Search results for: client classification

Authors: Sejin Jung, Dongho Kang, Byungsik Kim

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Assessing the impact of climate change requires the use of a multi-model ensemble (MME) to quantify uncertainties between scenarios and produce downscaled outlines for simulation of climate under the influence of different factors, including topography. This study decreases climate change scenarios from the 13 global climate models (GCMs) to assess the impacts of future climate change. Unlike South Korea, North Korea lacks in studies using climate change scenarios of the CoupledModelIntercomparisonProject (CMIP5), and only recently did the country start the projection of extreme precipitation episodes. One of the main purposes of this study is to predict changes in the average climatic conditions of North Korea in the future. The result of comparing downscaled climate change scenarios with observation data for a reference period indicates high applicability of the Multi-Model Ensemble (MME). Furthermore, the study classifies climatic zones by applying the Köppen-Geiger climate classification system to the MME, which is validated for future precipitation and temperature. The result suggests that the continental climate (D) that covers the inland area for the reference climate is expected to shift into the temperate climate (C). The coefficient of variation (CVs) in the temperature ensemble is particularly low for the southern coast of the Korean peninsula, and accordingly, a high possibility of the shifting climatic zone of the coast is predicted. This research was supported by a grant (MOIS-DP-2015-05) of Disaster Prediction and Mitigation Technology Development Program funded by Ministry of Interior and Safety (MOIS, Korea).

Keywords: MME, North Korea, Koppen–Geiger, climatic zones, coefficient of variation, CV

Procedia PDF Downloads 93

Authors: Marc Solé, Francesc Giné, Magda Valls, Nina Bijedic

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What people say on social media has turned into a rich source of information to understand social behavior. Specifically, the growing use of Twitter social media for political communication has arisen high opportunities to know the opinion of large numbers of politically active individuals in real time and predict the global political tendencies of a specific country. It has led to an increasing body of research on this topic. The majority of these studies have been focused on polarized political contexts characterized by only two alternatives. Unlike them, this paper tackles the challenge of forecasting Spanish political trends, characterized by multiple political parties, by means of analyzing the Twitters Users political tendency. According to this, a new strategy, named Tweets Analysis Strategy (TAS), is proposed. This is based on analyzing the users tweets by means of discovering its sentiment (positive, negative or neutral) and classifying them according to the political party they support. From this individual political tendency, the global political prediction for each political party is calculated. In order to do this, two different strategies for analyzing the sentiment analysis are proposed: one is based on Positive and Negative words Matching (PNM) and the second one is based on a Neural Networks Strategy (NNS). The complete TAS strategy has been performed in a Big-Data environment. The experimental results presented in this paper reveal that NNS strategy performs much better than PNM strategy to analyze the tweet sentiment. In addition, this research analyzes the viability of the TAS strategy to obtain the global trend in a political context make up by multiple parties with an error lower than 23%.

Keywords: political tendency, prediction, sentiment analysis, Twitter

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Authors: Hong Geng, Zaiyu Fan

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With the acceleration of urbanization and motorization in China, the scale of cities and the travel distance of residents are constantly expanding, and the number of cars is continuously increasing, so the urban traffic problem is more and more serious. Traffic congestion, environmental pollution, energy consumption, travel safety and direct interference between traffic and other urban activities are increasingly prominent problems brought about by motorized development. This not only has a serious impact on the lives of the residents but also has a major impact on the healthy development of the city. The paper found that, in order to solve the development of motorization, a number of problems will arise; urban planning and traffic planning and design in residential planning often take into account the development of motorized traffic but neglects the demand for street life. This kind of planning has resulted in the destruction of the traditional communication space of the residential area, the pollution of noise and exhaust gas, and the potential safety risks of the residential area, which has disturbed the previously quiet and comfortable life of the residential area, resulting in the inconvenience of residents' life and the loss of street vitality. Based on these facts, this paper takes a residential area in Wuhan as the research object, through the actual investigation and research, from the perspective of micro-transformation analysis, combined with the concept of traffic micro-reconstruction governance. And research puts forward the residential traffic optimization strategies such as strengthening the interaction and connection between the residential area and the urban street system, street traffic classification and organization.

Keywords: micro-transformation, residential traffic, residents demand, traffic microcirculation

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Authors: Ammar Said Ahmed, M. Reissmann, R. Bortfeldt, G. A. Brockmann

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Milk protein genetic variants are of interest for characterizing domesticated mammalian species and breeds, and for studying associations with economic traits. The aim of this work was to analyze milk protein genetic variation in the Sudanese native cattle breeds, which have been gradually declining in numbers over the last years due to the breed substitution, and indiscriminate crossbreeding. The genetic variation at three milk protein genes αS1-casein (CSN1S1), αS2-casein (CSN1S2) and ƙ-casein (CSN3) was investigated in 250 animals belonging to five Bos indicus cattle breeds of Sudan (Butana, Kenana, White-nile, Erashy and Elgash). Allele specific primers were designed for five SNPs determine the CSN1S1 variants B and C, the CSN1S2 variants A and B, the CSN3 variants A, B and H. Allele, haplotype frequencies and genetic distances (D) were calculated and the phylogenetic tree was constructed. All breeds were found to be polymorphic for the studied genes. The CSN1S1*C variant was found very frequently (>0.63) in all analyzed breeds with highest frequency (0.82) in White-nile cattle. The CSN1S2*A variant (0.77) and CSN3*A variant (0.79) had highest frequency in Kenana cattle. Eleven haplotypes in casein gene cluster were inferred. Six of all haplotypes occurred in all breeds with remarkably deferent frequencies. The estimated D ranged from 0.004 to 0.049. The most distant breeds were White-nile and Kenana (D 0.0479). The results presented contribute to the genetic knowledge of indigenous cattle and can be used for proper definition and classification of the Sudanese cattle breeds as well as breeding, utilization, and potential development of conservation strategies for local breeds.

Keywords: milk protein, genetic variation, casein haplotype, Bos indicus

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Authors: Xiaohua Zhu, Lingling Ma, Yongguang Zhao

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Leaf Area Index (LAI) is a key structural characteristic of crops and plays a significant role in precision agricultural management and farmland ecosystem modeling. However, LAI retrieved from different resolution data contain a scaling bias due to the spatial heterogeneity and model non-linearity, that is, there is scale effect during multi-scale LAI estimate. In this article, a typical farmland in semi-arid regions of Chinese Inner Mongolia is taken as the study area, based on the combination of PROSPECT model and SAIL model, a multiple dimensional Look-Up-Table (LUT) is generated for multiple crops LAI estimation from unmanned aerial vehicle (UAV) hyperspectral data. Based on Taylor expansion method and computational geometry model, a scale transfer model considering both difference between inter- and intra-class is constructed for scale effect analysis of LAI inversion over inhomogeneous surface. The results indicate that, (1) the LUT method based on classification and parameter sensitive analysis is useful for LAI retrieval of corn, potato, sunflower and melon on the typical farmland, with correlation coefficient R2 of 0.82 and root mean square error RMSE of 0.43m2/m-2. (2) The scale effect of LAI is becoming obvious with the decrease of image resolution, and maximum scale bias is more than 45%. (3) The scale effect of inter-classes is higher than that of intra-class, which can be corrected efficiently by the scale transfer model established based Taylor expansion and Computational geometry. After corrected, the maximum scale bias can be reduced to 1.2%.

Keywords: leaf area index (LAI), scale effect, UAV-based hyperspectral data, look-up-table (LUT), remote sensing

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Authors: M. Yosra, A. Mohamed, T. Sami

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Over the past few years, mobile communication technologies have seen significant evolution. This fact promoted the implementation of many systems in a multi-technological setting. From one system to another, the Quality of Service (QoS) provided to mobile consumers gets better. The growing number of normalized standards extends the available services for each consumer, moreover, most of the available radio frequencies have already been allocated, such as 3G, Wifi, Wimax, and LTE. A study by the Federal Communications Commission (FCC) found that certain frequency bands are partially occupied in particular locations and times. So, the idea of Cognitive Radio (CR) is to share the spectrum between a primary user (PU) and a secondary user (SU). The main objective of this spectrum management is to achieve a maximum rate of exploitation of the radio spectrum. In general, the CR can greatly improve the quality of service (QoS) and improve the reliability of the link. The problem will reside in the possibility of proposing a technique to improve the reliability of the wireless link by using the CR with some routing protocols. However, users declared that the links were unreliable and that it was an incompatibility with QoS. In our case, we choose the QoS parameter "bandwidth" to perform a supervised classification. In this paper, we propose a comparative study between some routing protocols, taking into account the variation of different technologies on the existing spectral bandwidth like 3G, WIFI, WIMAX, and LTE. Due to the simulation results, we observe that LTE has significantly higher availability bandwidth compared with other technologies. The performance of the OLSR protocol is better than other on-demand routing protocols (DSR, AODV and DSDV), in LTE technology because of the proper receiving of packets, less packet drop and the throughput. Numerous simulations of routing protocols have been made using simulators such as NS3.

Keywords: cognitive radio, multi technology, network simulator (NS3), routing protocol

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Authors: Tahani Aljohani, Jialin Yu, Alexandra. I. Cristea

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The more an educational system knows about a learner, the more personalised interaction it can provide, which leads to better learning. However, asking a learner directly is potentially disruptive, and often ignored by learners. Especially in the booming realm of MOOC Massive Online Learning platforms, only a very low percentage of users disclose demographic information about themselves. Thus, in this paper, we aim to predict learners’ demographic characteristics, by proposing an approach using linguistically motivated Deep Learning Architectures for Learner Profiling, particularly targeting gender prediction on a FutureLearn MOOC platform. Additionally, we tackle here the difficult problem of predicting the gender of learners based on their comments only – which are often available across MOOCs. The most common current approaches to text classification use the Long Short-Term Memory (LSTM) model, considering sentences as sequences. However, human language also has structures. In this research, rather than considering sentences as plain sequences, we hypothesise that higher semantic - and syntactic level sentence processing based on linguistics will render a richer representation. We thus evaluate, the traditional LSTM versus other bleeding edge models, which take into account syntactic structure, such as tree-structured LSTM, Stack-augmented Parser-Interpreter Neural Network (SPINN) and the Structure-Aware Tag Augmented model (SATA). Additionally, we explore using different word-level encoding functions. We have implemented these methods on Our MOOC dataset, which is the most performant one comparing with a public dataset on sentiment analysis that is further used as a cross-examining for the models' results.

Keywords: deep learning, data mining, gender predication, MOOCs

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Authors: Leila M. Carvalho, Maria Erica S. Oliveira, Arnoud C. Neto, Elza I. Ida, Massami Shimokomaki, Marta S. Madruga

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The quality of broiler breast meat is changing as a result of the continuing emphasis on genetic selection for a more efficient meat production. Breast meat has been classified as PSE (pale, soft, exudative), DFD (dark, firm, dry) and normal color meat, and recently a third group has emerged: the so-called PFN (pale, firm, non-exudative) meat. This classification was based on pH, color and functional properties. The aim of this work was to confirm the existence of PFN and PSE meat by biochemical characterization and functional properties. Twenty four hours of refrigerated fillet, Pectoralis major, m. samples (n= 838) were taken from Cobb flocks 42-48 days old, obtained in Northeastern Brazil tropical region, the Northeastern, considered to have only dry and wet seasons. Color (L*), pH, water holding capacity (WHC), values were evaluated and compared with PSE group samples. These samples were classified as Normal (465.8), PSE meat (L*≥53; pH<5.8) and PFN (L*≥53; pH>5.8). The occurrence of control meat, PSE and PFN was 69.09%, 11.10% and 19.81%, respectively. Samples from PFN presented 4.0-5.0% higher WHC in relation to PSE meat and similar to control group. These results are explained by the fact that PSE meat syndrome occurs because of higher protein denaturation as the consequence of a simultaneous lower pH values under warm carcass sooner after slaughtering impairing the myofibril proteins functional properties. Conversely, PFN samples follow normal glycolysis rate maintaining the normal proteins activities. In conclusion, the results reported herein confirm the existence of this emerging broiler meat group with similar properties as control group and it should be considered as normal breast meat group.

Keywords: broiler breast meat, funcional properties, PFN, PSE

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Authors: Rita Bagheri, Javed Ahmed, Humayra Bashir, M. Irfan Qureshi

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Agriculture lands suffer from a combination of stresses such as salinity and metal contamination including cadmium at the same time. Under such condition of multiple stresses, plant may exhibit unique responses different from the stress occurring individually. Thus, it would be interesting to investigate that how plant respond to combined stress at level of antioxidants and proteome expression, and identifying the proteins which are involved in imparting stress tolerance. With an approach of comparative proteomics and antioxidant analysis, present study investigates the response of Spinacia oleracea to salt (NaCl), cadmium (Cd), and their combination (NaCl+Cd) stress. Two-dimensional gel electrophoresis was used for resolving leaf proteome, and proteins of interest were identified using PDQuest software. A number of proteins expressed differentially, those indicated towards their roles in imparting stress tolerance, were digested by trypsin and analyzed on mass spectrometer for peptide mass fingerprinting (PMF). Data signals were then matched with protein databases using MASCOT. Results show that NaCl, Cd and both together (NaCl+Cd) induce oxidative stress which was highest in combined stress of Cd+NaCl. Correspondingly, the activities of enzymatic antioxidants viz., SOD, APX, GR and CAT, and non-enzymatic antioxidants had highest changes under combined stress compares to single stress over their respective controls. Among the identified proteins, several interesting proteins were identified that may be have role in Spinacia oleracia tolerance in individual and combinatorial stress of salt and cadmium. The functional classification of identified proteins indicates the importance and necessity of keeping higher ratio of defence and disease responsive proteins.

Keywords: Spinacia oleracea, Cd, salinity, proteomics, antioxidants, combinatorial stress

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Authors: Sanghamitra Roy

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In recent times, India has experienced the phenomenal rise in the number of registered vehicles and vehicular trips, particularly intra-city trips in most of its urban areas. The increase in vehicle ownership and use have increased parking demand immensely and accommodating the same is now a matter of big concern. Most cities do not have adequate off-street parking facilities thus forcing people to park on the streets. This has resulted in decreased carrying capacity, decreased traffic speed, increased congestion, and increased environmental problems. While integrated multi-modal transportation system is the answer to such problems, parking issues will continue to exist. In Kolkata, only 6.4% land is devoted for roads. The consequences of this huge crunch in road spaces coupled with increased parking demand are severe particularly in the CBD and major commercial areas, making the role of off-street parking facilities in Kolkata even more critical. To meaningfully address parking issues, it is important to identify the factors that influence parking demand so that it can be assessed and comprehensive parking policies and plans for the city can be formulated. This paper aims at identifying the factors that contribute towards parking demand for offices in Kolkata and their degree of correlation with parking demand. The study is limited to home-to-work trips located within Kolkata Municipal Corporation (KMC) where parking related issues are most pronounced. The data for the study is collected through personal interviews, questionnaires and direct observations from offices across the wards of KMC. SPSS is used for classification of the data and analyses of the same. The findings of this study will help in re-assessment of the parking requirements specified in The Kolkata Municipal Corporation Building Rules as a step towards alleviating parking related issues in the city.

Keywords: building rules, office spaces, parking demand, urbanization

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Authors: Thewodros K. Geberemariam

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The rapid physical expansion of urbanization coupled with aging infrastructure presents a unique decision and management challenges for many big city municipalities. Cities must therefore upgrade and maintain the existing aging urban drainage infrastructure systems to keep up with the demands. Given the overall contribution of assets to municipal revenue and the importance of infrastructure to the success of a livable city, many municipalities are currently looking for a robust and smart urban drainage infrastructure asset management solution that combines management, financial, engineering and technical practices. This robust decision-making shall rely on sound, complete, current and relevant data that enables asset valuation, impairment testing, lifecycle modeling, and forecasting across the multiple asset portfolios. On this paper, predictive computational intelligence (CI) and multi-class machine learning (ML) coupled with online, offline, and historical record data that are collected from an array of multi-parameter sensors are used for the extraction of different operational and non-conforming patterns hidden in structured and unstructured data to determine and produce actionable insight on the current and future states of the network. This paper aims to improve the strategic decision-making process by identifying all possible alternatives; evaluate the risk of each alternative, and choose the alternative most likely to attain the required goal in a cost-effective manner using historical and near real-time urban drainage infrastructure data for urban drainage infrastructures assets that have previously not benefited from computational intelligence and machine learning advancements.

Keywords: computational intelligence, machine learning, urban drainage infrastructure, machine learning, classification, prediction, asset management space

Procedia PDF Downloads 127

Authors: Chi-Ching Lee, Po-Jung Huang, Kuo-Yang Huang, Petrus Tang

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Background: Recent advances in high-throughput research technologies such as new-generation sequencing and multi-dimensional liquid chromatography makes it possible to dissect the complete transcriptome and proteome in a single run for the first time. However, it is almost impossible for many laboratories to handle and analysis these “BIG” data without the support from a bioinformatics team. We aimed to provide a web-based analysis platform for users with only limited knowledge on bio-computing to study the functional genomics and proteomics. Method: We use NCI-60 as an example dataset to demonstrate the power of the web-based analysis platform and data delivering system: C-eXpress takes a simple text file that contain the standard NCBI gene or protein ID and expression levels (rpkm or fold) as input file to generate a distribution map of gene/protein expression levels in a heatmap diagram organized by color gradients. The diagram is hyper-linked to a dynamic html table that allows the users to filter the datasets based on various gene features. A dynamic summary chart is generated automatically after each filtering process. Results: We implemented an integrated database that contain pre-defined annotations such as gene/protein properties (ID, name, length, MW, pI); pathways based on KEGG and GO biological process; subcellular localization based on GO cellular component; functional classification based on GO molecular function, kinase, peptidase and transporter. Multiple ways of sorting of column and rows is also provided for comparative analysis and visualization of multiple samples.

Keywords: cancer, visualization, database, functional annotation

Procedia PDF Downloads 591

Authors: C. M. Williams, R. English, P. King, I. M. Brown

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Introduction: Pseudoangiomatous Stromal Hyperplasia (PASH) is a benign fibrous proliferation of breast stroma affecting predominantly premenopausal women with no significant increased risk of breast cancer. Informal recommendations for management have continued to evolve over recent years from surgical excision to observation, although there are no specific national guidelines. This study assesses the safety of a non-surgical approach to PASH management by review of cases at a single centre. Methods: Retrospective case series review (January 2011 – August 2016) was conducted on consecutive PASH cases. Diagnostic classification (clinical, radiological and histological), management outcomes, and breast cancer incidence were recorded. Results: 43 patients were followed up for median of 25 months (3-64) with 75% symptomatic at presentation. 12% of cases (n=5) had a radiological score (BIRADS MMG or US) ≥ 4 of which 3 were confirmed malignant. One further malignancy was detected and proven radiologically occult and contralateral. No patients were diagnosed with a malignancy during follow-up. Treatment evolved from 67% surgical in 2011 to 33% in 2016. Conclusions: The management of PASH has transitioned in line with other published experience. The preliminary findings suggest this appears safe with no evidence of missed malignancies; however, longer follow up is required to confirm long-term safety. Recommendations: PASH with suspicious radiological findings ( ≥ U4/R4) warrants multidisciplinary discussion for excision. In the absence of histological or radiological suspicion of malignancy, PASH can be safely managed without surgery.

Keywords: benign breast disease, conservative management, malignancy, pseudoangiomatous stromal hyperplasia, surgical excision

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Authors: Wei Wen Yang, Emily Chia Yu Su

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Dengue fever is a mosquito-borne tropical infectious disease caused by the dengue virus. After infection, symptoms usually start from three to fourteen days. Dengue virus may cause a high fever and at least two of the following symptoms, severe headache, severe eye pain, joint pains, muscle or bone pain, vomiting, feature skin rash, and mild bleeding manifestation. In addition, recovery will take at least two to seven days. Dengue fever has rapidly spread in tropical and subtropical areas in recent years. Several phenomena around the world such as global warming, urbanization, and international travel are the main reasons in boosting the spread of dengue. In Taiwan, epidemics occur annually, especially during summer and fall seasons. On the other side, Singapore government also has announced the amounts number of dengue cases spreading in Singapore. As the serious epidemic of dengue fever outbreaks in Taiwan and Singapore, countries around the Asia-Pacific region are becoming high risks of susceptible to the outbreaks and local hub of spreading the virus. To improve public safety and public health issues, firstly, we are going to use Microsoft Excel and SAS EG to do data preprocessing. Secondly, using support vector machines and decision trees builds predict model, and analyzes the infectious cases between Taiwan and Singapore. By comparing different factors causing vector mosquito from model classification and regression, we can find similar spreading patterns where the disease occurred most frequently. The result can provide sufficient information to predict the future dengue infection outbreaks and control the diffusion of dengue fever among countries.

Keywords: dengue fever, Taiwan, Singapore, Aedes aegypti

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Authors: Shamsulhaq Amin

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Drought can arise from several hydrometeorological phenomena that result in insufficient precipitation, soil moisture, and surface and groundwater flow, leading to conditions that are considerably drier than the usual water content or availability. Drought is often assessed using indices that are associated with meteorological, agricultural, and hydrological phenomena. In order to effectively handle drought disasters, it is essential to accurately determine the kind, intensity, and extent of the drought using drought characterization. This information is critical for managing the drought before, during, and after the rehabilitation process. Over a hundred drought assessments have been created in literature to evaluate drought disasters, encompassing a range of factors and variables. Some models utilise solely hydrometeorological drivers, while others employ remote sensing technology, and some incorporate a combination of both. Comprehending the entire notion of drought and taking into account drought indices along with their calculation processes are crucial for researchers in this discipline. Examining several drought metrics in different studies requires additional time and concentration. Hence, it is crucial to conduct a thorough examination of approaches used in drought indices in order to identify the most straightforward approach to avoid any discrepancies in numerous scientific studies. In case of practical application in real-world, categorizing indices relative to their usage in meteorological, agricultural, and hydrological phenomena might help researchers maximize their efficiency. Users have the ability to explore different indexes at the same time, allowing them to compare the convenience of use and evaluate the benefits and drawbacks of each. Moreover, certain indices exhibit interdependence, which enhances comprehension of their connections and assists in making informed decisions about their suitability in various scenarios. This study provides a comprehensive assessment of various drought indices, analysing their types and computation methodologies in a detailed and systematic manner.

Keywords: drought classification, drought severity, drought indices, agricultur, hydrological

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Authors: Brindha Senthil Kumar, Payel Chakraborty, Senthil Kumar Nachimuthu, Arindam Maitra, Prem Nath

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Gastric cancer is predominantly caused by demographic and diet factors as compared to other cancer types. The aim of the study is to predict Early Gastric Cancer (ECG) from diet and lifestyle factors using supervised machine learning algorithms. For this study, 160 healthy individual and 80 cases were selected who had been followed for 3 years (2016-2019), at Civil Hospital, Aizawl, Mizoram. A dataset containing 11 features that are core risk factors for the gastric cancer were extracted. Supervised machine algorithms: Logistic Regression, Naive Bayes, Support Vector Machine (SVM), Multilayer perceptron, and Random Forest were used to analyze the dataset using Python Jupyter Notebook Version 3. The obtained classified results had been evaluated using metrics parameters: minimum_false_positives, brier_score, accuracy, precision, recall, F1_score, and Receiver Operating Characteristics (ROC) curve. Data analysis results showed Naive Bayes - 88, 0.11; Random Forest - 83, 0.16; SVM - 77, 0.22; Logistic Regression - 75, 0.25 and Multilayer perceptron - 72, 0.27 with respect to accuracy and brier_score in percent. Naive Bayes algorithm out performs with very low false positive rates as well as brier_score and good accuracy. Naive Bayes algorithm classification results in predicting ECG showed very satisfactory results using only diet cum lifestyle factors which will be very helpful for the physicians to educate the patients and public, thereby mortality of gastric cancer can be reduced/avoided with this knowledge mining work.

Keywords: Early Gastric cancer, Machine Learning, Diet, Lifestyle Characteristics

Procedia PDF Downloads 134

Authors: Aldrin Avergas, Quennie Mariel Peñaranda, Niña Karen San Miguel, Alexis Katrina Agustin, Peralta Xusha Mae, Maria Luisa Sison

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The research is entitled “The Relationship of Socioeconomic Status and Levels of Delinquency among Senior High School Students with Secured Attachment to their Mothers”. The researchers had explored the relationship between socioeconomic status and delinquent tendencies among grade 11 students. The objective of the research is to discover if delinquent behavior will have a relationship with the current socio-economic status of an adolescent student having a warm relationship with their mothers. The researchers utilized three questionnaires that would measure the three variables of the study, namely: (1) 1SEC 2012: The New Philippines Socioeconomic Classification System was used to show the current socioeconomic status of the respondents, (2) Self-Reported Delinquency – Problem Behavior Frequency Scale was utilized to determine the individual's frequency in engaging to delinquent behavior, and (3) Inventory of Parent and Peer Attachment Revised (IPPA-R) was used to determine the attachment style of the respondents. The researchers utilized a quantitative research design, specifically correlation research. The study concluded that there is no significant relationship between socioeconomic status and academic delinquency despite the fact that these participants had secured attachment to their mother hence this research implies that delinquency is not just a problem for students belonging in the lower socio-economic status and that even having a warm and close relationship with their mothers is not sufficient enough for these students to completely be free from engaging in delinquent acts. There must be other factors (such as peer pressure, emotional quotient, self-esteem or etc.) that are might be contributing to delinquent behaviors.

Keywords: adolescents, delinquency, high school students, secured attachment style, socioeconomic status

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Authors: Atour Taghipour

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In the real world each manufacturing company is an independent business unit. These business units are linked to each other through upstream and downstream linkages. The management of these linkages is called coordination which, could be considered as a difficult engineering task. The degree of difficulty of coordination depends on the type and the nature of information exchanged between partners as well as the structure of relationship from mutual to the network structure. The literature of manufacturing systems comprises a wide range of varieties of methods and approaches of coordination. In fact, two main streams of research can be distinguished: central coordination versus decentralized coordination. In the centralized systems a high degree of information exchanges is required. The high degree of information exchanges sometimes leads to difficulties when independent members do not want to share information. In order to address these difficulties, decentralized approaches of coordination of operations planning decisions based on some minimal information sharing have been proposed in many academic disciplines. This paper first proposes a framework of analysis in order to analyze the proposed approaches in the literature, based on this framework which includes the similarities between approaches we categorize the existing approaches. This classification can be used as a research map for future researches. The result of our paper highlights several opportunities for future research. First, it is proposed to develop more dynamic and stochastic mechanisms of planning coordination of manufacturing units. Second, in order to exploit the complementarities of approaches proposed by diverse science discipline, we propose to integrate the techniques of coordination. Finally, based on our approach we proposed to develop coordination standards to guaranty both the complementarity of these approaches as well as the freedom of companies to adopt any planning tools.

Keywords: network coordination, manufacturing, operations planning, supply chain

Procedia PDF Downloads 259

Authors: Ethan James

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Over one billion people worldwide suffer from some level of vision loss or blindness as a result of progressive retinal diseases. Many patients, particularly in developing areas, are incorrectly diagnosed or undiagnosed whatsoever due to unconventional diagnostic tools and screening methods. Artificial intelligence (AI) based on deep learning (DL) convolutional neural networks (CNN) have recently gained a high interest in ophthalmology for its computer-imaging diagnosis, disease prognosis, and risk assessment. Optical coherence tomography (OCT) is a popular imaging technique used to capture high-resolution cross-sections of retinas. In ophthalmology, DL has been applied to fundus photographs, optical coherence tomography, and visual fields, achieving robust classification performance in the detection of various retinal diseases including macular degeneration, diabetic retinopathy, and retinitis pigmentosa. However, there is no complete diagnostic model to analyze these retinal images that provide a diagnostic accuracy above 90%. Thus, the purpose of this project was to develop an AI model that utilizes machine learning techniques to automatically diagnose specific retinal diseases from OCT scans. The algorithm consists of neural network architecture that was trained from a dataset of over 20,000 real-world OCT images to train the robust model to utilize residual neural networks with cyclic pooling. This DL model can ultimately aid ophthalmologists in diagnosing patients with these retinal diseases more quickly and more accurately, therefore facilitating earlier treatment, which results in improved post-treatment outcomes.

Keywords: artificial intelligence, deep learning, imaging, medical devices, ophthalmic devices, ophthalmology, retina

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Authors: Mohammad Saber Eslamlou

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Morphology of Islamic cities has been extensively studied by researchers of Islamic cities and different theories could be found about it. In this regard, there exist much difference in method of analysis, classification, recognition, confrontation and comparative method of urban morphology. The present paper aims to examine the previous methods, approaches and insights and that how Dr. Giulia Annalinda Neglia dealt with the analysis of morphology of Islamic cities. Neglia is assistant professor in University of Bari, Italy (UNIBA) who has published numerous papers and books on Islamic cities. I introduce her works in the field of morphology of Islamic cities. And then, her thoughts, insights and research methodologies are presented and analyzed in critical perspective. This is a qualitative research on her written works, which have been classified in three major categories. The first category consists mainly of her works on morphology and physical shape of Islamic cities. The results of her works’ review suggest that she has used Moratoria typology in investigating morphology of Islamic cities. Moreover, overall structure of the cities under investigation is often described linear; however, she’s against to define a single framework for the recognition of morphology in Islamic cities. She states that ‘to understand the physical complexity and irregularities in Islamic cities, it is necessary to study the urban fabric by typology method, focusing on transformation processes of the buildings’ form and their surrounding open spaces’ and she believes that fabric of each region in the city follows from the principles of an specific period or urban pattern, in particular, Hellenistic and Roman structures. Furthermore, she believes that it is impossible to understand the morphology of a city without taking into account the obvious and hidden developments associated with it, because form of building and their surrounding open spaces are written history of the city.

Keywords: city, Islamic city, Giulia Annalinda Neglia, morphology

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Authors: Qingsong Xiong, Cheng Yuan, Qingzhao Kong, Haibei Xiong

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Deep-learning-driven approaches based on vibration responses have attracted larger attention in rapid structural condition assessment while obtaining sufficient measured training data with corresponding labels is relevantly costly and even inaccessible in practical engineering. This study proposes an integrated label propagation network for structural condition assessment, which is able to diffuse the labels from continuously-generating measurements by intact structure to those of missing labels of damage scenarios. The integrated network is embedded with damage-sensitive features extraction by deep autoencoder and pseudo-labels propagation by optimized fuzzy clustering, the architecture and mechanism which are elaborated. With a sophisticated network design and specified strategies for improving performance, the present network achieves to extends the superiority of self-supervised representation learning, unsupervised fuzzy clustering and supervised classification algorithms into an integration aiming at assessing damage conditions. Both numerical simulations and full-scale laboratory shaking table tests of a two-story building structure were conducted to validate its capability of detecting post-earthquake damage. The identifying accuracy of a present network was 0.95 in numerical validations and an average 0.86 in laboratory case studies, respectively. It should be noted that the whole training procedure of all involved models in the network stringently doesn’t rely upon any labeled data of damage scenarios but only several samples of intact structure, which indicates a significant superiority in model adaptability and feasible applicability in practice.

Keywords: autoencoder, condition assessment, fuzzy clustering, label propagation

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Authors: Ayu Dwi Hardiyanti, Bernardus Anggit Winahyu, I. Gusti Agung Ayu Sugita Sari, Lestari Sutra Simamora, I. Wayan Warmada

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The Penosogan Formation sandstone, that has Middle Miosen age, has been deemed as a reservoir potential based on sample data from sandstone outcrop in Kebakalan and Kedawung villages, Karangsambung sub-district, Kebumen Regency, Central Java. This research employs the following analytical methods; petrography, X-ray diffraction (XRD), and porosity test. Based on the presence of micritic sandstone, muddy micrite, and muddy sandstone, the Penosogan Formation sandstone has a fine-coarse granular size and middle-to-fine sorting. The composition of the sandstone is mostly made up of plagioclase, skeletal grain, and traces of micrite. The percentage of clay minerals based on petrographic analysis is 10% and appears to envelop grain, resulting enveloping grain which reduces the porosity of rocks. The porosity types as follows: interparticle, vuggy, channel, and shelter, with an equant form of cement. Moreover, the diagenesis process involves compaction, cementation, authigenic mineral growth, and dissolving due to feldspar alteration. The maturity of the reservoir can be seen through the X-ray diffraction analysis results, using ethylene glycol solution for clay minerals fraction transformed from smectite–illite. Porosity test analysis showed that the Penosogan Formation sandstones has a porosity value of 22% based on the Koeseomadinata classification, 1980. That shows high maturity is very influential for the quality of reservoirs sandstone of the Penosogan Formation.

Keywords: sandstone reservoir, Penosogan Formation, smectite, XRD

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Authors: S. Abdi Goudazri, R. Ziaie Moayed, A. Nazeri

Abstract:

Loose soils normally are of weak bearing capacity due to their structural nature. Being exposed to heavy traffic loads, they would fail in most cases. To tackle the aforementioned issue, geotechnical engineers have come up with different approaches; one of which is making use of geosynthetic-reinforced soil-aggregate systems. As these polymeric reinforcements have highlighted economic and environmentally-friendly features, they have become widespread in practice during the last decades. The present research investigates the efficiency of four different types of these reinforcements in increasing the bearing capacity of two-layered soil sections using a series California Bearing Ratio (CBR) test. The studied sections are comprised of a 10 cm-thick layer of no. 161 Firouzkooh sand (weak subgrade) and a 10 cm-thick layer of compacted aggregate materials (base course) classified as SP and GW according to the United Soil Classification System (USCS), respectively. The aggregate layer was compacted to the relative density (Dr) of 95% at the optimum water content (Wopt) of 6.5%. The applied reinforcements were including two kinds of geocomposites (type A and B), a geotextile, and a geogrid that were embedded at the interface of the lower and the upper layers of the soil-aggregate system. As the standard CBR mold was not appropriate in height for this study, the mold used for soaked CBR tests were utilized. To make a comparison between the results of stress-settlement behavior in the studied specimens, CBR values pertinent to the penetrations of 2.5 mm and 5 mm were considered. The obtained results demonstrated 21% and 24.5% increments in the amount of CBR value in the presence of geocomposite type A and geogrid, respectively. On the other hand, the effect of both geotextile and geocomposite type B on CBR values was generally insignificant in this research.

Keywords: geosynthetics, geogrid, geotextile, CBR test, increasing bearing capacity

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Authors: Parul Johri, Mala Trivedi

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Major Intrinsic proteins (MIPs), actively involved in the passive transport of small polar molecules across the membranes of almost all living organisms. MIPs that specifically transport water molecules are named aquaporins (AQPs). The permeability of membranes is actively controlled by the regulation of the amount of different MIPs present but also in some cases by phosphorylation and dephosphorylation of the channel. Based on sequence similarity, MIPs have been classified into many categories. All of the proteins are made up of the 20 amino acids, the only difference is there in their orientations. Again all the 20 amino acids are made up of the basic five elements namely: carbon, hydrogen, oxygen, sulphur and nitrogen. These elements are responsible for giving the amino acids the properties of hydrophilicity/hydrophobicity which play an important role in protein interactions. The hydrophobic amino acids characteristically have greater number of carbon atoms as carbon is the main element which contributes to hydrophobic interactions in proteins. It is observed that the carbon level of proteins in different species is different. In the present work, we have taken a sample set of 150 aquaporins proteins from Uniprot database and a dynamic programming code was written to calculate the carbon percentage for each sequence. This carbon percentage was further used to barcode the aqauporins of animals and plants. The protein taken from Oryza sativa, Zea mays and Arabidopsis thaliana preferred to have carbon percentage of 31.8 to 35, whereas on the other hand sequences taken from Mus musculus, Saccharomyces cerevisiae, Homo sapiens, Bos Taurus, and Rattus norvegicus preferred to have carbon percentage of 31 to 33.7. This clearly demarks the carbon range in the aquaporin proteins from plant and animal origin. Hence the atom level analysis of protein sequences can provide us with better results as compared to the residue level comparison.

Keywords: aquaporins, carbon, dynamic prgramming, MIPs

Procedia PDF Downloads 343

Authors: Juan Manuel Macia

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The Montreal Port Authority (MPA) wanted to build a new road link via Souligny Avenue to increase the fluidity of goods transported by truck in the Viau Street area of Montreal and to mitigate the current traffic problems on Notre-Dame Street. With the purpose of having a better integration and acceptance of this project with the neighboring residential surroundings, this project needed to include an architectural integration, bringing some artistic components to the bridge design along with some landscaping components. The MPA is required primarily to provide direct truck access to Port of Montreal with a direct connection to the future Assomption Boulevard planned by the City of Montreal and, thus, direct access to Souligny Avenue. The MPA also required other key aspects to be considered for the proposal and development of the project, such as the layout of road and rail configurations, the reconstruction of underground structures, the relocation of power lines, the installation of lighting systems, the traffic signage and communication systems improvement, the construction of new access ramps, the pavement reconstruction and a summary assessment of the structural capacity of an existing service tunnel. The identification of the various possible scenarios began by identifying all the constraints related to the numerous infrastructures located in the area of the future link between the port and the future extension of Souligny Avenue, involving interaction with several disciplines and technical specialties. Several viaduct- and tunnel-type geometries were studied to link the port road to the right-of-way north of Notre-Dame Street and to improve traffic flow at the railway corridor. The proposed design took into account the existing access points to Port of Montreal, the built environment of the MPA site, the provincial and municipal rights-of-way, and the future Notre-Dame Street layout planned by the City of Montreal. These considerations required the installation of an engineering structure with a span of over 60 m to free up a corridor for the future urban fabric of Notre-Dame Street. The best option for crossing this span length was identified by the design and construction of a curved bridge over Notre-Dame Street, which is essentially a structure with a deck formed by a reinforced concrete slab on steel box girders with a single span of 63.5m. The foundation units were defined as pier-cap type abutments on drilled shafts to bedrock with rock sockets, with MSE-type walls at the approaches. The configuration of a single-span curved structure posed significant design and construction challenges, considering the major constraints of the project site, a design for durability approach, and the need to guarantee optimum performance over a 75-year service life in accordance with the client's needs and the recommendations and requirements defined by the standards used for the project. These aspects and the need to include architectural and artistic components in this project made it possible to design, build, and integrate a signature infrastructure project with a sustainable approach, from which the MPA, the commuters, and the city of Montreal and its residents will benefit.

Keywords: curved bridge, steel box girder, medium span, simply supported, industrial and urban environment, architectural integration, design for durability

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Authors: Silvija Šiljeg, Ivan Marić, Ante Šiljeg

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The accessibility of urban green spaces (UGS) is an integral element in the quality of life. Due to rapid urbanization, UGS studies have become a key element in urban planning. The potential benefits of space for its inhabitants are frequently analysed. A functional transport network system and the optimal spatial distribution of urban green surfaces are the prerequisites for maintaining the environmental equilibrium of the urban landscape. An accessibility analysis was conducted as part of the Urban Green Belts Project (UGB). The development of a GIS database for Zadar was the first step in generating the UGS accessibility indicator. Data were collected using the supervised classification method of multispectral LANDSAT images and manual vectorization of digital orthophoto images (DOF). An analysis of UGS accessibility according to the ANGst standard was conducted in the first phase of research. The accessibility indicator was generated on the basis of seven objective measurements, which included average UGS surface per capita and accessibility according to six functional levels of green surfaces. The generated indicator was compared with subjective measurements obtained by conducting a survey (718 respondents) within statistical units. The collected data reflected individual assessments and subjective evaluations of UGS accessibility. This study highlighted the importance of using objective and subjective measures in the process of understanding the accessibility of urban green surfaces. It may be concluded that when evaluating UGS accessibility, residents emphasize the immediate residential environment, ignoring higher UGS functional levels. It was also concluded that large areas of UGS within a city do not necessarily generate similar satisfaction with accessibility. The heterogeneity of output results may serve as guidelines for the further development of a functional UGS city network.

Keywords: urban green spaces (UGS), accessibility indicator, subjective and objective measurements, Zadar

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Authors: Vinay Kumar, M. Kishor, Sathyanarayana Rao Ts

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Background: Growth of elderly people in the general population in recent years is termed as ‘greying of the world’ where there is a shift from high mortality & fertility to low mortality and fertility, resulting in an increased proportion of older people as seen in India. Improved health care promises longevity but socio-economic factors like poverty, joint families and poor services pose a psychological threat. Epidemiological data regarding the prevalence of mental disorders in geriatric population with physical illness is required for proper health planning. Methods: Sixty consecutive elderly patients aged 60 years or above of both sexes, reporting with physical illness to general outpatient registration counter of JSS Medical College and Hospital, Mysore, India, were considered for the Study. With informed consent, they were screened with General Health Questionnaire (GHQ-12) and were further evaluated for diagnosing mental disorders according to WHO International Classification of Diseases (ICD-10) criteria. Results: Mental disorders were detected in 48.3%, predominantly depressive disorders, nicotine dependence, generalized anxiety disorder, alcohol dependence and least was dementia. Most common physical illness was cardiovascular disease followed by metabolic, respiratory and other diseases. Depressive disorders, substance dependence and dementia were more associated with cardiovascular disease compared to metabolic disease and respiratory diseases were more associated with nicotine dependence. Conclusions: Depression and Substance use disorders among elderly population is of concern, which needs to be further studied with larger population. Psychiatric morbidity will adversely have an impact on physical illness which needs proper assessment and management. This will enhance our understanding and prioritize our planning for future.

Keywords: Geriatric, mental disorders, physical illness, psychiatry

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Authors: Isaac K. E. Ampomah, Seong-Bae Park, Sang-Jo Lee

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Over the past decade, there have been promising developments in Natural Language Processing (NLP) with several investigations of approaches focusing on Recognizing Textual Entailment (RTE). These models include models based on lexical similarities, models based on formal reasoning, and most recently deep neural models. In this paper, we present a sentence encoding model that exploits the sentence-to-sentence relation information for RTE. In terms of sentence modeling, Convolutional neural network (CNN) and recurrent neural networks (RNNs) adopt different approaches. RNNs are known to be well suited for sequence modeling, whilst CNN is suited for the extraction of n-gram features through the filters and can learn ranges of relations via the pooling mechanism. We combine the strength of RNN and CNN as stated above to present a unified model for the RTE task. Our model basically combines relation vectors computed from the phrasal representation of each sentence and final encoded sentence representations. Firstly, we pass each sentence through a convolutional layer to extract a sequence of higher-level phrase representation for each sentence from which the first relation vector is computed. Secondly, the phrasal representation of each sentence from the convolutional layer is fed into a Bidirectional Long Short Term Memory (Bi-LSTM) to obtain the final sentence representations from which a second relation vector is computed. The relations vectors are combined and then used in then used in the same fashion as attention mechanism over the Bi-LSTM outputs to yield the final sentence representations for the classification. Experiment on the Stanford Natural Language Inference (SNLI) corpus suggests that this is a promising technique for RTE.

Keywords: deep neural models, natural language inference, recognizing textual entailment (RTE), sentence-to-sentence relation

Procedia PDF Downloads 326

Authors: Wendu Pang, Yaxin Luo, Junhong Li, Yu Zhao, Danni Cheng, Yufang Rao, Minzi Mao, Ke Qiu, Yijun Dong, Fei Chen, Jun Liu, Jian Zou, Haiyang Wang, Wei Xu, Jianjun Ren

Abstract:

We aimed to compare the prognostic prediction value of positive lymph node number (PLNN) to the American Joint Committee on Cancer (AJCC) tumor, lymph node, and metastasis (TNM) staging system for patients with hypopharyngeal squamous cell carcinoma (HPSCC). A total of 826 patients with HPSCC from the Surveillance, Epidemiology, and End Results database (2004–2015) were identified and split into two independent cohorts: training (n=461) and validation (n=365). Univariate and multivariate Cox regression analyses were used to evaluate the prognostic effects of PLNN in patients with HPSCC. We further applied six Cox regression models to compare the survival predictive values of the PLNN and AJCC TNM staging system. PLNN showed a significant association with overall survival (OS) and cancer-specific survival (CSS) (P < 0.001) in both univariate and multivariable analyses, and was divided into three groups (PLNN 0, PLNN 1-5, and PLNN>5). In the training cohort, multivariate analysis revealed that the increased PLNN of HPSCC gave rise to significantly poor OS and CSS after adjusting for age, sex, tumor size, and cancer stage; this trend was also verified by the validation cohort. Additionally, the survival model incorporating a composite of PLNN and TNM classification (C-index, 0.705, 0.734) performed better than the PLNN and AJCC TNM models. PLNN can serve as a powerful survival predictor for patients with HPSCC and is a surrogate supplement for cancer staging systems.

Keywords: hypopharyngeal squamous cell carcinoma, positive lymph nodes number, prognosis, prediction models, survival predictive values

Procedia PDF Downloads 118

Authors: P. Massanyi, L. Kichi, T. Slanina, E. Kolesar, J. Danko, N. Lukac, E. Tvrda, R. Stawarz, A. Kolesarova

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Target of this study was the analysis of the impact of crude glycerol on canine spermatozoa motility, morphology, viability, and membrane integrity. Experiments were realized in vitro. In the study, semen from 5 large dog breeds was used. They were typical representatives of large breeds, coming from healthy rearing, regularly vaccinated and integrated to the further breeding. Semen collections were realized at the owners of animals and in the veterinary clinic. Subsequently the experiments were realized at the Department of Animal Physiology of the SUA in Nitra. The spermatozoa motility was evaluated using CASA analyzer (SpermVisionTM, Minitub, Germany) at the temperature 5 and 37°C for 5 hours. In the study, 13 motility parameters were evaluated. Generally, crude glycerol has generally negative effect on spermatozoa motility. Morphological analysis was realized using Hancock staining and the preparations were evaluated at magnification 1000x using classification tables of morphologically changed spermatozoa. Data clearly detected the highest number of morphologically changed spermatozoa in the experimental groups (know twisted tails, tail torso and tail coiling). For acrosome alterations swelled acrosomes, removed acrosomes and acrosomes with undulated membrane were detected. In this study also the effect of crude glycerol on spermatozoa membrane integrity were analyzed. The highest crude glycerol concentration significantly affects spermatozoa integrity. Results of this study show that crude glycerol has effect of spermatozoa motility, viability, and membrane integrity. Detected changes are related to crude glycerol concentration, temperature, as well as time of incubation.

Keywords: dog, semen, spermatozoa, acrosome, glycerol, CASA, viability

Procedia PDF Downloads 294