Search results for: emerging trends

Authors: Ki-Yeo Kim

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Trends in genetics are transforming in order to identify differential coexpressions of correlated gene expression rather than the significant individual gene. Moreover, it is known that a combined biomarker pattern improves the discrimination of a specific cancer. The identification of the combined biomarker is also necessary for the early detection of invasive oral squamous cell carcinoma (OSCC). To identify the combined biomarker that could improve the discrimination of OSCC, we explored an appropriate number of genes in a combined gene set in order to attain the highest level of accuracy. After detecting a significant gene set, including the pre-defined number of genes, a combined expression was identified using the weights of genes in a gene set. We used the Principal Component Analysis (PCA) for the weight calculation. In this process, we used three public microarray datasets. One dataset was used for identifying the combined biomarker, and the other two datasets were used for validation. The discrimination accuracy was measured by the out-of-bag (OOB) error. There was no relation between the significance and the discrimination accuracy in each individual gene. The identified gene set included both significant and insignificant genes. One of the most significant gene sets in the classification of normal and OSCC included MMP1, SOCS3 and ACOX1. Furthermore, in the case of oral dysplasia and OSCC discrimination, two combined biomarkers were identified. The combined genomic expression achieved better performance in the discrimination of different conditions than in a single significant gene. Therefore, it could be expected that accurate diagnosis for cancer could be possible with a combined biomarker.

Keywords: oral squamous cell carcinoma, combined biomarker, microarray dataset, correlated genes

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Authors: A. H. M. Zehadul Karim

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Cameron Highlands (4°28’N, 101°23’E) is an attractive mountainous region with steep slopes located in the state of Pahang, Malaysia stretching between 1070 and 1830m above sea level with a total land area of 71,218ha. It is one of the few places in Malaysia that has a tropical highland climate as the mean annual temperature of it is 18 °C (64 °F) thus making the atmosphere perfect for specialized agriculture. Being ecologically suitable, Cameron Highlands has recently been identified as a very strategic farming area, producing multifarious vegetables, flowers and tea with a commercial motive of marketing them to Singapore and all over the urban areas of Malaysia to meet the domestic and international demands. The main intricacies of this plantation agriculture are fully dependent on the policies formulated by a group of emerging entrepreneurs who employ foreign labourers to make these agricultural activities a success in the agrarian sector in Malaysia. Based on the socio-anthropological perspective, the paper entirely relies on empirical field data generated by interviewing 10 farm owners and 200 foreign workers to find out the intricacies of this plantation agriculture which makes the research innovative and pragmatically significant. The paper deals with important issues relating to this productive plantation agriculture of Cameron Highlands and as such, narrates the various exceptional and holistic skills adopted for this type of farming.

Keywords: Cameron Highlands Malaysia, plantation agriculture, issues and challenges, mechanisms

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Authors: A. D. Tayal

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The energy industry is undergoing significant disruption. This research outlines that, whilst challenging; this disruption is also an emerging opportunity for electricity utilities. One such opportunity is leveraging the developments in data analytics and machine learning. As the uptake of renewable energy technologies and complimentary control systems increases, electricity grids will likely transform towards dense microgrids with high penetration of renewable generation sources, rich in network and customer data, and linked through intelligent, wireless communications. Data digitisation and analytics have already impacted numerous industries, and its influence on the energy sector is growing, as computational capabilities increase to manage big data, and as machines develop algorithms to solve the energy challenges of the future. The objective of this paper is to address how far the uptake of renewable technologies can go given the constraints of existing grid infrastructure and provides a qualitative assessment of how higher levels of renewable energy penetration can be facilitated by incorporating even broader technological advances in the fields of data analytics and machine learning. Western Australia is used as a contextualised case study, given its abundance and diverse renewable resources (solar, wind, biomass, and wave) and isolated networks, making a high penetration of renewables a feasible target for policy makers over coming decades.

Keywords: data, innovation, renewable, solar

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Authors: Yantao Li, Jun Zheng

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Food poison caused by consumption of contaminated food, especially seafood, is one of most serious public health threats worldwide. Vibrio parahaemolyticus is emerging bacterial pathogen and the leading cause of human gastroenteritis associated with food poison, especially in the southern coastal region of China. To successfully cause disease in host, bacterial pathogens need to overcome the host-derived stresses encountered during infection. One of the toxic chemical species elaborated by the host is nitric oxide (NO). NO is generated by acidified nitrite in the stomach and by enzymes of the inducible NO synthase (iNOS) in the host cell, and is toxic to bacteria. Bacterial pathogens have evolved some mechanisms to battle with this toxic stress. Such mechanisms include genes to sense NO produced from immune system and activate others to detoxify NO toxicity, and genes to repair the damage caused by toxic reactive nitrogen species (RNS) generated during NO toxic stress. However, little is known about the NO resistance in V. parahaemolyticus. In this study, a transposon coupled with next generation sequencing (Tn-seq) technology will be utilized to identify genes for NO resistance in V. parahaemolyticus. Our strategy will include construction the saturating transposon insertion library, transposon library challenging with NO, next generation sequencing (NGS), bioinformatics analysis and verification of the identified genes in vitro and in vivo.

Keywords: vibrio parahaemolyticus, nitric oxide, tn-seq, virulence

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Authors: A. Guellati, F-M Lounis, N. Guemras, K. Daoud

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Micro droplet formation is considered as a growing emerging area of research due to its wide-range application in chemistry as well as biology. The mechanism of micro droplet formation using two immiscible liquids running through a T-junction has been widely studied. We believe that the flow of these two immiscible phases can be of greater important factor that could have an impact on out-flow hydrodynamic behavior, the droplets generated and the size of the droplets. In this study, the type of the capillary tubes used also represents another important factor that can have an impact on the generation of micro droplets. The tygon capillary tubing with hydrophilic inner surface doesn't allow regular out-flows due to the fact that the continuous phase doesn't adhere to the wall of the capillary inner surface. Teflon capillary tubing, presents better wettability than tygon tubing, and allows to obtain steady and regular regimes of out-flow, and the micro droplets are homogeneoussize. The size of the droplets is directly dependent on the flows of the continuous and dispersed phases. Thus, as increasing the flow of the continuous phase, to flow of the dispersed phase stationary, the size of the drops decreases. Inversely, while increasing the flow of the dispersed phase, to flow of the continuous phase stationary, the size of the droplet increases.

Keywords: microfluidic system, micro droplets generation, t-junction, fluids engineering

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Authors: Ahmed Negm, George Aggidis, Xiandong Ma

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With the diverse nature of urban cities, customer demand patterns, landscape topologies or even seasonal weather trends; managing our water distribution networks (WDNs) has proved a complex task. These unpredictable circumstances manifest as pipe failures, intermittent supply and burst events thus adding to water loss, energy waste and increased carbon emissions. Whilst these events are unavoidable, advanced pressure management has proved an effective tool to control and mitigate them. Henceforth, water utilities have struggled with developing a real-time control method that is resilient when confronting the challenges of water distribution. In this paper we use deep reinforcement learning (DRL) algorithms as a novel pressure control strategy to minimise pressure violations and leakage under both burst and background leakage conditions. Agents based on asynchronous actor critic (A2C) and recurrent proximal policy optimisation (Recurrent PPO) were trained and compared to benchmarked optimisation algorithms (differential evolution, particle swarm optimisation. A2C manages to minimise leakage by 32.48% under burst conditions and 67.17% under background conditions which was the highest performance in the DRL algorithms. A2C and Recurrent PPO performed well in comparison to the benchmarks with higher processing speed and lower computational effort.

Keywords: deep reinforcement learning, pressure management, water distribution networks, leakage management

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Authors: J. Jebunnesa, Y. Budd, T. Mason

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COVID-19 changed the pedagogical expectations of early childhood education as many teachers across Australia had to quickly adapt to new teaching practices such as remote teaching. An important factor in the successful implementation of any new teaching and learning approach is teacher preparation, however, due to the pandemic, the transformation to remote teaching was immediate. A timely question to be asked is how early childhood teachers managed the transition from face-to-face teaching to remote teaching and what was learned through this time. This study explores the experiences of early childhood educators in Australia during COVID-19 lockdowns. Data were collected from an online survey conducted through the official Facebook forum of “Early Childhood Education and Care Australia,” and a constructivist grounded theory methodology was used to analyse the data. Initial research results suggest changing expectations of teachers’ roles and responsibilities during the lockdown, with a significant category related to transitioning teacher identities emerging. The concept of transitioning represents the shift from the role of early childhood educator to educational innovator, essential worker, social worker, and health officer. The findings illustrate the complexity of early childhood educators’ roles during the pandemic.

Keywords: changing role of teachers, constructivist grounded theory, lessons learned, teaching during COVID-19

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Authors: Anukriti Kumar, Tanmay Singh, Dinesh Kumar Vishwakarma

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Traffic control has been one of the most common and irritating problems since the time automobiles have hit the roads. Problems like traffic congestion have led to a significant time burden around the world and one significant solution to these problems can be the proper implementation of the Intelligent Transport System (ITS). It involves the integration of various tools like smart sensors, artificial intelligence, position technologies and mobile data services to manage traffic flow, reduce congestion and enhance driver's ability to avoid accidents during adverse weather. Road and traffic signs’ recognition is an emerging field of research in ITS. Classification problem of traffic signs needs to be solved as it is a major step in our journey towards building semi-autonomous/autonomous driving systems. The purpose of this work focuses on implementing an approach to solve the problem of traffic sign classification by developing a Convolutional Neural Network (CNN) classifier using the GTSRB (German Traffic Sign Recognition Benchmark) dataset. Rather than using hand-crafted features, our model addresses the concern of exploding huge parameters and data method augmentations. Our model achieved an accuracy of around 97.6% which is comparable to various state-of-the-art architectures.

Keywords: multiclass classification, convolution neural network, OpenCV

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Authors: Diamantino Ribeiro, António Pedro Costa, Jorge Remondes

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Dubai and the UAE governments have been investing in technology and digital communication for a long time. These governments are pioneers in introducing innovative strategies, policies and projects. They are also recognized worldwide for defining and implementing long term public programs. In terms of eGovernment Dubai and the UAE rank among the world’s most advanced. Both governments have surprised the world a few years ago by creating a Happiness Ministry. This paper focuses on UAE’s government digital strategies and its approach to the next era. The main goal of this exploratory study is to understand the new era of eGovernment and transfer of the happiness and wellness programs. Data were collected from the corpus latente and analysis was anchored in qualitative methodology using content analysis and observation as analysis techniques. The study allowed to highlight that the 2020 government reshuffle has a strong focus on digital reorganisation and digital sustainability, one of the newest trends in sustainability. Regarding happiness and wellbeing portfolio, we were able to observe that there has been a major change within the government organisation: The Ministry of Happiness was extinct and the Ministry of Community Development will manage the so-called ‘Happiness Portfolio’. Additionally, our observation allowed to note the government dual approach to governance: one through digital transformation, thus enhancing the digital sustainability process and, the second one trough government development.

Keywords: ministry of happiness, eGovernment, communication, digital sustainability

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Authors: Demisash, Abate Getnet, Gudisa, Ababo Geleta, Daba, Berhane Olani

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As consumption trends of fish are rising in Ethiopia, assessment of the environmental performance of Fisheries becomes vital. Hence, Cleaner Production Assessment was conducted on Lake Tana No.1 Fish Supply Association. This paper focuses on determining the characteristics, quantity, and setting up cleaner production options for the site with the experimental investigation. The survey analysis showed that illegal waste dumping in Lake Tana is common practice in the area, and some of the main reasons raised were they have no option than doing this for dis-charging fish wastes. Quantifying a fish waste by examination of records at the point of generation resulted in a generation rate of 72,822.61 kg per year, which is a significant amount of waste and needs management system. The result of the proximate analysis showed high free fat content of about 12.33%, and this was a good candidate for the production of biodiesel that has been set as an option for fish waste utilization. Among the different waste management options, waste reduction by product optimization, which involves biodiesel production, was chosen as a potential method. Laboratory scale experiments were performed to produce a renewable energy source from the wastes. The resulting biodiesel was characterized and found to have a density of 0.756kg/L, viscosity 0.24p, and 153°C flashpoints, which shows the product has values in compliance with the American Society for Testing and Materials (ASTM) standards.

Keywords: biodiesel, cleaner production, renewable energy, waste management

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Authors: Wesam Rababa, Jamal Al-Qawasmi

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The global climate is negatively impacted by CO2 emissions, which are mostly produced by buildings. Several green building rating systems (GBRS) have been proposed to impose low-carbon criteria in order to address this problem. The Green Globes certification is one such system that evaluates a building's sustainability level by assessing different categories of environmental impact and emerging concepts aimed at reducing environmental harm. Therefore, assessment tools at the national level are crucial in the developing world, where specific local conditions require a more precise evaluation. This study analyzed eight sustainable building assessment systems from different regions of the world, comparing a comprehensive list of CO2-related indicators with a various assessment system for conducting coverage analysis. The results show that GBRS includes both direct and indirect indicators in this regard. It reveals deep variation between examined practices, and a lack of consensus not only on the type and the optimal number of indicators used in a system, but also on the depth and breadth of coverage of various sustainable building SB attributes. Generally, the results show that most of the examined systems reflect a low comprehensive coverage, the highest of which is found in materials category. On the other hand, the most of the examined systems reveal a very low representative coverage.

Keywords: Assessment tools, CO2-related indicators, Comparative study, Green Building Rating Systems

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Authors: Lipsa Dash, Gyanendra Sahu

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Intelligence is the cornerstone of humans, and now they have created a counterpart of themselves artificially. Our understanding of the word intelligence is a very perspective based and mostly superior understanding of what we read, write, perceive and understand the adversities around better. A wide range of industrial sectors have also started involving the technology to perceive, reason and act. Similarly, intellectual property is the product of human intelligence and creativity. The World Intellectual Property Organisation is currently working on technology trends across the globe, and AI tops the list in the digital frontier that will have a profound impact on the world, transforming the way we live and work. Coming to Intellectual Property, patents and creations of the AI’s itself have constantly been in question. This paper explores whether AI’s can fit in the flexibilities of Trade Related Intellectual Property Studies and gaps in the existing IP laws or rthere is a need of amendment to include them in the ambit. The researcher also explores the right of AI’s who create things out of their intelligence and whether they could qualify to be legal persons making the other laws applicable on them. Differentiation between AI creations and human creations are explored in the paper, and the need of amendments to determine authorship, ownership, inventorship, protection, and identification of beneficiary for remuneration or even for determining liability. The humans and humanoids are all indulged in matters related to Privacy, and that attracts another constitutional legal issue to be addressed. The authors will be focusing on the legal conundrums of AI, transhumanism, and the Internet of things.

Keywords: artificial intelligence, humanoids, healthcare, privacy, legal conundrums, transhumanism

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Authors: Sheng-Hung Chen, Grace Mmametena Mahlangu, Hui-Cheng Wang

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This study aims to explore the trends of the Entrepreneurial Framework Conditions (EFCs) in the five African regions. The Global Entrepreneur Monitor (GEM) is the primary source of data. The data drawn were organized into a panel (2000-2021) and obtained from the National Expert Survey (NES) databases as harmonized by the (GEM). The Methodology used is descriptive and uses mainly charts and tables; this is in line with the approach used by the GEM. The GEM draws its data from the National Expert Survey (NES). The survey by the NES is administered to experts in each country. The GEM collects entrepreneurship data specific to each country. It provides information about entrepreneurial ecosystems and their impact on entrepreneurship. The secondary source is from the literature review. This study focuses on the following GEM indicators: Financing for Entrepreneurs, Government support and Policies, Taxes and Bureaucracy, Government programs, Basic School Entrepreneurial Education and Training, Post school Entrepreneurial Education and Training, R&D Transfer, Commercial And Professional Infrastructure, Internal Market Dynamics, Internal Market Openness, Physical and Service Infrastructure, and Cultural And Social Norms, based on GEM Report 2020/21. The limitation of the study is the lack of updated data from some countries. Countries have to fund their own regional studies; African countries do not regularly participate due to a lack of resources.

Keywords: trend analysis, entrepreneurial framework conditions (EFCs), African region, government programs

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Authors: Malik Abdul Jabbar Zen, Wiwik Misaco Yuniarti, Azisya Amalia Karimasari, Novita Priandini

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Zoonos is as an infectiontransmitted froma nimals to human sand vice versa currently having increased in the last 20 years. The experts/scientists predict that zoonosis will be a threat to the community in the future since it leads on 70% emerging infectious diseases (EID) and the high mortality of 50%-90%. The zoonosis’ spread from animal to human is caused by contaminated food known as foodborne disease. One World One Health, as the conceptual prevention toward zoonosis, requires the crossed disciplines cooperation to accelerate and streamlinethe handling ofanimal-based disease. E-Vet Smart Rapid System is an integrated innovation in the veterinary expertise application is able to facilitate the prevention, treatment, and educationagainst pandemic diseases and zoonosis. This system is constructed by Decision Support System (DSS) method provides a database of knowledge that is expected to facilitate the identification of disease rapidly, precisely, and accurately as well as to identify the deduction. The testingis conducted through a black box test case and questionnaire (N=30) by validity and reliability approach. Based on the black box test case reveals that E-Vet Rapid System is able to deliver the results in accordance with system design, and questionnaire shows that this system is valid (r > 0.361) and has a reliability (α > 0.3610).

Keywords: diagnosis, disease, expert systems, livestock, zoonosis

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Authors: Andrea Bellucci, Martina Tofi

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The aim of paper is to analyze business models of bancassurance in Italy for life business. The life insurance business is very developed in the Italian market and banks branches have 80% of the market share. Given its maturity, the life insurance market needs to consolidate its organizational form to allow for the development of non-life business, which nowadays collects few premiums but represents a great opportunity to enlarge the market share of bancassurance using its strength in the distribution channel while the market share of independent agents is decreasing. Starting with the main business model of bancassurance for life business, this paper will analyze the performances of life companies in the Italian market by balance sheet indicators and by main discriminant variables of business models. The study will observe trends from 2013 to 2015 for the Italian market by exploiting a database managed by Associazione Nazionale delle Imprese di Assicurazione (ANIA). The applied approach is based on a bottom-up analysis starting with variables and indicators to define business models’ classification. The statistical classification algorithm proposed by Ward is employed to design business models’ profiles. Results from the analysis will be a representation of the main business models built by their profile related to indicators. In that way, an unsupervised analysis is developed that has the limit of its judgmental dimension based on research opinion, but it is possible to obtain a design of effective business models.

Keywords: bancassurance, business model, non life bancassurance, insurance business value drivers

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Authors: Elias Kodjo Kekesi

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This study seeks to investigate a critical but neglected area in disaster and emergency management in Ghana. It explores aspects of work within one of the safety-critical work environments that expose workers to psychological, social and physical harm. With much attention to crises’ survivors, deceased and their families, this research attempts to answer a key question: ‘What happens to the rescuer’? Emergency response is associated with immense and unprecedented pressure that puts responders’ physical, mental and social well-being at risk. Despite the negative psychological outcomes, scholars argue that being in a traumatic situation may trigger positive outcomes for some people. Thus, the study also focuses on the positive impact of working in a risky crisis environment. Additionally, people’s interpretation of negative experiences or exposure to adverse conditions differ owing to their personal resources which explains why some people may be negatively affected whiles others are positively impacted. To examine these complex nuances, an exploratory sequential mixed method design is adopted. This paper will highlight the findings of study one, which explores the underlying themes emerging from the Ghanaian disaster and emergency response environment regarding psychosocial hazard exposures and the corresponding outcomes.

Keywords: psychosocial hazards, organisational outcomes, qualitative research, Ghana

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Authors: Thinh Huu Nguyen, Shahab Sharfaei, Jindřich Soukup

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In the pursuit of reducing production costs, offshoring has been a major trend throughout global value chains for many decades. However, with the rise of advanced technologies, new opportunities to automate their production are changing the motivation of multinational firms to go offshore. Instead, many firms are working to relocate their offshored activities from developing economies back to their home countries. This phenomenon, known as reshoring, has recently garnered much attention as it becomes clear that automation in advanced countries might have major implications not only on their own economies but also through international trade on the economy of low-income countries, including their labor market outcomes and their comparative advantages. Thus, while using robots to substitute human labor may lower the relative costs of producing at home, it has the potential to decrease employment and demand for exports from developing economies through reshoring. In this paper, we investigate the recent literature to provide a further understanding of the relationships between robotization and the reshoring of production. Moreover, we analyze the impact of robot adoption on international trade in both developed and emerging markets. Finally, we identify the research gaps and provide avenues for future research in international economics. This study is a part of the project funded by the Internal Grant Agency (IGA) of the Faculty of Business Administration, Prague University of Economics and Business.

Keywords: automation, robotization, reshoring, international trade

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Authors: Nidhi Bansal, Upasna Agarwal

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Innovation is a key driver for economic growth and well-being of developed as well as emerging economies like India. Very few studies examined the relationship between IWB and work-family enrichment. Therefore, the present study examines the relationship between work-family enrichment (WFE) and innovative workplace behavior (IWB) and whether it is mediated by positive emotions. Social exchange theory and broaden and build theory explain the proposed relationships. Data were collected from 250 full time dual working parents in different Indian organizations through a survey questionnaire. Snowball technique was used for approaching respondents. Mediation analysis was assessed through PROCESS macro (Hayes, 2012) in SPSS. With correlational analysis, it was explored that all three variables were significantly and positively related. Analysis suggests that work-family enrichment is significantly related to innovative workplace behavior and this relationship is partially mediated by positive emotions. A cross-sectional design, use of self-reported questions and data collected only from dual working parents are few limitations of the study. This is one of the few studies to examine the innovative workplace behavior in response to work-family enrichment and first attempt to examine the mediation effect of emotions between these two variables.

Keywords: dual working parents, emotions, innovative workplace behavior, work-family enrichment

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Authors: Zhongyang Sun, Shu Zhang, Manjiang Xie

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Emerging evidence indicates that microRNAs (miRNAs) play important roles in modulating osteoblast function and bone formation. However, the influence of miRNA on osteoblast proliferation and the possible mechanisms underlying remain to be defined. In this study, we aimed to investigate whether miR-103 regulates osteoblast proliferation under simulated microgravity condition through regulating Cav1.2, the primary subunit of L-type voltage sensitive calcium channels (LTCCs). We first investigated the effect of simulated microgravity on osteoblast proliferation and the outcomes clearly demonstrated that the mechanical unloading inhibits MC3T3-E1 osteoblast-like cells proliferation. Using quantitative Real-Time PCR (qRT-PCR), we provided data showing that miR-103 was up-regulated in response to simulated microgravity. In addition, we observed that up-regulation of miR-103 inhibited and down-regulation of miR-103 promoted osteoblast proliferation under simulated microgravity condition. Furthermore, knocking-down or over-expressing miR-103, respectively, up- or down-regulated the level of Cav1.2 expression and LTCCs currents, suggesting that miR-103 acts as an endogenous attenuator of Cav1.2 in osteoblasts under the condition of simulated microgravity. More importantly, we showed that the effect of miR-103 on osteoblast proliferation was diminished in simulated microgravity, when co-transfecting miR-103 mimic or inhibitor with Cav1.2 siRNA. Taken together, our data suggest that miR-103 inhibits osteoblast proliferation mainly through suppression of Cav1.2 expression under simulated microgravity condition. This work may provide a novel mechanism of microgravity-induced detrimental effects on osteoblast, identifying miR-103 as a novel possible therapeutic target in bone remodeling disorders in this mechanical unloading.

Keywords: microRNA, osteoblasts, cell proliferation, Cav1.2, simulated microgravity

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Authors: Satyam Raikwar, Thomas Herlitzius, Jens Fehrmann

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In recent years, the autonomous navigation of orchard and field robots is an emerging technology of the mobile robotics in agriculture. One of the core aspects of autonomous navigation builds upon path planning, which is still a crucial issue. Generally, for simple representation, the path planning for a mobile robot is performed in a two-dimensional space, which creates a path between the start and goal point. This paper presents the automatic path planning approach for robots used in orchards and vineyards using occupancy grid maps with field consideration. The orchards and vineyards are usually structured environment and their topology is assumed to be constant over time; therefore, in this approach, an RGB image of a field is used as a working environment. These images undergone different image processing operations and then discretized into two-dimensional grid matrices. The individual grid or cell of these grid matrices represents the occupancy of the space, whether it is free or occupied. The grid matrix represents the robot workspace for motion and path planning. After the grid matrix is described, a probabilistic roadmap (PRM) path algorithm is used to create the obstacle-free path over these occupancy grids. The path created by this method was successfully verified in the test area. Furthermore, this approach is used in the navigation of the orchard robot.

Keywords: orchard robots, automatic path planning, occupancy grid, probabilistic roadmap

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Authors: Akinbo Razak Yinka, Adesanya Kehinde Kazeem, Oladokun Oluwagbenga Peter

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Time series data are sequences of observations collected over a period of time. Such data can be used to predict health outcomes, such as disease progression, mortality, hospitalization, etc. The Statistical approach is based on mathematical models that capture the patterns and trends of the data, such as autocorrelation, seasonality, and noise, while Neural methods are based on artificial neural networks, which are computational models that mimic the structure and function of biological neurons. This paper compared both parametric and non-parametric time series models of patients treated for malaria in Maternal and Child Health Centres in Lagos State, Nigeria. The forecast methods considered linear regression, Integrated Moving Average, ARIMA and SARIMA Modeling for the parametric approach, while Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) Network were used for the non-parametric model. The performance of each method is evaluated using the Mean Absolute Error (MAE), R-squared (R2) and Root Mean Square Error (RMSE) as criteria to determine the accuracy of each model. The study revealed that the best performance in terms of error was found in MLP, followed by the LSTM and ARIMA models. In addition, the Bootstrap Aggregating technique was used to make robust forecasts when there are uncertainties in the data.

Keywords: ARIMA, bootstrap aggregation, MLP, LSTM, SARIMA, time-series analysis

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Authors: Nur Mohammad Ali, Md. Shafiqul Alam, Jayanta Bhusan Deb, Nowrin Sharmin

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The past ten years have seen significant expansion in the aviation sector, which during the previous five years has steadily pushed emerging countries closer to economic independence. It is crucial to accurately forecast the potential demand for air travel to make long-term financial plans. To forecast market demand for low-cost passenger carriers, this study suggests working with low-cost airlines, airports, consultancies, and governmental institutions' strategic planning divisions. The study aims to develop an artificial intelligence-based methods, notably fuzzy inference systems (FIS), to determine the most accurate forecasting technique for domestic low-cost carrier demand in Bangladesh. To give end users real-world applications, the study includes nine variables, two sub-FIS, and one final Mamdani Fuzzy Inference System utilizing a graphical user interface (GUI) made with the app designer tool. The evaluation criteria used in this inquiry included mean square error (MSE), accuracy, precision, sensitivity, and specificity. The effectiveness of the developed air passenger demand prediction FIS is assessed using 240 data sets, and the accuracy, precision, sensitivity, specificity, and MSE values are 90.83%, 91.09%, 90.77%, and 2.09%, respectively.

Keywords: aviation industry, fuzzy inference system, membership function, graphical user interference

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Authors: Adel A. Ghobbar, Ahmad Bakkar

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Although special care is given to maintenance, aircraft systems fail, and these failures cause delays and cancellations. The occurrence of Delays and Cancellations affects operators and manufacturers negatively. To reduce technical delays and cancellations, one should be able to determine the important systems causing them. The goal of this research is to find a method to define the most expensive delays and cancellations systems for Airline operators. A predictive model was introduced to forecast the failure and their impact after carrying out research that identifies relevant information to tackle the problems faced while answering the questions of this paper. Data were obtained from the manufacturers’ services reliability team database. Subsequently, delays and cancellations evaluation methods were identified. No cost estimation methods were used due to their complexity. The model was developed, and it takes into account the frequency of delays and cancellations and uses weighting factors to give an indication of the severity of their duration. The weighting factors are based on customer experience. The data Analysis approach has shown that delays and cancellations events are not seasonal and do not follow any specific trends. The use of weighting factor does have an influence on the shortlist over short periods (Monthly) but not the analyzed period of three years. Landing gear and the navigation system are among the top 3 factors causing delays and cancellations for all three aircraft types. The results did confirm that the cooperation between certain operators and manufacture reduce the impact of delays and cancellations.

Keywords: reliability, availability, delays & cancellations, aircraft maintenance

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Authors: Jana Gebhartova, Tomas Duda, Ivan Benes

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World is characterized by constantly emerging new links, increasing complexity and speed of processes in the society. The globalized world needs (except political and financial mechanisms and institutions) functional supply chains. Transport and supply chains can be interrupted in case of natural disasters, conflicts and civil disorders, sudden demand shocks, export/import restrictions, terrorism. Long-term interruption of crucial services for human existence can results in breakdown of the whole society. If global supply chains can be interrupted, the ability to survive a crisis situation depends on local self-sufficiency, it means ensuring water, food and energy. In the world of 21st century, new way of thinking (based on the concept of resilience) is needed. Planning for self-sufficiency and resilience must be part of the agenda of local governments. The paper presents first results of research project VF20112015518 “Security of population – crisis management” that deals with issue of critical infrastructure, ensuring regional self-sufficiency in crisis situations and issues related to population protection and water, energy and food security. The project is being solved within Security Research of Ministry of the Interior of the Czech Republic in 2011-2015.

Keywords: crisis management, resilience, indicators of self-sufficiency, continuity of supplies

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Authors: Belayihun Missaw

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Cotton cationization is an emerging area that solves the environmental problems associated with the reactive dyeing of cotton. In this study, keratin hydrolysate cationizing agent from chicken feather was extracted and optimized to eliminate the usage of salt during dyeing. Cationization of cotton using the extracted keratin hydrolysate and dyeing of the cationized cotton without salt was made. The effect of extraction parametric conditions like concentration of caustic soda, temperature and time were studied on the yield of protein from chicken feather and colour strength (K/S) values, and these process conditions were optimized. The optimum extraction conditions were. 25g/l caustic soda, at 500C temperature and 105 minutes with average yield = 91.2% and 4.32 colour strength value. The effect of salt addition, pH and concentration of cationizing agent on yield colour strength was also studied and optimized. It was observed that slightly acidic condition with 4% (% owf) concentration of cationizing agent gives a better dyeability as compared to normal cotton reactive dyeing. The physical properties of cationized-dyed fabric were assessed, and the result reveals that the cationization has a similar effect as normal dyeing of cotton. The cationization of cotton with keratin extract was found to be successful and economically viable.

Keywords: cotton materials, cationization, reactive dye, keratin hydrolysate

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Authors: T. Salami, O. S. Idowu, N. J. Bello

Abstract:

Climatic variability is an essential issue when dealing with the issue of climate change. Variability of some climate parameter helps to determine how variable the climatic condition of a region will behave. The most important of these climatic variables which help to determine the climatic condition in an area are both the Temperature and Precipitation. This research deals with Longterm climatic variability in Nigeria. Variables examined in this analysis include near-surface temperature, near surface minimum temperature, maximum temperature, relative humidity, vapour pressure, precipitation, wet-day frequency and cloud cover using data ranging between 1901-2010. Analyses were carried out and the following methods were used: - Regression and EOF analysis. Results show that the annual average, minimum and maximum near-surface temperature all gradually increases from 1901 to 2010. And they are in the same case in a wet season and dry season. Minimum near-surface temperature, with its linear trends are significant for annual, wet season and dry season means. However, the diurnal temperature range decreases in the recent 100 years imply that the minimum near-surface temperature has increased more than the maximum. Both precipitation and wet day frequency decline from the analysis, demonstrating that Nigeria has become dryer than before by the way of rainfall. Temperature and precipitation variability has become very high during these periods especially in the Northern areas. Areas which had excessive rainfall were confronted with flooding and other related issues while area that had less precipitation were all confronted with drought. More practical issues will be presented.

Keywords: climate, variability, flooding, excessive rainfall

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Authors: Shahla Ali

Abstract:

In the context of promoting green infrastructure development, new opportunities are emerging to re-examine sustainable development practices. This paper presents an initial exploration of the development of community-investor dispute prevention and facilitation mechanisms in the context of the Belt and Road Initiative (BRI) spanning Asia, Africa, and Europe. Given the widescale impact of China’s multi-jurisdictional development initiative, learning how to coordinate with local communities is vital to realizing inclusive and sustainable growth. In the 20 years since the development of the first multilateral community-investor dispute resolution mechanism developed by the International Finance Centre/World Bank, much has been learned about public facilitation, community engagement, and dispute prevention during the early stages of major infrastructure development programs. This paper will explore initial findings as they relate to initiatives underway along the BRI within the Asian Infrastructure Investment Bank and the Asian Development Bank. Given the borderless nature of sustainability concerns, insights from diverse regions are critical to deepening insights into best practices. Drawing on a case-based methodology, this paper will explore the achievements, challenges, and lessons learned in community-investor dispute prevention and resolution for major infrastructure projects in the greater China region.

Keywords: law and development, dispute prevention, sustainable development, mitigation

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Authors: E. Kharaishvili, G. Erkomaishvili, M. Chavleishvili

Abstract:

The paper evaluates the natural and resource potential of agriculture, a traditional sector for Georgia. It is concluded that despite favorable conditions the rate of development of the sector is lower compared to other sectors of the economy, self-sufficiency rate for locally produced agricultural products is low; on average, import of food is 4 times higher compared to export, and the country faces considerable challenges in this regard. Tendencies of self-sufficiency rates are studied, and it is concluded that the indicators of export and import of agro-food products increase in accordance with the tendency of increasing production in agricultural sector. The paper substantiates stimulating impact of international trade on agricultural development. Two alternative strategies are assessed in this respect: 1) export stimulation, and 2) import replacement strategies. It is concluded that significant tendencies are observed in agro-food sector of Georgia; in particular, productivity is low; import volume significantly exceeds the export volume. It is considered that the growth of export will allow Georgia to overcome limited opportunities of local market and encourage increasing competitiveness. Various tools of economic policy are suggested for achieving these goals; in particular to subsidize export, optimize trade barriers, manage exchange rates effectively, offer special financial services, provide insurance for export, etc.

Keywords: agro-food sector, trend of production, export stimulation, economic policy

Procedia PDF Downloads 206

Authors: Lin Yanyan

Abstract:

This research set out to understand early childhood practitioners’ perceptions of continuous professional development (CPD) opportunities and its relationship to career progression and leadership roles in Singapore. The small-scale qualitative inductive study was conducted in two phases. Phase one used close-ended questionnaires with a total of 24 early years practitioner participants, while phase two included a total of 5 participants who were invited to participate in the second part of the data collection. Semi-structured interviews were used at phase two to elicit deeper responses from parents and teachers. Findings from the study were then thematically coded and analysed. The findings from both questionnaires and interviews showed that early years practitioners perceived CPD to be important to their professional growth, but there was no conclusive link that CPD necessarily led to the progression of leadership roles in the early years. Participants experience of CPD was strongly determined by their employer- the preschool operator, being government-funded or a private entity, which resulted in key differences emerging between their responses. Participants also experienced road blocks in their pursuit of CPD, in the form of staff shortage, budget constraints and lack of autonomy as their employers imposed specific CPD courses on them to suit the organisational needs, rather than their personal or professional needs.

Keywords: continuous professional development (CPD), early years practitioners (EYP), career progression, leadership

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Authors: Rajvir Kaur, Jeewani Anupama Ginige

Abstract:

With recent trends in Big Data and advancements in Information and Communication Technologies, the healthcare industry is at the stage of its transition from clinician oriented to technology oriented. Many people around the world die of cancer because the diagnosis of disease was not done at an early stage. Nowadays, the computational methods in the form of Machine Learning (ML) are used to develop automated decision support systems that can diagnose cancer with high confidence in a timely manner. This paper aims to carry out the comparative evaluation of a selected set of ML classifiers on two existing datasets: breast cancer and cervical cancer. The ML classifiers compared in this study are Decision Tree (DT), Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Logistic Regression, Ensemble (Bagged Tree) and Artificial Neural Networks (ANN). The evaluation is carried out based on standard evaluation metrics Precision (P), Recall (R), F1-score and Accuracy. The experimental results based on the evaluation metrics show that ANN showed the highest-level accuracy (99.4%) when tested with breast cancer dataset. On the other hand, when these ML classifiers are tested with the cervical cancer dataset, Ensemble (Bagged Tree) technique gave better accuracy (93.1%) in comparison to other classifiers.

Keywords: artificial neural networks, breast cancer, classifiers, cervical cancer, f-score, machine learning, precision, recall

Procedia PDF Downloads 277