ACS ES&T Water, Год журнала: 2025, Номер unknown
Опубликована: Янв. 28, 2025
Язык: Английский
Опубликована: Апрель 19, 2023
The application of Artificial Intelligence (AI) across a wide range domains comes with both high expectations its benefits and dire predictions misuse. While AI systems have largely been driven by technology-centered design approach, the potential societal consequences mobilized HCI researchers towards researching human-centered artificial intelligence (HCAI). However, there remains considerable ambiguity about what it means to frame, evaluate HCAI. This paper presents critical review large corpus peer-reviewed literature emerging on HCAI in order characterize community is defining as Our contributes an overview map research based work that explicitly mentions terms 'human-centered intelligence' or machine learning' their variations, suggests future challenges directions. reveals breadth happening HCAI, established clusters areas Interaction Ethical AI. new definition calls for greater collaboration between research, constructs.
Язык: Английский
Процитировано
75
Water Research, Год журнала: 2022, Номер 218, С. 118451 - 118451
Опубликована: Апрель 13, 2022
Язык: Английский
Процитировано
74
The Science of The Total Environment, Год журнала: 2024, Номер 917, С. 170085 - 170085
Опубликована: Янв. 15, 2024
Язык: Английский
Процитировано
32
Antibiotics, Год журнала: 2025, Номер 14(2), С. 134 - 134
Опубликована: Янв. 30, 2025
Antimicrobial resistance (AMR) poses a critical global health threat, necessitating innovative approaches in antimicrobial stewardship (AMS). Artificial intelligence (AI) and machine learning (ML) have emerged as transformative tools this domain, enabling data-driven interventions to optimize antibiotic use combat resistance. This comprehensive review explores the multifaceted role of AI ML models enhancing efforts across healthcare systems. AI-powered predictive analytics can identify patterns resistance, forecast outbreaks, guide personalized therapies by leveraging large-scale clinical epidemiological data. algorithms facilitate rapid pathogen identification, profiling, real-time monitoring, precise decision making. These technologies also support development advanced diagnostic tools, reducing reliance on broad-spectrum antibiotics fostering timely, targeted treatments. In public health, AI-driven surveillance systems improve detection AMR trends enhance monitoring capabilities. By integrating diverse data sources—such electronic records, laboratory results, environmental data—ML provide actionable insights policymakers, providers, officials. Additionally, applications programs (ASPs) promote adherence prescribing guidelines, evaluate intervention outcomes, resource allocation. Despite these advancements, challenges such quality, algorithm transparency, ethical considerations must be addressed maximize potential field. Future research should focus developing interpretable interdisciplinary collaborations ensure equitable sustainable integration into initiatives.
Язык: Английский
Процитировано
2
Environmental Pollution, Год журнала: 2021, Номер 292, С. 118299 - 118299
Опубликована: Окт. 7, 2021
Язык: Английский
Процитировано
67
Environmental Research, Год журнала: 2022, Номер 210, С. 112953 - 112953
Опубликована: Фев. 17, 2022
Язык: Английский
Процитировано
56
IEEE Access, Год журнала: 2022, Номер 10, С. 18583 - 18595
Опубликована: Янв. 1, 2022
In this study, a new blockchain protocol and novel architecture that integrate the advantages offered by edge computing, artificial intelligence (AI), IoT end-devices, were designed, developed, validated. This has ability to monitor environment, collect data, analyze it, process it using an AI-expert engine, provide predictions actionable outcomes, finally share on public platform. For use-case implementation, pandemic caused wide rapid spread of coronavirus COVID-19 was used test evaluate proposed system. Recently, various authors traced viruses in sewage water studied how can be as tracking Early warning notifications allow governments organizations take appropriate actions at earliest stages possible. The system validated experimentally 14 Raspberry Pis, results analyses proved is able utilize low-cost low-power flexible hardware processing layer detect predict its AI with accuracy 95%, outcome over accomplished when platform secured honesty-based distributed proof authority (HDPoA) without any substantial impact devices’ power sources, there only consumption increase 7% Pi for mining 14% produce prediction.
Язык: Английский
Процитировано
38
Discover Nano, Год журнала: 2023, Номер 18(1)
Опубликована: Апрель 1, 2023
Abstract Recent years have witnessed the emergence of several viruses and other pathogens. Some these infectious diseases spread globally, resulting in pandemics. Although biosensors various types been utilized for virus detection, their limited sensitivity remains an issue. Therefore, development better diagnostic tools that facilitate more efficient detection pathogens has become important. Nanotechnology recognized as a powerful tool viruses, it is expected to change landscape analysis. Recently, nanomaterials gained enormous attention value improving biosensor performance owing high surface-to-volume ratio quantum size effects. This article reviews impact nanotechnology on design, development, sensors viruses. Special paid nanoscale materials, nanobiosensors, internet medical things, artificial intelligence-based viral techniques.
Язык: Английский
Процитировано
27
American Journal of Infection Control, Год журнала: 2024, Номер 52(2), С. 141 - 146
Опубликована: Янв. 25, 2024
•Viral aerosols from toilet flushing pose a possible route of pathogen transmission.•Toilet lid closure prior to is believed mitigate cross-contamination.•We show does not cross-contamination.•Brushing bowl without disinfectant results in contamination surfaces.•The use during cleaning reduces cross-contamination surfaces. BackgroundViral generated represent potential transmission. The goal this study was determine the impact on generation viral and restroom fomites.MethodsA surrogate for human enteric viruses (bacteriophage MS2) added household public bowls flushed. resulting other surfaces then determined.ResultsAfter inoculated toilets, seat bottoms averaged >107 PFU/100 cm2. Viral did depend position (up or down). After were cleaned using brush with commercial product (hydrochloric acid), >4 log10 (>99.99%) reduction water observed versus no product. Bowl reduced by 1.6 (97.64%) when used product.ConclusionsThese demonstrate that closing risk contaminating bathroom disinfection all (ie, rim, floors) may be necessary after virus cross-contamination. fomites. A determined. These
Язык: Английский
Процитировано
11
Journal of Environmental Engineering, Год журнала: 2024, Номер 150(4)
Опубликована: Янв. 25, 2024
Machine learning (ML) is increasingly implemented to model water infrastructure dynamics. Common ML models are primarily data-driven and require a significant amount of data for robust training. Often, obtaining at higher temporal spatial resolutions in systems can be challenging due cost time considerations. In such scenarios, integrating the existing scientific knowledge into an as physics-informed advantageous enhance predictive capability generalizability. This study examines generalizability common typical unit operation process (UOP) system dynamics urban treatment. The studied (1) continuous stirred-tank reactor, (2) activated sludge (3) fixed-bed granular adsorption reactor. Applications neural networks (PINNs) presented. Results demonstrate when availability limited: not necessarily predicting dynamics, except exhibit simpler periodic patterns. also do generalize across different loading conditions. contrast, developed PINN yield high Benefiting from embedded prior knowledge, PINNs significantly reduced sets predictions. These results suggest hybridizing physics principles domain framework critical UOP modeling.
Язык: Английский
Процитировано
8