Third-party Sources Research Articles

Predictive Optimization of Patient No-Show Management in Primary Healthcare Using Machine Learning.

The "no-show" problem in healthcare refers to the prevalent phenomenon where patients schedule appointments with healthcare providers but fail to attend them without prior cancellation or rescheduling. In addressing this issue, our study delves into a multivariate analysis over a five-year period involving 21,969 patients. Our study introduces a predictive model framework that offers a holistic approach to managing the no-show problem in healthcare, incorporating elements into the objective function that address not only the accurate prediction of no-shows but also the management of service capacity, overbooking, and idle resource allocation resulting from mispredictions. Our approach simplifies preprocessing and eliminates the need for expert judgment in variable selection, thereby enhancing the model's usability in routine healthcare operations. Our research revealed that key predictors of no-shows are consistent across various studies. We employed semi-automatic feature selection techniques, achieving results comparable to state-of-the-art approaches but with significantly reduced complexity in their selection. This method not only streamlines the feature selection process but also enhances the overall efficiency and scalability of our predictive models, making them more adaptable to diverse healthcare settings. This comprehensive strategy enables healthcare providers to optimize resource allocation and improve service delivery, making our findings relevant for healthcare systems globally facing similar challenges. Future work aims to expand the analysis by incorporating additional third-party data sources, such as weather and commuting activities, to explore the broader impacts of external factors on patient no-show behavior. To the best of our knowledge, this innovative approach is expected to provide deeper insights and further enhance the predictability and effectiveness of no-show mitigation strategies in healthcare systems.

Reading Between the Tabloids: An Objective Analysis of the Quality and Readability of Patient Encountered Online Material for Face Transplantation.

Face transplantation is a highly sensationalized procedure in the media. The purpose of this study is to assess the content and readability of online materials that prospective patients/public encounter regarding face transplantation. A search for face transplantation was performed on Google. Sites were categorized under 3 groups: established face transplant programs, informational third-party sources (eg, Wikipedia), and news article/tabloid sites. Each site was assessed for readability using 6 different readability metrics, while quality was assessed utilizing JAMA benchmark criteria and DISCERN instrument. One-way ANOVA with post hoc Tukey's multiple comparisons test was used for analysis. News sources were significantly easier to read than face transplant program sites (10.4 grade reading level vs. 12.4). For the JAMA benchmark, face transplant programs demonstrated the lowest average score relative to third-party sites, and news sources (2.05 vs. 2.91 vs. 3.67, respectively; P<0.001), but had significantly greater DISCERN scores than news sources (53.50 vs. 45.83, P=0.019). News sources were significantly more accessible, readable, and offered greater transparency of authorship compared with reputable sources, despite their lack of expertise on face transplantation. Face transplant programs should update their websites to ensure readability and accessibility of the information provided to the public.

The Demographic Frame

The Demographic Frame is a comprehensive, secure, access-controlled database of person-level data consisting of demographic, social, and economic characteristics of individuals derived from census, survey, administrative, and third-party data sources. The Demographic Frame is facilitating innovations in data collection, processing, analyses, and products for census and survey programs.

Likelihood of COVID-19 Outbreaks in US Immigration and Customs Enforcement (ICE) Detention Centers, 2020‒2021.

Objectives. To determine facility-level factors associated with COVID-19 outbreaks in US Immigration and Customs Enforcement (ICE) detention centers. Methods. We obtained COVID-19 case counts at 88 ICE detention facilities from May 6, 2020, through June 21, 2021, from the COVID Prison Project. We obtained information about facility population size, facility type (dedicated to immigrants or mixed with other incarcerated populations), and facility operator (public vs private contractor) from third-party sources. We defined the threshold for a COVID-19 outbreak as a cumulative 3-week incidence of 10% or more of the detained population. Results. Sixty-three facilities (72%) had at least 1 outbreak. Facilities with any outbreak were significantly more likely to be privately operated (P < .001), to have larger populations (113 vs 37; P = .002), and to have greater changes in their population size over the study period (‒56% vs -26%; P < .001). Conclusions. Several facility-level factors were associated with the occurrence of COVID-19 outbreaks in ICE facilities. Public Health Implications. Structural and organizational factors that promote respiratory infection spread in ICE facilities must be addressed to protect detainee health. (Am J Public Health. 2024;114(9):909-912. https://doi.org/10.2105/AJPH.2024.307704).

Organizational resiliency through practice innovation: forced brand evolution in a prolonged exogenous service ecosystem disruption

PurposeThis paper aims to explore practice innovation and organizational resiliency during exogenous service ecosystem disruptions. This inquiry focuses on the extreme disruption caused by the COVID-19 pandemic, which required service firms to recodify long-established service scripts, adapt digital and physical material elements of the service encounter and ultimately reconfigure a system of practices. The specific context is forced practice innovation in Starbucks servicescape (kiosks and coffeehouses). Starbucks is best known for its custom beverages and third-place strategy. Their strict adherence to a complex service script and unique ordering practices altered during pandemic stay-home disease prevention mandates.Design/methodology/approachThematic coding consistent with prior research on practice innovation and diffusion and a grounded theory methodology was conducted. Data were triangulated and analyzed within and across a variety of sources. These include field notes from direct observation, interviews, focus groups, firm-authored collateral in the form of marketing communications and third-party authored secondary sources such as news, social media, blogs and forums.FindingsData reveal how practice innovation occurs through the reconfiguration of a system of practices, which support organizational resiliency and can force brand evolution, in prolonged exogenous service ecosystem disruptions. The COVID-19 pandemic required service industries to adapt and recodify service scripts and alter physical and digital elements of service encounters. While the pandemic affected all firms in the sector, we argue that Starbucks' established scripts and third-place strategies, which characterized the brand experience, were particularly vulnerable. We find that practice innovation occurs through the reconfiguration of practice elements – competences, meanings and materiality – and restructures the service encounter. Practice codification, transposition, adaptation and stabilization support organizational resiliency and brand evolution. We find that Starbucks' brand experience emphasis on the third place is reconceptualized from an in-person community-based retailscape to a platform-based strategy necessitating script recodification and practice adaptation. Our analysis of Starbucks' kiosks and coffeehouses illuminates how a distinctly branded service encounter is constituted by a system of practices that can be reconfigured and diffused anew in the face of disruption.Originality/valueThe conceptualization of practice innovation as systems reconfiguration establishes a novel approach to understanding innovation in service ecosystems. The COVID-19 pandemic is a unique context to study a sector-wide exogenous extended service disruption. We focus on a firm with an elaborate pre-pandemic service script and commitment to a third-place brand experience guiding its system of practices. We reveal unique insights on practice innovation within service ecosystems during exogenous prolonged disruptions in which brands evolve through the recodification of service scripts and sustained reconfiguration of systems of practice.

KG-CFSA: a comprehensive approach for analyzing multi-source heterogeneous social network knowledge graph

Analyzing opinions, extracting and modeling information, and performing network analysis in online information studies are challenging tasks with multi-source social network data. This complexity arises from the difficulty in harnessing data across various platforms and the absence of a unified data modeling approach. Although social network analysis has used a multiplex approach to study complex networks, no previous work has integrated data from multiple social networks, knowledge graph fusion, and contextual focal structure analysis (CFSA) for an online study. This study has developed a multi-source graph model and applied a Cartesian merge to model relations across multiple documents, entities, and topics. We improved the information modeled with third-party data sources such as WikiData and DiffBot. This approach has created a multiplex network instance for CFSA detection, incorporating topic-topic, entity-entity, and document-document models. We applied this method to a dataset from the Indo-Pacific region and identified 40,000 unique focal sets of influential topics, entities, and documents. The top sets discussed economics, elections, and policies such as the Indo-Pacific Economic Framework, Ekonomi baru, #NKRIHargaMati, #IndonesiaJaya, and the Xinjiang Supply Chain. Our model tracks information spread across multiple social media platforms and enhances the visibility of vital information using various relationships. The results underscore the effectiveness of KG-CFSA in contextualizing large-scale information from multiple sources.

FDNet: Imperceptible backdoor attacks via frequency domain steganography and negative sampling

Backdoor attacks against Deep Neural Networks (DNNs) have surfaced as a substantial and concerning security challenge. These backdoor vulnerabilities in DNNs can be introduced by third-party sources through maliciously manipulated training data. Existing backdoor attacks are primarily built on perturbation trigger patterns in the spatial domain, which makes practical deployment arduous due to the ease of detection by inspectors. Moreover, shortcut learning renders the backdoor network less robust against defense methods. This work advances an effective and adaptable approach to backdoor attacks, situated in the frequency domain. This methodology involves the incorporation of specific natural perturbations within the frequency domain of images. Remarkably, these introduced triggers yield minimal alterations in the image’s semantic content, rendering them nearly imperceptible to human observers. To evade detection by machine-based defenders, we introduce a new training paradigm that incorporates negative sampling techniques. This approach compels the neural network to learn richer differences as trigger patterns. We evaluate our attacks on popular convolutional neural networks, visual transformers, and MLP-Mixer models as well as four standard datasets including MNIST, CIFAR-10, GTSRB, and ImageNet. Experimental results demonstrate that the trained networks can be successfully injected with backdoors. Our attack methods exhibit remarkable efficacy, achieving high attack success rates in both All-to-one (near 100% on all datasets) and All-to-all (over 90% except on ImageNet) scenarios and also demonstrate robustness against contemporary state-of-the-art defense mechanisms. Furthermore, our work reveals that DNNs can capture discrepancies in the frequency components of images that are barely perceptible to humans.

DTT: An Example-Driven Tabular Transformer for Joinability by Leveraging Large Language Models

Many organizations rely on data from government and third-party sources, and those sources rarely follow the same data formatting. This introduces challenges in integrating data from multiple sources or aligning external sources with internal databases. Commercial database systems do not offer adequate support for integrating data from heterogeneous sources, and manual integration is both time-consuming and inefficient. State-of-the-art data integration approaches that rely on similarity functions and textual transformations often fail to handle challenging cases where multiple mappings are required, or the mappings go beyond simple textual transformations. In this paper, we study the potentials of deep neural models for transforming tables for joinability. In particular, we cast the problem as a prediction task and develop a framework that leverages large deep-learning language models to transform tabular data from a source formatting to a desired target representation. Our framework can efficiently learn the patterns for mapping a source formatting into an expected target using just a few examples, which can then be used for tasks such as table joining, filling in missing values, and error detection. Compared to state-of-the-art mapping and joining approaches, our framework delivers noticeably more accurate and scalable performance on both real-world and synthetic datasets. Our experimental evaluation also shows that the performance of the proposed framework using our fine-tuned model is at par or better than large language models such as GPT-3, despite the significant difference in size, and that using large language models within our framework improves their performance.

Epistemic Injustice at the ICC?

Abstract This article empirically assesses the role third-party evidence has played in the Office of the Prosecutor’s (OTP) initiation of an investigation into the Afghanistan Situation at the International Criminal Court (ICC). After finding that most of the evidence upon which the OTP relied in its request to initiate an investigation is from third parties, this article classifies and ranks the categories of third party upon which the OTP relied. Critically analysing the OTP’s use of third-party sources, this article uncovers the way potential biases towards dominant languages, affluent locations and prestigious institutions influence the choice of which information to use as evidence, possibly evincing an epistemic injustice at the OTP. Heeding Naz Modirzadeh’s call to use Third World Approaches to International Law (TWAIL) to empower Global South states, this article concludes with recommendations to the OTP and third parties on how they can maximize their collaboration while avoiding capitulation to the politics of exclusion. In addition, the article’s findings reveal opportunities for stakeholders to enhance evidence collection and use by particular third-party categories in Afghanistan and in similar situations.

Publicity’s Misinformation Problem

This paper argues that everyday practices crucial for ensuring politically engaged citizens such as sharing news articles or deliberating about potential laws can also be responsible for undermining the state’s efforts to publicize the law. Theorists view publicity—a requirement that laws should be public and accessible—as having crucial normative and practical importance in liberal democracy and, more broadly, in ensuring the rule of law. Due to egalitarian concerns, laws are often long, complex, and specific to ensure that street-level bureaucrats exercise low levels of discretion in applying the law. This—what I deem the institutional publicity problem—means that the law is so inaccessible that busy, everyday citizens must turn to third-party sources to understand policymaking. These intermediaries often make mistakes promulgating the law. Misinformation is hard to counteract, and pre-existing beliefs affect information acceptance. This all represents a behavioral publicity problem: morally and legally permissible actions can complicate and undermine reasonable efforts of citizens to learn about the law. I argue that the state is caught between a rock and a hard place. While there are benefits to having the state fight against misinformation, it also raises serious concerns about democratic engagement.

Cross-Calibration of HY-1D/COCTS Thermal Emissive Bands in the South China Sea

Haiyang-1D (HY-1D) is the second operational satellite in China’s Haiyang-1 series of satellites, carrying the Chinese Ocean Color and Temperature Scanner (COCTS) to provide ocean color and temperature observations. The radiometric calibration is a prerequisite to guarantee the quality of the satellite observations and the derived products, and the radiometric calibration of the thermal emissive bands of HY-1D/COCTS can effectively improve the accuracy of sea surface temperature (SST) derived from the thermal infrared data. In this paper, a study on the regional cross-calibration of the COCTS thermal emissive bands is conducted for high-accuracy SST observations in the South China Sea. The Visible Infrared Imaging Radiometer Suite (VIIRS) on board the NOAA-20 satellite launched by the National Oceanic and Atmospheric Administration (NOAA) is selected as the calibration reference sensor, and a double-difference cross-calibration method is used for HY-1D/COCTS thermal infrared brightness temperature (BT) evaluation. The results show that the bias of the 11 µm and 12 µm thermal emissive bands of COCTS and VIIRS in the South China Sea are 0.101 K and 0.892 K, respectively, and the differences in BTs between the two sensors show temperature dependence. The cross-calibration coefficients are obtained and used to correct the BT of the COCTS thermal emissive bands. The bias of the BT of the 11 µm and 12 µm bands of COCTS are about 0.01 K after cross-calibration. To further validate the results, COCTS post-calibration data were examined using the NOAA-20 Cross-track Infrared Sounder (CrIS) data as a third-party source. The BT is calculated with the spectral response functions of the COCTS thermal emissive bands using the convolution calculation of the CrIS hyperspectral region observations. The comparison shows a small bias between the post-calibration COCTS thermal emissive band observations and CrIS, which is consistent with the comparison between VIIRS and CrIS. The accuracy of the post-calibration COCTS thermal emissive band BT data in the South China Sea has been significantly improved.

Leveraging Salesforce Analytics for Enhanced Business Intelligence

Salesforce Analytics is a strong business intelligence (BI) solution that turns raw data into actionable insights. Today's data-driven world requires fast, reliable data analysis for business choices. Salesforce Analytics' broad range of solutions helps organizations use data for better decision-making, operational efficiency, and strategic planning. Integration of Salesforce Analytics into company processes has several benefits. First, it gives organizations real-time knowledge to adapt quickly to market developments and client requests. Organizations may track KPIs and trends using customisable dashboards, automatic reporting, and predictive analytics. Real-time visibility empowers decision-makers to prevent concerns from becoming major ones. Salesforce Analytics' unified data access and analysis platform improves departmental cooperation. Team members may easily exchange insights and reports, breaking down silos and promoting data-driven culture. On-demand report creation and sharing guarantees that all stakeholders have the same information, resulting in better aligned and informed decision-making. Customer relationship management is another important Salesforce Analytics function. The software analyzes touchpoint data to help companies understand their consumers. Businesses may detect client preferences, forecast behavior, and tailor marketing by using this data, improving customer happiness and loyalty. Salesforce's AI-powered analytics help foresee client wants and personalize offerings. Salesforce Analytics also improves forecasting and planning. Organizations may forecast future performance better by evaluating previous data and patterns. This skill is crucial in sales forecasting, inventory management, and financial planning, where precise projections ensure operational efficiency and profitability. Another benefit of Salesforce Analytics is third-party data integration. To get a complete picture of their operations, businesses may mix data from social media, email marketing, and e-commerce platforms. Integration improves strategic choices and corporate results by enabling more complete analysis. The platform's versatility lets analytics tools be customized to match corporate demands, boosting its BI solution value. Data security is crucial in the digital era, and Salesforce Analytics takes it seriously. The platform protects critical company data using encryption, access restrictions, and industry standards. Businesses may use their data confidently because of this security assurance. Finally, Salesforce Analytics improves business intelligence by offering real-time insights, collaboration, customer knowledge, and precise forecasting and planning. In today's fast-paced corporate world, its ability to interact with third-party data sources and secure data analysis makes it a useful tool. Salesforce Analytics will allow data-driven decision-making and strategic planning to help companies accomplish their objectives and expand sustainably as they navigate the contemporary market.

Global indicators of progress on climate change education: Non-state actor data collaboration for SDG 4

Despite their limitations as proxy measures, indicators can serve as leverage points in encouraging policy change. Facing the global urgency of climate change and increasingly recognizing the role of education in providing the social and political impetus for climate action, indicators on climate change education can help. However, despite the encompassing of climate change education in several Sustainable Development Goal (SDG) targets, the associated global indicators currently rely on country self-reported and otherwise inadequate data. This paper describes a collaborative process of developing publicly available third-party data sources to support more robust indicators of climate change education, including in relation to the SDGs.

Anti-money laundering and customer due diligence: empirical evidence from South Africa

PurposeThe purpose of this study is to examine the South African banks’ customer due diligence (CDD) practices in the fintech era to mitigate money laundering (ML) risks and ensure financial stability. Financial technologies have brought substantial transformations to the financial services sector. However, such technologies have exposed the sector to emerging risks that threaten the integrity and stability of the financial system globally. Before any bank–customer relationship is established, proper customer background checks must be conducted. These background checks enable financial institutions to validate information provided and ensure customers are properly risk profiled. Failure to risk profile customers could result in financial institutions being used as conduits for ML. Undoubtedly, CDD procedures are pivotal to overall anti-money laundering efforts and curbing financing terrorism in a regulatory framework.Design/methodology/approachA qualitative research approach was adopted to address the research questions of the study. Given the confidentiality associated with the financial services sector, data triangulation was used in blending mainly secondary and primary data sources. Secondary data sources used in the study were published reports available in the public domain that were corroborated with subject matter experts’ interviews.FindingsBased on the findings of this study, it is concluded that in South Africa, technological solutions have been incorporated into CDD functions, which is now risk-based (enhanced due diligence). Also, legally, South Africa has incorporated the biometrics, integration with Department of Home Affairs and Companies and Intellectual Property Commission databases, customer consent to third-party sources with the Financial Intelligence Centre Act and the Protection of Personal Information Act.Originality/valueThe shift towards digital banking in South Africa results in increased data and dynamic risk profiling. This study advocates a policy shift requiring a risk-based approach to mitigating emerging ML risks (in particular digital laundering), especially in the wake of South Africa’s recent greylisting by the Financial Action Task Force.

Third-Party Source Switches: Objective Valuation or Fair Value Opinion Shopping?

ABSTRACT Fair value estimates from external third-party sources are generally considered more reliable than internal estimates based on managerial inputs. However, even externally sourced estimates are subject to managerial opportunism, because firms can switch from one external source to another. In the context of life insurance companies that mostly rely on external sources, we posit that such source switches could be driven by managerial incentive either to faithfully report fair values (objective valuation) or to inflate estimates to avoid OTTI (opinion shopping). Our results support both motives. In instances in which the two incentives yield conflicting predictions for source switching, we find the opinion-shopping motive dominates. We also find that switches that increase fair value estimates are associated with a reduced OTTI likelihood and magnitude, especially for high-impairment-risk securities. On balance, our evidence suggests that opportunism with respect to source switching can compromise the reliability of externally sourced fair value estimates. Data Availability: Data are available from the public sources cited in the text. JEL Classifications: G22; M41.

Measuring the effects of electronic government services and corrupt practices on information exchange: a comparison of public and private hospitals

PurposeHealth Information Exchange (HIE) is essential for the efficient and cost-effective delivery of health-care services. The provider’s administrative structure and external environment can substantially influence adopting technologies involving inter-organizational linkages, such as HIE. Using the theoretical lens of institutional theory, this study aims to compare how public and private hospitals' engagement in HIE is influenced by corruption and government online services or e-government usage.Design/methodology/approachThe study uses the positivist research design of secondary data analysis to test the six hypotheses proposed. Data from multiple third-party reliable sources, including the European Commission and World Bank, are combined into the final dataset consisting of observations from 1,442 hospitals across 30 countries in Europe. A multilevel modeling approach is used to associate country and hospital-level variables and test the hypothesis.FindingsThe study finds that, on average, a 10% increase in corruption leads to a 6.3% decrease, while a 10% increase in e-government leads to a 7% increase in the probability of HIE engagement for a hospital. The negative impact of corruption on average is 18% more in public than private hospitals, while the positive impact of e-government is 75% stronger in public in comparison to private hospitals. The study also finds that HIE engagements in health systems with predominantly public hospitals are more sensitive to corruption and e-government.Originality/valueTo the best of the authors’ knowledge, the study is one of the first to use the institutional view to test the influence of government actions and public providers' concentration on HIE engagement. The comparison of public and private institutions enriches our understanding of promoters and inhibitors of HIE.

Support for resource management: The role of information sources and affinitive trust

Public trust in natural resources agencies is critical for successful resource management and can facilitate an environment of management innovation and experimentation. However, building public trust in natural resources management is often challenging, particularly in resource management systems where people may receive information from a variety of different sources. We used a mail survey to analyze the relationship between affinitive trust (a measure of perceived shared values), information source use, and overall support for management in marine fisheries in the United States. The results of our mixed linear model showed that higher levels of affinitive trust and more use of official sources were most strongly associated with higher levels of support for management. The use of website sources (e.g., fishing websites and blogs), third-party sources (e.g., fishing organizations, advocacy groups, tv, and bait or tackle shops), older age, and higher levels of income were most associated with lower levels of support. The results imply that engendering higher levels of affinitive trust by promoting institutional values may lead to higher support for management.

A practically feasible transfer learning method for deep-temporal urban change monitoring

ABSTRACT Neural networks have shown their potential to monitor urban changes with deep-temporal remote sensing data, which simultaneously considers a large number of observations within a given window. However, training these networks with supervision is a challenge due to the low availability of third-party sources with sufficient spatio-temporal resolution to label each window individually. To remedy this problem, we developed a novel approach utilizing transfer learning (TL) on a set of deep-temporal windows. We demonstrate that labelling of multiple windows simultaneously can be practically viable, even with a low amount of high spatial resolution third-party data. The overall process provides a trade-off between labour resources and the ability to train a network on existing systems, despite its intensive memory requirements. As a demonstration, an existing previously trained (pre-trained) network was used to transfer knowledge to a new target location. We demonstrate our method with combined Sentinel 1 and 2 observations for the area of Liège (Belgium) for the time period spanning 2017–2020. This is underpinned by our use of common metrics in machine learning and remote sensing, and in our discussion of selected examples. Three independent transfers of the same pre-trained model and their combination were carried out, all of which showed an improvement in terms of these metrics.

Leveraging Large Language Models (LLM) for the Plastic Surgery Resident Training: Do They Have a Role?

Introduction Large language models (LLMs) are designed for recognizing, summarizing, translating, predicting, and generating text-based content from knowledge gained from extensive data sets. ChatGPT4 (Generative Pre-trained Transformer 4) (OpenAI, San Francisco, California, United States) is a transformer-based LLM model pretrained on public data as well as data obtained from third-party sources using deep learning techniques of fine tuning and reinforcement learning from human feedback to predict the next text. We wanted to explore the role of LLM as a teaching assistant (TA) in plastic surgery. Material and Methods TA roles were first identified in available literature, and based on the roles, a list of suitable tasks was created where LLM could be used to perform the task. Prompts designed to be fed in to the LLM (specifically ChatGPT) to generate appropriate output, were then created and fed to the ChatGPT model. The outputs generated were scored by evaluators and compared for interobserver agreement. Results A final set of eight TA roles were identified where a LLM could be utilized to generate content. These contents were scored for usefulness and accuracy. These were scored independently by the eight study authors in a scoring sheet created for the study. Interobserver agreements for content accuracy, usefulness, and clarity were 100% for content generated for the following: interactive case studies (generation), simulation of preoperative consultations, and generation of ethical considerations. Discussion LLMs in general and ChatGPT (on which this study is based) in specific, can generate answers to questions and prompts based on huge amount of text fed into the model for training the underlying language model. The answers generated have been found to be accurate, readable, and even indistinguishable from human-generated text. This capability of automated content synthesis can be exploited to generate summaries to text, answer short and long answers, and generate case scenarios. We could identify a few such scenarios where the LLM could in general be utilized to play the role of a TA and aid plastic surgery residents in particular. In addition, these models could also be used by students to obtain feedback and gain reflection which itself stimulates critical thinking. Conclusion Incorporating LLMs into the educational arsenal of plastic surgery residency programs can provide a dynamic, interactive, and individualized learning experience for residents and prove to be worthy TAs of future.

EFFICIENCY OF USING RENEWABLE ENERGY SOURCES FOR AN AUTONOMOUS HEAT SUPPLY SYSTEM

Projects for the integrated use of renewable energy sources were considered. The efficiency of using renewable energy sources in autonomous heat supply systems with minimal use of third-party energy sources and reducing the specific cost of hourly heat supply was assessed. A new software and information complex for modeling autonomous heat supply systems with minimal use of third-party energy sources was developed. With the help of the developed software and information complex, the optimized operating modes of the autonomous heat supply system were modeled and numerical estimates of the efficiency of using renewable energy sources in the system were obtained.