Miracle-dz/UniFAR-FTUs huggingface dataset:
Ejection Fraction Estimation from Echocardiograms Using Optimal Left Ventricle Feature Extraction Based on Clinical Methods

Echocardiography is one of the imaging systems most often utilized for assessing heart anatomy and function. Left ventricle ejection fraction (LVEF) is an important clinical variable assessed from echocardiography via the measurement of left ventricle (LV) parameters. Significant inter-observer and intra-observer variability is seen when LVEF is quantified by cardiologists using huge echocardiography data. Machine learning algorithms have the capability to analyze such extensive datasets and identify intricate patterns of structure and function of the heart that highly skilled observers might overlook, hence paving the way for computer-assisted diagnostics in this field. In this study, LV segmentation is performed on echocardiogram data followed by feature extraction from the left ventricle based on clinical methods. The extracted features are then subjected to analysis using both neural networks and traditional machine learning algorithms to estimate the LVEF. The results indicate that employing machine learning techniques on the extracted features from the left ventricle leads to higher accuracy than the utilization of Simpson’s method for estimating the LVEF. The evaluations are performed on a publicly available echocardiogram dataset, EchoNet-Dynamic. The best results are obtained when DeepLab, a convolutional neural network architecture, is used for LV segmentation along with Long Short-Term Memory Networks (LSTM) for the regression of LVEF, obtaining a dice similarity coefficient of 0.92 and a mean absolute error of 5.736%.
Causation-based process monitoring and diagnosis for multivariate categorical processes

ABSTRACT As many manufacturing and service processes nowadays involve multiple categorical quality characteristics, statistical surveillance for multivariate categorical processes has attracted increasing attention recently. However, in the literature there are only a few research papers that focus on the monitoring and diagnosis of such processes. This may be partly due to the challenges and limitations in describing the correlation relationships among categorical variables. In many applications, causal relationships may exist among categorical variables, in which the shifts at upstream, or cause, variables will propagate to their downstream, or effect, variables based on the causal structure. In such cases, a causation-based rather than correlation-based description would better account for the relationship among multiple categorical variables. This provides a new opportunity to establish improved monitoring and diagnosis schemes. In this article, we employ a Bayesian network to characterize such causal relationships and integrate it with a statistical process control technique. We propose two control charts for detecting shifts in the conditional probabilities of the multiple categorical variables that are embedded in the Bayesian network. The first chart provides a general tool, and the second chart integrates directional information, which also leads to a diagnostic prescription of shift locations. Both simulation and real case studies are used to demonstrate the effectiveness of the proposed monitoring and diagnostic schemes.
Viewing optics for immersive near-eye displays: pupil swim/size and weight/stray light

Near-eye display performance is usually summarized with a few simple metrics such as field of view, resolution, brightness, size, and weight, which are derived from the display industry. In practice, near-eye displays often suffer from image artifacts not captured in traditional display metrics. This work defines several immersive near-eye display metrics such as gaze resolution, pupil swim, image contrast, and stray light. We will discuss these metrics and their trade-offs through review of a few families of viewing optics. Fresnel lenses are used in most commercial virtual reality near-eye displays in part due to their light weight, low volume and acceptable pupil swim performance. However, Fresnel lenses can suffer from significant stray light artifacts. We will share our measurements of several lenses and demonstrate ways to improve performance. Smooth refractive lens systems offer the option for lower stray-light viewing but usually at the cost of a much larger size and weight in order to get to the same pupil swim performance. This can be addressed by using a curved image plane but requires new display technology. Polarization-based pancake optics is promising and can provide excellent image resolution and pupil swim performance within an attractive form-factor. This approach, however, generally results in low light efficiency and poor image contrast due to severe ghosting. We will discuss some of the main limitations of that technology.
A Class of Exact Solutions of (3+1)-Dimensional Generalized B-Type Kadomtsev–Petviashvili Equation

Abstract By employing the generalized (G′/G) $(G'/G)$-expansion method and symbolic computation, we obtain new exact solutions of the (3 + 1) dimensional generalized B-type Kadomtsev–Petviashvili equation, which include the traveling wave exact solutions and the non-traveling wave exact solutions showed by the hyperbolic function and the trigonometric function. Meanwhile, some interesting physics structure are discussed.
Actuator fault reconstruction based on a robust adaptive observer

A fault reconstruction method based on a robust adaptive observer is proposed to handle the actuator additive faults that exist in a non-linear descriptor system that satisfies the Lipschitz conditions. First, a robust adaptive fault reconstruction observer is proposed. This method has the desired robust performance and can achieve accurate reconstructions of both abrupt and time-varying faults in the presence of an external unknown disturbance. Then, to improve the transition process of fault reconstruction, an improved robust adaptive fault reconstruction observer is proposed. The improved robust adaptive fault reconstruction algorithm, which uses different coefficients for the proportion output errors and the derivative output errors, can enhance the rapidity and accuracy of fault reconstruction. The robust adaptive and improved robust adaptive observers are based on the linear matrix inequality technique. According to the Lyapunov stability theory, the stability conditions of the methods are derived. Additionally, the influence of an external unknown disturbance on the actuator fault reconstruction is suppressed using a robust method. Finally, two different examples are used to demonstrate the effectiveness of the proposed fault reconstruction methods.
A Multi-User Virtual World with Music Recommendations and Mood-Based Virtual Effects

The SEND/RETURN (S/R) project is created to explore the efficacy of content-based music recommendations alongside a uniquely generated Unreal Engine 5 (UE5) virtual environment based on audio features. S/R employs both a k-means clustering algorithm using audio features and a fast pattern matching (FPM) algorithm using 30-second audio signals to find similar-sounding songs to recommend to users. The feature values of the recommended song are then communicated via HTTP to the UE5 virtual environment, which changes a number of effects in real-time. All of this is being replicated from a listen-server to other clients to create a multiplayer audio session. S/R successfully creates a lightweight online environment that replicates song information to all clients and suggests new songs that alter the world around you. In this work, we extend S/R by training a convolutional neural network using Mel-spectrograms of 30-second audio samples to predict the mood of a song. This model can then orchestrate the post-processing effect in the UE5 virtual environment. The developed convolutional model had a validation accuracy of 67.5% in predicting 4 moods ('calm', 'energetic', 'happy', 'sad').
WORK LIFE BALANCE, BURNOUT, AND JOB CHARACTERISTICS OF WOMEN EMPLOYEES ON JOB SATISFACTION

Job satisfaction is something that all employees who work in an organization want to achieve, so it is necessary to pay attention to the main aspects to achieve job satisfaction. The purpose of this study was to explore work life balance, burnout and job characteristics associated with job satisfaction of female employees at the Center for Food and Drug Administration - Balai Besar Pengawasan Obat dan Makanan (BBPOM) in Pekanbaru City. This study uses a descriptive study with the intention of explaining work life balance, burnout, job characteristics and job satisfaction felt by BBPOM Pekanbaru female employees. Purposive sampling is used to determine the number of samples to be studied. There were 60 female employees of BBPOM in Pekanbaru City who met the characteristics of the sample. Questionnaire data in the form of statements were analyzed descriptively. The results showed that there were still female employees of BBPOM Pekanbaru City who lacked a sense of work life balance and still felt bored at work. The workload at the office piles up plus the burden on household chores that must be completed. As a result of the imbalance of work life and boredom experienced, some employees do not feel satisfied at work. However, in terms of job characteristics, they feel that the work assigned is in accordance with their expertise. This study can be used as input for organizations to achieve job satisfaction for female employees of BBPOM Pekanbaru City by paying attention to work-life balance.
Coronal plasma diagnostics from ground‐based observations

In this paper we discuss the potential of ground‐based visible observations of the solar corona to address the key open problems in the physics of the solar atmosphere and of solar activity. We first compare the diagnostic potential of visible observations with those of high‐resolution spectrometers and narrowband imagers working in the EUV and X‐ray wavelength ranges. We then review the main diagnostic techniques (and introduce a few new ones) that can be applied to line and continuum emission in the solar atmosphere, and the physical problems that they enable us to address. Finally, we briefly review the main features of ground‐based coronographic instrumentation currently being developed and planned.
Porcine epidemic diarrhea viruses from Vietnam: isolation, characterization, and neutralizing activity

Porcine epidemic diarrhea (PED) is characterized by acute enteritis, watery diarrhea, weight loss, dehydration, and death, with high mortality in neonatal piglets. In this study, 3 virus isolates collected in Vietnam between 2016 and 2017 were propagated successfully in Vero cells at high virus titers. Sequence analysis of the full-length spike (S) gene showed that all 3 isolates belong to genogroup 2b, which is closely related to other prevalent Asian strains. A comparison of the amino acid sequence revealed a 98.19% to 99.13% homology with the Vietnam isolates circulating during 2013–2015, suggesting that field PED viruses (PEDVs) are evolving continuously. Experiments in animals showed that the antisera from guinea pigs immunized with the vaccine strain resulted in higher levels (5 log2) of neutralizing antibodies against the homologous strain and a relatively moderate level of neutralizing antibodies against the field isolates. This finding would be helpful in selecting a PEDV strain for vaccine development.
Autonomous System for EEG-Based Multiple Abnormal Mental States Classification Using Hybrid Deep Neural Networks Under Flight Environment

Detection of the pilots’ mental states is particularly critical because their abnormal mental states (AbSs) could cause catastrophic accidents. In this study, we presented the feasibility of classifying the various specific AbSs (namely, low fatigue, high fatigue, low workload, high workload, low distraction, and high distraction) by applying the deep learning method. To the best of our knowledge, this study is the first attempt to classify multiple AbSs of pilots. We proposed the hybrid deep neural networks with five convolutional blocks and two long short-term memory layers for decoding multiple AbSs. We designed the model to extract the informative features from electroencephalography signals. A total of ten pilots conducted the experiment in a simulated flight environment. Compared with five conventional models, our proposed model achieved the highest grand-average accuracy of 68.04(± 5.26)% which is at least 6.55% higher than other conventional models for classifying seven mental states across all subjects. Our proposed model could distinguish and classify low and high levels for each status category and give appropriate feedback to the subjects. In addition, we found nine indicators that showed the statistically significant differences between two mental states ( ${p} < 0.05$ ). Hence, we believe that it will contribute significantly to autonomous driving or autopilot advances based on artificial intelligence technology in the future.
Effect of Penning Ionization on the Balance of Charged Particles in Plasma of a Stationary Reflex Discharge

A model of stationary reflex discharge, which is based on the volume-averaged (global) one, has been considered in the case of weakly ionized non-isothermal plasma. The temperature and the density of plasma electrons are calculated. The process of cathode sputtering and the formation of sputtered ions by means of the electron impact ionization, Penning ionization, and sputtered ion charge exchange at gas ions in plasma are analyzed. The dependence of the ionization degree of sputtered atoms on plasma parameters is determined. The influence of the Penning ionization on the balance of charged particles in plasma is considered.
Reduced‐order observer‐based fault estimation and fault‐tolerant control for a class of discrete Lipschitz nonlinear systems

This paper addresses an issue on reduced‐order observer‐based robust fault estimation and fault‐tolerant control for a class of uncertain nonlinear discrete‐time systems. By introducing a nonsingular coordinate transformation, a new nonlinear reduced‐order fault estimation observer (RFEO) is proposed with a wide application range in order to achieve an accurate estimation of both states and faults. Next, an improved algorithm is given to obtain the optimal estimation by using a novel iterative linear matrix inequality technique. Furthermore, an RFEO‐based output feedback fault‐tolerant controller, which is independent of the RFEO, can maintain the stability and performance of the faulty system. Simulation results of an aircraft application show the effectiveness of the proposed method. Copyright © 2016 John Wiley & Sons, Ltd.
The development of Radon transforms associated to Compton scatter imaging concepts

For more than three decades, the exploitation of Compton scattering has led to significant progress in imaging the inner parts of an optically opaque object. In this imaging process, the Compton scattering angle plays an essential role as it labels the recorded data, suppressing the motion of the scanning camera, as well as generates new geometric manifolds on which radiation measurements are made and thereby introducing new relevant Radon transforms. The present paper is aimed to synthetize the conceptual development of Compton scattering imaging in order to gain a constructive overview and possibly induce a further innovative growth to it. We believe this may lay foundation to new mathematical tools as well as lead to a great diversity of applications.
Windowed SSA （Singular Spectral Analysis） for Geophysical Time Series Analysis

Although the SSA (singular spectral analysis) is a potential tool for analysing time series of different physical processes, the processing of large geophysical data set requires more time and is found to be computationally expansive. In particular for the SVD (singular value decomposition) of large trajectory matrix, the processing units require huge memory and high performance computing system. In the present work, we propose an alternative scheme based on WSSA (windowed singular spectral analysis), which is robust for analysing long data sets without losing any valuable low-frequency information contained in the data. The underlying scheme reduces the floating point operations in SVD computations as the size of the trajectory matrix is small in windowed processing. In order to test the efficiency, the authors applied the proposed method on two geophysical data sets i.e., the climatic record with 30,000 data points and seismic reflection trace with 8,000 data points. The authors have shown that without distorting any physical information, the low-frequency contents of the data are well preserved after the windowed processing in both the cases.
A Biohybrid Interaction Framework for the Integration of Robots in Animal Societies

The collective behavior of animals has been traditionally studied through observation, quantitative models of behavior, and devices of low intelligence. Nowadays, the advancements in the field of robotics allow for closed-loop experiments that occur in real-time, and for artificial agents that intelligently and autonomously blend into hybrid groups of animals and robots (i.e., biohybrid groups). Such systems provide scientists with the opportunity to study the social interactions that govern collective behavior from within the group of animals, either by mimicking them or by introducing precise stimuli to elicit and study their response. In this work, we introduce an open source Biohybrid Observation and Interaction (BOBI) platform for small animals, which consists of 3 main components: 1) an experimental setup; 2) a wheeled mobile robot called LureBot; 3) their software. We demonstrate the software and hardware design aspects that make BOBI stand out compared to our previous system and similarly purposed systems. Finally, we demonstrate our methodologies and the new robot’s agility, by conducting a set of preliminary experiments with rummy-nose tetra (Hemigrammus rhodostomus) fish, where we study: 1) the response of one fish swimming with a biomimetic and a non-biomimetic lure; 2) the robot behavior when the robot interacts biomimetically and in closed loop with the wall alone, with 1 fish, and with 4 fish. The experiments show that fish strongly prefer biomimetic lures, and that the robot is consistently successful in engaging in social interactions with the fish.
Kick Cat Effect: Social Context Shapes the Form and Extent of Emotional Contagion

Emotional contagion refers to the transmission and interaction of emotions among people. Researchers have mainly focused on its process and mechanism, often simplifying its social background due to its complexity. Therefore, in this study, we attempt to explore whether the presence and clarity of social context affect emotional contagion and the related neural mechanisms. In Experiment 1, participants were asked to report their subjective experiences after being exposed to the facial expressions of emotional expressers, with or without the corresponding social context being presented. The results revealed that positive or negative expressions from the expressers elicited corresponding emotional experiences in the receivers, regardless of the presence of social context. However, when the social context was absent, the degree of emotional contagion was greater. In Experiment 2, we further investigated the effect of the clarity of social contexts on emotional contagion and its neural mechanisms. The results showed an effect consistent with those in Experiment 1 and highlighted the special role of N1, N2, P3, and LPP components in this process. According to the emotions as social information theory, individuals may rely more on social appraisal when they lack sufficient contextual information. By referencing the expressions of others and maintaining emotional convergence with them, individuals can adapt more appropriately to their current environment.
Characteristics of audit committees and public accountants on audit fees

The purpose of this research is to obtain empirical what are factors that affect audit fees with audit fees as dependent variable and audit committee effectiveness as independent variable. This research uses manufacturing companies listed on the Indonesia Stock Exchange (IDX) from 2018 to 2020 period. Data analysis was carried out using multiple regression analysis, with a selected sample of 198 companies of three years observation. The results indicates that audit committee size influence audit fees, while audit committee meeting and audit committee expertise are not influence audit fees. Therefore, company could focus on aspects which can influence the audit fees.
Towards the assessment of myelination using time-dependent diffusion MRI indices

Synopsis We study the sensitivity of time-dependent diffusion MRI indices or qτ-indices to demyelination in the mouse brain. For this, we acquire in vivo four-dimentional diffusion-weighted images-varying over gradient strength, direction and diffusion time-and estimate the qτ-indices from the corpus callosum. First order Taylor approximation of each index gives fitting coefficients α and β whose variance we investigate. Results indicate that, cuprizone intoxication affects mainly index coefficient β by introducing inequality of variances between the two mice groups, most significantly in the splenium and that MSD increases and RTOP decreases over diffusion time τ.
Efficacy and Safety of a Parenteral Nutrition Program for Patients with RAS Wild-Type Metastatic Colorectal Cancer Administered First-Line Cetuximab Plus Chemotherapy: A Propensity Score Matching Study

Malnutrition is a common problem in patients with metastatic colorectal cancer (mCRC) receiving targeted therapy plus chemotherapy, resulting in severe toxicity and decreased survival rates. This retrospective study employing propensity score matching (PSM) examined the efficacy and safety of a supplemental home parenteral nutrition (HPN) program for patients with RAS wild-type mCRC receiving cetuximab plus chemotherapy. This retrospective nationwide registry study included data from 14 medical centers/hospitals across Taiwan, and the data period ranged from November 2016 to December 2020. Patients with RAS wild-type mCRC receiving cetuximab plus chemotherapy as their first-line therapy were included and divided into HPN and non-HPN program groups. HPN was initiated based on patient-specific factors, such as baseline nutritional status, treatment-related toxicities, and comorbidities. Clinical outcomes were evaluated using response to therapy, duration of response (DoR), progression-free survival (PFS), and overall survival (OS). This study recruited 758 patients, of whom 110 and 648 were included in the HPN and non-HPN program groups, respectively. After 1:3 PSM, the data of 109 and 327 patients from the HPN and non-HPN program groups were analyzed, respectively. The HPN program group had a higher metastasectomy rate (33.9% vs. 20.2%, p = 0.005), and longer duration of treatment and DoR than the non-HPN program group (13.6 vs. 10.3 and 13.6 vs. 9.9 months, p = 0.001 and < 0.001, respectively). The HPN program group tended to have a longer median PFS (18.2 vs. 13.9 months, p = 0.102). Moreover, we noted a significant improvement in the median OS in the same group (53.4 vs. 34.6 months, p = 0.002). Supplemental HPN programs may be recommended for select patients with mCRC receiving targeted therapy plus chemotherapy to improve oncological outcomes.