Miracle-dz/UniFAR-FTUs huggingface dataset:
Dyspnea Severity Assessment Based on Vocalization Behavior with Deep Learning on the Telephone

In this paper, a system to assess dyspnea with the mMRC scale, on the phone, via deep learning, is proposed. The method is based on modeling the spontaneous behavior of subjects while pronouncing controlled phonetization. These vocalizations were designed, or chosen, to deal with the stationary noise suppression of cellular handsets, to provoke different rates of exhaled air, and to stimulate different levels of fluency. Time-independent and time-dependent engineered features were proposed and selected, and a k-fold scheme with double validation was adopted to select the models with the greatest potential for generalization. Moreover, score fusion methods were also investigated to optimize the complementarity of the controlled phonetizations and features that were engineered and selected. The results reported here were obtained from 104 participants, where 34 corresponded to healthy individuals and 70 were patients with respiratory conditions. The subjects’ vocalizations were recorded with a telephone call (i.e., with an IVR server). The system provided an accuracy of 59% (i.e., estimating the correct mMRC), a root mean square error equal to 0.98, false positive rate of 6%, false negative rate of 11%, and an area under the ROC curve equal to 0.97. Finally, a prototype was developed and implemented, with an ASR-based automatic segmentation scheme, to estimate dyspnea on line.
Analysis of coding unit partitioning and complexity reduction at intra-prediction mode of HEVC

ABSTRACT The emerging trend of ultra-high definition (4 K, UHD) video services necessitates coding efficiency, which is much higher than H.264. High-Efficiency Video Coding (HEVC) has been designed with the intention of drastically reducing the bit rate without compromising the video quality. The key feature is the introduction of a larger block structure with flexible block sizes from 64 × 64 to 8 × 8 samples. The enhanced feature has imposed higher complexity, specifically at the intra-prediction mode level of HEVC. The proposed research work has presented a novel hybrid approach to reducing the number of candidate Coding Units (CU) passing through irrelevant partition patterns and time-consuming iterative Rate-Distortion (RD) cost estimation process. The hybrid approach is implemented in two steps. In the first step gradient evaluation is performed and the CU is categorized as homogeneous or non-homogeneous. In the second step Early Termination is adopted by using machine learning for partitioning non-homogeneous CU. As the number of candidate Coding Units undergoing the time-consuming RD process is reduced by using the two-step hybrid approach, the proposed approach proves to be the best of its kind. The reduction in the number of candidate units undergoing the RD process reduces the complexity of the intra-prediction process. Encoding time is the measure of complexity and the proposed hybrid approach reduces the encoding time by an average of 36% compared with the HEVC reference model and also by an average of 26% as compared with the previous work. The Peak Signal to Noise Ratio (PSNR) reduced by an average of 0.1% indicates a slight reduction in the encoded image quality. The proposed approach significantly reduced the encoding time without affecting the RD performance of the encoder. The image quality is not compromised substantially when considering the improvement in the encoding time and the reduction in the complexity at an intra-prediction level.
Agglomeration vs amenities? Unraveling the latent engine of growth in metropolitan Greece

Economic downturns, social change, and migrations shape population expansion and shrinkage, making city life cycles particularly complex over time and intrinsically diversified over space. Identifying local drivers of population change plays a major role when addressing metropolitan cycles of growth and decline and provides insights to any policy and planning strategy aimed at promoting together local development, economic competitiveness, and socio-environmental sustainability at large. Timing of metropolitan cycles is, however, heterogeneous and reflects the individual development path of any city. Assuming economic downturns and the associated social processes at the base of spatially heterogeneous patterns of population growth and decline in Mediterranean Europe, we adopted a spatial econometric approach investigating short-term and long-term demographic dynamics (1960–2010) in metropolitan Athens (Greece), with the aim at identifying contextual drivers of population change. Spatial regressions evaluated the role of economic and non-economic dimensions of metropolitan growth, quantifying the impact of agglomeration, scale, accessibility, and amenities at different phases of the city life cycle. Settlement models grounded on scale and agglomeration processes—with growing population in high- and medium-density municipalities—were observed under economic expansion. Recession consolidated a settlement model with population growth in socially dynamic and accessible (low density) districts with natural/cultural amenities, reflecting the inherent decline of agglomeration economies. Based on such dynamics, the polarized hierarchy of central and peripheral locations resulting from radio-centric population expansion was replaced with a settlement model grounded on population increase in “intermediate-density,” attractive locations.
A Bifunctional Fluorescence Probe Based on AIE-ICT Strategy for Visual Detection of Cu2+/Co2+ in Complex Matrix

A novel fluorescence chemical sensor-based probe 1-{[(E)-(2-aminophenyl)azanylidene]methyl}naphthalen-2-ol (AMN) was designed and synthesized, which performed a “naked eye” detection ability toward Cu2+ and Co2+ based on aggregation-induced emission (AIE) fluorescence strategy. It has sensitive detection ability for Cu2+ and Co2+. In addition, the color changed from yellow-green to orange under the sunlight, realizing the rapid identification of Cu2+/Co2+, which has the potential of on-site visual detection under the “naked eye”. Moreover, different “on” and “off” fluorescence expressions were exhibited under excessive glutathione (GSH) in AMN-Cu2+ and AMN-Co2+ systems, which could be employed to distinguish Cu2+ from Co2+. The detection limits for Cu2+ and Co2+ were measured to be 8.29 × 10−8 M and 9.13 × 10−8 M, respectively. The binding mode of AMN was calculated to be 2:1 by Jobs’ plot method analysis. Ultimately, the new fluorescence sensor was applied to detect Cu2+ and Co2+ in real samples (tap water, river water, and yellow croaker), and the results were satisfying. Therefore, this high-efficiency bifunctional chemical sensor platform based on “on–off” fluorescence detection will provide significant guidance for the advance development of single-molecule sensors for multi-ion detection.
Construction of a Novel Diagnostic Model Based on Ferroptosis-Related Genes for Hepatocellular Carcinoma Using Machine and Deep Learning Methods

Hepatocellular carcinoma (HCC) is one of the most general malignant tumors. Ferroptosis, a type of necrotic cell death that is oxidative and iron-dependent, has a strong correlation with the development of tumors and the progression of cancer. The present study was designed to identify potential diagnostic Ferroptosis-related genes (FRGs) using machine learning. From GEO datasets, two publicly available gene expression profiles (GSE65372 and GSE84402) from HCC and nontumor tissues were retrieved. The GSE65372 database was used to screen for FRGs with differential expression between HCC cases and nontumor specimens. Following this, a pathway enrichment analysis of FRGs was carried out. In order to locate potential biomarkers, an analysis using the support vector machine recursive feature elimination (SVM-RFE) model and the LASSO regression model were carried out. The levels of the novel biomarkers were validated further using data from the GSE84402 dataset and the TCGA datasets. In this study, 40 of 237 FRGs exhibited a dysregulated level between HCC specimens and nontumor specimens from GSE65372, including 27 increased and 13 decreased genes. The results of KEGG assays indicated that the 40 differential expressed FRGs were mainly enriched in the longevity regulating pathway, AMPK signaling pathway, the mTOR signaling pathway, and hepatocellular carcinoma. Subsequently, HSPB1, CDKN2A, LPIN1, MTDH, DCAF7, TRIM26, PIR, BCAT2, EZH2, and ADAMTS13 were identified as potential diagnostic biomarkers. ROC assays confirmed the diagnostic value of the new model. The expression of some FRGs among 11 FRGs was further confirmed by the GSE84402 dataset and TCGA datasets. Overall, our findings provided a novel diagnostic model using FRGs. Prior to its application in a clinical context, there is a need for additional research to evaluate the diagnostic value for HCC.
Rate Integrating Hemispherical Resonator Gyroscope Detection Error Analysis and Compensation

Compensation for the hemispherical resonator gyroscope (HRG) detection error significantly improves gyroscope performance as well as provides a basis for the implementation of high-precision control algorithms. In this article, an identification method is proposed in which the precession factor and the detection error parameters, including gain error, nonorthogonal angle error, and phase error, can be identified simultaneously in a single test. First, we analyze the detection error caused by gyroscope manufacturing as well as the inconsistency in circuit device parameters. Second, the detection error is modeled, and the impact of the error on the standing wave azimuth detection and the control system is discussed. Third, a method is presented for identifying the error parameters based on the nonlinear least squares. Finally, experiments are conducted to verify the effectiveness of the identification and compensation method. As a result of compensation, the bias instability decreases by 33 times from 5.08°/h to 0.15°/h, and the scale factor nonlinearity decreases by four times from 50.26 to 10.23 ppm.
Naples Prognostic Score as an Independent Predictor of Survival Outcomes for Resected Locally Advanced Non-Small Cell Lung Cancer Patients After Neoadjuvant Treatment

Background The Naples Prognostic Score (NPS) can reflect patient’s nutritional and inflammatory status, which is identified as a prognostic indicator for various malignant tumors. However, its significance in patients with resected locally advanced non-small cell lung cancer (LA-NSCLC) patients who receive neoadjuvant treatment remains unclear so far. Methods A total of 165 LA-NSCLC patients surgically treated from May 2012 to November 2017 were retrospectively investigated. The LA-NSCLC patients were divided into three groups according to NPS scores. The receiver operating curve (ROC) analysis was performed to reveal the discriminatory ability of NPS and other indicators for predicting the survival. The NPS and clinicopathological variables were further evaluated the prognostic value by univariate and multivariate Cox analysis. Results The NPS was related to age (P = 0.046), smoking history (P = 0.004), Eastern Cooperative Oncology Group (ECOG) score (P = 0.005), and adjuvant treatment (P = 0.017). Patients with high NPS scores had worse overall survival (OS) (group 1 vs 0, P = 0.006; group 2 vs 0, P < 0.001) and disease-free survival (DFS) (group 1 vs 0, P < 0.001; group 2 vs 0, P < 0.001). The ROC analysis demonstrated that NPS had better predictive ability than other prognostic indicators. Multivariate analysis revealed that NPS was independent prognostic indicator of OS (group 1 vs 0, hazard ratio [HR] =2.591, P = 0.023; group 2 vs 0, HR = 8.744, P = 0.001) and DFS (group 1 vs 0, HR =3.754, P < 0.001; group 2 vs 0, HR = 9.673, P < 0.001). Conclusion The NPS could be an independent prognostic indicator in patients with resected LA-NSCLC receiving neoadjuvant treatment and more reliable than the other nutritional and inflammatory indicators.
Leading-edge effects in bypass transition

The effect of a blunt leading edge on bypass transition is studied by numerical simulation. A mixed direct and large-eddy simulation of a flat plate with a super-ellipse leading edge is carried out at various conditions. Onset and completion of transition is seen to move upstream with increasing bluntness. For sharper leading edges, at lower levels of turbulence, transition usually occurs through instabilities on low-speed streaks as observed by Jacobs & Durbin (2001) and Brandt et al. (2004) whereas increasing either the turbulence intensity or the leading-edge bluntness brings into play another mechanism. Free-stream vortices are amplified at the leading edge because of stretching. In the case of particularly strong vortices, this interaction induces a localized streamwise vortical disturbance in the boundary layer which then grows as it convects downstream and eventually breaks down to form a turbulent spot. These disturbances, which are localized and hence wavepacket-like, move at speeds in the range 0.55 U∞–0.65 U∞ and occur in the lower portion of the boundary layer. Simulations conducted with isolated vortices confirm such a response of the boundary layer.
Analysis of Laminar Heat Transfer in Internally Finned Tubes with Uniform Outside Wall Temperature

An analysis is presented for fully developed laminar convective heat transfer in tubes with internal longitudinal fins and uniform outside wall temperature. The governing momentum and energy equations were solved numerically, with the influence of fin conductance accounted for by a single parameter. The distributions of fin temperature, fluid temperature and local heat flux (both at the fin and unfinned surfaces) are presented. These are shown to be strongly dependent on finned tube geometry and, in some cases, on the fin conductance parameter as well. Based on average heat transfer per unit area, the various fins proved more effective than the unfinned surfaces. Values for overall Nusselt number indicated significant heat transfer enhancement over smooth tube conditions.
Natural frequency detection of smart composite structure by small diameter fiber Bragg grating

Structural health monitoring (SHM) in-service is very important and definitely demanded for the safe working of high performance composite structures in situ. Fiber Bragg grating-based sensors were usually embedded inside a composite to compose a smart structure for a real time monitor. However, the mechanical performance of the materials would be unfavorably influenced. To lower the influence, a smaller diameter optical fiber is preferred. In this paper, a new non-destructive evaluation sensor system based on small diameter fiber Bragg grating (SDFBG) was proposed. The impact results show that the SDFBG sensor could be used in dynamic strain measurement and detect the natural frequency of the structure. Furthermore, the sensor would be applied in SHM.
Feasibility, coverage, and inter-rater reliability of the assessment of therapeutic interaction by a humanoid robot providing arm rehabilitation to stroke survivors using the instrument THER-I-ACT

Objective: The instrument THERapy-related InterACTion (THER-I-ACT) was developed to document therapeutic interactions comprehensively in the human therapist–patient setting. Here, we investigate whether the instrument can also reliably be used to characterise therapeutic interactions when a digital system with a humanoid robot as a therapeutic assistant is used. Methods: Participants and therapy: Seventeen stroke survivors receiving arm rehabilitation (i.e., arm basis training (ABT) for moderate-to-severe arm paresis [n = 9] or arm ability training (AAT) for mild arm paresis [n = 8]) using the digital therapy system E-BRAiN over a course of nine sessions. Analysis of the therapeutic interaction: A total of 34 therapy sessions were videotaped. All therapeutic interactions provided by the humanoid robot during the first and the last (9th) session of daily training were documented both in terms of their frequency and time used for that type of interaction using THER-I-ACT. Any additional therapeutic interaction spontaneously given by the supervising staff or a human helper providing physical assistance (ABT only) was also documented. All ratings were performed by two trained independent raters. Statistical analyses: Intraclass correlation coefficients (ICCs) were calculated for the frequency of occurrence and time used for each category of interaction observed. Results: Therapeutic interactions could comprehensively be documented and were observed across the dimensions provision of information, feedback, and bond-related interactions. ICCs for therapeutic interaction category assessments from 34 therapy sessions by two independent raters were high (ICC ≥0.90) for almost all categories of the therapeutic interaction observed, both for the occurrence frequency and time used for categories of therapeutic interactions, and both for the therapeutic interaction performed by the robot and, even though much less frequently observed, additional spontaneous therapeutic interactions by the supervisory staff and a helper being present. The ICC was similarly high for an overall subjective rating of the concentration and engagement of patients (0.87). Conclusion: Therapeutic interactions can comprehensively and reliably be documented by trained raters using the instrument THER-I-ACT not only in the traditional patient–therapist setting, as previously shown, but also in a digital therapy setting with a humanoid robot as the therapeutic agent and for more complex therapeutic settings with more than one therapeutic agent being present.
The NAC transcription factors play core roles in flowering and ripening fundamental to fruit yield and quality

Fruits are derived from flowers and play an important role in human food, nutrition, and health. In general, flowers determine the crop yield, and ripening affects the fruit quality. Although transcription factors (TFs) only account for a small part of plant transcriptomes, they control the global gene expression and regulation. The plant-specific NAC (NAM, ATAF, and CUC) TFs constitute a large family evolving concurrently with the transition of both aquatic-to-terrestrial plants and vegetative-to-reproductive growth. Thus, NACs play an important role in fruit yield and quality by determining shoot apical meristem (SAM) inflorescence and controlling ripening. The present review focuses on the various properties of NACs together with their function and regulation in flower formation and fruit ripening. Hitherto, we have a better understanding of the molecular mechanisms of NACs in ripening through abscisic acid (ABA) and ethylene (ETH), but how NACs regulate the expression of the inflorescence formation-related genes is largely unknown. In the future, we should focus on the analysis of NAC redundancy and identify the pivotal regulators of flowering and ripening. NACs are potentially vital manipulation targets for improving fruit quantity and quality.
Epidemiological Characteristics of Notifiable Respiratory Infectious Diseases in Mainland China from 2010 to 2018

Respiratory infectious diseases (RIDs) pose threats to people’s health, some of which are serious public health problems. The aim of our study was to explore epidemic situations regarding notifiable RIDs and the epidemiological characteristics of the six most common RIDs in mainland China. We first collected the surveillance data of all 12 statutory notifiable RIDs for 31 provinces in mainland China that reported between 2010 and 2018, and then the six most prevalent RIDs were selected to analyze their temporal, seasonal, spatiotemporal and population distribution characteristics. From 2010 to 2018, there were 13,985,040 notifiable cases and 25,548 deaths from RIDs in mainland China. The incidence rate of RIDs increased from 109.85/100,000 in 2010 to 140.85/100,000 in 2018. The mortality from RIDs ranged from 0.18/100,000 to 0.24/100,000. The most common RIDs in class B were pulmonary tuberculosis (PTB), pertussis, and measles, while those in class C were seasonal influenza, mumps and rubella. From 2010 to 2018, the incidence rate of PTB and rubella decreased; however, pertussis and seasonal influenza increased, with irregular changes in measles and mumps. The mortality from PTB increased from 2015 to 2018, and the mortality from seasonal influenza changed irregularly. PTB was mainly prevalent among people over 15 years old, while the other five common RIDs mostly occurred among people younger than 15 years old. The incidence of the six common RIDs mostly occurred in winter and spring, and they were spatiotemporally clustered in different areas and periods. In conclusion, PTB, seasonal influenza and mumps remain as public health problems in China, suggesting that continuous government input, more precise interventions, and a high-tech digital/intelligent surveillance and warning system are required to rapidly identify emerging events and timely response.
Effect of inter-element apodization on passive cavitation images

Acoustic cavitation has been correlated with a variety of ultrasound-mediated bioeffects. Recently developed passive cavitation imaging methods provide spatially resolved maps of cavitation activity with good azimuthal resolution but poor axial resolution. Here, inter-element apodization is investigated as a means of improving image quality. Cavitation was induced from echogenic liposomes in a flow phantom exposed to 6 MHz Doppler ultrasound (Philips HDI-5000). The resulting acoustic emissions were passively recorded on 64 elements of a linear array (L8-3 transducer, Zonare z.one ultra scanner). Amplitude scaling of each waveform by its root-mean-square value improved axial resolution at the expense of creating an ‘X-shaped’ artifact. Cosine amplitude apodization of the received waveforms across the array and centered about the azimuthal location of the beamformed image pixel was found to reduce grating lobe artifacts. Numerical time reversal of the received waveforms, using the Fresnel approximation for .
The Effect of Training and Individual Characteristics on Employee Performance Mediated By Employee Competence at the Office of the Class I Airport Organizing Unit in Papua

Due to the vital role of airports in regional development and progress, this study examines the effect of training, individual characteristics, and competencies on employee performance at the Papua Class I Airport Organizing Unit (UPBU) Office. In addition, this study also tested competency variables as mediators. This research is a quantitative study. The research data was obtained by distributing questionnaires to 220 employees at the UPBU Class I Offices in Merauke, Wamena, and Sorong. Only 129 questionnaires can be used as research data. The data analysis method used is path analysis with the help of SPSS software version 25. The results of this study show that training and individual characteristics have a positive and significant effect on employee performance and competence. Competence affects employee performance and can mediate the relationship between training and individual characteristics to employee performance.
Variable Conceptualisation and Measurement in Environmental Research

This paper discusses the importance of variable conceptualisation and measurement in environmental research. The paper explains how wrong application of concepts can mislead the researcher when conducting research, and the resultant effects on each stage of the environmental research process. The paper is motivated by the problems behind many research students pursuing their masters or doctoral degree programmes face, especially with change in dissertations or theses titles and methods to match the contents of their reports. In this paper, the authors demystify the challenges encountered by unskilful researchers and students when trying to make their readers have a clear understanding of their research reports (dissertations or theses). Therefore, the paper may serve as a guide in planning and conducting environmental research by university degree students and early career researchers.