Search results for: online learning environments

Authors: Sagir M. Yusuf, Chris Baber

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We describe issues bedeviling the coordination of heterogeneous (different sensors carrying agents) multi-agent missions such as belief conflict, situation reasoning, etc. We applied Bayesian and agents' presumptions inferential reasoning to solve the outlined issues with the heterogeneous multi-agent belief variation and situational-base reasoning. Bayesian Belief Network (BBN) was used in modeling the agents' belief conflict due to sensor variations. Simulation experiments were designed, and cases from agents’ missions were used in training the BBN using gradient descent and expectation-maximization algorithms. The output network is a well-trained BBN for making inferences for both agents and human experts. We claim that the Bayesian learning algorithm prediction capacity improves by the number of training data and argue that it enhances multi-agents robustness and solve agents’ sensor conflicts.

Keywords: distributed constraint optimization problem, multi-agent system, multi-robot coordination, autonomous system, swarm intelligence

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Authors: Yosra Mefteh Rekik

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A computational agent-based model of financial markets stresses interactions and dynamics among a very diverse set of traders. The growing body of research in this area relies heavily on computational tools which by-pass the restrictions of an analytical method. The main goal of this research is to understand how the stock market operates and behaves how to invest in the stock market and to study traders’ behavior within the context of the artificial stock markets populated by heterogeneous agents. All agents are characterized by adaptive learning behavior represented by the Artificial Neuron Networks. By using agent-based simulations on artificial market, we show that the existence of heterogeneous agents can explain the price dynamics in the financial market. We investigate the relation between market diversity and market efficiency. Our empirical findings demonstrate that greater market heterogeneity play key roles in market efficiency.

Keywords: agent-based modeling, artificial stock market, heterogeneous expectations, financial stylized facts, computational finance

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Authors: İrem Yetkin, Feray Maden, Seda Tosun, Yenal Akgün, Özgür Kilit, Koray Korkmaz, Gökhan Kiper, Mustafa Gündüzalp

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Forced migration, whether caused by conflicts or other factors, frequently places individuals in vulnerable situations, necessitating immediate access to shelter. To promptly address the immediate needs of affected individuals, temporary shelters are often established. These shelters are characterized by their adaptable and functional nature, encompassing lightweight and sustainable structural systems, rapid assembly capabilities, modularity, and transportability. The shelter design is contingent upon demand, resulting in distinct phases for different structural forms. A multi-phased shelter approach covers emergency response, temporary shelter, and permanent reconstruction. Emergency shelters play a critical role in providing immediate life-saving aid, while temporary and transitional shelters, which are also called “t-shelters,” offer longer-term living environments during the recovery and rebuilding phases. Among these, temporary shelters are more extensively covered in the literature due to their diverse inhabiting functions. The roles of emergency shelters and temporary shelters are inherently separate, addressing distinct aspects of sheltering processes. Given their prolonged usage, temporary shelters are built for greater durability compared to emergency shelters. Nonetheless, inadequacies in temporary shelters can lead to challenges in ensuring habitability. Issues like non-expandable structures unsuitable for accommodating large families, the use of short-term shelters that worsen conditions, non-waterproof materials providing insufficient protection against bad weather conditions, and complex installation systems contribute to these problems. Given the aforementioned problems, there arises a need to develop adaptive shelters featuring lightweight components for ease of transport, possess the ability for rapid assembly, and utilize durable materials to withstand adverse weather conditions. In this study, first, the state-of-the-art on temporary shelters is presented. Then, an adaptive temporary shelter composed of foldable plates is proposed, which can easily be assembled and transportable. The proposed shelter is deliberated upon its movement capacity, transportability, and flexibility. This study makes a valuable contribution to the literature since it not only offers a systematic analysis of temporary shelters utilizing kinetic systems but also presents a practical solution that meets the necessary design requirements.

Keywords: deployable structures, foldable plates, forced migration, temporary shelters

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Authors: Kevin. S. Badni

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Due to the rapid advances in the use of information technology and students’ familiarity with technology, learning styles in higher education are being reshaped. One of the technology developments that has gained considerable attention in recent years is Augmented Reality (AR), where technology is used to combine overlays of digital data on physical real-world settings. While AR is being heavily promoted for entertainment by mobile phone manufacturers, it has had little adoption in higher education due to the required upfront investment that an instructor needs to undertake in creating relevant AR applications. This paper discusses a case study that uses a low upfront development approach and examines the impact on generation-Z students’ motivation whilst studying design history over a four-semester period. Even though the upfront investment in creating the AR support was minimal, the results showed a noticeable increase in student motivation. The approach used in this paper can be easily transferred to other disciplines and other areas of design education.

Keywords: augmented reality, history, motivation, technology

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Authors: Ahmed Mohamed Elnady Mohamed Elsafty

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This paper explains a developed applied objective model to measure the culture qualitatively and quantitatively, whether in individuals or in teams, in order to be able to use culture correctly or modify it efficiently. This model provides precise measurements and consistent interpretations by being comprehensive, updateable, and protected from being misled by imitations. Methodically, the provided model divides the culture into seven dimensions (total 43 cultural factors): First dimension is outcome-orientation which consists of five factors and should be highest in leaders. Second dimension is details-orientation which consists of eight factors and should be in highest intelligence members. Third dimension is team-orientation which consists of five factors and should be highest in instructors or coaches. Fourth dimension is change-orientation which consists of five factors and should be highest in soldiers. Fifth dimension is people-orientation which consists of eight factors and should be highest in media members. Sixth dimension is masculinity which consists of seven factors and should be highest in hard workers. Last dimension is stability which consists of seven factors and should be highest in soft workers. In this paper, the details of all cultural factors are explained. Practically, information collection about each cultural factor in the targeted person or team is essential in order to calculate the degrees of all cultural factors using the suggested equation of multiplying 'the score of factor presence' by 'the score of factor strength'. In this paper, the details of how to build each score are explained. Based on the highest degrees - to identify which cultural dimension is the prominent - choosing the tested individual or team in the supposedly right position at the right time will provide a chance to use minimal efforts to make everyone aligned to the organization’s objectives. In other words, making everyone self-motivated by setting him/her at the right source of motivation is the most effective and efficient method to achieve high levels of competency, commitment, and sustainability. Modifying a team culture can be achieved by excluding or including new members with relatively high or low degrees in specific cultural factors. For conclusion, culture is considered as the software of the human beings and it is one of the major compression factors on the managerial discretion. It represents the behaviors, attitudes, and motivations of the human resources which are vital to enhance quality and safety, expanding the market share, and defending against attacks from external environments. Thus, it is tremendously essential and useful to use such a comprehensive model to measure, use, and modify culture.

Keywords: culture dimensions, culture factors, culture measurement, cultural analysis, cultural modification, self-motivation, alignment to objectives, competency, sustainability

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Authors: Stefan Peters, Phillip Roetman

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The global loss of biodiversity is threatening the benefits nature provides to human populations and has become a more pressing issue than climate change and requires immediate attention. While there have been successful global agreements for environmental protection, such as the Montreal Protocol, these are rare, and we cannot rely on them solely. Thus, it is crucial to take national and local actions to support biodiversity. Australia is one of the 17 countries in the world with a high level of biodiversity, and its cities are vital habitats for endangered species, with more of them found in urban areas than in non-urban ones. However, the protection of biodiversity in metropolitan Adelaide has been inadequate, with over 130 species disappearing since European colonization in 1836. In this research project we conceptualized, developed and implemented a framework for wildlife Habitat Hotspots and Habitat Corridor modelling in an urban context using geographic data and GIS modelling and analysis. We used detailed topographic and other geographic data provided by a local council, including spatial and attributive properties of trees, parcels, water features, vegetated areas, roads, verges, traffic, and census data. Weighted factors considered in our raster-based Habitat Hotspot model include parcel size, parcel shape, population density, canopy cover, habitat quality and proximity to habitats and water features. Weighted factors considered in our raster-based Habitat Corridor model include habitat potential (resulting from the Habitat Hotspot model), verge size, road hierarchy, road widths, human density, and presence of remnant indigenous vegetation species. We developed a GIS model, using Python scripting and ArcGIS-Pro Model-Builder, to establish an automated reproducible and adjustable geoprocessing workflow, adaptable to any study area of interest. Our habitat hotspot and corridor modelling framework allow to determine and map existing habitat hotspots and wildlife habitat corridors. Our research had been applied to the study case of Burnside, a local council in Adelaide, Australia, which encompass an area of 30 km2. We applied end-user expertise-based category weightings to refine our models and optimize the use of our habitat map outputs towards informing local strategic decision-making.

Keywords: biodiversity, GIS modeling, habitat hotspot, wildlife corridor

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Authors: Lillian M. Seo, Curtis L. Petersen, Ryan J. Halter, David Kotz, John A. Batsis

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Background: Resistance-based exercises effectively enhance muscle strength, which is especially important in older populations as it reduces the risk of disability. Our group developed a Bluetooth-enabled handle for resistance exercise bands that wirelessly transmits relative force data through low-energy Bluetooth to a local smartphone or similar device. The system has the potential to measure home-based exercise interventions, allowing health professionals to monitor compliance. Its feasibility has already been demonstrated in both clinical and field-based settings, but it remained unclear whether the system’s usability persisted upon repeated use. The current study sought to assess the usability of this system and its users’ satisfaction with repeated use by deploying the device among younger adults to gather formative information that can ultimately improve the device’s design for older adults. Methods: A usability study was conducted in which 32 participants used the above system. Participants executed 10 repetitions of four commonly performed exercises: bicep flexion, shoulder abduction, elbow extension, and triceps extension. Each completed three exercise sessions, separated by at least 24 hours to minimize muscle fatigue. At its conclusion, subjects completed an adapted version of the usefulness, satisfaction, and ease (USE) questionnaire – assessing the system across four domains: usability, satisfaction, ease of use, and ease of learning. The 20-item questionnaire examined how strongly a participant agrees with positive statements about the device on a seven-point Likert scale, with one representing ‘strongly disagree’ and seven representing ‘strongly agree.’ Participants’ data were aggregated to calculate mean response values for each question and domain, effectively assessing the device’s performance across different facets of the user experience. Summary force data were visualized using a custom web application. Finally, an optional prompt at the end of the questionnaire allowed for written comments and feedback from participants to elicit qualitative indicators of usability. Results: Of the n=32 participants, 13 (41%) were female; their mean age was 32.4 ± 11.8 years, and no participants had a physical impairment. No usability questions received a mean score < 5 of seven. The four domains’ mean scores were: usefulness 5.66 ± 0.35; satisfaction 6.23 ± 0.06; ease of use 6.25 ± 0.43; and ease of learning 6.50 ± 0.19. Representative quotes of the open-ended feedback include: ‘A non-rigid strap-style handle might be useful for some exercises,’ and, ‘Would need different bands for each exercise as they use different muscle groups with different strength levels.’ General impressions were favorable, supporting the expectation that the device would be a useful tool in exercise interventions. Conclusions: A simple usability assessment of a Bluetooth-enabled resistance exercise band supports a consistent and positive user experience among young adults. This study provides adequate formative data, assuring the next steps can be taken to continue testing and development for the target population of older adults.

Keywords: Bluetooth, exercise, mobile health, mHealth, usability

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Authors: Guoyu Wang, Meilian Zhang, Chunhui LI, Bing Ren

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In recent decades, a variety of floating structures have played a crucial role in ocean and marine engineering, such as ships, offshore platforms, floating breakwaters, fish farms, floating airports, etc. It is common for floating structures to suffer from loadings under waves, and the responses of the structures mounted in marine environments have a significant relation to the wave impacts. The interaction between surface waves and floating structures is one of the important issues in ship or marine structure design to increase performance and efficiency. With the progress of computational fluid dynamics, a number of numerical models based on the NS equations in the time domain have been developed to explore the above problem, such as the finite difference method or the finite volume method. Those traditional numerical simulation techniques for moving bodies are grid-based, which may encounter some difficulties when treating a large free surface deformation and a moving boundary. In these models, the moving structures in a Lagrangian formulation need to be appropriately described in grids, and the special treatment of the moving boundary is inevitable. Nevertheless, in the mesh-based models, the movement of the grid near the structure or the communication between the moving Lagrangian structure and Eulerian meshes will increase the algorithm complexity. Fortunately, these challenges can be avoided by the meshless particle methods. In the present study, a moving particle semi-implicit model is explored for the numerical simulation of fluid–structure interaction with surface flows, especially for coupling of fluid and moving rigid body. The equivalent momentum transfer method is proposed and derived for the coupling of fluid and rigid moving body. The structure is discretized into a group of solid particles, which are assumed as fluid particles involved in solving the NS equation altogether with the surrounding fluid particles. The momentum conservation is ensured by the transfer from those fluid particles to the corresponding solid particles. Then, the position of the solid particles is updated to keep the initial shape of the structure. Using the proposed method, the motions of a free-floating body in regular waves are numerically studied. The wave surface evaluation and the dynamic response of the floating body are presented. There is good agreement when the numerical results, such as the sway, heave, and roll of the floating body, are compared with the experimental and other numerical data. It is demonstrated that the presented MPS model is effective for the numerical simulation of fluid-structure interaction.

Keywords: floating body, fluid structure interaction, MPS, particle method, waves

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Authors: Sreelekha Prakash, Yulong Gu

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Background: About 61% of adults surveyed across 25 states reported they had experienced at least one type of Adverse Childhood Experience (ACE) before 18 years of age. Relationships between ACEs and a variety of substance-related behaviors and behavioral health have been reported in previous studies. ACEs can have lasting, negative effects on health, well-being, as well as life opportunities such as education and job potential. Objectives: For the current research, the aim was to assess the factors affecting substance use behavior in young adults. The further onset of drug use and its association was analyzed with ACEs and mental health. Method: The young adults from a county in the north-eastern United States were invited to participate in an online questionnaire survey with prior consent through an IRB approved study. The Survey included questions related to social determinants of health, 10 item ACE questionnaire, and substance use related to Alcohol, Marijuana, Opioids, Stimulants, and other drugs. PHQ-9 questionnaire was used to assess cognitive health. Results: Data was analyzed for the 244 completed surveys {68% (165) were females, and 78% (190) were Whites}. The average age of the participants was 26.7 years, and approximately 80% were lifelong residents of the county or year-round residents. Of the respondents, 50% (122) were high school graduates with some college education, and 56% (136) had a full-time jobs. Past 30-day usage for alcohol was 76% (72), and marijuana was 28.4% (27). The data showed that the higher the ACE scores, the younger they start using any substance (p < 0.0001). The data for PHQ-9 and ACE scores showed that the higher the ACE score, the higher the PHQ-9 score, with a significant p-value (p 0.0001). The current data also showed a significant association with other drugs; marijuana use showed significance for 30 days of use (p 0.0001), stimulant use (0.0008), prescription drug misuse (0.01), and opioids (0.01). Conclusion: These findings further support the association between ACEs and initiation of drug use and its correlation with mental health symptoms. Promoting a safe and supportive environment for children and youth in their earlier ages can prevent the youth and young adults from the effects of drug use and create healthy living habits for young adults.

Keywords: subtance use, young adults, adverse childhood experience, PHQ-9

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Authors: Sujay Kotwale, Ramasubba Reddy M.

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Electrocardiogram (ECG) is a non-invasive technique used to study and analyze various heart diseases. Cardiac arrhythmia is a serious heart disease which leads to death of the patients, when left untreated. An early-time detection of cardiac arrhythmia would help the doctors to do proper treatment of the heart. In the past, various algorithms and machine learning (ML) models were used to early-time detection of cardiac arrhythmia, but few of them have achieved better results. In order to improve the performance, this paper implements principal component analysis (PCA) along with XGBoost model. The PCA was implemented to the raw ECG signals which suppress redundancy information and extracted significant features. The obtained significant ECG features were fed into XGBoost model and the performance of the model was evaluated. In order to valid the proposed technique, raw ECG signals obtained from standard MIT-BIH database were employed for the analysis. The result shows that the performance of proposed method is superior to the several state-of-the-arts techniques.

Keywords: cardiac arrhythmia, electrocardiogram, principal component analysis, XGBoost

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Authors: Faye Cosgrove, Donna Peacock

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This paper examines current state strategies for dealing with vulnerable people in police custody and identifies the underpinning discourses and practices which inform these strategies. It has previously been argued that the state has utilised contradictory and conflicting responses to the control of crime, by employing opposing strategies of denial and adaptation in order to simultaneously both display sovereignty and disclaim responsibility. This paper argues that these contradictory strategies are still being employed in contemporary criminal justice, although the focus and the purpose have now shifted. The focus is upon the ‘vulnerable’ suspect, whose social identity is as incongruous, complex and contradictory as his social environment, and the purpose is to redirect attention away from negative state practices, whilst simultaneously displaying a compassionate and benevolent countenance in order to appeal to the voting public. The findings presented here result from intensive qualitative research with police officers, with health care professionals, and with civilian volunteers who work within police custodial environments. The data has been gathered over a three-year period and includes observational and interview data which has been thematically analysed to expose the underpinning mechanisms from which the properties of the system emerge. What is revealed is evidence of contemporary state practices of denial relating to the harms of austerity and the structural relations of vulnerability, whilst simultaneously adapting through processes of ‘othering’ of the vulnerable, ‘responsibilisation’ of citizens, defining deviance down through diversionary practices, and managing success through redefining the aims of the system. The ‘vulnerable’ suspect is subject to individual pathologising, and yet the nature of risk is aggregated. ‘Vulnerable’ suspects are supported in police custody by private citizens, by multi-agency partnerships, and by for-profit organisations, while the state seeks to collate and control services, and thereby to retain a veneer of control. Late modern ambivalence to crime control and the associated contradictory practices of abjuration and adjustment have extended to state responses to vulnerable suspects. The support available in the custody environment operates to control and minimise operational and procedural risk, rather than for the welfare of the detained person, and in fact, the support available is discovered to be detrimental to the very people that it claims to benefit. The ‘vulnerable’ suspect is now subject to the bifurcated logics employed at the new limits of the sovereign state.

Keywords: custody, policing, sovereign state, vulnerability

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Authors: Nicole Scott, Emily Dwyer, Cara Patrissy, Samantha Bonventre, Lina Begdache

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Across North America, the use and abuse of Attention Deficit Hyperactivity Disorder (ADHD) medication, cannabis, nicotine, and other psychoactive substances across college campuses have become an increasingly prevalent problem. Students frequently use these substances to aid their studying or deal with their mental health issues. However, it is still unknown what psychoactive substances are likely to be abused when college students illicitly use ADHD medication. In addition, it is not clear which psychoactive substance is associated with mental distress. Thus, the purpose of this study is to fill these gaps by assessing the use of different psychoactive substances when illicit ADHD medication is used; and how this association relates to mental stress. A total of 702 undergraduate students from different college campuses in the U.S. completed an anonymous survey distributed online. Data were self-reported on demographics, the use of ADHD medications, cannabis, nicotine, other psychoactive drugs, and mental distress, and feelings and opinions on the use of illicit study drugs were all included in the survey. Mental distress was assessed using the Kessler Psychological Distress 6 Scale. Data were analyzed in SPSS, Version 25.0, using Pearson’s Correlation Coefficient. Our results show that use of ADHD medication, cannabis use (non-frequent and very frequent), and nicotine use (non-frequent and very frequent), there were both statistically significant positive and negative correlations to specific psychoactive substances and their corresponding frequencies. Along the same lines, ADHD medication, cannabis use (non-frequent and very frequent), and nicotine use (non-frequent and very frequent) had statistically significant positive and negative correlations to specific mental distress experiences. As these findings are combined, a vicious loop can initiate a cycle where individuals who abuse psychoactive substances may or may not be inclined to use other psychoactive substances. This may later inhibit brain functions in those main areas of the brain stem, amygdala, and prefrontal cortex where this vicious cycle may or may not impact their mental distress. Addressing the impact of study drug abuse and its potential to be associated with further substance abuse may provide an educational framework and support proactive approaches to promote awareness among college students.

Keywords: stimulant, depressant, nicotine, ADHD medication, psychoactive substances, mental health, illicit, ecstasy, adrenochrome

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Authors: Yang Zhang, Jian He

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Heart disease is one of the leading causes of death in the world, and coronary heart disease (CHD) is one of the major heart diseases. Electrocardiogram (ECG) is widely used in the detection of heart diseases, but the traditional manual method for CHD prediction by analyzing ECG requires lots of professional knowledge for doctors. This paper introduces sliding window and continuous wavelet transform (CWT) to transform ECG signals into images, and then ResNet and Bi-LSTM are introduced to build the ECG feature extraction network (namely ECGNet). At last, an auxiliary system for coronary heart disease prediction was developed based on modified ResNet18 and Bi-LSTM, and the public ECG dataset of CHD from MIMIC-3 was used to train and test the system. The experimental results show that the accuracy of the method is 83%, and the F1-score is 83%. Compared with the available methods for CHD prediction based on ECG, such as kNN, decision tree, VGGNet, etc., this method not only improves the prediction accuracy but also could avoid the degradation phenomenon of the deep learning network.

Keywords: Bi-LSTM, CHD, ECG, ResNet, sliding window

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Authors: N. Schneider, M. Bruchhage, M. Hartweg, G. Mutungi, J. O Regan, S. Deoni

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Observational neuroimaging studies suggest differences between breast-fed and formula-fed infants in developmental myelination, a key brain process for learning and cognitive development. However, the possible effects of a nutrient formulation on myelin development in healthy term infants in an intervention study have not been investigated. Objective was, therefore, to investigate the efficacy of a nutrient formulation with higher levels of myelin-relevant nutrients as compared to a control formulation with lower levels of the same nutrients on brain myelination and cognitive development in the first 6 months of life. The study is an ongoing randomized, controlled, double-blind, two-center, parallel-group clinical trial with a nonrandomized, non-blinded arm of exclusively breastfed infants. The current findings result from a staged statistical analysis at 6 months; the recruitment and intervention period has been completed for all participants. Follow-up visits at 12, 18 and 24 months are still ongoing. N= 81 enrolled full term, neurotypical infants of both sexes were randomized into either the investigational (N= 42) or the control group (N= 39), and N= 108 children in the breast-fed arm served as a natural reference group. The effect of a blend of docosahexaenoic acid, arachidonic acid, iron, vitamin B12, folic acid as well as sphingomyelin from a uniquely proceed whey protein concentrate enriched in alpha-lactalbumin and phospholipids in an infant nutrition product matrix was investigated. The main outcomes for the staged statistical analyses at 6 months included brain myelination measures derived from MRI. Additional outcomes were brain volume, cognitive development and safety. The full analyses set at 6 months comprised N= 66 infants. Higher levels of myelin-relevant nutrients compared to lower levels resulted in significant differences in myelin structure, volume, and rate of myelination as early as 3 and 6 months of life. The cross-sectional change of means between groups for whole-brain myelin volume was 8.4% for investigational versus control formulation (3.5% versus the breastfeeding reference) group at 3 months and increased to 36.4% for investigational versus control formulation (14.1% versus breastfeeding reference) at 6 months. No statistically significant differences were detected for early cognition scores. Safety findings were largely similar across groups. This is the first pediatric nutritional neuroimaging study demonstrating the efficacy of a myelin nutrient blend on developmental myelination in well-nourished term infants. Myelination is a critical process in learning and development. The effects were demonstrated across the brain, particularly in temporal and parietal regions, known to be functionally involved in sensory, motor and language skills. These first results add to the field of nutritional neuroscience by demonstrating early life nutrition benefits for brain architecture which may be foundational for later cognitive and behavioral outcomes. ClinicalTrials.gov Identifier: NCT03111927 (Infant Nutrition and Brain Development - Full-Text View - ClinicalTrials.gov).

Keywords: brain development, infant nutrition, MRI, myelination

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Authors: Francesca Maria Melucci, Marco Panettieri, Rocco Roma

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Cities are at the forefront of achieving the sustainable development goals set out in the Sustainable Development Goals of Agenda 2030. For these reasons, increasing attention has been given to the creation of resilient, sustainable, inclusive and green cities and finding solutions to these problems is one of the greatest challenges faced by researchers today. In particular urban green infrastructures, including green roofs, play a key role in tackling environmental, social and economic problems. The starting point was an extensive literature review on 1. research developments on the benefits (environmental, economic and social) and implications of green roofs; 2. sustainability assessment and applied methodologies; 3. specific indicators to measure impacts on urban sustainability. Through this review, the appropriate qualitative and quantitative characteristics that are part of the complex 'green roof' system were identified, as studies that holistically capture its multifunctional nature are still lacking. So, this paper aims to find a method to improve community participation in green roof initiatives and support local governance processes in developing efficient proposals to achieve better sustainability and resilience of cities. To this aim, the multidimensional indicator-based framework, presented by Tapia in 2021, has been tested for the first time in the case of a green roof in the city of Madrid. The framework's set of indicators was implemented with other indicators such as those of waste management and circularity (OECD Inventory of Circular Economy indicators) and sustainability performance. The specific indicators to be used in the case study were decided after a consultation phase with relevant stakeholders. Data on the community's willingness to participate in green roof implementation initiatives were collected through interviews and online surveys with a heterogeneous sample of citizens. The results of the application of the framework suggest how the different aspects of sustainability influence the choice of a green roof and provide input on the main mechanisms involved in citizens' willingness to participate in such initiatives.

Keywords: urban agriculture, green roof, urban sustainability, indicators, multi-criteria analysis

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Authors: Tesfaye Mengistu

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Power cables play a crucial role in the transmission and distribution of electrical energy. As the electricity generation, transmission, distribution, and storage systems become smarter, there is a growing emphasis on incorporating intelligent approaches to ensure the reliability of power cables. Various types of electrical cables are employed for transmitting and distributing electrical energy, with cross-linked polyethylene (XLPE) cables being widely utilized due to their exceptional electrical and mechanical properties. However, insulation defects can occur in XLPE cables due to subpar manufacturing techniques during production and cable joint installation. To address this issue, experts have proposed different methods for monitoring XLPE cables. Some suggest the use of interdigital capacitive (IDC) technology for online monitoring, while others propose employing continuous wave (CW) terahertz (THz) imaging systems to detect internal defects in XLPE plates used for power cable insulation. In this study, we have developed models that employ a custom dataset collected locally to classify the physical safety status of individual power cables. Our models aim to replace physical inspections with computer vision and image processing techniques to classify defective power cables from non-defective ones. The implementation of our project utilized the Python programming language along with the TensorFlow package and a convolutional neural network (CNN). The CNN-based algorithm was specifically chosen for power cable defect classification. The results of our project demonstrate the effectiveness of CNNs in accurately classifying power cable defects. We recommend the utilization of similar or additional datasets to further enhance and refine our models. Additionally, we believe that our models could be used to develop methodologies for detecting power cable defects from live video feeds. We firmly believe that our work makes a significant contribution to the field of power cable inspection and maintenance. Our models offer a more efficient and cost-effective approach to detecting power cable defects, thereby improving the reliability and safety of power grids.

Keywords: artificial intelligence, computer vision, defect detection, convolutional neural net

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Authors: Gueidi Afef, Gharsellaoui Hamza, Ben Ahmed Samir

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This paper deals with the secret of the continual progression data that new data management solutions have been emerged: The NoSQL databases. They crossed several areas like personalization, profile management, big data in real-time, content management, catalog, view of customers, mobile applications, internet of things, digital communication and fraud detection. Nowadays, these database management systems are increasing. These systems store data very well and with the trend of big data, a new challenge’s store demands new structures and methods for managing enterprise data. The new intelligent machine in the e-learning sector, thrives on more data, so smart machines can learn more and faster. The robotics are our use case to focus on our test. The implementation of NoSQL for Robotics wrestle all the data they acquire into usable form because with the ordinary type of robotics; we are facing very big limits to manage and find the exact information in real-time. Our original proposed approach was demonstrated by experimental studies and running example used as a use case.

Keywords: NoSQL databases, database management systems, robotics, big data

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Authors: Samia Dahmani

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The research investigated the process and outcomes of an ongoing project “Den Grønne Rute”: a network of recreational rooms to be implemented throughout a former vulnerable neighborhood: Trekanten in the city of Holstebro in Denmark. The aim of the project is to better integrate Trekanten in the city and which initiated reflections upon the (dis)connection and the integration in relation to urban planning and city management, as well as the extent to which the project considers diversity since Holstebro is a growing multicultural city. With this research, it was first verified if Trekanten is splintered from Holstebro city, and secondly if planning for diversity, by engaging people in the process via place-making approaches, can help redress the disconnection between the neighborhood and the rest of the city. More specifically, the paper aims at exploring the role of place-making “Den Grønne Rute” in Trekanten in promoting planning for diversity and reassembling the splintering in the city. The theoretical and conceptual framework served to analyze the relationship between the splintering urbanism concept and the community involvement’s role in an inclusive process. The field study examines the detachment between Trekanten and Holstebro and the extent to which the project can overcome the disconnection. Methodologically a mix-methods approach was adopted where two semi-structured interviews, a focus group, and an online survey were conducted. Contrary to prior assumptions, the results showed that not only is Trekanten splintered from the city, but also the city is greatly disconnected from Trekanten, and hence Holstebro is a splintered city. The surprise was that Trekanten is moreover inner-splintered. The splintering urbanism accordingly has different dimensions. Even though the project’s design seemed to incorporate diverse ages and groups of people, its process lacks an understanding of the diversity’s relevance in promoting inclusiveness. In fact, the analysis revealed socio-cultural and psychological splintering. Since place-making, as a collaborative approach in planning, is itself an expression of diversity (since it brings differences into play), reconsidering diversity within the process by engaging people at the early stages of planning was recommended. Another suggestion was not to limit the project to a destination but more as an experience to remember and a story to tell. Only by bringing people together in re-imagining the place can Trekanten reassemble with Holstebro and vice versa. The aim of the research was to add a new perspective to the splintering urbanism and planning for diversity so to advance place-making as an approach in promoting the latter and redressing the former.

Keywords: the splintering urbanism, place-making, planning for diversity, Den Grønne Rute, Trekanten, Holstebro

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Authors: Danying Gu, Xiaoyan Li, Yuanlei He

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As a knowledge-intensive industry, nuclear industry highly values the importance of safety and quality. The life cycle of a NPP (Nuclear Power Plant) can last 100 years from the initial research and design to its decommissioning. How to implement the high-quality knowledge management and how to contribute to a more safe, advanced and economic NPP (Nuclear Power Plant) is the most important issue and responsibility for knowledge management. As the lead of nuclear industry, nuclear research and design institute has competitive advantages of its advanced technology, knowledge and information, DKM (Design Knowledge Management) of nuclear research and design institute is the core of the knowledge management in the whole nuclear industry. In this paper, the study and practice on DKM and knowledge transfer across the life cycle of a new-built NPP in China is introduced. For this digital intelligent NPP, the whole design process is based on a digital design platform which includes NPP engineering and design dynamic analyzer, visualization engineering verification platform, digital operation maintenance support platform and digital equipment design, manufacture integrated collaborative platform. In order to make all the design data and information transfer across design, construction, commissioning and operation, the overall architecture of new-built digital NPP should become a modern knowledge management system. So a digital information transfer model across the NPP life cycle is proposed in this paper. The challenges related to design knowledge transfer is also discussed, such as digital information handover, data center and data sorting, unified data coding system. On the other hand, effective delivery of design information during the construction and operation phase will contribute to the comprehensive understanding of design ideas and components and systems for the construction contractor and operation unit, largely increasing the safety, quality and economic benefits during the life cycle. The operation and maintenance records generated from the NPP operation process have great significance for maintaining the operating state of NPP, especially the comprehensiveness, validity and traceability of the records. So the requirements of an online monitoring and smart diagnosis system of NPP is also proposed, to help utility-owners to improve the safety and efficiency.

Keywords: design knowledge management, digital nuclear power plant, knowledge transfer, life cycle

Procedia PDF Downloads 265

Authors: Christian Louis Kühner, Florian Pfeffel, Valentin Nickolai

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Taking care of children, dependents, or pets can be a difficult and time-consuming task. Especially for part- and full-time employees, it can feel exhausting and overwhelming to meet these obligations besides working a job. Thus, working mostly at home and not having to drive to the company can save valuable time and stress. This study aims to show the influence that the working model has on the job satisfaction of employees with care responsibilities in comparison to employees who do not have such obligations. Using structural equation modeling (SEM), the three work models, “work from home”, “working remotely”, and a hybrid model, have been analyzed based on 13 influencing constructs on job satisfaction. These 13 factors have been further summarized into three groups “classic influencing factors”, “influencing factors changed by remote working”, and “new remote working influencing factors”. Based on the influencing factors on job satisfaction, an online survey was conducted with n = 684 employees from the service sector. Here, Cronbach’s alpha of the individual constructs was shown to be suitable. Furthermore, the construct validity of the constructs was confirmed by face validity, content validity, convergent validity (AVE > 0.5: CR > 0.7), and discriminant validity. In addition, confirmatory factor analysis (CFA) confirmed the model fit for the investigated sample (CMIN/DF: 2.567; CFI: 0.927; RMSEA: 0.048). The SEM-analysis has shown that the most significant influencing factor on job satisfaction is “identification with the work” with β = 0.540, followed by “Appreciation” (β = 0.151), “Compensation” (β = 0.124), “Work-Life-Balance” (β = 0.116), and “Communication and Exchange of Information” (β = 0.105). While the significance of each factor can vary depending on the work model, the SEM-analysis shows that the identification with the work is the most significant factor in all three work models and, in the case of the traditional office work model, it is the only significant influencing factor. The study shows that among the employees with care responsibilities, the higher the proportion of working from home in comparison to working from the office, the more satisfied the employees are with their job. Since the work models that meet the requirements of comprehensive care led to higher job satisfaction amongst employees with such obligations, adapting as a company to such private obligations by employees can be crucial to sustained success. Conversely, the satisfaction level of the working model where employees work at the office is higher for workers without caregiving responsibilities.

Keywords: care responsibilities, home office, job satisfaction, structural equation modeling

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Authors: Ruchi Sharma, Rajasekhar Balasubramanian

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Motor vehicles emit a number of air pollutants, among which fine particulate matter (PM₂.₅) is of major concern in cities with high population density due to its negative impacts on air quality and human health. Typically, people spend more than 80% of their time indoors. Consequently, human exposure to traffic-related PM₂.₅ in indoor environments has received considerable attention. Most of the public residential buildings in tropical countries are designed for natural ventilation where indoor air quality tends to be strongly affected by the migration of air pollutants of outdoor origin. However, most of the previously reported traffic-related PM₂.₅ exposure assessment studies relied on ambient PM₂.₅ concentrations and thus, the health impact of traffic-related PM₂.₅ on occupants in naturally ventilated buildings remains largely unknown. Therefore, a systematic field study was conducted to assess indoor human exposure to traffic-related PM₂.₅ with and without mitigation measures in a typical naturally ventilated residential apartment situated near a road carrying a large volume of traffic. Three PM₂.₅ exposure scenarios were simulated in this study, i.e., Case 1: keeping all windows open with a ceiling fan on as per the usual practice, Case 2: keeping all windows fully closed as a mitigation measure, and Case 3: keeping all windows fully closed with the operation of a portable indoor air cleaner as an additional mitigation measure. The indoor to outdoor (I/O) ratios for PM₂.₅ mass concentrations were assessed and the effectiveness of using the indoor air cleaner was quantified. Additionally, potential human health risk based on the bioavailable fraction of toxic trace elements was also estimated for the three cases in order to identify a suitable mitigation measure for reducing PM₂.₅ exposure indoors. Traffic-related PM₂.₅ levels indoors exceeded the air quality guidelines (12 µg/m³) in Case 1, i.e., under natural ventilation conditions due to advective flow of outdoor air into the indoor environment. However, while using the indoor air cleaner, a significant reduction (p < 0.05) in the PM₂.₅ exposure levels was noticed indoors. Specifically, the effectiveness of the air cleaner in terms of reducing indoor PM₂.₅ exposure was estimated to be about 74%. Moreover, potential human health risk assessment also indicated a substantial reduction in potential health risk while using the air cleaner. This is the first study of its kind that evaluated the indoor human exposure to traffic-related PM₂.₅ and identified a suitable exposure mitigation measure that can be implemented in densely populated cities to realize health benefits.

Keywords: fine particulate matter, indoor air cleaner, potential human health risk, vehicular emissions

Procedia PDF Downloads 114

Authors: Laura Micheli, Majd Hijazi, Mahmoud Faytarouni

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The damage reported by oil and gas industrial facilities revealed the utmost vulnerability of steel liquid storage tanks to seismic events. The failure of steel storage tanks may yield devastating and long-lasting consequences on built and natural environments, including the release of hazardous substances, uncontrolled fires, and soil contamination with hazardous materials. It is, therefore, fundamental to reliably predict the damage that steel liquid storage tanks will likely experience under future seismic hazard events. The seismic performance of steel liquid storage tanks is usually assessed using vulnerability curves obtained from the numerical simulation of a tank under different hazard scenarios. However, the computational demand of high-fidelity numerical simulation models, such as finite element models, makes the vulnerability assessment of liquid storage tanks time-consuming and often impractical. As a solution, this paper presents a surrogate model-based strategy for predicting seismic-induced damage in steel liquid storage tanks. In the proposed strategy, the surrogate model is leveraged to reduce the computational demand of time-consuming numerical simulations. To create the data set for training the surrogate model, field damage data from past earthquakes reconnaissance surveys and reports are collected. Features representative of steel liquid storage tank characteristics (e.g., diameter, height, liquid level, yielding stress) and seismic excitation parameters (e.g., peak ground acceleration, magnitude) are extracted from the field damage data. The collected data are then utilized to train a surrogate model that maps the relationship between tank characteristics, seismic hazard parameters, and seismic-induced damage via a data-driven surrogate model. Different types of surrogate algorithms, including naïve Bayes, k-nearest neighbors, decision tree, and random forest, are investigated, and results in terms of accuracy are reported. The model that yields the most accurate predictions is employed to predict future damage as a function of tank characteristics and seismic hazard intensity level. Results show that the proposed approach can be used to estimate the extent of damage in steel liquid storage tanks, where the use of data-driven surrogates represents a viable alternative to computationally expensive numerical simulation models.

Keywords: damage prediction , data-driven model, seismic performance, steel liquid storage tanks, surrogate model

Procedia PDF Downloads 136

Authors: Tolga Barisik, Ali Fuat Guneri, K. Dastan

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Major industrial facilities have a high potential for fire risk. In particular, the indices used for the detection of hidden fire are used very effectively in order to prevent the fire from becoming dangerous in the initial stage. These indices provide the opportunity to prevent or intervene early by determining the stage of the fire, the potential for hazard, and the type of the combustion agent with the percentage values of the ambient air components. In this system, artificial neural network will be modeled with the input data determined using the Levenberg-Marquardt algorithm, which is a multi-layer sensor (CAA) (teacher-learning) type, before modeling the modeling methods in the literature. The actual values produced by the indices will be compared with the outputs produced by the network. Using the neural network and the curves to be created from the resulting values, the feasibility of performance determination will be investigated.

Keywords: artifical neural networks, fire, Graham Index, levenberg-marquardt algoritm, oxygen decrease percentage index, risk assessment, Trickett Index

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Authors: Thomas Ogilegwu Orohu

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This paper presents agricultural education as a vocational study, an impetus for youths, economic empowerment. The survival of Nigeria’s agriculture rests squarely on the youth who are the farmers and leaders of tomorrow. Hitherto, the teaching and learning of agriculture has proceeded in such a manner that graduates of such programs have failed to make the successful launch into the world of agricultural enterprises (entrepreneurship). Major constraints that predisposed this anomalous situation were identified to include poor policy framework, socio-economic pressures, undue parental and peer influences, improper value orientation and of course, the nature of curricula. In response to the situation, some programs and/or initiatives aimed at inculcating entrepreneurial skills were proposed by this paper with identified target beneficiaries. The initiatives bordered on curricular reorientation that integrate entrepreneurship/enterprise education, retraining of graduates, financial support system among others.

Keywords: Program initiatives. vocational agriculture, youths’ empowerment, introduction

Procedia PDF Downloads 294

Authors: Samia Dahmani

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The research investigated the process and outcomes of an ongoing project, “Den Grønne Rute”: a network of recreational rooms to be implemented throughout a former vulnerable neighborhood: Trekanten, in the city of Holstebro in Denmark. The aim of the project is to better integrate Trekanten in the city and which initiated reflections upon the (dis)connection and the integration in relation to urban planning and city management, as well as the extent to which the project considers diversity since Holstebro is a growing multicultural city. With this research, it was first verified if Trekanten is splintered from Holstebro city, and secondly if planning for diversity, by engaging people in the process via place-making approaches, can help redress the disconnection between the neighborhood and the rest of the city. More specifically, the paper aims at exploring the role of place-making “Den Grønne Rute” in Trekanten in promoting planning for diversity and reassembling the splintering in the city. The theoretical and conceptual framework served to analyze the relationship between the splintering urbanism concept and the community involvement’s role for an inclusive process. The field study examines the detachment between Trekanten and Holstebro and the extent to which the project can overcome the disconnection. Methodologically a mix-methods approach was adopted where two semi-structured interviews, a focus group, and an online survey were conducted. Contrary to prior assumptions, the results showed that not only is Trekanten splintered from the city, but also the city is greatly disconnected from Trekanten, and hence Holstebro is a splintered city. The surprise was that Trekanten is moreover inner-splintered. The splintering urbanism accordingly has different dimensions. Even though the project’s design seemed to incorporate diverse ages and groups of people, its process lacks an understanding of the diversity’s relevance in promoting inclusiveness. In fact, the analysis revealed socio-cultural and psychological splintering. Since place-making, as a collaborative approach in planning, is itself an expression of diversity (since it brings differences into play), reconsidering diversity within the process by engaging people at the early sages of planning was recommended. Another suggestion was not to limit the project to a destination but more as an experience to remember and a story to tell. Only by bringing people together in re-imagining the place, can Trekanten reassemble with Holstebro and vice versa. The aim with the research was to add a new perspective to the splintering urbanism and planning for diversity so to advance place-making as an approach in promoting the latter and redressing the former.

Keywords: the splintering urbanism, placemaking, planning for diversity, den grønne rute, trekanten, holstebro

Procedia PDF Downloads 94

Authors: Sandra So, Mohd Roslan Ismail, Safurah Jaafar

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With the rapid proliferation of data in healthcare has provided an opportune platform creation of Artificial Intelligence (AI). AI has brought a paradigm shift for healthcare professionals, promising improvement in delivery and quality. This study aims to determine the perception of healthcare personnel on perceived ease of use, perceived usefulness, and subjective norm toward attitude for artificial intelligence acceptance. A cross-sectional single institutional study of employees’ perception of adopting AI in the hospital was conducted. The survey was conducted using a questionnaire adapted from Technology Acceptance Model and a four-point Likert scale was used. There were 96 or 75.5% of the total population responded. This study has shown the significant relationship and the importance of ease of use, perceived usefulness, and subjective norm to the acceptance of AI. In the study results, it concluded that the determining factor to the strong acceptance of AI in their practices is mostly those respondents with the most interaction with the patients and clinical management.

Keywords: artificial intelligence, machine learning, perceived ease of use, perceived usefulness, subjective norm

Procedia PDF Downloads 212

Authors: S. Kawther, C. Marshall

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Educational neuroscience has the intention of transforming research findings of the underpinning neural processes of learning to educational practices. A main criticism of the field, hitherto, is that less focus has been put on studying the in-progress practical application of these findings. Therefore, this study aims to gain a better understanding of teachers’ perceptions of the practical application and utilization of brain knowledge. This was approached by investigating the answer to 'What are the facilitators and barriers for bringing research from neuroscience to bear on education?'. Following a qualitative design, semi-structured interviews were conducted with 12 teachers who had a proficient course in educational neuroscience. Thematic analysis was performed on the transcribed data applying Braun & Clark’s steps. Findings emerged with four main themes: time, knowledge, teacher’s involvement, and system. These themes revealed that some effective brain-based practices are being engaged in by the teachers. However, the lack of guidance and challenges regarding this implementation were also found. This study discusses findings in light of the development of educational neuroscience implementation.

Keywords: brain-based, educational neuroscience, neuroeducation, neuroscience-informed

Procedia PDF Downloads 153

Authors: Zhizhong Yuan, Lili Wang, Chenya Zheng, Wuqi Yang

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How to encourage and support peer-to-peer (P2P) network lending, and effectively monitor the risk of P2P network lending, has become the focus of the Chinese government departments, industrialists, experts and scholars in recent years. The reason is that this convenient online micro-credit service brings a series of credit risks and other issues. Avoiding the risks brought by the P2P network lending model, it can better play a benign role and help China's small and medium-sized private enterprises with vigorous development to solve the capital needs; otherwise, it will bring confusion to the normal financial order. As a form of financial services, P2P network lending has injected new blood into China's non-government finance in the past ten years, and has found a way out for idle funds and made up for the shortage of traditional financial services in China. However, it lacks feasible measures in credit evaluation and government supervision. This paper collects a large amount of data about P2P network lending of China. The data collection comes from the official media of the Chinese government, the public achievements of existing researchers and the analysis and collation of correlation data by the authors. The research content of this paper includes literature review; the current situation of China's P2P network lending development; the risk analysis of P2P network lending in China; the risk prevention strategy of P2P network lending in China. The focus of this paper is to try to find a specific program to strengthen supervision and avoid risks from the perspective of government regulators, operators of P2P network lending platform, investors and users of funds. These main measures include: China needs to develop self-discipline organization of P2P network lending industry and formulate self-discipline norms as soon as possible; establish a regular information disclosure system of P2P network lending platform; establish censorship of credit rating of borrowers; rectify the P2P network lending platform in compliance through the implementation of bank deposition. The results and solutions will benefit all the P2P network lending platforms, creditors, debtors, bankers, independent auditors and government agencies of China and other countries.

Keywords: peer-to-peer(P2P), regulation, risk prevention, supervision

Procedia PDF Downloads 154

Authors: Manuela Caciula, Luis Torres, Simion Tomoiaga

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Introduction: The SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), more commonly referred to as COVID-19, has wreaked havoc on all facets of higher education since its inception in late 2019. College students, in particular, significantly reduced their daily energy expenditure and increased the time spent sitting to listen to online classes and complete their studies from home. This change, in combination with the associated COVID-19 lockdown, presumably decreased physical activity levels, increased mental health symptoms, and led to the promotion of unhealthy eating habits. Objectives: The main objective of this study was to determine the current self-reported physical activity levels, mental health symptoms, and body composition of college students after the COVID-19 lockdown in order to develop future interventions for the overall improvement of health. Methods: All participants completed pre-existing, well-validated surveys for both physical activity (International Physical Activity Questionnaire - long form) and mental health (Hospital Anxiety and Depression Scale). Body composition was assessed in person with the use of an Inbody 570 device. Results: Of the 90 American college students (M age = 22.52 ± 4.54, 50 females) who participated in this study, depressive and anxious symptom scores consistent with 58% (N = 52) heightened symptomatology, 17% (N = 15) moderate borderline symptomatology, and 25% (N = 23) asymptomatology were reported. In regard to physical activity, 79% (N = 71) of the students were highly physically active, 18% (N = 16) were moderately active, and 3% (N = 3) reported low levels of physical activity. Additionally, 46% (N = 41) of the students maintained an unhealthy body fat percentage based on World Health Organization recommendations. Strong, significant relationships were found between anxiety and depression symptomatology and body fat percentage (P = .003) and skeletal muscle mass (P = .015), with said symptomatology increasing with added body fat and decreasing with added skeletal muscle mass. Conclusions: Future health interventions for American college students should be focused on strategies to reduce stress, anxiety, and depressive characteristics, as well as nutritional information on healthy eating, regardless of self-reported physical activity levels.

Keywords: physical activity, mental health, body composition, COVID-19

Procedia PDF Downloads 83

Authors: Kamyar Tolouei, Ehsan Moosavi

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In open-pit mines, the long-term production scheduling optimization problem (LTPSOP) is a complicated problem that contains constraints, large datasets, and uncertainties. Uncertainty in the output is caused by several geological, economic, or technical factors. Due to its dimensions and NP-hard nature, it is usually difficult to find an ideal solution to the LTPSOP. The optimal schedule generally restricts the ore, metal, and waste tonnages, average grades, and cash flows of each period. Past decades have witnessed important measurements of long-term production scheduling and optimal algorithms since researchers have become highly cognizant of the issue. In fact, it is not possible to consider LTPSOP as a well-solved problem. Traditional production scheduling methods in open-pit mines apply an estimated orebody model to produce optimal schedules. The smoothing result of some geostatistical estimation procedures causes most of the mine schedules and production predictions to be unrealistic and imperfect. With the expansion of simulation procedures, the risks from grade uncertainty in ore reserves can be evaluated and organized through a set of equally probable orebody realizations. In this paper, to synthesize grade uncertainty into the strategic mine schedule, a stochastic integer programming framework is presented to LTPSOP. The objective function of the model is to maximize the net present value and minimize the risk of deviation from the production targets considering grade uncertainty simultaneously while satisfying all technical constraints and operational requirements. Instead of applying one estimated orebody model as input to optimize the production schedule, a set of equally probable orebody realizations are applied to synthesize grade uncertainty in the strategic mine schedule and to produce a more profitable and risk-based production schedule. A mixture of metaheuristic procedures and mathematical methods paves the way to achieve an appropriate solution. This paper introduced a hybrid model between the augmented Lagrangian relaxation (ALR) method and the metaheuristic algorithm, the Harris Hawks optimization (HHO), to solve the LTPSOP under grade uncertainty conditions. In this study, the HHO is experienced to update Lagrange coefficients. Besides, a machine learning method called Random Forest is applied to estimate gold grade in a mineral deposit. The Monte Carlo method is used as the simulation method with 20 realizations. The results specify that the progressive versions have been considerably developed in comparison with the traditional methods. The outcomes were also compared with the ALR-genetic algorithm and ALR-sub-gradient. To indicate the applicability of the model, a case study on an open-pit gold mining operation is implemented. The framework displays the capability to minimize risk and improvement in the expected net present value and financial profitability for LTPSOP. The framework could control geological risk more effectively than the traditional procedure considering grade uncertainty in the hybrid model framework.

Keywords: grade uncertainty, metaheuristic algorithms, open-pit mine, production scheduling optimization

Procedia PDF Downloads 91