Experimental dataset on water levels, sediment depths and wave front celerity values in the study of multiphase shock wave for different initial up- and down-stream conditions

This data article presents a rich original experimental video sources and wide collections of laboratory data on water levels, sediment depths and wave front celerity values arose from different multiphase dam-break scenarios. The required data of dam-break shock waves in highly silted-up reservoirs with various initial up- and down-stream hydraulic conditions is obtained directly from high-quality videos. The multi-layer shock waves were recorded by three professional cameras mounted along the laboratory channel. The extracted video images were rigorously scrutinized, and the datasets were obtained through the images via image processing method. Different sediment depths in the upstream reservoir and dry- or wet-bed downstream conditions were considered as initial conditions, compromising a total of 32 different scenarios. A total of 198 original experimental videos are made available online in the public repository "Mendeley Data" in 8 groups based on 8 different initial upstream sediment depths [1], [2], [3], [4], [5], [6], [7], [8]. 20 locations along the flume and 15 time snaps after the dam breaks were considered for data collecting. Consequently, a total of 18,000 water level and sediment depth data points were collected to prepare four datasets, which are uploaded in the public repository "Mendeley Data". A total of 9600 water level data points could be accessed in [9], [10], while 8400 sediment depth data points are available online in [11], [12] and could be utilized for validation and practical purposes by other researchers. This data article is related to another research article entitled "Experimental study and numerical verification of silted-up dam-break" [13]

Digital turn and its implications on teacher’s professional achievement: learning communities formation among teachers

Nowadays, digital technology has reduced teachers’ professional isolation by facilitating communications and interactions. In the modern knowledge-based world, teachers as learners participate in professional learning communities to seek knowledge resources for more effective teaching and consequently the improvement of their students’ learning. In fact, professional learning communities are as context for teachers’ professional development. The goal of this research is discovering achievements that teachers achieve by participating in learning communities. Method of Research is the literature review.  Results show that teachers’ participation in learning communities leads to valuable achievements for them

Effect of lumbopelvic control on landing mechanics and lower extremity muscles' activities in female professional athletes: implications for injury prevention.

BACKGROUND: Lumbopelvic control (LPC) has recently been associated with function, kinesiology, and load distribution on the limb. However, poor LPC has not been studied as a risk factor for lower limb injury in sports requiring frequent jump landings. The present study investigated the effects of LPC on landing mechanics and lower limb muscle activity in professional athletes engaged in sport requiring frequent landing. METHODS: This study was conducted on 34 professional female athletes aged 18.29 ± 3.29 years with the height and body mass of 173.5 ± 7.23 cm and 66.79 ± 13.37 kg, respectively. The landing error scoring system (LESS) and ImageJ software were used to assess landing mechanics. Wireless electromyography was also used to record the activity of the gluteus medius (GMed), rectus femoris, and semitendinosus. Lumbopelvic control was evaluated using the knee lift abdominal test, bent knee fall-out, active straight leg raising, and the PRONE test using a pressure biofeedback unit. Based on the LPC tests results, the participants were divided into two groups of proper LPC (n = 17) and poor LPC (n = 17). RESULTS: There were significant differences between the groups with proper and poor LPC in terms of the LESS test scores (P = 0.0001), lateral trunk flexion (P = 0.0001), knee abduction (P = 0.0001), knee flexion (P = 0.001), trunk flexion (P = 0.01), and GMed muscle activity (P = 0.03). There were no significant differences in the activity of the rectus femoris and semitendinosus muscles, and ankle dorsiflexion (P > 0.05). CONCLUSIONS: Poor lumbopelvic control affects the kinematics and activity of the lower limb muscles, and may be a risk factor for lower limb injuries, especially of the knee

Analysis and Prediction of COVID-19 Using SIR, SEIQR, and Machine Learning Models: Australia, Italy, and UK Cases

The novel coronavirus disease, also known as COVID-19, is a disease outbreak that was first identified in Wuhan, a Central Chinese city. In this report, a short analysis focusing on Australia, Italy, and UK is conducted. The analysis includes confirmed and recovered cases and deaths, the growth rate in Australia compared with that in Italy and UK, and the trend of the disease in different Australian regions. Mathematical approaches based on susceptible, infected, and recovered (SIR) cases and susceptible, exposed, infected, quarantined, and recovered (SEIQR) cases models are proposed to predict epidemiology in the above-mentioned countries. Since the performance of the classic forms of SIR and SEIQR depends on parameter settings, some optimization algorithms, namely Broyden–Fletcher–Goldfarb–Shanno (BFGS), conjugate gradients (CG), limited memory bound constrained BFGS (L-BFGS-B), and Nelder–Mead, are proposed to optimize the parameters and the predictive capabilities of the SIR and SEIQR models. The results of the optimized SIR and SEIQR models were compared with those of two well-known machine learning algorithms, i.e., the Prophet algorithm and logistic function. The results demonstrate the different behaviors of these algorithms in different countries as well as the better performance of the improved SIR and SEIQR models. Moreover, the Prophet algorithm was found to provide better prediction performance than the logistic function, as well as better prediction performance for Italy and UK cases than for Australian cases. Therefore, it seems that the Prophet algorithm is suitable for data with an increasing trend in the context of a pandemic. Optimization of SIR and SEIQR model parameters yielded a significant improvement in the prediction accuracy of the models. Despite the availability of several algorithms for trend predictions in this pandemic, there is no single algorithm that would be optimal for all cases

Evaluating the Quality of Machine Learning Explanations: A Survey on Methods and Metrics

The most successful Machine Learning (ML) systems remain complex black boxes to end-users, and even experts are often unable to understand the rationale behind their decisions. The lack of transparency of such systems can have severe consequences or poor uses of limited valuable resources in medical diagnosis, financial decision-making, and in other high-stake domains. Therefore, the issue of ML explanation has experienced a surge in interest from the research community to application domains. While numerous explanation methods have been explored, there is a need for evaluations to quantify the quality of explanation methods to determine whether and to what extent the offered explainability achieves the defined objective, and compare available explanation methods and suggest the best explanation from the comparison for a specific task. This survey paper presents a comprehensive overview of methods proposed in the current literature for the evaluation of ML explanations. We identify properties of explainability from the review of definitions of explainability. The identified properties of explainability are used as objectives that evaluation metrics should achieve. The survey found that the quantitative metrics for both model-based and example-based explanations are primarily used to evaluate the parsimony/simplicity of interpretability, while the quantitative metrics for attribution-based explanations are primarily used to evaluate the soundness of fidelity of explainability. The survey also demonstrated that subjective measures, such as trust and confidence, have been embraced as the focal point for the human-centered evaluation of explainable systems. The paper concludes that the evaluation of ML explanations is a multidisciplinary research topic. It is also not possible to define an implementation of evaluation metrics, which can be applied to all explanation methods.</jats:p

COVID-19 and cancer: A comparative case series

Background: Cancer patients, with an incidence of more than 18 million new cases per year, may constitute a significant portion of the COVID-19 infected population. In the pandemic situation, these patients are considered highly vulnerable to infectious complications due to their immunocompromised state. Material & Methods: In this retrospective case series, the documents of solid cancer patients infected by SARS-CoV-2, hospitalized in Shariati hospital between 20 February and 20 April 2020, were evaluated. The diagnosis of COVID-19 was based on laboratory-confirmed COVID-19 and/or features of chest CT scan highly suggestive for SARS-CoV-2. Results: A total of 33 COVID-19-infected cancer patients were included. Mean age was 63.9 years, and 54.5 of the patients were male. LDH level was significantly higher (1487.5 ± 1392.8 vs. 932.3 ± 324.7 U/L, P-value=0.016) and also serum albumin was significantly lower in non-survivors (3.6 ± 0.5 vs. 2.9 ± 0.6 g/dL, p-value=0.03). Among 16 patients with stage IV cancer, thirteen patients died, which was significantly higher compared to stage I-III cancer patients (81.3 vs. 18.8 P-value= <0.001). In terms of developing complications, sepsis, invasive ventilation and mortality was significantly higher in patients who received cytotoxic chemotherapy within the last 14 days. Conclusion: In this study, we showed that the mortality rate among cancer patients affected by COVID-19 was higher than general population and this rate has a significant correlation with factors including the stage of the disease, the type of cancer, the activity of cancer and finally receiving cytotoxic chemotherapy within 14 days before diagnosis of COVID-19. © 202

Data science in economics: Comprehensive review of advanced machine learning and deep learning methods

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This paper provides a comprehensive state-of-the-art investigation of the recent advances in data science in emerging economic applications. The analysis is performed on the novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a broad and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, is used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which outperform other learning algorithms. It is further expected that the trends will converge toward the evolution of sophisticated hybrid deep learning models

Byzantine Fireflies

This paper addresses the problem of synchronous beeping, as addressed by swarms of fireflies. We present Byzantine-resilient algorithms ensuring that the correct processes eventually beep synchronously despite a subset of nodes beeping asynchronously. We assume that n > 2f (n is the number of processes and f is the number of Byzantine processes) and that the initial state of the processes can be arbitrary (self-stabilization). We distinguish the cases where the beeping period is known, unknown or approximately known. We also consider the situation where the processes can produce light continuously. © Springer-Verlag Berlin Heidelberg 2015

Driving Innovation through Big Open Linked Data (BOLD): Exploring Antecedents using Interpretive Structural Modelling

YesInnovation is vital to find new solutions to problems, increase quality, and improve profitability. Big open linked data (BOLD) is a fledgling and rapidly evolving field that creates new opportunities for innovation. However, none of the existing literature has yet considered the interrelationships between antecedents of innovation through BOLD. This research contributes to knowledge building through utilising interpretive structural modelling to organise nineteen factors linked to innovation using BOLD identified by experts in the field. The findings show that almost all the variables fall within the linkage cluster, thus having high driving and dependence powers, demonstrating the volatility of the process. It was also found that technical infrastructure, data quality, and external pressure form the fundamental foundations for innovation through BOLD. Deriving a framework to encourage and manage innovation through BOLD offers important theoretical and practical contributions