Miracle-dz/UniFAR-FTUs huggingface dataset:
GRIT AND MINDSET – UNRELATED TO PERFORMANCE IN A MOTOR LEARNING TASK

Objective Research has shown grit and mindset can be improved, but no known research has investigated the effect of learning a new skill on grit and growth mindset. While grit and mindset seem to predict success in education and success, it is also not clear if initial grit or mindset have an effect on motor skill acquisition. The purposes of this research is to investigate if grit and growth mindset are predictors of learning a new skill and if learning a new skill leads to improvement in grit and growth mindset. Methods 14 students enrolled in a motor learning course participated in a six week intervention where they practiced juggling during class via Zoom. Concepts discussed in class as part of the course were incorporated into practice sessions by the teaching associate/researcher. Grit and growth mindset were measured using the short grit (Grit-S) scale and Dweck Mindset Instrument (DMI) respectively and compared in individuals before and after four weeks practicing juggling. The highest number of successful catches by each subject was recorded after each week of practice. The correlation of improvement in successful ball catches and initial grit and growth mindset were analyzed. A pre/post analysis was done using a dependent T-test and correlation between number of juggling catches and initial grit and growth mindset were analyzed. All data was collected virtually over Zoom and Google Sheets. Results: A negative, moderate relationship was found between initial grit and percent improvement to week 4 (R = -.517; p=.029) and no significant relationship
Special Purpose Entities in Megaprojects: Empty Boxes or Real Companies?

Megaprojects involve organizations called “Special Purpose Entities” (SPEs) also known as “special purpose vehicles. ” Despite their relevance, particularly for governance, SPEs are under-investigated. In the project management literature, there is neither a widely accepted definition of SPEs nor a clear understanding of what it does. This article presents an extensive literature review, which considers three domains: legal, financial, and project management. Four outcomes are presented: the definition of SPE, the typology of existing SPEs, comparisons of existing SPEs, and descriptions of SPE uses in megaprojects.
Time Series Forecasting using Genetic Algorithm A Case Study of Maintenance Cost Data For Tunnel Fans

— Time series forecasting is widely used as a basis for economic planning, production planning, production control and optimizing industrial processes. The aim of this study has been to develop a novel two-level Genetic Algorithm (GA) to optimize time series forecasting in order to forecast cost data for fans used in road tunnels by the Swedish Transport Administration (Trafikverket). The first level of the GA is responsible for the process of forecasting time series cost data, while the second level evaluates the forecasting. The first level implements GA based on the Autoregressive Integrated Moving Average (ARIMA) model. The second level utilizes a GA based on forecasting error rate to identify proper forecasting. The results show that GA based on the ARIMA model produces good forecasting results for the labor cost data objects. It was found that a multi-objective GA based on the ARIMA model showed an improved performance. The forecasted data can be used for Life Cycle Cost (LCC) analysis.
Multilateral and Multidimensional Wellness Measurement in the Absence of Cardinal Measure: Health, Loneliness, Ageing and Gender in 21st Century China.

Comparing the wellbeing of groups using self reported measures of wellbeing can be challenging. The scale dependency of many summary statistics applied to arbitrary Cantril scales attached to ordinal categorical data can engender a lack of coherency in results based upon alternative, equally valid scales. Furthermore, the conditions under which results will be robust across alternative scales seldom prevail in practice. Here scale independent methods for the multilateral and multidimensional wellness measurement and comparison of groups are proposed and implemented in a study of the health-loneliness-aging-gender nexus in 21 st century China. The results indicate that poor health and loneliness appears to increase with age, though not monotonically. Improved health status is always associated with better un-loneliness outcomes and improved un-loneliness status is always associated with better health outcomes. While a large portion of the population are not affected by loneliness, of those who are, ill health is generally more likely to be reported. With regard to the health - loneliness joint distribution, generally, males enjoy better joint outcomes than their female counterparts in almost every comparison and urban dwellers enjoy better outcomes than their rural counterparts.
6D Posture Estimation of Road Vehicles from Color Images

 Abstract — Currently, in the field of object posture estimation, there is research on estimating the position and angle of an object by storing a 3D model of the object to be estimated in advance in a computer and matching it with the model. However, in this research, we have succeeded in creating a module that is much simpler, smaller in scale, and faster in operation. Our 6D pose estimation model consists of two different networks – a classification network and a regression network. From a single RGB image, the trained model estimates the class of the object in the image, the coordinates of the object, and its rotation angle in 3D space. In addition, we compared the estimation accuracy of each camera position, i.e., the angle from which the object was captured. The highest accuracy was recorded when the camera position was 75°, the accuracy of the classification was about 87.3%, and that of regression was about 98.9%.
Establishing the laws of preferential choice behavior

Mathematical and computational decision models are powerful tools for studying choice behavior, and hundreds of distinct decision models have been proposed over the long interdisciplinary history of decision making research. The existence of so many models has led to theoretical fragmentation and redundancy, obscuring key insights into choice behavior, and preventing consensus about the essential properties of preferential choice. We provide a synthesis of formal models of risky, multiattribute, and intertemporal choice, three important domains in decision making. We identify recurring insights discovered by scholars of different generations and different disciplines across these three domains, and use these insights to classify over 150 existing models as involving various combinations of eight key mathematical and computational properties. These properties capture the main avenues of theoretical development in decision making research and can be used to understand the similarities and differences between decision models, aiding both theoretical analyses and empirical tests.
Dynamic and Thermodynamic Air–Sea Coupling Associated with the Indian Ocean Dipole Diagnosed from 23 WCRP CMIP3 Models*

The performance of 23 World Climate Research Programme (WCRP) Coupled Model Intercomparison Project, phase 3 (CMIP3) models in the simulation of the Indian Ocean dipole (IOD) is evaluated, and the results show large diversity in the simulated IOD intensity. A detailed diagnosis is carried out to understand the role of the Bjerknes dynamic air–sea feedback and the thermodynamic air–sea coupling in shaping the different model behaviors. The Bjerknes feedback processes include the equatorial zonal wind response to SST, the thermocline response to the equatorial zonal wind, and the ocean subsurface temperature response to the thermocline variation. The thermodynamic feedback examined includes the wind–evaporation–SST and cloud–radiation–SST feedbacks. A combined Bjerknes and thermodynamic feedback intensity index is introduced. This index well reﬂects the simulated IOD strength contrast among the strong, moderate, and weak model groups. It gives a quantitative measure of the relative contribution of the dynamic and thermodynamic feedback processes. The distinctive features in the dynamic and thermodynamic coupling strength are closely related to the mean state difference in the coupled models. A shallower (deeper) equatorial mean thermocline, a stronger (weaker) background vertical temperature gradient, and a greater (smaller) mean vertical upwelling velocity are found in the strong (weak) IOD simulation group. Thus, the mean state biases greatly affect the air–sea coupling strength on the interannual time scale. A number of models failed to simulate the observed positive wind–evaporation–SST feedback during the IOD developing phase. Analysis indicates that the bias arises from a greater contribution to the surface latent heat ﬂux anomaly by the sea–air speciﬁc humidity difference than by the wind speed anomaly.
Enabling the use of Environmental DNA for Regulatory Policymaking in the Marine Industrial Revolution

New industrial activities are exacerbating anthropogenic impacts on the marine environment and imposing an unprecedented combination of stressors on ocean ecosystems. The classical methodologies for monitoring impacts on marine life, based on morphological species identification, cannot keep pace with the urgent need for more detailed information required to monitor biodiversity changes, and to promptly deliver easily-understandable data to time and budget-constrained environmental compliance and enforcement practitioners. This is particularly true for small-sized organisms that are hard to identify and that dominate in some environments, such as deep sea. Knowledge limitations affect not only the ability to remediate environmental degradation, but also the lack of adequate understanding of how the cumulative impact of human activities at sea is affecting the provision of the ocean’s many ecosystem services that are fundamental for the sustainability of our planet. High-throughput sequencing (HTS) technologies applied to environmental DNA (eDNA) holds great potential to confront these knowledge gaps, although their use in marine environmental impact assessments (EIAs) is still limited. The project Monitoring Marine Biodiversity in Genomic Era, funded by the Swiss Network for International Studies (SNIS), has explored the potential of the eDNA HTS approach, so called eDNA metabarcoding, for assessing the health of marine ecosystems and the impact of major human actions on them. The project concludes that 1) emerging eDNA-based technologies should be deployed alongside traditional morphological species identification techniques to provide relevant information to ocean resources managers, and environmental compliance and enforcement practitioners; 2) there are no significant legal constraints to its use; and 3) its decreasing costs open new opportunities to enhance human knowledge of complex marine ecosystems and the effects of cumulative impacts on them in both coastal and deep-ocean settings. However, the project also highlights the fact that more research is needed to improve interpretation of eDNA data for impact assessment. Particular concerns include the needs to ground truth species identities and interactions, infer abundance data and develop molecular indices of ecological status. Major challenges to overcome in eDNA data analysis are associated with signal persistence over time and in non-living material, as well as the transportation of eDNA in water over long distances.
The D4Dairy-project – How digitalization and data integration pave the way to dairy health improvement

Lameness in dairy cattle still remains a widespread animal welfare and economic issue. Early detection and prediction of lameness events may help to reduce negative effects for farms. However, integrating lameness assessment into the daily work routine is not feasible for many farms and may be prone to subjective bias for use in genetics. External assessment, carried out by veterinarians or claw trimmers, is often not done regularly and thus lameness may not be detected before it becomes clinical. Lameness is caused by a variety of factors including housing, feeding and management and may be associated with changes in milk performance or behavior. Technical advances and growing digitalization in various areas of dairy farming increased the amount and quality of data availability on farm supporting data-driven decision making. These advances happened incrementally with the consequence that data from different management areas is available in different formats or software programs and thus mainly used to make informed decisions in the respective area. Integrating these different data sources may enable more informed decisions by considering information from other management areas, which would be missed otherwise. Thus, one aim of the D4Dairy project is the integration of various data to enhance early lameness detection and decision-support on individual farms. Our approach is based on the idea that lame cows show changes in performance and behavior, even before they show clinical signs of lameness. By integrating already existing data from national performance recordings, farm records, veterinary records, claw trimmings and different kinds of milking and sensor systems measuring e.g. activity, temperature or rumination (smaXtec, SCR by Allflex) a decision-support tool for early lameness detection and prediction should be developed. Subsequently, the outcomes of this approach should be used for the definition of auxiliary traits for claw health to be included into the breeding value estimation. Aside from the integration of different farm data this also requires the integration of data across farms, considering different systems, such as husbandry (e.g. free stall vs. pasture), milking (e.g. automatic milking system vs. conventional milking parlor with fixed milking times) or sensor systems (e.g. activity and temperature vs. activity and rumination monitoring). In our contribution we want to show the steps towards data integration, which challenges we encountered and how we handled them. Furthermore, we present how we aim to use the outcomes for including claw health into the current breeding program and breeding value estimation.
A Method for Group Decision Making with Multiplicative Consistent Interval-valued Intuitionistic Fuzzy Preference Relation

—Interval-valued intuitionistic fuzzy preference relation (IVIFPR) is an appropriate tool for describing the group decision making (GDM) problems with complex and uncertain information because of its inclusiveness and ﬂexibility. Based on the newly deﬁned multiplicative consistency, this paper investigates an algorithm for the GDM method with IVIFPRs. Firstly, a novel multiplicative consistency concept is proposed, which is proved to satisfy an important property: robustness. A conversion formula is devised to accomplish the multiplicative consistent IVIFPRs by utilizing the normalized interval-valued intuitionistic fuzzy (IVIF) weights. Subsequently, a consistency measure and inconsistent repairing process are put forward to ensure that every individual IVIFPR is of acceptable multiplicative consistency. Afterward, in the context of minimizing the deviations between the given IVIFPRs and their corresponding consistent IVIFPRs, two fractional programming models are constructed to generate the normalized individual IVIF weights and collective ones, where the experts are considered as individuals and a group, respectively. Finally, an example is cited and comparative analyses with previous approaches are conducted to demonstrate the applicability and validity of the proposed method.
Motion Blur Detection and Removal in Images

:- Image blur detection and removal have been one of the major topics of research in image processing in the recent years. These blur detection and removal algorithms have many real world applications including image restoration and image enhancement. Image blur can include motion blur and out-of-focus blur or blurring due to lens imperfections. This paper covers an overview of recent methods and advancements made in the fields of motion blur detection as well as motion blur removal. This paper also proposes an approach for motion blur detection and removal involving Convolutional Neural Network(CNN) and Generative Adversarial Network(GAN).
Using Adaptive Enterprise Architecture Framework for Defining the Adaptable Identity Ecosystem Architecture

Digital identity management is often used to handle fraud detection and hence reduce identity thefts. However, using digital identity management presents additional challenges in terms of privacy of the identity owner meanwhile managing the security of the verification. In this paper, drawing on adaptive enterprise architecture (EA) with an ecosystem approach to digital identity, we describe an identity ecosystem (IdE) architecture to handle identity management (IdM) while safeguarding security and privacy. This study is a part of the larger action design research project with our industry partner DZ. We have used adaptive EA as a theoretical lens to define a privacy aware adaptive IdM with a view to improve the Id operations and delivery of services in the public and private sector. The value of the anticipated architecture is in its generic yet comprehensive structure, component orientation and layered approach which aim to enable the contemporary IdM.
A Discussion of Implicit Verses Explicit CSR Discourse: A case study of Ford, Volkswagen and Toyota.

A growing body of scholarly work has been emerging in the field of comparative capitalism due to its importance for multinational corporations. National Business Systems theory (NBS) explains how certain business practices are embedded in its national context. The Implicit verses Explicit framework differentiates CSR practices within different NBS. As more research emphasise convergence between NBS. This research aims to explore the language of CSR disclosures and how this language might differ between different institutional environments. By integrating literature from national business systems and corporate social responsibility, this research attempts to understand how converging national business systems as a result of continued globalisation (Dore, 2000), influences CSR discourse. Matten and Moon (2008), explain the relationship, since CSR is embedded within the institutions and environment. Furthermore, converging or diverging would have a profound effect on CSR, as companies may well have to adapt their practices to engage stakeholders (Marano & Kostova, 2016) and gain legitimacy (Bansal & Roth, 2000). The critical discourse analysis of Ford, Volkswagen and Toyota’s CSR reports allowed for identification of how discourse differs between companies originating from different NBS. As well as change in discourse between the period of 1999 and 2018. The fin dings indicate that Volkswagen and Toyota’s CSR language are showing more similarity to Fords’ CSR language. This research hopes to inspire further research in CSR disclosures and converging national business systems.
Kako napovedati težavno oskrbo dihalne poti How to predict a difficult airway

According to The American Society of Anaesthesiologists Task Force, a difficult airway is defined as a clinical situation in which a conventionally–trained anaesthesiologist experiences difficulty with face mask ventilation and/or difficulty with tracheal intubation. Failing at efficient airway management is a source of major apprehension for physicians. Predicting difficult airway management is a mandatory part of clinical practice for paediatric and adult patients so as to avoid unexpected difficult airway management and adverse outcomes. Predicting a difficult airway is based on a focused medical history, review of prior medical records, physical examination, and specific tests for airway assessment. Each feature or airway test has its own sensitivity and specificity for the prediction of a difficult airway. All airway assessment tests are characterised by pred
Library Management Using Voice Assistant

:- A Personal Assistant is a computer application that uses Artificial Intelligence (AI) to assist humans with their tasks. In today's world, the use of a voice personal assistant is becoming more common. Modern IPAs are capable of a broad range of functions, from simple ones like opening an app or setting an alarm to more complicated ones like taking notes. Siri from Apple, Google Assistant and Alexa from Amazon are all examples of AI assistants.
Human toxocariasis: A rare parasitosis (about 16 cases)

Toxocariasis is a helminthozoonosis with cosmopolitan distribution, due to the presence in the human body of larvae of a nematode of the genus Toxocara. (T.)Spp. Two nematodes are responsible for human toxocariasis: T. canis and T. catis. The objective of our study is to describe the epidemiological, clinical, diagnostic, therapeutic and evolutionary characteristics of human toxocariasis.
Applying Multi-Armed Bandit on top of content similarity recommendation engine

One of the goals of every news publisher is to prolong users’ browsing sessions by convincing them to read additional content besides the initially requested. Consequently, most news publishers use recommendation engines to keep users engaged by recommending them content which they might like to read next. In this paper we present how to improve an existing news recommendation system deployed on the mobile news site of one of Zemanta’s partners. With this pub-lisher the users are browsing news sequentially by scrolling through a continuous stream of items on their mobile devices. Our goal is to order the recommended items in a way to increase probability that the users will click one them and read them. The existing system orders the recommendations by similarity to the article that the user is currently reading. We build on top of the content-similarity approach by also estimating the probability that the recommended item will be clicked. We use click-through data, and take a Bayesian approach to estimate the probability of a click; moreover, we propose the use of hierarchical models as a way of including data from contextually similar sessions into the estimation. The recommendations are performed using a multi-armed bandit approach. We try to ﬁnd a trade-oﬀ between ”ex-ploitation” of items likely to perform well and ”exploration” of items where not much data is available. When evaluated on data collected from a mobile news publisher, the proposed methods showed signiﬁcant improvements compared to the existing system.
Gadour An Investigation into Libyan Teachers’ Views of Children’s Emotional and Behavioural

 Abstract — A great number of children in mainstream schools across Libya is currently living with emotional, behavioural difficulties. This study aims to explore teachers’ perceptions of children’s emotional and behavioural difficulties (EBD) and their attributions of the causes of EBD. The relevance of this area of study to current educational practice is illustrated in the fact that primary school teachers in Libya find classroom behaviour problems one of the major difficulties they face. The information presented in this study was gathered from 182 teachers that responded back to the survey, of whom, 27 teachers were later interviewed. In general, teachers’ perceptions of EBD reflect personal experience, training, and attitudes. Teachers appear from this study to use words such as indifferent, frightened, withdrawn, aggressive, disobedient, hyperactive, less ambitious, lacking concentration, and academically weak to describe pupils with EBD. The implications of this study are envisaged as being extremely important to support teachers addressing children’s EBD and shed light on the contributing factors to EBD for a successful teaching-learning process in Libyan primary