Miracle-dz/UniFAR-FTUs huggingface dataset:
The comparative analysis of the indoor air pollutants in occupied apartments at residential area and industrial area in dubai, united arab emirates

Most of the research on indoor air pollutants in the United Arab Emirates focus on the state before moving in. There are no studies on the evaluation and characteristics of Indoor Air Quality in living conditions. This study aims to obtain primary data by measuring the concentration of indoor hazardous chemical substances in apartment housings with different area characteristics and to identify the external and internal pollutants that affect them. As a methodology, a comparative analysis was conducted between residential (Business Bay) and industrial areas (Ras Al Khor) to investigate the indoor air pollutants level with the comparison of the outdoor environment, elapsed time after construction, finished materials, temperature, relative humidity, renovation, purchase of furniture and electric appliances, built-in closets, and air cleaning methods. The result showed that Benzene (C6H6), Toluene (C7H8), Ethylbenzene (C8H10), Xylene (C8H10), and Styrene (C8H8) were at a stable level. However, in the case of formaldehyde (HCHO), points exceeding or close to the WHO IAQ standard were found, suggesting that long-term attention is required. The differences between residential and industrial areas were marginal in terms of the size of the house, temperature and humidity, and the building materials. It was proven that the emission of indoor air pollutants from building materials lessened under 18 months after construction.
The impact of follower leadership position on transformational leadership as moderator of the association between work-related ambiguity and job satisfaction

This two-part study examined if the buffering effect of transformational leadership on the association between work-related ambiguity and job satisfaction is contingent upon whether a follower holds a formal leadership position him/herself. Data from two separate surveys were employed: Study 1: A sample of 845 respondents from Belgium. Study 2: A national probability sample of 1,608 Norwegian employees. Study 1 showed that task ambiguity had a significant negative relation with job satisfaction, but that transformational leadership did only buffer the association between task ambiguity and job satisfaction among employees holding a formal position as a supervisor or manager. Study 2 extended Study 1 by adjusting for age and job tenure of subordinates as a confounding variable. Study 2 confirmed that transformational leadership had a significantly stronger impact on the observed association between role ambiguity and job satisfaction among respondents holding a supervisor or manager position. In conclusion, when considering job satisfaction as an outcome of work-related ambiguity, transformational leadership is mainly beneficial for followers holding a formal supervisor or manager position themselves. Our findings thereby question assumptions about the general effectiveness of transformational leadership.
An EMT-based risk score thoroughly predicts the clinical prognosis, tumor immune microenvironment and molecular subtypes of bladder cancer

Background Epithelial mesenchymal transition (EMT) is closely related to the occurrence, development, metastasis and antitumor immunity of tumors. However, comprehensive studies correlating EMT and prognosis, tumor microenvironment (TME) and molecular subtypes of bladder cancer (BLCA) are lacking. Methods TCGA-BLCA was chosen as our training cohort, while Xiangya cohort, GSE13507, GSE48075 were selected as our validation cohorts. Prognostic genes were screened out using univariate Cox analysis and the least absolute shrinkage and selection operator (LASSO) algorithm. Then we developed an EMT risk score based on these prognostic genes and systematically correlated the risk score with prognosis, TME and molecular subtypes of BLCA. Results Based on EMT related genes, we developed two different EMT patterns, named EMT cluster 1 and cluster 2, and found that cluster 2 showed a worse prognosis and an inflammatory TME phenotype. For personalized prognosis and TME phenotypes predicting, we developed and validated an EMT-based risk score by 7 candidate genes (ANXA10, CNTN1, FAM180A, FN1, IGFL2, KANK4 and TOX3). Patients with high EMT risk scores had lower overall survival (OS) with high predictive accuracy both in the training cohort and validation cohort. In addition, we comprehensively correlated the EMT risk score with TME and molecular subtype, and found that high EMT risk score suggested higher levels of immune cell infiltration and more inclined to present the basal molecular subtype. It was noteworthy that the same results also appeared in the validation of Xiangya cohort. Conclusions EMT related genes play an important role in tumor progression and immunity in BLCA. Our EMT risk score could accurately predict prognosis and immunophenotype of a single patient, which could guide more effective precision medical strategies.
The effects of acute high-intensity aerobic exercise on cognitive performance: A structured narrative review

It is well established that acute moderate-intensity exercise improves cognitive performance. However, the effects of acute high-intensity aerobic exercise on cognitive performance have not been well characterized. In this review, we summarize the literature investigating the exercise-cognition interaction, especially focusing on high-intensity aerobic exercise. We discuss methodological and physiological factors that potentially mediate cognitive performance in response to high-intensity exercise. We propose that the effects of high-intensity exercise on cognitive performance are primarily affected by the timing of cognitive task (during vs. after exercise, and the time delay after exercise). In particular, cognitive performance is more likely to be impaired during high-intensity exercise when both cognitive and physiological demands are high and completed simultaneously (i.e., the dual-task paradigm). The effects may also be affected by the type of cognitive task, physical fitness, exercise mode/duration, and age. Second, we suggest that interactions between changes in regional cerebral blood flow (CBF), cerebral oxygenation, cerebral metabolism, neuromodulation by neurotransmitters/neurotrophic factors, and a variety of psychological factors are promising candidates that determine cognitive performance in response to acute high-intensity exercise. The present review has implications for recreational, sporting, and occupational activities where high cognitive and physiological demands are required to be completed concurrently.
Executive functioning and spoken language skills in young children with hearing aids and cochlear implants: Longitudinal findings

Deaf or hard-of-hearing (DHH) children who use auditory-oral communication display considerable variability in spoken language and executive functioning outcomes. Furthermore, language and executive functioning skills are strongly associated with each other in DHH children, which may be relevant for explaining this variability in outcomes. However, longitudinal investigations of language and executive functioning during the important preschool period of development in DHH children are rare. This study examined the predictive, reciprocal associations between executive functioning and spoken language over a 1-year period in samples of 53 DHH and 59 typically hearing (TH) children between ages 3–8 years at baseline. Participants were assessed on measures of receptive spoken language (vocabulary, sentence comprehension, and following spoken directions) and caregiver-completed executive functioning child behavior checklists during two in-person home visits separated by 1 year. In the sample of DHH children, better executive functioning at baseline (Time 1) was associated with better performance on the higher-order language measures (sentence comprehension and following spoken directions) 1 year later (Time 2). In contrast, none of the Time 1 language measures were associated with better executive functioning in Time 2 in the DHH sample. TH children showed no significant language-executive functioning correlations over the 1-year study period. In regression analyses controlling for Time 1 language scores, Time 1 executive functioning predicted Time 2 language outcomes in the combined DHH and TH samples, and for vocabulary, that association was stronger in the DHH than in the TH sample. In contrast, after controlling for Time 1 executive functioning, none of the regression analyses predicting Time 2 executive functioning from Time 1 language were statistically significant. These results are the first findings to demonstrate that everyday parent-rated executive functioning behaviors predict basic (vocabulary) and higher-order (comprehension, following directions) spoken language development 1 year later in young (3–8 year old) DHH children, even after accounting for initial baseline language skills.
Advances in non-invasive biosensing measures to monitor wound healing progression

Impaired wound healing is a significant financial and medical burden. The synthesis and deposition of extracellular matrix (ECM) in a new wound is a dynamic process that is constantly changing and adapting to the biochemical and biomechanical signaling from the extracellular microenvironments of the wound. This drives either a regenerative or fibrotic and scar-forming healing outcome. Disruptions in ECM deposition, structure, and composition lead to impaired healing in diseased states, such as in diabetes. Valid measures of the principal determinants of successful ECM deposition and wound healing include lack of bacterial contamination, good tissue perfusion, and reduced mechanical injury and strain. These measures are used by wound-care providers to intervene upon the healing wound to steer healing toward a more functional phenotype with improved structural integrity and healing outcomes and to prevent adverse wound developments. In this review, we discuss bioengineering advances in 1) non-invasive detection of biologic and physiologic factors of the healing wound, 2) visualizing and modeling the ECM, and 3) computational tools that efficiently evaluate the complex data acquired from the wounds based on basic science, preclinical, translational and clinical studies, that would allow us to prognosticate healing outcomes and intervene effectively. We focus on bioelectronics and biologic interfaces of the sensors and actuators for real time biosensing and actuation of the tissues. We also discuss high-resolution, advanced imaging techniques, which go beyond traditional confocal and fluorescence microscopy to visualize microscopic details of the composition of the wound matrix, linearity of collagen, and live tracking of components within the wound microenvironment. Computational modeling of the wound matrix, including partial differential equation datasets as well as machine learning models that can serve as powerful tools for physicians to guide their decision-making process are discussed.
Dynamic patterns of postprandial metabolic responses to three dietary challenges

Food intake triggers extensive changes in the blood metabolome. The kinetics of these changes depend on meal composition and on intrinsic, health-related characteristics of each individual, making the assessment of changes in the postprandial metabolome an opportunity to assess someone's metabolic status. To enable the usage of dietary challenges as diagnostic tools, profound knowledge about changes that occur in the postprandial period in healthy individuals is needed. In this study, we characterize the time-resolved changes in plasma levels of 634 metabolites in response to an oral glucose tolerance test (OGTT), an oral lipid tolerance test (OLTT), and a mixed meal (SLD) in healthy young males (n = 15). Metabolite levels for samples taken at different time points (20 per individual) during the challenges were available from targeted (132 metabolites) and non-targeted (502 metabolites) metabolomics. Almost half of the profiled metabolites (n = 308) showed a significant change in at least one challenge, thereof 111 metabolites responded exclusively to one particular challenge. Examples include azelate, which is linked to ω-oxidation and increased only in OLTT, and a fibrinogen cleavage peptide that has been linked to a higher risk of cardiovascular events in diabetes patients and increased only in OGTT, making its postprandial dynamics a potential target for risk management. A pool of 89 metabolites changed their plasma levels during all three challenges and represents the core postprandial response to food intake regardless of macronutrient composition. We used fuzzy c-means clustering to group these metabolites into eight clusters based on commonalities of their dynamic response patterns, with each cluster following one of four primary response patterns: (i) “decrease-increase” (valley-like) with fatty acids and acylcarnitines indicating the suppression of lipolysis, (ii) “increase-decrease” (mountain-like) including a cluster of conjugated bile acids and the glucose/insulin cluster, (iii) “steady decrease” with metabolites reflecting a carryover from meals prior to the study, and (iv) “mixed” decreasing after the glucose challenge and increasing otherwise. Despite the small number of subjects, the diversity of the challenges and the wealth of metabolomic data make this study an important step toward the characterization of postprandial responses and the identification of markers of metabolic processes regulated by food intake.
Ill-posedness results in critical spaces for some equations arising in hydrodynamics

Many questions related to well-posedness/ill-posedness in critical spaces for hydrodynamic equations have been open for many years. In this article we give a new approach to studying norm inflation (in some critical spaces) for a wide class of equations arising in hydrodynamics. As an application, we prove strong ill-posedness of the $n$-dimensional Euler equations in the class $C^1\cap L^2 (\Omega)$ and also in $C^k \cap L^2(\Omega)$ where $\Omega$ can be the whole space, a smooth bounded domain, or the torus. We also apply our method to the Oldroyd B, surface quasi-geostrophic, and Boussinesq systems.
Resource-Efficiency and Fairness Tradeoff in Downlink NOMA Systems

Non-orthogonal multiple access (NOMA) is a promising and key wireless technology for the next generation of mobile communications. Considering that spectral efficiency (SE) and energy efficiency (EE) are the main metrics for designing wireless networks, many previous works have focused on the relationship between EE and SE, and provided a good insight into the resource efficiency (RE), i.e. the joint EE-SE tradeoff in NOMA networks. Moreover, fairness among the devices in terms of resources sharing should be particularly concerned because of the characteristic of NOMA. In this paper, we formulate a multi-objective optimization (MOO) problem to achieve a good trade-off between EE, SE and fairness. It is a new paradigm called the RE-fairness tradeoff. In order to obtain the feasible (Pareto-optimal) solutions, the equivalent single objective optimization (SOO) problem is explored by combining the multi-objectives through a weighted-sum objective function. Then based on the sequential convex approximation (SCA) technique, the optimal and suboptimal approaches are proposed separately. Simulation results reveal that the proposed approaches can perform the RE-fairness tradeoff flexibly compared to the only RE design, while meeting the requirements of quality-of-service (QoS) and the upper limit for the number of users in a NOMA group.
Three Phase Weighted Least Square Power System State Estimation Based on Fair Distribution Weight Function method

In general most conventional power system weighted least squares (WLS) state estimation algorithms used same weights for the weighting matrix. This in effect causes a deviation from the true values of the measurements, bringing about inaccuracies in the solution. Again, most of the methods used to overcome this problem from the literature are mostly performed in single phase power system modeling. To overcome all these problems, we propose a three-phase state estimation method that improves these short-comings using a three-phase fair distribution weight function from the literature. Again, the fair weight function is included in the normal equation of the weighted least squares state estimation procedure from the second iteration, and it is updated till the solution converges, thereby minimizing the error in the state estimation process. Furthermore, both the Conventional State Estimation (CSE) method and the proposed method (fair) are modelled and analyzed using the IEEE 13-bus system in MATLAB, with an error of 0.02% added to each method, and are both compared with the actual solution of a three-phase state estimator with no error. Finally, it is observed that the proposed method (fair) has a better accuracy and computational efficiency when compared to the conventional state estimation method.
Dynamic Radio Resource Slicing for Service-Oriented 5G/B5G C-V2X Networks

With the significant development of 5G/B5G cellular vehicle-to-everything (C-V2X) techniques, the number of connected vehicles with various vehicular services grows rapidly. However, due to the spectrum scarcity, how to consider the different vehicular quality of service (QoS) requirements and provide customized vehicular services in 5G/B5G C-V2X becomes challenging. To this end, in the paper, we propose an online radio resource slicing approach for base station association and spectrum allocation in C-V2X. The long-term time-averaged total delay of delay-sensitive vehicles is minimized subject to the throughput constraints of rate-sensitive vehicles. Since the original optimization problem is a mixed integer nonlinear stochastic optimization problem, we leverage Lyapunov optimization to transform it into a series of real-time combinatorial optimizations, and apply Markov approximation technique to obtain near-optimal solution. Simulation results indicate that proposed slicing method has a significant improvement compared with the benchmark.
Resource Allocation for Cell-Free Massive MIMO-Enabled URLLC Downlink Systems

Ultra-Reliable and low-latency communication (URLLC) is a pivotal technique for enabling the wireless control over industrial Internet-of-Things (IIoT) devices. By deploying distributed access points (APs), cell-free massive multiple-input and multiple-output (CF mMIMO) has great potential to provide URLLC services for IIoT devices. In this paper, we investigate CF mMIMO-enabled URLLC in a smart factory. Lower bounds (LBs) of downlink ergodic data rate under finite channel blocklength (FCBL) with imperfect channel state information (CSI) are derived for maximum-ratio transmission (MRT), full-pilot zero-forcing (FZF), and local zero-forcing (LZF) precoding schemes. Meanwhile, the weighted sum rate is maximized by jointly optimizing the pilot power and transmission power based on the derived LBs. Specifically, we first provide the globally optimal solution of the pilot power, and then introduce some approximations to transform the original problems into a series of subproblems, which can be expressed in a geometric programming (GP) form that can be readily solved. Finally, an iterative algorithm is proposed to optimize the power allocation based on various precoding schemes. Simulation results demonstrate that the proposed algorithm is superior to the existing algorithms, and that the quality of URLLC services will benefit by deploying more APs, except for the FZF precoding scheme.
Linear Stability and Acoustics of a Subsonic Plane Jet Flow

Noise generated in subsonic jet flow is caused by wave like structures. These structures are shaped similar to the eigenfunctions of growing modes that are computable by means of the linear stability theory. The compressible linear stability solver itself provides acoustic eigenmodes with eigenfunctions that have support over the whole physical domain. It can be assumed, that a profound investigation of these acoustic modes together with the unstable growing modes improves the understanding of the fundamental mechanisms of noise generation. For these purposes a linear stability solver for two-dimensional, compressible, viscous flow is developed and implemented in Matlab. The validation of the solver with results available in literature shows an excellent agreement. Nonreflective boundary conditions are implemented to correctly describe the behavior of acoustic waves propagating towards the artificial boundary. The disturbance energy balance for compressible, viscous flow with open boundaries is derived. The corresponding energy method allows to decompose the temporal growth rate ωi into production, dissipation, and flux terms and provides thereby deep insight into the effective mechanisms. The investigation on a subsonic plane jet flow shows that the varicose and sinuous Kelvin– Helmholtz modes are the only temporally and spatially growing modes. These Kelvin–Helmholtz modes live in the shear layer of the jet and are extensively characterized in this work. A comparison of the spatial growth rate between the varicose plane mode with the axisymmetric mode for the cylindrical problem given in literature shows a good qualitative agreement. By means of the energy method it is demonstrated that the interaction between Reynolds stress and base flow velocity gradient is the dominating mechanism describing the temporal growth and that thermal affects play a minor role for subsonic jets. The choice of the boundary condition at the far field boundary has an important effect on the acoustic eigenmodes: Whereas the eigenvalues of the acoustic spectrum are purely real for the reflective wall condition, the nonreflective boundary condition yields negative temporal growth rates ωi < 0. The effect of negative temporal growth rates is caused by the energy leakage due to acoustic waves leaving the system through the nonreflective boundary, which can be detected by the proposed energy method. Negative temporal growth rates imply acoustic eigenfunctions that spatially grow towards the boundary. This interrelation between temporal decay and spatial growth is proven analytically.
Isometric Convolutional Neural Networks for Bone Suppression of Multi-Planar Dual Energy Chest Radiograph

Multi-planar bone suppression is an extended version of conventional anterior view bone suppression problem. It is expected to provide more information about the chest radiograph for medical diagnosis and for 3D reconstruction. In this study, convolutional neural networks with isometric architecture are explored to solve multi-planar bone suppression problem from dual energy chest radiographs and compared with conventional architecture, pyramidal architecture. As a result, the proposed isometric architecture has generally better performance and could produce medically diagnosable multi-planar bone-suppressed chest radiographs.
Statistics for Psychology

1. Displaying the order in a group of numbers. 2. Central tendency and variability. 3. Some key ingredients for inferential statistics: Z scores, the normal curve, sample versus population, and probability. 4. Introduction to hypothesis testing. 5. Hypothesis testing with means of samples. 6. Making sense of statistical significance: Effect size and statistical power. 7. Introduction to the t test: Single sample and dependent means. 8. The t test for independent means. 9. Introduction to the analysis of variance. 10. Factorial analysis of variance. 11. Correlation. 12. Prediction. 13. Chi-square tests. 14. Strategies when population distributions are not normal: Data transformations and rank-order tests. 15. Integration and the general linear model. 16. Making sense of advanced statistical procedures in research articles.