Miracle-dz/UniFAR-FTUs huggingface dataset:
Hitherto Ignored Effects of Chain Length on the Flory-Huggins Interaction Parameters in Concentrated Polymer Solutions

Equilibrium vapor pressures were measured for the systems toluene/poly(dimethylsiloxane) [two different polymers, 35-55°C] and cyclohexane/poly(vinyl methyl ether) [three polymers, 35-65°C] by combining a head-space sampler with a gas chromatograph. The Flory-Huggins interaction parameters X calculated therefrom as a function of the volume fraction φ 2 of polymer were complemented by literature data on inverse gas chromatography and by X values at high dilution from osmometry in the former case and from light-scattering measurements in the latter. These results demonstrate that the effects of differing chain lengths can still be felt at φ 2 > 0.5, in contrast to the general perception. This observation is tentatively explained in terms of changes in coil dimension taking place in the region of high polymer concentrations. Furthermore, X(φ 2 ) may exhibit a minimum even with the common polymers of present interest. In all cases the concentration dependence of X is described well by a series expansion of X with respect to φ 2 up to the fourth term, if two parameters are adjusted. The temperature dependence of X at different concentrations demonstrates that the heats of dilution generally increase with increasing φ 2 , whereas the noncombinatorial entropy of dilution decreases. This situation leads to a linear interdependence of the enthalpy and entropy part of X. With the system toluene/poly(dimethylsiloxane) one observes an inversion of the heat effects upon an increase of φ 2 from exothermal to endothermal at ca. 13 vol %.
A Unified Architecture for Accelerating Distributed DNN Training in Heterogeneous GPU/CPU Clusters

Data center clusters that run DNN training jobs are inherently heterogeneous. They have GPUs and CPUs for computation and network bandwidth for distributed training. However, existing distributed DNN training architectures, all-reduce and Parameter Server (PS), cannot fully utilize such heterogeneous resources. In this paper, we present a new distributed DNN training architecture called BytePS. BytePS can leverage spare CPU and bandwidth resources in the cluster to accelerate distributed DNN training tasks running on GPUs. It provides a communication framework that is both proved optimal and unified – existing all-reduce and PS become two special cases of BytePS. To achieve the proved optimality in practice, BytePS further splits the functionalities of a parameter optimizer. It introduces a Summation Service abstraction for aggregating gradients, which is common for all the optimizers. Summation Service can be accelerated by AVX instructions and can be efficiently run on CPUs, while DNN model-related optimizer algorithms are run on GPUs for computation acceleration. BytePS can accelerate DNN training for major frameworks including TensorFlow, PyTorch and MXNet. For representative DNN training jobs with up to 256 GPUs, BytePS outperforms the state-of-the-art open source all-reduce and PS by up to 84% and 245%, respectively.
Deep Griffin–Lim Iteration: Trainable Iterative Phase Reconstruction Using Neural Network

In this paper, we propose a phase reconstruction framework, named Deep Griffin–Lim Iteration (DeGLI). Phase reconstruction is a fundamental technique for improving the quality of sound obtained through some process in the time-frequency domain. It has been shown that the recent methods using deep neural networks (DNN) outperformed the conventional iterative phase reconstruction methods such as the Griffin–Lim algorithm (GLA). However, the computational cost of DNN-based methods is not adjustable at the time of inference, which may limit the range of applications. To address this problem, we combine the iterative structure of GLA with a DNN so that the computational cost becomes adjustable by changing the number of iterations of the proposed DNN-based component. A training method that is independent of the number of iterations for inference is also proposed to minimize the computational cost of the training. This training method, named sub-block training by denoising (SBTD), avoids recursive use of the DNN and enables training of DeGLI with a single sub-block (corresponding to one GLA iteration). Furthermore, we propose a complex DNN based on complex convolution layers with gated mechanisms and investigated its performance in terms of the proposed framework. Through several experiments, we found that DeGLI significantly improved both objective and subjective measures from GLA by incorporating the DNN, and its sound quality was comparable to those of neural vocoders.
Application of Machine Learning for Fatigue Prediction of Flexible Risers - Digital Twin Approach

Flexible pipes have a range of potential failure modes, however fatigue damage of the tensile, and eventually, the pressure armour, is one of the most common problems affecting the longevity of service life and the OPEX due to the common need for flexible riser replacement. With increasing utilisation of flexible pipe for current and future field developments, compounded by the recurrent need for field life extension, it is essential to monitor the riser fatigue regularly to maintain integrity, maximise asset life and to allow for informed appraisal before extending its operational life. This paper presents a novel method of using the refined finite element analysis (FEA) in combination with Artificial Neural Network (ANN) to develop a riser digital twin that can be utilised as an operational decision-making tool for integrity management and life extension. A digital twin model is trained on a subset of available metocean and vessel motion data utilising advanced neural networks which can then be utilised to predict fatigue under the full spectrum of metocean and internal pressure conditions. This approach allows for a significant reduction in the estimation time of the fatigue damage compared to conventional FEA as well as improved accuracy of prediction. The methodology presented in the paper has been primarily developed with the view of deepwater riser applications but is easily adaptable to shallow water application in combination with various floating vessels. A case study is presented to demonstrate how this technology is being deployed offshore. A comparison of FEA and digital twin approach is also presented to highlight the speed and efficiency of digital twin model whereby real-time insights on fatigue life can be evaluated for informed operational decisions.
Phenotypic correlation, path coefficient and multivariate analysis for yield and yield-associated traits in groundnut accessions

Abstract Yield is a complex quantitative trait largely influenced by the environment. Direct selection for grain yield is less efficient in improving groundnut productivity. The selection efficiency can be enhanced by exploiting the relationship between yield and its related traits. Moreover, the use of genetically diverse parents is essential to generate genetic variation for successful selection of genotypes in a breeding program. Therefore, the study aimed at analysing the relationship between grain yield and its related traits and determining the morphological diversity among selected groundnut genotypes under natural rosette disease (GRD) infestation. The genotypes were evaluated in a 7 × 4 alpha lattice design with three replications. Data were collected on yield and yield-related traits. Correlation, path coefficient and multivariate analyses were done. The results revealed that yield was directly associated with plant height, number of pods per plant, hundred seed weight, GRD incidence and number of secondary branches. Therefore, these traits should be considered in selection when improving groundnut for yield. Cluster analysis revealed existence of diversity among the evaluated groundnut genotypes with no influence of geographical origin to the clustering pattern. The Principal Components Analysis (PCA) biplot was effective in showing the genetic distance among the genotypes and the results were comparable with those of the cluster analysis. Moreover, Shannon-Weaver diversity indices revealed existence of high diversity among the genotypes, an implication that groundnut improvement for yield is possible through selection in breeding.
Tagged to Teach Ag Day: Evaluating the Influence of a Recruitment Event on Students’ Perceptions of Campus Climate

In an effort to measure the influence of an immersive recruitment event, we sought to evaluate high school students’ perceptions toward university climate before and after engaging in a day-long event hosted as part of National Teach Ag Day. The Inviting School Survey-Revised (ISS-R) was administered via paper and pencil when the students arrived and again at the conclusion of the event. Pre-test scores indicated students had favorable opinions of all aspects of [University’s] climate factors before attending the event; however, mean scores were consistently higher on the post-test than on the pretest for all school climate factor areas. We recommend agricultural education departments explore the possibility of implementing a similar immersive recruitment event tailored specifically for agricultural education students. Further, we recommend future research use experimental methods and larger sample sizes to expand the generalizability of these findings.
Duration of rheumatoid arthritis and the risk of developing interstitial lung disease

Rheumatoid arthritis is an autoimmune disease that classically presents as a symmetrical inflammatory polyarthritis. Extra-articular manifestations are prevalent, with the lungs being the most common site [1], and interstitial lung disease (ILD) being the most severe form of pulmonary involvement. In some studies, the median survival of patients with rheumatoid arthritis and a usual interstitial pneumonia (UIP) pattern on high-resolution computed tomography (HRCT) is around 3 years (similar to that in idiopathic pulmonary fibrosis (IPF) [2, 3]); however, in other studies, median survival is >7 years [4, 5]. When present, ILD accounts for 10–20% of all deaths in rheumatoid arthritis [6]. Age of ILD onset is similar in patients with RA-UIP and RA-NSIP but duration of RA before ILD onset differs https://bit.ly/3lgjfDJ
Research of “double desserts” seismic prediction method for tight sandstone - Taking Linxing block on the east edge of Ordos Basin as an example

The determination of the sweet spot of tight sandstone reservoir is the primary problem in the exploration and development of tight sandstone reservoir. Practice has proved that the prediction of reservoir spatial pore characteristics, thickness distribution and oil and gas potential (i.e. geological sweet spot) of tight sandstone reservoir can not meet the demand of exploration and development of tight sandstone reservoir. In view of the requirements of tight sandstone fracturing engineering, it is also necessary to analyze the engineering field of the reservoir. In this paper, based on seismic data preprocessing and petrophysical analysis, thin reservoir quantitative characterization and AVO fluid prediction are used to evaluate the tight reservoir. At the same time, combining with the fracture analysis method of FMI imaging logging, pre stack fracture description and brittleness prediction, the “engineering sweet spot” prediction is carried out. Finally, the multi-attribute RGB digital fusion visualization research method is used to comprehensively evaluate the tight reservoir. The practice shows that the research method has guiding significance for the exploration and development of tight reservoir.
Study of the oxygen‐sensitive behavior of yellow lead oxide by defect chemistry

The dependence of electrical conductivity on ambient oxygen partial pressure of yellow lead oxide ceramics is investigated. A conduction type transition is observed for pure PbO as the oxygen partial pressure varies through a certain value that corresponds to the minimum conductivity measured, and the conductivity is found to vary with −1/4 and +1/4 power of the oxygen partial pressure in the n region and the p region, respectively. It is also found that the conduction transition point can be moved to lower or higher oxygen partial pressures with acceptor or donor impurities doped into PbO. A point defect model of doubly charged oxygen vacancies with concentration affected by impurities is presented for PbO in the investigation and it is demonstrated that the theory is in good agreement with the experimental results.
An antibacterial and injectable calcium phosphate scaffold delivering human periodontal ligament stem cells for bone tissue engineering

Osteomyelitis and post-operative infections are major problems in orthopedic, dental and craniofacial surgeries. It is highly desirable for a tissue engineering construct to kill bacteria, while simultaneously delivering stem cells and enhancing cell function and tissue regeneration. The objectives of this study were to: (1) develop a novel injectable calcium phosphate cement (CPC) scaffold containing antibiotic ornidazole (ORZ) while encapsulating human periodontal ligament stem cells (hPDLSCs), and (2) investigate the inhibition efficacy against Staphylococcus aureus (S. aureus) and the promotion of hPDLSC function for osteogenesis for the first time. ORZ was incorporated into a CPC-chitosan scaffold. hPDLSCs were encapsulated in alginate microbeads (denoted hPDLSCbeads). The ORZ-loaded CPCC+hPDLSCbeads scaffold was fully injectable, and had a flexural strength of 3.50 ± 0.92 MPa and an elastic modulus of 1.30 ± 0.45 GPa, matching those of natural cancellous bone. With 6 days of sustained ORZ release, the CPCC+10ORZ (10% ORZ) scaffold had strong antibacterial effects on S. aureus, with an inhibition zone of 12.47 ± 1.01 mm. No colonies were observed in the CPCC+10ORZ group from 3 to 7 days. ORZ-containing scaffolds were biocompatible with hPDLSCs. CPCC+10ORZ+hPDLSCbeads scaffold with osteogenic medium had 2.4-fold increase in alkaline phosphatase (ALP) activity and bone mineral synthesis by hPDLSCs, as compared to the control group (p < 0.05). In conclusion, the novel antibacterial construct with stem cell delivery had injectability, good strength, strong antibacterial effects and biocompatibility, supporting osteogenic differentiation and bone mineral synthesis of hPDLSCs. The injectable and mechanically-strong CPCC+10ORZ+hPDLSCbeads construct has great potential for treating bone infections and promoting bone regeneration.
A lysimeter experiment to investigate the effect of surface sealing on hydrology and pesticide loss from the reconstructed profile of a clay soil. 1. Hydrological characteristics

Abstract. A laboratory experiment was designed to assess the impact of surface seal development on the hydrological response of a clay soil. The influence of surface sealing on vertical macropore flow and lateral throughflow was of particular interest. The extent and development of the surface seal in repacked lysimeters was designed to match that recorded over two growing seasons at a clay field site in Essex, and was not extensive enough to reduce significantly the infiltration capacity of the soil. Consequently, the hydrological response of the lysimeters was similar under sealed and unsealed conditions, with a more rapid wetting response under sealed conditions being attributed to the higher soil moisture content required to create the surface seal. Macropore flow was initiated at the A/B soil boundary of the lysimeters, in response to the development of a saturated layer. The rate of macropore and throughflow in the soil was dictated by rainfall intensity at the soil surface as this controlled the depth of water in the perched water table. Simulation of the tensiometer response in the lysimeters demonstrated that it was possible to attribute the rapid movement of water through the A horizon to water displacement processes alone, without recourse to preferential flow processes.
Catalyst-free Heteroepitaxy of III-V Semiconductor Nanowires on Silicon, Graphene, and Molybdenum Disulfide

Kate Gleason College of Engineering Rochester Institute of Technology Degree: Doctor of Philosophy Program: Microsystems Engineering Author’s Name: Mohadeseh Asadolahi Baboli Advisor’s Name: Parsian K. Mohseni, Ph.D. Dissertation Title: Catalyst-free Heteroepitaxy of III-V Semiconductor Nanowires on Silicon, Graphene, and Molybdenum Disulfide The research presented in this dissertation pioneered three novel nano-material systems, including vertically aligned InAlAs nanowires (NWs) on two-dimensional (2-D) graphene, InAs NWs on 2-D MoS2, and GaAsP NWs on Si using metalorganic chemical vapor deposition (MOCVD). Bottom up integratiton of NW structures enable heteroepitaxy of largely dissimilar III-V compounds on foreign substrates and provide a basis for design of high-performance devices that are otherwise inaccessible using thinfilms or planar geometries. During conventional heteroepitaxy of planar geometries, strict constraints are imposed by the need to match lattice parameters, thermal expansion coefficients, and polar coherence between adjacent dissimilar materials. Semiconductor III-V NWs with small substrate footprints can permit relief of lattice mismatch-induced strain in heteroepitaxial systems. Thus, high crystalline quality III-V compound semiconductor NWs can be monolithically integrated with foreign substrates for novel electronic and optoelectronic device designs. This dissertation presents wafer-scale production of vertically oriented InAsyP1-y and InxAl1-xAs NWs on single layer graphene (SLG) and MoS2 substrates, grown via pseudo-van der Waals epitaxy (vdWE). The morphology, areal density, and crystal
Research on Printing Process and Perfomance of CNT/PDMS Soft Sensing Material

： According to the requirements of the application of CNT/PDMS in soft ciruit and complex structure design, the direct writing printing technology is combined with the molding preparation method of the hybrid conductive material, and the printing process of CNT/PDMS is studied. Firstly, the printing ink of mixed materials with different CNT content is configured, and the rheological property and material conductivity are measured. All the printing inks are shear thinned and viscoelastic, among which the printing ink with CNT content of 7% is of good conductivity, easy to extrude and formed. The printer parameters are set as follows: scanning speed is 10 mm/s, extrusion pressure is 0.3 MP, and the printing process is stable. On this basis, the simple planar strip, two-dimensional mesh structure and three-dimensional grid structure are printed, and the temperature and uniaxial tensile experiments are carried out on the printed strip structure. The printed sample shows resistance sensing characteristics at different temperatures and tensile rates. The flexible circuit performance of the grid-like circuit is tested. When the voltage of about 6 V is applied, the grid-like circuit shows good electrical conductivity in the stretching or bending state. The pressure sensing experiment on the printed 3D grid structure shows that the composite material with good pressure sensitivity. The results of performance characterization proves that the printing process is feasible, which laid a foundation for the subsequent complex structure manufacturing of the material and its application in flexible sensing electronic equipment.
A 4–20-Gb/s 1.87-pJ/b Continuous-Rate Digital CDR Circuit With Unlimited Frequency Acquisition Capability in 65-nm CMOS

A continuous-rate referenceless clock and data recovery (CDR) circuit with an unlimited frequency acquisition capability is presented. The proposed frequency detector (FD) is derived from a multi-phase oversampling FD. Through accurate analysis of the root causes limiting the capture range, the extension techniques are proposed and digitally implemented by a small hardware overhead. The FD achieves the unlimited frequency detection capability and exhibits robust operation regardless of the initial clock frequency. The effect of the quadrature error is analyzed and verified by simulation. In addition, a frequency lock detector is implemented to control the loop gain for fast frequency acquisition. The prototype CDR circuit was designed and fabricated in a 65-nm CMOS process, occupying an active area of 0.045 mm2. The CDR circuit achieves a capture range from 4 Gb/s to 20 Gb/s, which is limited only by the oscillator operating range. The worst case acquisition time is 25 $\mu \text{s}$ with a PRBS31 pattern. The CDR circuit achieves a BER less than 10−12 and an energy efficiency of 1.87 pJ/b.
A Distributed Reactivity Model for Sorption by Soils and Sediments. 11. Slow Concentration-Dependent Sorption Rates

Long-term temporal phase distribution relationships (PDRs) were measured for sorption of a hydrophobic organic contaminant probe by seven EPA reference soils and sediments and six shale and kerogen samples. The times required for attainment of apparent sorption equilibrium by the phenanthrene probe were found to be highly dependent upon the aqueous phase-solute concentration, C(t), for a given sorbent, and the type of soil organic matter (SOM) associated with a particular sorbent. Organic-carbon-normalized single-point temporal distribution ratios corresponding to low residual solution phase concentrations were found to approach their respective apparent equilibrium values after times ranging from several days to 90 days for the EPA soils and sediments and from 90 days to ≥ 368 days for the shales and kerogens. Conversely, at residual solution phase concentrations 2 orders of magnitude larger, apparent equilibrium conditions were attained within a few hours for the EPA soils and sediments and within a yea.
Prediction of ENSO Beyond Spring Predictability Barrier Using Deep Convolutional LSTM Networks

An accurate prediction of El Niño Southern Oscillation (ENSO) holds the key to produce skillful seasonal weather predictions across the globe. All the statistical and dynamical ENSO models developed in the past four decades face a common problem, spring predictability barrier, which is the sudden drop in the skill of the ENSO prediction when the forecast is initiated before the onset of boreal summer. Recent studies suggest that data-driven machine learning models can overcome the spring predictability barrier. We show that using a convolutional long short-term memory (ConvLSTM) network, the monthly mean Nino3.4 index can be skillfully predicted up to one year ahead. The model was also able to predict strong El Niño cases, such as 1997–1998 and 2015–2016 a year ahead. Our results suggest that the proposed ConvLSTM model has significant skill in multiseasonal weather predictions.
Iterative Enhanced Multivariance Products Representation for Effective Compression of Hyperspectral Images

Effective compression of hyperspectral (HS) images is essential due to their large data volume. Since these images are high dimensional, processing them is also another challenging issue. In this work, an efficient lossy HS image compression method based on enhanced multivariance products representation (EMPR) is proposed. As an efficient data decomposition method, EMPR enables us to represent the given multidimensional data with lower-dimensional entities. EMPR, as a finite expansion with relevant approximations, can be acquired by truncating this expansion at certain levels. Thus, EMPR can be utilized as a highly effective lossy compression algorithm for hyper spectral images. In addition to these, an efficient variety of EMPR is also introduced in this article, in order to increase the compression efficiency. The results are benchmarked with several state-of-the-art lossy compression methods. It is observed that both higher peak signal-to-noise ratio values and improved classification accuracy are achieved from EMPR-based methods.