Miracle-dz/UniFAR-FTUs huggingface dataset:
The Evolution of Blockchain: From Lit to Dark

Transactions submitted through the blockchain peer-to-peer (P2P) network may leak out exploitable information. We study the economic incentives behind the adoption of blockchain dark venues, where users' transactions are observable only by miners on these venues. We show that miners may not fully adopt dark venues to preserve rents extracted from arbitrageurs, hence creating execution risk for users. The dark venue neither eliminates frontrunning risk nor reduces transaction costs. It strictly increases the payoff of miners, weakly increases the payoff of users, and weakly reduces arbitrageurs' profits. We provide empirical support for our main implications, and show that they are economically significant. A 1% increase in the probability of being frontrun raises users' adoption rate of the dark venue by 0.6%. Arbitrageurs' cost-to-revenue ratio increases by a third with a dark venue.
Approaches to evaluation of affordable housing initiatives in Australia

The release of the Framework for National Action on Affordable Housing (the Framework) in 2005 has provided an opportunity to consider how evaluation could be built from the outset into a major future initiative in the housing field. In response, this report first provides an overview of ideas and developments in evaluation theory and methods, drawing mainly on a recent proposal by two sociologists, Ray Pawson and Nick Tilley (1997).
An Application of Instantaneous Spectral Entropy for the Condition Monitoring of Wind Turbines

For economic and environmental reasons, the use of renewable energy sources is a key aspect of the ongoing transition to a sustainable industrialised society. Wind energy represents a major player among these natural, carbon-neutral sources. Nevertheless, wind turbines are often subject to mechanical faults, especially due to ageing. To alleviate Operation and Maintenance costs, Vibration-Based Inspection and Condition Monitoring have been proposed in recent times. This research proposes Instantaneous Spectral Entropy and Continuous Wavelet Transform for anomaly detection and fault diagnosis, departing from gearbox vibration time histories. The approach is validated on experimental data recorded from a turbine suffering bearing failure and total gearbox replacement. From a computational point of view, the proposed algorithm was found to be efficient and therefore even potentially applicable for real-time monitoring.
Training Teaching Assistants in Assessment of Lab Assignments in Computer Science at a Swedish University

Teaching assistants (TAs), students who assist the faculty, are widely used in computer science (CS) courses. Previous studies have, however, shown that TAs could be poorly prepared and need training. Particularly, an interview study has shown that one of the areas where the TAs experience uncertainty is when assessing students’ oral presentations of their lab assignments. Based on that result and by interviewing course coordinators, we have developed and offered training workshops about assessment in CS. We invited our TAs in the introductory CS courses to participate on a voluntary basis. By distributing pre-workshop surveys at the beginning of each semester, and post-workshop surveys at the end of the semesters, to both workshop attendees (50) and non-attendees (44), we studied how the TAs conducted the assessments and what impact the training workshop had on their self-reported practice. Both surveys had 11 identical statements that the TAs were asked to rate on a 7-point Likert scale. We also conducted interviews with four workshop attendees and three non-attendees. The results showed a significant difference between the two groups in the post-workshop survey: workshop attendees disagreed more with the statement “I try to assess students' understanding rather than the program”, which is more in line with the instructions given. In addition, when comparing pre- and post-workshop answers, the workshop attendees stated that they were less inclined to ask for help, experienced that the lab instructions were not detailed enough, and were more inclined to ask questions that convinced them that the students had written the program themselves. In the control group, no significant differences between pre- and post-tests were found.
Does Expertise Reduce Rates of Inattentional Blindness? A Meta-Analysis

Inattentional blindness occurs when one fails to notice a fully visible stimulus because one's attention is on another task. Researchers have suggested that expertise at this other task should reduce rates of inattentional blindness. However, research on the topic has produced mixed findings. To gain clarity on the issue, we meta-analyzed the extant studies (K = 14; N = 1153). On average, experts showed only a slight reduction in rates of inattentional blindness: 62% of novices experienced inattentional blindness compared to 56% of experts, weighted odds ratio = 1.33, 95% CI [0.78, 2.28]. The relevance of the stimuli to the experts’ domain of expertise showed no notable moderating effects. The low number of the included studies, and the small sample sizes of the original studies, weaken our conclusions. Nonetheless, when taken together, the available evidence provides little support for any reliable influence of expertise on rates of inattentional blindness.
The Missing Pieces to the Cold-Stored Platelet Puzzle

Cold-stored platelets are making a comeback. They were abandoned in the late 1960s in favor of room-temperature stored platelets due to the need for longer post-transfusion platelet recoverability and survivability in patients with chronic thrombocytopenia. However, the current needs for platelet transfusions are rapidly changing. Today, more platelets are given to patients who are actively bleeding, such as ones receiving cardiac surgeries. It has been established that cold-stored platelets are more hemostatically effective, have reduced bacterial growth, and have longer potential shelf lives. These compelling characteristics led to the recent interest in bringing back cold-stored platelets to the blood systems. However, before reinstating cold-stored platelets in the clinics again, a thorough investigation of in vitro storage characteristics and in vivo transfusion effects is required. This review aims to provide an update on the recent research efforts into the storage characteristics and functions of cold-stored platelets using modern investigative tools. We will also discuss efforts made to improve cold-stored platelets to be a better and safer product. Finally, we will finish off with discussing the relevance of in vitro data to in vivo transfusion results and provide insights and directions for future investigations of cold-stored platelets.
The UP150: A Multifactorial Environmental Intervention to Promote Employee Physical and Mental Well-Being

Physical activity (PA) is a major health factor and studies suggest workplaces could promote PA by modifying office design, motivational strategies and technology. The present study aims to evaluate the efficiency of UP150, a multifactorial workplace intervention for the improvement and maintenance of the level of physical fitness (PF) and wellbeing. Forty-five employees were randomly divided into the experimental (EG) and control (CG) groups. The PF was assessed pre-post intervention using the cubo fitness test (CFT), the amount of PA was evaluated using the IPAQ questionnaire and accelerometers while the workload was assessed using the NASA-TLX questionnaire and psycho-physical health by using the SF-12 questionnaire. The EG worked in UP150 offices while the CG worked in their usual offices for 8 weeks. The EG and CG came back 4 weeks after the intervention for CFT retention. The EG improved CFT motor efficiency and the amount of moderate PA, while it reduced mental load. The EG retained reached motor efficiency levels 4 weeks after the intervention. No differences were found in IPAQ. The UP150 demonstrated to be a proactive environment and to be efficient in the promotion of PA, improving PF and mental health while decreasing mental load.
Excessive self-grooming of Shank3 mutant mice is associated with gene dysregulation and imbalance between the striosome and matrix compartments in the striatum

Autism spectrum disorders (ASD) are characterised by atypical social communication and stereotyped behaviours. Mutations in the gene encoding the synaptic scaffolding protein SHANK3 are detected in 1-2% of patients with ASD and intellectual disability (ID), but the mechanisms underpinning the symptoms remain largely unknown. Here, we characterized the behaviour of Shank3 mutant mice deleted for exon 11 (Shank3Δ11/Δ11) from three to twelve months of age. We observed decreased locomotor activity, increased stereotyped self-grooming and atypical socio-sexual interaction compared to wild-type littermates. We then used RNAseq on four brain regions of the same animals to identify differentially expressed genes (DEG). DEGs were identified mainly in the striatum and were associated with synaptic transmission (e.g. Grm2, Dlgap1), G-protein-signalling pathways (e.g. Gnal, Prkcg1, and Camk2g), as well as excitation/inhibition balance (e.g. Gad2). Downregulated and upregulated genes were respectively enriched in the gene clusters of medium-sized spiny neurons expressing the dopamine 1 (D1-MSN) and the dopamine 2 receptor (D2-MSN). Moreover, expression of DEGs reported as striosome markers within the striatum (Cnr1, Gnal1, Gad2, and Drd4) were positively correlated with excessive self-grooming. Finally, we showed that the striosome compartment of Shank3Δ11/Δ11 mice was enlarged and displayed higher expression of GAD65 compared to wild-type mice. Altogether, these results shed light on a possible role of the striosomes/matrix imbalance in excessive self-grooming in Shank3Δ11/Δ11 mice. Such striatal alterations could be present in a subgroup of patients with ASD and ID and could open the way to new therapeutic approaches.
Improving Small Molecule pK a Prediction Using Transfer Learning With Graph Neural Networks

Enumerating protonation states and calculating micro-state pKa values of small molecules is an important yet challenging task for lead optimization and molecular modeling. Commercial and non-commercial solutions have notable limitations such as restrictive and expensive licenses, high CPU/GPU hour requirements, or the need for expert knowledge to set up and use. We present a graph neural network model that is trained on 714,906 calculated mico-state pKa predictions from molecules obtained from the ChEMBL database. The model is fine-tuned on a set of 5,994 experimental pKa values significantly improving its performance on two challenging test sets. Combining the graph neural network model with Dimorphite-DL, an open-source program for enumerating ionization states, we have developed the open-source Python package pkasolver, which is able to generate and enumerate protonation states and calculate micro-state pKa values with high accuracy.
Classification Efficiency of Pre-Trained Deep CNN Models on Camera Trap Images

This paper presents the evaluation of 36 convolutional neural network (CNN) models, which were trained on the same dataset (ImageNet). The aim of this research was to evaluate the performance of pre-trained models on the binary classification of images in a “real-world” application. The classification of wildlife images was the use case, in particular, those of the Eurasian lynx (lat. “Lynx lynx”), which were collected by camera traps in various locations in Croatia. The collected images varied greatly in terms of image quality, while the dataset itself was highly imbalanced in terms of the percentage of images that depicted lynxes.
Quercetin Improves Liver Function, Decreases the Expression of Pro-apoptotic Proteins p53 and Bax and Increases the Antioxidant Defense of Hepatocytes in Aged Male Rats

Eman A. Al-Shahari1,2, Eman R. ElBealy3, Abdelhalim A. Alkhazendar4 and Abeer A. Alm-Eldeen4* 1Department of Biology, Faculty of Science and Arts, King Khaled University, Abha, Kingdom of Saudi Arabia 2Department of Biology, Faculty of Science, Ibb University, Ibb, Yemen 3Biology Department, College of Science for Girls, King Khaled University, Abha, Kingdom of Saudi Arabia 4Department of Zoology, Faculty of Science, Tanta University, Tanta, Egypt Article Information Received 17 Novmber 2019 Revised 22 June 2020 Accepted 18 April 2021 Available online 21 January 2022 (early access)
Between fearful homebodies and carefree travel lovers: identifying tourist segments during the Covid-19 pandemic in Finland, Germany, and Norway

ABSTRACT While an increase in risk sensitivity for travel in times of crisis has been widely studied, attention to changes in the perception of travel motives as a possible counterpart have fallen short. Hence, the research submitted here explores the interplay of negative and positive psychological reactions towards travel during the Covid-19 pandemic, taking three Northern European countries as an example. Data was collected via online consumer panels in Finland, Germany, and Norway (n = 4,649) during the first lockdown in May 2020. We identify tourist segments based on their perceptions of travel risk and the importance of travel for their own well-being. K-means cluster analysis reveals five different pandemic-related clusters that exemplify individual travel plans for the near future, their personal affectedness and sociodemographic aspects, such as nationalities. Results show that a high level of perceived importance of travel for well-being can offset a simultaneous high perception of travel risk: ‘Fearful travel lovers’ make travel plans despite the pandemic. Moreover, we find significant differences regarding the size of the five segments in the three countries. Findings provide important strategic implications for tourism managers seeking how to address relevant tourist groups in times of pandemics.
Literature Review on Artificial Intelligence Methods for Glaucoma Screening, Segmentation, and Classification

Artificial intelligence techniques are now being applied in different medical solutions ranging from disease screening to activity recognition and computer-aided diagnosis. The combination of computer science methods and medical knowledge facilitates and improves the accuracy of the different processes and tools. Inspired by these advances, this paper performs a literature review focused on state-of-the-art glaucoma screening, segmentation, and classification based on images of the papilla and excavation using deep learning techniques. These techniques have been shown to have high sensitivity and specificity in glaucoma screening based on papilla and excavation images. The automatic segmentation of the contours of the optic disc and the excavation then allows the identification and assessment of the glaucomatous disease’s progression. As a result, we verified whether deep learning techniques may be helpful in performing accurate and low-cost measurements related to glaucoma, which may promote patient empowerment and help medical doctors better monitor patients.
Fractional-Order Sliding Mode Guidance Law for Intercepting Hypersonic Vehicles

This paper addresses the problem of a low-speed missile intercepting a hypersonic vehicle in the longitudinal plane. Firstly, based on the concept of the zero of the angular rate of the line-of-sight (LOS) angle, the guidance system is established by defining the LOS angular rate as the state variable. Secondly, in view of the difficulty of precisely measuring the external disturbance caused by the hypersonic vehicle’s maneuver in the guidance system, a non-homogeneous disturbance observer is designed to precisely estimate the disturbance information. Then, by introducing the fractional-order operator into the sliding surface, a fractional-order fast power reaching (FOFPR) guidance law is proposed based on the fast power reaching law. Simulation examples are carried out in two different maneuver modes of the hypersonic vehicle: the bang-bang maneuver mode and sinusoidal maneuver mode. Besides, comparative experiments are conducted with the proportional navigation (PN) and the integer-order fast power reaching (IOFPR) guidance laws. Finally, the simulation results demonstrate the superiority of the effectiveness of the proposed guidance law.
Nickel oxide nanoparticles induce apoptosis and ferroptosis in airway epithelial cells via ATF3

Exposure to nickel oxide nanoparticles (NiONPs), which have been widely produced and applied in industry, leads to adverse pulmonary and systemic effects. The aim of this study is to investigate the involvement of apoptosis and ferroptosis in NiONPs‐induced acute lung injury (ALI). Intratracheal instillation of NiONPs into mice elevated the levels of pro‐inflammatory cytokines, neutrophils, and proteins in the bronchoalveolar lavage fluid, and triggered apoptosis and ferroptosis in the lung tissues. Consistently, NiONPs‐induced apoptosis and ferroptosis were observed in in vitro experiments using human lung epithelial cells. Activating transcription factor 3 (ATF3), a stress‐inducible transcription factor, was upregulated by NiONPs exposure in both murine lung tissues and human lung epithelial cells. Moreover, human lung epithelial cells with ATF3 deficiency exhibited a lower level of apoptosis and ferroptosis when exposed to NiONPs. Collectively, our findings demonstrated that ATF3 was responsive to NiONPs exposure, and promoted NiONPs‐induced apoptosis and ferroptosis in lung epithelial cells, indicating that ATF3 is a potential biomarker and therapeutic target for NiONPs‐associated ALI.
Physician Health Care Visits for Mental Health and Substance Use During the COVID-19 Pandemic in Ontario, Canada

Key Points Question Has the incidence of physicians seeking outpatient care for mental health and substance use changed during the COVID-19 pandemic? Findings In a cohort study of 34 055 physicians, the rate of outpatient visits for mental health and substance use increased on average by 13% per physician during the first 12 months of the pandemic compared with the prior 12 months. Meaning These findings suggest that the COVID-19 pandemic is associated with greater mental health services use among physicians.
Tests and classification methods in adaptive designs with applications

Statistical tests for biomarker identification and classification methods for patient grouping are two important topics in adaptive designs of clinical trials related to genomic studies. In this article, we evaluate four test methods for biomarker identification in the first stage of an adaptive design: a model-based identification method, the popular two-sided t-test, the nonparametric Wilcoxon Rank-Sum test (two-sided), and the Regularized Generalized Linear Models. For patients grouping in the second stage, we examine classification methods such as Random Forest, Elastic-net Regularized Generalized Linear Models, Support Vector Machine (SVM), Gradient Boosting Machine (GBM), and Extreme Gradient Boosting (XGBoost). Simulation studies are carried out to assess the performance of the different methods. The best identification methods are chosen based on the well-known score, while the best classification techniques are selected based on the area under a receiver operating characteristic curve (AUC). The chosen methods are then applied to the Adaptive Signature Design (ASD) with a real data set from breast cancer patients for the purpose of evaluating the performance of ASD in different situations.
Towards Open-Set Scene Graph Generation with Unknown Objects

Scene graph generation (SGG) aims to detect objects and their relationships in an image, thereby enabling a detailed understanding of a complex scene for various real-world applications. In SGG applications such as robot vision, it is important to correctly detect all objects without recognizing any object as something different or ignoring it. However, previous studies on SGG do not consider unknown objects whose classes are unseen in training. Consequently, current SGG methods wrongly classify them as known object classes or overlook them. In this paper, we propose a new problem named “open-set SGG” with unknown objects, focusing on detecting even unknown objects and their relationships. Specifically, we formally define this new problem and propose an evaluation protocol, including an extended dataset with unknown objects and novel evaluation metrics designed for the open-set setting. We also build baseline methods by employing and extending existing SGG methods and compare them through experiments to establish the current baseline performance of open-set SGG. Finally, we discuss the limitations of the current SGG methodology in the open-set setting and point out future research directions.
Introduction to a Review Series on Normal and Pathologic Erythropoiesis.

the different layers of the regulation of erythropoiesis, which from cytokine signaling mechanisms that enable extrinsic regulation of RBC production to intrinsic transcriptional necessary for effective erythropoiesis. they post-transcriptional processes that are important in the control of red cell production, including the regulation of ribosome levels. Caulier and underline a classical view of erythropoiesis involves differentiation and maturation through distinct stages of erythroid cells, of recent are consistent with a more continuous model of erythroid differentiation. their how single-cell resolution our providing
Usefulness and Satisfaction With the ERP Systems in BiH Companies

The mail goal of this paper is to research usefulness and satisfaction with the ERP (Enterprise Resource Planning) system from the end users' point of view in companies in Bosnia and Herzegovina (BiH). The research was conducted using a questionnaire, prepared on the findings from the literature. The data were statistically analysed in accordance with the research objectives. The results show that there is a significant difference in user satisfaction with ERP regard to numbers of modules in ERP. Research shows that end users in participated companies are relatively satisfied with their ERP solutions and consider them useful for business, but improvements are still possible.
Effects of family history of substance use disorder on reward processing in adolescents with and without attention‐deficit/hyperactivity disorder

Patients with attention‐deficit/hyperactivity disorder (ADHD) often develop early onset substance use disorder (SUD) and show poor treatment outcomes. Both disorders show similar reward‐processing alterations, but it is unclear whether these are associated with familial vulnerability to SUD. Our aim was to investigate effects of family history of SUD (FH) on reward processing in individuals with and without ADHD, without substance misuse. Behavioural and functional magnetic resonance imaging (fMRI) data from a modified monetary incentive delay task were compared between participants with and without FH (FH positive [FH+]: n = 76 and FH negative [FH−]: n = 69; 76 with ADHD, aged 16.74 ± 3.14, 82 males), while accounting for continuous ADHD scores. The main analysis showed distinct positive association between ADHD scores and reaction times during neutral versus reward condition. ADHD scores were also positively associated with anticipatory responses of dorsolateral prefrontal cortex, independent of FH. There were no main FH effects on brain activation. Yet, FH+ participants showed distinct neural alterations in ventrolateral prefrontal cortex (VLPFC), dependent on ADHD. This was driven by positive association between ADHD scores and VLPFC activation during reward outcome, only in FH+. Sensitivity analysis with stricter SUD index showed hyperactivation of anterior cingulate cortex for FH+, independent of ADHD, during reward anticipation. There were no FH or ADHD effects on activation of ventral striatum in any analysis. Findings suggest both FH and ADHD effects in circuits of reward and attention/memory during reward processing. Future studies should examine whether these relate to early substance use initiation in ADHD and explore the need for adjusted SUD prevention strategies.