Miracle-dz/UniFAR-FTUs huggingface dataset:
A step in the right direction: the potential role of smartwatches in supporting chronic disease prevention in health care

Australia is struggling with the everincreasing burden of chronic disease. Over $38 billion per year is spent on care for people with chronic diseases, such as cardiovascular disease, type 2 diabetes, and cancer.1 The majority of this funding is dedicated to acute care, and just 9.6% of health care investment supports disease prevention.1 Perversely, Australia’s health system is rewarded for increasing acute care activity (activitybased funding) to manage disease, which perpetuates inefficient break– fix models of care.2 The strain on acute care service provision has been accelerated by the COVID19 pandemic3 and Australia’s ageing population, and is forecast as unsustainable.2 Ageing increases total expenditure on hospitalisations, pathology provision, medical imaging, and cost to the Pharmaceutical Benefits Scheme.1 This burden of chronic disease — associated with disability and premature death — is becoming less and less sensitive to further extensions in health care spending on treatment. Prevention (as defined in the Supporting Information) is urgently needed; however, dedicated funding, policies and models of preventive care for chronic diseases in communities are minimal, especially for priority populations and high risk social and environmental settings.
Approximate Inference of Traffic Flow State at Signalized Intersections Using a Bayesian Learning Framework

Model-based traffic state estimation is used to reproduce traffic flow states from available observation data to assist traffic control and management. Owing to temporal and spatial observation limitations, numerous unknown traffic states and parameters exist in a nonlinear traffic flow model. These cannot be accurately estimated using filter methods with unavoidable parametric assumptions. In addition, the difficulty of estimation increases owing to an increased number of diverse traffic flow states and more unfavorable observation conditions at signalized intersections compared with those on the freeway. To overcome these problems, in this study, we developed switching state-space models to approximate the description of dynamic traffic flows at signalized intersections. By setting the traffic flow rate at the upstream and downstream boundaries of the road as observation data, we constructed a Bayesian learning framework in which all unknown variables in the models can be learned from the observed data. Finally, the utility of our method is demonstrated with the synthetic and the real NGSIM data. The result demonstrated that the proposed method could perform reasonably well in estimating the traffic flow dynamic process and states at signalized intersections, and could constitute an efficient scheme that avoids critical dependence on parametric calibrations and assumptions.
Wide Range CCT Laser-Based Illuminant With High Efficiency and Excellent Optical Performances

A wide range correlated color temperature (CCT) white light laser-based illuminant (LBI) consisting of dual blue laser diodes (LDs), yellow phosphor color converters and red LDs has been proposed. In addition to the conventional 455 nm blue LDs-pumped phosphor color converters, another blue LD is utilized for the adjustment of CCT and improvement of the color rendering index (CRI). Considering systematic manipulation of the power ratio of LDs and peak wavelength of tuning blue LDs from 440 nm to 480 nm, different combinations of spectral power distributions of LBIs were simulated. It shows that an optimized 465 nm tuning blue LD is employed to achieve a balance between optical performances and blue light hazard efficiency of radiation (BLHER). The first practical LBI comprised of Y3Al5O12:Ce $^{\mathrm {3+{}}}$ (YAG:Ce $^{3+}$ ) phosphor color converters and three monochromatic LDs of 455 nm, 465 nm and 635 nm was fabricated based on the stated simulations. In good agreement with the simulation results, the LBI has simultaneously achieved an excellent CRI beyond 85, a high luminous flux of approximately 2000 lm and a high luminous efficiency of radiation (LER) of over 300 lm/W at the fairly wide CCT range of 3300 K to 6000 K. The low BLHER was consistently below 0.237. The circadian action factor (CAF) was between 0.36 and 0.83, and its tunability was 2.31 above the CCT range, which favors the entrainment of circadian rhythm. Therefore, this work demonstrates a promising method for providing high-quality and human-oriented white light sources in applications requiring great brightness.
Prediction of Oncotype DX Recurrence Score Using Clinicopathological Variables in Estrogen Receptor-Positive/Human Epidermal Growth Factor Receptor 2-Negative Breast Cancer

Purpose Oncotype DX (ODX) is a well-validated multigene assay that is increasingly used in Korean clinical practice. This study aimed to develop a clinicopathological prediction (CPP) model for the ODX recurrence scores (RSs). Methods A total of 297 patients (study group, n = 175; external validation group, n = 122) with estrogen receptor-positive, human epidermal growth factor receptor 2 (HER2)-negative, T1-3N0-1M0 breast cancer, and available ODX test results were included in the study. Risk categorization as determined by ODX RSs concurred with the TAILORx study (low-risk, RS ≤ 25; high-risk, RS > 25). Univariate and multivariate logistic regression analyses were used to assess the relationships between clinicopathological variables and risk stratified by the ODX RSs. A CPP model was constructed based on regression coefficients (β values) for clinicopathological variables significant by multivariate regression analysis. Results Progesterone receptor (PR) negativity, high Ki-67 index, and nuclear grade (NG) 3 independently predicted high-risk RS, and these variables were used to construct the CPP model. The C-index, which represented the discriminatory ability of our CPP model for predicting a high-risk RS, was 0.915 (95% confidence interval [CI], 0.859–0.971). When the CPP model was applied to the external validation group, the C-index was 0.926 (95% CI, 0.873–0.978). Conclusion Our CPP model based on PR, Ki-67 index, and NG could aid in the selection of patients with breast cancer requiring an ODX test.
DIGITAL MEDIA USE IN PUBLIC SPACE – AN OBSERVATIONAL STUDY OF SELF-SERVICE TECHNOLOGY DURING COVID19-PANDEMIC.

The spread of digital media and devices in public spaces, so-called self-service technology (SST), is reaching more and more areas, so that it is now possible to interact with them for example when shopping, ordering or sending parcels. The use of SST is mostly supposed to be self-explanatory and intuitive, nevertheless it implicitly presupposes a certain degree of technical literacy. If this is not the case, digital stress can be triggered. But how do people deal with these devices, what role does the presence of others, the device itself and how it works, and the situational context plays? What influence do the measures to deal with the Covid-19 virus and the pandemic-related constraints have on the nexus of device, users and situation? This was investigated qualitatively in the present study by conducting participant observations. The advantage of this methodological approach is the consideration of contextual factors of the different practices and the direct observation of subjective evaluation processes on the use or even non-use of SST. This shows that characteristics of the devices – e.g. functionality, initiation and optionality – , situational contextual conditions – e.g. time and social pressure and spatial conditions –, and the subject themselves – e.g. age, routinisation and carried along baggage– play a role in dealing with SST and moderate the emergence of digital stress.
Reimagining Well-Being Research in HRD

In recent years, a significant interest on the relevance of well-being has spurred dialog at every level -- from mundane everyday conversations about how we are feeling, to broader discussions related to the well-being of our communities and society. Joining a conversation about well-being is not difficult as we can talk to each other about our experiences. Recognizing commonalities in well-being experiences brings us together and gives shared purpose and meaning. For example, within the Academy of Human Resource Development (AHRD), we must continue to focus on the well-being of our members, stakeholders, and broad audiences we serve. Clearly, the efforts we undertake as HRD practitioners and researchers can only enhance the relevance and importance of well-being research and practice. Below, I share key academic conversations concerning well-being, and how HRD can promote greater exchange of ideas, and enhance our collective knowledge both in research and practice.
Molecular Characterization of Resistance to Nicosulfuron in Setaria viridis

The green foxtail, Setaria viridis (L.) P. Beauv. (Poales: Poaceae), is a troublesome and widespread grass weed in China. The acetolactate synthase (ALS)-inhibiting herbicide nicosulfuron has been intensively used to manage S. viridis, and this has substantially increased the selection pressure. Here we confirmed a 35.8-fold resistance to nicosulfuron in an S. viridis population (R376 population) from China and characterized the resistance mechanism. Molecular analyses revealed an Asp-376-Glu mutation of the ALS gene in the R376 population. The participation of metabolic resistance in the R376 population was proved by cytochrome P450 monooxygenases (P450) inhibitor pre-treatment and metabolism experiments. To further elucidate the mechanism of metabolic resistance, eighteen genes that could be related to the metabolism of nicosulfuron were obtained bythe RNA sequencing. The results of quantitative real-time PCR validation indicated that three ATP-binding cassette (ABC) transporters (ABE2, ABC15, and ABC15-2), four P450 (C76C2, CYOS, C78A5, and C81Q32), and two UDP-glucosyltransferase (UGT) (UGT13248 and UGT73C3), and one glutathione S-transferases (GST) (GST3) were the major candidates that contributed to metabolic nicosulfuron resistance in S. viridis. However, the specific role of these ten genes in metabolic resistance requires more research. Collectively, ALS gene mutations and enhanced metabolism may be responsible for the resistance of R376 to nicosulfuron.
Arteriosclerosis and Atherosclerosis Assessment in Clinical Practice: Methods and Significance

Alongside cancer, cardiovascular disease (CVD) exhibits the highest rates of morbidity and mortality globally, in western society as well as in Asian countries. Aging is a serious problem for the Asian population as progression toward a super-aged society is moving at a remarkably high rate. This increased rate of aging leads to increased CVD risk and, consequently, high CVD incidence. However, aging is not the only deleterious factor of vascular problems; hypertension, hypercholesterolemia, diabetes mellitus, and kidney disease may induce atherosclerosis and arteriosclerosis (i.e., arterial stiffening), and the progression of these diseases ultimately leads to cardiovascular, cerebrovascular, chronic kidney, or peripheral artery disease. Despite the existence of several guidelines on the treatment of risk factors such as hypertension and CVD, there is still an ongoing debate regarding the clinical need for assessment of arteriosclerosis and atherosclerosis, which act as a bridge between cardiovascular risk factors and CVD. In other words, although arteriosclerosis and atherosclerosis are essential to our understanding of vascular diseases, the need for additional tests beyond the conventional diagnosis method remains disputed. This is presumably due to insufficient discussion on how to apply such tests in clinical practice. This study aimed to fill this gap.
Broadcast information diffusion processes on social media networks: exogenous events lead to more integrated public discourse

Understanding information diffusion is vital to explaining the good, bad, and ugly impacts of social media. Two types of processes govern information diffusion: broadcasting and viral spread. Viral spreading is when a message is diffused by peer-to-peer social connections, whereas broadcasting is characterized by influences that can come from outside of the peer-to-peer social network. How these processes shape public discourse is not well understood. Using a simulation study and real-world Twitter data (10,155 users, 18,000,929 tweets) gathered during 2020, we show that broadcast spreading is associated with more integrated discourse networks compared to viral spreading. Moreover, discourse oscillates between extended periods of segregation and punctuated periods of integration. These results defy simple interpretations of good or bad, and instead suggest that information diffusion dynamics on social media have the capacity to disrupt or amplify both prosocial and antisocial content.
A maximum likelihood estimator for frequency and decay rate

An extension of Rife's discrete Fourier transform (DFT) interpolation into a complex form is presented. The method has several advantages over Rife's original formula. It can estimate the decay rate of the input sequence as well as the frequency, and does not require a square-root operation. An error analysis suggests a powerful but simple refinement step which can nearly attain the Cramer-Rao (CR) bound. An interpolation formula is derived, and its iteration into a refinement procedure is discussed. The procedure also allows the use of arbitrary windows to suppress the interference of other frequency components in the data that are typically found with real and/or damped sequences. <<ETX>>
Design of PAM4 modulation circuit with 2-tap pre-emphasis for millimeter-wave application

Millimeter-wave (mmWave) technology has been employed in many applications due to abundant bandwidth resources and high interference immunity such as telecommunication, automotive radars, and imaging. In this paper, a mmWave transmitter link incorporating PAM4 modulation, resonant tunneling diode (RTD) based oscillator, power amplifier and antenna was proposed. To achieve good linearity and alleviate inter-symbol interference (ISI) caused by channel loss simultaneously, a PAM4 modulation circuit utilizing voltage mode driver and 2-tap pre-emphasis was designed and optimized in TSMC 28 nm CMOS process. The simulation results show that our PAM4 modulation circuit operates properly at 40Gb/s with a differential output swing of up to 800 mVpp under a supply voltage of 0.9 V. The overall power consumption is about 51.3 mW, corresponding to an energy efficiency of 1.28 pJ/bit.
Inclisiran, Low-Density Lipoprotein Cholesterol and Lipoprotein (a)

Dyslipidemia treatment is of major importance in reducing the risk of atherosclerotic cardiovascular disease (ASCVD), which is still the most common cause of death worldwide. During the last decade, a novel lipid-lowering drug category has emerged, i.e., proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors. Apart from the two available anti-PCSK9 monoclonal antibodies (alirocumab and evolocumab), other nucleic acid-based therapies that inhibit or “silence” the expression of PCSK9 are being developed. Among them, inclisiran is the first-in-class small interfering RNA (siRNA) against PCSK9 that has been approved by both the US Food and Drug Administration (FDA) and the European Medicines Agency (EMA) for the treatment of hypercholesterolemia. Importantly, inclisiran therapy may improve low-density lipoprotein cholesterol (LDL-C) target achievement by offering a prolonged and significant LDL-C-lowering effect with the administration of only two doses per year. The present narrative review discusses the ORION/VICTORION clinical trial program that has been designed to investigate the impact of inclisiran on atherogenic lipoproteins and major adverse cardiac events in different patient populations. The results of the completed clinical trials are presented, focusing on the effects of inclisiran on LDL-C and lipoprotein (a) (Lp(a)) levels as well as on other lipid parameters such as apolipoprotein B and non-high-density lipoprotein cholesterol (non-HDL-C). Ongoing clinical trials with inclisiran are also discussed.
A network combining deep residual shrinkage block for infrared and visible image fusion

The infrared images depicting the thermal radiation of objects are not affected by objective conditions such as natural environment and climate. Visible image has high spatial resolution, with a lot of details and high contrast. Infrared and visible image fusion takes the advantages of both optical bands to get a fusion image with clearly targets and rich background information. In this paper, we propose a novel deep learning based model by designing a new network structure and loss function. The network consists of an auto encoder and deep residual shrinkage modules. We introduce multiple deep residual shrinkage blocks into encoder to learn adaptive soft threshold parameters for denoising both infrared and visible images, Without affecting the complexity of the model, feature enhancement and extraction are implemented within the network to maximize the retention of practical information, and then the average fusion strategy is used to obtain the fusion features. Finally, the fused image is reconstructed by the decoder. In the design of the loss function, our loss function consists of pixel loss, structural similarity loss and gradient loss together, thus better preserving the texture details and edge information of the image. Experiments are performed on publicly available data sets. Qualitative results exhibit that the fused images obtained by our method are clearer and more natural, and in line with human visual perception. Quantitative results show that our proposed model has achieved the optimal or sub-optimal values compared to the state-of-the-art image fusion algorithms.
The evolution of the business cycles distribution of countries

With this paper we contribute to the empirical analysis of the distributions of the international GDP business cycles and its evolution. In order to measure volatility two types of proxies per country time series were used, the standard logarithmic definition of growth and the denominated cycles coming from filtering with the Hodrick-Prescott Filter. Under both types, we find out the known stylized facts of growth processes in economics, the tend-shape probability distribution and the inverse power law relation between volatility and the GDP size. The dynamic analysis shows that the tails of probability density functions evolve getting fatter together with a decrease of their variance, suggesting an increasing non null probability of finding high amplitude fluctuations. Also, it is observed that the densities were at the beginning asymmetric with relative higher probability of getting positive fluctuations and that the economic system evolves to a more symmetric scenario giving space to the probability of drawing in the negative part. In addition, it is found that the mentioned power law relation has not remained exactly constant. This fact enriches the discussion about the meaning of scaling relations in economic systems.
Parallel simulation via SPPARKS of on-lattice kinetic and Metropolis Monte Carlo models for materials processing

SPPARKS is an open-source parallel simulation code for developing and running various kinds of on-lattice Monte Carlo models at the atomic or meso scales. It can be used to study the properties of solid-state materials as well as model their dynamic evolution during processing. The modular nature of the code allows new models and diagnostic computations to be added without modification to its core functionality, including its parallel algorithms. A variety of models for microstructural evolution (grain growth), solid-state diffusion, thin film deposition, and additive manufacturing (AM) processes are included in the code. SPPARKS can also be used to implement grid-based algorithms such as phase field or cellular automata models, to run either in tandem with a Monte Carlo method or independently. For very large systems such as AM applications, the Stitch I/O library is included, which enables only a small portion of a huge system to be resident in memory. In this paper we describe SPPARKS and its parallel algorithms and performance, explain how new Monte Carlo models can be added, and highlight a variety of applications which have been developed within the code.
Spatial Dependence of Log-Transformed Electromyography–Force Relation: Model-Based Sensitivity Analysis and Experimental Study of Biceps Brachii

This study investigated electromyography (EMG)–force relations using both simulated and experimental approaches. A motor neuron pool model was first implemented to simulate EMG–force signals, focusing on three different conditions that test the effects of small or large motor units located more or less superficially in the muscle. It was found that the patterns of the EMG–force relations varied significantly across the simulated conditions, quantified by the slope (b) of the log-transformed EMG-force relation. b was significantly higher for large motor units, which were preferentially located superficially rather than for random depth or deep depth conditions (p < 0.001). The log-transformed EMG–force relations in the biceps brachii muscles of nine healthy subjects were examined using a high-density surface EMG. The slope (b) distribution of the relation across the electrode array showed a spatial dependence; b in the proximal region was significantly larger than the distal region, whereas b was not different between the lateral and medial regions. The findings of this study provide evidence that the log-transformed EMG–force relations are sensitive to different motor unit spatial distributions. The slope (b) of this relation may prove to be a useful adjunct measure in the investigation of muscle or motor unit changes associated with disease, injury, or aging.
Optimal Design of Cymbal Stack Transducer in a Piezoelectric Linear Actuator by Finite Element Method

Abstract The optimal design of a piezoelectric linear actuator using parametric optimum method-based finite element method (FEM) was presented. First, the FEM model of the cymbal stack transducer was generated with its initial configuration. The structural parameters were chosen as the design variables and the displacement on the top surface of the transducer taken as the objective function. Second, the zero-order optimization method was chosen as the basic tool of the structural updating. The structural optimization scheme of the cymbal stack transducer was carried out based on ANSYS parametric design language (APDL). Finally, an example of dynamic response analysis was performed on the cymbal stack transducer to verify the structural optimization scheme. The results show that the displacement on the top surface is increased by 32.9% compared with the case of initial configuration.