The current energy situation in Ukraine is researched, considering the analysis of damages from missile attacks during the war. One of the ways to improve Ukrainian energy security is defined as decentralization of energy resources comprised with digitalization, decarbonization, democratization and deregulation (5D) increasing flexibility of the sector. The objectives are to examine in detail the 5D strategy. Distributed renewable energy sources are defined within the frame of Energy Community (EC) which was developed to adapt to current shortages in Ukrainian centralized grid. The EC digitalization is based on Digital Twin (DT) concept targeting design, monitoring, operational modifications and developing recommendations for the stable and optimal operation of photovoltaic systems. Energy community development and optimization include technical, meteorological, social, financial and environmental factors. Criterion programming was chosen as an effective solution to complex optimization problems. Sensitivity theory application is the basis for simplifying and optimizing the process of EC management. A model energy community 'shelter city' was created. The article highlights the problem of PV modules biological corrosion and suggests ways to eliminate it. In cases when biological corrosion cannot be avoided, approaches reducing its harmful effects are proposed to help during the design stage of a PV plant.
- Keywords
- 5D strategy of energy transition, Biological corrosion, Digital twin, Energy community, Photovoltaic system, Sensitivity analyses,
- Publication type
- Journal Article MeSH
The systematic targeting of medical infrastructure, personnel, and casualty evacuation routes by Russia in Ukraine challenges whether the North Atlantic Treaty Organization (NATO) can still rely on the Geneva Conventions for the protection of medical assets and personnel. This necessitates a thorough review of NATO Standardization Agreements (STANAGs) and doctrine used for Medical Planning for future Large Scale Combat Operations (LSCO). However, drawing lessons from the Ukrainian experience requires consideration of cultural context, medical evidence, and human factors. Ukraine's military medical system is burdened by its inherited Soviet-era doctrine, which was centralized and resistant to change. Initial reforms, aided by foreign assistance, aimed to modernize this system, emphasizing decentralized medical supply and improved training. However, the ongoing conflict has revealed the persistence of cultural issues, such as false reporting, lack of critical thinking, poor accountability, and resistance to change. Negative experiences can lead to abandonment of proven medical interventions, and without a comprehensive trauma registry, the effectiveness of the military medical system cannot be assessed with certainty. Frontline medical personnel often face high attrition and lack comprehensive command and control, further exacerbating challenges in delivering effective medical support. Despite these challenges to human factors, examples of innovative problem-solving exist. These solutions will be lost if not put into peer-reviewed doctrine or shared in a formal process. Previous NATO engagements assumed air-dominance and safe casualty evacuation and treatment across all echelons of care. The Ukrainian battlefield has shown that a near-peer adversary willing to systematically target medical units and the casualty evacuation system has catastrophic effects on military and civilian healthcare. Near-total reliance on ground-based evacuation platforms has forced Ukrainians to repurpose a large variety of nonstandard vehicles for casualty evacuation, as military ambulances were destroyed and most tracked/armored vehicles are prioritized for combat operations. Future NATO doctrine should emphasize mine-drone-resistant evacuation platforms with versatile (electronic) countermeasures. With the destruction of critical medical facilities, coupled with a high operational tempo and a massive influx of battlefield casualties, conventional triage models had to be abandoned, and casualties may remain in the prehospital setting for hours or even days. Point-of-injury stabilization has, therefore, taken on an even greater role than envisioned in NATO doctrine, and Ukrainian medical personnel adopted high-mobility approaches, such as delivery of lifesaving materials using drones. As medical treatment facilities came under attack, Ukrainians have resorted to distributed and hidden "micro-hospitals" and highly mobile surgical teams to avoid detection. The logistical challenges faced by Ukraine's medical system exposed weaknesses in NATO's approach to medical supply chains, requiring a more decentralized and on-demand medical supply model. The high level of civil-military medical coordination seen in Ukraine is far beyond what NATO doctrine currently envisions. Planning capabilities and tactical decision-making should be included in doctrine and in the curriculum of all medical unit leaders, with guiding frameworks that balance overall operational goals, personal safety, triage, and timely delivery of care. Co-development using validated "lessons learned" from Ukraine can provide a reliable roadmap for strategic reform.
- Publication type
- Journal Article MeSH
Access to clean water remains a critical global challenge, particularly in under-resourced regions. This study introduces an autonomous water treatment system leveraging Industry 4.0 technologies, including advanced smart sensors, real-time monitoring, and automation. The system employs a multi-stage filtration process-mechanical, chemical, and UV sterilization-to treat water with varying contamination levels. Smart sensors play a pivotal role in ensuring precise control and adaptability across the entire process. Experimental validation was conducted on three water types: pond, river, and artificially contaminated water. Results revealed significant reductions in key contaminants such as PPM, pH, and electrical conductivity, achieving water quality standards set by the WHO. Statistical analyses confirmed the system's reliability and adaptability under diverse conditions. These findings underscore the potential of smart, sensor-integrated, decentralized water treatment systems to effectively address global water security challenges. Future research could focus on scalability, renewable energy integration, and long-term operational durability to enhance applicability in remote areas.
- Keywords
- Industry 4.0, autonomous water treatment, decentralized solutions, smart sensors, water quality,
- Publication type
- Journal Article MeSH
The integration of Electric Vehicles (EVs) into power grids introduces several critical challenges, such as limited scalability, inefficiencies in real-time demand management, and significant data privacy and security vulnerabilities within centralized architectures. Furthermore, the increasing demand for decentralized systems necessitates robust solutions to handle the growing volume of EVs while ensuring grid stability and optimizing energy utilization. To address these challenges, this paper presents the Demand Response and Load Balancing using Artificial intelligence (DR-LB-AI) framework. The proposed framework leverages Artificial intelligence (AI) for predictive demand forecasting and dynamic load distribution, enabling real-time optimization of EV charging infrastructure. Furthermore, Blockchain technology is employed to facilitate decentralized, secure communication, ensuring tamper-proof energy transactions while enhancing transparency and trust among stakeholders. The DR-LB-AI framework significantly enhances energy distribution efficiency, reducing grid overload during peak periods by 20%. Through advanced demand forecasting and autonomous load adjustments, the system improves grid stability and optimizes overall energy utilization. Blockchain integration further strengthens security and privacy, delivering a 97.71% improvement in data protection via its decentralized framework. Additionally, the system achieves a 98.43% scalability improvement, effectively managing the growing volume of EVs, and boosts transparency and trust by 96.24% through the use of immutable transaction records. Overall, the findings demonstrate that DR-LB-AI not only mitigates peak demand stress but also accelerates response times for Load Balancing, contributing to a more resilient, scalable, and sustainable EV charging infrastructure. These advancements are critical to the long-term viability of smart grids and the continued expansion of electric mobility.
- Keywords
- Artificial intelligence, Blockchain, Demand response, EV charging stations, Load balancing,
- Publication type
- Journal Article MeSH
Biomedicine today is experiencing a shift towards decentralized data collection, which promises enhanced reproducibility and collaboration across diverse laboratory environments. This inter-laboratory study evaluates the performance of biocytometry, a method utilizing engineered bioparticles for enumerating cells based on their surface antigen patterns. In centralized and aggregated inter-lab studies, biocytometry demonstrated significant statistical power in discriminating numbers of target cells at varying concentrations as low as 1 cell per 100,000 background cells. User skill levels varied from expert to beginner capturing a range of proficiencies. Measurement was performed in a decentralized environment without any instrument cross-calibration or advanced user training outside of a basic instruction manual. The results affirm biocytometry to be a viable solution for immunophenotyping applications demanding sensitivity as well as scalability and reproducibility and paves the way for decentralized analysis of rare cells in heterogeneous samples.
- MeSH
- Single-Cell Analysis * methods MeSH
- Immunophenotyping methods MeSH
- Laboratories standards MeSH
- Humans MeSH
- Flow Cytometry methods MeSH
- Reproducibility of Results MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
PURPOSE: To compare 3 capsulotomy centration methods. SETTING: Private clinic, Zlin, Czech Republic. DESIGN: Prospective, consecutive case series. METHODS: 180 eyes undergoing cataract surgery had anterior capsule staining with microfiltered 0.4% trypan blue solution before selective laser capsulotomy. The first 60 eyes (Group 1) had mydriatic dilated pupil centered capsulotomies. The next 60 eyes (Group 2) were centered on the trypan blue central landmark (TCL). The final 60 capsulotomies (Group 3) were centered on the patient fixated coaxial Purkinje reflex (CPR). Measurements between key anatomical landmarks and the TCL, CPR capsulotomies, and implanted intraocular lens (IOL) center were made. RESULTS: The TCL, observed in >94% of eyes in the study, coincided with the CPR with a displacement of <0.1 ± 0.1 mm. Group 1 capsulotomies were noticeably decentered on the IOLs by 0.3 ± 0.2 mm. The Group 2 symmetrical IOL relationship was maintained with a decentration of 0.15 ± 0.1 mm. Group 3 had a similar decentration with the IOLs with 0.15 ± 0.1 mm. Verification with IOLMaster 700 data and CALLISTO Eye System showed that the CPR and the TCL were coincident with the measured visual axis. CONCLUSIONS: The clearly visible TCL served as an alternate landmark to the patient fixated CPR, and being on the anterior capsule was not sensitive to tilt. Further patient compliance was not required. Both were superior to dilated pupil centration, to achieve symmetric IOL coverage. This has application for both capsulotomies and capsulorhexes.
- MeSH
- Anatomic Landmarks MeSH
- Coloring Agents * administration & dosage MeSH
- Capsulorhexis * methods MeSH
- Phacoemulsification * MeSH
- Lens Implantation, Intraocular MeSH
- Middle Aged MeSH
- Humans MeSH
- Lens Capsule, Crystalline surgery MeSH
- Anterior Capsule of the Lens surgery MeSH
- Prospective Studies MeSH
- Aged, 80 and over MeSH
- Aged MeSH
- Trypan Blue * MeSH
- Check Tag
- Middle Aged MeSH
- Humans MeSH
- Male MeSH
- Aged, 80 and over MeSH
- Aged MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Comparative Study MeSH
- Names of Substances
- Coloring Agents * MeSH
- Trypan Blue * MeSH
For the past decade, there has been a significant increase in customer usage of public transport applications in smart cities. These applications rely on various services, such as communication and computation, provided by additional nodes within the smart city environment. However, these services are delivered by a diverse range of cloud computing-based servers that are widely spread and heterogeneous, leading to cybersecurity becoming a crucial challenge among these servers. Numerous machine-learning approaches have been proposed in the literature to address the cybersecurity challenges in heterogeneous transport applications within smart cities. However, the centralized security and scheduling strategies suggested so far have yet to produce optimal results for transport applications. This work aims to present a secure decentralized infrastructure for transporting data in fog cloud networks. This paper introduces Multi-Objectives Reinforcement Federated Learning Blockchain (MORFLB) for Transport Infrastructure. MORFLB aims to minimize processing and transfer delays while maximizing long-term rewards by identifying known and unknown attacks on remote sensing data in-vehicle applications. MORFLB incorporates multi-agent policies, proof-of-work hashing validation, and decentralized deep neural network training to achieve minimal processing and transfer delays. It comprises vehicle applications, decentralized fog, and cloud nodes based on blockchain reinforcement federated learning, which improves rewards through trial and error. The study formulates a combinatorial problem that minimizes and maximizes various factors for vehicle applications. The experimental results demonstrate that MORFLB effectively reduces processing and transfer delays while maximizing rewards compared to existing studies. It provides a promising solution to address the cybersecurity challenges in intelligent transport applications within smart cities. In conclusion, this paper presents MORFLB, a combination of different schemes that ensure the execution of transport data under their constraints and achieve optimal results with the suggested decentralized infrastructure based on blockchain technology.
- Keywords
- Agents, Blockchain, Cloud, MORFLB, Self-autonomous vehicle, Training,
- Publication type
- Journal Article MeSH
The deployment of optical network infrastructure and development of new network services are growing rapidly for beyond 5/6G networks. However, optical networks are vulnerable to several types of security threats, such as single-point failure, wormhole attacks, and Sybil attacks. Since the uptake of e-commerce and e-services has seen an unprecedented surge in recent years, especially during the COVID-19 pandemic, the security of these transactions is essential. Blockchain is one of the most promising solutions because of its decentralized and distributed ledger technology, and has been employed to protect these transactions against such attacks. However, the security of blockchain relies on the computational complexity of certain mathematical functions, and because of the evolution of quantum computers, its security may be breached in real-time in the near future. Therefore, researchers are focusing on combining quantum key distribution (QKD) with blockchain to enhance blockchain network security. This new technology is known as quantum-secured blockchain. This article describes different attacks in optical networks and provides a solution to protect networks against security attacks by employing quantum-secured blockchain in optical networks. It provides a brief overview of blockchain technology with its security loopholes, and focuses on QKD, which makes blockchain technology more robust against quantum attacks. Next, the article provides a broad view of quantum-secured blockchain technology. It presents the network architecture for the future research and development of secure and trusted optical networks using quantum-secured blockchain. The article also highlights some research challenges and opportunities.
- Keywords
- attacks, blockchain, optical networks, quantum key distribution, quantum-secured blockchain, security,
- Publication type
- Journal Article MeSH
Dealing with the islanded operation of a microgrid (MG), the micro sources must cooperate autonomously to regulate the voltage and frequency of the local power grid. Droop controller-based primary control is a method typically used to self-regulate voltage and frequency. The first problem of the droop method is that in a steady state, the microgrid's frequency and voltage deviate from their nominal values. The second concerns the power-sharing issue related to mismatched power line impedances between Distribution Generators (DGs) and MGs. A Secondary Control Unit (SCU) must be used as a high-level controller for droop-based primary control to address the first problem. This paper proposed a decentralized SCU scheme to deal with this issue using optimized PI controllers based on a Genetic Algorithm (GA) and Artificial Neural Networks (ANNs). The GA provides the appropriate adjustment parameters for all adopted PI controllers in the primary control-based voltage and current control loops and SCU-based voltage and frequency loops. ANNs are additionally activated in SCUs to provide precise online control parameter modification. In the proposed control structure, a virtual impedance method is adopted in the primary control scheme to address the power-sharing problem of parallel DGs. Further, in this paper, one of the main objectives includes electricity transmission over long distances using Low-Voltage DC Transmission (LVDCT) systems to reduce power losses and eradicate reactive power problems. Voltage Source Inverters (VSIs) are adopted to convert the DC electrical energy into AC near the consumer loads. The simulation results illustrated the feasibility of the proposed solutions in restoring voltage and frequency deviations, reducing line losses, as well as achieving active and reactive power sharing among the DGs connected to the MG.
- Keywords
- artificial neural network, distribution generators, genetic algorithm, microgrid, power sharing, secondary control, virtual impedance,
- MeSH
- Electric Impedance MeSH
- Electricity * MeSH
- Neural Networks, Computer MeSH
- Computer Simulation MeSH
- Electric Power Supplies * MeSH
- Publication type
- Journal Article MeSH
Electronic Health Record (EHR) systems currently in use are not designed for widely interoperable longitudinal health data. Therefore, EHR data cannot be properly shared, managed and analyzed. In this article, we propose two approaches to making EHR data more comprehensive and FAIR (Findable, Accessible, Interoperable, and Reusable) and thus more useful for diagnosis and clinical research. Firstly, the data modeling based on the LinkML framework makes the data interoperability more realistic in diverse environments with various experts involved. We show the first results of how diverse health data can be integrated based on an easy-to-understand data model and without loss of available clinical knowledge. Secondly, decentralizing EHRs contributes to the higher availability of comprehensive and consistent EHR data. We propose a technology stack for decentralized EHRs and the reasons behind this proposal. Moreover, the two proposed approaches empower patients because their EHR data can become more available, understandable, and usable for them, and they can share their data according to their needs and preferences. Finally, we explore how the users of the proposed solution could be involved in the process of its validation and adoption.
- Keywords
- Distributed electronic health records, FAIR principles, HL7 FHIR, bio-data management, ontology,
- MeSH
- Data Management MeSH
- Electronic Health Records * MeSH
- Humans MeSH
- Semantic Web * MeSH
- Software MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
