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We present several characteristics for spatio-temporal point patterns when the spatial locations are restricted to a linear network. A nonparametric kernel-based intensity estimator is proposed to highlight the concentration of events within the network and time, either jointly or separately. We also provide second-order characteristics for spatio-temporal point patterns on linear networks such as K-function and pair correlation function to analyze the type of interaction between points. They are independent of network’s geometry and have known values for Poisson point processes. Finally, we consider some applications to traffic accidents and demonstrate our findings by analyzing datasets of Houston (United States), Medellín (Colombia), and Eastbourne (United Kingdom). Supplementary materials for this article are available online.

This investigation was aimed to assess nitrate concentration and its health risk in vegetable and fruit samples collected from Iran. The mean nitrate level in samples ranged from 66.70 to 1072.60 m...

The present study has been conducted to investigate the teacher’s attitudes towards the effects of lesson plan. Furthermore, the second goal of the study is to investigate the effects of lesson plan on classroom management. Similarly, the questionnaire had two parts the first part had 6 items and the second part had 6 items too and applied randomly. Besides, the data analyzing was performed in SPSS (version, 21) and bring out the mean and standard division. Finally, the findings reveled the importance and role of lesson planning on classroom management numbered as follow: (1) Planning is the most appropriate skill that the teacher needs to create a successful one. (2) The effective teacher is the one who plan effective lessons. (3) Preparation is the most important thing a teacher does. In addition, the following factors are related to the effects of lesson plan on classroom management. (1) Planning is a necessary skill to develop an organized learning environment. (2) Good planning minimizes class management problems. (3) A successful learning environment is the result of well lesson plan.

We propose a computationally efficient and statistically principled method for kernel smoothingof point pattern data on a linear network. The point locations, and the network itself, are convolvedwith a two-dimensional kernel and then combined into an intensity function on the network. Thiscan be computed rapidly using the fast Fourier transform, even on large networks and for largebandwidths, and is robust against errors in network geometry. The estimator is consistent, and itsstatistical efficiency is only slightly suboptimal. We discuss bias, variance, asymptotics, bandwidthselection, variance estimation, relative risk estimation and adaptive smoothing. The methods areused to analyse spatially varying frequency of traffic accidents in Western Australia and the relativerisk of different types of traffic accidents in Medellín, Colombia.

Community detection is an NP-hard problem. Therefore, evolutionary-based optimization methods are conventionally applied to cope with the problem. The primary challenge regarding the application of evolutionary-based approaches, specifically to handle large complex networks, is their relatively long execution time. In this respect, this article proposes an extension of a known genetic algorithm, Genetic Algorithm for Community Detection (GACD), for community detection. This new extension is supplied with a novel local search strategy to speed up the convergence and improve the accuracy of the GACD algorithm. To reduce the search space, a locus-based representation of the complex network, in which communities are to be detected, is applied. This type of representation incorporates domain-specific knowledge with the solutions through initialization and reproduction operators. In addition, it does not need to know the number of communities at the beginning of the search process. Our experiments with the real-world and Lancichinetti–Fortunato–Radicchi (LFR) network datasets demonstrate the relatively high capacity of our proposed genetic algorithm in detecting communities with relatively fewer generations and more precision.

Community detection from networks is an emerging topic in modern network science. Communities are defined as clusters of nodes or vertices that share higher concentration of edges among themselves than sharing with other nodes in the network. Community structure is an important property of real systems and detecting communities enables us to better understand the underlying structure of the system. The most widely used method for community detection is modularity maximization which works by optimizing a quality function named modularity of the network partition. However, traditional modularity-based approaches generally have a resolution limit that prevents them from detecting communities that are sufficiently smaller compared to the whole network. In this work, we target to overcome the resolution limit of the modularity function by incorporating a weight term in the modularity formulation. We propose a community detection approach based on a community quality metric, named as weighted modularity. We validate the performance of the proposed method in several benchmark networks and show that the proposed method is promising in different settings.

DOR: 98.1000/2383-1251.1397.5. 139.10.2.1606.1610 Extended abstract Introduction: Using of plant growth regulators is one of the methods can improve plant growth against environmental stresses such as salinity. Salicylic acid plays an important role in physiological processes regulation, including germination. Today, using of growth promoting bacteria has been increased and it causes to raise the seed vigor, uniformity, germination percentage and better seedling establishment. Growth promoting bacteria can be effect on increasing plant resistance to adverse environmental conditions by interposition in plant hormones production such as auxin, GA, cytokinins, and as well as the stabilization of nitrogen or phosphorus availability and other nutrients Materials and Methods: This experiment was conducted as factorial in a completely randomized design with three replications. Salicylic acid factor (SA) was selected at two levels (0 and 1 mM). The bacterial treatments included Azotobacter (AZ), Azospirilum (AZP), complex of Azotobacter and Azospirillum (AZ + AZP), and without inoculation (C) and salinity treatment (S) was at five levels (0, 50, 100, 150 and 200 mM). Results: Results showed that all treatments had no significant effect on germination percentage. Radicle and plumule length, seed vigor index and seedling fresh weight was significantly increased at 50 mM NaCl. Generally speaking, the elongation of plant organs when treated with low concentrations of salts may induce osmotic adjustment activity in the plants which may improve growth. Germination rate, Radicle and plumule length and seed vigor index were significantly increased by salicylic acid treatment. AZ and AZ+AZP increased germination parameters significantly than control. Generally germination factors were better improved by combination salicylic acid with AZ than AZP and AZ+AZP. These results could indicate the synergistic relationship between growth promoting bacteria and salicylic acid. Conclusion: According to the results pre-treatment of melon seeds by 1 mM salicylic acid and Azotobacter can be proposed to improve seed germination and seedling establishment under salinity stress. Highlights: Effect of salinity on seed germination characteristics of melon. Effect of biofertilizer and salicylic acid on germination and seedling growth of melon under salt stress.

Forkhead box P3 (FOXP3) gene (Gene ID: 50943, Xp11.23) is an X-linked gene that encodes FOXP3 protein, an essential transcription factor in CD4+ CD25+ FOXP3 regulatory T (Treg) cells. FOXP3 mutation has been linked with the pathogenesis of several tumours; however, little is known about the role of single-nucleotide polymorphism (SNP) in its promoter region and its correlation with brain tumour. In the present study, we have investigated the association between SNPs in the promoter region of FOXP3 gene, a promoter SNP, -2383 C/T (rs3761549) with susceptibility to brain cancer in a population of Iran. The distribution of case, control, age and sex was balanced and with rs3761549 C/T allele frequencies distribution also falling in Hardy-Weinberg equilibrium (P = 0.053 and 0.062). The allele C of rs3761549 is lower in the brain tumour cases when compared with the controls (364 vs 392, P = 0.005). The frequency of combined T variant genotype (TT + CT) was significantly higher in the brain cancer cases compared with the controls (28 vs 8, P = 0.001), which was consistent with the T allele distribution. When we used the CC genotype as a reference, we found that both CT and TT genotypes were associated with a higher risk of developing brain tumour (odds ratio [OR], 0.3583; 95% confidence interval [CI], 0.164-0.8197 and OR, 0; 95% CI, 0-0.4118, respectively).

The application of deep learning algorithms in medical imaging analysis is a steadily growing research area. While deep learning methods are thriving in the medical domain, they seldom utilize the rich knowledge associated with connected radiology reports. The knowledge derived from these reports can be utilized to enhance the performance of deep learning models. In this work, we used a comprehensive chest X-ray findings vocabulary to automatically annotate an extensive collection of chest X-rays using associated radiology reports and a vocabulary-driven concept annotation algorithm. The annotated X-rays are used to train a deep neural network classifier for finding detection. Finally, we developed a knowledge-driven reasoning algorithm that leverages knowledge learned from X-ray reports to improve upon the deep learning module's performance on finding detection. Our results suggest that combining deep learning and knowledge from radiology reports in a hybrid framework can significantly enhance overall performance in the CXR finding detection.

Rule-based Natural Language Processing (NLP) pipelines depend on robust domain knowledge. Given the long tail of important terminology in radiology reports, it is not uncommon for standard approaches to miss items critical for understanding the image. AI techniques can accelerate the concept expansion and phrasal grouping tasks to efficiently create a domain specific lexicon ontology for structuring reports. Using Chest X-ray (CXR) reports as an example, we demonstrate that with robust vocabulary, even a simple NLP pipeline can extract 83 directly mentioned abnormalities (Ave. recall=93.83%, precision=94.87%) and 47 abnormality/normality descriptions of key anatomies. The richer vocabulary enables identification of additional label mentions in 10 out of 13 labels (compared to baseline methods). Furthermore, it captures expert insight into critical differences between observed and inferred descriptions, and image quality issues in reports. Finally, we show how the CXR ontology can be used to anatomically structure labeled output.

We developed a rich dataset of Chest X-Ray (CXR) images to assist investigators in artificial intelligence. The data were collected using an eye-tracking system while a radiologist reviewed and reported on 1,083 CXR images. The dataset contains the following aligned data: CXR image, transcribed radiology report text, radiologist’s dictation audio and eye gaze coordinates data. We hope this dataset can contribute to various areas of research particularly towards explainable and multimodal deep learning/machine learning methods. Furthermore, investigators in disease classification and localization, automated radiology report generation, and human-machine interaction can benefit from these data. We report deep learning experiments that utilize the attention maps produced by the eye gaze dataset to show the potential utility of this dataset., Measurement(s) chest X-ray image • radiologist’s dictation audio data • radiologist’s eye gaze coordinates data Technology Type(s) eye tracking device • machine learning • Radiologist • Chest Radiography Sample Characteristic - Organism Homo sapiens Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.14035613

Transfer learning with pre-trained neural networks is a common strategy for training classifiers in medical image analysis. Without proper channel selections, this often results in unnecessarily large models that hinder deployment and explainability. In this paper, we propose a novel approach to efficiently build small and well performing networks by introducing the channel-scaling layers. A channel-scaling layer is attached to each frozen convolutional layer, with the trainable scaling weights inferring the importance of the corresponding feature channels. Unlike the fine-tuning approaches, we maintain the weights of the original channels and large datasets are not required. By imposing L1 regularization and thresholding on the scaling weights, this framework iteratively removes unnecessary feature channels from a pre-trained model. Using an ImageNet pre-trained VGG16 model, we demonstrate the capabilities of the proposed framework on classifying opacity from chest X-ray images. The results show that we can reduce the number of parameters by 95% while delivering a superior performance., This paper was accepted by the IEEE International Symposium on Biomedical Imaging (ISBI) 2021

Spatial point processes on linear networks are increasingly getting attention in different disciplines such as traffic accidents and street crime analysis. Dealing with a set of time-ordered point patterns on a linear network over a period, helps in obtaining a time series of estimated intensity images. In this article, we combine the problem of estimating the intensity and relative risk of point patterns on linear networks with trend detection in time-ordered observations. Taking the temporal autocorrelation between consecutive time-ordered intensity and relative risk images into account, we make use of the Mann–Kendall trend test to look for potential locations in the network where the estimated intensity and/or relative risk show evidence of a monotonic trend. The monthly time-ordered spatial point patterns of fatal traffic accidents and street crimes in the city of London, UK, in the period of January 2013 to December 2017, are used as an application.

We propose an adaptive-sliding-window approach (LACPD) for the problem of change-point detection in a set of time-ordered observations. The proposed method is combined with sub-sampling techniques to compensate for the lack of enough data near the time series’ tails. Through a simulation study, we analyse its behaviour in the presence of an early/middle/late change-point in the mean, and compare its performance with some of the frequently used and recently developed change-point detection methods in terms of power, type I error probability, area under the ROC curves (AUC), absolute bias, variance, and root-mean-square error (RMSE). We conclude that LACPD outperforms other methods by maintaining a low type I error probability. Unlike some other methods, the performance of LACPD does not depend on the time index of change-points, and it generally has lower bias than other alternative methods. Moreover, in terms of variance and RMSE, it outperforms other methods when change-points are close to the time series’ tails, whereas it shows a similar (sometimes slightly poorer) performance as other methods when change-points are close to the middle of time series. Finally, we apply our proposal to two sets of real data: the well-known example of annual flow of the Nile river in Awsan, Egypt, from 1871 to 1970, and a novel remote sensing data application consisting of a 34-year time-series of satellite images of the Normalised Difference Vegetation Index in Wadi As-Sirham valley, Saudi Arabia, from 1986 to 2019. We conclude that LACPD shows a good performance in detecting the presence of a change as well as the time and magnitude of change in real conditions. This work has been supported by Project MTM2017-82553-R (AEI/ FEDER, UE), Project PID2020-113125RB-I00 (AEI) and the Caixa Foundation (ID1000010434), Caja Navarra Foundation, and UNED Pamplona, under Agreement LCF/PR/PR15/51100007.

The purpose of this study was to investigate the impact of organizational factors based on the technology-organization-environment (TOE) framework on the applied levels and characteristics of audit analysis and internal audit performance. This study examined the factors that influence the use of audit analytics after applying these analyzes, as well as whether the use of audit analytics improves internal audit performance. This is a descriptive-correlational study. The statistical population of the study consisted of: Certified Public Accountants working in the Audit Organization, Institutions of Public Accountants Society using Cochran formula, 150 individuals were selected as sample. Data gathering tool was a 21-item researcher-made questionnaire whose validity was confirmed by face and structural methods and the reliability of the questionnaire was confirmed by Cronbach's alpha. Data analysis was performed using Amos 22 software and structural equation modeling method. The findings show that the complexity of information technology (IT), technology competency, managerial support and professional assistance have a positive and significant effect on the application and software level of audit analytics. The size of the organization and auditing standards have a significant and positive effect on the performance of the audited analysts. Functional auditing has a positive and significant effect on the level of (software) auditing analytics, and functional auditing and auditing analysis have a significant positive effect on internal audit performance.

Purpose: Lumbar Puncture (LP) is widely used for spinal and epidural anesthesia or Cerebrospinal fluid (CSF) sampling procedures. As this procedure is highly complicated and needs high experience to be performed correctly, it is necessary to teach this skill to the physicians. Considering the limitation of number of usage of rubber models and advantages of Virtual Reality (VR) environment for digital training of skills, we tried to investigate the capability of VR environment to train the LP procedures.with TLE. Materials and Methods: Geometrical model of the lumbar area of L2 to L5 are extracted from fusion of MR and CT imaging modalities. Also physical model of resistance of each layers against needle insertion at lumbar area are investigated through specially designed sensorized handle for LP needle and recorded from a 41-yearold female patient. Then geometrical and physical models of lumbar area are fused together and the Virtual Reality (VR) model of it, with insertion force rendering capability is extracted. Then the model is integrated with a haptic device and the complete VR environment is investigated. Results: In this work we introduced a robotic Lumbar Puncture Simulator (LP Sim) with force feedback which may be used for training the LP procedures .Using LP Sim, when the trainee insert the needle inside the lumbar area at the provided virtual reality environment, he/she may feel the insertion forces against his/her movement inside the lumbar area. Conclusion: The LP Sim is a virtual reality-enabled environment, with force feedback, that provides an appropriate framework for training this skill.

Automatic detection of findings and their locations in chest x-ray studies is an important research area for AI application in healthcare. Whereas for finding classification tasks image-level labeling suffices, additional annotation in the form of bounding boxes is required for detection of findings locations. However, the process of locally marking findings on chest xray images is both time consuming and costly as it needs to be performed by radiologists. To address this problem, weakly supervised approaches have been employed to depict finding locations by looking at attention maps produced by convolution networks trained for findings classification. However, these approaches have not shown much promise so far and raised concerns whether the networks are actually focusing on the right abnormality regions. With this in mind, in this paper we propose an automatic approach for labeling chest x-ray images for findings and locations by leveraging radiology reports. Our labeling approach is anatomically standardized to the upper, middle, and lower lung zones for the left and right lungs, and is composed of two stages. In the first stage, we use a lungs segmentation UNet model and an atlas of normal patients to mark the six lung zones on the image with standardized bounding boxes. In the second stage, the associated radiology report is used to label each lung zone as positive or negative for finding, resulting in a set of six labeled bounding boxes per image. Using this approach, we automatically annotated a dataset of 13911 CXR images in a matter of hours, with an average annotation recall of 0.881 and precision of 0.896 when evaluated on 300 dual validated images. Finally, we used this “silver” bounding boxes dataset to train an opacity detection model using a RetinaNet architecture, and obtained localization results on par with the state-of-the-art.

Deep learning has now become the de facto approach to the recognition of anomalies in medical imaging. Their 'black box' way of classifying medical images into anomaly labels poses problems for their acceptance, particularly with clinicians. Current explainable AI methods offer justifications through visualizations such as heat maps but cannot guarantee that the network is focusing on the relevant image region fully containing the anomaly. In this paper, we develop an approach to explainable AI in which the anomaly is assured to be overlapping the expected location when present. This is made possible by automatically extracting location-specific labels from textual reports and learning the association of expected locations to labels using a hybrid combination of Bi-Directional Long Short-Term Memory Recurrent Neural Networks (Bi-LSTM) and DenseNet-121. Use of this expected location to bias the subsequent attention-guided inference network based on ResNet101 results in the isolation of the anomaly at the expected location when present. The method is evaluated on a large chest X-ray dataset., Comment: 5 pages, Paper presented as a poster at the International Symposium on Biomedical Imaging, 2020, Paper Number 655

Detecting change-points and trends are common tasks in the analysis of remote sensing data. Over the years, many different methods have been proposed for those purposes, including (modified) Mann-Kendall and Cox-Stuart tests for detecting trends; and Pettitt, Buishand range, Buishand U, standard normal homogeneity (Snh), Meanvar, structure change (Strucchange), breaks for additive season and trend (BFAST), and hierarchical divisive (E. divisive) for detecting change-points. In this paper, we describe a simulation study based on including different artificial, abrupt changes at different time-periods of image time series to assess the performances of such methods. The power of the test, type I error probability, and mean absolute error (MAE) were used as performance criteria, although MAE was only calculated for change-point detection methods. The study reveals that if the magnitude of change (or trend slope) is high, and/or the change does not occur in the first or last time-periods, the methods generally have a high power and a low MAE. However, in the presence of temporal autocorrelation, MAE raises, and the probability of introducing false positives increases noticeably. The modified versions of the Mann-Kendall method for autocorrelated data reduce/moderate its type I error probability, but this reduction comes with an important power diminution. In conclusion, taking a trade-off between the power of the test and type I error probability, we conclude that the original Mann-Kendall test is generally the preferable choice. Although Mann-Kendall is not able to identify the time-period of abrupt changes, it is more reliable than other methods when detecting the existence of such changes. Finally, we look for trend/change-points in land surface temperature (LST), day and night, via monthly MODIS images in Navarre, Spain, from January 2001 to December 2018. This work has been supported by Project MTM2017-82553-R (AEI/ FEDER, UE). It has also received funding from la Caixa Foundation (ID1000010434), Caja Navarra Foundation, and UNED Pamplona, under agreement LCF/PR/PR15/51100007.

Basic deep learning classifiers used for medical images often produce global labels. While annotation for localized disease detection might be costly, the knowledge of prevalence of conditions in different anatomical areas can help improve the accuracy by limiting the classifier to relevant areas. However, this improvement provided by context knowledge, is usually offset by the errors of the segmentation map used to isolate the area of interest. This paper proposes a framework for disease classification consisting of a segmentation network, a segmentation quality assessment network, and two separate classifiers on whole image and relevant segmented area. The quality assessment network controls the impact of the two disease classifiers on the final outcome, utilizing the masked image only when segmentation is acceptable. We show that in a very large dataset of chest X-ray images, this framework produces a 2% increase in the area under ROC curve for classification compared to a baseline.

Background: The current study aimed to measure and decomposes socioeconomic-related inequalities in DMFT (decayed, missing, and filled teeth) index among adults in Iran. Methods: The study data was drawn from the adult component of Perspective Epidemiological research studies in IrAN (PERSIAN) from 17 centers in 14 different provinces of Iran. DMFT score was used as a measure of oral health among adults in Iran. The relative and generalized (absolute) concentration index (RC and GC, respectively) was used to quantify and decompose socioeconomic-related inequalities in DMFT among Iranian adults (35 years and older). Results: A total of 128813 adults aged 35 and older, who are enrolled in the Prospective Epidemiological Research Studies in IrAN (PERSIAN), were included in the study. The mean score of DMFT of the adults was 6.01 (SD=3.17). The findings suggested that DMFT was mainly concentrated among the poor in the 14 provinces included in the study (RC = -0.064; 95% confidence interval (CI), -0.066 to -0.063 and GC = -0.387; 95% CI, 0.397 to -0.377). In addition, SES, being male, older age and being widow or divorced were identified as the main factors contributing to the concentration of DMFT among the worse-off in Iran. Conclusions: It is recommended to focus in the oral health status of socioeconomically disadvantaged groups in order to reduce socioeconomic-related inequality in oral health among adults in Iran. Moreover, it should be noted that reducing socioeconomic-related inequalities in oral health should be accompanied by appropriate health promotion policies that focus actions on the fundamental socio-economical causes of dental disease.

Background Socioeconomic-related inequality in oral hygiene behaviors in Iran is poorly understood. This study aims to measure and decompose socioeconomic-related inequalities in oral hygiene behaviors among middle-aged and elderly adults in Iran. Methods A cross-sectional analysis was performed using data from the Prospective Epidemiological Research Studies in IrAN (PERSIAN), a large national cohort study. A total of 130,016 individuals aged 35 years and above from 17 cohort centers in Iran were included in the study. The normalized concentration index (Cn) was used to measure the magnitude of inequality in oral hygiene behaviors, i.e. brushing at least twice and flossing once daily, among middle-aged and elderly Iranian adults included in the cohort centers. Decomposition analysis was performed to quantify the contribution of each determinant to the observed inequality in oral hygiene behaviors. Results Totally, 65.5% of middle-aged and elderly adults brushed their teeth twice a day or more, 7.6% flossed at least once a day and 3.48% had both habits. The estimated Cn of the two habits combined, i.e. tooth brushing and dental flossing, for all provinces taken part in the PERSIAN cohort study was 0.399 (95% confidence interval [CI]: 0.383 to 0.417), indicating that the prevalence of the two habits combined is more concentrated among individuals with higher socioeconomic status. Inequality in oral hygiene behaviors was pro-rich in all cohort centers. The decomposition results suggested socioeconomic status as the main factor contributing to the overall inequality, followed by the level of education, and the province of residence. Conclusion A low prevalence of oral hygiene behaviors among middle-aged and elderly Iranian adults was observed. There was also a pro-rich inequality in oral hygiene behaviors among middle-aged and elderly adults in all cohort centers. These results suggest an urgent need for targeted policy interventions to increase the prevalence of preventive oral hygiene behaviors among the poor and less-educated middle-aged and elderly adults in Iran.

Community detection a crucial task in the study of complex networks aims at identifying structural patterns of the networks. Recently, evolutionary methods are successfully applied to reveal communities of complex networks. Most of them employ only one quality measure in their search processes. Since each objective covers a different aspect of network’s property, investigating this problem with more than one objectives results in identifying more accurate community structure. To handle this issue in this paper, a multi-objective genetic algorithm integrated with a local search strategy called Enhanced Multi-Objective Genetic Algorithm for Community Detection (EMOGACD) is proposed. The main goal of using the local search strategy is speeding up the convergence and improving the accuracy of the proposed method. the proposed method uses the vector-based method is used to represent the solutions. This type of representation reduces the search space and does not need to know the number of communities at the beginning of the search process. Performed experiments performed on both real-world and synthetic networks demonstrate the relatively high capacity of the proposed method in detecting high quality communities within lower generations.

Obtaining automated preliminary read reports for common exams such as chest X-rays will expedite clinical workflows and improve operational efficiencies in hospitals. However, the quality of reports generated by current automated approaches is not yet clinically acceptable as they cannot ensure the correct detection of a broad spectrum of radiographic findings nor describe them accurately in terms of laterality, anatomical location, severity, etc. In this work, we present a domain-aware automatic chest X-ray radiology report generation algorithm that learns fine-grained description of findings from images and uses their pattern of occurrences to retrieve and customize similar reports from a large report database. We also develop an automatic labeling algorithm for assigning such descriptors to images and build a novel deep learning network that recognizes both coarse and fine-grained descriptions of findings. The resulting report generation algorithm significantly outperforms the state of the art using established metrics.

Sina is a robotic telesurgery system which can be used for performing general surgeries. The system has a reconfigurable surgery console which may be used both in sitting, semisitting, or standing surgeon postures. At the surgeon side are surgery handles of scissor, grasper, hammer, or stylus type which may be changed to perform different tasks during a single surgery. The slave subsystem has a modular and open architecture design for placement of surgical robots at one or both sides of the surgical bed and which can be integrated to each other. Noninterruptive reorienting the patient during general surgeries of deformable soft tissues is the most important advantage of such a modular and integrable design. The Sina cameraman robot (RoboLens) can smartly track the surgery instruments which are 5 mm in diameter and fully articulated. Also, the system benefits from tremor reduction, movement scaling, and three degrees of freedom haptic feedback plus pinch force for each master handle. Any available operating room equipment such as electrosurgery devices and vision systems may be integrated into the Sina surgical system. Sina also benefits from both single and reusable instruments to reduce the cost of surgeries, which is one of the main bottlenecks in the generalization of robotic surgeries.

Chest radiography is the most common medical image examination for screening and diagnosis in hospitals. Automatic interpretation of chest X-rays at the level of an entry-level radiologist can greatly benefit work prioritization and assist in analyzing a larger population. Subsequently, several datasets and deep learning-based solutions have been proposed to identify diseases based on chest X-ray images. However, these methods are shown to be vulnerable to shift in the source of data: a deep learning model performing well when tested on the same dataset as training data, starts to perform poorly when it is tested on a dataset from a different source. In this work, we address this challenge of generalization to a new source by forcing the network to learn a source-invariant representation. By employing an adversarial training strategy, we show that a network can be forced to learn a source-invariant representation. Through pneumonia-classification experiments on multi-source chest X-ray datasets, we show that this algorithm helps in improving classification accuracy on a new source of X-ray dataset.

We propose a novel deep neural network architecture for normalcy detection in chest X-ray images. This architecture treats the problem as fine-grained binary classification in which the normal cases are well-defined as a class while leaving all other cases in the broad class of abnormal. It employs several components that allow generalization and prevent overfitting across demographics. The model is trained and validated on a large public dataset of frontal chest X-ray images. It is then tested independently on images from a clinical institution of differing patient demographics using a three radiologist consensus for ground truth labeling. The model provides an area under ROC curve of 0.96 when tested on 1271 images. We can automatically remove nearly a third of disease-free chest X-ray screening images from the workflow, without introducing any false negatives (100% sensitivity to disease) thus raising the potential of expediting radiology workflows in hospitals in future., Comment: This paper was accepted by IEEE ISBI 2020

OBJECTIVES Obesity is a considerable and growing public health concern worldwide. The present study aimed to quantify socioeconomic inequalities in adult obesity in western Iran. METHODS A total of 10 086 participants, aged 35-65 years, from the Ravansar Non-communicable Disease Cohort Study (2014-2016) were included in the study to examine socioeconomic inequalities in obesity. We defined obesity as a body mass index ≥30 kg/m2 . The concentration index and concentration curve were used to illustrate and measure wealth-related inequality in obesity. Additionally, we decomposed the concentration index to identify factors that explained wealth-related inequality in obesity. RESULTS Overall, the prevalence of obesity in the total sample was 26.7%. The concentration index of obesity was 0.04; indicating that obesity was more concentrated among the rich (p

Energy is one of the essential resources for human life and mainly classified as non-renewable resources. Since huge amounts of energy are consumed in the agriculture sector, an energy audit is an essential strategy in countries. Conservation agriculture as a tool for sustainable development can lead to saving agricultural resources. In the current investigation, energy audit for wheat conservation and conventional production systems was performed. For this purpose, 48 farms were selected randomly in 2016, and their energy performance was evaluated and compared. The data were analyzed to calculate energy parameters. Also, data envelopment analysis technique was used to identify the possible ways to achieve higher efficiency in farms. To this end, current and optimum situations and saving energy in different cultivation systems were determined using Charnes, Cooper, and Rhodes (CCR) model. The research results showed that the average energy ratio, net energy gain, specific energy, and energy productivity for conservation farms were 4.31, 137,656 MJ ha−1, 5.56 MJ kg−1, and 0.18 kg MJ−1, respectively. Corresponded values for conventional farms were measured to be 3.03, 90,101 MJ ha−1, 7.69 MJ kg−1, and 0.13 kg MJ−1, respectively. Data envelopment analysis results revealed that the highest saving energy in conventional system belongs to diesel fuel and irrigation inputs, and the least amount of energy saving was seen in human labor input. While for the conservation system, the highest and the least amount of energy saving belongs to nitrogen and human labor, respectively.

Micro-grids are the key technologies known to solve challenges such as increased electric demand, fatigue electric installations, electrical leakage and pressures and opposition from environmental advocacy groups. The current article is presenting an improved optimization algorithm based on a differential evolution algorithm to achieve the optimal response for managing distributed energy resources in micro-grids. The simulation results show that: 1) The final cost of network management in systems based on the agent is very favorable compared to a network regardless of the agent and also are economically much more useful and effective in coordinating various energy sources. 2) The results of the proposed algorithm are much better in comparison with the results of the Fireflies optimization algorithm, a differential evolution algorithm and the particle swarm algorithm. This comparison proves the high performance of the algorithm.

For the purposes of government policy concerning energy security, optimal taxation, and climate change, precise estimates of the effective factors in price elasticity of electricity demand are of principal importance. In this regard, this paper explores the effect of different factors including proportion of income spent-level, consumer academic-level, demand-types, demand time, possibility of postponing demand-level, price-level, demand-level, and awareness of participation benefits-level on price elasticity of electricity demand for Iran power system. To achieve this, a nonlinear-empirical model based on response surface methodology is proposed. Thereafter, these factors were prioritized based on their effect on price elasticity. Analysis results reveal that proportion of income spent-level has the most significant effect on the price elasticity of electricity demand while consumer academic-level has the least effect. These results could be applied by policy makers as a tool in making decisions on how to set the price of electricity.

Hallaq’s answer to one of the most controversial questions is simple and categorical: the Islamic State, judged by any standard definition of what the modern State represents, is both impossible and contradictory in terms. Hallaq’s preference of the Hobbesian-Schmittian reading of modern state, beside his legendary rather than historically realistic account of Muslim’s governance, led him to the above definite and categorical conclusion. Given the importance of this theory, we shall, in this article, explore an alternative explanation for the Modern State by examining its new Kantian reading, in which there exists an original room for the rule of law, as well as the genuine respect of human dignity.

This research attempts to investigate the effects of concentration and radius ratio on convective heat transfer and entropy generation of a non-Newtonian hybrid nanofluid flowing through a concentric annulus. The nanofluid is prepared by suspending tetramethylammonium hydroxide (TMAH) coated Fe3O4 (magnetite) nanoparticles and gum arabic (GA) coated carbon nanotubes (CNTs) in water. Variable thermal conductivity and viscosity are used in simulations. The convective heat transfer coefficient of inner and outer walls, and total entropy generation augment with increasing Fe3O4 and CNT concentrations. Increasing radius ratio from 0.2 to 0.8, at CNT concentration of 1.1% and Fe3O4 concentration of 0.7%, decreases the heat transfer coefficient of inner wall by 85.05%, while increases that of outer wall by 35.49%. Models of convective heat transfer coefficient of both walls and total entropy generation are developed using neural network. Genetic algorithm is used with compromise programming to achieve optimal cases with maximum heat transfer and minimum entropy generation. In this method, the objective functions are mixed and the problem transforms into a single-objective optimization. Finally, applying the nanofluid with high concentrations is recommended for all conditions except the cases in which importance of entropy generation is considered much greater than that of heat transfer.