Search results for: consumer purchasing decision

Authors: F. Lhafiane, A. Elbyed, M. Bouchoum

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The design of Reverse logistics Network has attracted growing attention with the stringent pressures from both environmental awareness and business sustainability. Reverse logistical activities include return, remanufacture, disassemble and dispose of products can be quite complex to manage. In addition, demand can be difficult to predict, and decision making is one of the challenges tasks. This complexity has amplified the need to develop an integrated architecture for product return as an enterprise system. The main purpose of this paper is to design Multi agent system (MAS) architecture using the Prometheus methodology to efficiently manage reverse logistics processes. The proposed MAS architecture includes five types of agents: Gate keeping Agent, Collection Agent, Sorting Agent, Processing Agent and Disposal Agent which act respectively during the five steps of reverse logistics Network.

Keywords: reverse logistics, multi agent system, prometheus methodology

Procedia PDF Downloads 460

Authors: Mohammad A. Alolayan, John S. Evans, James K. Hammitt

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The value of statistical life has been estimated for a middle eastern country with high economical subsidization system. In this study, in-person interviews were conducted on a stratified random sample to estimate the value of mortality risk. Double-bounded dichotomous choice questions followed by open-ended question were used in the interview to investigate the willingness to pay of the respondent for mortality risk reduction. High willingness to pay was found to be associated with high income and education. Also, females were found to have lower willingness to pay than males. The estimated value of statistical life is larger than the ones estimated for western countries where taxation system exists. This estimate provides a baseline for monetizing the health benefits for proposed policy or program to the decision makers in an eastern country. Also, the value of statistical life for a country in the region can be extrapolated from this this estimate by using the benefit transfer method.

Keywords: mortality, risk, VSL, willingness-to-pay

Procedia PDF Downloads 309

Authors: Ali Cheraghian, Farshid Hajati, Soheila Gheisari, Yongsheng Gao

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In this paper, we present a novel 2.5D face recognition method based on Gabor Discrete Cosine Transform (GDCT). In the proposed method, the Gabor filter is applied to extract feature vectors from the texture and the depth information. Then, Discrete Cosine Transform (DCT) is used for dimensionality and redundancy reduction to improve computational efficiency. The system is combined texture and depth information in the decision level, which presents higher performance compared to methods, which use texture and depth information, separately. The proposed algorithm is examined on publically available Bosphorus database including models with pose variation. The experimental results show that the proposed method has a higher performance compared to the benchmark.

Keywords: Gabor filter, discrete cosine transform, 2.5d face recognition, pose

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Authors: Horea-Serban Raluca-Ioana, Istrate Marinela

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The first part of the paper analyzes the dynamics of the total fertility rate both at the national and regional level, pointing out the regional disparities in the distribution of this indicator. At the same time, we also focus on the collapse of the number of live births, on the changes in the fertility rate by birth rank, as well as on the failure of acquiring the desired number of children. The second part of the study centres upon a survey applied to urban families with 3 and more than 3 offspring. The preliminary analysis highlights the fact that an increased fertility (more than 3rd rank) is triggered by the parents’ above the average material condition and superior education. The current situation of Romania, which is still passing through a period of relatively rapid demographic changes, marked by numerous convulsions, requires a new approach, in compliance with the recent interpretations appropriate to a new post-transitional demographic regime.

Keywords: fertility rate, family size intention, third birth rank, regional disparities

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Authors: Amit Kumar Dwivedi, Priyanko Ghosh

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Data Envelopment analysis (DEA) has been used to calculate the technical and scale efficiency measures of the public and private sugar manufacturing firms of the Indian Sugar Industry (2006 to 2010). Within DEA framework, the input & Output oriented Variable Returns to Scale (VRS) & Constant Return to Scale (CRS) model is employed for the study of Decision making units (DMUs). A representative sample of 43 firms which account for major portion of the total market share is studied. The selection criterion for the inclusion of a firm in the analysis was the total sales of INR 5,000 million or more in the year 2010. After reviewing the literature it is found that no study has been conducted in the context of Indian sugar manufacturing firms in the Post-liberalization era which motivates us to initiate the study.

Keywords: technical efficiency, Indian sugar manufacturing units, DEA, input output oriented

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Authors: Chen Xi, Lao Xuerui, Li Junjie, Jiang Yike, Wang Hanwei, Zeng Zihao

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To further promote the development of smart cities, the microscopic "nerve endings" of the City Intelligent Model (CIM) are extended to be more sensitive. In this paper, we develop a pedestrian trajectory twin terminal based on the CIM and CNN technology. It also uses 5G networks, architectural and geoinformatics technologies, convolutional neural networks, combined with deep learning networks for human behaviour recognition models, to provide empirical data such as 'pedestrian flow data and human behavioural characteristics data', and ultimately form spatial performance evaluation criteria and spatial performance warning systems, to make the empirical data accurate and intelligent for prediction and decision making.

Keywords: urban planning, urban governance, CIM, artificial intelligence, convolutional neural network

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Authors: Saleem Abuleil, Khalid S. Alsamara

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Events represent a significant source of information on the web; they deliver information about events that occurred around the world in all kind of subjects and areas. These events can be collected and organized to provide valuable and useful information for decision makers, researchers, as well as any person seeking knowledge. In this paper, we discuss an ongoing research to target stock markets domain to observe and record changes (events) when they happen, collect them, understand the meaning of each one of them, and organize the information along with meaning in a well-structured format. By using Semantic Role Labeling (SRL) technique, we identified four factors for each event in this paper: verb of action and three roles associated with it, entity name, attribute, and attribute value. We have generated a set of rules and techniques to support our approach to analyze and understand the meaning of the events taking place in stock markets.

Keywords: natuaral language processing, Arabic language, event extraction and understanding, sematic role labeling, stock market

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Authors: Maria A. Castillo H., Andres R. Leandro, Jose F. Bienvenido B.

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During the last 20 years, the globalization of economies, population growth, and the increase in the consumption of fresh agricultural products have generated greater demand for ornamentals, flowers, fresh fruits, and vegetables, mainly from tropical areas. This market situation has demanded greater competitiveness and control over production, with more efficient protected agriculture technologies, which provide greater productivity and allow us to guarantee the quality and quantity that is required in a constant and sustainable way. Guatemala, located in the north of Central America, is one of the largest exporters of agricultural products in the region and exports fresh vegetables, flowers, fruits, ornamental plants, and foliage, most of which were grown in greenhouses. Although there are no official agricultural statistics on greenhouse production, several thesis works, and congress reports have presented consistent estimates. A wide range of protection structures and roofing materials are used, from the most basic and simple ones for rain control to highly technical and automated structures connected with remote sensors for monitoring and control of crops. With this breadth of technological models, it is necessary to analyze georeferenced data related to the cultivated area, to the different existing models, and to the covering materials, integrated with altitude, climate, and soil data. The georeferenced registration of the production units, the data collection with digital tools, the use of satellite images, and geographic information systems (GIS) provide reliable tools to elaborate more complete, agile, and dynamic information maps. This study details a methodology proposed for gathering georeferenced data of high protection structures (greenhouses) in Guatemala, structured in four phases: diagnosis of available information, the definition of the geographic frame, selection of satellite images, and integration with an information system geographic (GIS). It especially takes account of the actual lack of complete data in order to obtain a reliable decision-making system; this gap is solved through the proposed methodology. A summary of the results is presented in each phase, and finally, an evaluation with some improvements and tentative recommendations for further research is added. The main contribution of this study is to propose a methodology that allows to reduce the gap of georeferenced data in protected agriculture in this specific area where data is not generally available and to provide data of better quality, traceability, accuracy, and certainty for the strategic agricultural decision öaking, applicable to other crops, production models and similar/neighboring geographic areas.

Keywords: greenhouses, protected agriculture, GIS, Guatemala, satellite image, digital tools, precision agriculture

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Authors: Syed Asif Hassan, Tabrej Khan, Ibrahim Manssor Al Najar, Mohammed Nasser

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The incredible accomplishment achieved by e-commerce in consumer durable area encouraged us to implement the online e-commerce model to tap the business benefits of electronic pharmacy in Saudi Arabia. The Kingdom of Saudi Arabia is famous for traditional herbal medicine. The rich heritage of traditional medicine has helped the mushrooming of regional pharmaceutical industries manufacturing drugs and other therapeutic against various diseases. However, the implementation of e-commerce in pharmacy has not been employed in the Kingdom of Saudi Arabia. The electronic pharmacy (E-Pharm) is an important sector that is flourishing across the globe and providing benefits of E-Pharm to the customers and suppliers all around the world. In this context, our web-based application of electronic pharmacy is the one of its kind in the Kingdom of Saudi Arabia. Surveys and personal interviews were used to identify key objectives of the proposed web-based portal. As per the findings of the surveys and personal interviews, following key objectives were identified: (a) The online platform will be used for ordering of prescription based medications for consumers. (b) The e-portal will provide space for pharmaceutical retailers who do not have an electronic platform to upload and sell their therapeutic products in an organized way. (c) The web portal will provide a tracking system to track the customer’s behavior like choice, offer, order, shipment, payment, etc. The web-based e-pharmacy portal will be developed using MySQL and PHP. The development of e-pharmacy web portal and e-prescription practices will not only improve the growth of electronic pharmacy but would also decrease the possibility of prescription alteration thus providing safety and improving the quality of service provided to the patient or consumers.

Keywords: e-commerce, E-Pharm, MySQL, PHP

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Authors: Kvnkc Sharma

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Globalization, coupled with increase in competition is compelling organizations to adopt innovative strategies and identify core competencies in order to distinguish themselves from the competition. The capability of an organization is no longer determined by their products or services alone. The intellectual assets and quality of the human resource are fast emerging as key differentiators. Corporations are now positioning themselves as ‘brands’ not solely to market their products and services, but also to lure and to retain the best talent in the business. This paper identifies leadership as the ‘key element’ in developing an organization’s brand, which has a significant influence on the employee’s eventual perception of this external brand as portrayed by the organization. External branding incorporates innovation, consumer concern, trust, quality and sustainability. The paper contends that employees are indeed an organization’s ‘brand ambassadors. Internal branding involves taking care of these ambassadors of corporate brand i.e. human resource. If employees of an organization are not exposed to the organization’s branding (an ongoing process that functionally aligns, motivates and empower employees at all levels to consistently provide a satisfying customer experience), the external brand could be jeopardized. Internal branding, on the other hand, refers to employee’s perception of the organization’s brand. The current business environment can at best, be termed as volatile. Employees with the right technical and behavioral skills remain a scarce resource and the employers need to be ready to capture the attention, interest and commitment of the best and brightest candidates. This paper attempts to review and understand the relationship between employer branding and employee retention. The paper also seeks to identify potential impact of employer branding across all the factors affecting employees.

Keywords: external branding, human resource, internal branding, leadership

Procedia PDF Downloads 237

Authors: J. Bravo, F. Cabrera Suárez, B. Vega, L. Román, M. Martel, F. Acosta

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Ciguatera fish poisoning (CFP) is the most common non-bacterial intoxication in the world caused by ingestion of fish with bio-accumulated ciguatoxins (CTXs). It is typical in tropical and subtropical areas, mainly affecting the Caribbean Sea, Polynesia and other areas in the Pacific and Indian Oceans. Interest in Europe by the CFP is increasing in recent years as more and more cases in European hospitals are appearing, usually by people who have consumed ciguatoxin imported fish or have travelled to areas of risk for this poisoning. Since 2004 a series of poisonings raised the question of a possible occurrence of ciguatoxin in Europe, especially in the area of Macaronesia in the East Atlantic temperate zone. Furthermore, some studies have identified the presence of Gambierdiscus spp. in waters surrounding the Canary Islands and Madeira, a toxic dinoflagellate related to this poisoning. The toxin accumulates and concentrates through the food chain and affects to the end of the chain, the human consumer. Fish were collected from the Canary Islands waters and the toxin has been extracted and purified by using acetone and liquid/liquid partition in order to eliminate the excess of fatty acids that may interfere with the detection of the toxin. The fish extracts were inoculated in Neuroblastoma (neuro-2a) cells. After 24-h cell viability was used as an endpoint for cytotoxic effects measurement. Since 2011 our laboratory is collecting data for species such Seriola spp., Epinephelus spp., Makaira spp., Pomatomus spp., Xiphias spp., and Acantocybium spp., from all islands and including the sports fishing and professional activities, we obtained a 8% of fish that have ciguatoxin in their muscle. With these results, we conclude that the island where fishing and fish size affects the probability of catching a fish with the ciguatoxin.

Keywords: Canary Islands, ciguatera fish poisoning, ciguatoxin, Europe

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Authors: Ruvaitha Banu, Amaladhithyan Krishnamoorthy

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IoT networks today solve various consumer problems, from home automation systems to aiding in driving autonomous vehicles with the exploration of multiple devices. For example, in an autonomous vehicle environment, multiple sensors are available on roads to monitor weather and road conditions and interact with each other to aid the vehicle in reaching its destination safely and timely. IoT systems are predominantly dependent on the cloud environment for data storage, and computing needs that result in latency problems. With the advent of Fog networks, some of this storage and computing is pushed to the edge/fog nodes, saving the network bandwidth and reducing the latency proportionally. Managing the data stored in these fog nodes becomes crucial as it might also store sensitive information required for a certain application. Data management in fog nodes is strenuous because Fog networks are dynamic in terms of their availability and hardware capability. It becomes more challenging when the nodes in the network also live a short span, detaching and joining frequently. When an end-user or Fog Node wants to access, read, or write data stored in another Fog Node, then a new protocol becomes necessary to access/manage the data stored in the fog devices as a conventional static way of managing the data doesn’t work in Fog Networks. The proposed solution discusses a protocol that acts by defining sensitivity levels for the data being written and read. Additionally, a distinct data distribution and replication model among the Fog nodes is established to decentralize the access mechanism. In this paper, the proposed model implements stewardship towards the data stored in the Fog node using the application of Reinforcement Learning so that access to the data is determined dynamically based on the requests.

Keywords: IoT, fog networks, data stewardship, dynamic access policy

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Authors: Brady Manescau, Ilyas Sellami, Khaled Chetehouna, Charles De Izarra, Rachid Nait-Said, Fati Zidani

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During a fire in an oil and gas refinery, several thermal accidents can occur and cause serious damage to people and environment. Among these accidents, the BLEVE (Boiling Liquid Expanding Vapor Explosion) is most observed and remains a major concern for risk decision-makers. It corresponds to a violent vaporization of explosive nature following the rupture of a vessel containing a liquid at a temperature significantly higher than its normal boiling point at atmospheric pressure. Their effects on the environment generally appear in three ways: blast overpressure, radiation from the fireball if the liquid involved is flammable and fragment hazards. In order to estimate the potential damage that would be caused by such an explosion, risk decision-makers often use quantitative risk analysis (QRA). This analysis is a rigorous and advanced approach that requires a reliable data in order to obtain a good estimate and control of risks. However, in most cases, the data used in QRA are obtained from the empirical correlations. These empirical correlations generally overestimate BLEVE effects because they are based on simplifications and do not take into account real parameters like the geometry effect. Considering that these risk analyses are based on an assessment of BLEVE effects on human life and plant equipment, more precise and reliable data should be provided. From this point of view, the CFD modeling of BLEVE effects appears as a solution to the empirical law limitations. In this context, the main objective is to develop a numerical tool in order to predict BLEVE thermal effects using the CFD code FDS version 6. Simulations are carried out with a mesh size of 1 m. The fireball source is modeled as a vertical release of hot fuel in a short time. The modeling of fireball dynamics is based on a single step combustion using an EDC model coupled with the default LES turbulence model. Fireball characteristics (diameter, height, heat flux and lifetime) issued from the large scale BAM experiment are used to demonstrate the ability of FDS to simulate the various steps of the BLEVE phenomenon from ignition up to total burnout. The influence of release parameters such as the injection rate and the radiative fraction on the fireball heat flux is also presented. Predictions are very encouraging and show good agreement in comparison with BAM experiment data. In addition, a numerical study is carried out on an operational propane accumulator in an Algerian gas processing plant of SONATRACH company located in the Hassi R’Mel Gas Field (the largest gas field in Algeria).

Keywords: BLEVE effects, CFD, FDS, fireball, LES, QRA

Procedia PDF Downloads 181

Authors: Bagireanu Astrid, Bros-Williamson Julio, Duncheva Mila, Currie John

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Digital Twins (DT) are an innovative concept of cyber-physical integration of data between an asset and its virtual replica. They have originated in established industries such as manufacturing and aviation and have garnered increasing attention as a potentially transformative technology within the built environment. With the potential to support decision-making, real-time simulations, forecasting abilities and managing operations, DT do not fall under a singular scope. This makes defining and leveraging the potential uses of DT a potential missed opportunity. Despite its recognised potential in established industries, literature on DT in the built environment remains limited. Inadequate attention has been given to the implementation of DT in construction projects, as opposed to its operational stage applications. Additionally, the absence of a standardised definition has resulted in inconsistent interpretations of DT in both industry and academia. There is a need to consolidate research to foster a unified understanding of the DT. Such consolidation is indispensable to ensure that future research is undertaken with a solid foundation. This paper aims to present a comprehensive systematic literature review on the role of DT in the built environment. To accomplish this objective, a review and thematic analysis was conducted, encompassing relevant papers from the last five years. The identified papers are categorised based on their specific areas of focus, and the content of these papers was translated into a through classification of DT. In characterising DT and the associated data processes identified, this systematic literature review has identified 6 DT opportunities specifically relevant to the built environment: Facilitating collaborative procurement methods, Supporting net-zero and decarbonization goals, Supporting Modern Methods of Construction (MMC) and off-site manufacturing (OSM), Providing increased transparency and stakeholders collaboration, Supporting complex decision making (real-time simulations and forecasting abilities) and Seamless integration with Internet of Things (IoT), data analytics and other DT. Finally, a discussion of each area of research is provided. A table of definitions of DT across the reviewed literature is provided, seeking to delineate the current state of DT implementation in the built environment context. Gaps in knowledge are identified, as well as research challenges and opportunities for further advancements in the implementation of DT within the built environment. This paper critically assesses the existing literature to identify the potential of DT applications, aiming to harness the transformative capabilities of data in the built environment. By fostering a unified comprehension of DT, this paper contributes to advancing the effective adoption and utilisation of this technology, accelerating progress towards the realisation of smart cities, decarbonisation, and other envisioned roles for DT in the construction domain.

Keywords: built environment, design, digital twins, literature review

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Authors: Carlos Roncero-Parra, Alfonso Parreño-Torres, Jorge Mateo Sotos, Alejandro L. Borja

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In this research, different methods based on machine/deep learning algorithms are presented for the classification and diagnosis of patients with mental disorders such as alzheimer. For this purpose, the signals obtained from 32 unipolar electrodes identified by non-invasive EEG were examined, and their basic properties were obtained. More specifically, different well-known machine learning based classifiers have been used, i.e., support vector machine (SVM), Bayesian linear discriminant analysis (BLDA), decision tree (DT), Gaussian Naïve Bayes (GNB), K-nearest neighbor (KNN) and Convolutional Neural Network (CNN). A total of 668 patients from five different hospitals have been studied in the period from 2011 to 2021. The best accuracy is obtained was around 93 % in both ADM and ADA classifications. It can be concluded that such a classification will enable the training of algorithms that can be used to identify and classify different mental disorders with high accuracy.

Keywords: alzheimer, machine learning, deep learning, EEG

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Authors: Amirhossein Karamoozian, Desheng Wu, Behzad Abbasnejad

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Building information modelling (BIM) is a new technology to enhance the efficiency of project management in the construction industry. In addition to the potential benefits of this useful technology, there are various risks and obstacles to applying it in construction projects. In this study, a decision making approach is presented for risk assessment in BIM adoption in construction projects. Various risk factors of exerting BIM during different phases of the project lifecycle are identified with the help of Delphi method, experts’ opinions and related literature. Afterward, Shannon’s entropy and Fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Situation) are applied to derive priorities of the identified risk factors. Results indicated that lack of knowledge between professional engineers about workflows in BIM and conflict of opinions between different stakeholders are the risk factors with the highest priority.

Keywords: risk, BIM, fuzzy TOPSIS, construction projects

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Authors: Lubos Rojka

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The common perception of morally responsible behavior is that it presupposes freedom of choice, and that free decisions and actions are not determined by natural events, but by a person. In other words, the moral agent has the ability and the possibility of doing otherwise when making morally responsible decisions, and natural causal determinism cannot fully account for morally significant freedom. The incompatibility between a person’s morally significant freedom and causal determinism appears to be a natural position. Nevertheless, some of the most influential philosophical theories on moral responsibility are compatibilist or semi-compatibilist, and they exclude the requirement of alternative possibilities, which contradicts the claims of classical incompatibilism. The compatibilists often employ Frankfurt-style thought experiments to prove their theory. The goal of this paper is to examine the role of imaginary Frankfurt-style examples in compatibilist accounts. More specifically, the compatibilist accounts defended by John Martin Fischer and Michael McKenna will be inserted into the broader understanding of a person elaborated by Harry Frankfurt, Robert Kane and Walter Glannon. Deeper analysis reveals that the exclusion of alternative possibilities based on Frankfurt-style examples is problematic and misleading. A more comprehensive account of moral responsibility and morally significant (source) freedom requires higher order complex theories of human will and consciousness, in which rational and self-creative abilities and a real possibility to choose otherwise, at least on some occasions during a lifetime, are necessary. Theoretical moral reasons and their logical relations seem to require a sort of higher-order agent-causal incompatibilism. The ability of theoretical or abstract moral reasoning requires complex (strongly emergent) mental and conscious properties, among which an effective free will, together with first and second-order desires. Such a hierarchical theoretical model unifies reasons-responsiveness, mesh theory and emergentism. It is incompatible with physical causal determinism, because such determinism only allows non-systematic processes that may be hard to predict, but not complex (strongly) emergent systems. An agent’s effective will and conscious reflectivity is the starting point of a morally responsible action, which explains why a decision is 'up to the subject'. A free decision does not always have a complete causal history. This kind of an emergentist source hyper-incompatibilism seems to be the best direction of the search for an adequate explanation of moral responsibility in the traditional (merit-based) sense. Physical causal determinism as a universal theory would exclude morally significant freedom and responsibility in the traditional sense because it would exclude the emergence of and supervenience by the essential complex properties of human consciousness.

Keywords: consciousness, free will, determinism, emergence, moral responsibility

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Authors: Somchai Buaroong

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This research attempted to study information system success in dimensions of the user satisfaction level and to find the association between the independent factors of the user experiences, user knowledge, and user attitude. The study sample was selected using simple random sampling that comprised of 190 users who had used the Bangkok HIS. The data were reported from 165 questionnaires. The results found that the user satisfaction was at a moderate level, user satisfaction on the information quality and system quality was at a moderate level, while satisfaction on service quality was at a high level. The computer knowledge of the user was at a moderate level, and the user attitude was at a positive level. The participation of the user was at a low level and the participation in decision and in evaluation was at a low level; however participation in implementation and in benefit was at a moderate.

Keywords: information system success, hospital information system, user attitude, user satisfaction

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Authors: Michael Mederle, Heinz Bernhardt

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The Management of machine fleets or autonomous vehicle control will considerably increase efficiency in future agricultural production. Especially entire process chains, e.g. harvesting complexes with several interacting combine harvesters, grain carts, and removal trucks, provide lots of optimization potential. Organization and pre-planning ensure to get these efficiency reserves accessible. One way to achieve this is to optimize infield path planning. Particularly autonomous machinery requires precise specifications about infield logistics to be navigated effectively and process optimized in the fields individually or in machine complexes. In the past, a lot of theoretical optimization has been done regarding infield logistics, mainly based on field geometry. However, there are reasons why farmers often do not apply the infield strategy suggested by mathematical route planning tools. To make the computational optimization more useful for farmers this study focuses on these influencing factors by expert interviews. As a result practice-oriented navigation not only to the field but also within the field will be possible. The survey study is intended to cover the entire range of German agriculture. Rural mixed farms with simple technology equipment are considered as well as large agricultural cooperatives which farm thousands of hectares using track guidance and various other electronic assistance systems. First results show that farm managers using guidance systems increasingly attune their infield-logistics on direction giving obstacles such as power lines. In consequence, they can avoid inefficient boom flippings while doing plant protection with the sprayer. Livestock farmers rather focus on the application of organic manure with its specific requirements concerning road conditions, landscape terrain or field access points. Cultivation of sugar beets makes great demands on infield patterns because of its particularities such as the row crop system or high logistics demands. Furthermore, several machines working in the same field simultaneously influence each other, regardless whether or not they are of the equal type. Specific infield strategies always are based on interactions of several different influences and decision criteria. Single working steps like tillage, seeding, plant protection or harvest mostly cannot be considered each individually. The entire production process has to be taken into consideration to detect the right infield logistics. One long-term objective of this examination is to integrate the obtained influences on infield strategies as decision criteria into an infield navigation tool. In this way, path planning will become more practical for farmers which is a basic requirement for automatic vehicle control and increasing process efficiency.

Keywords: autonomous vehicle control, infield logistics, path planning, process optimizing

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Authors: Tobias Milz, L. Macpherson, Varvara Vetrova

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The law is a fundamental pillar of human societies as it shapes, controls and governs how humans conduct business, behave and interact with each other. Recent advances in computer-assisted technologies such as NLP, data science and AI are creating opportunities to support the practice, research and study of this pervasive domain. It is therefore not surprising that there has been an increase in investments into supporting technologies for the legal industry (also known as “legal tech” or “law tech”) over the last decade. A sub-discipline of particular appeal is concerned with assisted legal research. Supporting law researchers and practitioners to retrieve information from the vast amount of ever-growing legal documentation is of natural interest to the legal research community. One tool that has been in use for this purpose since the early nineteenth century is legal citation indexing. Among other use cases, they provided an effective means to discover new precedent cases. Nowadays, computer-assisted network analysis tools can allow for new and more efficient ways to reveal the “hidden” information that is conveyed through citation behavior. Unfortunately, access to openly available legal data is still lacking in New Zealand and access to such networks is only commercially available via providers such as LexisNexis. Consequently, there is a need to create, analyze and provide a legal citation network with sufficient data to support legal research tasks. This paper describes the development and analysis of a legal citation Network for New Zealand containing over 300.000 decisions from 125 different courts of all areas of law and jurisdiction. Using python, the authors assembled web crawlers, scrapers and an OCR pipeline to collect and convert court decisions from openly available sources such as NZLII into uniform and machine-readable text. This facilitated the use of regular expressions to identify references to other court decisions from within the decision text. The data was then imported into a graph-based database (Neo4j) with the courts and their respective cases represented as nodes and the extracted citations as links. Furthermore, additional links between courts of connected cases were added to indicate an indirect citation between the courts. Neo4j, as a graph-based database, allows efficient querying and use of network algorithms such as PageRank to reveal the most influential/most cited courts and court decisions over time. This paper shows that the in-degree distribution of the New Zealand legal citation network resembles a power-law distribution, which indicates a possible scale-free behavior of the network. This is in line with findings of the respective citation networks of the U.S. Supreme Court, Austria and Germany. The authors of this paper provide the database as an openly available data source to support further legal research. The decision texts can be exported from the database to be used for NLP-related legal research, while the network can be used for in-depth analysis. For example, users of the database can specify the network algorithms and metrics to only include specific courts to filter the results to the area of law of interest.

Keywords: case citation network, citation analysis, network analysis, Neo4j

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Authors: Benhaddadi M., Déry D.

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Electric motors are the single biggest consumer of electricity, and the consumption will have more than to double by 2050. Meanwhile, the existing technologies offer the potential to reduce the motor energy demand by up to 30 %, whereas the know-how to realise energy savings is not extensively applied. That is why the authors first conducted a detailed analysis of the regulation of the electric motor market in North America To illustrate the colossal energy savings potential permitted by the VFD, the authors have equipped experimental setup, based on centrifugal pump, simultaneously equipped with regulating throttle valves and variable frequency drive VFD. The obtained experimental results for 1.5 HP motor pump are extended to another motor powers, as centrifugal pumps that are different in power may have similar operational characteristics if they are located in a similar kind of process, permitting the simulations for 5 HP and 100 HP motors. According to the obtained results, VFDs tend to be most cost-effective when fitted to larger motor pumps, in addition to higher duty cycle of the motor and relative time operating at lower than full load. The energy saving permitted by the VFD use is huge, and the payback period for drive investment is short. Nonetheless, it’s important to highlight that there is no general rule of thumb that can be used to obtain the impact of the relative time operating at lower than full load. Indeed, in terms of energy-saving differences, 50 % flow regulation is tremendously better than 75 % regulation, but a slightly enhanced relative to 25 %. Two main distinct reasons can explain this somewhat not anticipated results: the characteristics of the process and the drop in efficiency when motor is operating at low speed.

Keywords: motor, drive, energy efficiency, centrifugal pump

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Authors: Valentin Père, Mathieu Milhé, Fabien Baillon, Jean-Louis Dirion

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Microgrids offer potential solutions to meet the need for local grid stability and increase isolated networks autonomy with the integration of intermittent renewable energy production and storage facilities. In such a context, sizing production and storage for a given network is a complex task, highly depending on input data such as power load profile and renewable resource availability. This work aims at developing an operating cost computation methodology for different microgrid designs based on the use of deep reinforcement learning (RL) algorithms to tackle the optimal operation problem in stochastic environments. RL is a data-based sequential decision control method based on Markov decision processes that enable the consideration of random variables for control at a chosen time scale. Agents trained via RL constitute a promising class of Energy Management Systems (EMS) for the operation of microgrids with energy storage. Microgrid sizing (or design) is generally performed by minimizing investment costs and operational costs arising from the EMS behavior. The latter might include economic aspects (power purchase, facilities aging), social aspects (load curtailment), and ecological aspects (carbon emissions). Sizing variables are related to major constraints on the optimal operation of the network by the EMS. In this work, an islanded mode microgrid is considered. Renewable generation is done with photovoltaic panels; an electrochemical battery ensures short-term electricity storage. The controllable unit is a hydrogen tank that is used as a long-term storage unit. The proposed approach focus on the transfer of agent learning for the near-optimal operating cost approximation with deep RL for each microgrid size. Like most data-based algorithms, the training step in RL leads to important computer time. The objective of this work is thus to study the potential of Batch-Constrained Q-learning (BCQ) for the optimal sizing of microgrids and especially to reduce the computation time of operating cost estimation in several microgrid configurations. BCQ is an off-line RL algorithm that is known to be data efficient and can learn better policies than on-line RL algorithms on the same buffer. The general idea is to use the learned policy of agents trained in similar environments to constitute a buffer. The latter is used to train BCQ, and thus the agent learning can be performed without update during interaction sampling. A comparison between online RL and the presented method is performed based on the score by environment and on the computation time.

Keywords: batch-constrained reinforcement learning, control, design, optimal

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Authors: Yekini N. Asafe, Haastrup A. Victor, Lawal N. Olawale, Okikiola F. Mercy

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Recent increase in access to personal computer and networking systems have made it feasible to perform much of cumbersome and costly paper-based administration in all organization. Desktop computers, networking systems, high capacity storage devices and telecommunications system is currently allowing the transfer of various format of data to be processed, stored and dissemination for the purpose of decision making. Going paperless is more of benefits compare to full paper-based office. This paper proposed a model for design and implementation of e-administration system (paperless campus) for an institution of learning. If this model is design and implemented it will reduced cost and complexity of educational administration also eliminate menaces and environmental hazards attributed to paper-based administration within schools and colleges.

Keywords: e-administration, educational administration, paperless campus, paper-based administration

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Authors: Kyujin Shim, Soojin Kim

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This study identifies and examines the impact of factors on two types of CSR outcomes, consumers’ perceptions of corporate authenticity and their pro-firm behavior intentions. Specifically we investigated the roles of two factors - the consumers’ perceptions of CSR motives of a company (i.e. business-oriented vs. society-oriented) and their ethical orientations (i.e. deontology vs. consequentialism). A web-based survey was conducted in South Korea and the United States respectively to compare the differences of consumer reactions between the two countries. The results show that consumers in two countries behave differently to a firm’s CSR motives. In the United States, when consumers perceive a company’s CSR motive as society-oriented, they are more likely to perceive the company authentic and as a result more likely to engage in pro-firm behavior. However, when consumers’ ethical orientation is considered, only consumers’ consequential orientation led to their pro-firm behavioral intention. In South Korea, interpretation of two different CSR motives affects the valence in consumers’ perceptions of corporate authenticity (i.e. society-oriented CSR motive and positive perception of corporate authenticity vs. business-oriented CSR motive and negative perception of corporate authenticity). Korean consumers also showed same pattern in terms of relationship among society-oriented CSR motive, perception of corporate authenticity, and pro-firm behavior intention. Interestingly, Korean consumers’ consequential orientation affects both their perception of corporate authenticity and their pro-firm behavior intention positively. In addition, there was an interaction effect of business-oriented CSR motive and deontological orientation on perception of corporate authenticity. Theoretical and practical implications will be discussed.

Keywords: corporate authenticity, corporate social responsibility, consequentialist ethics, CSR motives, deontological ethics

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Authors: Abdelrahman A. Ramzy, Bassel S. Abdallah, Mohamed E. Bahgat, Sarah M. Abdelkader, Sherif H. ElGohary

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Attention-Deficit/Hyperactivity Disorder (ADHD), epilepsy, and autism affect millions of children worldwide, many of which are undiagnosed despite the fact that all of these disorders are detectable in early childhood. Late diagnosis can cause severe problems due to the late treatment and to the misconceptions and lack of awareness as a whole towards these disorders. Moreover, electroencephalography (EEG) has played a vital role in the assessment of neural function in children. Therefore, quantitative EEG measurement will be utilized as a tool for use in the evaluation of patients who may have ADHD, epilepsy, and autism. We propose a screening tool that uses EEG signals and machine learning algorithms to detect these disorders at an early age in an automated manner. The proposed classifiers used with epilepsy as a step taken for the work done so far, provided an accuracy of approximately 97% using SVM, Naïve Bayes and Decision tree, while 98% using KNN, which gives hope for the work yet to be conducted.

Keywords: ADHD, autism, epilepsy, EEG, SVM

Procedia PDF Downloads 185

Authors: Ines Kovacic, Dijana Pavicic-Hamer, Petra Buric, Maja Levak Zorinc, Daniel M. Lyons

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Silver nanoparticles (AgNPs), owing to their unique physical and chemical properties, have become one of the most widely used nanoparticles in consumer products. Despite the increased use of AgNPs in science and industry over the past twenty years, only relatively recently has concern been raised over their entering brackish and marine environments. However, data on their potential impact on marine organisms, especially invertebrates are very limited. This study aimed to examine the effects of 60 nm AgNPs (10, 100, 500 and 1000 µg/l) and silver ions (100, 1000 µg/l) on the Mediterranean green crab Carcinus aestuarii Nardo, 1847. The crab mortality was assessed during seven days of exposure. After the exposure, total haemocytes (THC) and differential haemocytes number (DHC) were counted (immune response), in addition to histological examination of gills stained with haematoxylin and eosin. The effect of AgNPs and silver ions resulted in a dose dependent mortality and destruction of gills epithelium with haemocytes infiltration in the gills lacuna. Total haemocyte count was greater with increasing concentration of AgNPs, at concentrations from 10 to 500 µg/l. Hyalinocytes were the most common immunological cells noted in the crab hemolymph, while granulocytes and semigranulocytes were suppressed with increasing concentration of AgNPs (500 and 1000 µg/l). Thus, as crabs are filter feeders, they are susceptible to uptake of AgNPs by direct accumulation in gills mucus or indirectly via circulation of haemocytes in their open vascular system. Results of this study on crabs add to knowledge of the effects of AgNPs in the marine environment.

Keywords: crab, immune response, histological alteration, silver nanoparticles

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Authors: Ouzayr Rabhi, Ibtissam Arrassen

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To provide a complete analysis of the organization and to help decision-making, leaders need to have relevant data; Data Warehouses (DW) are designed to meet such needs. However, designing DW is not trivial and there is no formal method to derive a multidimensional schema from heterogeneous databases. In this article, we present a Model-Driven based approach concerning the design of data warehouses. We describe a multidimensional meta-model and also specify a set of transformations starting from a Unified Modeling Language (UML) metamodel. In this approach, the UML metamodel and the multidimensional one are both considered as a platform-independent model (PIM). The first meta-model is mapped into the second one through transformation rules carried out by the Query View Transformation (QVT) language. This proposal is validated through the application of our approach to generating a multidimensional schema of a Balanced Scorecard (BSC) DW. We are interested in the BSC perspectives, which are highly linked to the vision and the strategies of an organization.

Keywords: data warehouse, meta-model, model-driven architecture, transformation, UML

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Authors: Ayman M. Mansour

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In this paper, we propose a multi-agent intelligent system that is used for monitoring the health conditions of elderly people. Monitoring the health condition of elderly people is a complex problem that involves different medical units and requires continuous monitoring. Such expert system is highly needed in rural areas because of inadequate number of available specialized physicians or nurses. Such monitoring must have autonomous interactions between these medical units in order to be effective. A multi-agent system is formed by a community of agents that exchange information and proactively help one another to achieve the goal of elderly monitoring. The agents in the developed system are equipped with intelligent decision maker that arms them with the rule-based reasoning capability that can assist the physicians in making decisions regarding the medical condition of elderly people.

Keywords: fuzzy logic, inference system, monitoring system, multi-agent system

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Authors: M. K. Hossain, J. Keidel, O. Hensel, M. Diakite

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Fat reduced dairy products are holding a potential market due to health reason. Due to less creamy, and pleasantness, reduced and/or low-fat dairy products are getting less consumer acceptance whereas the fat molecule provides smooth, creamy and a pleasant mouthfeel in dairy products especially yogurt & ice cream. This study was aimed to investigate whether the application of microparticulated whey proteins (MWPs) processed by extrusion cooking, the reduced fat yogurt can achieve similar or higher creaminess compared to whole milk (3.8% fat) and skimmed milk (0.5% fat) yogurt. Full cream and skimmed milk were used to prepare natural stirred yogurt, as well as the dry matter content, also adjusted up to 16% with skimmed milk powder. Whey protein concentrates (WPC80) were used to produce MWPs in particle size of d50 > 5 µm, d50 3<5 µm and d50 < 3 µm through the hot-extrusion process with a screw speed of 400, 600 and 1000 rpm respectively. Furthermore, the commercially available microparticulated whey protein called Simplesse® was also applied in order to compare with extruded MWPs. The rheological and sensory properties of yogurt were assessed, and data were analyzed statistically. The applications of extruded MWPs with 600 and 1000 rpm were achieved significantly (p < 0.05) higher creaminess and preference compared to the whole and skimmed milk yogurt whereas, 400 rpm got lower preference. On the other hand, Simplesse® obtained the lowest creaminess and preference compared to other yogurts, although the contribution of dry matter in yogurt was same as extruded MWPs. The creaminess and viscosities were strongly (r = 0.62) correlated, furthermore, the viscosity from sensory evaluation and the dynamic viscosity of yogurt was also significantly (r = 0.72) correlated which clarifies that the performance of sensory panelists as well as the quality of the products.

Keywords: microparticulation, hot-extrusion, reduced-fat yogurt, whey protein concentrate

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Authors: Kolade Olawande Owoeye, George R. S. Weir

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The widespread of terrorism and extremism activities on the internet has become a major threat to the government and national securities due to their potential dangers which have necessitated the need for intelligence gathering via web and real-time monitoring of potential websites for extremist activities. However, the manual classification for such contents is practically difficult or time-consuming. In response to this challenge, an automated classification system called composite technique was developed. This is a computational framework that explores the combination of both semantics and syntactic features of textual contents of a web. We implemented the framework on a set of extremist webpages dataset that has been subjected to the manual classification process. Therein, we developed a classification model on the data using J48 decision algorithm, this is to generate a measure of how well each page can be classified into their appropriate classes. The classification result obtained from our method when compared with other states of arts, indicated a 96% success rate in classifying overall webpages when matched against the manual classification.

Keywords: extremist, web pages, classification, semantics, posit

Procedia PDF Downloads 139