Miracle-dz/UniFAR-FTUs huggingface dataset:
A Triplex Ultrasound Evaluation of Preclinical Changes in Type 2 Diabetes in Foot Arteries

Background and objective: Type 2 diabetes mellitus (T2DM) is a significant health problem that is becoming more prevalent worldwide. This study aimed to assess hemodynamic and morphological parameters in diabetic patients' foot arteries and compare them to those obtained in asymptomatic control group. Materials and methods: This is a cross-sectional case-control study. B-mode ultrasound, color Doppler, and pulse wave Doppler were conducted to assess the dorsalis pedis arteries (DPAs) and posterior tibial arteries (PTAs). The morphological, total vascular diameter, wall thickness, and flow Doppler indices were measured. A total of 200 hundred participants were selected randomly using a random sampling technique. One hundred diabetic patients and 100 non-diabetic persons were determined. Results: In diabetic patients, the overall grayscale diameter and wall thickness of foot arteries were statistically significantly larger than the asymptomatic group in the right DPA (p<0.01), left DPA (p<0.001), right PTA (p<0.001), and left PTA (p<0.001). In the diabetic group, the level of hemoglobin A1c (HbA1c) was positively correlated with blood flow resistive index (RI) in the right DPA (r=0.839; p<0.001), left DPA (r=0.801; p<0.001), right PTA (r=0.801; p<0.001), and left PTA (r=0.801; p<0.001). No significant differences were noted in both groups in blood flow Doppler parameters - pulsatility index (PI) and resistive index (RI). Conclusion: Overall grayscale diameters of foot arteries are larger in the diabetes group than in the control group, indicating arterial wall thickening as an early indicator of diabetes-related alterations. PI of both DPA and RI of right DPA were increased in diabetic patients more than the control group. The level of glycosylatedhemoglobin A1c (HbA1c) was strongly linked with the blood flow resistive index in diabetes patients.
Effectiveness of Mindfulness-based Relapse Prevention Group Therapy and Treatment as Usual on Shame and Self-criticism in Individuals with Opioid Use Disorder: A Comparative Investigation

Background: Shame and self-criticism are two variables that refer to negative self-assessment. Such negative self-assessments cause unpleasant emotions, which are among the known factors involved in drug abuse and relapse. Objectives: This study aimed to compare mindfulness-based relapse prevention (MBRP) group therapy and treatment as usual (TAU) in terms of their effect on shame and self-criticism in individuals with opioid use disorder (OUD). Patients and Methods: The current quasi-experimental study with a pretest-posttest and follow-up design was conducted on 36 patients who met the diagnostic and statistical manual of mental disorders (DSM-5) criteria for OUD. The participants were randomly assigned to two groups. One group received a combination of MBRP group therapy and TAU, and the other group was treated only by TAU. The participants filled out the Other as Shamer Scale and the Forms of Self-Criticizing/Attacking and Self-Reassuring Scale in the pretest, posttest, and follow-up stages. The data were statistically analyzed using a mixed-design analysis of variance. Results: Our results indicated that the mean score of shame significantly decreased in the follow-up compared to pretest and posttest. Moreover, the combination of MBRP group therapy and TAU was significantly more effective than TAU alone. The posttest and follow-up mean scores of the participants who received a combination of MBRP group therapy and TAU were significantly lower than those treated only with TAU. Conclusions: The study results showed that MBRP could be considered an effective therapeutic approach for reducing shame and self-criticism in individuals with OUD.
Development and validation of the Online Instructor Support Survey (OISS)

Online instructors play a critical role in online student success, and need various forms of institutional support to succeed in online teaching. This article describes the creation, validation, and results of the Online Instructor Support Survey (OISS) consisting of six sections (a) Technology and technical support (b) Pedagogical (Course Development and Teaching) support (c) Online Education Academic Support Services (d) Institutional Policies for Online Education, and (e) Online Instructor Recognition, Rewards, and Incentives. Online instructor (N=275) responses highlight areas of support that are largely prevalent and areas where further support and awareness of such support is needed at higher education institutions.
Factors affecting the application of high technology in agriculture production of farmers in Ho Chi Minh City, Vietnam

The article analyzes the factors affecting the decision to apply technology in agricultural production of farmers in Ho Chi Minh City. The logit model was used for the analysis. The results of the regression analysis showed that factors such as gender, education level, family labor, cultivated area, non-agricultural income, agricultural extension services, credit, farm training and economic efficiency from the application of high technology has a positive impact on the decision to apply technology in agricultural production of farmers in Ho Chi Minh City. While factors such as age, profit orientation, risk aversion, input costs, complexity of technology, and popularity of technology have a negative impact on the decision to apply technology of farmers.
High‐resolution simulation of a severe case of low‐level windshear at the Hong Kong International Airport: Turbulence intensity and sensitivity to turbulence parameterization scheme

Large eddy simulations are performed for a severe windshear case of terrain‐induced airflow disturbances at the Hong Kong International Airport. The objectives of the study include: (i) to investigate the performance of large eddy simulation in capturing the turbulence intensity to be encountered by the aircraft; and (ii) to find out the impact of the choice of turbulence parameterization scheme on the simulation results. The structure functions of the headwind profiles are calculated, and the simulated velocity and the LIDAR data are found to have similar behavior. The cube root of eddy dissipation rate is also calculated, which is taken to be the metric of turbulence intensity in aviation application. All EDR calculated has similar behavior. They have good correlation, but the best‐fit‐straight‐line of the scatter plot is found to have a slope far away from unity. The reason for this needs to be studied further. Finally, a simulation with the use of Smagorinsky scheme is performed. It is found to have better performance in capturing pilot windshear reports in terms of the relative operating characteristic curve. The use of Smagorinsky scheme alone may be an alternative in the choice of turbulence parameterization scheme in the alerting of windshear using large eddy dissipation, but the computational cost is too expensive. It is important to note that the present results are based on one case only and more cases would need to be studied in order to establish the robustness of the conclusions. More experiments would also need to be conducted, for example, in which nest(s) to use the Smagorinsky scheme.
Effects of plant growth regulator from seaweed extracts on expression of genes regulating the oil and related metabolites productions in oil palm

Abstract. Kresnawaty I, Santoso D,Hudiyono S. 2022. Effects of plant growth regulator from seaweed extracts on expression of genes regulating the oil and related metabolites productions in oil palm. Biodiversitas 23: 793-800. Plant growth regulators (PGRs) have been used in agricultural practicesto regulate various processes, such as germination, vegetative growth, reproduction, maturation, leaf senescence and post-harvest. Seaweed extract as natural PGRs source containsauxin, cytokines, and gibberellins. Although PGRs have been used for decades, the effects of these compounds on the production of secondary metabolites, proteins and related gene-expressionare not yetwell knownin some perennial crops.To study those,treatments consist of trunk injection of oil palm trees with biostimulant that contain PGRs from seaweed extract. Effects of treatments compared were biostimulant (B), biostimulant with activator for oil accumulation (BA) and control (C). The results indicated that the biostimulant treatments affected ACCase Biotin Carboxylase and HMG-CoA genes expression ratio andwere maximum at4 weeks after treatments (WAT) for biostimulant with activators treatments and 8 WAT for biostimulants without activator. Effect of biostimulants treatments in inducing the gene expression was not significant after 8 WAT. Moreover samples with addition biostimulant in 1 and 7 had the highest values ? ofthe gibberellin (GID) gene expression. Oil accumulation showed increase in 4 WAT dan had optimum level in 8 WATbecause of activators addition, especially increased of palmitic and linoleic acid.
Output Torque Optimization of Switched Reluctance Motors

The major advantages of Switched Reluctance Motors (SRM) are the simple and rugged motor construction, higher efficiency and low cost power electronics controller. However, SRM has essentially the high torque ripple and the magnetic noise due to its saliency.This paper presents the examination of a special combination of space harmonics of inductance and time harmonics of current from the standpoint of instantaneous torque. The space harmonics distribution of inductance are closely related to the design of teeth configuration and the analysis shows the optimum combination of motor teeth dimension with current control strategy can improve the torque-per-(amp2) ratio with reducing the torque ripple. The experimental studies using the 1.5kW SRM show the combination of the 3rd space harmonic of inductance with the 3rd time harmonic of current improves the torque-per-(amp2) ratio, while the torque ripple can be suppressed within a reasonable range.
A Long‐Lasting Precipitation Deficit in South China During Autumn‐Winter 2020/2021: Combined Effect of ENSO and Arctic Sea Ice

A severe persistent drought occurred in South China during autumn–winter 2020/2021, causing enormous economic losses and severe water shortage in South China. Focusing on this event, this study analyzed the climate anomalies associated with this event, and investigated the underlying mechanisms as well as their relationship with the leading modes of the autumn and winter precipitation in eastern China. The results suggest that the drought in South China during autumn 2020 is mainly attributed to the water vapor divergence anomalies induced by an anomalous anticyclone over eastern China and an anomalous cyclone over the South China Sea, which are caused by an anomalous Eurasian atmospheric wave‐train triggered by the negative sea ice anomalies in the Barents Sea‐Kara Sea region and a La Niña‐like condition in the tropical Pacific sea surface temperatures, respectively. The continued drought in South China during winter 2020/2021 is mainly attributed to the water vapor divergence anomalies induced by an anomalous cyclone over the western North Pacific, which is caused by the continued La Niña‐like condition. The mechanisms of the first two leading modes of autumn precipitation in eastern China both contributed to the drought in South China during autumn 2020, whereas the mechanisms of the first leading mode of winter precipitation in eastern China played an important role in causing the drought in South China during winter 2020/2021. Numerical experiments demonstrate important impact of the ENSO and Arctic sea ice anomalies on the East Asian climate anomalies during autumn–winter 2020/2021.
Quantitative and qualitative diversity of chili (Capsicum spp.) genotypes

Abstract. Sahid ZD, Syukur M, Maharijaya A, Nurcholis W. 2022. Quantitative and qualitative diversity of chili (Capsicum spp.) genotypes. Biodiversitas 23: 895-901. The diversity of chili species in tropical climate areas, especially Indonesia, is very diverse. Information on the performance of chili both quantitatively and qualitatively is needed to form the basis for future research. This study aimed to obtain qualitative and quantitative character information on several chili genotypes. The results showed that there were differences in chili genotypes based on qualitative and quantitative characters. The genotype that excelled quantitatively in terms of fruit weight was CK 12. In addition, this genotype had a square fruit shape that was absent in other genotypes. The highest number of fruit in this study was indicated by pure lines F8 145291-14-9-3-12-1-1 and Bara. Cluster analysis based on quantitative characters (HCA Analysis) grouped all genotypes into three major groups where the CK 12 genotype separated into one group of its own. The information in this study is very useful for research and development of chili plants.
Lead-Lag PSS Design Based on H∞ Control Theory and Genetic Algorithm

The aim of this paper is to explain the way to scale back the dimension of an influence system stabilizer (PSS) supported H∞ management theory. In recent years, sturdy PSS styles that adopt associate in nursing H∞ controller are investigated so as to ensure the performance once the state of the system configuration and power flow modification. However the H∞ controller has not been wide adopted into sensible use attributable to the tangled nature of its theory and structure. We tends to think about the H∞ management downside below the condition that PSS structure is mounted to be a lead-lag compensator. Infinity norm of transfer function from disturbance to output is subjected to be minimized via searching and evolutionary computation. The resulted optimal parameters make the system stable and also guarantee robust performance. We applied the evolutionary robust controller to a pneumatic servo system. For performances comparison, three controllers; PID with derivative first order filter controller, PI controller and H- loop shaping controller are investigated. We tends to optimize the parameters of the Lead-Lag PSS by a genetic algorithm program that has associate in nursing analysis perform that takes under consideration a closed-loop system H∞ norm and a desired response. During this manner, we tends to design a PSS that encompasses a standard managementler structure and guarantees its control performance.
Identification and characterization of strawberry FvGELP1 (Fragaria vesca GDSL esterase/lipase)

Abstract. Witasari LD, Huang FC, Schwab W. 2022. Identification and characterization of strawberry FvGELP1 (Fragaria vesca GDSL esterase/lipase). Biodiversitas 23: 907-915. Fruit softening is primarily the result of the hydrolytic enzymes activity. Esterases are reasonable candidates due to their putative role in cell wall components degradation. This study aimed to identify and characterize a new enzyme related to strawberry fruit ripening. Esterase activity was detected in the native PAGE of protein extract isolated from Fragaria ×ananassared fruit. Amino acid sequence analysis of the protein band revealed several esterases as possible candidates. GDSL esterase/lipase from Fragaria vesca(gene27964; FvGELP1) was chosen for cloning purposes and further analysis. Quantitative RT-PCR of FvGELP1in plant tissues of F. vescaindicated high expression levels in fruit, in particular in early developmental stages. The gene FvGELP1(1,161 bp) was amplified from F. vescafruit cDNA and expressed as a 43 kDa HisTag fusion protein in Saccharomyces cerevisiae.FvGELP1 possesses four conserved residues Gly43-Asp44-Ser45-Asn46. FvGELP1 contains Ser45, Asp364, and His367 as the catalytic triad. Esterase assays of FvGELP1 resulted in high levels of the aromatic alcohol products by applying ? -naphthyl acetate (? NA), p-nitrophenyl acetate (pNPA), phenyl acetate, and benzyl acetate as substrates. It could be suspected that FvGELP1 plays a role in strawberry fruit ripening and might be involved in the hemicellulose degradation, presumably by deacetylation of the polysaccharide.
Learning to Detect Salient Object

The project aims to implement a solution to the salient object detection problem for images as proposed in [2]. The problem is formulated as a binary labeling task where the salient object is separated from the background. A set of features, namely, multiscale contrast, center-surround histogram and color spatial distribution, are obtained to describe a salient object locally, regionally, and globally. A conditional random ﬁeld is learned to combine these features for salient object detection. The learning is done over a dataset of labelled images and the results thus obtained are put up.
Strahlung von Sendeanlagen und Gesundheit : Bewertung von wissenschaftlichen Studien im Niedrigdosisbereich (Stand: Dezember 2012)

This report updates the assessment of the health effects of high-frequency radiation based on newly published studies on human beings in the period from October 2006 to December 2012. It takes account of experimental field studies, epidemiological studies of population groups in their everyday environment and experimental studies with short-term exposure under controlled conditions in the laboratory, in which the influence of high-frequency radiation from fixed facilities such as broadcasting transmitters and mobile telephone base stations was studied. In the past six years, approximately 50 studies on this topic were published. Generally speaking, exposure to high-frequency radiation from fixed transmitters is pleasingly low, and at this exposure level no harmful effects on health have been scientifically established to date. In view of this, there is no call for a modification of the ambient limit values specified in the Ordinance relating to Protection from Non-Ionising Radiation (ONIR). With respect to the nonthermal effects of high-frequency radiation, there are still many gaps in scientific knowledge. In particular, there are almost no methodologically sound long-term observations. It is therefore not possible to make a reliable assessment of any potential longterm risks at this time, and consequently caution is called for, especially with regard to long-term exposure to high-frequency radiation.
On the Impact of Computer Vision Algorithms on Sport Training Automation: Proof of Concept for Shadow Boxing Virtual Instructor

We overview several XR applications of deep convolutional neural networks to the opportunity for creating an automated sports training process. From smart soccer and basketballs to automated fitness training programs, the progress of computer vision methods combined with personalized recommender systems and specific data science algorithms allows one to train non-contact training processes in a semi-automated manner with virtual mirrors and XR devices. We overview modern progress in this area and also present our own prototype of Shadow Boxing simulator for Virtual Mirror aiming to match part of boxer training with automated control and gamification process. We show that the trend of automating training instructors in sport leads to a positive shift in sportsmen and trainees’ view of artificial intelligence in common life.
Model-free Value Iteration Algorithm for Continuous-time Stochastic Linear Quadratic Optimal Control Problems

This paper presents a novel value iteration (VI) algorithm for finding the optimal control for a kind of infinitehorizon stochastic linear quadratic (SLQ) problem with unknown systems. First, an off-line algorithm is estabilished to obtain the optimal feedback control of our problem. Then, based on the off-line algorithm, the VI-based model-free algorithm and its convergence proof is provided. The main feature of the model-free algorithm is that a stabilizing control is not needed to initiate the algorithm. Finally, we validate our results with a simulation example.
On Data Augmentation in Point Process Models Based on Thinning Procedures

Many models for point process data are defined through a thinning procedure where locations of a base process (often Poisson) are either kept (observed) or discarded (thinned). In this paper, we go back to the fundamentals of the distribution theory for point processes and provide a colouring theorem that characterizes the joint density of thinned and observed locations in any of such models. In practice, the marginal model of observed points is often intractable, but thinned locations can be instantiated from their conditional distribution and typical data augmentation schemes can be employed to circumvent this problem. Such approaches have been employed in recent publications, but conceptual flaws have been introduced in this literature. We concentrate on an example: the so-called sigmoidal Gaussian Cox process. We apply our general theory to resolve what are contradicting viewpoints in the data augmentation step of the inference procedures therein. Finally, we provide a multitype extension to this process and conduct Bayesian inference on data consisting of positions of 2 different species of trees in Lansing Woods, Illinois.
Existence, structure, and robustness of ground states of a NLSE in 3D with a point defect

We study the ground states for the Schrödinger equation with a focusing nonlinearity and a point interaction in dimension three. We establish that ground states exist for every value of the mass; moreover, they are positive, radially symmetric, and decreasing along the radial direction and show a Coulombian singularity at the location of the point interaction. Remarkably, the existence of the ground states is independent of the attractive or repulsive character of the point interaction.
Depth-Aware Generative Adversarial Network for Talking Head Video Generation

Talking head video generation aims to produce a synthetic human face video that contains the identity and pose information respectively from a given source image and a driving video. Existing works for this task heavily rely on 2D representations (e.g. appearance and motion) learned from the input images. However, dense 3D facial geometry (e.g. pixel-wise depth) is extremely important for this task as it is particularly beneficial for us to essentially generate accurate 3D face structures and distinguish noisy information from the possibly cluttered background. Nevertheless, dense 3D geometry annotations are prohibitively costly for videos and are typically not available for this video generation task. In this paper, we introduce a self-supervised face-depth learning method to automatically recover dense 3D facial geometry (i.e. depth) from the face videos without the requirement of any expensive 3D annotation data. Based on the learned dense depth maps, we further propose to leverage them to estimate sparse facial keypoints that capture the critical movement of the human head. In a more dense way, the depth is also utilized to learn 3D-aware cross-modal (i.e. appearance and depth) attention to guide the generation of motion fields for warping source image representations. All these contributions compose a novel depth-aware generative adversarial network (DaGAN) for talking head generation. Extensive experiments conducted demonstrate that our proposed method can generate highly realistic faces, and achieve significant results on the unseen human faces. 11https://github.com/harlanhong/CVPR2022-DaGAN