Miracle-dz/UniFAR-FTUs huggingface dataset:
Detection, Location and Quantification of Structural Faults in a Two-Story Building Using the Artificial Immunological System

Large buildings and skyscrapers are vulnerable to environmental, temporal, and anthropological stresses, generating wear and tear that can lead to this social and economic impediment's collapse. The technological improvements of the fourth industrial revolution have resulted in changes in the connection between physical space and man, known as the cyber physic model, which necessitates monitoring systems to protect the structural branch and so correct this structural vulnerability. Thus, the structural health monitoring system is the exact measure of the evolution required by the cyber physic model in construction and the protection of the monumental buildings, ensuring not only their economic development but also the safety of society. Therefore, this research work presents the innovative proposal of the cyber-physical structural health monitoring system aimed at buildings and skyscrapers, based on and differentiated by intelligent computing techniques, using the negative selection algorithm to perform the analysis and monitoring of structural integrity, overcoming the existing traditional work. The cyber-physical structural health monitoring system will be applied to experimental data obtained from the shear building model that represents these imposing skyscrapers. An artificial immune system will be developed and used in the decision-making process based on the acquisition and processing of the obtained signals to perform the identification, localization, and quantification of possible structural damage. Observing the results, this work proved to be efficient, robust, and economically feasible, having high performance and overcoming the shortcomings of traditional techniques. It represents the perfect measure of cyber physics in the monitoring of large buildings and skyscrapers.
Utility Grid Interfaced Solar Water Pumping System Using PMSM Drive

– This work presents a solar photovoltaic (PV) array fed grid interfaced, encoder-less, permanent magnet synchronous motor (PMSM) based solar water pumping (SWP) system. This system mitigates the intermittency issues associated with the solar PV energy. In the presence of grid, the SWP system provides an uninterrupted water flow irrespective of available solar insolation. In condition of grid failure, the output water flow is a function of available solar insolation. A boost converter is used on the grid side to facilitate power transfer from the grid and enabling the unity power factor (UPF) operation using unit vector template (UVT) theory. The speed of the PMSM, is regulated using sensor-less vector control. A double second order generalized integrator quadrature signal generator (DSOGI- QSG) is used for extracting the fundamental signal from the distorted grid voltage. Performance of the proposed system is validated using a laboratory developed prototype under varying solar insolation, during grid failure, during voltage sag, voltage swell and distorted grid voltage conditions.
Investigate Effect of Ag2O Adding on Y134 Superconductor

In this paper . W e synthesized Y134 superconductor doped Ag2O by solid state reaction and investigated the effect of Ag2O addition on the critical temperature of Y134 superconductor and SEM micrographs shown Y134 doped Ag2O has rather smooth and small pores feature . The maximum critical temperature found at an optimal doping ,0.1 Ag, with Tc onset =97 K.
A Mode Optimization Method for Vortex Electromagnetic Wave Radar Imaging

Since the radar echo of the space target satisfies the sparseness, the compressed sensing (CS) theory can be used to reconstruct the target information with data much lower than the Nyquist sampling rate. For the vortex electromagnetic wave (VEMW) radar, since the azimuth of the target also satisfies the sparseness, the use of CS can also significantly reduce the requirement of the azimuth resolution on the amount of mode data. Based on the abovementioned analysis, this letter proposes a CS-based VEMW radar imaging mode sequence optimization method. First, the imaging model is deduced, and the beam steering method is performed to adjust the direction of the main lobe of the VEMW in different modes, so that the target can be fully illuminated. On the basis, a sparse model of the VEMW radar is established, and the analysis shows that the essence of mode sequence optimization is to optimize the observation matrix in CS. Finally, after establishing the optimization model of the observation matrix, an optimization method is proposed to solve the model, and the 3D information of the target is reconstructed at last. Simulation experiment results prove the effectiveness of this method.
V2G-based Smart Autonomous Vehicle For Urban Mobility using Renewable Energy

IRSEEM (Institute for Research in Embedded Systems ESIGELEC, Rouen, France) is coordinator of a research program SaveMore aiming to develop and demonstrate the viability and effectiveness of systems for electrical transport and urban logistics based on autonomous robotic electric vehicles operating within a smart grid electrical power distribution framework. As a part of this project, our work focuses on the study of the coupling of electric vehicles with renewable energy. At the scale of a city, electric vehicles can be considered as a means of intermittent storage of electric power which can be distributed to the network when it is required (e.g., at times of the date when demand spikes). When these vehicles belong to a controlled and intelligent fleet, network organization is dynamic and leads to a smart grid. The widespread use of electric vehicles in cities coupled with renewable energy appears as a powerful tool to help local and regional authorities in the implementation of the European Agenda for low-carbon, reduced air pollution and encourage energy savings. In this paper, we present a Vehicle to Grid model which implements the interaction between an electric vehicle and a smart grid. The model takes into account several kinds of parameters related to the battery, the charging station, the size of the fleet and the power grid as the expansion coefficient. A statistical approach is adopted for the setting of these parameters to determine the significant parameters (parameters that have an impact of the system when their values change). Several simulations are performed to validate the model. In first step, we have studied the behavior of the model on a typical day of a person who has traveled from his home to work (in France). As a second step, and in order to study the power consumption behavior of the model, we have tested it during several seasons. The results show the effectiveness of the model developed.
Pteridophyte species richness in the central Himalaya is limited by cold climate extremes at high elevations and rainfall seasonality at low elevations

Abstract There is a consensus that climate factors strongly influence species richness along elevation gradients, but which factors are crucial and how they operate are still elusive. Here, we assess the relative importance of temperature‐related versus precipitation‐related variables and the relative importance of extreme climate versus climate seasonality in driving pteridophyte species richness. We used correlation and regression analyses to relate species richness of pteridophytes, and their two major groups (lycophytes, ferns), in fifty 100‐m vertical bands to climatic factors representing different aspects of climatic conditions (general climate, stressful climate, and climate seasonality). Variation partitioning analysis was used to determine the relative importance of each group of climatic factors on species richness. Across the entire elevational gradient, species richness had a parabolic response to mean annual temperature (adjusted R 2 = .87−.91), and a linear response to annual precipitation (adjusted R 2 = .82). Mean annual temperature and annual precipitation in the second‐order polynomial model together explained 96.3%−98.7% of the variation in species richness. The variation in species richness uniquely explained by minimum temperature of the coldest month was much greater than that uniquely explained by temperature seasonality, but the variation in species richness uniquely explained by precipitation during the driest month was much smaller than that uniquely explained by precipitation seasonality. Overall, extreme climate variables explained slightly more variation than did climate seasonality. Our study suggests that pteridophyte richness along the elevational gradient is largely driven by a combination of both temperature‐ and precipitation‐related parameters, although precipitation‐related variables play a slightly stronger role, and that extreme low temperature events (at high elevations) and seasonal precipitation variability (at low elevations) are the strongest determinants of pteridophyte species richness.
Low Frequency Noises of Hydrogen Sensors

Abstract: The investigated sensors have sandwich structure metal/porous silicon/crystalline silicon/Al. The contact metal on porous silicon was from Au and from Pd. Porosity of the samples is 57 % and 63 %. Current-voltage characteristics and low frequency noises spectra of crystalline silicon sensors with nano-pores surface layer before, during and after influence of hydrogen gas flow are studied. Sensitivity of porous silicon on H 2 is determined both by the traditional resistance modulation method and by the noise spectroscopy method. Analyses of gas sensitivity show advantages of the noise method. Noise spectra of the samples with porous silicon layer in general have 1 forms where f γ0. 7 ≤ γ≤1. For low frequency noise modeling and for explanation of its behavior “Correlated number-mobility fluctuation model” is used. Copyright © 2009 IFSA. Keywords: Low frequency noise, Hydrogen sensor, Porous silicon 1. Introduction Design and manufacture of control and monitoring systems permitted to receive important information about composition of the several gaseous medium. This is very important for the environment monitoring. The basic part of the control and monitoring systems is the gas sensor. At present there are gas sensors on the base of several semiconductor materials and principles of operation. They are used for threshold detection of the several gas escapes and for control of the foul gas concentration at the technological processes. Extensive search for new materials and compounds, new techniques for increasing of sensitivity, selectivity and detectivity of semiconductor based gas sensors have great interest [1-7]. Porous silicon (PS) has high resistivity and therefore it is very sensitive to external influences, such as gaseous medium. Porous silicon/silicon substrates have been used for sensing
An interactive VR system based on full-body tracking and gesture recognition

Most current virtual reality (VR) interactions are realized with the hand-held input device which leads to a low degree of presence. There is other solutions using sensors like Leap Motion to recognize the gestures of users in order to interact in a more natural way, but the navigation in these systems is still a problem, because they fail to map the actual walking to virtual walking only with a partial body of the user represented in the synthetic environment. Therefore, we propose a system in which users can walk around in the virtual environment as a humanoid model, selecting menu items and manipulating with the virtual objects using natural hand gestures. With a Kinect depth camera, the system tracks the joints of the user, mapping them to a full virtual body which follows the move of the tracked user. The movements of the feet can be detected to determine whether the user is in walking state, so that the walking of model in the virtual world can be activated and stopped by means of animation control in Unity engine. This method frees the hands of users comparing to traditional navigation way using hand-held device. We use the point cloud data getting from Kinect depth camera to recognize the gestures of users, such as swiping, pressing and manipulating virtual objects. Combining the full body tracking and gestures recognition using Kinect, we achieve our interactive VR system in Unity engine with a high degree of presence.
Multi-Domain Neumann Network with Sensitivity Maps for Parallel MRI Reconstruction

MRI is an imaging technology that non-invasively obtains high-quality medical images for diagnosis. However, MRI has the major disadvantage of long scan times which cause patient discomfort and image artifacts. As one of the methods for reducing the long scan time of MRI, the parallel MRI method for reconstructing a high-fidelity MR image from under-sampled multi-coil k-space data is widely used. In this study, we propose a method to reconstruct a high-fidelity MR image from under-sampled multi-coil k-space data using deep-learning. The proposed multi-domain Neumann network with sensitivity maps (MDNNSM) is based on the Neumann network and uses a forward model including coil sensitivity maps for parallel MRI reconstruction. The MDNNSM consists of three main structures: the CNN-based sensitivity reconstruction block estimates coil sensitivity maps from multi-coil under-sampled k-space data; the recursive MR image reconstruction block reconstructs the MR image; and the skip connection accumulates each output and produces the final result. Experiments using the fastMRI T1-weighted brain image dataset were conducted at acceleration factors of 2, 4, and 8. Qualitative and quantitative experimental results show that the proposed MDNNSM method reconstructs MR images more accurately than other methods, including the generalized autocalibrating partially parallel acquisitions (GRAPPA) method and the original Neumann network.
“It’s About Going From Good to Great”: Expert Approaches to Conducting a First Sport Psychology Session

The aim of this study was to investigate expert practitioners’ approaches to conducting a first sport psychology session with individual clients as there is sparse empirical literature on this topic. Nine expert Certified Mental Performance Consultants completed a semistructured interview where they discussed experiences conducting a first meeting with an athlete. Primary objectives included establishing the relationship, setting guidelines and expectations, understanding the client’s background, identifying presenting concerns, and formulating the treatment plan and building skills. Building rapport was an aspect used to establish the relationship while discussing confidentiality was utilized to set guidelines. Important strategies employed to increase the perceived benefits to services included conveying the consulting approach and philosophy. Lessons learned centered around doing too much and not appreciating individual differences of clients. Findings show expert consultants aim to achieve similar broad objectives in the first session and provide a basis for best practices in this area.
Deep potential development of transition-metal-rich carbides

In this study, we developed DeepPot-SE type potentials according to the High-Entropy Strategy for Cr23C6\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathrm{Cr}}_{23}{\mathrm{C}}_{6}$$\end{document}, Fe23C6\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathrm{Fe}}_{23}{\mathrm{C}}_{6}$$\end{document}, W23C6\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathrm{W}}_{23}{\mathrm{C}}_{6}$$\end{document}, and Mo23C6\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathrm{Mo}}_{23}{\mathrm{C}}_{6}$$\end{document} systems. Predictive molecular dynamics are then conducted according to various elastic and mechanical properties. Training dataset accuracy is confirmed both numerically and visually. Elastic constants, Poisson Ratio, and bulk modulus are determined and compared to the literature with percent errors ranging between 0.45 and 13.33%. Ground-state lattice constants yield 0.111–1.375% percent error. Linear lattice constant thermal expansion trends are found from 300 to 1500 K as expected. Melting behaviors with clear melting points are observed for each binary. The Birch–Murnaghan Equation of State is suitably fit, providing additional verification of the bulk modulus calculations. Polycrystalline thermal stability is also verified for each binary potential.
Excitation of resonant plasmonic cavities by integrated waveguides for sensing applications

Optical sensors represent a large growing market which is nowadays focusing onto advancement in mobile technology. Innovations in the field of optical sensors are mostly driven by the technological advancements in the domain of micro & nanofabrication. One key to the miniaturization of optical sensors is their integration onto small chips having their own light sources and detectors. This thesis shows two separate applications of integrated optical sensors which benefit from the implementation of optical nano-structures. A first study investigates a biosensor based on a plasmonic slot waveguide cavity for the detection of changes in refractive index in femto-liter volumes. By integrating the biosensor onto a silicon-on-insulator platform, we could confine the light excitation of the cavity into a single-mode silicon strip waveguide. In a first step realized by simulation, we showed the efficient coupling of the fundamental quasi-transverse electric mode of the waveguide to the plasmonic slot waveguide cavity. We showed that the strong light confinement into the slot is an intrinsic property of the plasmonic slot waveguide which is based on the excitation of a guided wave at a metal-insulator-metal interface. We investigated the surface sensitivity of this biosensor which revealed its potential to detect single-molecules at high concentrations. Moreover, we reported a high bulk sensitivity of up to 600nm per refractive index units. In a second step, we developed a multi-step process based on electron beam lithography to fabricate the sensor. In a third step, we characterized the propagation properties of the fabricated waveguides. Finally, we measured the transmission properties of the integrated sensor has well as the far-field scattering of the plasmonic cavity. A second study focused on a new architecture of a standing-wave integrated Fourier transform spectrometer. This type of spectrometer uses nano-samplers (metallic nano-structures) to probe the intensity of a standing wave generated inside a single-mode waveguide terminated by a mirror. To enhance the well known bandwidth limitation of this type of spectrometer, we implemented a scanning mirror enabling the sub-sampling of the interferogram between each fixed nano-sampler. We fabricated a chip containing a 1D array of low delta n single-mode waveguides made out of epoxy-based "EpoCore" polymer. Equidistant metallic nano-samplers were patterned on top of the waveguides thanks to electron beam lithography. Micro-lenses were fabricated, aligned and glued to the facet of the chip to enable the free space coupling of the waveguides. We implemented a mechanical setup which included a closed-loop piezo actuated mirror to induce an additional phase shift to the interferogram. The realization of an optical setup taking care of the readout of the interferogram showed a 2D multiplexing potential of the spectrometer by realizing the simultaneous detection of independent waveguides. We also investigated the calibration procedures to overcome the fabrication uncertainties by an adapted post-processing step.
Segmentation and Centerline Extraction of the Coronary Arteries with GPU Processing

Coronary artery disease is one of the most common causes of death in Norway, and the world. While diagnosing this disease, and treating it is a common procedure at many hospitals, discovering and documenting a faster, safer and cheaper way to detect the disease could be critical for many patients. Our goal is to implement an algorithm that can automatically detect and produce a segmentation of the coronary arteries from Computed Tomography Angiography (CTA) images. In this thesis we explore and discuss several different methods and approaches for segmentation of the coronary arteries and vessels in general in relation to compatibility for parallel execution on a GPU, accuracy and practicality. A suitable method is chosen based on the discussion and implemented. The results show that the algorithm is able to segment large portions of the coronary arteries. But the produced results are vulnerable to noise, artifacts and irregular vessels. The most significant improvement would be a centerline selection addition, where the centerlines that are chosen to produce the segmentation is chosen based on their location in relation to the heart. Or some other method that is more reliable then the current bio-mechanical method. Our implementation is able to produce a segmentation and a centerline in around 10 minutes. The method implemented in this thesis is a general approach to vessel segmentation, but tuned for coronary artery segmentation. The approach could potentially be adapted and used for extraction of other tubular structures.
Strengthening Saudi Arabia’s Primary Health Care through an e-Referral System: A Case Study

Health systems are becoming more complex, regulatory bodies are increasing their vigilance, and reimbursement practices are shifting toward value, making closing the referral loop an imperative for patient safety, regulatory oversight, and financial viability. The aim of this study was to examine the referral pattern in PHC services and whether a significant variation exists among them based on geographic accessibility to a referred hospital. This was a cross-sectional retrospective study that included all sequentially referred patients between 1 January 2019 and 30 December 2021. A pre-initiative comparison could not be performed, as previous data on the traditional referral system could not be collected. The primary outcome measures considered in this study were the referral rate, and the proportion of the documented appointment date. The healthcare facilities’ geographic locations and data of the hospital departments to which the patients were referred were also available. Between 2019 and 2021, the hospital received 52,143 referrals from the 9 designated PHC centres covering 34 districts. In the PHC centres located within the ≤13 km zone, 1 in every 14 patients were referred to the hospital, whereas 1 in every 20 patients visited PHC centres outside this zone. Since the introduction of the Ehalati e-referral system, the number of documented appointment schedules of the referred patients has improved over time by 16.1% (from 79.6% to 95.7%, p < 0.001). Ophthalmologic (17.1%) and dental services (15.4%) received the most referrals among all other specialties, whereas the referral rate for cardiology services was the lowest (2.5%). The documented appointment scheduling record of referred patients has improved significantly since the introduction of the Ehalati e-referral system. However, the results of this study indicate that the proximity of PHC centres to specialised hospitals is more likely associated with higher referral and documented appointment scheduling rates. Strategies that improve scheduling, decrease variation among clinics, and improve patient access will likely improve the closing rates of the referral loop.