Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Solver for a Simplified Model of the Multi-Physics Heat Transfer Problem in Radio Frequency Ablation of Hepatic Tumors

Mathematical modeling and computer simulation of heat transfer in the liver play a key role in prediction of radio frequency ablation outcome. While multi-physics modeling should be used for accurate simulation of heat transfer in a highly perfused organ like the liver, the computational cost of such a problem could have a negative impact on the simulation time. This article extends a highly parallel finite element-based method to simulate the multi-physics heat transfer problem in the liver efficiently. A message passing interface (MPI)-based strong coupling method is developed and tested on cluster nodes. The scalability of this work is assessed by performing simulations on up to 1024 cores.
Heavy flavour production and propagation

In this talk, I discuss production of heavy quarks in heavy-ion collisons, their thermalisation, diﬀerent energy-loss formalisms based on pQCD including their theoretical foundation, limitations and kinematic domain of applicability and some of the important observables for quark-gluon plasma.
Robust ecological analysis of camera trap data labelled by a machine learning model

Ecological data are collected over vast geographic areas using digital sensors such as camera traps and bioacoustic recorders. Camera traps have become the standard method for surveying many terrestrial mammals and birds, but camera trap arrays often generate millions of images that are time‐consuming to label. This causes significant latency between data collection and subsequent inference, which impedes conservation at a time of ecological crisis. Machine learning algorithms have been developed to improve the speed of labelling camera trap data, but it is uncertain how the outputs of these models can be used in ecological analyses without secondary validation by a human. Here, we present our approach to developing, testing and applying a machine learning model to camera trap data for the purpose of achieving fully automated ecological analyses. As a case‐study, we built a model to classify 26 Central African forest mammal and bird species (or groups). The model generalizes to new spatially and temporally independent data (n = 227 camera stations, n = 23,868 images), and outperforms humans in several respects (e.g. detecting ‘invisible’ animals). We demonstrate how ecologists can evaluate a machine learning model's precision and accuracy in an ecological context by comparing species richness, activity patterns (n = 4 species tested) and occupancy (n = 4 species tested) derived from machine learning labels with the same estimates derived from expert labels. Results show that fully automated species labels can be equivalent to expert labels when calculating species richness, activity patterns (n = 4 species tested) and estimating occupancy (n = 3 of 4 species tested) in a large, completely out‐of‐sample test dataset. Simple thresholding using the Softmax values (i.e. excluding ‘uncertain’ labels) improved the model's performance when calculating activity patterns and estimating occupancy but did not improve estimates of species richness. We conclude that, with adequate testing and evaluation in an ecological context, a machine learning model can generate labels for direct use in ecological analyses without the need for manual validation. We provide the user‐community with a multi‐platform, multi‐language graphical user interface that can be used to run our model offline.
Prediction of Neutralization Depth of R.C. Bridges Using Machine Learning Methods

Machine learning techniques have become a popular solution to prediction problems. These approaches show excellent performance without being explicitly programmed. In this paper, 448 sets of data were collected to predict the neutralization depth of concrete bridges in China. Random forest was used for parameter selection. Besides this, four machine learning methods, such as support vector machine (SVM), k-nearest neighbor (KNN) and XGBoost, were adopted to develop models. The results show that machine learning models obtain a high accuracy (>80%) and an acceptable macro recall rate (>80%) even with only four parameters. For SVM models, the radial basis function has a better performance than other kernel functions. The radial basis kernel SVM method has the highest verification accuracy (91%) and the highest macro recall rate (86%). Besides this, the preference of different methods is revealed in this study.
Effects of nutrient omission on the reproduction of Meloidogyne javanica in soybean

Abstract With the expansion of soybean (Glycine max [L.] Merrill) crop to more sandy soils with lower organic matter (OM) content, the presence of nematodes is one of the main limiting factors for grain yield. The objective of this study was to evaluate the effects of nutrient omission in soybean in relation to the reproduction of Meloidogyne javanica. The experiment was carried out using a completely randomized design in a 2 × 12 factorial arrangement. The treatments were with (+) and without (-) nematode inoculation and nutrient omission (complete, -N, -P, -K, -limestone, -S, -B, -Cu, -Fe, -Mn, -Zn, and control without fertilization), and six replicates. Nematode reproduction (NR), number of nematodes in the roots (NNR), root length (RL), root fresh weight (RFW), shoot fresh weight (SFW), shoot dry weight (SDW), root length, number of pods and flowers, chlorophyll content, and nutritional status of the plant were observed. The reproduction factor and number of nematodes were related to RFW. Apart from RL, in the absence of -N, -limestone, and -S, the presence of the nematode in the substrate caused a reduction in all yield components, and N omission presented the greatest restrictive effect, with reductions of 25.3%, 81.6%, and 84.8% in RFW, SFW, and SDW, respectively, compared to control treatment.
Clinical, Molecular and Histopathological Evaluation of the Effect Autologus PRP & PRF on the Second Intension Wound Healing in Dogs

A new trend of regenerative medicine was introduced recently in the field of second intension wound healing. The PRP and PRF represented a corner stone in this clinical practice, were it admit many factors for acceleration of wound healing, enhancement of wound epithelization and neovascularization. PRP and PRF are considered as a great concentrated source of growth factors (PDGF, TGF-β, VEGF, bFGF) and cytokines which are fundamental fro wound healing. Nine adult dogs were used in this study; we induced a 3cm diameter full thickness cutaneous injury at the right chest region. The animals were divided into three groups equally and the wounds were treated twice weekly for three successive weeks. Group A received only normal saline (control group). The members of second group treated by PRP (group B) while, the group C treated by PRF. Clinical evaluation, Molecular studies of IL10 & TGF-β and histopathological examination were used to demonstrate the difference between the three treatment regimes. Results showed non-significant negative low correlation between loss of weight and WHR%, and showed significant high positive correlation between treatment cost either by PRP or PRF with IL10 (0.79*) and WHR%(0.996**). We concluded that the PRP and PRF exhibited higher regeneration capacity and accelerate the quality of wound healing. The PRP was more superior to PRF, but not significantly different. The IL10 was significant increase in expression in PRP, while TGF-b was non-significant increased in PRF group
Maternal Supply of Cysteamine during Late Gestation Alleviates Oxidative Stress and Enhances Angiogenesis in Porcine Placenta

Background: Oxidative stress in placenta is associated with the occurrence of adverse pregnancy outcomes in sow, but there are few satisfactory treatment strategies for these conditions. This study investigated the potential of cysteamine (CS) as an antioxidant protectant for regulating the reproductive performance, redox status, and placental angiogenesis of sows.Methods: The placental oxidative stress status and vascular density of piglets with different birth weights: <1.0 kg (low birth weight, LBW) and 1.4-1.6 kg (normal birth weight, NBW) were evaluated, followed by allotting 84 sows to four treatments (n=21) and feeding them with a basal diet supplemented with 0, 100, 300, or 500 mg/kg of CS (CON, CS100, CS300, and CS500 diet) from day 85 of gestation to day 21 of lactation, respectively. Placentae, serum and colostrum, and blood samples of sows or piglets were collected, and the characteristics of sows and piglets were recorded. Furthermore, the in vivo results were validated using porcine vascular endothelial cells (PVECs). Results: The placentae for the LBW piglets had higher oxidative damage and lower vascular density than those for the NBW piglets (P < 0.05). Further experiments with sows showed that compared with the CON group, the CS100 group was lower in the stillbirth and invalid rates, and higher in the piglet birth weight and placental efficiency (P < 0.05). Meanwhile, the CS100 group also displayed higher glutathione and lower malondialdehyde in both the serum and colostrum of sows (P < 0.05). Interestingly, compared to the CON group, the LBW placentae of the CS100 group showed a decrease in oxidative damage, while an increase in vascular density (P < 0.05), as well as the mRNA level of vascular endothelial growth factor A and the immunostaining intensity of platelet endothelial cell adhesion molecule-1 (P < 0.05). Furthermore, the in vitro experiment indicated that CS pre-treatment could significantly reverse the NADPH oxidase 2-ROS-mediated inactivation of signal transducer and activator of transcription-3 (Stat3) signaling pathway induced by H2O2 inhibition of the proliferation, tube formation, and migration of PVECs (P < 0.05). Conclusions: The results indicated that oxidative stress and impaired angiogenesis might contribute to the occurrence of low-birth-weight piglets during pregnancy, but CS supplementation at 100 mg/kg during late gestation and lactation of sows could alleviate oxidative stress and enhance angiogenesis in placenta, thereby improving pregnancy outcome. The in vitro data showed that the underlying mechanism for the positive effects of CS may be related to the activation of Stat3.
Integration of sub 50 nm features based on EVG SmartNIL for 8-inch substrates

The NanoImprint Lithography (NIL) technology by using a soft stamp is today ready for high volume manufacturing (HVM) with the global solution proposed by EVG1. This UV-based imprint, using a transparent stamp is now a standard technology and the most common option for the full wafer imprint, meaning the size of the stamp correspond to the size of the wafer to print. Previous work has shown promising results with strong repeatability and uniformity in terms of critical dimension (CD)2. In 2017, larges features, bigger than 500 nm period, and shallow aspect ratio were qualified3. Latter in 20194, lithography and etching through a Si/SiO2 stack were demonstrated for 25 wafers imprinted in a single run: - Depending on several diameters contact (from 100 to 50 nm) and densities (from 1:3 to 1:15). - For line and space arrays with a density of 1:4 and variable spaces widths (from 100 to 50 nm). In this paper we demonstrate that the limit of the patterns dimension can be pushed to sub-50 nm features thanks to EVG SmartNIL technology, the optimized EVGNIL-UV/AS2 soft stamp material with matching resist as well as the improvement of pattern transfer by dry etching. Based on CDSEM metrology, and SEM cross-sections, high fidelity and reproducibility were demonstrated, with 25 replications in a single run using the same soft stamp. Transfer compatibility of the imprint material was validated until 45 nm line, with 1:4 density. Furthermore, the process window of this NIL technology and its compatibility with applications as photonics and 3D patterning are discussed. The specific developments achieved around stripping of the substrates and the perspectives for low defectivity process are pointed out.
Wheel flat analogue fault detector verification study under dynamic testing conditions using a scaled bogie test rig

ABSTRACT Advanced health monitoring of unpowered heavy haul and general freight railway wagons is still an emerging field, limited by the lack of electrical power on-board the vehicles and the cost of instrumenting massive fleets. This paper presents a dynamic verification of an on-board wheel flat detection technique that, using analogue signal processing, reduces the power consumption and hardware costs of condition monitoring sensor nodes. A 1:4 scale bogie test rig was used to record bearing adapter acceleration signals of healthy and defective wheelsets. The data were then used to verify the wheel flat detection technique, which effectively distinguished between healthy and defective acceleration signals, using analogue computing only, without the need for software or a complex algorithm and corresponding hardware. This technique is promising for further development of low-cost and ultra-low power sensor nodes systems that require numerous sensor nodes, such as heavy haul and general freight railway applications.
Sensors and actuators B : Chemical Integrated platform for detecting pathogenic DNA via magnetic tunneling junction-based biosensors

In recent years, the development of portable platforms for performing fast and point-of-care analyses has drawn considerable attention for their wide variety of applications in life science. In this framework, tools combining magnetoresistive biosensors with magnetic markers have been widely studied in order to detect concentrations of specific molecules, demonstrating high sensitivity and ease of integration with conventional electronics. In this work, first, we develop a protocol for efficient hybridization of natural DNA; then, we show the detection of hybridization events involving natural DNA, namely genomic DNA extracted from the pathogenic bacterium Listeria monocytogenes, via a compact magnetic tunneling junction (MTJ)-based biosensing apparatus. The platform comprises dedicated portable electronics and microfluidic setups, enabling point-of-care biological assays. A sensitivity below the nM range is demonstrated. This work constitutes a step forward towards the development of portable lab-on-chip platforms, for the multiplexed detection of pathogenic health threats in food and food processing environment.
Modulating Vapor Pressure Deficit in the Plant Micro-Environment May Enhance the Bioactive Value of Lettuce

Growing demand for horticultural products of accentuated sensory, nutritional, and functional quality traits has been driven by the turn observed in affluent societies toward a healthy and sustainable lifestyle relying principally on plant-based food. Growing plants under protected cultivation facilitates more precise and efficient modulation of the plant microenvironment, which is essential for improving vegetable quality. Among the environmental parameters that have been researched for optimization over the past, air relative humidity has always been in the background and it is still unclear if and how it can be modulated to improve plants’ quality. In this respect, two differentially pigmented (green and red) Salanova® cultivars (Lactuca sativa L. var. capitata) were grown under two different Vapor Pressure Deficits (VPDs; 0.69 and 1.76 kPa) in a controlled environment chamber in order to appraise possible changes in mineral and phytochemical composition and in antioxidant capacity. Growth and morpho-physiological parameters were also analyzed to better understand lettuce development and acclimation mechanisms under these two VPD regimes. Results showed that even though Salanova plants grown at low VPD (0.69 kPa) increased their biomass, area, number of leaves and enhanced Fv/Fm ratio, plants at high VPD increased the levels of phytochemicals, especially in the red cultivar. Based on these results, we have discussed the role of high VPD facilitated by controlled environment agriculture as a mild stress aimed to enhance the quality of leafy greens.
Value Co-Creation Process and Measurement in 4.0 SMEs: An Exploratory Research in a B2B Marketing Innovation Context

The purpose of this exploratory research is to contribute to the lack of empirical research exploring techniques and protocols that can be used to measure the level of value obtained from using these technologies in the various marketing processes and in a business-to-business (B2B) context. By doing so, firms operating in fast changing dynamic environments can develop the right means to continuously adapt, integrate, reconfigure, and redeploy resources and capabilities to become more competitive and disruptive in their offerings. The phenomenon of interest is described by applying a case study qualitative approach to three 4.0 companies which use virtual reality (VR)/augmented reality (AR) technologies and by carrying out ten in-depth interviews to managers in those organizations. Results show that some small and medium sized enterprises (SMEs) are more rigorous about performance and tracking metrics compared to other companies providing similar technological services. In this line, results show two stages where potential value can be generated and measured when utilizing VR/AR technologies. The first is during testing and development of VR/AR simulations. In this stage, only the ones with strong research and academic background have been developing and using tracking systems, guidelines, and protocols as resources to measure the value obtained from using these technologies in marketing innovation processes. The second moment where value creation can be measured is during the implementation of the VR/AR simulation with the target user. Further research is needed to develop standardization guidelines and protocols that guarantee the success of the simulations delivered to the hiring firms.
Photovoltaic System Power Reserve Determination Using Parabolic Approximation of Frequency Response

The most commonly used method for including photovoltaic system in the control of system frequency is to operate a PV power plant at reduced power while maintaining a specific amount of available power reserve. Such operating regime, often referred to as de-loaded, can cause additional costs and, consequently, determining a required power reduction becomes vitally important. This paper introduces a method for determining the required minimum de-loaded reserve of a photovoltaic system. To achieve the desired result a parabolic approximation of the system frequency response is developed to derive an explicit function of the frequency nadir. Taking this approximation into consideration, a method for determining the required minimum de-loaded margin was developed and tested in a case-study which observed a small power system consisting of several power plants. To demonstrate the accuracy of the proposed parabolic approximation, the calculated approximate values of the frequency nadir were compared to the values of the frequency nadir obtained from the frequency response simulations performed on the power system model developed in MATLAB/Simulink.
Evaluation of Sirt1 And Foxo’s In Primary Tumor And Distant Organ Metastasis Invaded By Benign And Highly Metastatic Breast Carcinoma Cells Under In Vitro And In Vivo Conditions.

Breast cancer is the second most common cancer in women. In malignant breast cancers, tumor cells have the potential to metastasize to distant organs through the lymphatic system and blood circulation. The aim of this study is to evaluate the expression of SIRT1 and FoxO proteins in metastatic and nonmetastatic breast cancer cells and distant organs metastasis. In our study, SIRT1, p53, p21, and FoxO proteins have been evaluated in metastatic 4TLM and non-metastatic 67NR cell lines by immunocytochemistry in vitro and also in mice breast cancer model in vivo. Cells were orthotopically injected to mammary fat pads of 8-10 weeks old Balb/c female mice. Primary tumor, lung and liver tissues were removed and expressions of these proteins were evaluated by immunohistochemistry, western blot and RT-PCR. In addition, signal pathways that are related to SIRT and FoxO proteins were examined by using IPA core analysis. TCGA database was browsed for investigation of different genes.In primary tumors, SIRT1, p21, p53, E2F1 and FoxO expressions were higher in 67NR compared to 4TLM. In metastatic lung and liver tissues, the expression levels of SIRT1, FoxO1, FoxO3a and FoxO4 proteins were increased in 4TLM compared to 67NR. IPA and TCGA analysis have also revealed that SIRT1 and FoxO proteins are lower in primary tumors, but increased in metastatic stages. In conclusion, in primary tumors SIRT1 and FoxO expressions were decreased in 4TLM compared to 67NR. Moreover, SIRT1 and FoxO, especially expressed in metastatic cells. High level of FoxO expressions in metastatic stages in TNBC patients also supports its association with metastasis. Our findings suggest that SIRT1 and FoxO’s have crucial role in tumor progression metastatic process in breast cancer.
The Geometry and Sensitivity of Ion-Beam Sculpted Nanopores for Single Molecule DNA Analysis

In this dissertation, the relationship between the geometry of ion-beam sculpted solid-state nanopores and their ability to analyze single DNA molecules using resistive pulse sensing is investigated. To accomplish this, the three dimensional shape of the nanopore is determined using energy filtered and tomographic transmission electron microscopy. It is shown that this information enables the prediction of the ionic current passing through a voltage biased nanopore and improves the prediction of the magnitude of current drop signals when the nanopore interacts with single DNA molecules. The dimensional stability of nanopores in solution is monitored using this information and is improved by modifying the pore’s fabrication procedure. Furthermore, the correlation between noise sources present in the nanopore and the noble gas used to form the ion beam during fabrication is investigated. Finally, the polymerase chain reaction is used to verify that DNA translocates through ion-beam sculpted nanopores. This dissertation is approved for recommendation to the Graduate Council Dissertation Director: —————————————————————————– Dr. Jiali Li, Ph.D. Dissertation Committee: —————————————————————————– Dr. Michael Lieber, Ph.D. —————————————————————————– Dr. David McNabb, Ph.D. —————————————————————————– Dr. William ‘Lin’ Oliver, Ph.D. —————————————————————————– Dr. Gregory Salamo, Ph.D. —————————————————————————– Dr. Chao-Hung ‘Steve’ Tung, Ph.D. c ©2013 by Ryan Connor Rollings All Rights Reserved Dissertation Duplication Release I hereby authorize the University of Arkansas Libraries to duplicate this dissertation when needed for research and/or scholarship. Agreed Ryan Connor Rollings Refused Ryan Connor Rollings
Active and Reactive Value Dimensions: A Dynamic-Based Perspective in the Hotel Sector

Despite the wide acknowledgment of consumer value as a dynamic concept in marketing and tourism literature, few studies have addressed its dynamicity. This article provides an approach to the dynamic nature of value in a hotel experience by proposing a conceptual framework that seeks to explain the influence of value types on customer satisfaction and loyalty as being concatenated rather than simultaneous effects, as more usually described. The concatenation of effects is based on the distinction between active versus reactive values and is tested in a structural model consisting of eight types of value (Efficiency, Service Quality, Status, Esteem, Entertainment, Aesthetics, Ethics, and Escapism). The experiences of 585 hotel guests were analyzed with SEM-PLS (structural equation modeling–partial least squares). Although a direct effect persists between Service Quality and Satisfaction, and Efficiency does not directly affect Loyalty, the chain of Reactive Values–Active Values–Satisfaction–Loyalty is empirically validated shedding light on research in value co-creation processes.