Release Planning Research Articles

What Can Radiation Protection Experts Contribute to the Issue of the Treated Water Stored in the Damaged Fukushima Daiichi Nuclear Power Plant?

Decommissioning efforts are underway at the reactor where the accident occurred, namely the damaged Tokyo Electric Power Company (TEPCO) Fukushima Daiichi Nuclear Power Plant (FDNPP). However, a large amount of groundwater flowing into the site has become contaminated with radioactive substances and is stored in tanks on site, which has hampered the decommissioning work. Although the inflow of groundwater has been greatly reduced through measures such as the construction of frost walls, approximately 170 m3 of water treated by the Advanced Liquid Processing System (ALPS) is being stored in tanks, each day. The tanks used to store this treated water are expected to become full by around the summer of 2022. It is not easy to get people to understand the efforts of all concerned parties, and providing clear information to these concerned parties is also a challenge. Questions have also been raised regarding whether other alternatives have been fully explored in the ALPS subcommittee. Some people have commented that the answers to the questions raised regarding the biological effects of tritium transmutation are inadequate. Some suspect that the answers are too detailed and incomprehensible, and that the respondents may be manipulating the public with some malicious intent. In any case, each possible plan presents both advantages and disadvantages, depending on the people who are involved. That makes it an ethical and vexing issue that can sway decisions, as perspectives change. While the environmental release plan is scientifically safe, it may represent a painful alternative. On the other hand, a more careful and imaginative approach to the idea of continued storage in tanks or other forms of storage may reveal some troublesome hidden disadvantages. Under these circumstances, experts must be prepared to answer people's questions in a comprehensive and robust manner.

A survey on the current status and future perspective of informed consent management in the MIRACUM consortium of the German Medical Informatics Initiative

BackgroundThe consent management is an essential component for supporting the implementation of consents and withdrawals and thus, the realisation of patient’s rights. In MIRACUM, one of the four consortia of the Medical Informatics Initiative (MII), ten university hospitals intend to integrate the generic Informed Consent Service® (gICS) in their Data Integration Center (DIC). To provide a tool that supports the local workflows of the MIRACUM sites, the gICS should be improved.MethodsWe used three standardised questionnaires with 46 questions to elicit requirements from the ten sites. Each site answered the questions from the current and the desired future perspective. This made it possible to understand the individual processes at each site and it was possible to identify features and improvements that were generally necessary.ResultsThe results of the survey were classified according to their impact on the gICS. Feature requests of new functionalities, improvements of already implemented functionalities and conceptual support for implementing processes were identified. This is the basis for an improved gICS release to support the ten sites’ individual consent management processes.ConclusionsA release plan for the feature requests and improvements was coordinated with all sites. All sites have confirmed that the implementation of these features and enhancements will support their software-based consent management processes.

Analysis of the multi-objective release plan rescheduling problem

Software project development is a dynamic process by nature, since it is common that events such as changes in the software requirements and in the development team occur during the execution of the original project plan. When such disruptive events occur, the project plan needs to be adjusted, and the activity of adjusting the original plan in order to comply with the date and costs constraints of the project is called project rescheduling. This situation is not exclusive to whole-project plan-driven approaches to software development, as it is also present when software projects are developed following agile methodologies such as Scrum. In Scrum, shorter period project plans, called release plans, are used and when disruptive events occur release plan rescheduling needs to be performed. Plan rescheduling can be modeled as a multi-objective optimization problem, however, there have been no studies that investigate the release plan rescheduling problem (RPRP) and its solution by means of multi-objective optimizers. The study presented in this paper intends to address the lack of studies of the release plan rescheduling problem and covers three fundamental topics. First, a model for the release plan rescheduling problem is proposed, which considers metrics that are particularly important to define a release plan, including story points and velocity. Second, three evolutionary multi-objective algorithms are utilized for solving a number of problem instances and an appropriate in-depth analysis of their solutions is provided. Finally, a careful examination of the solutions with respect to their practicability is performed.

Supporting people leaving prisons during COVID-19: perspectives from peer health mentors

PurposeCurrently, people leaving prisons face concurrent risks from the COVID-19 pandemic and the overdose public health emergency. The closure or reduction of community services people rely on after release such as treatment centres and shelters has exacerbated the risks of poor health outcomes and harms. This paper aims to learn from peer health mentors (PHM) about changes to their work during overlapping health emergencies, as well as barriers and opportunities to support people leaving prison in this context.Design/methodology/approachThe Unlocking the Gates (UTG) Peer Health Mentoring Program supports people leaving prison in British Columbia during the first three days after release. The authors conducted two focus groups with PHM over video conference in May 2020. Focus groups were recorded and transcribed, and themes were iteratively developed using narrative thematic analysis.FindingsThe findings highlighted the importance of peer health mentorship for people leaving prisons. PHM discussed increased opportunities for collaboration, ways the pandemic has changed how they are able to provide support, and how PHM are able to remain responsive and flexible to meet client needs. Additionally, PHM illuminated ways that COVID-19 has exacerbated existing barriers and identified specific actions needed to support client health, including increased housing and recovery beds, and tools for social and emotional well-being.Originality/valueThis study contributes to our understanding of peer health mentorship during the COVID-19 pandemic from the perspective of mentors. PHM expertise can support release planning, improved health and well-being of people leaving prison and facilitate policy-supported pandemic responses.

Skills Required in the Care of Cancer Patients Who Undergo Surgery in the Hospital-Home Transition

It is essential to recognize what care is required by patients undergoing surgery in the transition from hospital to home to provide guidance for plans for release and follow-up and to achieve patient adherence to these plans. The objective of this study is to describe the skills required for the care of cancer patients who undergo surgery after hospital discharge. An exploratory-type descriptive approach was adopted, including 290 cancer patients who underwent surgery at a reference center in Bogotá, Colombia. Hospital discharge was followed by 4 weeks of telephone follow-up to investigate the skills required for care on the basis of the CUIDAR tool. The participants had a mean age of 59.3 years, with the majority being female and having low levels of education. The most prevalent type of cancer found was breast cancer, followed by colon and rectal, prostate, stomach, cervical, lung, and ovarian cancer. The first follow-up identified needs for care in most of the CUIDAR dimensions, predominantly instrumentation, knowledge, and anticipation. The fourth follow-up, which found reduced needs, focused on knowledge of diet and eating, physical activity, the management of sadness and anxiety, a permanent telephone hotline, and sharing with loved ones. Cancer patients who underwent surgery require skills for at-home care that need to be addressed in hospital discharge programs and with structured telephone follow-up. Telephone follow-up interventions need to be consolidated in hospital release or hospital discharge programs that address these care needs.

Maintaining Connections: An Exploratory Analysis of the Predictors of Prison Visitation with Children and the Post-Release Plans of Incarcerated Mothers

Parental incarceration has been found to negatively impact families and communities. This study examined characteristics that impact receiving in-person visits among a sample of incarcerated mothers of minor children in a large Southern prison system. Factors impacting post release plans to live with children were also examined. Contact with their children, race/ethnicity, offense type, previous incarceration, and sentence length predicted whether incarcerated mothers received in-person visits from their children. Receiving visits from their children, race/ethnicity, having custody prior to arrest, and offense type predicted whether incarcerated mothers planned to live with their children after release from prison. Implications for policy and future research are presented.

Microservices Backlog–A Genetic Programming Technique for Identification and Evaluation of Microservices From User Stories

The microservice granularity directly affects the quality attributes and usage of computational resources of the system, determining optimal microservice granularity is an open research topic. Microservices granularity is defined by the number of operations exposed by the microservice, the number of microservices that compose the whole application, and its complexity and dependencies. This paper describes “Microservice Backlog (MB)”, a semiautomatic model for defining and evaluating the granularity of microservice-based applications; MB uses genetic programming technique to calculate at design time the granularity of each microservice from the user stories in the “product backlog” or release planning; the genetic algorithm combined coupling, cohesion, granularity, semantic similarity, and complexity metrics to define the number of microservices, and the user stories associated with each microservice. MB decomposes the candidate microservices, allowing to analyze graphically the size of each microservice, as well as its complexity, dependencies, coupling, cohesion metrics, and the number of calls or requests between microservices. The resulting decomposition (number of microservices and their granularity) performed by MB shows less coupling, higher cohesion, less complexity, fewer user stories associated with each microservice, and fewer calls among microservices. MB was validated against three existing methods, using two state-of-the-art applications (Cargo Tracking and JPet-Store), and one real-life application (Foristom Conferences). The development team and/or architect can use metrics to identify the critical points of the system and determine at design time how the microservice-based application will be implemented.

1953 Amnesty According to GULAG Statistics: Its Structure and Dynamics

The article studies the dynamics of 1953 amnesty and analyzes its structural characteristics. The 1953 amnesty was the largest in the entire history of the Soviet penitentiary system. Therefore, it is of particular interest to know the stages of the prisoners’ release process and the way such a large-scale problem could be solved. The author focuses on the structure of prisoners to be released as well as the structural changes of prisoners caused by the amnesty and their impact on the system as a whole. Despite a large number of domestic and foreign studies addressing the GULAG, the process of amnesty implementation has not been studied as it is yet. The author traces the implementation of the USSR Supreme Soviet Presidium Decree dated 03.27.1953 "On amnesty." The methodological basis for the study is statistical analysis of data on the implementation of the release plan. The result of the study is the conclusion that the 1953 amnesty was a turning point in the functioning of the USSR camp system. However, its process was uneven and accompanied by a number of difficulties caused by the need to carry out serious control and accounting work and involve additional sources to make decisions on the release of individual prisoners.

Is Bug Severity in Line with Bug Fixing Change Complexity?

Both complexity of code change for bug fixing and bug severity play an important role in release planning when considering which bugs should be fixed in a specific release under certain constraints. This work investigates whether there are significant differences between bugs of different severity levels regarding the complexity of code change for fixing the bugs. Code change complexity is measured by the number of modified lines of code, source files, and packages, as well as the entropy of code change. We performed a case study on 20 Apache open source software (OSS) projects using commit records and bug reports. The study results show that (1) for bugs of high severity levels (i.e. Blocker, Critical and Major in JIRA), there is no significant difference on the complexity of code change for fixing bugs of different severity levels for most projects, while (2) for bugs of low severity levels (i.e. Major, Minor and Trivial in JIRA), fixing bugs of a higher severity level needs significantly more complex code change than fixing bugs of a lower severity level for most projects. These findings provide useful and practical insights for effort estimation and release planning of OSS development.

An Exploration of Correctional Counselor Workloads in a Midwestern State

Time studies have been conducted across a variety of occupations. However, no known research has examined the workload of correctional counselors. In this study, the Iowa Department of Corrections, in partnership with the American Probation and Parole Association, performed a workload evaluation of over 100 correctional counselors who participated in a time study. The most common activities concerned inmate requests, classification, assessment, release planning, treatment group work, and administrative tasks. Most concerning, respondents indicated anywhere from 20% to 50% of the activities engaged in were unsatisfactorily completed.

Aspect‐based requirements mining technique to improve prioritisation process: multi‐stakeholder perspective

Requirement prioritisation and selection is an essential activity in modern-day large software development. Optimal prioritisation process is critical for successful implementation and release planning in a software development project. Requirement prioritisation becomes more challenging in projects having large sets of requirements and stakeholders, having diverse perspectives resulting in irrelevancy and ambiguity during features extraction. This study aims to improve requirement prioritisation process using text mining and clustering techniques for accurate extraction of features and requirement prioritisation in multi-stakeholder context. The proposed framework developed to avoid incompleteness in requirements and disagreement among development teams and stakeholders. Thus, the proposed framework compared with other requirement prioritisation techniques (i.e. Analytical Heretical Process, Commutative Voting and Wiegers) to highlight the significance of the proposed framework while conducting an experimental study. The results show that the proposed framework outperformed the traditional techniques and enhanced the prioritisation process with complete semantic information of extracted features and taking into account the diverse perspective of stakeholders.

Assessment of technological resources for the production of composite products based on mathematical methods

This article is devoted to the selection and justification of the use of the corresponding mathematical methods in the formation of a composite product production program and the assessment of the importance of provided technological resources. The choice of marginal profit as criteria of the optimality of the production program is proved. The formalized mathematical formulations of integer and dual tasks of linear programming are presented. The tasks of analyzing the annual release plan for a range of six products is solved. It is concluded that the usage of mathematical methods of linear programming and the theory of dual is successful decision in the field of development of composite production programs.

Optimization of Software Release Planning Considering Architectural Dependencies, Cost, and Value

Within any incremental development paradigm, there exists a tension between the desire to deliver value to the customer early and the desire to reduce cost by avoiding architectural refactoring and rework in subsequent releases. What is lacking is an analytical framework that quantifies opportunities and risks of choosing one or the other of these strategies or a blend of the two. This article demonstrates the use of design structure and domain mapping matrices for analyzing architectural dependencies and proposes an optimization-based decision-making technique to support effective release planning. The optimization models recommend the order in which architectural elements and features should be implemented across different releases so as to: (a) minimize rework cost; (b) maximize early value delivery; or (c) optimize an integrated measure of cost and value. These analytic models can be applied earlier in the life cycle and, hence, provide timely information about the progress and changes that occur at each iteration.

A Hybrid Equity Release Plan for Retirement Financing

Abstract There are two main equity release plans for retirement financing: reverse mortgage plan and home reversion plan. Both plans entitle the homeowners not only to release cash from their properties but also to allow them living there for life. In the lease buyback scheme (LBS) recently introduced in Singapore, the home owner sells the tail-end of the property lease to the government in exchange for a cash payment upfront. Unlike the two main equity release plans, the LBS only allows the owner to stay in the property for the front part of the lease but not for life. In this paper, we propose a hybrid home equity release plan that incorporates features of the home reversion plan and the LBS. We provide an actuarial framework to analyze the pricing of the hybrid plan, as well as the LBS and home reversion plan. Some numerical illustrations are presented to show the actuarial values of the plan under different choices of plan parameters, such as the lease period and the portion of property value sold. The hybrid plan provides enhanced flexibility to plan takers to meet their retirement needs.

COVID-19: digital equivalence of health care in English prisons

COVID-19: digital equivalence of health care in English prisons

The 16th Data Release of the Sloan Digital Sky Surveys: First Release from the APOGEE-2 Southern Survey and Full Release of eBOSS Spectra

Abstract This paper documents the 16th data release (DR16) from the Sloan Digital Sky Surveys (SDSS), the fourth and penultimate from the fourth phase (SDSS-IV). This is the first release of data from the Southern Hemisphere survey of the Apache Point Observatory Galactic Evolution Experiment 2 (APOGEE-2); new data from APOGEE-2 North are also included. DR16 is also notable as the final data release for the main cosmological program of the Extended Baryon Oscillation Spectroscopic Survey (eBOSS), and all raw and reduced spectra from that project are released here. DR16 also includes all the data from the Time Domain Spectroscopic Survey and new data from the SPectroscopic IDentification of ERosita Survey programs, both of which were co-observed on eBOSS plates. DR16 has no new data from the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey (or the MaNGA Stellar Library “MaStar”). We also preview future SDSS-V operations (due to start in 2020), and summarize plans for the final SDSS-IV data release (DR17).

Enhancing Software Evolution Requirements Engineering Based on User Feedback

End-user feedback has an essential role in the requirement’s identification, prioritization, and management of the software evolution process. Several approaches are proposed for utilizing user-pushed feedback collected from social media, forums, and review systems. The collected feedback via the online channels contains a variety of information. Thus, the researchers proposed analytical approaches to classify feedback according to the data it holds. Still, recent results indicate that no single classifier works best for all feedback types and information sources. Also, online feedback does not have a direct mapping to the requirements, and it does not contain user context data. This causes wasting in developers’ effort in understanding and analyzing feedback. On the other hand, online feedback cannot be used to explore user satisfaction and acceptance of the implemented and planned requirements. Likewise, the developer cannot collect feedback from a specific segment of the users. To overcome the deficiency of online feedback, this paper proposes a novel approach that utilizes pulling feedback from users while using the software. The proposed approach consists of a model and process for structuring feedback requests, linking feedback to the requirements, embedding feedback with the user context information, specifying the target audience for the feedback request, analyzing collected feedback depending on predefined interpretation rules, which provide insights that support developers in release planning. The feedback request model and process are implemented by a tool called FeatureEcho which was evaluated in a software company by conducting a case study for upgrading a governmental internet portal. The results indicate that FeatureEcho is a valuable step towards increasing the understanding of the end-users needs which supports the decision-making procedure of software evolution.

A Simple Model of Capacity Contention During New Product Introductions

We consider the problem of determining an optimal release schedule for a production facility as it transitions from producing an older product to a newer one. We use simple queueing ideas to model the impact of the new product introduction on the throughput rate and cycle time of both products. We then incorporate the learning gained from accumulated experience in producing the new product to formulate a deterministic optimization model for release planning during product transitions. Since the model is nonlinear and non-convex we use a genetic algorithm to obtain near-optimal solutions. The structure of the optimal solution provides insights into the relationships between the different costs involved in the model. Computational experiments show that the release planning model produces high-quality solutions in reasonable CPU times, and reflects the behavior of the system realistically. The principal insight is that careful management of releases during product transitions has the potential to mitigate many of the adverse effects frequently observed in practice such as longer cycle times for both old and new products.

Discrete event-driven model predictive control for real-time work-in-process optimization in serial production systems

Advanced technologies (e.g., distributed sensors, RFID, and auto-identification) can gather processing information (e.g., system status, uncertain machine breakdown, and uncertain job demand) accurately and in real-time. By combining this transparent, detailed, and real-time production information with production system physical properties, an intelligent event-driven feedback control can be designed to reschedule the release plan of jobs in real-time without work-in-process (WIP) explosion. This controller should obtain the operational benefits of pull (e.g., Toyota’s Kanban system) and still develop a coherent planning structure (e.g., MRPII). This paper focuses on this purpose by constructing a discrete event-driven model predictive control (e-MPC) for real-time WIP (r-WIP) optimization. The discrete e-MPC addresses three key modelling problems of serial production systems: (1) establish a max-plus linear model to describe dynamic transition behaviors of serial production systems, (2) formulate a model-based event-driven production loss identification method to provide feedback signals for r-WIP optimization, and (3) design a discrete e-MPC to generate the optimal job release plan. Based on a case from an industrial sewing machine production plant, the advantages of the discrete e-MPC are compared with the other two r-WIP control strategies: Kanban and MRPII. The results show that the discrete e-MPC can rapidly and cost-effectively reconfigure production logic. It can decrease the r-WIP without deteriorating system throughput. The proposed e-MPC utilizes the available transparent sensor data to facilitate real-time production decisions. The effort is a step forward in smart manufacturing to achieve improved system responsiveness and efficiency.

Dominance Conditions for Optimal Order-Lot Matching in the Make-To-Order Production System

Order-lot matching is the process of assigning items in lots being processed in the make-to-order production system to meet the due dates of the orders. In this study, an order-lot matching problem (OLMP) is considered to minimize the total tardiness of orders with different due dates. In the OLMP considered in this study, we need to not only determine the allocation of items to lots in the production facility but also generate a lot release plan for the given time horizon. We show that the OLMP can be considered as a special type of machine scheduling problem with many similarities to the single machine total tardiness scheduling problem ( 1 | | ∑ T i ). We suggest dominance conditions for the OLMP by modifying those for 1 | | ∑ T i and a dynamic programming (DP) model based on the dominance conditions. With two example problems, we show that the DP model can solve small-sized OLMPs optimally.