Miracle-dz/UniFAR-FTUs huggingface dataset:
Flickr data for analysing tourists’ spatial behaviour and movement patterns

The purpose of this study is to analyse the suitability of photo-sharing platforms, such as Flickr, to extract relevant knowledge on tourists’ spatial movement and point of interest (POI) visitation behaviour and compare the most prominent clustering approaches to identify POIs in various application scenarios. ,The study, first, extracts photo metadata from Flickr, such as upload time, location and user. Then, photo uploads are assigned to latent POIs by density-based spatial clustering of applications with noise (DBSCAN) and k-means clustering algorithms. Finally, association rule analysis (FP-growth algorithm) and sequential pattern mining (generalised sequential pattern algorithm) are used to identify tourists’ behavioural patterns. ,The approach has been demonstrated for the city of Munich, extracting 13,545 photos for the year 2015. POIs, identified by DBSCAN and k-means clustering, could be meaningfully assigned to well-known POIs. By doing so, both techniques show specific advantages for different usage scenarios. Association rule analysis revealed strong rules (support: 1.0-4.6 per cent; lift: 1.4-32.1 per cent), and sequential pattern mining identified relevant frequent visitation sequences (support: 0.6-1.7 per cent). ,As a theoretic contribution, this study comparatively analyses the suitability of different clustering techniques to appropriately identify POIs based on photo upload data as an input to association rule analysis and sequential pattern mining as an alternative but also complementary techniques to analyse tourists’ spatial behaviour. ,From a practical perspective, the study highlights that big data sources, such as Flickr, show the potential to effectively substitute traditional data sources for analysing tourists’ spatial behaviour and movement patterns within a destination. Especially, the approach offers the advantage of being fully automatic and executable in a real-time environment. ,The study presents an approach to identify POIs by clustering photo uploads on social media platforms and to analyse tourists’ spatial behaviour by association rule analysis and sequential pattern mining. The study gains novel insights into the suitability of different clustering techniques to identify POIs in different application scenarios. ,本论文旨在分析图片分享平台Flickr对截取游客空间动线信息和景点（POI）游览行为的适用性, 并且对比最知名的几种聚类分析手段, 以确定不同情况下的POI。 ,本论文首先从Flickr上摘录下图片大数据, 比如上传时间、地点、用户等。 其次, 本论文使用DBSCAN和k-means聚类分析参数来将上传图片分配给POI隐性变量。 最后, 本论文采用关联规则挖掘分析（FP-growth参数）和序列样式勘探分析（GSP参数）以确认游客行为模式。 ,本论文以慕尼黑城市为样本, 截取2015年13,545张图片。 POIs由DBSCAN和k-means聚类分析将其分配到有名的POIs。 由此, 本论文证明了两种技术对不同用法的各自优势。 关联规则挖掘分析显示了显著联系（support：1%−4.6%；lift：1.4%−32.1%）, 序列样式勘探分析确立了相关频率游览次序（support：0.6%−1.7%。 ,本论文的理论贡献在于, 根据图片数据, 通过对比分析不同聚类分析技术对确立POIs, 并且证明关联规则挖掘分析和序列样式勘探分析各有千秋又互相补充的分析技术以确立游客空间行为。 ,本论文的现实意义在于, 强调了大数据的来源, 比如Flickr,证明了其对于有效代替传统数据的潜力, 以分析在游客在一个旅游目的地的空间行为和动线模式。 特别是这种方法实现了实时自动可操作性等优势。 ,本论文展示了一种方法, 这种方法通过聚类分析社交媒体上的上传图片以确立POIs, 以及通过关联规则挖掘分析和序列样式勘探分析来分析游客空间行为。 本论文对于不同聚类分析以确立不同适用情况下的POIs的确立提出了独到见解。
Transcriptional regulation of the Nε-fructoselysine metabolism in Escherichia coli by global and substrate-specific cues

Thermally processed food is an important part of the human diet. Heat-treatment, however, promotes the formation of so-called Amadori rearrangement products (ARPs), such as fructoselysine. The gut microbiota including Escherichia coli can utilize these compounds as a nutrient source. While the degradation route for fructoselysine is well described, regulation of the corresponding pathway genes frlABCD remained poorly understood. Here we use bioinformatics combined with molecular and biochemical analyses and show that in E. coli, fructoselysine metabolism is tightly controlled at the transcriptional level. The global regulator Crp (CAP), as well as the alternative sigma factor σ32 (RpoH) contribute to promoter activation at high cAMP-levels and heat stress, respectively. In addition, we identified and characterized a transcriptional regulator FrlR, encoded adjacent to frlABCD, as fructoselysine-6-phosphate specific roadblock repressor. Our study provides profound evidence that the interplay of global and substrate-specific regulation is a perfect adaptation strategy to efficiently utilize unusual substrates within the human gut environment. Abbreviated Summary Thermal food processing promotes the formation of Amadori rearrangement products (ARPs), such as fructoselysine. The gut microbiota including Escherichia coli can utilize these compounds as a nutrient source. We show that in E. coli, fructoselysine metabolism is tightly controlled at the transcriptional level by global and substrate-specific regulators. Their interplay is a perfect adaptation strategy to efficiently utilize fructoselysine within the human gut environment. Graphical Abstract
APPLYING MULTI AGENT SYSTEM TO TRACK UAV MOVEMENT

The thesis introduces an innovative UAV detection system. The commercial UAV market is booming. Meanwhile, the risks and threats from improper UAV usages are also booming. CUAS is to protect the public and facilities. The problem is a lack of an intelligent platform which can adapt many sensors for UAV detection. The hypothesis is that, the system can track the UAV’s movement by applying the multi-agent system (MAS) to UAV route track. The experiment proves that the multi-agent system benefits for the UAV track.
A Pedagogy to Develop Effective Virtual Teams

ABSTRACT While virtual teams dominate business environments, little attention has been paid to how team leadership, in particular shared leadership, can be reinforced in classrooms. There is a consensus that shared leadership impacts the performance of virtual groups in a positive manner. While higher education in business is aiming to prepare graduates for virtual team leadership and effectiveness, pedagogical practices to orient students to virtual leadership based on quantitative research are lacking. Business school educators need to develop a classroom pedagogy that encourages the growth of shared leadership in virtual groups within students. This paper presents the results of preliminary research that demonstrates that one aspect of the skills of shared leadership, in the form of expertise-based teamwork, contributes to the success of virtual teams in a business class. How this pedagogical practice can be applied to the business setting is discussed. Replication of this work is planned.
Ladybird Cobbitty 2017 Brassica Dataset

2020-04-02 - Dataset Updated Update README.txt files for JFR reference. Include annotation and calibration files. Include example code. ChangeLog.txt: Changelog added. autonomous/annotations/: Added directory containing bounding-box image annotations of cauliflower and broccoli across all four beds and all ten weeks. autonomous/calibrations/: Added directory containing Ladybird calibration data. autonomous/example_code/: Added directory of Jupyter Notebooks (Python3) for loading and viewing Ladybird sensor data. 2019-03-21 - Original upload
Experimental and Model‐Based Investigation of the Effect of the Free‐Surface Flow Regime on the Detection Threshold of Warm Water Inflows

Heat has been used as a tracer to identify and quantify groundwater inflows into streams. Over the last decade, a few methods have used fiber‐optic distributed temperature sensing to facilitate assessment of such inflows into small streams. However, these methods focused mainly on the groundwater percentage and the thermal contrast between groundwater and surface water without considering the flow regime of the surface water. In this study, artificial water inflows into a controlled flume were examined using fiber‐optic distributed temperature sensing to quantify the thermal anomalies induced as a function of the flow regime (turbulent or laminar). Computer simulations were then performed to widen the range of the parameters tested and provide insight into the physical processes involved. Experiments conducted under the turbulent regime were in accordance with results and uncertainties of previous studies. Under the laminar regime, however, the inflow‐induced thermal anomalies were always smaller than those under the turbulent regime for a given inflow percentage. Therefore, the actual inflow percentage may be underestimated when using a classic method under a laminar regime.
Cross-Modal Subspace Clustering via Deep Canonical Correlation Analysis

For cross-modal subspace clustering, the key point is how to exploit the correlation information between cross-modal data. However, most hierarchical and structural correlation information among cross-modal data cannot be well exploited due to its high-dimensional non-linear property. To tackle this problem, in this paper, we propose an unsupervised framework named Cross-Modal Subspace Clustering via Deep Canonical Correlation Analysis (CMSC-DCCA), which incorporates the correlation constraint with a self-expressive layer to make full use of information among the inter-modal data and the intra-modal data. More specifically, the proposed model consists of three components: 1) deep canonical correlation analysis (Deep CCA) model; 2) self-expressive layer; 3) Deep CCA decoders. The Deep CCA model consists of convolutional encoders and correlation constraint. Convolutional encoders are used to obtain the latent representations of cross-modal data, while adding the correlation constraint for the latent representations can make full use of the information of the inter-modal data. Furthermore, self-expressive layer works on latent representations and constrain it perform self-expression properties, which makes the shared coefficient matrix could capture the hierarchical intra-modal correlations of each modality. Then Deep CCA decoders reconstruct data to ensure that the encoded features can preserve the structure of the original data. Experimental results on several real-world datasets demonstrate the proposed method outperforms the state-of-the-art methods.
Exaggerated Cortical Representation of Speech in Older Listeners: Mutual Information Analysis

Aging is associated with an exaggerated representation of the speech envelope in auditory cortex. The relationship between this age-related exaggerated response and a listener’s ability to understand speech in noise remains an open question. Here, information-theory-based analysis methods are applied to magnetoencephalography (MEG) recordings of human listeners, investigating their cortical responses to continuous speech, using the novel non-linear measure of phase-locked mutual information between the speech stimuli and cortical responses. The cortex of older listeners shows an exaggerated level of mutual information, compared to younger listeners, for both attended and unattended speakers. The mutual information peaks for several distinct latencies: early (∼50 ms), middle (∼100 ms) and late (∼200 ms). For the late component, the neural enhancement of attended over unattended speech is affected by stimulus SNR, but the direction of this dependency is reversed by aging. Critically, in older listeners and for the same late component, greater cortical exaggeration is correlated with decreased behavioral inhibitory control. This negative correlation also carries over to speech intelligibility in noise, where greater cortical exaggeration in older listeners is correlated with worse speech intelligibility scores. Finally, an age-related lateralization difference is also seen for the ∼100 ms latency peaks, where older listeners show a bilateral response compared to younger listeners’ right-lateralization. Thus, this information-theory-based analysis provides new, and less coarse-grained, results regarding age-related change in auditory cortical speech processing, and its correlation with cognitive measures, compared to related linear measures. New & Noteworthy Cortical representations of natural speech are investigated using a novel non-linear approach based on mutual information. Cortical responses, phase-locked to the speech envelope, show an exaggerated level of mutual information associated with aging, appearing at several distinct latencies (∼50, ∼100 and ∼200 ms). Critically, for older listeners only, the ∼200 ms latency response components are correlated with specific behavioral measures, including behavioral inhibition and speech comprehension.
Analysis of the sideband current harmonics and vibro‐acoustics in the PMSM with SVPWM

To control the high-frequency acoustic noise of the permanent magnet synchronous machine (PMSM) with the space vector pulse-width modulation (SVPWM), an investigation into the mechanism of the sideband vibro-acoustics is presented in this study. Especially, the relationship is developed between the sideband electromagnetic components and the mechanical vibro-acoustics, which paves the fundamentals to reduce the noise at the sources. Firstly, the analytical derivations of the sideband voltage and current harmonics are expressed, while the magnetic components of permanent magnet and armature reaction are also discussed in different carrier frequency domains. Secondly, the corresponding radial electromagnetic force analysis is established to acquire the pattern of the vibro-acoustic. Finally, the proposed multi-physics models are reconstructed by coupling spatial and temporal features, where the amplitudes are also calculated. A 12-slot10-pole prototype PMSM is considered as a case study to testify the accuracy of the proposed method, where the corresponding electromagnetic components are calculated and measured under different conditions, together with the vibration and acoustics. This investigation may provide a new route for the acoustics prediction and the precise optimisation of PMSM.
Machine learning based approach to detection of anomalous data from sensors in cyber-physical water supply systems

The paper proposes an approach to detection of anomalous data from sensors in cyber-physical systems on an example of a water supply supply system. The approach is based on methods of machine learning and modeling of technical systems. The primary data for machine learning was obtained on the developed software/hardware prototype of the water supply system by using a number of microcontrollers, sensors and actuators. The experiments confirmed the feasibility of the proposed approach. Several machine learning methods from scikit-learn library of the Python programming language were tested. As a result of the experiments we identified a learning method and its parameters ensuring the highest accuracy of the abnormal situations recognition.
La vaginose bactérienne et ses conséquences en santé publique.

La vaginose bacterienne (VB) est l'infection genitale basse la plus frequente chez la femme. Relevant d'une alteration de l'ecosysteme vaginal, elle voit le remplacement de la flore normale ou dominent les lactobacilles par d'autres especes bacteriennes de cette flore (Gardnerella vaginalis, Mycoplasma hominis, especes anaerobies diverses) qui se multiplient anormalement. La cause precise de ce remplacement n'est pas connue, mais le role de certains facteurs a ete mis en evidence: disparition des lactobacilles probiotiques (producteurs de H 2 O 2 ) ou perte de la capacite d'adhesion, traitements antibiotiques, antiseptiques et toilette gynecologique drastique. Les consequences de la vaginose bacterienne, longtemps consideree comme une affection denuee de toute gravite, apparaissent aujourd'hui comme devant etre prises en consideration en matiere de sante publique. Comme d'autres infections genitales bacteriennes, elle peut etre a l'origine de complications impliquant le haut appareil genital: endometrite et salpingite. Chez la femme enceinte, son role dans les risques de chorioamniotite, d'infections intra-amniotiques et d'accouchements prematures est etabli. Enfin, elle semble etre associee a des risques accrus vis-a-vis de l'infection par le virus de l'immunodeficience humaine, tant en matiere de susceptibilite que d'infectiosite.
Deep Learning for Black-Box Modeling of Audio Effects

Virtual analog modeling of audio effects consists of emulating the sound of an audio processor reference device. This digital simulation is normally done by designing mathematical models of these systems. It is often difficult because it seeks to accurately model all components within the effect unit, which usually contains various nonlinearities and time-varying components. Most existing methods for audio effects modeling are either simplified or optimized to a very specific circuit or type of audio effect and cannot be efficiently translated to other types of audio effects. Recently, deep neural networks have been explored as black-box modeling strategies to solve this task, i.e., by using only input–output measurements. We analyse different state-of-the-art deep learning models based on convolutional and recurrent neural networks, feedforward WaveNet architectures and we also introduce a new model based on the combination of the aforementioned models. Through objective perceptual-based metrics and subjective listening tests we explore the performance of these models when modeling various analog audio effects. Thus, we show virtual analog models of nonlinear effects, such as a tube preamplifier; nonlinear effects with memory, such as a transistor-based limiter and nonlinear time-varying effects, such as the rotating horn and rotating woofer of a Leslie speaker cabinet.
The complete chloroplast genome of an endangered plant-Nomocharis aperta

Abstract Nomocharis aperta is an endangered and endemic species with high ornamental value in China. In this study, we reported a complete chloroplast genome of N. aperta, which was de novo assembled using the next-generation sequencing data. The complete chloroplast genome is 152,845 in length, including a large single copy region of 70,506 bp and a small single copy region of 17,468 bp and two inverted repeat regions of 26,520 bp. A total of 130 functional genes were encoded, consisting of 84 protein-coding genes, 36 transfer RNA genes, and 8 ribosomal RNA genes. The overall AT content of the chloroplast genome is 63.00%. In addition, phylogenetic analysis with the reported chloroplast genomes showed that N. aperta is nested within Lilium and close to L. henricii, L. bakerianum and L. taliense. It indicates that the study on the relationship between Nomocharis and Lilium needs more Nomocharis and Lilium complete chloroplast genome, especially some key species like N. aperta.
Powered wind tunnel tests setup of the IRON innovative turboprop aircraft

This paper describes the powered wind tunnel tests setup of the innovative configuration of the IRON regional turboprop aircraft. The objective of the tests is the evaluation of propulsive effects on aircraft stability and control characteristics. During the setup process, several aerodynamic issues have been anticipated and here illustrated. A scaled engine deck has been derived from the full-scale data provided by the IRON powerplant consortium partner. From two representative flight conditions, the characteristics of the scaled motor as RPM, torque and power have been calculated, providing a choice for the electric motors to install in the test section. The motors’ operating voltage and current determined the sizing of the power, acquisition and control system. Similarly, the desired propeller coefficients were the target of a propeller design process, which was performed with XROTOR, MATLAB®, XFOIL and validated with RANS analyses. Finally, to directly evaluate the propeller thrust and normal force, motors’ supporting structures with load cells have been conceptually designed.
Fabrication and characterization of Au/n-type InP Schottky barrier diode with monolayer graphene interlayer

Au/graphene/n-InP Schottky barrier diode (SBD) was fabricated by the use of spray pyrolysis technique with a monolayer graphene interlayer, and the temperature dependent characteristics was performed in a wide temperature range from 60 to 300 K with steps. The Au/GR/n-InP SBD exhibited excellent rectifying behavior however barrier height (Φb0) of the device increased with increasing temperature while ideality factor (n) and series resistance (Rs) decreased. The strong temperature dependency of SBH and the deviation from theoretical value of Richardson constant clarified by considering a Gaussian distribution model of the SBH which was caused by the BH inhomogeneities. The mean BH Φ ¯ b 0 of 0.94 and 0.59 eV was estimated with the standard deviation of 0.011 and 0.004 eV attributed to the presence of a double GD of SBD. The modified Richardson plots gave mean BH value of 0.98 and 0.70 eV and the Richardson constant values of 8.10, 13,38 A K−2 cm−2 was very close to its theoretical value of 9.4 A K−2 cm−2. These results yields that carrier transport mechanism of Au/GR/n-InP SBD can be clarified by Thermionic-emission-diffusion (TED) mechanism with a double Gaussian distribution of the SBHs. In addition, the I–V characteristics of SBD under dark and light ambiance indicates that Au/graphene/n-InP is strongly dependent on the light effect. Thus, we can conclude that graphene/n-InP device is a potential candidate for photovoltaic systems. Moreover, the UV–vis spectroscopy of the graphene film exhibit a strong absorption as A = 9.45% compared to the literature, that is, Au/GR/n-InP SBD grown by spray pyrolysis has excellent optical properties for optoelectronic applications.
Estimation and Projection of Petroleum Demand and Tax Collection from Petroleum Sector in India

Taxes from the petroleum sector constitute a significant share in indirect tax collection of the union as well as state governments in India. Understanding prospective revenue from petroleum taxes could help governments in better public finance management. The importance of revenue from the petroleum sector has increased after the introduction of Goods and Services Tax (GST) in India, as fiscal autonomy of the governments (both federal and provincial) to augment tax collection through unilateral policy changes has been curtailed with harmonisation of the tax system. Revenue mobilisation from petroleum taxes is dependent on consumption (sales) of petroleum products, and, therefore, understanding consumption of petroleum products is important to understand prospective revenue from the petroleum sector. The objective of this article is to estimate the petroleum consumption function and revenue (tax collection) function. Based on the estimated functions, we project the petroleum demand and tax collection for the period from 2017–18 to 2024–25. JEL Classification: H25, Q41, Q47
Saddle–splay elasticity induced chiral structures in achiral liquid crystals within cylindrical geometry

ABSTRACT On the basis of Landau–de Gennes theory and the finite-difference iterative method, spontaneous chiral structures in cylinders with degenerate planar boundary conditions are investigated. A double-twist director configuration can be achieved by the saddle–splay contribution, corresponding to the L24 term in Landau–de Gennes theory. The twist angle increases as the radius R of the cylinder is reduced because the curvature of the cylindrical surface becomes larger. Moreover, we find fine structures of the domain walls and point defects between opposite-handed domains. An energy comparison shows that domain walls are the stable state for K24/K> 0.6 (K11 = K33 = K, K11, K33 and K24 are the splay, bend, saddle–splay elastic constants in Frank theory), whereas point defects are the stable state for K24/K< 0.6. Graphical abstract Schematic of the chiral director configuration for domain walls (a, c) and point defects (b, d), induced by saddle-splay elasticity.