Miracle-dz/UniFAR-FTUs huggingface dataset:
Secure Health Care Data Transmission and Prevention Based on Hybrid Robin Public Key Signature Algorithm

Due to the present environmental condition, a chronicle disease was increasing frequently to take care of their own health most of the people prefer IoT. Internet of things (IoT) which plays major role in every field that makes the life easy. In medical field also IoT plays major role by frequently monitoring the health condition of patient in remote area. Even though IoT gives solution for smarter lifestyle, it also has some drawbacks while transmitting the data which is similar type of threat occurs during the data transmissions in WSN. Comparing to all other type of networks, the data transmitted in the smart health monitoring is very sensitive. There are different types of attacks which occur during data transmission such as sinkhole, worm hole and grey hole etc. To overcome those attacks, hybrid Rabin algorithm and modified routing algorithm are proposed for securing the data. These algorithms give 82% accuracy in data delivery detecting the attack and gives more efficiency.
EduVis: Visualização Interativa de Dados Educacionais

Resumo Uma análise efetiva dos processos de ensino permite proporcionar à comunidade educativa um meio para analisar os resultados dos processos de ensino, apresentando potencial para ajudar a prevenir e corrigir situações problemáticas, levando a uma educação mais eficaz e bem sucedida. A aplicação de técnicas de data mining no âmbito da educação fornece ferramentas para analisar dados de ambientes educacionais, desde o comportamento dos alunos até às estratégias de ensino e coordenação de programas de estudos. No entanto, estes resultados incluem um amplo conjunto de comportamentos que consistem em padrões simbólicos, muitas vezes representados sob a forma textual, que normalmente é difı́cil de entender. Dado o potencial da visualização para representar grandes quantidades de dados, esta pode superar essa limitação, aliviando a carga cognitiva associada à interpretação de informação. Assim, utilizando os resultados de técnicas de data mining educacional que já tinha sido aplicado para analisar a interdependência entre as disciplinas de um curso universitário, foi desenvolvida uma visualização que permite a análise desses mesmos padrões. A visualização consiste em dois mecanismos distintos, mas coordenados, de representação de informação. O primeiro corresponde a uma visualização interativa multi-nı́vel das disciplinas dos vários semestres do curso, em que as relações de dependência são representadas através de conetores visuais. A segunda ferramenta, que consiste em matrizes multicamada, permite aplicar mecanismos de filtragem e explorar mais detalhadamente as relações entre as disciplinas. A complementaridade dos dois métodos de visualização permite tornar vários padrões imediatamente percetı́veis e permite a navegação e análise de informação. A elaboração de testes com utilizadores reiterou estas caracterı́sticas, provando o potencial do sistema para possibilitar uma solução eficaz para a visualização de padrões educacionais.
A NEW APPROACH USING COMPUTER VISION FOR DRONE DETECTION

Ngày nay, một cá nhân hay tổ chức có thể dễ dàng có được một máy bay không người lái (drone) với mức ngân sách chấp nhận được. Với khả năng mang theo những vật liệu nổ, các camera và các vật phi pháp, các drone có thể trở thành các mối đe dọa về anh ninh đối với các tổ chức quân và dân sự. Phát hiện các drone xuất hiện trong các khu vực không được phép trở thành một bài toán cấp thiết. Bài báo này thực hiện các nghiên cứu thực nghiệm cho việc huấn luyện mạng nơ-ron tích chập nhiều tầng Faster R-CNN để Faster-CNN sau khi huấn luyện có thể phát hiện chính xác nhất các drone trong ảnh. Faster R-CNN sau khi huấn luyện có thể sử dụng trong các hệ thống phát hiện, cảnh báo và phòng thủ drone cho các khu vực nhạy cảm. Mạng Faster R-CNN được huấn luyện sử dụng tập dữ liệu ảnh với các hộp giới hạn gán nhãn drone và các lựa chọn huấn luyện khác nhau. Với các lựa chọn huấn luyện hợp lý được xác định thông qua các thực nghiệm, Faster R-CNN sau khi huấn luyện có thể phát hiện drone với độ chính xác trung bình lên tới 0,774, cao hơn 83% so với Fast R-CNN với độ chính xác trung bình là 0,420 trên cùng một tập dữ liệu.
Psychological Networks can Identify Potential Pathways to Specific Intervention Targets for Anxiety in Response to Coronavirus Disease 2019 (COVID-19)

Addressing mental health challenges related to the COVID-19 outbreak can be facilitated through research that characterizes the needs of subpopulations and identifies specific pathways to targeted intervention. Toward this aim, we examined the impact of the COVID-19 outbreak on anxiety symptoms among college students (N = 487) and explored the relative impact of coping strategies using a psychological network approach, which models complex interactions to identify potential pathways to symptom-level intervention. Although students showed several significant fluctuations in pre- to post-outbreak anxiety symptom levels measured with the GAD-7, anxiety network connectivity was not significantly different across timepoints. Consistent with hypotheses, the post-outbreak symptom+coping network revealed that increased use of the adaptive coping strategies of acceptance, behavioral activation, and values-based action was associated with lower levels of fear, restlessness, and trouble relaxing. The symptom+coping network also revealed that increased use of the maladaptive strategies of excessive cleaning, reassurance seeking, and excessive checking was associated with higher levels of irritability and fear. Surprisingly, the use of reappraisal and avoidance, two strategies with putatively opposing adaptive value, highly overlapped and showed positive associations with fear and irritability. These symptom+coping associations can guide the assessment and treatment of anxiety in the face of COVID-19.
3D Holographic Visualization for Pre-procedural Planning of Spatially Complex Brain Aneurysm Treatment

The purpose of this study was to develop and demonstrate a novel imaging platform that post-processes volumetric imaging data (e.g. Computed Tomography (CT) or Magnetic Resonance angiography) to provide holographic visualization for pre-procedural treatment planning of a morphologically complex brain aneurysm. Digital CT images were segmented, using the Materialise Mimics software, into three-dimensional digital models that were imported into a prototype application for the Microsoft HoloLens. Feedback from testing the prototype indicated potential for augmented reality to assist an interventionalist in spatial understanding and depth perception of spatially complex anatomy and could increase confidence in pre-procedural planning. Future studies will be conducted with additional cases to further validate the utility of the platform in surgical planning decisions and to expand the platform for patient/resident education, telemedicine, and intra-operative use.
The Effectiveness of Multimedia-Based Blended Learning Method for Analysis of the Concept Understanding and Characters Development of Students in Hydrocarbon Compound Subject Matter

The development of communication and information technology has become an opportunity in the development of learning. Conventional learning is no longer effective in improving student learning outcomes. So it is important to apply technology integration as a medium and resources in a blended learning system. This study aims to measure how effective the application of blended learning in improving the learning outcomes in hydrocarbon compound. Using quantitative methods, this research is experimental research. The research object was divided into control class and experimental class, data mining was carried out with test and questionnaire. The analysis technique uses N-Gain Score and One-sample T-Test with counting technique assisted by IBM SPSS Statistics 22. The results showed that the application of blended learning to increase students' conceptual understanding in the category of "quite effective" with an increase in the percentage of concept understanding scores in the experimental class by 57.80% was far higher than the increase in the control class by 15.51%. Increasing the character development of students through blended learning is high. The lowest value (lower) is 3.6095 while the highest value (upper) is 3.6858 with an average value of 3.64 can be categorized as high.
A Simple-To-Use Nomogram to Predict Survival After Acute Respiratory Distress Syndrome

Background: The aim of this study to construct and validate a simple-to-use nomogram to predict the survival of patients with acute respiratory distress syndrome.Methods: A total of 197 patients with acute respiratory distress syndrome were selected from the Dryad Digital Repository. All eligible individuals were randomly stratified into the training set (n=133) and the testing set (n=64) as 2: 1 ratio. LASSO regression analysis was used to select the optimal predictors, and receiver operating characteristic and calibration curves were used to evaluate accuracy and discrimination of the model. Clinical usefulness of the nomogram was also assessed using decision curve analysis and Kaplan–Meier analysis.Results: Age, albumin, platelet count, Acute Physiology and Chronic Health Evaluation II score, PaO2/FiO2, lactate dehydrogenase, high-resolution computed tomography score, and syndrome etiology were identified as independent prognostic factors on LASSO regression analysis; these factors were integrated for the construction of the nomogram. Results of calibration plots, decision curve analysis, and receiver operating characteristic analysis showed that this model has good predictive ability of patient survival in acute respiratory distress syndrome. Moreover, a significant difference in the 28-day survival was shown between the patients stratified into different risk groups (P < 0.001). Conclusions: We satisfactorily constructed a simple-to-use nomogram based on eight relevant factors to predict survival and prognosis of patients with acute respiratory distress syndrome. This model can aid personalized treatment and clinical decision-making.
Formation of stable inverse sheath in ion–ion plasma by strong negative ion emission

The effect of strong charged particle emission on plasma–wall interactions is a classical, yet unresolved question in plasma physics. Previous studies on secondary electron emission have shown that with different emission coefficients, there are classical, space-charge-limited, and inverse sheaths. In this letter, we demonstrate that a stable ion–ion inverse sheath and ion–ion plasma are formed with strong surface emission of negative ions. The continuous space-charge-limited to inverse ion–ion sheath transition is observed, and the plasma near the surface consequently transforms into pure ion–ion plasma. The results may explain the long-puzzled experimental observation that the density of negative ions depends on only charge not mass in negative ion sources.
Iron-doped ZnO as a support for Pt-based catalysts to improve activity and stability: enhancement of metal–support interaction by the doping effect

In heterogeneous catalysis, the role of the interface between a metal and a metal oxide in deciding catalytic performance has remained a long-standing question. Out of many molecular-scale factors that affect the properties of metal–oxide interfaces, doping or impurities in the oxides can result in excess charge carriers or oxygen vacancies on the oxides, which lead to a change in catalytic activity. For a model system with a tunable dopant, we employed Pt nanoparticles with Fe doping. We synthesized a series of Fe-doped ZnO with different Fe loadings (i.e., 0, 1, and 4%) using the co-precipitation method, and then deposited Pt nanoparticles onto these supports. The Pt-based catalysts were employed to investigate the effect of the dopant to promote the catalytic performance for the CO oxidation reaction. The 4% Fe loading sample showed the highest catalytic activity among the catalysts, with a turnover frequency of 5.37 s−1 at 126 °C. The dopant was found to enhance the interaction between the Pt nanoparticles and the catalyst support, including the prevention of metal sintering, which resulted in an improvement of catalytic activity.
Pure hydrophilic block copolymer vesicles with redox- and pH-cleavable crosslinks

The self-assembly of a novel double hydrophilic block copolymer consisting of biocompatible blocks, namely pullulan-b-poly(N-vinylpyrrolidone), is presented. Completely hydrophilic spherical structures with an average apparent radius of 800 nm at increased concentrations in water are observed via dynamic light scattering as well as cryo scanning electron microscopy and confocal laser scanning microscopy techniques. Moreover, the pullulan block is converted to present aldehyde groups acting as anchor point for crosslinker attachments. It is demonstrated, that the oxidized self-assembled particles could be crosslinked via the bifunctional crosslinker cystamine forming dynamic covalent imine linkages with aldehyde groups. The afforded vesicles with an average diameter of 700 nm are stable upon high dilution and could be observed via cryo scanning electron microscopy and transmission electron microscopy. Furthermore, it is possible to cleave the crosslinking bonds with the treatment of acid or the application of a reducing agent. The responsivity is a key feature taking future applications in the biomedical sector into account.
Removal of information from working memory is not related to inhibition

Working memory (WM) is a system for maintenance of and access to a limited number of goal-relevant representations in the service of higher cognition. Because of its limited capacity, WM requires interference-control processes, allowing us to avoid being distracted by irrelevant information. Recent research has proposed two interference-control processes, which are conceptually similar: (1) an active, item-wise removal process assumed to remove no-longer relevant information from WM, and (2) an inhibitory process assumed to suppress the activation of distractors against competing, goal-relevant representations. The purpose of this study was to determine the extent to which removal and inhibition are the same construct. Results showed acceptable to good reliabilities for nearly all measures. Similar to previous studies, a structural equation modeling approach identified a reliable latent variable of removal. However, also similar to some previous studies, no latent variable of inhibition could be established. This was the case even when the correlation matrix used to compute the latent variable of inhibition was disattenuated for imperfect reliability. Critically, the individual measures of inhibition were unrelated to the latent variable of removal. These results provide tentative support for the notion that removal is not an inhibitory process. This suggests that the removal process should be conceptualized as a process independent of inhibition, as proposed in computational WM models that implement removal as the “unbinding” of a WM item from the context in which it occurred.
Environment 4.0: How digitalization and machine learning can improve the environmental footprint of the steel production processes

The concepts of Circular Economy and Industrial Symbiosis are nowadays considered by policy makers a key for the sustainability of the whole European Industry. However, in the era of Industry4.0, this results into an extremely complex scenario requiring new business models and involve the whole value chain, and representing an opportunity as well. Moreover, in order to properly consider the environmental pillar of sustainability, the quality of available information represents a challenge in taking appropriate decisions, considering inhomogeneity of data sources, asynchronous nature of data sampling in terms of clock time and frequency, and different available volumes. In this sense, Big Data techniques and tools are fundamental in order to handle, analyze and process such heterogeneity, to provide a timely and meaningful data and information interpretation for making exploitation of Machine Learning and Artificial Intelligence possible. Handling and fully exploiting the complexity of the current monitoring and automation systems calls for deep exploitation of advanced modelling and simulation techniques to define and develop proper Environmental Decision Support Systems. Such systems are expected to extensively support plant managers and operators in taking better, faster and more focused decisions for improving the environmental footprint of production processes, while preserving optimal product quality and smooth process operation. The paper describes a vision from the steel industry on the way in which the above concepts can be implemented in the steel sector through some application examples aimed at improving socio-economic and environmental sustainability of production cycles.
Characteristics of Cuticular Wax and Analysis of Wax Biosynthesis Related Genes in Lanzhou Lily(Lilium Davidii Var. Unicolor) Under Drought Stress

Background: Plant wax is the general term of cuticle lipid components on the outer surface of plant tissue, which is closely related to drought resistance of plants. Lanzhou lily has the characteristics of wide adaptability, strong drought resistance, high ornamental and edible value. Plants were grown under three drought intensity treatments, namely, being watered at intervals of 5, 15, and 25 days. In this study, we analyzed the structure and component of cuticular wax of Lanzhou lily and Tresor under drought stress by scanning electron microscopy (SEM) and gas chromatography–mass spectrometry (GC-MS). We employed RNA sequencing (RNA-Seq) to investigate transcriptomic changes in the Lanzhou lily in response to drought stress.Results: In present study, the wax crystals of Lanzhou lily were mainly irregular flakes and filaments, while the wax crystals of Tresor lily were mainly granular. There were four kinds of compounds in the cuticle wax of Lanzhou lily and Tresor lily, which were acids, esters, alkanes and alcohols. Among them, the main components of Lanzhou lily were alkanes, and the content of acids in Tresor lily was more. Phylogenetic tree analysis showed that KCS homologous gene TRINITY_ DN101578_ c0_ g3 and TRINITY _ DN98845_ c1_ g2 genes were clustered into one group, TRINITY_ DN95975_ c0_ g3 and dicotyledons were clustered into the same branch. In addition, transcriptome analysis of Lanzhou lily showed that the metabolic pathway of fatty acid elongation was significant under severe drought stress. KCS1, KCS3, KCS6 and KCS11 encoding fatty acid elongation were significantly expressed under moderate drought stress. The expression of MYB96 transcription factor was only significant under severe drought stress. Conclusions: The wax content and components of Lanzhou lily were more than those of Tresor lily. Drought stress increased the wax content of Lanzhou lily and enhanced its drought resistance. Our research has revealed some important significant expression genes not only improved the drought tolerance of Lanzhou lily, but also had high application value in the development of drought tolerant varieties.
A pH-responsive Self-healing Gel with Crosslinking of Cucurbituril (CB[n]) via Hydrogen Bonding

A novel supramolecular gel has been prepared via hydrogen-bonding interaction between cucurbit[7]uril (CB[7]) and polyacrylamide (PAAm). The amino groups of PAAm are delinked to carbonyls of CB[7] by hydrogen bonding, and the hydrogen-bond interaction was the main driving force for the formation of the supramolecular gel. The sol–gel phase transition, mechanical properties, self-healing property, and the response to pH were examined. We found that CB-PAAm gel has good mechanical properties; besides, pH could control the sol–gel transition and self-assembly of the CB-PAAm gel.
Sentiment Analysis: A Tool for Mining Opinions and Emotions

Sentiment analysis is studying and analyzing the opinion, views, emotions etc. of people on different products, services or topics. Movie reviews, product review, tweets etc. can be taken as data and users emotions can be analyzed whether positive, negative or neutral. Sentiment analysis can be done using many techniques and on different levels. Depending on the need whole sentence or document can be analyzed. Organizations can use the results of sentiment analysis for improving the quality of their products or services and sentiment analysis can aid in decision making. This paper discuss few techniques which have been used for sentiment analysis.
Adversarial network embedding on heterogeneous information networks

Network embedding has been proven to be helpful for solving real-world problems. Moreover, real-world networks are often heterogeneous information networks(HINs). In this paper, we propose a new adversarial framework for heterogeneous network embedding, namely AGNE-HIN. AGNE-HIN can learn latent code distribution in the network through a generative adversarial way. What’s more, to reduce the global smoothness of the embedded vector caused by GAN, we apply perturbation to the input to form adversarial data. Experimental results verify our design and demonstrate the effectiveness of the proposed method in node clustering, link prediction and similarity ranking tasks.
Comparative Study of the Contribution of Hop (Humulus Lupulus L.) Hard Resins Extracted from Different Hop Varieties to Beer Quality Parameters

Hop (Humulus lupulus L.) hard resin is mainly composed of deterioration products that arise by oxidation of the soft resin (mainly α- and β-acids). The prenylated chalcone xanthohumol is also naturally found in the hop hard resins. To date, no clear distinctions have been made among the brewing properties of hard resins extracted from different hop varieties. Hard resins extracted from bittering and aroma hops were compared and rated based on their brewing value and their contributions to beer quality. Brewing trials were conducted in which hop pellets were replaced with resin-rich extracts and added to the wort at different time points. It was thereby possible to evaluate and establish the brewing potential of the hop hard resin as well as the varietal impact and the effect of the hopping technology. The sensory evaluations of the beers proved that hard resins have a positive impact on taste and bitterness. The data indicated that the hard-resin brews are not only influenced by the hop variety used but also by the boiling regime applied. Hard resins from bittering hops showed better brewing properties than those extracted from the aroma variety.
Gibb’s free energy of the spinning black holes

The present gives a model for the change in Gibb’s free energy of spinning black holes with corresponding change in the event horizon using the first law of black hole mechanics, mass-energy equivalence relation applied to the Gibb’s free energy of the Reissnner-Nordstrom black hole and concludes that the magnitude of change in free energy with corresponding change in the event horizon is approximately similar to the magnitude of change in temperature of spinning black holes with corresponding change in the mass in XRBs.
Autenticação e autorização de dispositivos IoT e IIoT em infraestrutura de redes de Identidade Federadas

Resumo. Realizar a liberação de acesso à Internet para dispositivos IoT nas instituições de ensino é um desafio para a cibersegurança, pois comumente é adotado o compartilhamento de chaves ou utilizadas as credenciais de uma pessoa. Usar uma infraestrutura de autenticação de Federação de Identidades, tal qual a CAFe da RNP, pode facilitar a identificação, autenticação e autorização de dispositivos Internet of Things (IoT) desassociando-o da credencial da pessoa. Este trabalho busca propor uma arquitetura usando Federações de Identidade para realizar o processo de autenticação de dispositivos IoT nas instituições que utilizam a eduroam. Para isto, uma proposta de alteração na arquitetura do ambiente eduroam para registro de dispositivos é o objeto deste estudo.