Search results for: governance framework

Authors: Shaikh Farhad Hossain, Liakot Ali

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Technology advances accelerate the quality and type of services provided for health care and especially for monitoring health conditions. Sensors have turned out to be more effective to detect diverse physiological signs and can be worn on the human body utilizing remote correspondence modules. An assortment of programming devices have been created to help in preparing a difference rundown of essential signs by examining and envisioning information produced by different sensors. In this proposition, we presented a Health signs and Activity acknowledgment monitoring system. Utilizing off-the-rack sensors, we executed a movement location system for identifying five sorts of action: falling, lying down, sitting, standing, and walking. The framework collects and analyzes sensory data in real-time, and provides different feedback to the users. In addition, it can generate alerts based on the detected events and store the data collected to a medical server.

Keywords: ADL, SVM, TRIL , MEMS

Procedia PDF Downloads 387

Authors: Mehdi Ghommem, Daniel Garcia, Nathan Collier, Victor Calo

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We develop a fast, user-friendly implementation of a potential flow solver based on the unsteady vortex lattice method (UVLM). The computational framework uses the Python programming language which has easy integration with the scripts requiring computationally-expensive operations written in Fortran. The mixed-language approach enables high performance in terms of solution time and high flexibility in terms of easiness of code adaptation to different system configurations and applications. This computational tool is intended to predict the unsteady aerodynamic behavior of multiple moving bodies (e.g., flapping wings, rotating blades, suspension bridges...) subject to an incoming air. We simulate different aerodynamic problems to validate and illustrate the usefulness and effectiveness of the developed computational tool.

Keywords: unsteady aerodynamics, numerical simulations, mixed-language approach, potential flow

Procedia PDF Downloads 278

Authors: Bahram Fathi, Seyedhossein Sajadifar, Naser Khiabani

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Data envelopment analysis (DEA) has recently gained popularity in energy efficiency analysis. A common feature of the previously proposed DEA models for measuring energy efficiency performance is that they treat energy consumption as an input within a production framework without considering undesirable outputs. However, energy use results in the generation of undesirable outputs as byproducts of producing desirable outputs. Within a joint production framework of both desirable and undesirable outputs, this paper presents several DEA-type linear programming models for measuring energy efficiency performance. In addition to considering undesirable outputs, our models treat different energy sources as different inputs so that changes in energy mix could be accounted for in evaluating energy efficiency. The proposed models are applied to measure the energy efficiency performances of 12 OPEC countries and the results obtained are presented.

Keywords: energy efficiency, undesirable outputs, data envelopment analysis

Procedia PDF Downloads 722

Authors: Deepak Jugran

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The last century has witnessed rapid scientific growth, and social policies mainly targeted to increase the “life expectancy” of the people. As a result of these developments, the aging as well as ailing population, is increasing by every day. Despite an increase in “life expectancy”, we have not recorded compression in morbidity numbers as the age of onset of the majority of health issues has not increased substantially. In recent years, the prevalence of chronic diseases along with the improved treatment has also resulted in the increase of people living with chronic diseases. The last decade has also focused on social policies to increase the life expectancy in the population; however, in recent decades, social policies and biomedical research are gradually shifting on the potential of increasing healthy life or healthspan. In this article, we review the existing framework of lifespan and healthspan and wish to ignite a discussion among social scientists and public health experts to propose a wholistic framework to balance the trade-offs on social policies for “lifespan” and “healthspan”.

Keywords: lifespan, healthspan, chronic diseases, social policies

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Authors: Mijoh A. Gbededo, Kapila Liyanage, Ilias Oraifige

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Decision making for sustainable manufacturing design and management requires critical considerations due to the complexity and partly conflicting issues of economic, social and environmental factors. Although there are tools capable of assessing the combination of one or two of the sustainability factors, the frameworks have not adequately integrated all the three factors. Case study and review of existing simulation applications also shows the approach lacks integration of the sustainability factors. In this paper we discussed the development of a simulation based framework for support of a holistic assessment of sustainable manufacturing design and management. To achieve this, a strategic approach is introduced to investigate the strengths and weaknesses of the existing decision supporting tools. Investigation reveals that Discrete Event Simulation (DES) can serve as a rock base for other Life Cycle Analysis frameworks. Simio-DES application optimizes systems for both economic and competitive advantage, Granta CES EduPack and SimaPro collate data for Material Flow Analysis and environmental Life Cycle Assessment, while social and stakeholders’ analysis is supported by Analytical Hierarchy Process, a Multi-Criteria Decision Analysis method. Such a common and integrated framework creates a platform for companies to build a computer simulation model of a real system and assess the impact of alternative solutions before implementing a chosen solution.

Keywords: discrete event simulation, life cycle sustainability analysis, manufacturing, sustainability

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Authors: Shoohira Aftab, Hammad Afzal

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Information integration is a well known procedure to provide consolidated view on sets of heterogeneous information sources. It not only provides better statistical analysis of information but also facilitates users to query without any knowledge on the underlying heterogeneous information sources The problem of providing a consolidated view of information can be handled using Semantic data (information stored in such a way that is understandable by machines and integrate-able without manual human intervention). However, integrating information using semantic web technology without any access management enforced, will results in increase of privacy and confidentiality concerns. In this research we have designed and developed a framework that would allow information from heterogeneous formats to be consolidated, thus resolving the issue of interoperability. We have also devised an access control system for defining explicit privacy constraints. We designed and applied our framework on both semantic and non-semantic data from heterogeneous resources. Our approach is validated using scenario based testing.

Keywords: information integration, semantic data, interoperability, security, access control system

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Authors: Abel Sichinava

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The need for organ transplantation is vigorously increasing worldwide. The numbers on the waiting lists grow, but the number of donors is not keeping up with the demand even though there is a legal possibility of decreasing the gap between the demand and supply. Most countries around the globe are facing an organ donation problem (living or deceased); however, the extent of the problem differs based on how well developed a country is. The determining issues seem to be centered on how aware the society is about the concept of organ donation, as well as cultural and religious factors. Even if people are aware of the benefits of organ donation, they may still have fears that keep them from being in complete agreement with the idea. Some believe that in the case of deceased organ donation: “the brain dead human body may recover from its injuries” or “the sick might get less appropriate treatment if doctors know they are potential donors.” In the case of living organ donations, people sometimes fear that after the donation, “it might reduce work efficiency, cause health deterioration or even death.” Another major obstacle in the organ shortage is a lack of a well developed ethical framework. In reality, there are truly an immense number of people on the waiting list, and they have only two options in order to receive a suitable organ. First is the legal way, which is to wait until their turn. Sadly, numerous patients die while on the waiting list before an appropriate organ becomes available for transplant. The second option is an illegal way: seeking an organ in a country where they can possibly get. To tell the truth, in people’s desire to live, they may choose the second option if their resources are sufficient. This process automatically involves “organ brokers.” These are people who get organs from vulnerable poor people by force or betrayal. As mentioned earlier, the high demand and low supply leads to human trafficking. The subject of the study was the large number of society from different backgrounds of their belief, culture, nationality, level of education, socio-economic status. The great majority of them interviewed online used “Google Drive Survey” and others in person. All statistics and information gathered from trusted sources annotated in the reference list and above mentioned considerable testimonies shared by the respondents are the fundamental evidence of a lack of the well developed ethical framework. In conclusion, the continuously increasing number of people on the waiting list and an irrelevant ethical framework, lead people to commit to atrocious, dehumanizing crimes. Therefore, world society should be equally obligated to think carefully and make vital decisions together for the advancement of an organ donations and its ethical framework.

Keywords: donation, ethical framwork, organ, transplant

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Authors: M. Mohemmed Sha, T. Manesh, A. Ahmed Mohamed Mustaq

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In this corporate world, the technology of Web services has grown rapidly and its significance for the development of web based applications gradually rises over time. The success of Business to Business integration rely on finding novel partners and their services in a global business environment. But the selection of the most suitable Web service from the list of services with the identical functionality is more vital. The satisfaction level of the customer and the provider’s reputation of the Web service are primarily depending on the range it reaches the customer’s requirements. In most cases the customer of the Web service feels that he is spending for the service which is undelivered. This is because the customer always thinks that the real functionality of the web service is not reached. This will lead to change of the service frequently. In this paper, a framework is proposed to evaluate the Quality of Service (QoS) and its cost that makes the optimal correlation between each other. Also this research work proposes some management decision against the functional deviancy of the web service that are guaranteed at time of selection.

Keywords: web service, service level agreement, quality of a service, cost of a service, QoS, CoS, SOA, WSLA, WsRF

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Authors: Jindong Gu, Matthias Schubert, Volker Tresp

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In many outlier detection tasks, only training data belonging to one class, i.e., the positive class, is available. The task is then to predict a new data point as belonging either to the positive class or to the negative class, in which case the data point is considered an outlier. For this task, we propose a novel corrupted Generative Adversarial Network (CorGAN). In the adversarial process of training CorGAN, the Generator generates outlier samples for the negative class, and the Discriminator is trained to distinguish the positive training data from the generated negative data. The proposed framework is evaluated using an image dataset and a real-world network intrusion dataset. Our outlier-detection method achieves state-of-the-art performance on both tasks.

Keywords: one-class classification, outlier detection, generative adversary networks, semi-supervised learning

Procedia PDF Downloads 137

Authors: I. Pina, M. Painho

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Increased knowledge about the nature of place and the conditions under which space becomes place is a key factor for better urban planning and place-making. Although there is a broad consensus on the relevance of this knowledge, difficulties remain in relating the theoretical framework about place and urban management. Issues related to representation of places are among the greatest obstacles to overcome this gap. With this critical discussion, based on literature review, we intended to explore, in a common framework for geographical analysis, the potential of stories to spell out place meanings, bringing together qualitative text analysis and text mining in order to capture and represent the singularity contained in each person's life history, and the patterns of social processes that shape places. The development of this reasoning is based on the extensive geographical thought about place, and in the theoretical advances in the field of Geographic Information Science (GISc).

Keywords: discourse analysis, geographic information science place, place-making, stories

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Authors: Abeba Nigussie Turi, Xiangming Samuel Li

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Data-driven business models are more typical for established businesses than early-stage startups that strive to penetrate a market. This paper provided an extensive discussion on the principles of data analytics for early-stage digital entrepreneurial businesses. Here, we developed data-driven decision-making (DDDM) framework that applies to startups prone to multifaceted barriers in the form of poor data access, technical and financial constraints, to state some. The startup DDDM framework proposed in this paper is novel in its form encompassing startup data analytics enablers and metrics aligning with startups' business models ranging from customer-centric product development to servitization which is the future of modern digital entrepreneurship.

Keywords: startup data analytics, data-driven decision-making, data acquisition, data generation, digital entrepreneurship

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Authors: Rachel Koshy, Nazrila Hairizan Bt. Nasir, Samsiah Bt. Awang, Kamaliah Bt. Mohamad Noh

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Malaysia collaborated as a ‘trailblazer’ country with PHCPI (Primary Health Care Performance Initiative) to populate the Primary Health Care (PHC) Vital Signs Profile (VSP) for the country. The PHC VSP provides an innovative snapshot of the primary health care system's performance. Four domains were assessed: system financing, system capacity, system performance, and system equity, and completed in 2019. There were two phases using a mixed method study design. The first phase involved a quantitative study, utilising existing secondary data from national and international sources. In the case of unavailability of data for any indicators, comparable alternative indicators were used. The second phase was a mixed quantitative-qualitative approach to measure the functional capacity based on governance and leadership, population health needs, inputs, population health management, and facility organisation and management. PHC spending constituted 35% of overall health spending in Malaysia, with a per capita PHC spending of $152. The capacity domain was strong in the three subdomains of governance and leadership, information system, and funds management. The two subdomains of drugs & supplies and facility organisation & management had low scores, but the lowest score was in empanelment of the population under the population health management. The PHC system performed with an access index of 98%, quality index of 84%, and service coverage of 62%. In the equity domain, there was little fluctuation in the coverage of reproductive, maternal, newborn, and child health services by mother’s level of education and under-five child mortality between urban and rural areas. The public sector was stronger in the capacity domain as compared to the private sector. This is due to the different financing, organisational structures, and service delivery mechanism. The VSP has identified areas for improvement in the effort to provide high-quality PHC for the population. The gaps in PHC can be addressed through the system approach and the positioning of public and private primary health care delivery systems.

Keywords: primary health care, health system, system domains, vital signs profile

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Authors: Celine Jaspers

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The Belgian legal system is slowly evolving to mandatory mediation to promote ADR. One of the reasons for this evolution is the lack of use of alternative methods in relation to their possible benefits. Especially in divorce cases, ADR can play a beneficial role in resolving disputes, since the emotional component is very much present. When children are involved, a solution provided by the parent may be more adapted to the child’s best interest than a court order. In the first part, the lack of use of voluntary ADR and the evolution toward mandatory ADR in Belgium will be indicated by sources of legislation, jurisprudence and social-scientific sources, with special attention to divorce cases. One of the reasons is lack of knowledge on ADR, despite the continuing efforts of the Belgian legislator to promote ADR. One of the last acts of ADR-promotion, was the implementation of an Act in 2018 which gives the judge the possibility to refer parties to mediation if at least one party wants to during the judicial procedure. This referral is subject to some conditions. The parties will be sent to a private mediator, recognized by the Federal Mediation Commission, to try to resolve their conflict. This means that at least one party can be mandated to try mediation (indicated as “semi-mandatory mediation”). The main goal is to establish the factors and elements that Belgium has to take into account in their further development of mandatory ADR, with consideration of the human rights perspective and the EU perspective. Furthermore it is also essential to detect some dangerous pitfalls other systems have encountered with their process design. Therefore, the second part, the comparative component, will discuss the existing framework in California, USA to establish the necessary elements, possible pitfalls and considerations the Belgian legislator can take into account when further developing the framework of mandatory ADR. The contrasting and functional method will be used to create key elements and possible pitfalls, to help Belgium improve its existing framework. The existing mandatory system in California has been in place since 1981 and is still up and running, and can thus provide valuable lessons and considerations for the Belgian system. Thirdly, the key elements from a human rights perspective and from a European Union perspective (e.g. the right to access to a judge, the right to privacy) will be discussed too, since the basic human rights and European legislation and jurisprudence play a significant part in Belgian legislation as well. The main sources for this part will be the international and European treaties, legislation, jurisprudence and soft law. In the last and concluding part, the paper will list the most important elements of a mandatory ADR-system design with special attention to the dangers of these elements (e.g. to include or exclude domestic violence cases in the mandatory ADR-framework and the consequences thereof), and with special attention for the necessary the international and European rights, prohibitions and guidelines.

Keywords: Belgium, divorce, framework, mandatory ADR

Procedia PDF Downloads 136

Authors: D. Szinay, O. Perski, A. Jones, T. Chadborn, J. Brown, F. Naughton

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Background: The uptake of health and wellbeing smartphone apps is largely influenced by popularity indicators (e.g., rankings), rather than evidence-based content. Rapid disengagement is common. This study aims to explore how and why potential users 1) select and 2) engage with such apps, and 3) how increased engagement could be promoted. Methods: Semi-structured interviews and a think-aloud approach were used to allow participants to verbalise their thoughts whilst searching for a health or wellbeing app online, followed by a guided search in the UK National Health Service (NHS) 'Apps Library' and Public Health England’s (PHE) 'One You' website. Recruitment took place between June and August 2019. Adults interested in using an app for behaviour change were recruited through social media. Data were analysed using the framework approach. The analysis is both inductive and deductive, with the coding framework being informed by the Theoretical Domains Framework. The results are further mapped onto the COM-B (Capability, Opportunity, Motivation - Behaviour) model. The study protocol is registered on the Open Science Framework (https://osf.io/jrkd3/). Results: The following targets were identified as playing a key role in increasing the uptake of and engagement with health and wellbeing apps: 1) psychological capability (e.g., reduced cognitive load); 2) physical opportunity (e.g., low financial cost); 3) social opportunity (e.g., embedded social media); 4) automatic motivation (e.g., positive feedback). Participants believed that the promotion of evidence-based apps on NHS-related websites could be enhanced through active promotion on social media, adverts on the internet, and in general practitioner practices. Future Implications: These results can inform the development of interventions aiming to promote the uptake of and engagement with evidence-based health and wellbeing apps, a priority within the UK NHS Long Term Plan ('digital first'). The targets identified across the COM-B domains could help organisations that provide platforms for such apps to increase impact through better selection of apps.

Keywords: behaviour change, COM-B model, digital health, mhealth

Procedia PDF Downloads 147

Authors: Nyong Princely Awazi, Gadinga Walter Forje, Barnabas Neba Nfornkah, Ndzifon Jude Kimengsi

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Ecotourism is documented as a sustainable measure of bridging conservation goals and livelihood sustenance around protected areas, due to its ability of not just providing alternative livelihood, but also in providing the necessary resources that can help finance conservation initiatives. In Cameroon, all ecotourism activities around national parks are aimed at generating revenue through the conservation service while providing sustainable livelihood options to the local population. There exists an information lacuna regarding the contribution of ecotourism finances to conservation efforts in the country. This study was aimed at establishing the contribution of ecotourism finances to conservation initiatives in and around the Campo Ma’an National Park (CMNP). Data were collected through the administering of 120 structured questionnaires to ecotourism actors and 15 key/expert interviews with tourism and conservation actors in the Campo Ma’an landscape. Chi-square test, Spearman’s rank correlation and regressions were used for data analysis. The study revealed that the main sources of ecotourism financing to the park service are through entrance fees, cameras and vehicle fees paid by tourists as well as ecotourism project financing through NGOs. Calculations from the tourism register of the park showed that the park was able to raise as much as 1,576,000 FCFA (US$ 3,152) annually. It was further established that ecotourism revenue has not greatly supported conservation, with 54% of respondents perceiving ecotourism not contributing to biodiversity conservation. Chi Square test results highlighted poor ecotourism governance, low level of ecotourism development, corruption from park management staff, obsolete nature of the current finance law on the management of protected area revenue as key factors hindering ecotourism financing in conservation. For ecotourism financing to contribute to biodiversity conservation in the CMNP and in Cameroon’s protected areas, the government needs to revise the finance law on the management of revenue generated from protected areas, improve park governance to fight corruption and enhance transparency, invest in the development and marketing of the Campo Ma’an national park as a tourism destination in the country.

Keywords: Cameroon, Campo Ma’an National Park, conservation, ecotourism, ecotourism financing

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Authors: Waheed Oseni

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This article reviews emergent global trends in intellectual property protection and identifies patenting as a strategic initiative. Recent developments in software and method of doing business patenting are fast transforming the e‐business landscape. The article discusses the emergent global regulatory framework concerning intellectual property rights and the strategic value of patenting. Important features of a corporate patenting portfolio are described. Superficially, the e‐commerce landscape appears to be dominated by dotcom start-ups or the “dotcomization” of existing brick and mortar companies. But, in reality, at its very bedrock is intellectual property (IP). In this connection, the recent avalanche of patenting of software and method‐of‐doing‐business (MDB) in the USA is a very significant development with regard to rules governing IP rights and, therefore, e‐commerce. Together with the World Trade Organization’s (WTO) IP rules, there is an emerging global regulatory framework for IP rights, an understanding of which is necessary for designing effective e‐commerce strategies.

Keywords: intellectual property, patents, methods, computer software

Procedia PDF Downloads 509

Authors: Aminath Waseela, Vinesh Chandra, Shaun Nykvist,

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The success of technology integration initiatives hinges on the knowledge and skills of teachers to effectively integrate technology in classroom teaching. Consequently, gaining an understanding of teachers' technology knowledge and its integration can provide useful insights on strategies that can be adopted to enhance teaching and learning, especially in developing country contexts where research is scant. This paper extends existing knowledge on teachers' use of technology by developing a conceptual framework that recognises how three key types of knowledge; content, pedagogy, technology, and their integration are at the crux of teachers' technology use while at the same time is amenable to empirical studies. Although the aforementioned knowledge is important for effective use of technology that can result in enhanced student engagement, literature on how this knowledge leads to effective technology use and enhanced student engagement is limited. Thus, this theoretical paper proposes a framework to explore teachers' knowledge through the lens of the Technological Pedagogical and Content Knowledge (TPACK); the integration of technology in classroom teaching through the Substitution Augmentation Modification and Redefinition (SAMR) model and how this affects students' learning through the Bloom's Digital Taxonomy (BDT) lens. Studies using this framework could inform the design of professional development to support teachers to develop skills for effective use of available technology that can enhance student learning engagement.

Keywords: information and communication technology, ICT, in-service training, small island developing states, SIDS, student engagement, technology integration, technology professional development training, technological pedagogical and content knowledge, TPACK

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Authors: Prem Bahadur Giri, Mathinee Yucharoen

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Community Forest User Groups (CFUGs) have been the core stone of forest management efforts in Nepal. Due to the lack of a smooth transition into the local governance structure in 2017, policy instruments have not been effectively cascaded to the local level, creating ambiguity and inconsistency in forest governance. Descriptive mixed-method research was performed with community users and stakeholders of Tarpakha community forest, Siranchowk Rural Municipality, to understand the role of the political economy in CFUG management. A household survey was conducted among 100 households (who also are existing members of the Tarpakha CFUG) to understand and document their energy consumption preferences and practices. Likewise, ten key informant interviews and five focus group discussions with the municipality and forest management officials were also conducted to have a wider overview of the factors and political, socio-economic, and religious contexts behind the utilization of renewable energy for sustainable development. Findings from our study suggest only 3% of households use biogas as the main source of energy. The rest of the households mention liquid petroleum gas (LPG), electricity and firewood as major sources of energy for domestic purposes. Community members highlighted the difficulty in accessing firewood due to strict regulations from the CFUG, lack of cattle and manpower to rear cattle to produce cow dung (for biogas), lack of technical expertise at the community level for the operation and maintenance of solar energy, among others as challenges of resource. Likewise, key informants have mentioned policy loopholes at both the federal and local levels, especially with regard to the promotion of alternative or renewable energy, as there are no clear mandates and provisions to regulate the renewable energy industry. The study recommends doing an in-depth study on the feasibility of renewable energy sources, especially in the context of CFUGs, where biodiversity conservation aspects need to be equally taken into consideration while thinking of the promotion and expansion of renewable energy sources.

Keywords: community forest, renewable energy, sustainable development, Nepal

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Authors: Michael A. Ifarajimi

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The transition from being a graduate teacher to a highly accomplished teacher has been widely portrayed in literature as challenging. Pre-service teachers are troubled with complex issues such as implementing, assessment, meeting prescribed learning outcomes, taking risks, supporting eco sustainability, etc. This list is not exhaustive as they are further complicated when the concerns extend beyond the classroom into the broader school setting and community. Meanwhile, the pre-service teacher education programme as is currently run in Nigeria, cannot adequately prepare newly trained teachers for the realities of classroom teaching. And there appears to be no formal structure in place for mentoring such teachers by the more seasoned teachers in schools. The central research question of the study, therefore, is which institutional framework can be distinguished for enactment in mentoring practices in teacher education? The study was conducted in five colleges of education in South-West Nigeria, and a sample of 1000 pre-service teachers on their final year practicum was randomly selected from the colleges of education. A pre-service teacher mentorship programme (PTMP) framework was designed and implemented, with a focus on the impact of transformative inquiry on the pre-service teacher support system. The study discovered a significant impact of mentoring on pre-service teacher’s professional transformation. The study concluded that institutionalizing mentorship through transformative inquiry is a means to sustainable teacher education, professional growth, and effective classroom practice. The study recommended that the government should enact policies that will promote mentoring in teacher education and establish a framework for the implementation of mentoring practices in the colleges of education in Nigeria.

Keywords: institutionalization, mentoring, pre-service teachers teacher education, transformative inquiry

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

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

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

Procedia PDF Downloads 264

Authors: Richard Ren

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Air pollution is a serious danger to international well-being and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.

Keywords: air quality prediction, air pollution, artificial intelligence, machine learning algorithms

Procedia PDF Downloads 108

Authors: Sarbjit Singh

Abstract:

This framework development explored the status of GSCM in manufacturing SMEs and concluded that there was a significant gap w.r.t carbon emissions measurement in the supply chain activities. The measurement of carbon emissions within supply chains is important green initiative toward its reduction. The majority of the SMEs were facing the problem to quantify the green house gas emissions in its supply chain & to make it a low carbon supply chain or GSCM. Thus, the carbon management initiatives were amalgamated with the supply chain activities in order to measure and reduce the carbon emissions, confirming the GHG protocol scopes. Henceforth, it covers the development of carbon management software (CMS) tool to quantify carbon emissions for effective carbon management. This tool is cheap and easy to use for the industries for the management of their carbon emissions within the supply chain.

Keywords: w.r.t carbon emissions, carbon management software, supply chain management, Indian Industry

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Authors: Joshua Denning, Kapila Liyanage

Abstract:

Digital transformation has the power to change the way we live and work. Within the manufacturing sector many companies have been quick to adapt to the new digital ways of working especially after the covid-19 pandemic has accelerated adoption. Within the manufacturing sector the umbrella term of industry 4.0 does a good job at summarising specific technologies that can be adopted as part of a digital transformation to leverage many benefits to the business. These technologies include but are not limited to big data, advanced robotics, IoT, digital twins, augmented reality and many more. As larger companies with abundant resource and knowledge in these areas have adopted these new technologies they pave the way for SME’s within the industry to follow suit however they are stalling in doing so do to many factors limiting their ability to progress. This paper explores the critical success factors to digital transformation under industry 4.0 for manufacturing SME’s. It proposes a detailed framework to enable businesses to implement industry 4.0 in a sustainable and agile way catering specifically to the needs of SME’s.

Keywords: digital transformation, industry 4.0, manufacturing SME, sustainability, business agility

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Authors: Tim Wollert, Fabian Behrendt

Abstract:

Value Stream Management (VSM) is a widely used methodology in the context of Lean Management for improving end-to-end material and information flows from a supplier to a customer from a company’s perspective. Whereas the design principles, e.g. Pull, value-adding, customer-orientation and further ones are still valid against the background of an increasing digitalized and dynamic environment, the methodology itself for mapping a value stream is characterized as time- and resource-intensive due to the high degree of manual activities. The digitalization of processes in the context of Industry 4.0 enables new opportunities to reduce these manual efforts and make the VSM approach more agile. The paper at hand aims at providing a modular data and calculation framework, utilizing the available business data, provided by information and communication technologies for automizing the value stream mapping process with focus on the manufacturing process.

Keywords: lean management 4.0, value stream management (VSM) 4.0, dynamic value stream mapping, enterprise resource planning (ERP)

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Authors: Modestus Queen

Abstract:

As we know, diplomatic languages carry the political ideology and cultural stance of the country. Knowing that China's diplomatic discourse is complicated and is heavily flavored with Chinese characteristics, one of the core goals of President Xi's administration is to properly tell the story of China. This cannot be done without proper translation or interpretation of major Chinese diplomatic concepts. Therefore, this research seeks to interpret the relevance of "Yi" as used in "Zhèngquè Yì Lì Guān". The author argues that it is not enough to translate a document but that it must be properly interpreted to portray it as political, economic, cultural and diplomatic relevant to the target audience, in this case, African people. The first finding in the current study indicates that literal translation is a bad strategy, especially in Chinese diplomatic discourses. The second finding indicates that "Yi" can be used as a framework to analyze Sino-Africa relations from economic, social and political perspectives, and the third finding indicates that "Yi" is the guiding principle of China's foreign policy towards Africa.

Keywords: Yi, justice, China-Africa, interpretation, diplomatic discourse, discourse reconstruction

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Authors: Taha Anjamrooz, Sareh Rajabi, Salwa Bheiry

Abstract:

Selection between the available projects in bidding processes for the contractor is one of the essential areas to concentrate on. It is important for the contractor to choose the right projects within its portfolio during the tendering stage based on certain criteria. It should align the bidding process with its origination strategies and goals as a screening process to have the right portfolio pool to start with. Secondly, it should set the proper framework and use a suitable technique in order to optimize its selection process for concertation purpose and higher efforts during the tender stage with goals of success and winning. In this research paper, a two steps framework proposed to increase the efficiency of the contractor’s bidding process and the winning chance of getting the new projects awarded. In this framework, initially, all the projects pass through the first stage screening process, in which the portfolio basket will be evaluated and adjusted in accordance with the organization strategies to the reduced version of the portfolio pool, which is in line with organization activities. In the second stage, the contractor uses linear programming to optimize the portfolio pool based on available resources such as manpower, light equipment, heavy equipment, financial capability, return on investment, and success rate of winning the bid. Therefore, this optimization model will assist the contractor in utilizing its internal resource to its maximum and increase its winning chance for the new project considering past experience with clients, built-relation between two parties, and complexity in the exertion of the projects. The objective of this research will be to increase the contractor's winning chance in the bidding process based on the success rate and expected return on investment.

Keywords: bidding process, internal resources, optimization, contracting portfolio management

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Authors: Raja Asad Zaheer, Aisha Tanveer, Hafza Mehreen Fatima

Abstract:

Global Software Development (GSD) is becoming a common norm in software industry, despite of the fact that global distribution of the teams presents special issues for effective communication and coordination of the teams. Now trends are changing and project management for distributed teams is no longer in a limbo. GSD can be effectively established using agile and project managers can use different agile techniques/tools for solving the problems associated with distributed teams. Agile methodologies like scrum and XP have been successfully used with distributed teams. We have employed exploratory research method to analyze different recent studies related to challenges of GSD and their proposed solutions. In our study, we had deep insight in six commonly faced challenges: communication and coordination, temporal differences, cultural differences, knowledge sharing/group awareness, speed and communication tools. We have established that each of these challenges cannot be neglected for distributed teams of any kind. They are interlinked and as an aggregated whole can cause the failure of projects. In this paper we have focused on creating a scalable framework for detecting and overcoming these commonly faced challenges. In the proposed solution, our objective is to suggest agile techniques/tools relevant to a particular problem faced by the organizations related to the management of distributed teams. We focused mainly on scrum and XP techniques/tools because they are widely accepted and used in the industry. Our solution identifies the problem and suggests an appropriate technique/tool to help solve the problem based on globally shared knowledgebase. We can establish a cause and effect relationship using a fishbone diagram based on the inputs provided for issues commonly faced by organizations. Based on the identified cause, suitable tool is suggested, our framework suggests a suitable tool. Hence, a scalable, extensible, self-learning, intelligent framework proposed will help implement and assess GSD to achieve maximum out of it. Globally shared knowledgebase will help new organizations to easily adapt best practices set forth by the practicing organizations.

Keywords: agile project management, agile tools/techniques, distributed teams, global software development

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Authors: Maulana Ayoub Ali, Pradeep Kulshrestha

Abstract:

Laws of any given secular state have a huge contribution in the growth of the Islamic banking system because the system uses conventional laws to govern its activities. Therefore, the former should be ready to accommodate the latter in order to make the Islamic banking system work properly without affecting the current conventional banking system and therefore without affecting its system. Islamic financial rules have been practiced since the birth of Islam. Following the recent world economic challenges in the financial sector, a quick rebirth of the contemporary Islamic ethical banking system took place. The coming of the Islamic banking system is due to various reasons including but not limited to the failure of the interest based economy in solving financial problems around the globe. Therefore, the Islamic banking system has been adopted as an alternative banking system in order to recover the highly damaged global financial sector. But the Islamic banking system has been facing a number of challenges which hinder its smooth operation in different parts of the world. It has not been the aim of this paper to discuss other challenges rather than the legal ones, but the same was partly discussed when it was justified that it was proper to do so. Generally, there are so many things which have been discovered in the course of writing this paper. The most important part is the issue of the regulatory and supervisory framework for the Islamic banking system in Tanzania and in other nations is considered to be a crucial part for the development of the Islamic banking industry. This paper analyses what has been observed in the study on that area and recommends for necessary actions to be taken on board in a bid to make Islamic banking system reach its climax of serving the larger community by providing ethical, equitable, affordable, interest-free and society cantered banking system around the globe.

Keywords: Islamic banking, interest free banking, ethical banking, legal framework

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Authors: Mohammed Saif Al-Saidi, John P. T. Mo

Abstract:

Modern engineering assets are complex and very high in value. They are expected to function for years to come, with ability to handle the change in technology and ageing modification. The aging of an engineering asset and continues increase of vendors and contractors numbers forces the asset operation management (or Owner) to design an asset system which can capture these changes. Furthermore, an accurate performance measurement and risk evaluation processes are highly needed. Therefore, this paper explores the nature of the asset management system performance evaluation for an engineering asset based on the System Support Engineering (SSE) principles. The research work explores the asset support system from a range of perspectives, interviewing managers from across a refinery organisation. The factors contributing to complexity of an asset management system are described in context which clusters them into several key areas. It is proposed that SSE framework may then be used as a tool for analysis and management of asset. The paper will conclude with discussion of potential application of the framework and opportunities for future research.

Keywords: asset management, performance, evaluation, modern engineering, System Support Engineering (SSE)

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Authors: Shuqi Zhang

Abstract:

Federated learning allows users to participate in collaborative model training without sending data to third-party servers, reducing the risk of user data privacy leakage, and is widely used in smart finance and smart healthcare. However, the distributed architecture design of federation learning itself and the existence of secure aggregation protocols make it inherently vulnerable to backdoor attacks. To solve this problem, the federated learning backdoor defense framework FLEX based on group aggregation, cluster analysis, and neuron pruning is proposed, and inter-compatibility with secure aggregation protocols is achieved. The good performance of FLEX is verified by building a horizontal federated learning framework on the CIFAR-10 dataset for experiments, which achieves 98% success rate of backdoor detection and reduces the success rate of backdoor tasks to 0% ~ 10%.

Keywords: federated learning, secure aggregation, backdoor attack, cluster analysis, neuron pruning

Procedia PDF Downloads 86