Software Support Tools Research Articles

Forecasting software indicators: an industry-academia collaboration

ContextNowadays software-development organizations are urged to exploit their data for empowering their decision-making processes. Such data may be used to monitor the status of meaningful software indicators (e.g., software quality, productivity and on-time delivery) that are relevant for their decision-making processes. Forecasting the values of such indicators may provide evidence of a potentially high risk or opportunity that could help to anticipate actions accordingly. Most of the existing forecasting proposals in software engineering use open-source data rather than data from industrial projects. Therefore, there is a lack of evidence on how these proposals fit the particular needs of a software-development organization and how they can be automated into the organization’s infrastructure.ObjectiveTo enable software indicators´ forecasting in a software-development organization (Modeliosoft).MethodWe designed an industry-academia collaboration based on Action Design Research (ADR) to address Modeliosoft’s forecasting challenges.ResultsA tool-supported method called FOSI (Forecasting Of Software Indicators) for enabling forecasting in Modeliosoft. We obtained positive results regarding its suitability and technical feasibility in a pilot project of the organization. In addition, we provide details and reflections on the potential usefulness of the method for addressing similar field problems.ConclusionsThe procedures and results detailed in this paper are valuable to: 1) address Modeliosoft’s forecasting challenges 2) inspire other software-development organizations on how to deal with similar problems and even reuse some procedures and software support tools resulted from this work, 3) promote the win-win benefits of industry-academia collaborations.

Towards a holistic assessment of circular economy strategies: The 9R circularity index

Our planet faces mounting environmental burdens due to linear production and consumption. Circular economy strategies offer a promising alternative, but evaluating their effectiveness requires robust measurement tools. Existing approaches lack a comprehensive framework incorporating the hierarchical strategies proposed by the 9R framework and the butterfly diagram. Based on the current circular economy indicators, this study gathers a set of indicators designed to assess the efficacy of circular economy strategies, considering the hierarchical levels outlined in the 9R framework. Moreover, it leverages the Analytic Hierarchy Process (AHP) fundamental scale as an integration tool to consolidate the suggested indicators into a unified metric termed the “9R circularity index”. This approach relies on a bottom-up approach to measure circular economy at various levels. The “9R circularity index” enhances the ability to compare results from a material flow analysis perspective. In addition, this paper presents a step-by-step approach and a supporting software tool that aims to facilitate the measurement, selection and comparison between circular and linear models. Ultimately, this proposed approach offers a workable, data-driven tool to support the transition towards a circular economy.

Health Inequality Data Repository

The Health Inequality Data Repository from the World Health Organization (WHO) is a free, open-access selection of publicly available disaggregated data about health and indicators of health. It includes regional and continental data sets from 1950 to 2022, covering all world regions, from 16 sources on more than 2,000 indicators. As of November 2023, the available data set categories are Sustainable Development Goals; WHO Global Health Observatory; COVID-19; Reproductive, Maternal, and Child Health; Immunization; HIV, Tuberculosis, and Malaria; Adult Health; Health Care; Burden of Disease; Disability; Migration; Environmental Health; WHO General Programme of Work; and Beyond the Health Sector (Health Inequality Monitor 2023). The sources of the data sets include publicly available data from the WHO and numerous United Nations (UN) agencies, the Organisation for Economic Co-operation and Development (OECD), the European Union, the US Agency for International Development (USAID), and more. In addition to the data sets, two software options are available. The Health Equity Assessment Toolkit (HEAT) allows users to view the data in various chart and graph formats, and HEAT Plus allows users to upload their own disaggregated data in addition to the available data sets. The Health Inequality Data Repository and its supporting software tools are housed within the Health Inequality Monitor section of the WHO website, and it has numerous educational options related to its resources and goals.

Hydrological Regime Alteration Assessment in the Context of WFD 2000/60: A European and Global Review

Although the impact of hydrology on the ecological status of surface water bodies has been highly recognised, the hydrological regime alteration assessment has proven to be a challenging task. In this context, an extensive structured review analysis was used as a research method to investigate the strength and limitations of the hydrological regime alteration assessment methods as adopted by each member of the European Environment Agency and the cooperating countries, according to the Water Framework Directive 2000/60, as well as to propose future directions. The review was also widened to include the methods currently used worldwide in the hydrological alteration studies and the supporting software tools developed. The implementation of a common methodology on a European scale is not applicable, since a single approach would not be able to cope with the regional needs and conditions. The main limitation in almost all the methods developed by European countries and worldwide is the need for a flow time series of high temporal resolution, so as to also capture the systems’ extreme high and low flows. Automatic monitoring systems for rivers can provide a solution. Additionally, hydrological modelling may provide the necessary data for the definition of the reference conditions. Nevertheless, the main limitations of the methodologies reviewed and the challenge for future development are the incorporation of the groundwater contribution to the hydrological regime and the development of quantitative relationships between flow alteration and ecological response.

Artificial intelligence systems in surgery: A review of opportunities, limitations, and prospects

Artificial intelligence technologies are increasingly being applied in a variety of medical disciplines. After reviewing 278 publications from 1985 to 2023, 99 articles were selected from the databases elibrary, PubMed, Medline, WoS, Nature, Springer, and Wiley J Database to present the main approaches and a modern picture of the application of artificial intelligence methods and technologies in pediatric surgery and intensive care. The article examines many facets of artificial intelligence systems for medical uses, namely, computer decision support systems or supporting the surgeon throughout the surgical intervention procedure. Computer analysis of 3D visualization and 3D anatomical modeling of images obtained from computed tomography and magnetic resonance imaging investigations can be used to plan operations. Because of the possibilities of sufficiently accurate 3D models and methods for organs and pathological processes, various methodologies and software tools for preoperative planning and intraoperative support of surgical intervention have been developed. Computer (technical) vision analyzes high-quality medical images and interprets them in multimodal three-dimensional images for computer diagnoses and operations under visual control, including augmented reality methods. Robotic surgery involves manipulators, including remotely controlled ones, and intellectualized complexes that independently perform specific actions of the second assistant surgeon. In intensive care, artificial intelligence technologies are being investigated to merge data from bedside monitors and other information about patients conditions to identify critical situations and control mechanical ventilation. Simultaneously, several obstacles impede the adoption of artificial intelligence in surgery. The nature and standardization of the initial data required for their integration, taking into consideration atypical cases, the possibility of bias in the sample used, and the transparency of the decision-making process in machine learning models are examples. The explanation of solutions presented in machine learning models and the transition to full-fledged validation of the systems being built define the prospects for developing and using artificial intelligence systems.

Use of supporting software tool for decision-making during low-probability severe accident management at nuclear power plants

In the project NARSIS – New Approach to Reactor Safety ImprovementS – possible advances in safety assessment of nuclear power plants (NPPs) were considered, which also included possible improvements in the field of management of low probability accident scenarios. As a part of it, a supporting software tool for making decisions under severe accident management was developed.
 The mentioned tool, named Severa, is a prototype demonstration-level decision supporting system, aimed for the use by the technical support center (TSC) while managing a severe accident, or for the training purposes. Severa interprets, stores and monitors key physical measurements during accident sequence progression. It assesses the current state of physical barriers: core, reactor coolant system, reactor pressure vessel and containment. The tool gives predictions regarding accident progression in the case that no action is taken by the TSC. It provides a list of possible recovery strategies and courses of action. The applicability and feasibility of possible action courses in the given situation are addressed. For each action course, Severa assesses consequences in terms of probability of the containment failure and estimated time window for failure. At the end, Severa evaluates and ranks the feasible actions, providing recommendations for the TSC. The verification and validation of Severa has been performed in the project and is also described in this paper. Although largely simplified in its current state, Severa successfully demonstrated its potential for supporting accident management and pointed toward the next steps needed with regard to further advancements in this field.

Software Development and Tool Support for Curriculum Design: A Systematic Mapping Study

Curriculum design is the systematic process of establishing how a learning process is designed, developed, supported, and delivered. This process is supported by software tools which can help improve curriculum alignment and facilitate the design of courses or programs. This article aims to analyze software proposals for curriculum design support that consider using models, methods, and techniques in software development. To do this, a systematic mapping of studies was conducted, including six research questions. This study includes 45 articles published from 2011 to 2022. The results indicate that 60% use some model, method, or technique in software development. Most software uses some models, such as ontologies, UML diagrams, or IMS-1D models. Although most articles use some model, method, or technique, there is a lack of use of software engineering models such as UML diagrams, which are standard in the software industry and research.

Requirements engineering for sustainable software systems: a systematic mapping study

Various approaches toward the development of sustainable software systems have been proposed by the requirements engineering community over the last decade. We conducted a systematic mapping study, analyzed 55 publications, and identified 29 approaches that have been published since the year 2000. We analyzed how the approaches evolved over time and how the publications and authors are influenced by each other. Furthermore, the approaches are analyzed in terms of their supported requirements engineering activities, along with the evidence provided in the publications. Additionally, we also analyzed which sustainability definitions have been used, if an iterative application of the approaches is discussed, and if the approaches also provide a tool-support for practitioners. We noticed an increase of publications on requirements engineering approaches toward sustainability in the last years, whereas a majority discuss sustainability based on the same multi-dimensional concept. Although different case studies have been already conducted, we noticed a lack of an industrial application. Our main findings concern the need of an evaluation on how the proposed requirements engineering approaches can also be applied in agile software development processes. Additionally, we also promote the development of supporting software tools to support practitioners in adapting the proposed approaches.

Software-Tool Support for Collaborative, Virtual, Multi-Site Molecular Tumor Boards

The availability of high-throughput molecular diagnostics builds the foundation for Molecular Tumor Boards (MTBs). Although more fine-grained data is expected to support decision making of oncologists, assessment of data is complex and time-consuming slowing down the implementation of MTBs, e.g., due to retrieval of the latest medical publications, assessment of clinical evidence, or linkage to the latest clinical guidelines. We share our findings from analysis of existing tumor board processes and defininion of clinical processes for the adoption of MTBs. Building on our findings, we have developed a real-world software prototype together with oncologists and medical professionals, which supports the preparation and conduct of MTBs and enables collaboration between medical experts by sharing medical knowledge even across the hospital locations. We worked in interdisciplinary teams of clinicians, oncologists, medical experts, medical informaticians, and software engineers using design thinking methodology. With their input, we identified challenges and limitations of the current MTB approaches, derived clinical process models using Business Process and Modeling Notation (BMPN), and defined personas, functional and non-functional requirements for software tool support. Based on it, we developed software prototypes and evaluated them with clinical experts from major university hospitals across Germany. We extended the Kanban methodology enabling holistic tracking of patient cases from “backlog” to “follow-up” in our app. The feedback from interviewed medical professionals showed that our clinical process models and software prototype provide suitable process support for the preparation and conduction of molecular tumor boards. The combination of oncology knowledge across hospitals and the documentation of treatment decision can be used to form a unique medical knowledge base by oncologists for oncologists. Due to the high heterogeneity of tumor diseases and the spread of the latest medical knowledge, a cooperative decision-making process including insights from similar patient cases was considered as a very valuable feature. The ability to transform prepared case data into a screen presentation was recognized as an essential feature speeding up the preparation process. Oncologists require special software tool support to incorporate and assess molecular data for the decision-making process. In particular, the need for linkage to the latest medical knowledge, clinical evidence, and collaborative tools to discuss individual cases were named to be of importance. With the experiences from the COVID-19 pandemic, the acceptance of online tools and collaborative working is expected to grow. Our virtual multi-site approach proved to allow a collaborative decision-making process for the first time, which we consider to have a positive impact on the overall treatment quality.

LipidOz enables automated elucidation of lipid carbon–carbon double bond positions from ozone-induced dissociation mass spectrometry data

Lipids play essential roles in many biological processes and disease pathology, but unambiguous identification of lipids is complicated by the presence of multiple isomeric species differing by fatty acyl chain length, stereospecifically numbered (sn) position, and position/stereochemistry of double bonds. Conventional liquid chromatography-mass spectrometry (LC-MS/MS) analyses enable the determination of fatty acyl chain lengths (and in some cases sn position) and number of double bonds, but not carbon-carbon double bond positions. Ozone-induced dissociation (OzID) is a gas-phase oxidation reaction that produces characteristic fragments from lipids containing double bonds. OzID can be incorporated into ion mobility spectrometry (IMS)-MS instruments for the structural characterization of lipids, including additional isomer separation and confident assignment of double bond positions. The complexity and repetitive nature of OzID data analysis and lack of software tool support have limited the application of OzID for routine lipidomics studies. Here, we present an open-source Python tool, LipidOz, for the automated determination of lipid double bond positions from OzID-IMS-MS data, which employs a combination of traditional automation and deep learning approaches. Our results demonstrate the ability of LipidOz to robustly assign double bond positions for lipid standard mixtures and complex lipid extracts, enabling practical application of OzID for future lipidomics.

Cardiothoracic Radiologist Workload, Work Capacity, and Burnout Post-COVID: Results of a Survey From the Society of Thoracic Radiology.

In this report and analysis of the results of a late 2021 post-COVID pandemic survey of members of the Society of Thoracic Radiology, we compared cardiothoracic radiologist workloads and burnout rates with those obtained from a prepandemic survey of society members. The more recent survey also asked respondents to provide a subjective assessment of their individual workload capacity should they be required to read cases at a section average daily case work volume, and this assessment was correlated with burnout rates. To measure nonrelative value unit workload, we requested data on non-case-related work responsibilities including teaching and multidisciplinary conferences that were not assessed in the first survey. In addition, we asked respondents to provide information on the availability of support services, personnel, and hardware and software tools that could improve work efficiency and reduce radiologist stress levels thereby mitigating burnout. We found that postpandemic case workload and cardiothoracic radiologists' burnout rates were similarly high compared with prepandemic levels with an overall burnout rate of 88% including a 100% burnout rate among women which had significantly increased. The range of radiologists' workload capacity is broad, although 80% of respondents reported that reading at an average sectional case volume was at or above their capacity, and the perceived capacity correlated with burnout measures. The presence of fellows and computer-aided diagnosis/artificial intelligence tools were each associated with significant decreases in burnout, providing 2 potential strategies that could be employed to address high cardiothoracic radiologist burnout rates.

Real-time operating decision support system for reservoir operation using weather forecast and a hydrological model

This paper presents the application of a real-time decision support system (DSS) for power generation and flood control at the Thac Xang hydropower plant in Lang Son province, Vietnam. The DSS consists of three key components: real-time operation and visualization, forecast, and optimized decision modules. The real-time operation and visualization module provides operators with a graphical interface that displays real-time data on the plant's status, such as water levels, reservoir levels, and power output. The weather forecasting module provides information about future precipitation. This information is used as input data for the inflow forecasting model. The real-time computing module takes into account current conditions, such as the water level in the reservoir to compute relevant variables such as current inflow rate or outflow rates from the reservoir. The simulation module is used to test different operating scenarios taking current and future inflow rates into account. This helps the plant operators to understand the impact of their decisions on the plant's performance. The simulation module can also be used to identify potential problems and develop contingency plans. Therefore, a mathematical model is developed. Particularly, this model uses real-time information of dams. The main objective is to maximize economic value over the time horizon by producing electricity when it is most valuable. An approach of a simulated annealing algorithm is used to solve this model. We also present a decision support software tool for small hydropower systems that provide hydropower equipment operators with the information required to optimize the dam's performance in terms of power efficiency and effectiveness.

Whole life cycle environmental impact assessment of buildings: Developing software tool and database support for the EU framework Level(s)

The European Commission has published a framework on the environmental sustainability of buildings, called Level(s), a set of indicators addressing the resource efficiency and environmental impact of buildings. This framework is now a key element of European initiatives to develop a circular economy and aims to address whole life cycle greenhouse gas (GHG) emissions and to drive a renovation wave for existing buildings. Analysis of the whole life cycle environmental impact of buildings, with an emphasis on GHG emissions, is also the focus of initiatives in several EU Member States. To support users, Level(s) was accompanied by a list of Life Cycle Assessment (LCA) software tools and databases for calculating such indicators. A need was identified for practical, user-orientated information about the scope, consistency, cost, and accessibility of LCA software tools and databases. Therefore, a list of LCA tools and databases used in Europe for the assessment of the whole life cycle environmental impacts of buildings was compiled in 2017–2020 and their characteristics analysed based on a set of criteria. This paper describes the policy context of Level(s) and discusses criteria that can be used for the characterisation of LCA software tools and databases from the list compiled for Level(s). The methods included a literature review, surveys/interviews, and the co-creation of criteria for the categorization of tools and databases. The results are criteria including construction-specificity, system boundaries & scope, indicators, modelling granularity, methodological adherence to Level(s) and EN standards, data quality, transparency and verification, accessibility, data exchange and interoperability, cost, training and support, as well as additional information. By providing greater visibility regarding the features of tools and databases, this paper contributes to the mainstream use of Level(s) and also to the implementation of key EU policy initiatives aimed at enhancing the environmental sustainability of the built environment, such as the EU Renovation Wave, the New European Bauhaus, and the EU taxonomy for sustainable activities.

Implementation of a virtual, on-demand, molecular tumor board at a large, multi-clinic, community oncology practice.

96 Background: Despite the availability of molecularly-targeted agents for the treatment of many cancer types, gaps remain in integrating comprehensive precision oncology decision support tools and services into routine clinical practice. Molecular Tumor Boards (MTBs) have been shown to improve accurate incorporation of precision oncology and oncologic clinical trial enrolment into clinical practice. However, the traditional MTB model is a didactic meeting occurring at regular pre-scheduled cadences which may not align with treatment decisions or schedules of community-based general medical oncologists and advanced practice providers (APPs) without protected time away from clinic. Herein, we report on the utilization of an on-demand virtual MTB (vMTB) implemented at Tennessee Oncology (TO) powered by the Personalized Medicine (PM) team at the Sarah Cannon Research Institute (SCRI). Methods: “MolecularHelp” (MH) decision support services were implemented in September 2021 for oncology providers at TO – a network of over 100 oncologists and 86 APPs practicing across 34 clinics in Tennessee. The MH services request was a structured order that could be placed directly within the electronic health record (EHR) or through patient-protected email within the practice. MH orders initiated a virtual, on-demand interpretation of comprehensive genomic profiling (CGP) reports by a centralized vMTB run by SCRI's PM team and supported by Genospace, SCRI's precision medicine software platform. The PM team – comprised of pharmacologists, cell biologists, human geneticists, and molecular biologists – analyze CGP results and provide expert advice on both standard-of-care (SOC) and clinical research therapeutic options. Both SOC and clinical research targeted therapy options were relayed back to the treating physician and embedded within the EHR. Herein, we report key metrics including MH order frequency, average turnaround time, and subsequent clinical trial enrolment between September 2021 and March 2022. Results: CGP reports from 120 unique patients were reviewed by the vMTB during the collection period. MH orders were initiated by 30 TO providers from 14 different clinic locations across Middle and East Tennessee. The average turnaround time from referral to vMTB interpretation was less than 10 hours. Of the 120 patients reviewed, actionable mutations were identified by the MTB in 103 patients, of whom 27 subsequently enrolled onto clinical trials (15 phase 1 and 12 phase 2/3). Conclusions: An on-demand vMTB is feasible within an engaged community oncology practice with investments in bioinformatics, decision support software tools, and a team of precision oncology experts supported by a robust clinical trial menu. On-demand vMTBs can be widely adopted to enhance clinical trial enrolment. Future directions include studying the impact of vMTBs on patient outcomes over time.

Navigating the Digital World - the Impact on the New World of Work, A Multigen Perspective

Digital technology impacts the 21st century, and it shapes our lifestyle, the way we think and the way we act. This paper presents aspects of the digital transformation of the workplace and how this process requires the creation and development of new skills and competencies for employees today but especially in the future. In this new context, an essential role is played by education, by educational systems that need to adapt to new requirements, precisely so that educators have the skills needed to be more productive. From this perspective, in the paper, the authors present a teaching and learning method called the TADEO method, which encourages learning the skills needed for Education 4.0. This method is based on a technology support software tool to help teachers build their teaching plan, teaching duration, teaching materials, and assessment method. Hybrid work is an opportunity that brings new challenges: the work environment is changing rapidly, and automation is replacing human tasks in order for organizations to thrive. Recruitment has become complicated with the digital transformation, and new challenges are emerging for employers as they need people with the skills needed to cope with a changing work environment. The study was conducted on a sample of respondents from a multinational service company based in western Romania, where the accounting department employees were interviewed to see what they think about artificial intelligence, the implications of robots, and the future at work. This research is based on four objectives that have been achieved and three hypotheses that have been tested. The study results showed that about half (43.7%) of the employees who answered this questionnaire worked in the office. Only 28.2% consider themselves very satisfied with the job, and 37.9% see themselves working at the same company for 10 years.

A comparative study on multi-agent and service-oriented microgrid automation systems from energy internet perspective

The current advancements of energy, information, communication, and automation technologies and their integration have provided ways for the energy industry to transform into cleaner energy systems. This transition has contributed to the concept called energy internet. The recent energy technologies provide clean energy generation, storage and demand response through distributed energy resources. Information, communication, and automation technologies aim to provide supporting software tools and enabling mechanisms to automate the operation and control of those resources in a coordinated way. Thus, researchers and the software industry are developing software frameworks and platforms to support energy system automation. Commonly, most of the frameworks follow the design principles of either multi-agent systems (MAS) or service-oriented architecture (SOA). However, there are many frameworks and no straightforward criteria to select which one to implement in energy systems’ automation applications to fulfill the energy internet vision. This study provides a conceptual investigation of MAS- and SOA-based software solutions by designing a use case for microgrid application automation considering its expansion for enabling energy internet. Two software frameworks, RIAPS and Arrowhead, have been selected as the candidates of MAS and SOA from the literature study. This study shows that neither MAS or SOA approach alone might not meet the requirements of microgrid automation and energy internet. Consequently, a combined approach of MAS and SOA is proposed.

Decision-Making Support Software Tools Based on Original Authoring Bayesian Probabilistic Models

The article presents programs that implement original approaches to group expert rating estimation and fuzzy inference. They implement probabilistic models based on Bayes’ Formula, previously proposed and published in the works of the authors. In these models, the estimated input data are interpreted as evidence in favor of one or another hypothesis from the set of possible ones, determined by the specifics of the model. All the evidence obtained is, in one way or another, transformed into a set of Bayesian conditional probabilities calculated under the assumption that the corresponding hypothesis is true, and the posterior probability distribution on the set of these hypotheses is used as the output. This distribution is used either directly as a result for decision making, or as a basis for calculating the final result. The features of the software implementation of models on the Java platform are discussed, the advantages of the models, confirmed or identified in the process of software implementation, are noted. The developed programs have a convenient graphical user interface and can be used as decision-making support tools to solve applied problems in the field of expert rating estimation and fuzzy inference.

SCADA-Compatible and Scaleable Visualization Tool for Corrosion Monitoring of Offshore Wind Turbine Structures

The exploitation of offshore windfarms (WFs) goes hand in hand with large capital expenditures (CAPEX) and operational expenditures (OPEX), as these mechanical installations operate continuously for multiple decades in harsh, saline conditions. OPEX can account for up to 30% of the levelised cost of energy (LCoE) for a deployed offshore wind farm. To maintain the cost-competitiveness of deployed offshore WFs versus other renewable energy sources, their LCoE has to be kept in check, both by minimising the OPEX and optimising the offshore wind energy production. As corrosion, in particular uniform corrosion, is a major cause of failure of offshore wind turbine structures, there is an urgent need for corrosion management systems for deployed offshore wind turbine structures (WTs). Despite the fact that initial corrosion protection solutions are already integrated on some critical structural components such as WT towers, WT transition pieces or WT sub-structure (fixed or floating platforms), these components can still be harshly damaged by the corrosive environmental offshore conditions. The traditional preventive maintenance strategy, in which regular manual inspections by experts are necessary, is widely implemented nowadays in wind farm applications. Unfortunately, for such challenging operating environments, regular human inspections have a significant cost, which eventually increase the OPEX. To minimise the OPEX, remote corrosion monitoring solutions combined with supporting software (SW) tools are thus necessary. This paper focuses on the development of a software (SW) tool for the visualisation of corrosion measurement data. To this end, criteria for efficient structural corrosion analysis were identified, namely a scaleable, SCADA-compatible, secure, web accessible tool that can visualise 3D relationships. In order to be effective, the SW tool requires a tight integration with decision support tools. This paper provides three insights: Firstly, through a literature study and non-exhaustive market study, it is shown that a combined visualisation and decision SW tool is currently non-existing in the market. This gap motivates a need for the development of a custom SW tool. Secondly, the capabilities of the developed custom software tool, consisting of a backend layer and visualisation browser designed for this task are demonstrated and discussed in this paper. This indicates that a SCADA-compatible visualisation software tool is possible, and can be a major stepping stone towards a semi-automated decision support toolchain for offshore wind turbine corrosion monitoring.

Off-design modeling of a microturbine combined heat & power system

There is increasing interest in decision support software tools that evaluate the techno-economic potential of distributed energy system technologies for deployment in combined heat and power (CHP) applications, such as commercial buildings, microgrids, and connected communities. This trend is particularly evident among end-users looking to integrate renewable resources with more traditional CHP technologies for on-site generation and enhanced resiliency. However, many of these software tools do not account for part-load and off-design characteristics of prime mover and heat recovery equipment, resulting in lower accuracy for both the optimal selection and sizing and the economic value proposition of integrated hybrid renewable energy-CHP systems. Microturbine technology is an attractive distributed energy resource whose performance is sensitive to both part-load and off-design (i.e., excursions of ambient conditions away from the design point temperature and pressure) operating conditions but is widely unreported. In the present work, we address this deficiency by developing a detailed, thermodynamic model of a commercial 200 kW microturbine CHP system and paramaterizing results for incorporation into the Renewable Energy Optimization (REopt) tool developed by the National Renewable Energy Laboratory. The details of the microturbine model, including model benchmarking and validation, are presented. Microturbine off-design modeling includes part-load analysis, assessment of ambient sensitivities, and mapping heat recovery heat exchanger response to hot water grade requirements. The model is then exercised in several illustrative examples to depict the numerous factors which alter electric power, electric efficiency, and hot water recovery. Model results show, for example, predictions of a 4 percentage point electric efficiency range occurring between intake air temperatures of -18 to 50 °C. Likewise, the model closely predicts the 25 % loss in heat recovery while at -18 °C and the 8 % gain in heat recovery while at 35 °C. An additional case shows how heat recovery may increase or decrease by 9.5 % of the design value when changing the return water temperature plus-or-minus 22 °C. In particular, the impact of ambient conditions on best-possible microturbine performance is conveyed through hourly simulations of the system subjected to the weather of different geographic locations within the United States. • A model of a 200 kW microturbine configured for hot water recovery is developed. • Part-load modeling approach and simulation results for both electric efficiency and waste heat recovery are detailed. • Off-design performance as functions of ambient temperature and pressure is explored. • Heat recovery performance maps as functions of return and supply hot water temperatures are developed.

The intellectual support efficiency methods evaluation in the sphere of social infrastructure accessibility managing for low-mobile population groups

Modern society significant part is made up of People with Limited Mobility (PLM). This category of citizens is limited in movement and experiences problems of interaction with social infrastructure: first of all, these are people with disabilities (disabled people), who, according to the World Health Organization (WHO), make up more than 15% of the world's population, as well as people moving, for example, from baggage or stroller. Researchers identify three main problems in the interaction of PLM with social infrastructure: the first is related to the physical barriers of social infrastructure facilities (SIF) the second is the inaccessibility of information about SIF, and the third includes a number of social problems caused by the attitude towards PLM within society. Efficiency improving in the field of urban mana¬gement and ensuring the information availability of SIF for PLM is possible through the use of geographic information technologies, electronic maps and digital city information systems, as well as specialized decision support systems. The purpose of research is to solve an urgent scientific and technical problem of implementing a support system for making informed management decisions in the field of ensuring the availability of information systems for PLM, as well as developing a methodology for assessing the effectiveness of the DSS. Materials and methods. Control theory for the development of an intelligent decision support method, software and hardware implementation tools, as well as an analytical, computational and graphical method for evaluating the effectiveness of development. Results. A DSS has been developed in the field of managing the availability of SIF for PLM. An assessment of the DSS functioning quality and efficiency was made, the analytical and graphical results of the efficiency assessment were reflected. Conclusion. The paper describes a methodology for the development of a DSS for managing the accessibility of SIF for PLM, including the introduction into the existing urban management system and evaluating the effectiveness of intellectual support methods in managing the accessibility of social infrastructure for PLM.