Miracle-dz/UniFAR-FTUs huggingface dataset:
Definition of the Clinical Characteristics of Patients with Moderate and Severe Atopic Dermatitis for Whom Narrow-Band UVB (NB-UVB) and Medium-Dose UVA1 Phototherapies Are Still Valuable Treatment Options at the Age of Biologics

Narrow-band (NB) UVB and UVA1 have been successfully used for the treatment of atopic dermatitis (AD) since the 1980s, but the clinical indications for their use “at the age of biologics” remain to be assessed. From 2013 to 2017, 145 patients underwent a first treatment cycle with phototherapy. They achieved a median final EASI score of 9.90 with UVA1 and 13.70 with NB-UVB. The rates of patients achieving an IGA score of 0/1 persistent for at least 6 months were 33% with UVA1 and 28% with NB-UVB, and the rates with an EASI90 improvement were 10.9% with UVA1 and 11.0% with NB-UVB. The cut-off baseline EASI values for a good probability to achieve a 0/1 IGA were 24.4 with UVA1 and 24.7 with NB-UVB. A 0/1 IGA persistent for at least 6 months was more likely to be achieved by patients with a history of flares interspersed with periods of mild or no disease. From 2018, we only enrolled patients with the above-mentioned characteristics. The number of treated patients was lower, but the final EASI score, the rate of patients achieving IGA 0/1 persistent for at least 6 months, and EASI90 were significantly higher. Medium-dose UVA1 and NB-UVB phototherapies remain useful for the treatment of AD patients with a baseline EASI score lower than 24.4 and 24.7, respectively, and a medical history of flares followed by prolonged periods of complete or near-complete remission.
July effect in clinical outcomes of esophagogastroduodenoscopy performed at teaching hospitals in the United States

Abstract Background Esophagogastroduodenoscopy (EGD) is a common procedure used for both diagnosis and treatment, but carries risks such as bleeding and perforation. The “July effect”—described as increased complication rates during the transition of new trainees—has been studied in other procedures, but has not been thoroughly evaluated for EGD. Methods We used the National Inpatient Sample database for 2016 to 2018 to compare outcomes in EGD performed between July to September and April to June. Results Approximately 0.91 million patients in the study received EGD between July to September (49.35%) and April to June (50.65%), with no significant differences between the two groups in terms of age, gender, race, income, or insurance status. Of the 911,235 patients, 19,280 died during the study period following EGD, 2.14% (July–September) vs 1.95% (April–June), with an adjusted odds ratio of 1.09 (P < 0.01). The adjusted total hospitalization charge was $2052 higher in July–September ($81,597) vs April to June ($79,023) (P < 0.005). The mean length of stay was 6.8 days (July–September) vs 6.6 days (April–June) (P < 0.001). Conclusions The results of this study are reassuring as the July effect on inpatient outcomes for EGDs was not significantly different according to our study. We recommend seeking prompt treatment and improving new trainee training and interspecialty communication for better patient outcomes.
A Collection of Datasets and Simulation Frameworks for Industrial Control System Research

This paper documents the release of several testbeds, which model control systems from various critical infrastructure industries. Included with the models is a set of data logs related to normal operations and cyberattacks exploited against these systems. The normal operations for each system were a constrained random set of scenarios that operate the system in normal manual and automated modes as well as attack scenarios. The cyberattacks include reconnaissance, denial-of-service, man-in-the-middle, and injection; some attacks fall under more than one of these categories. Use cases of these testbeds include obtaining datasets to train defensive applications that utilize machine learning.
Improved Target Laser Capture Technology for Hexagonal Honeycomb Scanning

In laser tracking systems, capturing moving targets is a prerequisite to guaranteeing a tracking system’s performance. Previous studies have confirmed that the capture probability of hexagonal spiral scanning is higher than that for other scanning methods, but there is still room for improvement. This article proposes an improved hexagonal honeycomb scanning capture method based on hexagonal spiral scanning for a prior moving target model with a Gaussian distribution positioned within the scanning range of the visual threshold. Through experimental verification, it was found that, within the same scanning field of view, the capture probability can be increased by 3% compared to that in traditional hexagonal spiral scanning, making the capture probability greater than 98%. The improved hexagonal honeycomb structure scanning method proposed in this article provides a new solution for target acquisition problems in fields such as laser communication, laser docking, and airborne radar.
Monotherapy Of Tamsulosin Vs Monotherapy of Solifenacin in The Treatment Of Ureter Stent Discomforts In Urology Patients: A Retrospective Study

DJ stent placement is a common and necessary urological procedure that can be used in a variety of ways to treat a variety of urinary tract conditions. It can cause lower urinary tract symptoms (LTUS). Pharmacologic therapy is one of several experiments that have been conducted to alleviate these symptoms, specifically the use of alpha-1A blockers (tamsulosin) and antimuscarinics (solifenacin) in the treatment of LUTS associated with DJ stents. The aim of our study was evaluating the monotherapy of tamsulosin vs monotherapy of solifenacin in ureter stent discomfort patients in department of Urology Sri Venkateswara Institute of Medical Sciences south Indian tertiary care teaching hospital Tirupati, Andhra Pradesh. A retrospective study was conducted in 187 patients with ureter stent discomfort patients. The data collected from 187 patients was initially assessed for their USSQ quesionarries, QoL and efficacy of monotherapy of tamsulosin vs monotherapy of solifenacin was assessed.
Machine Learning and Deep Learning based Stock Market Prediction considering Covid-19 as a Feature

India’s capital markets are witnessing intense uncertainty due to global market failures. Since the outbreak of COVID-19, risk asset prices have plummeted sharply. Risk assets declined half or more compared to the losses in 2008 and 2009. The high volatility is likely to continue in the short term; as a result, the Indian markets have declined sharply. In this paper, we have used different algorithms such as Gated Recurrent Unit, Long Short-Term Memory, Support Vector Regressor, Decision Tree, Random Forest, Lasso Regression, Ridge Regression, Bayesian Ridge Regression, Gradient Boost, and Stochastic Gradient Descent Algorithm to predict financial markets based on historical data available along with economic and financial features during this pandemic. According to our findings, deep learning models can accurately estimate financial indexes by utilizing non-linear transaction data. We found that the Gated Recurrent Unit performs better than the existing model.
Applications of eye-tracking technologies in military vehicle environment

Eye tracking technologies are becoming ubiquitous in the modern world and their use cases range from mobile devices to medical tests, from emotion recognition to operating vehicles and aircraft. However, the eye-tracking data acquisition process is still vulnerable and prone to multiple inconsistencies when reading gaze features. In this paper, we survey the most common challenges encountered when gathering gaze data using all types of state-of-the-art sensors and provide an overview of eye-tracking data applications in relation to the military vehicle environment.
Anti‑tumor effects of an aqueous extract of Ecklonia cava in BALB/cKorl syngeneic mice using colon carcinoma CT26 cells

Ecklonia cava (E. cava) is well known as one of edible alga that contains various unique polyphenols. The anti-tumor activity of an aqueous extract of E. cava (AEC) against colon carcinoma was evaluated by analyzing the alterations in tumor growth, histopathological structure and molecular mechanisms in CT26 tumor-bearing BALB/cKorl syngeneic mice after administrating AEC for five weeks. AEC contained high total phenolic contents and demonstrated significant scavenging activity against 2,2-diphenyl-1-picrylhydrazyl radicals. Marked anti-tumor effects were demonstrated in the AEC-treated CT26 cells. In the in vivo syngeneic model, the AEC treatment decreased the volume and weight of CT26 tumors, and expanded the necrotic region in the hematoxylin and eosin stained tumor sections. The inhibitory effects of AEC on tumor growth were reflected by the increased level of apoptotic proteins, inhibition of cell proliferation, suppression of metastasis ability and increase in tumor-suppressing activity in CT26 tumor-bearing BALB/cKorl syngeneic mice. The potential function of phlorotannin (PT), one of the primary active compounds in AEC, was demonstrated by the increased cytotoxicity, apoptosis and suppression of cell proliferation in PT-treated CT26 cells. Overall, the results of the present study provide novel scientific evidence that AEC can suppress the growth of CT26 colon cancer by activating apoptosis, suppressing cell proliferation, inhibiting cell migration and enhancing the tumor-suppressing activity.
Real-time Patient Monitoring System to Reduce Medical Error with the help of Database System

In the field of healthcare, continuous data collection and storage with the appropriate visualization tools can be an effective strategy for a wide range of illnesses. As a result of infor-mation gaps or discrepancies, underdeveloped nations and rural areas of wealthy countries experience medical errors, resulting in higher death rates. Using cutting-edge computing and cloud computing technologies alongside database and visualization tools, these problems will be adequately addressed. In addition, it will ensure superior medical care by enabling telehealth and remote monitoring. This study proposes a cloud-based patient monitoring system that can collect and display real-time data from a variety of sensors, including heart rate, electrocardiogram (ECG), blood oxygen level, and body temperature, among others. Minimal effort is required to integrate additional sensors into the device. The ability to store and visualize measured values in the cloud enables this system to mitigate clinical mistakes due to information gaps. With the help of visualization, the patient's state can be monitored by both the central hub physician and the designated physician. In addition, a GSM module will send a warning to the doctor if the critical level is either exceeded or dropped, shortening the time it takes for the doctor to take the necessary steps. The healthcare system as a whole stands to benefit from this prototype gadget.
Perhitungan Premi dengan Asumsi Waktu Antar Klaim Berdistribusi Eksponensial

In this paper, will searched how the insurance premium calculation equations assuming exponential distribution of time between the claim and the amount of claims Beta distribution. To obtain or seek the distribution of sum claims, first known form of the distribution of Ti. Because Ti valuable and continuous positive, meaning it can be assumed that the distribution of Ti is continuous and non-negative. In this paper, it is assumed that the Ti distribution is exponential. The function of the amount of the premium that is obtained by assuming an exponential and the amount
Research on Data Interpolation of Energy Storage Power Station Based on DC-GAN Network

With the decline in the proportion of domestic traditional coal power generation, more and more lithium battery power stations have been put into use. There are thousands batteries in one station, then the safety of the battery in the power station become more and more important. Therefore, the power stations have stricter requirements on safety, economy, and reliability. How to ensure the safety and stability of power stations has become an urgent problem. However, due to various factors, the data from power station always have low sampling precision and long sampling intervals, which makes the data cannot meet the accuracy requirements of energy storage safety products. At the same time, Traditional machine learning algorithms and many artificial intelligence algorithms are difficult to forecast or monitor for the power station in this situation. This paper proposes a method of using a Deep Convolutional Generative Adversarial Network(DCGAN) to learn the existing high-sampling and high-precision data, and filling in the missing data caused by the long sampling interval in the power station. The traditional loss function of GAN has been improved to speed up the convergence of the network, and to solve collapse problem when traditional GAN Network training. Finally, the method proposed in this paper is verified by comparing the interpolated data generated by different models with the real data with high-sampling. Compared with the different traditional interpolation methods and traditional Linear GAN, in the case of different sampling time intervals, the method proposed in this paper is obvious more effective and it has more practical application value.
Oblique Blind Faulting Underneath the Luzon Volcanic Arc During the 2022 Mw 7.0 Abra Earthquake, the Philippines

Unknown seismogenic structures lurking beneath convergent margins introduce substantial uncertainty in seismic hazard assessments. In northwestern Luzon, the Mw 7.0 Abra earthquake on 27 July 2022 highlights the seismic activity along an unmapped blind fault underneath the Cordillera Central. By integrating coseismic displacements constrained by radar satellite imagery and Global Navigation Satellite System, we image oblique coseismic slip at 11–22 km depth with peak slip of ∼1 m beneath the Philippine Fault Zone in northern Luzon. The southward propagation of coseismic slip and aftershocks terminated at a distance of 50 km from the northern end of the 1990 Luzon earthquake rupture, leaving a seismicity gap in between. Coulomb stress changes of reaching 2 bars are imparted at the shallow portions of the Vigan‐Aggao and Abra River faults, where the updated 100‐year seismic potential is increased to Mw 7.0–7.7, given the thick seismogenic layer of ∼30 km in northern Luzon.
Perceptions on using the International Caries Detection and Assessment System (ICDAS) among Malaysian private dentists: a qualitative study

Objective: to explore the perceptions of Malaysian private dentists on the use of the International Caries Detection and Assessment System (ICDAS) in their dental practice. Material and Methods: this qualitative study involved individual interviews with twelve general dental practitioners working in the private sector, who has been exposed to ICDAS training during their undergraduate study. Purposive sampling was carried out among private dentists in Malaysia from various states to reflect diversity. The interviews were recorded and transcribed. Data analysis was conducted by thematic analysis. Results: five main barriers were identified through the coding process, namely time factors, lack of training, having no effect on treatment planning, charting difficulties
Hill-Climbing Algorithm for Efficient Color-Based Image Segmentation

In this paper, we present a novel image segmentation method that produces a set of visually coherent regions. The method is based on a hill-climbing approach and achieves the segmentation by performing two main tasks. First, the hill-climbing algorithm detects local maxima of clusters in the global three-dimensional color histogram of an image. Then, the algorithm associates the pixels of an image with the detected local maxima; as a result, several visually coherent segments are generated. The segmentation algorithm is simple and fast. Moreover, the whole segmentation process is performed without any hand-tuning of parameters. This method does not assume any a priori knowledge on the number of clusters or the content of an image.
Effectiveness of vestibular exercises on balance impairment in patients affected with stroke - A systematic review

Introduction and Aim: The patients who have been suffered with stroke have severe balance problems, it is difficult to tell with certainty how balance performance in stroke patients is impacted by vestibular impairment. The study's purpose was to determine if oculomotor and gaze stability exercises may help post-stroke individuals improve their balance. Materials and Methods: This study includes all articles of various databases from 2000 to 2020 was included for study selection. The google scholar, research gate databases were comprehensively studied. 100 articles were searched and out of which 60 articles were selected to be assessed for eligibility in full text. Finally, 9 articles were chosen for inclusion and for analysis, although 7 of them did not match our proposed inclusion criteria. Results: Exercises for the vestibular system have been demonstrated to be effective for stroke patients whose balance has been compromised. Conclusion: The study finds that more high -quality experimental research on vestibular exercises and balance impairment after stroke is necessary because there is very little evidence from earlier experimental studies to draw firm conclusions about their effects.
Three-Dimensional Reconstruction of Secondary Arc Based on Improved Binocular Stereo Vision Method

The secondary arc’s 3-D spatial morphology is significant for the study of its suppression technology. However, the brightness of secondary arc is high and the spatial morphology is complex. In addition, the haze-like plasma causes shielding and interruption on arc reconstruction. The reconstruction quality is seriously affected. Consequently, to obtain accurate physical parameters of secondary arc, a 3-D reconstruction experimental platform based on binocular stereo vision was built. In view of the unique haze-like plasma occlusion and arc interruption phenomenon of secondary arc image, this article used the dehazing algorithm based on the atmospheric scattering model to dehaze and enhance the arc image. A double-threshold recovery algorithm based on the grayscale of secondary arc image was used to repair intermittent arcs. Then, based on the optimized disparity map, the 3-D image of secondary arc was reconstructed. Results show that there are spatial differences in the shape changes of secondary arc. Moreover, the variation in arc length and diameter is divided into two stages. Compared with the previous work, this article realizes the 3-D reconstruction of secondary arc, and the shape of the arc is more realistic, which can provide a reference for exploring the physical characteristics of secondary arc.
A New Objective Function for RPL Based on Combined Metrics in Mobile IoT

To satisfy the quality of service requirements for some applications in the Internet of Things (IoT) network with mobile nodes, routing protocols must be adaptable to make better routing decisions. Routing Protocol for Lowpower and Lossy Networks (RPL) is one of the routing protocols that support metrics and constraints while building a Destination Oriented Directed Acyclic Graphs (DODAG) using a specific Objective Function (OF). However, RPL metrics that use standard OFs do not guarantee the performance criteria for a specific mobile IoT in terms of received and lost packets, overhead, throughput and power consumption. In this paper, we propose a new version of mrhof based on a combination of metrics such as total energy, the number of neighbors, and expected transmission count (ETX) to evaluate RPL performances in a mobile context. Then, we compare the assessment of this function to existing OFs based on various mobility models (Random Waypoint (RWP), Reference Point Group Mobility (RPGM), Nomadic) and node densities. The results show that this new function is more efficient than the standards OF in terms of packets received and packets lost with Random Way Point and RPGM models. Regardless of the number of nodes, it also reduces ETX and traffic overhead for all mobility models, as well as convergence time in the Random Waypoint environment. Furthermore, this new function contributes to the improvement of Rtmetric, the enhancement of throughput at low density only in favor of RWP and RPGM models, and an effective power consumption reduction through all densities and mobility models.
Korean University Students’ Experiences and Beliefs about Medium of Instruction in English Speaking Classes

The purpose of this study is to further understand Korean university students’ perception of using English as a primary medium of instruction in English speaking classes, along with their beliefs about what language would prove the most beneficial as a medium of instruction. This study aims to present the beliefs that students hold about which language(s) should be employed and for what reasons, as well as whether these beliefs vary depending on the students’ language proficiency. This research was based on the responses given by students after having direct experience participating in English speaking classes taught in English.This study used a survey questionnaire as the primary source of data which contained multiple choice questions, a Likert-scale, and open-ended questions. Data was collected from a total of 135 predominantly first-year Korean university students who self-reported as being at the beginner and intermediate-levels. A combination of quantitative and qualitative approaches were used to analyze the data. Descriptive statistics and a series of independent sample t-tests were used for the multiple choice questions and Likert-scale questions and content analysis applied to open-ended questions.Data analysis revealed that students had positive experiences in English speaking classes taught in English. The advantages of the English-only classes included the English-rich environment, improved familiarity with English, a positive emotional response, increased attentiveness to English, and most importantly, improvement in English proficiency. At the same time, students also reported difficulties in understanding lectures and instructions and in communicating with their teachers in English. They coped by using various strategies, such as machine translators and by asking their teachers questions. However, beginner-level students were more self-reliant while intermediate-level students used a balanced use of self-sufficiency and human resources. Regarding their beliefs about what language should be used as the medium of instruction, both beginner and intermediate students believed that English should remain the primary language used in class with a moderate use of L1 being suggested. Specifically, students agreed that L1 may provide benefits in academic, affective, and managerial domains. However, beginner-level students found using L1 to be more advantageous than intermediate-level students.The findings of this study suggest employing an English medium of instruction across varying English proficiency levels in English speaking classes and discusses various methods that might be considered by teachers when employing an English medium of instruction, one of which would be allowing for the use of the students’ L1 to a moderate extent under certain circumstances.