Multiple Versions Research Articles

A Mission Profile-Based Reliability Modeling Framework for Fault-Tolerant Electric Propulsion

This article proposes a thermally enhanced reliability modeling framework which works from the mission profile of an electrified aviation propulsion system, incorporating known fault-tolerant power electronics, motor drives, control, and their pre and postfault behaviors. The reliability framework is presented in a hierarchical fashion. Multiphysics modeling is used, incorporating electromechanical states, power losses, and thermal behaviors. The approach includes the device thermal stress in healthy and postfault operating states in the failure rate calculations. Markov chain models are then used with the calculated failure rates to form system level reliability models. A case study of an electric vertical takeoff and landing urban aerial vehicle (eVTOL UAV) walks through the proposed method, modeling multiple versions of the propulsion system, with various levels of fault tolerance and redundancy. The proposed reliability modeling framework captures the operating temperatures and thermal swings seen by the components due to the flight mission, enhancing the failure rate calculation. Further, since the postfault operating mode of the fault tolerant system causes higher component currents and temperatures, different component failure rates are calculated for postfault operation. Finally, the Markov chain modeling allows for a reliability comparison between various UAV propulsion systems, including the effect of combining fault tolerance at the subsystem level, with redundancy at the system level.

The clinical management of hepatocellular carcinoma worldwide: A concise review and comparison of current guidelines: 2022 update.

Hepatocellular carcinoma (HCC) is the fifth most common malignancy and the second leading cause of cancer-related mortality worldwide. This review is an updated version that summarizes comprehensive guidelines published from January 2001 to January 2022 worldwide with a focus on the clinical management of HCC. The electronic databases MEDLINE, the Chinese SinoMed, and the Japanese CiNii were systematically searched. A total of 22 characteristic guidelines for HCC management were ultimately included, including 1 international guideline, 11 guidelines from Asia, 5 from Europe, 4 from the America, and 1 from Australia. If guidelines were published in multiple versions, the most recent update was included, and surveillance, diagnosis, and treatment were compared. The composition of and recommendations in current guidelines on HCC varied, so these guidelines were regrouped and diagnostic and treatment algorithms were summarized graphically to provide the latest information to clinicians. The diagnostic criteria were grouped into 2 categories: a "Size-based pathway" and a "Non-size-based pathway". The treatment criteria were summarized according to different treatment algorithms, and mainstream treatment options were reviewed. Findings from comparison of current guidelines might help target and concentrate efforts to improve the clinical management of HCC. However, further studies are needed to improve the management and outcomes of HCC. More straightforward or refined guidelines would help guide doctors to make better decisions in the treatment of HCC in the future.

When more is less: evaluating multiple versions of the Geriatric Depression Scale.

When more is less: evaluating multiple versions of the Geriatric Depression Scale.

Regenerative Topology Optimization of Fine Lattice Structures.

We present a generative approach for creating three-dimensional lattice structures optimized for mass and deflection composed of thousands of one-dimensional strut primitives. Our approach draws inspiration from topology optimization principles. The proposed method iteratively determines unnecessary lattice struts through stress analysis, erodes those struts, and then randomly generates new struts across the entire structure. The objects resulting from this distributed optimization technique demonstrate high strength-to-weight ratios that are at par with state-of-the-art topology optimization approaches, but are qualitatively very different. We use a dynamics simulator that allows optimization of structures subject to dynamic load cases, such as vibrating structures and robotic components. Because optimization is performed simultaneously with simulation, the process scales efficiently on massively parallel graphics processing units. The intricate nature of the output lattices contributes to a new class of objects intended specifically for additive manufacturing. Our work contributes a highly parallel simulation method and simultaneous algorithm for analyzing and optimizing lattices with thousands of struts. In this study, we validate multiple versions of our algorithm across sample load cases, to show its potential for creating high-resolution objects with implicit optimized microstructural patterns.

TECHNOLOGICAL AND SCIENTIFIC DEVELOPMENTS TOWARDS USE OF BIG DATA IN HEALTH DATA MANAGEMENT – AN OVERVIEW

The healthcare industry has been using data and technology driven innovation for a long time now with Evidence Based Medicine (EBM) being at the helm. Effectively using technology is vital towards successful data management in healthcare. A convenient approach that can collect, store, evaluate, and analyze health data will be beneficial to all the stakeholders in the healthcare delivery chain. With the advancement, clubbed with the availability and affordability of technology, it has become an effective tool for providing the right- care backed with accurate, consolidated evidence. Such approach not only helps in improving the quality of care, it also helps in early detection of diseases and effective treatment, Big Data is providing the necessary data management tools for the same. A simple Extract-Transform-Load (ETL) procedure using Hadoop can be used. As Big data analyses large amounts of data to uncover hidden patterns, correlations and other insights, its role in making the right, predictive decisions for quality care of the patients and better health planning is possible. Big data technologies such as Hadoop bring significant cost advantages when it comes to storing large amounts of data and combined with the speed of Hadoop and in-memory analytics, with the ability to analyze new sources of data, we are able to analyze information instantly – and make decisions which is very essential in the healthcare provider segment. Hadoop does not only store, but also process any and many file data: big or small, can be plain text files or binary files like images and even multiple different version of some particular data format across different time periods. It consists of three core components: a distributed file system, a parallel programming framework, and a resource/job management system. In healthcare, patient records, health plans, insurance information and other types of information is extremely difficult to manage – but, this very data is full of key insights, the need is for an effective data management and analytics that can be pragmatic. By analyzing large amounts of information – both structured and unstructured – rapidly, health care providers can provide lifesaving diagnoses. Keywords: Big Data, EBM, ETL, Hadoop

Manipulating Common Method Variance via Experimental Conditions

Research data collected from single respondents may raise concerns regarding common method variance (CMV), which is believed to threaten the validity of findings. The primary concern is that CMV can inflate substantive relationships, such that they appear statistically significant when they are not. Thus, understanding the nature of CMV is critical, especially when one considers the popularity—and sometimes necessity—of using self-report data. Research examining CMV has found conflicting evidence about the impact of CMV. Researchers who believe CMV influences findings have proposed solutions to combat any real or perceived potential bias, including changing survey instructions and using marker variables, but few studies have examined the efficacy of these approaches. The purpose of this study is to examine the impact of these techniques and the nature of CMV using an experimental design. To conduct the experiment, multiple versions of a survey, which vary in their use of the remedial approaches, are utilized to collect data, which resulted in 1,069 usable responses. The experimental design was based on the faking literature and included instructions intended to induce or reduce the levels of CMV. Further, two different marker variables are used to determine the degree to which they create a psychological separation in substantive variables. Correlation analysis and measurement invariance are used to analyze the data. This study posits that, if CMV is a substantial concern for self-report data and these approaches are effective, then findings will differ in surveys that incorporate such approaches from surveys that do not. Results indicate few differences in experimental conditions, meaning that regardless of instructions or marker variable, substantive item correlations remained statistically similar. The results indicate this is likely due to the minimal impact of CMV, given that the proposed methods of correction did not significantly influence research findings. These findings have implications for researchers in that they do not support that CMV, or at least its proposed remedies, significantly alter findings. However, support for the null conclusions, in spite of appropriate statistical power, warrant future research examining the nature and impact of CMV.

Clinical Care of Hyperthyroidism Using Wearable Medical Devices in a Medical IoT Scenario.

This paper presents an in-depth study and analysis of clinical care of patients with hyperthyroidism using wearable medical devices in the context of medical IoT scenarios. According to the use scenario of the gateway and the connectivity of the equipment, the hardware architecture, hardware interfaces, functionality, and performance of the gateway were briefly designed, so as to monitor patients with hyperthyroidism more comprehensively and save labor costs. The gateway can provide access to different devices and adaptation functions to different hardware interfaces and provide hardware support for the subsequent deployment of the proposed new medical communication protocols and related information systems. A medical data convergence information system based on multidevice management and multiprotocol parsing was designed and implemented. The system enables the management and configuration of different medical devices and access to data through the targeted parsing of the underlying medical device communication protocols. The system also provides the automatic adaptation of multiple types of underlying medical device communication protocols and automatic parsing of multiple versions and can provide multiple devices to process fused data streams or device information and data from a single device. The use of event-driven asynchronous communication eliminates the tight dependency on service invocation in the synchronous communication approach. The use of a metadata-based data model structure enables model extensions to accommodate the impact of iterative business requirements on the database structure. Real-time patient physiological data transmission for intraoperative monitoring based on the MQTT protocol and video transmission for intraoperative patient monitoring based on the RTMP protocol were implemented. The development of the intelligent medical monitoring service system was completed, and the system was tested, optimized, and deployed. The functionality and performance of the system were tested, the performance issue of slow query speed was optimized, and the deployment of the project using Docker containers was automated.

Efficient microscopy image analysis on CPU-GPU systems with cost-aware irregular data partitioning

The analysis of high resolution whole slide tissue images is a computationally expensive task, which adversely impacts effective use of pathology imaging data in research. We propose runtime solutions to enable efficient execution of pathology image analysis applications on modern distributed memory hybrid platforms equipped with both CPUs and GPUs. Hybrid systems offer significant computation capacity, but taking advantage of this computing power is complex. An application developer may have to implement multiple versions of data processing codes targeted for different computing devices. The developer also has to tackle the challenges of efficiently distributing computational load among the nodes of a distributed memory machine and among computing devices on a node. This is particularly difficult in analysis of high resolution images because of irregular computing costs of processing different image regions. In order to address these problems, we have leveraged a high-level image processing language (Halide) and integrated it into our runtime system called Region Templates (RT). The language simplifies the application development while generating code for multiple devices, such as CPU and GPU. The integration with RT allows for efficient multiple node hybrid execution. We also developed a novel cost-aware data partitioning (CADP) strategy that considers the workload irregularity to minimize load imbalance. Our experimental evaluation shows significant performance improvements on hybrid CPU-GPU machines, as compared with using a single processor (CPU or GPU), as well as on multi-GPU systems. CADP resulted in 1.7× better performance than other workload partitioning approaches (e.g., KD-Trees) on a hybrid machine and was up to 2.24× faster in multi-node settings.

Complementary Relationship for evaporation performance at different spatial and temporal scales

Several versions of the Complementary Relationship (CR) between actual regional evaporation and apparent potential evaporation have recently been proposed. Few studies have compared multiple CR versions side-by-side using datasets spanning various climates and land surfaces. Filling this lack is one purpose of this project. It also investigates how various CR versions respond to changes in spatial and temporal averaging. This study uses multiple years of data from seven eddy-covariance flux stations in Australia, representing a wide range of biomes, along with global ERA5 reanalysis data products. Daily and monthly averages were used for both datasets, and the Australian observations also used weekly and yearly averages. The ERA5 data represent a scale of about 30 km, much larger than the scale represented by the flux station data. A set of five questions regarding the impact of spatial and temporal scaling on CR parameter values and performance are asked and assessed using the two datasets. Four recent CR versions are considered in answering the questions. Due to important differences between FLUXNET and ERA5 data, questions regarding temporal scaling were answered with greater confidence than those regarding spatial scaling. With these data, rescaled versions of the CR performed best overall.

CHEX

In scientific computing and data science disciplines, it is often necessary to share application workflows and repeat results. Current tools containerize application workflows, and share the resulting container for repeating results. These tools, due to containerization, do improve sharing of results. However, they do not improve the efficiency of replay. In this paper, we present the multiversion replay problem, which arises when multiple versions of an application are containerized, and each version must be replayed to repeat results. To avoid executing each version separately, we develop CHEX , which checkpoints program state and determines when it is permissible to reuse program state across versions. It does so using system call-based execution lineage. Our capability to identify common computations across versions enables us to consider optimizing replay using an in-memory cache, based on a checkpoint-restore-switch system. We show the multiversion replay problem is NP-hard, and propose efficient heuristics for it. CHEX reduces overall replay time by sharing common computations but avoids storing a large number of checkpoints. We demonstrate that CHEX maintains lightweight package sharing, and improves the total time of multiversion replay by 50% on average.

Collaborative creativity among undergraduate students as game creators during gamification in a university-wide elective course

Collaborative creativity is an essential skill for unleashing university students’ skills in team building during complex problem-solving. The aim of this descriptive quantitative research was to examine undergraduate students’ perspectives regarding their collaborative creativity experience as game creators for a gamification group assignment within a university-wide elective course on learning disabilities. The Assessment Scale of Creative Collaboration (ASCC) questionnaire was completed by 14 students. The results provide insightful perspectives by undergraduate students regarding their experience in collaborative creativity during game creation. The findings from the closed-ended questions of the ASCC demonstrate that most of the students valued the diversity of knowledge and skills of their team members, shared different ideas, adapted their ideas and practices according to the project difficulties, trusted their team members, co-constructed the game together, overcame conflicts in positive ways, developed multiple versions of their game, and coordinated well for their team project. Data from the open-ended questions of the ASCC, which provided detailed but nuanced perspectives of the students, were analysed thematically. Three main themes were identified: the positive aspects of gamification, the constraints related to the gamification project, and teamwork and collaborative problem-solving approaches. This study provides evidence on the feasibility of game creation that is embedded within gamification group projects for fostering collaborative creativity among undergraduate students. Gamification has the potential in supporting the acquisition of 21st century learning and innovation skills in higher education. Implications of the application of gamification in higher education are discussed.

Relationality as a Lens for Policy Analysis: Preserving Harmony in a Triangular Cooperation Project to Strengthen Gender Mainstreaming in Fiji

Policy has been mostly approached as a rational project of setting goals and establishing rules and roles to achieve them. Alternative approaches to policy have been referred to as post-positivist, critical-reflexive and relational. They all emphasize emergent, co-evolutionary and relational aspects of policy work that cannot be reduced to rational choice and reasoning-based models alone. A shared element of such frameworks is the focus on relationships, which are seen not just in a narrow sense of the “logic of appropriateness,” but as a force that shapes actors’ identities, interests and power. Following relational analytical approaches, we analyze a triangular development cooperation project funded by the United States Agency for International Development (USAID) and the Government of Indonesia in order to strengthen gender mainstreaming (SGM) of Fijian government. Through attention to relationality as it shapes actors’ identities and narratives, we demonstrate how a different form of learning employed by each actor facilitated harmony in the project. A key mediating factor in the smooth project co-evolution that we observed, was the ambiguous project design and vaguely articulated goals, supported by fragmented project setup and reporting. Such ambiguity allowed formulation of multiple versions of the project’s outcomes for multiple audiences. However, it also resulted in little impact on the ground in Fiji. Our findings support persistent criticism of development aid projects in small island states for rarely addressing problems of target populations.

Development of a Pipeline for Adverse Drug Reaction Identification in Clinical Notes: Word Embedding Models and String Matching.

BackgroundKnowledge about adverse drug reactions (ADRs) in the population is limited because of underreporting, which hampers surveillance and assessment of drug safety. Therefore, gathering accurate information that can be retrieved from clinical notes about the incidence of ADRs is of great relevance. However, manual labeling of these notes is time-consuming, and automatization can improve the use of free-text clinical notes for the identification of ADRs. Furthermore, tools for language processing in languages other than English are not widely available.ObjectiveThe aim of this study is to design and evaluate a method for automatic extraction of medication and Adverse Drug Reaction Identification in Clinical Notes (ADRIN).MethodsDutch free-text clinical notes (N=277,398) and medication registrations (N=499,435) from the Cardiology Centers of the Netherlands database were used. All clinical notes were used to develop word embedding models. Vector representations of word embedding models and string matching with a medical dictionary (Medical Dictionary for Regulatory Activities [MedDRA]) were used for identification of ADRs and medication in a test set of clinical notes that were manually labeled. Several settings, including search area and punctuation, could be adjusted in the prototype to evaluate the optimal version of the prototype.ResultsThe ADRIN method was evaluated using a test set of 988 clinical notes written on the stop date of a drug. Multiple versions of the prototype were evaluated for a variety of tasks. Binary classification of ADR presence achieved the highest accuracy of 0.84. Reduced search area and inclusion of punctuation improved performance, whereas incorporation of the MedDRA did not improve the performance of the pipeline.ConclusionsThe ADRIN method and prototype are effective in recognizing ADRs in Dutch clinical notes from cardiac diagnostic screening centers. Surprisingly, incorporation of the MedDRA did not result in improved identification on top of word embedding models. The implementation of the ADRIN tool may help increase the identification of ADRs, resulting in better care and saving substantial health care costs.

Multiplicative version of eccentric connectivity index

The eccentric connectivity index is defined for a connected graph G as the summation of the terms ɛG(u)+ɛG(v) over all edges uv of G, in which ɛG(u) denotes the eccentricity of the vertex u in G. In this paper, we study a multiplicative version of this graph invariant under the name multiplicative eccentric connectivity index. We present a sharp lower bound on the multiplicative eccentric connectivity index of n-vertex trees with fixed diameter and a sharp upper bound on the multiplicative eccentric connectivity index of n-vertex trees with given number of pendent vertices. Using these results, the trees on n vertices with the first three minimum and maximum values of the multiplicative eccentric connectivity index are characterized. Moreover, we present sharp bounds on the multiplicative eccentric connectivity index of connected graphs in terms of certain graph parameters including the order, size, radius, diameter, and number of universal vertices, and study the relationship between this invariant and several other vertex-eccentricity-based invariants. In particular, the minimal graphs with respect to the multiplicative eccentric connectivity index in the set of all connected graphs of given order, connected graphs of given size, and unicyclic and bicyclic graphs of given order are characterized and a Nordhaus–Gaddum-type result for this invariant is given.

Testing resource allocation for software with multiple versions

Testing resource allocation for software with multiple versions

Multi-Level Visualization of Aggregated Information About Source Code Evolution Across Multiple Versions

Multi-Level Visualization of Aggregated Information About Source Code Evolution Across Multiple Versions

Developing Check-Point Mechanism to Protect Mobile Agent Free-Roaming Against Untrusted Hosts

Mobile Agent has many benefits over traditional distributed systems such as reducing latency, bandwidth, and costs. Mobile Agent Systems are not fully utilized due to security problems. This paper focuses on mobile agent protection against malicious hosts. A new security mechanism called Checkpoints has been proposed. Checkpoint Mechanism (CPM) aims to protect Mobile Agent against malicious hosts in case of Capturing and Integrity attacks. CPM assumes using a free-roaming mobility mechanism by Mobile agent systems. The main idea behind CPM is to generate multiple versions of Mobile Agent. The multiple version is used to recover Mobile Agent from Capturing and Integrity attacks by untrusted hosts. MA versions are kept in Recovery Host(RH). RH plays a key role in CPM by controlling and monitoring MAs’ recovery processes. A prototype method has been used to prove the feasibility of CPM. The prototype was implemented by using the. Net framework and C#. full discussion for several scenarios has been done to analyze the feasibility and performance for CPM. As found from this research, CPM has a strong ability to protect Mobile Agents from Capturing and Integrity attacks completely. In addition, there is no negative impact on the overall performance of the mobile agent system.

Testing resource allocation for software with multiple versions

Testing resource allocation for software with multiple versions

Influence of Risk Analysis as a Risk Management Practice on Project Performance in Kenya Commercial Banks

Undertaking an information technology project in a banking environment is a complex task. Studies globally and locally indicate a high failure rate of information technology projects. Standish group report 2019 states that 83.9 percent of information technology projects partially or completely fail. The majority of projects 52 percent were over budget, overdue, or lacked promised functionality. Previous studies indicate information technology projects in commercial Banks in Kenya experience the same project performance variations, as projects are either delayed, over budget or have issues with functionality. Risk is a factor that challenges project performance. Project risk management includes risk identification, analysis, response, and monitoring and control of risk in a project. The overall goal of this study was to examine the relationship of risk management and information technology project performance in Kenyan commercial banks, taking into account the moderating effect of project complexity and the mediating effect of risk culture, both of which had been largely overlooked in previous research hence filling a research vacuum. The target population was forty projects in Kenyan commercial banks in Kenya which made the unit of analysis. Stratified and simple random sampling technique was used. Stratified and simple random sampling technique was used. Questionnaires were used to collect the data from the targeted one hundred and eight respondents. The instrument was tested for reliability by use of Cronbach's alpha coefficient of internal consistency test and validity by use of selected information technology project professionals' review. Drop and pick method of administering questionnaires was used so as to allow respondents enough time to go through the questionnaires and give their responses. An option of online questionnaires was also available to respondents. Based on a survey, the research used both descriptive and explanatory analysis designs. The association between risk analysis and performance of information technology projects in Kenyan commercial banks was investigated using multiple regression. Quantitative data was analyzed using multiple regression analysis model software tool SPSS Version 25. The study adopted empirical model of least squares method while testing the hypotheses. The researcher conducted diagnostic tests of Normality, Linearity, Homoscedasticity and Multicollinearity to see if the data conforms to the basic assumptions of linear regression. The findings were presented using statistical parameter estimates. Tables and figures were used to present data, and supported by explanatory annotations. The results indicated that risk analysis had significant effects on the performance of information technology projects in the banking sector. The study recommends that banks should consider implementing and fully operationalize risk analysis in information technology projects. The Central Bank of Kenya should also consider putting in place an information technology projects risk policy framework to aid the banks in project undertakings. The finding in this research will aid project managers and different stakeholders in the banking and related sectors in managing information technology projects risks and hence increase the success rate of the projects. The knowledge gap is also addressed by scholarly work that has resulted from this research by providing statistical data analysis and explanations on the IT project performance in relation to risk analysis influence in commercial banks in Kenya.

Effect or Treatment Heterogeneity? Policy Evaluation with Aggregated and Disaggregated Treatments

Binary treatments in empirical practice are often (i) ex-post aggregates of multiple treatments or (ii) can be disaggregated into multiple treatment versions after assignment. In such cases it is unclear whether estimated heterogeneous effects are driven by effect heterogeneity or by treatment heterogeneity. This paper provides estimands to decompose canonical effect heterogeneity into the effect heterogeneity driven by different responses to underlying multiple treatments and potentially different compositions of these underlying effective treatments. This allows to avoid spurious discovery of heterogeneous effects, to detect potentially masked heterogeneity, and to evaluate the underlying assignment mechanism of treatment versions. A nonparametric method for estimation and statistical inference of the decomposition parameters is proposed. The framework allows for the use of machine learning techniques to adjust for high-dimensional confounding of the effective treatments. It can be used to conduct simple joint hypothesis tests for effect heterogeneity that consider all effective treatments simultaneously and circumvent multiple testing procedures. It requires weaker overlap assumptions compared to conventional multi-valued treatment effect analysis. The method is applied to a reevaluation of heterogeneous effects of smoking on birth weight. We find that parts of the differences between ethnic and age groups can be explained by different smoking intensities. We further reassess the gender gap in the effectiveness of the Job Corps training program and find that it is largely explained by gender differences in the type of vocational training received.