Search results for: complex and dynamic systems

Authors: Fernanda Rodriguez

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Breast cancer is pervasive among women, and an increasing number of women are opting for a mastectomy: a medical operation in which one or both breasts are removed with the intention of treating or averting breast cancer. However, there is an emerging population of cancer survivors in European nations that, rather than attempting to reconstruct their breasts to resemble as much as possible ‘normal’ breasts, have turned to dress their scars with decorative tattoos. At a practical level, this study hopes to improve the support systems of these women by possibly providing professionals in the medical field, tattoo artists, and family members of cancer survivors with a deeper understanding of their motivations and decision-making processes for choosing an alternative restorative route - such as decorative tattoos - after their mastectomy. At an intellectual level, however, this study aims to narrow a gap in the academic field concerning the relationship between mastectomies and alternative methods of healing, such as decorative tattoos, as well as to broaden the understanding regarding meaning-making and the ‘normal’ feminine body. Thus, by means of semi-structured interviews and a phenomenological standpoint, this research set itself the goal to understand why do women who have undergone a mastectomy choose to dress their scars with decorative tattoos instead of attempting to regain ‘normalcy’ through breast reconstruction or 3D areola tattoos? The results obtained from the interviews with fifteen women showed that the disillusionment with one part of the other of breast restoration techniques had led these women to find an alternative form of healing that allows them not only to close a painful chapter of their life but also to regain control over their bodies after a period of time in which agency was taking away from them. Decorative post-mastectomy tattoos allow these women to grant their bodies with new meanings and produce their own interpretation of their feminine body and identity.

Keywords: alternative femininity, decorative mastectomy tattoos, gender embodiment, social stigmatization

Procedia PDF Downloads 121

Authors: Mattia Testuzza

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Public health is a social endeavour, which involves many different actors: from extremely stratified, structured health systems to unofficial networks of people and knowledge. Health and diseases are an intertwined individual and social experiences. Both patients and health workers navigate this public space through relations of trust. Trust in healthcare goes from the personal trust between a patient and her/his doctor to the trust of both the patient and the health worker in the medical knowledge and the healthcare system. Trust it is not a given, but it is continuously negotiated, given and gained. The key to understand these essential relations of trust in health is to recognise them as a social practice, which therefore implies agency and power. In these terms, health is constantly public and made public, as trust emerges as a meaningfully political phenomenon. Trust as a power relation can be observed at play in the implementation of public health policies such as the WHO’s Directly-Observed Theraphy Short-course (DOTS), and with the increasing concern for drug-resistance that tuberculosis pose, looking at the role of trust in the healthcare delivery system and implementation of public health policies becomes significantly relevant. The ethnographic fieldwork was carried out in four months through observation of the daily practices at the National Tuberculosis Center of Nepal, and semi-structured interviews with MultiDrug-Resistant Tuberculosis (MDR-TB) patients at different stages of the treatment, their relatives, MDR-TB specialised nurses, and doctors. Throughout the research, the role which trust plays in tuberculosis treatment emerged as one fundamental ax that cuts through all the different factors intertwined with drug-resistance development, unfolding a tension between the DOTS policy, which undermines trust, and the day-to-day healthcare relations and practices which cannot function without trust. Trust also stands out as a key component of the solutions to unforeseen issues which develop from the overall uncertainty of the context - for example, political instability and extreme poverty - in which tuberculosis treatment is carried out in Nepal.

Keywords: trust, tuberculosis, drug-resistance, politics of health

Procedia PDF Downloads 257

Authors: Delia Gabriela Moisa, Demos Parapanos, Tim Heap

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The tourism sector is mainly comprised of small and medium-sized tourism enterprises (SMEs), representing approximately 80% of global businesses in this field. These entities require focused attention and support to address challenges, ensuring their competitiveness and relevance in a dynamic industry characterized by continuously changing customer preferences. To address these challenges, it becomes imperative to consider not only socio-demographic factors but also delve into the intricate interplay of psychological elements influencing consumer behavior. This study investigates the impact of personality traits and travel motivations on visitor activities in Cumbria, United Kingdom, an iconic region marked by UNESCO World Heritage Sites, including The Lake District National Park and Hadrian's Wall. With a £4.1 billion tourism industry primarily driven by SMEs, Cumbria serves as an ideal setting for examining the relationship between tourist psychology and activities. Employing the Big Five personality model and the Travel Career Pattern motivation theory, this study aims to explain the relationship between psychological factors and tourist activities. The study further explores SME perspectives on personality-based market segmentation, providing strategic insights into addressing evolving tourist preferences.This pioneering mixed-methods study integrates quantitative data from 330 visitor surveys, subsequently complemented by qualitative insights from tourism SME representatives. The findings unveil that socio-demographic factors do not exhibit statistically significant variations in the activities pursued by visitors in Cumbria. However, significant correlations emerge between personality traits and motivations with preferred visitor activities. Open-minded tourists gravitate towards events and cultural activities, while Conscientious individuals favor cultural pursuits. Extraverted tourists lean towards adventurous, recreational, and wellness activities, while Agreeable personalities opt for lake cruises. Interestingly, a contrasting trend emerges as Extraversion increases, leading to a decrease in interest in cultural activities. Similarly, heightened Agreeableness corresponds to a decrease in interest in adventurous activities. Furthermore, travel motivations, including nostalgia and building relationships, drive event participation, while self-improvement and novelty-seeking lead to adventurous activities. Additionally, qualitative insights from tourism SME representatives underscore the value of targeted messaging aligned with visitor personalities for enhancing loyalty and experiences. This study contributes significantly to scholarship through its novel framework, integrating tourist psychology with activities and industry perspectives. The proposed conceptual model holds substantial practical implications for SMEs to formulate personalized offerings, optimize marketing, and strategically allocate resources tailored to tourist personalities. While the focus is on Cumbria, the methodology's universal applicability offers valuable insights for destinations globally seeking a competitive advantage. Future research addressing scale reliability and geographic specificity limitations can further advance knowledge on this critical relationship between visitor psychology, individual preferences, and industry imperatives. Moreover, by extending the investigation to other districts, future studies could draw comparisons and contrasts in the results, providing a more nuanced understanding of the factors influencing visitor psychology and preferences.

Keywords: personality trait, SME, tourist behaviour, tourist motivation, visitor activity

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Authors: Getaneh Berie Tarekegn

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A precise localization system is crucial for many artificial intelligence Internet of Things (AI-IoT) applications in the era of smart cities. Their applications include traffic monitoring, emergency alarming, environmental monitoring, location-based advertising, intelligent transportation, and smart health care. The most common method for providing continuous positioning services in outdoor environments is by using a global navigation satellite system (GNSS). Due to nonline-of-sight, multipath, and weather conditions, GNSS systems do not perform well in dense urban, urban, and suburban areas.This paper proposes a generative AI-based positioning scheme for large-scale wireless settings using fingerprinting techniques. In this article, we presented a semi-supervised deep convolutional generative adversarial network (S-DCGAN)-based radio map construction method for real-time device localization. It also employed a reliable signal fingerprint feature extraction method with t-distributed stochastic neighbor embedding (t-SNE), which extracts dominant features while eliminating noise from hybrid WLAN and long-term evolution (LTE) fingerprints. The proposed scheme reduced the workload of site surveying required to build the fingerprint database by up to 78.5% and significantly improved positioning accuracy. The results show that the average positioning error of GAILoc is less than 0.39 m, and more than 90% of the errors are less than 0.82 m. According to numerical results, SRCLoc improves positioning performance and reduces radio map construction costs significantly compared to traditional methods.

Keywords: location-aware services, feature extraction technique, generative adversarial network, long short-term memory, support vector machine

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Authors: Jerome E. Abiemo, Takeshi Mizunoya

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The Aveyime community in the Volta region of Ghana is one of the major hubs for rice production. In the past, rice farmers applied organic pesticides to control pests, and compost as a soil amendment to improve fertility and productivity. However, the introduction of chemical pesticides and fertilizers have led many farmers to convert to inorganic system of rice production, without considering the social costs (e.g. groundwater contamination and health costs) related to the use of pesticides. The study estimates and compares the private and social BCRs of organic and inorganic systems of rice production. Both stratified and simple random sampling techniques were employed to select 300 organic and inorganic rice farmers and 50 pesticide applicators. The respondents were interviewed with pre-tested questionnaires. The Contingent Valuation Method (CVM) which elucidates organic farmers` Willingness-to-Pay (WTP) was employed to estimate the cost of groundwater contamination. The Cost of Illness (COI) analysis was used to estimate the health cost of pesticide-induced poisoning of applicators. The data collated, was analyzed with the aid of Microsoft excel. The study found that high private benefit (e.g. increase in farm yield and income) was the most influential factor for the rapid adoption of pesticides among rice farmers. The study also shows that the social costs of inorganic rice production were high. As such the social BCR of inorganic farming (0.2) was low as compared to organic farming (0.7). Based on the results, it was recommended that government should impose pesticide environmental tax, review current agricultural policies to favour organic farming and promote extension education to farmers on pesticide risk, to ensure agricultural and environmental sustainability.

Keywords: benefit-cost-ratio (BCR), inorganic farming, pesticides, social cost

Procedia PDF Downloads 481

Authors: Ru-Yu Lien, Shih-Hsin Hung, Shu-Fen Lu, Ju-Jen Shie, Wen-Ju Yang, Yuann-Meei Tzeng, Chien-Ying Wang

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Background: The quality of care in intensive care units (ICUs) is crucial, especially for terminally ill patients. Shared decision-making (SDM) with families is essential to ensure appropriate care and reduce suffering. Aim: This study explores the effectiveness of an end-of-life decision support Patient Decision Aid (PDA) in an ICU setting. Methods: This study employed a cross-sectional research design conducted in an ICU from August 2020 to June 2023. Participants included family members of end-of-life patients aged 20 or older. A total of 319 participants. Family members of end-of-life patients received the PDA, and data were collected after they made medical decisions. Data collection involved providing family members with a PDA during family meetings. A post-PDA questionnaire with 17 questions assessed PDA effectiveness and anxiety levels. Statistical analysis was performed using SPSS 22.0. Results: The PDA significantly reduced anxiety levels among family members (p < 0.001). It helped them organize their thoughts, prepare for discussions with doctors, and understand critical decision factors. Most importantly, it influenced decision outcomes, with a shift towards palliative care and withdrawal of life-sustaining treatment. Conclusion: This study highlights the importance of family-centered end-of-life care in ICUs. PDAs promote informed decision-making, reduce conflicts, and enhance patient and family involvement. These tools align patient values and goals with medical recommendations, ultimately leading to decisions that prioritize comfort and quality of life. Implementing PDAs in healthcare systems can ensure that patients' care aligns with their values.

Keywords: shared decision-making, patient decision aid, end-of-life care, intensive care unit, family-centered care

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Authors: M. Doyeni, S. Suproniene, V. Tilvikiene

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Agricultural wastes contribute significantly to global climate change through greenhouse gas emissions if not adequately recycled and sustainably managed. A recurring agricultural waste is livestock wastes that have consistently served as feedstock for biogas systems. The objective of this study was to access the influence of digestate fertilization on soil microbial activity and greenhouse gas emissions in agricultural fields. Wheat (Triticum spp. L.) was fertilized with different types of animal wastes digestates (organic fertilizers) and mineral nitrogen (inorganic fertilizer) for three years. The 170 kg N ha⁻¹ presented in digestates were split fertilized at an application rate of 90 and 80 kg N ha⁻¹. The soil microorganism activity could be predicted significantly using the dehydrogenase activity and soil microbial biomass carbon. By combining the two different monitoring approaches, the different methods applied in this study were sensitive to enzymatic activities and organic carbon in the living component of the soil organic matter. The emissions of greenhouse gasses (carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O) were monitored directly by a static chamber system. The soil and environmental variables were measured to determine their influence on greenhouse gas emissions. Emission peaks was observed in N₂O and CO₂ after the first application of fertilizers with the emissions flattening out over the cultivating season while CH₄ emission was negligible with no apparent patterns observed. Microbial biomass carbon and dehydrogenase activity were affected by the fertilized organic digestates. A significant difference was recorded between the control and the digestate treated soils for the microbial biomass carbon and dehydrogenase. Results also showed individual and cumulative emissions of CO₂, CH₄ and N₂O from the digestates were relatively low suggesting the digestate fertilization can be an efficient method for improving soil quality and reducing greenhouse gases from agricultural sources in temperate climate conditions.

Keywords: greenhouse gas emission, manure digestate, soil microbial activity, yield

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Authors: Washaya Soul, Mupangwa John, Mapfumo Lizwell, Muchenje Voster

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Ruminant animals contribute a considerable amount of methane to the atmosphere, which is a cause of concern for global warming. Two studies were conducted in beef and goats where the studies aimed to determine the enteric CH₄ levels from a herd of beef cows raised on semi-arid rangelands and to evaluate the effect of supplementing goats with forage legumes: Vigna unguiculata and Lablab purpureus on enteric methane production. A total of 24 cows were selected from Boran and Nguni cows (n = 12 per breed) from two different farms; parity (P1 – P4) and season (dry vs. wet) were considered predictor variables in the first experiment. Eighteen goats (weaners, 9 males, 9 females) were used, in which sex and forage species were predictor variables in the second experiment. Three treatment diets were used in goats. Methane was measured using a Laser methane detector [LMD] for six consecutive days and repeated once after every three months in beef cows and once every week for 6 weeks in goats during the post-adaptation period. Parity and breed had no effects on CH₄ production in beef cows; however, season significantly influenced CH₄ outputs. Methane production was higher (P<0.05) in the dry compared to the wet season, 31.1CH₄/DMI(g/kg) and 28.8 CH₄/DMI(g/kg) for the dry and wet seasons, respectively. In goats, forage species and sex of the animal affected enteric methane production (P<0.05). Animals produce more gas when ruminating than feeding or just standing for all treatments. The control treatment exhibited higher (P<0.05) methane emissions per kg of DMI. Male goats produced more methane compared to females (17.40L/day; 12.46 g/kg DMI and 0.126g/day) versus (15.47L/day, 12.28 g/kg DMI, 0.0109g/day) respectively. It was concluded that cows produce more CH₄/DMI during the dry season, while forage legumes reduce enteric methane production in goats, and male goats produce more gas compared to females. It is recommended to introduce forage legumes, particularly during the dry season, to reduce the amount of gas produced.

Keywords: beef cows, extensive grazing system, forage legumes, greenhouse gases, goats Laser methane detector.

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Authors: Adam N. Bell, Sarina J. Ergas, Michael Nystrom, Nathan P. Brennan, Kevan L. Main

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Integrated Multi-Trophic Aquaculture systems (IMTA) have the potential to improve the sustainability of seafood production, generate organic fertilizer and feed, remove waste discharges and reduce energy use. IMTA can include periphyton biofilters where algae and microbes grow on surfaces, along with caught detritus and amphipods. Periphyton biofilters provide many advantages: nitrification, denitrification, primary production and ecological diversity. The goal of this study was to determine how biofilter hydraulic residence time (τ) effects periphyton biomass production, dissolved oxygen (DO) and nutrient removal. A pilot scale recirculating aquaculture system (RAS) was designed, constructed and operated at different hydraulic residence times (τ= 1, 2, 4, 6, 8 hours per tank). For each τ, a conservative tracer study was conducted to investigate system hydrodynamics. Data on periphyton weights, pH, nitrogen species, phosphorus, temperature and DO were collected. The tracer study for τ =1 hour revealed that the normalized time < τ, indicating short-circuiting. Periphyton biomass production rate was relatively unaffected by τ (R_e<1 for all τ). Average ammonia nitrogen removal was > 75% for all trials. Nitrate and nitrite did not accumulate in the RAS for τ≥4 hours due to enhanced denitrification in anoxic zones. For τ≥4 hours DO concentration was at a maximum of 4 mg L-1 after 14:00, and decreased to 0 mg L-1 during nighttime. At τ=1 hour, the RAS stayed > 2 mg L-1 and DO was more evenly distributed. For the validation trial, the culture tank was stocked with Centropomus undecimalis (common snook) and the system was operated at τ= 1 hr. Preliminary results showed that a RAS with an integrated periphyton biofilter could support fish health with low nutrient concentrations DO > 6 mg L-1.

Keywords: sustainable aquaculture, resource recovery, nitrogen, microalgae, hydrodynamics, integrated multi-trophic aquaculture

Procedia PDF Downloads 136

Authors: Zahra Khodaee

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Environmental Justice (EJ) concept consists of multifaceted movements, community struggles, and discourses in contemporary societies that seek to reduce environmental risks, increase environmental protections, and generally reduce environmental inequalities suffered by minority and poor communities; a term that incorporates ‘environmental racism’ and ‘environmental classism,’ captures the idea that different racial and socioeconomic groups experience differential access to environmental quality. This article explores environmental justice as an urban phenomenon in urban planning and applies it in peri-urban environment of a metropolis. Tehran peri-urban environments which are the result of meeting the city- village- nature systems or «city-village junction» have gradually faced effects such as accelerated environmental decline, changes without land-use plan, and severe service deficiencies. These problems are instances of environmental injustice which make the planners to adjust the problems and use and apply the appropriate strategies and policies by looking for solutions and resorting to theories, techniques and methods related to environmental justice. In order to access to this goal, try to define environmental justice through justice and determining environmental justice indices to analysis environmental injustice in case study. Then, make an effort to introduce some criteria to select case study in two micro and micro levels. Qiyamdasht town as the peri-urban environment of Tehran metropolis is chosen and examined to show the existence of environmental injustice by questionnaire analysis and SPSS software. Finally, use AIDA technique to design a strategic plan and reduce environmental injustice in case study by introducing the better scenario to be used in policy and decision making areas.

Keywords: environmental justice, metropolis of Tehran, Qiyam, Dasht peri, urban settlement, analysis of interconnected decision area (AIDA)

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Authors: Ana Neves, John Leander, Ignacio Gonzalez, Raid Karoumi

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Systematic monitoring and inspection are needed to assess the present state of a structure and predict its future condition. If an irregularity is noticed, repair actions may take place and the adequate intervention will most probably reduce the future costs with maintenance, minimize downtime and increase safety by avoiding the failure of the structure as a whole or of one of its structural parts. For this to be possible decisions must be made at the right time, which implies using systems that can detect abnormalities in their early stage. In this sense, Structural Health Monitoring (SHM) is seen as an effective tool for improving the safety and reliability of infrastructures. This paper explores the decision-making problem in SHM regarding the maintenance of civil engineering structures. The aim is to assess the present condition of a bridge based exclusively on measurements using the suggested method in this paper, such that action is taken coherently with the information made available by the monitoring system. Artificial Neural Networks are trained and their ability to predict structural behavior is evaluated in the light of a case study where acceleration measurements are acquired from a bridge located in Stockholm, Sweden. This relatively old bridge is presently still in operation despite experiencing obvious problems already reported in previous inspections. The prediction errors provide a measure of the accuracy of the algorithm and are subjected to further investigation, which comprises concepts like clustering analysis and statistical hypothesis testing. These enable to interpret the obtained prediction errors, draw conclusions about the state of the structure and thus support decision making regarding its maintenance.

Keywords: artificial neural networks, clustering analysis, model-free damage detection, statistical hypothesis testing, structural health monitoring

Procedia PDF Downloads 211

Authors: Kavita Goel, Donald Winchester

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In a world of information overload and work complexities, academics often struggle to create an online instructional environment enabling efficient and effective student learning. Research has established that students’ learning styles are different, some learn faster when taught using audio and visual methods. Attributes like prior knowledge and mental effort affect their learning. ‘Cognitive load theory’, opines learners have limited processing capacity. Cognitive load depends on the learner’s prior knowledge, the complexity of content and tasks, and instructional environment. Hence, the proper allocation of cognitive resources is critical for students’ learning. Consequently, a lecturer needs to understand the limits and strengths of the human learning processes, various learning styles of students, and accommodate these requirements while designing online assessments. As acknowledged in the cognitive load theory literature, visual and auditory explanations of worked examples potentially lead to a reduction of cognitive load (effort) and increased facilitation of learning when compared to conventional sequential text problem solving. This will help learner to utilize both subcomponents of their working memory. Instructional design changes were introduced at the case site for the delivery of the postgraduate business subjects. To make effective use of auditory and visual modalities, video recorded lectures, and key concept webinars were delivered to students. Videos were prepared to free up student limited working memory from irrelevant mental effort as all elements in a visual screening can be viewed simultaneously, processed quickly, and facilitates greater psychological processing efficiency. Most case study students in the postgraduate programs are adults, working full-time at higher management levels, and studying part-time. Their learning style and needs are different from other tertiary students. The purpose of the audio and visual interventions was to lower the students cognitive load and provide an online environment supportive to their efficient learning. These changes were expected to impact the student’s learning experience, their academic performance and retention favourably. This paper posits that these changes to instruction design facilitates students to integrate new knowledge into their long-term memory. A mixed methods case study methodology was used in this investigation. Primary data were collected from interviews and survey(s) of students and academics. Secondary data were collected from the organisation’s databases and reports. Some evidence was found that the academic performance of students does improve when new instructional design changes are introduced although not statistically significant. However, the overall grade distribution of student’s academic performance has changed and skewed higher which shows deeper understanding of the content. It was identified from feedback received from students that recorded webinars served as better learning aids than material with text alone, especially with more complex content. The recorded webinars on the subject content and assessments provides flexibility to students to access this material any time from repositories, many times, and this enhances students learning style. Visual and audio information enters student’s working memory more effectively. Also as each assessment included the application of the concepts, conceptual knowledge interacted with the pre-existing schema in the long-term memory and lowered student’s cognitive load.

Keywords: cognitive load theory, learning style, instructional environment, working memory

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Authors: Abdallah Al-ZoubI, Mamoun Dmour

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The spectrum of IoT devices is becoming widely diversified, entering almost all possible fields and finding applications in industry, health, finance, logistics, education, to name a few. The IoT active endpoint sensors and devices exceeded the 12 billion mark in 2021 and are expected to reach 27 billion in 2025, with over $34 billion in total market value. This sheer rise in numbers and use of IoT devices bring with it considerable concerns regarding data storage, analysis, manipulation and protection. IoT Blockchain-based systems have recently been proposed as a decentralized solution for large-scale data storage and protection. COVID-19 has actually accelerated the desire to utilize IoT devices as it impacted both demand and supply and significantly affected several regions due to logistic reasons such as supply chain interruptions, shortage of shipping containers and port congestion. An IoT-blockchain system is proposed to handle big data generated by a distributed network of sensors and controllers in an interactive manner. The system is designed using the Ethereum platform, which utilizes smart contracts, programmed in solidity to execute and manage data generated by IoT sensors and devices. such as Raspberry Pi 4, Rasbpian, and add-on hardware security modules. The proposed system will run a number of applications hosted by a local machine used to validate transactions. It then sends data to the rest of the network through InterPlanetary File System (IPFS) and Ethereum Swarm, forming a closed IoT ecosystem run by blockchain where a number of distributed IoT devices can communicate and interact, thus forming a closed, controlled environment. A prototype has been deployed with three IoT handling units distributed over a wide geographical space in order to examine its feasibility, performance and costs. Initial results indicated that big IoT data retrieval and storage is feasible and interactivity is possible, provided that certain conditions of cost, speed and thorough put are met.

Keywords: IoT devices, blockchain, Ethereum, big data

Procedia PDF Downloads 151

Authors: Luleka Dlamini, Olivier Crespo, Jos van Dam

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Accurately estimating crop growth and yield is pivotal for timely sustainable agricultural management and ensuring food security. Crop models and remote sensing can complement each other and form a robust analysis tool to improve crop growth and yield estimations when combined. This study thus aims to systematically evaluate how research that exclusively focuses on assimilating RS data into crop models varies among countries, crops, data assimilation methods, and farming conditions. A strict search string was applied in the Scopus and Web of Science databases, and 497 potential publications were obtained. After screening for relevance with predefined inclusion/exclusion criteria, 123 publications were considered in the final review. Results indicate that over 81% of the studies were conducted in countries associated with high socio-economic and technological advancement, mainly China, the United States of America, France, Germany, and Italy. Many of these studies integrated MODIS or Landsat data into WOFOST to improve crop growth and yield estimation of staple crops at the field and regional scales. Most studies use recalibration or updating methods alongside various algorithms to assimilate remotely sensed leaf area index into crop models. However, these methods cannot account for the uncertainties in remote sensing observations and the crop model itself. l. Over 85% of the studies were based on commercial and irrigated farming systems. Despite a great global interest in data assimilation into crop models, limited research has been conducted in resource- and data-limited regions like Africa. We foresee a great potential for such application in those conditions. Hence facilitating and expanding the use of such an approach, from which developing farming communities could benefit.

Keywords: crop models, remote sensing, data assimilation, crop yield estimation

Procedia PDF Downloads 133

Authors: Luleka Dlamini, Olivier Crespo, Jos van Dam

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Accurately estimating crop growth and yield is pivotal for timely sustainable agricultural management and ensuring food security. Crop models and remote sensing can complement each other and form a robust analysis tool to improve crop growth and yield estimations when combined. This study thus aims to systematically evaluate how research that exclusively focuses on assimilating RS data into crop models varies among countries, crops, data assimilation methods, and farming conditions. A strict search string was applied in the Scopus and Web of Science databases, and 497 potential publications were obtained. After screening for relevance with predefined inclusion/exclusion criteria, 123 publications were considered in the final review. Results indicate that over 81% of the studies were conducted in countries associated with high socio-economic and technological advancement, mainly China, the United States of America, France, Germany, and Italy. Many of these studies integrated MODIS or Landsat data into WOFOST to improve crop growth and yield estimation of staple crops at the field and regional scales. Most studies use recalibration or updating methods alongside various algorithms to assimilate remotely sensed leaf area index into crop models. However, these methods cannot account for the uncertainties in remote sensing observations and the crop model itself. l. Over 85% of the studies were based on commercial and irrigated farming systems. Despite a great global interest in data assimilation into crop models, limited research has been conducted in resource- and data-limited regions like Africa. We foresee a great potential for such application in those conditions. Hence facilitating and expanding the use of such an approach, from which developing farming communities could benefit.

Keywords: crop models, remote sensing, data assimilation, crop yield estimation

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Authors: Zainab Mutlaq Ibrahim, Mohamed Bader-El-Den, Mihaela Cocea

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Students' textual feedback can hold unique patterns and useful information about learning process, it can hold information about advantages and disadvantages of teaching methods, assessment components, facilities, and other aspects of teaching. The results of analysing such a feedback can form a key point for institutions’ decision makers to advance and update their systems accordingly. This paper proposes a data mining framework for analysing end of unit general textual feedback using part of speech feature (PoS) with four machine learning algorithms: support vector machines, decision tree, random forest, and naive bays. The proposed framework has two tasks: first, to use the above algorithms to build an optimal model that automatically classifies the whole data set into two subsets, one subset is tailored to assessment practices (assessment related), and the other one is the non-assessment related data. Second task to use the same algorithms to build an optimal model for whole data set, and the new data subsets to automatically detect their sentiment. The significance of this paper is to compare the performance of the above four algorithms using part of speech feature to the performance of the same algorithms using n-grams feature. The paper follows Knowledge Discovery and Data Mining (KDDM) framework to construct the classification and sentiment analysis models, which is understanding the assessment domain, cleaning and pre-processing the data set, selecting and running the data mining algorithm, interpreting mined patterns, and consolidating the discovered knowledge. The results of this paper experiments show that both models which used both features performed very well regarding first task. But regarding the second task, models that used part of speech feature has underperformed in comparison with models that used unigrams and bigrams.

Keywords: assessment, part of speech, sentiment analysis, student feedback

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Authors: C. Mendoza Ramírez, M. Gambús Ordaz, R. Mercado Ojeda.

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Chemical solutions flooding with surfactants, based on their property of reducing the interfacial tension between crude oil and water, is a potential application of chemical enhanced oil recovery (CEOR), however, the high-rate retention of surfactants associated with adsorption in the porous medium and the complexity of the mineralogical composition of the reservoir rock generates a limitation in the efficiency of displacement of crude oil. This study evaluates the effect of the concentration of a mixture of anionic-non-ionic surfactants with silica nanoparticles, in a rock sample composed of 25.14% clay minerals of the kaolinite, chlorite, halloysite and montmorillonite type, according to the results of X-Ray Diffraction analysis and Scanning Electron Spectrometry (XRD and SEM, respectively). The amount of the surfactant mixture adsorbed on the clay rock minerals was analyzed from the construction of its calibration curve and the 4-Region Isotherm Model in a UV-Visible spectroscopy. The adsorption rate of the surfactant in the clay rock averages 32% across all concentrations, influenced by the presence of the surface area of the substrate with a value of 1.6 m2/g and by the mineralogical composition of the clay that increases the cation exchange capacity (CEC). In addition, on Region I and II a final concentration measurement is not evident in the UV-VIS, due to its ionic nature, its high affinity with the clay rock and its low concentration. Finally, for potential CEOR applications, the adsorption of these mixed surfactant systems is considered due to their industrial relevance and it is concluded that it is possible to use concentrations in Region III and IV; initially the adsorption has an increasing slope and then reaches zero in the equilibrium where interfacial tension values are reached in the order of x10-1 mN/m.

Keywords: anionic–nonionic surfactants, clay rock, adsorption, 4-region isotherm model, cation exchange capacity, critical micelle concentration, enhanced oil recovery

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Authors: Jefferson Camacho Mejía, Jenny Paola Forero Pachón, Luis Carlos Gómez Flórez

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Action research is a qualitative research methodology, which leads the researcher to delve into the problems of a community in order to understand its needs in depth and finally, to propose actions that lead to a change of social paradigm. Although this methodology had its beginnings in the human sciences, it has attracted increasing interest and acceptance in the field of information systems research since the 1990s. The countless possibilities offered nowadays by the use of Information Technologies (IT) in the development of different socio-economic activities have meant a change of social paradigm and the emergence of the so-called information and knowledge society. According to this, governments, large corporations, small entrepreneurs and in general, organizations of all kinds are using IT to virtualize their processes, taking them from the physical environment to the digital environment. However, there is a potential risk for organizations related with exposing valuable information without an appropriate framework for protecting it. This paper shows progress in the development of a methodological design to manage the information security risks associated with the IT-based processes virtualization, by applying the principles of the action research methodology and it is the result of a systematic review of the scientific literature. This design consists of seven fundamental stages. These are distributed in the three stages described in the action research methodology: 1) Observe, 2) Analyze and 3) Take actions. Finally, this paper aims to offer an alternative tool to traditional information security management methodologies with a view to being applied specifically in the planning stage of IT-based process virtualization in order to foresee risks and to establish security controls before formulating IT solutions in any type of organization.

Keywords: action research, information security, information technology, methodological design, process virtualization, risk management

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Authors: David L. Olson, Stephen B. H. Bruder, Adam S. Watkins, Cleon E. Davis

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In global navigation satellite systems (GNSS), denied settings such as indoor environments, autonomous mobile robots are often limited to dead-reckoning navigation techniques to determine their position, velocity, and attitude (PVA). Localization is typically accomplished by employing an inertial measurement unit (IMU), which, while precise in nature, accumulates errors rapidly and severely degrades the localization solution. Standard sensor fusion methods, such as Kalman filtering, aim to fuse precise IMU measurements with accurate aiding sensors to establish a precise and accurate solution. In indoor environments, where GNSS and no other a priori information is known about the environment, effective sensor fusion is difficult to achieve, as accurate aiding sensor choices are sparse. However, an opportunity arises by employing a depth camera in the indoor environment. A depth camera can capture point clouds of the surrounding floors and walls. Extracting attitude from these surfaces can serve as an accurate aiding source, which directly combats errors that arise due to gyroscope imperfections. This configuration for sensor fusion leads to a dramatic reduction of PVA error compared to traditional aiding sensor configurations. This paper provides the theoretical basis for the depth camera aiding sensor method, initial expectations of performance benefit via simulation, and hardware implementation, thus verifying its veracity. Hardware implementation is performed on the Quanser Qbot 2™ mobile robot, with a Vector-Nav VN-200™ IMU and Kinect™ camera from Microsoft.

Keywords: autonomous mobile robotics, dead reckoning, depth camera, inertial navigation, Kalman filtering, localization, sensor fusion

Procedia PDF Downloads 209

Authors: Srinivas Vangari

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In the era of big data, ensuring the accuracy, consistency, and reliability of critical data assets is crucial for data-driven enterprises. Master Data Management (MDM) plays a crucial role in this endeavor. This paper investigates the role of Artificial Intelligence (AI) in enhancing MDM, focusing on how AI-driven solutions can automate and optimize various stages of the master data lifecycle. By integrating AI (Quantitative and Qualitative Analysis) into processes such as data creation, maintenance, enrichment, and usage, organizations can achieve significant improvements in data quality and operational efficiency. Quantitative analysis is employed to measure the impact of AI on key metrics, including data accuracy, processing speed, and error reduction. For instance, our study demonstrates an 18% improvement in data accuracy and a 75% reduction in duplicate records across multiple systems post-AI implementation. Furthermore, AI’s predictive maintenance capabilities reduced data obsolescence by 22%, as indicated by statistical analyses of data usage patterns over a 12-month period. Complementing this, a qualitative analysis delves into the specific AI-driven strategies that enhance MDM practices, such as automating data entry and validation, which resulted in a 28% decrease in manual errors. Insights from case studies highlight how AI-driven data cleansing processes reduced inconsistencies by 25% and how AI-powered enrichment strategies improved data relevance by 24%, thus boosting decision-making accuracy. The findings demonstrate that AI significantly enhances data quality and integrity, leading to improved enterprise performance through cost reduction, increased compliance, and more accurate, real-time decision-making. These insights underscore the value of AI as a critical tool in modern data management strategies, offering a competitive edge to organizations that leverage its capabilities.

Keywords: artificial intelligence, master data management, data governance, data quality

Procedia PDF Downloads 22

Authors: Danyi Wang, Andrew Schriefer, Dennis O'Rourke, Brajendra Kumar, Yang Liu, Fei Zhong, Juergen Scheuenpflug, Zheng Feng

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Purpose: It has been recognized that the microbiome plays critical roles in disease pathogenesis, including cancer, autoimmune disease, and multiple sclerosis. To develop a clinical-grade assay for exploring microbiome-derived clinical biomarkers across disease areas, a two-phase approach is implemented. 1) Identification of the optimal sample preparation reagents using pre-mixed bacteria and healthy donor stool samples coupled with proprietary Sigma-Aldrich® bioinformatics solution. 2) Exploratory analysis of patient samples for enabling precision medicine. Study Procedure: In phase 1 study, we first compared the 16S sequencing results of two ATCC® microbiome standards (MSA 2002 and MSA 2003) across five different extraction kits (Kit A, B, C, D & E). Both microbiome standards samples were extracted in triplicate across all extraction kits. Following isolation, DNA quantity was determined by Qubit assay. DNA quality was assessed to determine purity and to confirm extracted DNA is of high molecular weight. Bacterial 16S ribosomal ribonucleic acid (rRNA) amplicons were generated via amplification of the V3/V4 hypervariable region of the 16S rRNA. Sequencing was performed using a 2x300 bp paired-end configuration on the Illumina MiSeq. Fastq files were analyzed using the Sigma-Aldrich® Microbiome Platform. The Microbiome Platform is a cloud-based service that offers best-in-class 16S-seq and WGS analysis pipelines and databases. The Platform and its methods have been extensively benchmarked using microbiome standards generated internally by MilliporeSigma and other external providers. Data Summary: The DNA yield using the extraction kit D and E is below the limit of detection (100 pg/µl) of Qubit assay as both extraction kits are intended for samples with low bacterial counts. The pre-mixed bacterial pellets at high concentrations with an input of 2 x106 cells for MSA-2002 and 1 x106 cells from MSA-2003 were not compatible with the kits. Among the remaining 3 extraction kits, kit A produced the greatest yield whereas kit B provided the least yield (Kit-A/MSA-2002: 174.25 ± 34.98; Kit-A/MSA-2003: 179.89 ± 30.18; Kit-B/MSA-2002: 27.86 ± 9.35; Kit-B/MSA-2003: 23.14 ± 6.39; Kit-C/MSA-2002: 55.19 ± 10.18; Kit-C/MSA-2003: 35.80 ± 11.41 (Mean ± SD)). Also, kit A produced the greatest yield, whereas kit B provided the least yield. The PCoA 3D visualization of the Weighted Unifrac beta diversity shows that kits A and C cluster closely together while kit B appears as an outlier. The kit A sequencing samples cluster more closely together than both the other kits. The taxonomic profiles of kit B have lower recall when compared to the known mixture profiles indicating that kit B was inefficient at detecting some of the bacteria. Conclusion: Our data demonstrated that the DNA extraction method impacts DNA concentration, purity, and microbial communities detected by next-generation sequencing analysis. Further microbiome analysis performance comparison of using healthy stool samples is underway; also, colorectal cancer patients' samples will be acquired for further explore the clinical utilities. Collectively, our comprehensive qualification approach, including the evaluation of optimal DNA extraction conditions, the inclusion of positive controls, and the implementation of a robust qualified bioinformatics pipeline, assures accurate characterization of the microbiota in a complex matrix for deciphering the deep biology and enabling precision medicine.

Keywords: 16S rRNA sequencing, analytical validation, bioinformatics pipeline, metagenomics

Procedia PDF Downloads 171

Authors: Sharda Kumari, Saiman Shetty

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Autonomous trucking is just one of the numerous significant shifts impacting fleet management services. The Society of Automotive Engineers (SAE) has defined six levels of vehicle automation that have been adopted internationally, including by the United States Department of Transportation. On public highways in the United States, organizations are testing driverless vehicles with at least Level 4 automation which indicates that a human is present in the vehicle and can disable automation, which is usually done while the trucks are not engaged in highway driving. However, completely driverless vehicles are presently being tested in the state of California. While autonomous trucking can increase safety, decrease trucking costs, provide solutions to trucker shortages, and improve efficiencies, logistics, too, requires advancements to keep up with trucking innovations. Given that artificial intelligence, machine learning, and automated procedures enable people to do their duties in other sectors with fewer resources, CRM (Customer Relationship Management) can be applied to the autonomous trucking business to provide the same level of efficiency. In a society witnessing significant digital disruptions, fleet management is likewise being transformed by technology. Utilizing strategic alliances to enhance core services is an effective technique for capitalizing on innovations and delivering enhanced services. Utilizing analytics on CRM systems improves cost control of fuel strategy, fleet maintenance, driver behavior, route planning, road safety compliance, and capacity utilization. Integration of autonomous trucks with automated fleet management, yard/terminal management, and customer service is possible, thus having significant power to redraw the lines between the public and private spheres in autonomous trucking logistics.

Keywords: autonomous vehicles, customer relationship management, customer experience, autonomous trucking, digital transformation

Procedia PDF Downloads 112

Authors: N. Stoeva, I. Spassova, R. Nickolov, M. Khristova

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Exhaust gases from stationary and mobile combustion sources contain nitrogen oxides that cause a variety of environmentally harmful effects. The most common approach of their elimination is the catalytic reaction in the exhaust using various reduction agents such as NH3, CO and hydrocarbons. Transition metals (Co, Ni, Cu, etc.) are the most widely used as active components for deposition on various supports. However, since the interaction between different catalyst components have been extensively studied in different types of reaction systems, the possible cooperation between active components and the support material and the underlying mechanisms have not been thoroughly investigated. The support structure may affect how these materials maintain an active phase. The objective is to investigate the addition of carbonaceous materials with different nature and texture characteristics on the properties of the resulting silica-carbon support and how it influences of the catalytic properties of the supported copper and cobalt catalysts for reduction of NO with CO. The versatility of the physico-chemical properties of the composites and the supported copper and cobalt catalysts are discussed with an emphasis on the relationship of the properties with the catalytic performance. The catalysts were prepared by sol-gel process and were characterized by XRD, XPS, AAS and BET analysis. The catalytic experiments were carried out in catalytic flow apparatus with isothermal flow reactor in the temperature range 20–300оС. After the catalytic test temperature-programmed desorption (TPD) was carried out. The transient response method was used to study the interaction of the gas phase with the catalyst surface. The role of the interaction between the support and the active phase on the catalyst’s activity in the studied reaction was discussed. We suppose the carbon particles with small sizes to participate in the formation of the active sites for the reduction of NO with CO along with their effect on the kind of deposited metal oxide phase. The existence of micropore texture for some of composites also influences by mass-transfer limitations.

Keywords: catalysts, no reduction, composites, bet analysis

Procedia PDF Downloads 426

Authors: S. Amirhossein Tabatabaei, Abdulghader A. Aldaeef, Mohammad T. Rayhani

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As the permafrost continues to melt in the northern regions due to global warming, a soil-water mixture is left behind with drastically lower strength; a phenomenon that directly impacts the resilience of existing structures and infrastructure systems. The frozen soil-structure interaction, which in ice-poor soils is controlled by both interface shear and ice-bonding, changes its nature into a sole frictional state. Adfreeze, the controlling mechanism in frozen soil-structure interaction, diminishes as the ground temperature approaches zero. The main purpose of this paper is to capture the altered behaviour of frozen interface with respect to rising temperature, especially near melting states. A series of pull-out tests are conducted on model piles inside a cold room to study how the strength parameters are influenced by the phase change in ice-poor soils. Steel model piles, embedded in artificially frozen cohesionless soil, are subjected to both sustained pull-out forces and constant rates of displacement to observe the creep behaviour and acquire load-deformation curves, respectively. Temperature, as the main variable of interest, is increased from a lower limit of -10°C up to the point of melting. During different stages of the temperature rise, both skin deformations and temperatures are recorded at various depths along the pile shaft. Significant reduction of pullout capacity and accelerated creep behaviour is found to be the primary consequences of rising temperature. By investigating the different pull-out capacities and deformations measured during step-wise temperature change, characteristics of the transition from frozen to unfrozen soil-structure interaction are studied.

Keywords: Adfreeze, frozen soil-structure interface, ice-poor soils, pull-out capacity, warming permafrost

Procedia PDF Downloads 112

Authors: Batul Kagalwala

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The nervous system is made up of complex delicate structures such as the spinal cord, peripheral nerves and the brain. These are prone to various types of injury ranging from neurodegenerative diseases to trauma leading to diseases like Parkinson's, Alzheimer's, multiple sclerosis, amyotrophic lateral sclerosis (ALS), multiple system atrophy etc. Unfortunately, because of the complicated structure of nervous system, spontaneous regeneration, repair and healing is seldom seen due to which brain damage, peripheral nerve damage and paralysis from spinal cord injury are often permanent and incapacitating. Hence, innovative and standardized approach is required for advance treatment of neurological injury. Nigella sativa (N. sativa), an annual flowering plant native to regions of southern Europe and Asia; has been suggested to have neuroprotective and anti-seizures properties. Neuroregeneration is found to occur in damaged cells when treated using extract of N. sativa. Due to its proven health benefits, lots of experiments are being conducted to extract all the benefits from the plant. The flowers are delicate and are usually pale blue and white in color with small black seeds. These seeds are the source of active components such as 30–40% fixed oils, 0.5–1.5% essential oils, pharmacologically active components containing thymoquinone (TQ), ditimoquinone (DTQ) and nigellin. In traditional medicine, this herb was identified to have healing properties and was extensively used Middle East and Far East for treating diseases such as head ache, back pain, asthma, infections, dysentery, hypertension, obesity and gastrointestinal problems. Literature studies have confirmed the extract of N. sativa seeds and TQ have inhibitory effects on inducible nitric oxide synthase and production of nitric oxide as well as anti-inflammatory and anticancer activities. Experimental investigation will be conducted to understand which ingredient of N. sativa causes neuroregeneration and roots to its healing property. An aqueous/ alcoholic extract of N. sativa will be made. Seed oil is also found to have used by researchers to prepare such extracts. For the alcoholic extracts, the seeds need to be powdered and soaked in alcohol for a period of time and the alcohol must be evaporated using rotary evaporator. For aqueous extracts, the powder must be dissolved in distilled water to obtain a pure extract. The mobile phase will be the extract while the suitable stationary phase (substance that is a good adsorbent e.g. silica gels, alumina, cellulose etc.) will be selected. Different ingredients of N. sativa will be separated using High Performance Liquid Chromatography (HPLC) for treating damaged cells. Damaged brain cells will be treated individually and in different combinations of 2 or 3 compounds for different intervals of time. The most suitable compound or a combination of compounds for the regeneration of cells will be determined using DOE methodology. Later the gene will also be determined and using Polymerase Chain Reaction (PCR) it will be replicated in a plasmid vector. This plasmid vector shall be inserted in the brain of the organism used and replicated within. The gene insertion can also be done by the gene gun method. The gene in question can be coated on a micro bullet of tungsten and bombarded in the area of interest and gene replication and coding shall be studied. Investigation on whether the gene replicates in the organism or not will be examined.

Keywords: black cumin, brain cells, damage, extract, neuroregeneration, PCR, plasmids, vectors

Procedia PDF Downloads 663

Authors: Alireza Taghdiri, Sara Ghanbarzade Ghomi

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Existing buildings are permanently subjected to change, continuously renovated and repaired in their long service life. Old buildings are destroyed and their material and components are recycled or reused for constructing new ones. In this process, the importance of sustainability principles for building construction is obviously known and great significance must be attached to the consumption of resources, resulting effects on the environment and economic costs. Utilization strategies for extending buildings service life and delay in destroying have a positive effect on environment protection. In addition, simpler alterability or expandability of buildings’ structures and reducing energy and natural resources consumption have benefits for users, producers and the environment. To solve these problems, by applying theories of open building, structural components of some conventional building systems have been analyzed and then, a new geometry adaptive building system is developed which can transform and support different imposed loads. In order to achieve this goal, various research methods and tools such as professional and scientific literatures review, comparative analysis, case study and computer simulation were applied and data interpretation was implemented using descriptive statistics and logical arguments. Therefore, hypothesis and proposed strategies were evaluated and an adaptable and reusable 2-dimensional building system was presented which can respond appropriately to dwellers and end-users needs and provide reusability of structural components of building system in new construction or function. Investigations showed that this incremental building system can be successfully applied in achieving the architectural design objectives and by small modifications on components and joints, it is easy to obtain different and adaptable load-optimized component alternatives for flexible spaces.

Keywords: adaptability, durability, open building, service life, structural building system

Procedia PDF Downloads 437

Authors: Zahrul Faizi M. S., Ali A., Norhuda A. M.

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Emission of greenhouse gases (GHG) from solid waste treatment and dependency on fossil fuel to produce electricity are the major concern in Malaysia as well as global. Innovation in downdraft gasification with combined heat and power (CHP) systems has the potential to minimize solid waste and reduce the emission of anthropogenic GHG from conventional fossil fuel power plants. However, the efficiency and capability of downdraft gasification to generate electricity from various alternative fuels, for instance, agriculture residues (i.e., woodchip, coconut shell) and municipal solid waste (MSW), are still controversial, on top of the toxicity level from the produced bottom ash. Thus this study evaluates the adaptability and reliability of the 20 kW downdraft gasification system to generate electricity (while considering environmental sustainability from the bottom ash) using flexible local feedstock at 20, 40, and 60% mixed ratio of MSW: agriculture residues. Feedstock properties such as feed particle size, moisture, and ash contents are also analyzed to identify optimal characteristics for the combination of feedstock (feedstock flexibility) to obtain maximum energy generation. Results show that the gasification system is capable to flexibly accommodate different feedstock compositions subjected to specific particle size (less than 2 inches) at a moisture content between 15 to 20%. These values exhibit enhance gasifier performance and provide a significant effect to the syngas composition utilizes by the internal combustion engine, which reflects energy production. The result obtained in this study is able to provide a new perspective on the transition of the conventional gasification system to a future reliable carbon-negative energy technology. Subsequently, promoting commercial scale-up of the downdraft gasification system.

Keywords: carbon-negative energy, feedstock flexibility, gasification, renewable energy

Procedia PDF Downloads 137

Authors: Waqqas-ur-Rehman Butt, Martin Servin, Marion Pause

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In recent years, object detection has gained much attention and very encouraging research area in the field of computer vision. The robust object boundaries detection in an image is demanded in numerous applications of human computer interaction and automated surveillance systems. Many methods and approaches have been developed for automatic object detection in various fields, such as automotive, quality control management and environmental services. Inappropriately, to the best of our knowledge, object detection under illumination with shadow consideration has not been well solved yet. Furthermore, this problem is also one of the major hurdles to keeping an object detection method from the practical applications. This paper presents an approach to automatic object detection in images under non-standardized environmental conditions. A key challenge is how to detect the object, particularly under uneven illumination conditions. Image capturing conditions the algorithms need to consider a variety of possible environmental factors as the colour information, lightening and shadows varies from image to image. Existing methods mostly failed to produce the appropriate result due to variation in colour information, lightening effects, threshold specifications, histogram dependencies and colour ranges. To overcome these limitations we propose an object detection algorithm, with pre-processing methods, to reduce the interference caused by shadow and illumination effects without fixed parameters. We use the Y CrCb colour model without any specific colour ranges and predefined threshold values. The segmented object regions are further classified using morphological operations (Erosion and Dilation) and contours. Proposed approach applied on a large image data set acquired under various environmental conditions for wood stack detection. Experiments show the promising result of the proposed approach in comparison with existing methods.

Keywords: image processing, illumination equalization, shadow filtering, object detection

Procedia PDF Downloads 217

Authors: Gaurav Kumar Sinha

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In an era marked by increasing concerns about energy sustainability, this research endeavors to address the pressing challenge of energy consumption in buildings and industries. This study delves into the transformative potential of AWS services in optimizing energy efficiency. The research is founded on the recognition that effective management of energy consumption is imperative for both environmental conservation and economic viability. Buildings and industries account for a substantial portion of global energy use, making it crucial to develop advanced techniques for analysis and reduction. This study sets out to explore the integration of AWS services with big data analytics to provide innovative solutions for energy consumption analysis. Leveraging AWS's cloud computing capabilities, scalable infrastructure, and data analytics tools, the research aims to develop efficient methods for collecting, processing, and analyzing energy data from diverse sources. The core focus is on creating predictive models and real-time monitoring systems that enable proactive energy management. By harnessing AWS's machine learning and data analytics capabilities, the research seeks to identify patterns, anomalies, and optimization opportunities within energy consumption data. Furthermore, this study aims to propose actionable recommendations for reducing energy consumption in buildings and industries. By combining AWS services with metrics-driven insights, the research strives to facilitate the implementation of energy-efficient practices, ultimately leading to reduced carbon emissions and cost savings. The integration of AWS services not only enhances the analytical capabilities but also offers scalable solutions that can be customized for different building and industrial contexts. The research also recognizes the potential for AWS-powered solutions to promote sustainable practices and support environmental stewardship.

Keywords: energy consumption analysis, big data analytics, AWS services, energy efficiency

Procedia PDF Downloads 65

Authors: Bahadır Erişti

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Values are the behavioral procedures of society based communication and interaction process that includes social and cultural backgrounds. The policy of learning and teaching in higher education is oriented towards constructing knowledge and skills, based on theorist framework of cognitive and psychomotor aspects. This approach makes people not to develop generosity, empathy, affection, solidarity, justice, equality and so on. But the sensorial gains of education system provide the integrity of society interaction. This situation carries out the necessity of values education’s in higher education. The current study aims to consider values education from the viewpoint of students in higher education. Within the framework of the current study, an open ended survey based scenario of higher education students was conducted with the students’ social, cognitive, affective and moral developments. In line with this purpose, the following situations of the higher education system were addressed based on the higher education students’ viewpoint: The views of higher education students’ regarding values that are tried to be gained at the higher education system; The higher education students’ suggestions regarding values education at the higher education system; The views of the higher education students’ regarding values that are imposed at the higher education system. In this study, descriptive qualitative research method was used. The study group of the research is composed of 20 higher education postgraduate students at Curriculum and Instruction Department of Educational Sciences at Anadolu University. An open-ended survey was applied for the purpose of collecting qualitative data. As a result of the study, value preferences, value judgments and value systems of the higher education students were constructed on prioritizes based on social, cultural and economic backgrounds and statues. Multi-dimensional process of value education in higher education need to be constructed on higher education-community-cultural background cooperation. Thus, the act of judgement upon values between higher education students based on the survey seems to be inherent in the system of education itself. The present study highlights the students’ value priorities and importance of values in higher education. If the purpose of the higher education system gains on values, it is possible to enable society to promote humanity.

Keywords: higher education, value, values education, values in higher education

Procedia PDF Downloads 342