Evaluation of diagnostic accuracy and dimensional measurements by using CBCT in mandibular first molars

Background: This study aimed to assess the diagnostic accuracy of cone beam computed tomography (CBCT) and quantitatively evaluate the morphology of mandibular first molars using CBCT. Material and Methods: Twenty-four double-rooted mandibular first molars were evaluated by NewTom VGi CBCT. The distance from the furcation and apex to the cementoenamel junction (CEJ), diameter and thickness of canal walls, the buccolingual (BL) to mesiodistal (MD) ratio (ΔD), prevalence of oval canals at different sections and taper of the canals were all determined. In order to assess the diagnostic accuracy of CBCT, distance from the furcation and apex to the CEJ and thickness of canal walls at the CEJ and apex were compared with the gold standard values (caliper and stereomicroscope). Statistical analyses were carried out using intraclass correlation coefficient (ICC), paired t-test and repeated measures ANOVA. Results: A high correlation existed between the CBCT and gold standard measurements ( P <0.001). In dimensional measurements, length of mesial root was higher than the distal root and lingual furcation was farther from the CEJ than the buccal furcation ( P <0.001). An important finding of this study was the mesiodistal taper of the mesiobuccal (MB) and mesiolingual (ML) canals; which was equal to 0.02. Conclusions: CBCT has acceptable diagnostic accuracy for measurement of canal wall thickness. Cleaning and shaping of the canals should be performed based on the unique anatomy of the respective canal; which necessitates the use of advanced imaging techniques for thorough assessment of root canal anatomy in a clinical settin

Vitamin D deficiency prevalence in summer compared to winter in a city with high humidity and a sultry climate

Background: Vitamin D deficiency is high in winter because of reduced exposure to sunlight. It seems that in places with high humidity and a sultry climate, exposure to sunlight in summer can be low too. This study was designed to determine the vitamin D deficiency prevalence in Sari, a city with a high humidity climate at the end of summer, and compare it to winter. Material and methods: This cross-sectional study was carried out on men and women aged 10 to 70. Clustered blood samples were received from 351 subjects who participated in this study toward the end of summer, and in winter. The levels of serum vitamin D, calcium, phosphorus, alkaline phosphatase and PTH were measured. T test and X2 were used for data analysis. Results: 351subjects (66.4% women, 33.6% men) aged 11 to 69 (mean age &#177; SD 37.11 &#177; 12.6) participated in the study. The mean 25-(OH) D concentration in summer was 13.41 &#177; 13, and in winter it was 11.7 &#177; 11, and the difference was statistically significant (p < 0.02). The prevalence of 25-OHvitamin D deficiency was 87.5% (307) in winter and 78.6% (276) in summer (p < 0.05). Conclusion: This study shows that although in this area with a high humidity climate, seasonal variation of vitamin D is statistically significant, the prevalence of Vitamin D insufficiency is as high in summer as in winter. (Pol J Endocrinol 2011; 62 (3): 249&#8211;251)Background: Vitamin D deficiency is high in winter because of reduced exposure to sunlight. It seems that in places with high humidity and a sultry climate, exposure to sunlight in summer can be low too. This study was designed to determine the vitamin D deficiency prevalence in Sari, a city with a high humidity climate at the end of summer, and compare it to winter. Material and methods: This cross-sectional study was carried out on men and women aged 10 to 70. Clustered blood samples were received from 351 subjects who participated in this study toward the end of summer, and in winter. The levels of serum vitamin D, calcium, phosphorus, alkaline phosphatase and PTH were measured. T test and X2 were used for data analysis. Results: 351subjects (66.4% women, 33.6% men) aged 11 to 69 (mean age &#177; SD 37.11 &#177; 12.6) participated in the study. The mean 25-(OH) D concentration in summer was 13.41 &#177; 13, and in winter it was 11.7 &#177; 11, and the difference was statistically significant (p < 0.02). The prevalence of 25-OHvitamin D deficiency was 87.5% (307) in winter and 78.6% (276) in summer (p < 0.05). Conclusion: This study shows that although in this area with a high humidity climate, seasonal variation of vitamin D is statistically significant, the prevalence of Vitamin D insufficiency is as high in summer as in winter. (Pol J Endocrinol 2011; 62 (3): 249&#8211;251

Nonlinear Dynamics Tools for Offline Signature Verification Using One-class Gaussian Process

One of the major problems in biometrics and in document forensics is the offline mode of signature verification. This study aims to present a novel approach of verifying an individual's signature through offline images of handwriting. The approach proposed here relies on a global method which considers signature images as waveforms. First, image decompositions are in terms of a series of wavelet sub-bands at some specific levels. Wavelet sub-bands are then extended so as to obtain waveforms. Each waveform is quantized by two Nonlinear Dynamics Tools in order to generate feature vectors. Multi-Resolution Box-Counting (MRBC) fractal dimension algorithm as well as probabilistic finite state automata (PFSA) are applied separately to signature waveforms. In the training and verification phase, we propose the one-class Gaussian process (GP) priors based on writer-independent approach. As one of the main parameters, optimal decision threshold is selected from False Accept Rate (FAR) and False Reject Rate (FRR) curves. The presented system was tested on two Persian databases (PHBC and UTSig) as well as on two Latin databases (MCYT-75 and CEDAR). In fact, the results produced by this method were generally better in terms of the four signature databases than the state-of-the-art results.Scopu

Process-oriented integration in industrial information systems

To cope with economic constraints, request for mass customization, globalization and cost reduction ... is increasingly enhanced and the development of manufacturing strategies towards a lean manufacturing strategy has gone. Lean strategy deployment in a company, leading to company reorganization in the logical chain of production, will be to avoid waste. In fact, focusing on the business allows the company to share costs and reduce development time, to control the quality of its products. In general, the circulation and flow of the right information in a company, will have an important role in achieving a lean strategy. currently Information Systems of a company consists of multiple software and designed systems. ERP, PLM, SCM , etc., each system designed to achieve the objectives in one aspect of the business. While a variety of products, all of which are required for the company, but Creates redundancy, heterogeneity and the increasing volume of information. This inconsistency can cause big problems in the field of communication inter- company. In this study, a method based on Service Oriented Architecture and Enterprise Service Bus (ESB) will be presented, including the following, and the solution is to solve these problems: 1. Intelligent Routing Module for organize the workshop resources 2. The monitoring & governance module for control on product performance, production and quality 3. mediation module for exchange data between different transactions in business and technology 4. The dynamic choreography module to combine and arrange the services action to achieve a particular goal in Industrial Service

Enhancing Smart Agriculture Monitoring via Connectivity Management Scheme and Dynamic Clustering Strategy

The evolution of agriculture towards a modern, intelligent system is crucial for achieving sustainable development and ensuring food security. In this context, leveraging the Internet of Things (IoT) stands as a pivotal strategy to enhance both crop quantity and quality while effectively managing natural resources such as water and fertilizer. Wireless sensor networks, the backbone of IoT-based smart agricultural infrastructure, gather ecosystem data and transmit them to sinks and drones. However, challenges persist, notably in network connectivity, energy consumption, and network lifetime, particularly when facing supernode and relay node failures. This paper introduces an innovative approach to address these challenges within heterogeneous wireless sensor network-based smart agriculture. The proposed solution comprises a novel connectivity management scheme and a dynamic clustering method facilitated by five distributed algorithms. The first and second algorithms focus on path collection, establishing connections between each node and m-supernodes via k-disjoint paths to ensure network robustness. The third and fourth algorithms provide sustained network connectivity during node and supernode failures by adjusting transmission powers and dynamically clustering agriculture sensors based on residual energy. In the fifth algorithm, an optimization algorithm is implemented on the dominating set problem to strategically position a subset of relay nodes as migration points for mobile supernodes to balance the network’s energy depletion. The suggested solution demonstrates superior performance in addressing connectivity, failure tolerance, load balancing, and network lifetime, ensuring optimal agricultural outcomes

Service Function Chaining Based on Grammar in Software Defined Networks

Service Function Chaining is an architecture for orchestrating network services that assign choice to the network. This architecture is essentially a policy structure that should form the proper chain of services. Managing these networks is susceptible to error due to the combination of services with dedicated configurations. Accordingly, solutions will be needed to provide an appropriate ambiance for such a situation. Therefore, before running, the chains must be fully controlled, which requires the definition of chaining rules. Among the issues raised in this architecture are: checking the accuracy of the chains, as well as reducing the number of combinations of service chains. To solve these issues, the grammar is used in this paper. In this way, based on the scenarios in the Internet Engineering Task Force, they first create them and then their grammar is obtained using Regular Expressions and Finite Automaton. Subsequently, using the Cocke–Younger–Kasami algorithm, the grammar evaluation is performed and the number of combinations of services is also shown. The results show that this grammar can be verified by checking the service chain and also significantly reducing the number of combinations of service chains

Deep learning framework to extract anatomy for mosquito image classification

Mosquitoes are the main cause of the spread of dangerous diseases such as malaria, yellow fever, dengue fever, and Zika. The most effective way to control these diseases is to correctly identify the types of mosquito species. In the traditional method of identifying mosquitoes, identification is based on morphological diagnoses by specialized human beings with special skills. The most important classification challenge is to reduce the number of experts and the great diversity of different species of mosquitoes. In order to overcome this challenge, developing an automated method based on deep learning architectures to identify and classify mosquitoes will be a valuable resource for non-specialists.This study proposes a convolutional network model that integrates the ResNet101 architecture and the Mask_RCNN technique to segment and classifies mosquito images. 2354 mosquito images of three species of Anopheles, Aedes, and Culex are compared with each other. In the proposed model, instead of entering the network as a complete image of a mosquito, first, the images are segmented, and then different parts of the abdomen, legs, wings, and head are given to the network as input. The corresponding binary mask of the described parts of the mosquito body is produced by the convolution network to extract the feature for each separate part and then calculate the loss value between the classified values and the image label. The evaluation results showed that the extraction of mosquito anatomy images affects the faster classification of images and the network performed better with 97.84% accuracy than normal

Depression and Its Association with Adherence to Gluten-Free Diet in Patients with Celiac Disease

Background and Objective: Celiac disease is a malabsorption disorder that could result in various psychological consequences if patients do not adhere to a gluten-free diet. This study aimed to determine the frequency of major depressive disorder and its relationship with adherence to a gluten-free diet among patients with celiac disease. Methods: This descriptive-analytical study was conducted on 47 patients with celiac disease (30 women and 17 men) with an average age of 40.88 ± 10.7 years who had been referred to the Golestan Research Center of Gastroenterology and Hepatology during the summer of 2019. Patients were invited to complete a 13-item Beck Inventory. Celiac Dietary Adherence Test (CDAT) was used to assess adherence from the patients' point of view, and Standardized Dietician Evaluation (SDE) was used to evaluate adherence to the diet from the interviewer's perspective. Results: Overall, 28 people (59.6%) with celiac disease reported some degree of depression. Based on the SDE, the adherence rate of patients to a gluten-free diet was 83%. The association between adherence to a gluten-free diet and the prevalence of depression was not significant. There was also no significant association between the prevalence of depression and the gender and age of patients. Conclusion: Based on the results, a high percentage of patients with celiac disease have symptoms of depression. However, there is no significant relationship between adherence to a gluten-free diet and the prevalence of depression