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30 pages, 2444 KB  
Systematic Review
The Decentralized AI Ecosystem in Healthcare: A Systematic Review of Technologies, Governance, and Implementation
by Antonio Pesqueira, Carmen Cucul, Thomas Egelhof, Stephanie Fuchs, Leilei Tang, Natalia Sofia and Andreia de Bem Machado
Systems 2026, 14(4), 414; https://doi.org/10.3390/systems14040414 (registering DOI) - 9 Apr 2026
Abstract
This research examines the emerging ecosystem of models that are developed and run across a distributed network of computers called decentralized artificial intelligence. The focus is to understand these models in the healthcare context and with a focus on their core components: technologies, [...] Read more.
This research examines the emerging ecosystem of models that are developed and run across a distributed network of computers called decentralized artificial intelligence. The focus is to understand these models in the healthcare context and with a focus on their core components: technologies, governance frameworks, and real-world applications. A systematic literature review was conducted, analyzing peer-reviewed studies from PubMed, Scopus, and Web of Science to map the current landscape of the field. The primary objective was to synthesize the current research on decentralized approaches in healthcare, including core approaches like federated learning and blockchain-based AI models, as well as emerging concepts such as agentic AI blockchain-based AI models and DAOs, to comprehend their application in clinical and operational settings. The research assesses the maturity of these implementations, ranging from pilot programs to large-scale organizational settings. It also identified the key computational and technical methods and platforms used and the key benefits and challenges influencing their adoption. The findings underscore the pivotal role of the decentralized paradigm in addressing the fundamental limitations of traditional AI, including data privacy, trust, institutional silos, and regulatory complexity. Insights are also offered for healthcare providers, technology developers, researchers, and policymakers aiming to navigate and leverage decentralized AI to build more equitable, efficient, and collaborative healthcare systems. Full article
(This article belongs to the Special Issue Leveraging AI Algorithms to Enhance Healthcare Systems)
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13 pages, 4411 KB  
Article
Design and Implementation of High-Capacity DDR3 Micro-Module Based on 3D TSV Advanced Packaging
by Haoyue Ji, Liang Zeng, Hongwen Qian, Wenchao Tian, Jingjing Lin and Yuhe Duan
Micromachines 2026, 17(4), 459; https://doi.org/10.3390/mi17040459 (registering DOI) - 9 Apr 2026
Abstract
To meet the demands for miniaturization, lightweight design, and high performance in modern electronic systems, advanced 3D TSV technology enables a substantial increase in storage capacity even within physically constrained form factors. This paper proposes a schematic design methodology and system-level integrated modeling [...] Read more.
To meet the demands for miniaturization, lightweight design, and high performance in modern electronic systems, advanced 3D TSV technology enables a substantial increase in storage capacity even within physically constrained form factors. This paper proposes a schematic design methodology and system-level integrated modeling approach for a four-layer stacked micro-module based on wafer-level packaging. By leveraging heterogeneous chip fan-out technology and TSV-based vertical stacking, the fabricated DDR3 micro-module achieves a compact footprint of 14 × 9 × 3.5 mm, a storage capacity of 4 GB, and a 64-bit bus width. Compared to conventional board-level mounting, the module reduces the footprint area by 95%. Following comprehensive multi-level testing, the micro-module fully complies with standard protocol requirements, enabling a paradigm shift in form factors for mobile computing devices while enhancing computational density and energy efficiency in data center server applications. Full article
(This article belongs to the Special Issue Micro/Nano Manufacturing of Electronic Devices)
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33 pages, 2020 KB  
Article
Machine Learning, Thematic Feature Grouping, and the Magnificent Seven: A Forecasting Analysis
by Mirarmia Jalali, Mohammad Najand and Andrew Cohen
J. Risk Financial Manag. 2026, 19(4), 274; https://doi.org/10.3390/jrfm19040274 (registering DOI) - 9 Apr 2026
Abstract
This study examines the predictability of monthly excess returns for the “Magnificent Seven” U.S. technology firms using machine learning and economically motivated thematic feature grouping. Framed as a focused study of the most systemically consequential equity panel in modern markets—seven firms representing over [...] Read more.
This study examines the predictability of monthly excess returns for the “Magnificent Seven” U.S. technology firms using machine learning and economically motivated thematic feature grouping. Framed as a focused study of the most systemically consequential equity panel in modern markets—seven firms representing over 30% of the S&P 500—the analysis confronts a small-N, large-P environment where economically structured dimensionality reduction is essential. Using 154 firm-level characteristics categorized into 13 economic themes, we evaluate linear, penalized, tree-based, and neural network models in a small-N, large-P setting. Unrestricted models suffer substantial overfitting and fail to outperform the historical average benchmark out-of-sample. In contrast, theme-based models generate economically meaningful and regime-dependent predictive gains. Short-Term Reversal and seasonality exhibit stronger expansion-period predictability, while size and profitability perform better during recessions. Regularized linear models provide the most stable performance in limited-data environments, whereas nonlinear ensemble methods improve only when training windows are extended. The findings underscore the importance of economically structured dimensionality reduction and adaptive factor allocation in managing concentration risk among systemically important mega-cap firms. Full article
(This article belongs to the Section Financial Markets)
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16 pages, 303 KB  
Article
Virtual Reality and the Sense of Belonging Among Distance Learners: A Study on Peer Relationships in Higher Education
by David Košatka, Alžběta Šašinková, Markéta Košatková, Tomáš Hunčík and Čeněk Šašinka
Virtual Worlds 2026, 5(2), 17; https://doi.org/10.3390/virtualworlds5020017 (registering DOI) - 9 Apr 2026
Abstract
Distance learners in higher education are often assumed to face limited peer interaction, potentially weakening their sense of belonging. This study examines peer relationships and belonging among students in distance and blended university programs, with attention to the role of virtual reality (VR) [...] Read more.
Distance learners in higher education are often assumed to face limited peer interaction, potentially weakening their sense of belonging. This study examines peer relationships and belonging among students in distance and blended university programs, with attention to the role of virtual reality (VR) within digitally mediated learning environments. Immersive VR teaching is included in the curriculum for distance learning students in the studied programs. Using a mixed-methods design, survey data and open-ended responses were collected from 17 students in Information Studies and Information Service Design. An adapted Classroom Community Scale was supplemented with items addressing the perceived contribution of different communication technologies. Contrary to expectations, fully distance learners did not report weaker agreement with statements reflecting belonging than blended students; on several items, they expressed stronger agreement, particularly regarding perceived peer support and learning opportunities. Results indicate that conventional 2D communication tools, particularly chats and video calls, are central to sustaining peer relationships. VR was not perceived as essential but described by some students as an added value supporting shared experience and group cohesion. Overall, belonging emerges as a socio-technical achievement shaped by communication practices rather than physical proximity. Full article
71 pages, 3197 KB  
Systematic Review
Applications of Artificial Intelligence in Renewable Energy Transition: A Systematic Literature Review
by Shahbaz Ahmad Saadi, Dhanashree Katekhaye and Róbert Magda
Energies 2026, 19(8), 1839; https://doi.org/10.3390/en19081839 (registering DOI) - 9 Apr 2026
Abstract
The renewable energy transition is a central component of global strategies to mitigate climate change and achieve sustainable development. However, the large-scale integration of renewable energy sources introduces significant challenges related to variability, system complexity, and operational efficiency. In recent years, artificial intelligence [...] Read more.
The renewable energy transition is a central component of global strategies to mitigate climate change and achieve sustainable development. However, the large-scale integration of renewable energy sources introduces significant challenges related to variability, system complexity, and operational efficiency. In recent years, artificial intelligence (AI) has emerged as a promising enabler for addressing these challenges through advanced data-driven forecasting, optimization, and decision-support capabilities. This study presents a systematic bibliometric and thematic review of peer-reviewed research on AI applications in the renewable energy transition published between 2015 and 2025, and was conducted following the PRISMA framework. Using the Scopus database, a total of 595 journal articles were analyzed through bibliometric performance indicators, network analysis, and thematic synthesis. The results reveal a rapidly growing and highly collaborative research field, characterized by strong international co-authorship and increasing methodological diversity. Early research predominantly focused on prediction and forecasting tasks, while more recent studies emphasize system-level optimization, energy management, and integrative AI applications across renewable technologies. The review further highlights key research trends, conceptual framing, and methodological orientations shaping the field. By consolidating dispersed literature and mapping its evolution, this study provides a structured overview that supports future research, policy development, and practical implementation of AI-enabled solutions for a sustainable energy transition. Full article
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142 KB  
Abstract
Continuous Glucose Monitoring Technologies in Children with Type 1 Diabetes: An Integrative Review of Efficacy and Adherence over the Past Five Years
by Letícia G. L. Morais, Rafaela T. Cruvinel, Maria F. O. Melo, Heitor C. Souza, Alicy C. Cruvinel, Letícia G. Franco, Maria E. F. Andrade, Isabela M. Pereira, Sávio C. Souza and Helen D. S. C. Souza
Proceedings 2026, 137(1), 147; https://doi.org/10.3390/proceedings2026137147 (registering DOI) - 8 Apr 2026
Abstract
Introduction: Type 1 diabetes mellitus (DM1) is a common chronic autoimmune disease in childhood, characterized by absolute insulin deficiency [...] Full article
(This article belongs to the Proceedings of The 6th International Congress on Health Innovation—INOVATEC 2025)
29 pages, 3165 KB  
Review
Thermal and Dynamic Behavior of Anaerobic Digesters Under Neotropical Conditions: A Review
by Ricardo Rios, Nacari Marin-Calvo and Euclides Deago
Energies 2026, 19(8), 1838; https://doi.org/10.3390/en19081838 (registering DOI) - 8 Apr 2026
Abstract
Anaerobic digesters operating under neotropical conditions face significant technological constraints. High humidity, intense solar radiation, and pronounced diurnal temperature variations increase conductive, convective, and radiative heat losses. These factors reduce internal thermal stability and directly affect methane production rates and overall energy efficiency. [...] Read more.
Anaerobic digesters operating under neotropical conditions face significant technological constraints. High humidity, intense solar radiation, and pronounced diurnal temperature variations increase conductive, convective, and radiative heat losses. These factors reduce internal thermal stability and directly affect methane production rates and overall energy efficiency. As a result, thermal instability becomes a recurrent operational bottleneck in biogas plants without active temperature control. This review examines the thermal and dynamic behavior of anaerobic reactors from a process-engineering perspective. It integrates energy balances, heat-transfer mechanisms, and computational fluid dynamics (CFD) modeling. The combined effects of temperature gradients, hydrodynamic mixing patterns, and structural material properties are analyzed to determine their influence on thermal homogeneity, microbial stability, and methane yield consistency under mesophilic conditions. Technological strategies to mitigate thermal losses are evaluated. These include passive insulation using low-conductivity materials, geometry optimization supported by numerical modeling, and thermal recirculation schemes, as these factors govern temperature distribution and process resilience. Current limitations are also discussed, particularly the frequent decoupling between ADM1-based kinetic models and transient heat-transfer analysis. This separation restricts predictive capability under real-scale diurnal temperature oscillations. The development and validation of coupled hydrodynamic–thermal–biokinetic models under fluctuating neotropical boundary conditions are proposed as critical steps. Such integrated approaches can enhance operational stability, ensure consistent methane production, and improve energy self-sufficiency in organic waste valorization systems. Full article
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29 pages, 542 KB  
Article
Beyond FinTech Adoption: How AI-Enabled Financial Process Digitalization Shapes Entrepreneurship
by Konstantinos S. Skandalis and Dimitra Skandali
FinTech 2026, 5(2), 31; https://doi.org/10.3390/fintech5020031 (registering DOI) - 8 Apr 2026
Abstract
The digital transformation of entrepreneurial finance has progressed beyond basic FinTech adoption toward the deeper digitalization of financial processes and the integration of artificial intelligence (AI). Yet, firms, particularly non-financial SMEs, vary substantially in their ability to convert these technologies into superior entrepreneurial, [...] Read more.
The digital transformation of entrepreneurial finance has progressed beyond basic FinTech adoption toward the deeper digitalization of financial processes and the integration of artificial intelligence (AI). Yet, firms, particularly non-financial SMEs, vary substantially in their ability to convert these technologies into superior entrepreneurial, market, and financial outcomes. This study develops and tests a capability-based model explaining how FinTech-enabled financial process digitalization (FPD) and AI use shape entrepreneurship by influencing entrepreneurial performance outcomes. In line with current developments in digital finance, AI use is conceptualized as an embedded and complementary feature of FinTech-enabled financial process digitalization rather than an independent technological category. Drawing on the resource-based view and behavioral finance, we propose digital financial capability (DFC) as a central mechanism through which FinTech-enabled digitalized finance creates value, while credit fear is conceptualized as a behavioral constraint that limits entrepreneurial outcomes. We further posit customer satisfaction as a market-facing outcome linking financial capabilities to firm performance. Using survey data from 318 non-financial SMEs operating in Greece and applying Partial Least Squares Structural Equation Modeling (PLS-SEM), the findings show that FPD and AI use significantly enhance DFC, which in turn increases customer satisfaction and entrepreneurial performance. In addition, financial process digitalization reduces credit fear, thereby mitigating its negative impact on entrepreneurial performance. By shifting the focus from technology adoption toward AI-supported capability development within digitally enabled financial processes and behavioral mechanisms, this study advances FinTech and entrepreneurship research and offers actionable insights for managers and policymakers seeking to leverage digital finance for sustainable entrepreneurial value creation. Full article
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25 pages, 1238 KB  
Article
Optimisation of Photovoltaic Generation and Energy Storage Systems in Portuguese Semi-Detached Households in Social-Housing Neighbourhoods to Mitigate Energy Poverty
by João M. P. Q. Delgado and Bárbara P. Costa
Appl. Sci. 2026, 16(8), 3657; https://doi.org/10.3390/app16083657 (registering DOI) - 8 Apr 2026
Abstract
The building sector is responsible for 40% of CO2 emissions in Portugal, making the integration of renewable energy systems increasingly relevant. Photovoltaic (PV) technologies have become more accessible due to declining levelized costs of energy, and when coupled with battery energy storage [...] Read more.
The building sector is responsible for 40% of CO2 emissions in Portugal, making the integration of renewable energy systems increasingly relevant. Photovoltaic (PV) technologies have become more accessible due to declining levelized costs of energy, and when coupled with battery energy storage systems (BESSs), they can enhance grid independence, reduce household energy expenses, and mitigate peak load stress. However, high upfront costs still limit adoption, particularly among vulnerable communities. This study evaluates the technical, economic, and environmental performance of PV systems, with and without BESSs, compared with an existing solar thermal configuration in a social-housing neighbourhood in Porto, Portugal. Numerical simulations were conducted for three scenarios, optimising system sizing and ensuring hourly energy flow balance between generation, storage, and grid supply. Results indicate that all configurations are technically feasible within Porto’s climate conditions, though with distinct investment needs, payback periods, and CO2 reduction outcomes. The findings offer practical guidance for designing renewable energy solutions tailored to social housing, supporting both decarbonization goals and long-term mitigation of energy poverty. Full article
(This article belongs to the Special Issue Energy Transition in Sustainable Buildings)
26 pages, 1171 KB  
Article
Evaluation Model for Determining the Level of E-Commerce Development in Romania Within the European Context, Using Advanced Data Mining and Artificial Intelligence (AI) Techniques
by Costel-Iliuță Negricea, Cristina Coculescu, Ana Maria Mihaela Iordache, Laura Daniela Roșca and Alexandru Dan Smedescu
J. Theor. Appl. Electron. Commer. Res. 2026, 21(4), 115; https://doi.org/10.3390/jtaer21040115 (registering DOI) - 8 Apr 2026
Abstract
In recent years, the e-commerce sector has undergone continuous adaptation to both consumer needs and the economic context. This adaptation is driven by technological advances and the development of new software products. The present study aimed at achieving two primary objectives. First, it [...] Read more.
In recent years, the e-commerce sector has undergone continuous adaptation to both consumer needs and the economic context. This adaptation is driven by technological advances and the development of new software products. The present study aimed at achieving two primary objectives. First, it sought to assess the current state of e-commerce development in Romania within the broader European context. Second, it identified the use of AI-driven automation as a potential strategy for improving e-commerce in the country. To this end, e-commerce indicators were extracted from the questionnaire “ICT Usage and E-commerce in Enterprises,” which was conducted by National Statistical Authorities and centralized at the level of the European Commission. The questionnaire was carried out on a sample of 157,000 companies. A range of sophisticated techniques were employed to these indicators with the aim of reducing their dimensionality and classification error, with the objective of achieving a robust classification with the lowest possible error rate. We then proceeded to analyze Romania’s position in this ranking and, given the structure of e-commerce companies in the country, proposed the use of AI-driven automation as a potential strategy for enhancing activity in this sector. Full article
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38 pages, 519 KB  
Review
Advancements in CO2 Capture and Storage: Technologies, Performance, and Strategic Pathways to Net-Zero by 2050
by Ahmed A. Bhran and Abeer M. Shoaib
Materials 2026, 19(8), 1497; https://doi.org/10.3390/ma19081497 - 8 Apr 2026
Abstract
In order to reach net-zero by 2050, we need to have strong decarbonization policies, especially in hard-to-abate clean-ups like steel (8% of the global emissions), cement (7%), and power generation (30%), and negative emissions through direct air capture (DAC) and bioenergy with carbon [...] Read more.
In order to reach net-zero by 2050, we need to have strong decarbonization policies, especially in hard-to-abate clean-ups like steel (8% of the global emissions), cement (7%), and power generation (30%), and negative emissions through direct air capture (DAC) and bioenergy with carbon capture and storage (BECCS). This review paper summarizes the progress in CO2 capture, compression, transportation, and storage technologies between 2020 and 2025, including energy penalty (20–40%) and cost (15–30%) reductions, with innovations such as metal–organic frameworks (MOFs), bio-inspired catalysts, ionic liquids, and artificial intelligence (AI)-based optimization. This paper, as a new input into the carbon capture and storage (CCS) field, uses the Weighted Sum Model (WSM) as a multi-criteria decision-making tool to rank the best technologies in the capture, storage, monitoring, and transportation sectors. The weights of the criteria are calculated based on Shannon entropy, and the assessment is performed in three conditions, namely, optimistic, pessimistic, and expected. The weights are computed with sensitivity analysis to make the assessment robust. The viability of key projects, such as Northern Lights (Norway, 1.5 MtCO2/year), Porthos (The Netherlands, 2.5 MtCO2/year), Quest (Canada, 1 MtCO2/year), and Petra Nova (USA, 1.6 MtCO2/year), is evident, and it is projected that, globally, CCS will reach 49 MtCO2/year across 43 plants in 2025. The review incorporates socio-economic and environmental justice, including barriers such as high costs ($30–600/MtCO2), energy penalties (1–10 GJ/tCO2), and opposition between people (20–40% in EU/US). In comparison with previous reviews, this article has a more comprehensive focus, provides quantitative synthesis through WSM, and discusses the implications for researchers, policymakers, and stakeholders towards achieving faster CCS implementation on the path to net-zero. Full article
(This article belongs to the Section Energy Materials)
24 pages, 955 KB  
Systematic Review
Telemedicine and 5G Technologies: A Systematic Global Review of Applications over the Past Decade
by Alessandra Franco, Francesca Angelone, Danilo Calderone, Alfonso Maria Ponsiglione, Maria Romano, Carlo Ricciardi and Francesco Amato
Bioengineering 2026, 13(4), 438; https://doi.org/10.3390/bioengineering13040438 (registering DOI) - 8 Apr 2026
Abstract
This systematic review analyzes how the introduction and progressive deployment of 5G networks have influenced the evolution of telemedicine between 2014 and 2024, focusing on their impact on performance, accessibility, and the feasibility of advanced clinical applications across the pre-COVID-19, COVID-19, and post-COVID-19 [...] Read more.
This systematic review analyzes how the introduction and progressive deployment of 5G networks have influenced the evolution of telemedicine between 2014 and 2024, focusing on their impact on performance, accessibility, and the feasibility of advanced clinical applications across the pre-COVID-19, COVID-19, and post-COVID-19 periods. The review was conducted in accordance with PRISMA guidelines and included publications retrieved from SCOPUS, PubMed, and Web of Science using a PICO-based search strategy. Studies were selected based on predefined inclusion and exclusion criteria, and extracted data included clinical parameters, network characteristics such as bandwidth and latency, geographic setting, and type of telemedicine service. A total of 45 studies met the inclusion criteria, with most published between 2020 and 2024. The most frequently reported applications were telediagnosis, particularly robotic ultrasound, followed by telesurgery and teleconsultation. The low latency enabled by 5G networks supported complex telesurgical procedures over distances exceeding 5000 km, while in ultra-remote areas, hybrid solutions combining 5G and fiber-optic networks were often adopted to ensure stable connections. The integration of robotic platforms and AI-based tools further enhanced the precision and reliability of remote procedures. Overall, 5G technology has significantly advanced telemedicine by enabling real-time, high-quality care over long distances, improving access to specialist services and supporting more equitable and efficient digital healthcare delivery, particularly in underserved regions. Full article
(This article belongs to the Section Biosignal Processing)
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17 pages, 2087 KB  
Review
Tackling Paediatric Dynapenia: AI-Guided Neuromuscular Active Break Model for Early-Year Primary School Students
by Andrew Sortwell, Carmel Mary Diezmann, Rodrigo Ramirez-Campillo and Aron J. Murphy
Appl. Sci. 2026, 16(8), 3654; https://doi.org/10.3390/app16083654 - 8 Apr 2026
Abstract
School-based neuromuscular training interventions have the potential to mitigate dynapenia in the paediatric population and enhance movement skill outcomes; however, translating research into practice in primary school settings has been slow due to the expertise and professional learning required for implementation. This review [...] Read more.
School-based neuromuscular training interventions have the potential to mitigate dynapenia in the paediatric population and enhance movement skill outcomes; however, translating research into practice in primary school settings has been slow due to the expertise and professional learning required for implementation. This review describes the new teacher-supported intervention ‘Kids Innovative Neuromuscular Enhancement & Teacher-supported Instructional Coaching with AI’ (Kinetic AI) and presents evidence supporting its use in primary school settings. The Scale for the Assessment of Narrative Review Articles (SANRA) was used to guide the narrative and conceptual review methodology employed to synthesise peer-reviewed literature on paediatric dynapenia, school-based neuromuscular training, and AI technology-supported instructional models. This synthesis informed the development of a conceptual approach to neuromuscular training delivery in primary schools. The newly developed Kinetic AI conceptual model provides a pathway to embed neuromuscular training within active class breaks, offering adaptive feedback and targeted teacher support to facilitate implementation. This approach has the potential to bridge gaps between research, access, and practice. The Kinetic AI application is designed to support children’s muscular fitness and movement skills through school-based neuromuscular training, while addressing barriers to research translation and teacher expertise. When applied during school breaks, this approach has the potential to reduce the risk of dynapenia and contribute to scalable improvements in paediatric health and wellbeing. Full article
(This article belongs to the Special Issue Children's Exercise Medicine: Bridging Science and Healthy Futures)
27 pages, 3191 KB  
Article
Business Process Optimization for a Greener Future: The Russian Experience in Operational Management
by Nadezhda Shmeleva, Tatyana Tolstykh, Tatiana Guseva, Tatiana Khoroshilova and Denis Lazarenko
Sustainability 2026, 18(8), 3691; https://doi.org/10.3390/su18083691 - 8 Apr 2026
Abstract
The relevance of this study is driven by the need to develop new mechanisms and tools aimed at improving the technological, resource, and economic efficiencies of industrial businesses while minimizing their negative environmental impacts and enhancing their environmental performances. Although such approaches as [...] Read more.
The relevance of this study is driven by the need to develop new mechanisms and tools aimed at improving the technological, resource, and economic efficiencies of industrial businesses while minimizing their negative environmental impacts and enhancing their environmental performances. Although such approaches as the theory of constraints, the concept of sustainable development, and principles of Best Available Techniques have garnered attention individually, their combined, interdisciplinary application to the streamlining of business processes in industry has not yet been fully explored. The purpose of this study is to demonstrate the advisability of managing business processes based on the principles of resource efficiency enhancement by preventing irrational resource consumption, production losses, pollution and waste in the context of the Sustainable Development Goals. This article analyzes the current state of research business process optimization for a greener future. The proposed methodological approach is based on ranking business processes according to their levels of resource efficiency. Business process engineering and an evaluation of its outcomes in terms of resource efficiency were conducted using a case study of a building materials manufacturer in Northwest Russia. Various business management scenarios were developed to improve resource efficiency through process engineering initiatives. The findings of this study can inform the development of strategic approaches for building materials manufacturers as they transition toward sustainable development. Full article
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26 pages, 3491 KB  
Article
Alternative Energy Source Integration in Medium-Capacity Gas Boiler Plant in Latvian Climate Conditions: Case Study for 6.38 MW Plant Servicing a Residential District
by Jānis Jākobsons, Filips Kukšinovs, Kristina Ļebedeva, Aleksandrs Zajacs and Jeļena Tihana
Energies 2026, 19(8), 1836; https://doi.org/10.3390/en19081836 - 8 Apr 2026
Abstract
One of the main goals of heat and electricity producers in Latvia is to reduce the use of fossil fuels and introduce alternative fuel types that could help in reducing carbon dioxide emissions. This work focuses on addressing the set issue for a [...] Read more.
One of the main goals of heat and electricity producers in Latvia is to reduce the use of fossil fuels and introduce alternative fuel types that could help in reducing carbon dioxide emissions. This work focuses on addressing the set issue for a medium-capacity automated gas boiler plant, which provides heat for a local residential district. The following solutions were selected for boiler plant optimization: an electric boiler, a heat storage system, and solar collectors. Operating mode simulations were conducted for the electric boiler and solar collectors using Excel and Polysun (Standard) software. Simulations were created based on energy resource demand data obtained from a residential district located in Latvia and local energy resource prices/heat energy tariffs for the year 2024. The results from the simulations were used for technical and economic calculations to determine the payback period of the project. The electric boiler, together with the thermal energy storage tank and solar collectors, can produce 5903.04 MWh/year (~70% of local district heat demand) of thermal energy. This reduces the CO2 emissions of the boiler plant by at least 1186.51 tCO2 per year, which, at an emission quota price of 63.80 EUR/tCO2, allows for savings of 75,699.34 EUR per year (12.82 EUR/MWh heat energy). The project’s discounted payback period is 4.12 years, considering the reduction in the cost of the CO2 emission quota. The results of this study show that the chosen technologies are straightforward solutions that can be used to optimize existing boiler plants with limited space and can provide financial benefits to heat energy producers. Full article
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