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BACKGROUND: Advances in plasma biomarkers to detect Alzheimers disease (AD) biology allows researchers to improve the efficiency of participant recruitment into preclinical trials. Recently, protein levels of plasma amyloid-beta and tau proteins have been shown to be predictive of elevated amyloid in brain. Online registries, such as the Alzheimers Prevention Trials (APT) Webstudy, include and follow participants using remote assessments to facilitate efficient screening and enrollment of large numbers of individuals who may be at higher risk for AD. OBJECTIVES: The AlzMatch Pilot Study investigated the feasibility of recruiting individuals from an online registry for blood sample collection at community-based phlebotomy centers and plasma biomarker quantification to assess an individuals eligibility for AD preclinical trials. DESIGN: Pilot feasibility study with co-primary outcomes. SETTING: This pilot feasibility study included participants from the APT Webstudy, the remote assessment arm of the Trial-ready cohort for Preclinical and Prodromal AD (TRC-PAD) Platform. Novel design included collection of electronic consent, use of community laboratories for plasma collection, mass spectrometry-based biomarker assay, and telephone communication of plasma biomarker screening eligibility. PARTICIPANTS: Participants invited to the AlzMatch pilot feasibility study were active in the APT Webstudy, 50 years of age or older, resided within 50 miles of both a Quest Diagnostics Patient Services Center (a national diagnostic laboratory with convenient locations for sample collection and processing) and one of six TRC-PAD vanguard clinical trial sites, had no self-reported dementia diagnosis, were able to communicate in English and engaged with the APT Webstudy within the prior 6 months. MEASUREMENTS: Primary feasibility outcomes were completion of electronic consent (e-consent) for invited participants and collection of usable blood samples. Additional feasibility outcomes included invitation response rate, plasma biomarker eligibility status (based on amyloid beta-42/40 [Aβ42/40] concentration ratio), ApoE proteotype, and trial inclusion criterion), and completion of telephone contact to learn eligibility to screen for a study. RESULTS: 300 APT Webstudy participants were invited to consent to the AlzMatch study. The AlzMatch e-consent rate was 39% (n=117) (95% CI of 33.5%-44.5%) overall, which was higher than the expected rate of 25%. Similar consent rates were observed across participants based on self-defined sex (41% Female (n=75), 37% Male (n=42)) and race and ethnicity (37% from underrepresented groups (URG) (n=36), 40% not from URG (n=79)). Among those that consented (n=117), plasma was successfully collected from 74% (n=87) (95% CI of 66%-82%), with similar rates across sex (76% Female (n=57), 71% Male (n=30)) and race and ethnicity (75% URG (n=27) and 75% not from URG (n=59)). 60% (n=51) of participants with plasma biomarker results were eligible to screen for future preclinical AD trials. CONCLUSION: Electronic consent of participants through an online registry, blood sample collection at community-based centers, plasma biomarker quantification and reporting, and biomarker assessments for study eligibility were all feasible with similar engagement rates across demographic groups. Although this pilot was a small and selective sample, participants engaged and consented at higher than expected rates. We conclude that collecting blood at community laboratories for biomarker analyses may improve accessibility beyond research, and may facilitate broader access for clinical use of AD plasma biomarkers. Based on our results, an expanded version of the AlzMatch study is underway, which involves expanding invitations to additional APT Webstudy participants and clinical trial sites.

Recently, artificial intelligence (AI) and blockchain have become two of the most trending and disruptive technologies. Blockchain technology can automate payment in cryptocurrency and provide access to a shared ledger of data, transactions, and logs in a decentralized, secure, and trusted manner. In addition, with smart contracts, blockchain has the ability to govern interactions among participants with no intermediary or a trusted third party. AI, on the other hand, offers intelligence and decision-making capabilities to machines similar to humans. This review presents a detailed survey on blockchain and AI basics and features. This paper provides a review of the literature and a brief on the integration of blockchain and AI applications in multiple areas. We also identify some sole cases of blockchain–AI integration in the energy sector with current use cases. Eventually, we discuss research advantages and challenges associated with integrating blockchain with AI in the energy domain. [ABSTRACT FROM AUTHOR]

Non‐trivial relationships link individual materials properties to device‐level performance. Device optimization therefore calls for new automation approaches beyond the laboratory bench with tight integration of different research methods. This study demonstrates a Materials Acceleration Platform (MAP) in the field of battery research based on the problem‐agnostic Fast INtention‐Agnostic LEarning Server (FINALES) framework, which integrates simulations and physical experiments while leaving the active control of the hardware and software resources executing experiments or simulations with the partners running the respective units. This decentralization of control is a distinctive feature of MAPs using the FINALES framework. The connected capabilities entail the formulation and characterization of electrolytes, cell assembly and testing, early lifetime prediction, and ontology‐mapped data storage provided by institutions distributed across Europe. The infrastructure is used to optimize the ionic conductivity of electrolytes and the End Of Life (EOL) of lithium‐ion coin cells by varying the electrolyte formulation. Trends in ionic conductivity are rediscovered and the effect of the electrolyte formulation on the EOL is investigated. Further, the capability of this MAP to bridge diverse research modalities, scales, and institutions enabling system‐level investigations under asynchronous conditions while handling concurrent workflows on the material‐ and system‐level is shown, demonstrating true intention‐agnosticism. [ABSTRACT FROM AUTHOR]

In view of the mushroom development of web-based business and green production, a few organizations have moved to produce ecological friendly items and utilized the online channel to build up the competition. This paper deals with a two-echelon sustainable dual-channel supply chain containing two manufacturers and a single retailer. One manufacturer of the investigated supply chain produces a traditional non-green item, and another one releases a substitutable green item. The demand of both the item at the online and offline channel is a function of the sales price, greenness level, sales effort of the retailer. Furthermore, we have discussed the criteria for optimal pricing and greening strategy of supply chain members and the sales effort level of the retailer under a centralized and decentralized model. To enhance the supply chain performance, a revenue-sharing contract has been proposed, subject to the pareto improvement of each channel member. The numerical result demonstrates that the proposed revenue-sharing contract can enhance the manufacturer's profit as well as system profit compared to the centralized and decentralized model and ensure higher profit for all the members than the decentralized model. Moreover, the item in the centralized model enjoys the highest green level than all other scenarios. Sensitivity analysis exhibits that a higher value of greening cost reduces the manufacturer's green level, decreasing the sales price. [ABSTRACT FROM AUTHOR]

Technological advancement, the emergence of more complex production systems, and the need for greater manufacturer competition have caused production systems to shift from a centralized environment to a decentralized one. Researchers have paid more attention to distributed flow shop scheduling problems and investigated various features and issues related to them in recent years. However, special types of these problems, in which the network structure is serial and inter-factory transportation is significant, have received less attention. This study investigated the two-machine decentralized flow shop scheduling problem, in which inter-factory transportation is handled by a batch delivery system. The goal was to simultaneously reduce the costs of makespan and batch delivery. A mixed-integer linear programming model capable of solving small-size instances in a logical running time was presented to better describe the problem. Then, in order to solve large-size instances in a logical running time, a fast branch and bound algorithm with a heuristic method were developed to obtain the appropriate upper bound as well as the tight lower bounds at each node. The computational results indicated that the B&B algorithm performed very well in terms of problem-solving running time. The findings also demonstrated that the heuristic method can solve the most complex instances by 100 jobs with less than 13% error. [ABSTRACT FROM AUTHOR]

Transaction transparency and trust are highly valued in the digital age. When conventional centralized systems are utilized, concerns arise regarding data security and intermediate control, as these systems frequently fail to meet these standards. In this regard, blockchain technology, which offers secure and decentralized solutions, stands out as a game-changer. The article examines the underlying principles of smart contracts and blockchain technology in order to gain an understanding of their potential to revolutionize online interactions. We delve into the decentralized architecture of the blockchain, focusing on its distributed ledger system that ensures the immutability and transparency of data. Determining the functions of consensus mechanisms such as Proof-of-Work and Proof-of-Stake, which form the technological foundation of blockchain, is crucial for preserving the security and integrity of the network. We further investigate the possibility that intelligent contracts can orchestrate agreements and enable trustless transactions in response to predefined conditions. This study showcases the practical applications of blockchain technology and smart contracts in establishing more reliable processes within the healthcare, supply chain management, and finance sectors. The study not only contemplates potential solutions and forthcoming improvements but also recognizes the constraints of the existing technology, specifically with regard to scalability and energy usage. The emergence of smart contracts and blockchain technology heralds a new era of decentralized and impenetrable digital security. Their revolutionary potential to redefine trust and empower both individuals and corporations is evident. In order to optimize their capabilities, we must work together to address existing challenges and enforce solid legal structures that regulate the ethical and transformative application of these state-of-the-art technologies.

An emerging area of study known as Cognitive IoT (CIoT) has emerged as a result of recent efforts to include cognition in the design of the Internet of Things (IoT). Several features and challenges from the IoT are carried over to the CIoT. A lot of applications in CIoT generate massive heterogeneous data, and they require inferential tasks with less computational overhead. Therefore, this study suggests a decentralized computing approach to handle multiple hypothesis testing in less computational time. The first stage is to minimize error at the cluster node by total variance regularization through the alternating direction method of multipliers. Subsequently, the fusion center handles the heterogeneity of data, minimizes model error, extracts the most informative data, and adjusts the p-value for multiple hypotheses testing. The experimental evaluation and cross-validation on six-month traces of the carbon monoxide dataset reveal the efficacy of the proposed algorithm over competing approaches. [ABSTRACT FROM AUTHOR]

This paper introduces ESMira, a server and mobile app (Android, iOS) developed for research projects using experience sampling method (ESM) designs. ESMira offers a very simple setup process and ease of use, while being free, decentralized, and open-source (source code is available on GitHub). The ongoing development of ESMira started in early 2019, with a focus on scientific requirements (e.g., informed consent, ethical considerations), data security (e.g., encryption), and data anonymity (e.g., completely anonymous data workflow). ESMira sets itself apart from other platforms by both being free of charge and providing study administrators with full control over study data without the need for specific technological skills (e.g., programming). This means that study administrators can have ESMira running on their own webspace without needing much technical knowledge, allowing them to remain independent from any third-party service. Furthermore, ESMira offers an extensive list of features (e.g., an anonymous built-in chat to contact participants; a reward system that allows participant incentivization without breaching anonymity; live graphical feedback for participants) and can deal with complex study designs (e.g., nested time-based sampling). In this paper, we illustrate the basic structure of ESMira, explain how to set up a new server and create studies, and introduce the platform's basic functionalities. [ABSTRACT FROM AUTHOR]

In robotics, achieving adaptivity in complex environments is challenging. Traditional robotic systems use stiff materials and computationally expensive centralized controllers, while nature often favors soft materials and embodied intelligence. Inspired by nature's distributed intelligence, this study explores a decentralized approach for robust behavior in soft robotic systems without knowledge of their shape or environment. It is demonstrated that only a few basic rules implemented in identical modules that shape the soft robotic system can enable whole‐body phototaxis, navigating on a surface toward a light source, without explicit communication between modules or prior system knowledge. The results reveal the method's effectiveness in generating robust and adaptive behavior in dynamic and challenging environments. Moreover, the approach's simplicity makes it possible to illustrate and understand the underlying mechanism of the observed behavior, paying particular attention to the geometry of the assembled system and the effect of learning parameters. Consequently, the findings offer insights into the development of adaptive, autonomous robotic systems with minimal computational power, paving the way for robust and useful behavior in soft and microscale robots, as well as robotic matter, that operate in real‐world environments. [ABSTRACT FROM AUTHOR]

The sustainable development goals (SDGs) are fundamental to circular economy, climate action, sustainable digital environment initiatives that addresses the higher need of shifting towards fully sustainable and renewable energy systems. The decentralized power system has been applied and interconnected to the system and accounting to the renewable energy mix, energy storage and distribution. A decentralized hybrid renewable energy system can be of much help in providing a deficit of power between energy generation and demand where a single renewable energy system is not sustainable and reliable. The aim of this study was to characterize the biomass, perform the biomethane potential test of the biomass, select suitable bio-digester using multi-criteria decision analysis, carry out simulation-modelling of the solar-anaerobic digestion system in achieving energy self-efficiency with the development of the dynamic models that of climatic condition, social-economic and technology feasible. The performance of the system was designed and optimised to get maximum output power and mitigation of the environment at a lower cost. The performance was analysed based on present cost, locally availability, environmentally friendly, cost of energy on the local tariffs, load satisfaction, fuel consumption savings, operation and maintenance cost, human and technology capital and pollutants saving rates. The results showed major energy consummation from the solar-biodigester system, technology solutions that maximize energy efficiency and reduce the heat loss in the AD process. Waste to energy (WtE) technology coupled with the solar system could be viewed as key to sustainable, affordable and clean electricity generation. [ABSTRACT FROM AUTHOR]

An electronic democratic framework can also fulfill all the rules and regulations of any governing body. The election is one of the important events for a robust democratic system in a country, but still, many people are not thinking of it as major concern for democracy. There are several issues of vote fraud, hacking of EVM (Electronic Voting Machine), election manipulation, and booth capturing within the current electoral system. In this paper, we proposed a framework for E-voting (Electronic Voting) which might resolve these issues. This framework uses Blockchain technology as a service along with an E-Voting system that addresses all restrictions. Blockchain advancements proposed a wide scope of different applications to reduce the sharing of monetary economies. The proposed framework uses proof of voter's identity (PoVI) consensus algorithm that makes the proposed system more secure and differentiates this model from other models of E-voting systems. The objective of this paper is to clarify the utilization of Blockchain innovation as assistance for an authentic electronic democratic framework. The proposed framework evaluates the capability of distributed record advances with the assistance of illustrative contextual analysis, specifically the cycle of a political decision that improves security and minimizes the expense of facilitating a cross-country casting a ballot framework. [ABSTRACT FROM AUTHOR]

The management of water resources and the related disposal of wastewater are essential for human existence and the advancement of contemporary society. Collecting and managing wastewater has a significant effect on local and global economies as well as the environment. Innovation in the field of the environment is of utmost importance in reducing the environmental impacts on systems and in making them more sustainable economically, environmentally and socially. Decentralization is considered an appropriate solution to sustainability problems in liquid waste management programs because it focuses on treating liquid waste on-site, recycling it locally, and taking advantage of the local resources available in domestic wastewater. This research analyzes the needs, appropriate technical methods, and support for water management through decentralized systems. Three considerations will be used to support the choice of a decentralized wastewater treatment system in the Khartoum Locality: $106,000,000. According to the BioWin results, the effectiveness of each alternative household wastewater treatment was comparable. Software such as MapInfo, GPS Area Calculator, BioWin, and GIS was used to reach the targets. Additionally, the results revealed that the decentralized wastewater treatment method, considering its costs, land requirements, and slope effects on the environment, is preferable to centralized wastewater treatment systems. These results serve as a guide for choosing the best wastewater treatment option to increase access to safe sanitation and to integrate decentralized wastewater management to upgrade and improve the environment in the Khartoum Locality. [ABSTRACT FROM AUTHOR]

Numerous consumers have become prosumers as a result of Government regulations and incentives designed to promote the production of zero-carbon electricity. These prosumers are now actively engaged in exporting surplus power generation to the grid and importing electricity from the grid during periods of increased demand. Two models are taken into consideration for analysis in this paper. In a decentralized model, the capital investment is made by the prosumer; whereas in an economically centralized model, on the other hand, the capital investment is made by the Renewable Energy Service Company (RESCO). The centralized model has two modes of operation: in mode 1, RESCO pays rent for the provided space, and in mode 2, RESCO offers a discounted rate for each unit the customer uses from RESCO. We look at the economics of both approach strategies in India that are relevant to home prosumers who install rooftop solar and storage systems. These two methods are used to quantify the profit and payback period for each stakeholder in this paper. The maximum profit is made by 70% of prosumers in the decentralized concept. The results demonstrate that for domestic prosumers with lower demand, the decentralized approach performs well. When significant energy demands exist, the centralized model is preferable. [ABSTRACT FROM AUTHOR]

The ever-increasing couplings between electricity and gas systems highlight the coordinated restoration decisions for interdependent electricity and gas system (IEGS) to enhance resilience. However, the distinct timescales of power and gas flow rates as well as information privacy concern cause additional complexities in making restoration decisions, and large calculation scale and nonconvexity also results in computational obstacles. To address these issues, this paper proposes a hybrid spatio-temporal scale decentralized restoration strategy for IEGS to enhance restoration efficiency and security. First, in the spatial scale, the network sectionalizing and the restoration processes in sectionalized electricity and gas systems are coordinated in a decentralized manner with limited boundary information interactions to respect information privacy. Next, the impacts of distinct timescales of power and gas flow rates are explored to accelerate restoration, and a linear gas flow dynamics model is also proposed to capture the gas transmission dynamics with high accuracy, enhancing restoration security. Furthermore, a decomposition-based alternating direction method of multipliers algorithm is proposed to efficiently solve the proposed IEGS restoration strategy, showing good calculation time and convergence performance. Finally, the effectiveness of the proposed restoration strategy is validated in a T118N20 test system and a real-world system, demonstrating the enhanced restoration efficiency, security alongside commendable computational performance.

As the use of artificial intelligence (AI) grows within the field of cybersecurity, so does the demand for secure and decentralized AI systems to protect against potential cyber threats. Blockchain technology has emerged as the ideal approach for increasing both the security and privacy of AI systems since it provides decentralized and immutable data storage. This systematic literature review focuses on the integration of blockchain technology with decentralized AI within cybersecurity. It provides a comprehensive taxonomy of blockchain technology and decentralized AI for cybersecurity, serving as the starting point for the study. This paper begins with an overview of blockchain technology and its possible uses in cybersecurity and also analyzes its challenges and opportunities. Decentralized AI is also covered in the study, along with its potential advantages and difficulties in decentralized AI cybersecurity. Building on that foundation, this study provides convincing findings highlighting the beneficial relationships between blockchain technology and decentralized AI for cybersecurity, contributing to a nuanced comprehension of their integration. Moreover, it offers real-world uses of blockchain-enabled, decentralized AI to discuss its usefulness in tackling cybersecurity concerns. Finally, this paper discusses future research directions for blockchain-enabled decentralized AI in cybersecurity, including potential applications and implications of this technology in the field, emphasizing the potential of blockchain-enabled decentralized AI in cybersecurity to enhance security, privacy, and trust in AI systems.

The increased decentralization of renewable energy has increased curtailment rates in stagnating demand zones, increasing the levelized cost of energy (LCOE). The geographically dynamic nature of gravity energy storage (GES) is emerging in the field of mechanical energy storage, over pumped hydro. However, GES costs vary geospatially, specifically in decentralized suburban areas, due to the impact of urban socioeconomics. This study aims to find a mathematical approximation of a cost-optimized location for suburban Solar–GES hybrid systems in curtailment-prone areas. A multi-parameterization model mathematically programmed land, transmission, supply chain and excavation costs into geospatial matrix approximations for suburban areas of 2500 km2 in Fukuoka and Ibaraki in Japan. It was found that SPV-GES location-dependent costs were mainly affected by distance from the city's economic center and flat plains in suburbs, and supply chain and transmission costs optimized the location-dependent cost for GES at a specific point. It was also found that flat terrains were more economical than mountainous terrains due to high GES supply chain costs. With GES found to be cost-competitive compared to other storage technologies in Japan, this study reveals that GES introduction benefits the LCOE of suburban, decentralized SPV when curtailment is >50% irrespective of terrain. [ABSTRACT FROM AUTHOR]

There are issues such as dishonest checking organizations and unreliable data storage in a deduplication scheme based on data popularity. This paper proposed a data popularity deduplication model for decentralized storage. To address the dishonesty of the checking agency, the model executed duplication checking and popularity checking of data by using smart contracts as the checking organization, which combined the non-tamper-ability of blockchain with the non-repudiation of smart contracts. It guaranteed the authenticity of the test results. To address the problem of unreliable data storage, it proposed a file chain storage structure. This structure satisfied the requirement of data popularity deduplication. Meanwhile, this structure established the logical relationship between the fragments which were distributed in different storage nodes to achieve physical/logical uploads by adding auxiliary information. The file chain storage structure provided the basis for popularity data in decentralized network storage. Besides, it added backup identifiers to the data block information, and divided the storage network into two virtual storage spaces with the help of the backup identifiers. Also, two virtual storage spaces were oriented for data detection and storage. It satisfied the user’s backup needs. Security analysis and performance analysis show that the scheme is feasible, guarantees the authenticity of the test results, and improves the reliability of data storage. [ABSTRACT FROM AUTHOR]

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Most e-voting schemes make use of central servers. Users are obliged to trust these servers, which represent a vulnerability of the scheme. In the last few years, a very small group of schemes has been published that overcomes this handicap by using a peer-to-peer (P2P) approach. These are known as boardroom e-voting schemes, whereby users take the role of the servers. They act as managers of the process: they cast votes, keep a record of them, and verify the cryptographic operations made by others. Nevertheless, ballots must fulfill certain constraints which conflict with the possibilities of recent debate tools. These tools allow users to decide what to vote on, thus enabling the ballot frame to remain unknown before the voting process. The scheme presented here is a new boardroom voting protocol. It provides privacy, eligibility, and verifiability among other relevant features. The key advantage of this system is its high degree of flexibility, due to the absence of a need to impose any constraint on the ballots. This paper includes experimental results with two debate groups. [ABSTRACT FROM AUTHOR]

Municipal solid waste management (MSWM) is perceived as a global issue regardless of the place of waste generation. The amount of unmanaged waste is increasing rapidly, along with its impact on the environment and human health. In hilly areas, specifically the North Eastern Region (NER) states of India, due to the unique topography coupled with socio-economic factors, there are inadequate waste management practices marked by insufficient infrastructure, minimal research studies, and limited data availability. This paper comprehensively reviews the existing status of MSWM practices and waste treatment technologies, identifies the challenges, and discusses the prospective approaches for MSWM in NER states of India. NER, is characterized by its hilly terrain and has the most diverse demographic profile in the country. The study highlights the notable increase in waste generation in the urban population in NER. The total amount of waste generated in NER is about 2907 tons per day, with a collection rate of 86.96%, treatment at 31.09%, and landfilling at 33.67%. The biodegradable fraction makes up the majority of waste composition (more than 50%) in NER, followed by recyclables and inert. The existing MSWM consists of waste collection, transportation, and disposal with limited source segregation and treatment. All the states of NER practice open dumping and burning as the primary waste treatment and disposal system. The study discusses the challenges and prospects to ensure effective MSWM in NER. This review is a region-specific study that considers cultural diversity, topography, and socio-economic dynamics. The outcome of this review will be helpful to the researchers and policymakers in making appropriate waste management plans and improve the MSWM system in NER. [ABSTRACT FROM AUTHOR]

Escalating events such as extreme weather conditions, geopolitical incidents, acts of war, cyberattacks, and the intermittence of renewable energy resources pose substantial challenges to the functionality of global electric grids. Consequently, research on enhancing the resilience of electric grids has become increasingly crucial. Concurrently, the decentralization of electric grids, driven by a heightened integration of distributed energy resources (DERs) and the imperative for decarbonization, has brought about significant transformations in grid topologies. These changes can profoundly impact flexibility, operability, and reliability. However, there is a lack of research on the impact of DERs on the electric grid's resilience, as well as a simple model to simulate the impact of any disturbance on the grid. Hence, to analyze the electric grid's resilience, this study employs an extrapolation of Leontief's input–output (IO) model, originally designed to study ripple effects in economic sectors. Nodes are treated as industries, and power transmission between nodes is considered as the relationship between industries. Our research compares operability changes in centralized, partially decentralized, and fully decentralized grids under identical fault conditions. Using grid inoperability as a key performance indicator (KPI), this study tests the three grid configurations under two fault scenarios. The results confirm the efficacy of decentralization in enhancing the resilience and security of electric grids. [ABSTRACT FROM AUTHOR]

The fourth industrial revolution has broadly transformed the manufacturing system. However, this transformation is somewhat lacking in traditional or manual production systems due to the absence of IT infrastructure. Such traditional industries need to have the advantage of real-time control and monitoring. This study has developed economic assembly planning, scheduling, and control for a traditional assembly system. We used the concept of the configurable virtual workstation as the digitalization framework. Then, we employed the decentralized scheduling concept to reduce the computational effort in scheduling the complex product. The implementation result showed that scheduling and planning have transformed the traditional assembly process into intelligent scheduling and control with low digitalization effort. [ABSTRACT FROM AUTHOR]

This study presents an environmentally friendly mechanism for task distribution designed explicitly for blockchain Proof of Authority (POA) consensus. This approach facilitates the selection of virtual machines for tasks such as data processing, transaction verification, and adding new blocks to the blockchain. Given the current lack of effective methods for integrating POA blockchain into the Cloud Industrial Internet of Things (CIIoT) due to their inefficiency and low throughput, we propose a novel algorithm that employs the Dynamic Voltage and Frequency Scaling (DVFS) technique, replacing the periodic transaction authentication process among validator candidates. Managing computer power consumption becomes a critical concern, especially within the Internet of Things ecosystem, where device power is constrained, and transaction scalability is crucial. Virtual machines must validate transactions (tasks) within specific time frames and deadlines. The DVFS technique efficiently reduces power consumption by intelligently scheduling and allocating tasks to virtual machines. Furthermore, we leverage artificial intelligence and neural networks to match tasks with suitable virtual machines. The simulation results demonstrate that our proposed approach harnesses migration and DVFS strategies to optimize virtual machine utilization, resulting in decreased energy and power consumption compared to non-DVFS methods. This achievement marks a significant stride towards seamlessly integrating blockchain and IoT, establishing an ecologically sustainable network. Our approach boasts additional benefits, including decentralization, enhanced data quality, and heightened security. We analyze simulation runtime and energy consumption in a comprehensive evaluation against existing techniques such as WPEG, IRMBBC, and BEMEC. The findings underscore the efficiency of our technique (LBDVFSb) across both criteria. [ABSTRACT FROM AUTHOR]

In this article, we propose a control scheme with predictors in a decentralized manner for coupled networked control systems (NCSs) under uncertain, large time-delays and event-triggered inputs. The network-induced delays are handled via the prediction; thus, the delay value is allowed to be large, and the burden of the network is relieved by the event-triggered input. Two methods are employed to deal with the large delay issue: the state and output feedback. When the state of each subsystem is measurable, full-state feedback is used, whereas when the plant state cannot be measured, output feedback is employed with the help of an observer, which is more common in practice. Instead of treating the interactive plants like a global system, the exponential stability of the coupled systems, under decentralized predictors with asynchronous sampled-data feedback, is analyzed in a decentralized way. Finally, the proposed methods are verified via an example of three interconnected cart–pendulum systems, while such systems would not be stabilizable by the traditional approach when the network-induced delays are relatively large. [ABSTRACT FROM AUTHOR]

The Internet of Vehicles (IoV) is an incipient topic within the wider domain of the Internet of Things (IoT). Using this technology intelligent transportation systems (ITS) can be developed, whose main purpose is to ensure more safety, faster travel, reduced energy consumption and improved vehicle upkeep. Devices connected within the IoV transmit a vast amount of data, which leads to additional costs on the communication network along with data security concerns. This research considers the potential uses of blockchain technology for improving communication between independent vehicles. In this paper, a decentralized system is proposed to enhance the security and performance of financial transactions in the network of autonomous cars based on the Ethereum blockchain. Our system is divided into two modules: car registration and message alert generating. New vehicles are allowed on the network using the smart contract that has been written and published on the Ethereum Remix IDE. This is the contract code attached to the Metamask wallet. Another module, the message alert-generating module, allows an administrator to send alert messages to all registered cars via a web-based system to their wallet for an Ethereum gas cost. It ensures the reliability of data transferred between the self-driving cars through the use of blockchain-based systems. The data is, therefore, confirmed and vetted using the proof of work and stake consensus algorithm to bring out the truth. The transaction being made, the system will see to it that the transaction made is of integrity and trustable. By synching all these technologies and means of reaching an agreement, the blockchain secures not only the means of keeping and retaining the information of the kept within self-driving vehicles but also establishes a strong foundation of highest trust and transparency in the interaction between the vehicles on the network. Accordingly, a well-defined and reliable conceptual design for the communication systems of autonomous vehicles has been proposed. Future work and the unresolved issues of using blockchain for autonomous vehicles have also been deliberated over in detail.

The Know Your Customer (KYC) process is a fundamental prerequisite for any financial institution’s compliance with the regulatory framework. Blockchain technology has emerged as a revolutionary solution to enhance the effectiveness of the KYC procedure. It ensures that the KYC process is transparent, secure, and immutable, thereby offering a robust solution to combat fraudulent activities. The potential of blockchain technology in revolutionizing the KYC process has been acknowledged globally. Blockchain technology provides a decentralized platform for storing customer data, enabling financial institutions to access the information seamlessly. Using ethereum blockchain technology in KYC procedures can enhance the efficiency of financial institutions, significantly reducing the time and cost associated with the process. This work aims to provide a viable and sustainable solution to the challenges that banks experience in implementing KYC procedures and onboarding new customers. The proposed solution involves the central bank maintaining a comprehensive register of all registered banks while closely monitoring their adherence to the existing regulations governing KYC and customer acquisition.

Government regulations and incentives supporting zero-carbon electricity production have significantly increased prosumers. This article conducts a techno-economic analysis, investigating the impact of significant factors such as fluctuating load needs, solar panel sizes, battery capacities, operating expenditures, and payback duration. A decentralized prosumer consortium comprised of home prosumers with roof-top solar generation is selected for the study. Utilizing the maximum renewable energy generated is a challenge in the prosumer consortium as the energy generation is not consistent throughout. The proposed model outlines a transformative approach to residential energy systems, introducing a prosumer consortium with a peer-to-peer energy trading network. Prosumers, equipped with solar panels and storage solutions, engage in active energy trading with consumers. This detailed economic analysis considers diverse prosumer and consumer profiles and accommodates dynamic load shifts based on prosumer load and generation patterns. The study explores three operating scenarios: a blended consortium of prosumers and consumers, prioritizing renewable energy usage during peak production, and inter-consortium energy pools for efficient resource utilization. Through a comprehensive evaluation of these scenarios, the system aims to identify economically optimal models for each case, ultimately determining the most profitable prosumer consortium model for participating entities. Compared to the traditional decentralized model, in the three scenarios, the utilization of surplus solar energy generated happened. The study shows that adopting decentralized prosumer consortium energy transactions, followed by a 25% to 30% load shifting, a load leveling-based demand response initiative, results in a 35% higher average profit in the system under consideration. When the system contains a suitable balance of consumers and prosumers, with consumers making up 70% to 80% of the overall consortium load, it reduces the average payback time by 40%.

Unmanned aerial vehicles (UAVs) have gained prominence across various sectors for their versatile applications. While their advantages are evident, addressing concerns associated with their deployment is essential to ensure reliability. This study presents an innovative approach for coordinating a group of UAVs in aerial survey missions. The decentralized strategy presented in this article allow UAVs to self-organize into linear formation, optimize their coverage paths, and adapt to agent failures, thereby ensuring efficient and adaptive mission execution. The strategy has been tested and validated on two different platforms: the inter-UAV communication performance is evaluated on NS-3 simulator to measure metrices such as packet delivery ratio, throughput, delay, and routing overhead within the UAV swarms, while mission efficiency and fault tolerance is analyzed on robot operating system framework, and visualized on Gazebo simulator with real-time parameters. Through experimental results, we show that, after proper tuning of control parameters, the approach succeeds in flock formation with high level of fault tolerance, offering higher efficiency in terms of mission time, transmission delay, packet delivery rate, and control overhead, when compared to the benchmark approaches.

Background: The common cold is a frequent, acute, and mild upper respiratory human disease. Nasal congestion has been considered the most bothersome symptom in the common cold, impacting quality of life (QoL). Topical decongestants containing steroids benefit QoL in allergic rhinitis, but no published research has assessed the impact of topical decongestants on QoL in the common cold. Objective: To evaluate the effects of xylometazoline hydrochloride 0.1% (Otrivin, GSK Consumer Healthcare SARL, Switzerland) for up to 7 days on QoL in participants with nasal congestion associated with the common cold. Design: This was a decentralized, longitudinal, open-label study. Methods: The study enrolled 136 participants (⩾18 years) with early symptoms of the common cold, of which 102 were included in the modified intention-to-treat (mITT) population. Within 24 h of study product receipt, participants confirmed a 'plugged nose' and ⩾1 other common cold symptom. Primary endpoints were Wisconsin Upper Respiratory Symptom Survey-21 (WURSS-21) total score, total and individual symptom scores, and total QoL score. Secondary endpoints were additional QoL scores. Exploratory and post hoc analyses included median days to resolution for each QoL factor and analyses of five QoL categories. Results: Consistent improvements in symptoms and QoL were seen in the mITT population. From day 1, improvements were seen in the 'plugged nose' symptom (p = 0.0023), WURSS-21 total QoL score, and all individual QoL scores (p < 0.0001 for all). After the last dose needed, significant improvements were seen in sleep quality (73%), vitality (76%), physical activity (71%), social activity (80%), and sensation (81%). No serious or unexpected adverse events were reported. Conclusion: This study is the first to demonstrate in a real-life setting that treating nasal congestion in adults with xylometazoline hydrochloride 0.1% during the common cold positively impacts QoL factors relevant to daily living [Otrivin: Quality of Life (QoL) Impact in a Real-World Setting; https://clinicaltrials.gov/study/NCT05556148]. Plain language summary: Impact of xylometazoline hydrochloride 0.1% on quality of life in people with blocked nose associated with the common cold The common cold is a widespread, mild respiratory illness for which a hallmark symptom is a blocked or stuffy nose, which makes breathing and sleeping difficult. This study focused on how a nasal spray called Otrivin (containing xylometazoline hydrochloride 0.1%) impacts the quality of life (QoL) of people suffering from nasal congestion due to the common cold. Participants answered a questionnaire called the Wisconsin Upper Respiratory Symptom Survey-21 (WURSS-21), which helped understand how people experience respiratory symptoms and how different aspects of QoL were impacted. Participants also responded to eight additional QoL questions not covered in the WURSS-21. The results showed that from the first day of using the nasal spray, participants experienced significant relief from the blocked nose symptom and reported an overall improvement in their QoL and well-being, such as in sleep quality, energy levels, senses, and physical and social activities. In conclusion, this real-world study demonstrated that using xylometazoline hydrochloride 0.1% nasal spray during the common cold can significantly improve nasal congestion and various aspects of a person's daily life. These findings provide valuable evidence for using this nasal spray to relieve symptoms and enhance the overall well-being of individuals with the common cold. [ABSTRACT FROM AUTHOR]