Effective Criteria for Seismic Rehabilitation Planning of Road Transportation Infrastructures

While seismic rehabilitation process for road infrastructures has been traditionally based on seismic factors, consideration of non-seismic factors is necessary for reliable project ranking. Non-seismic factors include socioeconomic criteria, determining the value of a project to its users’ community. Based on the information obtained from a questionnaire survey and literature review, this paper identifies a set of effective rehabilitation criteria (ERC) for seismic rehabilitation decision-making to develop a priority index that is applied to determine the rehabilitation priority. The identified RC will then be weighted for four types of road structures including bridges, tunnels, retaining walls, and buildings. The results can be generalized to provide valuable insights for policy makers concerned with transportation infrastructure planning, especially in developing countries where project prioritization is often an issue. To underline the value of the study, the weighted RC are applied in ranking road rehabilitation projects in an illustrative example

Deep learning models for improved accuracy of a multiphase flowmeter

Measurement of oil and gas two-phase flow with variable flow regimes relies to a large extent on flow patterns and their transitions. Using multiphase flowmeters in flows with high gas volume fractions is therefore usually associated with large uncertainties. This work presents a dynamic neural network method to measure the flow rate using a nonlinear autoregressive network with exogenous inputs (NARX). Total temperature and total pressure are used as network inputs and the obtained results are compared with a multilayer perceptron (MLP). Comparison between modeling results and the experimental data shows that the NARX network can predict oil and gas flow with variable flow regimes with less error compared to the MLP model, e.g. an absolute average percentage deviation (AAPD) of 0.68% instead of 1.02%. The present work can hence be seen as a proof-of-concept study that should motivate further applications of deep learning models to facilitate enhanced accuracy in flow metering

Post-Viral Aspergillosis

Post-viral aspergillosis (PVA) is a clinical form of Aspergillus infection that occurs after some viral infections. Aspergillus is the most common respiratory fungal co-pathogen in patients with viral infections. Most cases of PVA have been reported as invasive pulmonary aspergillosis (IPA) after influenza, COVID-19, and the cytomegalovirus infection. PVA is more commonly reported in critically ill patients with viral pneumonia. Suggested risk factors for PVA include cellular immune deficiency, ARDS, pulmonary tracts and parenchyma damage, and corticosteroid therapy. New pulmonary nodules such as dense, well-circumscribed lesions with or without a halo sign, air crescent sign, or cavity, or wedge-shaped and segmental or lobar consolidation on the chest CT scan can suggest PVA. As in the treatment of invasive aspergillosis in other settings, triazoles, such as voriconazole or isavuconazole, have been suggested as the first-line treatment for PVA. It seems that the presence of PVA has significantly decreased the survival rate in patients with viral infections

Nasal packing, periorbital edema and ecchymosis after septorhinoplasty

Introduction:Edema and ecchymosis after septorhinoplasty is an unpleasant manifestation for both the patient and surgeon. Although routine nasal packing is frequently done after septorhinoplasty, assessing the relevancy of post-surgical periorbital edema and ecchymosis with nasal packing eventually may helps to clarifying more prohibitable causes of these complications that are unintentionally perform. Materials and Methods: In an interval of 1.5 years, 124 patients whom were candidates of septorhinoplasty by one surgeon in a center of plastic and reconstructive surgery in Tehran participated in our study. Patients were randomly divided in two groups. For half of them at the end of operation bilateral routine nasal packing was done and for the rest a light dressing limited to the nostril was performed. Thereafter, sequentially in the 1st, 3rd, 7th and 30th postoperative day severity of periorbital edema and ecchymosis were recorded based on a scaling system by a third person who was not informed about the study. Results: Conventional nasal packing is relevant to an increasing number of cases with periorbital edema and ecchymosis after septorhinoplasty. The difference between patients whom were nasally packed or not was not significant at the first postoperative day but in the 3rd and 7th day it was meaningfully less in number and severity in the unpacked group. Discussion: This shows that it is not necessary to do pack in every patient after septorhinoplasty and performing a light dressing may suffice.

Cluster heads optimum choice and route discovery by using fuzzy logic in wireless sensor networks

Optimum energy consumption in wireless sensitive networks plays an important role on network management. In the proposed model of this paper, first all the nods send the energy, station distance and density parameters to their fuzzy modules. According to each nod's fuzzy module outputs, a timer is activated for all nodes, which start reverse-counting from obtained value from fuzzy module. Timer of better nod comes to zero sooner and two best nods are selected in each zone (with the distance of r). One of them is introduced as superior cluster head and the other nods are connected to the closest cluster head. In addition, the cluster head not introduced as superior cluster head first collects data from neighbor's nods and then sends it to the superior cluster head after classifying data as package. The performance of the proposed model of this paper is compared with other methods and the preliminary results indicate that the proposed algorithm has increased first nod death time compared with other methods in the literature

Layer‐Wise Titania Growth Within Dimeric Organic Functional Group Viologen Periodic Mesoporous Organosilica as Efficient Photocatalyst for Oxidative Formic Acid Decomposition

A bridge dimeric organic functional group viologen PMOs synthesized via layer by layer growth on titania (TiO2) has been unprecedently prepared as stable periodic mesoporous organosilica using surfactant under mild acidic conditions. The layer by layer TiO2 incorporation within the prepared organic functional group viologen‐PMO could successfully develop a new type of hybrid photo‐oxidation system for the mineralization of formic acid under sunlight irradiation conditions.The authors are thankful for financial supports (95849156) from Iran National foundation of Science (INSF). The publication has been prepared with support from RUDN University Program 5–100

A machine learning approach for differentiating bipolar disorder type II and borderline personality disorder using electroencephalography and cognitive abnormalities

This study addresses the challenge of differentiating between bipolar disorder II (BD II) and borderline personality disorder (BPD), which is complicated by overlapping symptoms. To overcome this, a multimodal machine learning approach was employed, incorporating both electroencephalography (EEG) patterns and cognitive abnormalities for enhanced classification. Data were collected from 45 participants, including 20 with BD II and 25 with BPD. Analysis involved utilizing EEG signals and cognitive tests, specifically the Wisconsin Card Sorting Test and Integrated Cognitive Assessment. The k-nearest neighbors (KNN) algorithm achieved a balanced accuracy of 93%, with EEG features proving to be crucial, while cognitive features had a lesser impact. Despite the strengths, such as diverse model usage, it’s important to note limitations, including a small sample size and reliance on DSM diagnoses. The study suggests that future research should explore multimodal data integration and employ advanced techniques to improve classification accuracy and gain a better understanding of the neurobiological distinctions between BD II and BPD

Clinical Significance and Different Expression of Dipeptidyl Peptidase IV and Procalcitonin in Mild and Severe COVID-19

Background: Coronavirus has become a global concern in 2019-20. The virus belongs to the coronavirus family, which has been able to infect many patients and victims around the world. The virus originated in the Chinese city of Wuhan, which eventually spread around the world and became a pandemic. Materials and Methods: A total of 60 Patients with severe (n=30) and mild (n=30) symptoms of COIVD-19 were included in this study. Peripheral blood samples were collected from the patients. Real-time PCR was used to compare the relative expression levels of Procalcitonin and dipeptidyl peptidase IV (DPPIV) in a patient with severe and mild Covid-19 infection. Results: Procalcitonin and dipeptidyl peptidase IV markers in the peripheral blood of patients with severe symptoms, were positive in 29 (96.60%) and 26 (86.60%), respectively (n=30); however, positive rates in the mild symptoms patients group were 27 (90%) and 25 (83.30%), respectively. There was a statistically significant difference between these two groups in terms of DDPIV and Procalcitonin (p<0.001). Conclusion: Procalcitonin and DPPIV increase in patients with COVID-19 infection, significantly higher in the patients with more severe clinical symptoms than those with milder ones. More studies will be needed to verify the reliability of the current findings. Keywords: Procalcitonin, DPPIV, Severe symptoms, Mild symptoms, COVID-1

Clinical, epidemiological, and mycological features of patients with candidemia: Experience in two tertiary referral centers in Iran

Background and purpose: Candidemia is a major cause of morbidity and mortality among patients receiving immunosuppressive therapy and those hospitalized with serious underlying diseases. Here, we investigated the epidemiological, clinical, and mycological features of candidemia in Tehran, Iran. Materials and methods: A prospective observational study of all patients diagnosed with candidemia was performed at two referral teaching hospitals in Tehran, Iran, from February to December 2018. Demographic characteristics, underlying diseases, risk factors, clinical symptoms, and laboratory analyses of candidemic patients with positive culture were mined. Candida isolates were molecularly identified by sequencing of the internal transcribed spacer region (ITS1-5.8S-ITS2). The antifungal susceptibility testing for fluconazole, itraconazole, voriconazole, posaconazole, amphotericin B, caspofungin, micafungin, and anidulafungin against the isolates was performed using CLSI broth microdilution reference method (M27-A3). Results: A total of 89 episodes were identified, with an incidence of 2.1 episodes/1000 admissions. The common underling disease were malignancy (46%), renal failure/dialysis (44%), and hypertension (40%). The overall crude mortality was 47%. C. albicans (44%) was the most frequent causative agent, followed by C. glabrata (21%), C. parapsilosis complex (15%), C. tropicalis (11%), and C. lusitaniae (3.5%). All the isolates were susceptible to amphotericin B. The activity of all four azoles was low against non-albicans Candida species, especially C. tropicalis. Conclusion: The increase in non-albicans Candida species with reduced susceptibility to antifungal drugs might be alarming in high-risk patients. Therefore, accurate knowledge of predisposing factors and epidemiological patterns in candidemia are effective steps for managing and decreasing the mortality rate in candidemia.This study has been funded and supported by Tehran University of Medical Sciences, Tehran, Iran (Grant no. 99-2-99-48944).S

A machine learning approach for differentiating bipolar disorder type II and borderline personality disorder using electroencephalography and cognitive abnormalities

This study addresses the challenge of differentiating between bipolar disorder II (BD II) and borderline personality disorder (BPD), which is complicated by overlapping symptoms. To overcome this, a multimodal machine learning approach was employed, incorporating both electroencephalography (EEG) patterns and cognitive abnormalities for enhanced classification. Data were collected from 45 participants, including 20 with BD II and 25 with BPD. Analysis involved utilizing EEG signals and cognitive tests, specifically the Wisconsin Card Sorting Test and Integrated Cognitive Assessment. The k-nearest neighbors (KNN) algorithm achieved a balanced accuracy of 93%, with EEG features proving to be crucial, while cognitive features had a lesser impact. Despite the strengths, such as diverse model usage, it’s important to note limitations, including a small sample size and reliance on DSM diagnoses. The study suggests that future research should explore multimodal data integration and employ advanced techniques to improve classification accuracy and gain a better understanding of the neurobiological distinctions between BD II and BPD