Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic and static postural alterations in HIV-related progressive multifocal leukoencephalopathy in a Latino-Hispanic male: a case study

Introduction: Human immunodeficiency virus (HIV) affects millions of people worldwide. Because of an impaired immune system, HIV-positive people are at a higher risk of contracting HIV-related complications, including progressive multifocal leukoencephalopathy (PML). PML is a progressive disease involving degeneration of the white matter in the central nervous system, causing profound adverse effects on gait and posture. This case report presents gait and posture deficiencies in a physically active 52-year-old Latino-Hispanic male with HIV-related PML. Case description: System examinations were conducted to evaluate confidence in balance, balance deficits, and fall risk, given the clinical presentation of HIV-related PML. The patient presented a profound balance and gait motor control impairments, with alterations in posture stability, particularly in mediolateral sway, and velocity were increased when adding vestibular input challenge and dual-tasks (counting backward) components. During gait assessment, the patient compensated by reducing speed during the turn and walk back to the sitting position. Conclusions: This case study strives to provide specific evidence for static and dynamic motor control in PML HIV-related case. Strengthening exercises focusing on posture musculature are highly recommended. Additionally, we encourage to focus on dynamic balance interventions to improve PML-related alterations such the ones presented in this case report. HIV AIDS Rev 2020; 19, 2: 139-145 DOI: https://doi.org/10.5114/hivar.2020. 96326
The formation of fat-derived flavour compounds during the ripening of Gouda-type cheese

Cheese flavour is an important quality attribute, and is mainly formed during cheese ripening. Besides compounds that are formed from protein and carbohydrates, milk fat-derived compounds are essential for cheese flavour. Before, but mainly during ripening, free fatty acids, lactones, ketones, esters, alcohols and aldehydes can be formed from milk fat, compounds that all contribute their own special character to the final cheese flavour. The formation mechanisms that lead to these compounds are not completely understood, and therefore cheese flavour formation is not under full control.
Inductive Power Transfer Systems With Digital Switch-Controlled Capacitor for Maximum Efficiency Point Tracking

Inductor–capacitor–capacitor-series (LCC-S) compensation topology has been widely used in inductive power transfer (IPT) systems due to its constant coil current regardless of the load and the coupling coefficient. Usually, the inductance of transmitter coil is sensitive to misalignments of the magnetic coupler when the asymmetrical coil structure with a smaller size receiver coil is adopted. The fluctuation of the inductance of transmitter coil will increase the power losses in the primary inverter. To compensate the fluctuation of the transmitter coil inductance, a digital switch-controlled capacitor (D-SCC) for maximum efficiency point tracking (MEPT) of LCC-S compensated IPT system is proposed. Consequently, the proposed LCC-S compensated IPT system can achieve high efficiency against the fluctuation of transmitter coil inductance. In traditional switch-controlled capacitor, high- frequency current sensor is required for the synchronization between the transmitter coil current and the driver signals of the switch-controlled capacitor. Nevertheless, the drive signals of D-SCC can be generated directly by the DSP controller in the primary side without detecting zero-cross points of the transmitter coil current, which can improve the system reliability and reduce the cost. Furthermore, the MEPT based on D-SCC is easy to be implemented because only the input dc current measurement is required. A 3.3-kW prototype is presented to verify the validity of the proposed D-SCC-based MEPT.
Monkey Features Location Identification Using Convolutional Neural Networks

Understanding animal behavior in its natural habitat is a challenging task. One of the primary step for analyzing animal behavior is feature detection. In this study, we propose the use of deep convolutional neural network (CNN) to locate monkey features from raw RGB images of monkey in its natural environment. We train the model to identify features such as the nose and shoulders of the monkey at about 0.01 model loss.
Gas exchanges and growth of maize as affected by aeration porosity and soil compaction

Assuming that less than 10% aeration porosity is sufficient to ensure plant development, provided that other aspects of porous space remain functional, and the magnitude of this effect is dependent on soil density, the objective of this study were to evaluate the physiological and growth responses in corn plants submitted to different aeration porosities and compaction levels. The experiment was conducted in a greenhouse, in randomized block design in factorial arrangement (2 x 5), two densities: 1.6 and 1.7 Mg m - ³, five aeration porosity: 7, 8, 9, 10 and 12%, with five repetitions per treatment. For each density were made columns of soil in hydraulic press, and the aeration porosities were monitored by mercury tensiometers. The interaction between the factors had no effect on the analyzed variables. Aeration porosity lower than 10% did not cause significant reduction in gas exchange and plant growth values, while in higher porosity there was a limitation in CO 2 , fixation, and as a consequence, reduced photosynthesis, accompanied by a reduction in plant height and biomass production of the aerial part. At higher density, photosynthesis and SPAD index were higher, but the increase in biomass was smaller. It is concluded that aeration porosity greater than 10% are more limiting to plants, and the range between 7 and 10% generally reflects similar values. The highest levels of chlorophyll b and carotenoids in the highest density are adaptive responses to the stress condition.
Extract Human Mobility Patterns Powered by City Semantic Diagram

With widespread deployment of GPS devices, massive spatiotemporal trajectories became more accessible. This booming trend paved the solid data ground for researchers to discover the regularities or patterns of human mobility. However, there are still three challenges in semantic pattern extraction including semantic absence, semantic bias and semantic complexity. In this paper, we invent and apply a novel data structure namely City Semantic Diagram to overcome above three challenges. First, our approach resolves semantic absence by exactly identifying semantic behaviours from raw trajectories. Second, the design of semantic purification helps us to detect semantic complexity from human mobility. Third, we avoid semantic bias using objective data source such as ubiquitous GPS trajectories. Comprehensive and massive experiments have been conducted based on real taxi trajectories and points of interest in Shanghai. Compared with existing approaches, City Semantic Diagram is able to discover fine-grained semantic patterns effectively and accurately.
Using Advanced Remote Sensing Technologies to Discriminate Patterns of Ancient Aboriginal Land Use in the Australian Arid Zone

Satellite-derived surface elevation models are an important resource for landscape archaeological studies. Digital elevation data are useful for classifying land features, characterizing terrain morphology, and discriminating the geomorphic context of archaeological phenomena. This paper shows how remotely sensed elevation data obtained from the Japan Aerospace Exploration Agency’s Advanced Land Observing Satellite was integrated with local land system spatial data to digitally classify the topographic slope position of seven broad land classes. The motivation of our research was to employ an objective method that would allow researchers to geomorphometrically discriminate the topographic context of Aboriginal stone arrangements, an important archaeological site type in the Pilbara region of northwest Australia. The resulting digital terrain model demonstrates that stone arrangement sites are strongly correlated with upper topographic land features, a finding that contradicts previous site recordings and fundamentally changes our understanding of where stone arrangement sites are likely to have been constructed. The outcome of this research provides investigators with a stronger foundation for testing hypotheses and developing archaeological models. To some degree, our results also hint at the possible functions of stone arrangements, which have largely remained enigmatic to researchers.
Regional Youth Unemployment in Turkey: Do Determinants Vary by Gender?

Economic theory addresses unemployment as one of the crucial macroeconomic problems. Given the contributions to the production process, labor market integration of youth population is one of the main focuses of the labor economics literature. In this direction, a great number of studies have analyzed the dynamics of youth unemployment in the Turkish economy. However, a limited number of studies have investigated the issue in the case of Turkey with respect to gender using micro-level data. Given this knowledge, this study attempts to examine the determinants of the youth unemployment in 26 regions categorized under NUTS-2 over the period 2014-2019, and investigate whether those determinants vary by gender. Estimation results indicate that per capita income negatively affects both the total youth unemployment and youth unemployment by gender. Relative cohort size and internal migration positively affects all of the three youth unemployment categories. An increase in inflation rate increases youth female unemployment whereas youth male unemployment decreases with inflation. In addition, while an increase in age at first marriage increases youth female unemployment, it decreases youth male unemployment. Overall, the findings reveal that the impact that regional youth unemployment dynamics has on youth female unemployment is greater than on youth male unemployment. This study emphasizes that gender-based differences should be taken into account while developing policies towards the integration of youth into the labor market.
Design and Modeling of an Interleaving Boost Converter with Quasi-Saturated Inductors for Electric Vehicles

This paper is focused on the analysis and modeling of DC-DC boost converters in interleaving configuration exploiting inductors in the quasi-saturation region. The converter is designed to optimize the power density by minimizing the size of the inductors. A simulation study for electric vehicle applications is conducted analyzing the effects of duty cycle and temperature variation on the ripple of the input current giving, moreover, information on the performances and dynamics of the converter for further implementation and optimizations.
An Electromagnetic Translational Vibration Energy Harvester Fabricated in MP35N Alloy

This paper presents a mechanically-robust high-power-density electromagnetic vibration energy harvester fabricated from MP35N alloy. Its primary focus is on the use of MP35N alloy, and the corresponding performance. It follows our prior work on a similar harvester fabricated in silicon that now provides a performance baseline. The optimized design flow developed in our prior work is applied here, yielding mechanical, electrical, and magnetic design decisions, and harvesting performance, that remain largely unchanged. Importantly, while supporting comparable harvesting performance, the new material significantly improves robustness and ruggedness as needed for practical applications. The MP35N harvester suspension is fabricated using a combination of water-jet and electrical-discharge machining. The resulting harvester has an active volume of 1.81 cm<sup>3</sup>, and an output power <inline-formula> <tex-math notation="LaTeX">$P_{Out}$ </tex-math></inline-formula> of 1.26 mW at 1.08 g and 107.7 Hz under matched load. This yields a power density (PD) of 0.7 mW/cm<sup>3</sup> and a normalized power density (NPD) of 0.6 mW/cm<sup>3</sup>/g<sup>2</sup>. Importantly, the new harvester survives a 6-foot drop, and ordinary handling during fabrication and operation. The addition of backiron is shown to reduce magnetic-path reluctance, increase magnetic coupling, and thus increase output power. The harvester with backiron has an active volume of 2.17 cm<sup>3</sup>, and a <inline-formula> <tex-math notation="LaTeX">$P_{Out}$ </tex-math></inline-formula> of 2.2 mW at 1.3 g under matched load, yielding a PD of 1.01 mW/cm<sup>3</sup> and an NPD of 0.6 mW/cm<sup>3</sup>/g<sup>2</sup>. [2020-0261]
Hierarchical Aggregated Deep Features for ALS Point Cloud Classification

Classification of airborne laser scanning (ALS) point clouds is needed in digital cities and 3-D modeling. To efficiently recognize objects in ALS point clouds, we propose a novel hierarchical aggregated deep feature representation method, which can adequately employ spatial association of multilevel structures and deep feature discrimination. In our method, a 3-D deep learning model is constructed to represent the discriminative feature of each point cluster in a hierarchical structure by decreasing the within-class distance and increasing the between-class distance. Our method aggregates the discriminative deep features in different levels into a hierarchical aggregated deep feature that considers the spatial hierarchy and feature distinctiveness. Lastly, we build a multichannel 1-D convolutional neural network to classify the unknown points. Our tests demonstrate that the proposed hierarchical aggregated deep feature method can enhance point cloud classification results. Comparing with seven state-of-the-art methods, those results also verified the superior performance of our method.
2020 Swiss Stata Conference

In this article, we describe the randregret command which implements a variety of Random Regret Minimization (RRM) models. The command allows the user to apply the classic RRM model introduced in Chorus (2010, European Journal of Transport and Infrastructure Research 10: 181–196), the Generalized RRM model introduced in Chorus (2014, Transportation Research Part B: Methodological 68: 224–238)
Service Evolution Analytics: Change and Evolution Mining of a Distributed System

Changeability and evolvability analysis can aid an engineer tasked with a maintenance or an evolution task. This article applies <italic>change mining</italic> and <italic>evolution mining</italic> to evolving distributed systems. First, we propose a <italic>Service Change Classifier</italic> based Interface Slicing algorithm that mines change information from two versions of an evolving distributed system. To compare old and new versions, the following change classification labels are used: inserted, deleted, and modified. These labels are then used to identify subsets of the operations in our newly proposed Interface (WSDL) Slicing algorithm. Second, we proposed four <italic>Service Evolution Metrics</italic> that capture the evolution of a system's <italic>Version Series</italic> VS = {<italic>V</italic><sub>1</sub>, <italic>V</italic><sub>2</sub>,…,<italic>V<sub>N</sub></italic>}. Combined the two form the basis of our proposed <italic>Service Evolution Analytics</italic> model, which includes learning during its development phase. We prototyped the model in an intelligent tool named AWSCM (Automatic Web Service Change Management). Finally, we present results from experiments with two well-known cloud services: Elastic Compute Cloud (EC2) from the Amazon Web Service (AWS), and Cluster Controller (CC) from Eucalyptus. These experiments demonstrate AWSCM's ability to exploit change and evolution mining.
Walking the tightrope or constructing a bridge? A study into effective partnership practices between an interstate boarding school community and a very remote Aboriginal Community

Access to secondary education for very remote Northern Territory Indigenous students is limited. Although many students attend distant boarding schools, very few stay to complete Year 12 (the final year of secondary school in Australia). Few families and communities are fully engaged in the whole transition process. This paper describes a case study of one very remote Indigenous community and its partnership with an interstate boarding College. The partnership is attributed with students from community staying to complete Year 12 and then seeking local employment pathways afterward. The study on which this paper is based, investigated how the elements within this partnership function. Using a qualitative methodology with a phenomenological design, two adults from the remote Indigenous Community and six staff from a partner boarding College were interviewed. Interviews were transcribed verbatim, thematically coded and participants were deidentified. Limitations included small sample size not completely representative of the students, families, Elders and staff from either the community or the college.
Semantic Unsupervised Change Detection of Natural Land Cover With Multitemporal Object-Based Analysis on SAR Images

Change detection is one of the most addressed topics in the remote sensing community. When performed on synthetic aperture radar images, the most critical issues are as follows: 1) the labeling of the identified changing patterns and 2) the scarce robustness of classic pixel-based approaches based on threshold segmentation of an appropriate change index, which tend to fail when multiple changes are present in the study area. In this work, a new methodology for unsupervised change detection in vegetation canopy is presented. It overcomes these limitations by exploiting multitemporal geographical object-based image analysis with the aim to make the intrinsic semantic of data emerge and direct the processing toward the identification of precise classes of changes through dictionary-based preclassification and fuzzy combination of class-specific information layers. The proposed methodology has been tested in ten different experiments covering agriculture and clear-cut deforestation applications. The results, validated against literature methods, highlighted the superiority of the proposed approach, which was quantitatively assessed in terms of standard classification quality parameters. On agriculture experiments, it allowed for an average increase in the detection accuracy of about 11% with respect to the best performing literature method, with an increment of the false alarm rate in the order of 0.5%. In case of deforestation, the registered detection accuracy was comparable to that achieved by the literature, while the most significant benefit was the reduction, of more than one-third, of the number of detected false deforestation patterns. Overall, the main characteristics of the proposed architecture are the robustness and the lack of any supervision, which makes it very well-suited for operational scenarios.
Change Detection in Electricity Consumption Patterns Utilizing Adaptive Information Theoretic Algorithms

The high-resolution data on electricity consumption, recorded by smart meters at customers’ premises, are valuable sources of operational information and consumption patterns. In addition, customers’ characterization plays an undeniable role in the implementation of demand response (DR) programs, as any changes in the consumption patterns could affect DR programs. Therefore, an accurate algorithm for detecting changes in the consumption patterns is very useful in not only the implementation of DR but also in other fields, such as load forecasting and peak shaving. This article proposes a reliable procedure for detecting changes in customers’ consumption patterns. For this reason, an adaptive algorithm is introduced to improve the clustering quality of customers’ consumption patterns by determining the optimum number of clusters, using a locally weighted entropy-based segmentation. Moreover, considering the customers’ consumption records in different time slots as the features, another adaptive algorithm is introduced for feature selection based on the mutual information concept. The proposed method is evaluated by applying a real dataset provided by the Irish Social Science Data Archive. The results corroborate the efficiency of the proposed procedure.
Bifurcation analysis for a class of fractional-order financial system with time delays

This paper analysis the bifurcation properties of a new class of fractional order financial system, respectively from the directions, the asymptotically stability and monotonicity of specific discussions, deduced the this new fractional order condition of the stability of the financial system and the bifurcation characteristic. Moreover, Some simulations are performed to verily the bifurcation characteristics of executive-order financial system around the bifurcation point which satisfy the conditions of the bifurcation properties.
Weighted Particle Swarm Clustering Algorithm for Self-Organizing Maps

The traditional K-means clustering algorithm is difficult to determine the cluster number, which is sensitive to the initialization of the clustering center and easy to fall into local optimum. This paper proposes a clustering algorithm based on self-organizing mapping network and weight particle swarm optimization SOM&WPSO (Self-Organization Map and Weight Particle Swarm Optimization). Firstly, the algorithm takes the competitive learning mechanism of a self-organizing mapping network to divide the data samples into coarse clusters and obtain the clustering center. Then, the obtained clustering center is used as the initialization parameter of the weight particle swarm optimization algorithm. The particle position of the WPSO algorithm is determined by the traditional clustering center is improved to the sample weight, and the cluster center is the “food” of the particle group. Each particle moves toward the nearest cluster center. Each iteration optimizes the particle position and velocity and uses K-means and K-medoids recalculates cluster centers and cluster partitions until the end of the algorithm convergence iteration. After a lot of experimental analysis on the commonly used UCI data set, this paper not only solves the shortcomings of K-means clustering algorithm, the problem of dependence of the initial clustering center, and improves the accuracy of clustering, but also avoids falling into the local optimum. The algorithm has good global convergence.
Analysis and Control of Wireless Motor Drives With a Single Inverter in Primary Side

In motor drives supplied through wireless power transfer (WPT) systems, a high frequency inverter and a low frequency one are commonly used in the primary and secondary sides of the system, respectively. This arrangement causes reduced reliability and efficiency, and high cost. Besides, using an electrolyte isolator like big dc capacitor, shorten the system lifetime. This capacitor is usually located in parallel with the motor drive system. So, the capacitor fail leads to a total system failure. In this article, a multi-functional single inverter used in the primary side dispatch as the high-frequency ac power via a magnetic link as well as driving an induction motor without any extra inverter in the secondary side. This is obtained by using a simple AC chopper in the secondary side as a wave shaping convertor. In this way, the desirable ac power can efficiently be injected into the motor whereas the WPT system efficiency is improved by up to 5%. The modeling and mathematical analysis of the proposed wireless drive are presented. Also, the performance of the drive under an open-loop V/F control system is verified by simulation and experimental results. AC/AC converters, electric vehicles, wireless motor drives, wireless power transfer.