Miracle-dz/UniFAR-FTUs huggingface dataset:
The Paradox of Conspiracy Theory: The Positive Impact of Beliefs in Conspiracy Theories on Preventive Actions and Vaccination Intentions during the COVID-19 Pandemic

This study aims to analyze the direct and indirect impact of beliefs in conspiracy theories on COVID-19-related preventive actions and vaccination intentions. The dominant theory in the literature is that beliefs in conspiracy theories have negative consequences. In particular, strong beliefs in conspiracy theories lower people’s intentions to engage in preventive actions and get vaccinated. Previous studies indicated that this dominant theory applies in Korea as well. However, we find that this dominant theory does not apply in Korea. Based on an analysis of survey data, we find that beliefs in conspiracy theories have positive impact on preventive actions and vaccination intentions. In addition, beliefs in conspiracy theories play indirect roles in these two responses to COVID-19. Specifically, when perceived benefits and trust in the government or science enhance preventive actions or increase vaccination intentions, strong beliefs in conspiracy theories promote this effect. This positive role of conspiracy theories is paradoxical because they are generally viewed as negative.
Center of Pressure Trajectory and Spatiotemporal Gait Parameters When Walking with Limited Knee Flexion

Stiff-knee, which indicates reduced range of knee flexion, may decrease gait stability. Although it is closely related to an increase in fall risk, the effect of limited knee flexion on the balance capacity during walking has not been well studied. This study aimed at examining how walking with limited knee flexion would influence the center of pressure (COP) trajectory and spatiotemporal gait parameters. Sixteen healthy young participants conducted four different walking conditions: normal walking and walking with limited knee flexion of their left knee up to 40 and 20 degrees, respectively. Results show that the participants walked significantly (p<0.05) slower with shorter stride length, wider step width, less cadence, and decreased stance phase when walking with limited knee flexion, compared to normal walking. The increase in the asymmetry and variability of the COP was also observed. It indicates that limited knee flexion during walking might affect the dynamic balance.
Iron Deficiency Anemia in Inflammatory Bowel Diseases—A Narrative Review

Inflammatory bowel disease (IBD), which includes Crohn’s disease and ulcerative colitis, is characterized by chronic inflammation of the gastrointestinal tract. IBD has been associated with numerous symptoms and complications, with the most common being iron deficiency anemia (IDA). Iron deficiency in IBD is caused by inadequate intake, malabsorption (including duodenal involvement and surgical removal), and chronic blood loss by mucosal ulcerations. Therefore, an appropriate diet should be enforced. Iron deficiency and iron supplementation have been associated with alterations to gut microbiota. IBD-associated anemia, in particular iron deficiency anemia, is associated with a significant decrease in quality of life and with clinical symptoms such as chronic fatigue, headaches and dizziness, reduced exercise tolerance, pale skin, nails, conjunctiva, and fainting. However, despite these numerous adverse symptoms, IDA remains undertreated. The European Crohn’s and Colitis Organisation (ECCO) guidelines state that patients should be monitored for anemia. Adequate treatment, whether oral or intravenous, should be implemented while taking into consideration C-reactive protein values (CRP), hemoglobin levels, and therapeutic response. It should be stressed that every case of anemia in IBD patients should be treated. Intravenous iron formulations, which are more superior compared to the oral form, should be used. There is a need to increase awareness and implementation of international guidelines on iron supplementation in patients with IBD.
Morphological Characterization and DNA Barcoding of Duckweed Species in Saudi Arabia

Duckweeds, or Lemnaceae, are widespread aquatic plants. Morphology-based identification of duckweed species is difficult because of their structural complexity. Hence, molecular tools provide significant advantages for characterizing and selecting species or clones for sustainable commercial use. In this study, we collected and characterized ten duckweed isolates from nine different regions in Saudi Arabia (SA). Based on the morphological characterization and phylogenetic analysis of intergenic spacer sequences of chloroplast DNA using six barcoding markers, the clones were classified into three genera, represented by seven species: Lemna gibba L. , Lemna minor L. , Lemna japonica Landolt, Lemna aequinoctialis Welw. , Lemna perpusilla Torr. , Spirodela polyryiza (L.) Schleid. , and Landoltia punctate G. Mey. Lemna gibba was revealed to be a distinct dominant duckweed species in many regions of SA. Five barcoding markers showed that L. gibba, L. minor, and L. punctata were the most widely distributed species in the country. However, L. punctata, L. perpusilla, and S. polyryiza were the dominant species in the Al-Qassim, Madinah-1, and Madinah-2 regions, respectively. Moreover, the morphological traits revealed variations for these clones, relative to other studied duckweed clones. According to the results obtained in this study, three out of six plastid markers (trnH-psbA, matK, and atpF-atpH) helped to identify the dominant duckweed species in Saudi Arabia. Further evaluation based on adaptability, molecular genetic studies, and functional genomics is needed for these species to be used at the commercial level in Saudi Arabia.
An Investigation of Team Inputs, Processes, And Emergent States on Performance in a Spaceship Bridge Simulation

This investigation serves to provide evidence on the effects of various input variables on intact teams through repeated team performance sessions in a team spaceship bridge simulation (i.e. Artemis). The Input Mediator/Moderator Output Input (IMOI) model provides a systems engineering an approach to understand various team and individual input variables contribution to the development of team processes and emergent states, ultimately leading to a team’s ability to perform together. While various prior research initiatives have served to contribute to the pool of evidence of which input variables are most highly predictive of a team’s overall performance, the need for further and recursive input to output investigations is needed. Our results indicate perceived team effectiveness and cohesion are significant predictors in team performance and that skill and knowledge of a simulated environment may overshadow team-specific effectiveness indicators as the team gains experience.
Methods of Modification of Mesenchymal Stem Cells and Conditions of Their Culturing for Hyaline Cartilage Tissue Engineering

The use of mesenchymal stromal cells (MSCs) for tissue engineering of hyaline cartilage is a topical area of regenerative medicine that has already entered clinical practice. The key stage of this procedure is to create conditions for chondrogenic differentiation of MSCs, increase the synthesis of hyaline cartilage extracellular matrix proteins by these cells and activate their proliferation. The first such works consisted in the indirect modification of cells, namely, in changing the conditions in which they are located, including microfracturing of the subchondral bone and the use of 3D biodegradable scaffolds. The most effective methods for modifying the cell culture of MSCs are protein and physical, which have already been partially introduced into clinical practice. Genetic methods for modifying MSCs, despite their effectiveness, have significant limitations. Techniques have not yet been developed that allow studying the effectiveness of their application even in limited groups of patients. The use of MSC modification methods allows precise regulation of cell culture proliferation, and in combination with the use of a 3D biodegradable scaffold, it allows obtaining a hyaline-like regenerate in the damaged area. This review is devoted to the consideration and comparison of various methods used to modify the cell culture of MSCs for their use in regenerative medicine of cartilage tissue.
Modeling Pilot Flight Performance in a Cognitive Architecture: Model Demonstration

Cognitive architecture models can support the simulation and prediction of human performance in complex human-machine systems. In the current work, we demonstrate a pilot model that can perform and simulate taxiing and takeoff tasks. The model was built in Queueing Network-Adaptive Control of Thought Rational (QN-ACTR) cognitive architecture and can be connected to flight simulators such as X-Plane to generate various data, including performance, mental workload, and situation awareness. The model results are determined in combination by the declarative knowledge chunks, production rules, and a set of parameters. Currently, the model can generate flight operation behavior similar to human pilots. We will collect human pilot data to examine further and validate model assumptions and parameter values. Once validated, such models can support interface evaluation and competency-based pilot training, providing a theory-based predictive approach complementary to human-in-the-loop experiments for aviation research and development.
Designing for Interaction: Creating Smart Applications and Clever Devices

Explore the new design discipline that is behind such products as the iPod and innovative Web sites like Flicer. While other books on this subject are either aimed at more seasoned practitioners or else are too focused on a particular medium like software, this guide will take a more holistic approach to the discipline, looking at interaction design for the Web, software, and devices. It isi? the only interaction design book that is coming from a designers point of view rather than that of an engineer. This much-needed guide is more than just a how-to manual. It covers interaction design fundamentals, approaches to designing, design research, and more, and spans all mediums-Internet, software, and devices. Even robots! Filled with tips, real-world projects, and interviews, you'll get a solid grounding in everything you need to successfully tackle interaction design.
Contagem de ovos do Aedes Aegypti em palhetas de ovitrampas baseada em Deep Learning

O Aedes aegypti é o vetor de um dos problemas de saúde pública mais difíceis de serem combatidos no mundo tropical: a proliferação da epidemia de dengue. Como não há vacina ou tratamento específico, e a erradicação do seu vetor se tornou praticamente impossível, a melhor maneira de se evitar a doença é realizando o controle do mosquito Aedes aegypti. Com isso, este trabalho apresenta uma metodologia computacional para a segmentação de ovos nas palhetas ovitrampas que visa auxiliar especialistas nessa contagem. Para o desenvolvimento deste trabalho realizamos testes com as redes neurais convolucional desenvolvida para segmentação: U-Net, Segnet e uma rede pré treinada. Em seguida, realizamos uma etapa de pós-processamento baseada em morfologia matemática. Os resultados alcançados foram promissores, e a rede U-Net foi a que apresentou melhor desempenho, com Acurácia de 98,65% na segmentação dos ovos e erro médio quadrado de 4,25% na contagem.
Training intensity distribution, physiological adaptation and immune function in endurance athletes

Exercise intensity and its distribution is probably the most important and most heavily debated variable of endurance training. Training induces adaptation but also induces stress responses. Controlling the training-intensity distribution may provide a mechanism for balancing these two effects. It has been reported that elite endurance athletes train with a high volume and load, relative to the sport. These athletes spend the vast majority (>80%) of training time at relatively low intensities (lower than the lactate threshold, zone one), and therefore <20% of training time above the lactate threshold (zones two and three). Experimental studies support the beneficial effects of a high training volume in zone one, and show detrimental effects of replacing zone one training with training in zone two. This is likely due to enhanced recovery from training in zone one compared with training in zone two. The acute recovery following training sessions in zones two and three has been reported to not be different, but the recovery following training in zone one has been reported to be faster. Improvements in physiological adaptation and endurance performance have been reported to be greater following training programmes with higher exercise intensities. Therefore, it has been suggested that a polarised training model, which includes ~80% of training in zone one with ~20% of training in zone three is more beneficial than a threshold training model, with the majority of training in zone two. However, research into an optimal training-intensity distribution is limited. Therefore, the aims of this thesis were to assess the effectiveness of training-intensity distribution on the improvements in physiological adaptation, endurance performance and assess if manipulating training-intensity distribution had an effect on immune function. Study one revealed that the lactate threshold, the lactate turnpoint and maximal performance measures in swimming, cycling and running, assessed using the methods outlined in the study, are reproducible in trained endurance athletes. These tests can therefore be used by trained endurance athletes as part of a physiological testing programme to assess not only endurance performance, but also to demarcate training intensity zones for exercise intensity prescription and monitor moderate to large adaptations to training. Practitioners should take care when deciding on the duration between tests to test for adaptations from training, as adaptations need to be greater than these detected test-retest variations to be considered physiologically meaningful. To the best of the author’s knowledge, study two was the first study to have assessed training-intensity distribution in a group of multisport athletes. Training was monitored over a 6-month period, and testing took place every two months to assess the effect of the training on physiological adaptation. Although speculative due to the number of variables involved, the results suggest that a greater proportion of training time spent in…
Basic Camp Management: An Introduction to Camp Administration

This practical, instructional guide serves both as a learning resource for the novice camp director and as a quick reference for the more experienced director. Covering 14 topics of study identified by the American Camping Association as core areas of knowledge for camping professionals, this book includes discussions of leadership, participation, program design, risk management, finances, marketing, site and facilities, and strategic planning. Interpretive charts, tables, and graphics illustrate both the basics and the nuances of camp administration. Each topic is followed by a series of checkpoints and questions pertinent to the previous discussion to be used as a tool for further review.
Does the monetary policy have any short-run and long-run effect on economic growth? A developing and a developed country perspective

PurposeThe study aims to empirically examine the relationship between monetary policy and economic growth, as well as to explore the long-run and the short-run effect of monetary policy on the economic growth of a developing country (Bangladesh) and a developed country (the United Kingdom). Design/methodology/approachDepending on data availability, the study employed secondary data covering the period of 1980–2019. The augmented Dickey–Fuller test and the Phillips–Perron test were used for the stationarity test. Further, the F-bounds test was run to justify the long-run relationship between monetary policy and economic growth. Thereafter, long-run coefficients were revealed from the auto-regressive distributed lag (ARDL) model and short-run coefficients from the error correction model. Furthermore, the vector error correction model (VECM) Granger causality approach was employed to demonstrate the causality of studied variables. Lastly, different diagnostics tests ensured the robustness of the models.FindingsF-bounds test outcomes suggest that monetary policy has a long-run relationship with economic growth in both countries. Long-run coefficients revealed that money supply has a positive long-run impact on economic growth in both countries. Unlike the UK, the exchange rate exhibits an adverse effect on the economic growth of Bangladesh. The bank rate seems to promote economic growth for the UK. Findings also depict that increase in lending interest rates hurts the economic growth for both countries. Besides, the short-run coefficients portray random effects at different lags in both cases. Lastly, causality among studied variables is revealed using the VECM Granger causality approach.Originality/valueThe novelty of this study lies in consideration of both developing and developed countries in the same study.
A comparative analysis of consensus protocols for dealing power theft issue in Pakistan

Abstract Power is considered as a boon for life, lifeline of a country's economy; a key instrument for the socio-economic evolution of any country. Pakistan faces the issue of power theft that worsens the economy of utilities. The purpose of research is to modernize Pakistan's power sector with the usage of Blockchain. Research is based on a comparative analysis of proof of stake (POS) and proof of authority (POA) consensus protocols for the power theft issue for small, medium, or large scales of consumers; results showing automated and pre-selected processing time of transactions running on different nodes of POS and POA along. By comparative analysis of POS and POA; it is shown that the performance of Blockchain remains unaffected by the consensus protocol. To further strengthen the findings the statistical analysis data is carried out to establish the uniqueness of sampled data. The result establishes that Blockchain performs equally well for different architectures of the proposed architecture. The consensus protocol helps in achieving a suitable governing style with no effect on Blockchain performance.
Especificações Alloy de Elementos da Lógica Proposicional

A elaboração de problemas únicos e com características específicas em disciplinas formais de graduação é uma tarefa tediosa, principalmente quando a disciplina é ofertada em Massive Open Online Courses (MOOCs). Diante deste cenário, foi desenvolvida uma técnica à geração automática de fórmulas e argumentos válidos da Lógica Proposicional. O maior desafio da técnica foi gerar argumentos válidos. Pois, estes argumentos são improváveis de surgir a partir da geração totalmente aleatória. A técnica faz uso de conceitos da área de Síntese de Programas. Para isto, os conceitos de síntese são utilizados em conjunto com especificações formais descritas em Alloy. Neste artigo, são apresentadas as especificações Alloy necessárias à geração dos elementos da Lógica.
Applying State-of-the-Art Deep-Learning Methods to Classify Urban Cities of the Developing World

This paper shows the efficacy of a novel urban categorization framework based on deep learning, and a novel categorization method customized for cities in the global south. The proposed categorization method assesses urban space broadly on two dimensions—the states of urbanization and the architectural form of the units observed. This paper shows how the sixteen sub-categories can be used by state-of-the-art deep learning modules (fully convolutional network FCN-8, U-Net, and DeepLabv3+) to categorize formal and informal urban areas in seven urban cities in the developing world—Dhaka, Nairobi, Jakarta, Guangzhou, Mumbai, Cairo, and Lima. Firstly, an expert visually annotated and categorized 50 × 50 km Google Earth images of the cities. Each urban space was divided into four socioeconomic categories: (1) highly informal area; (2) moderately informal area; (3) moderately formal area, and (4) highly formal area. Then, three models mentioned above were used to categorize urban spaces. Image encompassing 70% of the urban space was used to train the models, and the remaining 30% was used for testing and validation of each city. The DeepLabv3+ model can segment the test part with an average accuracy of 90.0% for Dhaka, 91.5% for Nairobi, 94.75% for Jakarta, 82.0% for Guangzhou city, 94.25% for Mumbai, 91.75% for Cairo, and 96.75% for Lima. These results are the best for the DeepLabv3+ model among all. Thus, DeepLabv3+ shows an overall high accuracy level for most of the measuring parameters for all cities, making it highly scalable, readily usable to understand the cities’ current conditions, forecast land use growth, and other computational modeling tasks. Therefore, the proposed categorization method is also suited for real-time socioeconomic comparative analysis among cities, making it an essential tool for the policymakers to plan future sustainable urban spaces.
A feature importance ranking based fault diagnosis method for variable-speed screw chiller

Fault detection and diagnosis (FDD) technology plays an important role in maintaining the stable and efficient operation of the chiller system, and screening the most important parameters for FDD is the key step that is far more critical than the selection of diagnosis methods. This study proposes a feature importance ranking method based on random forest (RF) to improve the FDD performance of a chiller while using fewer sensors, which is proved to be effective and efficient. When the product type or sensor configuration changes, the proposed method can be used to rank the feature importance efficiently based on the new fault simulation data, the FDD model using the features that are highly significant to fault indication and diagnosis can be established and the diagnosis performance can be promoted, accordingly. For verification and validation, fault simulation experiments of normal operation, refrigerant leakage and refrigerant overcharge have been carried out on a 200-ton variable-speed screw chiller. The importance of 15 parameters obtained from the experiments is ranked according to their contributions to the diagnosis of the corresponding faults using the proposed RF importance ranking method. Different critical features have also been discussed in terms of FDD performance and expertise knowledge for screw chiller. The optimal feature set for the investigated faults of the chiller is found to have just four parameters: Refrigerant Discharge Temperature (TR_dis), Condenser Water Temperature Difference (TWCD), Evaporator Water Temperature Difference (TWED) and Compressor Power (kW). The overall diagnostic accuracy reaches 99.90%, higher than using all the parameters. Fewer sensors achieve better performance.