Search results for: healthcare data security

Authors: Shalaka Kadam, Dinesh Apte, Sagar Mainkar

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The life cycle of products is becoming shorter and shorter due to increased competition in market, shorter product development time and increased product diversity. Short life cycles are normal in retail industry, style business, entertainment media, and telecom and semiconductor industry. The subject of accurate forecasting for demand of short lifecycle products is of special enthusiasm for many researchers and organizations. Due to short life cycle of products the amount of historical data that is available for forecasting is very minimal or even absent when new or modified products are launched in market. The companies dealing with such products want to increase the accuracy in demand forecasting so that they can utilize the full potential of the market at the same time do not oversupply. This provides the challenge to develop a forecasting model that can forecast accurately while handling large variations in data and consider the complex relationships between various parameters of data. Many statistical models have been proposed in literature for forecasting time series data. Traditional time series forecasting models do not work well for short life cycles due to lack of historical data. Also artificial neural networks (ANN) models are very time consuming to perform forecasting. We have studied the existing models that are used for forecasting and their limitations. This work proposes an effective and powerful forecasting approach for short life cycle time series forecasting. We have proposed an approach which takes into consideration different scenarios related to data availability for short lifecycle products. We then suggest a methodology which combines statistical analysis with structured judgement. Also the defined approach can be applied across domains. We then describe the method of creating a profile from analogous products. This profile can then be used for forecasting products with historical data of analogous products. We have designed an application which combines data, analytics and domain knowledge using point-and-click technology. The forecasting results generated are compared using MAPE, MSE and RMSE error scores. Conclusion: Based on the results it is observed that no one approach is sufficient for short life-cycle forecasting and we need to combine two or more approaches for achieving the desired accuracy.

Keywords: forecast, short life cycle product, structured judgement, time series

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Authors: Victor Bodell, Lukas Ekstrom, Somayeh Aghanavesi

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Fuel consumption (FC) is one of the key factors in determining expenses of operating a heavy-duty vehicle. A customer may therefore request an estimate of the FC of a desired vehicle. The modular design of heavy-duty vehicles allows their construction by specifying the building blocks, such as gear box, engine and chassis type. If the combination of building blocks is unprecedented, it is unfeasible to measure the FC, since this would first r equire the construction of the vehicle. This paper proposes a machine learning approach to predict FC. This study uses around 40,000 vehicles specific and o perational e nvironmental c onditions i nformation, such as road slopes and driver profiles. A ll v ehicles h ave d iesel engines and a mileage of more than 20,000 km. The data is used to investigate the accuracy of machine learning algorithms Linear regression (LR), K-nearest neighbor (KNN) and Artificial n eural n etworks (ANN) in predicting fuel consumption for heavy-duty vehicles. Performance of the algorithms is evaluated by reporting the prediction error on both simulated data and operational measurements. The performance of the algorithms is compared using nested cross-validation and statistical hypothesis testing. The statistical evaluation procedure finds that ANNs have the lowest prediction error compared to LR and KNN in estimating fuel consumption on both simulated and operational data. The models have a mean relative prediction error of 0.3% on simulated data, and 4.2% on operational data.

Keywords: artificial neural networks, fuel consumption, friedman test, machine learning, statistical hypothesis testing

Procedia PDF Downloads 175

Authors: Sinan Alsaadi, Mustafa Merdan

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Communication and access systems can be made with many methods in today’s world. These systems are such standards as Wifi, Wimax, Bluetooth, GPS and GPRS. Devices which use these standards also use system resources excessively in direct proportion to their transmission speed. However, large-scale data communication is not always needed. In such cases, a technology which will use system resources as little as possible and support smart network topologies has been needed in order to enable the transmissions of such small packet data and provide the control for this kind of devices. IEEE issued 802.15.4 standard upon this necessity and enabled the production of Zigbee protocol which takes these standards as its basis and devices which support this protocol. In our project, this communication protocol was preferred. The aim of this study is to provide the immediate data transmission of our robot from the field within the scope of the project. In addition, making the communication with the robot through Zigbee Protocol has also been aimed. While sitting on the computer, obtaining the desired data from the region where the robot is located has been taken as the basis. Arduino Uno R3 microcontroller which provides the control mechanism, 1298 shield as the motor driver.

Keywords: ZigBee, wireless network, remote monitoring, smart home, agricultural industry

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Authors: Massimiliano Condotta, Paolo Ruggeri, Chiara Scanagatta, Giovanni Borga

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Architects and urban planners, when designing and renewing cities, have to face a complex set of problems, including the issues of noise and air pollution which are considered as hot topics (i.e., the Clean Air Act of London and the Soundscape definition). It is usually taken for granted that these problems go by together because the noise pollution present in cities is often linked to traffic and industries, and these produce air pollutants as well. Traffic congestion can create both noise pollution and air pollution, because NO₂ is mostly created from the oxidation of NO, and these two are notoriously produced by processes of combustion at high temperatures (i.e., car engines or thermal power stations). We can see the same process for industrial plants as well. What have to be investigated – and is the topic of this paper – is whether or not there really is a correlation between noise pollution and air pollution (taking into account NO₂) in urban areas. To evaluate if there is a correlation, some low-cost methodologies will be used. For noise measurements, the OpeNoise App will be installed on an Android phone. The smartphone will be positioned inside a waterproof box, to stay outdoor, with an external battery to allow it to collect data continuously. The box will have a small hole to install an external microphone, connected to the smartphone, which will be calibrated to collect the most accurate data. For air, pollution measurements will be used the AirMonitor device, an Arduino board to which the sensors, and all the other components, are plugged. After assembling the sensors, they will be coupled (one noise and one air sensor) and placed in different critical locations in the area of Mestre (Venice) to map the existing situation. The sensors will collect data for a fixed period of time to have an input for both week and weekend days, in this way it will be possible to see the changes of the situation during the week. The novelty is that data will be compared to check if there is a correlation between the two pollutants using graphs that should show the percentage of pollution instead of the values obtained with the sensors. To do so, the data will be converted to fit on a scale that goes up to 100% and will be shown thru a mapping of the measurement using GIS methods. Another relevant aspect is that this comparison can help to choose which are the right mitigation solutions to be applied in the area of the analysis because it will make it possible to solve both the noise and the air pollution problem making only one intervention. The mitigation solutions must consider not only the health aspect but also how to create a more livable space for citizens. The paper will describe in detail the methodology and the technical solution adopted for the realization of the sensors, the data collection, noise and pollution mapping and analysis.

Keywords: air quality, data analysis, data collection, NO₂, noise mapping, noise pollution, particulate matter

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Authors: Shyh Ming Chern

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Cubic equations of state (EoS), popular due to their simple mathematical form, ease of use, semi-theoretical nature and, reasonable accuracy are normally fitted to vapor-liquid equilibrium P-v-T data. As a result, They often show poor accuracy in the region near and above the critical point. In this study, the performance of the renowned Peng-Robinson (PR) and Patel-Teja (PT) EoS’s around the critical area has been examined against the P-v-T data of water. Both of them display large deviations at critical point. For instance, PR-EoS exhibits discrepancies as high as 47% for the specific volume, 28% for the enthalpy departure and 43% for the entropy departure at critical point. It is shown that incorporating P-v-T data of the supercritical region into the retuning of a cubic EoS can improve its performance above the critical point dramatically. Adopting a retuned acentric factor of 0.5491 instead of its genuine value of 0.344 for water in PR-EoS and a new F of 0.8854 instead of its original value of 0.6898 for water in PT-EoS reduces the discrepancies to about one third or less.

Keywords: equation of state, EoS, supercritical water, SCW

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Authors: Ching Louis Liu, Edmund Kazmierczak, Tim Miller

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Safety analysis for multi-agent systems is complicated by the, potentially nonlinear, interactions between agents. This paper proposes a method for analyzing the safety of multi-agent systems by explicitly focusing on interactions and the accident data of systems that are similar in structure and function to the system being analyzed. The method creates a Bayesian network using the accident data from similar systems. A feature of our method is that the events in accident data are labeled with HAZOP guide words. Our method uses an Ontology to abstract away from the details of a multi-agent implementation. Using the ontology, our methods then constructs an “Interaction Map,” a graphical representation of the patterns of interactions between agents and other artifacts. Interaction maps combined with statistical data from accidents and the HAZOP classifications of events can be converted into a Bayesian Network. Bayesian networks allow designers to explore “what it” scenarios and make design trade-offs that maintain safety. We show how to use the Bayesian networks, and the interaction maps to improve multi-agent system designs.

Keywords: multi-agent system, safety analysis, safety model, integration map

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Authors: Jack R. McKenzie, Peter A. Appleby, Thomas House, Neil Walton

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Cold-start is a notoriously difficult problem which can occur in recommendation systems, and arises when there is insufficient information to draw inferences for users or items. To address this challenge, a contextual bandit algorithm – the Fast Approximate Bayesian Contextual Cold Start Learning algorithm (FAB-COST) – is proposed, which is designed to provide improved accuracy compared to the traditionally used Laplace approximation in the logistic contextual bandit, while controlling both algorithmic complexity and computational cost. To this end, FAB-COST uses a combination of two moment projection variational methods: Expectation Propagation (EP), which performs well at the cold start, but becomes slow as the amount of data increases; and Assumed Density Filtering (ADF), which has slower growth of computational cost with data size but requires more data to obtain an acceptable level of accuracy. By switching from EP to ADF when the dataset becomes large, it is able to exploit their complementary strengths. The empirical justification for FAB-COST is presented, and systematically compared to other approaches on simulated data. In a benchmark against the Laplace approximation on real data consisting of over 670, 000 impressions from autotrader.co.uk, FAB-COST demonstrates at one point increase of over 16% in user clicks. On the basis of these results, it is argued that FAB-COST is likely to be an attractive approach to cold-start recommendation systems in a variety of contexts.

Keywords: cold-start learning, expectation propagation, multi-armed bandits, Thompson Sampling, variational inference

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Authors: Merle Talvik, Taimi Tulva, Kristi Puusepp, Ulle Ernits

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Background data. This is a study in the field of health humanities. The 1930s were years of rapid cultural and economic development in Europe and in Estonia. The urban way of life the glamorous lifestyle gained popularity, although the society of Estonia in the 1930s had traditionally been agrarian. People's free time increased, which needed to be filled with activities either at home or outside the home. Therefore, the number of popular magazines aimed at housewives increased. More than 200 magazines and bulletins were published in the Republic of Estonia before the Second World War (in 1934, the population of Estonia was 1,126,000). In the 1930s, the Republic of Estonia faced several challenges in healthcare. Infectious diseases, alcoholism, prostitution and child mortality had to be dealt with. Healers without medical education operated in the villages. For the average person, medical care was quite expensive, and despite efforts, by 1940, only 20% of the population was covered by health insurance. Advice published in popular family magazines provided help in solving, understanding and preventing health problems. Aim. The aim of the study is to analyze the health counseling through magazines during the Republic of Estonia (1930-1940) in historical and cultural context. Method. In total, 420 magazine issues were processed. An extensive textual analysis, as well as an analysis of photographs and illustrations from the aspect of health advice was carried out to achieve the research objective. Results. Health counseling was written by well-known doctors of the time, leaders of the abstinence movement and others. There was advice in various areas: prevention of infectious and non-infectious diseases and their treatment with simple methods, first aid, combating sexually transmitted diseases, women's and children's health, mental health, folk medicine techniques, abstinence, healthy eating, skin care, hygiene, introducing pharmacy products. Advice was offered in both written and visual form. Photos and illustrations helped to empower the health advice. Folk heritage and health knowledge of the time were relied upon, and a scientific point of view was popularized. Aspirations towards a European lifestyle were reflected in articles and illustrations. Contribution. The article has an ethnological attitude, and its impact comes down to understanding the history of health care in its socio-cultural context. The health counseling topics of the 1930s are also applicable in today's health education and research. Health counseling builds on the legacy of the past, and it helps to understand that the past is in the future and the main principles of health counseling arise from our history and background.

Keywords: estonian republic, health counseling, lifestyle, magazines, media

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Authors: Nuo Duan, Yi Pik Cheng

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This paper presents the data of a series of two-dimensional Discrete Element Method (DEM) simulations of a large-diameter rigid monopile subjected to cyclic loading under a high gravitational force. At present, monopile foundations are widely used to support the tall and heavy wind turbines, which are also subjected to significant from wind and wave actions. A safe design must address issues such as rotations and changes in soil stiffness subject to these loadings conditions. Design guidance on the issue is limited, so are the availability of laboratory and field test data. The interpretation of these results in sand, such as the relation between loading and displacement, relies mainly on empirical correlations to pile properties. Regarding numerical models, most data from Finite Element Method (FEM) can be found. They are not comprehensive, and most of the FEM results are sensitive to input parameters. The micro scale behaviour could change the mechanism of the soil-structure interaction. A DEM model was used in this paper to study the cyclic lateral loads behaviour. A non-dimensional framework is presented and applied to interpret the simulation results. The DEM data compares well with various set of published experimental centrifuge model test data in terms of lateral deflection. The accumulated permanent pile lateral displacements induced by the cyclic lateral loads were found to be dependent on the characteristics of the applied cyclic load, such as the extent of the loading magnitudes and directions.

Keywords: cyclic loading, DEM, numerical modelling, sands

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Authors: Sumay Bhojwani, Ashutosh Chandra, Mamita Devaburman, Akriti Bhogal

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This article uses the Material flow analysis for estimating e-wastes in the Delhi/NCR region. The Material flow analysis is based on sales data obtained from various sources. Much of the data available for the sales is unreliable because of the existence of a huge informal sector. The informal sector in India accounts for more than 90%. Therefore, the scope of this study is only limited to the formal one. Also, for projection of the sales data till 2030, we have used regression (linear) to avoid complexity. The actual sales in the years following 2015 may vary non-linearly but we have assumed a basic linear relation. The purpose of this study was to know an approximate quantity of desktop e-wastes that we will have by the year 2030 so that we start preparing ourselves for the ineluctable investment in the treatment of these ever-rising e-wastes. The results of this study can be used to install a treatment plant for e-wastes in Delhi.

Keywords: e-wastes, Delhi, desktops, estimation

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Authors: Beatrice Arthur

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The rapid development of artificial intelligence (AI) has transformed various industries, enabling faster and more accurate decision-making processes. However, with these advancements come increased risks, including data privacy issues, systemic biases, and challenges related to transparency and accountability. As AI technologies become more integrated into business processes, there is a growing need for comprehensive auditing and assurance frameworks to manage these risks and ensure ethical use. This paper provides a literature review on AI auditing and assurance programs, highlighting the importance of adapting traditional audit methodologies to the complexities of AI-driven systems. Objective: The objective of this review is to explore current AI audit practices and their role in mitigating risks, ensuring accountability, and fostering trust in AI systems. The study aims to provide a structured framework for developing audit programs tailored to AI technologies while also investigating how AI impacts governance, risk management, and regulatory compliance in various sectors. Methodology: This research synthesizes findings from academic publications and industry reports from 2014 to 2024, focusing on the intersection of AI technologies and IT assurance practices. The study employs a qualitative review of existing audit methodologies and frameworks, particularly the COBIT 2019 framework, to understand how audit processes can be aligned with AI governance and compliance standards. The review also considers real-time auditing as an emerging necessity for influencing AI system design during early development stages. Outcomes: Preliminary findings indicate that while AI auditing is still in its infancy, it is rapidly gaining traction as both a risk management strategy and a potential driver of business innovation. Auditors are increasingly being called upon to develop controls that address the ethical and operational risks posed by AI systems. The study highlights the need for continuous monitoring and adaptable audit techniques to handle the dynamic nature of AI technologies. Future Directions: Future research will explore the development of AI-specific audit tools and real-time auditing capabilities that can keep pace with evolving technologies. There is also a need for cross-industry collaboration to establish universal standards for AI auditing, particularly in high-risk sectors like healthcare and finance. Further work will involve engaging with industry practitioners and policymakers to refine the proposed governance and audit frameworks. Funding/Support Acknowledgements: This research is supported by the Information Systems Assurance Management Program at Concordia University of Edmonton.

Keywords: AI auditing, assurance, risk management, governance, COBIT 2019, transparency, accountability, machine learning, compliance

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Authors: Varun Singh, Mainak Bandyopadhyay, Maharana Pratap Singh

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The shortest path (SP) problem concerns with finding the shortest path from a specific origin to a specified destination in a given network while minimizing the total cost associated with the path. This problem has widespread applications. Important applications of the SP problem include vehicle routing in transportation systems particularly in the field of in-vehicle Route Guidance System (RGS) and traffic assignment problem (in transportation planning). Well known applications of evolutionary methods like Genetic Algorithms (GA), Ant Colony Optimization, Particle Swarm Optimization (PSO) have come up to solve complex optimization problems to overcome the shortcomings of existing shortest path analysis methods. It has been reported by various researchers that PSO performs better than other evolutionary optimization algorithms in terms of success rate and solution quality. Further Geographic Information Systems (GIS) have emerged as key information systems for geospatial data analysis and visualization. This research paper is focused towards the application of PSO for solving the shortest path problem between multiple points of interest (POI) based on spatial data of Allahabad City and traffic speed data collected using GPS. Geovisualization of results of analysis is carried out in GIS.

Keywords: particle swarm optimization, GIS, traffic data, outliers

Procedia PDF Downloads 477

Authors: U. S. N. Raju, Kothuri Sai Kiran, Meena G. Kamal, Vinay Nikhil Pabba, Suresh Kanaparthi

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There is huge amount of lecture video data available for public use, and many more lecture videos are being created and uploaded every day. Searching for videos on required topics from this huge database is a challenging task. Therefore, an efficient method for video retrieval is needed. An approach for automated video indexing and video search in large lecture video archives is presented. As the amount of video lecture data is huge, it is very inefficient to do the processing in a centralized computation framework. Hence, Hadoop Framework for distributed computing for Big Video Data is used. First, step in the process is automatic video segmentation and key-frame detection to offer a visual guideline for the video content navigation. In the next step, we extract textual metadata by applying video Optical Character Recognition (OCR) technology on key-frames. The OCR and detected slide text line types are adopted for keyword extraction, by which both video- and segment-level keywords are extracted for content-based video browsing and search. The performance of the indexing process can be improved for a large database by using distributed computing on Hadoop framework.

Keywords: video lectures, big video data, video retrieval, hadoop

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Authors: K. Y. Chen, H. Chua, C. W. Kan

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Environmental investment is an important issue in many countries. In this study, we will first review the environmental issues related to India and their effect on the economical development. Secondly, economic data would be collected from government yearly statistics. The statistics would also include the environmental investment information of India. Finally, we would co-relate the data in order to find out the relationship between environmental investment and sustainable development in India. Therefore, in the paper, we aim to analyse the effect of an environmental investment on the sustainable development in India. Based on the economic data collected, India is in development status with fast population and GDP growth speed. India is facing the environment problems due to its high-speed development. However, the environment investment could give a positive impact on the sustainable development in India. The environmental investment is keeping in the same growth rate with GDP. Acknowledgment: Authors would like to thank the financial support from the Hong Kong Polytechnic University for this work.

Keywords: India, environmental investment, sustainable development, analysis

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Authors: R. Periyasamy, Deepak Joshi, Sneh Anand

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Foot posture assessment is important to evaluate foot type, causing gait and postural defects in all age groups. Although different methods are used for classification of foot arch type in clinical/research examination, there is no clear approach for selecting the most appropriate measurement system. Therefore, the aim of this study was to develop a system for evaluation of foot type as clinical decision-making aids for diagnosis of flat and normal arch based on the Arch Index (AI) and foot pressure distribution parameter - Power Ratio (PR) data. The accuracy of the system was evaluated for 27 subjects with age ranging from 24 to 65 years. Foot area measurements (hind foot, mid foot, and forefoot) were acquired simultaneously from foot pressure intensity image using portable PedoPowerGraph system and analysis of the image in frequency domain to obtain foot pressure distribution parameter - PR data. From our results, we obtain 100% classification accuracy of normal and flat foot by using the linear discriminant analysis method. We observe there is no misclassification of foot types because of incorporating foot pressure distribution data instead of only arch index (AI). We found that the mid-foot pressure distribution ratio data and arch index (AI) value are well correlated to foot arch type based on visual analysis. Therefore, this paper suggests that the proposed system is accurate and easy to determine foot arch type from arch index (AI), as well as incorporating mid-foot pressure distribution ratio data instead of physical area of contact. Hence, such computational tool based system can help the clinicians for assessment of foot structure and cross-check their diagnosis of flat foot from mid-foot pressure distribution.

Keywords: arch index, computational tool, static foot pressure intensity image, foot pressure distribution, linear discriminant analysis

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Authors: Vareesha Masood, Ammara Gul, Nabeeha Areej, Muhammad Asif Masood, Hamna Imran

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The universal use of PDF files has prompted hackers to use them for malicious intent by hiding malicious codes in their victim’s PDF machines. Machine learning has proven to be the most efficient in identifying benign files and detecting files with PDF malware. This paper has proposed an approach using a decision tree classifier with parameters. A modern, inclusive dataset CIC-Evasive-PDFMal2022, produced by Lockheed Martin’s Cyber Security wing is used. It is one of the most reliable datasets to use in this field. We designed a PDF malware detection system that achieved 99.2%. Comparing the suggested model to other cutting-edge models in the same study field, it has a great performance in detecting PDF malware. Accordingly, we provide the fastest, most reliable, and most efficient PDF Malware detection approach in this paper.

Keywords: PDF, PDF malware, decision tree classifier, random forest classifier

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Authors: Bahloul Amel

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The present study debates students’ perceptions of the use of technology in learning English as a Foreign Language. Its aim is to explore and understand students’ preparation and presentation of Posters, PowerPoint and Animated Videos by drawing attention to visual and oral elements. The data is collected through observations and semi-structured interviews and analyzed through phenomenological data analysis steps. The themes emerged from the data, visual learning satisfaction in using information and communication technology, providing structure to oral presentation, learning from peers’ presentations, draw attention to using Posters, PowerPoint and Animated Videos as each supports visual learning and organization of thoughts in oral presentations.

Keywords: EFL, posters, PowerPoint presentations, Animated Videos, visual learning

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Authors: S. K. Ashiquer Rahman

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the paper concentrations on the importance of hourly compensation, emphasizing the significance of the unemployment rate. There are the two most important factors of a nation these are its unemployment rate and hourly compensation. These are not merely statistics but they have profound effects on individual, families, and the economy. They are inversely related to one another. When we consider the unemployment rate that will probably decline as hourly compensations in manufacturing rise. But when we reduced the unemployment rates and increased job prospects could result from higher compensation. That’s why, the increased hourly compensation in the manufacturing sector that could have a favorable effect on job changing issues. Moreover, the relationship between hourly compensation and unemployment is complex and influenced by broader economic factors. In this paper, we use panel data regression models to evaluate the expected link between hourly compensation and unemployment rate in order to determine the effect of hourly compensation on unemployment rate. We estimate the fixed effects model, evaluate the error components, and determine which model (the FEM or ECM) is better by pooling all 60 observations. We then analysis and review the data by comparing 3 several countries (United States, Canada and the United Kingdom) using panel data regression models. Finally, we provide result, analysis and a summary of the extensive research on how the hourly compensation effects on the unemployment rate. Additionally, this paper offers relevant and useful informational to help the government and academic community use an econometrics and social approach to lessen on the effect of the hourly compensation on Unemployment rate to eliminate the problem.

Keywords: hourly compensation, Unemployment rate, panel data regression models, dummy variables, random effects model, fixed effects model, the linear regression model

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Authors: Fatemeh Noughani, Seyd Mehdi Sadat

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Medical tourism or travel therapy is travelling from one country to another to be under medical treatment, utilizing the health factors of natural sector like mineral water springs and so on. From 1990s medical tourism around the world developed and grew because of different factors like globalization and free trade in the fields of health services, changes in exchange rates in the world economy (which caused the desirability of Asian countries as a medical tourist attraction) in a way that currently there is a close competition in this field among famous countries in medical services to make them find a desirable place in medical tourism market of the world as a complicated and growing industry in a short time. Perhaps tourism is an attractive industry and a good support for the economy of Iran, if we try to merge oil earnings and tourism industry it would be better and more constructive than putting them in front of each other. Moving from oil toward tourism economy especially medical tourism, must be one of the prospects of Iran's government for the oil industry to provide a few percent of the yearly earnings of the country. Among the achievements in medical tourism we can name the prevention of brain drain to other countries and an increase in employment rate for healthcare staff, increase in foreign exchange earnings of the country because of the tourists' staying and followed by increasing the quality of life and cultural transmission as well as empowering the medical human resources.

Keywords: developing countries, health tourism, quality of life, cultural transmission

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Authors: Robertson Freeman

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The prime objective of this research is to examine income inequality and its effects on household livelihoods in Parker Paint. Many researchers failed to address the potential threat of income inequality on diverse household livelihood indicators, including health, food, housing, transport and many others. They examine and generalize the effects of income differentials on household livelihoods by addressing one indicator of livelihood security. This research fills the loopholes of previous research by examining the effects of income inequality and how it affects the livelihoods of households, taking into consideration livelihood indicators including health, food security, and transport. The researcher employed the mixed research method to analyze the distribution of income and solicit opinions of household heads on the effects of their monthly income on their livelihoods. Age and sex structure, household composition, type of employment and educational status influence income inequality. The level of income, Lorenz curve and the Gini coefficient was mutually employed to calculate and determine the level of income inequality. One hundred eighty-two representing 96% of household heads are employed while 8, representing 4%, are unemployed. However, out of a total number of 182 employed, representing 96%, 27 people representing 14%, are employed in the formal private sector, while 110, representing 58%, are employed in the private informal sector. Monthly average income, savings, investments and unexpected circumstances affect the livelihood of households. Infrastructural development and wellbeing should be pursued by reducing expenditure earmarked in other sectors and channeling the funds towards the provision of household needs. One of the potent tools for consolidating household livelihoods is to initiate livelihood empowerment programs. Government and private sector agencies should establish more health insurance schemes, providing mosquito nets, immunization services, public transport, as well as embarking on feeding programs, especially in the remote areas of Parker paint. To climax the research findings, self-employment, entrepreneurship and the general private sector employment is a transparent double-edged sword. If employed in the private sector, there is the likelihood to increase one’s income. However, this also induces the income gap between the rich and poor since many people are exploited by affluence, thereby relegating the poor from the wealth hierarchy. Age and sex structure, as well as type of employment, should not be overlooked since they all play fundamental roles in influencing income inequality. Savings and investments seem to play a positive role in reducing income inequality. However, savings and investment in this research affect livelihoods negatively. It behooves mankind to strive and work hard to the best of ability in earning sufficient income and embracing measures to retain his financial strength. In so doing, people will be able to provide basic household needs, celebrate the reduction in unemployment and dependence and finally ensure sustainable livelihoods.

Keywords: income, inequality, livelihood, pakerpaint

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Authors: Eteri Kharaishvili

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Introduction. The paper analyzes the main indicators of export and import of Georgian agri-food products; identifies positive and negative trends under the pandemic; based on the revealed problemssubstantiates the need formodernization ofin agri-food sector. It is argued that low production and productivity rates of food products negatively impact achieving the optimal export-to-import ratio; therefore, it leads toincreaseddependence on other countries andreduces the level of food security. Research objectives. The objective of the research is to identify the key challenges based on the analysis of export-import indicators of Georgian food products during the pandemic period and develop recommendations on the possibilities of post-pandemic perspectives. Research methods. Various theoretical and methodological research tools are used in the paper; in particular, a desk research is carried out on the research topic; endogenous and exogenous variables affecting export and import are determined through factor analysis; SWOT and PESTEL analysis are used to identify development opportunities; selection and groupingof data, identification of similarities and differences is carried outby using analysis, synthesis, sampling, induction and other methods; a qualitative study is conducted based on a survey of agri-food experts and exporters for clarifying the factors that impede export-import flows. Contributions. The factors that impede the export of Georgian agri-food products in the short run under COVID-19 pandemic are identified. These are: reduced income of farmers, delays in the supply of raw materials and supplies to the agri-food sectorfrom the neighboring industries, as well as in harvesting, processing, marketing, transportation, and other sectors; increased indirect costs, etc. The factors that impede the export in the long run areas follows loss of public confidence in the industry, risk of losing positions in traditional markets, etc. Conclusions are made on the problems in the field of export and import of Georgian agri-food products in terms of the pandemic; development opportunities are evaluated based on the analysis of the agri-food sector potential. Recommendations on the development opportunities for export and import of Georgian agri-food products in the post-pandemic period are proposed.

Keywords: agri-food products, export, and import, pandemic period, hindering factor, development potential

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Authors: Fatemeh Havas Beigi

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The purpose of this study is to identify the objectives of a multicultural curriculum for the primary education period from the perspective of ethnic teachers and education experts and cultural professionals. The research paradigm is interpretive, the research approach is qualitative, the research strategy is content analysis, the sampling method is purposeful and it is a snowball, and the sample of informants in the research for Iranian ethnic teachers and experts until the theoretical saturation was estimated to be 67 people. The data collection tools used were based on semi-structured interviews and individual interviews and focal interviews were used to collect information. The data format was also in audio format and the first period coding and the second coding were used to analyze the data. Based on data analysis 11 Objective: Paying attention to ethnic equality, expanding educational opportunities and justice, peaceful coexistence, anti-ethnic and racial discrimination education, paying attention to human value and dignity, accepting religious diversity, getting to know ethnicities and cultures, promoting teaching-learning, fostering self-confidence, building national unity, and developing cultural commonalities for a multicultural curriculum were identified.

Keywords: objective, multicultural curriculum, connect, elementary education period

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Authors: Benjaporn Yaemjamuang, Sasitorn Jetanont

Abstract:

The three star hotels in Sukhumvit area of Bangkok and the potential to be in the tourism industry joining the ASEAN Community were studied. The findings revealed that the representative samples satisfy the potential of hotel services at a high level in all aspects. The level of service satisfaction by gender is not different. On the other hand, for different ethnic origins, ages, occupations, levels of education, the satisfaction on the services varies in significance level of 0.05. Factors associated with satisfaction in the services of the hotel include a potential location and environment. It was also found that satisfaction with the service aspects are related as follows: services (r = .810), food (r = .807), booking service (r = .768), room condition (r = .762) and security (r =.756) which is aligned with the coefficient .826.

Keywords: three star hotel, ASEAN community, potential in tourism industry, Bangkok

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Authors: Katie Dolaghan, Christina Tran, Kim Hamilton, Amanda Beresford, Vicky Adams, Jamie Toole, John Marley

Abstract:

During the Covid 19 pandemic routine outpatient appointments were not possible face to face. That resulted in many branches of healthcare starting virtual clinics. These clinics have continued following the return to face to face patient appointments. With these new types of clinic it is important to ensure that a high standard of patient care is maintained. In order to improve patient and clinician experience of the telephone clinics a quality improvement project was carried out to ensure the patient and clinician experience of these clinics was enhanced whilst remaining a safe, effective and an efficient use of resources. The project began by developing a process map for the consultation process and agreed on the design of a driver diagram and tests of change. In plan do study act (PDSA) cycle1 a single consultant completed an online survey after every patient encounter over a 5 week period. Baseline patient responses were collected using a follow-up telephone survey for each patient. Piloting led to several iterations of both survey designs. Salient results of PDSA1 included; patients not receiving appointment letters, patients feeling more anxious about a virtual appointment and many would prefer a face to face appointment. The initial clinician data showed a positive response with a provisional diagnosis being reached in 96.4% of encounters. PDSA cycle 2 included provision of a patient information sheet and information leaflets relevant to the patients’ conditions were developed and sent following new patient telephone clinics with follow-up survey analysis as before to monitor for signals of change. We also introduced the ability for patients to send an images of their lesion prior to the consultation. Following the changes implemented we noted an improvement in patient satisfaction and, in fact, many patients preferring virtual clinics as it lead to less disruption of their working lives. The extra reading material both before and after the appointments eased patients’ anxiety around virtual clinics and helped them to prepare for their appointment. Following the patient feedback virtual clinics are now used for review patients as well, with all four consultants within the department continuing to utilise virtual clinics. During this presentation the progression of these clinics and the reasons that these clinics are still operating following the return to face to face appointments will be explored. The lessons that have been gained using a QI approach have helped to deliver an optimal service that is valid and reliable as well as being safe, effective and efficient for the patient along with helping reduce the pressures from ever increasing waiting lists. In summary our work in improving the quality of virtual clinics has resulted in improved patient satisfaction along with reduced pressures on the facilities of the health trust.

Keywords: clinic, satisfaction, telephone, virtual

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Authors: Liu Zhiyuan, Sun Zongdi

Abstract:

In this paper, the BP neural network model is established to predict the carbon trading price and carbon trading volume in Shanghai City. First of all, we find the data of carbon trading price and carbon trading volume in Shanghai City from September 30, 2015 to December 23, 2016. The carbon trading price and trading volume data were processed to get the average value of each 5, 10, 20, 30, and 60 carbon trading price and trading volume. Then, these data are used as input of BP neural network model. Finally, after the training of BP neural network, the prediction values of Shanghai carbon trading price and trading volume are obtained, and the model is tested.

Keywords: Carbon trading price, carbon trading volume, BP neural network model, Shanghai City

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Authors: Prajwal Eachempati, Laurent Muzellec, Ashish Kumar Jha

Abstract:

We use Facebook as a case study to investigate the complex relationship between the firm’s public discourse (and actions) surrounding data privacy and the performance of a business model based on monetizing user’s data. We do so by looking at the evolution of public discourse over time (2004–2021) and relate topics to revenue and stock market evolution Drawing from archival sources like Zuckerberg We use LDA topic modelling algorithm to reveal 19 topics regrouped in 6 major themes. We first show how, by using persuasive and convincing language that promises better protection of consumer data usage, but also emphasizes greater user control over their own data, the privacy issue is being reframed as one of greater user control and responsibility. Second, we aim to understand and put a value on the extent to which privacy disclosures have a potential impact on the financial performance of social media firms. There we found significant relationship between the topics pertaining to privacy and social media/technology, sentiment score and stock market prices. Revenue is found to be impacted by topics pertaining to politics and new product and service innovations while number of active users is not impacted by the topics unless moderated by external control variables like Return on Assets and Brand Equity.

Keywords: public discourses, data protection, social media, privacy, topic modeling, business models, financial performance

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Authors: Shashank Tiwari, S. P. Mahapatra

Abstract:

The aim of this technique is to reduce the steps of process scales up, save time & cost of the industries. This technique will minimise the steps of process scale up. The new steps are, Novel Lab Scale, Novel Lab Scale Trials, Novel Trial Batches, Novel Exhibit Batches, Novel Validation Batches. In these steps, it is not divided to validation batches in three parts but the data of trials batches, Exhibit Batches and Validation batches are use and compile for production and used for validation. It also increases the batch size of the trial, exhibit batches. The new size of trials batches is not less than fifty Thousand, the exhibit batches increase up to two lack and the validation batches up to five lack. After preparing the batches all their data & drugs use for stability & maintain the validation record and compile data for the technology transfer in production department for preparing the marketed size batches.

Keywords: batches, technique, preparation, scale up, validation

Procedia PDF Downloads 351

Authors: Balamurugan L.

Abstract:

Artificial intelligence (AI) and its applications in Intellectual Property Rights (IPR) has been significantly growing in recent years. In last couple of years, AI tools for Patent Research and Patent Analytics have been well-stabilized in terms of accuracy of references and representation of identified patent insights. However, AI tools for Patent Prosecution and Patent Litigation are still in the nascent stage and there may be a significant potential if such market is explored further. Our research is primarily focused on identifying potential whitespaces and schematic algorithms to automate the Patent Prosecution and Patent Litigation Process of the Intellectual Property. The schematic algorithms may assist leading AI tool developers, to explore such opportunities in the field of Intellectual Property. Our research is also focused on identification of pitfalls of the AI. For example, Information Security and its impact in IPR, and Potential remediations to sustain the IPR in the digital environment.

Keywords: artificial intelligence, patent analytics, patent drafting, patent litigation, patent prosecution, patent research

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Authors: Ghazal Faraj, Andras Micsik

Abstract:

The CIDOC Conceptual Reference Model (CRM) is an extensible ontology that provides integrated access to heterogeneous and digital datasets. The CIDOC-CRM offers a “semantic glue” intended to promote accessibility to several diverse and dispersed sources of cultural heritage data. That is achieved by providing a formal structure for the implicit and explicit concepts and their relationships in the cultural heritage field. The COURAGE (“Cultural Opposition – Understanding the CultuRal HeritAGE of Dissent in the Former Socialist Countries”) project aimed to explore methods about socialist-era cultural resistance during 1950-1990 and planned to serve as a basis for further narratives and digital humanities (DH) research. This project highlights the diversity of flourished alternative cultural scenes in Eastern Europe before 1989. Moreover, the dataset of COURAGE is an online RDF-based registry that consists of historical people, organizations, collections, and featured items. For increasing the inter-links between different datasets and retrieving more relevant data from various data silos, a shared federated ontology for reconciled data is needed. As a first step towards these goals, a full understanding of the CIDOC CRM ontology (target ontology), as well as the COURAGE dataset, was required to start the work. Subsequently, the queries toward the ontology were determined, and a table of equivalent properties from COURAGE and CIDOC CRM was created. The structural diagrams that clarify the mapping process and construct queries are on progress to map person, organization, and collection entities to the ontology. Through mapping the COURAGE dataset to CIDOC-CRM ontology, the dataset will have a common ontological foundation with several other datasets. Therefore, the expected results are: 1) retrieving more detailed data about existing entities, 2) retrieving new entities’ data, 3) aligning COURAGE dataset to a standard vocabulary, 4) running distributed SPARQL queries over several CIDOC-CRM datasets and testing the potentials of distributed query answering using SPARQL. The next plan is to map CIDOC-CRM to other upper-level ontologies or large datasets (e.g., DBpedia, Wikidata), and address similar questions on a wide variety of knowledge bases.

Keywords: CIDOC CRM, cultural heritage data, COURAGE dataset, ontology alignment

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Authors: Matodzi Rapalalani

Abstract:

In context with Spencer's (2018) classic labelling theory, labels influence the perceptions of both the individual and other members of society. That is, once labelled, the individual act in ways that confirm the stereotypes attached to the label. This study, therefore, investigates the understanding of gendered labelling and its effects on Vhavenda women. Gender socialization and patriarchy have been viewed as the core causes of the problem. The literature presented the development of gendered labelling, forms of it, and other aspects. A qualitative method of data collection was used in this study, and semi-structural interviews were conducted. A total of 6 participants were used as it is easy to deal with a small sample. Thematic analysis was used as the data was interpreted and analyzed. Ethical issues such as confidentiality, informed consent, and voluntary participation were considered. Through the analysis and data interpretation, causes such as lack of Christian values, insecurities, and lust were mentioned as well as some of the effects such as frustrations, increased divorce, and low self-esteem.

Keywords: gender, naming, Venda, women, African culture

Procedia PDF Downloads 88