Private Identification, Authentication and Key Agreement Protocol with Security Mode Setup

Identification, authentication and key agreement protocol of UMTS networks with security mode setup has some weaknesses in the case of mutual freshness of key agreement, DoS-attack resistance, and efficient bandwidth consumption. In this article we consider UMTS AKA and some other proposed schemes. Then we explain the known weaknesses of the previous frameworks suggested for the UMTS AKA protocol. After that we propose a new protocol called private identification, authentication, and key agreement protocol (PIAKAP), for UMTS mobile network. Our suggested protocol combines identification and AKA stages of UMTS AKA protocol while eliminates disadvantages of related works and brings some new features to improve the UMTS AKA mechanism. These features consist of reducing the interactive rounds of the UMTS AKA with security mode setup and user privacy establishment

Deep Learning for Condition Detection in Chest Radiographs: A Performance Comparison of Different Radiograph Views and Handling of Uncertain Labels

Chest radiographs are the initial diagnostic modality for lung or chest-related conditions. It is believed that radi- ologist’s availability is a bottleneck impacting patient’s safety because of long waiting times. With the arrival of machine learning and especially deep learning, the race for finding artificial intelligence (AI) based approaches that allow for the highest accuracy in detecting abnormalities on chest radiographs is at its peak. classification of radiographs as normal or ab- normal is based on the training and expertise of the reporting radiologist. The increase in the number of chest radiographs over a period of time and the lack of sufficient radiologists in the UK and worldwide have had an impact on the number of chest radiographs assessed and reported in a given time frame. Substantial work is dedicated to machine learning for classifying normal and abnormal radiographs based on a single pathology. The success of deep learning techniques in binary radiograph classification urges the medical imaging community to apply it to multi-label radiographs. Deep learning techniques often require huge datasets to train its underlying model. Recently, the availability of large multi-label datasets has ignited new efforts to overcome this challenging task. This work presents multiple convolutional neural networks (CNNs) based models trained on publically available CheXpert multi-label data. Based on common pathologies seen on chest radiographs and their clinical significance, we have chosen pathologies such as pulmonary odema, cardiomegaly, atelectasis, consolidation and pleural ef- fusion. We trained our models using different projections such as anteroposterior (AP), posteroanterior (PA), and lateral and compared the performance of our models for each projection. Our results demonstrate that the model for the AP projection outperforms the remaining models with an average AUC of 0.85. Furthermore, we use the samples with uncertain labels in CheXpert dataset and improve the model performance by removing the uncertainty using gaussian mixture models (GMM). The results show improvement in all three views with AUCs ranging from 0.91 for AP, 0.75 for PA and 0.85 on the lateral view.Peer reviewe

Evaluation of the effectiveness of electro-coagulation-flotation process for removal toxicity of olive oil mill wastewater

Background: The olive oil mill wastewater is one of the contaminating food industrial wastewaters. Olive oil extraction process imports wastewater with high phenolic chemicals into the environment. In this study the toxicity of raw olive oil mill wastewater and the effluent of electro-coagulation have been investigated.Material & Methods: Germination test was used for evaluating the toxicity of wastewater and effluent process. Electro-coagulation-flotation examinations performed in a plexiglas electrochemical reactor with 1750 ml practical volume. 4 blades of aluminum and titanium as anode and cathode electrodes with a gap of 2 cm were connected to DC power supply in monopolar parallel mode.Results: Based on the information from this study, the pollution load of phenolic compounds in olive oil mill wastewater is 1000 times more than municipal wastewater. Also, the evaluation toxicity of effluent obtained from the process in natural pH of wastewater (pH=5.2), 117 mA/m2 current density and 30 minutes time process, suggests that the effluent causes plant species growth, even without dilution.Conclusion: The results obtained from this research can warn us to the risks of releasing these wastewaters without treatment and toxic effects on the different species of plants. Results demonstrated although using of electro-coagulation-flotation process removes high percent of pollutants of olive oil mill wastewater, but, it can’t attain the discharge limitations, then it should be more treated with some other methods.Key words: Toxicity, Electro-Coagulation-Flotation, Olive Wastewate

A case of invasive aspergillosis in CGD patient successfully treated with Amphotericin B and INF-γ

BACKGROUND: Chronic granulomatous disease (CGD) is a rare disorder of phagocytes in which absence of superoxide and hydrogen peroxide production in phagocytes predisposes patients to bacterial and fungal infections. The most common fungal infections in these patients are caused by Aspergillus species. CASE PRESENTATION: Here, we describe Aspergillus osteomyelitis of the ribs and hepatic abscess in a 5-year-old boy. The patient was successfully treated with Amphotericin B and INF-γ. CONCLUSION: With respect to the high frequency of aspergillosis in the CGD patient, immune deficiency should be investigated in patients with invasive aspergillosis. Moreover, using antifungal drugs as prophylaxis can improve the quality of life in these patients

Prediction of Response to Neoadjuvant Chemoradiotherapy by MRI-Based Machine Learning Texture Analysis in Rectal Cancer Patients

Introduction Neoadjuvant chemoradiotherapy (nCRT) followed by surgical resection is the standard treatment for locally advanced rectal cancer (LARC). Radiomics can be used as noninvasive biomarker for prediction of response to therapy. The main aim of this study was to evaluate the association of MRI texture features of LARC with nCRT response and the effect of Laplacian of Gaussian (LoG) filter and feature selection algorithm in prediction process improvement. Methods All patients underwent MRI with a 3T clinical scanner, 1 week before nCRT. For each patient, intensity, shape, and texture-based features were derived from MRI images with LoG filter using the IBEX software and without preprocessing. We identified responder from a non-responder group using 9 machine learning classifiers. Then, the effect of preprocessing LoG filters with 0.5, 1 and 1.5 value on these classification algorithms' performance was investigated. Eventually, classification algorithm's results were compared in different feature selection methods. Result Sixty-seven patients with LARC were included in the study. Patients' nCRT responses included 11 patients with Grade 0, 19 with Grade 1, 26 with Grade 2, and 11 with Grade 3 according to AJCC/CAP pathologic grading. In MR Images which were not preprocessed, the best performance was for Ada boost classifier (AUC = 74.8) with T2W MR Images. In T1W MR Images, the best performance was for aba boost classifier (AUC = 78.1) with a sigma = 1 preprocessing LoG filter. In T2W MR Images, the best performance was for naive Bayesian network classifier (AUC = 85.1) with a sigma = 0.5 preprocessing LoG filter. Also, performance of machine learning models with CfsSubsetEval (CF SUB E) feature selection algorithm was better than others. Conclusion Machine learning can be used as a response predictor model in LARC patients, but its performance should be improved. A preprocessing LoG filter can improve the machine learning methods performance and at the end, the effect of feature selection algorithm on model's performance is clear. Keywords:MRI; Rectal cancer; Radiomics; Machine learnin

Extremes and Cultures: Investigating the Decline of the Chalcolithic Age in the Tehran Plain with the Environmental Archaeology Approach

Natural hazards in ancient times were among the factors central to the decline of human cultures and civilizations.Climate change periods are associated with increased extreme weather events such as torrential rains and prolongeddroughts, thus posing severe challenges to human societies. In the fourth millennium BCE, variable climaticconditions in the Tehran plain caused cultural dynamics to be disrupted. Through an environmental archaeologicalapproach, the present study discusses the possible causes of cultural decline and collapse in this plain in two stagesof climate change during the fourth millennium BCE. The data derives from the archaeological site of MafinAbad, where occurs a situation similar to a series of sites in North Central and Southwest Iran. High-resolutionpaleoclimate research has been used to reconstruct the climatic conditions of the fourth millennium BCE. Thisresearch reflects the importance of environmental sedimentology studies in archaeological sites to identify possibleenvironmental reasons for cultural prosperity and disintegration of prehistoric rural communities