Miracle-dz/UniFAR-FTUs huggingface dataset:
Developing target injection and tracking for inertial fusion energy power plants

Fuelling of a commercial inertial fusion energy (IFE) power plant consists of supplying about 500 000 fusion targets each day. The most challenging type of target in this regard is that for laser driven direct drive IFE power plants. Spherical capsules with cryogenic DT fuel must be injected into the centre of a reaction chamber operating at temperatures as high as 1500° C and possibly containing as much as 0.5 torr of xenon fill gas. The DT layer must remain highly symmetric, have a smooth inner ice surface finish and reach the chamber centre (CC) at a temperature of about 18.5 K. This target must be positioned at the centre of the chamber with a placement accuracy of ±5 mm. The accuracy of alignment of the laser driver beams and the target in its final position must be within ±20 μm. All this must be repeated six times per second. The method proposed to meet these requirements is to inject the targets into the reaction chamber at high speed ( ≈ 400 m/s), track them, and hit them in flight with steerable driver beams. The challenging scientific and technological issues associated with this task are being addressed through a combination of analyses, modelling, materials property measurements and demonstration tests with representative injection equipment. Measurements of relevant DT properties are planned at Los Alamos National Laboratory. An experimental target injection and tracking system is now being designed to support the development of survivable targets and demonstrate successful injection scenarios. Analyses of target heating are under way. Calculations have shown that a direct drive target must have a highly reflective outer surface to prevent excess heating by thermal radiation. In addition, heating by hot chamber fill gas during injection far outweighs that by the thermal radiation. It is concluded that the dry wall, gas filled reaction chambers must have gas pressures and wall temperatures less than previously assumed in order to prevent excessive heating in current direct drive target designs. An integrated power plant systems study to address this issue has been initiated.
LiDAR SLAM with a Wheel Encoder in a Featureless Tunnel Environment

Simultaneous localization and mapping (SLAM) represents a crucial algorithm in the autonomous navigation of ground vehicles. Several studies were conducted to improve the SLAM algorithm using various sensors and robot platforms. However, only a few works have focused on applications inside low-illuminated featureless tunnel environments. In this work, we present an improved SLAM algorithm using wheel encoder data from an autonomous ground vehicle (AGV) to obtain robust performance in a featureless tunnel environment. The improved SLAM system uses FAST-LIO2 LiDAR SLAM as the baseline algorithm, and the additional wheel encoder sensor data are integrated into the baseline SLAM structure using the extended Kalman filter (EKF) algorithm. The EKF algorithm is used after the LiDAR odometry estimation and before the mapping process of FAST-LIO2. The prediction step uses the wheel encoder and inertial measurement unit (IMU) data, while the correction step uses the FAST-LIO2 LiDAR state estimation. We used an AGV to conduct experiments in flat and inclined terrain sections in a tunnel environment. The results showed that the mapping and the localization process in the SLAM algorithm was greatly improved in a featureless tunnel environment considering both inclined and flat terrains.
Guaranteed cost control of uncertain discrete linear time-delay systems

Considers guaranteed cost control problem for a class of uncertain discrete-time linear systems with state delay. The uncertainty is assumed to be norm-bounded, and the attention is focused on the design of memoryless state feedback controllers such that the resulting closed-loop system is not only asymptotically stable but also guarantees an adequate level of performance. A sufficient condition for the existence of guaranteed cost controllers is derived and a parametrized characterization of guaranteed cost control laws is given in terms of the feasible solutions to a certain linear matrix inequality.
Use of piecewise linear value functions in interactive multicriteria decision support: a Monte Carlo study

This paper describes a Monte Carlo study conducted to evaluate the effects of modelling assumptions and design parameters on the behaviour of interactive methods for the discrete choice MCDM problem, based on explicit value function models. The purpose of the study is to identify those assumptions and parameters which lead to the most efficient use of preference judgements made by the decision maker, and to the greatest robustness to judgmental errors. It is concluded that nonlinearities in the value function need to be modelled, achieved here by use of a piecewise linear form. It was also found that search for indifference points, rather than using simple preference judgements alone, is of great advantage, best realized by expressing judgements in terms of pairwise trade-offs. Methods incorporating these features are highly robust to judgmental errors. Interactive methods of this class are compared with a priori fitting of similar value functions, and found to give a very similar quality of solution.
Gradient field coils for MR imaging with high spectral purity

Advanced magnetic field codes with implemented optimization features are presented which make it possible to find new, technically simple 3-D coil configurations with excellent field quality for magnetic resonance imaging (MRI). A family of solutions which require a minimum number of simple geometrical elements is presented, the most sophisticated having field error contributions of order nine and higher. >
A stable data‐adaptive method for matched‐field array processing in acoustic waveguides

The presence of a “modal noise” component leads to estimator instability when Capon's maximum likelihood (ML) method is applied to the processing of data from a vertical array in an acoustic waveguide. The physics of the waveguide forces signal vectors and noise vectors alike to be projected onto the span of the “mode” vectors, when the number of sensors (N) exceeds the number of propagating modes (M). The instability occurs whenever the (single snapshot) N × 1 data vectors have the form x = Us + Uγ + white noise, where the matrix U is N × M (sampling the normal modes at the hydrophone locations and independent of the actual acoustic disturbances present), and s and γ correspond to signal and ambient noise sources, respectively. This condition arises in normal‐mode and local normal‐mode propagation. The dominant eigenvectors of R−1 (where R is the cross‐spectral matrix) are sensitive to slight inaccuracies in the calculation of R−1 in ways that affect the performance of the ML estimator. Following transfo.
Environmental Toxocara spp. presence in crowded squares and public parks from San Juan Province, Argentina: A call for a “One Health” approach

Introduction Canine soil-transmitted helminth (cSTH) parasites need specific environmental conditions to complete their life cycle. Toxocara canis and T. cati are the most important zoonotic cSTH, since they are the causal agents of human toxocariasis. Canine STHs are dispersed in feces from infected domestic and wildlife canines. In this study, the presence of STH in canine feces was evaluated in 34 crowded public parks and squares from San Juan Province (Argentina). Methods Fecal samples were collected during different seasons in 2021–2022 and analyzed by standard coprological methods, including Sheather and Willis flotation and Telemann sedimentation. InfoStat 2020, OpenEpi V. 3.01 and R and RStudio® were used for statistical analysis and QGIS 3.16. 10 for mapping. Results From a total of 1,121 samples collected, 100 (8.9%) were positive for at least one intestinal parasite (IP) and three cSTH species were detected: Toxocara spp. , Toxascaris leonina and Trichuris vulpis. The most prevalent cSTH species was T. vulpis (64/1121; 0.057%), while the least prevalent was Toxocara spp. (19/1121; 0.017%). The detection of Toxocara spp. eggs was significantly different depending on the season. The geo-spatial variation of each cSTH per season is described. Discussion This is the first study in San Juan Province to identify environmental contamination of cSTHs in public areas. The specific localization of areas with the presence of cSTH eggs could provide information to guide strategies to reduce the cSTH infection burden in dogs and promote serological screening of the human population for Toxocara spp. Given the zoonotic nature of Toxocara spp. We hope this information will help to reinforce activities of control programs, focusing on the “One Health” approach.
On self-consistent determination of the quasi-average in statistical physics

The concept of quasi-average introduced by N. N. Bogoliubov for describing the states of many-particle systems with spontaneously broken symmetry (superfluid liquids, superconductors, crystals, etc.) is generalized so that the fields introduced into the initial Hamiltonian for breaking its symmetry are not presumed to be given from outside, but are determined by the properties of the physical object itself and can be found by solving the system of equations in the self-consistent field approximation.
Waterfowl recently infected with low pathogenic avian influenza exhibit reduced local movement and delayed migration

Understanding relationships between infection and wildlife movement patterns is important for predicting pathogen spread, especially for multispecies pathogens and those that can spread to humans and domestic animals, such as avian influenza viruses (AIVs). Although infection with low pathogenic AIVs is generally considered asymptomatic in wild birds, prior work has shown that influenza-infected birds occasionally delay migration and/or reduce local movements relative to their uninfected counterparts. However, most observational research to date has focused on a few species in northern Europe; given that influenza viruses are widespread globally and outbreaks of highly pathogenic strains are increasingly common, it is important to
Observational Determination of Cloud Mass Flux Distributions

Abstract Using Arakawa's spectral representation of cumulus convection, the cloud mass flux distributions and other cloud properties are diagnostically determined from large-scale observations. Observational data over the tropical Atlantic Ocean during the BOMEX Phase 3 (22–30 June 1969) are used. The analyzed period is subdivided into three parts according to different synoptic situations. The cloud properties and the manner of interaction between large-scale fields and cumulus convection under the different situations are examined. During the period in which a strong trade inversion lies near 800 mb, most clouds detrain below the trade inversion and cloud mass flux is confined below this level. The large apparent heat sink and apparent moisture source found near the inversion base are mainly due to the cooling and moistening effects of the detrainment from clouds. During the period affected by an upper-level extended trough, the trade inversion is weakened and some clouds penetrate through the 800-mb le.
Practical considerations on data patterns in Bayesian Maximum Entropy Estimation: A systematic and critical review

Objective: It is well known that some data features (sample size, skewness, among others) may determine method performance. The choice of those features depends on the researcher’s level of awareness on the statistical method. In this study, the level of awareness on the influence of spatial data key characteristics (sample size, skewness, spatial dependency and variogram model) in Bayesian Maximum Entropy (BME) was analyzed. Methodology: A systematic review was conducted that covers the period from 1990 (year of BME introduction) to 2019. Two main keywords “Bayesian Maximum Entropy” and “BME” were used for literature search. Publications which only mentioned the keywords without applying BME were excluded while those with application and/or BME theory discussion were considered. Six of the world’s leading Open Access sources of scientific literature were considered, namely: Science Direct, African Journals Online, Springer, Google Scholar, MPDI and Academic Journals. A total of 118 research articles from 62 journals were identified. The sample sizes screened shows that 25.4% of the published articles used few samples (less than 100), which implies the variogram might not yield accurate results. The analysis of the use of skewness showed that most researchers do not apply transformation on skewed data (82.2%) nor consider skewness in their descriptive statistics (90.7%). Even though 11% of theoretical papers have mentioned about spatial dependency level, 92.4% of them failed to consider it. Most researchers (68.64%) do not specify the variogram models but when they do, they mostly use exponential model (12.7%). It clearly appears in this review that most researchers do not consider the effect of sample size, skewness, and spatial dependency level when applying BME. Yet very few research works have focused on these aspects. This therefore calls for more in-depth studies on the effect of data characteristics on BME’s performance. Keywords: Bayesian Maximum Entropy, sample size, skewness, spatial dependency
Internet of Things (IoT)-Based Smart Healthcare System for Efficient Diagnostics of Health Parameters of Patients in Emergency Care

The Internet of Things (IoT) has been instrumental in bringing about several advancements and innovations in the domain of healthcare. Healthcare professionals are essentially life savers when it comes to handling emergency cases, such as accidents, heart attacks, etc. Only the patient’s vital parameters generally characterize emergency cases, and the doctors must wait for additional details for a wholesome diagnosis. As a result, the treatment processes and procedures sometimes get hastened and, in turn, put the patients’ lives at risk. It would always be helpful for doctors to be equipped with the medical requirements in advance for deciding the right course of action, thereby increasing the scope and chances of recovery. In this work, multimodel IoT (MMIoT) devices are deployed to monitor and collect health data from different body parts simultaneously. The healthcare data comprises signals and imagery captured from the MMIoT devices. Both the U-Net model and LSTM model are used to analyze the data automatically. The data processing is carried out by the server connected to the MMIoT network. All the medical IoT devices experimented with in this work are interconnected using a potential 5G network for optimal data transmission. The output obtained from the U-Net and the LSTM are channelized through a dense layer to classify the health anomalies accurately. It would not only facilitate but also educate medical professionals to handle unseen and typical cases in the future confidently. It can improve the overall quality of treatment and save lives with the best available resources.
Comparison of Madden-Julian oscillation in three super El Niño events

This paper investigated the characteristics of Madden-Julian Oscillation (MJO) in three super El Niño events (i.e., 1982/83, 1997/98 and 2015/16 El Niño events) based on reanalysis data. MJO with apparent eastward propagation can be observed during the developing stages of these three super El Niño events. Enhanced MJO zonal wind was observed over the western Pacific, especially in 1997/98 and 2015/16 El Niño events, which is mainly attributed to the effects of tropical background circulation and extratropical anomalous circulation. During the mature stages of 1982/83 and 1997/98 El Niño events, MJO with noticeable eastward propagation cannot be observed, and the MJO zonal wind amplitude at 850 hPa was weakened (enhanced) over the Indian Ocean and western Pacific (central and eastern Pacific). However, MJO zonal wind amplitude over the central and eastern Pacific was enhanced and the prominent eastward propagation was also found in the mature stage of 2015/16 El Niño. The eastward propagation of MJO was also observed during the decaying stages of the three super El Niño events, but its intensity was weaker compared with the developing and mature stages. The abnormal activity of MJO during the mature and decaying stages may be closely related to the characteristics in circulation and moisture anomalies caused by El Niño and the seasonal cycle of circulation and moisture. In addition, this study found that the RMM index and MJO zonal wind amplitude may lead to contradictory results in identifying the characteristics of MJO activity, especially during the developing and decaying stages.
Planning queueing simulations

Simple heuristic formulas are developed to estimate the simulation run lengths required to achieve desired statistical precision in queueing simulations. The formulas are intended to help in the early planning stages before any data have been collected. The queueing simulations considered are single replications (one long run) conducted to estimate steady-state characteristics such as expected equilibrium queue lengths. The formulas can be applied to design simulation experiments to develop and evaluate queueing approximations. In fact, this work was motivated by efforts to develop approximations for packet communication networks with multiple classes of traffic having different service characteristics and bursty arrival processes. In addition to indicating the approximate simulation run length required in each case of a designed experiment, the formulas can help determine what cases to consider, what statistical precision to aim for, and even whether to conduct the experiment at all. The formulas are based on heavy-traffic limits for queues (the limiting behavior as the traffic intensity approaches its upper limit for stability) and associated diffusion approximations. In particular, the formulas apply to stochastic processes that can be approximated by reflected Brownian motion, such as the queue-length process in the standard GI/G/1 model.
Image segmentation for blind lanes based on improved SegNet model

Abstract. The position of blind lanes must be correctly determined in order for blind people to travel safely. Aiming at the low accuracy and slow speed of traditional blind lanes image segmentation algorithms, a semantic segmentation method based on SegNet and MobileNetV3 is proposed. The main idea is to replace the coding part of the original SegNet model with the feature extraction part of MobileNetV3 and remove the pooling layer. Blind lanes images were collected through online search and self-shooting, and then the data were manually marked by LabelMe software and trained on TensorFlow deep learning framework. The experimental results show that the improved model has high segmentation accuracy and recognition speed. The pixel accuracy of blind lanes segmentation is 98.21%, the mean intersection over union is 96.29%, and the average time for processing a 416 × 416 image is 0.057 s, which meets the real-time requirements of the blind guidance system.
Slab optical waveguides in Er 3 + -doped tellurite glass by N + ion implantation at 1.5 MeV

Slab optical waveguides were fabricated in tung-sten-tellurite glass doped with Er3 + ions by means of nitrogen ion implantation at 1.5 MeV. A wide range of ion doses (from 5·1012 to 8·1016 ions/cm2) was used. Optical characterization, performed by dark-line spectroscopy, revealed that the waveguides were of optical barrier type: the implanted layer exhibited a decrease of the refractive index with respect to the virgin bulk glass, while the region comprised between the sample surface and the end of the ion track acted as an optical guiding structure. It was also demonstrated that a post-implantation annealing process, performed at various temperatures on the samples implanted at higher doses, contributes to the reduction of the barrier region.
A Recurrence Matrix Solution for the Dynamic Response of Elastic Aircraft

A systematic procedure is developed for the calculation of the structural response of an airplane subject to dynamic loads. Particular attention is given the problem of determining the stresses developed due to flight through gusts. Difference equivalents for derivatives and matrix notation are used to develop a recurrence relation that permits step-by-step calculation of the response and of the loads that occur on the structure. The chief feature of this recurrence approach is that the generality and physical aspects of the basic equilibrium relations of the problem are preserved without loss of ease in application. The use of difference equivalents for derivatives in the solution of dynamic problems is first illustrated by means of a simple damped oscillator example, and the application to the flexible aircraft structure is then made. For brevity, the case of wing bending and vertical motion of the airplane is treated, although the method may be readily extended to take into account also wing torsional deformations, pitching motion of the airplane, fuselage deflections, and tail forces of known character. Either a sharp-edge gust or a gust of arbitrary shape in the spanwise or flight directions may be treated. Some results obtained by application of the recurrence matrix relation are presented, and the advantages of this method over other methods of evaluating the dynamic response of an aircraft are discussed.
Neuroprotective Potential of Biflavone Ginkgetin: A Review

Neurological disorders are becoming more common, and there is an intense search for molecules that can help treat them. Several natural components, especially those from the flavonoid group, have shown promising results. Ginkgetin is the first known biflavonoid, a flavonoid dimer isolated from ginkgo (Ginkgo biloba L.). Later, its occurrence was discovered in more than 20 different plant species, most of which are known for their use in traditional medicine. Herein we have summarized the data on the neuroprotective potential of ginkgetin. There is evidence of protection against neuronal damage caused by ischemic strokes, neurotumors, Alzheimer’s disease (AD), and Parkinson’s disease (PD). Beneficial effects in ischemic strokes have been demonstrated in animal studies in which injection of ginkgetin before or after onset of the stoke showed protection from neuronal damage. AD protection has been the most studied to date. Possible mechanisms include inhibition of reactive oxygen species, inhibition of β-secretase, inhibition of Aβ fibril formation, amelioration of inflammation, and antimicrobial activity. Ginkgetin has also shown positive effects on the relief of PD symptoms in animal studies. Most of the available data are from in vitro or in vivo animal studies, where ginkgetin showed promising results, and further clinical studies should be conducted.
AN OBSERVATION ABOUT GOODNESS-OF-FIT TESTS OF DISTRIBUTIONS

In a univariate goodness-of-fit (GoF) test of distributions, one verifies if a random variable (RV) X follows a hypothesized distribution function F(x) = Pr{X ≤x} or otherwise. Another distribution may be considered as an alternative for the RV. Distributions may or may not be completely specified. Some parameters may be estimated from the sampled observations. Such GoF tests are of singular importance and have many applications in management, engineering and science. For example, we may try to see the statistical distribution for the lifetime of a machine and its reliability of operation at a particular time point. We may need to estimate repair times. Such distributions then may be used arrive at optimal maintenance policies. The estimated distribution of demand of an item may be used in production planning. GoF tests also arise in a regression analysis, which again is a topic of high practical significance.