Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards a Gateway-based Context-Aware and Self-Adaptive Security Management Model for IoT-based eHealth Systems

IoT-based systems have considerable dynamic behavior and heterogeneous technology participants. The corresponding threats and security operations are also complex to handle. Traditional security solutions may not be appropriate and effective in such ecosystems as they recognize and assess a limited context, they work well only with high-end and specific computing platforms, and implement manual response mechanisms. We have identified the security objectives of a potential IoT-eHealth system and have proposed a security model that can efficiently achieve them. The proposed model is a context-aware and self-adaptive security management model for IoT, in eHealth perspective that will monitor, analyze, and respond to a multitude of security contexts autonomously. As these operations are planned at the gateway level, the model exploits the advantages of computing in the Fog Layer. Moreover, the proposed model offers flexibility and open connectivity to allow any smart device or thing to be managed irrespective of their native design. We have also explained how our model can establish and serve the essential security objectives of an IoT-based environment.
Nicotiana tabacum callus studies. X. ABA increases resistance to cold damage

The effects of cis. trans abscisic acid on response to chilling was investigated in callused Nicotiana tabacum L. pith explants. Explants pretreated with 10-4M ABA underwent approximately 50% less cellular leakage when chilled at 2°C under short-day conditions for 10 d than the comparable non-treated tissue. Growth in terms of fresh and dry weights, although poor in comparison to non-chilled (20°C, long days) treatments, was more than twice that of the non-ABA-treated material. On an absolute dry weight basis proline content increased on chilling from 0.7 to 3.4 mg g-1 in non-ABA-treated explants, but rose to nearly 17 mg g-1 in the tissue treated with ABA. Only in the case of cold-hardened. ABA-treated tissue could some cells survive subzero temperatures and regenerate callus again. It is suggested that at least part of the ameliorating effects of ABA result from an increase in the level of proline.
The 5-hydroxytryptamine 6 receptor: localisation and function

The application of molecular biological techniques has revealed the existence of gene products encoding several novel putative receptors for the neurotransmitter 5-hydroxytryptamine (5-HT). For most of these novel receptors, no prior pharmacological or functional data existed, and the present challenges to pharmacologists are to identify tools to study these gene products, to determine whether or not they function as endogenous receptors and to determine potential therapeutic uses of ligands for these receptors. Here, we review work detailing the cloning and characterisation of the 5-HT6 receptor, its distribution and evidence for functional responses mediated by naturally-occurring 5-HT6 receptors.
Virtual Experiment Platform Based on Chinese Speech Interaction

In this work, we present a module of Chinese speech interaction specifically for the 3-dimensinal (3D) virtual experimental platform. The use of speech interaction can improve the user's experience and be more efficient and natural in assembling the complex experimental devices. The assembly relationship graph and assembly sequence tree are used to constrain the assembly relationships and order of the experimental instruments. Based on the assembly tree structures and semantic information which is combined with the virtual scene, the assembling process can be guided by voice and text prompts, which clarifies the user's assembly operation and improve the interaction efficiency.
Prevalence of Toxoplasma gondii Antibodies and DNA in Iranian HIV Patients

Background & Objective: Toxoplasma gondii infection has public health importance and can lead to serious diseases in immunosuppressed patients, such as HIV cases. Appropriate control of T. gondii infection in HIV patients requires information about the prevalence of T. gondii antibodies and DNA in different population. In this study, we aimed to determine the prevalence of Toxoplasma gondii antibodies and DNA in HIV patients in Tehran, Iran. Methods: A total of 149 HIV patients from the Iranian Research Center for HIV/AIDS, Tehran, Iran were enrolled in the study. Anti-Toxoplasma IgG and IgM were detected by ELISA and T. gondii DNA was evaluated by PCR and quantita- tive real-time PCR. IgG positive samples were also assessed for their avidity. Results: Anti-Toxoplasma IgG and IgM were positive in 46.3% and 2.7% of cases respectively. 92.7% of our patients showed past infection and 4.3% revealed recently acquired toxoplasmosis based on their IgG avidity test. T. gondii DNA was not detected by PCR but real-time PCR results showed DNA in 4.7% of total patients and 13.1% of the IgG seropositive cases. Conclusion: Our findings indicated that latent toxoplasmosis was relatively prevalent in our study population, but new T. gondii infection had low prevalence. Almost half of our patients were IgG negative and at risk of acquiring toxoplasma infection. Low copy numbers of DNA were detected in 4.7% of the cases without any clinical manifestation. Therefore, detection and monitoring of anti-Toxoplasma antibodies and DNA in HIV patients is substantial to estimate the risk of reactivation and new infection.
Integrative Approach Of Big Data And Network Attacks Analysis In Cloud Environment

Lately mining of information from online life is pulling in more consideration because of the blast in the development of Big Data. In security, Big Data manages an assortment of immense advanced data for investigating, envisioning and to draw the bits of knowledge for the expectation and anticipation of digital assaults. Big Data Analytics (BDA) is the term composed by experts to portray the art of dealing with, taking care of and gathering a great deal of data for future evaluation. Data is being made at an upsetting rate. The quick improvement of the Internet, Internet of Things (IoT) and other creative advances are the rule liable gatherings behind this proceeded with advancement. The data made is an impression of the earth, it is conveyed out of, along these lines can use the data got away from structures to understand the internal exercises of that system. This has become a significant element in cyber security where the objective is to secure resources. Moreover, the developing estimation of information has made large information a high worth objective. Right now, investigate ongoing exploration works in cyber security comparable to huge information and feature how Big information is secured and how huge information can likewise be utilized as a device for cyber security. Simultaneously, a Big Data based concentrated log investigation framework is actualized to distinguish the system traffic happened with assailants through DDOS, SQL Injection and Bruce Force assault. The log record is naturally transmitted to the brought together cloud server and big information is started in the investigation process.
Practice and Application of LSTM in Temperature Prediction of HVAC System

The optimization of HVAC system is an important part of building energy conservations. As a complex nonlinear system with large inertia and large lag, the traditional HVAC system adjusts temperature based on the current indoor temperature, so that the control does not involve future changes. Therefore, accurately predicting the building indoor temperature will help more stable control and provide a reference for system tuning. Air handling unit dataset, which is established by the US Department of Energy for experiments, is used in this article, and the long short-term memory (LSTM) model is also used to predict the temperature. The MAE of predictions of the final model is about 0.077 °F, and the RMSE is about 0.107 °F. The experimental results show that the LSTM can efficiently learn the trends and features of the data, and higher prediction accuracy than the traditional back propagation neural network (BP) and multi-layer perceptron (MLP).
A Virtual Nested MIMO Array Exploiting Fourth Order Difference Coarray

In active direction of arrivals (DOA) sensing, the multiple-input multiple-output (MIMO) radar can lead to more degrees of freedom (DOFs). The DOFs can be further increased when the fourth order cumulants are used in MIMO. However, most array geometries are not designed for fourth order cumulants based MIMO and this array geometry optimization problem is still an open issue. To have a simple guideline for this optimization problem, we reformulate it into two separate problems, which are a fourth order sum coarray optimization problem and a second order difference coarray problem. The fourth order sum coarray optimization is realized by solving a postage stamp problem and a virtual nested array can be obtained. The number of elements in this virtual nested array can be significantly increased with only a few physical sensors. The corresponding inter-element spacing of virtual nested array is expanded simultaneously. Then the second order difference coarray is adopted to the virtual nested array to further increase the DOFs. Numerical results validate the high DOFs achieved by our proposition.
An Ensemble of Deep Convolutional Neural Networks for Drunkenness Detection Using Thermal Infrared Facial Imagery

This paper proposes an original method for subject independent drunkenness detection using an ensemble of Deep Convolutional Neural Networks (DCNNs) for processing of thermal infrared facial imagery characterizing the subjects to be tested. The proposed neural system consists of an ensemble of two DCNNs modules for thermal infrared facial image processing; the first module is composed by 12 layers and the second one has 10 layers. The two DCNNs have been trained separately, using different architectures and different sets of parameters. The final decision is influenced by the confidence degrees of two CNN component modules. The proposed method is evaluated using the dataset of 400 thermal infrared facial images belonging to 10 subjects. For each subject the dataset contains 20 thermal images corresponding to sober condition and other 20 images for inebriation condition obtained 30 minutes after the subject has drunk 100 ml amount of whisky. The experiments of the proposed DCNN couple for subject independent drunkenness detection lead to the overall correct detection score of 95.75%. This confirms the effectiveness of the proposed approach.
Characterizing Packet Loss in City-Scale LoRaWAN Deployment: Analysis and Implications

With the popularity of LoRaWAN network deployments, it is critical to understand the packet transmission performance in various application settings. Previous efforts on LoRaWAN network measurement are either simulation-based or small-scale, which is not sufficient to reflect the true state of large operational LoRaWAN networks. In this paper, we provide an in-depth investigation of the packet loss rate (PLR) using the trace collected from a large-scale LoRaWAN network in Shanghai over eight months. We extract the performance parameters that have a direct impact on PLR from the trace information of packets and analyze the influence of these parameters on PLR. We also provide a comprehensive case study to find out in what circumstances the data packets are more likely to be lost. Our study shows the relationship between several indicators that are commonly considered to affect PLR. The lessons learned provide important guidelines for future LoRaWAN network optimization and deployments.
Anomaly Detection Method Based on Clustering Undersampling and Ensemble Learning

Detecting abnormal traffic is important for network management. Aiming at the problem of data imbalance in traffic anomaly detection, an undersampling method based on clustering is used to process imbalanced data sets. Set the number of clusters in normal flow samples to the number of abnormal flow samples, and then use the cluster center nearest neighbor sample points as retained sample individuals to achieve the purpose of under-sampling. The effective fusion of clustering undersampling and Adaboost algorithm makes the algorithm pay more attention to samples that are difficult to judge in the data set, and further improves the effect of network traffic anomaly detection. K-RUSboost algorithm is simulated in Moore data set and iscxvpn2016 data set. The experimental results show that using this algorithm improves the recall rate of abnormal flow and maintains the false positive rate of abnormal flow at a relatively low level.
Use of Ensemblers Learning for Prediction of Heart Disease

Among all fateful disease, heart diseases are considered as the utmost prevalent. Medical specialist oversight various audit of disease related to heart and segregate reports of heart patients, their symptoms and illness. Progressively reported about patients with common diseases who have typical symptoms. The current scenario people in globe want to people choose luxurious life rather than peaceful life a so they work harder trying to equalize with machine in order to secure high capital and to comfort their livelihoods which in results gives no rest and high pressure regards distinct health issues, because of this there intake of healthy and balanced diet modifies and also their complete to do and not to do list, in this type of living style there are distinct issues like getting depressed and suffering from blood pressure, sugar level issue at an initial age and not enough rest for themselves and eating habits as an outcome contributing with minor negligence leads to an extensive threat of disease like heart disease. Term ‘heart disease’ includes the divergent diseases that affect heart. Heart predictor system will use the data mining intelligence to give a user-adapting outcome to new and hidden design in the data. The Technology used to implement is serviceable for the skilful healthcare to get finest quality of utility and to shorten the extent of conflicting medicine consequence. Heart disease can be handled productively stabilizing the livelihood, remedy, medication, and final stage of Surgeries. With the right analysis, the manifestation of heart disease can be shortening and the operating of the heart gets improved. The proposed work focuses on Ensemblers Techniques which combines the weak learners to implement hybrid model and provide better results than individual classifier.
Degradation of Zein during Germination of Corn

The degradation of zein during germination of corn was investigated in special relevance to protease activity, Polyacrylamide gel electrophoresis demonstrated that two main subunits of zein were rapidly degraded as the germination proceeded. It was observed that the zein degradation was followed by the formation of free amino acids, especially phenylalanine and tyrosine. A protease activity increased remarkably during the germination. A crude extract was obtained which could degrade zein in vitro to form relatively large amounts of free phenylalanine and tyrosine. The crude extract showed a maximum activity for casein at pH 3 and for zein at pH 4.5. The activity was enhanced when determined in the presence of 2-mercapto- ethanol. A sulfhydryl protease was estimated to occur which might be responsible for the degradation of zein during germination.
A Unified Anisotropic Particle-Based Ocean Wave Simulation Framework for Marine Simulator Visual System

It is urgent to improve the physical reality of ocean scenes in marine simulator for the maritime industry. In this paper, we propose a unified anisotropic particle-based ocean wave simulation framework for marine simulator. In the unified framework, a novel hybrid SPH method is applied to model the ocean waves, which combines a series of nonlinear density constraints and divergence-free velocity field constraints. By solving density constraints, the particle position distribution is directly adjusted to keep a constant density. Experiments show that the hybrid SPH method has advantages in compressibility and stability. Moreover, a novel stochastic fluctuating wind field model is integrated into the hybrid SPH method. The Perlin noise and a modified log wind profile are introduced to enrich the details of wind field. We also introduce a novel oil spill model for marine simulator based on number density, which can achieve a desired sharp density changes at interfaces between multiple fluids. For real-time surface reconstruction, we propose an improved anisotropic particle transformation method based on the distribution of the neighboring particles. The problem of particle deformation near the boundary is solved by our piecewise correction function, with the sharp features and anisotropy maintained. When calculating the covariance matrix and weighted position, the contribution of neighboring particles in other fluid phases is considered to eliminate gaps between multiple fluids in the oil spill scenes. The simulation results show that our unified anisotropic framework based on the SPH concept can easily integrate other models, has strong expansibility, and is very applicable to simulate complex ocean scenes.
Defect Detection Method For Steel Based On Semantic Segmentation

The traditional methods used to detect defects in steel have always had many problems, such as long time consuming, causing a certain degree of damage to the surface of steel, and consuming more human resources. With the development of deep learning, theoretically, through semantic segmentation and image classification technology, deep neural network can be used to detect whether there are defects in steel, the categories of defects contained, and the locations of the defects, thereby solving the problems of traditional detection methods. The problem is destructive and time-consuming. Most recent researches on related aspects are mostly experiments with small data sets and small samples, and have achieved good performance. In this large-scale data set, based on the structure used by U-Net, the new network design is newly designed. For PSD U-Net, the model uses hole convolution and HDC design instead of the original convolution layer to preserve the picture details to the greatest extent under the premise of expanding the receptive field. The parametric sigmoid norm (PSN) layer is superimposed on the feature extraction network to further Improve recognition rate. Finally, the paper is based on three networks, U-Net, Mask R-CNN and PSD U-Net, and continuously transforms the feature extraction network for experiments. The results show that the PSD U-Net architecture and the network with superimposed PSN layers have better results for the rest of the isometric models.
CPS-Based Human-Vehicle Co-Pilot Switching Strategy Under Different Information Flow Topologies

Automated vehicles will still remain the form of human-vehicle cooperation in the foreseeable future. Switched cooperative driving mode makes improvements on preventing drivers from being out-of-the-loop by having them assume manual control at periodic intervals. This paper studies driving mode switching strategy under different communication topologies, which could be applied to different situations within the current traffic infrastructure, and proposes new switching strategies considering predecessor-following topology, leader-following topology, and leader-predecessor-following topology. The strategies could guarantee the driver’s driving time, then avoid fatigue and distraction caused by excessive automatic driving time.
Experimental Study on Gamification Teaching of Mathematics Classroom Practice Based on Flow Experience

Students in the state of flow can learn more efficiently. The research purpose of this paper is to find out methods teachers can use to improve learners' flow experience in teaching activities. Based on the experimental study of educational games in primary school mathematics classroom practice, this paper explores the strategies of educational games to improve learners' flow experience, and analyzes the experimental data of learning effect from four factors: fearless failure, loss of self-awareness, neglect of time passing and focus on the value of the activity itself, the neglect of the passage of time and the focus on the activity itself. combined with interview data, and finally came to the conclusion that educational games can improve learners' flow experience in classroom practice to a certain extent. This paper sums up three methods that can improve students' flow experience in teaching activities and four strategies about the design of mathematics classroom practice.
Infantile Hemangioma Detection using Deep Learning

Infantile hemangiomas are the most common type of benign tumor which appear in the first weeks of life. As currently there is no robust protocol to monitor and assess the hemangioma status, this study proposes a preliminary method to detect the lesion. Therefore, in this paper we describe a hemangiomas classifier based on a linear convolutional neural network architecture. The challenge was to achieve a good classification using a relatively small internal database of 240 images from 40 different patients. The results are promising as the CNN performance evaluation showed a level of accuracy on the test set of 93.84%. Five metrics were calculated to assess the proposed model performances: accuracy, positive predicted value, negative predicted value, sensitivity and specificity.
The effect of moisture content on physical properties of wheat.

Physical properties often required for designing the equipments for planting, harvesting and postharvesting operations of seeds. Several physical properties of three popular wheat varieties (Shiraz, Karoun and Shiroudy) were determined and compared for moisture content in 8, 12 and 18% w.b in 2007 in University of Tehran. The average length, width and thickness were 6.75, 3.26 and 2.77 mm at a moisture content of 8% w.b. , respectively. studies on rewetted wheat seeds showed that the thousand-kernel weight increased from 18.38 to 22.43g. The geometric and equivalent mean diameter, surface area, sphericity and aspect ratio at a moisture content of 8% w.b were 3.93, 3.94 mm, 48.68 mm , 0.58, 0.48, respectively. The 2 porosity increased from 0.43 to 0.45 %. Whereas the bulk density decreased from 0.72 to 0.66kg m and the -3 true density from 1.25 to 1.19 kg m , with an increasing in the moisture content range of 8B18% w.b. The -3 static and dynamic angle of repose varied from 37.28 to 47.33 and 29.89 to 36.5 . The mean of static friction o coefficient of three wheat varieties increased the linearly against surfaces of three structural materials, namely, compressed plastic (0.43 0.53), galvanized iron (0.33 0.53) and plywood (0.35 0.41) as the moisture content increased from 8 to 18% w.b.