Decentralized Clinical Trials in Early Drug Development-A Framework Proposal.

The COVID-19 pandemic has led to a rethinking of clinical trial design to maintain clinical research activity, with regulatory changes allowing for the wider implementation and development of decentralized design models. Evidence of the feasibility and benefits associated with a remote design comes mainly from observational studies or phase 2 and 3 clinical trials, in which implementation is easier with a better-established safety profile. Early drug development is a slow and expensive process in which accrual and safety are key aspects of success. Applying a decentralized model to phase 1 clinical trials could improve patient accrual by removing geographic barriers, improving patient population diversity, strengthening evidence for rare tumors, and reducing patients' financial and logistical burdens. However, safety monitoring, data quality, shipment, and administration of the investigational product are challenges to its implementation. Based on published data for decentralized clinical trials, we propose an exploratory framework of solutions to enable the conceptualization of a decentralized model for phase 1 clinical trials.

Sleep and cardiac autonomic modulation in older adults: Insights from an at-home study with auditory deep sleep stimulation.

The autonomic nervous system regulates cardiovascular activity during sleep, likely impacting cardiovascular health. Aging, a primary cardiovascular risk factor, is associated with cardiac autonomic disbalance and diminished sleep slow waves. Therefore, slow waves may be linked to aging, autonomic activity and cardiovascular health. However, it is unclear how sleep and slow waves are linked to cardiac autonomic profiles across multiple nights in older adults. We conducted a randomized, crossover trial involving healthy adults aged 62-78 years. Across 2 weeks, we applied auditory stimulation to enhance slow waves and compared it with a SHAM period. We measured sleep parameters using polysomnography and derived heart rate, heart rate variability approximating parasympathetic activity, and blood pulse wave approximating sympathetic activity from a wearable. Here, we report the results of 14 out of 33 enrolled participants, and show that heart rate, heart rate variability and blood pulse wave within sleep stages differ between the first and second half of sleep. Furthermore, baseline slow-wave activity was related to cardiac autonomic activity profiles during sleep. Moreover, we found auditory stimulation to reduce heart rate variability, while heart rate and blood pulse wave remained unchanged. Lastly, within subjects, higher heart rate coincided with increased slow-wave activity, indicating enhanced autonomic activation when slow waves are pronounced. Our study shows the potential of cardiac autonomic markers to offer insights into participants' baseline slow-wave activity when recorded over multiple nights. Furthermore, we highlight that averaging cardiac autonomic parameters across a night may potentially mask dynamic effects of auditory stimulation, potentially playing a role in maintaining a healthy cardiovascular system.

The Digital Platform and Its Emerging Role in Decentralized Clinical Trials.

Decentralized clinical trials (DCTs) are becoming increasingly popular. Digital clinical trial platforms are software environments where users complete designated clinical trial tasks, providing investigators and trial participants with efficient tools to support trial activities and streamline trial processes. In particular, digital platforms with a modular architecture lend themselves to DCTs, where individual trial activities can correspond to specific platform modules. While design features can allow users to customize their platform experience, the real strengths of digital platforms for DCTs are enabling centralized data capture and remote monitoring of trial participants and in using digital technologies to streamline workflows and improve trial management. When selecting a platform for use in a DCT, sponsors and investigators must consider the specific trial requirements. All digital platforms are limited in their functionality and technical capabilities. Integrating additional functional modules into a central platform may solve these challenges, but few commercial platforms are open to integrating third-party components. The lack of common data standardization protocols for clinical trials will likely limit the development of one-size-fits-all digital platforms for DCTs. This viewpoint summarizes the current role of digital platforms in supporting decentralized trial activities, including a discussion of the potential benefits and challenges of digital platforms for investigators and participants. We will highlight the role of the digital platform in the development of DCTs and emphasize where existing technology is functionally limiting. Finally, we will discuss the concept of the ideal fully integrated and unified DCT and the obstacles developers must address before it can be realized.

Advancing longevity research through decentralized science.

In an era marked by scientific stagnation, Decentralized Science (DeSci) challenges the inefficiencies of traditional funding and publishing systems. DeSci employs blockchain technology to address the misalignment of incentives in academic research, emphasizing transparency, rapid funding, and open-source principles. Centralized institutions have been linked to a deceleration of progress, which is acutely felt in the field of longevity science-a critical discipline as aging is the #1 risk factor for most diseases. DeSci proposes a transformative model where decentralized autonomous organizations (DAOs) facilitate community-driven funding, promoting high-risk, high-reward research. DeSci, particularly within longevity research, could catalyze a paradigm shift towards an equitable, efficient, and progressive scientific future.

A participatory multi-criteria decision analysis framework reveals transition potential towards non-grid wastewater management.

Many public environmental decisions are wicked problems due to high complexity and uncertainty. We test a participatory value-based framework based on multi-criteria decision analysis (MCDA) to tackle such problems. Our framework addresses two important gaps identified in reviews of MCDA applications to environmental problems: including stakeholders and treating uncertainty. We applied our framework in two complex real-world cases concerning a paradigm shift in the wastewater sector; the transition from centralized wastewater systems to decentralized non-grid systems. Non-grid systems may solve some problems of centralized systems by reducing costs, increasing flexibility, and addressing growing demands on environmental issues, especially in rural areas. But non-grid systems have rarely been implemented in OECD countries, because it is unclear whether a transition is recommendable, and whether stakeholders would accept this shift. This problem allows addressing several fundamental research questions. As theoretical contribution, we found that stakeholder participation in MCDA is necessary, because different preferences of stakeholders can lead to different best-performing options in the assessments. Compared to the typical integrated assessment (IA) approach that excludes stakeholders' preferences, the MCDA process led to clearer outcomes. Results indicate that including the uncertainty of predicted consequences of options with Monte Carlo simulation helped discriminate between options and identify best-performing options. Challenging the uncertainty of elicited stakeholder preferences with sensitivity analyses, we found that best-performing options were especially sensitive to the MCDA aggregation model. Despite the high uncertainty, it was possible to suggest robust consensus options that would perform reasonably well for all stakeholders. As practical contribution, results indicated that a transition from the centralized to decentralized non-grid systems seems feasible. Most stakeholders assigned highest weights to environmental protection objectives in decision-making workshops. These stakeholder preferences implemented in MCDA led to a generally better assessment of innovative non-grid systems, especially when including urine source separation. Stakeholders perceived the MCDA process as beneficial and found results plausible. We conclude that the proposed participatory value-based framework is rigorous, but still feasible in practice. The framework is certainly transferable to any context and is open to testing and refinement in various applications to wicked decision problems.

Performance of a pilot-scale microbial electrolysis cell coupled with biofilm-based reactor for household wastewater treatment: simultaneous pollutant removal and hydrogen production.

The septic tank is the most commonly used decentralized wastewater treatment systems for household wastewater treatment in on-site applications. The removal rate of various pollutants is lower in different septic tank configurations. The integration of a microbial electrolysis cells (MEC) into septic tank or biofilm-based reactors can be a green and sustainable technology for household wastewater treatment and energy production. In this study, a 50-L septic tank was converted into a 50-L MEC coupled with biofilm-based reactor for simultaneous household wastewater treatment and hydrogen production. The biofilm-based reactor was integrated by an anaerobic packed-bed biofilm reactor (APBBR) and an aerobic moving bed biofilm reactor (aeMBBR). The MEC/APBBR/aeMBBR was evaluated at different organic loading rates (OLRs) by applying voltage of 0.7 and 1.0 V. Result showed that the increase of OLRs from 0.2 to 0.44 kg COD/m3 d did not affect organic matter removals. Nutrient and solids removal decreased with increasing OLR up to 0.44 kg COD/m3 d. Global removal of chemical oxygen demand (COD), biochemical oxygen demand (BOD), total nitrogen (TN), ammoniacal nitrogen (NH4+), total phosphorus (TP) and total suspended solids (TSS) removal ranged from 81 to 84%, 84 to 85%, 53 to 68%, 88 to 98%, 11 to 30% and 76 to 88% respectively, was obtained in this study. The current density generated in the MEC from 0 to 0.41 A/m2 contributed to an increase in hydrogen production and pollutants removal. The maximum volumetric hydrogen production rate obtained in the MEC was 0.007 L/L.d (0.072 L/d). The integration of the MEC into biofilm-based reactors applying a voltage of 1.0 V generated different bioelectrochemical nitrogen and phosphorus transformations within the MEC, allowing a simultaneous denitrification-nitrification process with phosphorus removal.

Removal of antibiotic resistant bacteria and antibiotic resistance genes by an electrochemically driven UV/chlorine process for decentralized water treatment.

The UV/chlorine (UV/Cl2) process is a developing advanced oxidation process and can efficiently remove antibiotic resistant bacteria (ARB) and antibiotic resistance genes (ARGs). However, the transportation and storage of chlorine solutions limit the application of the UV/Cl2 process, especially for decentralized water treatment. To overcome the limitation, an electrochemically driven UV/Cl2 process (E-UV/Cl2) where Cl2 can be electrochemically produced in situ from anodic oxidation of chloride (Cl-) ubiquitously present in various water matrices was evaluated in this study. >5-log inactivation of the ARB (E. coli) was achieved within 5 s of the E-UV/Cl2 process, and no photoreactivation of the ARB was observed after the treatment. In addition to the ARB, intracellular and extracellular ARGs (tetA, sul1, sul2, and ermB) could be effectively degraded (e.g., log(C0/C) > 4 for i-ARGs) within 5 min of the E-UV/Cl2 process. Atomic force microscopy showed that the most of the i-ARGs were interrupted into short fragments (< 30 nm) during the E-UV/Cl2 process, which can thus effectively prevent the self-repair of i-ARGs and the horizontal gene transfer. Modelling results showed that the abatement efficiencies of i-ARG correlated positively with the exposures of •OH, Cl2-•, and ClO• during the E-UV/Cl2 process. Due to the short treatment time (5 min) required for ARB and ARG removal, insignificant concentrations of trihalomethanes (THMs) were generated during of the E-UV/Cl2 process, and the energy consumption (EEO) of ARG removal was ∼0.20‒0.27 kWh/m3-log, which is generally comparable to that of the UV/Cl2 process (0.18-0.23 kWh/m3-log). These results demonstrate that the E-UV/Cl2 process can provide a feasible and attractive alternative to the UV/Cl2 process for ARB and ARG removal in decentralized water treatment system.

Smartphone-based distress screening, information provision, and psychotherapy for reducing psychological distress among AYA cancer survivors: protocol for a fully decentralized multicenter randomized controlled clinical trial.

Fear of cancer recurrence (FCR) is a common and distressing condition among adolescents and young adults (AYAs). This study aims to investigate the efficacy of digital interventions, including distress screening-based information provision and smartphone problem-solving therapy, on common psychological distress, especially FCR, in AYA patients with cancer. Participants will be 224 AYA outpatients with cancer aged 15-39 years who will be randomly assigned to either an 8-week smartphone-based intervention or a waitlist control group. This intervention includes smartphone-based distress screening, information provision, and psychotherapy (problem-solving therapy). The primary endpoint will be the Fear of Cancer Recurrence Inventory-Short Form score at week 8. This study will be conducted as a fully decentralized, randomized, and multicenter trial. The study protocol was approved by the Institutional Review Board of Nagoya City University on 19 April 2024 (ID: 46-23-0005). Trial registration: UMIN-CTR: UMIN000054583.

Genetically engineered bacterial biofilm materials enhances portable whole cell sensing.

In recent years, whole-cell biosensors (WCBs) have emerged as a potent approach for environmental monitoring and on-site analyte detection. These biosensors harness the biological apparatus of microorganisms to identify specific analytes, offering advantages in sensitivity, specificity, and real-time monitoring capabilities. A critical hurdle in biosensor development lies in ensuring the robust attachment of cells to surfaces, a crucial step for practical utility. In this study, we present a comprehensive approach to tackle this challenge via engineering Escherichia coli cells for immobilization on paper through the Curli biofilm pathway. Furthermore, incorporating a cellulose-binding peptide domain to the CsgA biofilm protein enhances cell adhesion to paper surfaces, consequently boosting biosensor efficacy. To demonstrate the versatility of this platform, we developed a WCB for copper, optimized to exhibit a discernible response, even with the naked eye. To confirm its suitability for practical field use, we characterized our copper sensor under various environmental conditions-temperature, salinity, and pH-to mimic real-world scenarios. The biosensor-equipped paper discs can be freeze-dried for deployment in on-site applications, providing a practical method for long-term storage without loss of sensitivity paper discs demonstrate sustained functionality and viability even after months of storage with 5 µM limit of detection for copper with visible-to-naked-eye signal levels. Biofilm-mediated surface attachment and analyte sensing can be independently engineered, allowing for flexible utilization of this platform as required. With the implementation of copper sensing as a proof-of-concept study, we underscore the potential of WCBs as a promising avenue for the on-site detection of a multitude of analytes.

A Randomized, Double-Blind, Placebo-Controlled Trial to Assess the Effectiveness and Safety of Melatonin and Three Formulations of Floraworks Proprietary TruCBN™ for Improving Sleep.

The phytocannabinoid cannabinol (CBN) has a potential mechanism of action as an alternative sleep aid but there is minimal evidence to support its effectiveness. The aim of this randomized, double-blind, placebo-controlled study was to assess the safety and effects of three formulations of a hemp-derived CBN sleep aid, TruCBN™ [25 mg (n = 206), 50 mg (n = 205), 100 mg (n = 203)], on sleep quality (PROMIS Sleep Disturbance 8A), relative to placebo (n = 204). The effectiveness and safety of these formulations relative to 4 mg of melatonin (n = 202) was assessed. Exploratory measures were stress (PROMIS Stress 4A), anxiety (Anxiety 4A), pain (PROMIS™ PEG), and well-being (WHO 5). All groups and the 4 mg melatonin group experienced significant improvement in sleep quality relative to the placebo group with no significant differences between any group and the melatonin group. Participants taking 100 mg showed a larger decrease in stress compared to the placebo group. There were no significant differences in anxiety, pain, well-being, or the frequency of side effects between any group and the placebo group. There was no significant difference in improvements in sleep quality between any of the treatment groups and the 4 mg melatonin group. Orally ingested CBN, at 25 mg, 50 mg, and 100 mg, is a safe and effective alternative for the improvement of sleep.

Differentially Private Client Selection and Resource Allocation in Federated Learning for Medical Applications Using Graph Neural Networks.

Federated learning (FL) has emerged as a pivotal paradigm for training machine learning models across decentralized devices while maintaining data privacy. In the healthcare domain, FL enables collaborative training among diverse medical devices and institutions, enhancing model robustness and generalizability without compromising patient privacy. In this paper, we propose DPS-GAT, a novel approach integrating graph attention networks (GATs) with differentially private client selection and resource allocation strategies in FL. Our methodology addresses the challenges of data heterogeneity and limited communication resources inherent in medical applications. By employing graph neural networks (GNNs), we effectively capture the relational structures among clients, optimizing the selection process and ensuring efficient resource distribution. Differential privacy mechanisms are incorporated, to safeguard sensitive information throughout the training process. Our extensive experiments, based on the Regensburg pediatric appendicitis open dataset, demonstrated the superiority of our approach, in terms of model accuracy, privacy preservation, and resource efficiency, compared to traditional FL methods. The ability of DPS-GAT to maintain a high and stable number of client selections across various rounds and differential privacy budgets has significant practical implications, indicating that FL systems can achieve strong privacy guarantees without compromising client engagement and model performance. This balance is essential for real-world applications where both privacy and performance are paramount. This study suggests a promising direction for more secure and efficient FL medical applications, which could improve patient care through enhanced predictive models and collaborative data utilization.

The Artificial Neural Twin - Process optimization and continual learning in distributed process chains.

Industrial process optimization and control is crucial to increase economic and ecologic efficiency. However, data sovereignty, differing goals, or the required expert knowledge for implementation impede holistic implementation. Further, the increasing use of data-driven AI-methods in process models and industrial sensory often requires regular fine-tuning to accommodate distribution drifts. We propose the Artificial Neural Twin, which combines concepts from model predictive control, deep learning, and sensor networks to address these issues. Our approach introduces decentral, differentiable data fusion to estimate the state of distributed process steps and their dependence on input data. By treating the interconnected process steps as a quasi neural-network, we can backpropagate loss gradients for process optimization or model fine-tuning to process parameters or AI models respectively. The concept is demonstrated on a virtual machine park simulated in Unity, consisting of bulk material processes in plastic recycling.

A Fully Decentralized Randomized Controlled Study of As-Needed Albuterol-Budesonide Fixed-Dose Inhaler in Mild Asthma: The BATURA Study Design.

Purpose: Decentralized clinical trials, where trial-related activities occur at locations other than traditional clinical sites(eg participant homes, local healthcare facilities), have the potential to improve trial access for people for whom time and/or distance constraints may impede participation. Albuterol-budesonide 180/160 µg pressurized metered-dose inhaler (pMDI) is FDA approved for the as-needed treatment or prevention of bronchoconstriction and to reduce the risk of exacerbations in patients with asthma 18 years or older. BATURA (NCT05505734) is a fully decentralized study, investigating as-needed albuterol-budesonide in participants with mild asthma. Methods: BATURA is a fully decentralized, phase 3b, randomized, double-blind, event-driven exacerbation study conducted in the United States. Participants aged ≥12 years using as-needed short-acting ß2-agonist (SABA), alone or with low-dose inhaled corticosteroid or leukotriene receptor antagonist maintenance, are randomized 1:1 to as-needed albuterol-budesonide 180/160 µg or albuterol 180 µg pMDI for up to 52 weeks (minimum 12 weeks). Participants continue their current maintenance therapy, if applicable. Participants must have used SABA for ≥2 days in the 2 weeks pre-enrollment and have an Asthma Impairment Risk Questionnaire score ≥2 at screening and randomization. All trial-related visits, including screening and consent, are conducted virtually, with study medication shipped directly to each participant's residence. The primary objective is to evaluate the efficacy of as-needed albuterol-budesonide versus albuterol on severe asthma exacerbation risk, measured by time-to-first severe asthma exacerbation (primary endpoint). Secondary endpoints include annualized rate of severe asthma exacerbation and total systemic corticosteroid exposure. Study medication use is captured via a Hailie sensor attached to the study medication pMDI. The intended sample size is 2500 participants. Conclusion: BATURA evaluates as-needed albuterol-budesonide in participants with mild asthma. The decentralized study model enables the trial to move out of research sites into participant homes, reducing participant burden and improving access.

Accelerating urban energy transitions: Harnessing EEET framework dynamics for sustainable development in resource-based cities of Sichuan Basin, China.

This study examines managing urban energy systems in the Sichuan Basin, considering natural gas production. An integrated approach assessed the economy, energy, ecology, and technology (EEET) connections. The study identifies systemic issues and significant differences across locations and emphasizes the interdependent nature of economic expansion, energy innovations, and environmental commitment. Findings offer valuable insights for policymakers, providing strategic directions for overcoming barriers to sustainable city and energy advancements, and nurturing resilient urban environments. The EEET framework sheds light on the complex interactions of urban development, enabling officials to focus on areas for improvement. Addressing conflicts can create more cohesive and sustainable urban landscapes, uncovering serious environmental and energy-use issues in cities with dense industrial zones. Decision-makers could prioritize solutions like enforcing pollution controls and encouraging green energy use. Addressing disputes among urban development participants can craft strategies to encourage cooperation, finding win-win outcomes for sustainable city growth.

Secondary use of patient data within decentralized studies using the example of rare diseases in Germany: A data scientist's exploration of process and lessons learned.

Objective: Unlocking the potential of routine medical data for clinical research requires the analysis of data from multiple healthcare institutions. However, according to German data protection regulations, data can often not leave the individual institutions and decentralized approaches are needed. Decentralized studies face challenges regarding coordination, technical infrastructure, interoperability and regulatory compliance. Rare diseases are an important prototype research focus for decentralized data analyses, as patients are rare by definition and adequate cohort sizes can only be reached if data from multiple sites is combined. Methods: Within the project "Collaboration on Rare Diseases", decentralized studies focusing on four rare diseases (cystic fibrosis, phenylketonuria, Kawasaki disease, multisystem inflammatory syndrome in children) were conducted at 17 German university hospitals. Therefore, a data management process for decentralized studies was developed by an interdisciplinary team of experts from medicine, public health and data science. Along the process, lessons learned were formulated and discussed. Results: The process consists of eight steps and includes sub-processes for the definition of medical use cases, script development and data management. The lessons learned include on the one hand the organization and administration of the studies (collaboration of experts, use of standardized forms and publication of project information), and on the other hand the development of scripts and analysis (dependency on the database, use of standards and open source tools, feedback loops, anonymization). Conclusions: This work captures central challenges and describes possible solutions and can hence serve as a solid basis for the implementation and conduction of similar decentralized studies.

The Use of Blockchain Technology in Public Health: Lessons Learned.

Blockchain is a new technology utilized to develop creative solutions in different industries, such as health care. Blockchain is a decentralized and distributed encrypted system made up of interconnected blocks containing transaction-related information that can be shared with network participants. A blockchain network is utilized in the healthcare industry to safeguard and share patient information among hospitals, pharmacies, and doctors' diagnostic labs. Blockchain applications can precisely identify serious and potentially harmful mistakes within the medical sector. The objective is to comprehensively explore the potential use, present implementations, challenges, and future possibilities of blockchain in health management systems, and to provide information to researchers, policymakers, and practitioners on how to utilize new technology to enhance data security, efficiency, decentralization of data, authenticity of data, transparency, and verifiability of data compared to conventional databases in health management systems. Key review findings for blockchain technology in public health surveillance might include enhanced data security and accessibility of data, data storage and sharing, ensuring tamper-proof records are accessed, empowering patients, and improving overall healthcare outcomes. Its immutability proves to be important for securing healthcare data. It offers a safeguard for health records and clinical trial outcomes and ensures compliance with regulatory standards. This evaluation focuses on how it has transformed data protection, improved workflows, and safe health information interchange. Despite obstacles, further study and standardization initiatives have the potential to transform health care and guarantee patient care that is resilient and trustworthy. In the present healthcare industry, blockchain technology plays an essential role in healthcare systems. It can lead to computerized processes for collecting and validating data, accurate information collected from multiple sources, and data that are fixed, transparent to misuse, and secure, with a reduced risk of digital crimes. In addition, the study provides a detailed analysis of the potential applications for including the use of blockchain technology in transforming public health surveillance.

Blockchain-Based Caching Architecture for DApp Data Security and Delivery.

Decentralized applications (DApps) built on blockchain technology offer a promising solution to issues caused by centralization. However, traditional DApps leveraging off-chain storage face performance challenges due to factors such as storage location, network speed, and hardware conditions. For example, decentralized storage solutions such as IPFS suffer from diminished download performance due to I/O constraints influenced by data access patterns. Aiming to enhance the Quality of Service (QoS) in DApps built on blockchain technology, this paper proposes a blockchain node-based distributed caching architecture that guarantees real-time responsiveness for users. The proposed architecture ensures data integrity and user data ownership through blockchain while maintaining cache data consistency through local blockchain data. By implementing local cache clusters on blockchain nodes, our system achieves rapid response times. Additionally, attribute-based encryption is applied to stored content, enabling secure content sharing and access control, which prevents data leakage and unauthorized access in unreliable off-chain storage environments. Comparative analysis shows that our proposed system achieves a reduction in request processing latency of over 89% compared to existing off-chain solutions, maintaining cache data consistency and achieving response times within 65 ms. This demonstrates the model's effectiveness in providing secure and high-performance DApp solutions.

Innovative microbial water quality management in water distribution systems using in-pipe hydropowered UV disinfection: envisioning futuristic water-energy systems.

Hydropower UV disinfection has not been explored as a possible alternative for off-grid disinfection. Hydropowered UV LED technology was developed using off-the-shelf UV-C LEDs and pico - and femto-scale hydro turbine generators and evaluated across point-of-use relevant flow rates. Commercially available UV LED flow through reactors were subjected to microorganism challenge testing with 3 power schemes: wall-plug, hydropower, and hydropower-charged battery. UV LEDs powered by hydropower-charged battery demonstrated similar disinfection as wall-plug powered UV LEDs, achieving 0.5-1.8 MS2 log10 reduction at flow rates 0.5-2.3 L min-1, corresponding to reduction equivalent doses (RED) up to 16 or 30 mJ/cm2 for 254 and 285 nm, respectively. With hydropowered UV LEDs alone, MS2 log10 reduction decreased to <0.3 log10 reduction due to an underperforming and grossly inefficient turbine, with RED of 8 or 18 mJ/cm2 for 254 and 285 nm, respectively. Assessment of existing markets of UV disinfection systems and pico-hydro turbines demonstrated that hydropowered UV systems are already theoretically feasible for scales at point-of-entry (POE) and above. Economic feasibility will improve if turbines and/or UV system efficiencies improve. Prototype hydropower UV LED systems ranged from $145 to 220 depending on the UV LED reactor, and the battery system added $81. This study demonstrates the practicality of sustainable, renewable energy POU UV disinfection technology that can benefit decentralised, off-grid, rural and remote communities. The system may also scale up to provide renewable energy disinfection at larger scales, such as buildings and water distribution systems, for protecting human health in highly populated areas.

Distributed Broadcast Control of Multi-Agent Systems Using Hierarchical Coordination.

Broadcast control (BC) is a bio-inspired coordination technique for a swarm of agents in which a single coordinator broadcasts an identical scalar signal to all performing agents without discrimination, and the agents make appropriate moves towards the agents' collective optimal state without communicating with one another. The BC technique aims to accomplish a globally assigned task for which BC utilizes a stochastic optimization algorithm to coordinate a group of agents. However, the challenge intensifies as the system becomes larger: it requires a larger number of agents, which protracts the converging time for a single coordinator-based BC model. This paper proposes a revamped version of BC model, which assimilates distributed multiple coordinators to control a larger multi-agent system efficiently in a pragmatic manner. Precisely, in this hierarchical BC scheme, the distributed multiple sub-coordinators broadcast the identical feedback signal to the agents, which they receive from the global coordinator to accomplish the coverage control task of the ordinary agents. The dual role of sub-coordinators is manipulated by introducing weighted averaging of the gradient estimation under the stochastic optimization mechanism. The potency of the proposed model is analyzed with numerical simulation for a coverage control task, and various performance aspects are compared with the typical BC schemes to demonstrate its practicability and performance improvement. Particularly, the proposed scheme shows the same convergence with about 30% less traveling costs, and the near convergence is reached by only about one-third of iteration steps compared to the typical BC.