8,027 research outputs found

    LIFTS: Learning Featured Transition Systems

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    Artificial Intelligence in Sustainable Vertical Farming

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    As global challenges of population growth, climate change, and resource scarcity intensify, the agricultural landscape is at a critical juncture. Sustainable vertical farming emerges as a transformative solution to address these challenges by maximizing crop yields in controlled environments. This paradigm shift necessitates the integration of cutting-edge technologies, with Artificial Intelligence (AI) at the forefront. The paper provides a comprehensive exploration of the role of AI in sustainable vertical farming, investigating its potential, challenges, and opportunities. The review synthesizes the current state of AI applications, encompassing machine learning, computer vision, the Internet of Things (IoT), and robotics, in optimizing resource usage, automating tasks, and enhancing decision-making. It identifies gaps in research, emphasizing the need for optimized AI models, interdisciplinary collaboration, and the development of explainable AI in agriculture. The implications extend beyond efficiency gains, considering economic viability, reduced environmental impact, and increased food security. The paper concludes by offering insights for stakeholders and suggesting avenues for future research, aiming to guide the integration of AI technologies in sustainable vertical farming for a resilient and sustainable future in agriculture

    A multi-faceted intervention to reduce low value diagnostic studies in a medical intensive care unit.

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    Routine daily chest x-rays (CXRs) and laboratory studies have been identified as low-value care practices that contribute to the rising cost of healthcare without improving quality or outcomes. There is a large body of evidence as well as recommendations from multiple professional organizations for providers to not order unnecessary daily or routine diagnostic studies. Rather, these should be ordered in an on-demand fashion as a response to a specific clinical query. Despite the strength of recommendations, practice remains variable across the U.S. The reasons for resistance to practice change as well as the most effective strategies for implementing sustainable change are not well understood. The purpose of this quality improvement project is to evaluate the impact of a multifaceted intervention on the number of routine or daily chest x-rays and laboratory studies ordered by advanced practice providers and medical residents in a medical intensive care unit (MICU). The project aims are to 1) examine baseline ordering practices among MICU providers, 2) survey their knowledge, confidence, beliefs, and barriers surrounding daily diagnostic testing, 3) provide education to providers on current clinical guidelines, 4) implement the guidelines through the use of a clinical decision support tool, and 5) assess provider ordering practices post intervention . The primary outcome is to decrease the number of daily CXRs, BMPs, CBCs, and ABGs ordered unnecessarily in a medical intensive care unit

    Development, Implementation and Outcomes of a Quality Assurance System for the Provision of Continuous Renal Replacement Therapy in the Intensive Care Unit

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    Critically ill patients with requirement of continuous renal replacement therapy (CRRT) represent a growing intensive care unit (ICU) population. Optimal CRRT delivery demands continuous communication between stakeholders, iterative adjustment of therapy, and quality assurance systems. This Quality Improvement (QI) study reports the development, implementation and outcomes of a quality assurance system to support the provision of CRRT in the ICU. This study was carried out at the University of Kentucky Medical Center between September 2016 and June 2019. We implemented a quality assurance system using a step-wise approach based on the (a) assembly of a multidisciplinary team, (b) standardization of the CRRT protocol, (c) creation of electronic CRRT flowsheets, (d) selection, monitoring and reporting of quality metrics of CRRT deliverables, and (e) enhancement of education. We examined 34-month data comprising 1185 adult patients on CRRT (~โ€‰7420 patient-days of CRRT) and tracked selected QI outcomes/metrics of CRRT delivery. As a result of the QI interventions, we increased the number of multidisciplinary experts in the CRRT team and ensured a continuum of education to health care professionals. We maximized to 100% the use of continuous veno-venous hemodiafiltration and doubled the percentage of patients using regional citrate anticoagulation. The delivered CRRT effluent dose (~โ€‰30 ml/kg/h) and the delivered/prescribed effluent dose ratio (~โ€‰0.89) remained stable within the study period. The average filter life increased from 26 to 31 h (pโ€‰=โ€‰0.020), reducing the mean utilization of filters per patient from 3.56 to 2.67 (pโ€‰=โ€‰0.054) despite similar CRRT duration and mortality rates. The number of CRRT access alarms per treatment day was reduced by 43%. The improvement in filter utilization translated intoโ€‰~โ€‰20,000 USD gross savings in filter cost per 100-patient receiving CRRT. We satisfactorily developed and implemented a quality assurance system for the provision of CRRT in the ICU that enabled sustainable tracking of CRRT deliverables and reduced filter resource utilization at our institution

    Diverse perceptions of smart spaces

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    This is the era of smart technology and of โ€˜smartโ€™ as a meme, so we have run three workshops to examine the โ€˜smartโ€™ meme and the exploitation of smart environments. The literature relating to smart spaces focuses primarily on technologies and their capabilities. Our three workshops demonstrated that we require a stronger user focus if we are advantageously to exploit spaces ascribed as smart: we examined the concept of smartness from a variety of perspectives, in collaboration with a broad range of contributors. We have prepared this monograph mainly to report on the third workshop, held at Bournemouth University in April 2012, but do also consider the lessons learned from all three. We conclude with a roadmap for a fourth (and final) workshop, which is intended to emphasise the overarching importance of the humans using the spac

    Investigation of an embedded-optical-base system's functionality in detecting signal events for gait measurements

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    2018 Fall.Includes bibliographical references.Optical sensors have the potential to provide automated gait analysis and lameness detection in livestock. Measuring animals in motion while under field conditions is difficult for current gait analysis tools, such as plate and mat methods. This has caused a lack in commercially available systems. Additionally, a deficit of these systems and others is too much noise in their signal. Current sensor systems for static or in-motion measurements rely significantly on managing this noise as a source of error. From these problems, the primary objective of this body of work was to assess the use of an embedded-optical-base system (EOBS) and its ability to obtain real-time gait measurements from livestock. The research was composed of 3 field studies and 1 controlled study. Gait data was obtained using a commercial platform (2.4 m x 0.9 m; length x width) containing 1 EOBS. A signal-base-unit (SBU) and computer were setup near the EOBS platform by integrated cabling to collect real-time signal data. Signal fluctuation measurements (i.e., signal amplitude from hoof contact; 0 to 1 arbitrary units (au)) and kinematics (e.g., estimated speed, velocity and time duration) were recorded. The sensor detected hoof contact as signal amplitudes that could be examined in real time. Visual observations and video analyses were used for validating and classifying signal readings. The initial pilot study (field test) included 8 fistulated, crossbred steers (n = 8) tested over 1 d with 2 passes per animal over the EOBS platform. Pilot study data were used to evaluate initial signal fluctuations from animal contact. A second field study included 50 crossbred and purebred (n = 20, Angus; n = 10, Hereford; n = 20, Angus x Hereford) steers and heifers (n = 50; average BW = 292.5 kg) tested on 2 d over a 1-wk period with a total of 6 passes over the EOBS platform per animal. Steer and heifer normal walks, runs, and abnormal passes over the EOBS platform were analyzed. A third controlled study consisted of 3 mixed breed horses (n = 3) that had bilateral forelimb injections. Horses had both deep digital flexor muscles injected (1 with Botox and 1 with saline) with right and left forelimbs randomized. Horses were observed on 3 d over a 124-d period consisting of pre-treatment (baseline), post-treatment, and recovery test days with 10 passes over the EOBS platform per horse per day. Primary fluctuations, true (anomaly free) signal readings, from animal contact with the EOBS platform were analyzed. True signal readings were determined based on no influence observed from other limbs. A fourth field study consisted of 8 commercial bulls (n = 8) tested on 1 d with 3 passes over the EOBS platform per bull. Bulls were classified as either normal or abnormal in musculoskeletal structure and compared to one another to observe differences in signal fluctuation patterns. During the cattle studies, animals were not controlled and allowed to walk over the EOBS platform at their own pace. These studies formed the groundwork to determine the EOBS's functionality when animals passed over the platform. Signalment (i.e., breed, sex and age) and physiological characterizations were recorded. Temperature was also recorded for cattle field tests (e.g., min -6ยฐC to max 4ยฐC, respectively). For all 4 studies individual animal signal measurements were analyzed for each pass over the EOBS platform, compared to video data and classified for analysis. Results from all 4 studies showed intra- and inter-animal repeatability (qualitative observation) of observed signal readings. Though a variety of hoof contact signatures were obtained, repeating patterns were evident for both groups and individual animals. The embedded-optical-base system's (EOBS) functionality proved to be robust and operable under field trial conditions. Additionally, the signal showed extremely minimal noise. Lastly, the EOBS showed a stable baseline with clear deviations from it that could be correlated to hoof contact through video validation. Though the EOBS detected animal contact per pass, future work will investigate the system's operating readiness in accurately assessing variable gait measurements for lameness detection. Overall, data provides evidence that the embedded-optical-base system (EOBS) can detect hoof contact and differentiation between types of gait based on signal events

    ๋งˆ์ดํฌ๋กœ ์ „์‚ฐํ™” ๋‹จ์ธต์ดฌ์˜์„ ์ด์šฉํ•œ 3์ฐจ์› ๊ณจ-์ž„ํ”Œ๋ž€ํŠธ ์ ‘์ด‰๋ฅ  ์ธก์ • ์กฐ๊ฑด ํƒ์ƒ‰๊ณผ ์กฐ์งํ•™์  ์ธก์ •๋ฒ•๊ณผ์˜ ๋น„๊ต

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ์น˜๊ณผ๋Œ€ํ•™ ์น˜์˜๊ณผํ•™๊ณผ, 2023. 8. ์—ฌ์ธ์„ฑ.Purpose: Histological analysis is widely regarded as the gold standard method of evaluating osseointegration around a bone-implant. However, this approach requires invasive specimen preparation and is limited to representing only a single plane. By comparison, micro-computed tomography (ฮผCT) offers a rapid and convenient alternative that provides three-dimensional information, but is hampered by resolution and artifacts-related issue, making it a supplementary method for osseointegration analysis. To verify the reliability of ฮผCT for osseointegration evaluation, this animal model study compared bone-to-implant contact (BIC) ratios obtained by the gold standard histomorphometric method with those obtained by the ฮผCT method, using a rabbit tibia implant model. Materials and methods: A sandblasted, large-grit, acid-etched (SLA) implant and a machined surface implant were inserted into each tibia of two rabbits (giving eight implants in total). Bone-implant specimens were analyzed using ฮผCT with a spiral scan technique (SkyScan 1275) and histological sections were prepared thereafter. Three-dimensional (3D) reconstructed ฮผCT data and four two-dimensional (2D) ฮผCT sections, including one section corresponding to the histologic section and three additional sections rotated 45ยฐ, 90ยฐ, and 135ยฐ, were used to calculate the BIC ratio. The Pearsons test was used for correlation analysis at a significance level of 0.05. Results: The histomorphometric BIC and the 2D-ฮผCT BIC showed strong correlation (r = 0.762, P = 0.046), whereas the histomorphometric BIC and 3D-ฮผCT BIC did not (r = -0.375, P = 0.385). However, the mean BIC value of three or four 2D-ฮผCT sections showed a strong correlation with the 3D-ฮผCT BIC (three sections: r = 0.781, P = 0.038; four sections: r = 0.804, P = 0.029). Conclusion: The results of this animal model study indicate that ฮผCT can serve as a valuable complement to the histomorphometric method for bone-implant interface analyses. With the limitations of this study, 3D-ฮผCT analysis may even have a superior aspect by eliminating random variables that can arise as a consequence of the selected cutting direction.๋ชฉ ์  : ์น˜๊ณผ์šฉ ์ž„ํ”Œ๋ž€ํŠธ์˜ ๊ณจ์œ ์ฐฉ ํ‰๊ฐ€๋Š” ์กฐ์งํ•™์  ๋ถ„์„๋ฒ•์„ ํ†ตํ•˜์—ฌ ๊ณจ๊ณผ ์ž„ํ”Œ๋ž€ํŠธ์˜ ๋ถ€์ฐฉ๋ฅ ์„ ๋ถ„์„ํ•˜๋Š” ๊ฒƒ์ด ํ‘œ์ค€์œผ๋กœ ์—ฌ๊ฒจ์ง€๊ณ  ์žˆ์œผ๋‚˜, ์นจ์Šต์ ์ธ ์‹œํŽธ ์ค€๋น„๊ณผ์ •๊ณผ ํ•จ๊ป˜ 3์ฐจ์›์ ์ธ ์ž„ํ”Œ๋ž€ํŠธ์™€ ๊ณจ์˜ ๊ณ„๋ฉด ์ค‘ ํ•œ ๋‹จ๋ฉด๋งŒ์„ ํ‰๊ฐ€ํ•œ๋‹ค๋Š” ๋‹จ์ ์ด ์žˆ๋‹ค. ๊ทธ์— ๋”ฐ๋ผ, ๋ฏธ์„ธ ์ „์‚ฐํ™” ๋‹จ์ธต์ดฌ์˜(micro-CT)์„ ํ™œ์šฉํ•œ ๊ณจ์œ ์ฐฉ ๋ถ„์„๋ฒ•์ด ํ™œ์šฉ๋˜๊ณ  ์žˆ์œผ๋‚˜, ํ‘œ์ค€ํ™”๋œ ๋ฐฉ๋ฒ•์ด ์ œ์‹œ๋˜์–ด ์žˆ์ง€ ์•Š์œผ๋ฉฐ ํ•ด์ƒ๋„ ๋ฐ artifact๋“ฑ์˜ ๋ฌธ์ œ๋กœ ์ธํ•˜์—ฌ ๋ณด์กฐ์ ์ธ ๋ฐฉ๋ฒ•์œผ๋กœ๋งŒ ํ™œ์šฉ๋˜๊ณ  ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” spiral scanning์„ ํ™œ์šฉํ•œ micro-CT์˜ ๊ณจ-์ž„ํ”Œ๋ž€ํŠธ ๊ณ„๋ฉด ๋ถ„์„๋ฒ•์„ ๊ธฐ์กด์˜ ํ‘œ์ค€๋ฐฉ๋ฒ•์ธ ์กฐ์งํ•™์  ๋ถ„์„๋ฒ•๊ณผ ๋น„๊ตํ•˜๊ณ  ํ‰๊ฐ€ํ•˜์˜€๋‹ค. ๋ฐฉ ๋ฒ• : ํ† ๋ผ ๊ฒฝ๊ณจ ๋ชจ๋ธ์„ ์‚ฌ์šฉํ•˜์—ฌ, ์ด 2๋งˆ๋ฆฌ์˜ ํ† ๋ผ์˜ ์–‘์ชฝ ๊ฒฝ๊ณจ์— ๊ฐ๊ฐ 2๊ฐœ์”ฉ์˜ ์ž„ํ”Œ๋ž€ํŠธ๋ฅผ ์‹๋ฆฝํ•˜์—ฌ ์ด 8๊ฐœ์˜ ์ž„ํ”Œ๋ž€ํŠธ๋ฅผ ์‹๋ฆฝํ•˜์˜€๋‹ค. 8๊ฐœ์˜ ์ž„ํ”Œ๋ž€ํŠธ ์ค‘ 4๊ฐœ๋Š” SLA ํ‘œ๋ฉด์ฒ˜๋ฆฌ๊ฐ€ ๋˜์—ˆ๊ณ , ๋‚˜๋จธ์ง€ 4๊ฐœ๋Š” Turned ํ‘œ๋ฉด์„ ์‚ฌ์šฉํ•˜์˜€์œผ๋ฉฐ ๊ฐ ํ‘œ๋ฉด์˜ ์ž„ํ”Œ๋ž€ํŠธ๋Š” ๊ฒฝ๊ณจ์— ๊ต์ฐจ ์‹๋ฆฝ๋˜์—ˆ๋‹ค. ์‹๋ฆฝ 4์ฃผํ›„ ์‹คํ—˜๋™๋ฌผ์„ ํฌ์ƒํ•˜์—ฌ ๊ณจ-์ž„ํ”Œ๋ž€ํŠธ ์‹œํŽธ์„ ์ฒด์ทจํ•˜์˜€๊ณ , spiral scanning์„ ํ™œ์šฉํ•˜์—ฌ micro-CT (SkyScan 1275)๋กœ ์ดฌ์˜ํ•œ ๋’ค, ํ†ต์ƒ์ ์ธ ๋ฐฉ๋ฒ•์— ๋”ฐ๋ผ ์กฐ์ง ์‹œํŽธ์„ ์ œ์ž‘ํ•˜์˜€๋‹ค. Micro-CT์ดฌ์˜ ์ •๋ณด๋ฅผ ํ™œ์šฉํ•˜์—ฌ 3์ฐจ์›์ ์œผ๋กœ ๊ณจ-์ž„ํ”Œ๋ž€ํŠธ ๊ณ„๋ฉด์„ ์žฌ๊ตฌ์ถ•ํ•˜์˜€๊ณ , ์žฌ๊ตฌ์ถ•ํ•œ ์ž„ํ”Œ๋ž€ํŠธ ๊ณ„๋ฉด์ƒ์—์„œ ์ด 4๊ฐœ์˜ 2์ฐจ์› ๋‹จ๋ฉด์„ ๋‹ค์‹œ ์„ ํƒํ•˜์˜€๋‹ค. ๊ฐ 4๊ฐœ์˜ 2์ฐจ์› ๋‹จ๋ฉด์€ ์กฐ์ง ์‹œํŽธ๊ณผ ๋™์ผํ•œ ํ‰๋ฉด์„ ํฌํ•จํ•˜์—ฌ, ํ•ด๋‹น ํ‰๋ฉด์œผ๋กœ๋ถ€ํ„ฐ 45ยฐ, 90ยฐ, 135ยฐ ํšŒ์ „์‹œํ‚จ ํ‰๋ฉด์ด๋‹ค. 3์ฐจ์›์œผ๋กœ ์žฌ๊ตฌ์ถ•ํ•œ ๊ณจ-์ž„ํ”Œ๋ž€ํŠธ ๊ณ„๋ฉด (CT-3D), 4๊ฐœ์˜ 2์ฐจ์› ๋‹จ๋ฉด (CT-2D), ์กฐ์ง ์‹œํŽธ (histo-2D)์˜ ๊ณจ-์ž„ํ”Œ๋ž€ํŠธ ๋ถ€์ฐฉ๋ฅ ์„ ๊ตฌํ•˜์˜€๋‹ค. ๊ฐ ์ธก์ •๋ฐฉ๋ฒ•์˜ ์ƒ๊ด€๊ด€๊ณ„ ํ‰๊ฐ€๋ฅผ ์œ„ํ•ด Pearson ์ƒ๊ด€๊ณ„์ˆ˜ ๋ถ„์„์„ ์‚ฌ์šฉํ•˜์˜€์œผ๋ฉฐ, ์ž„ํ”Œ๋ž€ํŠธ ํ‘œ๋ฉด๊ฐ„์˜ ์ฐจ์ด๋ฅผ ํ‰๊ฐ€ํ•˜๊ธฐ ์œ„ํ•ด ๋…๋ฆฝ t-๊ฒ€์ •์ด ์‚ฌ์šฉ๋˜์—ˆ๋‹ค. ๊ฒฐ ๊ณผ : ๊ณจ-์ž„ํ”Œ๋ž€ํŠธ ๋ถ€์ฐฉ๋ฅ ์„ ๋ณด์•˜์„ ๋•Œ, histo-2D์™€ CT-2D ์ค‘ ์กฐ์ง ์‹œํŽธ๊ณผ ๋™์ผํ•œ ํ‰๋ฉด ๊ฐ„์—๋Š” ๊ฐ•ํ•œ ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ๋ณด์˜€์œผ๋‚˜ (r = 0.762, P = 0.046), histo-2D์™€ CT-3D ๊ฐ„์—๋Š” ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ๋ณด์ด์ง€ ์•Š์•˜๋‹ค (r = -0.375, P = 0.385). CT-2D์˜ ํ‰๋ฉด์—์„œ 3๊ฐœ๋‚˜ 4๊ฐœ์˜ ํ‰๋ฉด์˜ ํ‰๊ท ๊ฐ’์„ ์‚ฌ์šฉํ–ˆ์„ ๋•Œ๋Š” CT-3D์™€ ๊ฐ•ํ•œ ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ๋ณด์˜€๋‹ค. (3๊ฐœ์˜ ํ‰๋ฉด: r = 0.781, P = 0.038; 4๊ฐœ์˜ ํ‰๋ฉด: r = 0.804, P = 0.029). ๋‘ ์ข…๋ฅ˜์˜ ์ž„ํ”Œ๋ž€ํŠธ ํ‘œ๋ฉด๊ฐ„์—๋Š” ๊ณจ-์ž„ํ”Œ๋ž€ํŠธ ๋ถ€์ฐฉ๋ฅ  ๊ฐ„์— ์œ ์˜ํ•œ ์ฐจ์ด๋ฅผ ๋ณด์ด์ง€ ์•Š์•˜๋‹ค. ๊ฒฐ ๋ก  : ๋ณธ ๋™๋ฌผ ์‹คํ—˜์˜ ๊ฒฐ๊ณผ๋Š” micro-CT๊ฐ€ ๊ณจ-์ž„ํ”Œ๋ž€ํŠธ ๊ณ„๋ฉด ํ‰๊ฐ€์˜ ๋ณด์™„์ ์ธ ์ˆ˜๋‹จ์œผ๋กœ ์‚ฌ์šฉ๋  ์ˆ˜ ์žˆ์Œ์„ ์‹œ์‚ฌํ•œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ํ•œ๊ณ„ ํ•˜์—์„œ, micro-CT๋ฅผ ์ด์šฉํ•œ 3์ฐจ์› ๋ถ„์„์€ ์กฐ์ง ์‹œํŽธ ์ œ์ž‘์‹œ ์ ˆ์‚ญ ๋ฐฉํ–ฅ์— ๋”ฐ๋ผ ์„ ํƒ๋œ ๋‹จ๋ฉด์œผ๋กœ ์ธํ•œ ์ž„์˜์„ฑ ๋ณ€์ˆ˜๋ฅผ ์ œ๊ฑฐํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ์ ์—์„œ ์šฐ์ˆ˜ํ•œ ์ธก๋ฉด์„ ๊ฐ€์งˆ ์ˆ˜ ์žˆ๋‹ค.I. BACKGROUND 1 II. INTRODUCTION 5 III. MATERIALS AND METHODS 6 1. Specimen preparation and In vivo Implant surgery 7 2. Micro-CT scanning and data reconstruction 10 3. Undecalcified specimen preparation and histomorphometry 10 4. Analysis procedure for 2D and 3D Micro-CT 11 5. Statistics 16 IV. RESULTS 17 1. Clinical results of experimental animals 17 2. Histomorphometrical BIC ratio assessment 17 3. Measurement conditions of micro-CT analysis and 2D, 3D micro-CT BIC ratio assessment 19 4. Correlations between the BIC ratios determined using histomorphometry, 2D-ฮผCT with different cutting directions, and 3D-ฮผCT images 19 V. DISCUSSION 23 VI. CONCLUSIONS 29 VII. SUPPORTING INFORMATION 30 VIII. The published paper related to this study 32 REFERENCES 33 ABSTRACT IN KOREAN 40๋ฐ•
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