Miracle-dz/UniFAR-FTUs huggingface dataset:
A direct comparison of two measures of ordinal knowledge among 8-year-olds

Children’s knowledge of the ordinal relations among number symbols is related to their mathematical learning. Ordinal knowledge has been measured using judgment (i.e., decide whether a sequence of three digits is in order) and ordering tasks (i.e., order three digits from smallest to largest). However, the question remains whether performance on these two ordinal tasks tap into similar cognitive processes. Canadian children (N = 87; Age M = 8.7 years, Grade 3) completed symbolic number tasks (i.e., number comparison, ordering, and order judgment) and measures of arithmetic fluency (i.e., addition and subtraction) and working memory (i.e., digit span backward). For both ordinal tasks, there was a reverse distance effect for ordered sequences such that children responded faster to adjacent than to non-adjacent sequences (e.g., 2 3 4 vs. 4 7 9) and a canonical distance effect for unordered sequences such that children responded faster to non-adjacent than to adjacent sequences (e.g., 4 2 3 vs. 4 9 7). Working memory and number comparison each predicted unique variance in the ordinal measures (ordering, order judgment, and a latent ordinal factor based on the two measures). Furthermore, ordinal skills superseded the role of number comparison as the key predictor of arithmetic, controlling for children’s gender and working memory skills. In summary, although both ordering and order judgment tasks index ordinal knowledge, a latent factor that excludes task-specific error may be a better index than either task separately.
Predictive factors for improved diagnostic accuracy with the use of radial-probe EBUS

ABSTRACT Objective: To assess predictive factors for improved diagnostic accuracy with the use of radial-probe EBUS (RP-EBUS). Methods: This was a retrospective review of consecutive patients undergoing RP-EBUS between February of 2012 and January of 2020. Parameters including the presence of a bronchus sign on CT scans, the position of the radial EBUS probe, lesion size, lesion location, and lesion type were analyzed in relation to two defined outcomes (final diagnosis or no diagnosis). Univariate analysis was used in order to explore the individual effects of each parameter on diagnostic accuracy. Multivariate logistic regression was performed to identify significant predictors of diagnostic accuracy. Results: RP-EBUS was used for diagnostic purposes in 101 patients. The lesion was < 3 cm in size in 59 patients (58.4%) and predominantly solid in 60.3%. There was a positive correlation between radial EBUS probe position and diagnostic accuracy (p = 0.036), with 80.9% of the patients showing a bronchus sign on CT scans. Furthermore, 89% of the patients showed a bronchus sign on CT scans and a correlation with diagnostic accuracy (p = 0.030), with 65.8% of the lesions being located in the left/right upper lobe (p = 0.046). When the radial EBUS probe was within the target lesion, the diagnostic yield was = 80.8%. When the probe was adjacent to the lesion, the diagnostic yield was = 19.2%. A bronchus sign on CT scans was the only parameter that independently influenced diagnostic accuracy (adjusted OR, 3.20; 95% CI, 1.081-9.770; p = 0.036). Conclusions: A bronchus sign on CT scans is a powerful predictor of successful diagnosis by RP-EBUS.
AN ALTERNATIVE FOR MIXED MODEL ANALYSES OF LARGE, MESSY DATA SETS (MTDFREML)

Portable Fortran based programs (MTDFREML) were developed using a derivative-free algorithm to obtain REML estimates of (co )variance components. Computations are based on Henderson's mixed model equations for multiple-trait models with missing observations on some traits and incorporation of relationships among relatives. Many fixed and random factors are allowed with number oflevels dependent on computer memory. Data sets with more than 40,000 genetic effects have been analyzed. Options allow solving MME at convergence. Constraints are automatically imposed. Expectations, standard errors of contrasts of solutions for fixed effects and prediction error variances of solutions for random effects can be obtained. Dimensions can be changed to match data with computer capability. A Fortran compiler is necessary. No fee is charged but the University of Waterloo must certify a license has been obtained for sparse matrix subroutines (SP ARSP AK) used in the program. As an example, birth weights of 4891 progeny of389 sires nested within 12 breeds and of2893 dams nested within 3 breeds of dam were analyzed to estimate components of variance due to sires and dams and to estimate differences among breeds of sires. For MTDFREML the analysis was trivial but for PROC MIXED the analysis was impossible unless dams were dropped from the model.
Characterization of Antibiotic-producing Actinomycetes Isolated from River Tana and Lake Elementaita in Kenya

The rise of antibiotic-resistant bacteria has become a global health concern, necessitating the search for novel sources of antibiotics. Actinomycetes, a group of microorganisms, have been known for their ability to produce bioactive compounds with antimicrobial properties. This study aimed to isolate, identify, and characterize antibiotic-producing Actinomycetes from River Tana and Lake Elementaita. Samples were collected from the study sites, and Actinomycetes were isolated using serial dilution and spread plate techniques. The isolates were characterized based on their morphological and biochemical properties. Furthermore, their antibacterial activity against Escherichia coli, Staphylococcus aureus, and Salmonella typhi was evaluated using the agar well diffusion method. The zones of inhibition were measured (mm), and analysis was done to compare the activity of the isolates using Kruskal Wallis test and medians compared using Wilcoxon with Bonferroni correction at alpha = 0.05 in SAS version 9.4. Analysis of DNA sequences was done using the BLAST program and a phylogenetic tree was constructed using MEGA X version 11. Biochemical tests revealed positive results for catalase, indole, oxidase, and citrate utilization, while coagulase and methyl red tests were negative. In terms of antibacterial activity, 54.5% of the isolates showed activity against E. coli, 45.5% against S. aureus, and 45.5% against S. typhi. Isolate LEL2201 had significant (p < 0.05) higher zone of inhibition against S. aureus (inhibition zone of 25.0mm), while isolate RT2201 exhibited the highest activity against E. coli and S. typhi (inhibition zone of 8.5 mm and 8.6 mm, respectively). Molecular characterization through 16S rRNA gene sequencing identified the isolates as belonging to the Actinobacterium order. Phylogenetic analysis revealed their similarity to known Actinomycetes species including Actinomycetales bacterium, Streptomyces intermedius and Streptomyces flavomacrosporus from various countries. The findings of this study demonstrate the presence of antibiotic-producing Actinomycetes in River Tana and Lake Elementaita. Thus, further investigations are warranted to identify and characterize the specific antibacterial compounds produced by these isolates.
Neodymium-Doped Zinc Oxide Nanoparticles Catalytic Cathode for Enhanced Efficiency of Microbial Desalination Cells

The Microbial Desalination Cell is a novel method for desalinating water that also generates energy via substrate oxidation. The MDC comprises three chambers: the anode chamber, the desalination chamber, and the cathode chamber. The fundamental problem with the technology is that it generates very little power during the oxygen reduction reaction (ORR). One solution to this issue is to use a highly active cathode catalyst, which effectively increases the ORR rate. Neodymium-doped ZnO nanoparticles were produced and employed as a cathode catalyst in the three-chambered MDC1 to improve performance. Zn1−xNdxO nanocrystalline samples containing x = 0.0, 0.03, 0.6, and 0.10 were synthesized efficiently through the cost-efficient sol-gel method. Transmission electron microscopy (TEM) and X-ray diffraction techniques revealed the nanocrystalline nature and the phase purity of the Zn1−xNdxO samples. The structural properties of ZnO nanostructured materials were elucidated by Rietveld refinement of the XRD patterns, which showed displacement of Zn and O ions and revealed changes in the electron density around the Zn-O bond with Nd substitution. The local features of light emission from Zn1−xNdxO samples have been studied with photoluminescence. The UV and green-yellow emissions originate from the exciton transition and the transition between the Nd3+ deep level, oxygen vacancy and interstitial oxygen. The results were compared to MDC-2, which did not have a catalyst on the cathode. Both MDCs were tested using a saline water solution containing 15 g/L of NaCl to measure their desalination performance. The better reduction kinetics was confirmed by cyclic voltammetry of the MDC-1 cathode. MDC-1 had a higher desalination efficiency (77.02% ± 2.0%) due to the presence of an Nd-doped ZnO catalyst than MDC-2 (59.3% ± 8.3%). MDC-1’s maximum power density of 3.65 W/m3 was 2.78 times greater than MDC-2’s (0.78 W/m3). Furthermore, the coulombic efficiency of MDC-1 was found to be (8.8 ± 0.3%), which was much higher than that of MDC-2 (4.56 ± 0.2%). As a result, the Nd-doped ZnO-based catalyst developed in this study can potentially improve ORR in MDC cathodes, enabling them to generate more power.
Spectral Analysis of EEG Signals for Automatic Imagined Speech Recognition

Brain–computer interface (BCI) systems are intended to provide a means of communication for both the healthy and those suffering from neurological disorders. Imagined speech conveys users intentions. This article investigates the feasibility of spectral characteristics of the electroencephalogram (EEG) signals involved in imagined speech recognition. Eleven subjects were recruited to perform the speech imagination task. This article analyses the spectral features for binary and multiclass classification of imagined words in six different frequency bands (FBs). The 1-D EEG signals were converted into time–frequency representation (TFR) plots using smoothed pseudo-Wigner–Ville distribution (SPWVD) and classified using a convolutional neural network (CNN). In addition, the analysis was performed for subject-dependent, subject-independent, and leave-one-subject-out (LOSO) approaches along with the all-data approach. The proposed method achieved promising results in the gamma band with a binary classification accuracy of 82.04% ± 2.45%, 81.66% ± 4.93%, 78.97% ± 3.12%, and 81.04% ± 3.08% in all-data, subject-dependent, subject-independent, and LOSO approaches, respectively, and a multiclass classification accuracy of 51.44% ± 3.55%, 50.20% ± 1.35%, 49.93% ± 1.72%, and 50.42% ± 2.18% in all-data, subject-dependent, subject-independent, and LOSO approaches, respectively. Finally, the multiclass scalability in decoding the imagined words is investigated by increasing the number of classes from 2 to 15. The study’s findings demonstrate that the EEG-based imagined speech recognition using spectral analysis has the potential to be an effective tool for speech recognition in practical BCI applications. The contribution of this article lies in developing an EEG-based automatic imagined speech recognition (AISR) system that offers high accuracy and reliability while also providing a noninvasive method for speech recognition.
Effect of Xylazine Administration on Ketoprofen Toxicity in Mice

T HE goal of our study was to assess the influence of xylazine on the toxicity of ketoprofen in mice, then we determined the effect of co-administration of xylazine and ketoprofen on neurobehavioral toxicity and liver and kidney function as well as histopathological effects after repeated daily administration for 20 days. The LD 50 of ketoprofen & xylazine were respectively (375mg/kg, 75 mg/kg) respectively . The injection of ketoprofen alone at a dose of (0.126 mg/ kg) & ketoprofen & xylazine together at a dose of (0.663 mg/kg) caused the crossed squares & rearing to decline significantly. This was accompanied by a significant increase in the time it took the mouse to return to its normal position and a significant decline in the frequency of head poking. On the tenth and twenty-fifth day of administration of ketoprofen at (71.95 mg/ kg) & xylazine at (14.156 mg/kg i.p) there was a significant elevation in the concentration of liver enzymes(AST& ALT) & kidney function (Creatinine and uric acid ), as well as causing histological changes in the liver, kidney & brain. Our findings reveal that the xylazine caused an exacerbation of ketoprofen toxicity which appear as a depressant effect on the CNS , liver and kidney, furthermore histopathological changes in the liver, kidney, and brain.
Pinch size can affect the skinfold thickness measurement and interfere in the estimation and classification of body adiposity

Abstract The aim of this study was to verify the effect of pinch size on skinfold thickness measurement and the consequent interference in the estimation and classification of body adiposity components. Cross-sectional and quantitative study carried out with a sample of 29 subjects recruited from a university in the city of Fortaleza, Ceará, Brazil. Four measurement steps were performed at each site of the eight chosen skinfolds. The first step was performed with a subjective-landmark and the three subsequent steps with fixed-landmarks defined with an expanding secondary line at 2 cm intervals. Body adiposity components were determined from the skinfold thickness measured at each landmark. Repeated measures ANOVA and Bland-Altman agreement analysis were applied. The subjective-landmark was chosen as the dependent variable. The 6 cm-landmark showed similarity and statistical agreement with the subjective-landmark for all skinfolds except the thigh, and with the sums of five and eight skinfolds. All fixed-landmarks showed agreement below the cut-off point for the percentile classification of subcutaneous adiposity and normative relative body fat. Variation in pinch size is an important source of TEM that can affect the reproducibility of skinfold thickness measurements and interfere in the estimation and classification of the molecular and tissue component of body adiposity.
A reinforcement neural architecture search convolutional neural network for rolling bearing fault diagnosis

The complexity of machinery makes accurate identification of rolling bearing fault signals difficult. Convolutional neural networks (CNNs) have made some progress, but they rely on the expertise of the network designer and the iterative process of optimizing numerous parameters. Therefore, there is an urgent need to develop a method that reduces the threshold for designing CNNs for a given task. In this article, we propose a reinforcement neural architecture search CNN to address this problem. Firstly, we design a neural architecture search algorithm that can generate different types of sub-networks specifically for fault diagnosis tasks. Secondly, we execute a reinforcement learning-based search strategy to discover promising sub-networks. Furthermore, we enhance the performance of the sub-network by improving the optimizer and training parameters. We conduct extensive experiments using two different types of datasets and verify that the proposed method’s fault classification capability is superior to existing methods.
Assessing impact of a Mandela Washington Fellowship Institute for African entrepreneurs

The development of Sub-Saharan Africa (SSA) has been slow compared to the rest of the world, and a lack of investment in human capital development emerges as a prevalent reason. Oklahoma State University provided a six-week-long entrepreneurship education program (EEP) for participants from SSA; many had agricultural or food businesses. Approaches to entrepreneurship education is a well-debated topic in the literature, including how best to evaluate such. We conducted a study in which 12 entrepreneurs were interviewed about nine months after the program’s end. Transcripts were analyzed using Epoche, horizontalization, phenomenological reduction, and imaginative variation. Themes, sub-themes, and an essence emerged. The themes were improvements, changes, and challenges to their business models; self-improvement and empowerment; and social change. Building the capacity of socially responsible entrepreneurs for a new Africa was the phenomenon’s essence. Future research should assess the impact of business knowledge attainment on the long-term success of entrepreneurs and identify learning experiences motivating them to practice social entrepreneurship. EEPs should stress rigor and an ethos of accomplishment, conduct site visits, and provide opportunities for community service. Interventions to negate the marginalization frequently experienced by women entrepreneurs in developing contexts are also discussed.
The predictive values of admission characteristics for 28-day all-cause mortality in septic patients with diabetes mellitus: a study from the MIMIC database

Background Septic patients with diabetes mellitus (DM) are more venerable to subsequent complications and the resultant increase in associated mortality. Therefore, it is important to make tailored clinical decisions for this subpopulation at admission. Method Data from large-scale real-world databases named the Medical Information Mart for Intensive Care Database (MIMIC) were reviewed. The least absolute selection and shrinkage operator (LASSO) was performed with 10 times cross-validation methods to select the optimal prognostic factors. Multivariate COX regression analysis was conducted to identify the independent prognostic factors and nomogram construction. The nomogram was internally validated via the bootstrapping method and externally validated by the MIMIC III database with receiver operating characteristic (ROC), calibration curves, decision curve analysis (DCA), and Kaplan-Meier curves for robustness check. Results A total of 3,291 septic patients with DM were included in this study, 2,227 in the MIMIC IV database and 1,064 in the MIMIC III database, respectively. In the training cohort, the 28-day all-cause mortality rate is 23.9% septic patients with DM. The multivariate Cox regression analysis reveals age (hazard ratio (HR)=1.023, 95%CI: 1.016-1.031, p<0.001), respiratory failure (HR=1.872, 95%CI: 1.554-2.254, p<0.001), Sequential Organ Failure Assessment score (HR=1.056, 95%CI: 1.018-1.094, p=0.004); base excess (HR=0.980, 95%CI: 0.967-0.992, p=0.002), anion gap (HR=1.100, 95%CI: 1.080-1.120, p<0.001), albumin (HR=0.679, 95%CI: 0.574-0.802, p<0.001), international normalized ratio (HR=1.087, 95%CI: 1.027-1.150, p=0.004), red cell distribution width (HR=1.056, 95%CI: 1.021-1.092, p=0.001), temperature (HR=0.857, 95%CI: 0.789-0.932, p<0.001), and glycosylated hemoglobin (HR=1.358, 95%CI: 1.320-1.401, p<0.001) at admission are independent prognostic factors for 28-day all-cause mortality of septic patients with DM. The established nomogram shows satisfied accuracy and clinical utility with AUCs of 0.870 in the internal validation and 0.830 in the external validation cohort as well as 0.820 in the septic shock subpopulation, which is superior to the predictive value of the single SOFA score. Conclusion Our results suggest that admission characteristics show an optimal prediction value for short-term mortality in septic patients with DM. The established model can support intensive care unit physicians in making better initial clinical decisions for this subpopulation.
Chemical evolution of star-forming galaxies in the overdense environment

As the largest gravitationally bound systems in the Universe, galaxy clusters are a unique laboratory for studying the extremes of galaxy evolution. The general consensus is that the large cluster-scale halo and the hot intracluster medium plays a significant role in the morphological and colour transformation of galaxies. However, its effect on details of baryonic processes in cluster galaxies remains unknown. I use the gas-phase chemical abundance to trace the cumulative effect of the gas inflow/outflow modulated star formation history of a galaxy. Current observations of the gas-phase chemical abundance of star-forming galaxies in the cluster environment are highly uncertain. This thesis aims to answer whether, when and how environment affect the chemical evolution star-forming galaxies. I present the first observation of cluster-scale radial metallicity gradients from starforming galaxies in two CLASH clusters at z ∼ 0.35: MACS1115+0129 and RXJ1532+3021, using observations with the DEIMOS spectrograph on the Keck II telescope. I use observations for RXJ1532+3021 to quantify the systematic uncertainties in the flux calibration of DEIMOS spectrograph. We find that the gas-phase metallicity of galaxies decreases as a function of projected cluster-centric distance for MACSJ1115+0129, i.e., galaxies near the cluster core are metal-rich compared to galaxies in the cluster outskirts. Star-forming galaxies in MACSJ1115+0129 are 0.20 dex metal-rich compared to counterpart field galaxies. The negative clusterscale metallicity gradient in our observations is not driven by the stellar mass of galaxies. Star-forming galaxies in RXJ1532+3021 shows a bimodal radial metallicity distribution and their mass-metallicity distribution is consistent with local field galaxies. We suspect that either interloper galaxies or an in-plane merger causes the bimodality of the radial metallicity distribution in RXJ1532+30, indicating that the cluster-scale abundance gradient can probe the dynamical state of a cluster. We speculate that the negative cluster-scale metallicity gradient originates from ram-pressure stripping and/or strangulation processes in the cluster environments. The truncation of star formation in galactic outskirts due to ram pressure stripping (disk truncation) can observationally bias the aperture-based integrated metallicity measurements towards the central metal-rich part of the galaxy. To quantify this observational bias, I use a semi-analytic model of ram pressure stripping and conduct mock observations of disk truncated galaxies from the IFU survey of local galaxies. Our ram pressure stripping model predicts a typical cluster-scale metallicity gradient of −0.03 dex/Mpc and a minimal metallicity enhancement of +0.02 dex at a fixed
From Static to Dynamic Anomaly Detection With Application to Power System Cyber Security

Developing advanced diagnosis tools to detect cyber attacks is the key to security of power systems. It has been shown that multivariate data injection attacks can bypass bad data detection schemes typically built on static behavior of the systems, which misleads operators to disruptive decisions. In this article, we depart from the existing static viewpoint to develop a diagnosis filter that captures the dynamics signatures of such a multivariate intrusion. To this end, we introduce a dynamic residual generator approach formulated as robust optimization programs in order to detect a class of disruptive multivariate attacks that potentially remain stealthy in view of a static bad data detector. We investigate two possible desired features: (i) a non-zero transient and (ii) a non-zero steady-state behavior of the residual generator in the presence of an attack. In case (i), the problem is reformulated as a finite, but possibly non-convex, optimization program. We further develop a linear programming relaxation that improves the scalability, and as such practicality, of the diagnosis filter design. In case (ii), it turns out that the resulting robust program admits an exact convex reformulation, yielding a Nash equilibrium between the attacker and the residual generator. This assertion has an interesting implication: the proposed approach is not conservative in the sense that the additional knowledge of the worst-case attack does not improve the diagnosis performance. To illustrate our theoretical results, we implement the proposed diagnosis filter to detect multivariate attacks on the system measurements deployed to generate the so-called Automatic Generation Control signals in a three-area IEEE 39-bus system.