Miracle-dz/UniFAR-FTUs huggingface dataset:
Big data in support of the Sustainable Development Goals: a celebration of the establishment of the International Research Center of Big Data for Sustainable Development Goals (CBAS)

research on the use of big data analysis to quantify the utilization of urban green space in 366 cities in China. This analysis reveals that 94.01% of urban green spaces have not been utilized to their full potential. They observe that the difference between intra-urban green space and peri-urban green space is larger in Southwestern and Northwestern China than in Eastern China. These results will be essential to improving policies that enable better utilization of urban green spaces, with the potential to lift health and wellbeing indices in urban centers.
Sustainability in the wine-tourism sector – an analysis of perceived understanding and practices implemented by firms

ABSTRACT This study aims to identify how wine-tourism firms understand the concept of sustainability and what sustainable practices have been implemented. To do so, qualitative research was carried out, involving semi-structured interviews with nine Portuguese companies. The interviews were analysed through content analysis. The results revealed various definitions presented by the firms, and in the majority of them, sustainability is perceived as a set of practices aiming to conserve the environment and value the social dimension. The results also indicated that concerning the implementation of sustainable practices, the most common ones come within the environmental dimension. In addition, the firms are seen to be implementing gradually a number of sustainable practices in the three dimensions: environmental, social and economic.
The global pandemic is accelerating housing crises

Writing from home lockdown in Sydney, Australia, in our homes on the traditional lands of the Gadigal and Wiradjuri, Darug and Gundungurra people, we are reminded again of the central role of housing in this global pandemic (Rogers & Power, 2020). We have perhaps never needed rigorous housing journals and critically engaged housing scholars as much as we do right now. We are in the midst of a global pandemic wherein the house and home are both key actors and spaces through which to implement change (see Rogers & Power, 2020). Many housing systems around the world are in crisis across almost any analytical category you choose: socioeconomics, class, race, gender, generational questions, and the list goes on. There are media reports of a ‘global boom in house prices’ (Curran, 2021, p. 1), with ‘runaway house prices’ and ‘winners and losers’ from the global pandemic (Strauss & Smith, 2021, p. 1). Millennials are reported to have FOMO (i.e., fear of missing out) in a ‘hot housing market’ in New Zealand (Laurent, 2021, p. 1). In the United Kingdom, housing prices have unexpectedly risen during the COVID period (Meen, 2021, p. 1) and in the United States ‘rising housing costs’ are said to ‘leave homebuyers struggling’ (Dougherty & Thrush, 2021, p. 1). Homelessness is predicted to be ‘the next crisis’ (Booker, 2021, p. 1) in both the United States and the United Kingdom (Jayanetti, 2021, p. 1). There is a growing sense that these crises are accelerating not despite the pandemic, but because of it. Talking about public housing in the United States some time ago the French sociologist Loïc Wacquant (2012) called the American government a Centaur-state. Like the half-civilised man, half-wild horse of Greek mythology, Wacquant’s Centaur-state practised ‘liberalism [and freedom] at the top of the class structure and punitive paternalism at the bottom’. Like several other post-colonial settler states around the world, such as the United States, Canada, and New Zealand, in Australia we have our own Centaurlike housing system. Australia is different to the United States, Canada, and New Zealand in some important ways, and yet the Australian housing system is an equally strange Centaur-like creature of our own making, granting those who manage to ascend the so-called ‘housing ladder’ all sorts of housing and financial freedoms, while punishing those who treat their
A review on additive manufacturing of Al–Cu (2xxx) aluminium alloys, processes and defects

Aluminium alloys are commonly used in different areas and considerable interest has been given to additive manufacturing (AM) aluminium alloys recently. However, a few research studies succeeded in fabricating high-strength 2xxx alloys owing to porosity formation and their high cracking susceptibility. AM of these alloys is still suffering from different challenges. This review aimed at giving an insight into the current state-of-the-art of Al–Cu alloys AM and discusses the processes, challenges, and approaches in improving the integrity and performance of the parts manufactured in recent years. First, an introduction about the AM processes is given. Then, the main techniques used to fabricate parts are presented, and major defects found in additively manufactured parts are discussed afterward.
Time Series Forecasting Method Based on Finite State Machine

This paper considers the possibility of using deterministic finite state automatons for real time series forecasting. In order to solve this problem, the authors propose to modify a 10-readheads automaton which was designed to recognize multilinear sequences. The paper presents various modifications of this automaton and the results which were obtained using them for various time series. In addition, the paper presents a method for changing the algorithm of the automaton to enable its step by step execution.
Multilayer Slot Coupling Ultra-wideband Power Divider Based on Integrated Substrate Gap Waveguide

In this paper, based on the integrated substrate gap waveguide (ISGW), an ultra-wideband power divider using multilayer slot coupling technology is designed for 5G and satellite radar applications. The power divider used the ISGW which is composed of a via layer and a gap layer and transmitted energy from a coupling patch on transmission ridge of bottom layer through coupling slot to coupling patch on transmission ridge of top layer. This design has good isolation after adding the isolation resistor to a modified position. The simulated results show that return loss of the power divider is less than 10-dB, which the bandwidth can reach at least 15.7 GHz from 20.2GHz to 35.9 GHz. Also, the best insertion loss in a band is 3.53dB. The isolation degree is always better than 15-dB in 22.2GHz-35.4GHz.
Topology Morphing Control with Soft Transients for Multimode Series Resonant DC-DC Converter

The paper focuses on the galvanically isolated series-resonant dc-dc converter (SRC) operating at low values of the quality factor of the resonant tank. This topology is considered as a step-up front-end dc-dc converter with buck-boost functionality for integration of renewable energy sources in dc microgrids. The paper proposes the algorithm for soft transitions between half-bridge and full-bridge configurations in the input and output sides of the converter. The converter operates in a wide input voltage range with four control modes and performs soft transitions between them. The algorithm allows for linear recharging of blocking capacitors and stabilizing input voltage and current during transition intervals. The theoretical findings are corroborated with experimental study of converter capability to perform global maximum power point tracking of a photovoltaic module.
Spatial-Temporal Correlation-Concerned Measurement Manipulation Detection Based on Gramian Angular Summation Field and Convolutional Neural Networks

The growing integration of information and communication technology exposes more exploitable cyber-attack vectors to electric power systems. Measurement manipulation is an impactive type of cyber-attacks that can be launched to cause massive damages by interfering decision-making of control centers. Existing data-driven measurement manipulation detection models often bear criticisms due to the deficient mining of spatial-temporal correlations. In this paper, based on the Gramian Angular Summation Field (GASF) algorithm, various types of phasor time-series measured at different locations are transformed into a series of generalized multichannel images. From these generalized images, Convolutional Neural Networks (CNNs) are trained to discover suspicious patterns. GASF can visualize the temporal features of a single time-series and facilitate collaborative CNNs to figure out spatial-temporal correlations between different measurements. The improvements of the proposed framework in the detection performance are demonstrated through case studies.
Low Dose CT Image Restoration by Incremental Learning and Ant Colony Optimization

Patch priors play a key issue in patch based image restoration tasks. The main idea is to learn a good and adaptive image prior over small image patches. However, expected patch log ikelihood (EPLL) framework achieved a good performance in image restoration applications, it mainly employs the Gaussian Mixture model (GMM) prior over a large number of training patches which gains a huge computational cost. On the other hand, the GMM parameters are learned with the Expectation Maximization (EM) algorithm which usually suffers from local optimum problem. This paper proposes a post filter by extending the EPLL framework to reduce noise in CT images acquired on low radiation dose, in order to get a better quality image with fine details preserved. The basic idea is to incrementally estimate the mixture component parameters to skip over the significant drawback of the (EM) algorithm which greatly depends on the initialization of model parameters. Incremental method with split-merge mixtures learning (SMILE) is utilized for learning mixture models by consecutive split and merge operations. Then, the Ant Colony Optimization (ACO) algorithm is used to select homogeneous patches which tend to cluster together due to the local spatial smoothness in the image. Instead of restoring each patch separately, the maximum a posteriori (MAP) estimation is employed to denoise the homogeneous patches selected by ACO algorithm. The proposed method leads to better clustering results and superior denoised results.
Double-Layer Broadband and Wide Beam Microstrip Array Antenna For 79GHz Automotive Radar

A double-layer broadband and wide beam microstrip antenna is presented for 79GHz automotive radar. The antenna is composed of a microstrip line, a radiation patch and a parasitic patch. In addition, the radiation patch is fed by the microstrip line in the form of coupling feed. The parasitic patch on the top substrate is designed for wider band. Based on the double-layer elements, a 10-elements standing-wave linear array is constructed, which have broadband with 5 GHz from 76GHz to 81GHz. Besides, the array antenna can achieve a wide beam of 115° in the azimuth plane and a low side-lobe less than −20dB in the elevation plane.
Fast energy relaxation in the afterglow of a nanosecond capillary discharge in nitrogen/oxygen mixtures

The main aim of the present work was to investigate, how the plasma kinetics changes at the conditions of high specific energy deposition at high reduced electric fields. The nanosecond capillary discharge was used as an experimental tool. The measurements are performed for pure nitrogen and nitrogen/oxygen mixtures. Electric parameters of the discharge are measured: the applied voltage, the energy deposition, the electric field, and the electric current. The spectra of the optical emission of the discharge are obtained. The electron density in the discharge pulses is measured. The plasma decay is studied by the measurements of the electron density in the afterglow of the discharge as well. The processes controlling the plasma decay are determined. Depopulation of excited molecules on the example of N2(C) state of molecular nitrogen is studied in nitrogen/oxygen mixtures. The processes responsible for depopulation of excited species in the afterglow of the nanosecond discharge at high specific deposited energy at high reduced electric field are discussed. The radial distributions of the excited species at different gas compositions are investigated and compared. The experimental and numerical studies of the processes responsible for fast gas heating in pure nitrogen and in nitrogen/oxygen mixtures in the afterglow of nanosecond capillary discharge are performed. The peculiarities of the fast gas heating at conditions of the nanosecond discharge at high specific deposited energy at high reduced electric field are discussed. The modification of the kinetic scheme currently in use is proposed.
Torque Harmonic Elimination for Dual Three-Phase PMSMs Based on CPS-SVPWM

The traditional single three-phase two-level voltage source inverter introduces high-frequency current ripple due to pulse width modulation. It leads to high-frequency vibration and noise in motor control. To solve this problem, carrier phase-shift SVPWM (CPS-SVPWM) algorithm for a dual three-phase PMSM system is proposed in this paper. First, the reasons for the high-frequency torque ripple in the traditional three-phase PMSM system is analyzed. On this basis, a carrier phase shift algorithm is proposed for the dual three-phase PMSM system to reduce the motor torque ripple. Finally, finite element simulation verified the effectiveness of the algorithm.
A 38 GHz Modified Circular Microstrip Patch Antenna for 5G Mobile Systems

This paper proposes a single microstrip antenna designed to operate at the 38 GHz fifth generation (5G) allocated band. This element is an altered circular microstrip patch antenna that is created and optimized to function at the desired band of frequency. The performance of the individual element is simulated and measured and its performance is evaluated at the perspective desired frequency. The individual optimized element impedance matching bandwidth reached at the 38 GHz is 0.6391 GHz utilizing a minimum return loss from −20.62249 dB. The achieved gain for the individual element is 7.5 dB at 37.9 GHz. The obtained radiation pattern is omni-directional at the desired frequency band. The measured and simulated results conform to a large degree and confirm the good performance with respect to gain, return loss, bandwidth as well as the radiation pattern. This makes the suggested the antenna suitable for 5G applications.
Retinal Vessel Segmentation Using Deep Learning: A Review

This paper presents a comprehensive review of retinal blood vessel segmentation based on deep learning. The geometric characteristics of retinal vessels reflect the health status of patients and help to diagnose some diseases such as diabetes and hypertension. The accurate diagnosis and timing treatment of these diseases can prevent global blindness of patients. Recently, deep learning algorithms have been rapidly applied to retinal vessel segmentation due to their higher efficiency and accuracy, when compared with manual segmentation and other computer-aided diagnosis techniques. In this work, we reviewed recent publications for retinal vessel segmentation based on deep learning. We surveyed these proposed methods especially the network architectures and figured out the trend of models. We summarized obstacles and key aspects for applying deep learning to retinal vessel segmentation and indicated future research directions. This article will help researchers to construct more advanced and robust models.
A New Current Mirror Balancing Circuit for AC-LED Lighting

This paper proposes a new current balancing method for alternating current (AC) - light emitting diodes (LED). The proposed method uses the bi-directional current mirror circuit with negative feedback that consists of two reference currents, n+1 NPN transistors, n+1 PNP transistors, and 2(n+1) resistors for n pairs of anti-parallel LED strings. It can balance the LED currents in multiple AC-LED strings regardless of the polarity of the AC source while reducing the differences in LED currents caused by the early effect. In addition, the proposed circuit can achieve LED current balancing even in the variations of parameters among the transistors due to the negative feedback operation. Simulations and experiments were performed to verify the effectiveness of the proposed circuit. The balancing operation of the proposed circuit was simulated at low (50/60 Hz) and high (60 kHz) frequencies. In addition, open/short protection, power factor, and efficiency of an LED driver using the proposed circuit were analyzed and compared with a basic mirror circuit. For the experiments, a prototype was built and tested for 5W LED lighting consisting of three pairs of anti-parallel LED strings. The maximum LED current difference $\Delta i_{LED} $ was reduced from 45 mA to 4 mA by the balancing operation of the proposed circuit.
Moving Obstacle Segmentation with an Optical Flow-based DNN: an Implementation Case Study

Moving object detection is a crucial component of automotive systems’ safety functions. AI-based approaches for object detection are the most common solutions in the case of self-driving vehicles. For autonomous navigation in an industrial setting, a deep learning model that relies on stronger assumptions regarding the environment, can be implemented. This is due to the fact that an industrial environment is more strictly controlled than an urban area. However, the detection of moving obstacles is still a challenging task and its solution can serve as the basis for more advanced models. In this paper, we introduce an optical flow-based deep neural network approach for moving object segmentation and state of motion estimation in industrial environment as an implementation case study. The algorithm is based on our earlier optical flow egomotion filtering method and optical flow-based Deep Neural Network, called OFSNet. The aim of this paper is to introduce the main hardware and software modules of the moving object segmentation system, the integration process, the communication considerations and the logistics management system. The proposed system was installed and tested on a mobile robot platform in a mock warehouse environment. All of the program codes, documentations and installation steps are publicly available at GitHub.
Information Fusion in Attention Networks Using Adaptive and Multi-Level Factorized Bilinear Pooling for Audio-Visual Emotion Recognition

Multimodal emotion recognition is a challenging task in emotion computing as it is quite difficult to extract discriminative features to identify the subtle differences in human emotions with abstract concept and multiple expressions. Moreover, how to fully utilize both audio and visual information is still an open problem. In this paper, we propose a novel multimodal fusion attention network for audio-visual emotion recognition based on adaptive and multi-level factorized bilinear pooling (FBP). First, for the audio stream, a fully convolutional network (FCN) equipped with 1-D attention mechanism and local response normalization is designed for speech emotion recognition. Next, a global FBP (G-FBP) approach is presented to perform audio-visual information fusion by integrating self-attention based video stream with the proposed audio stream. To improve G-FBP, an adaptive strategy (AG-FBP) to dynamically calculate the fusion weight of two modalities is devised based on the emotion-related representation vectors from the attention mechanism of respective modalities. Finally, to fully utilize the local emotion information, adaptive and multi-level FBP (AM-FBP) is introduced by combining both global-trunk and intra-trunk data in one recording on top of AG-FBP. Tested on the IEMOCAP corpus for speech emotion recognition with only audio stream, the new FCN method outperforms the state-of-the-art results with an accuracy of 71.40%. Moreover, validated on the AFEW database of EmotiW2019 sub-challenge and the IEMOCAP corpus for audio-visual emotion recognition, the proposed AM-FBP approach achieves the best accuracy of 63.09% and 75.49% respectively on the test set.
Yield and Growth Parameters of Maize Hybrids As Influenced By Target Yield Approach

Maize a thermo-insensitive, long day kharif cereal adopts well to the diverse climatic conditions. With the introduction of high yielding varieties of maize, use of high analysis chemical fertilizer and monocropping practices gained popularity. This led to systematic mining of soils for major nutrients such as nitrogen, phosphorus and potassium resulting in wide spread low harvest of poor quality grains. Adequate nutrition therefore, is important but the emphasis should also be International Journal of Current Microbiology and Applied Sciences ISSN: 2319-7706 Volume 6 Number 9 (2017) pp. 220-229 Journal homepage: http://www.ijcmas. com
Feature Selection for Electrical Demand Forecasting and Analysis of Pearson Coefficient

Demand forecasting contributes to the stable operation of the electrical grid. Increasing computational complexity with the emerging big data in the interconnected electricity grid poses a challenge on the effective electrical demand forecasting. The emerging machine learning algorithms are capable of handling this big data, but their performance depends on the quality of the input features in the dataset. Feature selection filters relevant and informative attributes, improving the quality of the input dataset hence facilitating better prediction. In this article, the feature selection for electrical demand forecasting is presented. The relationship of input features with the electrical demand and relation between the input features are analysed. Correlation coefficient, variance inflation factor (VIF) and analysis of variance (ANOVA) based techniques are examined to identify the relevant attributes. Performance of ANOVA is analysed via sum of squares (SS), mean square (MS) and degree of freedom (df). A brief discussion on the key issues concerning multicollinearity and the application of Pearson coefficient is also presented. Challenges that require further attention in feature selection research are discussed at the end.
Informed Sampling Exploration Path Planner for 3D Reconstruction of Large Scenes

As vision-based navigation of small aircraft has been demonstrated to reach relative maturity, research into effective path-planning algorithms to complete the loop of autonomous navigation has been booming. Although the literature has seen some impressive works in this area, efficient path-planning that can be used in tasks such as inspection and coverage is still an open problem. In this spirit, this letter presents an online path-planning algorithm for fast exploration and 3D reconstruction of a previously unknown area of interest. Micro Aerial Vehicles (MAVs) are an ideal candidate for this task due to their maneuverability, but their limited computational power and endurance require efficient planning strategies. Popular sampling-based methods randomly sample the MAV's configuration space and evaluate viewpoints according to their expected information gain. Most often, however, valuable resources are spent on information gain calculations of unpromising viewpoints. This letter proposes a novel informed sampling approach that leverages surface frontiers to sample viewpoints only where high information gain is expected, leading to faster exploration. We study the impact of informed sampling in a wide range of photo-realistic scenes, and we show that our approach outperforms state-of-the-art exploration path planners in terms of both speed and reconstruction quality.