Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessing the Effect of Pedestrians’ Use of Cell Phones on Their Walking Behavior: A Study Based on Automated Video Analysis

The objective of the study is to assess the effect of the use of cell phones while walking at urban crosswalks. The methodology uses recent findings in health science concerning the relationship between tempo-spatial characteristics of gait and the cognitive abilities of pedestrians. Gait measures are shown to be affected by the complexity of the task (e.g., talking and texting) performed during walking. This study focuses on the effect of distraction states, distraction types (visual such as texting/reading and auditory such as talking/listening), and pedestrian-vehicle interactions on the gait parameters of pedestrians at crosswalks. Experiments are performed on a video data set near a college campus in the city of Kamloops, British Columbia. The analysis relies on automated video-based data collection using a computer vision technique. The benefits of such an automated system include the ability to capture the natural movement of pedestrians and minimizing the risk of disturbing their behavior. Results show that pedestrians distracted by texting/reading (visually) or talking/listening (auditory) while walking tend to reduce and control their walking speed by adjusting their step length or step frequency, respectively. Pedestrians distracted by texting/reading (visually) have significantly lower step length and are less stable in walking. Distracted pedestrians involved in interactions with approaching vehicles tend to reduce and control their walking speeds by adjusting their step frequencies. This research can find applications in pedestrian facility design, modeling and calibrating pedestrian simulations, and pedestrian safety intervention programs and legislative actions.
Combined Therapy Using Human Corneal Stromal Stem Cells and Quiescent Keratocytes to Prevent Corneal Scarring after Injury

Corneal blindness due to scarring is conventionally treated by corneal transplantation, but the shortage of donor materials has been a major issue affecting the global success of treatment. Pre-clinical and clinical studies have shown that cell-based therapies using either corneal stromal stem cells (CSSC) or corneal stromal keratocytes (CSK) suppress corneal scarring at lower levels. Further treatments or strategies are required to improve the treatment efficacy. This study examined a combined cell-based treatment using CSSC and CSK in a mouse model of anterior stromal injury. We hypothesize that the immuno-regulatory nature of CSSC is effective to control tissue inflammation and delay the onset of fibrosis, and a subsequent intrastromal CSK treatment deposited collagens and stromal specific proteoglycans to recover a native stromal matrix. Using optimized cell doses, our results showed that the effect of CSSC treatment for suppressing corneal opacities was augmented by an additional intrastromal CSK injection, resulting in better corneal clarity. These in vivo effects were substantiated by a further downregulated expression of stromal fibrosis genes and the restoration of stromal fibrillar organization and regularity. Hence, a combined treatment of CSSC and CSK could achieve a higher clinical efficacy and restore corneal transparency, when compared to a single CSSC treatment.
Prediction Error Analysis of Finite-Control-Set Model Predictive Current Control for IPMSMs

Finite-control-set model predictive current control (FCS-MPCC) has been widely investigated in the field of motor control. When the discrete motor prediction model is not obtained accurately, prediction error often occurs, which can result in improper determinations of optimal voltage vectors and can further affect the control performance of motor systems. However, papers evaluating the motor control performance employing FCS-MPCC rarely consider prediction error and its utilization to weaken the influence of inaccurate prediction model. This paper investigates in depth the prediction error caused by three influencing factors from the perspective of model accuracy—discretization method, prediction stepsize, and parameter mismatch. Firstly, the evaluation index, prediction error, is defined and its formulas considering the above three factors are derived based on interior permanent magnet synchronous motor (IPMSM). Then, the theoretical analysis of prediction error is provided. Finally, experimental results of an IPMSM drive system are presented to verify and complement the theoretical analysis. Both the theoretical analysis and experimental results fully elaborate the prediction error, which can offer practical guidelines for the evaluation and improvement of motor control performance, especially for FCS-MPCC in IPMSM applications.
The overall benefits of empagliflozin treatment in adult siblings with glycogen storage disease type Ib: one year experience

Introduction Recently published case reports suggest the benefit of empagliflozin use in subjects with glycogen storage disease Ib (GSD Ib). Methods We present the clinical and laboratory data of 2 adult brothers with GSD Ib treated with empagliflozin for 12 months. Results There was no severe infection during administration of empagliflozin. The improvement of clinical symptoms of inflammatory bowel disease and arthritis along with reduction in serum CRP levels and urinary albumin excretion was noted. Neutrophil count increased, allowing for reduction or temporary withdrawal of G-CSF treatment. Conclusions Empagliflozin may be a new safe treatment in GSD Ib patients with an advanced stage of the disease.
Modeling Neural Networks Training Process with Markov Decision Process

With the development of computer technology, statistics-based machine learning method has made great break-throughs, and also improved the development of artificial intelligence. Nevertheless, as a very influential model, neural networks are still treated as “black boxes”. The results of neural networks are extremely sensitive to the training samples, which lead to great challenges to the controllability of the algorithm. With the wide application of machine learning, demand for interpretability and controllability of neural networks algorithms is increasing. As a result, various scholars have tried to explain and verify neural networks algorithms based on formal methods in recent years. In this paper, a method (called MNNTP) is presented to model the training process of neural networks by using a Markov decision process (MDP). Through MNNTP, the neural networks are abstracted into the form of MDP, which makes notable contributions for verifying some mathematical properties of the neural networks.
The efficacy and safety of apatinib in patients with recurrent or metastatic nasopharyngeal carcinoma: a systematic review and meta-analysis

Background Apatinib is a small-molecule tyrosine kinase inhibitor targeting VEGFR-2, which was recently used in a phase II clinical trial for the treatment of recurrent or metastatic nasopharyngeal carcinoma (rmNPC). However, there is no consistent conclusion on its efficacy and safety on rmNPC. This study conducted a meta-analysis of clinical research on the efficacy and safety of apatinib in the treatment of rmNPC. Methods In April 2022, the PubMed, Web of Science, Scopus, Chinese National Knowledge Infrastructure (CNKI), CMB, and Wanfang databases were systematically searched, and relevant research literature were screened and analyzed. The clinical trial literatures using apatinib as the main single or combined treatment for rmNPC patients were selected and combined with objective response rate (ORR), disease control rate (DCR), progression-free survival (PFS), overall survival (OS) and other efficacy and safety indicators. Results The meta-analysis included 12 studies, including 408 patients with rmNPC. The methodological index for nonrandomized studies scale was used to evaluate the bias of the included literatures and found that the bias was low. A total of 408 rmNPC patients were included in the included literature, with 11 studies being a phase II single-arm trial and one being a phase II non-randomized controlled trial. The ORR of patients with rmNPC treated with apatinib was 41.5% (95% CI: 34.8%, 48.2%), and the DCR was 80.2% (95% CI: 70.9%, 89.6%). The median PFS was 6.4 months (95% CI: 5.3, 7.4), and the median OS was 14.8 months (95% CI: 10.7, 18.9). The incidence of hypertension, hand-foot skin reaction, and proteinuria was 31% (95% CI: 19–43%), 29% (95% CI: 20–39%), and 13% (95% CI: 6–20%), respectively. Discussion The efficacy of apatinib in the treatment of rmNPC patients is similar to that of the previous second-line chemotherapy drugs, but since most studies are phase II single-arm studies, the advantages and disadvantages of the existing second-line chemotherapy regimens cannot be determined.
Technical Vocabulary in Aviation: A Study of Private Pilot Students in Iceland

Communication in aviation is of vital importance. A large number of all aviation incident reports worldwide are the result of miscommunication. By standardizing phraseology and vocabulary used by pilots and air traffic control (ATC), communication can become safer and more efficient. Aviation personnel can, however, not only rely on phraseological knowledge as there needs to be underlying language proficiency that supports the user, especially in unforeseen circumstances. English is the international standard language in aviation. The International Civil Aviation Organization (ICAO) has set certain language proficiency requirements (LPRs) to set strict English proficiency guidelines for aviation personnel to adhere to. Pilots and ATC need to learn and use complex technical vocabulary that is confined to aviation. This highly technical aviation vocabulary can be hard to master and there are various methods in teaching it. This essay examines the vocabulary proficiency of private pilot students in an aviation academy in Iceland. It attempts to find correlations between prior education and own perception of language proficiency, with vocabulary ability. Twenty students, enrolled in a 220-hour theoretical Private Pilot License (PPL) course took part in the study. They answered a simple questionnaire about themselves and two vocabulary tests, one based on the academic word list (AWL) and the other on aviation vocabulary (AVT). The students scored lower than expected on both tests, scoring 47% and 59% respectively on average. Many students would not have passed the LRPs in vocabulary knowledge. The low scores on the AVT can be attributed to two external factors, shortage of flight instructors in the practical portion of the PPL program and to a move of operations. There is no link between prior education and scores in the sample, but own perception of proficiency does correlate to scores on both vocabulary tests.
Impact of Zinc on Oxidative Signaling Pathways in the Development of Pulmonary Vasoconstriction Induced by Hypobaric Hypoxia

Hypobaric hypoxia is a condition that occurs at high altitudes (>2500 m) where the partial pressure of gases, particularly oxygen (PO2), decreases. This condition triggers several physiological and molecular responses. One of the principal responses is pulmonary vascular contraction, which seeks to optimize gas exchange under this condition, known as hypoxic pulmonary vasoconstriction (HPV); however, when this physiological response is exacerbated, it contributes to the development of high-altitude pulmonary hypertension (HAPH). Increased levels of zinc (Zn2+) and oxidative stress (known as the “ROS hypothesis”) have been demonstrated in the vasoconstriction process. Therefore, the aim of this review is to determine the relationship between molecular pathways associated with altered Zn2+ levels and oxidative stress in HPV in hypobaric hypoxic conditions. The results indicate an increased level of Zn2+, which is related to increasing mitochondrial ROS (mtROS), alterations in nitric oxide (NO), metallothionein (MT), zinc-regulated, iron-regulated transporter-like protein (ZIP), and nicotinamide adenine dinucleotide phosphate (NADPH) oxidase-induced protein kinase C epsilon (PKCε) activation in the development of HPV. In conclusion, there is an association between elevated Zn2+ levels and oxidative stress in HPV under different models of hypoxia, which contribute to understanding the molecular mechanism involved in HPV to prevent the development of HAPH.
Subsampling OFDM-Based Ultrasonic Data Communication Through Metallic Channels for Monitoring of Cargo Containers

An enhanced ultrasonic communication system based on piezoelectric transducers for monitoring of goods in cargo containers is presented. The proposed system consists of several sensors placed inside the container whose data are collected and transmitted outside of it. Data transmission is carried out by an ultrasonic communication channel in order to avoid drilling the wall of the container. The proposed data communication system is based on the transmission of a 128-OFDM signal. This modulation has been chosen due to its robustness to channels with frequency-selective fading and its spectrum efficiency. In order to increase the signal bandwidth and to reduce the power consumption at the internal node (transmitter), the proposed system exploits the non-linearity of the metallic channel to transmit at higher resonance frequencies. Moreover, power consumption at the external node (receiver) is reduced by using a subsampling-based receiver, which allows its implementation through low-cost electronics.
Occurrence and Risk Assessment of per- and Polyfluoroalkyl Substances in Water Source Protection Area of Southeastern China

In order to clarify the pollution characteristics and human health risks of PFASs pollutants in typical drinking water sources in Zhejiang Province, this study relies on ultra -performance liquid chromatography-mass spectrometry (UPLC-MS/MS) technology to analyze the pollution of 26 PFASs in 7 reservoirs in Zhejiang Province. The detected concentrations of PFASs were evaluated to further assess the human health risks. Total PFASs concentrations in the seven reservoirs ranged from 1.30 ng L−1–24.90 ng L−1. Among the 26 PFASs pollutants analyzed, PFOA and PFBA were the main PFASs pollutants, the detected concentrations of PFOA and PFBS ranging from 0.50 ng L−1–13.70 ng L−1 and 0 ng L−1–1.70 ng L−1, respectively. Then we evaluated 15 PFASs and calculated the results of the HQ value of the reproductive toxicity and hepatotoxicity of the total PFASs in this study ranged from 2.30 × 10–8 to 1.16 × 10–4 and 9 × 10–8 to 5.24 × 10–4 respectively, which were both lower than 0.01, indicating that there is no significant risk to the human body.
Estimating epistemic uncertainty in soil parameters for nonlinear site response analyses: Introducing the Latin Hypercube Sampling technique

This study quantifies the effects of epistemic uncertainty in soil parameters on nonlinear (NL) site response analysis (SRA) results, validated against the data recorded at a well-instrumented geotechnical downhole array located in Japan. To this end, a one-dimensional soil column model of the Service Hall Array (SHA) near the Kashiwazaki-Kariwa Nuclear Power Plant (KKNPP) is developed using the finite element (FE) program LS-DYNA. The dynamic stress–strain relationship is characterized by a modified two-stage hyperbolic (MTH) NL backbone curve formulation capable of capturing soil behavior at both small- and large-shear strains. The model is then validated against the ground motion recordings to capture the model bias. The uncertainties associated with the shear-wave velocity profile (a small-strain soil property) and soil shear strength (a large-strain soil property) are incorporated in NL SRA to quantify their separate and joint randomization effects on the results. This study proposes using the Latin Hypercube Sampling (LHS) method as an efficient alternative to commonly used methods, such as Standard Monte Carlo (SMC), to account for uncertainty propagation in such reliability analysis. Both low-intensity and design-level records from the recordings at the SHA are applied to study the contribution of the small- and large-strain NL dynamic soil properties. Results from 46,200 NL FE analyses (23,100 per input motion) are presented. Measured and predicted site response, using recorded ground motions at this downhole array, is compared to assess the significance of soil parameter uncertainty on the observed ground motion dispersions. It is demonstrated that increasing the number of soft realizations and implementing higher level earthquake intensity lead to higher ground motion dispersion. Unlike past studies in randomization of Vs profiles with the SMC method, the LHS method is shown to have no significant effect on the predicted median surface response spectra and amplification factors (AFs) for this case study.
W-Band Low Profile Transmit Array Based on Wideband Partially Reflecting Surface

This paper presents a wideband low profile transmit array with a small height-to-diameter (H/D) ratio of 0.285 working at a center frequency of 100 GHz, which consists of a waveguide feed source, a metal ground plane, a partially reflective surface (PRS), and a transmission surface (TS). A PRS with not only a positive reflection phase gradient, but also a negative transverse reflection magnitude gradient is designed above the ground plane to reduce the height as well as to broaden the bandwidth of the transmit array (TA). The proposed TS is composed of 8 unit cells (UCs) with a high transmission magnitude and phase differences of 45° in a broadband range. Four passive antenna prototypes with different beam directions are fabricated to validate the design concepts, and good agreements are obtained between the radiation patterns, gain curves, and return losses. The measured peak gain is 20.7 dBi with aperture efficiency of 10.5% at 99.5 GHz. A relative bandwidth of 15.2% is obtained within 3-dB gain variations, making it a promising candidate for 6G wireless communication applications.
Biomarkers of Oxidative Stress Tethered to Cardiovascular Diseases

Cardiovascular disease (CVD) is a broad term that incorporated a group of conditions that affect the blood vessels and the heart. CVD is a foremost cause of fatalities around the world. Multiple pathophysiological mechanisms are involved in CVD; however, oxidative stress plays a vital role in generating reactive oxygen species (ROS). Oxidative stress occurs when the concentration of oxidants exceeds the potency of antioxidants within the body while producing reactive nitrogen species (RNS). ROS generated by oxidative stress disrupts cell signaling, DNA damage, lipids, and proteins, thereby resulting in inflammation and apoptosis. Mitochondria is the primary source of ROS production within cells. Increased ROS production reduces nitric oxide (NO) bioavailability, which elevates vasoconstriction within the arteries and contributes to the development of hypertension. ROS production has also been linked to the development of atherosclerotic plaque. Antioxidants can decrease oxidative stress in the body; however, various therapeutic drugs have been designed to treat oxidative stress damage due to CVD. The present review provides a detailed narrative of the oxidative stress and ROS generation with a primary focus on the oxidative stress biomarker and its association with CVD. We have also discussed the complex relationship between inflammation and endothelial dysfunction in CVD as well as oxidative stress-induced obesity in CVD. Finally, we discussed the role of antioxidants in reducing oxidative stress in CVD.
Deep Unsupervised Weighted Hashing for Remote Sensing Image Retrieval

Deep unsupervised hashing methods are gaining attention in the field of remote sensing (RS) image retrieval due to the rapid growth in the volume of unlabeled RS data. Most previous unsupervised hashing research use only natural image-based pre-trained models to generate label matrices, however, this method cannot capture the semantic information of RS images well and limits the accuracy of retrieval. To solve this problem, the authors propose a deep unsupervised weighted hashing (DUWH) model, which uses a similarity matrix updating strategy based on a weighted similarity structure to achieve the mutual optimization of the similarity matrix and hash network. The authors devise a novel combinatorial loss function to improve the hash performance, which can be used to obtain higher quality hash codes by assigning different weights to the sample pairs with different difficulties. Experiments were conducted on two RS datasets to verify the excellent performance of the proposed method.
Factors Affecting the Risk of Diabetes Development among Brazilian Residents in Japan

This study aimed to uncover the risk of diabetes and its associated factors among Brazilian residents in Japan. An anonymous questionnaire survey was conducted among Brazilians living in Japan who were aged >40 years. The questionnaire collected data on the participants’ Finnish Diabetes Risk Score (FINDRISC), basic demographics, and health conditions. The analysis was based on the FINDRISC to assess factors affecting the risk of diabetes. Among the 181 participants (mean age, 52.9 years), 77 and 104 were men and women, respectively; 26 (14.3%) people were diagnosed with diabetes. The participants were categorized into high- and low-risk groups based on a FINDRISC value of ≥12 and ≤11, respectively. The high-risk group, 26 [17.7%] patients, contained a significantly higher proportion whose education level was less than elementary school, who were unable to speak Japanese, and whose diets contained little feijoa than the low-risk group. Feijoa is a local Brazilian bean dish that is low in fat and calories but contains high protein content, which helps prevent diabetes. Our findings suggest that increased health literacy is important for controlling chronic diseases, including diabetes.
Research on Chinese Speech Emotion Recognition Based on Deep Neural Network and Acoustic Features

In recent years, the use of Artificial Intelligence for emotion recognition has attracted much attention. The industrial applicability of emotion recognition is quite comprehensive and has good development potential. This research uses voice emotion recognition technology to apply it to Chinese speech emotion recognition. The main purpose of this research is to transform gradually popularized smart home voice assistants or AI system service robots from a touch-sensitive interface to a voice operation. This research proposed a specifically designed Deep Neural Network (DNN) model to develop a Chinese speech emotion recognition system. In this research, 29 acoustic characteristics in acoustic theory are used as the training attributes of the proposed model. This research also proposes a variety of audio adjustment methods to amplify datasets and enhance training accuracy, including waveform adjustment, pitch adjustment, and pre-emphasize. This study achieved an average emotion recognition accuracy of 88.9% in the CASIA Chinese sentiment corpus. The results show that the deep learning model and audio adjustment method proposed in this study can effectively identify the emotions of Chinese short sentences and can be applied to Chinese voice assistants or integrated with other dialogue applications.