Miracle-dz/UniFAR-FTUs huggingface dataset:
Improvement of the performance of parabolic trough solar concentrator using freeform optics and CPV/T design

The developmental tendency of parabolic trough collector (PTC) is larger aperture area for energy harvest and novel optical design for higher solar concentration. Larger aperture faces a higher demand in tracking accuracy and lower tolerances with respect to wind loads, quality of mirrors, control, and mounting imprecisions. With the increase of reflection angle, the divergence size of concentrated focal spot gets enlarged on receiving surface, forming a Gaussian distribution. Receiving more energy and making best use of the concentrated solar power have become a key problem. In current study, a novel trough free-form solar concentrator (TFFC) has been developed by extending the aperture size with the aid of freeform optics and combined PV/thermal utilization. The structure model is composed by traditional parabolic trough with thermal tube, and extended freeform reflector with solar panel in slope configuration. The free-form surface is generated by geometric construction method for the sake of uniform heat flux distribution. The optical characteristics are validated by ray tracing method. The advantages will be revealed by compared with traditional system. The sensitivity analysis and error factors would be discussed as well. The initial results are promising and significant for the enhancement of trough type solar concentrator systems.
Role of Government to Enhance Digital Transformation in Small Service Business

In the digital economy era, small service business struggle to compete in a rapidly changing and dynamic market. Therefore, through digital transformation (DT), small service business seek to increase their competitive advantage, improve business performance, and achieve business growth. Having limited resources and capabilities, small service business must deal with several barriers and challenges. This study aims to discover the barriers, and the roles of government, for digital transformation in small service business. This study applied a qualitative approach involving semi-structured in-depth interviews with top management of small service business. Then, we employed the content analysis method to examine interview data and construct a model. This research reveals four main barriers to digital transformation in small service business: lack of funding, lack of digital capability, lack of human resources, and technical barriers. We found there are four government roles for supporting digital transformation in small service business: build a digital platform for small service business, promote mobile/digital payment, provide digital training, and build a digital collaboration ecosystem. Additionally, based on this study’s findings, a model for barriers and government support of digital transformation in small service business is presented. This study contributes to the conceptual framework and management implications in the digital transformation field. The study’s findings provide insights to practitioners and suggest that the government could alter the current policies and programs to support digital transformation in small service business.
The effect of university organizational culture on organizational silence and faculty–student interaction

Article history: Received: December 12, 2020 Received in revised format: December 29 2020 Accepted: February 20, 2021 Available online: February 20, 2021 Colleges/universities outside the Seoul metropolitan area (hereafter “non-Seoul universities”) are in a crisis of existence due to a decline in the school-aged population in South Korea. Most nonSeoul universities are making efforts to implement self-rescue measures for survival. This study examines the relationships among different types of organizational culture (innovative, collective, rational, and hierarchical), organizational silence (acquiescent, prosocial, and defensive), and faculty–student interaction as perceived by non-Seoul university professors to provide useful implications for effective human resource management plans for the professors, who are responsible for entrance examinations and education at non-Seoul universities in crisis. An online survey was employed to collect data from professors at private community colleges/universities outside the Seoul metropolitan area. Responses from 421 professors were used for data analysis. Results are as follows. First, when professors perceived innovative and collective cultures, their defensive silence decreased while prosocial silence increased. Second, when rational culture was perceived, defensive silence increased while prosocial silence decreased. Third, acquiescent silence increased while prosocial silence decreased when hierarchical culture was perceived. Fourth, faculty–student interaction was enhanced when innovative and collective cultures were prevalent. Finally, prosocial silence had a positive effect on faculty–student interaction. The findings imply that if university managers change policy to encourage professors to perceive the university culture as innovative and collective, prosocial silence, which is positive from the organization’s perspective, will increase, facilitating interactions between professors and students. © 2021 by the authors; licensee Growing Science, Canada
Awareness, Perception and Behavior Regarding Coronavirus Disease 2019 Among a Group of Egyptian Dental Students: A Cross Sectional Study

Purpose: To estimate awareness, perception and behavior regarding COVID 19 disease among a group of Egyptian dental students. Subjects and Methods: The study population consisted of a group of Egyptian fifth year dental students of Future University in Egypt. A specially designated questionnaire was sent to students in May this year. This questionnaire composed of a sequence of questions regarding students’ demographic information, students’ awareness of disease incubation period, symptoms, transmission modes of COVID 19 pandemic disease, infection and prevention control guidelines and measures for preventing COVID 19 and treatment of disease, the participants’ perception towards COVID 19 and lastly their behavior towards COVID 19 disease. Results: The current cross sectional study consisted of 115 students; 31(27%) males and 84 (73%) females. A little percentage 38 (33.9%) of students correctly reported that incubation period of this virus is 1 to 14 days. Most of the students were greatly aware of COVID 19 symptoms, transmission modes, prevention and treatment of COVID 19 disease and they had good perception and behavior towards COVID 19 pandemic disease. Conclusions: Generally most participants had good awareness, positive perception and behavior towards protective measures against COVID 19. However; in some points they had limited comprehension towards COVID 19 disease. Educational programs aiming to improve COVID 19 awareness, perception and behavior which are useful to dental students to gain positive behavior and preserve proper practices are highly required.
Estimation of Correlation and Path Analysis between Seed Yield and Various Quantitative Traits in Black Sesame (Sesamum indicum L.)

Sesame (Sesamum indicum L., 2n=26), an oleaginous seed of the family Pedaliaceae diploid species with 2n = 26 is one of the oldest oil crops and is widely cultivated in Asia and Africa, primarily for its high oil content and its distinctive flavor. The largest global producers of sesame seed are India, China and Sudan contributing approximately 60% of world production, and are highly valued for their oil. Sesame seed is commonly known as the “Queen of the Oil seeds”. The seeds of sesame contain 40 to 53 percent oil, which is rich in antioxidants and has amount of oleic and linoleic acids. The seed is additionally plentiful in protein, minerals, nutrients, including niacin and lignans, for example, sesamolin and sesamin.
Optimized Chinese Pronunciation Prediction byComponent-Based Statistical Machine Translation

To eliminate ambiguities in the existing methods to simplify Chinese pronunciation learning, we propose a model that can predict the pronunciation of Chinese characters automatically. The proposed model relies on a statistical machine translation (SMT) framework. In particular, we consider the components of Chinese characters as the basic unit and consider the pronunciation prediction as a machine translation procedure (the component sequence as a source sentence, the pronunciation, pinyin, as a target sentence). In addition to traditional features such as the bidirectional word translation and the n-gram language model, we also implement a component similarity feature to overcome some typos during practical use. We incorporate these features into a log-linear model. The experimental results show that our approach significantly outperforms other baseline models.
SLIC Superpixel Segmentation for Polarimetric SAR Images

Superpixel segmentation approaches for polarimetric synthetic aperture radar (SAR) images have only been studied in recent years. Simple linear iterative clustering (SLIC) is a simple and efficient superpixel segmentation method, first proposed for optical images. It basically includes three implementation steps, i.e., initialization, local $k$ -means clustering, and postprocessing. The challenge of applying SLIC to polarimetric SAR images lies in constructing the effective spatial and feature similarity and proposing the efficient segmentation procedure. In this study, to address both issues, we modify the SLIC clustering function to adapt the characteristics of polarimetric statistical measures. A new initialization method is proposed, which exploits the image gradient information to produce robust cluster centers. Furthermore, in an effort to give a comprehensive comparison and provide a fair assessment of the feature similarities for polarimetric SAR imagery, four classic statistical distances, among which two were not studied along with the SLIC previously, are embedded in the modified clustering function. The proposed method is validated by comparing with state-of-the-art SLIC-based algorithms and also the Ncut and TurboPixel algorithms. Experiments on extensive polarimetric SAR data sets show that the proposed method can significantly improve the segmentation results, with producing better boundary adherence and compact as well as uniform superpixels. We also obtain distinct conclusions that are different from the existing studies when investigating the performances of the statistical measures.
Feature Selection Based on Distance Measurement

: Every day we receive a large amount of information through different social media and software, and this data and information can be realized with the advent of data mining methods. In the process of data mining, to solve some high-dimensional problems, feature selection is carried out in limited training samples, and effective features are selected. This paper focuses on two Relief feature selection algorithms: Relief and ReliefF algorithm. The differences between them and their respective applicable scopes are analyzed. Based on Relief algorithm, the high weight feature subset is obtained, and the correlation between features is calculated according to the mutual information distance measure, and the high redundant features are removed to obtain the feature subset with higher quality. Experimental results on six datasets show the effectiveness of our method.
Treating Triple Thrombus-Pulmonary, Cardiac, and Cerebral: A Case Management with Calculated Conscience

1. Bickerstaff ER, Cloake PCP. Mesencephalitis and rhombencephalitis. Br Med J 1951;2:77‐81. 2. Moragas M, Martínez‐Yélamos S, Majós C, Fernández‐Viladrich P, Rubio F, Arbizu T. Rhombencephalitis: A series of 97 patients. Medicine (Baltimore) 2011;90:256‐61. 3. Duong M, Piroth L, Chavanet P, Comvernoux A, Grappin M, Portier H. A case of rhombencephalitis with isolation of cytomegalovirus and Mycobacterium avium complex in a woman with AIDS. AIDS 1994;8:1356‐7. 4. Arai Y, Kohno S, Takahashi Y, Miyajima Y, Tsutusi Y. Autopsy case of Neuro‐Behcet’s disease with multifocal neutrophilic perivascular inflammation. Neuropathology 2006;26:579‐85. 5. Kuntzer T, Bobousslavsky J, Miklossy J, Steck AF, Janzer R, Regli F. Borrelia rhombencephalomyelopathy. Arch Neurol 1991;48:832‐936. 6. Fu YC, Chi CS, Chiu YT, Hsu SL, Hwang B, Jan SL, et al. Cardiac complications of enterovirus rhombencephalitis. Arch Dis Child 2004;89:368‐73. 7. Nichter CA, Pavlakis SG, Shaikh U, Cherian KA, Dobrosyzcki J, Porricolo ME, et al. Rhombencephalitis caused by West Nile fever virus. Neurology 2000;55:153. 8. Zagardo MT, Shanholtz CB, Zoarski GH, Rothman MI. Rhombencephalitis caused by adenovirus: MR imaging appearance. Am J Neuroradiol 1998;19:1901‐3. 9. Khatri GD, Krishnan V, Antil N, Saigal G. Magnetic resonance imaging spectrum of intracranial tubercular lesions: One disease, many faces. Pol J Radiol 2018;83:524‐35.
Cross-View Locality Preserved Diversity and Consensus Learning for Multi-View Unsupervised Feature Selection

Although demonstrating great success, previous multi-view unsupervised feature selection (MV-UFS) methods often construct a view-specific similarity graph and characterize the local structure of data within each single view. In such a way, the cross-view information could be ignored. In addition, they usually assume that different feature views are projected from a latent feature space while the diversity of different views cannot be fully captured. In this work, we resent a MV-UFS model via cross-view local structure preserved diversity and consensus learning, referred to as CvLP-DCL briefly. In order to exploit both the shared and distinguishing information across different views, we project each view into a label space, which consists of a consensus part and a view-specific part. Therefore, we regularize the fact that different views represent same samples. Meanwhile, a cross-view similarity graph learning term with matrix-induced regularization is embedded to preserve the local structure of data in the label space. By imposing the <inline-formula><tex-math notation="LaTeX">$l_{2,1}$</tex-math><alternatives><mml:math><mml:msub><mml:mi>l</mml:mi><mml:mrow><mml:mn>2</mml:mn><mml:mo>,</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:math><inline-graphic xlink:href="tang-ieq1-3048678.gif"/></alternatives></inline-formula>-norm on the feature projection matrices for constraining row sparsity, discriminative features can be selected from different views. An efficient algorithm is designed to solve the resultant optimization problem and extensive experiments on six publicly datasets are conducted to validate the effectiveness of the proposed CvLP-DCL.
Effective Beneficiation of a Fine Coking Coal Using a Novel Flotation Scheme

ABSTRACT Effective flotation of fine coal remains challenging due to flotation inefficiency, of coarse, intergrowth particles, and contamination due to fine particles. In this article, a novel flotation scheme, named split-conditioning and bulk-flotation (SCBF), has been proposed to improve flotation efficiency of coarse and fine particles. The conditioning time and impellor speed of the flotation machine for coarse and fine particles were optimized using the nine well-planned orthogonal experiments. The results demonstrated that, by using the SCBF approach, an increase of 12.43% recovery for +0.074 mm particles and a decrease of 2.42% ash content in the -0.074 mm fractions were obtained compared to the conventional flotation. Based on the orthogonal experiments analysis, the optimal mixing conditions for +0.074 mm particles were an impellor speed of 2100 rpm and a conditioning time of 240 s, whereas, for the -0.074 mm size particles, an impellor speed of 2100 rpm and a conditioning time of 60 s were found to be the most important factors in SCBF. It was found that, for +0.074 mm particles, a higher impellor speed and a longer conditioning time improved the recovery of coarse particles, and a shorter conditioning time for the -0.074 mm particles reduced the contamination in clean coal.
Dynamic-Hierarchical Attention Distillation With Synergetic Instance Selection for Land Cover Classification Using Missing Heterogeneity Images

Optical and SAR modalities can provide the complementary information on the land properties, which usually lead to more robust and better classification performance. However, due to the restriction of imaging condition, not all modalities included into the training data sets could be available in real testing samples. Therefore, it is important to explore how to learn discriminative representations using multimodal data during the training stage, while achieving fine land cover classification using missing modalities at test time. In this article, we propose a novel dynamic-hierarchical attention distillation network (DH-ADNet) with multimodal synergetic instance selection (MSIS) for land cover classification using missing data modalities. First, the MSIS realizes the selection of the most representative multimodal instances to enhance the DH-ADNet’s ability of discriminative feature extraction. Then, the DH-ADNet is training on the basis of the curriculum learning strategy and promotes the hallucination stream to learn the privileged information. In particular, a novel dynamic-hierarchical attention distillation module (DH-ADM) is introduced, which adaptively highlights different contributions of multilayer attention distillation by carefully exploring the classification losses of multilayer features over the training iterations. Comprehensive evaluations on two coregistered optical and SAR data sets and report state-of-the-art results in the privileged information scenario.
Thermal Performance of Heat Exchanger Using Zeolite/vapour Adsorption

About 60% of the energy input in the chemical industry is discarded from the plant. Energy saving can be achieved in the entire plant by recovering these waste heats and reusing them as power and heat sources in the power plant. An adsorption heat pump has been developed for the purpose of regeneration of such unused energy. In this study, saturated humid air was supplied to a device packed with 13X zeolite particles of 4 mm in diameter. The time variation of temperature in the apparatus was measured experimentally. Then, the maximum temperature was estimated from the relationship between heat balance and adsorption equilibrium. The trend of the maximum temperature calculated from the heat balance is consistent with experiment. Further, it was found from the result of the heat balance equation that the sensible heat of the humid air supplied and the heat of adsorption of the zeolite are mainly distributed to the sensible heat of the zeolite. In the future, it is important to make effective use of the sensible heat of this zeolite. In order to extract more thermal energy from the device, it is necessary to improve the heat transfer between the packed bed and medium. A double pipe heat exchanger having a zeolite packed bed on the annular side was proposed as an apparatus. Flow direction of the humid air supplied to device was changed in two different ways. The one of them is supplying humid air radial flowly to the device and another is supplying the air in parallel flow. The influence of flow direction on heat transfer between packed bed and medium is studied with numerical simulation.
A Fault Diagnosis Method based on Improved Synthetic Minority Oversampling Technique and SVM for Unbalanced Data

Equipment usually breaks down suddenly and irregularly, so most of the data sets obtained for fault diagnosis have unbalanced characteristics, and the amount of data varies greatly from different fault types. In this paper, three problems in the application of synthetic minority oversampling technique (SMOTE) are studied, and the improved SMOTE algorithm combined with support vector machine (SVM) is proposed. The validity of the model is verified by CWRU bearing data compared with SVM and SMOTE+SVM methods, and the result of fault diagnosis is satisfactory.
Quality assessment of essential oils of Citrus Grandis peel obtained by different extraction methods

Essential oil of Citrus Grandis peel was obtained by steam distillation,organic solvent extraction and supercritical(carbon dioxide)extraction methods,respectively.The physical and chemical properties as well as the major functional group were compared and identified.It could be concluded from the results that the quality of the essential oil obtained by steam distillation was better than that obtained by the other two extraction methods.
Internet of Things Security: A Review of Enabled Application Challenges and Solutions

The Internet of Things (IoT) has been widely used in every aspect of life. The rapid development of IoT technologies raises concerns regarding security and privacy. IoT security is a critical concern in the preservation of the privacy and reliability of users’ private information. The privacy concern becomes the biggest barrier to further usage of IoT technology. This paper presents a review of IoT application areas in smart cities, smart homes, and smart healthcare that leverage such techniques from the point of view of security and privacy and present relevant challenges. In addition, we present potential tools to ensure the security and preservation of privacy for IoT applications. Furthermore, a review of relevant research studies has been carried out and discusses the security of IoT infrastructure, the protocols, the challenges, and the solutions. Finally, we provide insight into challenges in the current research and recommendations for future works. The reviewed IoT applications have made life easier, but IoT devices that use unencrypted networks are increasingly coming under attack by malicious hackers. This leads to access to sensitive personal data. There is still time to protect devices better by pursuing security solutions with this technology. The results illustrate several technological and security challenges, such as malware, secure privacy management, and non-security infrastructure for cloud storage that still require effective solutions.
Evaluation of Gutta Percha removal from different niti rotary file system: An in vitro study

The main goal of endodontic retreatment is cleaning and shaping of the root canal with removal of old root filling material. Hand instruments and rotary instruments are mainly used for removing this filling material; as hand instruments are time consuming and tedious so we used rotary instrumentation for removal of gutta percha. The main aim of this study is to compare the relative efficacy of three different NI TI rotary instrumentation systems for removal of gutta-percha from root canal during endodontic retreatment. Materials and Methods: Sixty freshly extracted, single-rooted human anterior teeth were instrumented with K-files, and each root canal was filled with gutta-percha and AH Plus (Dentsply Detrey, Konstanz, Germany) sealer using lateral compaction. Specimens were then divided into three experimental groups with ten specimens each. The removal of gutta-percha was performed using Pro-Taper retreatment files, Mtwo retreatment files, and Edge file XR. The amount of root canal filling material remnant in the coronal, middle and apical thirds was measured using stereomicroscope. Result: The data was evaluated statistically using analysis of variance. Pro-Taper group was found to have less remnant filling material as compared to the other groups in coronal and middle thirds, but a significant difference was observed between edge file XR and Mtwo. Conclusions: It was concluded that EDGE FILE XR, rotary retreatment system proved to be the most efficient method of removing gutta-percha and sealer in comparison to the other two retreatment files and M-TWO retreatment files required less time to remove root filling material than the other instruments.