Miracle-dz/UniFAR-FTUs huggingface dataset:
Identifying a possible stratification phenomenon in ionospheric F2 layer using the data observed by the DEMETER satellite: method and results

Abstract. Many studies have revealed the stratification phenomenon of the topside ionospheric F2 layer using ground-based or satellite-based ionograms, which can show direct signs of this phenomenon. However, it is difficult to identify this phenomenon using the satellite-based in situ electron density data. Therefore, a statistical method, using the shuffle resampling skill, is adopted in this paper. For the first time, in situ electron density data, recorded by the same Langmuir probe aboard the DEMETER (Detection of Electro-Magnetic Emission Transmitted from Earthquake Regions) satellite at different altitudes, are analyzed, and a possible stratification phenomenon is identified using the proposed method. Our results show that the nighttime stratification, possibly a permanent phenomenon, can cover most longitudes near the geomagnetic equator, which is not found from the daytime data. The arch-like nighttime stratification decreases slowly on the summer hemisphere and thus extends a larger latitudinal distance from the geomagnetic equator. All results, obtained by the proposed method, indicate that the stratification phenomenon is more complex than what has previously been found. The proposed method is thus an effective one, which can also be used in similar studies of comparing fluctuated data.
Life Cycle Assessment of New High Concentration Photovoltaic (HCPV) Modules and Multi-Junction Cells

Worldwide electricity consumption increases by 2.6% each year. Greenhouse gas emissions due to electricity production raise by 2.1% per year on average. The development of efficient low-carbon-footprint renewable energy systems is urgently needed. CPVMatch investigates the feasibility of mirror or lens-based High Concentration Photovoltaic (HCPV) systems. Thanks to innovative four junction solar cells, new glass coatings, Position Sensitive Detectors (PSD), and DC/DC converters, it is possible to reach concentration levels higher than 800× and a module efficiency between 36.7% and 41.6%. From a circular economy’s standpoint, the use of concentration technologies lowers the need in active material, increases recyclability, and reduces the risk of material contamination. By using the Life Cycle Assessment method, it is demonstrated that HCPV presents a carbon footprint ranking between 16.4 and 18.4 g CO2-eq/kWh. A comparison with other energy means for 16 impact categories including primary energy demand and particle emissions points out that the environmental footprint of HCPV is typically 50 to 100 times lower than fossil fuels footprint. HCPV’s footprint is also three times lower than that of crystalline photovoltaic solutions and is close to the environmental performance of wind power and hydropower.
Using plantar pressure to predict insole comfort in running

CAI individuals showed a significant ankle and knee kinematic and kinetic differences when wearing shoes, but only during the contact phase of walking. These results suggest that the deficits associated with CAI (increased ankle plantarflexion and a more inverted ankle walking pattern) as shown by the systematic review of Moisan et al. (2017) could be partially compensated by wearing shoes. Indeed, a decreased ankle plantarflexion and an increased ankle inversion moment were observed during the contact phase of shod walking. These results will help researchers and clinicians to better target the deficits associated with CAI that can be limited with shoes during rehabilitation.
Detection of Winding Asymmetries in Wound-Rotor Induction Motors via Transient Analysis of the External Magnetic Field

Over recent decades, the detection of faults in induction motors (IMs) has been mainly focused in cage motors due to their extensive use. However, in recent years, wound-rotor motors have received special attention because of their broad use as generators in wind turbine units, as well as in some large power applications in industrial plants. Some classical approaches perform the detection of certain faults based on the fast Fourier transform analysis of the steady state current (motor current signature analysis); they have been lately complemented with new transient time–frequency-based techniques to avoid false alarms. Nonetheless, there is still a need to improve the already existing methods to overcome some of their remaining drawbacks and increase the reliability of the diagnostic. In this regard, emergent technologies are being explored, such as the analysis of stray flux at the vicinity of the motor, which has been proven to be a promising option to diagnose the motor condition. Recently, this technique has been applied to detect broken rotor bar failures and misalignments in cage motors, offering the advantage of being a noninvasive tool with simple implementation and even avoiding some drawbacks of well-established tools. However, the application of these techniques to wound rotor IMs (WRIMs) has not been studied. This article explores the analysis of the external magnetic field under the starting to detect rotor winding asymmetry defects in WRIMs by using advanced signal processing techniques. Moreover, a new fault indicator based on this quantity is introduced, comparing different levels of fault and demonstrating the potential of this technique to quantify and monitor rotor winding asymmetries in WRIMs.
Studying Design Thinking as a Forthcoming Source to Innovation Speed

With complex technology-intense industries follows an ever-increasing need for rapid innovation processes. Yet, innovation speed and the time from idea to product realization can vary and be unpredictable.Design Thinking (DT) is suggested as a key driver to impact the speed of product innovation within product development projects. To understand and aid the road from early ideas and concepts to value- added products, this paper will provide a literature study on how Design Thinking can facilitate improved product innovation performance through innovation speed.The paper seeks to develop an overview of new insight on DT applicability for improved product innovation capability. This is done by identifying components that comprise DT´s innovative ability and appropriateness to product development contexts beyond the early creative phases of product development.As DT emphasize on visualization and re-framing problems, it contributes to enhanced clarity, meaning and confidence in ideas and decisions. DT in this way may impact strategy formulation and speed up complex innovation processes by pre-experiencing future situations.
Sustaining platforms as commons: perspectives on participation, infrastructure, and governance

ABSTRACT This work finds its place within Participatory Design (PD) as a specific approach to co-design that focuses on the politics of technological innovation and socio-technical transformations. In particular, the article contributes to the repositioning of co-design in the age of platform capitalism by engaging with the question: how can participatory designers approach interventions for the long-term sustainability of platforms as commons? As the contradictions and limitations of platform capitalism become increasingly evident, to engage with such a challenge is a way to pursue PD’s renewed political agenda. The article foregrounds the concept of platforms as commons to bring designers’ attention towards those platform arrangements which are antithetical to platform capitalism exploitative ones. By building on Free and Open Source Software (FOSS), as a paradigmatic case of platform as commons, the article outlines participation, infrastructure, and governance as relevant perspectives for framing broad areas of sustainability concerns; and it articulates them along four approaches for supporting long-term sustainability in practice: maintaining, scaling, replicating, and evolving. Ultimately, this article provides participatory designers with a map of possible orientations to frame and support their work, research or interventions around the long-term sustainability of platforms as commons.
A Proposed Solution of the Measurement Problem in Quantum Mechanics by a Hidden Variable Theory

The measurement problem in quantum mechanics is re-examined and it is shown that it cannot really be solved in a satisfactory way, within the framework of the usual interpretation of the theory. We then discuss von Neumann's attempts to prove that quantum mechanics is incompatible with the introduction of hidden variables, and develop a more detailed form of Bell's argument, showing that von Neumann's analysis is invalid. Using certain ideas that are implicit in the "differential-space" theory of Wiener and Siegel, we go on to propose a new deterministic equation of motion, describing a kind of coupling of the measuring instrument to the observed system that explains in detail how the wave packet is "reduced" during a measurement in a continuous and causally determined way. By averaging over the hidden parameters, we then recover the usual statistical results of quantum mechanics as a special case. However, a more detailed analysis of the theory shows that new experimental and theoretical questions can now be raised, which go outside the framework of the quantum theory as it is now formulated. These questions are examined briefly.
IMPACT OF A REHABILITATION PROGRAM ON THE URINARY MANAGEMENT OF PATIENTS AFFECTED BY CEREBROVASCULAR ACCIDENT

ABSTRACT Objective: to assess the effect of a Nursing Rehabilitation program on the urinary incontinence management of women after a CVA. Method: quantitative, quasi-experimental, and longitudinal study conducted in a convalescence unit in the Viana do Castelo district, Portugal, between September 2018 and March 2019. The sample included women (n=30) aged between 45 and 90, experiencing urinary incontinence after a CVA, assigned to two groups: experimental group (n=15) and control group (n=15). The functional rehabilitation program was applied to the experimental group for 4 weeks. The program comprises behavioral changes and an exercise plan to strengthen pelvic floor muscles, and the impact of urinary incontinence was assessed before and after the intervention. Results: a statistically significant correlation was found between the level of functional disability and the impact of urinary incontinence (r=-0.499; p=0.005). Hence, the level of functional disability influences the impact of urinary incontinence on quality of life. The functional rehabilitation program implemented in the experimental group obtained positive results and decreased the frequency (t=6.985, p=0.000) and amount (Z=-2.762, p=0.006) of urine loss. Conclusion: the functional rehabilitation program positively impacted and decreased the frequency and amount of urine loss.
Compressive sensing in diffusion MRI

This thesis is dedicated to the development of new acquisition and processing methods in diffusion MRI (dMRI) to characterize the diffusion of water molecules in white matter fiber bundles at the scale of a voxel. In particular, we focus our attention on the accurate recovery of the Ensemble Average Propagator (EAP), which represents the full 3D displacement of water molecule diffusion. Diffusion models such that the Diffusion Tensor or the Orientation Distribution Function (ODF) are largely used in the dMRI community in order to quantify water molecule diffusion. These models are partial EAP representations and have been developed due to the small number of measurement required for their estimations. It is thus of utmost importance to be able to accurately compute the EAP and order to acquire a better understanding of the brain mechanisms and to improve the diagnosis of neurological disorders. Estimating the full 3D EAP requires the acquisition of many diffusion images sensitized todifferent orientations in the q-space, which render the estimation of the EAP impossible in most of the clinical dMRI scanner. A surge of interest has been seen in order to decrease this time for acquisition. Some works focus on the development of new and efficient acquisition sequences. In this thesis, we use sparse coding techniques, and in particular Compressive Sensing (CS) to accelerate the computation of the EAP. Multiple aspects of the CS theory and its application to dMRI are presented in this thesis.
A Single-Stage Dual-Output Tri-Mode AC-DC Regulator for Inductively Powered Application

This paper presents a one-stage AC-DC voltage regulator with dual-output for inductively powered applications. In order to maximize the circuit performances, more than one independent regulated output voltage is often required to cater for different sub-modules in a system. The typical two-stage implementation which consists of a rectifier and a multiple-output DC-DC converter limits the overall power conversion efficiency of the receiver. A new architecture which is able to rectify and regulate two or more output voltages simultaneously in a single-stage is proposed and thereby improving the overall power conversion efficiency substantially. In addition, an energy-efficient control scheme is adopted to control the multiple switches in the architecture. Since no inductor is required in the proposed one-stage conversion structure, the cross-regulation problem that used to exist in a multiple-output switching converter has been eliminated. Apart from this, it is easier to be integrated with a system-on-chip (SoC) due to its minimized footprint. The tri-mode dual-output design was fabricated in standard $0.18~\mu \text{m}$ CMOS process. It delivers a maximum output power of 114mW. The measured peak power conversion efficiency is 91.7% at dual regulated output voltages of 1.8V and 2V.
Micro-Raman Spectroscopy, a Powerful Technique Allowing Sure Identification and Complete Characterization of Asbestiform Minerals

Micro-Raman spectroscopy has been applied to fibrous minerals regulated as “asbestos”—anthophyllite, actinolite, amosite, crocidolite, tremolite, and chrysotile—responsible of severe diseases affecting mainly, but not only, the respiratory system. The technique proved to be powerful in the identification of the mineral phase and in the recognition of particles of carbonaceous materials (CMs) lying on the “asbestos” fibers surface. Also, erionite, a zeolite mineral, from different outcrops has been analyzed. To erionite has been ascribed the peak of mesothelioma noticed in Cappadocia (Turkey) during the 1970s. On the fibers, micro-Raman spectroscopy allowed to recognize many grains, micrometric in size, of iron oxy-hydroxides or potassium iron sulphate, in erionite from Oregon, or particles of CMs, in erionite from North Dakota, lying on the crystal surface. Raman spectroscopy appears therefore to be the technique allowing, without preparation of the sample, a complete characterization of the minerals and of the associated phases.
ICBSI 2018 International Conference on Business Sustainability and Innovation MAPPING RESEARCH PUBLICATIONS RELATED TO BUSINESS INTELLIGENCE: AN OVERVIEW OF BIBLIOMETRIC DATA

Business intelligence is a relatively new concept as well as an attribute which deals with strategic initiatives of organization in justifying their complex business decision making as well as gaining competitive superiority. The purpose of this study is to explore a contemporary perspective on the emerging topics in business intelligence research publications using bibliometric approach. This involves the process of identifying and tagging business intelligence related topics as well as analyzing results of bibliometric indicators. To do so, this study used the Web of Science (WoS) and Scopus databases to extract related research publication outputs in the area of business intelligence studies. This included data on sources of articles, top universities or institutions that produce, high citation articles and have top authors with publications. This study manage to determine a total of 1103 research publications in WoS database and 2519 in Scopus that were published related to business intelligence context from the year 1980 to 2018. Out of this number, it was indentified that there were only 1744 journal articles and 1119 proceeding papers have been published. The results of analysis of these bibliometric data were highlighted and discussed.
Query-Based Video Synopsis for Intelligent Traffic Monitoring Applications

Synopsis of a long-duration video has many applications in intelligent transportation systems. It can help to monitor traffic with lesser manpower. However, generating meaningful synopsis of a long-duration video recording can be challenging. Often summarized outputs include redundant contents or activities that may not be helpful to the observer. Moving object trajectories are possible sources of information that can be used to generate the synopsis of long-duration videos. The synopsis generation faces challenges due to object tracking, grouping of the trajectories with respect to activity type, object category, and contextual information, and generating smooth synopsis according to a query. In this paper, we propose a method to generate meaningful and smooth synopsis of long-duration videos according to the users’ query. We have tracked moving objects and adopted deep learning to classify the objects into known categories (e.g., car, bike, and pedestrians). We then identify regions in the surveillance scene with the help of unsupervised clustering. Each tube (spatiotemporal object trajectory) is represented by the source and the destination. In the final stage, we take a query from the user and generate the synopsis video by smoothly blending the appropriate tubes over the background frame through energy minimization. The proposed method has been evaluated on two publicly available datasets and our own surveillance datasets. We have compared the method with popular state-of-the-art techniques. The experiments reveal that the proposed method is superior to the existing techniques and it produces visually seamless video synopsis.
Reliability evaluation for a multi‐commodity flow model under budget constraint

Abstract This paper is mainly to extend the multi‐commodity minimum cost flow problem from deterministic cases to stochastic cases. Under budget constraint, we propose an approach to calculate the probability that the required amount of multiple commodities can be transported simultaneously through a stochastic flow network. Such a probability is called system reliability. An algorithm is proposed first to find out all lower boundary points for the requirement in terms of minimal paths. The system reliability can then be calculated in terms of such lower boundary points.
Application of a Viscoelastic Model to Creep Settlement of High-Fill Embankments

In order to predict the creep settlement of high-fill embankments, the time-dependent viscoelastic model of Poynting–Thomson (the standard linear solid) has been chosen to represent the creep behavior of soils. In the present study, the hereditary integral was applied to calculate the strain while the load increase is varied with time. Calculation expressions of the creep settlement of an embankment during and after construction were obtained under one-dimensional compression conditions. Using this approach, the three parameters of every layer can be determined and adjusted to accommodate in situ monitoring data. The calculated results agreed well with those from the field, which imply that the method proposed in this paper can give a precise prediction of creep settlement of high-fill embankments.
A Coupled Inductor-Based Buck–Boost Type Grid Connected Transformerless PV Inverter Having the Ability to Control Two Subarrays Simultaneously

A coupled inductor-based buck–boost type single phase transformerless grid connected photo voltaic inverter which has the ability to extract maximum power simultaneously from two serially connected subarrays is proposed in this article. The series connection of the two subarrays, and the buck–boost nature of the inverter result in reduction of the number of modules which are connected in series in a subarray. Further, independent operation of the two subarrays enhances the overall power extracted from the subarrays while they are experiencing significant mismatch in their operating conditions. The deployment of the coupled inductors has reduced the component count and overall size, weight, and volume of the proposed inverter. On the other hand it helps to achieve moderately high overall operating efficiency at higher power level as compared to the single inductor-based buck–boost schemes. A new inductor current control technique which is independent of the mode of operation (CCM or DCM) is adopted to control the grid current. The topological structure of the inverter, and its control technique restrict the overall leakage current related to the subarrays within the permissible limit. A 1-kW experimental setup has been fabricated and thorough experimental analysis are performed to confirm the viability of the proposed inverter.
Automatic selection of optimal segmentation scales for high-resolution remote sensing images

To extract information from high resolution images is a challenge work.Compared tothe traditional pixel-based approach, the advantages of object-oriented classification methods are well documented. However, the appropriate scale parametersofthese methods are difficult to be determined, andthe choices of scale parametersareof high importance, whichwill havea strong effect on the segmentation effectiveness. Whereas the evaluations of the quality of a segmentation method are still mainly based onsubjective judgment, which is a complicated process and lacksstability and reliability. Thus, an objective and unsupervised method needs to beestablished for selecting suitable parameters for a multi-scale segmentation to ensure the bestresults. In this work, a novicemethod is introduced to choose the optimal parameter for themulti-scale segmentation. For large information in band itself and weak relationship among multispectral bands, valuable bands should be selected from original data and weighed by the degreeofcorrelation. Then thresholds of all 3 selected bands ranging from 20 to 200 (intervals of 10)are created in Definiens Professional 8.7. It considers that a segmentation has two desirable properties: each of the resulting segments should be internally homogeneous and should be distinguishable from its neighborhood. Therefore, the global intra-segment and inter-segment heterogeneity indexes are taken into account to identify the optimal segmentation scale. Finally, cubic spline interpolation is applied to select the optimalsegmentation scale. As a result, the measure combining a spatial autocorrelation indicator and a variance indicator shows that the method can improve the precision in global segmentation.
Benefits of Short-Distance Walking and Fast-Route Scheduling in Public Vehicle Service

Public vehicle service (PVS), as a paradigm to manage and share large-capacity vehicles for public passenger delivery, is promising to improve the quality of urban transportation. In the PVS system, a command center receives requests sent by passengers, periodically assigns them to public vehicles and schedules vehicle routes to serve the requests. However, in the PVS system, the passengers’ waiting time is not well utilized. Moreover, it is observed that the driving distance on low-speed roads accounts for a rather high percentage. These two factors impact on system efficiency. In order to utilize the waiting time, we propose to let passengers walk a short distance instead of standing at their origins. At the same time, the driving distance on low-speed roads will be reduced. In this paper, the closest meeting point algorithm is proposed to address the challenge of determining the best pick-up and drop-off locations. The large-scale simulations show that the passenger walking and the proposed fast-route scheduling strategy can shorten the total vehicle travel distance by 34%.