Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of deconvolution modelling applied to numerical combustion

A possible modelling approach in the large eddy simulation (LES) of reactive flows is to deconvolve resolved scalars. Indeed, by inverting the LES filter, scalars such as mass fractions are reconstructed. This information can be used to close budget terms of filtered species balance equations, such as the filtered reaction rate. Being ill-posed in the mathematical sense, the problem is very sensitive to any numerical perturbation. The objective of the present study is to assess the ability of this kind of methodology to capture the chemical structure of premixed flames. For that purpose, three deconvolution methods are tested on a one-dimensional filtered laminar premixed flame configuration: the approximate deconvolution method based on Van Cittert iterative deconvolution, a Taylor decomposition-based method, and the regularised deconvolution method based on the minimisation of a quadratic criterion. These methods are then extended to the reconstruction of subgrid scale profiles. Two methodologies are proposed: the first one relies on subgrid scale interpolation of deconvolved profiles and the second uses parametric functions to describe small scales. Conducted tests analyse the ability of the method to capture the chemical filtered flame structure and front propagation speed. Results show that the deconvolution model should include information about small scales in order to regularise the filter inversion. a priori and a posteriori tests showed that the filtered flame propagation speed and structure cannot be captured if the filter size is too large.
Ambulatory Management of Urinary Tract Infections in Children: Experience of the Pediatric Medical Emergency Department in Rabat

The aim of the study is to describe the clinical epidemiological characteristics and bacteriological profile of UTIs in infants and children, to determine the main risk factors for the occurrence of UTIs, and to identify the causes of UTIs urinary tract infections, to compare the interest of CRP and let procalcitonin assays for the diagnosis of febrile urinary tract infections as well as the interest of imaging (renal scintigraphy and ultrasound of the urinary tree) in the diagnosis of febrile urinary tract infections. This observational study of a cohort of 169 patients (children aged more than 28 days, 119 girls and 40 boys) conducted in the pediatric emergency department of the children's hospital of the university hospital of Rabat over a period of 6 months. The diagnosis of UTIs is based on the detection of germs in the urine, either indirectly by urine dipstick or by urine culture. However, the interpretation of microbiological results can be tricky: an inappropriate urine collection technique, particularly in neonates and young children, is a pitfall that the clinician must take into account to avoid misdiagnosing UTI, usually by over-diagnosis. The location of high UTI involving the renal parenchyma or low UTI limited to the bladder, which determines the morbidity of the infection, the therapeutic management and the imaging work-up, remains a matter of debate. DMSA renal scintigraphy, the reference examination in the case of high UTI, cannot be recommended as a first-line examination because of its cost and the practical difficulties of performing it. Biological markers of inflammation (leucocytosis, CRP, procalcitonin) do not always allow the diagnosis of ANP to be made with certainty.
Data Collection Algorithms for Model Training in Internet of Vehicles

In Internet of Vehicles (IoV), it is critical to collect sufficient data for model training, to support vehicular intelligent applications. However, the environment of IoV is highly dynamic due to the mobility of vehicles, making it challenging to efficiently allocate resources for data collection. Additionally, timely training of machine learning models with collected data is important for accurate representation in a constantly changing environment. This article aims to improve the performance of model training by collecting sufficient data from vehicles in IoV. A system throughput maximization problem is formulated under the limited bandwidth, storage, and computing resources, which is an NP-hard problem. To solve the problem, an iterative algorithm, namely, the iterative algorithm based on approaching minimum bandwidth (IAMB), is proposed to preferentially collect data from the vehicles with sufficient data and reliable communication quality. Besides, a genetic algorithm, namely, the genetic algorithm based on approaching minimum bandwidth (GAMB), is proposed to further improve the probability of superior individual by replacing operation. We also customize three greedy strategy-based algorithms as the baselines. Extensive experimental results show that our proposed algorithms outperform the baseline algorithms for all cases. Specifically, IAMB and GAMB can improve the throughput by up to 5% and 8%, respectively, compared with baseline algorithms. In addition, the customized genetic algorithm is also superior to the iterative algorithm on performance of system throughput. Moreover, the customized genetic algorithm is more stable than the proposed iterative algorithm in terms of system throughput for model training in the dynamic network environment.
The Relationship between Renin–Angiotensin–Aldosterone System (RAAS) Activity, Osteoporosis and Estrogen Deficiency in Type 2 Diabetes

Type 2 diabetes (T2D) is associated with a plethora of comorbidities, including osteoporosis, which occurs due to an imbalance between bone resorption and formation. Numerous mechanisms have been explored to understand this association, including the renin–angiotensin–aldosterone system (RAAS). An upregulated RAAS has been positively correlated with T2D and estrogen deficiency in comorbidities such as osteoporosis in humans and experimental studies. Therefore, research has focused on these associations in order to find ways to improve glucose handling, osteoporosis and the downstream effects of estrogen deficiency. Upregulation of RAAS may alter the bone microenvironment by altering the bone marrow inflammatory status by shifting the osteoprotegerin (OPG)/nuclear factor kappa-Β ligand (RANKL) ratio. The angiotensin-converting-enzyme/angiotensin II/Angiotensin II type 1 receptor (ACE/Ang II/AT1R) has been evidenced to promote osteoclastogenesis and decrease osteoblast formation and differentiation. ACE/Ang II/AT1R inhibits the wingless-related integration site (Wnt)/β-catenin pathway, which is integral in bone formation. While a lot of literature exists on the effects of RAAS and osteoporosis on T2D, the work is yet to be consolidated. Therefore, this review looks at RAAS activity in relation to osteoporosis and T2D. This review also highlights the relationship between RAAS activity, osteoporosis and estrogen deficiency in T2D.
Periodic momentum and thermal boundary layer mixed convection flow around the surface of a sphere in the presence of viscous dissipation

Numerical solutions for the periodic laminar boundary layer mixed convection flow around the surface of a heated sphere in the presence of viscous dissipation have been obtained by solving the governing equations using an implicit finite difference numerical technique. The fluid under consideration is assumed to be viscous and incompressible. Periodic momentum and thermal boundary layer profiles for different positions of x around the surface of the sphere are evaluated. The features of the obtained results for different values of mixed convection parameter λ, Prandtl number Pr, viscous dissipation parameter N, and frequency parameter ω are shown graphically. The obtained results confirm significant effect of all these mentioned parameters on periodic momentum and thermal boundary layer mixed convection flow around different positions of the sphere.
Divergence optimization based on trade-off between separation and extrapolation abilities in superresolution-based nonnegative matrix factorization ∗

In recent years, music and acoustic signal separation based on nonnegative matrix factorization (NMF) [1] has been a very active area of signal processing research. NMF for acoustical signals decomposes an input spectrogram into the product of a spectral basis matrix and its activation matrix. In particular, supervised NMF (SNMF) [2, 3], which includes a priori training with some sample sounds of a target instrument, can extract the target signal to some extent. However, for the case of a mixture consisting of many sources, the source extraction performance is markedly degraded when only single-channel observation is available. Multichannel NMF, which is a natural extension for multichannel signal, has been proposed as an unsupervised method [4]. However, such an unsupervised separation is a difficult problem because the decomposition is underspecified. Hence, these algorithms involve strong dependence on initial values and lack robustness. As another means for addressing multichannel signal separation, a hybrid method, which concatenates superresolution-based SNMF after directional clustering, has been proposed by the authors [5]. This method uses index information generated by binary masking of directional clustering to regard the spectral chasms as unseen observations, and finally reconstructs the target source components via spectrogram extrapolation using the supervised bases. In this paper, we extend the method [5] to be a more generalized form introducing new parametric cost functions for both separation and regularization based on β-divergence criterion. Also, we discuss the optimal divergence for this method.
Exploring Hyper-Parameters and Feature Selection for Predicting Non-Communicable Chronic Disease Using Stacking Classifier

Non-communicable disease, especially chronic disease, is the most common factor of complication of deteriorating physical health and the state of one’s mind. It is also a prominent cause of illness and mortality around the world. Primarily chronic disease is preventable at a particular stage though its occurrence is critical. To make clinical decisions, these illness prediction models were created to assist clinicians and patients. A chronic disease prediction model takes into account many risk variables to determine an individual’s illness risk. Machine learning approaches have made it possible to predict chronic disease early by collecting Electronic Health Record (EHR) data. This paper focuses on the diabetes dataset extracted from Kaggle and two unseen real datasets. In this paper, we have implemented Synthetic Minority Over-Sampling Technique (SMOTE) algorithm to balance the dataset. We have also explored Boruta as the feature selection method. To tune hyper-parameters of different algorithms, we have proposed an improved technique by combining the Grid Search method with the Grey Wolf Optimization algorithm. The Grid Search method requires extensive searching, while the Grey Wolf Optimization algorithm is easily linked, rapid to seek, and extremely exact. Nine conventional classification techniques have been evaluated in this paper. This research concentrates on the Stacking Classifier to assess the performance of the prediction model that produces the best results. The Proposed Model gave the highest F1-Score 98.84% on PIMA dataset, 98% after validation on the Synthetic dataset, 97.3% on ADRC dataset, 96.20% on FHD dataset. To the best of our knowledge, no previous work has focused on such a sort of technique and these two datasets. The outcomes of the comparison experiment on the PIMA dataset reveals that the proposed strategy performs better. This study also provides the interpretation of the proposed model. It conducts an ethical assessment of what explainability means for the use of Machine Learning models in clinical practice.
Autoimmune Encephalitis: A Literature Review

Abstract Autoimmune encephalitis (AIE) defines brain inflammation caused by a misdirected immune response against self-antigens expressed in the central nervous system. AIE encompasses a group of non-infectious immune-mediated inflammatory disorders of the brain parenchyma often involving the cortical or deep gray matter with or without involvement of the white matter, meninges, or the spinal cord. Suggested mechanisms that may trigger AIE include tumors (paraneoplastic), infections (para-infectious), or it may be cryptogenic. This study represents a review of the common forms of AIE, exploring their causes, diagnostic approaches, and management strategies. The previous and ongoing investigations in this field have been driven by the identification of several pathogenic autoantibodies that cause polysymptomatic neuropsychiatric and neurological diseases. AIE comprises a heterogeneous group of disorders that are at least as common as infectious causes of encephalitis. Early treatment is associated with better prognosis and is crucial for the prevention of severe complications. The underlying mechanisms for activation and autoimmune response in the CNS are still unclear. Further investigations are needed to better explain how immune mechanisms affect nervous system functions.
Effects of pre-anthesis low-temperature stress on the mineral components in wheat grains

Introduction The nutritional value of wheat is important to human health. Despite minerals being essential nutrients for the human body, they are often neglected in consideration of the nutritional quality of cereal grains. Extreme low-temperature events have become more frequent due to the current environmental unpredictability, and it is yet unknown how the mineral components in grains are affected by low temperature. Methods To provide valuable information for enhancing the nutritional quality of wheat under potential climatic conditions, we treated different cold-sensitive wheat cultivars at four low-temperature levels during the individual and combined stages of jointing and booting in controlled-environment phytotrons. Results and Discussion In general, the contents of P, K, Ca, and Zn in the cold-sensitive cultivar (Yangmai16) and K in the cold-tolerant cultivar (Xumai30) were enhanced by low temperature. However, the accumulation of minerals in mature grains was reduced under low-temperature treatment, except for P, Ca, and Zn in Yangmai16. In addition, the mineral content and accumulation in Yangmai16 (except for Fe) were more susceptible to low temperature during the combined stages, while the mineral content and accumulation of K, Fe, and Zn in Xumai30 were more susceptible to low temperature during the booting stage. Moreover, Yangmai16 under extremely low temperatures (T3 and T4) during booting and Xumai30 under all low-temperature treatments during the combined stages had lower comprehensive evaluation values. These findings offer a crucial reference for enhancing the nutritional quality of wheat grains under climate change.
The Moderating Effect of Values on the Relationship between Subjective Social Status and Depression: Evidence from MIDUS

The relationship between subjective social status (SSS) and mental health and its underlying mechanisms remain an area of interest in the social sciences. Using data from the Midlife in the United States 2 (MIDUS 2), we examine how individual differences in valuing achievement and autonomy moderate the relationship between SSS and symptoms of depression. We find evidence of a moderation effect; there is a weaker relationship between SSS and depression for individuals who strongly hold the values of achievement or autonomy. In addition, at low levels of SSS, there are significant differences in the number of depression symptoms depending on personal values which are not seen at higher rungs of the SSS ladder, indicating a difference in this relationship dependent on how strongly one holds values of achievement and autonomy. We conclude by speculating on the mechanisms by which values shape the link between SSS and mental well-being and suggest future directions in studying values.
Effect of conservation tillage on seedling emergence and crop growth-evidences from UAV observations

Abstract Over the past decades, intensive reclamation has caused significant land degradation, threatening both ecological and food security. The Chinese government has taken a series of novel Conservation Tillage (CT) measures according to regional climate and soil conditions to increase agriculture’s multifunctional and resilience. Previous studies have reported CT in northeast China can improve soil organic carbon and reduce ammonia emissions in northeast China. However, whether the regionally adapted CT can avoid its inherent limitations in seed emergence and crop growth is still lacking comprehensive understanding and investigation. By using high-resolution and time-series Unmanned Aerial Vehicle (UAV) observations, our study investigated and evaluated the performance of different advanced and regional adapted CT treatments in crop emergence and growth compared with conventional tillage during the whole growing season. Based on the field measurements, we found that although the residues can impact the seed emergence, there was no significant difference between CT treatments and conventional tillage, with some CT treatments can even raise the emergence rate. We also found that the high residues covered CT treatments obstructed the maize grown in the early growing season including the V3 and V6 period, while the crop growth in these treatments caught up with the conventional tillage in the following growing periods. Our study suggested that well-adjusted CT treatments in Northeast China have the potential to maintain crop production accompanied by enhancing the ecosystem’s multifunctionality. The results from this study provided observational evidence of crop growth impact from CT, which will have great implications in convincing the farmers and beneficial to the promotion of conservation tillage.
Direct-current electric invisibility through topology optimization

Based on the covariance matrix adaptation evolution strategy (CMA-ES), advanced designs of direct-current (DC) electric cloaks composed of bulk isotropic materials are presented through a topology optimization using a level set method. The designed DC electric cloaks succeed in providing DC electric invisibility of an electrical insulator in DC flow; specifically, an electric potential distribution is found that closely reproduces a distribution when no insulator is present. To produce this invisibility, we minimized the difference between distributions for the DC electric cloak and one without insulating obstacles as the objective function. CMA-ES explores optimal sets of level set functions as design variables that minimize the objective function with a perimeter constraint. In the best case in our simulation, the minimized objective function under cloaking reaches 0.00194% of that in the absence of cloaking. Toward multidirectional DC electric cloaks, a topology optimization subject to four-axial structural symmetries is demonstrated.Based on the covariance matrix adaptation evolution strategy (CMA-ES), advanced designs of direct-current (DC) electric cloaks composed of bulk isotropic materials are presented through a topology optimization using a level set method. The designed DC electric cloaks succeed in providing DC electric invisibility of an electrical insulator in DC flow; specifically, an electric potential distribution is found that closely reproduces a distribution when no insulator is present. To produce this invisibility, we minimized the difference between distributions for the DC electric cloak and one without insulating obstacles as the objective function. CMA-ES explores optimal sets of level set functions as design variables that minimize the objective function with a perimeter constraint. In the best case in our simulation, the minimized objective function under cloaking reaches 0.00194% of that in the absence of cloaking. Toward multidirectional DC electric cloaks, a topology optimization subject to four-axial struc.
Raman Scattering Study of Nano-Particle BaTiO3

BaTiO3 (hereafter BTO) particles with different sizes, diameters in the range 10-200 nm, were prepared by the hydrothermal synthesis method, and their Raman spectra were investigated in the temperature range 570 to -150℃; the spectra were obtained by micro-Raman spectroscopy. On the basis of the changes in the spectra, the phase diagram of particle size versus temperature was obtained. We can divide the region below the particle diameter of 200 nm into three regions. One is a bulk-like region with particle diameters in the range 100-200 nm; the spectra in this region are similar to that of a single crystal. The second region is a transient region with particle diameters in the range 60-90 nm. The third region is a highly distorted region with particle diameters below 50 nm, where the spectra do not change remarkable from -150 to 570℃. In the spectra of 70- and 85-nm particles, a new peak was observed; this peak corresponds to the vibration at the X-point of the Brillouin-zone boundary. Thus, the translational symmetry in the transient region is broken. The new peak is expected to contain information on the effect of particle size on the dielectric constant of BTO. This implies that it can indicate the maximum dielectric constant of BTO with a particle diameter of 70 nm.
State Observer for Optimal Control using White-box Building Models

In order to improve the energy efficiency of buildings, optimal control strategies, such as model predictive control (MPC), have proven to be potential techniques for intelligent operation of energy systems in buildings. However, in order to perform well, MPC needs an accurate controller model of the building to make correct predictions of the building thermal needs (feedforward) and the algorithm should ideally use measurement data to update the model to the actual state of the building (feedback). In this paper, a white-box approach is used to develop the controller model for an office building, leading to a model with more than 1000 states. As these states are not directly measurable, a state observer needs to be developed. In this paper, we compare three different state estimation techniques commonly applied to optimal control in buildings by applying them on a simulation model of the office building but fed with real measurement data. The considered observers are stationary Kalman Filter, time-varying Kalman Filter, and Moving Horizon Estimation. Summarizing the results, all estimators can achieve low output estimation error, but, in the case of the Kalman filters, the estimated state values are not physical. In the case of MHE, the model firstly had to be reduced to 200 states in order to evade the non-positive definite quadratic formulation present in the model and converge in tractable computation time.