Miracle-dz/UniFAR-FTUs huggingface dataset:
Design, Modeling, Control, and Experiments for a Fish-Robot-Based IoT Platform to Enable Smart Ocean

With the development of robotics, the underwater robot platform has been widely used in the Internet of Underwater Things (IoUT). An underwater robot platform equipped with multiple sensors is used as a mobile collector to build a reliable information collection system for IoUT. This article presents the mechatronic design, fabrication, modeling, control, simulation, and experiments of a robot IoUT platform to enable the smart ocean. Inspired by the design of both fin-actuated swimming of fish and buoyancy-driven gliding of underwater glider, a novel multilink gliding fish robot is proposed. The multilink gliding fish robot, which is called FishBot in this article, can swim flexibly and glide energy efficiently in three dimensions. In the FishBot, the body and/or caudal fin (BCF) with three degrees of freedom and buoyancy-driven system was equipped as the main propulsion device. Besides, a pair of pectoral fins was equipped to assist in regulating the gliding attitude and enhance the FishBot maneuverability in the vertical plane. The dynamic model that consists of cruise swimming motion, pure-pitching swimming motion, and 3-D swimming motion for control is established. Moreover, a behavioral control framework is developed to achieve a variety of fish-like swimming behaviors and gliding motion. Meanwhile, we proved the stability of the linear quadratic regulation controller and the locomotion controller is provided with exponential stability. The validity of the proposed model and the designed controller is demonstrated by numerical simulations. Finally, a series of experiments involving different fish-like behaviors and gliding motion elucidates the powerful locomotion ability of the FishBot.
Effects of Dynamic Topology Reconfiguration for Optimal Operation in Multi-Microgrid System

Multiple microgrids (MMG) system can reconfigure its topology by adjusting statuses of static switches and tie switches to improve reliability and economics of individual MGs. In this paper, a day-ahead optimal operation approach of MMG system in parallel model with topology reconfiguration is proposed, and a co-evolution algorithm (CEA) based approach is studied to manage multiple MGs more economically. In addition, proper feasible regions are set to ensure feasibility of the solution in CEA. Effectiveness of MMG system reconfiguration as well as cooperative operations of reconfiguration, energy storage systems (ESSs), and dispatchable distributed generations (DGs) are studied via numerical simulations. Specifically, simulation results indicate that MMG system reconfiguration in parallel mode can improve economic operation of MMGs and enhance efficient utilization of dispatchable DGs and ESSs.
Descriptive Answer Script Grading System using CNN-BiLSTM Network

Descriptive answer script assessment and rating program is an automated framework to evaluate the answer scripts correctly. There are several classification schemes in which a piece of text is evaluated on the basis of spelling, semantics and meaning. But, lots of these aren’t successful. Some of the models available to rate the response scripts include Simple Long Short Term Memory (LSTM), Deep LSTM. In addition to that Convolution Neural Network and Bi-directional LSTM is considered here to refine the result. The model uses convolutional neural networks and bidirectional LSTM networks to learn local information of words and capture long-term dependency information of contexts on the Tensorflow and Keras deep learning framework. The embedding semantic representation of texts can be used for computing semantic similarities between pieces of texts and to grade them based on the similarity score. The experiment used methods for data optimization, such as data normalization and dropout, and tested the model on an Automated Student Evaluation Short Response Scoring, a commonly used public dataset. By comparing with the existing systems, the proposed model has achieved the state-of-the-art performance and achieves better results in the accuracy of the test dataset.
Algodoo for online education: impulse and momentum activities

During the periods of sudden transition to online education, the opportunity to make applications that might attract students’ attention to the course has decreased even more. Although this deficiency tried to be eliminated with videos and simulations, it was not possible to ensure the active participation of students in some cases. In this study, the Algodoo program, which can increase the efficiency of the teaching environment by ensuring active participation of students in online lessons and the applications that can be done about impulse and momentum, are explained in detail. A total of six different applications were carried out, one related to the subject of impulse, one related to the momentum, two related to the relationship between impulse and momentum change, and two related to momentum conservation. At the same time, while developing these applications, the adjustments made on the simulation and the reasons are explained in detail. In this way, both the introduction of the program and the sample application suggestion were presented. The values obtained as a result of the applications were calculated and compared both theoretically and on simulation in different ways. As a result, it has been observed that the values have internal consistency with each other and are also compatible with theoretical calculations. The Algodoo program, which allows many interactive applications and can be downloaded for free, is a program that can be used both in lecturing and evaluation processes in physics lessons during the online education process.
Waste Management. The Disconnection between Normative and SMEs Reality

Waste generation is a critical factor in global environmental degradation, where SMEs have been historically relevant yet underestimated. This study covers the issue of the disconnection between the normative and SMEs reality. Therefore, this research aims to investigate the relationship between waste management norms and their application in Chilean SMEs. For context, Chile is the nation that generates the most amount of waste per capita in the region. Nevertheless, the country also has one of the most rigorous waste frameworks in the region, by means of the Extended Responsibility Producer (ERP) law, which has been gradually implemented since 2016. Data for the study were collected through in-depth interviews with 25 SMEs. The results show the practical limitations of SMEs in complying with the waste law, the lack of traceability in the waste management system, and the need for economic support and technical assistance to improve the use and management of sustainable raw materials. Therefore, this study contributes to the limited knowledge of how SMEs implement waste management norms and their importance in diminishing waste generation and promoting waste hierarchy.
The Role of Social Networks in the Formation of Social Lifestyle Changes Caused by the
Covid-19

Today, the outbreak of the coronavirus has become a major global crisis and has affected many countries. One of the consequences of the spread of this virus is the creation of social panic and rapid changes in people's lifestyles, which the social networks are noticing. The impact of social media, which plays an effective role even in people's lifestyles, is being examined in the Covid-19 Pandemic. The purpose of this paper is to investigate the role of social networks in lifestyle changes in the coronavirus pandemic period. The present research is quantitative in terms of approach and in terms of type and nature, it is a descriptive survey. The data collection tool was assumed to be the Twitter social network. A total of 100.000 cases have been investigated based on the support vector machine (SVM) method and its results have been compared with decision tree and naive Bayes methods. Data processing is done using Python software. The trained model of SVM has a success rate on accuracy as near to 97% and also has 92% in the F1 score. The results show that social networks have about a 30% effect on lifestyle changes and stress during the pandemic periods. In order to form logical and desirable behaviors instead of dramatic behaviors such as fear and social stress in the use of social networks, social agents have their priority in organizing information and knowledge and informing the target community about the constructive and harmful cases of these networks and place different social roles and activities in society. Accordingly, providing the right news and information through trusted and responsible channels and platforms can play an important role in the proper management of society.
Dynamics of inflation and its impact on economic growth in selected East African Countries: Ethiopia, Sudan and Kenya

Despite Ethiopia's, Sudan's, and Kenya's fastest-growing economies' performance over the last two decades, the overall average inflation rate remains double digits. It subsequently incorporates a detrimental impact to support economic growth in an extended period. This study aims to assess the dynamics of inflation and its impacts on economic growth Ethiopia, Kenya, and Sudan using time-series macroeconomic data collected from the African Development Bank. The research used the Autoregressive Distributed Lag (ARDL) econometrics model and investigated the presence of cointegration and long-term relationships between macroeconomic factors. The result indicates that the exchange rate and the supply of the long-run economic growth rate influence Ethiopia's money supply. Inflation rates and foreign direct investments have impacted economic growth rates in Kenya and Sudan. The economic growth rate in all counties is generally influenced by both the availability of money and the exchange rate—also, real per capita GDP affecting the economic growth rate of Sudan and Ethiopia. Policies on introducing new technologies, building capacities in public and private sectors, youth and gender parity, mobilizing domestic resources, and public participation are recommended for Ethiopia, Kenya, and Sudan. Key words: Inflation dynamics, real GDP growth rate, ARDL model, Ethiopia, Kenya and Sudan.
What Influences Students' Understanding of Scalability Issues in Parallel Computing?

Graduates with high performance computing (HPC) skills are more in demand than ever before, most recently fueled by the rise of artificial intelligence and big data technologies. However, students often find it challenging to grasp key HPC issues such as parallel scalability. The increased demand for processing large-scale scientific computing data makes more essential the importance of mastering parallelism, with scalability often being a crucial factor. This is even more challenging when non-computing majors require HPC skills. This paper presents the design of a parallel computing course offered to atmospheric science majors. It discusses how the design addressed challenges presented by non-computer science majors who lack a background in fundamental computer architecture, systems, and algorithms. The content of the course focuses on the concepts and methods of parallelization, testing, and the analysis of scalability. Considering all students have to confront many (non-HPC) scalability issues in the real world, and there may be similarities between real-world scalability and parallel computing scalability, the course design explores this similarity in an effort to improve students’ understanding of scalability issues in parallel computing. The authors present a set of assignments and projects that leverage the Tianhe-2A supercomputer, ranked #6 in the TOP500 list of supercomputers, for testing. We present pre-and post-questionnaires to explore the effectiveness of the class design and find an 11.7% improvement in correct answers and a decrease of 36.8% in obvious, but wrong, answers. The authors also find that students are in favor of this approach.
Sputter Deposited Metal Layers Embedded in Composites—From Fundamentals to Applications

Due to the low heat flux towards the substrate, magnetron sputter deposition offers the possibility to deposit thin films on heat sensitive materials such as fiber-reinforced polymers, also known as composite materials. Passive thermal probe measurements during the sputter deposition of metal layers show indeed that the temperature increase remains well below 25 °C for film thicknesses up to 600 nm. The latter thickness threshold is based on the influence of embedded metal films on the adhesion of the composite plies. Films thicker than this threshold deteriorate the mechanical integrity of the composite. The introduction of the uncured composite in the vacuum chamber strongly affects the base pressure by outgassing of impurities from the composite. The impurities affect the film properties as illustrated by their impact on the Seebeck coefficient of sputter deposited thermocouples. The restrictions to embed thin films in composites, as illustrated by both the heat flux measurements, and the study on the influence of impurities, are however not insurmountable. The possibility to use embedded thin films will be briefly demonstrated in different applications such as digital volume image correlation, thermocouples, and de-icing.
Amplification of pixels in medical image data for segmentation via deep learning object-oriented approach

Medical images serve as a very important tool for medical diagnosis. Medical image segmentation is an area of image processing that segments critical parts of a medical image for diagnosis purposes. The emergence of machine learning approach for medical image segmentation specifically by employing Convolutional Neural Network (CNN) has become a ubiquity as other approaches does not able to compete with its robustness and accuracy. However, this approach is very exhaustive in terms of time and computing resources. The CNN approach mostly emphasizes on the spatial information regarding the image without using much of the individual data contained withing the image. Therefore, this paper proposed a method to amplify the pixel data of medical images via Object-oriented Programming (OOP) approach for segmentation using a straightforward sequential deep learning approach. The results indicated that the proposed method allows more than 90 % faster training time with 33.8 seconds average and overall better segmentation performance of 0.744 for balanced-accuracy metric compared to recent state-of-the-art CNN segmentation models such as SegNet and U-Net Models.
Atom search-Jaya-based deep recurrent neural network for liver cancer detection

Automatic detection of liver cancer is the fundamental requirement of computer-aided diagnosis in the clinical sector. The traditional methods used in the liver detection process are not effective in accurately detecting the tumour region using a large-sized dataset. Moreover, segmenting the large intensity of the tumour region is a complex issue with the existing methods. To overcome these issues, an accurate and efﬁcient liver cancer detection method named atom search-Jaya-based deep recurrent neural network is proposed in this research. The proposed method mimics the atomic motion based on the interaction forces and the constraint forces of the hybrid molecules. The optimal solution is revealed through the ﬁtness measure, which in turn accepts the minimal error value as the optimal solution. The weights of the classiﬁer are optimally updated based on the position of the atom with respect to the iterations. The proposed atom search-Jaya-based deep recurrent neural network attained signiﬁcantly better performance in accurately detecting the tumor region using the exploration ability of atoms in the search space. The results obtained by the proposed model in terms of accuracy, speciﬁcity, sensitivity, and precision are 93.64%, 96%, 95%, and 94.88%, respectively, while considering the three features using 80% of training data.
Challenges faced by blood transfusion services in a tertiary care centre for recruitment of convalescent plasma donors for COVID-19

Background: The transfusion of convalescent blood products is an age old therapy in emerging infectious disease outbreaks. Convalescent plasma works on the principle of passive immunotherapy and Viral Neutralization. In the Current pandemic of corona virus disease (COVID-19), convalescent plasma transfusions can have therapeutic role as there is no specific preventative and therapeutic options. This study was done to see the challenges faced by blood transfusion services for recruitment of plasma donors. Methods: The retrospective study included challenges faced by blood transfusion services in recruiting Donors for convalescent plasma for COVID-19 and strategies followed for improving the donation at SMS Medical College and Hospital from April 2020 to October 2020. Results: This study evaluated challenges faced to recruit the recently recovered COVID-19 patients as donors, Setting up of plasmapheresis capacity, Role of social media in recruitment of Convalescent plasma donors, Financial aspects associated with Convalescent plasma donation, Role of NGOs in recruitment of donors and Success achieved in organization of donor mobilization and plasma collection. Conclusions: Recruitment of Convalescent Plasma donor from COVID-19 recovered patients is a big challenge for blood transfusion services. Different strategies to motivate donors should be implemented such as personnel communication, advertisement via classical mass media like radio, poster, newspaper and online media.
Cogging torque reduction based on segmented skewing magnetic poles with different combinations of pole‐arc coefficients in surface‐mounted permanent magnet synchronous motors

National Natural Science Foundation of China, Grant/Award Numbers: 51737008, 51707107 Abstract The calculation and reduction of cogging torque are fundamental to the evaluation and optimisation of motor performance. Considering that the accuracy of the cogging torque calculated by analytical method (AM) is low, an accurate calculation model of the effective air‐gap length considering the actual stator slot structure and suitable for any motor is proposed, the cogging torque of 6p36s surface‐mounted permanent magnet synchronous motor (SPMSM) are calculated accurately and verified by finite element method (FEM). Furthermore, the segmented skewing magnetic poles with different combinations of pole‐arc coefficients are designed to weaken cogging torque, and the optimal combination of pole‐arc coefficients and tilting angle of the adjacent magnetic poles are summarised by AM and particle swarm optimisation (PSO) algorithm. Finally, the cogging torques of six SPMSMs with different slot‐pole combinations including 6p36s, 4p36s, 6p9s, 8p9s, 8p12s and 10p12s are analysed, and the results indicate that the cogging torque can be greatly weakened based on segmented skewing magnetic poles with different combinations of pole‐arc coefficients, and other performances of the motors are basically not affected.
Drivers of Water Resources Carrying Capacity and Changes due to Climate Change in China

With basic knowledge of global sustainable development, water resources carrying capacity (WRCC) relates closely to society, economy and environment. It has important affection on the knowledge and construction of water resources security system. Based on most updated international and national research outcomes on drivers of WRCC and future changes due to climate change, this paper collects and summarizes the theory and drivers of WRCC, as well as water resources and sustainable development, water sources-society-environment coupling system and trends of water cycle in a changing climate representing by the drivers of WRCC. With an overall understanding of water sources carrying capacity, this paper analyzes the evaluation methods of WRCC study, including the system dynamics theory, the decision model with multiple factors and so on. Finally, this paper concludes the current problems of water resources representing by WRCC.
Common‐garden experiment reveals clinal trends of bud phenology in black spruce populations from a latitudinal gradient in the boreal forest

Climate warming is modifying the movement of air masses over Northern latitudes, producing warming and cooling events across the boreal regions. These new conditions changes may mismatch plant phenology from weather conditions, and affect the growing period of trees. Understanding the processes of local adaptation in bud phenology can help to predict the response of plants to these rapid and unexpected environmental changes. Our study monitored bud burst and bud set weekly during four growing seasons in black spruce [Picea mariana (Mill.) B.S.P.] saplings planted in a common garden and originating from five provenances representing the whole latitudinal distribution of the closed boreal forest in Quebec, Canada. We compared the variance in bud phenology among populations and years, and analysed the relationships with temperatures at the origin sites. Bud burst and bud set occurred in mid‐May and mid‐July, respectively, with a large variability among provenances and between the study years. A delayed bud phenology was observed in the provenances from warmer sites, with bud burst and bud set being 1.1 and 1.4 days later for every additional degree in mean annual temperature at the origin site respectively. Populations with earlier bud bursts also showed earlier bud sets, thus the growing season was similar among provenances. The heritability of bud set was higher than that of bud burst, with estimates of 0.26 and 0.21 respectively. On average, variance in bud phenology among provenances reached 5.3%, which was higher than that within provenances (2.6%). The factor year explained 37.7%–69.7% of the variance in bud phenology. Synthesis. The findings demonstrate the evolutionary response to climate variations in the boreal biome. Endogenous and environmental components have different effects on the phases of bud burst and bud set. This complex interaction among drivers of bud phenology may prevent reliable predictions of changes in the growing season under changing climate. An earlier growth reactivation due to higher spring temperatures could result in higher frost risks to the developing buds. Despite a longer period for photosynthesis expected for the evergreen black spruce under warmer conditions, the period spent for bud development could remain unchanged.