Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi‐GNSS Airborne Radio Occultation Observations as a Complement to Dropsondes in Atmospheric River Reconnaissance

Variations in the water vapor that atmospheric rivers (ARs) carry toward North America within Pacific storms strongly modulates the spatiotemporal distribution of west‐coast precipitation. The “AR Recon” program was established to improve forecasts of landfalling Pacific‐coast ARs and their associated precipitation. Dropsondes are deployed from weather reconnaissance aircraft and pressure sensors have been added to drifting ocean buoys to fill a major gap in standard weather observations, while research is being conducted on the potential for airborne Global Navigation Satellite System (GNSS) radio occultation (ARO) to also contribute to forecast improvement. ARO further expands the spatial coverage of the data collected during AR Recon flights. This study provides the first description of these data, which provide water vapor and temperature information typically as far as 300 km to the side of the aircraft. The first refractivity profiles from European Galileo satellites are provided and their accuracy is evaluated using the dropsondes. It is shown that spatial variations in the refractivity anomaly (difference from the climatological background) are modulated by AR features, including the low‐level jet and tropopause fold, illustrating the potential for RO measurements to represent key AR characteristics. It is demonstrated that assimilation of ARO refractivity profiles can influence the moisture used as initial conditions in a high‐resolution model. While the dropsonde measurements provide precise, in situ wind, temperature and water vapor vertical profiles beneath the aircraft, and the buoys provide surface pressure, ARO provides complementary thermodynamic information aloft in broad areas not otherwise sampled at no additional expendable cost.
A Conceptual Framework to Measure the Innovative Behaviour of Employees and To Automatically Analyze the Affecting Variables

The UAE’s organizations are driven to achieve the Vision 2021 to become a competitive knowledge economy and one of the key performance indicators is to keep improving the gross innovation index until UAE becomes the most innovative nation by the year 2021. To that effect, the organisations are experiencing an innovation movement and contributing through various innovation initiatives to achieve the national objective. Hence, it becomes quintessential to measure the innovative practices implemented in an organisation and the growth and sustainability of the innovation will be determined by the behaviour (innovative) of the employees. Research Objectives – The main research objective is to develop a conceptual framework to electronically measure the innovative practices and the innovative behaviour of the employees working in the higher education organisations in UAE. Methodology – The epistemological belief that the innovative behaviour of employees is contingent on the context of the organizational setting, the constructivist research paradigm has been applied to investigate the research. The primary data has been collected using the interview method and the secondary data is collected through a systematic review of literature extracted from the various database using the keyword search criteria. Thematic analysis and Narrative analysis has been conducted to analyse the data. in private organisations (local and international) the constructs of innovative behaviour, associated the variables. on micro and macro variables; the dominating ones being the organizational the degree of employee engagement and the leadership style. The be new practices or methods or ideas implemented by them on the official portal on pre-set frequency interval and record the variables that prompted them to implement the innovative practice and/or prevented them to implement the innovative practice. The conceptual framework to measure the innovative behaviour and the practical model to analyze the affecting variables will assist policymakers to capitalize on the behavioural promoter variables and take relevant measures to achieve organizational as well as national objectives.
Assessment of Environmental Pollution of Heavy Metals Deposited on the Leaves of Trees in Yazd Bus Terminals

There is a lack of information about urban transport activity in adding heavy elements to the environment. This research assesses concentration some of heavy elements in the deposited atmospheric dust in Yazd bus terminals. Dust samples were collected from the green space in the bus terminals of urban transportation. Following the digestion by nitric acid, determination of the total metal concentration of cadmium(Cd), cobalt(Co), copper(Cu), nickel(Ni), lead(Pb), zinc(Zn), chromium (Cr), and manganese(Mn) in the dust were performed via Atomic Absorption Spectrometry. The map representing the spatial distribution of metals was plotted and their source was identified using Pearson correlation coefficients, Principal Component Analysis(PCA), and Cluster Analysis(CA). The findings indicated that the mean concentration of heavy metals in the deposited dust on the leaves of the trees was in the following order; Cd < Co < Ni < Pb < Cu < Zn < Cr < Mn. The map representing the spatial distribution of heavy metal concentrations indicated that the abundance of metals in different stations varies according to their location. Two important sources for the concentration of heavy metals in the deposited dust on the leaves were identified. The sources of Co, Cd, Mn, and Ni were anthropogenic and lithogenic, and the sources of Pb, Zn, Cr and Cu were the traffic and industrial activities. The amounts of EF, CF, IPI and, RI Indicators in residential, commercial, green space and environmental uses were estimated at low to extremely high levels of pollution. The findings showed that the growth of industrialization and human activities had caused contamination of the environment by heavy metals emitted into the atmosphere of Yazd.
Interpersonal Violence (IV) in Sport and Mental Health Outcomes in Teenagers

The aim of the present study was to examine the associations between interpersonal violence (IV) experienced in the context of sport by teenagers and three mental health outcomes: self-esteem, psychological distress, and post-traumatic stress disorder (PTSD) symptoms. A convenience sample of 1055 French-Canadian athletes between the ages of 14 and 17 who were participating in an organized sport was recruited to participate in an online survey. Results showed that psychological violence and neglect as well as sexual violence were independently related to lower self-esteem while physical, psychological/neglect, and sexual violence were all independently related to higher psychological distress and PTSD symptoms. Early intervention programs for athletes that experience IV is critical as it may help prevent the development of subsequent mental health issues.
The effect of implementing an aseptic practice bundle for anaesthetists to reduce postoperative infections. Protocol for a stepped wedge, cluster randomised, multi-site trial. The Anaesthetists Be Cleaner (ABC) Study.

Postoperative infection is a serious problem in New Zealand, and internationally with considerable human and financial costs. Also, in New Zealand, certain factors that contribute to postoperative infection are more common in Māori and Pacific populations. To date, most efforts to reduce postoperative infection have focused on surgical aspects of care and on antibiotic prophylaxis, but recent research shows that anaesthesia providers may also have an impact on infection transmission. These providers sometimes exhibit imperfect hand hygiene and frequently transfer the blood or saliva of their patients to their work environment. In addition, intravenous medications may become contaminated while being drawn up and administered to patients. Working with relevant practitioners and other experts, we have developed an evidence-informed bundle to improve key aseptic practices by anaesthetists with the aim of reducing postoperative infection. The key elements of the bundle are the filtering of compatible drugs, context-relevant hand hygiene practices, and enhanced maintenance of clean work surfaces. We will seek support for implementation of the bundle from senior anaesthesia and hospital leadership and departmental “champions”. Anaesthetic teams and recovery room staff will be educated about the bundle and its potential benefits through presentations, written material, and exemplar videos. We will implement the bundle in operating rooms where hip or knee arthroplasty or cardiac surgery procedures are undertaken in a five-site, stepped wedge, cluster randomised, quality improvement design. We will compare outcomes between approximately 5,000 cases before and 5,000 cases after implementation of our bundle. Outcome data will be collected from existing national and hospital databases. Our primary outcome will be days alive and out of hospital to 90 days, which is expected to reflect all serious postoperative infections. Our secondary outcome will be the rate of surgical site infection. Aseptic practice will be observed in sampled cases in each cluster before and after implementation of the bundle.
Discrete Spherical Image Representation for CNN‐Based Spherical Camera Pose Estimation

A spherical image has a full field of view. Using spherical images can estimate camera pose more accurately than conventional perspective images. It is known that a sphere can be quasi‐uniform sampled by dividing a geodesic dome repeatedly; it results in a representation with rotational invariance approximately. Based on this fact, in this paper we proposed a method of estimating a spherical camera pose based on the geodesic‐dome‐division‐based discrete spherical image representation by modifying the conventional perspective‐image‐oriented convolutional neural network, called PoseNet. First, a spherical image is represented as a geodesic‐dome‐division‐based discrete spherical image. Then, to cope with the six‐connectivity of the pixels of a geodesic‐dome‐division‐based discrete spherical image using the conventional convolutional neural network framework, the discrete spherical image is flattened as a connected five parallelograms and resampled as an eight‐connectivity image. Consequently, via the comparative experiments, it is found that the geodesic‐dome‐division‐based discrete spherical image representation approach can achieve a better result of camera pose estimation than equirectangular image and CubeMap representations. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.
Some Notes on The Sample Size determination

Sample size determination is often important step in planning a statistical study. Studies that are too small sizes of samples may lead to inaccurate results but studies that are too large sizes of samples may waste time and resources. Because the appropriate sample size determination depends on the type of data being collected of the study (categorical or continuous) and the purpose of the study (estimate or test). Therefore, in this paper we discuss the important factors that affect on calculating the appropriate sample size. These factors, include: the type of data, level of precision, the confidence level, the degree of variability, effect size, type I error, Type II error, significance level, power of the test and other factors. Also, we discuss and illustrate the different procedures (formulas) for sample size calculation.
Response Surface Mesh with the Outer Input Method

Artificial intelligence in general and optimization tasks applied to the design of aerospace, space,and automotive structures, rely on response surfaces to forecast the output of functions, and are vital part of these methodologies. Yet they have important limitations, since greater precisions require greater data sets, thus, training or updating larger response surfaces become computationally expensive, sometimes unfeasible. This has been a bottle neck limitation to achieve more promising results, rendering many AI related task with a low efficiency.To solve this challenge, a new methodology created to segment response surfaces is hereby presented. Differently than other similar methodologies, the novel algorithm here presented named outer input method, has a very simple and robust operation. With only one operational parameter, maximum element size, it efficiently generates a near isopopulated mesh for any data set with any type of distribution, such as random, Cartesian, or clustered, for domains with any number of coordinates.Thus, it is possible to simplify the response surfaces by generating an ensemble of response surfaces, here denominated response surface mesh. This study demonstrates how a metamodel denominated Kriging, trained with a large data set, can be simplified with a response surface mesh, significantly reducing its often expensive computation costs> experiments here presented achieved an speed increase up to 180 times, while using a dual core parallel processingcomputer. This methodology can be applied to any metamodel, and metamodel elements can be easily parallelized and updated individually. Thus, its already faster training operation has its speed increased.
Chromatographic Applications in the Multi-Way Calibration Field

In this review, recent advances and applications using multi-way calibration protocols based on the processing of multi-dimensional chromatographic data are discussed. We first describe the various modes in which multi-way chromatographic data sets can be generated, including some important characteristics that should be taken into account for the selection of an adequate data processing model. We then discuss the different manners in which the collected instrumental data can be arranged, and the most usually applied models and algorithms for the decomposition of the data arrays. The latter activity leads to the estimation of surrogate variables (scores), useful for analyte quantitation in the presence of uncalibrated interferences, achieving the second-order advantage. Recent experimental reports based on multi-way liquid and gas chromatographic data are then reviewed. Finally, analytical figures of merit that should always accompany quantitative calibration reports are described.
Fast Computation of Green Function for Layered Seismic Field via Discrete Complex Image Method and Double Exponential Rules

A novel computational method to evaluate the Sommerfeld integral (SI) efficiently and accurately is presented. The method rewrites the SI into two parts, applying discrete complex image method (DCIM) to evaluate the infinite integral while using double exponential quadrature rules (DE rules) for the computation of the finite part. Estimation of signal parameters via rotational invariance techniques (ESPRIT) is used to improve the accuracy and efficiency of extracting DCIM compared to the generalized pencil of function (GPOF). Due to the symmetry of the horizontal layered media, the Green function, representing the seismic fields due to a point source, can be written in the form of Sommerfeld integral in cylindrical coordinate system and be calculated by the proposed method. The performance of the method is then compared to the DE rules with weighted average partition extrapolation (WA), which shows a good agreement, with computational time reduced by about 40%.
Turkish Version, Validity and Reliability of the Lumbar Spine Instability Questionnaire.

AIM Low back pain causes economic and moral losses in society and clinically represents a multifaceted dilemma. Lumbar instability is an important factor in back pain due to its high incidence. The aim of this research was to reveal the reliability and validity of the Turkish Lumbar Spine Instability Questionnaire for evaluating patients with lumbar instability and low back pain. MATERIAL AND METHODS A total of 100 patients with low back pain participated in the study. Test-retest and internal consistency analyses were used to assess the reliability of the questionnaire, and correlation analysis was used to determine its validity. For correlation analysis, the total scores of the Lumbar Spine Instability Questionnaire, Visual Analog Scale, Bournemouth Questionnaire, Tampa Scale of Kinesiophobia, and Roland Morris Disability Questionnaire were compared. RESULTS Reliability analysis showed that the internal consistency value of the questionnaire was 0.818, and the intraclass correlation coefficient (ICC) value was 0.839. Pearson correlation analysis showed that the questionnaire had a good correlation with the Tampa Scale of Kinesiophobia (0.520) and a stronger correlation with the Visual Analog Scale (0.702), Roland Morris Disability Questionnaire (0.767), and Bournemouth Questionnaire (0.667). CONCLUSION Our results confirmed that the Turkish version of the Lumbar Spine Instability Questionnaire is reliable and valid.
Health Impact and Risk Factors Affecting South and Southeast Asian Women Following Natural Disasters: A Systematic Review

(1) Background: Following natural disasters, women have a higher prevalence of adverse physical and mental health outcomes. Given that the South and Southeast Asia regions are highly disaster prone, a review was undertaken to identify the potential health impact and key risk factors affecting women after disasters in the countries located in South and Southeast Asia regions. (2) Methods: A systematic literature search of four databases yielded 16 studies meeting the inclusion criteria. The review was conducted according to the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidance, between July 2008 and March 2021. (3) Results: The majority of studies reported women’s negative/poor mental health, identifying a significant association of socio-demographics, during disaster exposure, post-disaster, and pre-existing risk factors. The six most-cited influences on women’s mental health found in the reviewed literature were being female, adult age group, having no formal education, poverty or low economic status, poor physical health/physical injuries, and death of family members. Women’s health during the post-disaster period was generally reported as poor among all the countries of the South and Southeast Asia regions. (4) Conclusions: Appropriate social support and the availability of free healthcare access for women are warranted in disaster-affected areas. This review offers a valuable contribution to the knowledge of women’s health complications/challenges and associated risk factors related to disasters, essential for the development of strategies to help reduce this burden in the future. Further research is required on natural disasters to identify ways to reduce women’s health impacts after natural disasters, especially in the context of low-income and lower-middle-income countries.
Ultrafiltration of α-Lactalbumin Protein: Acquaintance of the Filtration Performance by Membrane Structure and Surface Alteration

α-Lactalbumin is an essential protein with multiple roles in physiological and the nutritional functionalities, such as diabetic prevention, blood pressure stabilization, and cancer cell inhibition. In the present work, polyethersulfone (PES)-based membranes were developed by incorporating Pluronic F127 and carbon nanotubes with single- and multi-walled dimensions (Sw-Cnts and Mw-Cnts) as additives. The resulting membranes were evaluated for use in the filtration of α-lactalbumin protein solution. Four series of membranes, including PES pristine membrane, were fabricated via the phase inversion process. The characteristics of the membrane samples were analyzed in terms of morphology, membrane surface hydrophilicity and roughness, and surface chemistry. The characterization results show that the incorporation of additive increased the surface wettability by reducing the surface water contact angle from 80.4° to 64.1° by adding F127 and Mw-Cnt additives. The highest pure water permeability of 135 L/(m2·h·bar) was also exhibited by the PES/F127/Mw-Cnt membrane. The performance of the modified membranes was clearly better than the pristine PSF for α-lactalbumin solution filtration. The permeability of α-lactalbumin solution increased from 9.0 L/(m2·h·bar) for the pristine PES membrane to 10.5, 11.0 and 11.5 L/(m2·h·bar) for membranes loaded with Pluronic F127, Sw-Cnts, and Mw-Cnts, respectively. Those increments corresponded to 17, 22, and 28%. Such increments could be achieved without altering the α-lactalbumin rejections of 80%. Remarkably, the rejection for the membrane loaded with Sw-Cnts even increased to 89%.
Right Ventricular Structure and Function in Young Adults Born Preterm at Very Low Birth Weight

Being born preterm (PT, <37 weeks gestation) or at very low birth weight (VLBW, <1500 g) is associated with increased rates of cardiopulmonary disorders in childhood. As survivors age, late cardiac effects, including right ventricular (RV) remodelling and occult pulmonary hypertension are emerging. In this population-based study, we aimed to investigate right heart structure and function in young adults born PT at VLBW compared to normal-weight term-born controls. The New Zealand VLBW Study has followed all infants born in 1986 with birth weight <1500 g. All were born preterm from 24 to 37 weeks. A total of 229 (71% of survivors) had echocardiograms aged 26–30 years which were compared to age-matched, term-born, normal-weight controls (n = 100). Young adults born preterm at very low birth weight exhibited smaller RV dimensions compared to term-born peers. Standard echocardiographic measures of RV function did not differ, but mildly reduced function was detected by RV longitudinal strain. This difference was related to birth weight and gestational age but not lung function or left ventricular function. Echocardiographic strain imaging may be an important tool to detect differences in RV function preterm and VLBW.
Fault Prediction of Centrifugal Pump Based on Improved KNN

To effectively predict the faults of centrifugal pumps, the idea of machine learning k-nearest neighbor algorithm (KNN) was introduced into the traditional Mahalanobis distance fault discrimination, and an improved centrifugal pump fault prediction model of KNN based on the Mahalanobis distance is proposed. In this method, the Mahalanobis distance is used to replace the distance function in the conventional KNN algorithm. Grid search and cross-validation are used to determine the optimal K value of the prediction model. A centrifugal pump test rig was established to solve three common faults of centrifugal pumps: cavitation, impeller damage, and machine seal damage, and the method was verified. The results show that this method can effectively distinguish the specific fault types of centrifugal pumps based on vibration signals, and the fault prediction accuracy of the off-balance condition is up to 82%. This study provides a novel idea and method for centrifugal pump fault prediction and diagnosis and avoids the interaction between parameters when monitoring multiple parameters.