Search results for: Privacy and Data Protection Law

Authors: Renata Silva-Mann, Sheila Valéria Álvares Carvalho, Robério Anastácio Ferreira, Laura Jane Gomes

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There is growing interest in defining indicators of sustainability, which are important for monitoring the conservation of native forests, particularly in areas of permanent protection. These indicators are references for assessing the state of the forest and the status of the depredated area and its ability to maintain species populations. The aim of the present study was to select genetic parameters as indicators of sustainability for Schinus terebinthifolius Raddi. Fragments located in riparian areas between the Sergipe and Alagoas States in Brazil. This species has been exploited for traditional communities, which represent 20% of the incoming. This study was carried out using the indicators suggested by the Organization for Economic Cooperation and Development, which were identified as Driving-Pressure-State-Impact-Response (DPSIR) factors. The genetic parameters were obtained in five populations located on the shores and islands of the São Francisco River, one of the most important rivers in Brazil. The framework for Schinus conservation suggests seventeen indicators of sustainability. In accordance with genetic parameters, the populations are isolated, and these genetic parameters can be used to monitor the sustainability of those populations in riparian area with the aim of defining strategies for forest restoration.

Keywords: alleles, molecular markers, genetic diversity, biodiversity

Procedia PDF Downloads 286

Authors: Sara Alshehri, Bayan Alenzi, Atheer Alshehri, Samia Chelloug, Zainab Almry, Hussah Albugmai

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This paper concerns body area networks sensor that collect signals around a human body. The black hole attacks are the main security challenging problem because the data traffic can be dropped at any node. The focus of our proposed solution is to efficiently route data packets while detecting black hole nodes.

Keywords: body sensor networks, security, black hole, routing, broadcasting, OMNeT++

Procedia PDF Downloads 626

Authors: Maryam Panahiazar, Andrew M. Bishara, Yorick Chern, Roohallah Alizadehsani, Dexter Hadleye, Ramin E. Beygui

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Early detection plays a crucial role in enhancing the outcome for a patient with coronary artery disease (CAD). We utilized a big data analytics platform on ~23,000 patients with CAD from a total of 960,129 UCSF patients in 8 years. We traced the patients from their first encounter with a physician to diagnose and treat CAD. Characteristics such as demographic information, comorbidities, vital, lab tests, medications, and procedures are included. There are statistically significant gender-based differences in patients younger than 60 years old from the time of the first physician encounter to coronary artery bypass grafting (CABG) with a p-value=0.03. There are no significant differences between the patients between 60 and 80 years old (p-value=0.8) and older than 80 (p-value=0.4) with a 95% confidence interval. This recognition would affect significant changes in the guideline for referral of the patients for diagnostic tests expeditiously to improve the outcome by avoiding the delay in treatment.

Keywords: electronic medical records, coronary artery disease, data analytics, young women

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Authors: Amdouni Hamida, Gammoudi Mohamed Mohsen

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In order to help the expert to validate association rules extracted from data, some quality measures are proposed in the literature. We distinguish two categories: objective and subjective measures. The first one depends on a fixed threshold and on data quality from which the rules are extracted. The second one consists on providing to the expert some tools in the objective to explore and visualize rules during the evaluation step. However, the number of extracted rules to validate remains high. Thus, the manually mining rules task is very hard. To solve this problem, we propose, in this paper, a semi-automatic method to assist the expert during the association rule's validation. Our method uses rule-based classification as follow: (i) We transform association rules into classification rules (classifiers), (ii) We use the generated classifiers for data classification. (iii) We visualize association rules with their quality classification to give an idea to the expert and to assist him during validation process.

Keywords: association rules, rule-based classification, classification quality, validation

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Authors: Atoum Abdullah

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The purpose of this research is to determine the extent to which computer animation and narration affect students’ understanding of complex scientific concepts and improve their exam performance, this is compared to traditional lectures that include PowerPoints with texts and static images. A mixed-method design in data collection was used, including quantitative and qualitative data. Quantitative data was collected using a pre and post-test method and a close-ended questionnaire. Qualitative data was collected through an open-ended questionnaire. A pre and posttest strategy was used to measure the level of students’ understanding with and without the use of animation. The test included multiple-choice questions to test factual knowledge, open-ended questions to test conceptual knowledge, and to label the diagram questions to test application knowledge. The results showed that students on average, performed significantly higher on the posttest as compared to the pretest on all areas of acquired knowledge. However, the increase in the posttest score with respect to the acquisition of conceptual and application knowledge was higher compared to the increase in the posttest score with respect to the acquisition of factual knowledge. This result demonstrates that animation is more beneficial when acquiring deeper, conceptual, and cognitive knowledge than when only factual knowledge is acquired.

Keywords: animation, narration, science, teaching

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Authors: Chukiat Chaiboonsri, Satawat Wannapan

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This paper used an asymmetric informative concept to apply in the macroeconomic model estimation of the tourism sector in Thailand. The variables used to statistically analyze are Thailand international and domestic tourism revenues, the expenditures of foreign and domestic tourists, service investments by private sectors, service investments by the government of Thailand, Thailand service imports and exports, and net service income transfers. All of data is a time-series index which was observed between 2002 and 2015. Empirically, the tourism multiplier and accelerator were estimated by two statistical approaches. The first was the result of the Generalized Method of Moments model (GMM) based on the assumption which the tourism market in Thailand had perfect information (Symmetrical data). The second was the result of the Maximum Entropy Bootstrapping approach (MEboot) based on the process that attempted to deal with imperfect information and reduced uncertainty in data observations (Asymmetrical data). In addition, the tourism leakages were investigated by a simple model based on the injections and leakages concept. The empirical findings represented the parameters computed from the MEboot approach which is different from the GMM method. However, both of the MEboot estimation and GMM model suggests that Thailand’s tourism sectors are in a period capable of stimulating the economy.

Keywords: TThailand tourism, Maximum Entropy Bootstrapping approach, macroeconomic model, asymmetric information

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Authors: Fong-Zuo Lee, Jihn-Sung Lai, Yung-Bin Lin, Xiaoqin Liu, Kuo-Chun Chang, Zhi-Xian Yang, Wen-Dar Guo, Jian-Hao Hong

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In recent years, climate change is a major factor to increase rainfall intensity and extreme rainfall frequency. The increased rainfall intensity and extreme rainfall frequency will increase the probability of flash flood with abundant sediment transport in a river basin. The floods caused by heavy rainfall may cause damages to the bridge, embankment, hydraulic works, and the other disasters. Therefore, the foundation scouring of bridge pier, embankment and spur dike caused by floods has been a severe problem in the worldwide. This severe problem has happened in many East Asian countries such as Taiwan and Japan because of these areas are suffered in typhoons, earthquakes, and flood events every year. Results from the complex interaction between fluid flow patterns caused by hydraulic works and the sediment transportation leading to the formation of river morphology, it is extremely difficult to develop a reliable and durable sensor to measure river bed degradation and bridge pier scouring. Therefore, an innovative scour monitoring sensor using vibration-based Micro-Electro Mechanical Systems (MEMS) was developed. This vibration-based MEMS sensor was packaged inside a stainless sphere with the proper protection of the full-filled resin, which can measure free vibration signals to detect scouring/deposition processes at the bridge pier. In addition, a friendly operational system includes rainfall runoff model, one-dimensional and two-dimensional numerical model, and the applicability of sediment transport equation and local scour formulas of bridge pier are included in this research. The friendly operational system carries out the simulation results of flood events that includes the elevation changes of river bed erosion near the specified bridge pier and the erosion depth around bridge piers. In addition, the system is developed with easy operation and integrated interface, the system can supplies users to calibrate and verify numerical model and display simulation results through the interface comparing to the scour monitoring sensors. To achieve the forecast of the erosion depth of river bed and main bridge pier in the study area, the system also connects the rainfall forecast data from Taiwan Typhoon and Flood Research Institute. The results can be provided available information for the management unit of river and bridge engineering in advance.

Keywords: flash flood, river bed degradation, bridge pier scouring, a friendly operational system

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Authors: Masood Monjezi

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The English teachers’ pedagogical identity construction is the way teachers go through the process of becoming teachers and how they maintain their teaching selves. The pedagogical identity of teachers is influenced by several factors within the individual and the society. The purpose of this study was to compare the imagined social world of the pre-service teachers with the real experiences the in-service teachers had in the context of Iran to see how prepared the pre-service teachers are with a view to their identity being. This study used a qualitative approach to collection and analysis of the data. Structured and semi-structured interviews, focus groups and process logs were used to collect the data. Then, using open coding, the data were analyzed. The findings showed that the imagined world of the pre-service teachers partly corresponded with the real world experiences of the in-service teachers leaving the pre-service teachers unprepared for their real world teaching profession. The findings suggest that the current approaches to English teacher training are in need of modification to better prepare the pre-service teachers for the future that expects them.

Keywords: imagined professional world, in-service teachers, pre-service teachers, real experiences, community of practice, identity

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Authors: Tohid Eskandarzadeh, Nader Bahlouli, Turaj Behnam, Azra Jafarzadeh

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The purpose of the study reported in this paper was to propose an optimal pattern to evaluate the staff management performance of the water and sewage organization. The performance prism-model was used to evaluate the following significant dimensions of performance: organizational strategies, organizational processes, organization capabilities, stakeholders’ partnership and satisfaction. In the present study, a standard, valid and reliable questionnaire was used to obtain data about the five dimensions of the performance prism model. 169 sample respondents were used for responding the questionnaire who were selected from the staff of water and waste-water organization in western Azerbaijan, Iran. Also, Alpha coefficient was used to check the reliability of the data-collection instrument which was measured to be beyond 0.7. The obtained data were statistically analyzed by means of SPSS version 18. The results obtained from the data analysis indicated that the performance of the staff management of the water and waste-water organization in western Azerbaijan was acceptable in terms of organizational strategies, organizational process, stakeholders’ partnership and satisfaction. Nevertheless, it was found that the performance of the staff management with respect to organizational abilities was average. Indeed, the researchers drew the conclusion that the current performance of the staff management in this organization in western Azerbaijan was less than ideal performance.

Keywords: performance evaluation, performance prism model, water, waste-water organization

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Authors: B. V. Murlimanju, P. J. Jiji, Latha V. Prabhu, Mangala M. Pai

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Background: The scaphoid is the most commonly fractured bone of the wrist. The fracture may disrupt the vessels and end up as the avascular necrosis of the bone. The objective of the present study was to investigate the morphology and number of the nutrient foramina in the cadaveric dried scaphoid bones of the Indian population. Methods: The present study included 46 scaphoid bones (26 right sided and 20 left sided) which were obtained from the gross anatomy laboratory of our institution. The bones were macroscopically observed for the nutrient foramina and the data was collected with respect to their number. The tabulation of the data and analysis were done. Results: All of our specimens (100%) exhibited the nutrient foramina over the non-articular surfaces. The foramina were observed only over the palmar and dorsal surfaces of the scaphoid bones. The foramina were observed both proximal and distal to the mid waist of the scaphoid bone. The foramen ranged between 9 and 54 in each scaphoid bone. The foramina over the palmar surface ranged between, 2-24 in number. The foramina over the dorsal surface ranged between, 7-36 in number. The foramina proximal to the waist ranged between 2 and 24 in number and distal to the waist ranged between 3 and 39. Conclusion: We believe that the present study has provided additional data about the nutrient foramina of the scaphoid bones. The data is enlightening to the orthopedic surgeon and would help in the hand surgeries. The morphological knowledge of the vasculature, their foramina of entry and their number is required to understand the concepts in the avascular necrosis of the proximal scaphoid and non-union of the fracture at the waist of the scaphoid.

Keywords: avascular necrosis, nutrient, scaphoid, vascular

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Authors: Malathi Balakrishnan, Cheng Lee Ooi, Chander Vengadasalam

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The purpose of this study is to explore a case study of trainee teachers’ learning experience on innovating traditional games during the traditional game carnival. It explores issues arising from multiple case studies of trainee teachers learning experiences in innovating traditional games. A qualitative methodology was adopted through observations, semi-structured interviews and reflective journals’ content analysis of trainee teachers’ learning experiences creating and implementing innovative traditional games. Twelve groups of 36 trainee teachers who registered for Sports and Physical Education Management Course were the participants for this research during the traditional game carnival. Semi structured interviews were administrated after the trainee teachers learning experiences in creating innovative traditional games. Reflective journals were collected after carnival day and the content analyzed. Inductive data analysis was used to evaluate various data sources. All the collected data were then evaluated through the Nvivo data analysis process. Inductive reasoning was interpreted based on the Self Determination Theory (SDT). The findings showed that the trainee teachers had positive game participation experiences, game knowledge about traditional games and positive motivation to innovate the game. The data also revealed the influence of themes like cultural significance and creativity. It can be concluded from the findings that the organized game carnival, as a requirement of course work by the Institute of Teacher Training Malaysia, was able to enhance teacher trainers’ innovative thinking skills. The SDT, as a multidimensional approach to motivation, was utilized. Therefore, teacher trainers may have more learning experiences using the SDT.

Keywords: learning experiences, innovation, traditional games, trainee teachers

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Authors: Nasrin Hosseinzad

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Troxerutin, a trihydroxyethylated derived of rutin, is a flavonoid existing in tea, coffee, cereal grains, various fruits and vegetables have been conveyed to display radioprotective, antithrombotic, nephron-protective and hepato-protective possessions. Troxerutin, has been well-proved to utilize hepatoprotective assets. Troxerutin could upturn the resistance of hippocampal neurons alongside apoptosis by lessening the action of AChE and oxidative stress. Consequently, troxerutin may have advantageous properties in the administration of Alzheimer's disease and cancer. Troxerutin has been testified to have several welfares and medicinal stuffs. It could shelter the mouse kidney against d-gal-induced damage by refining renal utility, decreasing histopathologic changes, dropping ROS construction, reintroducing the activities of antioxidant enzymes and reducing DNA oxidative destruction. The DNA cleavage study clarifies that troxerutin showed DNA protection against hydroxyl radical persuaded DNA mutilation. Troxerutin uses anti-cancer effect in HuH-7 hepatocarcinoma cells conceivably through synchronized regulation of the molecular signalling pathways, Nrf2 and NF-κB. DNA binding at slight channel by troxerutin may have donated to feature breaks leading to improved radiation brought cell death. Furthermore, the mechanism principal the observed variance in the antioxidant activities of troxerutin and its esters was qualified to equally their free radical scavenging capabilities and dissemination on the cell membrane outward.

Keywords: troxerutin, DNA, oxidative stress, antioxidant, free radical

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Authors: Evan Hajani, Ataur Rahman, Khaled Haddad

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Climate change will affect the hydrological cycle in many different ways such as increase in evaporation and rainfalls. There have been growing interests among researchers to identify the nature of trends in historical rainfall data in many different parts of the world. This paper examines the trends in annual maximum rainfall data from 30 stations in New South Wales, Australia by using two non-parametric tests, Mann-Kendall (MK) and Spearman’s Rho (SR). Rainfall data were analyzed for fifteen different durations ranging from 6 min to 3 days. It is found that the sub-hourly durations (6, 12, 18, 24, 30, and 48 minutes) show statistically significant positive (upward) trends whereas longer duration (sub-daily and daily) events generally show a statistically significant negative (downward) trend. It is also found that the MK test and SR test provide notably different results for some rainfall event durations considered in this study. Since shorter duration sub-hourly rainfall events show positive trends at many stations, the design rainfall data based on stationary frequency analysis for these durations need to be adjusted to account for the impact of climate change. These shorter durations are more relevant to many urban development projects based on smaller catchments having a much shorter response time.

Keywords: climate change, Mann-Kendall test, Spearman’s Rho test, trends, design rainfall

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Authors: Ruiyi Zhang, Wanglin Yan

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Strict land-use plan is issued based on city planning act for controlling urbanization and conserving semi-natural landscape. And the agrarian land resource in the suburbs has indispensable socio-economic value and contributes to the sustainability of the regional environment. However, the agrarian hinterland of metropolitan is witnessing severe farmland conversion and abandonment, while the contribution of land-use planning to farmland conservation remains unclear in those areas. Hypothetically, current land-use plan contributes to farmland loss. So, this research investigated the relationship between farmland loss and land-use planning at municipality level to provide base data for zoning in the metropolitan suburbs, and help to develop a sustainable land-use plan that will conserve the agrarian hinterland. As data and methods, 1) Farmland data of Census of Agriculture and Forestry for 2005 to 2015 and population data of 2015 and 2018 were used to investigate spatial distribution feathers of farmland loss in Tokyo Metropolitan District (TMD) for two periods: 2005-2010;2010-2015. 2) And the samples were divided by four urbanization facts. 3) DID data and zoning data for 2006 to 2018 were used to specify urbanization level of zones for describing land-use plan. 4) Then we conducted multiple regression between farmland loss, both abandonment and conversion amounts, and the described land-use plan in each of the urbanization scenario and in each period. As the results, the study reveals land-use plan has unignorable relation with farmland loss in the metropolitan suburbs at ward-city-town-village level. 1) The urban promotion areas planned larger than necessity and unregulated urbanization promote both farmland conversion and abandonment, and the effect weakens from inner suburbs to outer suburbs. 2) And the effect of land-use plan on farmland abandonment is more obvious than that on farmland conversion. The study advocates that, optimizing land-use plan will hopefully help the farmland conservation in metropolitan suburbs, which contributes to sustainable regional policy making.

Keywords: Agrarian land resource, land-use planning, urbanization level, multiple regression

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Authors: Enrico Porfido

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The paper aims to investigate the relation between landscape and tourism, with a special focus on coastal protected areas of Albania. The relationship between tourism and landscape is bijective: There is no tourism without landscape attractive features and on the other side landscape needs economic resources to be conserved and protected. The survival of each component is strictly related to the other one. Today, the Albanian protected areas appear as isolated islands, too far away from each other to build an efficient network and to avoid waste in terms of energy, economy and working force. This study wants to stress out the importance of cooperation in terms of common strategies and the necessity of introducing a touristic sustainable model in Albania. Comparing the protection system laws of the neighbor countries of the Adriatic-Ionian region and through a desk review on the best practices of protected areas that benefit from touristic activities, the study proposes the creation of the Albanian Riviera Landscape Park. This action will impact positively the whole southern Albania territory, introducing a sustainable tourism network that aims to valorize the local heritage and to stop the coastal exploitation processes. The main output is the definition of future development scenarios in Albania with the establishment of new protected areas and the introduction of touristic initiatives.

Keywords: Adriatic-Ionian region, protected areas, tourism for landscape, sustainable tourism

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Authors: Gouda K. Helal, Marwa I. Shabayek, Heba A. El-Ramly, Heba A. Awida

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The adverse effects of Clindamycin (Clind.) / Ibuprofen (Ibu.) combination on liver, kidney, blood elements and the significances of antioxidants (N-acetylcysteine and Zinc) against these effects were evaluated. The study includes: Group I; control n=30, Group II; patients on Clind.300mg/Ibu.400mg twice daily for a week n=30, Group III; patients on Clind.300mg/Ibu.400mg+N-acetylcysteine 200mg twice daily for a week n=15 and Group IV; patients on Clind.300mg/Ibu.400mg+Zinc50mg twice daily for a week n=15. Serum malondialdehyde (MDA), alanine transferase (ALT), aspartate transferase (AST), γ glutamyl transferase (GGT), creatinine, blood urea nitrogen (BUN) were measured. Applying one way ANOVA followed by Tuckey Kramer post test, Group II showed significant increase in ALT, AST, GGT, BUN and decrease in Hb, RBCs, platelets than Group I. Group III showed significant decrease in ALT, AST, GGT, BUN than Group II. Moreover, Group IV showed significant decrease in ALT, AST, GGT and increase in Hb, RBCs, and platelets than Group II. Conclusively, Adding Zinc or N-acetylcysteine buffer the oxidative stress and improve the therapeutic outcome of Clindamycin/Ibuprofen combination.

Keywords: clindamycin, ibuprofen, adverse effects, antioxidant, zinc, N-acetylcysteine

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Authors: Diego De Castro Rodrigues, Marcelo Lisboa Rocha, Daniela M. De Q. Trevisan, Marcos Dias Da Conceicao, Gabriel Rosa, Rommel M. Barbosa

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The use of association rules algorithms within data mining is recognized as being of great value in the knowledge discovery in databases. Very often, the number of rules generated is high, sometimes even in databases with small volume, so the success in the analysis of results can be hampered by this quantity. The purpose of this research is to present a method for reducing the quantity of rules generated with association algorithms. Therefore, a computational algorithm was developed with the use of a Weka Application Programming Interface, which allows the execution of the method on different types of databases. After the development, tests were carried out on three types of databases: synthetic, model, and real. Efficient results were obtained in reducing the number of rules, where the worst case presented a gain of more than 50%, considering the concepts of support, confidence, and lift as measures. This study concluded that the proposed model is feasible and quite interesting, contributing to the analysis of the results of association rules generated from the use of algorithms.

Keywords: data mining, association rules, rules reduction, artificial intelligence

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Authors: Stella Chioma Nwizu

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The National Commission for Adult and Non Formal Education (NMEC) in Nigeria is charged with the reduction of illiteracy rate through the development, monitoring, and supervision of literacy programmes in Nigeria. In spite of various efforts by NMEC to reduce illiteracy, literature still shows that the illiteracy rate is still high. According to NMEC/UNICEF, about 60 million Nigerians are non-literate, and nearly two thirds of them are women. This situation forced the government to search for innovative and better approaches to literacy promotion and delivery. The literacy by radio project was adopted as an innovative intervention to literacy delivery in Nigeria because the radio is the cheapest and most easily affordable medium for non-literates. The project aimed at widening access to literacy programmes for the non-literate marginalized and disadvantaged groups in Nigeria by taking literacy programmes to their door steps. The literacy by radio has worked perfectly well in non-literacy reduction in Cuba. This innovative intervention of literacy by radio is anchored on the diffusion of innovation theory by Rogers. The literacy by radio has been going on for fifteen years and the efficacy and contributions of this innovation need to be investigated. Thus, the purpose of this research is to review the contributions of the literacy by radio in Nigeria. The researcher adopted the survey research design for the study. The population for the study consisted of 2,706 participants and 47 facilitators of the literacy by radio programme in the 10 pilot states in Nigeria. A sample of four states made up of 302 participants and eight facilitators were used for the study. Information was collected through Focus Group Discussion (FGD), interviews and content analysis of official documents. The data were analysed qualitatively to review the contributions of literacy by radio project and determine the efficacy of this innovative approach in facilitating literacy in Nigeria. Results from the field experience showed, among others, that more non-literates have better access to literacy programmes through this innovative approach. The pilot project was 88% successful; not less than 2,110 adults were made literate through the literacy by radio project in 2017. However, lack of enthusiasm and commitment on the part of the technical committee and facilitators due to non-payment of honorarium, poor signals from radio stations, interruption of lectures with adverts, low community involvement in decision making in the project are challenges to the success rate of the project. The researcher acknowledges the need to customize all materials and broadcasts in all the dialects of the participants and the inclusion of more civil rights, environmental protection and agricultural skills into the project. The study recommends among others, improved and timely funding of the project by the Federal Government to enable NMEC to fulfill her obligations towards the greater success of the programme, setting up of independent radio stations for airing the programmes and proper monitoring and evaluation of the project by NMEC and State Agencies for greater effectiveness. In an era of the knowledge-driven economy, no one should be allowed to get saddled with the weight of illiteracy.

Keywords: innovative approach, literacy, project, radio, survey

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Authors: Shirin Alabdulqader

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This research aims to investigate the concept of code-switching (CS) between English, Arabic, and the CS practices of Saudi online users via a Translanguaging (TL) lens for more inclusive view towards the nature of the data from the study. It employs Digitally Mediated Communication (DMC), specifically the WhatsApp and Twitter platforms, in order to understand how the users employ online resources to communicate with others on a daily basis. This project looks beyond language and considers the multimodal affordances (visual and audio means) that interlocutors utilise in their online communicative practices to shape their online social existence. This exploratory study is based on a data-driven interpretivist epistemology as it aims to understand how meaning (reality) is created by individuals within different contexts. This project used a mixed-method approach, combining a qualitative and a quantitative approach. In the former, data were collected from online chats and interview responses, while in the latter a questionnaire was employed to understand the frequency and relations between the participants’ linguistic and non-linguistic practices and their social behaviours. The participants were eight bilingual Saudi nationals (both men and women, aged between 20 and 50 years old) who interacted with others online. These participants provided their online interactions, participated in an interview and responded to a questionnaire. The study data were gathered from 194 WhatsApp chats and 122 Tweets. These data were analysed and interpreted according to three levels: conversational turn taking and CS; the linguistic description of the data; and CS and persona. This project contributes to the emerging field of analysing online Arabic data systematically, and the field of multimodality and bilingual sociolinguistics. The findings are reported for each of the three levels. For conversational turn taking, the CS analysis revealed that it was used to accomplish negotiation and develop meaning in the conversation. With regard to the linguistic practices of the CS data, the majority of the code-switched words were content morphemes. The third level of data interpretation is CS and its relationship with identity; two types of identity were indexed; absolute identity and contextual identity. This study contributes to the DMC literature and bridges some of the existing gaps. The findings of this study are that CS by its nature, and most of the findings, if not all, support the notion of TL that multiliteracy is one’s ability to decode multimodal communication, and that this multimodality contributes to the meaning. Either this is applicable to the online affordances used by monolinguals or multilinguals and perceived not only by specific generations but also by any online multiliterates, the study provides the linguistic features of CS utilised by Saudi bilinguals and it determines the relationship between these features and the contexts in which they appear.

Keywords: social media, code-switching, translanguaging, online interaction, saudi bilinguals

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Authors: Hanan Begali, Shahi Dost, Annett Ziegler, Markus Cornberg, Maria-Esther Vidal, Anke R. M. Kraft

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Chronic hepatitis B virus (HBV) infection can be treated with nucleot(s)ide analog (NA), for example, which inhibits HBV replication. However, they have hardly any influence on the functional cure of HBV, which is defined by hepatitis B surface antigen (HBsAg) loss. NA needs to be taken life-long, which is not available for all patients worldwide. Additionally, NA-treated patients are still at risk of developing cirrhosis, liver failure, or hepatocellular carcinoma (HCC). Although each patient has the same components of the immune system, immune responses vary between patients. Therefore, a deeper understanding of the immune response against HBV in different patients is necessary to understand the parameters leading to HBV cure and to use this knowledge to optimize HBV therapies. This requires seamless integration of an enormous amount of diverse and fine-grained data from viral markers, e.g., hepatitis B core-related antigen (HBcrAg) and hepatitis B surface antigen (HBsAg). The data integration system relies on the assumption that profiling human immune systems requires the analysis of various variables (e.g., demographic data, treatments, pre-existing conditions, immune cell response, or HLA-typing) rather than only one. However, the values of these variables are collected independently. They are presented in a myriad of formats, e.g., excel files, textual descriptions, lab book notes, and images of flow cytometry dot plots. Additionally, patients can be identified differently in these analyses. This heterogeneity complicates the integration of variables, as data management techniques are needed to create a unified view in which individual formats and identifiers are transparent when profiling the human immune systems. The proposed study (HBsRE) aims at integrating heterogeneous data sets of 87 chronically HBV-infected patients, e.g., clinical data, immune cell response, and HLA-typing, with knowledge encoded in biomedical ontologies and open-source databases into a knowledge-driven framework. This new technique enables us to harmonize and standardize heterogeneous datasets in the defined modeling of the data integration system, which will be evaluated in the knowledge graph (KG). KGs are data structures that represent the knowledge and data as factual statements using a graph data model. Finally, the analytic data model will be applied on top of KG in order to develop a deeper understanding of the immune profiles among various patients and to evaluate factors playing a role in a holistic profile of patients with HBsAg level loss. Additionally, our objective is to utilize this unified approach to stratify patients for new effective treatments. This study is developed in the context of the project “Transforming big data into knowledge: for deep immune profiling in vaccination, infectious diseases, and transplantation (ImProVIT)”, which is a multidisciplinary team composed of computer scientists, infection biologists, and immunologists.

Keywords: chronic hepatitis B infection, immune response, knowledge graphs, ontology

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Authors: Natalia Limones, Javier Marzo, Marcus Wijnen, Aleix Serrat-Capdevila

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In this research we developed a structured approach for the detection of areas under the highest levels of drought risk that is suitable for data-scarce environments. The methodology is based on recent scientific outcomes and methods and can be easily adapted to different contexts in successive exercises. The research reviews the history of drought in the south of Angola and characterizes the experienced hazard in the episode from 2012, focusing on the meteorological and the hydrological drought types. Only global open data information coming from modeling or remote sensing was used for the description of the hydroclimatological variables since there is almost no ground data in this part of the country. Also, the study intends to portray the socioeconomic vulnerabilities and the exposure to the phenomenon in the region to fully understand the risk. As a result, a map of the areas under the highest risk in the south of the country is produced, which is one of the main outputs of this work. It was also possible to confirm that the set of indicators used revealed different drought vulnerability profiles in the South of Angola and, as a result, several varieties of priority areas prone to distinctive impacts were recognized. The results demonstrated that most of the region experienced a severe multi-year meteorological drought that triggered an unprecedent exhaustion of the surface water resources, and that the majority of their socioeconomic impacts started soon after the identified onset of these processes.

Keywords: drought risk, exposure, hazard, vulnerability

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Authors: Jummalla Aashrith, N. L. Shiva Sai, K. Bhavya Sri

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The swift progress of web technology has not only amassed a vast reservoir of internet data but also triggered a substantial surge in data generation. The internet has metamorphosed into one of the dynamic hubs for online education, idea dissemination, as well as opinion-sharing. Notably, the widely utilized social networking platform Twitter is experiencing considerable expansion, providing users with the ability to share viewpoints, participate in discussions spanning diverse communities, and broadcast messages on a global scale. The upswing in online engagement has sparked a significant curiosity in subjective analysis, particularly when it comes to Twitter data. This research is committed to delving into sentiment analysis, focusing specifically on the realm of Twitter. It aims to offer valuable insights into deciphering information within tweets, where opinions manifest in a highly unstructured and diverse manner, spanning a spectrum from positivity to negativity, occasionally punctuated by neutrality expressions. Within this document, we offer a comprehensive exploration and comparative assessment of modern approaches to opinion mining. Employing a range of machine learning algorithms such as Naive Bayes and Logistic Regression, our investigation plunges into the domain of Twitter data streams. We delve into overarching challenges and applications inherent in the realm of subjectivity analysis over Twitter.

Keywords: machine learning, sentiment analysis, visualisation, python

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Authors: Majid Alizadeh, Sina Nematizadeh, Hassan Esmailpour

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The mutual effects of hospitality and the environment are undeniable, so that the tourism industry has major harmful effects on the environment. Hotels, as one of the most important pillars of the hospitality industry, have significant effects on the environment. Green marketing is a promising strategy in response to the growing concerns about the environment. A green hotel marketing model was proposed using a grounded theory approach in the hotel industry. The study was carried out as a mixed method study. Data gathering in the qualitative phase was done through literature review and In-depth, semi-structured interviews with 10 experts in green marketing using snowball technique. Following primary analysis, open, axial, and selective coding was done on the data, which yielded 69 concepts, 18 categories and six dimensions. Green hotel (green product) was adopted as the core phenomenon. In the quantitative phase, data were gleaned using 384 questionnaires filled-out by hotel guests and descriptive statistics and Structural equation modeling (SEM) were used for data analysis. The results indicated that the mediating role of behavioral response between the ecological literacy, trust, marketing mix and performance was significant. The green marketing mix, as a strategy, had a significant and positive effect on guests’ behavioral response, corporate green image, and financial and environmental performance of hotels.

Keywords: green marketing, sustainable development, hospitality, grounded theory, structural equations model

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Authors: Tawfig Falani, Abdulalaziz Saleh

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Air pollution has become a major environmental issue with definitive repercussions on human health. Global concerns have been raised about the health effects of deteriorating air quality due mainly to widespread industrialization and urbanization. To assess the quality of air in Brega, air quality indicators were calculated using the U.S. Environmental Protection Agency procedure. Air quality was monitored from 01/10/2019 to 28/02/2021 with a daily average measuring six pollutants of particulate matter <2.5µm (PM2.5), and <10µm (PM₁₀), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), ozone (O₃), and carbon monoxide (CO). The result indicated that air pollution at general air quality monitoring sites for sulphur dioxide, carbon monoxide, PM₁₀ and PM2.5 and nitrogen dioxide are always within the permissible limit. Referring to a monthly average of Pollutants in the Brega Industrial area, all months were out of AQG limit for NO₂, and the same with O₃ except for two months. For PM2.5 and PM₁₀ 7, 5 out of 17 months were out of limits, respectively. Relative AQI for ozone is found in the range of moderate category of general air pollution, and the worst month was Nov. 2020, which was marked as Very Unhealthy category, then the next two months (Dec. 2020 and Jan. 2021 ) were Unhealthy categories. It's the first time that we have used the AQI in SOC, and not usually used in Libya to identify the quality of air pollution. So, I think it will be useful if AQI is used as guidance for specified air pollution. That dictate putting monitoring stations beside any industrial activity that has emissions of the six major air pollutants.

Keywords: air quality, air pollutants, air quality index (AQI), particulate matter

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Authors: Maryam Ziaee, Himanshu Shee, Amrik Sohal

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Big Data Analytics (BDA) in supply chain management has recently drawn the attention of academics and practitioners. Big data refers to a massive amount of data from different sources, in different formats, generated at high speed through transactions in business environments and supply chain networks. Traditional statistical tools and techniques find it difficult to analyse this massive data. BDA can assist organisations to capture, store, and analyse data specifically in the field of supply chain. Currently, there is a paucity of research on BDA in the pharmaceutical supply chain context. In this research, the Australian pharmaceutical supply chain was selected as the case study. This industry is highly significant since the right medicine must reach the right patients, at the right time, in right quantity, in good condition, and at the right price to save lives. However, drug shortages remain a substantial problem for hospitals across Australia with implications on patient care, staff resourcing, and expenditure. Furthermore, a massive volume and variety of data is generated at fast speed from multiple sources in pharmaceutical supply chain, which needs to be captured and analysed to benefit operational decisions at every stage of supply chain processes. As the pharmaceutical industry lags behind other industries in using BDA, it raises the question of whether the use of BDA can improve transparency among pharmaceutical supply chain by enabling the partners to make informed-decisions across their operational activities. This presentation explores the impacts of BDA on supply chain management. An exploratory qualitative approach was adopted to analyse data collected through interviews. This study also explores the BDA potential in the whole pharmaceutical supply chain rather than focusing on a single entity. Twenty semi-structured interviews were undertaken with top managers in fifteen organisations (five pharmaceutical manufacturers, five wholesalers/distributors, and five public hospital pharmacies) to investigate their views on the use of BDA. The findings revealed that BDA can enable pharmaceutical entities to have improved visibility over the whole supply chain and also the market; it enables entities, especially manufacturers, to monitor consumption and the demand rate in real-time and make accurate demand forecasts which reduce drug shortages. Timely and precise decision-making can allow the entities to source and manage their stocks more effectively. This can likely address the drug demand at hospitals and respond to unanticipated issues such as drug shortages. Earlier studies explore BDA in the context of clinical healthcare; however, this presentation investigates the benefits of BDA in the Australian pharmaceutical supply chain. Furthermore, this research enhances managers’ insight into the potentials of BDA at every stage of supply chain processes and helps to improve decision-making in their supply chain operations. The findings will turn the rhetoric of data-driven decision into a reality where the managers may opt for analytics for improved decision-making in the supply chain processes.

Keywords: big data analytics, data-driven decision, pharmaceutical industry, supply chain management

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Authors: Joshua Harrell, Gideon Osei Bonsu, Susan Garza, Clarence Conner, Da’Neisha Harris, Emma Bukoswki, Zohreh Safari

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The Social Vulnerability Index (SVI) stands as a pivotal tool for gauging community resilience amidst diverse stressors, including pandemics like COVID-19. This paper synthesizes recent research and underscores the significance of SVI in elucidating the differential impacts of crises on communities. Drawing on studies by Fox et al. (2023) and Mah et al. (2023), we delve into the application of SVI alongside emerging data sources to uncover nuanced insights into community vulnerability. Specifically, we explore the utilization of SVI in conjunction with mobility data from platforms like SafeGraph to probe the intricate relationship between social vulnerability and mobility dynamics during the COVID-19 pandemic. By leveraging 16 community variables derived from the American Community Survey, including socioeconomic status and demographic characteristics, SVI offers actionable intelligence for guiding targeted interventions and resource allocation. Building upon recent advancements, this paper contributes to the discourse on harnessing AI techniques to mitigate health disparities and fortify public health resilience in the face of pandemics and other crises.

Keywords: social vulnerability index, mobility dynamics, data analytics, health equity, pandemic preparedness, targeted interventions, data integration

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Authors: Ruomeng Xiao, Zhulin Zong, Longfa Yang

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Aiming at the problem that the target signal is difficult to detect under the strong ground clutter environment, this paper proposes a clutter suppression algorithm based on the combination of singular value decomposition and the Mallat fast wavelet algorithm. The method first carries out singular value decomposition on the radar echo data matrix, realizes the initial separation of target and clutter through the threshold processing of singular value, and then carries out wavelet decomposition on the echo data to find out the target location, and adopts the discard method to select the appropriate decomposition layer to reconstruct the target signal, which ensures the minimum loss of target information while suppressing the clutter. After the verification of the measured data, the method has a significant effect on the target extraction under low SCR, and the target reconstruction can be realized without the prior position information of the target and the method also has a certain enhancement on the output SCR compared with the traditional single wavelet processing method.

Keywords: clutter suppression, singular value decomposition, wavelet transform, Mallat algorithm, low SCR

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Authors: Chunhua Liao, Jinfei Wang, Bo Shan, Yang Song, Yongjun He, Taifeng Dong

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Winter wheat is one of the main crops in Canada. Forecasting of within-field variability of yield in winter wheat at the early stages is essential for precision farming. However, the crop yield modelling based on high spatial resolution satellite data is generally affected by the lack of continuous satellite observations, resulting in reducing the generalization ability of the models and increasing the difficulty of crop yield forecasting at the early stages. In this study, the correlations between Sentinel-2 data (vegetation indices and reflectance) and yield data collected by combine harvester were investigated and a generalized multivariate linear regression (MLR) model was built and tested with data acquired in different years. It was found that the four-band reflectance (blue, green, red, near-infrared) performed better than their vegetation indices (NDVI, EVI, WDRVI and OSAVI) in wheat yield prediction. The optimum phenological stage for wheat yield prediction with highest accuracy was at the growing stages from the end of the flowering to the beginning of the filling stage. The best MLR model was therefore built to predict wheat yield before harvest using Sentinel-2 data acquired at the end of the flowering stage. Further, to improve the ability of the yield prediction at the early stages, three simple unsupervised domain adaptation (DA) methods were adopted to transform the reflectance data at the early stages to the optimum phenological stage. The winter wheat yield prediction using multiple vegetation indices showed higher accuracy than using single vegetation index. The optimum stage for winter wheat yield forecasting varied with different fields when using vegetation indices, while it was consistent when using multispectral reflectance and the optimum stage for winter wheat yield prediction was at the end of flowering stage. The average testing RMSE of the MLR model at the end of the flowering stage was 604.48 kg/ha. Near the booting stage, the average testing RMSE of yield prediction using the best MLR was reduced to 799.18 kg/ha when applying the mean matching domain adaptation approach to transform the data to the target domain (at the end of the flowering) compared to that using the original data based on the models developed at the booting stage directly (“MLR at the early stage”) (RMSE =1140.64 kg/ha). This study demonstrated that the simple mean matching (MM) performed better than other DA methods and it was found that “DA then MLR at the optimum stage” performed better than “MLR directly at the early stages” for winter wheat yield forecasting at the early stages. The results indicated that the DA had a great potential in near real-time crop yield forecasting at the early stages. This study indicated that the simple domain adaptation methods had a great potential in crop yield prediction at the early stages using remote sensing data.

Keywords: wheat yield prediction, domain adaptation, Sentinel-2, within-field scale

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Authors: Dan Kanmegne

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Africa will become the most populated continent by the end of the century, with around 4 billion inhabitants. Food security and climate changes will become continental issues since agricultural practices depend on climate but also contribute to global emissions and land degradation. Agroforestry has been identified as a cost-efficient and reliable strategy to address these two issues. It is defined as the integrated management of trees and crops/animals in the same land unit. Agroforestry provides benefits in terms of goods (fruits, medicine, wood, etc.) and services (windbreaks, fertility, etc.), and is acknowledged to have a great potential for carbon sequestration; therefore it can be integrated into reduction mechanisms of carbon emissions. Particularly in sub-Saharan Africa, the constraint stands in the lack of information about both areas under agroforestry and the characterization (composition, structure, and management) of each agroforestry system at the country level. This study describes and quantifies “what is where?”, earliest to the quantification of carbon stock in different systems. Remote sensing (RS) is the most efficient approach to map such a dynamic technology as agroforestry since it gives relatively adequate and consistent information over a large area at nearly no cost. RS data fulfill the good practice guidelines of the Intergovernmental Panel On Climate Change (IPCC) that is to be used in carbon estimation. Satellite data are getting more and more accessible, and the archives are growing exponentially. To retrieve useful information to support decision-making out of this large amount of data, satellite data needs to be organized so to ensure fast processing, quick accessibility, and ease of use. A new solution is a data cube, which can be understood as a multi-dimensional stack (space, time, data type) of spatially aligned pixels and used for efficient access and analysis. A data cube for Burkina Faso has been set up from the cooperation project between the international service provider WASCAL and Germany, which provides an accessible exploitation architecture of multi-temporal satellite data. The aim of this study is to map and characterize agroforestry systems using the Burkina Faso earth observation data cube. The approach in its initial stage is based on an unsupervised image classification of a normalized difference vegetation index (NDVI) time series from 2010 to 2018, to stratify the country based on the vegetation. Fifteen strata were identified, and four samples per location were randomly assigned to define the sampling units. For safety reasons, the northern part will not be part of the fieldwork. A total of 52 locations will be visited by the end of the dry season in February-March 2020. The field campaigns will consist of identifying and describing different agroforestry systems and qualitative interviews. A multi-temporal supervised image classification will be done with a random forest algorithm, and the field data will be used for both training the algorithm and accuracy assessment. The expected outputs are (i) map(s) of agroforestry dynamics, (ii) characteristics of different systems (main species, management, area, etc.); (iii) assessment report of Burkina Faso data cube.

Keywords: agroforestry systems, Burkina Faso, earth observation data cube, multi-temporal image classification

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Authors: Reggie Chandra

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Trafficaccidents and traffic signal optimization are correlated. However, 70-90% of the traffic signals across the USA are not synchronized. The reason behind that is insufficient resources to create and implement timing plans. In this work, we will discuss the use of a breakthrough Artificial Intelligence (AI) technology to optimize traffic flow and collect 24/7/365 accurate traffic data using a vehicle detection system. We will discuss what are recent advances in Artificial Intelligence technology, how does AI work in vehicles, pedestrians, and bike data collection, creating timing plans, and what is the best workflow for that. Apart from that, this paper will showcase how Artificial Intelligence makes signal timing affordable. We will introduce a technology that uses Convolutional Neural Networks (CNN) and deep learning algorithms to detect, collect data, develop timing plans and deploy them in the field. Convolutional Neural Networks are a class of deep learning networks inspired by the biological processes in the visual cortex. A neural net is modeled after the human brain. It consists of millions of densely connected processing nodes. It is a form of machine learning where the neural net learns to recognize vehicles through training - which is called Deep Learning. The well-trained algorithm overcomes most of the issues faced by other detection methods and provides nearly 100% traffic data accuracy. Through this continuous learning-based method, we can constantly update traffic patterns, generate an unlimited number of timing plans and thus improve vehicle flow. Convolutional Neural Networks not only outperform other detection algorithms but also, in cases such as classifying objects into fine-grained categories, outperform humans. Safety is of primary importance to traffic professionals, but they don't have the studies or data to support their decisions. Currently, one-third of transportation agencies do not collect pedestrian and bike data. We will discuss how the use of Artificial Intelligence for data collection can help reduce pedestrian fatalities and enhance the safety of all vulnerable road users. Moreover, it provides traffic engineers with tools that allow them to unleash their potential, instead of dealing with constant complaints, a snapshot of limited handpicked data, dealing with multiple systems requiring additional work for adaptation. The methodologies used and proposed in the research contain a camera model identification method based on deep Convolutional Neural Networks. The proposed application was evaluated on our data sets acquired through a variety of daily real-world road conditions and compared with the performance of the commonly used methods requiring data collection by counting, evaluating, and adapting it, and running it through well-established algorithms, and then deploying it to the field. This work explores themes such as how technologies powered by Artificial Intelligence can benefit your community and how to translate the complex and often overwhelming benefits into a language accessible to elected officials, community leaders, and the public. Exploring such topics empowers citizens with insider knowledge about the potential of better traffic technology to save lives and improve communities. The synergies that Artificial Intelligence brings to traffic signal control and data collection are unsurpassed.

Keywords: artificial intelligence, convolutional neural networks, data collection, signal control, traffic signal

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