Using PLS-SEM technique to model construction organizations' willingness to participate in e-bidding

The push for the use of e-bidding is on the increase. In many countries, e-bidding has become part of the public procurement reform. However, previous studies have reported the low uptake of e-bidding in the construction industry. This study develops a theoretical structural model representing the impact six latent variables on the willingness of construction organizations to participate in e-bidding. Data from a questionnaire survey of 64 respondents from clients, contractors, consultants, and suppliers organizations was used to analyze the model. The result of the data analysis suggests that perceived barriers, cost, the perceived benefits of e-bidding and security concerns are the factors influencing willingness participate in e-bidding with perceived barriers being the most significant influencing factor. The perception about the adequacy of the existing traditional paper-based tendering and the perception about the non-readiness of business partners are the underlying barriers that need to be addressed when introducing e-biding. This study also discovers that when the respondents concern about the cost of e-bidding is high, their reported willingness to participate in e-bidding is higher when the perceived benefits is high that when the perceived benefits is low. To enhance the participation of the industry in e-bidding, public sector and large private clients would need to take leadership by making bidder's capability for e-bidding and e-project management at all project phases a criteria in bid evaluation. E-bidding developers and promoters need to use test project cases to monitor, measure, and document the benefits of e-bidding. To disseminate the perceived benefits, promoters would need to work closely with industry professional associations to develop promotional programmes linked to continuous professional development (CPD) points. The analytical approach used in this study should assist other researchers who might wish to address a similar research problem under a similar data and model constraints.     

Robust and efficient memory management in Apache AsterixDB

Traditional relational database systems handle data by dividing their memory into sections such as a buffer cache and working memory, assigning a memory budget to each section to efficiently manage a limited amount of overall memory. They also assign memory budgets to memory-intensive operators such as sorts and joins and control the allocation of memory to these operators; each memory-intensive operator attempts to maximize its memory usage to reduce disk I/O cost. Implementing such memory-intensive operators requires a careful design and application of appropriate algorithms that properly utilize memory. Today's Big Data management systems need the ability to handle large amounts of data similarly, as it is unrealistic to assume that truly big data will fit into memory. In this article, we share our memory management experiences in Apache AsterixDB, an open-source Big Data management software platform that scales out horizontally on shared-nothing commodity computing clusters. We describe the implementation of AsterixDB's memory-intensive operators and their designs related to memory management. We also discuss memory management at the global (cluster) level. We conducted an experimental study using several synthetic and real datasets to explore the impact of this work. We believe that future Big Data management system builders can benefit from these experiences.     

MicroRNA-16 Restores Sensitivity to Tyrosine Kinase Inhibitors and Outperforms MEK Inhibitors in KRAS-Mutated Non-Small Cell Lung Cancer

Background: Non-small cell lung cancer (NSCLC) is the leading cause of cancer death worldwide. Chemotherapy, the treatment of choice in non-operable cases, achieves a dismal success rate, raising the need for new therapeutic options. In about 25% of NSCLC, the activating mutations of the KRAS oncogene define a subclass that cannot benefit from tyrosine kinase inhibitors (TKIs). The tumor suppressor miR-16 is downregulated in many human cancers, including NSCLC. The main objectives of this study were to evaluate miR-16 treatment to restore the TKI sensitivity and compare its efficacy to MEK inhibitors in KRAS-mutated NSCLC. Methods: We performed in vitro and in vivo studies to investigate whether miR-16 could be exploited to overcome TKI resistance in KRAS-mutated NSCLC. We had three goals: first, to identify the KRAS downstream effectors targeted by mir-16, second, to study the effects of miR-16 restoration on TKI resistance in KRAS-mutated NSCLC both in vitro and in vivo, and finally, to compare miR-16 and the MEK inhibitor selumetinib in reducing KRAS-mutated NSCLC growth in vitro and in vivo. Results: We demonstrated that miR-16 directly targets the three KRAS downstream effectors MAPK3, MAP2K1, and CRAF in NSCLC, restoring the sensitivity to erlotinib in KRAS-mutated NSCLC both in vitro and in vivo. We also provided evidence that the miR-16–erlotinib regimen is more effective than the selumetinib–erlotinib combination in KRAS-mutated NSCLC. Conclusions: Our findings support the biological preclinical rationale for using miR-16 in combination with erlotinib in the treatment of NSCLC with KRAS-activating mutations.     

A hybrid classifier based on support vector machine and Jaya algorithm for breast cancer classification

Neural Computing and Applications - The experts’ decisions and evaluating the patients’ data are the most significant parts affecting the breast cancer analysis. For early breast cancer...     

A review of pressure transient analysis in reservoirs with natural fractures,vugs and/or caves

A review of the pressure transient analysis of flow in reservoirs having natural fractures,vugs and/or caves is presented to provide an insight into how much kn...