Miracle-dz/UniFAR-FTUs huggingface dataset:
Time Series Modeling to Forecast on Consuming Electricity: A case study Analysis of electrical consumption in Erbil City from 2014 to 2018

Time series analysis and forecasting have become a major tool in different applications in hydrology and environmental management fields. Among the most effective approaches for analyzing time series data is the model introduced by Box and Jenkins, ARIMA (Autoregressive Integrated Moving Average). Approach: In this study, we used Box-Jenkins methodology to build ARIMA model for electricity consumption data taken for Erbil region station for the period from 2014-2018. Results: In this research, ARIMA (1, 1, 1) (0, 1, 1)12 model was developed. This model is used to forecasting the monthly consumption for the upcoming 2019 year in each month to help decision makers establish priorities in terms of electricity demand management. Conclusion/Recommendations: An intervention time series analysis could be used to forecast the peak values of producing electricity in megawatt for Erbil city.
Intelligent Risk Prediction System in IoT-Based Supply Chain Management in Logistics Sector

The Internet of Things (IoT) has resulted in substantial advances in the logistics sector, particularly in logistics storage management, communication systems, service quality, and supply chain management. The goal of this study is to create an intelligent supply chain (SC) management system that provides decision support to SC managers in order to achieve effective Internet of Things (IOT)-based logistics. Current research on predicting risks in shipping operations in the logistics sector during natural disasters has produced a variety of unexpected findings utilizing machine learning (ML) algorithms and traditional feature-encoding approaches. This has prompted a variety of concerns regarding the research’s validity. These previous attempts, like many others before them, used deep neural models to gain features without requiring the user to maintain track of all of the sequence information. This paper offers a hybrid deep learning (DL) approach, convolutional neural network (CNN) + bidirectional gating recurrent unit (BiGRU), to lessen the impact of natural disasters on shipping operations by addressing the question, “Can goods be shipped from a source location to a destination? ”. The suggested DL methodology is divided into four stages: data collection, de-noising or pre-processing, feature extraction, and prediction. When compared to the baseline work, the proposed CNN + BiGRU achieved an accuracy of up to 94%.
Pretend Play Therapy In-person Versus Online for Participation Improvement in Children with Attention Deficit Hyperactivity Disorder: Study Protocol for a Clinical Trial

Background: Attention deficit hyperactivity disorder (ADHD) is a common condition in children, affecting 10% of Iranian children. Attention deficit hyperactivity disorder affects many areas of children’s lives, especially their participation. The most important part of a child’s life is playing, which is affected by ADHD. Children with ADHD have difficulties in their playing. Play is a child’s work and the most critical occupation that a child has. However, there are a few bright protocols to improve play for the sake of play. Objectives: This protocol describes interventions to improve play in children with ADHD. Methods: This is a double-blind clinical trial in parallel groups of children with ADHD aged 4 to 6 years. Trained occupational therapists will deliver therapy in the clinic for eight weeks during 16 sessions of 45 minutes. A follow-up assessment will be done eight weeks after the last therapy session. Conclusions: According to the results of the trial, evidence will be provided about a bright and feasible protocol for occupational therapists to address the play of children with ADHD that can have significant effects on their quality of life, play skills, and participation.
A Technique for Piecewise Linear Approximation of 2D Digital Curve

This paper introduces the concept of curve segment after relaxing the definition of digital straight segment and uses this concept to decompose a digital curve into a sequence of curve segments. For each curve segment, the maximum deviation of it from its approximating line segment is computed and the segment with the highest maximum deviation is split into two at a point most distant from the line segment, if the deviation exceeds a threshold. Only one vertex is added to the existing set of vertices in iteration until no more vertices can be added to the approximation. Experiments show improvement over a similar work.
A Diet Enriched with Lacticaseibacillus rhamnosus HN001 and Milk Fat Globule Membrane Alters the Gut Microbiota and Decreases Amygdala GABA a Receptor Expression in Stress-Sensitive Rats

Brain signalling pathways involved in subclinical anxiety and depressed mood can be modulated via the gut brain axis (GBA), providing the potential for diet and dietary components to affect mood. We investigated behavioural, physiological and gut microbiome responses to the Lacticaseibacillus rhamnosus strain HN001 (LactoB HN001™), which has been shown to reduce postpartum anxiety and depression, and a milk fat globule membrane-enriched product, Lipid 70 (SurestartTM MFGM Lipid 70), which has been implicated in memory in stress-susceptible Wistar Kyoto rats. We examined behaviour in the open field, elevated plus maze and novel object recognition tests in conjunction with the expression of host genes in neuro-signalling pathways, and we also assessed brain lipidomics. Treatment-induced alterations in the caecal microbiome and short-chain fatty acid (SCFA) profiles were also assessed. Neither ingredient induced behavioural changes or altered the brain lipidome (separately or when combined). However, with regard to brain gene expression, the L. rhamnosus HN001 + Lipid 70 combination produced a synergistic effect, reducing GABAA subunit expression in the amygdala (Gabre, Gat3, Gabrg1) and hippocampus (Gabrd). Treatment with L. rhamnosus HN001 alone altered expression of the metabotropic glutamate receptor (Grm4) in the amygdala but produced only minor changes in gut microbiota composition. In contrast, Lipid 70 alone did not alter brain gene expression but produced a significant shift in the gut microbiota profile. Under the conditions used, there was no observed effect on rat behaviour for the ingredient combination. However, the enhancement of brain gene expression by L. rhamnosus HN001 + Lipid 70 implicates synergistic actions on region-specific neural pathways associated with fear, anxiety, depression and memory. A significant shift in the gut microbiota profile also occurred that was mainly attributable to Lipid 70.
Public Policy Analysis and The Aspirations Of The Community In The Process Of Driving Perda

This study discusses the role of community participation in making "PERDA", using the Normative Political Science (Political Philosophy) approach. The main idea is that "good governance" is a concretization of the idea of democracy at the local government level. The demand for the involvement of all "stakeholders" is the demand for democracy, with the framework of thinking that the owner of the government is not the government itself but all citizens. Therefore, all policy planning and implementation are not the business of the government alone but the affairs of the whole community. Local government is required to carry out good governance in its implementation. That means the local government must be organized democratically involving three elements, namely the government bureaucracy (state), parliament (political society), and the whole society (civil society). What is new here is the role of public participation in formulating policies in the form of "procedural" and at the same time "substantial" PERDA.
Minimal Surfaces in Sub-Riemannian Structures and Functional Geometry of the Visual Cortex

We develop geometrical models of vision consistent with the characteristics of the visual cortex and study geometric flows in the relevant model geometries. We provide a novel sub-Riemannian model of the primary visual cortex, which models orientation-frequency selective phase shifted cortex cell behavior and the associated horizontal connectivity. We develop an image enhancement algorithm using sub-Riemannian diffusion and Laplace-Beltrami flow in the model framework. We provide two geometric models for multi-scale orientation map and orientation-frequency preference map construction which employ Bargmann transform in high dimensional cortical spaces. We prove the uniqueness of the solution to sub-Riemannian mean curvature flow equation in the Heisenberg group geometry. An iterative diffusion process followed by a maximum selection mechanism was proposed by Citti and Sarti in the sub-Riemannian setting of the roto-translation group. They conjectured that this two-fold procedure is equivalent to a mean curvature flow. However a complete proof was missing, even in the Euclidean setting. We prove in the Euclidean setting that this two fold procedure is equivalent to mean curvature flow.
Statistical shape influence in geodesic active contours

A novel method of incorporating shape information into the image segmentation process is presented. We introduce a representation for deformable shapes and define a probability distribution over the variances of a set of training shapes. The segmentation process embeds an initial curve as the zero level set of a higher dimensional surface, and evolves the surface such that the zero level set converges on the boundary of the object to be segmented. At each step of the surface evolution, we estimate the maximum a posteriori (MAP) position and shape of the object in the image, based on the prior shape information and the image information. We then evolve the surface globally, towards the MAP estimate, and locally, based on image gradients and curvature. Results are demonstrated on synthetic data and medical imagery, in 2D and 3D.
Simulations and measurements of proton beam energy spectrum after energy degradation

At the proton therapy facility PROSCAN of the Paul Scherrer Institute the energy modulation of the cyclotron generated proton beam is performed via material insertion into the beam trajectory. The energy spectrum of the particles propagating forwards after such procedure has been simulated and measured. The current paper summarizes the results of these simulations and measurements and illustrates their significance for the future developments of a gantry for proton therapy at the Paul Scherrer Institute.
Estimation and Prediction for

A statistical approach to a posteriori blockmodeling for digraphs and valued digraphs is proposed. The probability model assumes that the vertices of the digraph are partitioned iuto several unobserved (latent) classes and that the probability distribution of the relation between two vertices depends only on the classes to which they belong. A Bayesian estimator based on Gibbs sampling is proposed. The basic model is not identified. because class labels are arbitrary. The resulting identifiability problems are solved by restricting inference to the posterior distributions of invariant functions of the parameters and the vertex class membership. In addition, models are considered where class labels are identified by prior distributions for the class membership of some of the vertices. The model is illustrated by an example from the social networks literature (Kapferer's tailor shop).
Impact of cosmological satellite galaxies on the dynamics of the Milky Way disc

I present a high resolution study of the impact of realistic satellite galaxies, extracted from cosmological simulations of Milky Way-like haloes including 6 Aquarius and one Via Lactea II suites, on the dynamics of the Galactic disc. The initial conditions for the multi-component Milky Way galaxy were generated, to ensure an isolated system in a dynamical equilibrium state. After analysing the statistical properties of the subhaloes, candidates likely to impact the disc with the initial mass,tidal mass > 10^8 solar mass, were identified, inserted into our high resolution N-body simulations and evolved for 2 Gyr. I quantified the vertical heating due to such impacts by measuring the disc thickness and squared vertical velocity dispersion across the disc. According to our analysis, the strength of the heating is strongly dependent on the high mass end of the subhalo distribution from cosmological simulations. The mean increase of the vertical dispersion is approximately 20 km^2/s^2/Gyr for R > 4 kpc with a flat radial profile. Excluding Aq-F2 results with a very massive perturber with tidal mass = 6.10^10 solar mass, the mean heating is less than 12 km^^2/s^2/Gyr. These heating values correspond to 28% and 17% of the observed vertical heating rate in the solar neighbourhood, respectively. Taking into account the statistical dispersion around the mean, we miss the observed heating rate by more than 3 sigma. I observed a general flaring of the disc height in the case of all 7 simulations in the outer parts. The dynamics of the disc in the presence of satellites was also studied in the final two chapters where the selected runs were extended to 4 Gyr. The infall of satellites can be responsible for delaying/advancing the bar-formation, depending on the crossing epoch. The passage influences the bar-formation, if the passage occurs between the end of the shot noise regime T1 (0.75 Gyr) and before the exponential growth T2. The Aq-F2 run with the most massive candidate, induces localised vertical heating in the outer disc with R > 15 kpc. I also observed signatures of radial oscillatory behaviour in the outer disc plane, with features similar to that of the Monoceros ring. Disc warping is prominent after 3 Gyr of evolution, reaching median z coordinate of approximately 4 kpc. The asymmetric m = 1 mode is dominant for R > 15 kpc in the form of vertical motion, with the m = 2 mode influencing the rotation of disc particles.
A texture-based rolling bearing fault diagnosis scheme using adaptive optimal kernel time frequency representation and uniform local binary patterns

This paper presents a new scheme for rolling bearing fault diagnosis using texture features extracted from the time–frequency representations (TFRs) of the signal. To derive the proposed texture features, firstly adaptive optimal kernel time frequency representation (AOK-TFR) is applied to extract TFRs of the signal which essentially describe the energy distribution characteristics of the signal over time and frequency domain. Since the AOK-TFR uses the signal-dependent radially Gaussian kernel that adapts over time, it can exactly track the minor variations in the signal and provide an excellent time–frequency concentration in noisy environment. Simulation experiments are furthermore performed in comparison with common time–frequency analysis methods under different noisy conditions. Secondly, the uniform local binary pattern (uLBP), which is a computationally simple and noise-resistant texture analysis method, is used to calculate the histograms from the TFRs to characterize rolling bearing fault information. Finally, the obtained histogram feature vectors are input into the multi-SVM classifier for pattern recognition. We validate the effectiveness of the proposed scheme by several experiments, and comparative results demonstrate that the new fault diagnosis technique performs better than most state-of-the-art techniques, and yet we find that the proposed algorithm possess the adaptivity and noise resistance qualities that could be very useful in real industrial applications.
The X-shooter Spectral Library (XSL) and its First Data Release

Stellar spectral libraries play a pivotal role in astrophysics, helping us to understand the physics of stars and build models of stellar populations in order to study distant star clusters and galaxies. Aspects of current stellar spectral libraries that require improvement are: better calibrations, more stars, higher spectral resolution and broader wavelength coverage. The X-shooter spectrograph is well suited to the task, and we are building the X-shooter Spectral Library (XSL) of more than 700 stars covering the entire Hertzsprung-Russell diagram in the wavelength range 300-2480 nm at a mean resolving power of 10 000. Here we describe the sample, observations and reduction of the data, concentrating on the flux-calibrated, telluric-corrected near-ultraviolet and optical spectra of the first 237 unique stars observed in the first year of the survey. These spectra, the first data release, are now available at http://xsl.u-strasbg.fr/.
Very High Energy astronomy from H.E.S.S. to CTA. Opening of a new astronomical window on the non-thermal Universe

The last ten years saw the emergence of Imaging Atmospheric Cherenkov Telescopes as a mature and efficient technique for the study of the Very High Energy Universe, leading to the successful opening, mainly by the HESS experiment, of our understanding of the non-thermal Universe. This habilitation thesis summaries ten years of research in Very High Energy gamma-ray astronomy with HESS and CTA. In the first part, instrumental aspects such as the experiment conception, its calibration, the reconstruction of the events and the data analysis are presented. The second parts draws a panorama of the main discoveries in the domain.
Uncertainty quantification for random parabolic equations with nonhomogeneous boundary conditions on a bounded domain via the approximation of the probability density function

This paper deals with the randomized heat equation defined on a general bounded interval [L1, L2] and with nonhomogeneous boundary conditions. The solution is a stochastic process that can be related, via changes of variable, with the solution stochastic process of the random heat equation defined on [0,1] with homogeneous boundary conditions. Results in the extant literature establish conditions under which the probability density function of the solution process to the random heat equation on [0,1] with homogeneous boundary conditions can be approximated. Via the changes of variable and the Random Variable Transformation technique, we set mild conditions under which the probability density function of the solution process to the random heat equation on a general bounded interval [L1, L2] and with nonhomogeneous boundary conditions can be approximated uniformly or pointwise. Furthermore, we provide sufficient conditions in order that the expectation and the variance of the solution stochastic process can be computed from the proposed approximations of the probability density function. Numerical examples are performed in the case that the initial condition process has a certain Karhunen‐Loève expansion, being Gaussian and non‐Gaussian.
Liquid for Fused Deposition Modeling Technique (L-FDM)—A Revolution in Application Chemicals to 3D Printing Technology: Color and Elements

This article presents a novel 3D printing technique called L-FDM (liquid for fused deposition modeling), which is based on the deposition of molten thermoplastic material. The new method allows for the direct introduction of chemicals and polymer filament modifications during the printing process. In contrast to traditional incremental methods, L-FDM eliminates the need for extra granulating, extrusion, and processing equipment, making it possible to introduce chemical additives to the polymer matrix directly. This opens up exciting possibilities for chemical laboratories to test and experiment with new and known chemicals through 3D printing. The article discusses the technical aspects of L-FDM and its potential applications and provides practical examples of direct filament modifications using the technique. The results of these modifications were verified using a colorimeter, electron microscopy (SEM/EDS), and optical microscopy.
Hybrid coyote predator with DL network for brain disorder detection using EEG signals

ABSTRACT Brain abnormality is one of the dominant health issues in recent years, and electroencephalogram (EEG) is a mostly employed method for identifying abnormalities in the brain. Brain abnormalities can be cured only when it is detected early stage. Even though there are various techniques for the detection of brain abnormalities, the health sector concentrates more on the advanced modern method to diagnose the abnormality very precisely. Advanced DL methods have attracted physicians very much due to the fascination with developing detection methods with minimal errors. Hence, this paper deeply concentrated on developing a well-structured technique to predict brain abnormalities using the hybrid coyote predator optimisation-dependent DCNN (hybrid coyote predator-DCNN). The results of this study demonstrate the value of hybrid coyote predator optimisation, which inherits qualities like a long jump and an intelligence ability to adapt to its surroundings and aids in fine-tuning the modal parameters of the DCNN. Additionally, to improve the detection model’s precision, the frequency bands from which the features are taken are separated to identify brain changes. Utilising the CAP dataset and the performance metrics, the analysis is carried out. Regarding the TP and k-fold, the hybrid coyote predator-DCNN model attained accuracy of 98.446% and 97.384%, respectively.
Development of Interactive Multimedia-Based Physical Fitness Learning Media in Junior High School

This work seeks to provide a new teaching material for instructors in the physical fitness material of speed to aid teachers and students in the learning process and learning in physical education, sports, and health topics. This research and development method uses the Research and Development (R&D) approach developed by Lee & Owen. Small group trial subjects consisted of 20 students, large group trial subjects consisted of 30 students. Based on the results of product validation and trials, it was obtained 93% learning experts, 87% physical fitness experts, 91% media experts, 83% class VII user experts, 93% class VIII user experts, the results of the five validators met the criteria and were included in the category very valid and does not need to be revised. While small group trials for class VII were 83%, small group trials for class VIII were 82%, large group trials for class VII were 84%, large group trials for class VIII were 82%. From these results it was concluded that the development of this learning met the very good criteria and could be used to help the physical education learning process, especially on physical fitness material on the element of speed.