Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Acute Fatigue on Cognitive Performance in Team Sport Players: Does It Change the Way They Perform? A Scoping Review

Fatigue caused by exercise or mentally demanding tasks can lead to an alteration in the cognitive functioning of athletes. Therefore, it is important to investigate whether and to what extent fatigue influences athletes cognitive performance in sports with high cognitive demands. This scoping review aims to map research articles dealing with the effects of acute fatigue on players cognitive performance in team sports. The main inclusion criterium was that studies had to examine the impact of any form of acute fatigue on (i) cognitive functions only, (ii) reactive agility, (iii) sport-specific skills with reactive components included. In total, 12 articles met our inclusion criteria. Results indicated that prolonged exercise causes a decline in the decision-making, attention, and perception abilities of players. Accuracy of sport-specific tasks with cognitive components included rather deteriorated after both exercise and mental fatigue inducement. However, alteration of players cognitive performance depends on the intensity and duration of fatigue-inducing tasks. Mental fatigue and consequent decision-making deterioration can be triggered by at least 30 min of a Stroop color-word task as well as smartphone application exposure. Analysis of the studies revealed a lack of research investigating the acute effect of fatigue on reactive agility, along with cognitive functions such as memory or learning. Due to possible acute negative effects of fatiguing exercise or mentally demanding tasks on human cognition, future research should consider the examination of different types and intensities of exercise on players’ cognitive performance.
Robust reinsurance contract with learning and ambiguity aversion

We investigate the robust reinsurance demand and price under learning and ambiguity aversion. In the reinsurance contract, the insurer is ambiguity neutral and believes that he is perfectly informed, and the reinsurer is a Bayesian learner and is aware that even the filtered model is the best description of the data-generating process, might not forecast the future claims correctly. The ambiguity-averse reinsurer has a preference for reinsurance contract which is robust to model misspecification. Closed-form expressions for the robust reinsurance demand and price are derived. We find that both the reinsurer's one-sided learning and ambiguity aversion influence the structures and levels of the optimal reinsurance demand and price. Moreover, if the ambiguity-averse reinsurer specifies the suboptimal reinsurance contract as an ambiguity-neutral decision-maker, it will result in significant utility loss and the utility loss increases with ambiguity aversion level and Bayesian volatility.
DGS-SLAM: A Fast and Robust RGBD SLAM in Dynamic Environments Combined by Geometric and Semantic Information

Visual Simultaneous Localization and Mapping (VSLAM) is a prerequisite for robots to accomplish fully autonomous movement and exploration in unknown environments. At present, many impressive VSLAM systems have emerged, but most of them rely on the static world assumption, which limits their application in real dynamic scenarios. To improve the robustness and efficiency of the system in dynamic environments, this paper proposes a dynamic RGBD SLAM based on a combination of geometric and semantic information (DGS-SLAM). First, a dynamic object detection module based on the multinomial residual model is proposed, which executes the motion segmentation of the scene by combining the motion residual information of adjacent frames and the potential motion information of the semantic segmentation module. Second, a camera pose tracking strategy using feature point classification results is designed to achieve robust system tracking. Finally, according to the results of dynamic segmentation and camera tracking, a semantic segmentation module based on a semantic frame selection strategy is designed for extracting potential moving targets in the scene. Extensive evaluation in public TUM and Bonn datasets demonstrates that DGS-SLAM has higher robustness and speed than state-of-the-art dynamic RGB-D SLAM systems in dynamic scenes.
THE FUTURE OF INTERNAL AUDITING: GARDENER OF GOVERNANCE

Abstract Internal Auditing is a profession at crossroads. On the micro level, in organizations and institutions, stakeholders may see less and less value in the contributions from internal auditing and on a macro level, this projection jeopardizes the legitimacy and relevance of internal auditing as a profession. The good and bad news is that the future is uncertain. This article suggests five main fields of action and focus for the IIA Global – The Institute of Internal Auditors, its local chapters, internal auditors and academia that will determine the future role of internal auditing, its legitimacy, its relevance and its organizational and societal significance. The purpose of this article is to provide comprehensive content suggesting five concrete paths for the betterment of internal auditing. The five directions that deserve development are planet, public, profession, prosperity and people. Ultimately, positioning internal auditing as Gardener of Governance is a promising metaphor to strengthen its value proposition, both on a micro and on a macro level. Metaphors are an important way to convey ideas and make ideas stick.
Automatic identification of intestinal parasites in reptiles using microscopic stool images and convolutional neural networks

Captive environments trigger the propagation and multiplication of parasites among different reptile species, thus weakening their immune response and causing infections and diseases. Technological advances of convolutional neural networks have opened a new field for detecting and classifying diseases which have shown great potential to overcome the shortcomings of manual detection performed by experts. Therefore, we propose an approach to identify six captive reptiles parasitic agents (Ophionyssus natricis, Blastocystis sp, Oxiurdo egg, Rhytidoides similis, Strongyloides, Taenia) from a microscope stool images dataset. Towards this end, we first use an image segmentation stage to detect the parasite within the image, which combines the Contrast Limited Adaptive Histogram Equalization (CLAHE) technique, the OTSU binarization method, and morphological operations. Then, we carry out a classification stage through Mobilenet CNN under a transfer learning scheme. This method was validated on a stool image dataset containing 3616 images data samples and 26 videos from the six parasites mentioned above. The results obtained indicate that our transfer learning-based approach can learn a helpful representation from the dataset. We obtained an average accuracy of 98.66% across the six parasitic agent classes, which statistically outperformed, at a 95% confidence level, a custom CNN trained from scratch.
Myosin 10 supports mitotic spindle bipolarity by promoting PCM integrity and supernumerary centrosome clustering

Myosin 10 (Myo10) is a member of the MyTH4/FERM domain family of unconventional, actin-based motor proteins. Studies have implicated Myo10 in supporting cell adhesion via its integrin-binding FERM domain, and spindle positioning and spindle pole integrity via its microtubule-binding MyTH4 domain. Here we characterized Myo10’s contribution to mitosis using Myo10 knockout HeLa cells and MEFs isolated from a Myo10 knockout mouse. Most notably, both of these knockout cells exhibit a pronounced increase in the frequency of multipolar spindles. Staining of unsynchronized metaphase cells showed that the primary driver of spindle multipolarity in knockout MEFs and knockout HeLa cells lacking supernumerary centrosomes is PCM fragmentation, which creates y-tubulin-positive, centriole-negative microtubule asters that serve as additional spindle poles. For HeLa cells possessing supernumerary centrosomes, Myo10 depletion further accentuates spindle multipolarity by impairing centrosome clustering. These results argue, therefore, that Myo10 supports spindle bipolarity by maintaining PCM integrity in both normal and cancer cells, and by promoting supernumerary centrosome clustering in cancer cells. Finally, we present evidence that the defect in spindle pole integrity in Myo10 knockout cells is likely due to a defect in pole stability rather than pole maturation, and that Myo10 promotes supernumerary centrosome clustering at least in part by promoting cell adhesion during mitosis.
Absorptive Capacity of Multinational Subsidiary in Intra-Firm Knowledge Transfer: Cases from Japanese Multinational Manufacturers

This paper discusses the concept of absorptive capacity as a determining factor of knowledge transfer difficulties by using cases Japanese multinational company between headquarters and their overseas subsidiaries. This concept is initially presented to show costs or difficulties of newly transferred R&D related knowledge from external environment, and defined as capability accumulated through prior experience to exploit newly transferred knowledge determines transfer cost. Then several studies improve this concept to apply a greater variety of knowledge. However, it can be said that studies in this field as still inadequate for several reasons. Therefore this study makes another attempt to improve this concept. This paper consists from four parts. Firstly, several studies conceptualization of “absorptive capacity” are reviewed, and neglected aspect of this concept is pointed out. Secondly, based on the theoretical argument, reconceptualized model of absorptive capacity is presented. Thirdly, in order to support newly proposed model, qualitative study is conducted. In this study, cases of knowledge transfer within Japanese multinational companies between headquarters and their subsidiaries are shown and analyzed. Finally, results of empirical study, conclusions, practical implications, limitation and future perspectives are presented.
Compact Bent-Corner Orthogonal Beam Switching Antenna Module for 5G Mobile Devices

Typically, users engage with smartphones in either single-hand or dual-hand mode. To design antennas that operate at 28 GHz and have high pattern integrity for both modes of operation, an orthogonal beam switching module is required as a single phased-arrays would fail. First, a corner bent corporate fed array operating at 28 GHz is proposed, which has a forward gain of 8.5 dBi and a high-front-to-back ratio of 20 dB. Second, a corner bent printed Yagi antenna that also operates in the 28 GHz band is proposed. Both the corner bent antennas are compatible with the panel edge of commercial smartphones. The radiation from both antennas is mostly directed away from the user. A corner bent co-polarized orthogonal beam switching module is presented and characterized. The antenna module also has a shared ground, making it a potential candidate for future 5G smartphones. Detailed results are presented with adequate justification.
Advances in research on the effects of platelet activation in acute lung injury (Review)

Acute lung injury (ALI) is an acute hypoxic respiratory insufficiency or failure caused by various factors inside and outside the lungs. ALI is associated with high morbidity and a poor prognosis in hospitalized patients. The lungs serve as a reservoir for platelet precursor megakaryocytes and are closely associated with platelets. Platelets not only play a central role in hemostasis, coagulation and wound healing, but can also act as inflammatory cells capable of stimulating non-hemostatic immune functions under inflammatory conditions, participating in the progression of various inflammatory diseases, and can result in tissue damage. Therefore, it was speculated that platelets may play an important role in the pathogenesis of ALI. In this review, the latest research progress on secretion of bioactive mediators from platelets, platelet activation-related signaling pathways, and the direct contact reactions between platelets and neutrophils with endothelial cells that result in ALI are described, providing evidence to support the importance of the consideration of platelets in the search for ALI interventional targets.
Behaviour Monitoring and Interpretation - BMI: Smart Environments, Volume 3 Ambient Intelligence and Smart Environments

This book is concerned with behaviour monitoring and interpretation with regard to two main areas of focus: the investigation of motion patterns and ambient assisted living. It presents state-of-the-art contributions on research in both these areas. The first section consists of chapters discussing recent developments in monitoring and representing behaviours, with a particular focus on movement-based behaviour. The next part of the volume is more application-driven. Several case studies present the monitoring and support of people with cognitive impairments in smart environments, showing in particular how AI techniques are applied in these contexts and also how ambient assisted physical activity systems help to increase the engagement of seniors in physical activities. Investigations to show how monitored behaviours can be interpreted in smart environments are then described: a survey on knowledge-intensive methods for intention recognition; the detection of high-level daily activities by analysis of team behaviours in smart environments and a model for how ambient intelligence systems can automatically discover patterns of user behaviours. Finally the publication discusses the infrastructure of smart environments.IOS Press is an international science, technical and medical publisher of high-quality books for academics, scientists, and professionals in all fields. Some of the areas we publish in: -Biomedicine -Oncology -Artificial intelligence -Databases and information systems -Maritime engineering -Nanotechnology -Geoengineering -All aspects of physics -E-governance -E-commerce -The knowledge economy -Urban studies -Arms control -Understanding and responding to terrorism -Medical informatics -Computer Sciences
Performance gap analysis case study of a non-domestic building

Addressing the energy performance of buildings is key to achieving global emission reduction targets. Buildings account for 30% of total greenhouse gas emissions globally. A barrier to increasing the energy efficiency of buildings is the energy use performance gap. This paper discusses the causes of the performance gap and focuses on the building–user interaction unique to non-domestic buildings. In particular, it analyses the conflicting desires of operators and occupants, which can occur in buildings that operate as part of a larger organisation and result in an increased energy demand. The nDeep framework has been developed, which outlines the relationships between contributory factors of the performance gap in a large organisation. In addition, a mixed methods approach is implemented that combines user response and in-use energy data. The proposed methodology is applied in a case study of a university building, which highlights the conflicts that emerge between meeting user needs and operating a build.
Impact of Hyperparameters on Deep Learning Model for Customer Churn Prediction in Telecommunication Sector

In this paper, in order to predict a customer churn in the telecommunication sector, we have analysed several published articles that had used machine learning (ML) techniques. Significant predictive performance had been seen by utilising deep learning techniques. However, we have seen a tremendous lack of empirically derived heuristic information where we had to influence the hyperparameters consequently. Here, we had demonstrated three experimental findings, where a Relu activation function was embedded and utilised successfully in the hidden layers of the deep network. We can also see that the output layer had the service ability of a sigmoid function, in which we had seen a significant performance of the neural network model and obviously it was improved. Furthermore, we had also seen that the model's performance was noticed to be even better, but it was only considered better though when the batch size in the model was taken less than the test dataset’s size, respectively. In terms of accuracy, the RemsProp optimizer beat out the other algorithms such as stochastic gradient descent (SGD). RemsProp was seen even better from the Adadelta algorithm, the Adam algorithm, the AdaGrad algorithm, and AdaMax algorithm as well.
Validation and Improvement of Procedures for Performance Testing of Anti-Graffiti Agents on Concrete Structures

Graffiti on concrete structures is an ongoing, costly and increasing problem. A number of various anti-graffiti agents available on the market are intended to be resistant to graffiti attack or provide a treatment that aids the removal of graffiti. In several European countries the testing of anti-graffiti agents on concrete structures is following the EN 1504-2. This method is not appropriate for the aimed result. The method tests if an anti-graffiti agent damages a concrete structure and focuses on drying index and frost resistance in comparison with untreated samples. The aspects of the efficacy against graffiti attack, other types of dirt and aesthetic changes due to anti-graffiti protection are not evaluated by the present method. Therefore a validation and improvement of the current method is proposed.
Effects of switching speed on arcing and contact erosions in residential circuit breakers

Contact erosions are critical for performance of circuit breakers. It has been generally accepted that switching the contacts open with fast speed helps reducing the arcing, and hence reducing the contact erosions. However, some observations during repetitive, low current switching have shown contradictory results. An analytical study to arc power during contact opening in residential circuit breakers is performed in this paper. It is found that opening speed of contacts has opposite effects on arc power, depending on the impedance of the circuit. At high current, low impedance, fast opening results in lower arc power, while at low current, high impedance, slow opening results in lower arc power. Experiments are performed to verify the results of the analytical study. Arc power, contact erosions and high speed videos are compared between high switching speed and low switching speed at low current conditions. At high current conditions, arc power is compared with different delay time and different circuit impedance with a fixed opening speed. The experiment results agree with the analytical study.
Application of optimal ZPETC to high precision linear servo tracking control

In high precision numerical control machine servo tracking control system,in order to make the output response track perfectly the input command,the phase error between output and input is required to be zero,at the same time the magnitude is required to be uniform.Generally zero phase error tracking(controller)(ZPETC) is used as feedforward controller to compensate for the phase error,but this will also cause small gain error.To improve the tracking performance of the ZPETC,the optimal design scheme of feedforward controller based on L_2-norm was proposed.The optimal digital prefilter(DPF) was designed by selecting proper objective function.By cascading the developed DPF to ZPETC,a new feedforward(controller) was obtained.The scheme greatly improves the gain performance of the system while maintaining zero phase error of the system,and thus the tracking accuracy of the system is enhanced.This(characteristic) has gotten proved from the simulation results.
Boosting climate change mitigation potential of perennial lignocellulosic crops grown on marginal lands

Nitrogen fertilizer (NF) is a major uncertainty surrounding the greenhouse gas (GHG) emissions of lignocellulosic biofuels. NF enhances agronomic yields and soil C inputs via plant litters, but results in soil organic carbon (SOC) decomposition, soil N2O fluxes, and a large fossil energy footprint. Thus, whether NF is beneficial or detrimental to the GHG mitigation of biofuels is unknown. Here, we show the potential GHG mitigation of fertilizing switchgrass (Panicum virgatum) at the NF rate that minimizes net GHG emissions across 7.1 million ha of marginal lands in the Midwest US, with long-term production advantages surpassing emitted GHG by 0.66 Mg CO2e ha−1 yr−1 on the aggregate. Marginal lands limited by poor N fertility could see a much greater benefit, but not SOC-rich lands, limited by low precipitation, or short growing seasons. The objectives of maximizing yield and minimizing GHG overlap only in a few environments, suggesting that maximum yield will reduce the climate benefit of cellulosic biofuels.
Elementary Computational Thinking Instruction and Assessment: A Learning Trajectory Perspective

There is little empirical research related to how elementary students develop computational thinking (CT) and how they apply CT in problem-solving. To address this gap in knowledge, this study made use of learning trajectories (LTs; hypothesized learning goals, progressions, and activities) in CT concept areas such as sequence, repetition, conditionals, and decomposition to better understand students’ CT. This study implemented eight math-CT integrated lessons aligned to U.S. national mathematics education standards and the LTs with third- and fourth-grade students. This basic interpretive qualitative study aimed at gaining a deeper understanding of elementary students’ CT by having students express and articulate their CT in cognitive interviews. Participants’ (n = 22) CT articulation was examined using a priori codes translated verbatim from the learning goals in the LTs and was mapped to the learning goals in the LTs. Results revealed a range of students’ CT in problem-solving, such as using precise and complete problem-solving instructions, recognizing repeating patterns, and decomposing arithmetic problems. By collecting empirical data on how students expressed and articulated their CT, this study makes theoretical contributions by generating initial empirical evidence to support the hypothesized learning goals and progressions in the LTs. This article also discusses the implications for integrated CT instruction and assessments at the elementary level.
The Peculiarities of Verbalisation of Emotions by the Teachers in Distance Learning

The purpose of the study is to identify and categorise the lexical units denoting teachers’ emotions in the context of distance learning during the COVID-19 lockdown. Research methods: discourse and content analysis, categorisation, questionnaire “The emotional aspects and attitudes of teachers to various aspects of reform in secondary education”. The sample consisted of 413 teachers (90.3% female) who participated in an online survey. Teachers taught at different educational levels (i.e., elementary, middle and secondary schools). They completed self-report questionnaire that assessed their feelings towards remote learning. Predominantly positive and sthenic emotions (interest and joy) were reported regarding distance learning. The perception of online learning as the only possible form has changed teachers’ discourse regarding interaction with students: there was a shift from positive emotions towards negatively marked asthenic lexical units (disappointment, anxiety and disapproval). The emotional component of teachers’ attitudes towards online learning was characterised as undifferentiated in the beginning of distance learning. As teachers’ involvement in online learning increased, the share of respondents with an indifferent attitude decreased, while the share with a positive attitude towards this innovation - increased. Teachers’ discourse is characterised by a positive emotional connotation at the start of online learning; however, the awareness of the inevitability of such a format led to the predominance of negatively marked, asthenic emotions. A decrease was measured in the frequency of such lexical units such as “interest” and “joy”, while “confidence in oneself” has sharply increased.