Who shares health and medical scholarly articles on Facebook?

Over a million journal articles had been shared on public Facebook pages by 2017, but little is known about who is sharing (posting links to) these papers and whether mention counts could be an impact indicator. This study classified users who had posted about 749 links on Facebook before October 2017 mentioning 500 medical and health‐related research articles, obtained using altmetric.com data. Most accounts (68%) belonged to groups, including online communities, journals, academic organizations, and societies. Of individual profiles, academics accounted for only 4%, but the largest group were health care professionals (16%). More than half (58%) of all Facebook accounts examined were not academic. The non‐academic dominance suggests that public Facebook posts linking to health‐related articles are mostly used to facilitate scientific knowledge flow between non‐academic professionals and the public. Therefore, Facebook mention counts may be a combined academic and non‐academic attention indicator in the health and medical domains.     

Mapping intrinsic electromechanical responses at the nanoscale via sequential excitation scanning probe microscopy empowered by deep data

Ever-increasing hardware capabilities and computation powers have enabled acquisition and analysis of big scientific data at the nanoscale routine, though much of the data acquired often turn out to be redundant, noisy and/or irrelevant to the problems of interest, and it remains nontrivial to draw clear mechanistic insights from pure data analytics. In this work, we use scanning probe microscopy (SPM) as an example to demonstrate deep data methodology for nanosciences, transitioning from brute-force analytics such as data mining, correlation analysis and unsupervised classification to informed and/or targeted causative data analytics built on sound physical understanding. Three key ingredients of such deep data analytics are presented. A sequential excitation scanning probe microscopy (SE-SPM) technique is first developed to acquire high-quality, efficient and physically relevant data, which can be easily implemented on any standard atomic force microscope (AFM). Brute-force physical analysis is then carried out using a simple harmonic oscillator (SHO) model, enabling us to derive intrinsic electromechanical coupling of interest. Finally, principal component analysis (PCA) is carried out, which not only speeds up the analysis by four orders of magnitude, but also allows a clear physical interpretation of its modes in combination with SHO analysis. A rough piezoelectric material has been probed using such a strategy, enabling us to map its intrinsic electromechanical properties at the nanoscale with high fidelity, where conventional methods fail. The SE in combination with deep data methodology can be easily adapted for other SPM techniques to probe a wide range of functional phenomena at the nanoscale.     

Robust adaptive finite-time stabilization control for a class of nonlinear switched systems based on finite-time disturbance observer

The problem of adaptive global finite-time stabilization control for a class of nonlinear switched systems in the presence of external perturbations and arbitrary switchings has been addressed in this research study. The proposed scheme has been designed based on a finite-time estimation technique in which during the control procedure, unknown imposed perturbations are accurately estimated by means of the designed finite-time disturbance observer (FTDO). Due to the exact estimation of the external disturbances within a given finite time, the encountered complications and adversities from loss of information in the Lyapunov parameter estimation (LPE) methods have been solved which are caused by the persistent switchings in the system. Furthermore, a new solution for the problem of chattering phenomenon in nonlinear switched systems has been presented by utilizing the designed FTDO, which can counteract the malfunctioning responses of the system caused by external disturbances and unmodeled dynamics. In this paper, an acknowledged class of nonlinear switched systems has been taken into account which is in the general form of canonical structure. In addition, the established design strategy is formulated for the control of perturbed nonlinear switched systems with one and only input and assures that the system states through the finite-time convergence characteristic, reach the equilibrium point of origin. Finally, numerical simulations are carried out on a mass-spring-damper (MSD) dynamical system to indicate advantages and superior efficiency of the suggested method.     

Role of Heme Oxygenase, Leptin, Coenzyme Q10 and Trace Elements in Pre-eclamptic Women

The objective of this study to evaluate heme oxygenase (COHb), leptin and coenzyme Q10 (CoQ10) in pre-eclamptic women. Also Zinc, copper, Iron, total iron binding capacity, Ferritin and uric acid were assessed. 120 female subjects were included in this study. They were divided into, 60 female with normal pregnancy attending the outpatient clinic, 60 pre-eclamptic patients were recruited from obstetrics and gynaecology department El-kasr El-Aini hospital. The results showed that in pre-eclampatic group, leptin level was significantly increased while COHb and CoQ10 was significantly decreased. It is concluded that hemeoxygenase, leptin and coenzyme CoQ10 can be considered as new markers for prediction of pre-eclampsia.     

Bearing capacity of thin-walled shallow foundations: an experimental and artificial intelligence-based study

Thin-walled spread foundations are used in coastal projects where the soil strength is relatively low. Developing a predictive model of bearing capacity for this kind of foundation is of interest due to the fact that the famous bearing capacity equations are proposed for conventional footings. Many studies underlined the applicability of artificial neural networks (ANNs) in predicting the bearing capacity of foundations. However, the majority of these models are built using conventional ANNs, which suffer from slow rate of learning as well as getting trapped in local minima. Moreover, they are mainly developed for conventional footings. The prime objective of this study is to propose an improved ANN-based predictive model of bearing capacity for thin-walled shallow foundations. In this regard, a relatively large dataset comprising 145 recorded cases of related footing load tests was compiled from the literature. The dataset includes bearing capacity (Q_u), friction angle, unit weight of sand, footing width, and thin-wall length to footing width ratio (L_w/B). Apart from Q_u, other parameters were set as model inputs. To enhance the diversity of the data, four more related laboratory footing load tests were conducted on the Johor Bahru sand, and results were added to the dataset. Experimental findings suggest an almost 0.5 times increase in the bearing capacity in loose and dense sands when L_w/B is increased from 0.5 to 1.12. Overall, findings show the feasibility of the ANN-based predictive model improved with particle swarm optimization (PSO). The correlation coefficient was 0.98 for testing data, suggesting that the model serves as a reliable tool in predicting the bearing capacity.     

A new heuristic for task scheduling in heterogeneous computing environment

Heterogeneous computing （HC） environment utilizes diverse resources with different computational capabilities to solve computing-intensive applications having diverse computational requirements and constraints. The task assignment problem in HC environment can be formally defined as for a given set of tasks and machines, assigning tasks to machines to achieve the minimum makespan. In this paper we propose a new task scheduling heuristic, high standard deviation first （HSTDF）, which considers the standard deviation of the expected execution time of a task as a selection criterion. Standard deviation of the expected execution time of a task represents the amount of variation in task execution time on different machines. Our conclusion is that tasks having high standard deviation must be assigned first for scheduling. A large number of experiments were carried out to check the effectiveness of the proposed heuristic in different scenarios, and the comparison with the existing heuristics （Max-min, Sufferage, Segmented Min-average, Segmented Min-min, and Segmented Max-min） clearly reveals that the proposed heuristic outperforms all existing heuristics in terms of average makespan.