Search results for: encrypted traffic classification

Authors: Sarah E. Nelson, Casey Weiner, Alexander Sigmon, Jun Hua, Haris I. Sair, Jose I. Suarez, Robert D. Stevens

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Graph-theoretical information from structural connectomes indicated significant connectivity changes and improved acute prognostication in a Random Forest (RF) model in aneurysmal subarachnoid hemorrhage (aSAH), which can lead to significant morbidity and mortality and has traditionally been fraught by poor methods to predict outcome. This study’s hypothesis was that structural connectivity changes occur in canonical brain networks of acute aSAH patients, and that these changes are associated with functional outcome at six months. In a prospective cohort of patients admitted to a single institution for management of acute aSAH, patients underwent diffusion tensor imaging (DTI) as part of a multimodal MRI scan. A weighted undirected structural connectome was created of each patient’s images using Constant Solid Angle (CSA) tractography, with 176 regions of interest (ROIs) defined by the Johns Hopkins Eve atlas. ROIs were sorted into four networks: Default Mode Network, Executive Control Network, Salience Network, and Whole Brain. The resulting nodes and edges were characterized using graph-theoretic features, including Node Strength (NS), Betweenness Centrality (BC), Network Degree (ND), and Connectedness (C). Clinical (including demographics and World Federation of Neurologic Surgeons scale) and graph features were used separately and in combination to train RF and Logistic Regression classifiers to predict two outcomes: dichotomized modified Rankin Score (mRS) at discharge and at six months after discharge (favorable outcome mRS 0-2, unfavorable outcome mRS 3-6). A total of 56 aSAH patients underwent DTI a median (IQR) of 7 (IQR=8.5) days after admission. The best performing model (RF) combining clinical and DTI graph features had a mean Area Under the Receiver Operator Characteristic Curve (AUROC) of 0.88 ± 0.00 and Area Under the Precision Recall Curve (AUPRC) of 0.95 ± 0.00 over 500 trials. The combined model performed better than the clinical model alone (AUROC 0.81 ± 0.01, AUPRC 0.91 ± 0.00). The highest-ranked graph features for prediction were NS, BC, and ND. These results indicate reorganization of the connectome early after aSAH. The performance of clinical prognostic models was increased significantly by the inclusion of DTI-derived graph connectivity metrics. This methodology could significantly improve prognostication of aSAH.

Keywords: connectomics, diffusion tensor imaging, graph theory, machine learning, subarachnoid hemorrhage

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Authors: Dijiang Lyu, Shaoying Li, Zhangzhi Tan, Zhifeng Wu, Feng Gao

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Nowadays, China is experiencing rapidly urban rail transit expansions in the world. The purpose of this study is to finely model factors influencing transit ridership at multi-time dimensions within transit stations’ pedestrian catchment area (PCA) in Guangzhou, China. This study was based on multi-sources spatial data, including smart card data, high spatial resolution images, points of interest (POIs), real-estate online data and building height data. Eight multiple linear regression models using backward stepwise method and Geographic Information System (GIS) were created at station-level. According to Chinese code for classification of urban land use and planning standards of development land, residential land-use were divided into three categories: first-level (e.g. villa), second-level (e.g. community) and third-level (e.g. urban villages). Finally, it concluded that: (1) four factors (CBD dummy, number of feeder bus route, number of entrance or exit and the years of station operation) were proved to be positively correlated with transit ridership, but the area of green land-use and water land-use negative correlated instead. (2) The area of education land-use, the second-level and third-level residential land-use were found to be highly connected to the average value of morning peak boarding and evening peak alighting ridership. But the area of commercial land-use and the average height of buildings, were significantly positive associated with the average value of morning peak alighting and evening peak boarding ridership. (3) The area of the second-level residential land-use was rarely correlated with ridership in other regression models. Because private car ownership is still large in Guangzhou now, and some residents living in the community around the stations go to work by transit at peak time, but others are much more willing to drive their own car at non-peak time. The area of the third-level residential land-use, like urban villages, was highly positive correlated with ridership in all models, indicating that residents who live in the third-level residential land-use are the main passenger source of the Guangzhou Metro. (4) The diversity of land-use was found to have a significant impact on the passenger flow on the weekend, but was non-related to weekday. The findings can be useful for station planning, management and policymaking.

Keywords: fine-scale modeling, Guangzhou city, multi-time dimensions, multi-sources spatial data, transit ridership

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Authors: Andreea Molnar, Amalia Ardeljan, Lexi Frankel, Marissa Dallara, Brittany Nagel, Omar Rashid

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Background: Escherichia coli is a gram-negative, facultative anaerobic bacteria that belongs to the family Enterobacteriaceae and resides in the intestinal tracts of individuals. E.Coli has numerous strains grouped into serogroups and serotypes based on differences in antigens in their cell walls (somatic, or “O” antigens) and flagella (“H” antigens). More than 700 serotypes of E. coli have been identified. Although most strains of E. coli are harmless, a few strains, such as E. coli O157:H7 which produces Shiga toxin, can cause intestinal infection with symptoms of severe abdominal cramps, bloody diarrhea, and vomiting. Infection with E. Coli can lead to the development of systemic inflammation as the toxin exerts its effects. Chronic inflammation is now known to contribute to cancer development in several organs, including the prostate. The purpose of this study was to evaluate the correlation between E. Coli and the incidence of prostate cancer. Methods: Data collected in this cohort study was provided by a Health Insurance Portability and Accountability Act (HIPAA) compliant national database to evaluate patients infected with E.Coli infection and prostate cancer using the International Classification of Disease (ICD-10 and ICD-9 codes). Permission to use the database was granted by Holy Cross Health, Fort Lauderdale for the purpose of academic research. Data analysis was conducted through the use of standard statistical methods. Results: Between January 2010 and December 2019, the query was analyzed and resulted in 81, 037 patients after matching in both infected and control groups, respectively. The two groups were matched by Age Range and CCI score. The incidence of prostate cancer was 2.07% and 1,680 patients in the E. Coli group compared to 5.19% and 4,206 patients in the control group. The difference was statistically significant by a p-value p<2.2x10-16 with an Odds Ratio of 0.53 and a 95% CI. Based on the specific treatment for E.Coli, the infected group vs control group were matched again with a result of 31,696 patients in each group. 827 out of 31,696 (2.60%) patients with a prior E.coli infection and treated with antibiotics were compared to 1634 out of 31,696 (5.15%) patients with no history of E.coli infection (control) and received antibiotic treatment. Both populations subsequently developed prostate carcinoma. Results remained statistically significant (p<2.2x10-16), Odds Ratio=0.55 (95% CI 0.51-0.59). Conclusion: This retrospective study shows a statistically significant correlation between E.Coli infection and a decreased incidence of prostate cancer. Further evaluation is needed in order to identify the impact of E.Coli infection and prostate cancer development.

Keywords: E. Coli, prostate cancer, protective, microbiology

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Authors: Jia Li, Xing Wei, Yuchen Hong, Yang Lu

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Detecting vehicle behavior has always been the focus of intelligent transportation, but with the explosive growth of the number of vehicles and the complexity of the road environment, the vehicle behavior videos captured by traditional surveillance have been unable to satisfy the study of vehicle behavior. The traditional method of manually labeling vehicle behavior is too time-consuming and labor-intensive, but the existing object detection and tracking algorithms have poor practicability and low behavioral location detection rate. This paper proposes a vehicle behavior detection algorithm based on the dual-stream convolution network and the multi-dimensional video dynamic detection network. In the videos, the straight-line behavior of the vehicle will default to the background behavior. The Changing lanes, turning and turning around are set as target behaviors. The purpose of this model is to automatically mark the target behavior of the vehicle from the untrimmed videos. First, the target behavior proposals in the long video are extracted through the dual-stream convolution network. The model uses a dual-stream convolutional network to generate a one-dimensional action score waveform, and then extract segments with scores above a given threshold M into preliminary vehicle behavior proposals. Second, the preliminary proposals are pruned and identified using the multi-dimensional video dynamic detection network. Referring to the hierarchical reinforcement learning, the multi-dimensional network includes a Timer module and a Spacer module, where the Timer module mines time information in the video stream and the Spacer module extracts spatial information in the video frame. The Timer and Spacer module are implemented by Long Short-Term Memory (LSTM) and start from an all-zero hidden state. The Timer module uses the Transformer mechanism to extract timing information from the video stream and extract features by linear mapping and other methods. Finally, the model fuses time information and spatial information and obtains the location and category of the behavior through the softmax layer. This paper uses recall and precision to measure the performance of the model. Extensive experiments show that based on the dataset of this paper, the proposed model has obvious advantages compared with the existing state-of-the-art behavior detection algorithms. When the Time Intersection over Union (TIoU) threshold is 0.5, the Average-Precision (MP) reaches 36.3% (the MP of baselines is 21.5%). In summary, this paper proposes a vehicle behavior detection model based on multi-dimensional dynamic detection network. This paper introduces spatial information and temporal information to extract vehicle behaviors in long videos. Experiments show that the proposed algorithm is advanced and accurate in-vehicle timing behavior detection. In the future, the focus will be on simultaneously detecting the timing behavior of multiple vehicles in complex traffic scenes (such as a busy street) while ensuring accuracy.

Keywords: vehicle behavior detection, convolutional neural network, long short-term memory, deep learning

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Authors: Alejandra G. Barbón, José Vila, Dania Vazquez

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This study seeks to contribute to the advancement of library and archival sciences in the areas of records management, knowledge organization, and information architecture, particularly focusing on the enhancement of bibliographical description through the incorporation of visual interactive designs aimed to enrich the library users’ experience. In an era of heightened awareness about the legacy of hiddenness across special and rare collections in libraries and archives, along with the need for inclusivity in academia, the University of Miami Libraries has embarked on an innovative project that intersects the realms of print culture, decolonization, and digital technology. This proposal presents an exciting initiative to revitalize the study of Artists’ Books collections by employing digital visual representations to decolonize bibliographic records of some of the most unique materials and foster a more holistic understanding of cultural heritage. Artists' Books, a dynamic and interdisciplinary art form, challenge conventional bibliographic classification systems, making them ripe for the exploration of alternative approaches. This project involves the creation of a digital platform that combines multimedia elements for digital representations, interactive information retrieval systems, innovative information architecture, trending bibliographic cataloging and metadata initiatives, and collaborative curation to transform how we engage with and understand these collections. By embracing the potential of technology, we aim to transcend traditional constraints and address the historical biases that have influenced bibliographic practices. In essence, this study showcases a groundbreaking endeavor at the University of Miami Libraries that seeks to not only enhance bibliographic practices but also confront the legacy of hiddenness across special and rare collections in libraries and archives while strengthening conventional bibliographic description. By embracing digital visualizations, we aim to provide new pathways for understanding Artists' Books collections in a manner that is more inclusive, dynamic, and forward-looking. This project exemplifies the University’s dedication to fostering critical engagement, embracing technological innovation, and promoting diverse and equitable classifications and representations of cultural heritage.

Keywords: decolonizing bibliographic cataloging frameworks, digital visualizations information architecture platforms, collaborative curation and inclusivity for records management, engagement and accessibility increasing interaction design and user experience

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Authors: Rideci Farias, Haroldo Paranhos, Joyce Silva, Elson Almeida, Hellen Silva, Lucas Silva

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The constant growth of the fleet of vehicles in the big cities, makes that the Engineering is dynamic, with respect to the new solutions for traffic flow in general. In the Federal District (DF), Brazil, it is no different. The city of Brasilia, Capital of Brazil, and Cultural Heritage of Humanity by UNESCO, is projected to 500 thousand inhabitants, and today circulates more than 3 million people in the city, and with a fleet of more than one vehicle for every two inhabitants. The growth of the city to the North region, made that the urban planning presented solutions for the fleet in constant growth. In this context, a complex of viaducts, road accesses, creation of new rolling roads and duplication of the Bragueto bridge over Paranoa lake in the northern part of the city was designed, giving access to the BR-020 highway, denominated Clover of North Triage (TTN). In the geopedological context, the region is composed of hydromorphic soils, with the presence of the water level at some times of the year. From the geotechnical point of view, are soils with SPT < 4 and Resistance not drained, Su < 50 kPa. According to urban planning in Brasília, special art works can not rise in the urban landscape, contrasting with the urban characteristics of the architects Lúcio Costa and Oscar Niemeyer. Architects hired to design the new Capital of Brazil. The urban criterion then created the technical impasse, resulting in the technical need to ‘bury’ the works of art and in turn the access greenhouses at different levels, in regions of low support soil and water level Outcrossing, generally inducing the need for this study and design. For the adoption of the appropriate solution, Standard Penetration Test (SPT), Vane Test, Diagnostic peritoneal lavage (DPL) and auger boring campaigns were carried out. With the comparison of the results of these tests, the profiles of resistance of the soils and water levels were created in the studied sections. Geometric factors such as existing sidewalks and lack of elevation for the discharge of deep drainage water have inhibited traditional techniques for total removal of soft soils, thus avoiding the use of temporary drawdown and shoring of excavations. Thus, a structural layer was designed to reinforce the subgrade by means of the ‘needling’ of the soft soil, without the need for longitudinal drains. In this context, the article presents the geological and geotechnical studies carried out, but also the dimensioning of the reinforcement layer on the soft soil with a view to the main objective of this solution that is to allow the execution of the civil works without the interference in the roads in use, Execution of services in rainy periods, presentation of solution compatible with drainage characteristics and soft soil reinforcement.

Keywords: layer, reinforcement, soft soil, clover of north triage

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Authors: Jun Won Kim

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Objective: Theta-phase gamma-amplitude coupling (TGC) was used as a novel evidence-based tool to reflect the dysfunctional cortico-thalamic interaction in patients with schizophrenia. However, to our best knowledge, no studies have reported the diagnostic utility of the TGC in the resting-state electroencephalographic (EEG) of neuroleptic-naive patients with schizophrenia compared to healthy controls. Thus, the purpose of this EEG study was to understand the underlying mechanisms in patients with schizophrenia by comparing the TGC at rest between two groups and to evaluate the diagnostic utility of TGC. Method: The subjects included 90 patients with schizophrenia and 90 healthy controls. All patients were diagnosed with schizophrenia according to the criteria of Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM-IV) by two independent psychiatrists using semi-structured clinical interviews. Because patients were either drug-naïve (first episode) or had not been taking psychoactive drugs for one month before the study, we could exclude the influence of medications. Five frequency bands were defined for spectral analyses: delta (1–4 Hz), theta (4–8 Hz), slow alpha (8–10 Hz), fast alpha (10–13.5 Hz), beta (13.5–30 Hz), and gamma (30-80 Hz). The spectral power of the EEG data was calculated with fast Fourier Transformation using the 'spectrogram.m' function of the signal processing toolbox in Matlab. An analysis of covariance (ANCOVA) was performed to compare the TGC results between the groups, which were adjusted using a Bonferroni correction (P < 0.05/19 = 0.0026). Receiver operator characteristic (ROC) analysis was conducted to examine the discriminating ability of the TGC data for schizophrenia diagnosis. Results: The patients with schizophrenia showed a significant increase in the resting-state TGC at all electrodes. The delta, theta, slow alpha, fast alpha, and beta powers showed low accuracies of 62.2%, 58.4%, 56.9%, 60.9%, and 59.0%, respectively, in discriminating the patients with schizophrenia from the healthy controls. The ROC analysis performed on the TGC data generated the most accurate result among the EEG measures, displaying an overall classification accuracy of 92.5%. Conclusion: As TGC includes phase, which contains information about neuronal interactions from the EEG recording, TGC is expected to be useful for understanding the mechanisms the dysfunctional cortico-thalamic interaction in patients with schizophrenia. The resting-state TGC value was increased in the patients with schizophrenia compared to that in the healthy controls and had a higher discriminating ability than the other parameters. These findings may be related to the compensatory hyper-arousal patterns of the dysfunctional default-mode network (DMN) in schizophrenia. Further research exploring the association between TGC and medical or psychiatric conditions that may confound EEG signals will help clarify the potential utility of TGC.

Keywords: quantitative electroencephalography (QEEG), theta-phase gamma-amplitude coupling (TGC), schizophrenia, diagnostic utility

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Authors: David L. Knott, Robert Kingsland, Alistair Hitchon

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The proposed Newcastle Light Rail is a key part of the revitalisation of Newcastle, NSW and will provide a frequent and reliable travel option throughout the city centre, running from Newcastle Interchange at Wickham to Pacific Park in Newcastle East, a total of 2.7 kilometers in length. Approximately one-third of the route, along Hunter and Scott Streets, is subject to potential shallow underground mine workings. The extent of mining and seams mined is unclear. Convicts mined the Yard Seam and overlying Dudley (Dirty) Seam in Newcastle sometime between 1800 and 1830. The Australian Agricultural Company mined the Yard Seam from about 1831 to the 1860s in the alignment area. The Yard Seam was about 3 feet (0.9m) thick, and therefore, known as the Yard Seam. Mine maps do not exist for the workings in the area of interest and it was unclear if both or just one seam was mined. Information from 1830s geological mapping and other data showing shaft locations were used along Scott Street and information from the 1908 Royal Commission was used along Hunter Street to develop an investigation program. In addition, mining was encountered for several sites to the south of the alignment at depths of about 7 m to 25 m. Based on the anticipated depths of mining, it was considered prudent to assess the potential for sinkhole development on the proposed alignment and realigned underground utilities and to obtain approval for the work from Subsidence Advisory NSW (SA NSW). The assessment consisted of a desktop study, followed by a subsurface investigation. Four boreholes were drilled along Scott Street and three boreholes were drilled along Hunter Street using HQ coring techniques in the rock. The placement of boreholes was complicated by the presence of utilities in the roadway and traffic constraints. All the boreholes encountered the Yard Seam, with conditions varying from unmined coal to an open void, indicating the presence of mining. The geotechnical information obtained from the boreholes was expanded by using various downhole techniques including; borehole camera, borehole sonar, and downhole geophysical logging. The camera provided views of the rock and helped to explain zones of no recovery. In addition, timber props within the void were observed. Borehole sonar was performed in the void and provided an indication of room size as well as the presence of timber props within the room. Downhole geophysical logging was performed in the boreholes to measure density, natural gamma, and borehole deviation. The data helped confirm that all the mining was in the Yard Seam and that the overlying Dudley Seam had been eroded in the past over much of the alignment. In summary, the assessment allowed the potential for sinkhole subsidence to be assessed and a mitigation approach developed to allow conditional approval by SA NSW. It also confirmed the presence of mining in the Yard Seam, the depth to the seam and mining conditions, and indicated that subsidence did not appear to have occurred in the past.

Keywords: downhole investigation techniques, drilling, mine subsidence, yard seam

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Authors: Ayesha Akbar

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Public service delivery has witnessed a significant improvement with the integration of information communication technology (ICT). It not only improves management structure with advanced technology for surveillance of service delivery but also provides evidence for informed decisions and policy. Pakistan’s public sector organizations have not been able to produce some good results to ensure service delivery. Notwithstanding, some of the public sector organizations in Pakistan has diffused modern technology and proved their credence by providing better service delivery standards. These good indicators provide sound basis to integrate technology in public sector organizations and shift of policy towards evidence based policy making. Rescue-1122 is a public sector organization which provides emergency services and proved to be a successful model for the provision of service delivery to save human lives and to ensure human development in Pakistan. The information about the organization has been received by employing qualitative research methodology. The information is broadly based on primary and secondary sources which includes Rescue-1122 website, official reports of organizations; UNDP (United Nation Development Program), WHO (World Health Organization) and by conducting 10 in-depth interviews with the high administrative staff of organizations who work in the Lahore offices. The information received has been incorporated with the study for the better understanding of the organization and their management procedures. Rescue-1122 represents a successful model in delivering the services in an efficient way to deal with the disaster management. The management of Rescue has strategized the policies and procedures in such a way to develop a comprehensive model with the integration of technology. This model provides efficient service delivery as well as maintains the standards of the organization. The service delivery model of rescue-1122 works on two fronts; front-office interface and the back-office interface. Back-office defines the procedures of operations and assures the compliance of the staff whereas, front-office equipped with the latest technology and good infrastructure handles the emergency calls. Both ends are integrated with satellite based vehicle tracking, wireless system, fleet monitoring system and IP camera which monitors every move of the staff to provide better services and to pinpoint the distortions in the services. The standard time of reaching to the emergency spot is 7 minutes, and during entertaining the case; driver‘s behavior, traffic volume and the technical assistance being provided to the emergency case is being monitored by front-office. Then the whole information get uploaded to the main dashboard of Lahore headquarter from the provincial offices. The latest technology is being materialized by Rescue-1122 for delivering the efficient services, investigating the flaws; if found, and to develop data to make informed decision making. The other public sector organizations of Pakistan can also develop such models to integrate technology for improving service delivery and to develop evidence for informed decisions and policy making.

Keywords: data, e-governance, evidence, policy

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Authors: Despoina Mouratidis, Katia Lida Kermanidis

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Machine translation (MT) is a very important task in Natural Language Processing (NLP). MT evaluation is crucial in MT development, as it constitutes the means to assess the success of an MT system, and also helps improve its performance. Several methods have been proposed for the evaluation of (MT) systems. Some of the most popular ones in automatic MT evaluation are score-based, such as the BLEU score, and others are based on lexical similarity or syntactic similarity between the MT outputs and the reference involving higher-level information like part of speech tagging (POS). This paper presents a language-independent machine learning framework for classifying pairwise translations. This framework uses vector representations of two machine-produced translations, one from a statistical machine translation model (SMT) and one from a neural machine translation model (NMT). The vector representations consist of automatically extracted word embeddings and string-like language-independent features. These vector representations used as an input to a multi-layer neural network (NN) that models the similarity between each MT output and the reference, as well as between the two MT outputs. To evaluate the proposed approach, a professional translation and a "ground-truth" annotation are used. The parallel corpora used are English-Greek (EN-GR) and English-Italian (EN-IT), in the educational domain and of informal genres (video lecture subtitles, course forum text, etc.) that are difficult to be reliably translated. They have tested three basic deep learning (DL) architectures to this schema: (i) fully-connected dense, (ii) Convolutional Neural Network (CNN), and (iii) Long Short-Term Memory (LSTM). Experiments show that all tested architectures achieved better results when compared against those of some of the well-known basic approaches, such as Random Forest (RF) and Support Vector Machine (SVM). Better accuracy results are obtained when LSTM layers are used in our schema. In terms of a balance between the results, better accuracy results are obtained when dense layers are used. The reason for this is that the model correctly classifies more sentences of the minority class (SMT). For a more integrated analysis of the accuracy results, a qualitative linguistic analysis is carried out. In this context, problems have been identified about some figures of speech, as the metaphors, or about certain linguistic phenomena, such as per etymology: paronyms. It is quite interesting to find out why all the classifiers led to worse accuracy results in Italian as compared to Greek, taking into account that the linguistic features employed are language independent.

Keywords: machine learning, machine translation evaluation, neural network architecture, pairwise classification

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Authors: Oluwafunmibi Omotayo Fasanya, Augustine Kena Adjei

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To develop a machine learning model to predict diabetes (DM) prevalence in the U.S. population using demographic characteristics, physical indicators, and lifestyle habits, and to analyze how these factors contribute to the likelihood of diabetes. We analyzed data from 23,546 participants aged 20 and older, who were non-pregnant, from the 2009-2018 National Health and Nutrition Examination Survey (NHANES). The dataset included key demographic (age, sex, ethnicity), physical (BMI, leg length, total cholesterol [TCHOL], fasting plasma glucose), and lifestyle indicators (smoking habits). A weighted sample was used to account for NHANES survey design features such as stratification and clustering. A classification machine learning model was trained to predict diabetes status. The target variable was binary (diabetes or non-diabetes) based on fasting plasma glucose measurements. The following models were evaluated: Logistic Regression (baseline), Random Forest Classifier, Gradient Boosting Machine (GBM), Support Vector Machine (SVM). Model performance was assessed using accuracy, F1-score, AUC-ROC, and precision-recall metrics. Feature importance was analyzed using SHAP values to interpret the contributions of variables such as age, BMI, ethnicity, and smoking status. The Gradient Boosting Machine (GBM) model outperformed other classifiers with an AUC-ROC score of 0.85. Feature importance analysis revealed the following key predictors: Age: The most significant predictor, with diabetes prevalence increasing with age, peaking around the 60s for males and 70s for females. BMI: Higher BMI was strongly associated with a higher risk of diabetes. Ethnicity: Black participants had the highest predicted prevalence of diabetes (14.6%), followed by Mexican-Americans (13.5%) and Whites (10.6%). TCHOL: Diabetics had lower total cholesterol levels, particularly among White participants (mean decline of 23.6 mg/dL). Smoking: Smoking showed a slight increase in diabetes risk among Whites (0.2%) but had a limited effect in other ethnic groups. Using machine learning models, we identified key demographic, physical, and lifestyle predictors of diabetes in the U.S. population. The results confirm that diabetes prevalence varies significantly across age, BMI, and ethnic groups, with lifestyle factors such as smoking contributing differently by ethnicity. These findings provide a basis for more targeted public health interventions and resource allocation for diabetes management.

Keywords: diabetes, NHANES, random forest, gradient boosting machine, support vector machine

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Authors: Christabel Ihedike, John Mooney, Monica Price

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Summary: This study aims to critically evaluate effect of photochemical smog on respiratory health in Nigeria. Cohort of chronic obstructive pulmonary disease (COPD) patients was recruited from two large hospitals in Abuja Nigeria. Respiratory health questionnaires, daily diaries, dyspnoea scale and lung function measurement were used to obtain health data and investigate the relationship with air quality data (principally ozone, NOx and particulate pollution). Concentrations of air pollutants were higher than WHO and Nigerian air quality standard. The result suggests a correlation between measured air quality and exacerbation of respiratory illness. Introduction: Photochemical smog is a significant health challenge in most cities and its effect on respiratory health is well acknowledged. This type of pollution is most harmful to the elderly, children and those with underlying respiratory disease. This study aims to investigate impact of increasing temperature and photo-chemically generated secondary air pollutants on respiratory health in Abuja Nigeria. Method and Result: Health data was collected using spirometry to measure lung function on routine attendance at the clinic, daily diaries kept by patients and information obtained using respiratory questionnaire. Questionnaire responses (obtained using an adapted and internally validated version of St George’s Hospital Respiratory Questionnaire), shows that ‘time of wheeze’ showed an association with participants activities: 30% had worse wheeze in the morning: 10% cannot shop, 15% take long-time to get washed, 25% walk slower, 15% if hurry have to stop and 5% cannot take-bath. There was also a decrease in Forced expiratory volume in the first second and Forced Vital Capacity, and daily change in the afternoon–morning may be associated with the concentration level of pollutants. Also, dyspnoea symptoms recorded that 60% of patients were on grade 3, 25% grade 2 and 15% grade 1. Daily frequency of the number of patients in the cohort that cough /brought sputum is 78%. Air pollution in the city is higher than Nigerian and WHO standards with NOx and PM10 concentrations of 693.59ug/m-3 and 748ugm-3 being measured respectively. The result shows that air pollution may increase occurrence and exacerbation of respiratory disease. Conclusion: High temperature and local climatic conditions in urban Nigeria encourages formation of Ozone, the major constituent of photochemical smog, resulting also in the formation of secondary air pollutants associated with health challenges. In this study we confirm the likely potency of the pattern of secondary air pollution in exacerbating COPD symptoms in vulnerable patient group in urban Nigeria. There is need for better regulation and measures to reduce ozone, particularly when local climatic conditions favour development of photochemical smog in such settings. Climate change and likely increasing temperatures add impetus and urgency for better air quality standards and measures (traffic-restrictions and emissions standards) in developing world settings such as Nigeria.

Keywords: Abuja-Nigeria, effect, photochemical smog, respiratory health

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Authors: Sawang Saenghirunvattana, Chao Saenghirunvattana, Maria Christina Gonzales, Wilai Srimuk, Chitchamai Siangpro, Kritsana Sutthisri

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Introduction: Thailand is one of the fastest growing countries in Asia. It has emerged from agricultural to industrialized economy. Work places have shifted from farms to factories, offices and streets were employees are exposed to certain chemicals and pollutants causing occupational diseases particularly asthma. Work-related diseases are major concern and many studies have been published to demonstrate certain professions and their exposures that elevate the risk of asthma. Workers who exhibit coughing, wheezing and difficulty of breathing are brought to a health care setting where Pulmonary Function Test (PFT) is performed and based from results, they are then diagnosed of asthma. These patients, known to have occupational asthma eventually get well when removed from the exposure of the environment. Our study, focused on performing PFT or specific challenge test in diagnosing workers of occupational asthma with them executing the test within their workplace, maintaining the environment and their daily exposure to certain levels of chemicals and pollutants. This has provided us with an understanding and reliable diagnosis of occupational asthma. Objective: To identify the prevalence of Thai workers who develop asthma caused by exposure to pollutants and chemicals from their working environment by conducting interview and performing PFT or specific challenge test in their work places. Materials and Methods: This study was performed from January-March 2015 in Bangkok, Thailand. The percentage of abnormal symptoms of 940 workers in 5 different areas (factories of plastic, fertilizer, animal food, office and streets) were collected through a questionnaire. The demographic information, occupational history, and the state of health were determined using a questionnaire and checklists. PFT was executed in their work places and results were measured and evaluated. Results: Pulmonary Function test was performed by 940 participants. The specific challenge test was done in factories of plastic, fertilizer, animal food, office environment and on the streets of Thailand. Of the 100 participants working in the plastic industry, 65% complained of having respiratory symptoms. None of them had an abnormal PFT. From the participants who worked with fertilizers and are exposed to sulfur dioxide, out of 200 participants, 20% complained of having symptoms and 8% had abnormal PFT. The 300 subjects working with animal food reported that 45% complained of respiratory symptoms and 15% had abnormal PFT results. From the office environment where there is indoor pollution, Out of 140 subjects, 7% had symptoms and 4% had abnormal PFT. The 200 workers exposed to traffic pollution, 24% reported respiratory symptoms and 12% had abnormal PFT. Conclusion: We were able to identify and diagnose participants of occupational asthma through their abnormal lung function test done at their work places. The chemical agents and exposures were determined therefore effective management of workers with occupational asthma were advised to avoid further exposure for better chances of recovery. Further studies identifying the risk factors and causative agents of asthma in workplaces should be developed to encourage interventional strategies and programs that will prevent occupation related diseases particularly asthma.

Keywords: occupational asthma, pulmonary function test, specific challenge test, Thailand

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Authors: Hemin Yuan, Yun Wang, Xiangchun Wang

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Gas hydrate is drawing attention due to the fact that it has an enormous amount all over the world, which is almost twice the conventional hydrocarbon reserves, making it a potential alternative source of energy. It is widely distributed in permafrost and continental ocean shelves, and many countries have launched national programs for investigating the gas hydrate. Gas hydrate is mainly explored through seismic methods, which include bottom simulating reflectors (BSR), amplitude blanking, and polarity reverse. These seismic methods are effective at finding the gas hydrate formations but usually contain large uncertainties when applying to invert the micro-scale petrophysical properties of the formations due to lack of constraints. Rock physics modeling links the micro-scale structures of the rocks to the macro-scale elastic properties and can work as effective constraints for the seismic methods. A number of rock physics models have been proposed for gas hydrate modeling, which addresses different mechanisms and applications. However, these models are generally not well classified, and it is confusing to determine the appropriate model for a specific study. Moreover, since the modeling usually involves multiple models and steps, it is difficult to determine the source of uncertainties. To solve these problems, we summarize the developed models/methods and make four classifications of the models according to the hydrate micro-scale morphology in sediments, the purpose of reservoir characterization, the stage of gas hydrate generation, and the lithology type of hosting sediments. Some sub-categories may overlap each other, but they have different priorities. Besides, we also analyze the priorities of different models, bring up the shortcomings, and explain the appropriate application scenarios. Moreover, by comparing the models, we summarize a general workflow of the modeling procedure, which includes rock matrix forming, dry rock frame generating, pore fluids mixing, and final fluid substitution in the rock frame. These procedures have been widely used in various gas hydrate modeling and have been confirmed to be effective. We also analyze the potential sources of uncertainties in each modeling step, which enables us to clearly recognize the potential uncertainties in the modeling. In the end, we explicate the general problems of the current models, including the influences of pressure and temperature, pore geometry, hydrate morphology, and rock structure change during gas hydrate dissociation and re-generation. We also point out that attenuation is also severely affected by gas hydrate in sediments and may work as an indicator to map gas hydrate concentration. Our work classifies rock physics models of gas hydrate into different categories, generalizes the modeling workflow, analyzes the modeling uncertainties and potential problems, which can facilitate the rock physics characterization of gas hydrate bearding sediments and provide hints for future studies.

Keywords: gas hydrate, rock physics model, modeling classification, hydrate morphology

Procedia PDF Downloads 160

Authors: Silvia Zanazzi

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Artificial intelligence (AI) has become an integral part of our world, transitioning from scientific exploration to practical applications that impact daily life. The emergence of generative AI is reshaping education, prompting new questions about the role of teachers, the nature of learning, and the overall purpose of schooling. While AI offers the potential for optimizing teaching and learning processes, concerns about discrimination and bias arising from training data and algorithmic decisions persist. There is a risk of a disconnect between the rapid development of AI and the goals of building inclusive educational environments. The prevailing discourse on AI in education often prioritizes efficiency and individual skill acquisition. This narrow focus can undermine the importance of collaborative learning and shared experiences. A growing body of research challenges this perspective, advocating for AI that enhances, rather than replaces, human interaction in education. This study aims to examine the relationship between AI and education critically. Reviewing existing research will identify both AI implementation’s potential benefits and risks. The goal is to develop a framework that supports the ethical and effective integration of AI into education, ensuring it serves the needs of all learners. The theoretical reflection will be developed based on a review of national and international scientific literature on artificial intelligence in education. The primary objective is to curate a selection of critical contributions from diverse disciplinary perspectives and/or an inter- and transdisciplinary viewpoint, providing a state-of-the-art overview and a critical analysis of potential future developments. Subsequently, the thematic analysis of these contributions will enable the creation of a framework for understanding and critically analyzing the role of artificial intelligence in schools and education, highlighting promising directions and potential pitfalls. The expected results are (1) a classification of the cognitive biases present in representations of AI in education and the associated risks and (2) a categorization of potentially beneficial interactions between AI applications and teaching and learning processes, including those already in use or under development. While not exhaustive, the proposed framework will serve as a guide for critically exploring the complexity of AI in education. It will help to reframe dystopian visions often associated with technology and facilitate discussions on fostering synergies that balance the ‘dream’ of quality education for all with the realities of AI implementation. The discourse on artificial intelligence in education, highlighting reductionist models rooted in fragmented and utilitarian views of knowledge, has the merit of stimulating the construction of alternative perspectives that can ‘return’ teaching and learning to education, human growth, and the well-being of individuals and communities.

Keywords: education, artificial intelligence, teaching, learning

Procedia PDF Downloads 22

Authors: Hamed Jafargholi

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Global warming presents an immense environmental risk, causing detrimental impacts on ecological systems and putting coastal areas at risk. Implementing efficient measures to minimize greenhouse gas emissions and the use of fossil fuels is essential to reducing global warming. Vapor compression heat pumps provide a practical method for harnessing energy from waste heat sources and reducing energy consumption. However, traditional working fluids used in these heat pumps generally contain a significant global warming potential (GWP), which might cause severe greenhouse effects if they are released. The goal of the emphasis on low-GWP (below 150) refrigerants is to further the vapor compression heat pumps. A classification system for vapor compression heat pumps is offered, with different boundaries based on the needed heat temperature and advancements in heat pump technology. A heat pump could be classified as a low temperature heat pump (LTHP), medium temperature heat pump (MTHP), high temperature heat pump (HTHP), or ultra-high temperature heat pump (UHTHP). The HTHP/UHTHP border is 160 °C, the MTHP/HTHP and LTHP/MTHP limits are 100 and 60 °C, respectively. The refrigerant is one of the most important parts of a vapor compression heat pump system. Presently, the main ways to choose a refrigerant are based on ozone depletion potential (ODP) and GWP, with GWP being the lowest possible value and ODP being zero. Pure low-GWP refrigerants, such as natural refrigerants (R718 and R744), hydrocarbons (R290, R600), hydrofluorocarbons (R152a and R161), hydrofluoroolefins (R1234yf, R1234ze(E)), and hydrochlorofluoroolefin (R1233zd(E)), were selected as candidates for vapor compression heat pump systems based on these selection principles. The performance, characteristics, and potential uses of these low-GWP refrigerants in heat pump systems are investigated in this paper. As vapor compression heat pumps with pure low-GWP refrigerants become more common, more and more low-grade heat can be recovered. This means that energy consumption would decrease. The research outputs showed that the refrigerants R718 for UHTHP application, R1233zd(E) for HTHP application, R600, R152a, R161, R1234ze(E) for MTHP, and R744, R290, and R1234yf for LTHP application are appropriate. The selection of an appropriate refrigerant should, in fact, take into consideration two different environmental and thermodynamic points of view. It might be argued that, depending on the situation, a trade-off between these two groups should constantly be considered. The environmental approach is now far stronger than it was previously, according to the European Union regulations. This will promote sustainable energy consumption and social development in addition to assisting in the reduction of greenhouse gas emissions and the management of global warming.

Keywords: vapor compression, global warming potential, heat pumps, greenhouse

Procedia PDF Downloads 37

Authors: Praniil Nagaraj

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This paper proposes a method to collect homeless data using crowdsourcing and presents an approach to analyze the data, demonstrating its potential to strengthen existing and future policies aimed at promoting socio-economic equilibrium. This paper's contributions can be categorized into three main areas. Firstly, a unique method for collecting homeless data is introduced, utilizing a user-friendly smartphone app (currently available for Android). The app enables the general public to quickly record information about homeless individuals, including the number of people and details about their living conditions. The collected data, including date, time, and location, is anonymized and securely transmitted to the cloud. It is anticipated that an increasing number of users motivated to contribute to society will adopt the app, thus expanding the data collection efforts. Duplicate data is addressed through simple classification methods, and historical data is utilized to fill in missing information. The second contribution of this paper is the description of data analysis techniques applied to the collected data. By combining this new data with existing information, statistical regression analysis is employed to gain insights into various aspects, such as distinguishing between unsheltered and sheltered homeless populations, as well as examining their correlation with factors like unemployment rates, housing affordability, and labor demand. Initial data is collected in San Francisco, while pre-existing information is drawn from three cities: San Francisco, New York City, and Washington D.C., facilitating the conduction of simulations. The third contribution focuses on demonstrating the practical implications of the data processing results. The challenges faced by key stakeholders, including charitable organizations and local city governments, are taken into consideration. Two case studies are presented as examples. The first case study explores improving the efficiency of food and necessities distribution, as well as medical assistance, driven by charitable organizations. The second case study examines the correlation between micro-geographic budget expenditure by local city governments and homeless information to justify budget allocation and expenditures. The ultimate objective of this endeavor is to enable the continuous enhancement of the quality of life for the underprivileged. It is hoped that through increased crowdsourcing of data from the public, the Generosity Curve and the Need Curve will intersect, leading to a better world for all.

Keywords: crowdsourcing, homelessness, socio-economic policies, statistical analysis

Procedia PDF Downloads 48

Authors: Begum Koru, Isik Aybay, Demet Celik Ulusoy

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The discipline of law is quite complex and has its own terminology. Apart from written legal rules, there is also living law, which refers to legal practice. Basic legal rules aim at the happiness of individuals in social life and have different characteristics in different branches such as public or private law. On the other hand, law is a national phenomenon. The law of one nation and the legal system applied on the territory of another nation may be completely different. People who are experts in a particular field of law in one country may have insufficient expertise in the law of another country. Today, in addition to the local nature of law, international and even supranational law rules are applied in order to protect basic human values and ensure the protection of human rights around the world. Systems that offer algorithmic solutions to legal problems using artificial intelligence (AI) tools will perhaps serve to produce very meaningful results in terms of human rights. However, algorithms to be used should not be developed by only computer experts, but also need the contribution of people who are familiar with law, values, judicial decisions, and even the social and political culture of the society to which it will provide solutions. Otherwise, even if the algorithm works perfectly, it may not be compatible with the values of the society in which it is applied. The latest developments involving the use of AI techniques in legal systems indicate that artificial law will emerge as a new field in the discipline of law. More AI systems are already being applied in the field of law, with examples such as predicting judicial decisions, text summarization, decision support systems, and classification of documents. Algorithms for legal systems employing AI tools, especially in the field of prediction of judicial decisions and decision support systems, have the capacity to create automatic decisions instead of judges. When the judge is removed from this equation, artificial intelligence-made law created by an intelligent algorithm on its own emerges, whether the domain is national or international law. In this work, the aim is to make a general analysis of this new topic. Such an analysis needs both a literature survey and a perspective from computer experts' and lawyers' point of view. In some societies, the use of prediction or decision support systems may be useful to integrate international human rights safeguards. In this case, artificial law can serve to produce more comprehensive and human rights-protective results than written or living law. In non-democratic countries, it may even be thought that direct decisions and artificial intelligence-made law would be more protective instead of a decision "support" system. Since the values of law are directed towards "human happiness or well-being", it requires that the AI algorithms should always be capable of serving this purpose and based on the rule of law, the principle of justice and equality, and the protection of human rights.

Keywords: AI and law, artificial law, protection of human rights, AI tools for legal systems

Procedia PDF Downloads 76

Authors: Angela U. Makolo

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Protein-coding and Non-coding regions determine the biology of a sequenced transcriptome. Research advances have shown that Non-coding regions are important in disease progression and clinical diagnosis. Existing bioinformatics tools have been targeted towards Protein-coding regions alone. Therefore, there are challenges associated with gaining biological insights from transcriptome sequence data. These tools are also limited to computationally intensive sequence alignment, which is inadequate and less accurate to identify both Protein-coding and Non-coding regions. Alignment-free techniques can overcome the limitation of identifying both regions. Therefore, this study was designed to develop an efficient sequence alignment-free model for identifying both Protein-coding and Non-coding regions in sequenced transcriptomes. Feature grouping and randomization procedures were applied to the input transcriptomes (37,503 data points). Successive iterations were carried out to compute the gradient vector that converged the developed Protein-coding and Non-coding Region Identifier (PNRI) model to the approximate coefficient vector. The logistic regression algorithm was used with a sigmoid activation function. A parameter vector was estimated for every sample in 37,503 data points in a bid to reduce the generalization error and cost. Maximum Likelihood Estimation (MLE) was used for parameter estimation by taking the log-likelihood of six features and combining them into a summation function. Dynamic thresholding was used to classify the Protein-coding and Non-coding regions, and the Receiver Operating Characteristic (ROC) curve was determined. The generalization performance of PNRI was determined in terms of F1 score, accuracy, sensitivity, and specificity. The average generalization performance of PNRI was determined using a benchmark of multi-species organisms. The generalization error for identifying Protein-coding and Non-coding regions decreased from 0.514 to 0.508 and to 0.378, respectively, after three iterations. The cost (difference between the predicted and the actual outcome) also decreased from 1.446 to 0.842 and to 0.718, respectively, for the first, second and third iterations. The iterations terminated at the 390th epoch, having an error of 0.036 and a cost of 0.316. The computed elements of the parameter vector that maximized the objective function were 0.043, 0.519, 0.715, 0.878, 1.157, and 2.575. The PNRI gave an ROC of 0.97, indicating an improved predictive ability. The PNRI identified both Protein-coding and Non-coding regions with an F1 score of 0.970, accuracy (0.969), sensitivity (0.966), and specificity of 0.973. Using 13 non-human multi-species model organisms, the average generalization performance of the traditional method was 74.4%, while that of the developed model was 85.2%, thereby making the developed model better in the identification of Protein-coding and Non-coding regions in transcriptomes. The developed Protein-coding and Non-coding region identifier model efficiently identified the Protein-coding and Non-coding transcriptomic regions. It could be used in genome annotation and in the analysis of transcriptomes.

Keywords: sequence alignment-free model, dynamic thresholding classification, input randomization, genome annotation

Procedia PDF Downloads 70

Authors: Geyang Xia

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In many countries, governments have responded to the growing demand for educational resources through school district systems, and there is substantial evidence that school district systems have been effective in promoting inter-district and inter-school equity in educational resources. However, the scarcity of quality educational resources has brought about varying levels of education among different school districts, making it a common choice for many parents to buy a house in the school district where a quality school is located, and they are even willing to bear huge commuting costs for this purpose. Moreover, this is evidenced by the fact that parents of families in school districts with quality education resources have longer average commute lengths and longer average commute distances than parents in average school districts. This "unequal traveling" under the influence of the school district system is more common in school districts at the primary level of education. This further reinforces the differential hierarchy of educational resources and raises issues of inequitable educational public services, education-led residential segregation, and gentrification of school district housing. Against this background, this paper takes Nanjing, a famous educational city in China, as a case study and selects the school districts where the top 10 public elementary schools are located. The study first identifies the spatio-temporal behavioral trajectory dataset of these high-quality school district households by using spatial vector data, decrypted cell phone signaling data, and census data. Then, by constructing a "house-school-work (HSW)" commuting pattern of the population in the school district where the high-quality educational resources are located, and based on the classification of the HSW commuting pattern of the population, school districts with long employment hours were identified. Ultimately, the mechanisms and patterns inherent in this unequal commuting are analyzed in terms of six aspects, including the centrality of school district location, functional diversity, and accessibility. The results reveal that the "unequal commuting" of Nanjing's high-quality school districts under the influence of the school district system occurs mainly in the peripheral areas of the city, and the schools matched with these high-quality school districts are mostly branches of prestigious schools in the built-up areas of the city's core. At the same time, the centrality of school district location and the diversity of functions are the most important influencing factors of unequal commuting in high-quality school districts. Based on the research results, this paper proposes strategies to optimize the spatial layout of high-quality educational resources and corresponding transportation policy measures.

Keywords: school-district system, high quality school district, commuting pattern, unequal traveling

Procedia PDF Downloads 99

Authors: Adrián Esteban Ortiz-Valera, Angélica Lozano

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In developing countries, informal trade is relevant, but it has been little studied in urban freight transport (UFT) context, although it is a challenge due to the non- contemplated demand it produces and the operational limitations it imposes. Hence, UFT operational improvements (initiatives) and freight attraction models must consider informal trade for developing countries. Afour phasesapproach for characterizing the commercial areas in developing countries (considering both formal and informal establishments) is proposed and applied to ten areas in Mexico City. This characterization is required to calculate real freight trip attraction and then select and/or adapt suitable initiatives. Phase 1 aims the delimitation of the study area. The following information is obtained for each establishment of a potential area: location or geographic coordinates, industrial sector, industrial subsector, and number of employees. Phase 2 characterizes the study area and proposes a set of indicators. This allows a broad view of the operations and constraints of UFT in the study area. Phase 3 classifies the study area according to seven indicators. Each indicator represents a level of conflict in the area due to the presence of formal (registered) and informal establishments on the sidewalks and streets, affecting urban freight transport (and other activities). Phase 4 determines preliminary initiatives which could be implemented in the study area to improve the operation of UFT. The indicators and initiatives relation allows a preliminary initiatives selection. This relation requires to know the following: a) the problems in the area (congested streets, lack of parking space for freight vehicles, etc.); b) the factors which limit initiatives due to informal establishments (reduced streets for freight vehicles; mobility and parking inability during a period, among others), c) the problems in the area due to its physical characteristics; and d) the factors which limit initiatives due to regulations of the area. Several differences in the study areas were observed. As the indicators increases, the areas tend to be less ordered, and the limitations for the initiatives become higher, causing a smaller number of susceptible initiatives. In ordered areas (similar to the commercial areas of developed countries), the current techniquesfor estimating freight trip attraction (FTA) can bedirectly applied, however, in the areas where the level of order is lower due to the presence of informal trade, this is not recommended because the real FTA would not be estimated. Therefore, a technique, which consider the characteristics of the areas in developing countries to obtain data and to estimate FTA, is required. This estimation can be the base for proposing feasible initiatives to such zones. The proposed approach provides a wide view of the needs of the commercial areas of developing countries. The knowledge of these needs would allow UFT´s operation to be improved and its negative impacts to be minimized.

Keywords: freight initiatives, freight trip attraction, informal trade, urban freight transport

Procedia PDF Downloads 144

Authors: V. Bezverbny

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The actuality of the project deals with stable growing of deviant behavior’ statistics among Moscow citizens. During the recent years the socioeconomic health, wealth and life expectation of Moscow residents is regularly growing up, but the limits of crime and drug addiction have grown up seriously. Another serious Moscow problem has been economical stratification of population. The cost of identical residential areas differs at 2.5 times. The project is aimed at complex research and the development of methodology for main factors and reasons evaluation of deviant behavior growing in Moscow. The main project objective is finding out the links between the urban environment quality and dynamics of citizens’ deviant behavior in regional and municipal aspect using the statistical research methods and GIS modeling. The conducted research allowed: 1) to evaluate the dynamics of deviant behavior in Moscow different administrative districts; 2) to describe the reasons of crime increasing, drugs addiction, alcoholism, suicides tendencies among the city population; 3) to develop the city districts classification based on the level of the crime rate; 4) to create the statistical database containing the main indicators of Moscow population deviant behavior in 2010-2015 including information regarding crime level, alcoholism, drug addiction, suicides; 5) to present statistical indicators that characterize the dynamics of Moscow population deviant behavior in condition of expanding the city territory; 6) to analyze the main sociological theories and factors of deviant behavior for concretization the deviation types; 7) to consider the main theoretical statements of the city sociology devoted to the reasons for deviant behavior in megalopolis conditions. To explore the level of deviant behavior’ factors differentiation, the questionnaire was worked out, and sociological survey involved more than 1000 people from different districts of the city was conducted. Sociological survey allowed to study the socio-economical and psychological factors of deviant behavior. It also included the Moscow residents’ open-ended answers regarding the most actual problems in their districts and reasons of wish to leave their place. The results of sociological survey lead to the conclusion that the main factors of deviant behavior in Moscow are high level of social inequality, large number of illegal migrants and bums, nearness of large transport hubs and stations on the territory, ineffective work of police, alcohol availability and drug accessibility, low level of psychological comfort for Moscow citizens, large number of building projects.

Keywords: deviant behavior, megapolis, Moscow, urban environment, social stratification

Procedia PDF Downloads 195

Authors: Natalie Hoyer

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Usually, steel structures are designed for the upper region of the steel toughness-temperature curve. To address the reduced toughness properties in the temperature transition range, additional safety assessments based on fracture mechanics are necessary. These assessments enable the appropriate selection of steel materials to prevent brittle fracture. In this context, recommendations were established in 2011 to regulate the appropriate selection of steel grades for bridge bearing components. However, these recommendations are no longer fully aligned with more recent insights: Designing bridge bearings and their components in accordance with DIN EN 1337 and the relevant sections of DIN EN 1993 has led to an increasing trend of using large plate thicknesses, especially for long-span bridges. However, these plate thicknesses surpass the application limits specified in the national appendix of DIN EN 1993-2. Furthermore, compliance with the regulations outlined in DIN EN 1993-1-10 regarding material toughness and through-thickness properties requires some further modifications. Therefore, these standards cannot be directly applied to the material selection for bearings without additional information. In addition, recent findings indicate that certain bridge bearing components are subjected to high fatigue loads, necessitating consideration in structural design, material selection, and calculations. To address this issue, the German Center for Rail Traffic Research initiated a research project aimed at developing a proposal to enhance the existing standards. This proposal seeks to establish guidelines for the selection of steel materials for bridge bearings to prevent brittle fracture, particularly for thick plates and components exposed to specific fatigue loads. The results derived from theoretical analyses, including finite element simulations and analytical calculations, are verified through component testing on a large-scale. During these large-scale tests, where a brittle failure is deliberately induced in a bearing component, an artificially generated defect is introduced into the specimen at the predetermined hotspot. Subsequently, a dynamic load is imposed until the crack initiation process transpires, replicating realistic conditions akin to a sharp notch resembling a fatigue crack. To stop the action of the dynamic load in time, it is important to precisely determine the point at which the crack size transitions from stable crack growth to unstable crack growth. To achieve this, the potential drop measurement method is employed. The proposed paper informs about the choice of measurement method (alternating current potential drop (ACPD) or direct current potential drop (DCPD)), presents results from correlations with created FE models, and may proposes a new approach to introduce beach marks into the fracture surface within the framework of potential drop measurement.

Keywords: beach marking, bridge bearing design, brittle fracture, design for fatigue, potential drop

Procedia PDF Downloads 44

Authors: Mishell E. Cabrera, Bryan G. Valencia, Anderson I. Guamán

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Human dependence on plastic products has led to global pollution, with plastic particles ranging in size from 0.001 to 5 millimeters, which are called microplastics (hereafter, MPs). The abundance of microplastics is used as an indicator of pollution. However, reports of pollution (abundance of MPs) in river sediments do not consider that the accumulation of sediments and MPs depends on the energy of the river. That is, the abundance of microplastics will be underestimated if the sediments analyzed come from places where the river flows with a lot of energy, and the abundance will be overestimated if the sediment analyzed comes from places where the river flows with less energy. This bias can generate an error greater than 300% of the MPs value reported for the same river and should increase when comparisons are made between 2 rivers with different characteristics. Sections where the river flows with higher energy allow sands to be deposited and limit the accumulation of MPs, while sections, where the same river has lower energy, allow fine sediments such as clays and silts to be deposited and should facilitate the accumulation of MPs particles. That is, the abundance of MPs in the same river is underrepresented when the sediment analyzed is sand, and the abundance of MPs is overrepresented if the sediment analyzed is silt or clay. The present investigation establishes a protocol aimed at incorporating sample granulometry to calibrate MPs quantification and eliminate over- or under-representation bias (hereafter granulometric bias). A total of 30 samples were collected by taking five samples within six work zones. The slope of the sampling points was less than 8 degrees, referred to as low slope areas, according to the Van Zuidam slope classification. During sampling, blanks were used to estimate possible contamination by MPs during sampling. Samples were dried at 60 degrees Celsius for three days. A flotation technique was employed to isolate the MPs using sodium metatungstate with a density of 2 gm/l. For organic matter digestion, 30% hydrogen peroxide and Fenton were used at a ratio of 6:1 for 24 hours. The samples were stained with rose bengal at a concentration of 200 mg/L and were subsequently dried in an oven at 60 degrees Celsius for 1 hour to be identified and photographed in a stereomicroscope with the following conditions: Eyepiece magnification: 10x, Zoom magnification (zoom knob): 4x, Objective lens magnification: 0.35x for analysis in ImageJ. A total of 630 fibers of MPs were identified, mainly red, black, blue, and transparent colors, with an overall average length of 474,310 µm and an overall median length of 368,474 µm. The particle size of the 30 samples was calculated using 100 g per sample using sieves with the following apertures: 2 mm, 1 mm, 500 µm, 250 µm, 125 µm and 0.63 µm. This sieving allowed a visual evaluation and a more precise quantification of the microplastics present. At the same time, the weight of sediment in each fraction was calculated, revealing an evident magnitude: as the presence of sediment in the < 63 µm fraction increases, a significant increase in the number of MPs particles is observed.

Keywords: microplastics, pollution, sediments, Tena River

Procedia PDF Downloads 74

Authors: Elia Favarelli, Enrico Testi, Andrea Giorgetti

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Autonomous structural health monitoring (SHM) of many structures and bridges became a topic of paramount importance for maintenance purposes and safety reasons. This paper proposes a set of machine learning (ML) tools to perform automatic feature selection and detection of anomalies in a bridge from vibrational data and compare different feature extraction schemes to increase the accuracy and reduce the amount of data collected. As a case study, the Z-24 bridge is considered because of the extensive database of accelerometric data in both standard and damaged conditions. The proposed framework starts from the first four fundamental frequencies extracted through operational modal analysis (OMA) and clustering, followed by density-based time-domain filtering (tracking). The fundamental frequencies extracted are then fed to a dimensionality reduction block implemented through two different approaches: feature selection (intelligent multiplexer) that tries to estimate the most reliable frequencies based on the evaluation of some statistical features (i.e., mean value, variance, kurtosis), and feature extraction (auto-associative neural network (ANN)) that combine the fundamental frequencies to extract new damage sensitive features in a low dimensional feature space. Finally, one class classifier (OCC) algorithms perform anomaly detection, trained with standard condition points, and tested with normal and anomaly ones. In particular, a new anomaly detector strategy is proposed, namely one class classifier neural network two (OCCNN2), which exploit the classification capability of standard classifiers in an anomaly detection problem, finding the standard class (the boundary of the features space in normal operating conditions) through a two-step approach: coarse and fine boundary estimation. The coarse estimation uses classics OCC techniques, while the fine estimation is performed through a feedforward neural network (NN) trained that exploits the boundaries estimated in the coarse step. The detection algorithms vare then compared with known methods based on principal component analysis (PCA), kernel principal component analysis (KPCA), and auto-associative neural network (ANN). In many cases, the proposed solution increases the performance with respect to the standard OCC algorithms in terms of F1 score and accuracy. In particular, by evaluating the correct features, the anomaly can be detected with accuracy and an F1 score greater than 96% with the proposed method.

Keywords: anomaly detection, frequencies selection, modal analysis, neural network, sensor network, structural health monitoring, vibration measurement

Procedia PDF Downloads 125

Authors: Nadjet Mekaouche, Hanane Zitouni, Fatma Boudia, Habiba Fetati, A. Saleh, A. Lardjam, H. Geniaux, A. Coubret, H. Toumi

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Medicines have undeniably played a major role in prolonging shelf life and improving quality. The absolute efficacy of the drug remains a lever for innovation, its benefit/risk balance is not always assured and it does not always have the expected effects. Prior to marketing, knowledge about adverse drug reactions is incomplete. Once on the market, phase IV drug studies begin. For years, the drug was prescribed with less care to a large number of very heterogeneous patients and often in combination with other drugs. It is at this point that previously unknown adverse effects may appear, hence the need for the implementation of a pharmacovigilance system. Pharmacovigilance represents all methods for detecting, evaluating, informing and preventing the risks of adverse drug reactions. The most severe adverse events occur frequently in hospital and that a significant proportion of adverse events result in hospitalizations. In addition, the consequences of hospital adverse events in terms of length of stay, mortality and costs are considerable. It, therefore, appears necessary to develop ‘hospital pharmacovigilance’ aimed at reducing the incidence of adverse reactions in hospitals. The most widely used monitoring method in pharmacovigilance is spontaneous notification. However, underreporting of adverse drug reactions is common in many countries and is a major obstacle to pharmacovigilance assessment. It is in this context that this study aims to describe the experience of the pharmacovigilance service at the University Hospital of Oran (EHUO). This is a retrospective study extending from 2011 to 2017, carried out on archived records of declarations collected at the level of the EHUO Pharmacovigilance Department. Reporting was collected by two methods: ‘spontaneous notification’ and ‘active pharmacovigilance’ targeting certain clinical services. We counted 217 statements. It involved 56% female patients and 46% male patients. Age ranged from 5 to 78 years with an average of 46 years. The most common adverse reaction was drug toxidermy. For the drugs in question, they were essentially according to the ATC classification of anti-infectives followed by anticancer drugs. As regards the evolution of declarations by year, a low rate of notification was noted in 2011. That is why we decided to set up an active approach at the level of some services where a resident of reference attended the staffs every week. This has resulted in an increase in the number of reports. The declarations came essentially from the services where the active approach was installed. This highlights the need for ongoing communication between all relevant health actors to stimulate reporting and secure drug treatments.

Keywords: adverse drug reactions, hospital, pharmacovigilance, spontaneous notification

Procedia PDF Downloads 176

Authors: Khin Thandar Soe, Mark Stephen Pulham

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Ballistic building materials play a crucial role in ensuring the safety of the occupants within protective structures. Traditional options like Ordinary Portland Cement (OPC)-based walls, including reinforced concrete walls, precast concrete walls, masonry walls, and concrete blocks, are frequently employed for ballistic protection, but they have several drawbacks such as being thick, heavy, costly, and challenging to construct. On the other hand, glass and composite materials offer lightweight and easier construction alternatives, but they come with a high price tag. There has been no reported test data on magnesium-based ballistic wall panels or modular wall systems so far. This paper presents groundbreaking small arms test data related to the development of the world’s first magnesia cement ballistic wall panels and modular wall system. Non-hydraulic magnesia cement exhibits several superior properties, such as lighter weight, flexibility, acoustics, and fire performance, compared to the traditional Portland Cement. However, magnesia cement is hydrophilic and may degrade in prolonged contact with water. In this research, modified magnesia cement for water resistant and durability from UBIQ Technology is applied. The specimens are made of a modified magnesia cement formula and prepared in the Laboratory of UBIQ Technology Pty Ltd. The specimens vary in thickness, and the tests cover various small arms threats in compliance with standards AS/NZS2343 and UL752 and are performed up to the maximum threat level of Classification R2 (NATO) and UL-Level 8(NATO) by the Accredited Test Centre, BMT (Ballistic and Mechanical Testing, VIC, Australia). In addition, the results of the test conducted on the specimens subjected to the small 12mm diameter steel ball projectile impact generated by a gas gun are also presented and discussed in this paper. Gas gun tests were performed in UNSW@ADFA, Canberra, Australia. The tested results of the magnesia panels and wall systems are compared with one of concrete and other wall panels documented in the literature. The conclusion drawn is that magnesia panels and wall systems exhibit several advantages over traditional OPC-based wall systems, and they include being lighter, thinner, and easier to construct, all while providing equivalent protection against threats. This makes magnesia cement-based materials a compelling choice of application where efficiency and performance are critical to create a protective environment.

Keywords: ballistics, small arms, gas gun, projectile, impact, wall panels, modular, magnesia cement

Procedia PDF Downloads 77

Authors: Felix Liedel

Abstract:

The rise of artificial intelligence has numerous and far-reaching consequences. In addition to the obvious consequences for entire professions, the increasing interaction with chatbots also has a wide range of social consequences and implications. We are already increasingly used to interacting with digital chatbots, be it in virtual consulting situations, creative development processes or even in building personal or intimate virtual relationships. A media-theoretical classification of these phenomena has so far been difficult, partly because the interactive element in the exchange with artificial intelligence has undeniable similarities to human-to-human communication but is not identical to it. The proposed study, therefore, aims to reformulate the concept of symbolic interaction in the tradition of George Herbert Mead as symbolic interactivity in communication with chatbots. In particular, Mead's socio-psychological considerations will be brought into dialog with the specific conditions of digital media, the special dispositive situation of chatbots and the characteristics of artificial intelligence. One example that illustrates this particular communication situation with chatbots is so-called consensus fiction: In face-to-face communication, we use symbols on the assumption that they will be interpreted in the same or a similar way by the other person. When briefing a chatbot, it quickly becomes clear that this is by no means the case: only the bot's response shows whether the initial request corresponds to the sender's actual intention. This makes it clear that chatbots do not just respond to requests. Rather, they function equally as projection surfaces for their communication partners but also as distillations of generalized social attitudes. The personalities of the chatbot avatars result, on the one hand, from the way we behave towards them and, on the other, from the content we have learned in advance. Similarly, we interpret the response behavior of the chatbots and make it the subject of our own actions with them. In conversation with the virtual chatbot, we enter into a dialog with ourselves but also with the content that the chatbot has previously learned. In our exchanges with chatbots, we, therefore, interpret socially influenced signs and behave towards them in an individual way according to the conditions that the medium deems acceptable. This leads to the emergence of situationally determined digital identities that are in exchange with the real self but are not identical to it: In conversation with digital chatbots, we bring our own impulses, which are brought into permanent negotiation with a generalized social attitude by the chatbot. This also leads to numerous media-ethical follow-up questions. The proposed approach is a continuation of my dissertation on moral decision-making in so-called interactive films. In this dissertation, I attempted to develop a concept of symbolic interactivity based on Mead. Current developments in artificial intelligence are now opening up new areas of application.

Keywords: artificial intelligence, chatbot, media theory, symbolic interactivity

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Authors: Md. Rafiul Biswas, Uzair Shah, Mohammad Alkayal, Zubair Shah, Othman Althawadi, Kamila Swart

Abstract:

Background: Social media platforms play a significant role in the mediated consumption of sport, especially so for sport mega-event. The characteristics of Twitter data (e.g., user mentions, retweets, likes, #hashtag) accumulate the users in one ground and spread information widely and quickly. Analysis of Twitter data can reflect the public attitudes, behavior, and sentiment toward a specific event on a larger scale than traditional surveys. Qatar is going to be the first Arab country to host the mega sports event FIFA World Cup 2022 (Q22). Qatar has hosted the FIFA Arab Cup 2021 (FAC21) to serve as a preparation for the mega-event. Objectives: This study investigates public sentiments and experiences about FAC21 and provides an insight to enhance the public experiences for the upcoming Q22. Method: FCA21-related tweets were downloaded using Twitter Academic research API between 01 October 2021 to 18 February 2022. Tweets were divided into three different periods: before T1 (01 Oct 2021 to 29 Nov 2021), during T2 (30 Nov 2021 -18 Dec 2021), and after the FAC21 T3 (19 Dec 2021-18 Feb 2022). The collected tweets were preprocessed in several steps to prepare for analysis; (1) removed duplicate and retweets, (2) removed emojis, punctuation, and stop words (3) normalized tweets using word lemmatization. Then, rule-based classification was applied to remove irrelevant tweets. Next, the twitter-XLM-roBERTa-base model from Huggingface was applied to identify the sentiment in the tweets. Further, state-of-the-art BertTopic modeling will be applied to identify trending topics over different periods. Results: We downloaded 8,669,875 Tweets posted by 2728220 unique users in different languages. Of those, 819,813 unique English tweets were selected in this study. After splitting into three periods, 541630, 138876, and 139307 were from T1, T2, and T3, respectively. Most of the sentiments were neutral, around 60% in different periods. However, the rate of negative sentiment (23%) was high compared to positive sentiment (18%). The analysis indicates negative concerns about FAC21. Therefore, we will apply BerTopic to identify public concerns. This study will permit the investigation of people’s expectations before FAC21 (e.g., stadium, transportation, accommodation, visa, tickets, travel, and other facilities) and ascertain whether these were met. Moreover, it will highlight public expectations and concerns. The findings of this study can assist the event organizers in enhancing implementation plans for Q22. Furthermore, this study can support policymakers with aligning strategies and plans to leverage outstanding outcomes.

Keywords: FIFA Arab Cup, FIFA, Twitter, machine learning

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Authors: Akindele Ojo, Olanike Ojo, Agatha Oseghale

Abstract:

The empirical study examined the allocative efficiency of small holder tuber crop farmers in North central, Nigeria. Data used for the study were obtained from primary source using a multi-stage sampling technique with structured questionnaires administered to 300 randomly selected tuber crop farmers from the study area. Descriptive statistics, data envelopment analysis and Tobit regression model were used to analyze the data. The DEA result on the classification of the farmers into efficient and inefficient farmers showed that 17.67% of the sampled tuber crop farmers in the study area were operating at frontier and optimum level of production with mean allocative efficiency of 1.00. This shows that 82.33% of the farmers in the study area can still improve on their level of efficiency through better utilization of available resources, given the current state of technology. The results of the Tobit model for factors influencing allocative inefficiency in the study area showed that as the year of farming experience, level of education, cooperative society membership, extension contacts, credit access and farm size increased in the study area, the allocative inefficiency of the farmers decreased. The results on effects of the significant determinants of allocative inefficiency at various distribution levels revealed that allocative efficiency increased from 22% to 34% as the farmer acquired more farming experience. The allocative efficiency index of farmers that belonged to cooperative society was 0.23 while their counterparts without cooperative society had index value of 0.21. The result also showed that allocative efficiency increased from 0.43 as farmer acquired high formal education and decreased to 0.16 with farmers with non-formal education. The efficiency level in the allocation of resources increased with more contact with extension services as the allocative efficeincy index increased from 0.16 to 0.31 with frequency of extension contact increasing from zero contact to maximum of twenty contacts per annum. These results confirm that increase in year of farming experience, level of education, cooperative society membership, extension contacts, credit access and farm size leads to increases efficiency. The results further show that the age of the farmers had 32% input to the efficiency but reduces to an average of 15%, as the farmer grows old. It is therefore recommended that enhanced research, extension delivery and farm advisory services should be put in place for farmers who did not attain optimum frontier level to learn how to attain the remaining 74.39% level of allocative efficiency through a better production practices from the robustly efficient farms. This will go a long way to increase the efficiency level of the farmers in the study area.

Keywords: allocative efficiency, DEA, Tobit regression, tuber crop

Procedia PDF Downloads 291