Comparison of Vibration Amplitude in Isfahan Subway Due to Track Structure- An Experimental Study

Increasing the stability of structures and reducing the maintenance cost of slab track superstructures compared to ballasted tracks are among the reasons for the tendency to use this category of superstructures in the railway industry. Vibration reduction methods can be divided into three categories, source, propagation path, and receiver. In general, the slab track structures in Iran are divided into three categories: direct fixation track (DFT), floating slab track (FST), and high resilient fastener (HRF). Although railway tracks are a safe, economical and fast transportation system and can lead to the strengthening of the tourism industry, in the long term, vibrations can damage many historical structures in the city of Isfahan. FST and HRF systems are used in the structure of Isfahan subway track. In this paper, the accelerations (longitudinal, lateral, and vertical) of the Isfahan subway vehicle were measured in 30 stations (15 go stations and 15 return stations). The results showed that the HRF system compared to the FST has a significant effect in reducing the range of vibrations and ultimately the safety of the train and the ride comfort. For example, in the area between Si-O-Se-Pol and Imam Hossein Square, due to the track structure type (HRF), the maximum acceleration and RMS acceleration are in the range of 1.5 and 0.3 m/s2, respectively, while in other stations these values were extracted up to 4 and 0.7 m/s2, respectively

Shear transfer between precast prestressed bridge beams and in-situ concrete crosshead in continuous structures

A detailed investigation was made to study the shear transfer between precast prestressed beams and in-situ concrete in a relatively new method of construction of continuous bridge decks where the ends of precast beams are connected to an integral in-situ crosshead away from the supports. Two series of tests were carried out. In the first series 1/3 scale models of the M. o. T, C&CA M-8 sections were used, and these were modified in the second series to study the effect of the beam's top flanges within the connection. One of the most important mechanisms of shear transfer proved to be the top flanges of the precast beam. For the precast beams with top flanges (first series), and with a 300mm beam embedment length, it was discovered that: a) The shear force is transferred from a small length at the end of the beam. b) The in-situ concrete nibs (concrete surrounding the web) can take this shear force without stirrups. c) There is no need either to project all the bars from the precast into the in-situ concrete or to prestress the connection transversely as a means of improving shear transfer. d) It was possible to transfer the whole shear force at the connection with a reduced embedment length of 100mm with nib stirrups. For the precast beam without top flanges, the transfer of the shear force at the connection required other improving details. In this respect transverse prestressing and web shear connectors were utilized effectively. The effect of projecting bars was also examined. In the general behaviour of composite continuous beams subjected to shear a detailed comparison was made between different Code predictions for the web cracking shear and web crushing strength. A mathematical model is also proposed to predict the stirrup stress according to shear span, effective depth and stirrup ratio when failure is controlled by web crushing. Stirrup stress measurement in the vicinity of continuous support made it possible to predict the enhanced shear strength and a design method is proposed for the continuous beams. A comparion is also made between different Code predictions in this respect. To obtain more information about the strength of web shear connectors used in the secod series, a separate dowel shear specimen was designed. Different interface conditions including bond, dowel bar size and strength and the effect of shrinkage were examined. A design method is proposed together with a comparison with different Code predictions

Sentiment aggregation of targeted features by capturing their dependencies: Making sense from customer reviews

Ideation is an important phase in the new product development process at which product designers innovate and select novel ideas that can be added as features to an existing product. One way to find novel ideas is to transfer uncommon features of products of other domains and integrate them into the product to be improved. However, before incorporating such targeted features into the product, they need to be evaluated against the customers’ acceptance in social media using sentiment aggregation tools. Despite the many studies in sentiment analysis, mapping the customers’ opinions towards both high-level and technical features of a product extracted from social media to their best corresponding component in that product is still a challenge. Furthermore, none of the existing approaches ascertains the sentiment value of a targeted feature by capturing its dependencies on other features. In this paper, to address these drawbacks, we propose the sentiment aggregation framework for targeted features (SA-TF). SA-TF determines the sentiment of a targeted feature by assisting product designers in the tasks of mapping the features discussed in the reviews to the right product components, sentiment aggregation and considering feature dependencies to determine their polarity. The superiority of the different phases of SA-TF is demonstrated with experiments and comparing it with an existing approach

Multiple pregnancy and its complication-induced hospitalization in Shaheed Beheshti and Shabihkhani hospitals, Kashan, 2000-01

Background : Considering the importance of multiple pregnancy and its influence on neonate's mortality and morbidity and also with respect to the lack of informative data on its prevalence, the present study was conducted in Shaheed Beheshti and Shabihkhani hospitals in Kashan, 2000-01, to determine the role of early diagnosis and treatment. Materials and methods : During a one-year period, all multiple pregnancies were included. Initial data including gestational age, sex, maternal age, complication-induced hospitalization, birth weight, previous history of multiple pregnancy, mortality, and history of ovarian-stimulating drug consumption were all recorded in a questionnaire. Results : Of 10011 deliveries, 142 (1.4) were twins, 8 (0.079) were triplets, and 1 (0.009) was quadruplets. Of these, 58 twins (40.8), 5 triplets (62.5) and 1 (100) quadruplets were hospitalized. Prematurity was the most common complication in twins (84). Mortality was reported in 15 twins (13.5) and 3 triplets (21.4). The most seasonal prevalence of twin pregnancy was winter (27.4). Conclusion : Prematurity was more common as compared to western data. Prematurity and low birth weight are the most frequent cause of mortality, thus prevention of preterm delivery is highly recommended

Sentiment Analysis of Specific Product's Features Using Product Tree for Application in New Product Development

New Product Development (NPD) is a multi-step process by which novel products are introduced in the market. Sentiment analysis, which ascertains the popularity of each new feature added to the product, is one of the key steps in this process. In this paper we present an approach by which product designers analyze users’ reviews from social media platforms to determine the popularity of a specific product’s feature in order to make a decision about adding it to the product’s next generation. Our proposed approach utilizes a product tree generated from a product specification document to facilitate forming an efficient link between features mentioned in the users’ reviews and those of the product designer’s interest. Furthermore, it captures the links/interactions between a feature of interest and its other related features in a product to ascertain its polarity

A fine-grained ontology-based sentiment aggregation approach

© 2019, Springer International Publishing AG, part of Springer Nature. Sentiment analysis techniques are widely used to capture the voice of customers about different products/services. Aspect or feature-based sentiment detection tools as one of the sentiment analyses’ types are developed to find the customers’ opinions about various features of a product. However, as a product may contain many features, presenting the final obtained results to the users is a challenge. Even though this issue is addressed in the literature by developing different sentiment aggregation methods, their results are mostly presented at the basic-level features of a product. This may cause in losing customers’ opinion about at minor sub-features. However, as the performance of a basic feature is dependent on those of its different sub-features, we propose an approach which aggregates the extracted results at a fine-grained level features using a product ontology tree. We interpret the polarity of each feature as a satisfaction score which can help managers in investigating the weaknesses of their products even at minor levels in a more informed way

Feature drift-based framework for novel idea recommendation in new product development

In these competitive times, designers need to constantly update their tangible products with new features and functionalities to retain market share. This is especially important for designers of emerging products, as customer expectations of them change more frequently. New Product Development (NPD) is an established process which assists product designers in achieving this aim. In the first step of this process, product designers find novel features for upgrading their products (idea generation) and select some ideas (idea selection) for developing a new product concept in the next steps of NPD process. This step plays a vital role in introducing newness into a product and consequently its success in the market. A recent trend in existing literature for generating novel ideas is transfer learning where ideas for improvements are inspired from products in other domains. However, they are heavy/process-centric and time-intensive for the designers of emerging products in finding novel ideas from products of other domains. Moreover, while enormous research has been done to accomplish the idea selection task, there are still no agreed evaluation metrics for the product designers to prioritize their ideas. To address this problem in this thesis a framework, namely FEATURE, is developed which assists product designers to accomplish idea generation and idea selection to enhance emerging products. FEATURE adopts the science and engineering research approach, and it consists of three modules, namely Novel Feature Finder (NFF), Feature Sentiment Analyser (FSA) and Targeted Feature Recommender (TFR). NFF generates ideas by searching the products of cross-domains for their novel features that can be integrated into the product to be improved. To diminish the risk of product failure, FSA then ascertains the popularity of the generated ideas by using the customers’ reviews in social media. Finally, TFR prioritizes ideas based on different decision-criteria. From an industry-applicability perspective, FEATURE provides the designers of emerging products with a systematic light-weight approach to identify and assess realistic ideas in a short time. The functionality and viability of the different tasks of FEATURE are validated by experiments and systematized by a prototype to highlight the effectiveness of the overall proposed solution

A decision support framework for identifying novel ideas in new product development from cross-domain analysis

© 2017 Elsevier Ltd In current competitive times, product manufacturers need not only to retain their existing customer base, but also to increase their market share. One way they can achieve this is by generating new ideas and developing novel products with new features. As highlighted in the literature, in generating new ideas to develop novel and innovative products, it is important that product designers satisfy the needs of both current customers and new customers. However, despite the large number of existing studies that identify novel features in the ideation phase, product designers do not have a systematic framework that utilises additional information relating to products from either far-field or related domains to generate such new ideas in the ideation phase. This paper presents our proposed framework FEATURE which provides just such a systemic framework for product designers in the ideation phase of new product development. FEATURE has three phases. The first phase identifies and recommends to the product designers novel features that can be added to the next version of a reference product. In order to incorporate the customer's voice into the ideation phase, the second phase ascertains the popularity of the proposed features by using social media. The third phase ranks the proposed features based on the designer's decision criteria to select those that should be considered further in the next phases of new product development. We explain the importance of each phase of FEATURE and show the working of its first module in detail