Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Delirium Prevention Program on Delirium Occurrence and the Length of Intensive Care Unit (ICU) Stay: A Systematic Review and Meta-Analysis

Background: ICU patients typically experience diverse kinds of distress factors, which make them vulnerable to delirium thereby resulting in a higher rate of delirium occurrence. We aimed to review systematically current states of preventive nursing intervention in ICUs, and to analyze delirium occurrence and the length of ICU stay to provide suggestions for the preventive nursing practice in ICU. Methods: Data collection was done with literature search databases including PubMed, Ovid-MEDLINE, EMBASE, CINAHL, KMbase, KoreaMed, Korean Studies Information Service System (KISS), Korea Institute of Science and Technology Information (KiSTi), Research Information Service System (RISS) and with hand searching, from Apr 20 to May 10, 2020. Two researchers independently selected research literature, and three researchers summarized and identified related variables based on data extraction methods. Results: Overall, 236 articles identified, 11 articles met the inclusion criteria for review. The systematic review of the contents of preventive nursing intervention other than drug administration rendered the four types of intervention. The Peto odds ratio (OR) of the rate of delirium occurrence appeared as 0.29 (95% Confidence Interval (CI)=0.20∼0.43) which was statistically significant (Z=6.23, P<.01) in both group. The magnitude of the effect for the length of ICU stay demonstrated insignificant values, Mean difference (MD) −0.23 (95% CI=−0.51∼0.05) (Z=1.60, P=.11). Conclusion: For management of delirium among ICU patients, multi-component intervention packages, suitable for care setting in ICUs, need to be taken into account for the preparation of nursing intervention for prevention of delirium applicable to nursing practices.
Dedicated services for Barrett’s esophagus—a survey and service assessment of provision in United Kingdom hospitals

Background: Specialist services in medicine are increasingly recognised as gold standard care, in gastroenterology, studies of Barrett’s esophagus (BE) suggest dedicated services show better adherence to surveillance protocols and concomitant dysplasia detection. The aim of this study was to gain insight into the BE practice in UK hospitals. Methods: Adult endoscopy units in the UK were contacted via phone or email and asked to complete a 9-question survey about their BE service. Results: Two hundred and sixty-five units (~95%) were contacted with a response rate of 61.9% (164/265), 56.3% (85/151) reported a dedicated BE service. Having a dedicated BE service was associated with the use of high-resolution white light (92.9% vs . 71.2%, P=0.001) and acetic acid (83.5% vs . 48.5%, P<0.001) and the availability of advanced endoscopic therapies such as radiofrequency ablation (43.5% vs . 19.7%, P=0.004). Trusts which reported a dedicated BE endoscopy service were more likely to have a dedicated BE clinic (52.9% vs . 16.7%, P<0.001) and a named BE lead (85.9% vs . 31.8%, P<0.001). Conclusions: Dedicated services for BE are available in the UK and associated with quality indicators, work is needed to determine the efficacy and efficiency of this model of service. carries a with a been one of the of need by Cancer Research UK and they estimate 59% of esophageal cancers are preventable (4). In order to detect dysplasia and early cancers, international and national guidelines advise regular surveillance endoscopy procedures performed every 2–5 years, during which visible abnormalities are targeted for biopsy and quadrantic biopsies every 1–2 cm (Seattle protocol biopsies) are performed to try to detect early changes (5-7). Retrospective studies have shown there are outcomes for patients on surveillance (8) and adherence to Seattle protocol leads greater for the detection of dysplasia (9).
Exact Bayesian Pairwise Preference Learning and Inference on the Uniform Convex Polytope

In Bayesian approaches to utility learning from preferences, the objective is to infer a posterior belief distribution over an agent’s utility function based on previously observed agent preferences. From this, one can then estimate quantities such as the expected utility of a decision or the probability of an unobserved preference, which can then be used to make or suggest future decisions on behalf of the agent. However, there remains an open question as to how one can represent beliefs over agent utilities, perform Bayesian updating based on observed agent pairwise preferences, and make inferences with this posterior distribution in an exact, closed-form. In this paper, we build on Bayesian pairwise preference learning models under the assumptions of linearly additive multi-attribute utility functions and a bounded uniform utility prior. These assumptions lead to a posterior form that is a uniform distribution over a convex polytope for which we then demonstrate how to perform exact, closed-form inference w.r. t. this posterior, i.e., without resorting to sampling or other approximation methods.
Structured controller synthesis using LMI and alternating projection method

In this paper, we propose new LMI-based conditions to design output-feedback controllers under structural constraints. The conditions are given in terms of LMI's with rank constraints and we solve them by means of the alternating projection algorithm. Even though the algorithm does not ensure global convergence, extensive numerical experiments demonstrate that the proposed design method is promising in designing structured and/or multiobjective controllers, provided that the dimension of the closed-loop systems is not excessively large.
Efficacy of critiques in an apparel design studio

ABSTRACT Critique is a central feature in the discipline of apparel design and product development. Thus, this study examined student-generated responses regarding their learning experiences with three different critique methods: (a) group critique, (b) peer critique and (c) desk critique with instructor. A convenient sample of 40 students enrolled in an introductory design studio at a Midwest university participated in each critique session in the course of three projects throughout the semester. An online survey with closed and open-ended questions was conducted followed by each critique. Content analysis and descriptive statistics were conducted for data analysis. Results showed that students’ perceived learning experiences, work improved and motivation increased through critiques with honest feedback. The findings confirm the importance of both instructor-led and student-led critiques. Strategically, instructors can integrate the efficacy of each critique method into course projects, thus, incorporating activity theory into the curriculum.
Metastatic Hypopharyngeal Carcinoma without Nodal Involvement

Introduction: In the UK, patients with hypopharyngeal carcinoma are managed via a multidisciplinary team approach based on UK National MDT Guidelines. Hypopharyngeal cancer often presents late, 80% of patients presenting with Stage III or IV disease, with PET-CT recommended for these patients to assess for metastases. Case: We present a case of an elderly ex-smoker with T2N0 hypopharyngeal carcinoma later upstaged to T3N0M1. Discussion: Our case highlights discrepancies between the MDT Guidelines and NICE guidance over when to perform staging scans. NICE recommends PET-CT for T4 or N3 disease compared to Stage III or IV disease in the National MDT guidelines. Our patient would not have qualified for PET-CT under NICE guidance and his metastatic disease would have been undiagnosed. This case highlights that lack of nodal involvement does not exclude metastases and there should be a low index of suspicion for distant metastases in patients with significant systemic symptoms.
Voltage-Controlled Variable Inductor for Fixed-Frequency Critical Conduction Mode Operation

This article proposes a novel voltage-controlled variable inductor (VI) for fixing the switching frequency of dc–dc converters in the critical conduction mode (CRM). It overcomes the limitations of conventional current-controlled VIs such as complex winding structures, difficulties in control, and high sensitivity to temperature. In the proposed voltage-controlled VI, the primary side inductance of a coupled inductor is regulated by an auxiliary voltage source on the secondary side. The auxiliary voltage source is implemented by an auxiliary dc–dc converter, which delivers power to the load in parallel with the main converter. The operating principle of the converter system for employing the proposed VI is presented, and a control loop is designed to fix the switching frequency, considering the small-signal model of the auxiliary converter. The validity of the proposed VI is experimentally verified using a prototype CRM boost converter with an output voltage of 400 V and a maximum power of 400 W. The switching frequency is fixed at 100 kHz and the soft switching of the main converter is maintained regardless of the input voltage and output power.
Scientometric Analysis and Research Mapping Knowledge of Coconut Fibers in Concrete

Biodegradable materials are appropriate for the environment and are gaining immense attention worldwide. The mechanical properties (such as elongation at break, density, and failure strain) of some natural fibers (such as Coir, Hemp, Jute, Ramie, and Sisal) are comparable with those of some synthetic fibers (such as E glass, aramid, or Kevlar). However, the toughness of coconut fibers is comparatively more than other natural fibers. Numerous studies suggest coconut fibers perform better to improve the concrete mechanical properties. However, the knowledge is dispersed, making it difficult for anyone to evaluate the compatibility of coconut fibers in concrete. This study aims to perform a scientometric review of coconut fiber applications in cementitious concrete to discover the various aspects of the literature. The typical conventional review studies are somehow limited in terms of their capacity for linking different literature elements entirely and precisely. Science mapping, co-occurrence, and co-citation are among a few primary challenging points in research at advanced levels. The highly innovative authors/researchers famous for citations, the sources having the highest number of articles, domains that are actively involved, and co-occurrences of keywords in the research on coconut-fiber-reinforced cementitious concrete are explored during the analysis. The bibliometric database with 235 published research studies, which are taken from the Scopus dataset, are analyzed using the VOSviewer application. This research will assist researchers in the development of joint ventures in addition to sharing novel approaches and ideas with the help of a statistical and graphical description of researchers and countries/regions that are contributing. In addition, the applicability of coconut fiber in concrete is explored for mechanical properties considering the literature, and this will benefit new researchers for its use in concrete.
Normality, Grubb’s and Runs Tests for the von Bertalanffy model used in the fitting of the growth of Bacillus cereus strain wwcp1 on malachite green dye

When diagnostic tests show that the residuals form a pattern, there are a few treatment alternatives to choose from. Two of these choices are running a nonparametric analysis and switching to a different model. In this study, the Wald-Wolfowitz runs test is utilized as a statistical diagnosis approach to determine whether or not the randomization criteria have been met. The Wald-Wolfowitz runs test was chosen as the best method to use for this research since it was necessary to examine the randomness of the residual for the von Bertalanffy model used in the fitting of the growth of Bacillus cereus strain wwcp1 on malachite green dye. The observations indicated that the residual series had an adequate number of runs; this was the outcome. The runs test discovered four runs, despite the fact that the randomization assumption predicted thirteen runs. This implies that the runs in the residual collection are only marginally meaningful. The fact that the p-value was less than 0.05 indicates that the null hypothesis is rejected; this implies that the residuals include convincing evidence of non-randomness. Furthermore, this demonstrates the need of looking at potential outliers. However, the test for the lack of an outlier does not demand reanalysis of the data, as indicated by Grubb's test findings. To resolve this discrepancy, either a different model needs to be used or more data needs to be added.
Multitarget Wireless Power Transfer System Using Metasurface for Quasi-Bessel Beams With Large Half Power Beam Length

We propose a multitarget wireless power transfer (WPT) system using a transmissive metasurface with quasi-nondiffraction Bessel beams. To provide similar powers for more different targets, a special metasurface is designed to achieve uniform energy distributions in the nondiffraction area of Bessel beams, which can increase the half-power beam length (HPBL) by 10% compared with ordinary Bessel beams. A transmissive metasurface unit with transmission coefficient of more than 90% and a phase shift range greater than 360° are presented to form the metasurface at 10 GHz, which further ensures the high efficiency of the generated Bessel beams. Various receiving parameters are analyzed at the energy receiving end to reduce the influence between different targets. Receiving antennas that can harvest energy are designed with different apertures at 10 GHz, and the system efficiencies with different receiving apertures and different spaces between the receiving targets are analyzed to determine the appropriate receiving parameters. Finally, a multitarget metasurface WPT system based on the quasi-Bessel beams is fabricated. Simulation and experimental results show that the system can achieve almost equal WPT efficiency for five targets. More than 91% of the incident power can be modulated to form the quasi-Bessel beams, and the efficiency of each target is greater than 4.4% for the simulated results and greater than 3.3% for the measured results. Analysis of the designed rectifier circuit further proves the rationality of the proposed WPT system, which can be used for multiple charging targets and maintain a uniform power transfer efficiency. The quasi-Bessel beams achieve the high-efficiency multitarget WPTs and simplify the circuit design of receiving targets, making WPT applicable to more scenarios.
Study on the Relationships between Doctor Characteristics and Online Consultation Volume in the Online Medical Community

Background. As a new medical service model, the online health community can integrate various medical resources to the maximum extent and improve the accessibility and utilization rate of hospital high-quality medical resources. Objective. Research based on the characteristics of doctors can enable doctors to display themselves on the network platform better, provide better services for patients, and improve the quality of medical services for doctors. Method. By crawling the characteristic data of doctors in Good Doctor Online, using dynamic analysis, correlation analysis and regression analysis, this study explores the relationships between each characteristic data and online consultation volume. Results. The doctor’s title and city level representing the static characteristics of the doctor have a weak impact on the doctor’s online consultation volume, and the doctor’s dynamic characteristics such as the number of patient completions, the number of gifts received, and the number of published articles can have a positive impact on the doctor’s online consultation volume. However, the recommended heat will negatively affect the online consultation volume, and the comment text has no significant impact on the doctor’s online consultation volume. Conclusion. Therefore, doctors should actively publicize and show their professional level and constantly optimize their dynamic characteristics, increasing the number of online consultations and thus improving their influence.
Performance Evaluation of PV Model-Based Maximum Power Point Tracking Techniques

Maximum power point tracking (MPPT) techniques extract the ultimate power from the photovoltaic (PV) source. Therefore, it is a fundamental control algorithm in any PV configuration. The research in this area is rich and many MPPT methods have been presented in the literature. However, in the current study, we focus on the PV model-based MPPT algorithms. In this regard, the classification of this category can be mainly divided into curve fitting methods and techniques based on the mathematical model or characteristics of the PV source. The objective of the PV model-based MPPT algorithm is to allocate the position of the maximum power point (MPP). Thus, no searching efforts are required to capture that point, which makes it simple and easy to implement. Consequently, the aim of this study is to give an overview of the most commonly utilized model-based MPPT methods. Furthermore, discussion and suggestions are also addressed to highlight the gap in this area. The main methods from the literature are compared together. The comparison and evaluation are validated using an experimental hardware-in-the-loop (HIL) system, where high efficiency (more than 99%) can be obtained with a simple calculation procedure and fast convergence speed.
ASSESSMENT OF CUMULUS PARAMETERIZATION SCHEMES IN MODELING METEOROLOGY ASSOCIATED WITH AN AIR POLLUTION EVENT IN THE ANDEAN REGION OF ECUADOR

Air quality results from the interaction between emissions and meteorology. When significant changes occur, such as a sudden drop in temperature, atmospheric stability can persist, promoting air pollution. Between 7 and 9 November 2020, there was a significant drop in temperature in Cuenca (2,500 masl), a city located in the Andean zone in southern Ecuador. At noon, decreases of up to 700 W m –2 and 10°C were recorded in the solar radiation and temperature levels, respectively, compared to the first days of the month. At the same time, increments of around 20 µg m –3 in PM 2.5 hourly concentrations were recorded. Cumulus convection is a process directly related to cloud formation and, therefore, to solar radiation and temperature. We simulated the meteorology from 2 to 11 November 2020, with the Weather Research and Forecasting model (WRF 4.0.3), with a resolution of 1 km, without cumulus parameterization, and with nine options for this component. Modeling without this parameterization provided acceptable results for solar radiation and temperature. However, this option overestimated wind speed at the surface. Globally, option 10 (Kain–Fritsch Cumulus Potential) presented the best modeling performance. Options 3 (Grell–Freitas) and 6 (Tiedtke) were better or similar than modeling without cumulus parameterization. None of the options adequately modeled the temperature and solar radiation on 9 November 2020, the day on which the lowest values of these variables were observed, suggesting that the model will not provide proper values for days with sudden decreases in solar radiation and temperature. Because most parameterized effects, such as the boundary layer, convection, microphysics, and surface schemes, are closely linked, it is necessary to study their influence. It is also necessary to assess the potential benefit of data assimilation and even the development of dedicated schemes for numerical simulation in the equatorial zone of the Andes.
PNNUAD: Perception Neural Networks Uncertainty Aware Decision-Making for Autonomous Vehicle

Most environment perception methods in autonomous vehicles rely on deep neural networks because of their impressive performance. However, neural networks have black-box characteristics in nature, which may lead to perception uncertainty and untrustworthy autonomous vehicles. Thus, this work proposes a decision-making method to adapt the potential perception uncertainty due to the sensor noises, fuzzy features, and unfamiliar inputs. The whole method is named as Perception Neural Networks Uncertainty Aware Decision-Making (PNNUAD) method. PNNUAD first uses the Monte Carlo dropout method to estimate the perception neural network uncertainty into a distribution around the original output. Then, the perception uncertainty will be considered in a designed reinforcement learning-based planner using a distributed value function. Finally, a backup policy will maintain the vehicle’s performance to avoid disastrous perception uncertainty. The evaluation section uses an augmented reality urban driving scenario; namely, the scenario builds in the CARLA simulator while the perception uncertainty comes from the real dataset. This case study focuses on the object class uncertainty of a widely used neural network, i.e., YOLO-V3. The results indicate that the proposed method can maintain AV safety even with poor perception performance. Meanwhile, the AV has not become too conservative by defending the perception uncertainty. This work is necessary for applying the statistics neural networks to safety-critical autonomous vehicles, and the source code will be open-source in this work.
Dual-Concentrated Network With Morphological Features for Tree Species Classification Using Hyperspectral Image

At present, deep learning is a hot topic in the field of the classification of hyperspectral image (HSI), and it has aroused wide attention. However, in fine-grained classification tasks, such as tree species classification, the uncertain spectrum remains the major factor restraining the classification performance. In order to solve the dilemma of forest tree species classification, a dual-concentrated network with morphological features (DNMF) is proposed. First, mathematical morphology is used to extract the morphological features of HSI. Then, coarse-grained information is extracted from the original hyperspectral data, and fine-grained information is extracted from morphological features. After that, both morphological representations and spectral inputs are fed into DNMF, and the overall evaluation index and visual image are obtained. The advantage of DNMF is that it decouples the spatial and spectral information, and a multisource information fusion process is then simulated. Accordingly, DNMF obtains high tree species classification accuracy. In order to verify the superiority of DNMF, we choose Gaofeng State-owned Forest Farm in Guangxi Province and the Belgium dataset, which was collected near the western part of Belgium as the research area. Related experiments demonstrate that the DNMF model achieves clearly better classification performance over other competitive baselines.