Uncertainty evaluation of delayed neutron decay parameters

In a nuclear reactor, delayed neutrons play a critical role in sustaining a controllable chain reaction. Delayed neutron’s relative yields and decay constants are very important for modeling reactivity control and have been studied for decades. Researchers have tried different experimental and numerical methods to assess these delayed neutron parameters. The reported parameter values vary widely, much more than the small statistical errors reported with these parameters. Interestingly, the reported parameters fit their individual measurement data well in spite of these differences. This dissertation focuses on evaluation of the errors and methods of delayed neutron relative yields and decay constants for thermal fission of U-235. Various numerical methods used to extract the delayed neutron parameter from the measured data, including Matrix Inverse, Levenberg-Marquardt, and Quasi-Newton methods, were studied extensively using simulated delayed neutron data. This simulated data was Poisson distributed around Keepin’s theoretical data. The extraction methods produced totally different results for the same data set, and some of the above numerical methods could not even find solutions for some data sets. Further investigation found that ill-conditioned matrices in the objective function were the reason for the inconsistent results. To find a reasonable solution with small variation, a regularization parameter was introduced using a numerical method called Ridge Regression. The results from the Ridge Regression method, in terms of goodness of fit to the data, were good and often better than the other methods. Due to the introduction of a regularization number in the algorithm, the fitted result contains a small additional bias, but this method can guarantee convergence no matter how large the coefficient matrix condition number. Both saturation and pulse modes were simulated to focus on different groups. Some of the factors that affect the solution stability were investigated including initial count rate, sample flight time, initial guess values. Finally, because comparing reported delayed neutron parameters among different experiments is useless to determine if their data actually differs, methods are proposed that can be used to compare the delayed neutron data sets

The contribution of Alu exons to the human proteome.

BackgroundAlu elements are major contributors to lineage-specific new exons in primate and human genomes. Recent studies indicate that some Alu exons have high transcript inclusion levels or tissue-specific splicing profiles, and may play important regulatory roles in modulating mRNA degradation or translational efficiency. However, the contribution of Alu exons to the human proteome remains unclear and controversial. The prevailing view is that exons derived from young repetitive elements, such as Alu elements, are restricted to regulatory functions and have not had adequate evolutionary time to be incorporated into stable, functional proteins.ResultsWe adopt a proteotranscriptomics approach to systematically assess the contribution of Alu exons to the human proteome. Using RNA sequencing, ribosome profiling, and proteomics data from human tissues and cell lines, we provide evidence for the translational activities of Alu exons and the presence of Alu exon derived peptides in human proteins. These Alu exon peptides represent species-specific protein differences between primates and other mammals, and in certain instances between humans and closely related primates. In the case of the RNA editing enzyme ADARB1, which contains an Alu exon peptide in its catalytic domain, RNA sequencing analyses of A-to-I editing demonstrate that both the Alu exon skipping and inclusion isoforms encode active enzymes. The Alu exon derived peptide may fine tune the overall editing activity and, in limited cases, the site selectivity of ADARB1 protein products.ConclusionsOur data indicate that Alu elements have contributed to the acquisition of novel protein sequences during primate and human evolution

Parsing is All You Need for Accurate Gait Recognition in the Wild

Binary silhouettes and keypoint-based skeletons have dominated human gait recognition studies for decades since they are easy to extract from video frames. Despite their success in gait recognition for in-the-lab environments, they usually fail in real-world scenarios due to their low information entropy for gait representations. To achieve accurate gait recognition in the wild, this paper presents a novel gait representation, named Gait Parsing Sequence (GPS). GPSs are sequences of fine-grained human segmentation, i.e., human parsing, extracted from video frames, so they have much higher information entropy to encode the shapes and dynamics of fine-grained human parts during walking. Moreover, to effectively explore the capability of the GPS representation, we propose a novel human parsing-based gait recognition framework, named ParsingGait. ParsingGait contains a Convolutional Neural Network (CNN)-based backbone and two light-weighted heads. The first head extracts global semantic features from GPSs, while the other one learns mutual information of part-level features through Graph Convolutional Networks to model the detailed dynamics of human walking. Furthermore, due to the lack of suitable datasets, we build the first parsing-based dataset for gait recognition in the wild, named Gait3D-Parsing, by extending the large-scale and challenging Gait3D dataset. Based on Gait3D-Parsing, we comprehensively evaluate our method and existing gait recognition methods. The experimental results show a significant improvement in accuracy brought by the GPS representation and the superiority of ParsingGait. The code and dataset are available at https://gait3d.github.io/gait3d-parsing-hp .Comment: 16 pages, 14 figures, ACM MM 2023 accepted, project page: https://gait3d.github.io/gait3d-parsing-h

Flexible Surface Acoustic Wave Humidity Sensor with on Chip Temperature Compensation

AbstractThis paper reports the development of flexible surface acoustic wave (SAW) based humidity sensors on polyimide substrate. The SAW devices have two resonant peaks, named the A0 and S0 Lamb modes, which have different temperature coefficients of frequency. Graphene oxide (GO) is used as the sensing layer owing to its large surface area and hydrophilcity to water. The sensors show high sensitivity up to 145.83ppm/%RH, comparable to those on rigid substrates, and fast response time of 4.4s. The sensitivity increases with the increase of GO thickness and resonant frequency. By utilizing the S0 mode as the temperature reference, a SAW Humidity-sensor with an on chip temperature compensation capability is demonstrated. The humidity sensors also show the ability to work under severe bending condition, demonstrated its great potential for flexible/wearable applications

High sensitivity flexible Lamb-wave humidity sensors with a graphene oxide sensing layer

This paper reports high performance flexible Lamb wave humidity sensors with a graphene oxide sensing layer. The devices were fabricated on piezoelectric ZnO thin films deposited on flexible polyimide substrates. Two resonant peaks, namely the zero order antisymmetric (A0) and symmetric (S0) mode Lamb waves, were observed and fitted well with the theoretical analysis and modelling. With graphene oxide microflakes as the sensing layer, the sensing performance of both wave modes was investigated. The humidity sensitivity of the A0 mode is 145.83 ppm per %RH (at humidity 85%RH), higher than that of S0 mode of 89.35 ppm per %RH. For the first time, we have demonstrated that the flexible humidity sensors work as usual without noticeable deterioration in performance even under severe bending conditions up to 1500 με. Also the sensors showed an excellent stability upon repeated bending for thousand times. All the results demonstrated that the Lamb wave flexible humidity sensors have a great potential for application in flexible electronics

Transcriptome landscape of the human placenta

<p>Abstract</p> <p>Background</p> <p>The placenta is a key component in understanding the physiological processes involved in pregnancy. Characterizing genes critical for placental function can serve as a basis for identifying mechanisms underlying both normal and pathologic pregnancies. Detailing the placental tissue transcriptome could provide a valuable resource for genomic studies related to placental disease.</p> <p>Results</p> <p>We have conducted a deep RNA sequencing (RNA-Seq) study on three tissue components (amnion, chorion, and decidua) of 5 human placentas from normal term pregnancies. We compared the placental RNA-Seq data to that of 16 other human tissues and observed a wide spectrum of transcriptome differences both between placenta and other human tissues and between distinct compartments of the placenta. Exon-level analysis of the RNA-Seq data revealed a large number of exons with differential splicing activities between placenta and other tissues, and 79% (27 out of 34) of the events selected for RT-PCR test were validated. The master splicing regulator <it>ESRP1 </it>is expressed at a proportionately higher level in amnion compared to all other analyzed human tissues, and there is a significant enrichment of ESRP1-regulated exons with tissue-specific splicing activities in amnion. This suggests an important role of alternative splicing in regulating gene function and activity in specific placental compartments. Importantly, genes with differential expression or splicing in the placenta are significantly enriched for genes implicated in placental abnormalities and preterm birth. In addition, we identified 604-1007 novel transcripts and 494-585 novel exons expressed in each of the three placental compartments.</p> <p>Conclusions</p> <p>Our data demonstrate unique aspects of gene expression and splicing in placental tissues that provide a basis for disease investigation related to disruption of these mechanisms. These data are publicly available providing the community with a rich resource for placental physiology and disease-related studies.</p

Vanadium (V) bio-detoxification based on washing water of rice as microbial and carbon sources

Mining and smelting result in vanadium (V) being released into the environment. Biologically removing V(V) with washing water of rice (WWR) was investigated in this study. Over a 7-d trial, the V(V) removal efficiency increased with dosing washing water of rice dosage up to 56.6%. The results demonstrated that washing water of rice could be used as carbon and microbial sources for biologically reducing V(V). Using domesticated sludge as the inoculum could enhance V(V) detoxification performance, and 95.5% of V(V) was removed in the inoculated system for 5 d. Soluble V(V) was transformed into insoluble V(IV) (VO2), which could be further removed with precipitation. In addition to ABC transporters, a two-component system was also involved in V(V) reduction. The study confirmed that washing water of rice could be utilized for V(V) bio-detoxification

Strength properties of soils treated with calcium-based flocculants and their impact on vacuum preloading

In this study, a series of unconfined compressive strength tests were conducted to investigate the effect of calcium-based flocculants on the strength and deformation properties of slurry. The test results indicated that the presence of calcium-based flocculants [Ca(OH)2 or CaCl2] significantly enhanced the unconfined compressive strength (qu) of soil. A non-linear relationship was observed between qu and CaCl2 content, revealing that the maximum value of qu is obtained at a CaCl2 concentration of 24.8%. qu exhibited a high increase rate at early curing time in the presence of Ca(OH)2, with a lower increase rate after a longer curing time and high Ca(OH)2 content. The deformation modulus E50 showed an increasing trend with increasing CaCl2 content at 3 and 7 d, followed by a decreasing trend with increasing CaCl2 content at 14 and 28 d. However, the failure strain εf for CaCl2-treated soil ranges from 2.4% to 4.8%, showing no relation with qu. A significant increase in E50 for Ca(OH)2-treated soil in the early curing stages (3 and 7 d) was observed because of the increase in qu. After 14 d of curing, E50 tended to decrease with increasing Ca(OH)2 content. A positive relationship between the degree of vacuum and qu in the presence of calcium-based flocculants was proposed, indicating that a higher qu of treated soil leads to a lower water content after vacuum treatment for the same preloading period, emphasizing that the vacuum treatment efficiency increases with an increase in the qu of calcium-based flocculant-treated soil