Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance of a new symptom checker in patient triage: Canadian cohort study

Background Computerized algorithms known as symptom checkers aim to help patients decide what to do should they have a new medical concern. However, despite widespread implementation, most studies on symptom checkers have involved simulated patients. Only limited evidence currently exists about symptom checker safety or accuracy when used by real patients. We developed a new prototype symptom checker and assessed its safety and accuracy in a prospective cohort of patients presenting to primary care and emergency departments with new medical concerns. Method A prospective cohort study was done to assess the prototype’s performance. The cohort consisted of adult patients (≥16 years old) who presented to hospital emergency departments and family physician clinics. Primary outcomes were safety and accuracy of triage recommendations to seek hospital care, seek primary care, or manage symptoms at home. Results Data from 281 hospital patients and 300 clinic patients were collected and analyzed. Sensitivity to emergencies was 100% (10/10 encounters). Sensitivity to urgencies was 90% (73/81) and 97% (34/35) for hospital and primary care patients, respectively. The prototype was significantly more accurate than patients at triage (73% versus 58%, p<0.01). Compliance with triage recommendations in this cohort using this iteration of the symptom checker would have reduced hospital visits by 55% but cause potential harm in 2–3% from delay in care. Interpretation The prototype symptom checker was superior to patients in deciding the most appropriate treatment setting for medical issues. This symptom checker could reduce a significant number of unnecessary hospital visits, with accuracy and safety outcomes comparable to existing data on telephone triage.
Thermal stress computation for steam-electric generator dispatch

Calculation of thermal stress in loading and unloading steam-electric generators can permit dispatch at higher ramp rates without increasing turbine rotor fatigue. The calculations are made from a thermal model of the rotor that does not require temperature sensors. The equations are validated against the fatigue index curves provided by turbine manufacturers. Examples are presented to show the advantage of using this model for dispatch and for the retrospective analysis of power output records. >
Efficient Symptom Inquiring and Diagnosis via Adaptive Alignment of Reinforcement Learning and Classification

Medical automatic diagnosis aims to organize real-world diagnostic processes similar to those from human doctors and to achieve accurate diagnoses by interacting with patients. The task is formulated as a sequential decision-making problem with a series of information inquiry steps (asking about symptoms and ordering examinations) and the final diagnosis. Recent research has studied incorporating reinforcement learning for information inquiry and classification techniques for disease diagnosis, respectively. However, studies on efficiently and effectively combining the two procedures are still lacking. To address this issue, we devised an adaptive mechanism to align reinforcement learning and classification methods using distribution entropy as the medium. Additionally, we created a new dataset for patient simulation to address the lack of large-scale evaluation benchmarks. The dataset is extracted from the MedlinePlus knowledge base and contains significantly more diseases and more comprehensive symptom and examination information than existing datasets. Experimental evaluation shows that our method outperforms three current state-of-the-art methods on different datasets by achieving higher medical diagnostic accuracy with fewer inquiring turns.
Energy Saving Speed and Charge/Discharge Control of a Railway Vehicle with On‐board Energy Storage by Means of an Optimization Model

The optimal operation of rail vehicle minimizing total energy consumption is discussed in this paper. In recent years, the energy storage devices have enough energy and power density to use in trains as on‐board energy storage. The on‐board storage can assist the acceleration/deceleration of the train and may decrease energy consumption. Many works on the application of the energy storage devices to trains were reported, however, they did not deal enough with the optimality of the control of the devices. The authors pointed out that the charging/discharging command and vehicle speed profile should be optimized together based on the optimality analysis. The authors have developed the mathematical model based on a general optimization technique, sequential quadratic programming. The proposed method can determine the optimal acceleration/deceleration and current commands at every sampling point under fixed conditions of transfer time and distance. Using the proposed method, simulations were implemented in some cases. The electric double layer capacitor (EDLC) is assumed as an energy storage device in our study, because of its high power density etc. The trend of optimal solutions such as values of control inputs and energy consumption is finally discussed. Copyright © 2009 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
The time-resolved atomic, molecular and optical science instrument at the Linac Coherent Light Source

The newly constructed time-resolved atomic, molecular and optical science instrument (TMO), configured to take full advantage of both linear accelerators at SLAC National Accelerator Laboratory, the copper accelerator operating at a repetition rate of 120 Hz providing high per-pulse energy as well as the superconducting accelerator operating at a repetition rate of about 1 MHz providing high average intensity, is described.
Quantum advantage in learning from experiments

Quantum technology promises to revolutionize how we learn about the physical world. An experiment that processes quantum data with a quantum computer could have substantial advantages over conventional experiments in which quantum states are measured and outcomes are processed with a classical computer. We proved that quantum machines could learn from exponentially fewer experiments than the number required by conventional experiments. This exponential advantage is shown for predicting properties of physical systems, performing quantum principal component analysis, and learning about physical dynamics. Furthermore, the quantum resources needed for achieving an exponential advantage are quite modest in some cases. Conducting experiments with 40 superconducting qubits and 1300 quantum gates, we demonstrated that a substantial quantum advantage is possible with today’s quantum processors. Description Learning from quantum experiments There is considerable interest in extending the recent success of quantum computers in outperforming their conventional classical counterparts (quantum advantage) from some model mathematical problems to more meaningful tasks. Huang et al. show how manipulating multiple quantum states can provide an exponential advantage over classical processing of measurements of single-quantum states for certain learning tasks. These include predicting properties of physical systems, performing quantum principal component analysis on noisy states, and learning approximate models of physical dynamics (see the Perspective by Dunjko). In their proof-of-principle experiments using up to 40 qubits on a Google Sycamore quantum processor, the authors achieved almost four orders of magnitude of reduction in the required number of experiments over the best-known classical lower bounds. —YS Quantum-enhanced strategies can provide a dramatic performance boost in learning useful information from quantum experiments.
Improving Teacher-Student Interactions in Online Educational Forums using a Markov Chain based Stackelberg Game Model

With the rapid proliferation of the Internet, the area of education has undergone a massive transformation in terms of how students and instructors interact in a classroom. Online learning now takes more than one form, including the use of technology to enhance a face-to-face class, a hybrid class that combines both face-to-face meetings and online work, and fully online courses. Further, online classrooms are usually composed of an online education forum (OEF) where students and instructor discuss open-ended questions for gaining better understanding of the subject. However, empirical studies have repeatedly shown that the dropout rates in these online courses are very high partly due to the lack of motivation among the enrolled students. We undertake an empirical comparison of student behavior in OEFs associated with a graduate-level course during two terms. We identify key parameters dictating the dynamics of OEFs like effective incentive design, student heterogeneity, and super-posters phenomenon. Motivated by empirical observations, we propose an analytical model based on continuous time Markov chains (CTMCs) to capture instructor-student interactions in an OEF. Using concepts from lumpability of CTMCs, we compute steady state and transient probabilities along with expected net-rewards for the instructor and the students. We formulate a mixed-integer linear program which views an OEF as a single-leader-multiple-followers Stackelberg game. Through simulations, we observe that students exhibit varied degree of non-monotonicity in their participation (with increasing instructor involvement). We also study the effect of instructor bias and budget on the student participation levels. Our model exhibits the empirically observed super-poster phenomenon under certain parameter configurations and recommends an optimal plan to the instructor for maximizing student participation in OEFs.
A new approach to distribution system protection-adaptive relaying

The authors outline the design of an adaptive relaying system for the distribution network of the City of Saskatoon. Overcurrent and directional overcurrent relays are used to protect the network. Important design aspects of the system are emphasised. The developed software and hardware are described. The scheme, implemented in the Power System Research Laboratory at the University of Saskatchewan, is briefly outlined. Also examined are the consequences of partial and total failure of communications between relays and the station computers, and the station computers and the central computer. Measures to alleviate such situations are discussed.
K-band Imaging of the Nearby Clumpy, Turbulent Disk Galaxy DYNAMO G04-1

We present a case study of the stellar clumps in G04-1—a clumpy, turbulent disk galaxy located at z = 0.13—from the DYnamics of Newly-Assembled Massive Objects sample, using adaptive optics-enabled K-band imaging (∼2.25 kpc arcsec−1) with Keck-NIRC2. We identify 15 stellar clumps in G04-1 with a range of masses from 3.6 × 106–2.7 × 108 M ⊙, and a median mass of ∼ 2.9 × 107 M ⊙. Note that these masses decrease by about half when we apply a light correction for the underlying stellar disk. A majority (12 of 15) of the clumps observed in the K P -band imaging have associated components in Hα maps (∼2.75 kpc arcsec−1; ∼ 500 pc) and appear colocated ( Δx¯∼0.″1 ). Using Hubble Space Telescope observations from the Wide Field Camera on the Advanced Camera for Surveys, with the F336W and F467M filters, we also find evidence of radial trends in the stellar properties of the clumps: the clumps closer to the center of G04-1 are more massive (consistent with observations in high-z systems) and appear more red, suggesting they may be more evolved. Using our high-resolution data, we construct a star-forming main sequence for G04-1 in terms of spatially resolved quantities, and find that all regions (both clump and intraclump) within the galaxy are experiencing an enhanced mode of star formation routinely observed in galaxies at high-z. In comparison to recent simulations, our observations of a number of clumps with masses of 107–108 M ⊙ are not consistent with strong radiative feedback in this galaxy.
Effects of the Noncoding Subgenomic RNA of Red Clover Necrotic Mosaic Virus in Virus Infection

Knowledge of how RNA viruses manipulate host and viral gene expression is crucial to our understanding of infection and disease. Unlike viral protein-host interactions, little is known about the control of gene expression by viral RNA. ABSTRACT In recent years, a new class of viral noncoding subgenomic RNA (ncsgRNA) has been identified. This RNA is generated as a stable degradation product via an exoribonuclease-resistant RNA (xrRNA) structure, which blocks the progression of 5′→3′ exoribonuclease on viral RNAs in infected cells. Here, we assess the effects of the ncsgRNA of red clover necrotic mosaic virus (RCNMV), called SR1f, in infected plants. We demonstrate the following: (i) the absence of SR1f reduces symptoms and decreases viral RNA accumulation in Nicotiana benthamiana and Arabidopsis thaliana plants; (ii) SR1f has an essential function other than suppression of RNA silencing; and (iii) the cytoplasmic exoribonuclease involved in mRNA turnover, XRN4, is not required for SR1f production or virus infection. A comparative transcriptomic analysis in N. benthamiana infected with wild-type RCNMV or an SR1f-deficient mutant RCNMV revealed that wild-type RCNMV infection, which produces SR1f and much higher levels of virus, has a greater and more significant impact on cellular gene expression than the SR1f-deficient mutant. Upregulated pathways include plant hormone signaling, plant-pathogen interaction, MAPK signaling, and several metabolic pathways, while photosynthesis-related genes were downregulated. We compare this to host genes known to participate in infection by other tombusvirids. Viral reads revealed a 10- to 100-fold ratio of positive to negative strand, and the abundance of reads of both strands mapping to the 3′ region of RCNMV RNA1 support the premature transcription termination mechanism of synthesis for the coding sgRNA. These results provide a framework for future studies of the interactions and functions of noncoding RNAs of plant viruses. IMPORTANCE Knowledge of how RNA viruses manipulate host and viral gene expression is crucial to our understanding of infection and disease. Unlike viral protein-host interactions, little is known about the control of gene expression by viral RNA. Here, we begin to address this question by investigating the noncoding subgenomic RNA (ncsgRNA) of red clover necrotic mosaic virus (RCNMV), called SR1f. Similar exoribonuclease-resistant RNAs of flaviviruses are well studied, but the roles of plant viral ncsgRNAs, and how they arise, are poorly understood. Surprisingly, we find the likely exonuclease candidate, XRN4, is not required to generate SR1f, and we assess the effects of SR1f on virus accumulation and symptom development. Finally, we compare the effects of infection by wild-type RCNMV versus an SR1f-deficient mutant on host gene expression in Nicotiana benthamiana, which reveals that ncsgRNAs such as SR1f are key players in virus-host interactions to facilitate productive infection.
Forward--Backward Splitting with Deviations for Monotone Inclusions

We propose and study a weakly convergent variant of the forward--backward algorithm for solving structured monotone inclusion problems. Our algorithm features a per-iteration deviation vector which provides additional degrees of freedom. The only requirement on the deviation vector to guarantee convergence is that its norm is bounded by a quantity that can be computed online. This approach provides great flexibility and opens up for the design of new and improved forward--backward-based algorithms, while retaining global convergence guarantees. These guarantees include linear convergence of our method under a metric subregularity assumption without the need to adapt the algorithm parameters. Choosing suitable monotone operators allows for incorporating deviations into other algorithms, such as Chambolle--Pock and Krasnoselsky--Mann iterations. We propose a novel inertial primal--dual algorithm by selecting the deviations along a momentum direction and deciding their size using the norm condition. Numerical experiments demonstrate our convergence claims and show that even this simple choice of deviation vector can improve the performance, compared, e.g., to the standard Chambolle--Pock algorithm.
Homotopy Based Reinforcement Learning with Maximum Entropy for Autonomous Air Combat

The Intelligent decision of the unmanned combat aerial vehicle (UCAV) has long been a challenging problem. The conventional search method can hardly satisfy the real-time demand during high dynamics air combat scenarios. The reinforcement learning (RL) method can significantly shorten the decision time via using neural networks. However, the sparse reward problem limits its convergence speed and the artificial prior experience reward can easily deviate its optimal convergent direction of the original task, which raises great difficulties for the RL air combat application. In this paper, we propose a homotopy-based soft actor-critic method (HSAC) which focuses on addressing these problems via following the homotopy path between the original task with sparse reward and the auxiliary task with artificial prior experience reward. The convergence and the feasibility of this method are also proved in this paper. To confirm our method feasibly, we construct a detailed 3D air combat simulation environment for the RL-based methods training firstly, and we implement our method in both the attack horizontal flight UCAV task and the self-play confrontation task. Experimental results show that our method performs better than the methods only utilizing the sparse reward or the artificial prior experience reward. The agent trained by our method can reach more than 98.3% win rate in the attack horizontal flight UCAV task and average 67.4% win rate when confronted with the agents trained by the other two methods.
Compassion fatigue in pediatric hematology, oncology, and bone marrow transplant healthcare providers: An integrative review

Abstract Objective Compassion fatigue (CF), which includes burnout and secondary traumatic stress, is highly prevalent among healthcare providers (HCPs). Ultimately, if left untreated, CF is often associated with absenteeism, decreased work performance, poor job satisfaction, and providers leaving their positions. To identify risk factors for developing CF and interventions to combat it in pediatric hematology, oncology, and bone marrow transplant (PHOB) HCPs. Methods An integrative review was conducted. Controlled vocabulary relevant to neoplasms, CF, pediatrics, and HCPs was used to search PubMed, Cumulative Index to Nursing and Allied Health Literature (CINAHL), PsycINFO, and Web of Science MEDLINE. Inclusion criteria were the following: English language and PHOB population. Exclusion criteria were the following: did not address question, wrong study population, mixed study population where PHOB HCPs were only part of the population, articles about moral distress as this is a similar but not the same topic as CF, conference abstracts, and book chapters. Results A total of 16 articles were reviewed: 3 qualitative, 6 quantitative, 3 mixed methods, and 4 non research. Three themes were explored: (1) high-risk populations for developing CF, (2) sources of stress in PHOB HCPs, and (3) workplace interventions to decrease CF. Significance of results PHOB HCPs are at high risk of developing CF due to high morbidity and mortality in their patient population. Various interventions, including the use of a clinical support nurse, debriefing, support groups, respite rooms, and retreats, have varying degrees of efficacy to decrease CF in this population.
Systematic assessment of acquisition and data-processing parameters in the suspect screening of veterinary drugs in archive matrices using LC-HRMS

ABSTRACT Monitoring strategies for veterinary drugs in products of animal origin are shifting towards a more risk-based approach. Such strategies not only target a limited number of predefined . substances but also facilitate detection of unexpected substances. By combining the use of archive matrices such as feather meal with suspect-screening methods, early detection of new hazards in the food and feed industry can be achieved. Effective application of such strategies is hampered by complex data interpretation and therefore, targeted data analysis is commonly applied. In this study, the performance of a suspect-screening data processing workflow using a suspect list or the online spectral database mzCloudTM was explored to facilitate detection of veterinary drugs in archive matrices. Data evaluation parameters specifically investigated for application of a suspect list were mass tolerance and the addition or omission of retention times. Application of a mass tolerance of 1.5 ppm leads to an increase in the number of false positives, as does omission of retention times in the suspect list. Different acquisition modes yielding different qualities of MS2 data were studied and proved to be a critical factor, where data-dependent acquisition is preferred when matching to the mzCloudTM database. Using this approach, it is possible to search for compounds on a dedicated suspect list based on the exact mass and retention times and, at the same time, detect unexpected compounds without a priori information. A pilot study was conducted and fourteen different antibiotics were detected (and confirmed by MS/MS). Three of these antibiotics were not included in the suspect list. The optimised suspect-screening method proved to be fit for the purpose of finding veterinary drugs in feather meal, which are not in the scope of the current monitoring methods and therefore, it gives added value in the perspective of a risk-based monitoring.
Dynamic Aphasia as a Variant of Frontotemporal Dementia

We describe two individuals with progressive verbal difficulty who exhibited impairment of propositional language, with relatively well-preserved auditory comprehension, naming, and repetition—a profile that is consistent with dynamic aphasia. By providing a brief review of pertinent literature and the results from our neurologic, speech and language, neuropsychological, and neuroimaging testing, this report sheds light on the infrequently reported dynamic aphasia in the context of frontotemporal dementia. Our patients’ insights into their verbal difficulty tend to support the notion that dynamic aphasia results from interference at the stage where thoughts are converted into verbal messages—that is, the thought–verbal interface.
Impact of the precursor gas ratio on dispersion engineering of broadband silicon nitride microresonator frequency combs

Microresonator frequency combs, or microcombs, have gained wide appeal for their rich nonlinear physics and wide range of applications. Stoichiometric silicon nitride films grown via low-pressure chemical vapor deposition (LPCVD), in particular, are widely used in chip-integrated Kerr microcombs. Critical to such devices is the ability to control the microresonator dispersion, which has contributions from both material refractive index dispersion and geometric confinement. Here, we show that modifications to the ratio of the gaseous precursors in LPCVD growth have a significant impact on material dispersion and hence the overall microresonator dispersion. In contrast to the many efforts focused on comparisons between Si-rich films and stoichiometric (Si3N4) films, here, we focus on films whose precursor gas ratios should nominally place them in the stoichiometric regime. We further show that microresonator geometric dispersion can be tuned to compensate for changes in the material dispersion. © 2021 Optical Society of America