Miracle-dz/UniFAR-FTUs huggingface dataset:
Pharmacologic Neuroprotection

Considerable progress has been made in the understanding of the consequences of brain anoxia/ischaemia and reperfusion on metabolism and neuronal viability. Neurons are particularly susceptible to ischemic injury because they have a higher demand for energy and limited energy stores that get depleted within 2 to 4 minutes of anoxia. Neuroprotection involves provision of the means to prevent or minimize injury to neurons using treatments used to protect neural tissue from cellular events induced by deprivation of oxygen or glucose or both to the brain. When confronted with the need for neuroprotection, a clear understanding of the underlying mechanisms of both injury and treatment are required to decide on the best approach. Neuronal tissue injury processes continue over a prolonged period of time, implying that neuroprotective therapies may be initiated beyond the acute post-injury phase. Drugs targeting prolonged injury mechanisms, such as mitochondrial dysfunction, axonal injury, and neuro inflammation have a better chance of limiting irreversible damage. Barbiturates, etomidate, propofol, isoflurane, methylprednisolone, tirilazad mesylate, nimodipine, nicardipine and mannitol have all been used for neuroprotection, combining physiologic (oxygen, hypothermia), pharmacologic (erythopoietin derivatives), thrombolytic and anesthetic therapies. The concept of an ischemic penumbra and the chemical brain retractor concept have aided the emergence of neuroprotective strategies that play important roles in perioperative situations such as cardiopulmonary bypass, deep hypothermic circulatory arrest, carotid surgery, and cerebral aneurysm surgery. Pharmacological neuroprotection is also exhibited in subarachnoid haemorrhage, stroke, brain trauma, spinal trauma, induced hypotension, and post cardiac arrest resuscitation. Important strategies in neuroprotection include maintenance of normoxia, adequate cerebral perfusion pressure, maintenance of mild hypothermia and timely surgical intervention.
Infrared and Visible Image Fusion Based on Iterative Control of Anisotropic Diffusion and Regional Gradient Structure

To improve the fusion performance of infrared and visible images and effectively retain the edge structure information of the image, a fusion algorithm based on iterative control of anisotropic diffusion and regional gradient structure is proposed. First, the iterative control operator is introduced into the anisotropic diffusion model to effectively control the number of iterations. Then, the image is decomposed into a structure layer containing detail information and a base layer containing residual energy information. According to the characteristics of different layers, different fusion schemes are utilized. The structure layer is fused by combining the regional structure operator and the structure tensor matrix, and the base layer is fused through the Visual Saliency Map. Finally, the fusion image is obtained by reconstructing the structure layer and the energy layer. Experimental results show that the proposed algorithm can not only effectively deal with the fusion of infrared and visible images but also has high efficiency in calculation.
A Qualitative Study of Microbiological Analysis of Raw Milk Samples and Yogurt from Different Selected Districts of Bangladesh

: In this study, the microbiological quality of raw milk and yogurt samples was investigated. A total of 50 samples were studied, including 25 raw milk samples and 25 yogurt samples were used. These samples were collected from different parts of some selected district in Bangladesh. The samples were tested for the Total Viable Bacterial Count (TVBC) and Total Coliform Count (TCC). The range of TVBC and TCC in raw milk samples were 2.26×10 6 to 7.56×10 6 and 1.43×10 4 to 7.46×10 4 respectively. And the TVBC and TCC ranged in yogurt samples were from 2.17×10 6 to 5.65×10 6 and 1.32×10 4 to 8.73×10 4 respectively.
Topical Learning Trends

: The concept of e-learning is a technology-mediated learning approach of great potential from the educational perspective and it has been one of the main research lines of educational technology in the last decades. Learning is a constant aspect of our lives. There is no broadening of knowledge without learning. We learn each second, in numerous ways. There is an understanding of the vitality and the implementation of the recent course of learning and education. This article is unique and gives an insight about the upcoming setup of tutoring. Education also helps in cognitive, affective and psychomotor skills. The cognitive aspect is the most dominating aspect and is considered the single most crucial aspect for humans. Effective and psychomotor factors also contribute a lot to individual success in life. Affective is an essential aspect of students’ development, often ignored by educators in the learning process. This aspect has a significant contribution to the social life of students [1] . This means that the affective part plays an essential role in shaping individual attitudes and behaviour in social interactions in society. Therefore, education is considered a very appropriate socialization agent about attitudes and behaviour as social class indicators. You will be pleased to go through the different ongoing trends of education I have mentioned in the article.
MALWARE DETECTION

Due to the openness of Android OS, malicious applications grow rapidly, which makes the Android malware detection more and more urgent. In this paper, eﬀorts are made to apply the deep belief network to Android malware detection. A deep belief network (DBN) model combined with a back propagation (BP) neural network classiﬁer is constructed to detect Android malware. A DBN-BP model is trained, and eﬀects of diﬀerent parameters are examined by experiments. The performance of the DBN-BP classiﬁer outperforms other four commonly used machine learning algorithms, including BP, KNN, SVM and Native Bayes. By training the DBN-BP model with 700 API features, TPR reaches 96%, TNR rises to 99.8%, and F-score exceeds 96.2%. The DBN-BP classiﬁer is an eﬀective choice when high dimensional Android API feature sets are used to detect malicious applications.
Effect of Marketing Mix Strategy on MSE‟s Sales performance-Gomo region, Ethiopia

The purpose of the study was to determine the effects of marketing mix strategies on sales performance of MSEs in Merab Abaya town, Gomo region, Ethiopia. The objective of the study is to examine the different marketing mix variables such as the product, pricing, promotion and placing strategies on sales performance of MSEs. The study was a descriptive research type and the target population of the study was 224 owners of micro and small enterprises registered and operated in Merab Abaya town, and the survey was conducted with sample population of 144 MSEs. The study was employed stratified random sampling from different business areas including manufacturing, trade, construction and urban agriculture by a structured questionnaire. The collected data were analyzed, tabulated and presented in the tables. A Statistical Package for Social Science (SPSS) versions 20.0 software was used to analyze the data. The findings of this study was among the four marketing strategy variables, the product and place strategies had a positive and significant effect on sales performance, and remaining other two variables such as pricing and promotion strategies had positive but not made a significant effect on sales performance of MSEs.
Towards efficient quantized inference for convolutional neural network on edge system

Convolutional Neural Networks (CNN) have made splendid achievements in various object detection tasks. To extend the applications of CNN detection models, the implementation of model inference on edge platforms, such as ASIC, FPGA and other embedded systems, has been intensively investigated in recent years. However, the huge model size and its enormous overhead constrain the deployment of detection model on edge platform which always has limited computational capability. Quantized inference of CNN model is one of the efficient approach to running model on edge platform. In this paper, we develop a hardware-friendly quantized inference scheme of detection model that is used for efficient inference on embedded FPGA systems. The proposed method contains several techniques that are able to optimize the quantized inference of detection model on FPGA device. The experimental results demonstrate that not only make the detection model quantized inference more efficient but also maintain the accuracy of object detection.
Research and Application of the Network Security Monitoring Capability Evaluation Model of Power Control System Based on AHP and Fuzzy Comprehensive Evaluation

In recent years, China’s cyberspace security is facing increasingly severe challenges. As the national key basic information infrastructure, power control system is very important which is related to the national economy and the people’s livelihood. So network security of power control system should be taken seriously. The network security monitoring capability of power control system has a direct impact on the security of power business such as power production and supply. However, there are few studies on the evaluation of its network security monitoring capability. This paper puts forward the network security monitoring capability evaluation model of power control system based on AHP and fuzzy comprehensive evaluation, according to the research of public network security management capability evaluation, the particularity of network security monitoring system of power control system and the investigation opinions of 20 expert teams in power industry’s network security field. Then, the model is used to evaluate the network security monitoring capability of three municipal power control systems, and the final scores are 93.09, 88.65 and 90.17 respectively. The model established in this paper realizes the quantitative evaluation of the network security monitoring capability of the power control system. And it will help the power company to carry out targeted rectification and improvement according to the calculating result of this model. So the research and application in this paper has a certain practical engineering significance.
Gaussian mixture modelling by exploiting competitive stop EM algorithm

To improve the robustness of order selection and parameter learning for Gaussian mixture model (GMM), this paper proposes a competitive stop expectation-maximization (EM) algorithm, which is based on two stop conditions. The first condition is a Lilliefors test based multivariate (MV) normality criterion, which is used to determine whether to split a component into two different components. The EM algorithm stops splitting when all components have MV normality. The minimum description length (MDL) criterion is used in the second condition, which competes with the first condition to prevent the EM algorithm from over-splitting. Simulation experiments verify the effectiveness of the proposed algorithm.
Analysis and design of precise testing instrument chassis considering electromagnetic shielding

In this work, the issues regarding the mechanism and structural design of electromagnetic shielding technology are introduced. In accordance with the use requirements of precise testing instruments, the precautions in the chassis design with respect to electromagnetic shielding are determined. Moreover, the approach to improve the chassis through experiments is proposed, and the test shows that the improved chassis can meet the requirements of shielding.
Research status of improvement based on DV-Hop non-range positioning algorithm

In order to solve the problem of poor positioning of the DV-Hop algorithm, it is a current research hotspot. In view of the three aspects of error distribution, the research plan for effective improvement is listed. In the field of the minimum number of hops, the RSSI-based weighting correction and the use of deviation factor improvement; in the average distance of hops, the calculation of distance error improvement and the use of angle value weighting processing; in the calculation of unknown nodes, the weighting of the least square method and the triangle centroid method two. This correction is estimated. Relevant scholars of the above methods can solve the problem of poor positioning of the classic DV-Hop algorithm through simulation experiments.
Outdoor real-time RGBD sensor fusion of stereo camera and sparse lidar

Autonomous driving technology relies on high-resolution, high-performance vision sensing technology. As the two most popular sensors in the industry, Lidar and stereo cameras play an important role in sensing, detection, control and planning decisions. At this stage, the Lidar can obtain more accurate depth map information, but with the increase of resolution, its cost increases sharply and cannot be applied in large scale in the market. At the same time, the depth detection scheme based on computer vision has high resolution, but the data accuracy is very low. In order to solve the practical application defects of the above two sensors, this paper proposes a new sensor fusion method for stereo camera and low-resolution Lidar, which has high resolution, high performance and low cost. This paper adopts new sensor design, multi-sensor calibration, classification and selection of Lidar pointcloud features, large-scale and efficient stereo image matching and depth map calculation, and method of filling missing information based on pointcloud segmentation. To verify the effectiveness of the method in this paper, this paper used high-resolution Lidar as ground truth for comparison. The results show that the fusion method can achieve an average improved accuracy of 30% within a close range of 30 meters, while achieving 98% resolution. In addition, this paper presents a scheme for visualizing multi-sensor image fusion, and encapsulates five modules: multi-sensor calibration, large-scale stereo depth calculation, low-resolution Lidar simulation, sensor data fusion, and fusion image and error visualization, in order to facilitate future secondary development.
Aircraft parameter estimation using a new filtering technique based upon a neural network and Gauss-Newton method

Abstract A new parameter estimation method based upon neural network is proposed. The method proposed here uses feed forward neural networks to establish a neural model that could be used to predict subsequent time histories given the suitable measured initial conditions. The proposed neural model would not represent a generic flight dynamic model. The neural model in this case develops point to point fitting of the input and the output data. Thus, it could at best be referred to as flight dynamic model in restricted sense. Gauss-Newton method is then used to obtain optimal values of the aerodynamic parameters by minimising a suitable defined error cost function. The method has been validated using longitudinal and lateral-directional flight data of various test aircraft. The results thus obtained were compared with those obtained through wind tunnel test, or those obtained using Maximum likelihood and/or Filter error methods. Unlike, most of the parameter estimation methods, the proposed method does not require a prior description of the model. It also bypasses the requirement of solving equations of motion. This feature of the proposed method may have special significance in handling flight data of an unstable aircraft.
Research of Binocular Visual Inertial Algorithm Based on Point and Line Features

To solve the problem of poor performance of the binocular visual inertial odometer VINS-Fusion in scenes with low texture and large luminosity changes, a binocular visual inertial odometer PLVINS-Fusion is designed that integrates line feature measurement information, which use line features to easy to extract in low-texture scenes, and have the advantage of more robust tracking performance in scenes with large luminosity changes. Point and line features are extracted in the front-end visual extraction at the same time, and line feature residuals are added to the back-end nonlinear optimization, construct a bag-of-words model combining point and line features in the loop detection module. On this basis, a real-time photometric calibration algorithm is adopted to jointly optimize the exposure time, the camera response function and the vignetting factor, and the stability of KLT optical flow tracking is improved by correcting the image brightness. Experiments on benchmark dataset show that the optimized algorithm has higher robustness and effectively improves the positioning accuracy, and meets the real-time performance requirement.
The Design of Intelligent Logistics System Based on Internet of Things

With the rapid development of Internet technology, as well as interoperability of RFID technology, the popularity of ZigBee technology, things and objects can be achieved, this article discusses the design of smart logistic system of things Based on A10 Andriod-based system platform. System hardware consists of A10 platform, RFID modules, ZigBee modules, servers, mobile terminal, etc. The ZigBee sensors within the warehouse can monitor multiple points environment (including temperature, humidity, smoke alarm, PIR), RFID module can manage the track of goods storage. ZigBee coordinator and RFID reader module collected data and transferred to the A10 platform real-time processing. Then the data uploaded to the web server through the internet, users can obtain relevant information through the mobile terminal or Web. Experimental results showed this approach could reduce dependence on staff greatly and have good effect in actual application.
A host–gut microbial co-metabolite of aromatic amino acids, p-cresol glucuronide, promotes blood–brain barrier integrity in vivo

Purpose The sequential activity of gut microbial and host processes can exert a powerful modulatory influence on dietary components, as exemplified by the metabolism of the amino acids tyrosine and phenylalanine to p-cresol by gut microbes, and then to p-cresol glucuronide (pCG) by host enzymes. Although such glucuronide conjugates are classically thought to be biologically inert, there is accumulating evidence that this may not always be the case. We investigated the activity of pCG, studying its interactions with the cerebral vasculature and the brain in vitro and in vivo. Methods Male C57Bl/6J mice were used to assess blood–brain barrier (BBB) permeability and whole brain transcriptomic changes in response to pCG treatment. Effects were then further explored using the human cerebromicrovascular endothelial cell line hCMEC/D3, assessing paracellular permeability, transendothelial electrical resistance and barrier protein expression. Results Mice exposed to pCG showed reduced BBB permeability and significant changes in whole brain transcriptome expression. Surprisingly, treatment of hCMEC/D3 cells with pCG had no notable effects until co-administered with bacterial lipopolysaccharide, at which point it was able to prevent the permeabilising effects of endotoxin. Further analysis suggested that pCG acts as an antagonist at the principal lipopolysaccharide receptor TLR4. Conclusion The amino acid phase II metabolic product pCG is biologically active at the BBB, highlighting the complexity of gut microbe to host communication and the gut–brain axis.
AbaniCO2: Un Método Simple y Efectivo para la Toma de Decisiones sobre la Adopción de la Huella del Carbono en la Gestión Sustentable de Emisiones en las Empresas

This work proposes a simple method named AbaniCO2 to unify the concept internationally used for estimating carbon footprint (CFP) a concept that does not have a universal accepted definition. The method organizes the different existing views in three major perspectives simplifying the understanding of the conceptual framework of the CFP and the analysis of the methodological complexities to consider in the process of adoption. First, the paper clarifies certain concepts that show the many factors that provoke the present situation of confusion and the lack of interest for adopting the CFP in the sustainable management of emissions of a company or a product, mainly in the Latin-American countries and in medium and small size enterprises. AbaniCO2 is the result of the systematization of previous works by the authors is in a simplified representation, in the form of a hand fan, of the different methodological streams that are nowadays available to quantify the CFP. The methodology consists of six clearly defined steps, starting with the definition of the objective behind the decision of adopting the CFP and ending with the algorithm of calculation to obtain a value for the CFP. It is concluded that AbaniCO2 simplifies the methodological complexities and reduces the existing barriers for the comprehension and managerial decision making about CFP adoption for the sustainable management in enterprises, organizations, and products.
The DCC receptor regulates astroglial development essential for telencephalic morphogenesis and corpus callosum formation

The forebrain hemispheres are predominantly separated during embryogenesis by the interhemispheric fissure (IHF). Radial astroglia remodel the IHF to form a continuous substrate between the hemispheres for midline crossing of the corpus callosum (CC) and hippocampal commissure (HC). DCC and NTN1 are molecules that have an evolutionarily conserved function in commissural axon guidance. The CC and HC are absent in Dcc and Ntn1 knockout mice, while other commissures are only partially affected, suggesting an additional aetiology in forebrain commissure formation. Here, we find that these molecules play a critical role in regulating astroglial development and IHF remodelling during CC and HC formation. Human subjects with DCC mutations display disrupted IHF remodelling associated with CC and HC malformations. Thus, axon guidance molecules such as DCC and NTN1 first regulate the formation of a midline substrate for dorsal commissures prior to their role in regulating axonal growth and guidance across it.
Cross Layer Analysis of Interference Effects in Wireless Systems

Wireless communication systems are nowadays employed in many fields. The research effort beneath this work is to investigate one of the main issues in a radio communication system: interference. Devices operating in the 2.4 GHz unlicensed Industrial, Scientific and Medical (ISM) band are considered and, in particular, wireless sensors networks compliant to IEEE 802.15. 4 standard are used to evaluate performance indices in the presence of interference. The analysis starts from a real wireless control application and a RF power meter application: a complete perspective involving theoretical formulas, simulations and experimental results is given. A cross layer approach for CSMA/CA based devices, merging interference effects at PHY and MAC layer, is presented. Subsequently effects of interference on channel status evaluation and RSSI circuitry are faced and, finally, the feasibility of a low cost RF power meter is discussed. The purpose of the work is to understand interference phenomena, using a real life test bed and a complex set of interferers comprising both arbitrary signal generators and real wireless networks. The interaction of interference with a device is manifold: it involves physical layer, hardware and protocols. The final aim of this work is to provide protocol and hardware designers with a set of performance metrics and perspectives useful to improve wireless devices’ behavior against interference.