Miracle-dz/UniFAR-FTUs huggingface dataset:
Carotid web: An under-diagnosed cause of recurrent ischemic strokes

Background: Carotid Webs (CW) are an under-diagnosed cause of embolic stroke especially in young patients with no vascular risk fac - tors. Strokes associated with CW are frequently severe, with a high proportion of Large Vessel Occlusion (LVO). They are frequently consid - ered cryptogenic, and sub sequently, treatment with aspirin is initiated as secondary prevention. However, strokes secondary to a CW have high recurrence rates despite antithrombotic therapy, where as carotid revascularization is effective in preventing recurrent strokes. Methods: This is a single case report of a patient who presented to the emergency department with a right-hemispheric is chemic stroke due to LVO, requiring treatment with Mechanical Thrombectomy (MT). No specific cause was found and aspirin was initiated. Two months later, the patient presented to the emergency department with yet an - other right-hemispheric is chemic stroke due to LVO, which required treatment with MT. The presence of the CW was confirmed with the digital subtraction angiography performed during the treatment. A retrospective evaluation of the angio -CT performed during the first stroke showed the presence of the CW, which was initially over looked. The patient was then treated with carotid stenting and presented no further recurrences. Conclusion: Physicians should systematically investigate the pres - ence of a CW in patients with cryptogenic strokes. The high recurrence rate despite medical treatment highlights the importance of an early diagnosis in order to opt for a more aggressive treatment. Carotid re - vascularization seems safe and effective in preventing recurrences, and it should probably be recommended as a first-line treatment in these patients.
µ -TWO: 3 × F ASTER M ULTI -M ODEL T RAINING WITH O RCHESTRATION AND M EMORY O PTIMIZATION

In this paper, we identify that modern GPUs - the key platform for developing neural networks - are being severely underutilized, with ∼ 50% utilization, that further drops as GPUs get faster. We show that state-of-the-art training techniques that employ operator fusion and larger mini-batch size to improve GPU utilization are limited by memory and do not scale with the size and number of models. Additionally, we show that using state-of-the art data swapping techniques (between GPU and host memory) to address GPU memory limitations lead to massive computation stalls as network sizes grow. We introduce µ -two, a novel compiler that maximizes GPU utilization. At the core of µ -two is an approach that leverages selective data swapping from GPU to host memory only when absolutely necessary, and maximally overlaps data movement with independent computation operations such that GPUs never have to wait for data. By collecting accurate run-time statistics and data dependencies, µ -two automatically fuses operators across different models, and precisely schedules data movement and computation operations to enable concurrent training of multiple models with minimum stall time. We show how to generate µ -two schedules for diverse neural network and GPU architectures and integrate µ -two into the PyTorch framework. Our experiments show that µ -two can achieve up to a 3 × speed-up across a range of network architectures and hardware, spanning vision, natural language processing, and recommendation applications.
Finite propagation speed for Leibenson’s equation on Riemannian manifolds

We consider on arbitrary Riemannian manifolds the Leibenson equation ∂ t u = Δ p u q . This equation comes from hydrodynamics where it describes filtration of a turbulent compressible liquid in porous medium. We prove that that, under optimal restrictions on p and q , weak subsolutions to this equation have finite propagation speed.
A Review of Deep Learning Solutions in 360° Video Streaming

The spread of virtual reality and 360° video applications has raised research interest in developing new streaming techniques. On one hand, 360° videos rely on strict network requirements compared to conventional 2D videos. Realizing an adequate user experience is subject to ultra-low latency and huge bitrate requirements. On the other hand, 360° videos have distinct characteristics that allow for innovative streaming solutions. These solutions have benefited from the advancements in deep learning for optimizing the transmission under restricted network resources. In this paper, we review existing works employing deep learning in 360° video transmission and we highlight the challenges associated with 360° video streaming.
Burn severity evaluation in black pine forests with topographical factors using Sentinel-2 in Kastamonu, Turkiye

Background: Forest fires are one of the most important natural disasters all over the world in terms of the damage they cause to the ecosystem. Turkey is also exposed to wildfires damaging large areas of forests every year. Wildfires endanger the sustainability of forest resources and cause significant biological and ecological damage to the forests. It is crucial to estimate fire behaviour characteristics to take effective interventions in the fire events. Obtaining data based on terrestrial measurements is very expensive and very time-consuming to predict fire behaviour characteristics. The use of remote sensing technologies is therefore very useful since the satellite images will be faster, more sensitive, and economical to determine the burning severity and burning areas. Results: In this study, the forest fire that occurred in the Kastamonu-Taşköprü district was analyzed with remote sensing techniques. The fire occurred in 2020 (06.09.2020). The fire satellite image was thus taken from the pre-fire period (26.08.2020) and post-fire period (13.10.2020). First of all, pre-fire and post-fire Sentinel-2 images of fire areas were used to determine the burned area using NBR (Normalized Burn Ratio) and dNBR (Differenced Normalized Burn Ratio) indices. Also, burned area rate and burn severity were evaluated depending on the altitude, aspect, and slope factors. Conclusion: The total burned area was 1504.9 ha. The maps showed that the burned areas were covered by moderate-and high-severity classes. The forest fire was more severe in the altitude range from 1170 to 1370m, at 20-33% slope and northerly aspects in the burned area. The existence of extreme meteorological factors and the horizontal and vertical continuity of the forest fuels have been the main factors in the effect of the fire on large areas. Estimation of forest fire risk, taking into account extreme meteorological conditions and fuel properties, will have an important place in forest ecology and management.
Coupling the Relationship between Land Subsidence and Groundwater Level, Ground Fissures in Xi'an City Using Multi-Orbit and Multi-Temporal InSAR

The Xi’an region of China has been suffering from groundwater depletion, ground fissure hazards, and surface subsidence for a long time. Due to the complex tectonics and frequent human and natural activities, land deformation in the region is aggravated, posing a threat to infrastructure and human life. This study adopted the multi-orbit and multi-temporal InSAR technology to measure multi-dimensional displacements and time-series displacements in Xi’an City. Through the multi-dimensional deformation verification, it was found that the control of groundwater flow direction by ground fissures is the cause of horizontal deformation. On the contrary, the flow direction of groundwater from west to east was inferred using multi-dimensional deformation. Further analysis was performed by calculating the deformation gradient of the cumulative deformation to obtain differential land subsidence and angular distortions, and it was quantitatively determined that the threshold for the generation of ground fissures caused by differential subsidence is 1/500. Then, through the mutual verification of the time series data and the groundwater level, a positive correlation was obtained. However, due to the inconsistent geological conditions and soil layers at the monitoring positions of Well 2 and Well 3, the lag time was 64 days and 4 days, respectively. Finally, the relationship between the surface deformation and the groundwater in the sustained uplift areas was explored. The Well 1 groundwater-level data with a monitoring period of 22 years and the corresponding monitoring points’ time series data were modeled; it was concluded that, in the future, the groundwater level will continue to rise and surface deformation will mainly increase, without a slowing trend. Therefore, research on the impact of surface uplift on infrastructure should be strengthened. By quantifying the relationship between land subsidence, ground fissures, and the groundwater level in Xi’an, the results of this study provide a reference for groundwater monitoring and management.
A lightweight distributed processing computer system

For the needs of real-time parallel processing of critical task and real-time state control in muti-node computing system, a lightweight distributed processing computer mechanism is designed for iterative computing type tasks based on theoretical techniques such as memory computing and distributed computing. Using the feature of distributed collaborative processing, the data is deployed to different nodes to reduce the burden of each node and. To support continuous computational iterations, temporary data for each computation is temporarily stored in memory to reduce the disk IO access time during the iteration. Before the start of the whole task, the transformation relationship between the saved data is defined by generating the run plan graph of this task. In addition to load balancing of processing nodes, a balanced scheduling mechanism of control nodes is introduced to monitor the operation status of each node in real time through load balancing and control the deployment of computation tasks. Tests show that the designed lightweight computing mechanism outperforms the traditional distributed computing mechanism. In addition, the performance outperforms the mainstream memory-based distributed computing mechanism when carrying multiple tasks.
Modulatory role of celery (Apium graveolens) leaves extract against mercuric chloride-induced gonadal dysfunction in female rats and their pups

Background: Mercury (Hg) is a toxic heavy metal that has been recognized as one of the strongest environmental pollutants. Exposure to Hg even in low doses can induce adverse effects on the structure and functions of the different body organs including gonads. Celery ( Apium graveolens ) leaves are rich with antioxidant compounds like apiin, apigenin, and quercetin, and vitamins like E and C. Aim: This work aimed to evaluate the possible modulatory role of celery leaves extract against gonadal toxicity induced by HgCl 2 in female rats and their pups during gestation and lactation periods. Material &methods: Twenty-four female Wistar rats (180-200g) were used in this experiment. They were mated with males. After confirmation of the gestation test, the pregnant rats were divided into four groups (six for each) as follows: control, celery extract (CE) supplement (200 mg/kg b.wt), HgCl 2 (2mg/kg b.wt) and HgCl 2 co-supplemented with CE groups. The experiment extended from the 4 th day of gestation till the end of the weaning period. At the end of weaning, the female rats and their pups were weighed and sacrificed to collect the blood and remove the gonads for estimation of biochemical and histopathological changes. Results: The obtained results revealed that exposure of female rats to HgCl 2 resulted in a significant decrease in the activities of antioxidant enzymes (SOD, CAT, and GPx) and sexual hormones (FSH, LH, estrogen, progesterone, and testosterone) in both mother rats and their pups if compared with control. Further, a light microscopic investigation revealed that exposure to HgCl2 during gestation and lactation periods induced deleterious histopathological changes in the ovaries of mothers as well as in the ovaries and testes of their offspring. Additionally, the ovarian sections from mothers and their offspring displayed weak to negative expression for calretinin antibody, however, the testicular sections from offspring appeared strongly stained with COX-2 antibody (inflammatory marker) and weakly stained with Ki67 antibody (proliferative marker). Further study by flow cytometric technique revealed a highly significant increase in the mean % value of apoptotic cells in both ovaries and testes. On the other hand, co-supplementation of CE to HgCl 2 treated group successfully alleviated the altered antioxidants and hormones as well as the histological changes near to the normal as control. Conclusion : Celery extract has a powerful ameliorative role against HgCl 2 -induced deleterious biochemical and histopathological changes in the gonads of female rats and their pups.
Euler-Maruyama method for solving first order uncertain stochastic differential equations

Two forms of uncertainty are identified to be associated with dynamical systems, which are randomness and belief degree. The uncertain stochastic differential equation (USDE) is used to describe dynamical systems driven simultaneously by randomness and human uncertainty (belief degree). In this paper, the Euler-Maruyama method for solving USDEs is examined. The method is used to solve a stock pricing problem and the results are compared with those of Runge Kutta of order 4. The Euler-Maruyama method yields lower stock prices, while the stock prices from the Runge Kutta method proved to converge faster to those from the analytical method. At α = 0.5 where α ∈ (0, 1), the USDE reverts to the stochastic differential equation, with the uncertain component eliminated, showing that the USDE is indeed a hybrid of the uncertain differential equation and stochastic differential equation.
Analysis and Design of a Single Input and Multiple Output WPT System

This paper presents a single input and multiple output wireless power transfer (WPT) system. The system realizes two channels of constant voltage output through the two rectifiers (i.e., full bridge rectifier and the voltage doubling rectifier). Meanwhile, each channel is designed to meet various charging requirements while closed-loop control of only one channel is required. The closed-loop control is realized by phase-shift control. Finally, the practicability and effectiveness of the designed system is verified by circuit simulation.
Bifurcation Analysis in a Harvested Modified Leslie–Gower Model Incorporated with the Fear Factor and Prey Refuge

Fear and prey refuges are two significant topics in the ecological community because they are closely associated with the connectivity of natural resources. The effect of fear on prey populations and prey refuges (proportional to both the prey and predator) is investigated in the nonlinear-type predator-harvested Leslie–Gower model. This type of prey refuge is much more sensible and realistic than the constant prey refuge model. Because there is less research on the dynamics of this type of prey refuge, the current study has been considered to strengthen the existing literature. The number and stability properties of all positive equilibria are examined. Since the calculations for the determinant and trace of the Jacobian matrix are quite complicated at these equilibria, the stability of certain positive equilibria is evaluated using a numerical simulation process. Sotomayor’s theorem is used to derive a precise mathematical confirmation of the appearance of saddle-node bifurcation and transcritical bifurcation. Furthermore, numerical simulations are provided to visually demonstrate the dynamics of the system and the stability of the limit cycle is discussed with the help of the first Lyapunov number. We perform some sensitivity investigations on our model solutions in relation to three key model parameters: the fear impact, prey refuges, and harvesting. Our findings could facilitate some biological understanding of the interactions between predators and prey.
Climate Change and the Geography of the U.S. Economy

This paper examines how the spatial distribution of people and jobs in the United States has been and will be impacted by climate change. Using novel county-level weather data from 1951 to 2020, we estimate the longer-run effects of weather on local population, employment, wages, and house prices using a panel distributed lag model. The historical results point to long-lasting negative effects of extreme temperatures on each of these outcomes. We highlight that a long lag structure is necessary to appropriately capture the longer-run effects of climate change, as short-run effects are often small and imprecisely estimated. Using county-level weather projections based on alternative greenhouse gas emissions scenarios, we use the estimated models to project the spatial distribution of these local economic outcomes out to 2050. The results point to substantial reallocations of people and jobs across the country over the next three decades, with mobility increasing by between 33 and 100 percent depending on the scenario. Population and employment are projected to shift away from the Sunbelt and toward the North and Mountain West. We document that this would, in fact, be a continuation of a historical pattern: Over the past four decades the relationship between population growth and hot climates across the United States has turned from strongly positive to slightly negative. We present a spatial equilibrium model to interpret the results, highlighting the impacts of climate change on amenities and productivity, and find significant roles for both channels in accounting for our empirical findings.
Transmission Network Planning in Super Smart Grids: A Survey

For a utility company to reduce $CO_{2}$ emissions along with managing load demands, it must strive for a 100 percent renewable electrical power generation. Europe takes initiatives to achieve this goal by developing a super smart grid (SSG) based on renewable energy resources (RERs) by 2050. The SSG is based on two exclusive alternatives: wide area and decentralized power generation using large number of RERs. Before developing such SSG, there is a need to address the critical issues associated with RERs, i.e., load flow balancing and transients stability. Considering a reliability issue involved with RERs and the random deviations between demand response and generation response patterns, load flow balancing and transient stability become challenging research issues in SSGs. These technical issues are also considered to be more challenging, if an unexpected outage in the form of an occurrence of three phase (L-L-L) faults (TPF) arises in SSGs, due to power quality disturbances. To address this problem, load flow balancing probabilistic modeling is performed in this research paper in order to formulate the complexity of randomness between generation and demand response patterns through transmission network planning (TNP) in the form of a super smart node (SSN) transmission network infrastructure. Moreover, a further optimization in SSN transmission network has been done with the addition of a cooperative control strategies in terms of an integrating vehicle to grid (V2G) technology in SSN transmission network in order to achieve further enhancement in load flow balancing and transient stability in SSGs. Furthermore, as SSGs power infrastructure is based on different clusters, therefore in order to accommodate various clusters for load flow balancing, a continuous spinning reserve (CSRs) probabilistic modeling has also been performed in this paper in terms of its integration in SSN transmission network. Considering above probabilistic analysis, future contingencies are easily predictable, before any kind of disruptive changes arises in a SSGs due an occurrence of a TPF. Moreover, from simulation results as performed in this paper, we can easily verified that our proposed probabilistic algorithm of load flow balancing and transients stability outperforms existing literature work and can also achieved near optimal performance, even for a broad range of variations in load and also in case of an arising of significant power quality disturbances in SSGs due to an occurrence of TPF.
Nurses' Knowledge, Interpretation and Practice Regard Measuring End-Carbon Dioxide by Capnography: Self Learning Package

Background: Capnography is the gold standard for confirming endotracheal tube placement, help guide ventilation in conjunction with blood gases, as it provides an excellent picture of the respiratory process. Aim : Evaluate the effect of self-learning package on nurses' knowledge, interpretation and practice regard measuring End-Carbon dioxide by capnography. Design : A quasi-experimental (Pretest-Posttest design) was used. Setting : The study was carried in medicine intensive care units at Ain Shams University hospital. Subjects : All available nurses (45) in the previous mentioned setting. Tools : Structured nurses' assessment tool; included: part (I): Nurses’ characteristics, part (II): Nurses’ knowledge assessment questionnaire, part (III): Nurses’ capnography waveforms diagram interpretation and Part (IV) : Nurses’ practice observational checklist. Results: showed that there was an obvious improvement in the studied nurses’ total satisfactory level of knowledge, interpretation and practice post implementation of the self-learning package with a positive correlation between the independent variables of the study (knowledge, interpretation & practice) r = 0.71 at P< 0.001, r = 0.07 at P< 0.04 & r=0.52 at P< 0.05 respectively. Conclusion: This study concluded that self-learning package implementation had a positive effect in improving nurses’ knowledge, interpretation and practice regard measuring end-carbon dioxide by capnography. Recommendation: Provide periodic in-service training based on best practice guidelines for nurses who work in intensive care units and emergency units regarding measuring End-Carbon dioxide by capnography.
The Antioxidant Ability and Extraction Yield of Beta Carotene

Beta carotene (β-Carotene), one of the most abundant carotenoids in fruit and vegetables, is an essential source of antioxidants. The antioxidation ability of β-Carotene has been through in vitro and in vivo tests, which proved that the compound could prevent the oxidation of singlet and triplet oxygen, oil, and fats. Since β-Carotene is unstable and prone to oxidation when exposed to high temperature and high light intensity, various extraction methods have been tested and applied to get to the maximised yield and purity of the extract. Among all the extraction methods, enzyme-assisted extraction (EAE) is the most efficient, with almost 100% extraction yield. The need for antioxidants by the public promoted the production of β-Carotene nutraceuticals and cosmetics. This essay compared the efficacy of different extraction methods and bioactivity of β-Carotene in different research papers and concluded the extraction method with the highest efficiency.