services quality and patient outcome. The problem is getting worse as we have even more healthcare accesses from different sources such as mobile phones, wearable devices, etc. In an attempt to defragment health data, we are proposing the utilization of distributed transaction ledgers (DTL) technology by IOTA-Tangle which facilitates communication between patients and care providers. Through this technology the gap between health-care delivery and population health can be bridged so the quality of health services and patient outcomes may be improved. Our approach provides complete picture for each patient by sharing data between healthcare participants.

In a modern society, we are surrounded by state-of-the-art technologies that help our lives more convenient and secure than ever before. With the advancement from the fourth industrial revolution, sensor networks are added to our already well-connected society and now we are practically living in a super connected society where everything is communicating with each other. However, when it comes to the delivery of healthcare, the connectivity becomes challenging and patients suffer from the discontinuity of clinical information due to lack of communication and fragmented and isolated care delivery from within and across care settings. The resulting consequences include poor patient outcome, astronomical medical expenses and even deaths by preventable medical mistakes. According to the research from Johns Hopkins University (2016), there have been about 250,000 deaths reported per year by preventable medical mistakes. Also the US government spends more than a trillion dollars for treating chronic diseases using tax payers' money and the amount could reach $6 trillion by 2050 if no action such as rigorous preventive care is taken. The preventable medical mistakes indicated that there is a failure in the coordination among the caregivers and in sharing of the medical records at right time, with right people, in right place. The astronomical medical expense suggested that effective and accessible preventive care must be provided to each individual before any serious disease is fully developed. In this talk, Id like to discuss about current applicable health IT technologies and their applications for the delivery of smart and connected healthcare for everyone.

Chronic diseases such as heart diseases, cancer, diabetes, and asthma continue to increase in general public these days. With monitoring of observed symptoms along with reasonable levels of knowledge on those diseases, those may be detected early and managed properly. For that to happen, the awareness of the symptoms and proper knowledge about the diseases need to be provided to each individual. In order to acquire related health knowledge, individuals may need to collect necessary health information from various sources such as the Internet, articles, or some type of e-learning systems. However, the available information is often overwhelming and is mostly unorganized. Patients or learners are then struggling to find a way to retrieve relevant health information from such unorganized chunks of collected information. In this study, we attempt to provide only the relevant learning materials specific to each individual's symptoms or clinical conditions through a carefully designed e-learning system that provides customized learning. In our approach, we utilized observed symptoms and vital signs to identify potential diseases of each patient. Such factors are used to build patient profiles that are used to provide specific sets of learning materials called study plans. Such customized learning enables patients to take control of their symptoms and potential diseases to help improve patient outcomes as a result.

Patient generated data or personal clinical data in general is considered an important aspect in improving patient outcomes. However, personal clinical data is difficult to collect and manage due to their distributed nature, i.e., located over multiple places such as doctor's office, radiology center, hospitals, or some clinics, and heterogeneous data types such as text, image, chart, or paper based documents. In case of emergency, this situation makes necessary personal clinical data retrieval almost impossible. In addition, since the amount and types of personal clinical data continue to grow, finding relevant clinical data when needed is getting more difficult if no actions are taken. In response to such scenarios, we propose an approach that manages personal health data by utilizing meta-data for organization and easy retrieval of clinical data and cloud storage for easy access and sharing with caregivers to implement the continuity of care and evidence-based treatment. In case of emergency, we make critical medical information such as current medication and allergies available to relevant caregivers with valid license numbers only.

Personal health record (PHR) is considered a crucial part in improving patient outcomes. However the adoption rate by the general public in the US still remains low. To find out the barriers in adopting PHR, we have surveyed articles related to personal health record system (PHRS) from 2008 to 2016 and categorized them into 6 different categories such as motivation, barriers, ownerships, interoperability, privacy, and security and portability. In this paper, we propose a framework that can help lift such barriers and motivate people to adopt PHRS so they can manage their health by monitoring and controlling their clinical data using PHRS.

Modern healthcare systems are still struggling to provide patient-centered healthcare instead of clinician-centered, as it is essential to implement major aspects of modern healthcare such as continuity of care, evidence-based treatment and more importantly preventing medical mistakes. In order to implement patient-centered, it is necessary to collect all the relevant clinical /non-clinical data for each patient and make those available at the time of needs. However, such task requires lots of personal effort and the coordination from the care givers so it is not being done properly. In this talk, Ill present medical sensor and cloud based personal clinical data collection, analysis, and patient education based on the collected data. The collection of such clinical data will be stored in the personal health record system and make it available for query and update so it can be helpful in making important medical decisions. Ill also discuss about the barriers in personal health record adoption and how to lift such barriers so patient-centered healthcare may be possible.

In the U.S., preventable medical errors claim the lives of about 400,000 patients per year. Much of it is blamed for the lack of interoperable medical information at right time. Often times, personal medical information is stored in multiple clinical institutions in non-sharable format such as paper based. To provide effective and continuity of care, personal medical data need to be shared and patients must be in control of their own medical record - no decision about me without me. In this talk, I propose a model that addresses such issues. It allows patients to monitor and control their own health using medical data collection tool, ontological diagnosis support and personalized medical education. It also allows the communication with clinical institutions and physicians using standardized health record format such as HL7 CDA to implement continuity of care.

One of the main goals of the electronic health record (EHR) system is to empower patients to access to their own medical decisions. However, medical data is largely coming from clinical institutions so there is no way for them to control or maintain their own medical record. Patients or guardians may need to keep track of their medical data such as observed symptoms or measurements that may not be available in the EHR. Additionally, clinical decision without patient medical history can be error-prone and even be detrimental. Personal medical condition history is considered as the one of the weakest links in the current healthcare systems. For those reasons, it is necessary to have an effective and efficient personal health record system (PHRS) that allows patients or guardians to constantly monitor and control the personal health record. We propose a cloud based personal health record system that allows constant monitoring capability by supporting dynamic creation of clinical document architecture (CDA) document from a mobile device. The generated CDA document may be used to assess current health against major diseases through a clinical decision support system. We provide constant monitoring capability by using easy uploading module and decision support system. Our proposed system uses medical coding standards such as ICD-9-CM, SNOMED CT, etc. to achieve interoperability between different electronic health record systems.

With the recent advancement of science and technology, the modern industry-oriented competency requirements in mechanical engineering demand higher education for essential computer based knowledge. Production processes in modern factories these days, for example, mostly are controlled and operated by computers but current mechanical engineering curriculum doesn't provide such knowledge. In this paper, we proposed an interdisciplinary curriculum that is designed for undergraduate mechanical engineering major students so they may meet the competency requirements for the industry. An ideal industrial production process management method, where computer networking and cloud computing concepts are considered in the design, was proposed to illustrate such needs. The proposed method is designed to solve the problems that the modern production industries are experiencing. The proposed curriculum is designed to provide such computer knowledge to the mechanical engineering major students, which attempts to satisfy the modern industry-oriented competency requirements.

Thanks to the advances in development technologies and the services in learning management systems (LMS), e-learning has become the popular choice in academia, government, and private industries when delivering learning contents. Sharable Content Object Reference Model (SCORM), a standard for LMS, was initially specified by Advanced Distributed Learning (ADL), to meet the needs of the U.S. Department of Defense(DoD) for reusable, accessible, interoperable and durable web-based learning contents. In order to make efficient use of the learning contents, it is necessary for LMS to create directories by subject and to provide a federated search that covers heterogeneous information distributed over various locations. In this paper, we propose a SCORM based on LMS model that performs hybrid reasoning using hybrid base. It utilizes both rule base and case base when present search results.

Dynamic impact analysis based on program executions has shown promise in aiding tasks in the life cycle of large-scale systems. Dynamic impact analysis techniques have shown to produce more precise results than static impact analysis [1]. However, current dynamic impact analysis techniques lack important features such as analysis of dependency among program entities and consideration of object-oriented programs ' features. Thus they may produce imprecise results. In this paper, we present a precise dynamic impact analysis approach for object-oriented programs. This approach considers the characteristics of object- oriented programs and performs dependency analysis which may potentially reduce the impact sets by eliminating elements that do not have dependency on the changed elements. Even though our discussion in this paper is based on JavaTMprogramming language, this approach can be carried out in a language independent manner for broader applications.

Impact analysis predicts and determines the parts of a software system that can be affected by changes of the system. Before or after such changes are made, impact analysis helps reduce the risk and costs caused by unwanted impact from changes. Traditional static impact analysis techniques, based on static system information, tend to produce imprecise results that are hardly useful. Dynamic impact analysis techniques are based on dynamic system behaviors, thus produce more precise and useful results. Existing dynamic impact analysis techniques impose various amounts of overhead costs in time and space, and produce impact sets of different degree of precision. In this paper, we propose a new dynamic impact analysis technique that is less expensive in both time and space than existing techniques, and produce safe and precise impact set relative to the dynamic information used in calculation.

RosettaNet standards are industry standards for B2B integration. Although it has been adopted by many companies in informational technology, electronic components, and semiconductor manufacturing industries, etc., there are still many questions need to be answered. The paper introduces the typical scenario and benefits of using RosettaNet standards, provides a survey on the architectures supporting RosettaNet. We also provide a comparison of these architectures, and propose an architecture supporting RosettaNet using Web services architecture

Enterprise Architectures (EA’s) usually capture the information technology architecture of the organization including hardware, software, and networking standardizations, if any, that serves as the basis for all information systems developed within an organization. Usually EA’s are closely aligned to the Strategic Enterprise Plan of the organization. Any information system developed within the organization is derived, on the other hand, from the Strategic Information Systems Plan of the organization. The initial stages of developing an information system, after approval by the executive sponsors, include the scope definition, problem analysis and the requirements analysis phases. It is during the requirements analysis phase that the requirements of the new system are elicited from the stakeholders and analyzed – the analysis includes, among other aspects, the development of candidate system architectures (SA) that considers different ways of allocating the requirements between hardware, software and the network. One way of selecting the optimal architecture among the candidate system architectures is to determine the compliance of the architectures with the enterprise architecture. In this paper we provide a framework, called the Propagatory Framework, for establishing the traceability between SA and the EA that is based on the NFR Approach where NFR stands for Non-Functional Requirement. We demonstrate the practicality of the Propagatory framework by applying it to determine the traceability of the system architectures for a Home Appliance Control System (HACS).

This paper explores the use of program slicing as a tool for "component mining" of Java/spl trade/ source code. We define component mining to be the extraction of an executable slice from source code, which satisfies a specific use case (or set of use cases) and provides a standard component interface for its use. However, before a component can be generated, the desired features themselves must be isolated from the source code. Traditionally, software slicing has concerned itself with the value of a variable of interest (or set of variables) at a specific point of execution. This severely limits the usefulness of the traditional definition of a slicing criterion as a tool for feature isolation. We propose the repurposing of software "unit-tests" to aid in the isolation of features of interest within source code. By executing a target application in our JPDA (Java platform debugging architecture) based slicer, a selected unit-test may then serve as the slicing criterion.

Retrofitting security requirement into an existing system tends to result in less wanted qualities. So, it is a preferred practice to design with security in mind right from the beginning of the development process. An NFR framework has been established to incorporate non-functional requirements (NFRs) (L. Chung et al., 2000) that are crucial to secure system design into the development process. In this paper, we propose a methodology that utilizes the NFR framework to come up with secure design by selecting security design patterns for the domain specific application such as e-commerce system.

The present work practices a previously published layered middleware design architecture that can integrate a rich set of Java APIs into the OMG CORBA audio/video streaming framework. In this design, JMF, CORBA audio/video streaming service, CORBA, and JRE are functioned as a programming language domain-specific middleware, a common middleware service, a distribution middleware, and a host infrastructure middleware layers, respectively. The current work utilizes the OMG audio/video stream binding mechanism in order to provide a standardized interoperability of streams between CORBA and JMF environments. Through a generalized mapping, CORBA objects could directly manipulate JMF classes. Although the current high level layered design work is only applicable to OMG compliant Java ORBs, this design concept could be extended into other language specific ORBs by utilizing other programming language domain specific service layers, i.e., other programming language APIs.

A lock mechanism is essential for synchronization on the multiprocessor systems. The conventional queuing lock has two bus traffics that are the initial and retry of the lock-read. This paper proposes the new locking protocol, called WPV (waiting processor variable) lock mechanism, which has only one lock-read bus traffic command. The WPV mechanism accesses the shared data in the initial lock-read phase that is held in the pipelined protocol until the shared data is transferred. The WPV mechanism also uses the cache state lock mechanism to reduce the locking overhead and guarantees the FIFO lock operations in the multiple lock contentions. In this paper, we also derive the analytical model of WPV lock mechanism as well as conventional memory and cache queuing lock mechanisms. The simulation results on the WPV lock mechanism show that about 50% of access time is reduced comparing with the conventional queuing lock mechanism.

There have been numerous suggestions for improving the quality of service (QoS) of the Internet amid network congestion. However, due to the nature of the Internet service - best-effort delivery and distributed - it has been difficult to achieve. One dominant idea of solving the congestion problem is to have varying price for Internet access. The concept of smart market, proposed by MacKie-Mason et al. (1994), can help build an efficient Internet pricing structure that can be used to provide QoS when the network is congested. Efficient pricing structure can improve QoS of the Internet service by discouraging unnecessary usage of the Internet while giving access to the user who has "need-to-use" of the Internet through the pricing mechanism. In this paper, we propose a conceptual model for the pricing structure that utilizes auction market model as an implementation of the smart market. As a proof of concept, we have implemented a network sorting algorithm in CLIPS that implements auction part of the model.

There has been extensive research on establishing a non-functional requirement (NFR) framework (Chung et al., 2000) and applying it systematically in selecting software architectural design alternatives. However there is still a gap between software architecture and concrete detailed design. This paper presents a way to come up with more detailed designs by selecting a set of applicable design patterns. The method in selecting design patterns is applied step by step systematically in a defined process. After a preliminary selection of a set of potentially applicable design patterns based on existing knowledge, analysis of their applicability is conducted on each of the design patterns. In each analysis process, the potentially applicable design pattern and the chosen architectural design are decomposed; the traceability from software architecture to design patterns is analyzed. This method is applied in the case study on keyword in context system (KWIC) (Shaw and Garlan, 1996) and (Chung et al., 2000). The notation of NFR approach (Chung et al., 2003) is further refined.

This paper presents the results of experiments to study throughput behavior and determine the maximum attainable throughput in an 802.11g wireless LAN under UDP traffic. The focus is on observing the measured throughput over time when the network is flooded with a continuous stream of UDP data. Whereas previous studies investigate 802.11b/a performance, or use analytical or simulation methods to study 802.11g performance, this study measures the throughput by generating actual 802.11g traffic. In order to study 802.11g behavior exclusively, minimize environmental effects, and keep the network as simple as possible, the experimental setup is restricted to an isolated wireless LAN with a single access point configured for 802.11g operation. The studies show that in almost all cases the observed throughput is well below 50% of the 802.11g maximum data rate of 54 Mbps even under ideal and controlled conditions. Although network card implementation and use of RTS/CTS have a significant impact on throughput, access point distance has little effect. We also give a formula for computing the expected throughput and compare the values it gives with the measured values.

Software architectural design is becoming increasingly important in software engineering, as being manifested through various recent developments in the field such as the component-based software engineering paradigm and the distributed and collaborative computing paradigm. Abstraction is such a mechanism as the key concept underpinning software architecture, namely hiding the immense amount of details. Despite its long-recognized benefits, however abstraction can also pose difficulties with the understanding and analysis of software architecture since one architecture can result in potentially an infinite number of different system behaviors. In order to alleviate such difficulties, we introduce the notion of dynamic software architecture slicing (DSAS), a methodology for using the notion, and an algorithm to generate dynamic software architecture slice. We demonstrate the feasibility and the expected benefits of the approach by using an illustrative example.