EXPERIMENTAL ANALYSIS FOR STEEL CONGESTION RELIEF IN CONCRETE STRUCTURES UNDER MONOTONIC AND SEISMIC LOADS

Since the beginning of this PhD research, the author has endeavored to determine how to resolve the issues involving steel congestion in reinforced concrete (RC) structures. Three potential solutions to this problem were researched in detail. In the first method, reinforced concrete (RC) was mixed with steel fibers. The use of steel fibers instead of shear reinforcement stirrups resulted in the reduction of steel congestion in a manner which was both effective in reducing the effects of congestion and which was practical to implement. In the second method, steel congestion in reinforced concrete (RC) was effectively reduced by the use of self-consolidating concrete (SCC), which does not require the use of vibrators in its casting. In the final method, steel congestion was effectively reduced by the use of headed bars instead of traditional hooked bars. This first and third approach is emerging as a research topic of special interest in the American Concrete Institute (ACI).In evaluating these three approaches, and in combining them in this study, varied types of concrete were used. Shear testing was conducted using a lightweight concrete mix. Flexural testing of lightweight prestressed concrete (PC) beams was conducted using self-consolidating concrete (SCC). Seismic testing of headed bars in RC beam-column connections was conducted using a normalweight concrete mix. These three experiments were the subject matter of this study. In these studies the experimental results were compared with the ACI 318-08 provisions and with existing modeling equations proposed by many researchers. New models were proposed which better correlated with the test results were proposed.Therefore, although other studies in the world may have dealt with the relief of steel congestion in RC and/or PC structures, in researching these three unique methods for the relief of steel congestion it was discovered that several variations and combinations of such methods can provide effective solutions for diverse conditions. Most of all, this study should prove important in providing the basis for additional research since the guidelines and codes regarding the relief of steel congestion are shown to be based upon previously limited data

Estimation of the Parameters of Burr Type III Distribution Based on Dual Generalized Order Statistics

The estimation of the parameters of Burr type III distribution based on dual generalized order statistics is considered by using the maximum likelihood (ML) approach as well as the Bayesian approach. The exact expression of the expected Fisher information matrix of the parameters in the distribution is obtained. Also, an approximation based on Lindley is used to obtain the Bayes estimator. To compare the maximum likelihood estimator and the Bayes estimator of the parameters, Monte Carlo simulation study is performed

Development of a Remote Testing System for Performance of Gas Leakage Detectors

In this research, we designed a remote system to test parameters of gas detectors such as gas concentration and initial response time. This testing system is available to measure two gas instruments simultaneously. First of all, we assembled an experimental jig with a square structure. Those parts are included with a glass flask, two high-quality cameras, and two Ethernet modems for transmitting data. This remote gas detector testing system extracts numerals from videos with continually various gas concentrations while LCDs show photographs from cameras. Extracted numeral data are received to a laptop computer through Ethernet modem. And then, the numerical data with gas concentrations and the measured initial response speeds are recorded and graphed. Our remote testing system will be diversely applied on gas detector’s test and will be certificated in domestic and international countries

A Fast Post-Training Pruning Framework for Transformers

Pruning is an effective way to reduce the huge inference cost of large Transformer models. However, prior work on model pruning requires retraining the model. This can add high cost and complexity to model deployment, making it difficult to use in many practical situations. To address this, we propose a fast post-training pruning framework for Transformers that does not require any retraining. Given a resource constraint and a sample dataset, our framework automatically prunes the Transformer model using structured sparsity methods. To retain high accuracy without retraining, we introduce three novel techniques: (i) a lightweight mask search algorithm that finds which heads and filters to prune based on the Fisher information; (ii) mask rearrangement that complements the search algorithm; and (iii) mask tuning that reconstructs the output activations for each layer. We apply our method to BERT-BASE and DistilBERT, and we evaluate its effectiveness on GLUE and SQuAD benchmarks. Our framework achieves up to 2.0x reduction in FLOPs and 1.56x speedup in inference latency, while maintaining < 1% loss in accuracy. Importantly, our framework prunes Transformers in less than 3 minutes on a single GPU, which is over two orders of magnitude faster than existing pruning approaches that retrain. Our code is publicly available

HTRgene: a computational method to perform the integrated analysis of multiple heterogeneous time-series data: case analysis of cold and heat stress response signaling genes in Arabidopsis

Background Integrated analysis that uses multiple sample gene expression data measured under the same stress can detect stress response genes more accurately than analysis of individual sample data. However, the integrated analysis is challenging since experimental conditions (strength of stress and the number of time points) are heterogeneous across multiple samples. Results HTRgene is a computational method to perform the integrated analysis of multiple heterogeneous time-series data measured under the same stress condition. The goal of HTRgene is to identify response order preserving DEGs that are defined as genes not only which are differentially expressed but also whose response order is preserved across multiple samples. The utility of HTRgene was demonstrated using 28 and 24 time-series sample gene expression data measured under cold and heat stress in Arabidopsis. HTRgene analysis successfully reproduced known biological mechanisms of cold and heat stress in Arabidopsis. Also, HTRgene showed higher accuracy in detecting the documented stress response genes than existing tools. Conclusions HTRgene, a method to find the ordering of response time of genes that are commonly observed among multiple time-series samples, successfully integrated multiple heterogeneous time-series gene expression datasets. It can be applied to many research problems related to the integration of time series data analysis.This work, including publication costs, was supported by National Research Foundation of Korea(NRF) funded by the Ministry of Science, ICT (No.NRF-2017M3C4A7065887). This work was also supported by the Collaborative Genome Program for Fostering New Post-Genome Industry of the National Research Foundation (NRF) funded by the Ministry of Science and ICT (MSIT) (No. NRF-2014M3C9A3063541), and a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HI15C3224). This work was supported for W.J. by the Agenda program (No.PJ012465032019), Rural Development of dministration of Republic of Korea

Role of intermediate phase for stable cycling of Na_7V_4(P_2O_7)_4PO_4 in sodium ion battery

Sodium ion batteries offer promising opportunities in emerging utility grid applications because of the low cost of raw materials, yet low energy density and limited cycle life remain critical drawbacks in their electrochemical operations. Herein, we report a vanadium-based ortho-diphosphate, Na_7V_4(P_2O_7)_4PO_4, or VODP, that significantly reduces all these drawbacks. Indeed, VODP exhibits single-valued voltage plateaus at 3.88 V vs. Na/Na+ while retaining substantial capacity (>78%) over 1,000 cycles. Electronic structure calculations reveal that the remarkable single plateau and cycle life originate from an intermediate phase (a very shallow voltage step) that is similar both in the energy level and lattice parameters to those of fully intercalated and deintercalated states. We propose a theoretical scheme in which the reaction barrier that arises from lattice mismatches can be evaluated by using a simple energetic consideration, suggesting that the presence of intermediate phases is beneficial for cell kinetics by buffering the differences in lattice parameters between initial and final phases. We expect these insights into the role of intermediate phases found for VODP hold in general and thus provide a helpful guideline in the further understanding and design of battery materials

Surgical treatment and long-term outcomes of low-grade myofibroblastic sarcoma: a single-center case series of 15 patients

Abstract Background Low-grade myofibroblastic sarcoma (LGMS) is a poorly studied, rare, soft tissue sarcoma. LGMS is characterized by a low malignancy potential, tendency for local recurrence, and low likelihood of distant metastases. However, no studies have reported on the surgical treatment method and its long-term outcomes. Methods We included all patients treated for LGMS at our institution between March 2010 and March 2021. Medical charts were retrospectively reviewed to collect demographic information, as well as information about the clinical course, tumor characteristics, and outcomes. Statistical analysis was performed to identify the factors associated with the recurrence rate. Results Fifteen patients who underwent surgical treatment were enrolled in this study. There were seven cases in the upper extremities, four in the trunk area, three in the lower extremities, and one in the head and neck area. There were no metastatic cases and two cases of local recurrence. Conclusions The incidence of LGMS in the extremities or trunk may be higher than expected based on the current literature. Univariate analysis showed that local tissue invasion and surgical method could be associated with local recurrence. Although further large studies are needed to establish risk factors of local recurrence or extent of resection margins, based on our study, wide local excision under the proper diagnosis is the most important treatment

Privacy-Preserving Machine Learning with Fully Homomorphic Encryption for Deep Neural Network

Fully homomorphic encryption (FHE) is one of the prospective tools for privacypreserving machine learning (PPML), and several PPML models have been proposed based on various FHE schemes and approaches. Although the FHE schemes are known as suitable tools to implement PPML models, previous PPML models on FHE encrypted data are limited to only simple and non-standard types of machine learning models. These non-standard machine learning models are not proven efficient and accurate with more practical and advanced datasets. Previous PPML schemes replace non-arithmetic activation functions with simple arithmetic functions instead of adopting approximation methods and do not use bootstrapping, which enables continuous homomorphic evaluations. Thus, they could not use standard activation functions and could not employ a large number of layers. The maximum classification accuracy of the existing PPML model with the FHE for the CIFAR-10 dataset was only 77% until now. In this work, we firstly implement the standard ResNet-20 model with the RNS-CKKS FHE with bootstrapping and verify the implemented model with the CIFAR-10 dataset and the plaintext model parameters. Instead of replacing the non-arithmetic functions with the simple arithmetic function, we use state-of-the-art approximation methods to evaluate these non-arithmetic functions, such as the ReLU, with sufficient precision [1]. Further, for the first time, we use the bootstrapping technique of the RNS-CKKS scheme in the proposed model, which enables us to evaluate a deep learning model on the encrypted data. We numerically verify that the proposed model with the CIFAR-10 dataset shows 98.67% identical results to the original ResNet-20 model with non-encrypted data. The classification accuracy of the proposed model is 90.67%, which is pretty close to that of the original ResNet-20 CNN model...Comment: 12 pages, 4 figure

ATR inhibition facilitates targeting of leukemia dependence on convergent nucleotide biosynthetic pathways.

Leukemia cells rely on two nucleotide biosynthetic pathways, de novo and salvage, to produce dNTPs for DNA replication. Here, using metabolomic, proteomic, and phosphoproteomic approaches, we show that inhibition of the replication stress sensing kinase ataxia telangiectasia and Rad3-related protein (ATR) reduces the output of both de novo and salvage pathways by regulating the activity of their respective rate-limiting enzymes, ribonucleotide reductase (RNR) and deoxycytidine kinase (dCK), via distinct molecular mechanisms. Quantification of nucleotide biosynthesis in ATR-inhibited acute lymphoblastic leukemia (ALL) cells reveals substantial remaining de novo and salvage activities, and could not eliminate the disease in vivo. However, targeting these remaining activities with RNR and dCK inhibitors triggers lethal replication stress in vitro and long-term disease-free survival in mice with B-ALL, without detectable toxicity. Thus the functional interplay between alternative nucleotide biosynthetic routes and ATR provides therapeutic opportunities in leukemia and potentially other cancers.Leukemic cells depend on the nucleotide synthesis pathway to proliferate. Here the authors use metabolomics and proteomics to show that inhibition of ATR reduced the activity of these pathways thus providing a valuable therapeutic target in leukemia

StressGenePred: a twin prediction model architecture for classifying the stress types of samples and discovering stress-related genes in arabidopsis

Background Recently, a number of studies have been conducted to investigate how plants respond to stress at the cellular molecular level by measuring gene expression profiles over time. As a result, a set of time-series gene expression data for the stress response are available in databases. With the data, an integrated analysis of multiple stresses is possible, which identifies stress-responsive genes with higher specificity because considering multiple stress can capture the effect of interference between stresses. To analyze such data, a machine learning model needs to be built. Results In this study, we developed StressGenePred, a neural network-based machine learning method, to integrate time-series transcriptome data of multiple stress types. StressGenePred is designed to detect single stress-specific biomarker genes by using a simple feature embedding method, a twin neural network model, and Confident Multiple Choice Learning (CMCL) loss. The twin neural network model consists of a biomarker gene discovery and a stress type prediction model that share the same logical layer to reduce training complexity. The CMCL loss is used to make the twin model select biomarker genes that respond specifically to a single stress. In experiments using Arabidopsis gene expression data for four major environmental stresses, such as heat, cold, salt, and drought, StressGenePred classified the types of stress more accurately than the limma feature embedding method and the support vector machine and random forest classification methods. In addition, StressGenePred discovered known stress-related genes with higher specificity than the Fisher method. Conclusions StressGenePred is a machine learning method for identifying stress-related genes and predicting stress types for an integrated analysis of multiple stress time-series transcriptome data. This method can be used to other phenotype-gene associated studies.This work and publication costs were supported by National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT (No. NRF2017M3C4A7065887), and the Collaborative Genome Program for Fostering New Post-Genome Industry of the National Research Foundation (NRF) funded by the Ministry of Science and ICT (MSIT) (No. NRF-2014M3C9A3063541). This work was supported for W.J. by the Agenda program (No. PJ014307), Rural Development of Administration of Republic of Korea