Search results for: fields extraction from administrative documents

Authors: Oualid Walid Ben Ali

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Big Data has become one of the buzzwords today. The recent explosion of digital data has led the organization, either private or public, to a new era towards a more efficient decision making. At some point, business decided to use that concept in order to learn what make their clients tick with phrases like ‘sales funnel’ analysis, ‘actionable insights’, and ‘positive business impact’. So, it stands to reason that Big Data was viewed through green (read: money) colored lenses. Somewhere along the line, however someone realized that collecting and processing data doesn’t have to be for business purpose only, but also could be used for other purposes to assist law enforcement or to improve policing or in road safety. This paper presents briefly, how Big Data have been used in the fields of policing order to improve the decision making process in the daily operation of the police. As example, we present a big-data driven system which is sued to accurately dispatch the patrol cars in a geographic environment. The system is also used to allocate, in real-time, the nearest patrol car to the location of an incident. This system has been implemented and applied in the Emirate of Abu Dhabi in the UAE.

Keywords: big data, big data analytics, patrol car allocation, dispatching, GIS, intelligent, Abu Dhabi, police, UAE

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Authors: Nadia Masood Khan, Muhammad Salman Khan, Gul Muhammad Khan

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Cardiologists perform cardiac auscultation to detect abnormalities in heart sounds. Since accurate auscultation is a crucial first step in screening patients with heart diseases, there is a need to develop computer-aided detection/diagnosis (CAD) systems to assist cardiologists in interpreting heart sounds and provide second opinions. In this paper different algorithms are implemented for automated heart sound classification using unsegmented phonocardiogram (PCG) signals. Support vector machine (SVM), artificial neural network (ANN) and cartesian genetic programming evolved artificial neural network (CGPANN) without the application of any segmentation algorithm has been explored in this study. The signals are first pre-processed to remove any unwanted frequencies. Both time and frequency domain features are then extracted for training the different models. The different algorithms are tested in multiple scenarios and their strengths and weaknesses are discussed. Results indicate that SVM outperforms the rest with an accuracy of 73.64%.

Keywords: pattern recognition, machine learning, computer aided diagnosis, heart sound classification, and feature extraction

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Authors: Malki Fatima Zahra Nadia, Kellal Cheiimaa, Brahimi Houria

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Total Quality Management (TQM) is one of the most important modern management systems in marketing, that help organizations to survive and remain competitive in the dynamic market with frequent changes. It assists them in gaining a competitive advantage, growth, and excellence compared to their competitors. To understand the impact of TQM on competitive advantage in economic companies, a study was conducted in Ooredoo Telecommunications Company. A questionnaire was designed and distributed to OOredoo' 75 employees in each of the departments of leadership, quality assurance, quality control, research and development, production, customer service, Similarly, resulting in the retrieval of 72 questionnaires. To analyze the descriptive results of the study, the SPSS software version 25 was used. Additionally, Structural Equation Modeling (SEM) with the help of Smart Pls4 software was utilized to test the study's hypotheses. The study concluded that there is an impact between total quality management and competitive advantage in Ooredoo company to different degrees. On this basis, the study recommended the need to implement the total quality management system at the level of all organizations and in various fields.

Keywords: total quality management, ISO system, competitive advantage, competitive strategies

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Authors: Min-Ha Hong, Kee-Won Kim, Seung-Hoon Kim

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The character in the story-based content, such as novels and movies, is one of the core elements to understand the story. In particular, the character’s tendency is an important factor to analyze the story-based content, because it has a significant influence on the storyline. If readers have the knowledge of the tendency of characters before reading a novel, it will be helpful to understand the structure of conflict, episode and relationship between characters in the novel. It may therefore help readers to select novel that the reader wants to read. In this paper, we propose a method of extracting the tendency of the characters from a novel written in Korean. In advance, we build the dictionary with pairs of the emotional words in Korean and English since the emotion words in the novel’s sentences express character’s feelings. We rate the degree of polarity (positive or negative) of words in our emotional words dictionary based on SenticNet. Then we extract characters and emotion words from sentences in a novel. Since the polarity of a word grows strong or weak due to sentence features such as quotations and modifiers, our proposed method consider them to calculate the polarity of characters. The information of the extracted character’s polarity can be used in the book search service or book recommendation service.

Keywords: character tendency, data mining, emotion word, Korean novel

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Authors: Soraya Hoornam, Zeinab Sanaee

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Lithium-ion batteries are widely used for providing energy for mobile electronic devices. Graphite is a traditional anode material that was used in almost all commercialized lithium-ion batteries. It gives a specific capacity of 372 mAh/g for lithium storage. But there are multiple better choices for storing lithium that propose significantly higher specific capacities. As an example, silicon-based materials can be mentioned. In this regard, SiO₂ material can offer a huge specific capacity of 1965 mAh/g. Due to this high lithium storage ability, large volume change occurs in this electrode material during insertion and extraction of lithium, which may lead to cracking and destruction of the electrode. The use of nanomaterials instead of bulk material can significantly solve this problem. In addition, if we insert lithium in the active material of the battery before its cycling, which is called pre-lithiation, a further enhancement in the performance is expected. Here, we have fabricated an anode electrode of the battery using SiO₂ nanomaterial mixed with Graphite and assembled a lithium-ion battery half-cell with this electrode. Next, a pre-lithiation was performed on the SiO₂ nanoparticle-containing electrode, and the resulting anode material was investigated. This electrode has great potential for high-performance lithium-ion batteries.

Keywords: SiO₂ nanoparticles, lithium-ion battery, pre-lithiation, anode material

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Authors: Ouafek Nora, Keghouche Nassira, Dehdouh Heider, Untidt Carlos

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A lot of knowledge has been accumulated on the metal clusters supported on oxide surfaces because of their multiple applications in microelectronics, heterogeneous catalysis, and magnetic devices. In this work, the surface state of Ni / MgO has been studied by XPS (X-ray Photoemission Spectroscopy). The samples were prepared by impregnation with ion exchange Ni²⁺ / MgO, followed by either a thermal treatment in air (T = 100 -350 ° C) or a gamma irradiation (dose 100 kGy, 25 kGy dose rate h -1). The obtained samples are named after impregnation NMI, NMR after irradiation, and finally NMC(T) after calcination at the temperature T (T = 100-600 °C). A structural study by XRD and HRTEM reveals the presence of nanoscaled Ni-Mg intermetallic phases (Mg₂Ni, MgNi₂, and Mg₆Ni) and magnesium hydroxide. Mg(OH)₂ in nanometric range (2- 4 nm). Mg-Ni compounds are of great interest in energy fields (hydrogen storage…). XPS spectra show two Ni2p peaks at energies of about 856.1 and 861.9 eV, indicating that the nickel is primarily in an oxidized state on the surface. The shift of the main peak relative to the pure NiO (856.1 instead of 854.0 eV) suggests that in addition to oxygen, nickel is engaged in another link with magnesium. This is in agreement with the O1s spectra which present an overlap of peaks corresponds to NiO and MgO, at a calcination temperature T ≤ 300 °C.

Keywords: XPS, XRD, nanoparticules, Ni-MgO

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Authors: Sudhir K. Gupta

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Introduction: When a forensic doctor determines that a suspicious death is a suicide, homicide, or accident, the decision virtually becomes incontestable by the investigating police officer, and it becomes an issue whether the medical opinion was created with necessary checks and balances on the other probabilities of the case. It is suggested that the opinion of Forensic Medical experts is conventional, mutable, and shifting from one expert to another. The determination of suicide, accident, or homicide is mandatorily required, which is the Gold Standard for conducting death investigations. Forensic investigations serve many audiences, but the court is by far the most critical. The likely questions on direct and cross-examination determine how forensic doctors gather and handle evidence and what conclusions they reach. Methodology: The author interacted with the investigative authority, and a crime scene visit was also done along with the perusal of the Postmortem report, subsequent opinion, and crime scene photographs and statements of the witness and accused. Further analysis of all relevant scientific documents and opinions of other forensic doctors, forensic scientists, and ballistic experts involved in these cases was done to arrive at an opinion with scientific justification. Findings: The opinions arrived at by the author and how they helped the judiciary in delivering justice in these cases have been discussed in this article. This can help the readers to understand the process involved in formulating a credible forensic medical expert opinion for investigators and the judiciary. Conclusion: A criminal case might be won or lost over doubt cast on the chain of custody. Medically trained forensic doctors, therefore, learn to practice their profession in legally appropriate ways, and opinions must be based on medical justifications with credible references.

Keywords: forensic doctor, professional credibility, investigation, expert opinion

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Authors: Amber Dhoot, Tarush Gupta, Andrea Gurr, William Jenkins, Sandro Pietrunti, Alexis Tang

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Background: The COVID-19 pandemic has driven pharmacovigilance towards a new paradigm. Nowadays, more people than ever before are recognising and reporting adverse reactions from medications, treatments, and vaccines. In the modern era, with over 3.8 billion users, social media has become the most accessible medium for people to voice their opinions and so provides an opportunity to engage with more patient-centric and accessible pharmacovigilance. However, the pharmaceutical industry has been slow to incorporate social media into its modern pharmacovigilance strategy. This project aims to make social media a more effective tool in pharmacovigilance, and so reduce drug costs, improve drug safety and improve patient outcomes. This will be achieved by firstly uncovering and categorising the barriers facing the widespread adoption of social media in pharmacovigilance. Following this, the potential opportunities of social media will be explored. We will then propose realistic, practical recommendations to make social media a more effective tool for pharmacovigilance. Methodology: A comprehensive systematic literature review was conducted to produce a categorised summary of these barriers. This was followed by conducting 11 semi-structured interviews with pharmacovigilance experts to confirm the literature review findings whilst also exploring the unpublished and real-life challenges faced by those in the pharmaceutical industry. Finally, a survey of the general public (n = 112) ascertained public knowledge, perception, and opinion regarding the use of their social media data for pharmacovigilance purposes. This project stands out by offering perspectives from the public and pharmaceutical industry that fill the research gaps identified in the literature review. Results: Our results gave rise to several key analysis points. Firstly, inadequacies of current Natural Language Processing algorithms hinder effective pharmacovigilance data extraction from social media, and where data extraction is possible, there are significant questions over its quality. Social media also contains a variety of biases towards common drugs, mild adverse drug reactions, and the younger generation. Additionally, outdated regulations for social media pharmacovigilance do not align with new, modern General Data Protection Regulations (GDPR), creating ethical ambiguity about data privacy and level of access. This leads to an underlying mindset of avoidance within the pharmaceutical industry, as firms are disincentivised by the legal, financial, and reputational risks associated with breaking ambiguous regulations. Conclusion: Our project uncovered several barriers that prevent effective pharmacovigilance on social media. As such, social media should be used to complement traditional sources of pharmacovigilance rather than as a sole source of pharmacovigilance data. However, this project adds further value by proposing five practical recommendations that improve the effectiveness of social media pharmacovigilance. These include: prioritising health-orientated social media; improving technical capabilities through investment and strategic partnerships; setting clear regulatory guidelines using multi-stakeholder processes; creating an adverse drug reaction reporting interface inbuilt into social media platforms; and, finally, developing educational campaigns to raise awareness of the use of social media in pharmacovigilance. Implementation of these recommendations would speed up the efficient, ethical, and systematic adoption of social media in pharmacovigilance.

Keywords: adverse drug reaction, drug safety, pharmacovigilance, social media

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Authors: Yu Zhang, Pedro Desouza

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Since big data has become substantially more accessible and manageable due to the development of powerful tools for dealing with unstructured data, people are eager to mine information from social media resources that could not be handled in the past. Sentiment analysis, as a novel branch of text mining, has in the last decade become increasingly important in marketing analysis, customer risk prediction and other fields. Scientists and researchers have undertaken significant work in creating and improving their sentiment models. In this paper, we present a concept of selecting appropriate classifiers based on the features and qualities of data sources by comparing the performances of five classifiers with three popular social media data sources: Twitter, Amazon Customer Reviews, and Movie Reviews. We introduced a couple of innovative models that outperform traditional sentiment classifiers for these data sources, and provide insights on how to further improve the predictive power of sentiment analysis. The modelling and testing work was done in R and Greenplum in-database analytic tools.

Keywords: sentiment analysis, social media, Twitter, Amazon, data mining, machine learning, text mining

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Authors: Hao-Hsiang Ku, Ching-Ho Chi

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Big data applications have become an imperative for many fields. Many researchers have been devoted into increasing correct rates and reducing time complexities. Hence, the study designs and proposes an Ontology-based backpropagation neural network classification and reasoning strategy for NoSQL big data applications, which is called ON4NoSQL. ON4NoSQL is responsible for enhancing the performances of classifications in NoSQL and SQL databases to build up mass behavior models. Mass behavior models are made by MapReduce techniques and Hadoop distributed file system based on Hadoop service platform. The reference engine of ON4NoSQL is the ontology-based backpropagation neural network classification and reasoning strategy. Simulation results indicate that ON4NoSQL can efficiently achieve to construct a high performance environment for data storing, searching, and retrieving.

Keywords: Hadoop, NoSQL, ontology, back propagation neural network, high distributed file system

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Authors: Beilei Xu, Wencheng Wu, Lei Lin, Rachel Melnyk, Ahmed Ghazi

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In operating rooms, excessive cognitive stress can impede the performance of a surgeon, while low engagement can lead to unavoidable mistakes due to complacency. As a consequence, there is a strong desire in the surgical community to be able to monitor and quantify the cognitive stress of a surgeon while performing surgical procedures. Quantitative cognitiveload-based feedback can also provide valuable insights during surgical training to optimize training efficiency and effectiveness. Various physiological measures have been evaluated for quantifying cognitive stress for different mental challenges. In this paper, we present a study using the cognitive stress measured by the task evoked pupillary response extracted from the time series eye-tracking measurements to predict task difficulties in a virtual reality based robotic surgery training environment. In particular, we proposed a differential-task-difficulty scale, utilized a comprehensive feature extraction approach, and implemented a multitask learning framework and compared the regression accuracy between the conventional single-task-based and three multitask approaches across subjects.

Keywords: surgical metric, task evoked pupillary response, multitask learning, TSFresh

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Authors: Genaro Bolívar, Manuel Mas, Maria Tortolero, Jorge Salazar

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Due to the extensive use of water injection for oil displacement and pressure maintenance in oil fields, many reservoirs experience the problem of scale deposition when injection water starts to break through. In most cases the scaled-up wells are caused by the formation of sulfate and carbonate scales of calcium and strontium. Due to their relative hardness and low solubility, there are limited processes available for their removal and preventive measures such as the “squeeze” inhibitor treatment have to be taken. It is, therefore, important to gain a proper understanding of the kinetics of scale formation and its detrimental effects on formation damage under both inhibited and uninhibited conditions. Recently, the production of chitosan was started in our country and in the PDVSA-Intevep laboratories was synthesized and evaluated the properties of carboxymethyl chitosan (CMQ) as chelating agent of Ca2 + ions in water injection. In this regard, the characterization of the biopolymer by 13C - NMR, FTIR, TGA, and TM0374-2007 standard laboratory test has demonstrated the ability to remove up to 70% calcium ions in solution and shows a behavior that approaches that of commercial products.

Keywords: carboxymethyl chitosan, scale, calcium carbonate scale deposition, water injection

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Authors: N. Metidji, O. Badaoui, A. Djebli, H. Bendjebbas, R. Sellami

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The agro-industry is considered as one of the most waste producing industrial fields as a result of food processing. Upgrading and reuse of these wastes as animal or poultry food seems to be a promising alternative. Combined with the use of clean energy resources, the recovery process would contribute more to the environment protection. It is in this framework that a new solar dryer has been designed in the Unit of Solar Equipment Development. Direct solar drying has, also, many advantages compared to natural sun drying. In fact, the first does not cause product degradation as it is protected by the drying chamber from direct sun, insects and exterior environment. The aim of this work is to study the drying kinetics of waste, generated during the processing of pepper, by using a direct natural convection solar dryer at 35◦C and 55◦C. The rate of moisture removal from the product to be dried has been found to be directly related to temperature, humidity and flow rate. The characterization of these parameters has allowed the determination of the appropriate drying time for this product namely peppers waste.

Keywords: solar energy, solar dryer, energy conversion, pepper drying, forced convection solar dryer

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Authors: O. P. Tripathi, P. L. Verma

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Coronal Mass Ejections (CMEs) are mostly reached on Earth from 1 to 5 days from the Sun. As a consequence, slow CMEs are accelerated toward the speed of solar wind and fast CMEs are decelerated toward the speed of the solar wind. Coronal mass ejections (CMEs) are bursts of solar material i.e. clouds of plasma and magnetic fields that shoot off the sun’s surface. Other solar events include solar wind streams that come from the coronal holes on the Sun and solar energetic particles that are primarily released by CMEs. We have studied geomagnetic storms (DST ≤ - 80nT) during 1997-2012 with halo and partial halo coronal mass ejections and found that 73.28% CMEs (halo and partial halo coronal mass ejections) are associated with geomagnetic storms. The association rate of halo and partial halo coronal mass ejections are found 67.06% and 32.94% with geomagnetic storms respectively. We have also determined positive co-relation between magnitude of geomagnetic storms and speed of coronal mass ejection with correlation co-efficient 0.23.

Keywords: geomagnetic storms, coronal mass ejections (CMEs), disturbance storm time (Dst), interplanetary magnetic field (IMF)

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Authors: Abraham O. Olaniyi, Oluyemi Oke, Atilade Otunla

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The structural performance of bridges decreases progressively throughout their service life due to many contributing factors (fatigue, carbonation, fire incidents etc.). Around the world, numerous bridges have attained their estimated service life and many have approached this limit. The structural integrity assessment of the burnt composite bridge located at CH57+450, Koita village along Abuja-Abaji-Lokoja road, Nigeria, is presented as a case study and shall be forthwith referred to as the 'Koita bridge' in this paper. From the technical evaluation, the residual compressive strength of the concrete piers was found to be below 16.0 N/mm2. This value is very low compared to the expected design value of 30.0 N/mm2. The pier capping beam at pier location 1 has a very low residual compressive strength. The cover to the reinforcement of certain capping beams has an outline of reinforcement which signifies poor concrete cover and the mean compressive strength is also less than 20.0 N/mm2. The steel girder indicated black colouration as a result of the fire incident without any significant structural defect like buckling or warping of the steel section. This paper reviews the structural integrity assessment and repair methodology of the Koita bridge; a composite bridge damaged by fire, highlighting the various challenges of limited obtainable guidance documents about the bridge. The objectives are to increase the understanding of processes and versatile equipment required to test and assess a fire-damaged bridge in order to improve the quality of structural appraisal and rehabilitation; thus, eliminating the prejudice associated with current visual inspection techniques.

Keywords: assessment, bridge, rehabilitation, sustainability

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Authors: Ahmed Mohamed Al-Abdali

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Providing English for Specific Purposes (ESP) courses that are more closely geared to the learners’ needs and requirements in their fields of study undoubtedly enhance learners’ interest and success in a highly academic environment. While needs analysis is crucial to the success of ESP courses, it has not received sufficient attention from researchers in the Arab world. Oman is no exception from the Arab countries as this fact is realised in the ESP practices in the Omani higher educational context. This presentation, however, discusses the perceptions of the Language Centre (LC) students at Sultan Qaboos University (SQU), Oman, in relation to the requirements of their science colleges. The discussion of the presentation will be based on a mixed-method-approach study, which included semi-structured interviews, questionnaires and document analyses. These mixed methods have allowed for closer investigation of the participants' views, backgrounds and experiences. It is hoped that the findings of this study will be used to recommend changes to the ESP curriculum in the LC of SQU so that it better meets the needs of its students and requirements of the science colleges.

Keywords: curriculum, ESP, ELT, needs analysis, college requirements

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Authors: Alonso-Cañadas J., Galán-Valdivieso F., Saraite-Sariene L., García-Tabuyo M., Alonso-Morales N.

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Green Public Procurement (GPP) has recently gained attention in the academic and policy arenas since climate change has shown the need to be addressed by both private companies and public entities. Such growing interest motivates this article, aiming to explore the most influential journals, publishers, categories, and topics, as well as the recent trends and future research lines in GPP. Based on the Web of Science database, 578 articles from 2004 to February 2022 devoted to GPP are analyzed using Bibliometrix, an R-tool to perform bibliometric analysis, and Google’s Big Query and Data Studio. This article introduces a variety of findings. First, the most influential journals by far are “Journal of Cleaner Production” and “Sustainability,” differing in that the latter is open access while the former publishes via traditional subscription. This result also occurs regarding the main publishers (Elsevier and MDPI). These features lead us to split the sample into open-access journals and traditional journals to deepen into the similarities and differences between them, confirming that traditional journals exhibit a higher degree of influence in the literature than their open-access counterparts in terms of the number of documents, number of citations and impact (according to the H index). Second, this research also highlights the recent emergence of green-related terms (sustainable, environment) and, parallelly, the increase in categorizing GPP papers in “green” WoS categories, particularly since 2019. Finally, a number of related topics are emerging and will lead the research, such as food security, infrastructures, and implementation barriers of GPP.

Keywords: bibliometric analysis, green public procurement, open access, traditional journals

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Authors: Adedayo Jeremiah Adeyekun, Samuel Oluwagbemiga Ishola,

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This paper is an assessment of factors influencing the cost and time performance of subcontractors and the need for effective performance of subcontractors at the project sites. The factors influencing the performance of subcontractors are grouped, similar to those identified with the project or an organization and on another hand, there are significant factors influencing the performance of the subcontractors. These factors incorporate management level leadership, time required to complete the project, profit, staff capability/expertise, reputation, installment method, organization history, and project procurement method strategy, security, bidding technique, insurance, bond and relationship with the major contractors. The factors influencing the management of subcontractors in building development projects includes performance of significant past projects, standard of workmanship, consistence with guidelines, regular payment to labourers, adherence to program, regularity and viability of communication with main contractor, adherence to subcontract necessities. Other factors comprise adherence to statutory environmental regulations, number of experienced sites administrative staff, inspection and maintenance of good workplace, number of artisans and workers, quality of as-built and shop drawings and ability to carry out the quantity of work and so on. This study also aimed to suggest a way forward to improve the performance of subcontractors which is the reason for exceeding budget at the project sites. To carry out this study, a questionnaire was drafted to derive information on the causes of low performance of subcontractors and the implication to cost.

Keywords: performance, contractor, subcontractors, construction

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Authors: Paulo Cesar Pereira Junior, Alexandre Monteiro, Rafael da Luz Ribeiro, Antonio Carlos Sobieranski, Aldo von Wangenheim

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In this paper, we present a comparison between convolutional neural networks and classical computer vision approaches, for the specific precision agriculture problem of weed mapping on sugarcane fields aerial images. A systematic literature review was conducted to find which computer vision methods are being used on this specific problem. The most cited methods were implemented, as well as four models of convolutional neural networks. All implemented approaches were tested using the same dataset, and their results were quantitatively and qualitatively analyzed. The obtained results were compared to a human expert made ground truth for validation. The results indicate that the convolutional neural networks present better precision and generalize better than the classical models.

Keywords: convolutional neural networks, deep learning, digital image processing, precision agriculture, semantic segmentation, unmanned aerial vehicles

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Authors: Eleni Giouli

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Distance learning provides a flexible approach to education, enabling busy learners to complete their coursework at their own pace, on their own schedule, and from a convenient location. This flexibility combined with a series of other issues; make the benefits of lifelong distance learning numerous. The purpose of the paper is to investigate whether distance education can contribute to the improvement of adult skills in Greece, highlighting in this way the necessity of the lifelong distance learning. To investigate this goal, a questionnaire is constructed and analyzed based on responses from 3,016 attendees of lifelong distance learning programs in the e-learning of the National and Kapodistrian University of Athens in Greece. In order to do so, a series of relationships is examined including the effects of a) the gender, b) the previous educational level, c) the current employment status, and d) the method used in the distance learning program, on the development of new general, technical, administrative, social, cultural, entrepreneurial and green skills. The basic conclusions that emerge after using a binary logistic framework are that the following factors are critical in order to develop new skills: the gender, the education level and the educational method used in the lifelong distance learning program. The skills more significantly affected by those factors are the acquiring new skills in general, as well as acquiring general, language and cultural, entrepreneurial and green skills, while for technical and social skills only gender and educational method play a crucial role. Moreover, routine skills and social skills are not affected by the four factors included in the analysis.

Keywords: adult skills, distance learning, education, lifelong learning

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Authors: Minttu Laukkanen, Janne Huiskonen

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This study investigates the sharing economy business models as examples of the sustainable business models. The aim is to contribute to the limited literature on sharing economy in connection with sustainable business models by explaining sharing economy business models value capturing. Specifically, this research answers the following question: How business model choices affect captured sustainable value? A multiple case study approach is applied in this study. Twenty different successful sharing economy business models focusing on consumer business and covering four main areas, accommodation, mobility, food, and consumer goods, are selected for analysis. The secondary data available on companies’ websites, previous research, reports, and other public documents are used. All twenty cases are analyzed through the sharing economy business model framework and sustainable value analysis framework using qualitative data analysis. This study represents general sharing economy business model value attributes and their specifications, i.e. sustainable value propositions for different stakeholders, and further explains the sustainability impacts of different sharing economy business models through captured and uncaptured value. In conclusion, this study represents how business model choices affect sustainable value capturing through eight business model attributes identified in this study. This paper contributes to the research on sustainable business models and sharing economy by examining how business model choices affect captured sustainable value. This study highlights the importance of careful business model and sustainability impacts analyses including the triple bottom line, multiple stakeholders and value captured and uncaptured perspectives as well as sustainability trade-offs. It is not self-evident that sharing economy business models advance sustainability, and business model choices does matter.

Keywords: sharing economy, sustainable business model innovation, sustainable value, value capturing

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Authors: Sher Muhammad, Mirza Muhammad Waqar

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It is necessary to monitor and identify mangroves types and spatial extent near coastal areas because it plays an important role in coastal ecosystem and environmental protection. This research aims at identifying and mapping mangroves types along Karachi coast ranging from 24.79 to 24.85 degree in latitude and 66.91 to 66.97 degree in longitude using hyperspectral remote sensing data and techniques. Image acquired during February, 2012 through Hyperion sensor have been used for this research. Image preprocessing includes geometric and radiometric correction followed by Minimum Noise Fraction (MNF) and Pixel Purity Index (PPI). The output of MNF and PPI has been analyzed by visualizing it in n-dimensions for end-member extraction. Well-distributed clusters on the n-dimensional scatter plot have been selected with the region of interest (ROI) tool as end members. These end members have been used as an input for classification techniques applied to identify and map mangroves species including Spectral Angle Mapper (SAM), Spectral Feature Fitting (SFF), and Spectral Information Diversion (SID). Only two types of mangroves namely Avicennia Marina (white mangroves) and Avicennia Germinans (black mangroves) have been observed throughout the study area.

Keywords: mangrove, hyperspectral, hyperion, SAM, SFF, SID

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Authors: Alam Zeb, Glen W. Armstrong, Muhammad Qasim

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Loss of forest cover is one of the most important land cover changes and has been of great concern to policy makers. This study quantified forest cover changes over pre logging ban (1973-1993) and post logging ban (1993-2015) to examine the role of geophysical factors and spatial attributes of land in the two periods. We show that despite a complete ban on green felling, forest cover decreased by 28% and mostly converted to rangeland. Nevertheless, the logging ban was completely effective in controlling agriculture expansion. The binary logistic regression revealed that the south facing aspects at low elevation witnessed more deforestation in the pre-ban period compared to post-ban. Opposite to deforestation, forest degradation was more prominent on the northern aspects at higher elevation during the policy period. Agriculture expansion was widespread in the low elevation flat areas with gentle slope, while during the policy period agriculture contraction in the form of regeneration was observed on the low elevation areas of north facing slopes. All proximity variables, except distance to administrative boundary, showed a similar trend across the two periods and were important explanatory variables in understanding forest and agriculture expansion. The changes in determinants of forest and agriculture expansion and contraction over the two periods might be attributed to the influence of policy and a general decrease in resource availability.

Keywords: forest conservation , wood harvesting ban, logistic regression, deforestation, forest degradation, agriculture expansion, Chitral, Pakistan

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Authors: D. Šedivý, S. Fialová

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The main purpose of this study is to show differences between the numerical solution of the flow through the artificial heart valve using Newtonian or non-Newtonian fluid. The simulation was carried out by a commercial computational fluid dynamics (CFD) package based on finite-volume method. An aortic bileaflet heart valve (Sorin Bicarbon) was used as a pattern for model of real heart valve replacement. Computed tomography (CT) was used to gain the accurate parameters of the valve. Data from CT were transferred in the commercial 3D designer, where the model for CFD was made. Carreau rheology model was applied as non-Newtonian fluid. Physiological data of cardiac cycle were used as boundary conditions. Outputs were taken the leaflets excursion from opening to closure and the fluid dynamics through the valve. This study also includes experimental measurement of pressure fields in ambience of valve for verification numerical outputs. Results put in evidence a favorable comparison between the computational solutions of flow through the mechanical heart valve using Newtonian and non-Newtonian fluid.

Keywords: computational modeling, dynamic mesh, mechanical heart valve, non-Newtonian fluid

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Authors: Strahinja Kovačević, Jelena Vladić, Senka Vidović, Zoran Zeković, Lidija Jevrić, Sanja Podunavac Kuzmanović

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Supercritical extracts of highly valuated medicinal plant Satureja montana were prepared by application of supercritical carbon dioxide extraction in the carbon dioxide pressure range from 125 to 350 bar and temperature range from 40 to 60°C. Using GC/MS method of analysis chemical profiles (aromatic constituents) of S. montana extracts were obtained. Self-training artificial neural networks were applied to predict the retention time of the analyzed terpenes in GC/MS system. The best ANN model obtained was multilayer perceptron (MLP 11-11-1). Hidden activation was tanh and output activation was identity with Broyden–Fletcher–Goldfarb–Shanno training algorithm. Correlation measures of the obtained network were the following: R(training) = 0.9975, R(test) = 0.9971 and R(validation) = 0.9999. The comparison of the experimental and predicted retention times of the analyzed compounds showed very high correlation (R = 0.9913) and significant predictive power of the established neural network.

Keywords: ANN regression, GC/MS, Satureja montana, terpenes

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Authors: Fatmawati Adam, Saidatul Syaima Mat Tari, Saiful Nizam Tajuddin, Nurul Salwa Azliyana Hamzah

Abstract:

Essential oil of agarwood or known as Gaharu in Malay is highly prized for its value as luxury fragrances and incense. However, the complexities of the chemical composition of agarwood itself is the main challenge for establishment of an effective recovery method, which is able to ensure uniform qualities and standard for each batch of essential oil production. Agarwood markers are actually a blend of volatile and non-volatile compounds. While volatile molecules could be easily retrieved by the present distillation technique, the high solubility properties are the limiting factor for the latter. With regard to this, an elementary chemistry resolution study had been performed on commercial agarwood essential oil-water mixture, by the application of preparative HPLC and FTIR. Interpretation of the results leads to the theoretical postulation that, agarwood water mixture comprise of agarospirol, jinkohol, jinkoh eremol and khusenol. This study provides a pinpoint on the chemical characteristics of water soluble (non-volatile) agarwood compounds, therefore, will be an insight for researchers to develop a more strategic technique for their extraction. Thereafter the optimum quality of this essential oil could be controlled in a more improved way.

Keywords: Agarwood, Aquillaria Malaccensis, agarospirol, jinkohol, jinkoh eremol, khusenol

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Authors: D. Hilton

Abstract:

An analysis of the Australian Diabetes Screening Study estimated undiagnosed diabetes mellitus [DM] prevalence in a high risk general practice based cohort. DM prevalence varied from 9.4% to 18.1% depending upon the diagnostic criteria utilised with age being a highly significant risk factor. Utilising the gold standard oral glucose tolerance test, the prevalence of DM was 22-23% in those aged >= 70 years and <15% in those aged 40-59 years. Opportunistic screening in Australian general practice potentially can identify many persons with undiagnosed type 2 DM. An Australian Bureau of Statistics document published three years ago, reported the highest rate of DM in men aged 65-74 years [19%] whereas the rate for women was highest in those over 75 years [13%]. If you consider that the Australian Bureau of Statistics report in 2007 found that 13% of the population was over 65 years of age and that this will increase to 23-25% by 2056 with a further projected increase to 25-28% by 2101, obviously this information has to be factored into the equation when age related diabetes prevalence predictions are calculated. This 10-15% proportional increase of elderly persons within the population demographics has dramatic implications for the estimated number of elderly persons with DM in these age groupings. Computational methodology showing the age related demographic changes reported in these official statistical documents will be done showing estimates for 2056 and 2101 for different age groups. This has relevance for future diabetes prevalence rates and shows that along with many countries worldwide Australia is facing an increasing pandemic. In contrast Japan is expected to have a decrease in the next twenty years in the number of persons with diabetes.

Keywords: epidemiological methods, aging, prevalence, diabetes mellitus

Procedia PDF Downloads 362

Authors: Sanusi Abubakar Sadiq, Rebecca William Chiwar

Abstract:

Wildlife Park stands a chance of contributing to tourism development in different ways, but available infrastructure, and facilities required by visitors when they arrive, access road to the destination, and resources to facilitate positive experience are lacking in certain areas. The study set out to find out the need to develop Sumu Wildlife Park Bauchi State, to an international standard. The study focused on identifying the existing facilities and infrastructure at the park and to further identify the available resources used by visitors. In attempt to find out the impact of developing Sumu Wildlife Park and ways of filling the gap of the actual standard data were obtained from fifteen administrative staff of Sumu Wildlife Park, ten staff of Bauchi state Tourism Board and twenty-five residents of the community in Kafin Madaki, Bauchi. Relevant literature were reviewed in the study; data collected were organized and analyzed using Statistical Package of Social Sciences (SPSS), software for analysis. Findings revealed that though Sumu Wildlife Park has attractions to keep visitors patronage but has insufficient facilities to maintain visitors and has not been developed to an expected standard. The problem faced by the management of Sumu wildlife Park is lack of adequate facilities, infrastructure and resources. The need to develop Sumu Wildlife Park has enormous benefits in increasing patronage. Provision of more funds would help improve standard as there would be more activities within and around the park. Regular maintenance of those facilities protects the life span of the park.

Keywords: attractions, facilities, infrastructure, resources

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Authors: Yaxin Bi

Abstract:

Accurate prediction and understanding of the evolution mechanisms of earthquakes remain challenging in the fields of geology, geophysics, and seismology. This study leverages Long Short-Term Memory (LSTM) networks and Generative Adversarial Networks (GANs), a generative model tailored to time-series data, for generating synthetic time series data based on Swarm satellite data, which will be used for detecting seismic anomalies. LSTMs demonstrated commendable predictive performance in generating synthetic data across multiple countries. In contrast, the GAN models struggled to generate synthetic data, often producing non-informative values, although they were able to capture the data distribution of the time series. These findings highlight both the promise and challenges associated with applying deep learning techniques to generate synthetic data, underscoring the potential of deep learning in generating synthetic electromagnetic satellite data.

Keywords: LSTM, GAN, earthquake, synthetic data, generative AI, seismic precursors

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Authors: Insaf Bellamine, Hamid Tairi

Abstract:

Detecting moving objects in sequences is an essential step for video analysis. This paper mainly contributes to the Color Space-Time Interest Points (CSTIP) extraction and detection. We propose a new method for detection of moving objects. Two main steps compose the proposed method. First, we suggest to apply the algorithm of the detection of Color Space-Time Interest Points (CSTIP) on both components of the Color Structure-Texture Image Decomposition which is based on a Partial Differential Equation (PDE): a color geometric structure component and a color texture component. A descriptor is associated to each of these points. In a second stage, we address the problem of grouping the points (CSTIP) into clusters. Experiments and comparison to other motion detection methods on challenging sequences show the performance of the proposed method and its utility for video analysis. Experimental results are obtained from very different types of videos, namely sport videos and animation movies.

Keywords: Color Space-Time Interest Points (CSTIP), Color Structure-Texture Image Decomposition, Motion Detection, clustering

Procedia PDF Downloads 363