Numerical study on the flexural performance of precast segmental concrete beams with unbonded internal steel tendons

This study presents a numerical investigation of the flexural performance of precast segmental concrete beams (PSBs) with unbonded internal steel tendons. Numerical models developed in this study using Abaqus software capture well the responses of the PSBs reported in previous studies. This is the first time a three-dimensional numerical model is built and successfully validated against experimental results of PSBs in literature. Based on the verified numerical model, intensive simulations of performances of segmental beams with different parameters and various conditions, i.e. tension-controlled, compression-controlled and balanced sections, are carried out. Based on the numerical results, the flexural behaviour of PSBs under four-point loading is extensively discussed regarding the failure modes, joint opening, stress increment in the tendon and the stress transfer mechanism. A parametric study is also conducted and the results show that the effective prestress, prestressing steel reinforcement ratio, and span length-to-tendon depth ratio strongly affect the load-carrying capacity, ductility, tendon stress increment, joint opening and failure modes of PSBs with unbonded tendons, while the loading type, concrete strength and the number of joints show insignificant effects on the flexural performance of the structure

Electrochemical copper recovery from galvanic sludge

The feasibility of recovering copper from electronic industrial waste mud (galvanic sludge) using a combination of leaching and electrowinning has been examined. Leaching with sulfuric acid was found to be the most efficient and cost-effective way of extracting the copper from the sludge, and the optimum acid concentration and time were determined. The copper was then extracted by electrowinning in a batch recirculation electrochemical reactor (Porocell™) employing a three-dimensional carbon felt cathode. The influence of applied current, flow rate and the presence of other metal ion contaminates on the rate and current efficiency of copper electrowinning was investigated. An analysis of the experimental data showed that the current efficiency was lower than unity even though the limiting current for copper deposition was not exceeded. This low current efficiency was attributed to the occurrence of a side reaction, most likely the reduction of dissolved oxygen or oxygen-induced corrosion. The influence of this side reaction can be minimized by operating at relatively high currents and low flow rates

New method for edges detection of magnetic sources using logistic function

The tilt angle of the analytic signal amplitude (TA) is defined as the arctangent of the ratio of the first vertical derivative to the total horizontal derivative of the analytic signal amplitude. It is commonly used as a useful tool to estimate edges of magnetic sources because its value is slightly dependence on the direction of magnetization vector, and it is more effective in estimating the edges of the bodies than the analytic signal amplitude and the standard tilt angle. Based on logistic function (L) that has the same shape with the shape of arctangent function, and the derivatives of the analytic signal amplitude, we introduce some new filters which also can reduce the effect of the magnetization direction.Угол наклона амплитуды аналитического сигнала (TA) определяют как арктангенс отношения первой производной вертикального градиента к суммарной горизонтальной производной амплитуды аналитического сигнала. Определение этого угла обычно используют как полезный метод для оценки граней магнитных источников, поскольку его величина незначительно зависит от направления намагниченности. По аналитической функцией (L), что имеет одинаковую форму с формой функции арктангенс, введены некоторые новые фильтры, которые также могут уменьшить эффект направления намагниченности.Кут нахилу амплітуди аналітичного сигналу (TA) визначають як арктангенс відношення першої похідної вертикального градієнта до сумарної горизонтальної похідної амплітуди аналітичного сигналу. Визначення цього кута зазвичай використовують як корисний метод для оцінювання граней магнітних джерел, оскільки його величина незначно залежить від напрямку намагніченості. За аналітичною функцією (L), що має однакову форму з формою функції арктангенсу, введено деякі нові фільтри, які також можуть зменшити ефект напрямку намагніченості

Factors influencing farmers' forestland-use changes over 15 years (2005–2020) in Thua Thien Hue province, Vietnam

Over the last decades, Vietnam has seen substantial shifts in forest landscape uses and associated livelihoods. We document the livelihood transformations in Nam Dong, a mountainous district of Central Vietnam, where land uses have changed from the utilisation of products from natural forests and shifting cultivation (swidden agriculture) to acacia tree-dominated plantation forestry. Forestry policies (forestland allocation, plantation development agendas), the increase in the economic value of acacia, and household livelihood assets are the primary factors driving these changes. We also found that there are differences in the access to and ownership of forestland with regard to households of different communities and between poor vs wealthy households. Therefore, careful attention needs to be paid to guide future land use policies in the area to foster social and ecological sustainability. HIGHLIGHTS • Major livelihood and forestland-use changes have taken place in central Vietnam over the last two decades. • There has been widespread conversion of forestland (degraded natural forests, swidden land) and cropland to acacia plantations. • Household-scale forestland use changes were primarily driven by forestry policies, the market for woodchips, and land resource access. • There is inequality in access to and ownership of forestland between poor and wealthier households in the mountain district of Vietnam. • Cases of illegal forestland conversions pose challenges to ensuring sustainable forest landscapes

Computational methods for cancer driver discovery: A survey

Identifying the genes responsible for driving cancer is of critical importance for directing treatment. Accordingly, multiple computational tools have been developed to facilitate this task. Due to the different methods employed by these tools, different data considered by the tools, and the rapidly evolving nature of the field, the selection of an appropriate tool for cancer driver discovery is not straightforward. This survey seeks to provide a comprehensive review of the different computational methods for discovering cancer drivers. We categorise the methods into three groups; methods for single driver identification, methods for driver module identification, and methods for identifying personalised cancer drivers. In addition to providing a “one-stop” reference of these methods, by evaluating and comparing their performance, we also provide readers the information about the different capabilities of the methods in identifying biologically significant cancer drivers. The biologically relevant information identified by these tools can be seen through the enrichment of discovered cancer drivers in GO biological processes and KEGG pathways and through our identification of a small cancer-driver cohort that is capable of stratifying patient survivalities and quality of life in Australian men and women with diagnosed and undiagnosed high-risk obstructive sleep apnea.Vu Viet Hoang Pham, Lin Liu, Cameron Bracken, Gregory Goodall, Jiuyong Li, Thuc Duy L

CBNA: a control theory based method for identifying coding and non-coding cancer drivers

A key task in cancer genomics research is to identify cancer driver genes. As these genes initialise and progress cancer, understanding them is critical in designing effective cancer interventions. Although there are several methods developed to discover cancer drivers, most of them only identify coding drivers. However, non-coding RNAs can regulate driver mutations to develop cancer. Hence, novel methods are required to reveal both coding and non-coding cancer drivers. In this paper, we develop a novel framework named Controllability based Biological Network Analysis (CBNA) to uncover coding and non-coding cancer drivers (i.e. miRNA cancer drivers). CBNA integrates different genomic data types, including gene expression, gene network, mutation data, and contains a two-stage process: (1) Building a network for a condition (e.g. cancer condition) and (2) Identifying drivers. The application of CBNA to the BRCA dataset demonstrates that it is more effective than the existing methods in detecting coding cancer drivers. In addition, CBNA also predicts 17 miRNA drivers for breast cancer. Some of these predicted miRNA drivers have been validated by literature and the rest can be good candidates for wet-lab validation. We further use CBNA to detect subtype-specific cancer drivers and several predicted drivers have been confirmed to be related to breast cancer subtypes. Another application of CBNA is to discover epithelial-mesenchymal transition (EMT) drivers. Of the predicted EMT drivers, 7 coding and 6 miRNA drivers are in the known EMT gene lists.Vu V. H. Pham, Lin Liu, Cameron P. Bracken, Gregory J. Goodall, Qi Long, Jiuyong Li, Thuc D. L

Fast automated cell phenotype image classification

BACKGROUND: The genomic revolution has led to rapid growth in sequencing of genes and proteins, and attention is now turning to the function of the encoded proteins. In this respect, microscope imaging of a protein's sub-cellular localisation is proving invaluable, and recent advances in automated fluorescent microscopy allow protein localisations to be imaged in high throughput. Hence there is a need for large scale automated computational techniques to efficiently quantify, distinguish and classify sub-cellular images. While image statistics have proved highly successful in distinguishing localisation, commonly used measures suffer from being relatively slow to compute, and often require cells to be individually selected from experimental images, thus limiting both throughput and the range of potential applications. Here we introduce threshold adjacency statistics, the essence which is to threshold the image and to count the number of above threshold pixels with a given number of above threshold pixels adjacent. These novel measures are shown to distinguish and classify images of distinct sub-cellular localization with high speed and accuracy without image cropping. RESULTS: Threshold adjacency statistics are applied to classification of protein sub-cellular localization images. They are tested on two image sets (available for download), one for which fluorescently tagged proteins are endogenously expressed in 10 sub-cellular locations, and another for which proteins are transfected into 11 locations. For each image set, a support vector machine was trained and tested. Classification accuracies of 94.4% and 86.6% are obtained on the endogenous and transfected sets, respectively. Threshold adjacency statistics are found to provide comparable or higher accuracy than other commonly used statistics while being an order of magnitude faster to calculate. Further, threshold adjacency statistics in combination with Haralick measures give accuracies of 98.2% and 93.2% on the endogenous and transfected sets, respectively. CONCLUSION: Threshold adjacency statistics have the potential to greatly extend the scale and range of applications of image statistics in computational image analysis. They remove the need for cropping of individual cells from images, and are an order of magnitude faster to calculate than other commonly used statistics while providing comparable or better classification accuracy, both essential requirements for application to large-scale approaches