Study on the Influence of Steel Fiber Distribution on the Mechanical Properties of Perfobond Leiste (PBL) Shear Connectors.

In order to study the influence of steel fibers on the mechanical properties of Perfobond Leiste (PBL) shear connectors and improve the utilization of steel fibers in this structure, four push-out test specimens and eight finite element numerical models were produced to study PBL-type shear connector specimens with different steel fiber blending amounts and blending forms. The results show that in this structure, when the blending amount of steel fiber was 0.5% to 1.5%, the ultimate bearing capacity of the specimen improved linearly, and the steel fiber helped to give full play to the performance of the PBL shear connector. The steel fibers distributed in the Z-direction have a significant impact on the mechanical properties of the PBL shear connector, and the steel fibers distributed in this direction have a significant effect on increasing the ultimate bearing capacity of the specimen. Steel fibers distributed in the Y-direction can greatly improve the plasticity of concrete. In addition, the effective action area of steel fibers is the triangular area from the bottom of the PBL shear connector to the two tops of the concrete.

Electrostatic discharge induced degradation of optical-electrical properties and defect evolution of GaAs-based oxide-confined VCSELs.

GaAs-based oxide-confined vertical-cavity surface-emitting lasers (VCSELs) exhibit relatively low resistance against reliability-related damage. In order to gain a deeper understanding of the degradation and failure mechanism in oxide-confined VCSELs caused by electrostatic discharge (ESD)-induced defect proliferation, we investigated the effects of ESD stress on the degradation of optical-electrical characteristics and the evolution of defects in VCSELs under human body model test condition. The degradation threshold values for forward and reverse ESD pulse amplitudes were estimated to be 200 V and -50 V, respectively. Notably, VCSELs demonstrated greater sensitivity to reverse bias ESD compared to forward bias ESD. Analysis of optical emission and microstructure provided evidence that the device failure is attributed to an increase in ESD current density, leading to the multiplication of dark line defects (DLDs) originating from the edge of the device's oxide aperture. The formation of defects occurred suddenly in discrete events within small regions, rather than progressing gradually and uniformly. These defects propagated and led to damage across the entire active region. We believe that our results would be meaningful for improving the reliability of VCSEL in the future.

XA4C: eXplainable representation learning via Autoencoders revealing Critical genes.

Machine Learning models have been frequently used in transcriptome analyses. Particularly, Representation Learning (RL), e.g., autoencoders, are effective in learning critical representations in noisy data. However, learned representations, e.g., the "latent variables" in an autoencoder, are difficult to interpret, not to mention prioritizing essential genes for functional follow-up. In contrast, in traditional analyses, one may identify important genes such as Differentially Expressed (DiffEx), Differentially Co-Expressed (DiffCoEx), and Hub genes. Intuitively, the complex gene-gene interactions may be beyond the capture of marginal effects (DiffEx) or correlations (DiffCoEx and Hub), indicating the need of powerful RL models. However, the lack of interpretability and individual target genes is an obstacle for RL's broad use in practice. To facilitate interpretable analysis and gene-identification using RL, we propose "Critical genes", defined as genes that contribute highly to learned representations (e.g., latent variables in an autoencoder). As a proof-of-concept, supported by eXplainable Artificial Intelligence (XAI), we implemented eXplainable Autoencoder for Critical genes (XA4C) that quantifies each gene's contribution to latent variables, based on which Critical genes are prioritized. Applying XA4C to gene expression data in six cancers showed that Critical genes capture essential pathways underlying cancers. Remarkably, Critical genes has little overlap with Hub or DiffEx genes, however, has a higher enrichment in a comprehensive disease gene database (DisGeNET) and a cancer-specific database (COSMIC), evidencing its potential to disclose massive unknown biology. As an example, we discovered five Critical genes sitting in the center of Lysine degradation (hsa00310) pathway, displaying distinct interaction patterns in tumor and normal tissues. In conclusion, XA4C facilitates explainable analysis using RL and Critical genes discovered by explainable RL empowers the study of complex interactions.

Photoelectric characteristics of hydrogen-terminated polycrystalline diamond MESFETs.

In the field of diamond MESFETs, this work is what we believe to be the first to investigate the optoelectronic properties of hydrogen-terminated polycrystalline diamond MESFETs under visible and near-UV light irradiation. It is shown that the diamond MESFETs are well suited for weak light detection in the near-ultraviolet region around the wavelength of 368 nm, with a responsivity of 6.14 × 106 A/W and an external quantum efficiency of 2.1 × 107 when the incident light power at 368.7 nm is only 0.75 µW/cm2. For incident light at 275.1 nm, the device's sensitivity and EQE increase as the incident light power increases; at an incident light power of 175.32 µW/cm2 and a VGS of -1 V, the device's sensitivity is 2.9 × 105 A/W and the EQE is 1.3 × 106. For incident light in the wavelength range of 660 nm to 404 nm with an optical power of 70 µW/cm2, the device achieves an average responsivity of 1.21 × 105 A/W. This indicates that hydrogen-terminated polycrystalline diamond MESFETs are suitable for visible and near-UV light detection, especially for weak near-UV light detection. However, the transient response test of the device shows a long relaxation time of about 0.2 s, so it is not yet suitable for high-speed UV communication or detection.

OCMA: Fast, Memory-Efficient Factorization of Prohibitively Large Relationship Matrices.

Matrices representing genetic relatedness among individuals (i.e., Genomic Relationship Matrices, GRMs) play a central role in genetic analysis. The eigen-decomposition of GRMs (or its alternative that generates fewer top singular values using genotype matrices) is a necessary step for many analyses including estimation of SNP-heritability, Principal Component Analysis (PCA), and genomic prediction. However, the GRMs and genotype matrices provided by modern biobanks are too large to be stored in active memory. To accommodate the current and future "bigger-data", we develop a disk-based tool, Out-of-Core Matrices Analyzer (OCMA), using state-of-the-art computational techniques that can nimbly perform eigen and Singular Value Decomposition (SVD) analyses. By integrating memory mapping (mmap) and the latest matrix factorization libraries, our tool is fast and memory-efficient. To demonstrate the impressive performance of OCMA, we test it on a personal computer. For full eigen-decomposition, it solves an ordinary GRM (N = 10,000) in 55 sec. For SVD, a commonly used faster alternative of full eigen-decomposition in genomic analyses, OCMA solves the top 200 singular values (SVs) in half an hour, top 2,000 SVs in 0.95 hr, and all 5,000 SVs in 1.77 hr based on a very large genotype matrix (N = 1,000,000, M = 5,000) on the same personal computer. OCMA also supports multi-threading when running in a desktop or HPC cluster. Our OCMA tool can thus alleviate the computing bottleneck of classical analyses on large genomic matrices, and make it possible to scale up current and emerging analytical methods to big genomics data using lightweight computing resources.

Vehicle emission trends in China's Guangdong Province from 1994 to 2014.

Exploring vehicle emission trends within and outside the Pearl River Delta (PRD) region during a long period was scientific and practical, for the economic rapid unbalanced development, continuous implements of severe reducing vehicle emissions measures in Guangdong province. Multi-year inventories of vehicle emissions from 1994 to 2014 were estimated based on the emissions factors of different emissions standards and vehicle kilometers travelled for all types of vehicles. The trends and characteristics of the emissions of carbon monoxide (CO), volatile organic compounds (VOCs), nitrogen oxides (NOx), fine particulate matter (PM2.5) and course particulate matter (PM10) were then analyzed within and outside the PRD region. In the above two regions, the total amount of the five pollutant emissions varied greatly with gross domestic product (GDP) from 1994 to 2014, showing the overall performance of the first increasing up to 1.6-3.0 times before 2002, and then decreasing. However, the five pollutant emissions in the PRD region were 2.4-3.3 times more than in the non-PRD region. In both regions, light passenger cars and motorcycles were the main contributors to CO and VOC emissions (65%-80%), and heavy duty trucks and passenger cars were the main contributors to NOx, PM2.5 and PM10 emissions (around 42%-50%). Moreover, compared to CO and VOCs emissions, the changes in the contribution of every vehicles type to NOx, PM2.5 and PM10 emissions were more obvious, and coincided with the implementation time of emission and fuel standards in the non-PRD region. It was noted that CO and VOC emission variations was correlated closely with the population of yellow-label light passenger cars and motorcycles, whereas those of NOx and PM2.5 was coincided that of yellow-label heavy passenger cars and trucks.

Assessment of Co-benefits of vehicle emission reduction measures for 2015-2020 in the Pearl River Delta region, China.

Vehicle emissions have become one of the key factors affecting the urban air quality and climate change in the Pearl River Delta (PRD) region, so it is important to design policies of emission reduction based on quantitative Co-benefits for air pollutants and greenhouse gas (GHG). Emissions of air pollutants and GHG by 2020 was predicted firstly based on the no-control scenario, and five vehicle emissions reduction scenarios were designed in view of the economy, technology and policy, whose emissions reduction were calculated. Then Co-benefits between air pollutants and GHG were quantitatively analyzed by the methods of coordinate system and cross-elasticity. Results show that the emissions reduction effects and the Co-benefits of different measures vary greatly in 2015-2020. If no control scheme was applied, most air pollutants and GHG would increase substantially by 20-64% by 2020, with the exception of CO, VOC and PM2.5. Different control measures had different reduction effects for single air pollutant and GHG. The worst reduction measure was Eliminating Motorcycles with average reducing rate 0.09% for air pollutants and GHG, while the rate from Updated Emission Standard was 41.74%. Eliminating Yellow-label Vehicle scenario had an obvious reduction effect for every single pollutant in the earlier years, but Co-benefits would descent to zero in later by 2020. From the perspective of emission reductions and co-control effect, Updated Emission Standard scenario was best for reducing air pollutants and GHG substantially (tanα=1.43 and Els=1.77).

Model checking optimal finite-horizon control for probabilistic gene regulatory networks.

BACKGROUND: Probabilistic Boolean networks (PBNs) have been proposed for analyzing external control in gene regulatory networks with incorporation of uncertainty. A context-sensitive PBN with perturbation (CS-PBNp), extending a PBN with context-sensitivity to reflect the inherent biological stability and random perturbations to express the impact of external stimuli, is considered to be more suitable for modeling small biological systems intervened by conditions from the outside. In this paper, we apply probabilistic model checking, a formal verification technique, to optimal control for a CS-PBNp that minimizes the expected cost over a finite control horizon. RESULTS: We first describe a procedure of modeling a CS-PBNp using the language provided by a widely used probabilistic model checker PRISM. We then analyze the reward-based temporal properties and the computation in probabilistic model checking; based on the analysis, we provide a method to formulate the optimal control problem as minimum reachability reward properties. Furthermore, we incorporate control and state cost information into the PRISM code of a CS-PBNp such that automated model checking a minimum reachability reward property on the code gives the solution to the optimal control problem. We conduct experiments on two examples, an apoptosis network and a WNT5A network. Preliminary experiment results show the feasibility and effectiveness of our approach. CONCLUSIONS: The approach based on probabilistic model checking for optimal control avoids explicit computation of large-size state transition relations associated with PBNs. It enables a natural depiction of the dynamics of gene regulatory networks, and provides a canonical form to formulate optimal control problems using temporal properties that can be automated solved by leveraging the analysis power of underlying model checking engines. This work will be helpful for further utilization of the advances in formal verification techniques in system biology.

Analysis of particle-wall interactions during particle free fall.

In this study, the vertical motion of a particle in a quiescent fluid falling toward a horizontal plane wall is analyzed, based on simplified models. Using the distance between the particle and wall as a parameter, the effects of various forces acting on the particle and the particle motion are examined. Without the colloidal and Brownian forces being included, the velocity of small particles is found to be approximately equal to the inverse of the drag force correction function used in this study as the particle approaches the near-wall region. Colloidal force is added to the particle equation of motion as the particle moves a distance comparable to its size. It is found that the particle might become suspended above or deposited onto the wall, depending on the Hamaker constant, the surface potentials of the particle and wall, and the thickness of the electrical double layer (EDL). For strong EDL repulsive force and weaker van der Waals (VDW) attractive force, the particle will become suspended above the wall at a distance at which the particle velocity is zero. This location is referred to as the equilibrium distance. The equilibrium distance is found to increase with increased in EDL thickness when a repulsive force barrier appears in the colloidal force interaction. For the weak EDL repulsive force and strong VDW attractive force case, the particle can become deposited onto the wall without the Brownian motion effect. The Brownian jump length was found to be very small. Many Brownian jumps would be required in a direction toward the wall for a suspended particle to become deposited.