Search results for: public transportation network

Authors: William Ho, Agus Wicaksana

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Anecdotal evidence in the last decade shows that the occurrence of disruptive events and uncertainties in the supply chain is increasing. The coupling of these events with the nature of an increasingly complex and interdependent business environment leads to devastating impacts that quickly propagate within and across organizations. For example, the recent COVID-19 pandemic increased the global supply chain disruption frequency by at least 20% in 2020 and is projected to have an accumulative cost of $13.8 trillion by 2024. This crisis raises attention to organizational resilience to weather business uncertainty. However, the concept has been criticized for being vague and lacking a consistent definition, thus reducing the significance of the concept for practice and research. This study is intended to solve that issue by providing a comprehensive review of the conceptualization, measurement, and antecedents of operational resilience that have been discussed in the supply chain risk management literature (SCRM). We performed a Scholarly Network Analysis, combining citation-based and text-based approaches, on 252 articles published from 2000 to 2021 in top-tier journals based on three parameters: AJG ranking and ABS ranking, UT Dallas and FT50 list, and editorial board review. We utilized a hybrid scholarly network analysis by combining citation-based and text-based approaches to understand the conceptualization, measurement, and antecedents of operational resilience in the SCRM literature. Specifically, we employed a Bibliographic Coupling Analysis in the research cluster formation stage and a Co-words Analysis in the research cluster interpretation and analysis stage. Our analysis reveals three major research clusters of resilience research in the SCRM literature, namely (1) supply chain network design and optimization, (2) organizational capabilities, and (3) digital technologies. We portray the research process in the last two decades in terms of the exemplar studies, problems studied, commonly used approaches and theories, and solutions provided in each cluster. We then provide a conceptual framework on the conceptualization and antecedents of resilience based on studies in these clusters and highlight potential areas that need to be studied further. Finally, we leverage the concept of abnormal operating performance to propose a new measurement strategy for resilience. This measurement overcomes the limitation of most current measurements that are event-dependent and focus on the resistance or recovery stage - without capturing the growth stage. In conclusion, this study provides a robust literature review through a scholarly network analysis that increases the completeness and accuracy of research cluster identification and analysis to understand conceptualization, antecedents, and measurement of resilience. It also enables us to perform a comprehensive review of resilience research in SCRM literature by including research articles published during the pandemic and connects this development with a plethora of articles published in the last two decades. From the managerial perspective, this study provides practitioners with clarity on the conceptualization and critical success factors of firm resilience from the SCRM perspective.

Keywords: supply chain risk management, organizational resilience, scholarly network analysis, systematic literature review

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Authors: Shi-Bo Feng, Sheng-Jie Zhang, Jin Zhou

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The relationship between microbial diversity and algal bloom has received considerable attention for decades. Microbes undoubtedly affect annual bloom events and impact the physiology of both partners, as well as shape ecosystem diversity. However, knowledge about interactions and network correlations among broader-spectrum microbes that lead to the dynamics in a complete bloom cycle are limited. In this study, pyrosequencing and network approaches simultaneously assessed the associate patterns among bacteria, archaea, and microeukaryotes in surface water and sediments in response to a natural dinoflagellate (Alexandrium sp.) bloom. In surface water, among the bacterial community, Gamma-Proteobacteria and Bacteroidetes dominated in the initial bloom stage, while Alpha-Proteobacteria, Cyanobacteria, and Actinobacteria become the most abundant taxa during the post-stage. In the archaea biosphere, it clustered predominantly with Methanogenic members in the early pre-bloom period while the majority of species identified in the later-bloom stage were ammonia-oxidizing archaea and Halobacteriales. In eukaryotes, dinoflagellate (Alexandrium sp.) was dominated in the onset stage, whereas multiply species (such as microzooplankton, diatom, green algae, and rotifera) coexistence in bloom collapse stag. In sediments, the microbial species biomass and richness are much higher than the water body. Only Flavobacteriales and Rhodobacterales showed a slight response to bloom stages. Unlike the bacteria, there are small fluctuations of archaeal and eukaryotic structure in the sediment. The network analyses among the inter-specific associations show that bacteria (Alteromonadaceae, Oceanospirillaceae, Cryomorphaceae, and Piscirickettsiaceae) and some zooplankton (Mediophyceae, Mamiellophyceae, Dictyochophyceae and Trebouxiophyceae) have a stronger impact on the structuring of phytoplankton communities than archaeal effects. The changes in population were also significantly shaped by water temperature and substrate availability (N & P resources). The results suggest that clades are specialized at different time-periods and that the pre-bloom succession was mainly a bottom-up controlled, and late-bloom period was controlled by top-down patterns. Additionally, phytoplankton and prokaryotic communities correlated better with each other, which indicate interactions among microorganisms are critical in controlling plankton dynamics and fates. Our results supplied a wider view (temporal and spatial scales) to understand the microbial ecological responses and their network association during algal blooming. It gives us a potential multidisciplinary explanation for algal-microbe interaction and helps us beyond the traditional view linked to patterns of algal bloom initiation, development, decline, and biogeochemistry.

Keywords: microbial community, harmful algal bloom, ecological process, network

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Authors: Denise Levy, Anna Lucia C. H. Villavicencio

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Food irradiation is a processing and preservation technique to eliminate insects and parasites and reduce disease-causing microorganisms. Moreover, the process helps to inhibit sprouting and delay ripening, extending fresh fruits and vegetables shelf-life. Nevertheless, most Brazilian consumers seem to misunderstand the difference between irradiated food and radioactive food and the general public has major concerns about the negative health effects and environmental contamination. Society´s judgment and decision making are directly linked to perceived benefits and risks. The web-based project entitled ‘Scientific information about food irradiation: Internet as a tool to approach science and society’ was created by the Nuclear and Energetic Research Institute (IPEN), in order to offer an interdisciplinary approach to science education, integrating economic, ethical, social and political aspects of food irradiation. This project takes into account that, misinformation and unfounded preconceived ideas impact heavily on the acceptance of irradiated food and purchase intention by the Brazilian consumer. Taking advantage of the potential value of the Internet to enhance communication and education among general public, a research study was carried out regarding the possibilities and trends of Information and Communication Technologies among the Brazilian population. The content includes concepts, definitions and Frequently Asked Questions (FAQ) about processes, safety, advantages, limitations and the possibilities of food irradiation, including health issues, as well as its impacts on the environment. The project counts on eight self-instructional interactive web courses, situating scientific content in relevant social contexts in order to encourage self-learning and further reflections. Communication is a must to improve public understanding of science. The use of information technology for quality scientific divulgation shall contribute greatly to provide information throughout the country, spreading information to as many people as possible, minimizing geographic distances and stimulating communication and development.

Keywords: food irradiation, multimedia learning tools, nuclear science, society and education

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Authors: Muhammad Ali Sarwar, Muhammad Farooq, Nayab Hassan, Hammad Hassan

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Pakistan is highly recognized for its agriculture and is well known for producing substantial amounts of wheat, cotton, and sugarcane. However, some factors contribute to a decline in crop quality and a reduction in overall output. One of the main factors contributing to this decline is the presence of weed and its late detection. This process of detection is manual and demands a detailed inspection to be done by the farmer itself. But by the time detection of weed, the farmer will be able to save its cost and can increase the overall production. The focus of this research is to identify and classify the four main types of weeds (Small-Flowered Cranesbill, Chick Weed, Prickly Acacia, and Black-Grass) that are prevalent in our region’s major crops. In this work, we implemented three different deep learning techniques: YOLO-v5, Inception-v3, and Deep CNN on the same Dataset, and have concluded that deep convolutions neural network performed better with an accuracy of 97.45% for such classification. In relative to the state of the art, our proposed approach yields 2% better results. We devised the architecture in an efficient way such that it can be used in real-time.

Keywords: deep convolution networks, Yolo, machine learning, agriculture

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Authors: Javier Fernandez, Mayte Medina, Rafael Fernandez de Canete, Nuria Alcain, Juan Carlos Ramos-Diaz

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At present, the experimentation of anesthetic drugs on patients requires a regulation protocol, and the response of each patient to several doses of entry drug must be well known. Therefore, the development of pharmacological dose control systems is a promising field of research in anesthesiology. In this paper, it has been developed a non-linear compartmental the pharmacokinetic-pharmacodynamical model which describes the anesthesia depth effect in a sufficiently reliable way over a set of patients with the depth effect quantified by the Bi-Spectral Index. Afterwards, an Artificial Neural Network (ANN) predictive controller has been designed based on the depth of anesthesia model so as to keep the patient in the optimum condition while he undergoes surgical treatment. For the purpose of quantifying the efficiency of the neural predictive controller, a classical proportional-integral-derivative controller has also been developed to compare both strategies. Results show the superior performance of predictive neural controller during BiSpectral Index reference tracking.

Keywords: anesthesia, bi-spectral index, neural network control, pharmacokinetic-pharmacodynamical model

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Authors: Andrea Ghermandi

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Natural treatment systems such as constructed wetlands and waste stabilization ponds are increasingly used to treat water and wastewater from a variety of sources, including stormwater and polluted surface water. The provision of ancillary benefits in the form of cultural ecosystem services makes these systems unique among water and wastewater treatment technologies and greatly contributes to determine their potential role in promoting sustainable water management practices. A quantitative analysis of these benefits, however, has been lacking in the literature. Here, a critical assessment of the recreational and educational benefits in natural treatment systems is provided, which combines observed public use from a survey of managers and operators with estimated public use as obtained using geotagged photos from social media as a proxy for visitation rates. Geographic Information Systems (GIS) are used to characterize the spatial boundaries of 273 natural treatment systems worldwide. Such boundaries are used as input for the Application Program Interfaces (APIs) of two popular photo-sharing websites (Flickr and Panoramio) in order to derive the number of photo-user-days, i.e., the number of yearly visits by individual photo users in each site. The adequateness and predictive power of four univariate calibration models using the crowdsourced data as a proxy for visitation are evaluated. A high correlation is found between photo-user-days and observed annual visitors (Pearson's r = 0.811; p-value < 0.001; N = 62). Standardized Major Axis (SMA) regression is found to outperform Ordinary Least Squares regression and count data models in terms of predictive power insofar as standard verification statistics – such as the root mean square error of prediction (RMSEP), the mean absolute error of prediction (MAEP), the reduction of error (RE), and the coefficient of efficiency (CE) – are concerned. The SMA regression model is used to estimate the intensity of public use in all 273 natural treatment systems. System type, influent water quality, and area are found to statistically affect public use, consistently with a priori expectations. Publicly available information regarding the home location of the sampled visitors is derived from their social media profiles and used to infer the distance they are willing to travel to visit the natural treatment systems in the database. Such information is analyzed using the travel cost method to derive monetary estimates of the recreational benefits of the investigated natural treatment systems. Overall, the findings confirm the opportunities arising from an integrated design and management of natural treatment systems, which combines the objectives of water quality enhancement and provision of cultural ecosystem services through public use in a multi-functional approach and compatibly with the need to protect public health.

Keywords: constructed wetlands, cultural ecosystem services, ecological engineering, waste stabilization ponds

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Authors: Piotr Tylec, Alicja Dudek, Grzegorz Torbicz, Magdalena Mizera, Natalia Gajewska, Michael Su, Tanawat Vongsurbchart, Tomasz Stefura, Magdalena Pisarska, Mateusz Rubinkiewicz, Piotr Malczak, Piotr Major, Michal Pedziwiatr

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Introduction: Quality of life after bariatric surgery is an important factor when evaluating the final result of the treatment. Considering the vast surgical options, we tried to globally compare available methods in terms of quality of following the surgery. The aim of the study is to compare the quality of life a year after bariatric intervention using network meta-analysis methods. Material and Methods: We performed a systematic review according to PRISMA guidelines with Bayesian network meta-analysis. Inclusion criteria were: studies comparing at least two methods of weight loss treatment of which at least one is surgical, assessment of the quality of life one year after surgery by validated questionnaires. Primary outcomes were quality of life one year after bariatric procedure. The following aspects of quality of life were analyzed: physical, emotional, general health, vitality, role physical, social, mental, and bodily pain. All questionnaires were standardized and pooled to a single scale. Lifestyle intervention was considered as a referenced point. Results: An initial reference search yielded 5636 articles. 18 studies were evaluated. In comparison of total score of quality of life, we observed that laparoscopic sleeve gastrectomy (LSG) (median (M): 3.606, Credible Interval 97.5% (CrI): 1.039; 6.191), laparoscopic Roux en-Y gastric by-pass (LRYGB) (M: 4.973, CrI: 2.627; 7.317) and open Roux en-Y gastric by-pass (RYGB) (M: 9.735, CrI: 6.708; 12.760) had better results than other bariatric intervention in relation to lifestyle interventions. In the analysis of the physical aspects of quality of life, we notice better results in LSG (M: 3.348, CrI: 0.548; 6.147) and in LRYGB procedure (M: 5.070, CrI: 2.896; 7.208) than control intervention, and worst results in open RYGB (M: -9.212, CrI: -11.610; -6.844). Analyzing emotional aspects, we found better results than control intervention in LSG, in LRYGB, in open RYGB, and laparoscopic gastric plication. In general health better results were in LSG (M: 9.144, CrI: 4.704; 13.470), in LRYGB (M: 6.451, CrI: 10.240; 13.830) and in single-anastomosis gastric by-pass (M: 8.671, CrI: 1.986; 15.310), and worst results in open RYGB (M: -4.048, CrI: -7.984; -0.305). In social and vital aspects of quality of life, better results were observed in LSG and LRYGB than control intervention. We did not find any differences between bariatric interventions in physical role, mental and bodily aspects of quality of life. Conclusion: The network meta-analysis revealed that better quality of life in total score one year after bariatric interventions were after LSG, LRYGB, open RYGB. In physical and general health aspects worst quality of life was in open RYGB procedure. Other interventions did not significantly affect the quality of life after a year compared to dietary intervention.

Keywords: bariatric surgery, network meta-analysis, quality of life, one year follow-up

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Authors: Ja-Keoung Koo, Kensuke Nakamura, Hyohun Kim, Dongwha Shin, Yeonseok Kim, Ji-Su Ahn, Byung-Woo Hong

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The machine learning techniques based on a convolutional neural network (CNN) have been actively developed and successfully applied to a variety of image analysis tasks including reconstruction, noise reduction, resolution enhancement, segmentation, motion estimation, object recognition. The classical visual information processing that ranges from low level tasks to high level ones has been widely developed in the deep learning framework. It is generally considered as a challenging problem to derive visual interpretation from high dimensional imagery data. A CNN is a class of feed-forward artificial neural network that usually consists of deep layers the connections of which are established by a series of non-linear operations. The CNN architecture is known to be shift invariant due to its shared weights and translation invariance characteristics. However, it is often computationally intractable to optimize the network in particular with a large number of convolution layers due to a large number of unknowns to be optimized with respect to the training set that is generally required to be large enough to effectively generalize the model under consideration. It is also necessary to limit the size of convolution kernels due to the computational expense despite of the recent development of effective parallel processing machinery, which leads to the use of the constantly small size of the convolution kernels throughout the deep CNN architecture. However, it is often desired to consider different scales in the analysis of visual features at different layers in the network. Thus, we propose a CNN model where different sizes of the convolution kernels are applied at each layer based on the random projection. We apply random filters with varying sizes and associate the filter responses with scalar weights that correspond to the standard deviation of the random filters. We are allowed to use large number of random filters with the cost of one scalar unknown for each filter. The computational cost in the back-propagation procedure does not increase with the larger size of the filters even though the additional computational cost is required in the computation of convolution in the feed-forward procedure. The use of random kernels with varying sizes allows to effectively analyze image features at multiple scales leading to a better generalization. The robustness and effectiveness of the proposed CNN based on random kernels are demonstrated by numerical experiments where the quantitative comparison of the well-known CNN architectures and our models that simply replace the convolution kernels with the random filters is performed. The experimental results indicate that our model achieves better performance with less number of unknown weights. The proposed algorithm has a high potential in the application of a variety of visual tasks based on the CNN framework. Acknowledgement—This work was supported by the MISP (Ministry of Science and ICT), Korea, under the National Program for Excellence in SW (20170001000011001) supervised by IITP, and NRF-2014R1A2A1A11051941, NRF2017R1A2B4006023.

Keywords: deep learning, convolutional neural network, random kernel, random projection, dimensionality reduction, object recognition

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Authors: Lauren Zammerilla Westcott, Ronald C. Jones, James W. Fleshman

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The surgical treatment of breast cancer has rapidly evolved over the past 50 years, progressing from Halsted’s radical mastectomy to a public campaign of surgical options, aesthetic reconstruction, and patient empowerment. This article examines the happenings that led to the transition of breast surgery as a subset of general surgery to its own specialized field. Sparked by the research of Dr. Bernard Fisher and the first National Surgical Adjuvant Breast and Bowel Project trial in 1971, the field of breast surgery underwent significant growth over the next several decades, enabling general surgeons to limit their practices to the breast. High surgical volumes eventually led to the development of the first formal breast surgical oncology fellowship in a large community-based hospital at Baylor University Medical Center in 1982. The establishment of the American Society of Breast Surgeons, as well several landmark clinical trials and public campaign efforts, further contributed to the advancement of breast surgery, making it the specialized field of the current era.

Keywords: breast cancer, breast fellowship, breast surgery, surgical history

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Authors: Hamidreza Joudaki, Yousefali Ziari

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The appearance of city and urbanism is one of the important processes which have affected social communities. Being industrialized urbanism developed along with each other in the history. In addition, they have had simple relationship for more than six thousand years, that is, from the appearance of the first cities. In 18th century by coming out of industrial capitalism, progressive development took place in urbanism in the world. In Iran, the city of each region made its decision by itself and the capital of region (downtown) was the only central part and also the regional city without any hierarchy, controlled its realm. However, this method of ruling during these three decays, because of changing in political, social and economic issues that have caused changes in rural and urban relationship. Moreover, it has changed the variety of performance of cities and systematic urban network in Iran. Today, urban system has very vast imbalanced apace and performance. In Isfahan, the trend of urbanism is like the other part of Iran and systematic urban hierarchy is not suitable and normal. This article is a quantitative and analytical. The statistical communities are Isfahan Province cities and the changes in urban network and its hierarchy during the period of fifty years (1956 -2006) has been surveyed. In addition, those data have been analyzed by model of Rank and size and Entropy index. In this article Iran cities and also the factor of entropy of primate city and urban hierarchy of Isfahan Province have been introduced. Urban residents of this Province have been reached from 55 percent to 83% (2006). As we see the analytical data reflects that there is mismatching and imbalance between cities. Because the entropy index was.91 in 1956.And it decreased to.63 in 2006. Isfahan city is the primate city in the whole of these periods. Moreover, the second and the third cities have population gap with regard to the other cities and finally, they do not follow the system of rank-size.

Keywords: urban network, urban hierarchy, primate city, Isfahan province, urbanism, first cities

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Authors: Amanda Maitland

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This preliminary study has examined ways in which social media may help cause stalker murder by individuals with personality disorders and a strong sense of sexual propriety. A public display on social media by the intended victim was felt to be a trigger that instigated interpersonal violence. To identify behavioural paradigms, case studies of intimate partner murders were explored using news media sources and documentaries. In all of the case studies, social media interaction and social media postings occurred shortly before the murder. The evidence suggested a preponderance of correlations between the social media postings, stalking behaviours, personality disorders, and the murder of an intimate partner. In addition to this, a profile for of Facebook/social media murder was gleaned from the paradigms of behavior found in the case studies. The evidence showed a complex relationship between severe violence, stalking, borderline personality, and intimate partner violence was identified through the study. The struggle clients have in dealing with the: public, ambiguous and unrelenting nature of social media postings was also observed. The murderers anguish and rage appeared to be further intensified by attitudes of sexual propriety and entitlement. These attitudes were evident in all the case studies. The study concluded with further research on how the public can protect themselves from entering situations where social media postings might trigger a violent response. Further to this, psychological approaches were identified that might support client’s with personality disorders to cope with perceived provocative and distressing data on the internet. Thus, the findings of this study will be of interest to: therapists, psychologists, nurses, criminologists and social workers.

Keywords: social media, borderline personality, murder, cyberstalking, intimate partner violence, sexual propriety, Facebook

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Authors: Farideh Halakou, Emel Sen, Attila Gursoy, Ozlem Keskin

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Protein–Protein Interactions (PPIs) mediate major biological processes in living cells. The study of PPIs as networks and analyze the network properties contribute to the identification of genes and proteins associated with diseases. In this study, we have created the sub-networks of brain and lung metastasis from primary tumor in breast cancer. To do so, we used seed genes known to cause metastasis, and produced their interactions through a network-topology based prioritization method named GUILDify. In order to have the experimental support for the sub-networks, we further curated them using STRING database. We proceeded by modeling structures for the interactions lacking complex forms in Protein Data Bank (PDB). The functional enrichment analysis shows that KEGG pathways associated with the immune system and infectious diseases, particularly the chemokine signaling pathway, are important for lung metastasis. On the other hand, pathways related to genetic information processing are more involved in brain metastasis. The structural analyses of the sub-networks vividly demonstrated their difference in terms of using specific interfaces in lung and brain metastasis. Furthermore, the topological analysis identified genes such as RPL5, MMP2, CCR5 and DPP4, which are already known to be associated with lung or brain metastasis. Additionally, we found 6 and 9 putative genes that are specific for lung and brain metastasis, respectively. Our analysis suggests that variations in genes and pathways contributing to these different breast metastasis types may arise due to change in tissue microenvironment. To show the benefits of using structural PPI networks instead of traditional node and edge presentation, we inspect two case studies showing the mutual exclusiveness of interactions and effects of mutations on protein conformation which lead to different signaling.

Keywords: breast cancer, metastasis, PPI networks, protein conformational changes

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Authors: Andreea Anamaria Anghel, Flaviu Mihai Frigura-Lliasa, Attila Simo

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In recent years, there has been a renewed interest in revitalizing the historical area of Timisoara, a city located in western Romania, with a focus on preserving its architectural heritage while also promoting sustainable urban development. This has led to several initiatives aimed at improving public spaces, promoting sustainable transport, and encouraging the use of green infrastructure, such as green interior courtyards, to enhance the livability and sustainability of the area. A preliminary study regarding history, characteristics and current condition was carried out by the authors regarding these interior courtyards in the historical areas of Timisoara, the European Capital of Culture, in 2023, highlighting their potential to contribute to the sustainable development of the city. Modern interventions in interior historical courtyards should aim to preserve the historic character of these spaces while also promoting their sustainable and functional use in the 21st century. By doing so, these courtyards can continue to serve as vital urban oases and cultural landmarks for generations to come.

Keywords: architectural heritage, green interior courtyards, public spaces, sustainable development

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Authors: Jamaiah H. Yahaya, Aziz Deraman, Abdul Razak Hamdan, Yusmadi Yah Jusoh

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One of the most essential issues in software products is to maintain it relevancy to the dynamics of the user’s requirements and expectation. Many studies have been carried out in quality aspect of software products to overcome these problems. Previous software quality assessment models and metrics have been introduced with strengths and limitations. In order to enhance the assurance and buoyancy of the software products, certification models have been introduced and developed. From our previous experiences in certification exercises and case studies collaborating with several agencies in Malaysia, the requirements for user based software certification approach is identified and demanded. The emergence of social network applications, the new development approach such as agile method and other varieties of software in the market have led to the domination of users over the software. As software become more accessible to the public through internet applications, users are becoming more critical in the quality of the services provided by the software. There are several categories of users in web-based systems with different interests and perspectives. The classifications and metrics are identified through brain storming approach with includes researchers, users and experts in this area. The new paradigm in software quality assessment is the main focus in our research. This paper discusses the classifications of users in web-based software system assessment and their associated factors and metrics for quality measurement. The quality model is derived based on IEEE structure and FCM model. The developments are beneficial and valuable to overcome the constraints and improve the application of software certification model in future.

Keywords: software certification model, user centric approach, software quality factors, metrics and measurements, web-based system

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Authors: Laura Struik, Sarah Dow-Fleisner, Robert Janke

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Introduction: The use of e-cigarettes, also known as vaping, has risen exponentially among North American youth and young adults (YYA) in recent years and has become a critical public health concern. The marketing strategies used by e-cigarette companies have been associated with the uptick in use among YYA, with video advertisements on TV and other electronic platforms being the most pervasive strategy. It is unknown if or how these advertisements capitalize on the recently documented multi-faceted influences that contribute to the initiation of vaping among this demographic (e.g., stress, anxiety, gender, peers, etc.), which is examined in this study. Methods: This content analysis is phase one of a two-phased research project that aims to inform meaningful approaches to anti-vaping messaging and campaigns. As part of this first phase, a scoping review has been conducted to identify various influences (environmental, cognitive, contextual, social, and emotional) on e-cigarette uptake among YYA. The results of this scoping review will inform the development of a coding framework to analyze the multiple influences present in vaping advertisements, as seen on two popular television channels (Discovery and AMC). In addition, advertisement characteristics will be incorporated into the coding framework (e.g., the number of people present, demographic details, context, and setting, etc.), and analyzed. Findings: Findings will reveal the types of influences being leveraged in vaping advertisements, and identify the underlying messages that may be particularly attractive to YYA. This will contribute to a more nuanced understanding of how e-cigarette companies market their products and to whom. The results will also inform the next phase of this research project, which will encompass an analysis of anti-vaping advertisements and how the underpinning strategies align with those of the pro-vaping advertisements. Conclusions: Findings of this will study bring forward important implications for developing effective anti-vaping messages, and assist public health professionals in providing more comprehensive prevention and cessation support as it relates to e-cigarette use. Understanding which marketing strategies e-cigarette companies use is vital to our understanding of how to combat them. Findings will inform recommendations for public health efforts aimed at curbing e-cigarette use among YYA, and ultimately contribute to the health and well-being of YYA.

Keywords: e-cigarettes, youth and young adults, advertisements, public health

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Authors: Penrug Pengsombut, Worawut Hamarn, Teerawuth Suwannasri, Kittiphong Songrukkiat, Kanatip Ratanachoo

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PTT Public Company Limited or simply PTT has played an important role in strengthening national energy security of the Kingdom of Thailand by transporting natural gas to customers in power, industrial and commercial sectors since 1981. PTT has been constructing and operating natural gas pipeline system of over 4,500-km network length both onshore and offshore laid through different area classifications i.e., marine, forest, agriculture, rural, urban, and city areas. During project development phase, an Environmental Impact Assessment (EIA) is conducted and submitted to the Office of Natural Resources and Environmental Policy and Planning (ONEP) for approval before project construction commencement. Knowledge and experiences gained and revealed from EIA in the past projects definitely are developed to further advance EIA study process for newly 5th Transmission Natural Gas Pipeline Project (5TP) with approximately 415 kilometers length. The preferred pipeline route is selected and justified by SMARTi map, an advance digital one-map platform with consists of multiple layers geographic and environmental information. Sensitive area impact focus (SAIF) is a practicable impact assessment methodology which appropriate for a particular long distance infrastructure project such as 5TP. An environmental modeling simulation is adopted into SAIF methodology for impact quantified in all sensitive areas whereas other area along pipeline right-of-ways is typically assessed as an impact representative. Resulting time and cost deduction is beneficial to project for early start.

Keywords: environmental impact assessment, EIA, natural gas pipeline, sensitive area impact focus, SAIF

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Authors: Adina Dudau, Georgios Kominis, Melinda Szocs

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This paper links distinct bodies of literature around innovation and public services by examining a case of perceived innovation failure. Through a mixed methodology investigating student attitudes to, and behaviour around, technological innovation in higher education, the paper makes a contribution to the public service innovation literature by focusing on the duality of innovation outcomes, suggestive of an innovation typology in public services. The study was conducted in a UK Russell Group university and it focused on a technological process innovation. The innovation consisted of the provision of feedback to students in the form of a digital video (mp4), tailored to each individual submission, with extended voice-over commentary from the course coordinator and visual cues intended to help students see the relevance of comments to their submissions. The sample of the study consisted of a class of 79 undergraduate students. To investigate student attainment, we designed a field (also known as quasi or natural) experiment, essentially a manipulation of a social setting (in this case, the form of feedback given to students), but as part of a naturally occurring social arrangement (a real course which students attend and in which they are assessed). A two group control group design (see figure 3) was utilised to examine the effectiveness of the feedback innovation (video feedback). Two outcome variables of the service innovation were measured: student satisfaction and student attainment. In other words, the study examined not only students’ perceptions of whether VF was deemed to be beneficial towards their subsequent assignments; but also evidence of actual incremental benefits in students’ performance from one assignment to the next after VF was provided. The results were baffling and indicating competing agendas in higher education.

Keywords: higher education, audio-video, feedback, innovation

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Authors: Mai Alshareef

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The Saudi government considers the modernisation of the education system as a critical component of the national development plan, Saudi Vision 2030; however, this sudden reform creates tension amongst Saudis. This study examines first the reflection of the social and digital education reform on stakeholders and the general Saudi public, and second, the influence of information and communication technologies (ICTs) on the ethnoreligious conflict in Saudi Arabia. This study employs Cyberconflict theory to examine conflicts in the real world and cyberspace. The findings are based on a qualitative case study methodology that uses netnography, an analysis of 3,750 Twitter posts and semi-structural interviews with 30 individuals, including key actors in the Saudi education sector and Twitter activists during 2019\2020. The methods utilised are guided by thematic analysis to map an understanding of factors that influence societal conflicts in Saudi Arabia, which in this case include religious, national, and gender identity. Elements of Cyberconflict theory are used to better understand how conflicting groups build their identities in connection to their ethnic/religious/cultural differences and competing national identities. The findings correspond to the ethnoreligious components of the Cyberconflict theory. Twitter became a battleground for liberals, conservatives, the Saudi public and elites, and it is used in a novel way to influence public opinion and to challenge the media monopoly. Opposing groups relied heavily on a discourse of exclusion and inclusion and showed ethnic and religious affiliations, national identity, and chauvinism. The findings add to existing knowledge in the cyberconflict field of study, and they also reveal outcomes that are critical to the Saudi Arabian national context.

Keywords: education, cyberconflict, Twitter, national identity

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Authors: Sridara Tipian, Henry Skates Jr., Antika Sawadsri

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It is widely recognized that universal design should be implemented as broadly as possible to benefit as many groups and sub groups of people within a society. In Thailand, public buildings such as public universities are obvious places where the benefits of universal design principles are easily appreciated and applied, but there are other building types such as private universities where the benefits may not be just as obvious. In these buildings, the implementation of universal design is not always achieved. There are many reasons given for this among which is the perceived additional cost of implementation. This paper argues that social capital should be taken into consideration when such decisions are being made. The paper investigates the background, principles and theories pertaining to universal design and using a case study of a private university, investigates the implementation of universal design against the background of current legislation and the perceptions of the private university administrators. The study examines the physical facilities of the case study university in the context of current theories and principles of universal design alongside the legal requirements for same. A survey of building users evaluates knowledge of and attitudes to universal design. The research shows that although administrators perceive the initial cost of investment to be prohibitive in the short term, in the long term, changes in societal values in relation to social inclusiveness are changing and that the social capital of investing in universal design should not be underestimated. The results of this study should provide greater incentive for the enforcement of the legal requirements for universal design in Thailand.

Keywords: public buildings, physical facilities, social capital private university, investment, decision making, value, enforcement, legal requirements

Procedia PDF Downloads 275

Authors: Silvius Stanciu

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Romanian food products sold on European markets have been accused of several non-conformities of quality and safety. Most products incriminated last period were those of animal origin, especially meat and meat products. The study proposed an analysis of the notifications made by network members through Rapid Alert System for Food and Feed on products originating in Romania. As a source of information, the Rapid Alert System portal and the official communications of the National Sanitary Veterinary and Food Safety Authority were used. The research results showed that nearly a quarter of network notifications were rejected and were withdrawn by the European Authority. Although national authorities present these issues as success stories of national quality policies, the large number of notifications related to the volume of exported products is worrying. The paper is of practical and applicative importance for both the business environment and the academic environment, laying the basis for a wider research on the quality differences between Romanian and imported products.

Keywords: food, quality, RASFF, Rapid Alert System for Food and Feed, Romania

Procedia PDF Downloads 160

Authors: R. J. Kuo, W. C. Cheng, W. C. Lien, T. J. Yang

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Taiwan is a hyper endemic area for the Hepatitis B virus (HBV). The estimated total number of HBsAg carriers in the general population who are more than 20 years old is more than 3 million. Therefore, a case record review is conducted from January 2003 to June 2007 for all patients with a diagnosis of acute hepatitis who were admitted to the Emergency Department (ED) of a well-known teaching hospital. The cost for the use of medical resources is defined as the total medical fee. In this study, principal component analysis (PCA) is firstly employed to reduce the number of dimensions. Support vector regression (SVR) and artificial neural network (ANN) are then used to develop the forecasting model. A total of 117 patients meet the inclusion criteria. 61% patients involved in this study are hepatitis B related. The computational result shows that the proposed PCA-SVR model has superior performance than other compared algorithms. In conclusion, the Child-Pugh score and echogram can both be used to predict the cost of medical resources for patients with acute hepatitis in the ED.

Keywords: acute hepatitis, medical resource cost, artificial neural network, support vector regression

Procedia PDF Downloads 422

Authors: Hongmei Wang, Ziyun Xiang, Ying liu, Li Yu, Dongsheng Yue

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Intro: The COVID-19 (Corona Virus Disease 2019) has greatly changed the global economic, political and financial ecology. The mutation of the coronavirus in the UK in December 2020 has brought new panic to the world. Deep learning was performed on Chest Computed tomography (CT) of COVID-19 and Influenza and describes their characteristics. The predominant features of COVID-19 pneumonia was ground-glass opacification, followed by consolidation. Lesion density: most lesions appear as ground-glass shadows, and some lesions coexist with solid lesions. Lesion distribution: the focus is mainly on the dorsal side of the periphery of the lung, with the lower lobe of the lungs as the focus, and it is often close to the pleura. Other features it has are grid-like shadows in ground glass lesions, thickening signs of diseased vessels, air bronchi signs and halo signs. The severe disease involves whole bilateral lungs, showing white lung signs, air bronchograms can be seen, and there can be a small amount of pleural effusion in the bilateral chest cavity. At the same time, this year's flu season could be near its peak after surging throughout the United States for months. Chest CT for Influenza infection is characterized by focal ground glass shadows in the lungs, with or without patchy consolidation, and bronchiole air bronchograms are visible in the concentration. There are patchy ground-glass shadows, consolidation, air bronchus signs, mosaic lung perfusion, etc. The lesions are mostly fused, which is prominent near the hilar and two lungs. Grid-like shadows and small patchy ground-glass shadows are visible. Deep neural networks have great potential in image analysis and diagnosis that traditional machine learning algorithms do not. Method: Aiming at the two major infectious diseases COVID-19 and influenza, which are currently circulating in the world, the chest CT of patients with two infectious diseases is classified and diagnosed using deep learning algorithms. The residual network is proposed to solve the problem of network degradation when there are too many hidden layers in a deep neural network (DNN). The proposed deep residual system (ResNet) is a milestone in the history of the Convolutional neural network (CNN) images, which solves the problem of difficult training of deep CNN models. Many visual tasks can get excellent results through fine-tuning ResNet. The pre-trained convolutional neural network ResNet is introduced as a feature extractor, eliminating the need to design complex models and time-consuming training. Fastai is based on Pytorch, packaging best practices for in-depth learning strategies, and finding the best way to handle diagnoses issues. Based on the one-cycle approach of the Fastai algorithm, the classification diagnosis of lung CT for two infectious diseases is realized, and a higher recognition rate is obtained. Results: A deep learning model was developed to efficiently identify the differences between COVID-19 and influenza using chest CT.

Keywords: COVID-19, Fastai, influenza, transfer network

Procedia PDF Downloads 142

Authors: Raheel Shakoor Siddiqui, Sanjay Narayana Murthy, Manikandar Srinivas Cheruvu, Kash Akhtar

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Major trauma is a dynamic public health epidemic that is continuously evolving. Major trauma care services rely on multi-disciplinary team input involving highly trained pre and in-hospital critical care teams. Pre-hospital critical care teams (PHCCTs), major trauma centres (MTCs), trauma units, and rehabilitation facilities all form an efficient and organised trauma system. England comprises 27 MTCs funded by the National Health Service (NHS). Major trauma care entails enhanced resuscitation protocols coupled with the expertise of dedicated trauma teams and rapid radiological imaging to improve trauma outcomes. Literature reports a change in the demographic of major trauma as elderly patients (silver trauma) with injuries sustained from a fall of 2 metres or less commonly present to services. Evidence of an increasing population age with multiple comorbidities necessitates treatment within the first hour of injury (golden hour) to improve trauma survival outcomes. Staffing and funding pressures within the NHS have subsequently led to a shortfall of available physician-led PHCCTs. Thus, there is a strong emphasis on targeted research and funding to appropriately deploy resources to deprived areas. This review article will discuss the current English trauma system whilst critically appraising present challenges, identifying insufficiencies, and recommending aims for an improved future trauma system in England.

Keywords: trauma, orthopaedics, major trauma, trauma system, trauma network

Procedia PDF Downloads 187

Authors: Hadjer Sadoune, Liza Lamini, Scherazade Krim, Amel Djouadi, Rachida Rihani

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Deep neural networks are highly regarded for their accuracy in predicting intricate fermentation processes. Their ability to learn from a large amount of datasets through artificial intelligence makes them particularly effective models. The primary obstacle in improving the performance of these models is to carefully choose the suitable hyperparameters, including the neural network architecture (number of hidden layers and hidden units), activation function, optimizer, learning rate, and other relevant factors. This study predicts biogas production from real wastewater treatment plant data using a sophisticated approach: hybrid Bayesian optimization with a tree-structured Parzen estimator (BO-TPE) for an optimised deep neural network (DNN) model. The plant utilizes an Upflow Anaerobic Sludge Blanket (UASB) digester that treats industrial wastewater from soft drinks and breweries. The digester has a working volume of 1574 m3 and a total volume of 1914 m3. Its internal diameter and height were 19 and 7.14 m, respectively. The data preprocessing was conducted with meticulous attention to preserving data quality while avoiding data reduction. Three normalization techniques were applied to the pre-processed data (MinMaxScaler, RobustScaler and StandardScaler) and compared with the Non-Normalized data. The RobustScaler approach has strong predictive ability for estimating the volume of biogas produced. The highest predicted biogas volume was 2236.105 Nm³/d, with coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE) values of 0.712, 164.610, and 223.429, respectively.

Keywords: anaerobic digestion, biogas production, deep neural network, hybrid bo-tpe, hyperparameters tuning

Procedia PDF Downloads 38

Authors: Galal Omer, Onisimo Mutanga, Elfatih M. Abdel-Rahman, Elhadi Adam

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Threatened tree species (TTS) play a significant role in ecosystem functioning and services, land use dynamics, and other socio-economic aspects. Such aspects include ecological, economic, livelihood, security-based, and well-being benefits. The development of techniques for mapping and monitoring TTS is thus critical for understanding the functioning of ecosystems. The advent of advanced imaging systems and supervised learning algorithms has provided an opportunity to classify TTS over fragmenting landscape. Recently, vegetation maps have been produced using advanced imaging systems such as WorldView-2 (WV-2) and robust classification algorithms such as support vectors machines (SVM) and artificial neural network (ANN). However, delineation of TTS in a fragmenting landscape using high resolution imagery has widely remained elusive due to the complexity of the species structure and their distribution. Therefore, the objective of the current study was to examine the utility of the advanced WV-2 data for mapping TTS in the fragmenting Dukuduku indigenous forest of South Africa using SVM and ANN classification algorithms. The results showed the robustness of the two machine learning algorithms with an overall accuracy (OA) of 77.00% (total disagreement = 23.00%) for SVM and 75.00% (total disagreement = 25.00%) for ANN using all eight bands of WV-2 (8B). This study concludes that SVM and ANN classification algorithms with WV-2 8B have the potential to classify TTS in the Dukuduku indigenous forest. This study offers relatively accurate information that is important for forest managers to make informed decisions regarding management and conservation protocols of TTS.

Keywords: artificial neural network, threatened tree species, indigenous forest, support vector machines

Procedia PDF Downloads 515

Authors: Dato Surmanidze, Dato Antadze, Tornike Partenadze

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Public online services in Georgia are concentrated on main target segments: public administration, business, population, non-governmental and other interested organizations. Therefore, the strategy of digital Georgia is focused on providing G2C, G2B/B2G, G2NGO and G2G services. In G2C framework sophisticated and high-technological online services have been developed in order to provide passports, identity cards, documentations concerning residence and civil acts (birth, marriage, divorce, child adoption, change of name and surname, death, etc) as well as other services. Websites like my.gov.ge and sda.gov.ge have distance services like electronic application, processing and decision making. In line with international standards automatic services like electronic tenders, product catalogues, invoices and payment have been developed. This creates better investment climate for foreign companies in Georgia in the framework of G2B politics. The website mybusiness.gov.ge creates better conditions for local business. Among electronic services is e-NRMS (electronic system for national resource management) which was introduced by the Ministry of Finance of Georgia. The system was created in order to ensure management of national resources by state and business organizations. It is integrated with bank services and provides G2C, G2B and B2G representatives with electronic services. Also a portal meteo.gov.ge was created which gives electronic services concerning air, geological, environmental and pollution issues. Also worknet.gov.ge should be mentioned which is an electronic hub of information management for employers and employees. The information portal of labor market will facilitate receipt of information, its analysis and delivery to interested people like employers and employees. However, nowadays it’s been two years that only employees portal is activated. Therefore, awareness about the portal, its competitiveness and success is undermined.

Keywords: electronic services, public administration, information technology, information society

Procedia PDF Downloads 268

Authors: Hassan El Ghazi, Mohammed El Jourmi, Tayeb Sadiki, Esmail Ahouzi

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The purpose of this paper is to model and analyze a geostationary satellite communication system based on VSAT network and Multicarrier CDMA system scheme which presents a combination of multicarrier modulation scheme and CDMA concepts. In this study the channel coding strategies (Turbo codes and LDPC codes) are adopted to achieve good performance due to iterative decoding. The envisaged system is examined for a transmission over Multipath channel with use of Ku band in the uplink case. The simulation results are obtained for each different case. The performance of the system is given in terms of Bit Error Rate (BER) and energy per bit to noise power spectral density ratio (Eb/N0). The performance results of designed system shown that the communication system coded with LDPC codes can achieve better error rate performance compared to VSAT MC-CDMA system coded with Turbo codes.

Keywords: satellite communication, VSAT Network, MC-CDMA, LDPC codes, turbo codes, uplink

Procedia PDF Downloads 504

Authors: Parvin Foroughifar

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The concept of sense of community refers to residents’ sense of attachment and commitment to the other residents in a residential community. It is implicitly indicative of the mental image of a physical environment in which the residents enjoy strong social ties. Sense of community, a crucial factor in improving quality of life and social welfare, leads to life satisfaction in a residential community. Despite the important functions of such a notion, few empirical studies, to the best of the authors' knowledge, have been so far carried out in Iran to investigate the effective factors in sharpening the sense of community in residential communities. This survey research examined sense of community in 360 above 20-year old residents of three residential communities in Tehran, Iran using cluster sampling and questionnaire. The study yielded the result that variables of local social ties, social control and trust, sense of security, length of residence, use of public spaces, and mixed land use have a significant relationship with sense of community.

Keywords: sense of community, local social ties, sense of security, public space, residential community, Tehran

Procedia PDF Downloads 189

Authors: Ling Ai Wong, Hussain Shareef, Azah Mohamed, Ahmad Asrul Ibrahim

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The installation of photovoltaic based distributed generation (PVDG) in active distribution system can lead to voltage fluctuation due to the intermittent and unpredictable PVDG output power. This paper presented a method in mitigating the voltage rise by optimally locating and sizing the battery energy storage system (BESS) in PVDG integrated distribution network. The improved firefly algorithm is used to perform optimal placement and sizing. Three objective functions are presented considering the voltage deviation and BESS off-time with state of charge as the constraint. The performance of the proposed method is compared with another optimization method such as the original firefly algorithm and gravitational search algorithm. Simulation results show that the proposed optimum BESS location and size improve the voltage stability.

Keywords: BESS, firefly algorithm, PVDG, voltage fluctuation

Procedia PDF Downloads 321

Authors: Marcelo T. Okano, Oduvaldo Vendrametto, Osmildo S. Santos, Marcelo E. Fernandes, Heide Landi

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The literature on the interorganizational relationship between companies and organizations has increased in recent years, but there are still doubts about the various settings. The interorganizational networks are important in economic life, the fact facilitate the complex interdependence between transactional and cooperative organizations. A need identified in the literature is the lack of indicators to measure and identify the types of existing networks. The objective of this research is to examine the interorganizational relationships of two milk chains through indicators proposed by the theories of the four authors, characterizing them as network or not and what the benefits obtained by the chain organization. To achieve the objective of this work was carried out a survey of milk producers in two regions of the state of São Paulo. To collect the information needed for the analysis, exploratory research, qualitative nature was used. The research instrument of this work consists of a roadmap of semistructured interviews with open questions. Some of the answers were directed by the interviewer in the form of performance notes aimed at detecting the degree of importance, according to the perception of intensity to that regard. The results showed that interorganizational relationships are small and largely limited to the sale of milk or dairy cooperatives. These relationships relate only to trade relations between the owner and purchaser of milk. But when the producers are organized in associations or networks, interorganizational relationships and increase benefits for all participants in the network. The various visits and interviews in several dairy farms in the regions of São Pau-lo (indicated that the inter-relationships are small and largely limited to the sale of milk to cooperatives or dairy. These relationships refer only to trade relations between the owner and the purchaser of milk. But when the producers are organized in associations or networks, interorganizational relationships increase and bring benefits to all participants in the network.

Keywords: interorganizational networks, dairy chain, interorganizational system, São Pau-lo

Procedia PDF Downloads 580