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The present study examined the dependability of three newly developed direct behavior rating multi-item scales (DBR-MIS) of academic enablers (i.e., academic engagement, interpersonal skills, and study skills). Twenty-two K-5 teachers completed all three 5-item DBR-MIS daily for 1 week for one student in their class. Teachers' ratings on each item during the first occasion were used to create individualized DBR scales with 1-4 items. Items with the lowest ratings (indicating least frequent academic enablers) were included first and subsequent items were added in ascending order. Dependability of both full DBR-MIS and individualized DBR scales was evaluated using generalizability theory. Results indicated that the full DBR-MIS demonstrated high dependability and required only 1-4 assessment occasions (i.e., < 10 as the criterion) to inform absolute decision-making for progress monitoring. The three- and four-item individualized DBR-MIS demonstrated comparable dependability to their respective full five-item DBR-MIS. Dependability estimates of individualized scales in general were higher than standard D study-derived estimates with the same number of items (i.e., dependability estimates obtained by manipulating the number of items from the full standard scales modeled in D studies). Results support continued investigation of the DBR-MIS as a viable progress monitoring tool for school-based applications. Further research and implications for practice were discussed., (Copyright © 2024 Society for the Study of School Psychology. Published by Elsevier Ltd. All rights reserved.)

Elasticity and dependability factors are considered fundamental elements that have a significant and direct impact on reducing waste in manufacturing companies' resources as well as their supply chains (SCs) in order to improve their overall performance, as well as improving the performance of these companies' supply chains. This is in addition to not overlooking the effective and important strategic relationship and the role of suppliers to manufacturing companies as a mediating factor in the relationship between the qualities of the multi-directional relationship and mitigating the risks that it may be exposed to during its operational and production process. 156 samples were included in this study out of 743 small to large manufacturing companies in the Middle East region, including Jordan, Turkey, Saudi Arabia, and Egypt, where an experimental examination was conducted of the companies targeted in this study to prove or reject the proposed hypotheses. Structural equation modeling was applied and used to examine the proposed hypotheses. The results of this study indicated that the elements of flexibility and dependability have positive, direct, and statistically significant effects on mitigating supply risks, and this therefore works to improve the performance of the supply chain of manufacturing companies, especially small and medium-sized companies. In addition, the results of this study showed that building strong strategic partnerships with dependable, certified, high-performance, and flexible suppliers has a positive impact on moderating the potential supply risks that manufacturing companies may face in their operational processes, while the results showed that there is no statistically significant effect on the relationship between dependability and mitigate supply risks. This research paper offers empirical evidence for using the quality of the multi-directional relationship within supply risk dilution of manufacturing firms' context in developing countries for enhancing their supply chain performance. This study provides a clear roadmap and basis for managers and decision-makers in manufacturing companies to focus on the importance of the quality of relationships with suppliers and business partners in their business dealings to limit and mitigate the risks that they may face, which may lead to interruption of the necessary supplies to them., Competing Interests: I hereby declare that the disclosed information is correct and that no other situation of real, potential or apparent conflict of interest is known to me. I undertake to inform you of any change in these circumstances, including if an issue arises that is related to my paper. No funding was received for this work., (© 2023 The Author.)

The Athens Insomnia Scale (AIS) is a widely used self-report measure to evaluate insomnia symptoms based on the International Classification of Diseases criteria version 10 (ICD-10). Despite its extensive use in clinical and research settings, the reliability of the AIS has not been systematically investigated. This systematic review reports a reliability generalization meta-analysis study to assess the internal consistency and the test-retest reliability of the AIS across various populations and settings. A systematic literature search was conducted to identify studies reporting Cronbach's alpha and test-retest coefficients for the AIS. Pooled estimates of reliability, along with moderator analyses, were calculated. The AIS has an excellent internal consistency of 0.84 (95% CI: 0.81 to 0.86), and re-test reliability of 0.86 (95% CI: 0.80 to 0.92). The significant heterogeneity levels support the recommendation that future studies using the AIS include and discuss reliability estimates based on their own data., Competing Interests: Declaration of competing interest All authors declare no conflict of interest., (Copyright © 2023 Elsevier B.V. All rights reserved.)

This article proposes an adaptable path tracking control system, based on reinforcement learning (RL), for autonomous cars. A four-parameter controller shapes the behaviour of the vehicle to navigate lane changes and roundabouts. The tuning of the tracker uses an 'educated' Q-Learning algorithm to minimize the lateral and steering trajectory errors, this being a key contribution of this article. The CARLA (CAR Learning to Act) simulator was used both for training and testing. The results show the vehicle is able to adapt its behaviour to the different types of reference trajectories, navigating safely with low tracking errors. The use of a robot operating system (ROS) bridge between CARLA and the tracker (i) results in a realistic system, and (ii) simplifies the replacement of CARLA by a real vehicle, as in a hardware-in-the-loop system. Another contribution of this article is the framework for the dependability of the overall architecture based on stability results of non-smooth systems, presented at the end of this article., Competing Interests: The authors declare there are no competing interests., (©2023 Vilaça Carrasco and Silva Sequeira.)

Distributed Embedded Systems (DESs) carrying out critical tasks must be highly reliable and hard in real-time. Moreover, to operate in dynamic operational contexts in an effective and efficient manner, they must also be adaptive. Adaptivity is particularly interesting from a dependability perspective, as it can be used to develop dynamic fault tolerance mechanisms, which, in combination with static ones, make it possible to provide better and more efficient fault tolerance. However, constructing a DES with such complexity presents many challenges. This is because all the mechanisms that support fault tolerance, real-time, and adaptivity must be designed to operate in a coordinated manner. This paper presents the Dynamic Fault Tolerance for Flexible Time-Triggered Ethernet (DFT4FTT), a self-reconfigurable infrastructure for implementing highly reliable adaptive DES. Here, we describe the design of its hardware and software architecture and the main set of mechanisms, with a focus on fault tolerance.

Fault tolerance in IoT systems is challenging to overcome due to its complexity, dynamicity, and heterogeneity. IoT systems are typically designed and constructed in layers. Every layer has its requirements and fault tolerance strategies. However, errors in one layer can propagate and cause effects on others. Thus, it is impractical to consider a centralized fault tolerance approach for an entire system. Consequently, it is vital to consider multiple layers in order to enable collaboration and information exchange when addressing fault tolerance. The purpose of this study is to propose a multi-layer fault tolerance approach, granting interconnection among IoT system layers, allowing information exchange and collaboration in order to attain the property of dependability. Therefore, we define an event-driven framework called FaTEMa (Fault Tolerance Event Manager) that creates a dedicated fault-related communication channel in order to propagate events across the levels of the system. The implemented framework assist with error detection and continued service. Additionally, it offers extension points to support heterogeneous communication protocols and evolve new capabilities. Our empirical results show that introducing FaTEMa provided improvements to the error detection and error resolution time, consequently improving system availability. In addition, the use of Fatema provided a reliability improvement and a reduction in the number of failures produced.

Through the latest technological and conceptual developments, the centralized cloud-computing approach has moved to structures such as edge, fog, and the Internet of Things (IoT), approaching end users. As mobile network operators (MNOs) implement the new 5G standards, enterprise computing function shifts to the edge. In parallel to interconnection topics, there is the issue of global impact over the environment. The idea is to develop IoT devices to eliminate the greenhouse effect of current applications. Radio-frequency identification (RFID) is the technology that has this potential, and it can be used in applications ranging from identifying a person to granting access in a building. Past studies have focused on how to improve RFID communication or to achieve maximal throughput. However, for many applications, system latency and availability are critical aspects. This paper examines, through stochastic Petri nets (SPNs), the availability, dependability, and latency of an object-identification system that uses RFID tags. Through the performed analysis, the optimal balance between latency and throughput was identified. Analyzing multiple communication scenarios revealed the availability of such a system when deployed at the edge layer.

Robots can be very different, from humanoids to intelligent self-driving cars or just IoT systems that collect and process local sensors' information. This paper presents a way to increase dependability for information exchange and processing in systems with Cloud-Fog-Edge architectures. In an ideal interconnected world, the recognized and registered robots must be able to communicate with each other if they are close enough, or through the Fog access points without overloading the Cloud. In essence, the presented work addresses the Edge area and how the devices can communicate in a safe and secure environment using cryptographic methods for structured systems. The presented work emphasizes the importance of security in a system's dependability and offers a communication mechanism for several robots without overburdening the Cloud. This solution is ideal to be used where various monitoring and control aspects demand extra degrees of safety. The extra private keys employed by this procedure further enhance algorithm complexity, limiting the probability that the method may be broken by brute force or systemic attacks.

Recently, vast investments have been made worldwide in developing Cyber-Physical Systems (CPS) as solutions to key socio-economic challenges. The Internet-of-Things (IoT) has also enjoyed widespread adoption, mostly for its ability to add "sensing" and "actuation" capabilities to existing CPS infrastructures. However, attention must be paid to the impact of IoT protocols on the dependability of CPS infrastructures. We address the issues of CPS dependability by using an epidemic model of the underlying dynamics within the CPS' IoT subsystem (CPS-IoT) and an interference-aware routing reconfiguration. These help to efficiently monitor CPS infrastructure-avoiding routing oscillation, while improving its safety. The contributions of this paper are threefold. Firstly, a CPS orchestration model is proposed that relies upon: (i) Inbound surveillance and outbound actuation to improve dependability and (ii) a novel information diffusion model that uses epidemic states and diffusion sets to produce diffusion patterns across the CPS-IoT. Secondly, the proposed CPS orchestration model is numerically analysed to show its dependability for both sensitive and non-sensitive applications. Finally, a novel interference-aware clustering protocol called "INMP", which enables network reconfiguration through migration of nodes across clusters, is proposed. It is then bench-marked against prominent IoT protocols to assess its impact on the dependability of the CPS.

In critical industrial monitoring and control applications, dependability evaluation will be usually required. For wireless sensor networks deployed in industrial plants, dependability evaluation can provide valuable information, enabling proper preventive or contingency measures to assure their correct and safe operation. However, when employing sensor nodes equipped with cameras, visual coverage failures may have a deep impact on the perceived quality of industrial applications, besides the already expected impacts of hardware and connectivity failures. This article proposes a comprehensive mathematical model for dependability evaluation centered on the concept of Quality of Monitoring (QoM), processing availability, reliability and effective coverage parameters in a combined way. Practical evaluation issues are discussed and simulation results are presented to demonstrate how the proposed model can be applied in wireless industrial sensor networks when assessing and enhancing their dependability.

The dependability of systems and networks has been the target of research for many years now. In the 1970s, what is now known as the top conference on dependability-The IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)-emerged gathering international researchers and sparking the interest of the scientific community. Although it started in niche systems, nowadays dependability is viewed as highly important in most computer systems. The goal of this work is to analyze the research published in the proceedings of well-established dependability conferences (i.e., DSN, International Symposium on Software Reliability Engineering (ISSRE), International Symposium on Reliable Distributed Systems (SRDS), European Dependable Computing Conference (EDCC), Latin-American Symposium on Dependable Computing (LADC), Pacific Rim International Symposium on Dependable Computing (PRDC)), while using Natural Language Processing (NLP) and namely the Latent Dirichlet Allocation (LDA) algorithm to identify active, collapsing, ephemeral, and new lines of research in the dependability field. Results show a strong emphasis on terms, like 'security', despite the general focus of the conferences in dependability and new trends that are related with 'machine learning' and 'blockchain'. We used the PRDC conference as a use case, which showed similarity with the overall set of conferences, although we also found specific terms, like 'cyber-physical', being popular at PRDC and not in the overall dataset.

Objective: The profession of surgery is entering a new era of "big data," where analyses of longitudinal trainee assessment data will be used to inform ongoing efforts to improve surgical education. Given the high-stakes implications of these types of analyses, researchers must define the conditions under which estimates derived from these large datasets remain valid. With this study, we determine the number of assessments of residents' performances needed to reliably assess the difficulty of "Core" surgical procedures., Design: Using the SIMPL smartphone application from the Procedural Learning and Safety Collaborative, 402 attending surgeons directly observed and provided workplace-based assessments for 488 categorical residents after 5259 performances of 87 Core surgical procedures performed at 14 institutions. We used these faculty ratings to construct a linear mixed model with resident performance as the outcome variable and multiple predictors including, most significantly, the operative procedure as a random effect. We interpreted the variance in performance ratings attributable to the procedure, after controlling for other variables, as the "difficulty" of performing the procedure. We conducted a generalizability analysis and decision study to estimate the number of SIMPL performance ratings needed to reliably estimate the difficulty of a typical Core procedure., Results: Twenty-four faculty ratings of resident operative performance were necessary to reliably estimate the difficulty of a typical Core surgical procedure (mean dependability coefficient 0.80, 95% confidence interval 0.73-0.87)., Conclusions: At least 24 operative performance ratings are required to reliably estimate the difficulty of a typical Core surgical procedure. Future research using performance ratings to establish procedure difficulty should include adequate numbers of ratings given the high-stakes implications of those results for curriculum design and policy., (Copyright © 2019 Association of Program Directors in Surgery. Published by Elsevier Inc. All rights reserved.)

A fire detection system requires accurate and fast mechanisms to make the right decision in a fire situation. Since most commercial fire detection systems use a simple sensor, their fire recognition accuracy is deficient because of the limitations of the detection capability of the sensor. Existing proposals, which use rule-based algorithms or image-based machine learning can hardly adapt to the changes in the environment because of their static features. Since the legacy fire detection systems and network services do not guarantee data transfer latency, the required need for promptness is unmet. In this paper, we propose a new fire detection system with a multifunctional artificial intelligence framework and a data transfer delay minimization mechanism for the safety of smart cities. The framework includes a set of multiple machine learning algorithms and an adaptive fuzzy algorithm. In addition, Direct-MQTT based on SDN is introduced to solve the traffic concentration problems of the traditional MQTT. We verify the performance of the proposed system in terms of accuracy and delay time and found a fire detection accuracy of over 95%. The end-to-end delay, which comprises the transfer and decision delays, is reduced by an average of 72%.

Cyber Physical Systems (CPS) has been a popular research area in the last decade. The dependability of CPS is still a critical issue, and few surveys have been published in this domain. CPS is a dynamic complex system, which involves various multidisciplinary technologies. To avoid human errors and to simplify management, self-management CPS (SCPS) is a wise choice. To achieve dependable self-management, systematic solutions are necessary to verify the design and to guarantee the safety of self-adaptation decisions, as well as to maintain the health of SCPS. This survey first recalls the concepts of dependability, and proposes a generic environment-in-loop processing flow of self-management CPS, and then analyzes the error sources and challenges of self-management through the formal feedback flow. Focusing on reducing the complexity, we first survey the self-adaptive architecture approaches and applied dependability means, then we introduce a hybrid multi-role self-adaptive architecture, and discuss the supporting technologies for dependable self-management at the architecture level. Focus on dependable environment-centered adaptation, we investigate the verification and validation (V&V) methods for making safe self-adaptation decision and the solutions for processing decision dependably. For system-centered adaptation, the comprehensive self-healing methods are summarized. Finally, we analyze the missing pieces of the technology puzzle and the future directions. In this survey, the technical trends for dependable CPS design and maintenance are discussed, an all-in-one solution is proposed to integrate these technologies and build a dependable organic SCPS. To the best of our knowledge, this is the first comprehensive survey on dependable SCPS building and evaluation.

Recent industrial developments in autonomous systems, or agents, which assume that humans and the agents share the same space or even work in close proximity, open for new challenges in robotics, especially in motion planning and control. In these settings, the control system should be able to provide these agents a reliable path following control when they are working in a group or in collaboration with one or several humans in complex and dynamic environments. In such scenarios, these agents are not only moving to reach their goals, i.e., locations, they are also aware of the movements of other entities to find a collision-free path. Thus, this paper proposes a dependable, i.e., safe, reliable and effective, path planning algorithm for a group of agents that share their working space with humans. Firstly, the method employs the Theta * algorithm to initialize the paths from a starting point to a goal for a set of agents. As Theta * algorithm is computationally heavy, it only reruns when there is a significant change of the environment. To deal with the movements of the agents, a static flow field along the configured path is defined. This field is used by the agents to navigate and reach their goals even if the planned trajectories are changed. Secondly, a dipole field is calculated to avoid the collision of agents with other agents and human subjects. In this approach, each agent is assumed to be a source of a magnetic dipole field in which the magnetic moment is aligned with the moving direction of the agent. The magnetic dipole-dipole interactions between these agents generate repulsive forces to help them to avoid collision. The effectiveness of the proposed approach has been evaluated with extensive simulations. The results show that the static flow field is able to drive agents to the goals with a small number of requirements to update the path of agents. Meanwhile, the dipole flow field plays an important role to prevent collisions. The combination of these two fields results in a safe path planning algorithm, with a deterministic outcome, to navigate agents to their desired goals.

Cyber Physical Systems (CPSs) need to interact with the changeable environment under various interferences. To provide continuous and high quality services, a self-managed CPS should automatically reconstruct itself to adapt to these changes and recover from failures. Such dynamic adaptation behavior introduces systemic challenges for CPS design, advice evaluation and decision process arrangement. In this paper, a formal compositional framework is proposed to systematically improve the dependability of the decision process. To guarantee the consistent observation of event orders for causal reasoning, this work first proposes a relative time-based method to improve the composability and compositionality of the timing property of events. Based on the relative time solution, a formal reference framework is introduced for self-managed CPSs, which includes a compositional FSM-based actor model (subsystems of CPS), actor-based advice and runtime decomposable decisions. To simplify self-management, a self-similar recursive actor interface is proposed for decision (actor) composition. We provide constraints and seven patterns for the composition of reliability and process time requirements. Further, two decentralized decision process strategies are proposed based on our framework, and we compare the reliability with the static strategy and the centralized processing strategy. The simulation results show that the one-order feedback strategy has high reliability, scalability and stability against the complexity of decision and random failure. This paper also shows a way to simplify the evaluation for dynamic system by improving the composability and compositionality of the subsystem.

Wireless sensor networks are being increasingly used in several application areas, particularly to collect data and monitor physical processes. Non-functional requirements, like reliability, security or availability, are often important and must be accounted for in the application development. For that purpose, there is a large body of knowledge on dependability techniques for distributed systems, which provide a good basis to understand how to satisfy these non-functional requirements of WSN-based monitoring applications. Given the data-centric nature of monitoring applications, it is of particular importance to ensure that data are reliable or, more generically, that they have the necessary quality. In this survey, we look into the problem of ensuring the desired quality of data for dependable monitoring using WSNs. We take a dependability-oriented perspective, reviewing the possible impairments to dependability and the prominent existing solutions to solve or mitigate these impairments. Despite the variety of components that may form a WSN-based monitoring system, we give particular attention to understanding which faults can affect sensors, how they can affect the quality of the information and how this quality can be improved and quantified., Competing Interests: The authors declare no conflict of interest.

Failing to consider psychometric issues related to reliability and validity, differential deficits, and statistical power potentially undermines the conclusions of a study. In research using event-related brain potentials (ERPs), numerous contextual factors (population sampled, task, data recording, analysis pipeline, etc.) can impact the reliability of ERP scores. The present review considers the contextual factors that influence ERP score reliability and the downstream effects that reliability has on statistical analyses. Given the context-dependent nature of ERPs, it is recommended that ERP score reliability be formally assessed on a study-by-study basis. Recommended guidelines for ERP studies include 1) reporting the threshold of acceptable reliability and reliability estimates for observed scores, 2) specifying the approach used to estimate reliability, and 3) justifying how trial-count minima were chosen. A reliability threshold for internal consistency of at least 0.70 is recommended, and a threshold of 0.80 is preferred. The review also advocates the use of generalizability theory for estimating score dependability (the generalizability theory analog to reliability) as an improvement on classical test theory reliability estimates, suggesting that the latter is less well suited to ERP research. To facilitate the calculation and reporting of dependability estimates, an open-source Matlab program, the ERP Reliability Analysis Toolbox, is presented., (Copyright © 2016 Elsevier B.V. All rights reserved.)

Generalizability theory (G theory) provides a flexible, multifaceted approach to estimating score reliability. G theory's approach to estimating score reliability has important advantages over classical test theory that are relevant for research using event-related brain potentials (ERPs). For example, G theory does not require parallel forms (i.e., equal means, variances, and covariances), can handle unbalanced designs, and provides a single reliability estimate for designs with multiple sources of error. This monograph provides a detailed description of the conceptual framework of G theory using examples relevant to ERP researchers, presents the algorithms needed to estimate ERP score reliability, and provides a detailed walkthrough of newly-developed software, the ERP Reliability Analysis (ERA) Toolbox, that calculates score reliability using G theory. The ERA Toolbox is open-source, Matlab software that uses G theory to estimate the contribution of the number of trials retained for averaging, group, and/or event types on ERP score reliability. The toolbox facilitates the rigorous evaluation of psychometric properties of ERP scores recommended elsewhere in this special issue., (Copyright © 2016 Elsevier B.V. All rights reserved.)

Wireless Sensor Networks (WSNs) are being increasingly adopted in critical applications, where verifying the correct operation of sensor nodes is a major concern. Undesired events may undermine the mission of the WSNs. Hence, their effects need to be properly assessed before deployment, to obtain a good level of expected performance; and during the operation, in order to avoid dangerous unexpected results. In this paper, we propose a methodology that aims at assessing and improving the dependability level of WSNs by means of an event-based formal verification technique. The methodology includes a process to guide designers towards the realization of a dependable WSN and a tool ("ADVISES") to simplify its adoption. The tool is applicable to homogeneous WSNs with static routing topologies. It allows the automatic generation of formal specifications used to check correctness properties and evaluate dependability metrics at design time and at runtime for WSNs where an acceptable percentage of faults can be defined. During the runtime, we can check the behavior of the WSN accordingly to the results obtained at design time and we can detect sudden and unexpected failures, in order to trigger recovery procedures. The effectiveness of the methodology is shown in the context of two case studies, as proof-of-concept, aiming to illustrate how the tool is helpful to drive design choices and to check the correctness properties of the WSN at runtime. Although the method scales up to very large WSNs, the applicability of the methodology may be compromised by the state space explosion of the reasoning model, which must be faced by partitioning large topologies into sub-topologies., Competing Interests: The authors declare no conflict of interest.

Criteria for determining the trustworthiness of qualitative research were introduced by Guba and Lincoln in the 1980s when they replaced terminology for achieving rigor, reliability, validity, and generalizability with dependability, credibility, and transferability. Strategies for achieving trustworthiness were also introduced. This landmark contribution to qualitative research remains in use today, with only minor modifications in format. Despite the significance of this contribution over the past four decades, the strategies recommended to achieve trustworthiness have not been critically examined. Recommendations for where, why, and how to use these strategies have not been developed, and how well they achieve their intended goal has not been examined. We do not know, for example, what impact these strategies have on the completed research. In this article, I critique these strategies. I recommend that qualitative researchers return to the terminology of social sciences, using rigor, reliability, validity, and generalizability. I then make recommendations for the appropriate use of the strategies recommended to achieve rigor: prolonged engagement, persistent observation, and thick, rich description; inter-rater reliability, negative case analysis; peer review or debriefing; clarifying researcher bias; member checking; external audits; and triangulation., (© The Author(s) 2015.)

We present the first implementation of a distributed clock generation scheme for Systems-on-Chip that recovers from an unbounded number of arbitrary transient faults despite a large number of arbitrary permanent faults. We devise self-stabilizing hardware building blocks and a hybrid synchronous/asynchronous state machine enabling metastability-free transitions of the algorithm's states. We provide a comprehensive modeling approach that permits to prove, given correctness of the constructed low-level building blocks, the high-level properties of the synchronization algorithm (which have been established in a more abstract model). We believe this approach to be of interest in its own right, since this is the first technique permitting to mathematically verify, at manageable complexity, high-level properties of a fault-prone system in terms of its very basic components. We evaluate a prototype implementation, which has been designed in VHDL, using the Petrify tool in conjunction with some extensions, and synthesized for an Altera Cyclone FPGA.

Generalizability Theory (GT) offers increased utility for assessment research given the ability to concurrently examine multiple sources of variance, inform both relative and absolute decision making, and determine both the consistency and generalizability of results. Despite these strengths, assessment researchers within the fields of education and psychology have been slow to adopt and utilize a GT approach. This underutilization may be due to an incomplete understanding of the conceptual underpinnings of GT, the actual steps involved in designing and implementing generalizability studies, or some combination of both issues. The goal of the current article is therefore two-fold: (a) to provide readers with the conceptual background and terminology related to the use of GT and (b) to facilitate understanding of the range of issues that need to be considered in the design, implementation, and interpretation of generalizability and dependability studies. Given the relevance of this analytic approach to applied assessment contexts, there exists a need to ensure that GT is both accessible to, and understood by, researchers in education and psychology. Important methodological and analytical considerations are presented and implications for applied use are described., (Copyright © 2013 Society for the Study of School Psychology. Published by Elsevier Ltd. All rights reserved.)

Now, information systems are developed as open system that depend on each other. Assurance cases are expected to confirm a dependability of open systems. D*Framework is a method that can make assurance case for open system. In this paper we defined d*Framework formally. Furthermore we apply this method with case study and made discussion.

The design and realization of health monitoring applications has attracted the interest of large communities both from industry and academia. Several research challenges have been faced and issues tackled in order to realize effective applications for the management and monitoring of people with chronic diseases, people with disabilities, elderly people. However, there is a lack of efficient tools that enable rapid and possibly cheap realization of reliable health monitoring applications. The paper presents Uranus, a service oriented middleware architecture, which provides basic functions for the integration of different kinds of biomedical sensors. Uranus has also distinguishing characteristics like services for the run-time verification of the correctness of running applications and mechanisms for the recovery from failures. The paper concludes with two case studies as proof of concept.