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Yogurt is one of the most widely consumed fermented products obtained from milk that has a positive effect on health due to its high nutritional value. Yogurt enrichment can play a major role in the developmentof consumers’ health level. Spirulina platensisis a blue-green microalga and is rich in protein, essential fatty acids, vitamins, minerals and antioxidant compounds, such as phycocyanin. The aim of the present study was to investigate the effect of phycocyanin extracted from S. platensis and persimmon powder on physicochemical and sensory characteristics of yogurt. For this purpose, the yoghurt samples were analyzed for their physicochemical characteristics. Samples without persimmon powder and containing higher amounts of phycocyanin (1% and 1.5%) had the highest acidity. The acidity values of all samples increased over time. The highest pH belonged to the control sample and the addition of different amounts of phycocyanin and persimmon powder caused a significant reduction in pH.In all time intervals, with the increase of persimmon powder and phycocyanin, the syneresis rate of samplesincreased though it was not significant compared to the control sample. Samples containing higher amounts of persimmon powder (codes 6 and 7) had higher dry matter, protein, and water holding capacity (WHC).On both studied days, the highest and the lowest viscosity belonged to treatment code 7 andcode 2, respectively (p≤0.05). Additionally, the viscosity of all treatments increased significantly from the first day to the fifteenth day over time (p≤0.05). The control sample had the highest lightness (L* ) and the treatment code 8 had the highest redness (a * ‌). In all time intervals, the highest hardness and springiness belonged to the control sample (p≤0.05). The results of sensory evaluation of the samples showed that on the first day, the color score of the control sample and treatment codes 5, 6 and 7 was significantly higher than other treatments (p≤0.05). On all days studied, the taste scores of the control sample and treatment codes 5 and 6 and the texture score of the control sample and treatment codes 6, 7, and 8 were significantly higher than other treatments (p≤0.05).Considering the sensory evaluation and protein content of the yogurt samples, treatment code 6 was selected as the best treatment. The results showed that phycocyanin extracted from S. platensis and persimmon powder is effective on the physicochemical and sensory properties of yogurt and can be used as an effective additive.

There is a growing demand for the consumption of nutritious and safe food products. Cold plasma is a novel non-thermal technology that in recent years, has found numerous applications in the food industry. Study on the applications of this technology and its effects on food quality is increasing. Like any other technology, using cold plasma for the processing of foods can be associated with food quality challenges. This paper reviews the effect of cold plasma on the chemical structure of different food constituents as well as its influence on food characteristics. The emphasis is on the recent studies about the plasma mechanisms of action and chemical alterations of different food components. The studies show that the interaction of plasma-reactive species with food components depends on process conditions. Developing the functional characteristics and reducing the anti-nutritional compounds are of promising potentials of cold plasma. Finally, the research gaps, the salient drawbacks, and future prospects of this technology are highlighted.

Knowing the near real-time pose of the construction equipment is an important necessity for improving safety and productivity of construction processes. A safer construction site can be achieved by accurately capturing the movements of the equipment and their parts using motion tracking techniques. Recently, the application of Computer Vision (CV) techniques is growing, especially considering that other techniques have difficulties with the deployment and high cost. From the productivity point of view, knowing the pose of the equipment helps to estimate the time that the operator spent on each state of his/her operations. For this purpose, each part or joint of the equipment must be recognized and tracked. Extracting this information requires either using depth sensors or stereo vision, which includes the image information of two or more Red Green Blue (RGB) cameras that have overlapping views. Since the reading range of the common and affordable depth sensors are limited to few meters, this research chooses to use RGB cameras. The most challenging step for this purpose is to process the data of each camera individually for extracting the 2D skeleton of the equipment. After having the 2D skeleton of the equipment from each camera, the 3D pose can be further estimated using the relative rotation and translation information between the cameras coordinate systems. This paper mainly focuses on determining the 2D skeleton of excavators based on the videos received from the cameras available on the site. The method takes advantage of synthetic images of each excavator's part to train the parts' detectors. After detecting the parts, the backgrounds of the detected parts are subtracted. The remaining pixels from the previous step are processed to estimate the skeleton of each part. The final skeleton of the excavator is derived by connecting the individual skeletons of each part to their adjacent parts considering the kinematic information of the excavator.

The recognition of construction equipment is always necessary and important to monitor the progress and the safety of a construction project. Recently, the potentials of computer vision (CV) techniques have been investigated to facilitate the current equipment recognition method. However, the process of manually collecting and annotating a large image dataset of different equipment is one of the most time-consuming tasks that may delay the application of the CV techniques for construction equipment recognition. Moreover, collecting effective negative samples brings more difficulties for training the object detectors. This research aims to introduce an automated method for creating and annotating synthetic images of construction equipment while significantly reducing the required time. The synthetic images of the equipment are created from the three-dimensional (3D) models of construction machines combined with various background images taken from construction sites. The location of the equipment in the images is known since that equipment is the only object over the single-color background. This location can be extracted by applying segmentation techniques and then used for the annotation purpose. Furthermore, an automated negative image sampler is introduced in this paper to automatically generate many negative samples with different sizes out of one general image of a construction site in a way that the samples do not include the target object. The test results show that the proposed method is able to reduce the required time for annotating the images in comparison with traditional annotation methods while improving the detection accuracy.

We discussed the needs for adding definitions of RFID components to a BIM.We investigated scenarios to identify the related attributes of RFID system components.We investigated the relationships of RFID components with the assets/spaces.We proposed an IFC extension to incorporate the new definitions of RFID components.We validated and demonstrated the applicability of the proposed method through a real-world case study. Building Information Modeling (BIM) is emerging as a method of creating, sharing, exchanging and managing the building information throughout the lifecycle between all stakeholders. Radio Frequency Identification (RFID), on the other hand, has emerged as an automatic data collection and information storage technology, and has been used in different applications in the AEC/FM (Architecture, Engineering, Construction, and Facilities Management) industry. RFID tags are attached to building assets throughout their lifecycle and used to store lifecycle and context aware information taken from a BIM. Consequently, there is a need for a standard and formal definition of RFID technology components in BIM. The goal of this paper is to add the definitions for RFID components to the BIM standard and to map the data to be stored in RFID memory to the associated entries in a BIM database. The paper defines new entities, data types, and properties to be added to the BIM. Furthermore, the paper identifies the relationships between RFID tags and building elements. These predefined relationships facilitate the linkage between BIM data and RFID data. Eventually, the data that are required to be saved on RFID tags can be automatically selected using the defined relationships in a BIM. A real-world case study has been implemented to validate the proposed method using available BIM software.

The application of computer vision (CV) in construction projects has been investigated for many years, resulting in several advanced algorithms and methods. However, there is still a need t...

Construction sites are changing every day, which brings some difficulties for different contractors to do their tasks properly. One of the key points for all entities who work on the same site is the location of resources including materials, tools, and equipment. Therefore, the lack of an integrated localization system leads to an increase in the time wasted on searching for resources. In this research, a localization method based on Radio Frequency Identification (RFID) systems which does not need infrastructure is proposed to overcome this problem. This paper investigates the usage of active RFID technology for the localization of movable objects (e.g. components, equipment, and tools) equipped with RFID tags using handheld readers by extending a Cluster-based Movable Tag Localization (CMTL) technique which uses a k-Nearest Neighbor (k-NN) algorithm. CMTL uses a multidimensional clustering technique that considers signal pattern similarity between target and reference tags together with spatial distribution of reference tags for detecting the region where the target tag is located. The paper proposes applying an irregular bilinear interpolation method to form a grid of virtual reference tags within the selected cluster of real reference tags. Moreover, the proposed method uses artificial neural networks (ANNs) for positioning the target tag, as opposed to empirical weighted averaging formulas used in similar k-NN based methods. Comparative analysis is performed to quantify the improvement of the proposed method over similar k-NN-based methods using a simulation environment. A case study is performed to analyze the performance of the proposed method.

Introduction: Allergic asthma is a chronic airway inflammatory disease often determined with degrees of inflammation, hypersensitivity, bronchial constriction, and airway changes. Th1, Th2, and Th17 cells are the main cells involved in asthma pathophysiology. To evaluate Th1, Th2, and Th17 functions by assessing INF-γ, IL-4, and IL-17 gene and protein levels in asthma patients and healthy controls. Materials and Methods: In total, 44 individuals of Iranian ethnicity including 24 patients with allergic asthma and 20 healthy controls were enrolled. Peripheral blood mononuclear cells of all participants were isolated and cDNA was synthesized following RNA extraction. Gene expression and protein levels of INF-γ, IL-4, and IL-17 were evaluated by real-time polymerase chain reaction and sandwich ELISA, respectively. Results: The results of this study showed that the gene expression of IL-4 and IL-17 in patients was increased significantly compared to the control group (p = 0.046 and 0.03, respectively) whereas that of IFN-γ was significantly decreased in the group of patients (p = 0.021). Compared to the healthy controls, serum levels of IL-17 and IL-4 were significantly increased in asthma patients (p = 0.015 and 0.03, respectively). Conclusion: Higher IL-17 and IL-4 mRNA expression and serum levels in asthma patients than healthy controls highlight the role of Th2 and Th17 cells in asthma pathogenesis and their potential as therapeutic targets.

Indoor localization has gained importance as it has the potential to improve various processes related to the lifecycle management of facilities and to deliver personalized and location-based services (LBSs). Radio Frequency Identification (RFID) based systems, on the other hand, have been widely used in different applications in construction and maintenance. This paper investigates the usage of RFID technology for indoor localization of RFID equipped assets during the operation phase of facilities. The location-related data on RFID tags attached to assets are extracted from a Building Information Model (BIM) and can provide context-aware information inside the building which can improve Facilities Management (FM) processes. First, using the current location of the assets saved on the tags attached to fixed assets, an FM personnel is able to read tags from a distance and locate them on a floor plan. Fixed tags with known positions act as reference tags for RFID reader localization techniques. In this scenario, the user can also estimate his/her location by scanning the surrounding reference tags. Furthermore, the paper investigates an approach to locate moveable assets using received signals from available reference tags in the building based on pattern matching and clustering algorithms. As a result, a user equipped with an RFID reader is able to estimate his/her location, as well as the location of target assets, without having access to any Real-Time Location System (RTLS) infrastructure. Several case studies are used to demonstrate the feasibility of the proposed methods.

H pylori is the main causative agent of Gastric cancer and chronic gastritis. Genetic diversity of H. pylori has major contribution in its pathogenesis. We investigated the prevalence of oipA and iceA1/iceA2 positive strains of H. pylori among patients with gastric cancer and gastritis.Sampling performed by means of endoscopy from 86 patients. DNA was extracted from tissue samples using DNA extraction kit. PCR assay was performed and products were monitored by Agarose Gel Electrophoresis.Urease Test and 16S rRNA PCR did not show significant differences in detection of H. pylori. The frequency of iceA1 allele in patients with gastric cancer was significantly higher than those with gastritis (p0.05). However, there was no significant difference in prevalence of oipA and iceA2 genes among the two groups of patients (p0.05).The iceA1 gene, but the oipA and iceA2 genes , is associated with H. pylori-induced gastric cancer. However, confirmatory studies must be performed in future.

Dental practitioners are susceptible to Musculoskeletal Disorders (MSDs). The symptoms may begin from education courses. The aim of this study was to assess prevalence of musculoskeletal pain reports and correlated factors among the Mashhad Dental School students, Iran. A total of 177 undergraduate and postgraduate dental students, who were involved in educational clinical training, completed a questionnaire focusing on pain reports of different body anatomical regions. Variables such as gender, academic grade, academic year, clinical working hour, regular exercise times and also pain characteristics including pain duration intensity and frequency were evaluated. As results 82% of undergraduate students and 90% of postgraduate students reported body pain in at least one region. The most prevalent pain locations were: chest/shoulder (46.9%), head/neck (41.8%), middle back (33.9%) and right hand (25.4%). Severity of reported pain was increased due to performing dental work, increased working stress and working fatigue. Regular exercise was associated with alleviated some pain characteristics. As conclusions musculoskeletal pain reports were highly prevalent among dental students. Attention to prevention of musculoskeletal disorders should be considered as priority in dental schools. For dental students exercise training courses for strengthening muscles of shoulder/chest, neck and back are necessary.

Indoor localization has gained importance as it has the potential to improve various processes related to the lifecycle management of facilities and to deliver personalized and location-based services. Radio Frequency Identification (RFID) based systems, on the other hand, have been widely used in different applications in construction and maintenance. This paper investigates the usage of RFID technology for indoor localization of RFID equipped movable assets during the operation phase of facilities. The location-related data on RFID tags attached to fixed assets are extracted from a Building Information Model (BIM) and can provide context-aware information inside the building which can improve Facilities Management (FM) processes. The paper proposes a new approach to use received signals from available reference tags in the building attached to fixed assets to locate movable assets. The approach uses signal pattern matching and clustering algorithms for localization. As a result, a user equipped with an RFID reader is able to estimate the location of target assets, without having access to any RealTime Location System (RTLS) infrastructure. A case study is performed to demonstrate the feasibility of proposed methods.