Miracle-dz/UniFAR-FTUs huggingface dataset:
Speech intelligibility with various head-related transfer functions: A computational modeling approach

Speech intelligibility is known to be affected by the relative spatial position between target and interferers. The benefit of a spatial separation is, along with other factors, related to the head-related transfer function (HRTF). The HRTF is individually different and thus, the cues that improve speech intelligibility might also be different. In the current study an auditory model was employed to predict speech intelligibility with a variety of HRTFs. The predicted speech intelligibility was found to vary across HRTFs. Thus, individual listeners might have different access to cues that are important for speech intelligibility.
Neuroinflammation in the normal appearing white matter of multiple sclerosis brain causes abnormalities at the node of Ranvier

Changes to the structure of nodes of Ranvier in the normal-appearing white matter (NAWM) of MS brains are associated with chronic inflammation. We show that the paranodal domains in MS NAWM are longer on average than control, with Kv1.2 channels dislocated into the paranode. These pathological features are reproduced in a model of chronic meningeal inflammation generated by the injection of lentiviral vectors for the lymphotoxin-α (LTα) and interferon-γ (IFNγ) genes. We show that tumour necrosis factor (TNF), IFNγ and glutamate can provoke paranodal elongation in cerebellar slice cultures, which could be reversed by an NMDA blocker. When these changes were inserted into a computational model to simulate axonal conduction, a rapid decrease in velocity was observed, reaching conduction failure in small diameter axons. We suggest that glial cells activated by proinflammatory cytokines can produce high levels of glutamate, which triggers paranodal pathology, contributing to axonal damage and conduction deficits.
Worldwide strains of the nematophagous fungus Pochonia chlamydosporia are endophytic in banana roots and promote plant growth

The biocontrol fungus, Pochonia chlamydosporia, colonizes endophytically banana roots. Root hairs and root surface were found colonize by the fungus using a stable GFP transformant. Hyphal penetration of root cells was also observed. Spores of P. chlamydosporia 123, significantly increase root and leaf length and weight in banana plantlets (Musa acuminata cv. ‘Dwarf Cavendish’) in growth chamber experiments 30 days post-inoculation (dpi). In greenhouse 8L pot experiments, P. chlamydosporia 123 spore inoculation significantly increases leaf and root length and leaf weight in banana plants (75 dpi). Spore inoculation of P. chlamydosporia strains from worldwide origin (Pc21 Italy, Pc123 Spain, Pc399 China, and Pccat Cuba), significantly increases root, corm and leaf length and weight in banana plantlets. Pc21 was the best colonizer of banana roots. Consequently, this strain significantly increases most banana root and leaf length. Root colonization by P. chlamydosporia was also detected using cultural techniques and qPCR.
Title: Muscle torques provide more sensitive measures of post-stroke movement deficits than joint angles.

The whole repertoire of complex human motion is enabled by forces applied by our muscles and controlled by the nervous system. The of stroke on the complex multi-joint motor control is difficult to quantify in a meaningful way that informs about the underlying deficit in the active motor control and intersegmental coordination. We tested the idea that post-stroke deficit can be quantified with high sensitivity using motion capture and inverse modeling of a broad range of reaching movements. Our hypothesis is that muscle moments estimated based on active joint torques provide a more sensitive measure of post-stroke motor deficits than joint angle and angular velocity. The motion of twenty-two participants was captured while performing reaching movements in a center-out task, presented in virtual reality. We used inverse dynamics analysis to derive active joint torques that were the result of muscle contractions, termed muscle torques, that caused the recorded multi-joint motion. We then applied a novel analysis to separate the component of muscle torque related to gravity compensation from that related to intersegmental dynamics. Our results show that individual reaching movements can be characterized with higher information content using muscle torques rather than joint angles. Moreover, muscle torques allow for distinguishing between the individual motor deficits due to aging or stroke from the typical differences in reaching between healthy individuals. This novel quantitative assessment method may be used in conjunction with home-based gaming motion-capture technology for remote monitoring of motor deficits and inform the development of evidence-based robotic therapy interventions.
Dmp1Cre-directed knockdown of PTHrP in murine decidua is associated with increased bone width and a life-long increase in strength specific to male progeny

Parathyroid hormone related-protein (PTHrP) is a pleiotropic regulator of tissue homeostasis. In bone, knockdown in osteocytes by Dmp1Cre-targeted deletion causes osteopenia and impaired strength. We report that this outcome depends on parental genotype. Adult Dmp1Cre.Pthlhf/f mice from homozygous parents (Dmp1Cre.Pthlhf/f(hom)) have stronger bones, with 40% more trabecular bone mass and 30% greater femoral width than controls. At 12 days old, greater bone width was also found in male and female Dmp1Cre.Pthlhf/f(hom) mice, but not in gene-matched mice from heterozygous parents, suggesting a maternal influence before weaning. Milk PTHrP levels were normal, but decidua from mothers of Dmp1Cre.Pthlhf/f(hom) mice were smaller, with low PTHrP levels. Moreover, Dmp1Cre.Pthlhf/f(hom) embryonic bone was more mineralized and wider than control. We conclude that Dmp1Cre leads to gene recombination in decidua, and that decidual PTHrP influences decidual cell maturation and limits embryonic bone growth. This identifies a maternal-derived developmental origin of adult bone strength.
Hydroxychloroquine Proves Ineffective in Hamsters and Macaques Infected with SARS-CoV-2

We remain largely without effective prophylactic/therapeutic interventions for COVID-19. Although many human clinical trials are ongoing, there remains a deficiency of supportive preclinical drug efficacy studies. Here we assessed the prophylactic/therapeutic efficacy of hydroxychloroquine (HCQ), a drug of interest for COVID-19 management, in two animal models. When used for prophylaxis or treatment neither the standard human malaria dose (6.5 mg/kg) nor a high dose (50 mg/kg) of HCQ had any beneficial effect on clinical disease or SARS-CoV-2 kinetics (replication/shedding) in the Syrian hamster disease model. Similarly, HCQ prophylaxis/treatment (6.5 mg/kg) did not significantly benefit clinical outcome nor reduce SARS-CoV-2 replication/shedding in the upper and lower respiratory tract in the rhesus macaque disease model. In conclusion, our preclinical animal studies do not support the use of HCQ in prophylaxis/treatment of COVID-19. One Sentence Summary Hydroxychloroquine prophylaxis/treatment showed no beneficial effect in SARS-CoV-2 hamster and macaque disease models.
What Determines Fiscal Deficits in Europe? The Impact and Interaction of EMU Membership, Fiscal Rules, Fiscal Councils and Creative Accounting

Abstract We search for drivers of fiscal deficits in Europe using a data panel containing annual data of 27 EU countries in the years 1991–2012. Our special focus is on the influence of fiscal rules as well as on fiscal councils, i. e. institutions that may help to reduce deficits and enforce fiscal rules by advising governments. We distinguish between internal fiscal rules and external rules that result from EMU membership. In addition, we consider the impact of “creative accounting”, i. e. measures that help to circumvent fiscal rules, which we approximate by so called stock-flow-adjustments. We especially analyze the interactive influence of the mentioned variables on the budget balance.
Application of statistical methods in Internet financial information analysis

Abstract In today’s modern globalised world, we are faced with numerous data which form the basis for many crucial decisions. The ability to perform the analysis and interpret data correctly is, therefore, of essential importance in practically every field. The field of financial Information is one of such fields, especially the segment of information that is used by companies in corporate communication. This article is focused solely on the information that refers to the Internet financial reporting, which is important particularly for the external stakeholders. The research was made on the basis of the Internet Financial Reporting (IFR) Index for 27 Slovenian companies listed on the Ljubljana Stock Exchange. The article demonstrates the methodological approach to the creation of the IFR Index, which served as the new variable in the following steps of this research, for which various statistical analyses were performed (univariate, bivariate, and multivariate). Based on the descriptive statistics, the main characteristics of the IFR Index are identified, and, by using the t-test for two independent samples, it was found out that there is a difference among the companies listed in the two different listings. Moreover, through the use of the correlation analysis, the correlation between the IFRC and the IFR-P variables was verified, and, in the end, through the use of the multiple regression analysis, it was discovered that the size of the company is the factor which influences the level of the IFR Index. The purpose of this article is to show the awareness of the alternative research methods, and to facilitate the selection of the most appropriate method for addressing particular research questions in the Internet Financial Reporting.
What do we Know About Lobbying in Germany?

Abstract Contribution payments and information are two important channels of lobbying influence. This survey analyzes what is known about these channels in Germany from a political economy perspective. Based on a discussion of the empirical literature and the available data, I argue that the state of empirical knowledge about lobbying in Germany is rather limited. To improve our understanding of the lobbying process, I identify research gaps and discuss potential methodological approaches which can be suitable to address these questions. Also, I discuss the role of party donations and temporary employment of bureaucratic staff, based on two novel data sets. The analysis indicates that party donations might play a role as a means to achieve access; in contrast, the temporary exchange of employees in the public administration seems to be of rather less importance in informational lobbying.
Workplace Ostracism as a Mediating Variable in the Relationship between Paradoxical Leader Behaviours and Organizational Inertia

Abstract Background and Purpose: Many organizations suffer from an increased in the paradoxical behaviours of leaders, leading to followers to feel lonely and the workplace ostracism, which negatively effects the emergence of organizational inertia. The main purpose of this study is to examine direct and indirect effect of paradoxical leader behaviours on organizational inertia through the mediating role of the workplace ostracism. Design/Methodology/Approach: Using the convenience sampling technique, a self-administered survey was conducted at the level of a sample (n = 564) of employees in the factories of the State Company for Textile and Leather Industry in Iraq. Structural equation modelling (SEM) was employed to test the proposed research model using the AMOS v.24 software. Results: The findings revealed that paradoxical leader behaviours have a significant effect on workplace ostracism and organizational inertia. Further, workplace ostracism significant effect on organizational inertia. In addition, the study empirically supports the mediating effect of workplace ostracism on the relationship between paradoxical leader behaviours and organizational inertia. Conclusion: When paradoxical behaviours arise in the behaviour of an organization’s leaders, it will have an influence on the emergence of workplace ostracism amongst employees, hence the subsequent emergence of organizational inertia in general in the work environment. Based on these results, this study should be of help to leaders in avoiding paradoxical behaviours in terms of maintaining a certain equilibrium in dealing with employees to find a positive work environment that reduces staff ostracism, and thus organizational inertia.
Use of Microsatellites in Genetic Diversity Assessment, Parentage Testing and Individual Identification of the Kangal Shepherd Dog

Abstract The Kangal Shepherd Dog is considered the most common dog breed of Turkish origin. This study investigated variations in ten autosomal microsatellite markers (PEZ01, PEZ03, PEZ05, PEZ06, PEZ08, PEZ12, PEZ20, FHC2010, FHC2054 and FHC2079) for the purposes of genetic diversity assessment of the Kangal breed. In addition, the use of markers was assessed in parentage testing and individual identification within the Kangal breed. The microsatellite markers were typed in 51 Kangal dogs. The total number of alleles in the study population was 69. The mean number of alleles per locus was 6.9, and varied from four (FHC2079) to 12 (PEZ12). The polymorphic information content (PIC) ranged from 0.52 (FHC2079) to 0.87 (PEZ12), with the mean value for all loci of 0.717. Power of exclusion (PE) in 10 microsatellites investigated varied between 0.143 (FHC2054) and 0.472 (PEZ08) per locus. In order to determine the efficiency of using microsatellites for individual identification in the Kangal breed, power of discrimination (PD) and matching probability (MP) were calculated for each microsatellite marker. The panel achieved high combined MP (6.77 × 10-10) and high combined PD value of 99.99999%. The obtained results may contribute to further recognition of the Kangal breed, and confirm that the investigated microsatellites enable a reliable parentage testing and individual identification of the breed.
Application of African Couchgrass in Manufacturing of Medium Density Fiberboard

Abstract Worldwide, the medium density fiberboard (MDF) materials have been extensively used for furniture as well as a normal building material. The MDF production is based on wood materials that are obtained from the tree cutting, and this process may conduct to gradual deforestation. Different works have shown the advantages of using other renewable biomass sources for MDF manufacturing. This study aimed at assessing the potential of using the African couchgrass, as one of worldwide available grass, and which has been one of the most worrying weeds for agriculture areas. The test with the manufactured MDF checked such material properties like density, moisture content, water absorption, and thickness swelling. The established results were 727.143 kg/m3, 5.81%, 9.18%, and 7.6% respectively. All those results are in the range of standard values recommended by ASTM D1037, and they give optimism for the application of couchgrass in the manufacturing of MDF. This new material was planned to be used as a partition component. Therefore, further studies should evaluate other important properties, like fire and sound resistance, while a proposal for potential replacement of the used industrial wood glue is also welcome.
The YouTube Algorithm and the Alt-Right Filter Bubble

Abstract The YouTube algorithm is a combination of programmed directives from engineers along with learned behaviors that have evolved through the opaque process of machine learning which makes the algorithm’s directives and programming hard to understand. Independent tests to replicate the algorithm have shown that the algorithm has a strong bias towards right-leaning politics videos, including those racist views expressed by the alt-right community. While the algorithm seems to be pushing users towards the alt-right video content merely in an attempt to keep users in a cycle of video watching, the end result makes YouTube a powerful recruiting tool for Neo-Nazis and the alt-right. The filter bubble effect that this creates pushes users into a loop that reinforces radicalism instead of level-headed factual resources.
Sounds in the classroom: Auditory context-dependent memory in school-aged children

Abstract Context-dependent memory (CDM) is the effect whereby information is retrieved more accurately in the presence of the contextual information that was present during encoding than in the absence of that contextual information. Most previous CDM experiments have focused on spatial location, but contexts such as sights, smells, and sounds have also been shown to be effective mnemonic cues, although the research is more limited. In relation to auditory contexts, much of the previous research has focused on music and on adults. We were interested in determining whether auditory CDM effects could be found in a classroom setting in school-aged children using background noises. Across two experiments we found that the reinstatement of the auditory context improved memory performance for 2nd, 3rd, and 5th grade students. Sounds, not just musical pieces, are stored in memory and can be effective contextual mnemonic cues. Further, (auditory) CDM effects can be found in young children. Teachers should be aware of the influence of contextual auditory cues in the classroom setting, and how this information is stored along with the focus of the teaching lesson.
RRNet: Repetition-Reduction Network for Energy Efficient Depth Estimation

Lightweight neural networks that employ depthwise convolution have a significant computational advantage over those that use standard convolution because they involve fewer parameters; however, they also require more time, even with graphics processing units (GPUs). We propose a Repetition-Reduction Network (RRNet) in which the number of depthwise channels is large enough to reduce computation time while simultaneously being small enough to reduce GPU latency. RRNet also reduces power consumption and memory usage, not only in the encoder but also in the residual connections to the decoder. We apply RRNet to the problem of resource-constrained depth estimation, where it proves to be significantly more efficient than other methods in terms of energy consumption, memory usage, and computation. It has two key modules: the Repetition-Reduction (RR) block, which is a set of repeated lightweight convolutions that can be used for feature extraction in the encoder, and the Condensed Decoding Connection (CDC), which can replace the skip connection, delivering features to the decoder while significantly reducing the channel depth of the decoder layers. Experimental results on the KITTI dataset show that RRNet consumes <inline-formula> <tex-math notation="LaTeX">$3.84\times $ </tex-math></inline-formula> less energy and <inline-formula> <tex-math notation="LaTeX">$3.06\times $ </tex-math></inline-formula> less memory than conventional schemes, and that it is <inline-formula> <tex-math notation="LaTeX">$2.21\times $ </tex-math></inline-formula> faster on a commercial mobile GPU without increasing the demand on hardware resources relative to the baseline network. Furthermore, RRNet outperforms state-of-the-art lightweight models such as MobileNets, PyDNet, DiCENet, DABNet, and EfficientNet.
Prediction of future citation count with machine learning and neural network

Prediction of long-term citation counts of paper is increasingly important in academic community. In this work, the prediction of future citation counts is considered as binary classification task. The study is based on 2600 papers of physiology extracted from Web of Science. Eight bibliometric features of citing paper in the first three years after the publication of paper were selected. Three machine learning model and a neural network are built to test whether these features are effective in prediction of future citation counts. The experimental result shows that the selected features are valuable in the prediction of long-term citation counts. And the machine learning and neural network are useful for the task of predicating future citation counts.
Dynamic Maintenance, Production and Inspection Policies, for a Single-Stage, Multi-State Production System

Over the past 20 years, integrated decision making for production systems has gained the interest of researchers and practitioners. Many studies have shown that integrated decision making can lead to substantial amount of savings. Yet, a few research work has been conducted on the areas of integrated maintenance, production and quality in dynamic environments. This paper provides an integrated multi-period, maintenance, production and quality-inspection scheduling model, which is formulated as a Markov decision process. The model minimizes the total expected maintenance, production and quality inspection costs. The structural properties of the proposed model are mathematically investigated and with using sensitivity analysis, practical insights are also provided. We mathematically provide conditions to guarantee that the optimal inspection policy is monotone non-decreasing in the state of the machine. Furthermore, we show that the optimal production policy decreases by one unit as the state of inventory increases by one unit. Sensitivity analysis demonstrates that the production parameters affect both, maintenance and inspection decisions. In addition, the maintenance parameters affect inspection decisions. Finally, it is found that among the inspection parameters (i.e., cost-of-inspection and inspection-errors), type-II error mainly affects maintenance decisions.
A Fuzzy-Theory-Based Cellular Automata Model for Pedestrian Evacuation From a Multiple-Exit Room

The exit selection behavior of pedestrians plays an important part in the process of evacuation. This paper proposes a cellular automata model based on fuzzy logic method for simulating the evacuation of pedestrians from a multiple-exit room. When pedestrians select the exit, the distance and density are adopted as two important input variables in the fuzzy logic method, and the probability of selecting each exit is defined as the output variable of fuzzy logic method. The output variable of fuzzy logic, exit width and herding behavior are combined to determine the target exit. The competitiveness of each pedestrian is calculated by logit model to solve the position conflicts among pedestrians. The validation of the model is demonstrated by comparing the simulation data with the real data. The effects of attributes of pedestrians, exits and obstacles on evacuation are studied in simulations. Results show that large public facilities should control the inflows of pedestrians, and the reasonable increase of the exit quantity and exit width are effective for improving the evacuation efficiency. In the design of buildings, obstacles need to be designed reasonably, which should not be too large or too small. At the same time, obstacles should be kept at a certain distance from the exit, so as to ease the exit congestion and improve the evacuation efficiency. This paper takes the advantages of fuzzy logic method to solve the exit selection problem, which can effectively integrate the robustness with physiological-based “perception-action” behavior, the experience knowledge of pedestrians and the perception information of the surrounding environment into the decision-making process.