Search results for: open data

Authors: Maher J. Tadros, Yaseen Ananbeh

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The study area is located in Ajluon Forest Reserve northern part of Jordan. It consists of Mediterranean hills dominated by open woodlands of oak and pistachio. The aims of the study were to investigate the positive and negative relationships between the locals and the protected area and how it can affect the long-term forest conservation. The main research objectives are to review the impact of establishing Ajloun Forest Reserve on nature conservation and on the livelihood level of local communities around the reserve. The Ajloun forest reserve plays a fundamental role in Ajloun area development. The existence of initiatives of nature conservation in the area supports various socio-economic activities around the reserve that contribute towards the development of local communities in Ajloun area. A part of this research was to conduct a survey to study the impact of Ajloun forest reserve on biodiversity composition. Also, studying the biodiversity content especially for vegetation to determine the economic impacts of Ajloun forest reserve on its surroundings was studied. In this study, several methods were used to fill the objectives including point-centered quarter method which involves selecting randomly 50 plots at the study site. The collected data from the field showed that the absolute density was (1031.24 plant per hectare). Density was recorded and found to be the highest for Quecus coccifera, and relative density of (73.7%), this was followed by Arbutus andrachne and relative density (7.1%), Pistacia palaestina and relative density (10.5%) and Crataegus azarulus (82.5 p/ha) and relative density (5.1%),

Keywords: composition, density, frequency, importance value, point-centered quarter, structure, tree cover

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Authors: Sammani Danwawu Abdullahi

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Vertex Enumeration Algorithms explore the methods and procedures of generating the vertices of general polyhedra formed by system of equations or inequalities. These problems of enumerating the extreme points (vertices) of general polyhedra are shown to be NP-Hard. This lead to exploring how to count the vertices of general polyhedra without listing them. This is also shown to be #P-Complete. Some fully polynomial randomized approximation schemes (fpras) of counting the vertices of some special classes of polyhedra associated with Down-Sets, Independent Sets, 2-Knapsack problems and 2 x n transportation problems are presented together with some discovered open problems.

Keywords: counting with uncertainties, mathematical programming, optimization, vertex enumeration

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Authors: Kunal Shah, Swati Patel

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Many real world problems involve data which can be considered as multi-label data streams. Efficient methods exist for multi-label classification in non streaming scenarios. However, learning in evolving streaming scenarios is more challenging, as the learners must be able to adapt to change using limited time and memory. Classification is used to predict class of unseen instance as accurate as possible. Multi label classification is a variant of single label classification where set of labels associated with single instance. Multi label classification is used by modern applications, such as text classification, functional genomics, image classification, music categorization etc. This paper introduces the task of multi-label classification, methods for multi-label classification and evolution measure for multi-label classification. Also, comparative analysis of multi label classification methods on the basis of theoretical study, and then on the basis of simulation was done on various data sets.

Keywords: binary relevance, concept drift, data stream mining, MLSC, multiple window with buffer

Procedia PDF Downloads 584

Authors: Kerem Ok, Serafettin Senturk, Serdar Aktas, Cem Cevikbas

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Mobile technology is very popular nowadays and it provides a digital world where users can experience many value-added services. Service Providers are also eager to offer diverse value-added services to users such as digital identity, mobile financial services and so on. In this context, the security of data storage in smartphones and the security of communication between the smartphone and service provider are critical for the success of these services. In order to provide the required security functions, the SIM card is one acceptable alternative. Since SIM cards include a Secure Element, they are able to store sensitive data, create cryptographically secure keys, encrypt and decrypt data. In this paper, we design and implement a SIM and a smartphone framework that uses a SIM card for secure key generation, key storage, data encryption, data decryption and digital signing for mobile financial services. Our frameworks show that the SIM card can be used as a controlled Secure Element to provide required security functions for popular e-services such as mobile financial services.

Keywords: SIM card, mobile financial services, cryptography, secure data storage

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Authors: Srinivas Peri, Siva Abhishek Sirivella, Tejaswini Kallakuri, Uzair Ahmad

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Smart cities and intelligent transportation systems rely heavily on effective traffic management and infrastructure planning. This research tackles the data scarcity challenge by generating realistically synthetic traffic data from the PeMS-Bay dataset, enhancing predictive modeling accuracy and reliability. Advanced techniques like TimeGAN and GaussianCopula are utilized to create synthetic data that mimics the statistical and structural characteristics of real-world traffic. The future integration of Spatial-Temporal Generative Adversarial Networks (ST-GAN) is anticipated to capture both spatial and temporal correlations, further improving data quality and realism. Each synthetic data generation model's performance is evaluated against real-world data to identify the most effective models for accurately replicating traffic patterns. Long Short-Term Memory (LSTM) networks are employed to model and predict complex temporal dependencies within traffic patterns. This holistic approach aims to identify areas with low vehicle counts, reveal underlying traffic issues, and guide targeted infrastructure interventions. By combining GAN-based synthetic data generation with LSTM-based traffic modeling, this study facilitates data-driven decision-making that improves urban mobility, safety, and the overall efficiency of city planning initiatives.

Keywords: GAN, long short-term memory (LSTM), synthetic data generation, traffic management

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Authors: A. Ham, A. Kuckert-Wostheinrich

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Demographic changes, like the ageing population in European countries and shortage of nursing staff, the increasing number of people with severe cognitive impairment, and elderly socially isolated people raise important questions about who will provide long-term care for ageing citizens. Due to the so-called refugee crisis in 2015, some health care institutions for ageing citizens in Europe invited first generation immigrants to start a nursing career and providing them language skills, nursing training, and internships. The aim of this ethnographic research was to explore the social processes affecting workforce integration and how newcomers enact good care in ageing citizens in a German nursing home. By ethnographic fieldwork, 200 hours of participant observations, 25 in-depth interviews with immigrants and established staff, 2 focus groups with 6 immigrants, and 6 established staff members, data were analysed. The health care institution provided the newcomers a nursing program on psychogeriatric theory and nursing skills in the psychogeriatric field and professional oriented language skills. Courses of health prevention and theater plays accompanied the training. The knowledge learned in education could be applied in internships on the wards. Additionally, diversity and inclusivity courses were given to established personal for cultural awareness and sensitivity. They learned to develop a collegial attitude of respect and appreciation, regardless of gender, nationality, ethnicity, religion or belief, age sexual orientation, or disability and identity. The qualitative data has shown that social processes affected workforce integration, like organizational constraints, staff shortages, and a demanding workload. However, zooming in on the interactions between newcomers and residents, we noticed how they tinkered to enact good care by embodied caring, playing games, singing and dancing. By situational acting and practical wisdom in nursing care, the newcomers could meet the needs of ageing residents. Thus, when health care institutions open up nursing programs for newcomers with refugees’ backgrounds and focus on talent instead of shortcomings, we might as well stimulate the unknown competencies, attitudes, skills, and expertise of newcomers and create excellent nurses for excellent care.

Keywords: established staff, Germany, nursing, refugees

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Authors: Moshiur Rahman, Tahmida Jakia

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Bangladesh, a densely populated South Asian country is home to more than 160 million people. In two decades ago, the people of this developing nation drank heavily contaminated surface water. Over the past thirty years, the country, and its development partners, has undertaken extensive efforts to provide microbiologically safe groundwater based drinking water through the use of tube-wells. About 85% of the people now drink tube-well water from about 11 million tube-wells/hand pumps. However, diarrhoeal and other water-related diseases are still reported among the major causes of morbidity and mortality among Bangladeshi children. This implies that the mode of transmission of pathogens through water and/or other modes continue. In addition, massive scale arsenic contamination has been recently reported in the ground water. Thirty five million people may be at risk of consuming arsenic contaminated water exceeding 0.05 mg/l in Bangladesh. Drinking of arsenic contaminated water has been linked with skin problems, cancer, cardiovascular diseases, neurological diseases, eye problems, cancer of the internal organs, and other diseases. In the study area, Narail district, recent investigations about existing water quality situations indicated presence of low to high levels of arsenic, salinity, iron, manganese and bacteriological contamination risks. As challenges for safe water exist; it is likely that sanitation and food hygiene practices are poor which lead threat to ensure food security.The main attempt of this study is to find out the challenges to ensure food security andprovide probable solutions to ensure food safety towards 0.7 million of people in study area. A survey has been conducted at Lohagara and Kalia sub district of Narail district with a pretested questionnaire. Primary data are collected through a questionnaire, while secondary data are collected from pertinent offices as well as academic journals. FGD has also been done to know the knowledge regarding water, sanitation as well as food preparation and consumption practice of community people in study area. The major focus of this study is to assess the state of sanitation and food hygiene condition of rural people. It is found that most of the villagers have lack of knowledge about food safety. Open defecation rate is high which lead threat to ensure food security.

Keywords: food safety, challenges, hygiene, Bangladesh

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Authors: Andrea Treviño Gavito, Diego Klabjan, Sanjiv J. Shah

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Echocardiograms serve as pivotal resources for clinicians in diagnosing cardiac conditions, offering non-invasive insights into a heart’s structure and function. When echocardiographic studies are conducted, no standardized labeling of the acquired views is performed. Employing machine learning algorithms for automated echocardiogram view detection has emerged as a promising solution to enhance efficiency in echocardiogram use for diagnosis. However, existing approaches predominantly rely on supervised learning, necessitating labor-intensive expert labeling. In this paper, we introduce a fully unsupervised echocardiographic view detection framework that leverages convolutional autoencoders to obtain lower dimensional representations and the K-means algorithm for clustering them into view-related groups. Our approach focuses on discriminative patches from echocardiographic frames. Additionally, we propose a trainable inverse average layer to optimize decoding of average operations. By integrating both public and proprietary datasets, we obtain a marked improvement in model performance when compared to utilizing a proprietary dataset alone. Our experiments show boosts of 15.5% in accuracy and 9.0% in the F-1 score for frame-based clustering, and 25.9% in accuracy and 19.8% in the F-1 score for view-based clustering. Our research highlights the potential of unsupervised learning methodologies and the utilization of open-sourced data in addressing the complexities of echocardiogram interpretation, paving the way for more accurate and efficient cardiac diagnoses.

Keywords: artificial intelligence, echocardiographic view detection, echocardiography, machine learning, self-supervised representation learning, unsupervised learning

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Authors: V. V. Fomin, A. P. Mikhailovich, E. M. Agapitov, V. E. Rogachev, E. A. Kostousova, E. S. Perekhodova

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Ground-level landscape photos can be used as a source of objective data on woody vegetation and vegetation dynamics. We proposed a method for processing, analyzing, and presenting ground photographs, which has the following advantages: 1) researcher has to form holistic representation of the study area in form of a set of interlapping ground-level landscape photographs; 2) it is necessary to define or obtain characteristics of the landscape, objects, and phenomena present on the photographs; 3) it is necessary to create new or supplement existing textual descriptions and annotations for the ground-level landscape photographs; 4) single or multiple ground-level landscape photographs can be used to develop specialized geoinformation layers, schematic maps or thematic maps; 5) it is necessary to determine quantitative data that describes both images as a whole, and displayed objects and phenomena, using algorithms for automated image analysis. It is suggested to match each photo with a polygonal geoinformation layer, which is a sector consisting of areas corresponding with parts of the landscape visible in the photos. Calculation of visibility areas is performed in a geoinformation system within a sector using a digital model of a study area relief and visibility analysis functions. Superposition of the visibility sectors corresponding with various camera viewpoints allows matching landscape photos with each other to create a complete and wholesome representation of the space in question. It is suggested to user-defined data or phenomenons on the images with the following superposition over the visibility sector in the form of map symbols. The technology of geoinformation layers’ spatial superposition over the visibility sector creates opportunities for image geotagging using quantitative data obtained from raster or vector layers within the sector with the ability to generate annotations in natural language. The proposed method has proven itself well for relatively open and clearly visible areas with well-defined relief, for example, in mountainous areas in the treeline ecotone. When the polygonal layers of visibility sectors for a large number of different points of photography are topologically superimposed, a layer of visibility of sections of the entire study area is formed, which is displayed in the photographs. Also, as a result of this overlapping of sectors, areas that did not appear in the photo will be assessed as gaps. According to the results of this procedure, it becomes possible to obtain information about the photos that display a specific area and from which points of photography it is visible. This information may be obtained either as a query on the map or as a query for the attribute table of the layer. The method was tested using repeated photos taken from forty camera viewpoints located on Ray-Iz mountain massif (Polar Urals, Russia) from 1960 until 2023. It has been successfully used in combination with other ground-based and remote sensing methods of studying the climate-driven dynamics of woody vegetation in the Polar Urals. Acknowledgment: This research was collaboratively funded by the Russian Ministry for Science and Education project No. FEUG-2023-0002 (image representation) and Russian Science Foundation project No. 24-24-00235 (automated textual description).

Keywords: woody, vegetation, repeated, photographs

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Authors: Siwarak Kitchanapaibul, Warren Gillibrand, Rob Burton

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The prevalence of diabetes mellitus has been rising all over the world. Self-management is required for diabetes mellitus patients. The objective of this study is to explore the self-management among the ethnic groups with type 2 diabetes mellitus in Thailand, an upper middle-income country which is located in South East Asia. The ethnic groups in Thailand are a minority group which has limited education and a different culture, language, costume and lifestyle from Thai people. The qualitative exploratory study was used in this study. In-depth interviews with semi-structured open questions were conducted by 20 participants from purposive sampling. These participants were the ethnic groups who have type 2 diabetes mellitus, received the services from a region hospital, understood Thai and were willing to participate. Content analysis was adopted for the study. The results showed that all of the participants controlled their diet before the appointment day and never miss their appointment. Only 3 participants did their exercise while 2 participants stated that they occasionally forgot to take medicine. 10 participants use the herbs for reducing the sugar level. 12 participants drank a lot of water after a lapse in the diet because they believed that water could dilute the sugar. The findings identified 5 themes; ‘controlling diet before appointment day’; ‘drinking water after a lapse in diet’; ‘medication being a vital importance’; ‘exercise is unimportant’; and ‘taking herbs for sugar reduction’. The results of this study are important to the health professionals to understand the self-management of Ethnic groups and use the data to create the appropriate intervention for promoting health among the ethnic groups with type 2 diabetes mellitus in Thailand. The findings will lead to the revision of health policy and the procedure for promoting health in this special ethnic groups.

Keywords: self-management, diabetes, ethnic groups, Thailand

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Authors: Salma El Hajjami, Jamal Malki, Alain Bouju, Mohammed Berrada

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With the expansion of machine learning and data mining in the context of Big Data analytics, the common problem that affects data is class imbalance. It refers to an imbalanced distribution of instances belonging to each class. This problem is present in many real world applications such as fraud detection, network intrusion detection, medical diagnostics, etc. In these cases, data instances labeled negatively are significantly more numerous than the instances labeled positively. When this difference is too large, the learning system may face difficulty when tackling this problem, since it is initially designed to work in relatively balanced class distribution scenarios. Another important problem, which usually accompanies these imbalanced data, is the overlapping instances between the two classes. It is commonly referred to as noise or overlapping data. In this article, we propose an approach called: One Side Behavioral Noise Reduction (OSBNR). This approach presents a way to deal with the problem of class imbalance in the presence of a high noise level. OSBNR is based on two steps. Firstly, a cluster analysis is applied to groups similar instances from the minority class into several behavior clusters. Secondly, we select and eliminate the instances of the majority class, considered as behavioral noise, which overlap with behavior clusters of the minority class. The results of experiments carried out on a representative public dataset confirm that the proposed approach is efficient for the treatment of class imbalances in the presence of noise.

Keywords: machine learning, imbalanced data, data mining, big data

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Authors: Areej Salaymeh, Loren Schwiebert, Stephen Remias, Jonathan Waddell

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Extracting information from new data sources has emerged as a crucial task in many traffic planning processes, such as identifying traffic patterns, route planning, traffic forecasting, and locating infrastructure improvements. Given the advanced technologies used to collect Global Positioning System (GPS) data from dedicated GPS devices, GPS equipped phones, and navigation tools, intelligent data analysis methodologies are necessary to mine this raw data. In this research, an automatic detection framework is proposed to help identify and classify the locations of stopped GPS waypoints into two main categories: signalized intersections or highway congestion. The Delaunay triangulation is used to perform this assessment in the clustering phase. While most of the existing clustering algorithms need assumptions about the data distribution, the effectiveness of the Delaunay triangulation relies on triangulating geographical data points without such assumptions. Our proposed method starts by cleaning noise from the data and normalizing it. Next, the framework will identify stoppage points by calculating the traveled distance. The last step is to use clustering to form groups of waypoints for signalized traffic and highway congestion. Next, a binary classifier was applied to find distinguish highway congestion from signalized stop points. The binary classifier uses the length of the cluster to find congestion. The proposed framework shows high accuracy for identifying the stop positions and congestion points in around 99.2% of trials. We show that it is possible, using limited GPS data, to distinguish with high accuracy.

Keywords: Delaunay triangulation, clustering, intelligent transportation systems, GPS data

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Authors: Gayathri Nagarajan, L. D. Dhinesh Babu

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Health care is one of the prominent industries that generate voluminous data thereby finding the need of machine learning techniques with big data solutions for efficient processing and prediction. Missing data, incomplete data, real time streaming data, sensitive data, privacy, heterogeneity are few of the common challenges to be addressed for efficient processing and mining of health care data. In comparison with other applications, accuracy and fast processing are of higher importance for health care applications as they are related to the human life directly. Though there are many machine learning techniques and big data solutions used for efficient processing and prediction in health care data, different techniques and different frameworks are proved to be effective for different applications largely depending on the characteristics of the datasets. In this paper, we present a framework that uses ensemble machine learning technique gradient boosted trees for data classification in health care big data. The framework is built on Spark platform which is fast in comparison with other traditional frameworks. Unlike other works that focus on a single technique, our work presents a comparison of six different machine learning techniques along with gradient boosted trees on datasets of different characteristics. Five benchmark health care datasets are considered for experimentation, and the results of different machine learning techniques are discussed in comparison with gradient boosted trees. The metric chosen for comparison is misclassification error rate and the run time of the algorithms. The goal of this paper is to i) Compare the performance of gradient boosted trees with other machine learning techniques in Spark platform specifically for health care big data and ii) Discuss the results from the experiments conducted on datasets of different characteristics thereby drawing inference and conclusion. The experimental results show that the accuracy is largely dependent on the characteristics of the datasets for other machine learning techniques whereas gradient boosting trees yields reasonably stable results in terms of accuracy without largely depending on the dataset characteristics.

Keywords: big data analytics, ensemble machine learning, gradient boosted trees, Spark platform

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Authors: Razak Zakariya, Fazliana Mustajap, Lenny Sharinee Sakai

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A sediment map is quite important in the marine environment. The sediment itself contains thousands of information that can be used for other research. This study was conducted by using a multibeam echo sounder Reson T20 on 15 August 2020 at the Karang Sela (coral reef area) at Pulau Bidong. The study aims to identify the sediment type around the coral reef by using bathymetry and backscatter data. The sediment in the study area was collected as ground truthing data to verify the classification of the seabed. A dry sieving method was used to analyze the sediment sample by using a sieve shaker. PDS 2000 software was used for data acquisition, and Qimera QPS version 2.4.5 was used for processing the bathymetry data. Meanwhile, FMGT QPS version 7.10 processes the backscatter data. Then, backscatter data were analyzed by using the maximum likelihood classification tool in ArcGIS version 10.8 software. The result identified three types of sediments around the coral which were very coarse sand, coarse sand, and medium sand.

Keywords: sediment type, MBES echo sounder, backscatter, ArcGIS

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Authors: Mustafa Gülen, Eylem Güzel, Soner Guler

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The studies concentrated on the historical background of Van show the fact that Van has had a significant position as a settlement since ancient times and that it has hosted many civilizations during history. With the dominance of Ottoman Empire in 16th century, the region had been re-constructed by building new walls at the southern side of Van Castle. These construction activities had mostly been fulfilled by the usage of adobe which had been a fundamental material for thousands of years. As a result of natural disasters, battles and the move at the threshold of 20th century to the new settlement which is 9 kilometers away from the Ancient City Van is an open-air museum with the ruins of churches, mosques and baths. In this study, the usage of adobe in historical structures of Van city is evaluated in detail.

Keywords: historical structures, adobe, Van city, adobe

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Authors: Sedat Bilgili, Alper K. Demir

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The current Internet has become the dominant use with continuing growth in the home, medical, health, smart cities and industrial automation applications. Internet of Things (IoT) is an emerging technology to enable such applications in our lives. Moreover, Named Data Networking (NDN) is also emerging as a Future Internet architecture where it fits the communication needs of IoT networks. The aim of this study is to provide an NDN protocol stack implementation running on the Contiki operating system (OS). Contiki OS is an OS that is developed for constrained IoT devices. In this study, an NDN protocol stack that can work on top of IEEE 802.15.4 link and physical layers have been developed and presented.

Keywords: internet of things (IoT), named-data, named data networking (NDN), operating system

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Authors: Nashed Atef Nashed Farag

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Introduction: Reporting adverse events following vaccination remains a challenge. WHO has mandated pharmacovigilance centers around the world to submit Adverse Events Following Immunization (AEFI) reports from different countries to a large electronic database of adverse drug event data called Vigibase. Despite sufficient information about AEFIs on Vigibase, they are not available to the general public. However, the WHO has an alternative website called VigiAccess, an open-access website that serves as an archive for reported adverse reactions and AEFIs. The aim of the study was to establish a reporting model for a number of commonly used vaccines in the VigiAccess system. Methods: On February 5, 2018, VigiAccess comprehensively searched for ESSI reports on the measles vaccine, oral polio vaccine (OPV), yellow fever vaccine, pneumococcal vaccine, rotavirus vaccine, meningococcal vaccine, tetanus vaccine, and tuberculosis vaccine (BCG). These are reports from all pharmacovigilance centers around the world since they joined the WHO Drug Monitoring Program. Results: After an extensive search, VigiAccess found 9,062 AEFIs from the measles vaccine, 185,829 AEFIs from the OPV vaccine, 24,577 AEFIs from the yellow fever vaccine, 317,208 AEFIs from the pneumococcal vaccine, 73,513 AEFIs from the rotavirus vaccine, and 145,447 AEFIs from meningococcal cal vaccine, 22,781 EI FI vaccines against tetanus and 35,556 BCG vaccines against AEFI. Conclusion: The study found that among the eight vaccines examined, pneumococcal vaccines were associated with the highest number of AEFIs, while measles vaccines were associated with the fewest AEFIs.

Keywords: surgical approach, anatomical approach, decompression, axillary nerve, quadrangular space adverse events following immunization, cameroon, COVID-19 vaccines, nOPV, ODK vaccines, adverse reactions, VigiAccess, adverse event reporting

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Authors: Johanna Sagner, María José Sandoval

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At the early stages of adolescence, young people, regardless of a disability or not, start to establish closer friendship ties. Unlike previous developmental phases, these ties are rather reciprocal, more committed, and require more time. Friendship ties during adolescence allow the development of social and personal skills, specifically the skills to start constructing identity. In an inclusive context that incorporates young people with a disability, friendship among peers also takes place. Nonetheless, the relation is shaped, among others, by the alterity construction about the other with disability. Research about peers’ relation between young people with and without disability in an inclusive context has shown that the relation tends to become a helper-helpee relation, where those with a disability are seen as people in need. Prejudices about the others’ condition or distancing from the other because of his/hers disability are common. In this sense, the helper-helpee relation, as a non-reciprocal and protective relation, will not promote friendship between classmates, but a rather asymmetric alterity. Our research is an explorative case study that wants to know how the relation between peers is shaped within a different inclusive program, were also the integrated group has special educational needs. Therefore, we analyze from a qualitative and quantitative approach the data of an inverted inclusive program. This is a unique case of a special public school for visual disability in Germany that includes young people from a mainstream school who had learning difficulties. For the research, we analyze data from interviews, focal interviews and open-ended questions with an interpretative phenomenological analysis approach. The questionnaires include a five point Likert scale, for which we calculate the acceptance rate. The findings show that the alterity relation between pupils is less asymmetrical and represents a rather horizontal alterity. The helper-helpee relation is marked by exchange, since both groups have special educational needs and therefore, those with visual disability and those with learning difficulties help each other indistinctly. Friendship is more present among classmates. The horizontal alterity peers’ relation is influenced by a sort of tie, where none of the groups need more or less help than other groups. Both groups identify that they themselves and the other have special needs. The axiological axe of alterity is not of superiority or inferiority, recognizing each other’s differences and otherness. Another influential factor relates with the amount of time they spend together, since the program does not have a resource room or a teacher who teaches parallel lessons. Two probable causes for that rather equal peer relation might be the constellation of fewer pupils per classroom and the differentiated lessons taught by teachers with a special educational formation.

Keywords: alterity, disability, inverted inclusion, peers’ relation

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Authors: Yasin I. Tayem, Jamil Ahmed, Mahmood Bahzad, Abdullah Alnama, Fahad Al Asfoor, Mahmood A. Jalil, Mohammed Radhi, Ahmed Alenezi, Khalid A. J. Al-Khaja

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Drug-drug interactions (DDIs) represent a significant cause of patient’s morbidity and mortality. The rate of DDIs is rapidly increasing worldwide with the increasing proportion of ageing population and frequent requirement of polypharmacy-prescription of multiple drugs to treat comorbidities. Prescribing physicians are responsible for checking their prescriptions for the presence and severity of DDIs. However, since a large number of new drugs are approved and marketed every year, new interactions between medications are increasingly reported. Consequently, it is no longer practical for physicians to rely only upon their previous knowledge of medicine to avoid potential DDIs. The aim of this study was to explore the perceptions of physicians working at primary healthcare centers in Bahrain towards DDIs and how they manage them during their practice. Methodology: In this cross-sectional study, physicians working at all governmental primary healthcare centers in Bahrain were invited to voluntarily, privately and anonymously respond to a self-administered questionnaire. The questionnaire aims to assess their self-reported knowledge of DDIs and how they check for them in their practice. The participants were requested to provide socio demographic data and information related to their attitudes towards DDIs including strategies they employ for detecting and managing them, and their awareness of drugs which commonly cause DDIs. At the end of the questionnaire, an open-ended item was added to allow participants to further add any comment. Findings and Conclusions: The study is going on currently, and the results and conclusions will be presented at the conference.

Keywords: awareness, drug interactions, health centres, physicians

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Authors: Marietta Kékes Szabó

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The patterns of personality (mal)function and the individuals’ psychosocial environment influence the healthy status collectively and may lie in the background of psychosomatic disorders. Although the patients with their diversified symptoms usually do not have any organic problems, the experienced complaint, the fear of serious illness and the lack of social support often lead to increased anxiety and further enigmatic symptoms. The role of the family system and its atmosphere seem to be very important in this process. More studies explored the characteristics of dysfunctional family organization: inflexible family structure, hidden conflicts that are not spoken about by the family members during their daily interactions, undefined role boundaries, neglect or overprotection of the children by the parents and coalition between generations. However, questionnaires that are used to measure the properties of the family system are able to explore only its unit and cannot pay attention to the dyadic interactions, while the representation of the family structure by a figure placing test gives us a new perspective to better understand the organization of the (sub)system(s). Furthermore, its dynamic form opens new perspectives to explore the family members’ joint representations, which gives us the opportunity to know more about the flexibility of cohesion and hierarchy of the given family system. In this way, the communication among the family members can be also examined. The aim of my study was to collect a great number of information about the organization of psychosomatic families. In our research we used Gehring’s Family System Test (FAST) both in static and dynamic forms to mobilize the family members’ mental representations about their family and to get data in connection with their individual representations as well as cooperation. There were four families in our study, all of them with a young adult person. Two families with healthy participants and two families with asthmatic patient(s) were involved in our research. The family members’ behavior that could be observed during the dynamic situation was recorded on video for further data analysis with Noldus Observer XT 8.0 program software. In accordance with the previous studies, our results show that the family structure of the families with at least one psychosomatic patient is more rigid than it was found in the control group and the certain (typical, ideal, and conflict) dynamic representations reflected mainly the most dominant family member’s individual concept. The behavior analysis also confirmed the intensified role of the dominant person(s) in the family life, thereby influencing the family decisions, the place of the other family members, as well as the atmosphere of the interactions, which could also be grasped well by the applied methods. However, further research is needed to learn more about the phenomenon that can open the door for new therapeutic approaches.

Keywords: psychosomatic families, family structure, family system test (FAST), static and dynamic representations, behavior analysis

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Authors: Prabhvir Singh Marway, Kai Lok Chan, Maria-Ruxandra Jinga, Rachel Bok Ying Lee, Matthew Bok Kit Lee, Krishan Nandapalan, Sze Yi Beh, Harry Carr, Christopher Kui

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We piloted a structured series of online workshops on the 3D segmentation of anatomical structures from CT scans. 33 participants were recruited from four UK universities for two-day workshops between 2020 and 2021. Open-source software (3D-Slicer) was used. We hypothesized that active participation via real-time screen-sharing and voice-communication via Discord would enable improved engagement and learning, despite national lockdowns. Written feedback indicated positive learning experiences, with subjective measures of anatomical understanding and software confidence improving.

Keywords: medical education, workshop, segmentation, anatomy

Procedia PDF Downloads 200

Authors: Zsofia Patyi

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While empirical studies under socialism in Hungary focused on the lawyer society as a whole, current research deals with law students in specific. The change of regime and the mutation of legal education have influenced the motivation, efficiency, social background and self-concept of law students. This shift needs to be acknowledged, and the education system improved for students and together with students. A new law student society requires a different legal education system, different legal studies, or, at the minimum, a different approach to teaching law. This is to ensure that competitive lawyers be trained who understand the constantly changing nature of the law and, as a result, can potentially transform or create legislation themselves. A number of developments can affect law students’ awareness of legal relations in a democratic state. In today’s Hungary, these decisive factors are primarily the new regulation of the financing of law students, and secondly, the new Hungarian constitution (henceforth: Alaptörvény), which has modified the base of the Hungarian legal system. These circumstances necessitate a new, comprehensive, and empirical, investigation of law students. To this end, our research team (comprising a professor, a Ph.D. student, and two law students), is conducting a new type of study in February 2017. The first stage of the research project uses the desktop method to open up the research antecedents. Afterward, a structured questionnaire draft will be designed and sent to the Head of Department of Sociology and the Associate Professor of the Department of Constitutional Law at the University of Szeged to have the draft checked and amended. Next, an open workshop for students and teachers will be organized with the aim to discuss the draft and create the final questionnaire. The research team will then contact each Hungarian university with a Faculty of Law to reach all 1st- and 4th-year law students. 1st-year students have not yet studied the Alaptörvény, while 4th-year students have. All students will be asked to fill in the questionnaire (in February). Results are expected to be in at the end of February. In March, the research team will report the results and present the conclusions. In addition, the results will be compared to previous researches. The outcome will help us answer the following research question: How should legal studies and legal education in Hungary be reformed in accordance with law students and the future lawyer society? The aim of the research is to (1) help create a new student- and career-centered teaching method of legal studies, (2) offer a new perspective on legal education, and (3) create a helpful and useful de lege ferenda proposal for the attorney general as regards legal education as part of higher education.

Keywords: change, constitution, investigation, law students, lawyer society, legal education, legal studies, motivation, reform

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Authors: Daniel R. dos Santos, Mateus S. Maldonado, Estevão J. R. Batista

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The military commanders increasingly dependent on spatial awareness, as knowing where enemy are, understanding how war battle scenarios change over time, and visualizing these trends in ways that offer insights for decision-making. Thanks to advancements in geospatial technologies and artificial intelligence algorithms, the commanders are now able to modernize military operations on a universal scale. Thus, geovisualisation has become an essential asset in the defense sector. It has become indispensable for better decisionmaking in dynamic/temporal scenarios, operation planning and management for the war field, situational awareness, effective planning, monitoring, and others. For example, a 3D visualization of war field data contributes to intelligence analysis, evaluation of postmission outcomes, and creation of predictive models to enhance decision-making and strategic planning capabilities. However, old-school visualization methods are slow, expensive, and unscalable. Despite modern technologies in generating 3D point clouds, such as LIDAR and stereo sensors, monocular depth values based on deep learning can offer a faster and more detailed view of the environment, transforming single images into visual information for valuable insights. We propose a dedicated monocular depth reconstruction approach via deep learning techniques for 3D geovisualisation of satellite images. It introduces scalability in terrain reconstruction and data visualization. First, a dataset with more than 7,000 satellite images and associated digital elevation model (DEM) is created. It is based on high resolution optical and radar imageries collected from Planet and Copernicus, on which we fuse highresolution topographic data obtained using technologies such as LiDAR and the associated geographic coordinates. Second, we developed an imagery-DEM fusion strategy that combine feature maps from two encoder-decoder networks. One network is trained with radar and optical bands, while the other is trained with DEM features to compute dense 3D depth. Finally, we constructed a benchmark with sparse depth annotations to facilitate future research. To demonstrate the proposed method's versatility, we evaluated its performance on no annotated satellite images and implemented an enclosed environment useful for Geovisualisation applications. The algorithms were developed in Python 3.0, employing open-source computing libraries, i.e., Open3D, TensorFlow, and Pythorch3D. The proposed method provides fast and accurate decision-making with GIS for localization of troops, position of the enemy, terrain and climate conditions. This analysis enhances situational consciousness, enabling commanders to fine-tune the strategies and distribute the resources proficiently.

Keywords: depth, deep learning, geovisualisation, satellite images

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Authors: Peshawa Jalal Mohammed

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Following the recent political conflict in the Middle East, the number of refugees and internally displaced people increased in the last decades. The flood of displaced people became a big issue for the host communities in the neighbouring countries and Europe. The need for research about the education and integration of the refugees became urgent. After the appearance of the Islamic State and displacing millions of Syrian people, the Kurdistan Region of Iraq became a safe shelter for hundreds of thousands of Syrians and international organisations helping the refugees. This study focuses on the factors of parental involvement among the host community and refugee parents and its role in the academic success of children. The setting is the three provinces of Iraqi Kurdistan (Erbil, Sulaimani, and Dohuk), including the refugee camps in the three provinces. Based on the purpose of the study, the study was designed as a descriptive survey study with a mixed approach, qualitative (open-ended), and quantitative (questionnaire) questions and both forms of data were integrated and analysed. The current study participants were 8th and 9th graders at the basic school level, studying at public schools and their parents. The sampling design was the selection of local schools and schools in the refugee camps in the region's three provinces. The number of participants for each of the two groups was 250 students and 250 parents. The results showed that parents' socioeconomic status, gender, and place of residency have significant roles in students' parental involvement and academic success of their students. The results also show the characteristics of parental inspiration to their children's future and their expectations from education.

Keywords: refugee, education, parental involvement, socioeconomic

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Authors: Saeed Ullah Jan, Muhammad Ali, Misbah Ullah Awan

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Purpose: This study investigated the information needs and seeking behaviour, and hurdles to information seeking of Post Graduate students of Kohat University of Science and Technology (KUST), Khyber Pakhtunkhwa. It focused on the information requirements of the post-graduate students of the university, the pattern they use for seeking information, and the difficulties they face while seeking information. Design/Methodology/approach: This study used a quantitative approach, adapting a survey questionnaire method for data collection. The population of this study was composed of M.Phil. and Ph.D. students of 2019 and 2020 in the faculties of Physical and Numerical Sciences, Chemical and Pharmaceutical Sciences, Biological Sciences, and Social Sciences of KUST. The sample size was 260. Students were selected randomly. The study response rate was 77%, and data were analyzed through SPSS (22 versions). Key findings: The study revealed that Most students' information needs were for study and research activities, new knowledge, and career development. To fulfill these needs, the scholars use various sources and resources. The sources they used for information needs were journal articles, textbooks, and research projects commonly. For the information-seeking purpose, often, students prefer books that have some importance. The other factors that played an essential role in selecting material were topical relevance, Novelty, Recommended by colleagues, and publisher's reputation. Most of the students thought that Book Exhibitions, Open Access systems in the Library, and the Display of new arrivals could enhance the students' information-seeking. The main problem seeking information was faced by them was a shortage of printed information resources. Overall they wanted more facilities, enhancement in the library collection, and better services. Delimitations of the study: This study has not included 1) BS and M.Sc. Students of KUST; 2) The colleges and institutions affiliated with KUST; 3) This study was delimited only to the Post Graduate students of KUST. Practical implication(s): The findings of the study motivate the policymakers and authorities of KUST to restructure the information literacy programs to fulfill the scholars' information needs. It may inform the policymakers to know the difficulties faced by scholars during information seeking. Contribution to the knowledge: No significant work has been done on the students' information needs and seeking behaviour at KUST. The study analyzed the information needs and seeking behaviour of post graduate students. It brought a clear picture of information needs and seeking behaviour of scholars and addressed the problems faced by them during the seeking process.

Keywords: information needs of Pakistan, information-seeking behaviors, postgraduate students, university libraries, Kohat university of science and technology, Khyber Pakhtunkhwa, Pakistan

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Authors: Ying Ying Liu, Austin Cooke, Parimala Thulasiraman

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Internet of Things (IoT) has attracted a recent spark in research on Internet of Vehicles (IoV). In this paper, we focus on one research area in IoV: preserving location privacy of vehicle data. We discuss existing location privacy preserving techniques and provide a scheme for evaluating these techniques under IoV traffic condition. We propose a different strategy in applying Differential Privacy using k-d tree data structure to preserve location privacy and experiment on real world Gowalla data set. We show that our strategy produces differentially private data, good preservation of utility by achieving similar regression accuracy to the original dataset on an LSTM (Long Term Short Term Memory) neural network traffic predictor.

Keywords: differential privacy, internet of things, internet of vehicles, location privacy, privacy preservation scheme

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Authors: Shorena Pharjiani

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The Present empirical paper investigates the relationship between FDI and economic growth by 10 selected industries in 10 Central and Eastern European countries from the period 1995 to 2012. Different estimation approaches were used to explore the connection between FDI and economic growth, for example OLS, RE, FE with and without time dummies. Obtained empirical results leads to some main consequences: First, the Central and East European countries (CEEC) attracted foreign direct investment, which raised the productivity of industries they entered in. It should be concluded that the linkage between FDI and output growth by industries is positive and significant enough to suggest that foreign firm’s participation enhanced the productivity of the industries they occupied. There had been an endogeneity problem in the regression and fixed effects estimation approach was used which partially corrected the regression analysis in order to make the results less biased. Second, it should be stressed that the results show that time has an important role in making FDI operational for enhancing output growth by industries via total factor productivity. Third, R&D positively affected economic growth and at the same time, it should take some time for research and development to influence economic growth. Fourth, the general trends masked crucial differences at the country level: over the last 20 years, the analysis of the tables and figures at the country level show that the main recipients of FDI of the 11 Central and Eastern European countries were Hungary, Poland and the Czech Republic. The main reason was that these countries had more open door policies for attracting the FDI. Fifth, according to the graphical analysis, while Hungary had the highest FDI inflow in this region, it was not reflected in the GDP growth as much as in other Central and Eastern European countries.

Keywords: central and East European countries (CEEC), economic growth, FDI, panel data

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Authors: Yuhanis Yusof, Zuriani Mustaffa, Siti Sakira Kamaruddin

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Data mining is a fundamental technique in identifying patterns from large data sets. The extracted facts and patterns contribute in various domains such as marketing, forecasting, and medical. Prior to that, data are consolidated so that the resulting mining process may be more efficient. This study investigates the effect of different data normalization techniques, which are Min-max, Z-score, and decimal scaling, on Swarm-based forecasting models. Recent swarm intelligence algorithms employed includes the Grey Wolf Optimizer (GWO) and Artificial Bee Colony (ABC). Forecasting models are later developed to predict the daily spot price of crude oil and gasoline. Results showed that GWO works better with Z-score normalization technique while ABC produces better accuracy with the Min-Max. Nevertheless, the GWO is more superior that ABC as its model generates the highest accuracy for both crude oil and gasoline price. Such a result indicates that GWO is a promising competitor in the family of swarm intelligence algorithms.

Keywords: artificial bee colony, data normalization, forecasting, Grey Wolf optimizer

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Authors: Anuta Mukherjee, Saswati Mukherjee

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Data is growing everyday. Social networking sites such as Twitter are becoming an integral part of our daily lives, contributing a large increase in the growth of data. It is a rich source especially for sentiment detection or mining since people often express honest opinion through tweets. However, although sentiment analysis is a well-researched topic in text, this analysis using Twitter data poses additional challenges since these are unstructured data with abbreviations and without a strict grammatical correctness. We have employed collision theory to achieve sentiment analysis in Twitter data. We have also incorporated discourse analysis in the collision theory based model to detect accurate sentiment from tweets. We have also used the retweet field to assign weights to certain tweets and obtained the overall weightage of a topic provided in the form of a query. Hadoop has been exploited for speed. Our experiments show effective results.

Keywords: sentiment analysis, twitter, collision theory, discourse analysis

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Authors: Zahid Ullah, Atlas Khan

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The rapid advancements in data collection, storage, and processing capabilities have led to an explosion of data in various domains. In this era of big data, mathematical sciences play a crucial role in uncovering valuable insights and driving informed decision-making through data analytics. The purpose of this abstract is to present the latest advances in mathematical sciences and their application in harnessing the power of data analytics. This abstract highlights the interdisciplinary nature of data analytics, showcasing how mathematics intersects with statistics, computer science, and other related fields to develop cutting-edge methodologies. It explores key mathematical techniques such as optimization, mathematical modeling, network analysis, and computational algorithms that underpin effective data analysis and interpretation. The abstract emphasizes the role of mathematical sciences in addressing real-world challenges across different sectors, including finance, healthcare, engineering, social sciences, and beyond. It showcases how mathematical models and statistical methods extract meaningful insights from complex datasets, facilitating evidence-based decision-making and driving innovation. Furthermore, the abstract emphasizes the importance of collaboration and knowledge exchange among researchers, practitioners, and industry professionals. It recognizes the value of interdisciplinary collaborations and the need to bridge the gap between academia and industry to ensure the practical application of mathematical advancements in data analytics. The abstract highlights the significance of ongoing research in mathematical sciences and its impact on data analytics. It emphasizes the need for continued exploration and innovation in mathematical methodologies to tackle emerging challenges in the era of big data and digital transformation. In summary, this abstract sheds light on the advances in mathematical sciences and their pivotal role in unveiling the power of data analytics. It calls for interdisciplinary collaboration, knowledge exchange, and ongoing research to further unlock the potential of mathematical methodologies in addressing complex problems and driving data-driven decision-making in various domains.

Keywords: mathematical sciences, data analytics, advances, unveiling

Procedia PDF Downloads 94