Miracle-dz/UniFAR-FTUs huggingface dataset:
Procalcitonin as a Candidate Biomarker for Malarial Infection and Severe Malaria: A Meta-Analysis

Procalcitonin (PCT), as a marker of malaria severity, remains to be investigated. The present study collated and compared the levels of PCT between patients with severe malaria, uncomplicated malaria, and control participants to assess their role in predicting malaria infection and disease severity. The systematic review was registered at PROSPERO with registration number CRD42021297243. The search for relevant studies that reported PCT in patients with malaria was performed in PubMed, Scopus, and Web of Science. The following meta-analyses were conducted; (1) the pooled mean PCT levels in patients with severe and uncomplicated malaria, and (2) the pooled mean difference in PCT levels between patients with severe and uncomplicated malaria. Fifteen studies were included for qualitative and quantitative syntheses. The meta-analysis results show that the pooled mean PCT levels in patients with uncomplicated malaria were 3.92 ng/mL (95% CI: 2.26–5.58 ng/mL, I2: 96.5, five studies), whereas the pooled mean PCT levels in patients with severe malaria were 14.13 ng/mL (95% CI: 8.75–19.5 ng/mL, I2: 92.6, six studies). The meta-analysis showed that patients with severe malaria had an equal mean of PCT compared to those with uncomplicated malaria when the random-effects model was used (p: 0.055, weighted mean difference: 6.93, 95% CI: −0.16–14.02, I2: 84.6%, four studies). There were probable correlations between the level of parasitemia, immunity level, and possibly bacterial or other parasitic co-infection that could affect the PCT level among different clinical severities of malaria. Therefore, the PCT level alone does not seem to be a suitable biomarker to discriminate the severe/uncomplicated or infected/uninfected cases. Further studies should investigate the increased PCT levels in combination with other markers in association with malaria infection and severity.
Top Executives’ Overconfidence and Investment Efficiency Based on Data Analysis by Regression Method and CSMAR: Evidence from China

This paper studies the impact of management's overconfidence on corporate investment and decision-making. Using the China A-share listed companies and the data from the CSMAR database, applying the regression method to estimate the investment efficiency of the enterprises, this research studies the quantitative relationship between the executive overconfidence and the panel data model and packet regression. This paper finds that management overconfidence is positively associated with an inefficient corporate investment. Strictly controlling the confidence level of enterprise management and strengthening supervision are important measures to ensure the good operation of enterprises, reduce operational risks and achieve sustainable development.
Persistent Transmission of HCV among Men Who Have Sex with Men despite Widespread Screening and Treatment with Direct-Acting Antivirals

Background: In the Netherlands, unrestricted access to direct-acting antivirals (DAAs) halved the incidence of acute hepatitis C virus (HCV) infections among HIV-infected men who have sex with men (MSM). To develop strategies that can further reduce the spread of HCV, it is important to understand the transmission dynamics of HCV. We used phylogenetic analysis of a dense sample of MSM to provide insight into the impact of unrestricted access to DAAs on HCV transmission in the Netherlands and in Belgium. Methods: We included 89 MSM that were recently infected with HCV genotype 1a in ten Dutch and one Belgian HIV treatment centers. Sequences were generated using next gene sequencing and Sanger sequencing. Maximum likelihood phylogenetic analysis (general time reversible model) was performed on concatenated NS5A and NS5B sequences and a reference set of 389 highly similar control sequences selected from GenBank. A cluster was based on a minimum bootstrap support of 90% and a 3% genetic distance threshold. Results: We found that 78 (88%) of individuals were part of seven major clusters. All clusters included individuals from across the study region, however, different cities were part of different clusters. In three clusters, HIV-negative MSM clustered with sequences from HIV-positive MSM. All clusters that were observed before the introduction of DAAs persisted after unrestricted access to DAAs became available. Conclusion: Recently acquired HCV infections among MSM in the Netherlands and Belgium are strongly clustered and therefore highly suitable for targeted prevention strategies, such as contact tracing and partner notification. Importantly, despite an HCV incidence reduction after high DAA uptake and continuously monitoring, HCV transmission persisted in the same clusters.
THE MODELING OF UNSTEADY TURBULENT FLOWS IN TURBOMACHINES

The suitability of existing models for the simulation of flow through turbomachines is investigated and compared with a recently proposed adaptive turbulence model. Discussed are the improvements in accuracy that can be achieved by using non-linear turbulence models and unsteady calculations. The adaptive turbulence model is based on two equation turbulence modeling. It uses the temporal and spatial scales of the flow field to automatically adapt itself to the unresolved turbulent fluctuations. At its asymptotic limits it reduces either to a Direct Numerical Simulation - when the turbulent scales are in the order of the Kolmogorov micro scale - or to a standard two equation model - when the fluctuations are not resolved at all. In order to compare the quality of the presented models two cases have been considered: the flow past a cylinder and a subsonic as well as transonic flow past the VKI turbine blade. Calculations have been performed for each case using all the models and the results have been compared with measurements. The unsteady calculations gave better agreement with the experimental data demonstrating the superiority over steady state calculations for turbomachines. The simulation of complex flow fields, especially in turbomachines, implies a com- promise between the attained accuracy and required computational resources. From this point of view ignoring the time evolution was for a while an acceptable approximation. The comparison of the results obtained in the past with the experimental work showed that for this special case the approximation was too coarse. The results of steady state calcula- tions can not predict the pressure loss accurately enough. The question was if an unsteady approach is more suitable. It has recently been shown that the unsteady approach gave an improved agreement of the calculated flow field with the measurement data compared to steady state computations (Chen and Leschziner (1), Magagnato (2)). The prediction of the large scale vortices generated by thick trailing edges of turbine blades plays an important role here. In another work attention was drawn toward developing a model that would permit the calculation of an unsteady simulation of flows in turbomachines with acceptable computational eort (time and memory usage; Magagnato and Gabi (3)).
Thermal Expansion of 3C-SiC Obtained from In-Situ X-ray Diffraction at High Temperature and First-Principal Calculations

In situ X-ray crystallography powder diffraction studies on beta silicon carbide (3C-SiC) in the temperature range 25–800 °C at the maximum peak (111) are reported. At 25 °C, it was found that the lattice parameter is 4.596 Å, and coefficient thermal expansion (CTE) is 2.4 ×10−6/°C. The coefficient of thermal expansion along a-direction was established to follow a second order polynomial relationship with temperature (α11=−1.423×10−12T2+4.973×10−9T+2.269×10−6). CASTEP codes were utilized to calculate the phonon frequency of 3C-SiC at various pressures using density function theory. Using the Gruneisen formalism, the computational coefficient of thermal expansion was found to be 2.2 ×10−6/°C. The novelty of this work lies in the adoption of two-step thermal expansion determination for 3C-SiC using both experimental and computational techniques.
Targeting the Structural Integrity of Extracellular Vesicles via Nano Electrospray Gas-Phase Electrophoretic Mobility Molecular Analysis (nES GEMMA)

Extracellular vesicles (EVs) are in the scientific spotlight due to their potential application in the medical field, ranging from medical diagnosis to therapy. These applications rely on EV stability during isolation and purification—ideally, these steps should not impact vesicle integrity. In this context, we investigated EV stability and particle numbers via nano electrospray gas-phase electrophoretic mobility molecular analysis (nES GEMMA) and nanoparticle tracking analysis (NTA). In nES GEMMA, native, surface-dry analytes are separated in the gas-phase according to the particle size. Besides information on size and particle heterogeneity, particle number concentrations are obtained in accordance with recommendations of the European Commission for nanoparticle characterization (2011/696/EU, 18 October 2011). Likewise, and in contrast to NTA, nES GEMMA enables detection of co-purified proteins. On the other hand, NTA, yielding data on hydrodynamic size distributions, is able to relate particle concentrations, omitting electrolyte exchange (and resulting EV loss), which is prerequisite for nES GEMMA. Focusing on EVs of different origin, we compared vesicles concentrations and stability, especially after electrolyte exchange and size exclusion chromatography (SEC). Co-isolated proteins were detected in most samples, and the vesicle amount varied in dependence on the EV source. We found that depletion of co-purified proteins was achievable via SEC, but was associated with a loss of EVs and—most importantly—with decreased vesicle stability, as detected via a reduced nES GEMMA measurement repeatability. Ultimately, we propose the repeatability of nES GEMMA to yield information on EV stability, and, as a result, we propose that nES GEMMA can yield additional valuable information in EV research.
A Novel Window Reluctance Calculation to Improve Leakage Inductance Estimation of “E3E” Integrated Magnetic Components

Integrated magnetic components (IMCs) are used to save volume and increase the efficiency of many dc power converter topologies. However, their design is not a straightforward procedure, and it is possible to find different modeling strategies in the literature. Using one or another modeling approximation significantly impacts the analytical results' accuracy and may lead to erroneous solutions and to the need for a time-consuming redesigning process. In this study, an IMC with an EE-type structure and three air gaps, named E3E, is described and modeled under different modeling alternatives with an increasing degree of complexity. Additionally, a new expression to analytically model the window reluctance, key for coupling coefficient estimation, is proposed inspired by the E3E structure finite element analysis. The error of using each E3E modeling alternative is studied, by comparing the analytical results with the measurements obtained for several assemblies, including one for a dc–dc converter application case.
YOLOv5-Tassel: Detecting Tassels in RGB UAV Imagery With Improved YOLOv5 Based on Transfer Learning

Unmanned aerial vehicles (UAVs) equipped with lightweight sensors, such as RGB cameras and LiDAR, have significant potential in precision agriculture, including object detection. Tassel detection in maize is an essential trait given its relevance as the beginning of the reproductive stage of growth and development of the plants. However, compared with general object detection, tassel detection based on RGB imagery acquired by UAVs is more challenging due to the small size, time-dependent variable shape, and complexity of the objects of interest. A novel algorithm referred to as YOLOv5-tassel is proposed to detect tassels in UAV-based RGB imagery. A bidirectional feature pyramid network is adopted for the path-aggregation neck to effectively fuse cross-scale features. The robust attention module of SimAM is introduced to extract the features of interest before each detection head. An additional detection head is also introduced to improve small-size tassel detection based on the original YOLOv5. Annotation is performed with guidance from center points derived from CenterNet to improve the selection of the bounding boxes for tassels. Finally, to address the issue of limited reference data, transfer learning based on the VisDrone dataset is adopted. Testing results for our proposed YOLOv5-tassel method achieved the mAP value of 44.7%, which is better than well-known object detection approaches, such as FCOS, RetinaNet, and YOLOv5.
Long-Distance Contribution to {\Delta}{\Gamma}s/{\Gamma}s of the $B_s-\bar{B}_s$ System

We estimate the long-distance contribution to the \Delta{\Gamma}s of B_s system, based mainly on two-body D^(*)_s\Dbar^(*)_s modes and three-body D^(*)_s\Dbar^(*)\Kbar^(*) modes (and their CP conjugates). Some higher c\bar{s} resonances are also considered. The contribution to \Delta{\Gamma}s/{\Gamma}s by two-body modes is 10.2+-3.0%, slightly smaller than the short-distance result of 13.3+-3.2%. The contribution to width difference by D*_s0(2317), D_s1(2460), and D_s1(2536) resonances is negligible. For the three-body modes, we adopt the factorization formalism and model the form factors with off-shell D^(*)_s poles, the D_{sJ}(2700) resonance, and non-resonant (NR) contributions. These three-body modes can arise through current-produced or transition diagrams, but only SU(3)-related D^(*)_{u,d} \Dbar^(*)\Kbar modes from current diagram have been measured so far. The pole model results for D^*_{u,d} \Dbar^(*)\Kbar agree well with data, while D_{u,d} \Dbar^(*)\Kbar rates agree with data only within a factor of 2 to 3. All measured three-body rates can be reproduced by including NR contribution. The total \Delta{\Gamma}s/{\Gamma}s obtained is 16.7+-8.5%, which agrees with the short-distance result within uncertainties. For illustration, we also demonstrate the effect of D_{sJ}(2700) in modes with D^(*)K^*. In all scenarios, the total width difference remain consistence to the short-distance result. Our result indicates that (a) the operator product expansion (OPE) in short-distance picture is a valid assumption, (b) approximating the two-body decays to saturate width difference has a large correction, (c) the effect of three-body modes cannot be neglected, and (d) in addition to D^(*)_s poles, the D_{sJ}(2700) resonance also plays an important role in three-body modes. Future experiments are necessary to improve the estimation of \Delta{\Gamma}s from long-distance picture.
“Abnormal mental health” and a blameless state—Canadian media representations of Indigenous suicide

Indigenous suicide rates are high in Canada compared to the total Canadian rate and have been for some time. I argue it is necessary and salient to gather how Indigenous suicide is depicted in Canada. The media has a powerful influence on public perceptions, which can be a driving force that shapes suicide prevention policy. Using content analysis, media articles that reported on Indigenous suicide over the past 10 years were examined. It was found that media depictions represented forms of present colonialism and symbolic violence. The overwhelming portrayal of Indigenous suicide as a result of abnormal mental health diverts attention away from injustices, such as continuing racism and discrimination in healthcare, perhaps allowing these entities to persist. The Canadian media perceives that Indigenous suicide is a phenomenon that can be well understood within the confines of a colonial, dominant view of health.
Online Identification of Inductance and Flux Linkage for Inverter-Fed SPMSMs Using Switching State Functions

This article presents an online method to acquire the stator inductance and flux linkage for inverter-fed surface-mounted permanent magnet synchronous motors (SPMSMs) using switching state functions instead of average models. Given the switching state functions when different voltage vectors are applied to the motors, the information of inductance and flux linkage is revealed. Based on the dc bus voltage and current derivatives obtained from either hardware methods like Rogowski coil and differential circuit, or software methods such as tracking differentiator, stator inductance, and flux linkage are able to be figured out directly using the redundant or more practical methods with the help of recursive least square (RLS). Simulations and experiments are carried out to prove the effectiveness of the proposed methods, besides, dc voltage and operating speed variation as well as the impact of resistance are taken into account. Finally, compared with the conventional parameter identification based on the average models either in α–β or d–q coordinate, the proposed methods with switching state functions have remarkable advantages especially when the rotor position error exists. What needs further effort is the challenging case at very low speed, e.g., it is hard to identify flux linkage due to the small back electromotive force (EMF).
Dynamics of a Photochromic-Actuated Slot Microring Photonic Memristor

In electronic circuits, memristors have been defined as resistors whose resistance depends on past signals. Such elements have promise as low-power weighting and self-learning elements in electronic neuromorphic circuits. Photonic memristors, elements whose transmission depends on past optical signals, are experiencing renewed study alongside the rise in interest in neuromorphic photonics. One potential route to creating photonic memristors involves incorporating photochromic materials, whose optical properties continuously change with optical illumination, into photonic integrated circuits (PICs). In this manuscript we lay out a theoretical model to study the transmission dynamics of devices incorporating photochromic compounds into SiN planar microring resonators which utilize slot waveguide structure, and show that such devices fulfill the criteria for memristive behaviour. This represents a practical path towards incorporating photonic memristors into a technologically mature material platform with minimal additional fabrication processes.
Systematic Evaluation of Voriconazole Pharmacokinetic Models without Pharmacogenetic Information for Bayesian Forecasting in Critically Ill Patients

Voriconazole (VRC) is used as first line antifungal agent against invasive aspergillosis. Model-based approaches might optimize VRC therapy. This study aimed to investigate the predictive performance of pharmacokinetic models of VRC without pharmacogenetic information for their suitability for model-informed precision dosing. Seven PopPK models were selected from a systematic literature review. A total of 66 measured VRC plasma concentrations from 33 critically ill patients was employed for analysis. The second measurement per patient was used to calculate relative Bias (rBias), mean error (ME), relative root mean squared error (rRMSE) and mean absolute error (MAE) (i) only based on patient characteristics and dosing history (a priori) and (ii) integrating the first measured concentration to predict the second concentration (Bayesian forecasting). The a priori rBias/ME and rRMSE/MAE varied substantially between the models, ranging from −15.4 to 124.6%/−0.70 to 8.01 mg/L and from 89.3 to 139.1%/1.45 to 8.11 mg/L, respectively. The integration of the first TDM sample improved the predictive performance of all models, with the model by Chen (85.0%) showing the best predictive performance (rRMSE: 85.0%; rBias: 4.0%). Our study revealed a certain degree of imprecision for all investigated models, so their sole use is not recommendable. Models with a higher performance would be necessary for clinical use.
A Study of Terrain Reductions, Density Anomalies and Geophysical Inversion Methods in Gravity Field Modelling

Abstract : The general principles of the use of known density anomalies for gravity field modelling are reviewed with special emphasis on local applications and utilization of high degree and order spherical harmonic reference fields. The natural extension to include also unknown density anomalies will be studied within the framework of geophysical inversion methods, and the prospects for hybrid gravity field modelling/inversion methods will be outlined. A very simple case of such methods is the determination of representative topographic densities through collocation with parameters.
The strategy–identity nexus: The relevance of their temporal interplay to climate change

Grand Challenges have stimulated a search for new solutions at the interplay between fields and disciplines which previously have been separated. In this essay, I argue that a further development of temporal interplay between strategy and organizational identity may enrich studies of Grand Challenges, exemplified by how actors respond to climate change. This interplay is motivated by recent elaborations of the differences in temporality between strategy and organizational identity from a distinction between a dominant focus on the future (strategy) or the past (organizational identity) to a conceptualization of the differences in temporal structures between them. Using management research to contribute to the fight against climate change, sustained temporal interplay between strategy and organizational identity can advance our understanding of how organizations may act now for future climate goals. I suggest questions for future research focused on making an impact.
Neural Research on Depth Perception and Stereoscopic Visual Fatigue in Virtual Reality

Virtual reality (VR) technology provides highly immersive depth perception experiences; nevertheless, stereoscopic visual fatigue (SVF) has become an important factor currently hindering the development of VR applications. However, there is scant research on the underlying neural mechanism of SVF, especially those induced by VR displays, which need further research. In this paper, a Go/NoGo paradigm based on disparity variations is proposed to induce SVF associated with depth perception, and the underlying neural mechanism of SVF in a VR environment was investigated. The effects of disparity variations as well as SVF on the temporal characteristics of visual evoked potentials (VEPs) were explored. Point-by-point permutation statistical with repeated measures ANOVA results revealed that the amplitudes and latencies of the posterior VEP component P2 were modulated by disparities, and posterior P2 amplitudes were modulated differently by SVF in different depth perception situations. Cortical source localization analysis was performed to explore the original cortex areas related to certain fatigue levels and disparities, and the results showed that posterior P2 generated from the precuneus could represent depth perception in binocular vision, and therefore could be performed to distinguish SVF induced by disparity variations. Our findings could help to extend an understanding of the neural mechanisms underlying depth perception and SVF as well as providing beneficial information for improving the visual experience in VR applications.