Search results for: ABC-VED inventory classification

Authors: Phan Minh Trang, Joacim Rocklöv, Kim Bao Giang, Gunnar Kullgren, Maria Nilsson

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There are recent studies from high income countries reporting an association between heat waves and hospital admissions for mental health disorders. It is not previously studied if such relations exist in sub-tropical and tropical low- and middle-income countries. In this study from Vietnam, the assumption was that hospital admissions for mental disorders may be triggered, or exacerbated, by heat exposure and heat waves. A database from Hanoi Mental Hospital with mental disorders diagnosed by the International Classification of Diseases 10, spanning over five years, was used to estimate the heatwave-related impacts on admissions for mental disorders. The relationship was analysed by a Negative Binomial regression model accounting for year, month, and days of week. The focus of the study was heat-wave events with periods of three or seven consecutive days above the threshold of 35oC daily maximum temperature. The preliminary study results indicated that heat-waves increased the risks for hospital admission for mental disorders (F00-79) from heat-waves of three and seven days with relative risks (RRs) of 1.16 (1.01–1.33) and 1.42 (1.02–1.99) respectively, when compared with non-heat-wave periods. Heatwave-related admissions for mental disorders increased statistically significantly among men, among residents in rural communities and in elderly. Moreover, cases for organic mental disorders including symptomatic illnesses (F0-9) and mental retardation (F70-79) raised in high risks during heat waves. The findings are novel studying a sub-tropical middle-income city, facing rapid urbanisation and epidemiological and demographic transitions.

Keywords: mental disorders, admissions for F0-9 or F70-79, maximum temperature, heat waves

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Authors: Sujata Upgupta, Prasoon Kumar Singh

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The expansion of large scale coal mining into forest areas is a potential hazard for the local biodiversity and wildlife. The objective of this study is to provide a picture of the threat that coal mining poses to the forests of the East Bokaro landscape. The vulnerable forest areas at risk have been assessed and the priority areas for conservation have been presented. The forested areas at risk in the current scenario have been assessed and compared with the past conditions using classification and buffer based overlay approach. Forest vulnerability has been assessed using an analytical framework based on systematic indicators and composite vulnerability index values. The results indicate that more than 4 km² of forests have been lost from 1973 to 2016. Large patches of forests have been diverted for coal mining projects. Forests in the northern part of the coal field within 1-3 km radius around the coal mines are at immediate risk. The original contiguous forests have been converted into fragmented and degraded forest patches. Most of the collieries are located within or very close to the forests thus threatening the biodiversity and hydrology of the surrounding regions. Based on the vulnerability values estimated, it was concluded that more than 90% of the forested grids in East Bokaro are highly vulnerable to mining. The forests in the sub-districts of Bermo and Chandrapura have been identified as the most vulnerable to coal mining activities. This case study would add to the capacity of the forest managers and mine managers to address the risk and vulnerability of forests at a small landscape level in order to achieve sustainable development.

Keywords: forest, coal mining, indicators, vulnerability

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Authors: Michael Kho Lim

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This paper examines the status of the film industry as an industry in the Philippines—where or how it is classified in the Philippine industrial classification system and how this positioning gives the film industry an identity (or not) and affects (film) policy development and impacts the larger national economy. It is important to look at how the national government recognises Philippine cinema officially, as this will have a direct and indirect impact on the industry in terms of its representation, conduct of business, international relations, and most especially its implications on policy development and implementation. Therefore, it is imperative that the ‘identity’ of Philippine cinema be clearly established and defined in the overall industrial landscape. Having a clear understanding of Philippine cinema’s industry status provides a better view of the bigger picture and helps us determine cinema’s position in the national agenda in terms of priority setting, future direction and how the state perceives and thereby values the film industry as an industry. This will then serve as a frame of reference that will anchor the succeeding discussion. Once the Philippine film industry status is identified, the paper will then clarify how cultural policy is defined, understood, and applied in the Philippines in relation to Philippine cinema by reviewing and analyzing existing policy documents and pending bills in the Philippine Congress and Senate. Lastly, the paper delves into the roles that (national) cultural institutions and industry organisations play as primary drivers or support mechanisms and how they become platforms (or not) for the upliftment of the independent film sector and towards the sustainability of the film industry. The paper concludes by arguing that the role of the government and how government officials perceive and treats culture is far more important than cultural policy itself, as these policies emanate from them.

Keywords: cultural and creative industries, cultural policy, film industry, Philippine cinema

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Authors: Bentayeb-Ait Lounis Saïda, Cheref Zahia, Cherifi Thizi, Ri Kahina Bahmed, Kahina Hallali Yasmine Abdellaoui, Kenza Adli

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Algeria is the leading consumer of dairy products in North Africa. This is a fact. However, the nutritional quality of the latter remains unknown. The aim of this study is to characterise the dairy products available on the Algerian market in order to assess whether they constitute a healthy and safe choice. To do this, it collected data on the labelling of 390 dairy products, including cheese, yoghurt, UHT milk and milk drinks, infant formula and dairy creams. We assessed their degree of processing according to the NOVA classification, as well as the proportion of imitation products. The study was carried out between March 2020 and August 2023. The results show that 88% are ultra-processed; 84% for 'cheese', 92% for dairy creams, 92% for 'yoghurt', 100% for infant formula, 92% for margarines and 36% for UHT milk/dairy drinks. As for imitation/analogue dairy products, the study revealed the following proportions: 100% for infant formula, 78% for butter/margarine, 18% for UHT milk/milk-based drinks, 54% for cheese, 2% for camembert and 75% for dairy cream. The harmful effects of consuming ultra-processed products on long-term health are increasingly documented in dozens of publications. The findings of this study sound the alarm about the health risks to which Algerian consumers are exposed. Various scientific, economic and industrial bodies need to be involved in order to safeguard consumer health in both the short and long term. Food awareness and education campaigns should be organised.

Keywords: dairy, UPF, NOVA, yoghurt, cheese

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Authors: Hyuk-Jin Lee

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Korea is a country with thousands of years of history, like its neighbors China and Japan. However, compared to China, which is famous for its ancient fantasy novel "Journey to the West", and Japan, which is famous for its monsters, its “monster culture” is not actively used for tourism. The reason is that the culture closest to the present, from the 17th to 20th centuries, was the Joseon Dynasty, when Neo-Confucianism, which suppressed a monster culture, was the strongest. This trend became stronger after Neo-Confucianism became dogmatic in the mid-17th century. However, Korea, which has a history of Taoism for thousands of years, clearly has many literatures on monsters that can be used as tourism resources. The problem is that these data are buried in texts and are unfamiliar even to Koreans. This study examines the possibility of developing them into attractive tourism resources based on the literary records of the so-called 'monsters densely located in Namsan Mountain, located in the center of Seoul' buried in texts from the 16th to early 17th centuries. In particular, we introduce the surprising consistency in the description of the area north of Namsan Mountain in terms of 'feng shui geography', an oriental philosophy, in a contemporary Korean newspaper. Finally, based on the theoretical foundation through the phenomenological classification table of cultural heritage, we examine phenomenologically how important this ‘visualization of imaginary or text-based entities’ is to changes in the perception of specific cultural resources in a society. In addition, we will deeply analyze related cases, including Japan's ninja culture.

Keywords: monster culture, Namsan mountain, neo-confucianism, phenomenology, tourism

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Authors: Milton Ayoki, Edward Bbaale

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This paper analyses the relationship between innovation and employment at firm level with the objective of understanding the contribution of the different innovation strategies in fostering employment growth in Uganda. We use National Innovation Survey (micro-data of 705 Ugandan firms) for the period 2011-2014 and follow closely Harrison et al. (2014) structured approach, and relate employment growth to process innovations and to the growth of sales separately due to innovative and unchanged products. We find positive effects of product innovation on employment at firm level, while process innovation has no discernable impact on employment. Although there is evidence to suggest displacement of labour in some cases where firms only introduce new process, this effect is compensated by growth in employment from new products, which for most firms are introduced simultaneously with new process. Results suggest that source of innovation as well as size of innovating firms or end users of innovation matter for job growth. Innovation that develops from within the firm itself (user) and involving larger firms has greater impact on employment than that developed from outside or coming from within smaller firms. In addition, innovative firms are one and half times more likely to survive in the innovation driven economy environment than those that do not innovate. These results have important implications for policymakers and stakeholders in innovation ecosystem. Supporting policies need to be correctly tailored since the impacts depend on the innovation strategy (type) and characteristics and sector of the innovative firms (small, large, industry, etc.). Policies to spur investment, particularly in innovative sectors and firms with high growth potential would have long lasting effects on job creation. JEL Classification: D24, J0, J20, L20, O30.

Keywords: employment, process innovation, product innovation, Sub-Saharan Africa

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Authors: Junbeom Lee, Jaehyuck Cho, Wookyeong Jeong, Jonghan Won, Jungmin Hwang, Youngseok Song, Taikyeong Jeong

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This paper investigates and analyzes meta-learning technologies that use multiple-cameras to monitor and check abnormal behavior in people in real-time in the area of healthcare fields. Advances in artificial intelligence and computer vision technologies have confirmed that cameras can be useful for individual health monitoring and abnormal behavior detection. Through this, it is possible to establish a system that can respond early by automatically detecting abnormal behavior of the elderly, such as patients and the elderly. In this paper, we use a technique called meta-learning to analyze image data collected from cameras and develop a commercial product to determine abnormal behavior. Meta-learning applies machine learning algorithms to help systems learn and adapt quickly to new real data. Through this, the accuracy and reliability of the abnormal behavior discrimination system can be improved. In addition, this study proposes a meta-learning-based abnormal behavior detection system that includes steps such as data collection and preprocessing, feature extraction and selection, and classification model development. Various healthcare scenarios and experiments analyze the performance of the proposed system and demonstrate excellence compared to other existing methods. Through this study, we present the possibility that camera-based meta-learning technology can be useful for monitoring and testing abnormal behavior in the healthcare area.

Keywords: artificial intelligence, abnormal behavior, early detection, health monitoring

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Authors: Hamed Yousefi, Farnaz Mohammadi, Niloufar Mirian, Navid Amini

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Investigating possible capacities of visual functions where adapted mechanisms can enhance the capability of sports trainees is a promising area of research, not only from the cognitive viewpoint but also in terms of unlimited applications in sports training. In this paper, the visual evoked potential (VEP) and event-related potential (ERP) signals of amateur and trained volleyball players in a pilot study were processed. Two groups of amateur and trained subjects are asked to imagine themselves in the state of receiving a ball while they are shown a simulated volleyball field. The proposed method is based on a set of time-frequency features using algorithms such as Gabor filter, continuous wavelet transform, and a multi-stage wavelet decomposition that are extracted from VEP signals that can be indicative of being amateur or trained. The linear discriminant classifier achieves the accuracy, sensitivity, and specificity of 100% when the average of the repetitions of the signal corresponding to the task is used. The main purpose of this study is to investigate the feasibility of a fast, robust, and reliable feature/model determination as a neurofeedback parameter to be utilized for improving the volleyball players’ performance. The proposed measure has potential applications in brain-computer interface technology where a real-time biomarker is needed.

Keywords: visual evoked potential, time-frequency feature extraction, short-time Fourier transform, event-related spectrum potential classification, linear discriminant analysis

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Authors: Palash Bandyopadhyay

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Background and significance of the study: Crude oil and natural gas is of crucial importance due to its increasing demand in India. The demand has been increased because of change of lifestyle overtime. Since India has poor utilization of oil production capacity, constantly the import of it has been increased progressively day by day. This ultimately hit the foreign exchange reserves of India, however it negatively affect the Indian economy as well. The financial performance of crude oil and natural gas companies in India has been trimmed down year after year because of underutilization of production capacity, enhancement of demand, change in life style, and change in import bill and outflows of foreign currencies. In this background, the current study seeks to measure the financial performance of crude oil and natural gas companies of India in the post liberalization period. Keeping in view of this, this study assesses the financial performance in terms of liquidity management, solvency, efficiency, financial stability, and profitability of the companies under study. Methodology: This research work is encircled on yearly ratio data collected from Centre for Monitoring Indian Economy (CMIE) Prowess database for the periods between 1993-94 and 2012-13 with 20 observations using liquidity, solvency and efficiency indicators, profitability indicators and financial stability indicators of all the major crude oil and natural gas companies in India. In the course of analysis, descriptive statistics, correlation statistics, and linear regression test have been utilized. Major findings: Descriptive statistics indicate that liquidity position is satisfactory in case of three crude oil and natural gas companies (Oil and Natural Gas Companies Videsh Limited, Oil India Limited and Selan exploration and transportation Limited) out of selected companies under study but solvency position is satisfactory only for one company (Oil and Natural Gas Companies Videsh Limited). However, efficiency analysis points out that Oil and Natural Gas Companies Videsh Limited performs effectively the management of inventory, receivables, and payables, but the overall liquidity management is not well. Profitability position is very much satisfactory in case of all the companies except Tata Petrodyne Limited, but profitability management is not satisfactory for all the companies under study. Financial stability analysis shows that all the companies are more dependent on debt capital, which bears a financial risk. Correlation and regression test results illustrates that profitability is positively and negatively associated with liquidity, solvency, efficiency, and financial stability indicators. Concluding statement: Management of liquidity and profitability of crude oil and natural gas companies in India should have been improved through controlling unnecessary imports in spite of the heavy demand of crude oil and natural gas in India and proper utilization of domestic oil reserves. At the same time, Indian government has to concern about rupee depreciation and interest rates.

Keywords: financial performance, crude oil and natural gas companies, India, linear regression

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Authors: Jyoti Brahma

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Folk epics of India are found in various Indian languages. The study of folk epics and its importance in folkloristic study in India came into prominence only during the nineteenth century. The British administrators and missionaries collected and documented folk epics from various parts of the country. The paper is an attempt to investigate how colonial outpost appears to penetrate the interiors of Indian land and society and triggered off the Indian Renaissance. It takes into account the compositions of the epics of India and the attention it received during the nineteenth century, which in turn gave, rise to the national consciousness shaping the culture of India. Composed as oral traditions these folk epics are now seen as repositories of historical consciousness whereas in earlier times societies without literacy were said to be without history. So, there is an urgent need to re-examine the British impact on Indian literary traditions. The Bhakti poets through their nuanced responses in their efforts to change the behavior of Indian society gives us the perfect example of deferment in the clear cut distinction between the folk and the classical in the context of India. It evades a pure categorization and classification of the classical and constitutes part of the folk traditions of the cultural heritage of India. Therefore, the ethical question of what is ontologically known as ordinary discourse in the case of the “folk” forms metaphors and folk language gains importance once more. The paper also thus seeks simultaneously to outline the significant factors responsible for shaping the destiny of folklore in South India particularly the four political states of the Indian Union: Andhra Pradesh, Karnataka, Kerala and Tamil Nadu, what could be termed as South Indian “cultural zones”.

Keywords: colonial, folk, folklore, tradition

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Authors: Arwa Alzughaibi

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Human Motion detection is a challenging task due to a number of factors including variable appearance, posture and a wide range of illumination conditions and background. So, the first need of such a model is a reliable feature set that can discriminate between a human and a non-human form with a fair amount of confidence even under difficult conditions. By having richer representations, the classification task becomes easier and improved results can be achieved. The Aim of this paper is to investigate the reliable and accurate human motion detection models that are able to detect the human motions accurately under varying illumination levels and backgrounds. Different sets of features are tried and tested including Histogram of Oriented Gradients (HOG), Deformable Parts Model (DPM), Local Decorrelated Channel Feature (LDCF) and Aggregate Channel Feature (ACF). However, we propose an efficient and reliable human motion detection approach by combining Histogram of oriented gradients (HOG) and local phase quantization (LPQ) as the feature set, and implementing search pruning algorithm based on optical flow to reduce the number of false positive. Experimental results show the effectiveness of combining local phase quantization descriptor and the histogram of gradient to perform perfectly well for a large range of illumination conditions and backgrounds than the state-of-the-art human detectors. Areaunder th ROC Curve (AUC) of the proposed method achieved 0.781 for UCF dataset and 0.826 for CDW dataset which indicates that it performs comparably better than HOG, DPM, LDCF and ACF methods.

Keywords: human motion detection, histograms of oriented gradient, local phase quantization, local phase quantization

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Authors: Adamu Abdullahi, On Francisca, Saidu Isah Rambo, G. N. Obunadike, D. T. Chinyio

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The paper discusses the potential threat of Denial of Service (DoS) attacks in the Internet of Things (IoT) networks on constrained application protocols (CoAP). As billions of IoT devices are expected to be connected to the internet in the coming years, the security of these devices is vulnerable to attacks, disrupting their functioning. This research aims to tackle this issue by applying mixed methods of qualitative and quantitative for feature selection, extraction, and cluster algorithms to detect DoS attacks in the Constrained Application Protocol (CoAP) using the Machine Learning Algorithm (MLA). The main objective of the research is to enhance the security scheme for CoAP in the IoT environment by analyzing the nature of DoS attacks and identifying a new set of features for detecting them in the IoT network environment. The aim is to demonstrate the effectiveness of the MLA in detecting DoS attacks and compare it with conventional intrusion detection systems for securing the CoAP in the IoT environment. Findings: The research identifies the appropriate node to detect DoS attacks in the IoT network environment and demonstrates how to detect the attacks through the MLA. The accuracy detection in both classification and network simulation environments shows that the k-means algorithm scored the highest percentage in the training and testing of the evaluation. The network simulation platform also achieved the highest percentage of 99.93% in overall accuracy. This work reviews conventional intrusion detection systems for securing the CoAP in the IoT environment. The DoS security issues associated with the CoAP are discussed.

Keywords: algorithm, CoAP, DoS, IoT, machine learning

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Authors: Jacob J. Shila, Xiaoyu O. Wu

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As the applications of unmanned aerial vehicles (UAV) continue to increase across the world, it is critical to understand the factors that contribute to incidents and accidents associated with these systems. Given the variety of daily applications that could utilize the operations of the UAV (e.g., medical, security operations, construction activities, landscape activities), the main discussion has been how to safely incorporate the UAV into the national airspace system. The types of UAV incidents being reported range from near sightings by other pilots to actual collisions with aircraft or UAV. These incidents have the potential to impact the rest of aviation operations in a variety of ways, including human lives, liability costs, and delay costs. One of the largest causes of these incidents cited is the human factor; other causes cited include maintenance, aircraft, and others. This work investigates the key human factors associated with UAV incidents. To that end, the data related to UAV incidents that have occurred in the United States is both reviewed and analyzed to identify key human factors related to UAV incidents. The data utilized in this work is gathered from the Federal Aviation Administration (FAA) drone database. This study adopts the human factor analysis and classification system (HFACS) to identify key human factors that have contributed to some of the UAV failures to date. The uniqueness of this work is the incorporation of UAV incident data from a variety of applications and not just military data. In addition, identifying the specific human factors is crucial towards developing safety operational models and human factor guidelines for the UAV. The findings of these common human factors are also compared to similar studies in other countries to determine whether these factors are common internationally.

Keywords: human factors, incidents and accidents, safety, UAS, UAV

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Authors: Kasidis Chomrat, Nopasit Chakpitak, Anukul Tamprasirt, Annop Thananchana

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Internet of Things devices or IoT and Edge Computing has become one of the biggest things happening in innovations and one of the most discussed of the potential to improve and disrupt traditional business and industry alike. With rises of new hang cliff challenges like COVID-19 pandemic that posed a danger to workforce and business process of the system. Along with drastically changing landscape in business that left ruined aftermath of global COVID-19 pandemic, looming with the threat of global energy crisis, global warming, more heating global politic that posed a threat to become new Cold War. How emerging technology like edge computing and usage of specialized design visual processing units will be great opportunities for business. The literature reviewed on how the internet of things and disruptive wave will affect business, which explains is how all these new events is an effect on the current business and how would the business need to be adapting to change in the market and world, and example test benchmarking for consumer marketed of newer devices like the internet of things devices equipped with new edge computing devices will be increase efficiency and reducing posing a risk from a current and looming crisis. Throughout the whole paper, we will explain the technologies that lead the present technologies and the current situation why these technologies will be innovations that change the traditional practice through brief introductions to the technologies such as cloud computing, edge computing, Internet of Things and how it will be leading into future.

Keywords: internet of things, edge computing, machine learning, pattern recognition, image classification

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Authors: D. Jayalakshmi, S. S. Bhosale

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This paper, in its first part, presents the state-of-the-art literature of design approaches for geosynthetic reinforced unpaved roads. The literature starting since 1970 and the critical appraisal of flexible pavement design by Giroud and Han (2004) and Jonathan Fannin (2006) is presented. The design example is illustrated for Indian conditions. The example emphasizes the results computed by Giroud and Han's (2004) design method with the Indian road congress guidelines by IRC SP 72 -2015. The input data considered are related to the subgrade soil condition of Maharashtra State in India. The unified soil classification of the subgrade soil is inorganic clay with high plasticity (CH), which is expansive with a California bearing ratio (CBR) of 2% to 3%. The example exhibits the unreinforced case and geotextile as reinforcement by varying the rut depth from 25 mm to 100 mm. The present result reveals the base thickness for the unreinforced case from the IRC design catalogs is in good agreement with Giroud and Han (2004) approach for a range of 75 mm to 100 mm rut depth. Since Giroud and Han (2004) method is applicable for both reinforced and unreinforced cases, for the same data with appropriate Nc factor, for the same rut depth, the base thickness for the reinforced case has arrived for the Indian condition. From this trial, for the CBR of 2%, the base thickness reduction due to geotextile inclusion is 35%. For the CBR range of 2% to 5% with different stiffness in geosynthetics, the reduction in base course thickness will be evaluated, and the validation will be executed by the full-scale accelerated pavement testing set up at the College of Engineering Pune (COE), India.

Keywords: base thickness, design approach, equation, full scale accelerated pavement set up, Indian condition

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Authors: Stefano Iannello, Massimiliano Materazzi

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Fluidized bed reactor technologies are one of the most valuable pathways for thermochemical conversions of biogenic fuels due to their good operating flexibility. Nevertheless, there are still issues related to the mixing and separation of heterogeneous phases during operation with highly volatile feedstocks, including biomass and waste. At high temperatures, the volatile content of the feedstock is released in the form of the so-called endogenous bubbles, which generally exert a “lift” effect on the particle itself by dragging it up to the bed surface. Such phenomenon leads to high release of volatile matter into the freeboard and limited mass and heat transfer with particles of the bed inventory. The aim of this work is to get a better understanding of the behaviour of a single reacting particle in a hot fluidized bed reactor during the devolatilization stage. The analysis has been undertaken at different fluidization regimes and temperatures to closely mirror the operating conditions of waste-to-energy processes. Beechwood and polypropylene particles were used to resemble the biomass and plastic fractions present in waste materials, respectively. The non-invasive X-ray technique was coupled to particle tracking algorithms to characterize the motion of a single feedstock particle during the devolatilization with high resolution. A high-energy X-ray beam passes through the vessel where absorption occurs, depending on the distribution and amount of solids and fluids along the beam path. A high-speed video camera is synchronised to the beam and provides frame-by-frame imaging of the flow patterns of fluids and solids within the fluidized bed up to 72 fps (frames per second). A comprehensive mathematical model has been developed in order to validate the experimental results. Beech wood and polypropylene particles have shown a very different dynamic behaviour during the pyrolysis stage. When the feedstock is fed from the bottom, the plastic material tends to spend more time within the bed than the biomass. This behaviour can be attributed to the presence of the endogenous bubbles, which drag effect is more pronounced during the devolatilization of biomass, resulting in a lower residence time of the particle within the bed. At the typical operating temperatures of thermochemical conversions, the synthetic polymer softens and melts, and the bed particles attach on its outer surface, generating a wet plastic-sand agglomerate. Consequently, this additional layer of sand may hinder the rapid evolution of volatiles in the form of endogenous bubbles, and therefore the establishment of a poor drag effect acting on the feedstock itself. Information about the mixing and segregation of solid feedstock is of prime importance for the design and development of more efficient industrial-scale operations.

Keywords: fluidized bed, pyrolysis, waste feedstock, X-ray

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Authors: Qasim Munye, Juned Islam, Haseeb Qureshi, Syed Jung

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Lung nodules are commonly identified in computed tomography (CT) scans by experienced radiologists at a relatively late stage. Early diagnosis can greatly increase survival. We propose using a pix2pix conditional generative adversarial network to generate realistic images simulating early-stage lung nodule growth. We have applied deep images prior to 2341 slices from 895 computed tomography (CT) scans from the Lung Image Database Consortium (LIDC) dataset to generate pseudo-healthy medical images. From these images, 819 were chosen to train a pix2pix network. We observed that for most of the images, the pix2pix network was able to generate images where the nodule increased in size and intensity across epochs. To evaluate the images, 400 generated images were chosen at random and shown to a medical student beside their corresponding original image. Of these 400 generated images, 384 were defined as satisfactory - meaning they resembled a nodule and were visually similar to the corresponding image. We believe that this generated dataset could be used as training data for neural networks to detect lung nodules at an early stage or to improve the accuracy of such networks. This is particularly significant as datasets containing the growth of early-stage nodules are scarce. This project shows that the combination of deep image prior and generative models could potentially open the door to creating larger datasets than currently possible and has the potential to increase the accuracy of medical classification tasks.

Keywords: medical technology, artificial intelligence, radiology, lung cancer

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Authors: Saba Gachpaz, Hamid Reza Heidari

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The sharp increase in population growth leads to more pressure on agricultural areas to satisfy the food supply. To achieve this, more resources should be consumed and, besides other environmental concerns, highlight sustainable agricultural development. Land-use management is a crucial factor in obtaining optimum productivity. Machine learning is a widely used technique in the agricultural sector, from yield prediction to customer behavior. This method focuses on learning and provides patterns and correlations from our data set. In this study, nine physical control factors, namely, soil classification, electrical conductivity, normalized difference water index (NDWI), groundwater level, elevation, annual precipitation, pH of water, annual mean temperature, and slope in the alluvial plain in Khuzestan (an agricultural hotspot in Iran) are used to decide the best agricultural land use for both rainfed and irrigated agriculture for ten different crops. For this purpose, each variable was imported into Arc GIS, and a raster layer was obtained. In the next level, by using training samples, all layers were imported into the python environment. A random forest model was applied, and the weight of each variable was specified. In the final step, results were visualized using a digital elevation model, and the importance of all factors for each one of the crops was obtained. Our results show that despite 62% of the study area being allocated to agricultural purposes, only 42.9% of these areas can be defined as a suitable class for cultivation purposes.

Keywords: land suitability, machine learning, random forest, sustainable agriculture

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Authors: Iva Zdrilić, Petra Došenović Bonča, Darija Aleksić

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Since the boundaryless career, with its emphasis on cross-employer movements, was introduced as a new paradigm of career development, inter-organizational employee mobility has been increasing. Although this phenomenon may have positive implications for individual careers and destination organizations, the consequences for the source organizations losing workers are less clear. The aim of this paper is thus to develop a comprehensive typology of possible inter-organizational employee mobility determinants. Since the most common classification differentiates between mobility determinants at different levels (i.e., economic, organizational, and individual), this paper focuses on building a comprehensive multi-level typology of inter-organizational mobility determinants across diverse sectors and industries. By using a structured literature review approach and bibliometric analysis, the paper reveals both intricate relationships between different mobility determinants and the complexity of inter-organizational networks and social ties. The latter appears as both a mobility determinant (at the organizational and individual level) and a mobility effect. Indeed, inter-organizational employee mobility leads to the formation of networks between source and destination organizations. These networks are practically based on the social ties between mobile employees and their colleagues and, in this way, they close the "inter-organizational employee mobility - inter-organizational network/ties" circle. The paper contributes to the career development literature by uncovering hitherto underexplored diverse determinants of intra- and inter-sectoral mobility as well as the conflicting results of the existing studies on some factors (e.g., inter-organizational networks and/or social ties) that appear both as a mobility determinant and a mobility effect.

Keywords: inter-organizational mobility, social ties, inter-organizational network, knowledge transfer

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Authors: A. A. M. A. S. S. Perera, L. A. Ranasinghe, T. K. H. Nimeshika, D. M. Dhanushka Dissanayake, Namalie Walgampaya

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Nowadays, skin fungal diseases are mostly found in people of tropical countries like Sri Lanka. A skin fungal disease is a particular kind of illness caused by fungus. These diseases have various dangerous effects on the skin and keep on spreading over time. It becomes important to identify these diseases at their initial stage to control it from spreading. This paper presents an automated skin fungal disease identification system implemented to speed up the diagnosis process by identifying skin fungal infections in digital images. An image of the diseased skin lesion is acquired and a comprehensive computer vision and image processing scheme is used to process the image for the disease identification. This includes colour analysis using RGB and HSV colour models, texture classification using Grey Level Run Length Matrix, Grey Level Co-Occurrence Matrix and Local Binary Pattern, Object detection, Shape Identification and many more. This paper presents the approach and its outcome for identification of four most common skin fungal infections, namely, Tinea Corporis, Sporotrichosis, Malassezia and Onychomycosis. The main intention of this research is to provide an automated skin fungal disease identification system that increase the diagnostic quality, shorten the time-to-diagnosis and improve the efficiency of detection and successful treatment for skin fungal diseases.

Keywords: Circularity Index, Grey Level Run Length Matrix, Grey Level Co-Occurrence Matrix, Local Binary Pattern, Object detection, Ring Detection, Shape Identification

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Authors: Xizhen Zhou, Yanjie Ji

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In advancing the management of integrated electric vehicle (EV) parking-charging behaviors, this study uses Changshu City in Suzhou as a case study to establish a data association mechanism for parking-charging platforms and to develop a database for EV parking-charging behaviors. Key indicators, such as charging start time, initial state of charge, final state of charge, and parking-charging time difference, are considered. Utilizing the K-S test method, the paper examines the heterogeneity of parking-charging behavior preferences among pure EV and non-pure EV users. The K-means clustering method is employed to analyze the characteristics of parking-charging behaviors for both user groups, thereby enhancing the overall understanding of these behaviors. The findings of this study reveal that using a classification model, the parking-charging behaviors of pure EVs can be classified into five distinct groups, while those of non-pure EVs can be separated into four groups. Among them, both types of EV users exhibit groups with low range anxiety for complete charging with special journeys, complete charging at destination, and partial charging. Additionally, both types have a group with high range anxiety, characterized by pure EV users displaying a preference for complete charging with specific journeys, while non-pure EV users exhibit a preference for complete charging. Notably, pure EV users also display a significant group engaging in nocturnal complete charging. The findings of this study can provide technical support for the scientific and rational layout and management of integrated parking and charging facilities for EVs.

Keywords: traffic engineering, potential preferences, cluster analysis, EV, parking-charging behavior

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Authors: Sandra Mitrovic, Gaurav Singh

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In many industries, including telecommunications, churn prediction has been a topic of active research. A lot of attention has been drawn on devising the most informative features, and this area of research has gained even more focus with spread of (social) network analytics. The call detail records (CDRs) have been used to construct customer networks and extract potentially useful features. However, to the best of our knowledge, no studies including network features have yet proposed a generic way of representing network information. Instead, ad-hoc and dataset dependent solutions have been suggested. In this work, we build upon a recently presented method (node2vec) to obtain representations for nodes in observed network. The proposed approach is generic and applicable to any network and domain. Unlike node2vec, which assumes a static network, we consider a dynamic and time-evolving network. To account for this, we propose an approach that constructs the feature representation of each node by generating its node2vec representations at different timestamps, concatenating them and finally compressing using an auto-encoder-like method in order to retain reasonably long and informative feature vectors. We test the proposed method on churn prediction task in telco domain. To predict churners at timestamp ts+1, we construct training and testing datasets consisting of feature vectors from time intervals [t1, ts-1] and [t2, ts] respectively, and use traditional supervised classification models like SVM and Logistic Regression. Observed results show the effectiveness of proposed approach as compared to ad-hoc feature selection based approaches and static node2vec.

Keywords: churn prediction, dynamic networks, node2vec, auto-encoders

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Authors: Cam Nguyen

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Rapid, accurate, and safe sensing and imaging of physical quantities or structures finds many applications and is of significant interest to society. Sensing and imaging using radio-frequency (RF) techniques, particularly, has gone through significant development and subsequently established itself as a unique territory in the sensing world. RF sensing and imaging has played a critical role in providing us many sensing and imaging abilities beyond our human capabilities, benefiting both civilian and military applications - for example, from sensing abnormal conditions underneath some structures’ surfaces to detection and classification of concealed items, hidden activities, and buried objects. We present the developments of several sensing and imaging systems implementing RF technologies like ultra-wide band (UWB), synthetic-pulse, and interferometry. These systems are fabricated completely using RF integrated circuits. The UWB impulse system operates over multiple pulse durations from 450 to 1170 ps with 5.5-GHz RF bandwidth. It performs well through tests of various samples, demonstrating its usefulness for subsurface sensing. The synthetic-pulse system operating from 0.6 to 5.6 GHz can assess accurately subsurface structures. The synthetic-pulse system operating from 29.72-37.7 GHz demonstrates abilities for various surface and near-surface sensing such as profile mapping, liquid-level monitoring, and anti-personnel mine locating. The interferometric system operating at 35.6 GHz demonstrates its multi-functional capability for measurement of displacements and slow velocities. These RF sensors are attractive and useful for various surface and subsurface sensing applications. This paper was made possible by NPRP grant # 6-241-2-102 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.

Keywords: RF sensors, radars, surface sensing, subsurface sensing

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Authors: Hiromi Kawai

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This study investigates the nature of EFL older children’s L2 perceptive and productive abilities using classroom data, in order to find a pedagogical solution to the teaching of L2 sounds at an early stage of learning in a formal school setting. It is still inconclusive whether older children with only EFL formal school instruction at the initial stage of L2 learning are able to attain native-like perception and production in English within the very limited amount of exposure to the target language available. Based on the notion of the lack of study of EFL Japanese children’s acquisition of English segments, the researcher uses a model of L1 speech processing which was developed for investigating L1 English children’s speech and literacy difficulties using a psycholinguistic framework. The model is composed of input channel, output channel, and lexical representation, and examines how a child receives information from spoken or written language, remembers and stores it within the lexical representations and how the child selects and produces spoken or written words. Concerning language universality and language specificity in the language acquisitional process, the aim of finding any sound errors in L1 English children seemed to conform to the author’s intention to find abilities of English sounds in older Japanese children at the novice level of English in an EFL setting. 104 students in Grade 5 (between the ages of 10 and 11 years old) of an elementary school in Tokyo participated in this study. Four tests to measure their perceptive ability and three oral repetition tests to measure their productive ability were conducted with/without reference to lexical representation. All the test items were analyzed to calculate item facility (IF) indices, and correlational analyses and Structural Equation Modeling (SEM) were conducted to examine the relationship between the receptive ability and the productive ability. IF analysis showed that (1) the participants were better at perceiving a segment than producing a segment, (2) they had difficulty in auditory discrimination of paired consonants when one of them does not exist in the Japanese inventory, (3) they had difficulty in both perceiving and producing English vowels, and (4) their L1 loan word knowledge had an influence on their ability to perceive and produce L2 sounds. The result of the Multiple Regression Modeling showed that the two production tests could predict the participants’ auditory ability of real words in English. The result of SEM showed that the hypothesis that perceptive ability affects productive ability was supported. Based on these findings, the author discusses the possible explicit method of teaching English segments to EFL older children in a formal school setting.

Keywords: EFL older children, english segments, perception, production, speech processing system

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Authors: Marina Bubalo, Davor Romić, Stjepan Husnjak, Helena Bakić

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Even though European Council Directive 91/676/EEC known as Nitrates Directive was adopted in 1991, the issue of water quality preservation in areas of intensive agricultural production still persist all over Europe. High nitrate nitrogen concentrations in surface and groundwater originating from diffuse sources are one of the most important environmental problems in modern intensive agriculture. The fate of nitrogen in soil, surface and groundwater in agricultural area is mostly affected by anthropogenic activity (i.e. agricultural practice) and hydrological and climatological conditions. The aim of this study was to identify impact of land use, soil type, soil vulnerability to pollutant percolation, and natural aquifer vulnerability to nitrate occurrence in surface and groundwater within an intensive agricultural area. The study was set in Varaždin County (northern Croatia), which is under significant influence of the large rivers Drava and Mura and due to that entire area is dominated by alluvial soil with shallow active profile mainly on gravel base. Negative agricultural impact on water quality in this area is evident therefore the half of selected county is a part of delineated nitrate vulnerable zones (NVZ). Data on water quality were collected from 7 surface and 8 groundwater monitoring stations in the County. Also, recent study of the area implied detailed inventory of agricultural production and fertilizers use with the aim to produce new agricultural land use database as one of dominant parameters. The analysis of this database done using ArcGIS 10.1 showed that 52,7% of total County area is agricultural land and 59,2% of agricultural land is used for intensive agricultural production. On the other hand, 56% of soil within the county is classified as soil vulnerable to pollutant percolation. The situation is similar with natural aquifer vulnerability; northern part of the county ranges from high to very high aquifer vulnerability. Statistical analysis of water quality data is done using SPSS 13.0. Cluster analysis group both surface and groundwater stations in two groups according to nitrate nitrogen concentrations. Mean nitrate nitrogen concentration in surface water – group 1 ranges from 4,2 to 5,5 mg/l and in surface water – group 2 from 24 to 42 mg/l. The results are similar, but evidently higher, in groundwater samples; mean nitrate nitrogen concentration in group 1 ranges from 3,9 to 17 mg/l and in group 2 from 36 to 96 mg/l. ANOVA analysis confirmed statistical significance between stations that are classified in the same group. The previously listed parameters (land use, soil type, etc.) were used in factorial correspondence analysis (FCA) to detect importance of each stated parameter in local water quality. Since stated parameters mostly cannot be altered, there is obvious necessity for more precise and more adapted land management in such conditions.

Keywords: agricultural area, nitrate, factorial correspondence analysis, water quality

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Authors: Joseph Gear, Yue Xu, Ernest Foo, Praveen Gauravaran, Zahra Jadidi, Leonie Simpson

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Deep learning methods have been seeing an increasing application to the long-standing security research goal of automatic vulnerability detection for source code. Attention, however, must still be paid to the task of producing vector representations for source code (code embeddings) as input for these deep learning models. Graphical representations of code, most predominantly Abstract Syntax Trees and Code Property Graphs, have received some use in this task of late; however, for very large graphs representing very large code snip- pets, learning becomes prohibitively computationally expensive. This expense may be reduced by intelligently pruning this input to only vulnerability-relevant information; however, little research in this area has been performed. Additionally, most existing work comprehends code based solely on the structure of the graph at the expense of the information contained by the node in the graph. This paper proposes Semantic-enhanced Code Embedding for Vulnerability Discovery (SCEVD), a deep learning model which uses semantic-based feature selection for its vulnerability classification model. It uses information from the nodes as well as the structure of the code graph in order to select features which are most indicative of the presence or absence of vulnerabilities. This model is implemented and experimentally tested using the SARD Juliet vulnerability test suite to determine its efficacy. It is able to improve on existing code graph feature selection methods, as demonstrated by its improved ability to discover vulnerabilities.

Keywords: code representation, deep learning, source code semantics, vulnerability discovery

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Authors: Abdullah Saeidi, Aydin Gülses, Christan Flörke

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Skin cancer of the face region is the most common type of malignancy and surgical excision is the preferred approach. However, the clinical long term results reported in the literature are still controversial. Objectives: To describe; 1. Demographical characteristics 2. Affected site, distribution and TNM classification regarding tumor type 3. Surgical aspects • Surgical removal: excision principles, safety margins, the need for secondary resection, primary reconstruction/ defect closure, anesthesia protocol, duration of hospital stay (if any) • Secondary intervention for defect closure/reconstruction: Flap technique, anesthesia protocol, duration of hospital stay (if any), postoperative wound management etc. 4. Tumor recurrences 5. Clinical outcomes 6. Studying the possible therapy approach throw Biostatistical relation and correlation between multiple Histological, diagnostics and clinical Faktors. following surgical ablation of the skin cancer of the head and neck region. Methods: Selection and statistical analysis of medical records of patients who had admitted to the Department of Oral and Maxillofacial Surgery, Universitätsklinikum Schleswig Holstein, Campus Kiel during the period of 2015-2019 will be retrospectively evaluated. Data will be collected via ORBIS Information-Management-System (ORBIS AG, Saarbrücken, Germany).

Keywords: non melanoma skin cancer, face skin cancer, skin reconstruction, non melanoma skin cancer recurrence, non melanoma skin cancer metastases

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Authors: Chi Wu, Dean Wu, Wen-Te Liu, Cheng-Yu Tsai, Shin-Mei Hsu, Yin-Tzu Lin, Ru-Yin Yang

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Background: The results of previous studies compared the benefits and risks of receiving insomnia medication. However, the effects between hypnotic drugs used and enhancement of sleep quality were still unclear. Objective: The aim of this study is to establish a prediction model for hypnotic drugs' dosage used for insomnia subjects and associated the relationship between sleep stage ratio change and drug types. Methodologies: According to American Academy of Sleep Medicine (AASM) guideline, sleep stages were classified and transformed to hypnogram via the polysomnography (PSG) in a hospital in New Taipei City (Taiwan). The subjects with diagnosis for insomnia without receiving hypnotic drugs treatment were be set as the comparison group. Conversely, hypnotic drugs dosage within the past three months was obtained from the clinical registration for each subject. Furthermore, the collecting subjects were divided into two groups for training and testing. After training convolution neuron network (CNN) to predict types of hypnotics used and dosages are taken, the test group was used to evaluate the accuracy of classification. Results: We recruited 76 subjects in this study, who had been done PSG for transforming hypnogram from their sleep stages. The accuracy of dosages obtained from confusion matrix on the test group by CNN is 81.94%, and accuracy of hypnotic drug types used is 74.22%. Moreover, the subjects with high ratio of wake stage were correctly classified as requiring medical treatment. Conclusion: CNN with hypnogram was potentially used for adjusting the dosage of hypnotic drugs and providing subjects to pre-screening the types of hypnotic drugs taken.

Keywords: convolution neuron network, hypnotic drugs, insomnia, polysomnography

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Authors: H. El-Messiry, M. El-Zainy, D. Abdelkhalek

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Cone-beam computed tomography (CBCT) provides high-quality 3-dimensional images of dental structures because of its high spatial resolution. The study of dental morphology is important in research as it provides information about diversities within a population. Many studies have shown different shapes and numbers of roots canals among different races, especially in molars. The aim of this study was to determine the morphology of root canals of mandibular first and third molars in a sample of Egyptian population using CBCT scanning. Fifty mandibular first Molars (M1) and fifty mandibular third (M3) extracted molars were collected. Thick rectangular molds were made using pink wax to hold the samples. Molars were embedded in the wax mold by aligning them in rows leaving arbitrary 0.5cm space between them. The molds with the samples in were submitted for CBCT scan. The number and morphology of root canals were assessed and classified according to Vertucci's classification. The mesial and the distal roots were examined separately. Finally, data was analyzed using Fisher exact test. The most prevalent mesial root canal frequency in M1 was type IV (60%) and type II (40 %), while M3 showed prevalence of type I (40%) and II (40%). Distal root canal morphology showed prevalence of type I in both M1 (66%) and M3 (86%). So, it can be concluded that CBCT scanning provides supplemental information about the root canal configurations of mandibular molars in a sample of Egyptian population. This study may help clinicians in the root canal treatment of mandibular molars.

Keywords: cone beam computed tomography, mandibular first molar, mandibular third molar, root canal morphology

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Authors: Rachna Agarwal, R. Naveen, Darpan Thakre, Rohit Shahi, Maryam Abbasi, Upendra Rathore, Latika Gupta

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Aim: Retinal changes are the window to systemic vasculature. Therefore, we explored retinal changes in patients with idiopathic inflammatory myopathies (IIM) as a surrogate for vascular health. Methods: Adult and juvenile IIM patients visiting a tertiary care centre in 2021 satisfying the International Myositis Classification Criteria were enrolled for detailed ophthalmic examination in comparison with healthy controls (HC). Patients with conditions that precluded thorough posterior chamber examination were excluded. Scale variables are expressed as median (IQR). Multivariate analysis (binary logistic regression-BLR) was conducted, adjusting for age, gender, and comorbidities besides factors significant in univariate analysis. Results: 43 patients with IIM [31 females; age 36 (23-45) years; disease duration 5.5 (2-12) months] were enrolled for participation. DM (44%) was the most common diagnosis. IIM patients exhibited frequent attenuation of retinal vessels (32.6% vs. 4.3%, p <0.001), AV nicking (14% vs. 2.2%, p=0.053), and vascular tortuosity (18.6% vs. 2.2%, p=0.012), besides decreased visual acuity (53.5% vs. 10.9%, p<0.001) and immature cataracts (34.9% vs. 2.2%, p<0.001). Attenuation of vessels [OR 10.9 (1.7-71), p=0.004] emerged as significantly different from HC after adjusting for covariates in BLR. Notably, adults with IIM were more predisposed to retinal abnormalities [21 (57%) vs. 1 (16%), p=0.068], especially attenuation of vessels [14(38%) vs. 0(0), p=0.067] than jIIM. However, no difference was found in retinal features amongst the subtypes of adult IIM, nor did they correlate with MDAAT, MDI, or HAQ-DI. Conclusion: Retinal microvasculopathy and diminution of vision occur in nearly one-third to half of the patients with IIM. Microvasculopathy occurs across subtypes of IIM, and more so in adults, calling for further investigation as a surrogate for damage assessment and potentially even systemic vascular health.

Keywords: idiopathic inflammatory myopathies, vascular health, retinal microvasculopathy, arterial attenuation

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