Search results for: decision distance

Authors: Rubens P. Costa, Marcelo A. Leigui de Oliveira

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The comprehension of features on the energy spectra, the chemical compositions, and the origins of Ultra-High Energy Cosmic Rays (UHECRs) - mainly atomic nuclei with energies above ~1.0 EeV (exa-electron volts) - are intrinsically linked to the problem of determining the magnitude of their deflections in cosmic magnetic fields on cosmological scales. In addition, as they propagate from the source to the observer, modifications are expected in their original energy spectra, anisotropy, and the chemical compositions due to interactions with low energy photons and matter. This means that any consistent interpretation of the nature and origin of UHECRs has to include the detailed knowledge of their propagation in a three-dimensional environment, taking into account the magnetic deflections and energy losses. The parameter space range for the magnetic fields in the universe is very large because the field strength and especially their orientation have big uncertainties. Particularly, the strength and morphology of the Extragalactic Magnetic Fields (EGMFs) remain largely unknown, because of the intrinsic difficulty of observing them. Monte Carlo simulations of charged particles traveling through a simulated magnetized universe is the straightforward way to study the influence of extragalactic magnetic fields on UHECRs propagation. However, this brings two major difficulties: an accurate numerical modeling of charged particles diffusion in magnetic fields, and an accurate numerical modeling of the magnetized Universe. Since magnetic fields do not cause energy losses, it is important to impose that the particle tracking method conserve the particle’s total energy and that the energy changes are results of the interactions with background photons only. Hence, special attention should be paid to computational effects. Additionally, because of the number of particles necessary to obtain a relevant statistical sample, the particle tracking method must be computationally efficient. In this work, we present an analysis of the propagation of ultra-high energy charged particles in the intergalactic medium. The EGMFs are considered to be coherent within cells of 1 Mpc (mega parsec) diameter, wherein they have uniform intensities of 1 nG (nano Gauss). Moreover, each cell has its field orientation randomly chosen, and a border region is defined such that at distances beyond 95% of the cell radius from the cell center smooth transitions have been applied in order to avoid discontinuities. The smooth transitions are simulated by weighting the magnetic field orientation by the particle's distance to the two nearby cells. The energy losses have been treated in the continuous approximation parameterizing the mean energy loss per unit path length by the energy loss length. We have shown, for a particle with the typical energy of interest the integration method performance in the relative error of Larmor radius, without energy losses and the relative error of energy. Additionally, we plotted the distance amplification from rectilinear propagation as a function of the traveled distance, particle's magnetic rigidity, without energy losses, and particle's energy, with energy losses, to study the influence of particle's species on these calculations. The results clearly show when it is necessary to use a full three-dimensional simulation.

Keywords: cosmic rays propagation, extragalactic magnetic fields, magnetic deflections, ultra-high energy

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Authors: Yani Shi, Jiaqi Yan

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A new product is important for companies to extend consumers and manifest competitiveness. New product often involves new features that consumers might not be familiar with while it may also have a competitive advantage to attract consumers compared to established products. However, although most online retailers employ recommendation agents (RA) to influence consumers’ product choice decision, recommended new products are not accepted and chosen as expected. We argue that it might also be caused by providing a new product recommendation in the wrong way at the wrong time. This study seeks to discuss how new product evaluations sourced from third parties could be employed in RAs as evidence of the superiority for the new product and how the new product recommendation could be provided to a consumer at the right time so that it can be accepted and finally chosen during the consumer’s decision-making process. A 2*2 controlled laboratory experiment was conducted to understand the selection of new product recommendation sources and recommendation timing. Human subjects were randomly assigned to one of the four treatments to minimize the effects of individual differences on the results. Participants were told to make purchase choices from our product categories. We find that a new product recommended right after a similar existing product and with the source of the expert review will be more likely to be accepted. Based on this study, both theoretical and practical contributions are provided regarding new product recommendation.

Keywords: new product recommendation, recommendation timing, recommendation source, recommendation agents

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Authors: Titik Wahyuningsih

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The folktale to discuss is Ande-Ande Lumut, a story that is believed to be a history of two kingdoms in East Java, Indonesia. The title refers to the main male character in the story. This research is a library research which aims to know the patriarchal values in Indonesia. The data for the research is the song in the story that is actually the conversation between Ande-Ande Lumut and a mom who adopts him. It is told in the lines that many beautiful girls come to propose Ande-Ande Lumut but he does not want to accept them and keeps on staying in his upstairs room. Finally, he says yes for Klething Kuning to whom his mom describes as a girl with ugly face. Ande-Ande Lumut's decision is taken as Klething Kuning is the only girl who doesn't let Yuyu Kangkang help her. Yuyu Kangkang is described as a very big crab that helps the girls to cross the river but ask them to kiss him. Through the lense of feminist approach, Ande-Ande Lumut shows the men’s preference and dominance to make final decision over women. Even though the girls are actively propose their future husband, but they do it without giving any requirements. Meanwhile, the future husband chooses a girl with a criterion that no male has ever touched her, although the male is a crab.

Keywords: future spouse, Indonesian folktale, acceptance, patriarchal

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Authors: Maryam Ziaee, Himanshu Shee, Amrik Sohal

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Big Data Analytics (BDA) in supply chain management has recently drawn the attention of academics and practitioners. Big data refers to a massive amount of data from different sources, in different formats, generated at high speed through transactions in business environments and supply chain networks. Traditional statistical tools and techniques find it difficult to analyse this massive data. BDA can assist organisations to capture, store, and analyse data specifically in the field of supply chain. Currently, there is a paucity of research on BDA in the pharmaceutical supply chain context. In this research, the Australian pharmaceutical supply chain was selected as the case study. This industry is highly significant since the right medicine must reach the right patients, at the right time, in right quantity, in good condition, and at the right price to save lives. However, drug shortages remain a substantial problem for hospitals across Australia with implications on patient care, staff resourcing, and expenditure. Furthermore, a massive volume and variety of data is generated at fast speed from multiple sources in pharmaceutical supply chain, which needs to be captured and analysed to benefit operational decisions at every stage of supply chain processes. As the pharmaceutical industry lags behind other industries in using BDA, it raises the question of whether the use of BDA can improve transparency among pharmaceutical supply chain by enabling the partners to make informed-decisions across their operational activities. This presentation explores the impacts of BDA on supply chain management. An exploratory qualitative approach was adopted to analyse data collected through interviews. This study also explores the BDA potential in the whole pharmaceutical supply chain rather than focusing on a single entity. Twenty semi-structured interviews were undertaken with top managers in fifteen organisations (five pharmaceutical manufacturers, five wholesalers/distributors, and five public hospital pharmacies) to investigate their views on the use of BDA. The findings revealed that BDA can enable pharmaceutical entities to have improved visibility over the whole supply chain and also the market; it enables entities, especially manufacturers, to monitor consumption and the demand rate in real-time and make accurate demand forecasts which reduce drug shortages. Timely and precise decision-making can allow the entities to source and manage their stocks more effectively. This can likely address the drug demand at hospitals and respond to unanticipated issues such as drug shortages. Earlier studies explore BDA in the context of clinical healthcare; however, this presentation investigates the benefits of BDA in the Australian pharmaceutical supply chain. Furthermore, this research enhances managers’ insight into the potentials of BDA at every stage of supply chain processes and helps to improve decision-making in their supply chain operations. The findings will turn the rhetoric of data-driven decision into a reality where the managers may opt for analytics for improved decision-making in the supply chain processes.

Keywords: big data analytics, data-driven decision, pharmaceutical industry, supply chain management

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Authors: Diego De Castro Rodrigues, Marcelo B. Nery, Sergio Adorno

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The project aims to develop a framework for social data analysis, particularly by mobilizing criminal records and applying descriptive computational techniques, such as associative algorithms and extraction of tree decision rules, among others. The methods and instruments discussed in this work will enable the discovery of patterns, providing a guided means to identify similarities between recurring situations in the social sphere using descriptive techniques and data visualization. The study area has been defined as the city of São Paulo, with the structuring of social data as the central idea, with a particular focus on the quality of the information. Given this, a set of tools will be validated, including the use of a database and tools for visualizing the results. Among the main deliverables related to products and the development of articles are the discoveries made during the research phase. The effectiveness and utility of the results will depend on studies involving real data, validated both by domain experts and by identifying and comparing the patterns found in this study with other phenomena described in the literature. The intention is to contribute to evidence-based understanding and decision-making in the social field.

Keywords: social data analysis, criminal records, computational techniques, data mining, big data

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Authors: Masoud Sheidai, Fahimeh Koohdar, Hashem Sharifi

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Juglans regia (L.) commonly known as Persian walnut of the genus Juglans L. (Juglandaceae) is one of the most important cultivated plant species due to its high-quality wood and edible nuts. The genetic diversity analysis is essential for conservation and management of tree species. Persian walnut is native from South-Eastern Europe to North-Western China through Tibet, Nepal, Northern India, Pakistan, and Iran. The species like Persian walnut, which has a wide range of geographical distribution, should harbor extensive genetic variability to adapt to environmental fluctuations they face. We aimed to study the population genetic structure of seven Persian walnut populations including three wild and four cultivated populations by using ISSR (Inter simple sequence repeats) and SRAP (Sequence related amplified polymorphism) molecular markers. We also aimed to compare the genetic variability revealed by ISSR neutral multilocus marker and rDNA ITS sequences. The studied populations differed in morphological features as the samples in each population were clustered together and were separate from the other populations. Three wild populations studied were placed close to each other. The mantel test after 5000 times permutation performed between geographical distance and morphological distance in Persian walnut populations produced significant correlation (r = 0.48, P = 0.002). Therefore, as the populations become farther apart, they become more divergent in morphological features. ISSR analysis produced 47 bands/ loci, while we obtained 15 SRAP bands. Gst and other differentiation statistics determined for these loci revealed that most of the ISSR and SRAP loci have very good discrimination power and can differentiate the studied populations. AMOVA performed for these loci produced a significant difference (< 0.05) supporting the above-said result. AMOVA produced significant genetic difference based on ISSR data among the studied populations (PhiPT = 0.52, P = 0.001). AMOVA revealed that 53% of the total variability is due to among population genetic difference, while 47% is due to within population genetic variability. The results showed that both multilocus molecular markers and ITS sequences can differentiate Persian walnut populations. The studied populations differed genetically and showed isolation by distance (IBD). ITS sequence based MP and Bayesian phylogenetic trees revealed that Iranian walnut cultivars form a distinct clade separated from the cultivars studied from elsewhere. Almost all clades obtained have high bootstrap value. The results indicated that a combination of multilpcus and sequencing molecular markers can be used in genetic differentiation of Persian walnut.

Keywords: genetic diversity, population, molecular markers, genetic difference

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Authors: Lilian A. Okeke, I. Okeke, N. Waziri, S. Balogun, P. Nguku, O. Fawole

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Background: Access to ante-natal care services promotes safe motherhood and delivery with improved maternal and neonatal outcome. We conducted this study to identify factors influencing the utilization of antenatal care (ANC) and health delivery services. Methods: We conducted a cross sectional study. Households were numbered and a one in three sample was selected using a systematic sampling method. One hundred and ninety eight women who were either pregnant or had previous deliveries were interviewed using pretested structured questionnaires to obtain information on their socio-demographic characteristics, and reasons for non-utilization of ANC and health delivery services. We performed univariate and bivariate analysis using Epi info version 3.5.3. Results: The age of respondents ranged from (17-55 years) with a median age of 29 years. One hundred and ninety two (97%) utilized antenatal care services. Ninety three (47.9%) attended ANC at second trimester. More than half (58.6%) had ≥ 4 visits to ANC. One hundred and thirty one (66.2%) had their last delivery at home by a traditional birth attendant. Factors associated with ANC and health facility delivery services utilization were: age group 45-55 (OR 0.01; 95% CI: 0.00-0.16) and > 55 years (OR 0.03; 95% CI: 0.00-0.60), wife’s educational status (OR 3.17; 95% CI: 1.66-8.30), husband’s permission (OR 11.8; 95% CI 2.19-63.62), and distance ≥ 5km (OR 0.33; 95% CI: 0.16-0.60). Conclusion: ANC services were well utilized. Most women did not book early and had their last delivery at home. Predictors of ANC use and health facility delivery were age, wife’s educational status, husband's permission and long distance from health facility. A one-day health sensitization of the benefits of ANC utilization and the dangers of delivering at home was implemented.

Keywords: ante natal care, health facility, delivery services, rural area, Plateau state

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Authors: Semachew M. Kassa, Africa M Geremew, Tezera F. Azmatch, Nandyala Darga Kumar

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Frequency ratio (FR) and analytical hierarchy process (AHP) methods are developed based on past landslide failure points to identify the landslide susceptibility mapping because landslides can seriously harm both the environment and society. However, it is still difficult to select the most efficient method and correctly identify the main driving factors for particular regions. In this study, we used fourteen landslide conditioning factors (LCFs) and five soft computing algorithms, including Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR), Artificial Neural Network (ANN), and Naïve Bayes (NB), to predict the landslide susceptibility at 12.5 m spatial scale. The performance of the RF (F1-score: 0.88, AUC: 0.94), ANN (F1-score: 0.85, AUC: 0.92), and SVM (F1-score: 0.82, AUC: 0.86) methods was significantly better than the LR (F1-score: 0.75, AUC: 0.76) and NB (F1-score: 0.73, AUC: 0.75) method, according to the classification results based on inventory landslide points. The findings also showed that around 35% of the study region was made up of places with high and very high landslide risk (susceptibility greater than 0.5). The very high-risk locations were primarily found in the western and southeastern regions, and all five models showed good agreement and similar geographic distribution patterns in landslide susceptibility. The towns with the highest landslide risk include Amhara Saint Town's western part, the Northern part, and St. Gebreal Church villages, with mean susceptibility values greater than 0.5. However, rainfall, distance to road, and slope were typically among the top leading factors for most villages. The primary contributing factors to landslide vulnerability were slightly varied for the five models. Decision-makers and policy planners can use the information from our study to make informed decisions and establish policies. It also suggests that various places should take different safeguards to reduce or prevent serious damage from landslide events.

Keywords: artificial neural network, logistic regression, landslide susceptibility, naïve Bayes, random forest, support vector machine

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Authors: Solmaz Jahed Shiran, John Hearne, Tayebeh Saghapour

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Daily travel behavior is strongly influenced by the location of the places of residence, education, and employment. Hence a change in those locations due to a move or changes in an occupation leads to a change in travel behavior. Given the interventions of housing mobility and travel behaviors, the hypothesis is that a mobile housing market allows households to move as a result of any change in their life course, allowing them to be closer to central services, public transport facilities and workplace and hence reducing the time spent by individuals on daily travel. Conversely, household’s immobility may lead to longer commutes of residents, for example, after a change of a job or a need for new services such as schools for children who have reached their school age. This paper aims to investigate the association between residential mobility and travel behavior. The Victorian Integrated Survey of Travel and Activity (VISTA) data is used for the empirical analysis. Car ownership and journey to work time and distance of employed people are used as indicators of travel behavior. Change of usual residence within the last five years used to identify movers and non-movers. Statistical analysis, including regression models, is used to compare the travel behavior of movers and non-movers. The results show travel time, and the distance does not differ for movers and non-movers. However, this is not the case when taking into account the residence tenure-type. In addition, car ownership rate and number found to be significantly higher for non-movers. It is hoped that the results from this study will contribute to a better understanding of factors other than common socioeconomic and built environment features influencing travel behavior.

Keywords: journey to work, regression models, residential mobility, commute mode, car ownership

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Authors: Yenie Norambuena

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Analogical reasoning is a cognitive process that consists of structured comparisons of mental representations and scheme construction. Because of its heuristic function, it is ubiquitous in cognition and could play an important role in language development. The use of analogies is expressed early in children and this behavior is also reflected in language, suggesting a possible way to understand the complex links between thought and language. The current research examines factors of verbal and non-verbal reasoning that should be taken into consideration in the study of language development for their relations and predictive value. The study was conducted with 48 Chilean preschoolers (Spanish speakers) from 4 to 6-year-old. We assessed children’s verbal analogical reasoning, non-verbal analogical reasoning and linguistics skills (Listening Comprehension, Phonemic awareness, Alphabetic principle, Syllabification, Lexical repetition and Lexical decision). The results evidenced significant correlations between analogical reasoning factors and linguistic skills and they can predict linguistic performance mainly on oral comprehension, lexical decision and phonological skills. These findings suggest a fundamental interrelationship between analogical reasoning and linguistic performance on children’s and points to the need to consider this cognitive process in comprehensive theories of children's language development.

Keywords: verbal analogical reasoning, non-verbal analogical reasoning, linguistic skills, language development

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Authors: Payam Mousavi, Andrew L. Stewart, Huiwen You, Aryeh F. G. Fayerman

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We present a decision-support tool to assist an operator in the detection and tracking of a suspect vehicle traveling to an unknown target destination. Multiple data sources, such as traffic cameras, traffic information, weather, etc., are integrated and processed in real-time to infer a suspect’s intended destination chosen from a list of pre-determined high-value targets. Previously, we presented our work in the detection and tracking of vehicles using traffic and airborne cameras. Here, we focus on the fusion and processing of that information to predict a suspect’s behavior. The network of cameras is represented by a directional graph, where the edges correspond to direct road connections between the nodes and the edge weights are proportional to the average time it takes to travel from one node to another. For our experiments, we construct our graph based on the greater Los Angeles subset of the Caltrans’s “Performance Measurement System” (PeMS) dataset. We propose a Bayesian approach where a posterior probability for each target is continuously updated based on detections of the suspect in the live video feeds. Additionally, we introduce the concept of ‘soft interventions’, inspired by the field of Causal Inference. Soft interventions are herein defined as interventions that do not immediately interfere with the suspect’s movements; rather, a soft intervention may induce the suspect into making a new decision, ultimately making their intent more transparent. For example, a soft intervention could be temporarily closing a road a few blocks from the suspect’s current location, which may require the suspect to change their current course. The objective of these interventions is to gain the maximum amount of information about the suspect’s intent in the shortest possible time. Our system currently operates in a human-on-the-loop mode where at each step, a set of recommendations are presented to the operator to aid in decision-making. In principle, the system could operate autonomously, only prompting the operator for critical decisions, allowing the system to significantly scale up to larger areas and multiple suspects. Once the intended target is identified with sufficient confidence, the vehicle is reported to the authorities to take further action. Other recommendations include a selection of road closures, i.e., soft interventions, or to continue monitoring. We evaluate the performance of the proposed system using simulated scenarios where the suspect, starting at random locations, takes a noisy shortest path to their intended target. In all scenarios, the suspect’s intended target is unknown to our system. The decision thresholds are selected to maximize the chances of determining the suspect’s intended target in the minimum amount of time and with the smallest number of interventions. We conclude by discussing the limitations of our current approach to motivate a machine learning approach, based on reinforcement learning in order to relax some of the current limiting assumptions.

Keywords: autonomous surveillance, Bayesian reasoning, decision support, interventions, patterns of life, predictive analytics, predictive insights

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Authors: Adejoke Omowumi Adediran

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The commitment of African states to liberalise civil aviation in Africa through the implementation of the Yamoussoukro Decision of 1999 was reiterated in 2015 at the African Union Assembly meeting. A declaration was made by African Heads of government at the meeting to ensure the immediate implementation of the decision towards the establishment of a Single African Air Transport Market (SAATM) by 2017. A SAATM will imply among others, a removal of all commercial restrictions for African airlines in Africa; access to any route in Africa by African airlines without any required permit or authorisation; and a common set of regulations for airlines in African member states. As the envisioned 2017 date for launching the SAATM could not be met, a new date of January 2018 has been set. The lack of political will by African States, however, remains a prominent challenge to the realisation of the SAATM. As at June 2017, only twenty-one states had signed the commitment to actualise the decision creating the SAATM. In actualisation of the SAATM, a regulatory framework has been established to efficiently manage the new African airline industry, and regulatory texts have been adopted as part of the legal regime. This legal regime is to regulate both interstate and domestic operations. Airlines in Nigeria are currently faced with certain challenges which ultimately affect their effectiveness and passengers as well do not enjoy utmost customer satisfaction with services rendered by the airlines. Although Nigeria has demonstrated support for the SAATM since 2015, as Nigeria alongside ten other states, signed the initial commitment, whether or not SAATM will eventually be beneficial to airlines and passengers has become an issue in the light of the challenges of the Nigerian airline industry. Remarkably, the benefit of the SAATM is to a large extent ultimately determined by its legal framework. Using doctrinal research, this paper examines the legal implications of the SAATM on airlines and passengers in Nigeria. This paper analyses the legal framework of SAATM and juxtaposes this with the particular issues affecting airlines and passengers in Nigeria such as financial difficulties on the part of airlines and consumer protection as regards passengers. Among others, it can be asserted that the legal regime affords an opportunity for business expansion and creates a fair environment for competition. This is beneficial not only to the airlines but to passengers as well. In addition, in the interest of passengers, consumer rights are prescribed, and the regulations also cater for situations where airlines interrupt their services, as losses arising from these situations will be mitigated. There is indeed no doubt that the SAATM will be of great utility to both airlines and passengers in Nigeria.

Keywords: airlines, civil aviation, competition, consumer protection, passengers, single African air transport market, yamoussoukro decision

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Authors: Nureni Amoo, Sunday Aroge, Oluranti Akintola, Hakeem Olujide, Ibrahim Alabi

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This study investigated the optimum landfill site in Osogbo so as to identify suitable solid waste dumpsite for proper waste management in the capital city. Despite an increase in alternative techniques for disposing of waste, landfilling remains the primary means of waste disposal. These changes in attitudes in many parts of the world have been supported by changes in laws and policies regarding the environment and waste disposal. Selecting the most suitable site for landfill can avoid any ecological and socio-economic effects. The increase in industrial and economic development, along with the increase of population growth in Osogbo town, generates a tremendous amount of solid waste within the region. Factors such as the scarcity of land, the lifespan of the landfill, and environmental considerations warrant that the scientific and fundamental studies are carried out in determining the suitability of a landfill site. The analysis of spatial data and consideration of regulations and accepted criteria are part of the important elements in the site selection. This paper presents a multi-criteria decision-making method using geographic information system (GIS) with the integration of the fuzzy logic multi-criteria decision making (FMCDM) technique for landfill suitability site evaluation. By using the fuzzy logic method (classification of suitable areas in the range of 0 to 1 scale), the superposing of the information layers related to drainage, soil, land use/land cover, slope, land use, and geology maps were performed in the study. Based on the result obtained in this study, five (5) potential sites are suitable for the construction of a landfill are proposed, two of which belong to the most suitable zone, and the existing waste disposal site belonged to the unsuitable zone.

Keywords: fuzzy logic multi-criteria decision making, geographic information system, landfill, suitable site, waste disposal

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Authors: Toshitaka Higashino, Naoki Wakamiya

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Human beings perform a task by perceiving information from outside, recognizing them, and responding them. There have been various attempts to analyze and understand internal processes behind the reaction to a given stimulus by conducting psychological experiments and analysis from multiple perspectives. Among these, we focused on Model Human Processor (MHP). However, it was built based on psychological experiments and thus the relation with brain activity was unclear so far. To verify the validity of the MHP and propose our model from a viewpoint of neuroscience, EEG (Electroencephalography) measurements are performed during experiments in this study. More specifically, first, experiments were conducted where Latin alphabet characters were used as visual stimuli. In addition to response time, ERPs (event-related potentials) such as N100 and P300 were measured by using EEG. By comparing cycle time predicted by the MHP and latency of ERPs, it was found that N100, related to perception of stimuli, appeared at the end of the perceptual processor. Furthermore, by conducting an additional experiment, it was revealed that P300, related to decision making, appeared during the response decision process, not at the end. Second, by experiments using Japanese Hiragana characters, i.e. Japan's own phonetic symbols, those findings were confirmed. Finally, Japanese Kanji characters were used as more complicated visual stimuli. A Kanji character usually has several readings and several meanings. Despite the difference, a reading-related task and a meaning-related task exhibited similar results, meaning that they involved similar information processing processes of the brain. Based on those results, our model was proposed which reflects response time and ERP latency. It consists of three processors: the perception processor from an input of a stimulus to appearance of N100, the cognitive processor from N100 to P300, and the decision-action processor from P300 to response. Using our model, an application system which reflects brain activity can be established.

Keywords: brain activity, EEG, information processing model, model human processor

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Authors: Hsin Lee, Hsuan Lee

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Legal artificial intelligence (LegalAI) has been increasing applications within legal systems, propelled by advancements in natural language processing (NLP). Compared with general documents, legal case documents are typically long text sequences with intrinsic logical structures. Most existing language models have difficulty understanding the long-distance dependencies between different structures. Another unique challenge is that while the Judiciary of Taiwan has released legal judgments from various levels of courts over the years, there remains a significant obstacle in the lack of labeled datasets. This deficiency makes it difficult to train models with strong generalization capabilities, as well as accurately evaluate model performance. To date, models in Taiwan have yet to be specifically trained on judgment data. Given these challenges, this research proposes a Longformer-based pre-trained language model explicitly devised for retrieving similar judgments in Taiwanese legal documents. This model is trained on a self-constructed dataset, which this research has independently labeled to measure judgment similarities, thereby addressing a void left by the lack of an existing labeled dataset for Taiwanese judgments. This research adopts strategies such as early stopping and gradient clipping to prevent overfitting and manage gradient explosion, respectively, thereby enhancing the model's performance. The model in this research is evaluated using both the dataset and the Average Entropy of Offense-charged Clustering (AEOC) metric, which utilizes the notion of similar case scenarios within the same type of legal cases. Our experimental results illustrate our model's significant advancements in handling similarity comparisons within extensive legal judgments. By enabling more efficient retrieval and analysis of legal case documents, our model holds the potential to facilitate legal research, aid legal decision-making, and contribute to the further development of LegalAI in Taiwan.

Keywords: legal artificial intelligence, computation and language, language model, Taiwanese legal cases

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Authors: Bahman Gorjian

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The notorious COVID-19 is known as an illness that is caused by a novel coronavirus. Since its breakthrough, most governments have decided to temporarily close educational institutions in an attempt to reduce the spread of this disease. Distance education in Iran, like other countries, started from the beginning of the pandemic and caused the closure of schools and universities as an immediate response to control the spread of the virus. The present study followed two aims: First, to investigate if Iraqi M.A students majoring in TEFL who have been studying in Iranian universities during the pandemic believe that COVID-19 had negative/positive effects on their educational achievement; and second, to find how frequently these Iraqi M.A students have experienced psychological problems (e.g., anxiety, numbness, nightmares, nervousness) during the COVID-19. The participants were both male and female students who were admitted for M.A. TEFL courses at 4 Iranian Universities (Abadan Brach, Ahvaz Branch, Science and Research Branch, and Shiraz Branch of Islamic Azad University) for the winter academic term of 2020. The start of their classes coincided with the global outbreak of COVID-19. They were invited to take part in the present study through snowball sampling and were asked to provide their views on two questionnaires. The instruments used for gathering the data were the educational achievement questionnaire and self-rating anxiety scale. The results of the analysis suggested that the participants believed in the negative effects of COVID-19 on their education; the results also suggested COVID-19 affected participants’ psychological states. The discussed findings may have implications for international students and experts interested in the online education system.

Keywords: COVID-19, distance education, Iraqi M.A. students, teaching English as a foreign language, educational impacts, psychological impacts

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Authors: James M. Wakiru, Liliane Pintelon, Peter Muchiri, Stanley Mburu

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Maintenance performance measurement (MPM) represents an integrated aspect that considers both operational and maintenance related aspects while evaluating the effectiveness and efficiency of maintenance to ensure assets are working as they should. Three salient issues require to be addressed for an asset-intensive organization to employ an MPM-based framework to optimize maintenance. Firstly, the organization should establish important perfomance metric(s), in this case the maintenance objective(s), which they will be focuss on. The second issue entails aligning the maintenance objective(s) with maintenance optimization. This is achieved by deriving maintenance performance indicators that subsequently form an objective function for the optimization program. Lastly, the objective function is employed in an optimization program to derive maintenance decision support. In this study, we develop a framework that initially identifies the crucial maintenance performance measures, and employs them to derive maintenance decision support. The proposed framework is demonstrated in a case study of a geothermal drilling rig, where the objective function is evaluated utilizing a simulation-based model whose parameters are derived from empirical maintenance data. Availability, reliability and maintenance inventory are depicted as essential objectives requiring further attention. A simulation model is developed mimicking a drilling rig operations and maintenance where the sub-systems are modelled undergoing imperfect maintenance, corrective (CM) and preventive (PM), with the total cost as the primary performance measurement. Moreover, three maintenance spare inventory policies are considered; classical (retaining stocks for a contractual period), vendor-managed inventory with consignment stock and periodic monitoring order-to-stock (s, S) policy. Optimization results infer that the adoption of (s, S) inventory policy, increased PM interval and reduced reliance of CM actions offers improved availability and total costs reduction.

Keywords: maintenance, vendor-managed, decision support, performance, optimization

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Authors: Wenzheng Fu, Yue Fu, Zhijiang Dong, Yujian Fu

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The integration of artificial intelligence (AI) and machine learning (ML) into project management is transforming how engineering projects are executed, monitored, and controlled. This paper provides a comprehensive survey of AI and ML applications in project management, systematically categorizing their use in key areas such as project data analytics, monitoring, tracking, scheduling, and reporting. As project management becomes increasingly data-driven, AI and ML offer powerful tools for improving decision-making, optimizing resource allocation, and predicting risks, leading to enhanced project outcomes. The review highlights recent research that demonstrates the ability of AI and ML to automate routine tasks, provide predictive insights, and support dynamic decision-making, which in turn increases project efficiency and reduces the likelihood of costly delays. This paper also examines the emerging trends and future opportunities in AI-driven project management, such as the growing emphasis on transparency, ethical governance, and data privacy concerns. The research suggests that AI and ML will continue to shape the future of project management by driving further automation and offering intelligent solutions for real-time project control. Additionally, the review underscores the need for ongoing innovation and the development of governance frameworks to ensure responsible AI deployment in project management. The significance of this review lies in its comprehensive analysis of AI and ML’s current contributions to project management, providing valuable insights for both researchers and practitioners. By offering a structured overview of AI applications across various project phases, this paper serves as a guide for the adoption of intelligent systems, helping organizations achieve greater efficiency, adaptability, and resilience in an increasingly complex project management landscape.

Keywords: artificial intelligence, decision support systems, machine learning, project management, resource optimization, risk prediction

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Authors: Jiri Barta

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At the present time, awareness, education, computer simulation and information systems protection are very serious and relevant topics. The article deals with perspectives and possibilities of implementation of emergence or natural hazard threats into the system which is developed for communication among members of crisis management staffs. The Czech Hydro-Meteorological Institute with its System of Integrated Warning Service resents the largest usable base of information. National information systems are connected to foreign systems, especially to flooding emergency systems of neighboring countries, systems of European Union and international organizations where the Czech Republic is a member. Use of outputs of particular information systems and computer simulations on a single communication interface of information system for communication among members of crisis management staff and setting the site interoperability in the net will lead to time savings in decision-making processes in solving extraordinary events and crisis situations. Faster managing of an extraordinary event or a crisis situation will bring positive effects and minimize the impact of negative effects on the environment.

Keywords: computer simulation, communication, continuity, critical infrastructure, information systems, safety

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Authors: Omer Shujat Bhatti, Afshan Huma

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Online educational system were considered a great opportunity for distance learning. In recent days of COVID19 pandemic, it enable the continuation of educational activities at all levels of education, from primary school to the top level universities. One of the key considered element in supporting the online educational system is video lectures. The current research explored the usefulness of the video lectures delivered to technical students of masters level with a focus on MSc Sustainable Environmental design students who have diverse backgrounds in the formal educational system. Hence they were unable to cope right away with the online system and faced communication and understanding issues in the lecture session due to internet and allied connectivity issues. Researcher used self prepared video lectures for respective subjects and provided them to the students using Youtube channel and subject based Whatsapp groups. Later, students were asked about the usefulness of the lectures towards a better understanding of the subject and an overall enhanced learning experience. More than 80% of the students appreciated the effort and requested it to be part of the overall system. Data collection was done using an online questionnaire which was prior briefed to the students with the purpose of research. It was concluded that video lectures should be considered an integral part of the lecture sessions and must be provided prior to the lecture session, ensuring a better quality of delivery. It was also recommended that the existing system must be upgraded to support the availability of these video lectures through the portal. Teachers training must be provided to help develop quality video content ensuring that is able to cover the content and courses taught.

Keywords: video lectures, online distance education, synchronous instruction, asynchronous communication

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Authors: Rattanakorn Phadungthin, Juthathip Haema

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This research presents an assessment of the criticality of the substation's power transformer using the Entropy Weight method to enable more effective maintenance planning. Typically, transformers fail due to heat, electricity, chemical reactions, mechanical stress, and extreme climatic conditions. Effective monitoring of the insulating oil is critical to prevent transformer failure. However, finding appropriate weights for dissolved gases is a major difficulty due to the lack of a defined baseline and the requirement for subjective expert opinion. To decrease expert prejudice and subjectivity, the Entropy Weight method is used to optimise the weightings of eleven key dissolved gases. The algorithm to assess the criticality operates through five steps: create a decision matrix, normalise the decision matrix, compute the entropy, calculate the weight, and calculate the criticality score. This study not only optimises gas weighing but also greatly minimises the need for expert judgment in transformer maintenance. It is expected to improve the efficiency and reliability of power transformers so failures and related economic costs are minimized. Furthermore, maintenance schemes and ranking are accomplished appropriately when the assessment of criticality is reached.

Keywords: criticality assessment, dissolved gas, maintenance scheme, power transformer

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Authors: Donghun Jeong, Somyoung Shin, Yeoil Yun

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A large-scale traffic accident refers to an accident in which more than three people die or more than thirty people are dead or injured. In order to prevent a large-scale traffic accident from causing a big loss of lives or establish effective improvement measures, it is important to analyze accident situations in-depth and understand the effects of major accident variables on an accident. This study aims to analyze the contribution of individual accident variables to accident results, based on the accurate reconstruction of traffic accidents using PC-Crash’s Multi-Body, which is an accident reconstruction program, and simulation of each scenario. Multi-Body system of PC-Crash accident reconstruction program is used for multi-body accident reconstruction that shows motions in diverse directions that were not approached previously. MB System is to design and reproduce a form of body, which shows realistic motions, using several bodies. Targeting the 'freight truck cargo drop accident around the Changwon Tunnel' that happened in November 2017, this study conducted a simulation of the freight truck cargo drop accident and analyzed the contribution of individual accident majors. Then on the basis of the driving speed, cargo load, and stacking method, six scenarios were devised. The simulation analysis result displayed that the freight car was driven at a speed of 118km/h(speed limit: 70km/h) right before the accident, carried 196 oil containers with a weight of 7,880kg (maximum load: 4,600kg) and was not fully equipped with anchoring equipment that could prevent a drop of cargo. The vehicle speed, cargo load, and cargo anchoring equipment were major accident variables, and the accident contribution analysis results of individual variables are as follows. When the freight car only obeyed the speed limit, the scattering distance of oil containers decreased by 15%, and the number of dropped oil containers decreased by 39%. When the freight car only obeyed the cargo load, the scattering distance of oil containers decreased by 5%, and the number of dropped oil containers decreased by 34%. When the freight car obeyed both the speed limit and cargo load, the scattering distance of oil containers fell by 38%, and the number of dropped oil containers fell by 64%. The analysis result of each scenario revealed that the overspeed and excessive cargo load of the freight car contributed to the dispersion of accident damage; in the case of a truck, which did not allow a fall of cargo, there was a different type of accident when driven too fast and carrying excessive cargo load, and when the freight car obeyed the speed limit and cargo load, there was the lowest possibility of causing an accident.

Keywords: accident reconstruction, large-scale traffic accident, PC-Crash, MB system

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Authors: Ayat-Allah Bouramdane

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Solar Photovoltaic (PV) and Concentrated Solar Power (CSP) do not burn fossil fuels and, therefore, could meet the world's needs for low-carbon power generation as they do not release greenhouse gases into the atmosphere as they generate electricity. The power output of the solar PV module and CSP collector is proportional to the temperature and the amount of solar radiation received by their surface. Hence, the determination of the most convenient locations of PV and CSP systems is crucial to maximizing their output power. This study aims to provide a hands-on and plausible approach to the multi-criteria evaluation of site suitability of PV and CSP plants using a combination of Geographic Referenced Information (GRI) and Analytic Hierarchy Process (AHP). Applying the GRI-based AHP approach is meant to specify the criteria and sub-criteria, to identify the unsuitable areas, the low-, moderate-, high- and very high suitable areas for each layer of GRI, to perform the pairwise comparison matrix at each level of the hierarchy structure based on experts' knowledge, and calculate the weights using AHP to create the final map of solar PV and CSP plants suitability in Morocco with a particular focus on the Dakhla city. The results recognize that solar irradiation is the main decision factor for the integration of these technologies on energy policy goals of Morocco but explicitly account for other factors that cannot only limit the potential of certain locations but can even exclude the Dakhla city classified as unsuitable area. We discuss the sensitivity of the PV and CSP site suitability to different aspects, such as the methodology, the climate conditions, and the technology used in each source, and provide the final recommendations to the Moroccan energy strategy by analyzing if actual Morocco's PV and CSP installations are located within areas deemed suitable and by discussing several cases to provide mutual benefits across the Food-Energy-Water nexus. The adapted methodology and conducted suitability map could be used by researchers or engineers to provide helpful information for decision-makers in terms of sites selection, design, and planning of future solar plants, especially in areas suffering from energy shortages, such as the Dakhla city, which is now one of Africa's most promising investment hubs and it is especially attractive to investors looking to root their operations in Africa and import to European markets.

Keywords: analytic hierarchy process, concentrated solar power, dakhla, geographic referenced information, Morocco, multi-criteria decision-making, photovoltaic, site suitability

Procedia PDF Downloads 175

Authors: Kuei-Ling Sun, Emily Chia-Yu Su

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Allergy is a hypersensitive overreaction of the immune system to environmental stimuli, and a major health problem. These overreactions include rashes, sneezing, fever, food allergies, anaphylaxis, asthmatic, shock, or other abnormal conditions. Allergies can be caused by food, insect stings, pollen, animal wool, and other allergens. Their development of allergies is due to both genetic and environmental factors. Allergies involve immunoglobulin E antibodies, a part of the body’s immune system. Immunoglobulin E antibodies will bind to an allergen and then transfer to a receptor on mast cells or basophils triggering the release of inflammatory chemicals such as histamine. Based on the increasingly serious problem of environmental change, changes in lifestyle, air pollution problem, and other factors, in this study, we both collect allergens and non-allergens from several databases and use several machine learning methods for classification, including logistic regression (LR), stepwise regression, decision tree (DT) and neural networks (NN) to do the model comparison and determine the permutations of allergen amino acid’s sequence.

Keywords: allergy, classification, decision tree, logistic regression, machine learning

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Authors: R. Whalley

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The heat transfer modelling for a diffusion process will be considered. Difficulties in computing the time-distance dynamics of the representation will be addressed. Incomplete and irrational Laplace function will be identified as the computational issue. Alternative approaches to the response evaluation process will be provided. An illustration application problem will be presented. Graphical results confirming the theoretical procedures employed will be provided.

Keywords: heat, transfer, diffusion, modelling, computation

Procedia PDF Downloads 553

Authors: Hiroshi Kinoshita, Yasuto Nakanishi, Ryuhei Okuno, Toshio Higashi

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Various kinds of pegboards have been developed and used widely in research and clinics of rehabilitation for evaluation and training of patient’s hand function. A common measure in these peg boards is a total time of performance execution assessed by a tester’s stopwatch. Introduction of electrical and automatic measurement technology to the apparatus, on the other hand, has been delayed. The present work introduces the development of a pegboard with an electric sensor to detect moments of individual peg’s insertion and removal. The work also gives fundamental data obtained from a group of healthy young individuals who performed peg transfer tasks using the pegboard developed. Through trails and errors in pilot tests, two 10-hole peg-board boxes installed with a small photo-reflector and a DC amplifier at the bottom of each hole were designed and built by the present authors. The amplified electric analogue signals from the 20 reflectors were automatically digitized at 500 Hz per channel, and stored in a PC. The boxes were set on a test table at different distances (25, 50, 75, and 125 mm) in parallel to examine the effect of hole-to-hole distance. Fifty healthy young volunteers (25 in each gender) as subjects of the study performed successive fast 80 time peg transfers at each distance using their dominant and non-dominant hands. The data gathered showed a clear-cut light interruption/continuation moment by the pegs, allowing accurately (no tester’s error involved) and precisely (an order of milliseconds) to determine the pull out and insertion times of each peg. This further permitted computation of individual peg movement duration (PMD: from peg-lift-off to insertion) apart from hand reaching duration (HRD: from peg insertion to lift-off). An accidental drop of a peg led to an exceptionally long ( < mean + 3 SD) PMD, which was readily detected from an examination of data distribution. The PMD data were commonly right-skewed, suggesting that the median can be a better estimate of individual PMD than the mean. Repeated measures ANOVA using the median values revealed significant hole-to-hole distance, and hand dominance effects, suggesting that these need to be fixed in the accurate evaluation of PMD. The gender effect was non-significant. Performance consistency was also evaluated by the use of quartile variation coefficient values, which revealed no gender, hole-to-hole, and hand dominance effects. The measurement reliability was further examined using interclass correlation obtained from 14 subjects who performed the 25 and 125 mm hole distance tasks at two 7-10 days separate test sessions. Inter-class correlation values between the two tests showed fair reliability for PMD (0.65-0.75), and for HRD (0.77-0.94). We concluded that a sensor peg board developed in the present study could provide accurate (excluding tester’s errors), and precise (at a millisecond rate) time information of peg movement separated from that used for hand movement. It could also easily detect and automatically exclude erroneous execution data from his/her standard data. These would lead to a better evaluation of hand dexterity function compared to the widely used conventional used peg boards.

Keywords: hand, dexterity test, peg movement time, performance consistency

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Authors: F. Felipe

Abstract:

Air Defense Systems contain high-value assets that are expected to fulfill their mission for several years - in many cases, even decades - while operating in a fast-changing, technology-driven environment. Thus, it is paramount that decision-makers can assess how effective an Air Defense System is in the face of new developing threats, as well as to identify the bottlenecks that could jeopardize the security of the airspace of a country. Given the broad extent of activities and the great variety of assets necessary to achieve the strategic objectives, a systems approach was taken in order to delineate the core requirements and the physical architecture of an Air Defense System. Then, value-focused thinking helped in the definition of the measures of effectiveness. Furthermore, analytical methods were applied to create a formal structure that preliminarily assesses such measures. To validate the proposed methodology, a powerful simulation was also used to determine the measures of effectiveness, now in more complex environments that incorporate both uncertainty and multiple interactions of the entities. The results regarding the validity of this methodology suggest that the approach can support decisions aimed at enhancing the capabilities of Air Defense Systems. In conclusion, this paper sheds some light on how consolidated approaches of Systems Engineering and Operations Research can be used as valid techniques for solving problems regarding a complex and yet vital matter.

Keywords: air defense, effectiveness, system, simulation, decision-support

Procedia PDF Downloads 156

Authors: Yuanxiang Miao

Abstract:

Couchsurfing is a free homestay and social networking service accessible via the website and mobile app. Couchsurfers can directly request free accommodations from others and receive offers from each other. However, it is typically difficult for people to make a decision that accepts or declines a request when they receive it from Couchsurfers because they do not know each other at all. People are expected to meet up with some Couchsurfers who are kind, generous, and interesting while it is unavoidable to meet up with someone unfriendly. This paper utilized classification algorithms of Machine Learning to help people to find out the Good Couchsurfers and Not Good Couchsurfers on the Couchsurfing website. By knowing the prior experience, like Couchsurfer’s profiles, the latest references, and other factors, it became possible to recognize what kind of the Couchsurfers, and furthermore, it helps people to make a decision that whether to host the Couchsurfers or not. The value of this research lies in a case study in Kyoto, Japan in where the author has hosted 54 Couchsurfers, and the author collected relevant data from the 54 Couchsurfers, finally build a model based on classification algorithms for people to predict Couchsurfers. Lastly, the author offered some feasible suggestions for future research.

Keywords: Couchsurfing, Couchsurfers prediction, classification algorithm, hospitality tourism platform, hospitality sciences, machine learning

Procedia PDF Downloads 131

Authors: Pataraporn Kheawwan

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Preceptorship represents an important aspect in new nurse orientation. However, there was no formal preceptor training program developed for nurse preceptor in Thailand. The purposes of this study were to develop and evaluate formal preceptor training program for nurse preceptors in King Chulalongkorn Memorial Hospital, Thailand. A research and development study design was utilized in this study. Participants were 37 nurse preceptors. The program contents were delivered by e-learning material, class lecture, group discussion followed by simulation training. Knowledge of the participants was assessed pre and post program. Skill and critical thinking were assessed using Preceptor Skill and Decision Making Evaluation form at the end of program. Statistical significant difference in knowledge regarding preceptor role and coaching strategies between pre and post program were found. All participants had satisfied skill and decision making score after completed the program. Most of participants perceived benefits of preceptor training course. In conclusion, The results of this study reveal that the newly developed preceptorship course is an effective formal training course for nurse preceptors.

Keywords: preceptor, preceptorship, new nurse, clinical education

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Authors: Thorsten Auer, Sumaya Islam, Sabrina Plaß, Colin Wooldridge

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Whether individuals are more likely to forgo some ethical values if it is for a “great” social mission remains questionable. Research interest in the mechanism of moral disengagement has risen sharply in the organizational context over the last decades. Moral disengagement provides an explanatory approach to why individuals decide against their moral intent and describes the tendency to make unethical decisions due to a lack of self-regulation given various actions and their consequences. In our study, we examine the differences between individual decision-making given a commercial and social entrepreneurial context. Thereby, we investigate whether individuals in a social entrepreneurial context, characterized by pro-social goals and purpose beyond profit maximization, tend to make more or less “unethical” decisions in trade-off situations than those given a profit-focused commercial, entrepreneurial context. While a general priming effect may explain the tendency for individuals to make less unethical decisions given a social context, it remains unclear how individuals decide given a trade-off in that specific context. The trade-off in our study is characterized by the option to decide (un-) ethically to enhance the business purpose (in the social context, a social purpose, in the commercial context, a profit-maximization purpose). To investigate which characteristics of the context –and specifically of a trade-off – lead individuals to disregard and override their ethical values for a “greater good”, we design a conjoint analysis. This approach allows us to vary the attributes and scenarios and to test which attributes of a trade-off increase the probability of making an unethical choice. We add survey data to examine the individual propensity to morally disengage as an influencing factor to prefer certain attributes. Currently, we are in the final process of designing the conjoint analysis and plan to conduct the study by December 2022. We contribute to a better understanding of the role of moral disengagement in individual decision-making in a (social) entrepreneurial trade-off.

Keywords: moral disengagement, social entrepreneurship, unethical decision, conjoint analysis

Procedia PDF Downloads 87