Miracle-dz/UniFAR-FTUs huggingface dataset:
Studies on gas breakdown in pulsed radio frequency atmospheric pressure glow discharges

In pulsed RF atmospheric pressure glow discharges, the gas breakdown judged by the rapid drop in the amplitude of the pulsed RF voltage is no longer universally true. The steep increment of the plasma-absorbed RF power is proposed to determine the gas breakdown. The averaged plasma-absorbed RF power over a pulse period is used to evaluate effects of the preceding pulsed RF discharge on the breakdown voltage of the following one, finding that the breakdown voltage decreases with the increment in the averaged plasma-absorbed RF power under constant pulse duty ratio. Effects of the pulse off-time on the breakdown voltage and the breakdown delay time are also studied. The obtained dependence of the breakdown voltage on the pulse off-time is indicative of the transitional plasma diffusion processes in the afterglow. The breakdown voltage varies rapidly as the plasma diffuses fast in the region of moderate pulse off-time. The contribution of nitrogen atom recombination at the alumina surface is demonstrated in the prolonged memory effect on the breakdown delay time vs. the pulse off-time and experimentally validated by introducing a trace amount of nitrogen into argon at short and long pulse off-times.
Ovipositional behaviour and larval performance of Trichoferus campestris (Faldermann) (Coleoptera: Cerambycidae) on cut branches of four North American tree species

Velvet longhorned beetle Trichoferus campestris (Faldermann) (Coleoptera: Cerambycidae) is native to Asia. It is reported to be polyphagous in its native range, but hosts have not been screened in its introduced range in the United States. We conducted choice and no‐choice host experiments. For choice assays, gravid T. campestris oviposited freely on cut branches of four tree species: Juglans nigra, Malus domestica, Acer saccharum, and Pinus strobus. We investigated oviposition between apple branches with and without cankers. Half of the choice trials underwent winter temperatures. For no‐choice assays, eggs were placed on cut branches of these same four species. Half of these branches were later dissected to measure larval development. The rest were exposed to winter temperatures and T. campestris emergence was quantified. Overall, Trichoferus campestris oviposition and development occur more readily on J. nigra and M. domestica than on A. saccharum or P. strobus. The insect oviposited more on cankered than uncankered apple wood. Cold exposure during an overwintering period benefitted insect development in adults. The insect's high degree of polyphagy and ability to reproduce in dry wood has likely facilitated its establishment in multiple locales around the world.
CenterTube: Tracking Multiple 3D Objects With 4D Tubelets in Dynamic Point Clouds

3D Multi-Object Tracking (MOT) in dynamic point cloud sequences is a fundamental research problem for several downstream tasks such as motion planning and action recognition. Existing methods usually rely on the traditional tracking-by-detection (TBD) paradigm, which performs the tracking based on the results achieved by dedicated detectors. However, this two-stage framework usually cannot sufficiently exploit spatial-temporal information and end-to-end optimization, leading to sub-optimal tracking performance, especially when the object is partially or completely occluded. In this article, we propose a joint detection and tracking framework named CenterTube for dynamic point cloud sequences. The key to our approach is to formulate the problem of multiple object trajectory predictions as 4D tubelet detections. In particular, the proposed CenterTube is composed of three head branches, including a center branch, a regression branch, and a movement branch for the estimation of object center, object size, instance movement, and frame interval, respectively. Additionally, a Tube BEV-IoU (TB-IoU) is also presented to link the generated clip-level tubelets and form the final tracks. Extensive experiments conducted on the KITTI-MOT and nuScenes datasets demonstrate that our model achieves competitive performances even if no ready-made detection results is adopted.
The interaction of Escherichia coli with its surrounding three dimensional substrate measured by oscillatory optical tweezers

It has been known that the shape, the locomotion, and the growth of cells and bacteria are often affected by their interactions with extra cellular matrix (ECM). However, it is difficult to quantify such interactions with conventional biochemical methods. In this paper we report the application of oscillatory optical tweezers to trap and oscillate three types of E. coli, in 0.2% LB agar substrate to quantify the E. coli - substrate interactions in terms of the elasticity modulus G'. The three types of E. coli are BW25113 (wild-type, normal with flagellum), BW25113 (normal with flagellum, but subjected to UV light exposure for 1 hr to deactivate the flagellum), and JW1923 (a null-flagellum mutant of BW25113). Our results indicate that the value of G' for the later two is significantly higher than that for the normal wild-type (WT). We speculated that the interaction with the surrounding is perturbed, and hence reduced, mainly by the motion of the flagellum in the case of the WT.
Thin wetting film lensless imaging

Lensless imaging has recently attracted a lot of attention as a compact, easy-to-use method to image or detect biological objects like cells, but failed at detecting micron size objects like bacteria that often do not scatter enough light. In order to detect single bacterium, we have developed a method based on a thin wetting film that produces a micro-lens effect. Compared with previously reported results, a large improvement in signal to noise ratio is obtained due to the presence of a micro-lens on top of each bacterium. In these conditions, standard CMOS sensors are able to detect single bacterium, e.g. E.coli, Bacillus subtilis and Bacillus thuringiensis, with a large signal to noise ratio. This paper presents our sensor optimization to enhance the SNR; improve the detection of sub-micron objects; and increase the imaging FOV, from 4.3 mm2 to 12 mm2 to 24 mm2, which allows the detection of bacteria contained in 0. 5μl to 4μl to 10μl, respectively.
Combinatorial synthesis and high throughput evaluation of ferroelectric/dielectric thin-film libraries for microwave applications

A library of 256 differently doped thin films of (BaxSr1−x)TiO3 (where 0.5<x<1.0) was generated on a 1 in. ×1 in. LaAlO3 substrate using multistep thin-film deposition techniques together with a quaternary masking strategy. Appropriate postannealing processing afforded high-quality epitaxial thin films. The microwave properties, i.e., dielectric constant and loss tangent, of samples in the library were characterized with a scanning-tip microwave near-field microscope at 1 GHz, and the results were found to be consistent with measurements made with interdigital electrodes. Specific dopants were found to significantly affect the dielectric constant and tangent loss. Tungsten, in particular, reduces the tangent loss.
Soybean Replacement by Alternative Protein Sources in Pig Nutrition and Its Effect on Meat Quality

Simple Summary Soybean is the most common feed ingredient to supply protein to pigs. However, its production is linked to several issues related to environment (e.g., deforestation, water waste), production and transportation costs, as well as to ethical aspects including feed/food competition for land. In recent decades, research has endeavored to find alternatives to soybean in livestock feeds, especially for pigs. However, replacing this ingredient partially or totally may have consequences for animal growth rates, carcass yield and meat quality. The aim of this work was to review the research carried out in the last 10 years on this topic, identifying the alternative protein sources and their effects on pigs’ growth, carcass yield, and meat quality. Most of the ingredients recently studied are vegetable resources, especially local species of legumes well-adapted to specific climatic conditions. Moreover, some studies evaluated, as protein sources, oilseed by- and co-products, distillers’ dried grain with solubles, fish and animal proteins, and other co-products derived from other supply chains. Among innovative protein sources, microalgae and insects are gaining attention. Abstract Soybean is one of the most expensive and limiting feed ingredients in diet formulations; however, in pig farming, it represents the main source of protein. The production and supply of soybean are critical steps due to their environmental impact and feed/food competition for land use. Therefore, research is focusing on finding alternatives to replace soybean partially or totally. However, alternative ingredients should ensure similar growth performance, carcass traits, and meat quality characteristics compared to conventional soybean-based diets. The objective of this review was to evaluate the impact of different alternative protein sources to soybean in pig nutrition and their effects on growth performance, carcass, and meat quality traits. The review process was performed on Scopus®, and it considered research findings published from 2012 to the present on the Sus scrofa species. Articles without a control group fed with soybean were discarded. The main alternative protein sources identified were other legumes and distillers’ dried grain with solubles (fish and animal proteins, oilseed by- and co-products). Interesting innovative protein sources included by-products from other industries (residues), microalgae and insects. Nevertheless, in dietary formulations, close attention must be paid to address the nutritional requirements, balance the supply of amino acids, avoid anti-nutritional or toxic compounds occasionally present in alternative protein sources, as well as determine the availability of protein feed in specific geographical areas.
Remotely sensed-based analysis about climatic and landscape change effects on phytoplankton bloom in Barra Bonita Reservoir (São Paulo State, Brazil)

Abstract. The constant land use and land cover (LULC) changes combined with climatic factors are frequently assigned to anthropogenic eutrophication, one of the main ecological imbalances in aquatic systems characterized by dense phytoplankton proliferation. Beyond the degradation of freshwater ecosystems, some cyanobacterial and algae species produce toxins harmful to living beings. Distinct studies in the literature, usually supported by in situ data, have discussed the influence of LULC and climatic changes on phytoplankton bloom events. In this context, motivated by the importance of understanding the environmental mechanisms assigned to phytoplankton bloom events and considering the difficulties imposed by field data collection, our study focuses on analyzing the mentioned issue only using remotely sensed time series data. For this purpose, we performed a temporal analysis between 1985 and 2018 over a portion of the Barra Bonita Hydroelectric Reservoir, Brazil. Initially, we obtained the landscape occupation, precipitation, and temperature information from the MapBiomas, FLDAS, and CHIRPS projects, respectively. A fully automatic algorithm fed by Landsat image series and supported by Google Earth Engine functions was developed and employed to identify and quantify phytoplankton bloom events. Then, the obtained data were inspected by distinct statistical procedures, including correlation and trend analysis. Although there was an absence of a relationship between the climatic components and the emergence of phytoplankton blooms, it was identified using linear regression models (R2 ≥ 78 % ) an intensification of blooms after the increase in nonnatural forestry areas, reduction of pastures, and advance of agricultural areas. Furthermore, machine learning methods were employed to obtain nonlinear regression models (R2 ≥ 73 % ), making evident that the landscape changes are mainly responsible for the phytoplankton insurgences in the analyzed region. This result agrees with other studies found in the literature and highlights the possibility of investigating anthropogenic eutrophication only using remotely sensed data and automatic algorithms.
A Learning-based Correlated Graph Model for Spinal Cord Injury Prediction from Magnetic Resonance Spinal Images

— In epidemiological research on spine surgery, machine learning represents a promising new area. It is made up of several algorithms that work together to identify patterns in the data. Machine learning provides many benefits over traditional regression techniques, including a lower necessity for a priori predictor information and a higher capacity for managing huge datasets. Recent research has made significant progress toward using machine learning more effectively in spinal cord injury (SCI). Machine learning algorithms are employed to analyze non-traumatic and traumatic spinal cord injuries. Non-traumatic spinal cord injuries often reflect degenerative spine conditions that cause spinal cord compression, such as degenerative cervical myelopathy. This article proposes a novel correlated graph model (CGM) that adopts correlated learning to predict various outcomes published in traumatic and non-traumatic SCI. In the studies mentioned, machine learning is used for several purposes, including imaging analysis and epidemiological data set prediction. We discuss how these clinical predictive models are based on machine learning compared to traditional statistical prediction models. Finally, we outline the actions that must be taken in the future for machine learning to be a more prevalent statistical analysis method in SCI.
Is the leak head–discharge relationship in polyethylene pipes a bijective function?

Traditional characterization of the leak head–discharge relationship in pipeline systems relies on the use of power law functions to explain the increase in leakage rate with pressure when network and laboratory data are analysed. However, recent studies addressed that the leak behaviour is complex and influenced by pipe material and leak geometry. The experimental tests, carried out at Water Engineering Laboratory of the University of Perugia (WEL), Italy, show that the leak head–discharge relationship on polyethylene pipes is not a bijective function since it gives rise to different discharge values with respect to one value of the pressure. To explore this issue, two viscoelastic models of a leak in a polyethylene pipe are introduced and compared with other leakage models. The results show that the viscoelastic behaviour reflects the leak head–discharge relationship, and care should be taken in the use of bijective relationships.
A Snapshot-Stacked Ensemble and Optimization Approach for Vehicle Breakdown Prediction

Predicting breakdowns is becoming one of the main goals for vehicle manufacturers so as to better allocate resources, and to reduce costs and safety issues. At the core of the utilization of vehicle sensors is the fact that early detection of anomalies facilitates the prediction of potential breakdown issues, which, if otherwise undetected, could lead to breakdowns and warranty claims. However, the making of such predictions is too complex a challenge to solve using simple predictive models. The strength of heuristic optimization techniques in solving np-hard problems, and the recent success of ensemble approaches to various modeling problems, motivated us to investigate a hybrid optimization- and ensemble-based approach to tackle the complex task. In this study, we propose a snapshot-stacked ensemble deep neural network (SSED) approach to predict vehicle claims (in this study, we refer to a claim as being a breakdown or a fault) by considering vehicle operational life records. The approach includes three main modules: Data pre-processing, Dimensionality Reduction, and Ensemble Learning. The first module is developed to run a set of practices to integrate various sources of data, extract hidden information and segment the data into different time windows. In the second module, the most informative measurements to represent vehicle usage are selected through an adapted heuristic optimization approach. Finally, in the last module, the ensemble machine learning approach utilizes the selected measurements to map the vehicle usage to the breakdowns for the prediction. The proposed approach integrates, and uses, the following two sources of data, collected from thousands of heavy-duty trucks: Logged Vehicle Data (LVD) and Warranty Claim Data (WCD). The experimental results confirm the proposed system’s effectiveness in predicting vehicle breakdowns. By adapting the optimization and snapshot-stacked ensemble deep networks, we demonstrate how sensor data, in the form of vehicle usage history, contributes to claim predictions. The experimental evaluation of the system on other application domains also indicated the generality of the proposed approach.
Polymer-based stress sensor with integrated readout

We present a polymer-based mechanical sensor with an integrated strain sensor element. Conventionally, silicon has been used as a piezoresistive material due to its high gauge factor and thereby high sensitivity to strain changes in the sensor. By using the fact that the polymer SU-8 [1] is much softer than silicon and that a gold resistor is easily incorporated in SU-8, we have proven that a SU-8-based cantilever sensor is almost as sensitive to stress changes as the silicon piezoresistive cantilever. First, the surface stress sensing principle is discussed, from which it can be shown that the SU-8-based sensor is nearly as sensitive as the silicon based mechanical sensor. We hereafter demonstrate the chip fabrication technology of such a sensor, which includes multiple SU-8 and gold layer deposition. The SU-8-based mechanical sensor is finally characterized with respect to sensitivity, noise and device failure. The characterization shows that there is a good agreement between the expected and the obtained performance.
Arbitrary absolute vs. individualized running speed thresholds in team sports: A scoping review with evidence gap map

The aims of this scoping review were (i) to characterize the main methodological approaches to assessing individualized running speed thresholds in team sports players; (ii) to assess the use of traditional arbitrary (absolute) thresholds compared to individualized running speed thresholds in team sports players; (iii) to provide an evidence gap map (EGM) about the approaches and study designs employed in investigations in team sports and (iv) to provide directions for future research and practical applications for the strength and conditioning field. Methods studies were searched for in the following databases: (i) PubMed; (ii) Scopus; (iii) SPORTDiscus and (iv) Web of Science. The search was conducted on 15/07/2022. Risk of bias was assessed using the Risk of Bias Assessment Tool for Nonrandomized Studies (RoBANS). From 3,195 potentially relevant articles, 36 were eligible for inclusion in this review. Of the 36 included articles, 27 (75%) focused on the use of arbitrary and individualized running speed thresholds to describe the locomotor demands (e.g., high intensity running) of players. Thirty-four articles used individualized speed running thresholds based on physical fitness assessments (e.g., 40-m linear sprint) or physical performance (e.g., maximal acceleration). This scoping review supported the need for a greater focus to be placed on improving the methodological aspects of using individualized speed running thresholds in team sports. More than just creating alternatives to arbitrary thresholds, it is essential to increase the replicability of methodological conditions whilst ensuring that research comparing the most adequate measures and approaches to individualization takes into consideration the population and context of each study.
A survey of galaxy redshifts. V. The two-point position and velocity correlations.

We describe the results of a study of the two-point correlations in the 14.5 m/sub B/ CfA redshift survey. We use the distance information provided by the redshifts to estimate the two-point spatial correlation function zeta(r) in a way that is designed to be unbiased by peculiar velocities. The results agree well with what has been found from the deeper angular distributions. In the fiducial model zeta(r) = (r/sub 0//r)/sup . gamma. / with . gamma. = 1.77 we find r/sub 0/ = 5.4 +- 0.3 h/sup -1/ Mpc (H/sub 0/ = 100 h km s/sup -1/ Mpc/sup -1/). At r> or approx. =10 h/sup -1/ Mpc, zeta(r) seems to steepen and may in fact be negative at 20< or approx. =hr< or approx. =40 Mpc. In existing n-body simulations zeta(r) is poorly modeled by a power law, with more power on small scales and less power on large scales than the data. This confirms the visual impressions that the n-body clusters are too compact and the clusters too homogeneously distributed relative to the data.
Fluid‐loaded vibration of an elastic plate carrying a concentrated mass

The vibration of a simply supported rectangular plate with a discretely attached mass and fluid radiation loading is considered. The equations of motion are solved by utilizing the in vacuo eigenfunctions of a homogeneous panel as the basis for Fourier decomposition of both the fluid loading and attached mass loading. A modal matrix equation is established for the interactive system which includes the cross‐modal coupling induced by the discrete mass and acoustic fluid. The solution is exemplified by the numerical investigation of a centrally excited plate with a concentrated mass at center and off‐center locations. The influence of mass attachment upon both the plate response and radiated pressure is illustrated.
Population studies in groups and clusters of galaxies. II. A catalog of galaxies in the central 3. 5 deg of the Fornax Cluster

A catalog of 2678 galaxies within an area of about 40 sq deg centered on the Fornax Cluster has been compiled based on 26 deep large-scale plates obtained with the 2.5-m Las Campanas Observatory reflector. The catalog includes 340 likely cluster members and 2338 likely background galaxies. Radial velocities are given for 89 of the galaxies. The spatial distributions of various types of galaxies have been modeled as the sum of a King (1962) model cluster component superimposed on a uniform background. Using maximum-likelihood fits to these distributions, a core radius of 0.7 deg is found for a King model fit to the cluster, suggesting that there are few cluster members contained in the sample of background galaxies. 60 refs.
Stopping the "revolving door": "Zeida Laderech," a unique rehabilitation house for young adults with severe and enduring eating disorders.

A failure to maintain improvement achieved during acute treatment, followed by a "revolving door syndrome," is a common phenomenon in eating disorders (EDs). As a result, many patients develop a chronic course that is difficult to treat. To target these difficulties, we established a novel rehabilitation treatment model in Israel for patients with severe and enduring EDs, based on the "recovery theory" in mental health treatment. This paper describes the process of conceiving this model and specifies the components of this rehabilitation house, "Zeida Laderech" (Provisions for the Journey), that aims to provide a home-like environment, while assisting in developing a healthy and balanced lifestyle. Finally, the model is discussed in light of other residential programs established in the world. With the hope to improve the overall prognosis of individuals with EDs, it is suggested that the similar programs and ongoing innovation will continue to emerge internationally.