Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of a Rapid Inviscid/Boundary-Layer Aerodynamics Tool

Hypersonic vehicles, combined with scramjet propulsion, offer significant and unique flexibility, performance and reusability benefits over rockets. These characteristics will likely reduce the cost of access-to-space. However, the realisation of such vehicles is significantly complicated by engine performance requirements which dictate relatively low altitude, high dynamic pressure trajectories. The thick atmosphere and high velocities result in high aerodynamic drag and heating. This paper introduces a simple, aerothermodynamic model for the analysis of hypersonic vehicles using Cart3D to calculate the inviscid flow-field and provide edge conditions to boundary layer calculations. Comparisons are made between two viscous methods of varying fidelity; flat plate correlations for skin friction with a simplified running length calculation and integral methods applied along inviscid, surface streamlines. Three validation cases are presented; (1) a hypersonic, blunt body; (2) a delta-wing, lifting body at subsonic to hypersonic Mach numbers and (3) a hypersonic, realistic vehicle configuration with internal flow-paths. In general, Cart3D predicts the lift and pitching moment coefficients well but consistently under-predicts drag given the absence of shear stress. The viscous contribution to aerodynamic forces was found to be adequately modelled using flat plate correlations and a simple Euclidean distance in place of the true running length. Preliminary results, however, suggest predictions of surface heat transfer rates benefit from a streamline running length and higher fidelity boundary layer methods.
Creating memorable shopping experiences to meet phygital customers' needs: evidence from sporting goods stores

PurposeThis study focuses on memorable customer shopping experience design in the sporting goods retail setting. It aims to identify the phygital customers' needs and expectations that are satisfied through in-store technologies and to detect the in-store strategies that use these technologies to make the store attractive and experiential.Design/methodology/approachThis exploratory study adopted a qualitative research methodology, specifically a multiple-case study, by performing semi-structured interviews with sporting goods store managers.FindingsSporting goods retailers use various in-store technologies to create a phygital customer shopping experience, including devices, mobile apps, wireless communication technologies, in-store activations, support devices, intelligent stations, and sensors. To improve the phygital customer journey and the phygital shopping experience, retailers meet customers' needs for utilitarian, hedonic, social, and playfulness experiences. Purely physical or digital strategies, as well as phygital strategies, are identified. This research also proposes a model of in-store phygital customer shopping experience design for sporting goods retailers.Practical implicationsSporting goods managers can invest in multiple technologies by designing a physical environment according to the customers' needs for utilitarian, hedonic, social, and playful experiences. In addition, they can improve the phygital customer shopping experience with specific push strategies that increase customer engagement and, in turn, brand and store loyalty.Originality/valueThis study highlights how the phygital customer experiential journey can be created through new technologies and improved with specific reference to the sporting goods stores.
Electron Density Estimation by Electrostatic Probe for Plasma Generated Near the Spacecraft Returning to the Earth's Atmosphere

In this work, the electrostatic probe was utilized to estimate the density of electrons for plasma generated around reentry vehicles that have a geometrically blunt nose at high-altitude. The thermocouple uses to measured electron temperature, which is equal to the temperature of the gas, on board the MAC spacecraft. In the spacecraft backflow field, electrostatic probe measurements were taken at five separate regions 1 to 5 cm from the body of the spacecraft. Over an altitude range of 90 to 50 km with an electron density of 108 to 1012 1/cm3, respectively. The measured electron temperature ranged from 0.05 to 0.9 electron volts and the maximum re-entry velocity of the spacecraft was about 7048 m/s in the re-entry experiment. The cooling using water jet in the flow field, the relationship between electron density, angle of attack changes, and the combustion products of the spacecraft's nose is also discussed.
Correspondence of Yolk Sac and Embryonic Genotypes in F0 Mouse CRISPants

CRISPR-mediated genome editing in vivo can be accompanied by prolonged stability of the Cas9 protein in mouse embryos. Then, genome edited variant alleles will be induced as long as Cas9 protein is active, and unmodified wildtype target loci are available. The corollary is that CRISPR-modified alleles that arise after the first zygotic cell division potentially could be distributed asymmetrically to the cell lineages that are specified early during morula and blastocyst development. This has practical implications for the investigation of F0 generation individuals, as cells in embryonic and extraembryonic tissues, such as the visceral yolk sac, might end up inheriting different genotypes. We here investigated the hypothetically possible scenarios by genotyping individual F0 CRISPants and their associated visceral yolk sacs in parallel. In all cases, we found that embryonic genotype was accurately reflected by yolk sac genotyping, with the two tissues indicating genetic congruence, even when the conceptus was a mosaic of cells with distinct allele configurations. Nevertheless, low abundance of a variant allele may represent a private mutation occurring only in the yolk sac, and in those rare cases, additional genotyping to determine the mutational status of the embryo proper is warranted.
Fast truncated SVD of sparse and dense matrices on graphics processors

We investigate the solution of low-rank matrix approximation problems using the truncated singular value decomposition (SVD). For this purpose, we develop and optimize graphics processing unit (GPU) implementations for the randomized SVD and a blocked variant of the Lanczos approach. Our work takes advantage of the fact that the two methods are composed of very similar linear algebra building blocks, which can be assembled using numerical kernels from existing high-performance linear algebra libraries. Furthermore, the experiments with several sparse matrices arising in representative real-world applications and synthetic dense test matrices reveal a performance advantage of the block Lanczos algorithm when targeting the same approximation accuracy.
Matched Learning for Optimizing Individualized Treatment Strategies Using Electronic Health Records

ABSTRACT Current guidelines for treatment decision making largely rely on data from randomized controlled trials (RCTs) studying average treatment effects. They may be inadequate to make individualized treatment decisions in real-world settings. Large-scale electronic health records (EHR) provide opportunities to fulfill the goals of personalized medicine and learn individualized treatment rules (ITRs) depending on patient-specific characteristics from real-world patient data. In this work, we tackle challenges with EHRs and propose a machine learning approach based on matching (M-learning) to estimate optimal ITRs from EHRs. This new learning method performs matching instead of inverse probability weighting as commonly used in many existing methods for estimating ITRs to more accurately assess individuals’ treatment responses to alternative treatments and alleviate confounding. Matching-based value functions are proposed to compare matched pairs under a unified framework, where various types of outcomes for measuring treatment response (including continuous, ordinal, and discrete outcomes) can easily be accommodated. We establish the Fisher consistency and convergence rate of M-learning. Through extensive simulation studies, we show that M-learning outperforms existing methods when propensity scores are misspecified or when unmeasured confounders are present in certain scenarios. Lastly, we apply M-learning to estimate optimal personalized second-line treatments for type 2 diabetes patients to achieve better glycemic control or reduce major complications using EHRs from New York Presbyterian Hospital. Supplementary materials for this article are available online.
Interrater Reliability in Systematic Review Methodology: Exploring Variation in Coder Decision-Making

A methodologically sound systematic review is characterized by transparency, replicability, and a clear inclusion criterion. However, little attention has been paid to reporting the details of interrater reliability (IRR) when multiple coders are used to make decisions at various points in the screening and data extraction stages of a study. Prior research has mentioned the paucity of information on IRR including number of coders involved, at what stages and how IRR tests were conducted, and how disagreements were resolved. This article examines and reflects on the human factors that affect decision-making in systematic reviews via reporting on three IRR tests, conducted at three different points in the screening process, for two distinct reviews. Results of the two studies are discussed in the context of IRR and intrarater reliability in terms of the accuracy, precision, and reliability of coding behavior of multiple coders. Findings indicated that coding behavior changes both between and within individuals over time, emphasizing the importance of conducting regular and systematic IRR and intrarater reliability tests, especially when multiple coders are involved, to ensure consistency and clarity at the screening and coding stages. Implications for good practice while screening/coding for systematic reviews are discussed.
International Journal of Innovation in Teaching and Learning (IJITL) Volume IX- Issue I (June 2023)

This study examined the concept of Corporate Social Responsibility (CSR) and the effect of CSR initiatives on teaching and learning from teachers, students, and heads' perspectives. The study relied on in-depth interviews and focus-group discussions to obtain data. The participating schools were selected through a stratified sampling technique, with the individual respondents purposively sampled. Interviews and discussions were tape-recorded and subsequently transcribed, sorted to identify central and recurrent themes in the data. The study showed that corporate bodies have contributed immensely to the teaching and learning processes through improved school infrastructural facilities. It also found that CSR activities have enhanced teaching and learning processes. Thus, resulting in improved quality of teaching delivery, students' academic performance and beneficiary schools' societal status. The findings, however, focused mainly on the perspectives of teachers, students, and heads of schools. The findings are expected to maximize the effective deployment of CSR resources in education service and infrastructure provision in less-developed countries. The results are expected to engender further debate among corporations and other relevant stakeholders on the need for concerted efforts in tackling the school infrastructure deficit prevalent in less-developed nations. This study is one of its kinds on the effect of CSR initiatives on teaching and learning from direct beneficiaries' perspectives within the African context. This study also provides evidence of the need for a deliberate effort to encourage private-sector investment in school infrastructure.
"Integrating Location Information as Geohash Codes in Convolutional Neural Network-Based Satellite Image Classification"

In the past few years, there have been many research studies conducted in the field of Satellite Image Classification. The purposes of these studies included flood identification, forest fire monitoring, greenery land identification, and land-usage identification. In this field, finding suitable data is often considered problematic, and some research has also been done to identify and extract suitable datasets for classification. Although satellite data can be challenging to deal with, Convolutional Neural Networks (CNNs), which consist of multiple interconnected neurons, have shown promising results when applied to satellite imagery data. In the present work, first we have manually downloaded satellite images of four different classes in Florida locations using the TerraFly Mapping System, developed and managed by the High Performance Database Research Center at Florida International University. We then develop a CNN architecture suitable for extracting features and capable of multi-class classification in our dataset. We discuss the shortcomings in the classification due to the limited size of the dataset. To address this issue, we first employ data augmentation and then utilize transfer learning methodology for feature extraction with VGG16 and ResNet50 pretrained models. We use these features to classify satellite imagery of Florida. We analyze the misclassification in our model and, to address this issue, we introduce a location-based CNN model. We convert coordinates to geohash codes, use these codes as an additional feature vector and feed them into the CNN model. We believe that the new CNN model combined with geohash codes as location features provides a better accuracy for our dataset.
Characterization of noise and efficiency of the Pixirad-1/Pixie-III CdTe X-ray imaging detector

Thanks to their high detection efficiency and low intrinsic noise, direct conversion X-ray Photon Counting Detectors (XPCDs) are particularly suitable for low dose imaging applications such as mammography. The photon counting technology can implement also two or more acquisition thresholds to perform single-shot spectral imaging with one polychromatic source. However, XPCDs with thick high Z crystal sensor and small pixel size are affected by the charge sharing effect. This issue can induce multiple counts from a single interaction reducing both spectral and spatial resolutions. In a pure counting mode, multiple counts from charge sharing can be partially or totally removed by increasing the acquisition threshold at the cost of a loss of efficiency. Thus, the performances of XPCDs can be threshold-dependent. New generation XPCDs implement solutions to cope with charge sharing issue. If the charge sharing is properly corrected, no multiple counts are recorded and the performances (detection efficiency, spectral and spatial resolutions) of the acquisition system are expected to be independent from the acquisition threshold. This work presents a study of a Pixirad-1 detection system based on a CdTe Schottky sensor and PIXIE-III readout system. This chip can be configured in three different operation modes, two of which implement solutions to remove multiple counts. Using monochromatic radiation, the performances of these different operation modes have been compared trough a measure of the integral spectra and the Signal to Noise Ratio (SNR) as function of the acquisition threshold.
How has the landscape changed? Landscape transformation analysis of Ogrodzieniec-Podzamcze (POLAND) using landgraphy and landscape stratigraphy methods.

This article presents a combination of landscape biography and stratigraphy as a method for the visualisation of historical landscape change. The main goal was to analyse the evolution of the cultural landscape of part of the Ogrodzieniec municipality – Podzamcze (Częstochowa Upland, Poland). The evolution of the landscape was reconstructed based on cartographic materials (topographic maps from the last 200 years) and scholarly literature. The results show the dominance of the agricultural landscape in all studied periods. The forest and settlement landscapes expanded significantly within the studied period. The fortified landscape is clearly visible as a core running through all the years. The results indicate changes of an evolutionary type and heterogenous (genetically), heterotonic (in terms of land cover) form. The method can be used in well-documented areas to visualise the past and contemporary landscapes and to forecast future ones, so it can be useful in landscape planning.
Toxicity of Heavy Metals and Recent Advances in Their Removal: A Review

Natural and anthropogenic sources of metals in the ecosystem are perpetually increasing; consequently, heavy metal (HM) accumulation has become a major environmental concern. Human exposure to HMs has increased dramatically due to the industrial activities of the 20th century. Mercury, arsenic lead, chrome, and cadmium have been the most prevalent HMs that have caused human toxicity. Poisonings can be acute or chronic following exposure via water, air, or food. The bioaccumulation of these HMs results in a variety of toxic effects on various tissues and organs. Comparing the mechanisms of action reveals that these metals induce toxicity via similar pathways, including the production of reactive oxygen species, the inactivation of enzymes, and oxidative stress. The conventional techniques employed for the elimination of HMs are deemed inadequate when the HM concentration is less than 100 mg/L. In addition, these methods exhibit certain limitations, including the production of secondary pollutants, a high demand for energy and chemicals, and reduced cost-effectiveness. As a result, the employment of microbial bioremediation for the purpose of HM detoxification has emerged as a viable solution, given that microorganisms, including fungi and bacteria, exhibit superior biosorption and bio-accumulation capabilities. This review deals with HM uptake and toxicity mechanisms associated with HMs, and will increase our knowledge on their toxic effects on the body organs, leading to better management of metal poisoning. This review aims to enhance comprehension and offer sources for the judicious selection of microbial remediation technology for the detoxification of HMs. Microbial-based solutions that are sustainable could potentially offer crucial and cost-effective methods for reducing the toxicity of HMs.
Understanding the Lived Experiences of ESSU Guiuan Lecturers

Using a phenomenological qualitative approach, the researchers examined the components and lived experiences of six lecturers who have entered into the educational environment from schools of education with less than eight years of classroom teaching experience as contractual teachers. This study looked at what it means to be a lecturer in the classroom today, the challenges in the profession, how lecturers approach educating students, and the types of professional development, mentoring, or training they believe they need to educate students in the classroom effectively. This research used an in-depth interview strategy for data collection because it allows the participants to reflect on past, current, and relevant experiences in detail for the study. The one-on-one, in-depth interviews sought to engage the participants in conversations that would encourage responses that revealed the meanings of the participant's experiences in the education profession. The discussion summarizes the findings, the emerging themes included (1) the prevailing truths in pursuing a career of teaching, (2) side stories on new days of teaching,(3) transition, interaction, and relationship as perceived components in the journey of teaching, (4) challenges in instruction, student's behavior management, peer-relationship, and working environment, (5) aspiration to meet competence in teaching, (6) overwhelming consequences on the change of role of teachers amidst pandemic. Further, this study offers recommendations and discusses implications on the lived experiences of lecturers in the education profession.
Multiwave interaction solutions of the partial reverse Space-time nonlocal Mel’nikov equation

In this paper, we introduce appropriate rational and logarithmic transformations to transfer the partial reverse space-time nonlocal Mel’nikov equation in (2+1)-dimensions into its bilinear form. Then we extend the N-soliton decomposition algorithm and the inheritance solving strategy proposed by us to construct the higher order interaction solutions among solitons, periodic waves and rational waves for such type equation.
Identifying Psychological Distress Patterns during the COVID-19 Pandemic using an Intersectional Lens

_OBJECTIVE_. We inform an intersectional understanding of differences in psychological distress across the U.S. population during the early months of the COVID-19 pandemic by examining the unique and interactive influences of multiple social variables on levels of psychological distress. _METHODS_. The March and April 2020 waves of the American Trends Panel (N = 4,560) were analyzed using conditional inference trees and random forests to examine how complex interactions among social status variables influence psychological distress levels. _RESULTS_. Age, gender, socioeconomic status, and community attachment most influenced distress in March 2020, while race and ethnicity emerged as influential in April 2020, especially among older men. _CONCLUSIONS. _ The results provide insights into how multiple social statuses interact to shape psychological distress levels. By analyzing distress as a result of multiple pathways, we address theoretical mandates to consider the intersecting influence of social statuses on mental health. Targeted interventions by mental health specialists are discussed. _CONTRIBUTION. _ This study builds upon the extensive and ever-growing literature on the effects that the COVID-19 pandemic has had on health, while specifically approaching the analysis with an intersectional lens and using tree-based statistical modeling to better visualize the differential impact the early months of the pandemic had on mental health.
Performance Improvement of Consensus Tracking for Linear Multiagent Systems With Input Saturation: A Gain Scheduled Approach

For leader-following multiagent systems with input saturation, the existing protocols use a low gain feedback approach to achieve semi-global consensus. The main drawback of this approach is the ineffective utilization of the actuator potential, resulting in bad performance. To improve the transient performance of the consensus tracking, this paper proposes a gain scheduled approach for multiagent systems subject to the saturator saturations. A novel kind of scheduler-based protocols are proposed, which consists of state feedback controllers with time-varying gain and parameter schedulers. The role of the controllers is to achieve the consensus tracking, while the schedulers can accelerate this consensus progress by enlarging the gain parameter. To remove the dependence of the schedulers on global information, a minimum-value-based consensus algorithm is put forward, with idea of driving all values of agents throughout the network to their minimum value. Its implementation is guaranteed by the network-topology connectivity. Finally, our approach is further extended to the case where the leader’s control input is nonzero, time-varying, and bounded. The discontinuous protocol and its continuous approximation counterpart are designed, yielding the exact- and quasi-consensus tracking, respectively. Simulation results verify the theoretical analysis.