Review of Pedestrian Load Models for Vibration Serviceability Assessment of Floor Structures

This is the final version. Available on open access from MDPI via the DOI in this recordInnovative design and technological advancements in the construction industry have resulted in an increased use of large, slender and lightweight floors in contemporary office buildings. Compounded by an ever-increasing use of open-plan layouts with few internal partitions and thus lower damping, floor vibration is becoming a governing limit state in the modern structural design originating from dynamic footfall excitations. This could cause annoyance and discomfort to building occupants as well as knock-on management and financial consequences for facility owners. This article presents a comprehensive review pertinent to walking-induced dynamic loading of low-frequency floor structures. It is intended to introduce and explain key walking parameters in the field as well as summarise the development of previous walking models and methods for vibration serviceability assessment. Although a number of walking models and design procedures have been proposed, the literature survey highlights that further work is required in the following areas; (1) the development of a probabilistic multi-person loading model which accounts for inter- and intra-subject variabilities, (2) the identification of walking paths (routes accounting for the effect of occupancy patterns on office floors) coupled with spatial distribution of pedestrians and (3) the production of a statistical spatial response approach for vibration serviceability assessment. A stochastic approach, capable of taking into account uncertainties in loading model and vibration responses, appears to be a more reliable way forward compared to the deterministic approaches of the past and there is a clear need for further research in this areaEngineering and Physical Sciences Research Council (EPSRC)Qatar National Research Foundatio

Sustainable use of winter Durum wheat landraces under Mediterranean conditions

This research expected to determine new durum wheat germplasm resistant to biotic and abiotic stress factors. Eighty durum wheat lines selected from eighteen diverse landraces were tested together with eight durum wheat cultivars under reliable yellow rust epidemic during two successive years. Average infection coefficient of populations and cultivars was 32.44 in 2003 and 26.24 in 2004, showing severe epidemic condition which occurred at adult plant stage in 2003. Because of this the number of selected resistant and moderately resistant plant material greatly reduced. According to the yield trial study in which twenty resistant lines selected out of 30 resistant and moderately from sixteen populations were included, only two checks outperformed grand mean (2.48 t ha -1) and two lines selected from landrace population followed these with slightly lower yield difference. On the other hand, there were several lines which performed better than the grand mean of protein content (13.24%), SDS sedimentation (28.40 ml) and semolina color (24.35) and they ranked in the first group including the two checks cultivars. Bi- plot analysis showed that some promising lines with reasonable grain yields, good quality parameters, winter hardiness and drought tolerances among yellow rust resistance durum wheat landraces can be selected for semiarid conditions of Mediterranean countries for sustainable production

Existence of Solutions for a Class of Elliptic Systems in Involving the -Laplacian

In view of variational approach, we discuss a nonlinear elliptic system involving the p(x)-Laplacian. Establishing the suitable conditions on the nonlinearity, we proved the existence of nontrivial solutions

A PCE-based rheology modifier allows machining of solid cast green bodies of alumina

The performance of a poly(carboxylate ether) (PCE)-based superplasticizer to enable the machining of green bodies that are solid cast from suspensions of alumina was investigated. An alumina loading of 35 vol% in the presence of 1.25 wt% superplasticizer was established to be suitable for lathing and removal of significant amount of material through drilling. A reduction of 77% in the diameter of green bodies that corresponds to a 59% reduction in volume was achieved. The lathed green bodies exhibited smooth terraces without visible cracks. All of the green bodies were sintered without a polymer burnout step

Zoledronic acid induces apoptosis via stimulating the expressions of ERN1, TLR2, and IRF5 genes in glioma cells

Glioblastoma multiforme (GBM) is the most common and aggressive brain tumor that affects older people. Although the current therapeutic approaches for GBM include surgical resection, radiotherapy, and chemotherapeutic agent temozolomide, the median survival of patients is 14.6 months because of its aggressiveness. Zoledronic acid (ZA) is a nitrogen-containing bisphosphonate that exhibited anticancer activity in different cancers. The purpose of this study was to assess the potential effect of ZA in distinct signal transduction pathways in U87-MG cells. In this study, experiments performed on U87-MG cell line (Human glioblastoma-astrocytoma, epithelial-like cell line) which is an in vitro model of human glioblastoma cells to examine the cytotoxic and apoptotic effects of ZA. IC50 dose of ZA, 25 μM, applied on U87-MG cells during 72 h. ApoDIRECT In Situ DNA Fragmentation Assay was used to investigate apoptosis of U87MG cells. The quantitative reverse transcription polymerase chain reaction (qRT-PCR) (LightCycler480 System) was carried out for 48 gene expression like NF-κB, Toll-like receptors, cytokines, and inteferons. Our results indicated that ZA (IC50 dose) increased apoptosis 1.27-fold in U87MG cells according to control cells. According to qRT-PCR data, expression levels of the endoplasmic reticulum-nuclei-1 (ERN1), Toll-like receptor 2 (TLR2), and human IFN regulatory factor 5 (IRF5) tumor suppressor genes elevated 2.05-, 2.08-, and 2.3-fold by ZA, respectively, in U87MG cells. Our recent results indicated that ZA have a key role in GBM progression and might be considered as a potential agent in glioma treatment. © 2015, International Society of Oncology and BioMarkers (ISOBM)

Machine learning based prediction of squamous cell carcinoma in ex vivo confocal laser scanning microscopy

Image classification with convolutional neural networks (CNN) offers an unprecedented opportunity to medical imaging. Regulatory agencies in the USA and Europe have already cleared numerous deep learning/machine learning based medical devices and algorithms. While the field of radiology is on the forefront of artificial intelligence (AI) revolution, conventional pathology, which commonly relies on examination of tissue samples on a glass slide, is falling behind in leveraging this technology. On the other hand, ex vivo confocal laser scanning microscopy (ex vivo CLSM), owing to its digital workflow features, has a high potential to benefit from integrating AI tools into the assessment and decision-making process. Aim of this work was to explore a preliminary application of CNN in digitally stained ex vivo CLSM images of cutaneous squamous cell carcinoma (cSCC) for automated detection of tumor tissue. Thirty-four freshly excised tissue samples were prospectively collected and examined immediately after resection. After the histologically confirmed ex vivo CLSM diagnosis, the tumor tissue was annotated for segmentation by experts, in order to train the MobileNet CNN. The model was then trained and evaluated using cross validation. The overall sensitivity and specificity of the deep neural network for detecting cSCC and tumor free areas on ex vivo CLSM slides compared to expert evaluation were 0.76 and 0.91, respectively. The area under the ROC curve was equal to 0.90 and the area under the precision-recall curve was 0.85. The results demonstrate a high potential of deep learning models to detect cSCC regions on digitally stained ex vivo CLSM slides and to distinguish them from tumor-free skin

Bilkent University at TRECVID 2005

We describe our second-time participation, that includes one high-level feature extraction run, and three manual and one interactive search runs, to the TRECVID video retrieval evaluation. All of these runs have used a system trained on the common development collection. Only visual and textual information were used where visual information consisted of color, texture and edgebased low-level features and textual information consisted of the speech transcript provided in the collection. With the experience gained with our second-time participation, we are in the process of building a system for automatic classification and indexing of video archives