Miracle-dz/UniFAR-FTUs huggingface dataset:
High Gain Periodic 2-D Leaky-Wave Antenna With Backward Radiation for Millimeter-Wave Band

A two-dimensional (2D) periodic leaky-wave antenna (LWA) is proposed in this article. Using dielectric-filled wedge-like waveguide as medium for wave propagation, the quasi-TEM wave inside the guide is leaked through periodic rings of narrow slots, resulting in high gain frequency-dependent beam. Due to the two-dimensional nature of the antenna, along with the slots distribution, the radiation has low side lobe level (SLL) without the need to extend the length of the antenna as in the case of periodic one-dimensional (1D) LWA. The measured gain of the antenna is 20.34 dBi with SLL less than −13 dB and cross-polarization less the −28 dB. The antenna is proposed for millimeter-wave (mm-Wave) applications.
A Survey on Investment Demand Assessment Models for Power Grid Infrastructure

The investment demand forecast of the power grid is the further deepening of grid supply and demand management. Under the current reform of the power system in China, we are facing new challenges in analyzing and predicting the investment demand of the power grid. Firstly, the influencing factors of power grid investment demand from both internal and external aspects are analyzed in this paper. Then the selection principles of power grid investment demand factors by domestic and foreign scholars are summarized in this paper. Based on the above content, the forecast indicators and selection methods of domestic and foreign scholars on power grid investment demand are summarized in this paper. The common influencing factor mining algorithms are the grey correlation analysis, the core principal component analysis, and their combination methods. Moreover, the research status of domestic and foreign scholars in power grid investment demand, data mining technology, and forecasting technology is investigated. The current situation of power grid investment demand in China and the common forecasting methods are also elaborated. Finally, the key issues that need to be solved in future research on the investment demand of power grid infrastructure are summarized in this paper. This review will help power utilities find problems in power grid infrastructure investment demand and guide the future investment demand calculations of power utilities.
Effects of Irinotecan on Tumor Vasculature and Oxygenation: An in vivo Study on Colorectal Cancer Model

The effects of chemotherapy on the tumor microenvironment remain poorly investigated, although these are crucial for the drug delivery and anti-tumor activity and can modify physiological behavior of cancer cells. Optical coherence angiography and phosphorescence lifetime spectroscopy are promising optical methods to characterize both tumor vasculature and its oxygenation, respectively. In this study, we used these methods to explore the influence of irinotecan, a widely used chemotherapeutic agent, on tumor angiogenesis and oxygen content in a mouse model of colorectal cancer in vivo. For the first time, it was shown that irinotecan at conventional dosing reduces tumor oxygenation and blood perfusion via antiangiogenic mechanism of action. Immunohistochemistry for hypoxia and for the endothelial cell marker CD31 verified the in vivo findings. These data show the prospects of the optical techniques to monitor tumor microenvironment in the course of chemotherapy, which can help to optimize and individualize the treatment.
Copper, selenium and zinc content of canned and non- canned beverages in Nigeria

The levels of copper, selenium and zinc in beverages purchased in Nigeria were studied. Fifty samples of these beverages were digested in nitric acid and were analyzed using the Atomic Absorption Spectrophotometer (AAS). The zinc levels ranged from 0.0 - 1.34 mg/L for the canned and 0.01 - 1.11 mg/L for the non-canned beverages. All had zinc levels below the maximum contaminant level (MCL) of 5.0 mg/l set by United States Environmental Protection Agency (US EPA). The selenium levels ranged from 0.24 - 1.67 mg/L for the canned and 0.07 - 1.23 mg/L for non-canned beverages. 20 out of 21 (95%) canned beverages had selenium levels that exceeded the MCL whereas 90% of the non-canned products had selenium levels above the MCL. The copper levels ranged from 0.04 - 3.55 mg/L for the canned and 0.04 - 3.20 mg/L for non-canned beverages. 11 out a total of 21 (52%) canned beverages had copper levels that exceeded the MCL. 45% of the non-canned products had copper levels above the MCL. The mean and median values of copper and selenium exceeded the MCL in the beverages whereas the zinc levels did not exceed the MCL. The calculated amount of copper, selenium and zinc in three beverages were 14.57, 5.96 and 5.31 mg respectively. The worst-case scenario is assumed here to estimate the weekly intakes of Cu, Se and Zn, and results seem to pose some concern. 48 and 92% of the 50 beverages studied in March 2005 in Nigeria failed to meet the US EPA criteria for acceptable copper and selenium levels in consumer products. Key words : Copper, selenium, zinc, contaminants, beverages, Nigeria.
Virtual Reality to Study Pedestrian Wayfinding: Motivations and an Experiment on Usability

Despite the significance of pedestrian simulation in the design of the built environment and in the definition of crowd management procedures, we still lack data about several relevant human decision making processes. To tackle this issue, we propose a novel Virtual Reality system which allows to design controlled experiments in virtual settings, by tracking multiple users that wear Head Mounted Displays (HMD). We employed the system to experimentally evaluate the signage of a building of the University of of Milano–Bicocca and its implications on wayfinding decisions. We present here the results of a preliminary test made with the system, aiming at evaluating its usability and feeling of sickness due to the VR itself, as well as preliminary results on human wayfinding decisions.
Optimistic disclosure tone in corporate annual reporting and financial performance

The paper investigates the association between optimistic disclosure tone and corporate financial performance using a sample of listed firms in Gulf Cooperation Council (GCC) countries covering the period from 2012–2018 with a total of 779 observations. The optimistic disclosure tone is measured through DICTION 7.0 software. While, financial performance of firms is measured by return on equity, in addition, seven control variables. The paper found that firm performance has a positive association with the reported optimistic tone. This finding supports the agency and management impression theories.
LSTM Based Approach for Classifying Twitter Posts for Movie Success Prediction

Social media like Twitter contains rich information about people's preferences. There is a struggle to determine how to effectively utilize and interpret those data. Models are created using these large quantities of data to predict the behavior and tendencies. People share their thoughts about movies on Twitter. The movie industry has been a very important sector in the global market. So, it is important to maximize the profit by predicting the success of the movie. In this study, we proposed an approach to classify twitter posts predict the success of movies using Long short-term memory based approach. Our approach outperformed the existing approach by obtaining 83.97% accuracy.
A Performance Study of Principals MIMO Signal Detection Algorithms

The current trend in wireless communication systems is to transmit higher data rates and to guarantee high-quality services. This indicates the need to evolve new technologies to increase the capacity of the transmission channel. The use of MIMO systems represents a feasible solution to achieve these objectives. This article analyzes the performance of MIMO detection algorithms: zero-forcing (ZF), minimum-mean-square-error (MMSE), and maximum likelihood (ML), in terms of bit error rate (BER). It is showed that the ML detector is optimal for recovering the original signal but with high complexity, while the MMSE detector ensures a balance between reliability and complexity.
Improving diagnostic accuracy for breast cancer using prediction-based approaches

The objective of this study is to improve prediction outcome of breast cancer patients employing Machine Learning techniques so to be able to accurately classify between Benign or Malignant Tumor. The dataset taken for this experiment is an inclusion of extracted features of breast cancer patient cells and normal person cells that are extracted from digitized images of FNA (Fine-needle aspiration) tests performed on breast lumps. The dataset was exposed to Machine Learning models namely Support Vector Machine, Decision Tree, Logistic Regression, K- Nearest Neighbor, Naive Bayes, Random Forest and Neural Network based algorithm- Multilayer Perceptron to analyze the prediction results. The obtained results were also compared with ensemble- based learning techniques such as Gradient Boost, XGBoost and Adaboost classifiers to find the best performing algorithm. Further, this study aims to showcase to the clinicians the methodology of interpretation via Machine Learning and that it's routinely usage would certainly be beneficial to predict outcomes. The long-term goal of this type of study expects a slow and gradual realization of the importance of accurate tumor detection via Machine Learning models, as early detection of breast cancer can greatly improve prognosis and survival chances by promoting clinical treatment to patients as soon as possible.
Improvement and Implementation of a Multi-Path Management Algorithm based on MPTCP

The core idea of the Multi-Path Transmission Control Protocol (MPTCP) is to utilize multiple network connections by distributing payload data transmission among several sub-flows. Then, multiple paths in the underlying networks can be used to maximize the overall connection throughput. However, the concurrent transmission on only a subset of all possible sub-flows’ aggregation can improve network performance, because of performance differences between the subflow. In this paper, we propose a new FullMesh algorithm based on Path Characteristic and Data Characteristic (PCDC), in which a Subflow Impact Factor (IF) is used as a subflow characteristic to predict the impact of a subflow on the overall throughput. Then, different path sets are adopted for different sizes of traffic. The PCDC algorithm is evaluated in the NORNET CORE testbed, comparing it to the FullMesh algorithm. Our research results show that the PCDC algorithm can improve the network throughput and reduce the overall completion time of small data streams. 1 2 3
Gesture Recognition Using Reflected Visible and Infrared Lightwave Signals

In this article, we demonstrate the ability to recognize hand gestures in a noncontact wireless fashion using only incoherent light signals reflected from a human subject. Fundamentally distinguished from radar, lidar, and camera-based sensing systems, this sensing modality uses only a low-cost light source (e.g., LED) and a sensor (e.g., photodetector). The lightwave-based gesture recognition system identifies different gestures from the variations in light intensity reflected from the subject's hand within a short (20–35 cm) range. As users perform different gestures, scattered light forms unique, statistically repeatable, time-domain signatures. These signatures can be learned by repeated sampling to obtain the training model against which unknown gesture signals are tested and categorized. These time-domain variations of the lightwave signals reflected from hand are denoised, standardized, and then classified by using machine learning classification tools such as $K$-nearest neighbors and support vector machine. Performance evaluations have been conducted with eight gestures, five subjects, different distances and lighting conditions, and visible and infrared light sources. The results demonstrate the best hand gesture recognition performance of infrared sensing at 20 cm with an average of 96% accuracy. The developed gesture recognition system is low-cost, effective, and noncontact technology for numerous human–computer interaction applications.
Assessment Of Spatial Hazard And Impact Of PM10 Using Machine Learning

Air pollution is one of the major threats to human health and environment. When some substances in the atmosphere exceed a certain concentration it becomes harmful to the ecological system and the normal conditions of human existence. Particulate matter (PM) refers to small solid or liquid particles floating in the air. These small particles can move deeper into the respiratory tract, including the lungs this can lead to cough, asthma attacks, high blood pressure, heart attack, stroke and so on. Particulate matter is considered as the air pollutant of greatest concern to health. So as a first step to understand the seriousness of the issue is to monitor PM concentration. For the spatial hazard modeling of PM10 productive machine learning models such as Mixture Discriminant Analysis (MDA), Bagged Classification and Regression Trees (Bagged CART), Random Forest (RF), with accuracy 0.87, 0.92 and 0.93 respectively are used. However, these models cannot give accurate results with large samples and to overcome this eXtreme Gradient Boosting (XGBoost) method is used.
Development, presentation and tests of a hybrid thermal vibrational energy harvester based on lead free piezoelectric material

Piezoelectric and pyroelectric effects are well known and have been widely used in the field of energy harvesting, actuators or sensors. This paper presents a cantilever structure based on a lead free piezoelectric-pyroelectric hybrid transducer based on PVDF (Polyvinylidene fluoride) piezoelectric, a Lithium Niobate piezoelectric, and a SMA (Shape Memory Alloy) materials which is capable of simultaneously harvesting mechanical and thermal energies. The piezoelectric cantilever has been studied using finite element modeling for application in self-powered sensors in automobiles. The cantilever are able to resonate under 300 Hz of frequency range. We calculated the output voltage from both the thermal and vibrational excitations with the help of Comsol Multiphysics finite-element software. We shows that the configuration in which the PVDF is the top layer, then SMA as the middle layer and Lithium niobate as the bottom layer has the highest value of harvested energy with an output voltage up to 12Vpp.
Cross-Channel Graph Convolutional Networks for Skeleton-Based Action Recognition

In recent years, skeleton-based action recognition, graph convolutional networks, have achieved remarkable performance. In these existing works, the features of all nodes in the neighbor set are aggregated into the updated features of the root node, while these features are located in the same feature channel determined by the same $1\times 1$ convolution filter. This may not be optimal for capturing the features of spatial dimensions among adjacent vertices effectively. Besides, the effect of feature channels that are independent of the current action on the performance of the model is rarely investigated in existing methods. In this paper, we propose cross-channel graph convolutional networks for skeleton-based action recognition. The features fusion mechanism in our network is cross-channel, i.e, the updated feature of the root node is derived from different feature channels. Because different feature channels come from different $1 \times 1$ convolution filters, the cross-channel fusion mechanism significantly improves the ability of the model to capture local features among adjacent vertices. Moreover, by introducing a channel attention mechanism to our model, we suppress the influence of feature channels unrelated to action recognition on model performance, which improves the robustness of the model against the feature channels independent of the current action. Extensive experiments on the two large-scale datasets, NTU-RGB+D and Kinetics-Skeleton, demonstrate that the performance of our model exceeds the current mainstream methods.
SCAN: Smart Collaborative Attack in Named Data Networking

Named Data Networking is a promising Future Internet Architecture, where consumers fetch contents from the network using unique names. It has in-built security features like data integrity, and data origin authentication as contents are signed by the original producer. Though, NDN’s stateful forwarding plane helps to remove some of the existing DDoS attacks in the current IP architecture, still it can face one specific kind of attack called Interest flooding attack (IFA). In this paper, we focus on one variant of IFA where malicious consumers and malicious producers collaboratively launch an attack to degrade the QoS of legitimate consumers. Here we have shown how attackers can intelligently fix the Interest sending rate so that attack goes undetected easily. Simulation results demonstrate that with proper placement of malicious consumers and producers, the attackers can successfully launch the attack with moderate traffic and less number of malicious consumers.
A SLAM System Based on RGBD Image and Point-Line Feature

Most of the existing visual SLAM schemes rely solely on point or line features to estimate camera trajectory. In some scenes such as texture missing or motion blurring, it’s difficult to find a sufficient number of reliable features, resulting in low positioning accuracy. To extract more features, an RPL-SLAM solution is proposed to extract point features and line features respectively. Ulteriorly, the depth information of RGBD image is used to restore the 3D information of point and line features, improving the accuracy of camera track positioning. RPL-SLAM scheme mainly includes three modules: tracking, local mapping and loop detection. Tracking module extends the application of line feature on the basis of point feature extraction and matching. A SLD line segment extraction algorithm which can eliminate the micro segments and a DBM segment matching algorithm based on word bag are proposed respectively. These two algorithms improve the matching efficiency while ensuring the matching accuracy, and effectively track and locate the camera of each frame. In the local mapping and loop detection module, the Plucker coordinates are applied to express the spatial line and define the re-projection error of the straight line, so that the back-end optimization error model of point-line fusion is unified to solve the instability problem in optimization. RPL-SLAM is tested on TUM RGBD and ICL-NUIM data set respectively, and compared with ORB-SLAM2. The result shows that RPL-SLAM can effectively improve the accuracy of pose estimation and map reconstruction while maintaining real-time performance by fusing point-line features with depth images.
Elaboration on Occupancy Networks and Resizing Strategy

Occupancy networks introduces the decision boundary of neural network classifier to express arbitrarily complex three-dimensional topological structure, thereby solving the problem of incompatibility between the efficiency of calculation and the validity of storage in the field of 3D reconstruction. We briefly introduced the principle of occupancy network and its single-object 3D reconstruction effect. When further applying occupancy network to real online images, we propose a feasible resizing strategy to improve the versatility of occupancy network. We believe this will help occupancy network move towards a broader scene.
Electrophoretic mobility of DNA in gels. I. New biased reptation theory including fluctuations

In weak electric fields, the motion of DNA molecules undergoing gel electrophoresis may be described by biased reptation. We argue that the degree of molecular orientation induced by the field is determined by a competition between longitudinal fluctuations and drift of the molecule along the tube. A self‐consistent calculation shows that the end‐to‐end vector of long molecules varies with the square root of the field strength, and not linearly as previously supposed. This leads to a number of new predictions about the field dependence of the molecular mobility and the size limit of resolution. We present the results of computer simulations that support the predictions of the theory of biased reptation including fluctuations. Finally, we discuss the correspondence with experimental data and the implications of our findings for the optimization of DNA electrophoresis. © 1994 John Wiley & Sons, Inc.
Mining Graph-Fourier Transform Time Series for Anomaly Detection of Internet Traffic at Core and Metro Networks

This article proposes a framework to analyse traffic-data processes on a long-haul backbone infrastructure network providing internet services at a national level. This type of network requires low latency and fast speed, which means there is a large demand for research focusing on near real-time decision-making and resilience assessment. To this aim, this article proposes two innovative, complementary procedures: a multi-view approach for the topology analysis of a backbone network at a static level and a time-series mining approach of the graph signal for modelling the traffic dynamics. The combined framework provides a deeper understanding of a backbone network than classical models, allowing for backbone network optimisation operations and management at near real-time. This methodology was applied to the backbone infrastructure of a major UK internet service provider. Doing so increased accuracy and computational efficiency for detecting where and when anomalies and pattern irregularities occur in the network signal.
Stability Inspection of Isolated Hydro Power Plant with Cuttlefish Algorithm

The load frequency control of any system not merely depends upon the controlling technique although on the controller structure. The application of proportional-integral-derivative (PID) and proportional-integral-derivative with filter (PIDN) controller structures for load frequency investigation of isolated power generation plant is addressed in this paper. The dynamic response of the intricate control system is examined in simpler way by considering single power source. With an objective to obtain the stable operation of an isolated hydro power plant choice between PID and PIDN controller structure with cuttlefish algorithm is presented. The cuttlefish algorithm is optimizing the controller parameters to maintain the system stability. The robustness assessment of proposed algorithm validates the ability of algorithm to perform remarkably even with wide range of amendments in system parameters, random variations in power demand etc.
Customer Churn Analysis and Prediction in Banking Industry using Machine Learning

Customer Churning is also known as customer attrition. Nowadays, there are almost 1.5 million customers that are churning in a year that is rising every year. The Banking industry faces challenges to hold clients. The clients may shift over to different banks due to fluctuating reasons, for example, better financial services at lower charges, bank branch location, low-interest rates, etc. Thus, prediction models are utilized to predict clients who are probably going to churn in the future. Because serving long-term customers is less costly as compared to losing a client that leads to a loss in profit for the bank. Also, old customers create higher benefits and provide new referrals. In this paper, different models of machine learning such as Logistic regression (LR), decision tree (DT), K-nearest neighbor (KNN), random forest (RF), etc. are applied to the bank dataset to predict the probability of customer who is going to churn. The comparison in terms of performance like accuracy, recall, etc. is presented.