Search results for: rolling stock reliability

Authors: Tugba G. Isin Ozkan, Yoshiharu Fujii

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One of the most important constrains that decrease the crop yields are weeds. Increased amount and number of chemical herbicides are being utilized every day to control weeds. Chemical herbicides which cause environmental effects, and limitations on implementation of them have led to the nonchemical alternatives in the management of weeds. It is needed increasingly the application of allelopathy as a nonherbicidal innovation to control weed populations in integrated weed management. It is not only because of public concern about herbicide use, but also increased agricultural costs and herbicide resistance weeds. Allelopathy is defined as a common biological phenomenon, direct or indirect interaction which one plant or organism produces biochemicals influence the physiological processes of another neighboring plant or organism. Biochemicals involved in allelopathy are called allelochemicals that influence beneficially or detrimentally the growth, survival, development, and reproduction of other plant or organisms. All plant parts could have allelochemicals which are secondary plant metabolites. Allelochemicals are released to environment, influence the germination and seedling growth of neighbors' weeds; that is the way how allelopathy is applied for weed control. Crop cultivars have significantly different ability for inhibiting the growth of certain weeds. So, a high commercial value crop Corylus avellana L. and its byproducts were chosen to introduce for their allelopathic potential in this research. Edible nut of Corylus avellana L., commonly known as hazelnut is commercially valuable crop with byproducts; skin, hard shell, green leafy cover, and tree leaf. Research on allelopathic potential of a plant by using the sandwich bioassay method and investigation growth inhibitory activity is the first step to develop new and environmentally friendly alternatives for weed control. Thus, the objective of this research is to determine allelopathic potential of C. avellana L. and its byproducts by using sandwich method and to determine effective concentrations (EC) of their extracts for inducing half-maximum elongation inhibition on radicle of test plant, EC50. The sandwich method is reliable and fast bioassay, very useful for allelopathic screening under laboratory conditions. In experiments, lettuce (Lactuca sativa L.) seeds will be test plant, because of its high sensitivity to inhibition by allelochemicals and reliability for germination. In sandwich method, the radicle lengths of dry material treated lettuce seeds and control lettuce seeds will be measured and inhibition of radicle elongation will be determined. Lettuce seeds will also be treated by the methanol extracts of dry hazelnut parts to calculate EC₅₀ values, which are required to induce half-maximal inhibition of growth, as mg dry weight equivalent mL-1. Inhibitory activity of extracts against lettuce seedling elongation will be evaluated, like in sandwich method, by comparing the radicle lengths of treated seeds with that of control seeds and EC₅₀ values will be determined. Research samples are dry parts of Turkish hazelnut, C. avellana L. The results would suggest the opportunity for allelopathic potential of C. avellana L. with its byproducts in plant-plant interaction, might be utilized for further researches, could be beneficial in finding bioactive chemicals from natural products and developing of natural herbicides.

Keywords: allelopathy, Corylus avellana L., EC50, Lactuca sativa L., sandwich method, Turkish hazelnut

Procedia PDF Downloads 144

Authors: Leah Li Echiverri, Haoyu Shang, Yue Li

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Wenzhou-Kean University (WKU) is a Sino-US Cooperative University in China. It practices the English Immersion Program (EIP), where all the courses are taught in English. Class discussions and presentations are pervasively interwoven in designing students’ learning experiences. This WKU model has brought positive influences on students and is in some way ahead of traditional college English majors. However, literature to support the perceptions on the positive outcomes of this teaching and learning model remain scarce. The distinctive profile of Chinese-ESL students in an English Medium of Instruction (EMI) environment contributes further to the scarcity of literature compared to existing studies conducted among ESL learners in Western educational settings. Hence, the study investigated the students’ perceptions towards the English Immersion Program and determine how it influences Chinese-ESL students’ academic performance (AP). This research can provide empirical data that would be helpful to educators, teaching practitioners, university administrators, and other researchers in making informed decisions when developing curricular reforms, instructional and pedagogical methods, and university-wide support programs using this educational model. The purpose of the study was to establish the relationship between the English Immersion Program and Academic Performance among Chinese-ESL students enrolled at WKU for the academic year 2020-2021. Course length, immersion location, course type, and instructional design were the constructs of the English immersion program. English language learning, learning efficiency, and class participation were used to measure academic performance. Descriptive-correlational design was used in this cross-sectional research project. A quantitative approach for data analysis was applied to determine the relationship between the English immersion program and Chinese-ESL students’ academic performance. The research was conducted at WKU; a Chinese-American jointly established higher educational institution located in Wenzhou, Zhejiang province. Convenience, random, and snowball sampling of 283 students, a response rate of 10.5%, were applied to represent the WKU student population. The questionnaire was posted through the survey website named Wenjuanxing and shared to QQ or WeChat. Cronbach’s alpha was used to test the reliability of the research instrument. Findings revealed that when professors integrate technology (PowerPoint, videos, and audios) in teaching, students pay more attention. This contributes to the acquisition of more professional knowledge in their major courses. As to course immersion, students perceive WKU as a good place to study, providing them a high degree of confidence to talk with their professors in English. This also contributes to their English fluency and better pronunciation in their communication. In the construct of designing instruction, the use of pictures, video clips, and professors’ non-verbal communication, and demonstration of concern for students encouraged students to be more active in-class participation. Findings on course length and academic performance indicated that students’ perception regarding taking courses during fall and spring terms can moderately contribute to their academic performance. In conclusion, the findings revealed a significantly strong positive relationship between course type, immersion location, instructional design, and academic performance.

Keywords: class participation, English immersion program, English language learning, learning efficiency

Procedia PDF Downloads 143

Authors: Yana Snegireva

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Carbonate reservoirs are known for their heterogeneity, resulting from various geological processes such as diagenesis and fracturing. These complexities may cause great challenges in understanding fluid flow behavior and predicting the production performance of naturally fractured reservoirs. The investigation of carbonate reservoirs is crucial, as many petroleum reservoirs are naturally fractured, which can be difficult due to the complexity of their fracture networks. This can lead to geological uncertainties, which are important for global petroleum reserves. The problem outlines the key challenges in carbonate reservoir modeling, including the accurate representation of fractures and their connectivity, as well as capturing the impact of fractures on fluid flow and production. Traditional reservoir modeling techniques often oversimplify fracture networks, leading to inaccurate predictions. Therefore, there is a need for a modern approach that can capture the complexities of carbonate reservoirs and provide reliable predictions for effective reservoir management and production optimization. The modern approach to carbonate reservoir modeling involves the utilization of the hybrid fracture modeling approach, including the discrete fracture network (DFN) method and implicit fracture network, which offer enhanced accuracy and reliability in characterizing complex fracture systems within these reservoirs. This study focuses on the application of the hybrid method in the Nepsko-Botuobinskaya anticline of the Eastern Siberia field, aiming to prove the appropriateness of this method in these geological conditions. The DFN method is adopted to model the fracture network within the carbonate reservoir. This method considers fractures as discrete entities, capturing their geometry, orientation, and connectivity. But the method has significant disadvantages since the number of fractures in the field can be very high. Due to limitations in the amount of main memory, it is very difficult to represent these fractures explicitly. By integrating data from image logs (formation micro imager), core data, and fracture density logs, a discrete fracture network (DFN) model can be constructed to represent fracture characteristics for hydraulically relevant fractures. The results obtained from the DFN modeling approaches provide valuable insights into the East Siberia field's carbonate reservoir behavior. The DFN model accurately captures the fracture system, allowing for a better understanding of fluid flow pathways, connectivity, and potential production zones. The analysis of simulation results enables the identification of zones of increased fracturing and optimization opportunities for reservoir development with the potential application of enhanced oil recovery techniques, which were considered in further simulations on the dual porosity and dual permeability models. This approach considers fractures as separate, interconnected flow paths within the reservoir matrix, allowing for the characterization of dual-porosity media. The case study of the East Siberia field demonstrates the effectiveness of the hybrid model method in accurately representing fracture systems and predicting reservoir behavior. The findings from this study contribute to improved reservoir management and production optimization in carbonate reservoirs with the use of enhanced and improved oil recovery methods.

Keywords: carbonate reservoir, discrete fracture network, fracture modeling, dual porosity, enhanced oil recovery, implicit fracture model, hybrid fracture model

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Authors: Irina Mizeva, Elena Potapova, Viktor Dremin, Mikhail Mezentsev, Valeri Shupletsov

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The local tissue perfusion is regulated by the microvascular tone which is under the control of a number of physiological mechanisms. Laser Doppler flowmetry (LDF) together with wavelet analyses is the most commonly used technique to study the regulatory mechanisms of cutaneous microcirculation. External factors such as temperature, local pressure of the probe on the skin, etc. influence on the blood flow characteristics and are used as physiological tests to evaluate microvascular regulatory mechanisms. Local probe pressure influences on the microcirculation parameters measured by optical methods: diffuse reflectance spectroscopy, fluorescence spectroscopy, and LDF. Therefore, further study of probe pressure effects can be useful to improve the reliability of optical measurement. During pressure tests variation of the mean perfusion measured by means of LDF usually is estimated. An additional information concerning the physiological mechanisms of the vascular tone regulation system in response to local pressure can be obtained using spectral analyses of LDF samples. The aim of the present work was to develop protocol and algorithm of data processing appropriate for study physiological response to the local pressure test. Involving 6 subjects (20±2 years) and providing 5 measurements for every subject we estimated intersubject and-inter group variability of response of both averaged and oscillating parts of the LDF sample on external surface pressure. The final purpose of the work was to find special features which further can be used in wider clinic studies. The cutaneous perfusion measurements were carried out by LAKK-02 (SPE LAZMA Ltd., Russia), the skin loading was provided by the originally designed device which allows one to distribute the pressure around the LDF probe. The probe was installed on the dorsal part of the distal finger of the index figure. We collected measurements continuously for one hour and varied loading from 0 to 180mmHg stepwise with a step duration of 10 minutes. Further, we post-processed the samples using the wavelet transform and traced the energy of oscillations in five frequency bands over time. Weak loading leads to pressure-induced vasodilation, so one should take into account that the perfusion measured under pressure conditions will be overestimated. On the other hand, we revealed a decrease in endothelial associated fluctuations. Further loading (88 mmHg) induces amplification of pulsations in all frequency bands. We assume that such loading leads to a higher number of closed capillaries, higher input of arterioles in the LDF signal and as a consequence more vivid oscillations which mainly are formed in arterioles. External pressure higher than 144 mmHg leads to the decrease of oscillating components, after removing the loading very rapid restore of the tissue perfusion takes place. In this work, we have demonstrated that local skin loading influence on the microcirculation parameters measured by optic technique; this should be taken into account while developing portable electronic devices. The proposed protocol of local loading allows one to evaluate PIV as far as to trace dynamic of blood flow oscillations. This study was supported by the Russian Science Foundation under project N 18-15-00201.

Keywords: blood microcirculation, laser Doppler flowmetry, pressure-induced vasodilation, wavelet analyses blood

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Authors: Wesley Preßler, Lucie Schmidt

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Digital transformation is playing an increasingly important role in the development of smart residential quarters. In order to accompany and steer this process and ultimately make the success of the transformation efforts measurable, it is helpful to use an appropriate maturity model. However, conventional maturity models for digital transformation focus primarily on the evaluation of processes and neglect the information and power imbalances between the stakeholders, which affects the validity of the results. The Multi-Generation Smart Community (mGeSCo) research project is developing an interdisciplinary maturity model that integrates the dimensions of digital literacy, interpretive patterns, and technology acceptance to address this gap. As part of the mGeSCo project, the technological development of selected dimensions in the Smart Quarter Jena-Lobeda (Germany) is being investigated. A specific maturity model, based on Cohen's Smart Cities Wheel, evaluates the central dimensions Working, Living, Housing and Caring. To improve the reliability and relevance of the maturity assessment, the factors Digital Literacy, Interpretive Patterns and Technology Acceptance are integrated into the developed model. The digital literacy dimension examines stakeholders' skills in using digital technologies, which influence their perception and assessment of technological maturity. Digital literacy is measured by means of surveys, interviews, and participant observation, using the European Commission's Digital Literacy Framework (DigComp) as a basis. Interpretations of digital technologies provide information about how individuals perceive technologies and ascribe meaning to them. However, these are not mere assessments, prejudices, or stereotyped perceptions but collective patterns, rules, attributions of meaning and the cultural repertoire that leads to these opinions and attitudes. Understanding these interpretations helps in assessing the overarching readiness of stakeholders to digitally transform a/their neighborhood. This involves examining people's attitudes, beliefs, and values about technology adoption, as well as their perceptions of the benefits and risks associated with digital tools. These insights provide important data for a holistic view and inform the steps needed to prepare individuals in the neighborhood for a digital transformation. Technology acceptance is another crucial factor for successful digital transformation to examine the willingness of individuals to adopt and use new technologies. Surveys or questionnaires based on Davis' Technology Acceptance Model can be used to complement interpretive patterns to measure neighborhood acceptance of digital technologies. Integrating the dimensions of digital literacy, interpretive patterns and technology acceptance enables the development of a roadmap with clear prerequisites for initiating a digital transformation process in the neighborhood. During the process, maturity is measured at different points in time and compared with changes in the aforementioned dimensions to ensure sustainable transformation. Participation, co-creation, and co-production are essential concepts for a successful and inclusive digital transformation in the neighborhood context. This interdisciplinary maturity model helps to improve the assessment and monitoring of sustainable digital transformation processes in smart residential quarters. It enables a more comprehensive recording of the factors that influence the success of such processes and supports the development of targeted measures to promote digital transformation in the neighborhood context.

Keywords: digital transformation, interdisciplinary, maturity model, neighborhood

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Authors: Philipp Wenger, Michael Beltle, Stefan Tenbohlen, Uwe Riechert

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The integration of renewable energy introduces new challenges to the transmission grid, as the power generation is located far from load centers. The associated necessary long-range power transmission increases the demand for high voltage direct current (HVDC) transmission lines and DC distribution grids. HVDC gas-insulated switchgears (GIS) are considered being a key technology, due to the combination of the DC technology and the long operation experiences of AC-GIS. To ensure long-term reliability of such systems, insulation defects must be detected in an early stage. Operational experience with AC systems has proven evidence, that most failures, which can be attributed to breakdowns of the insulation system, can be detected and identified via partial discharge (PD) measurements beforehand. In AC systems the identification of defects relies on the phase resolved partial discharge pattern (PRPD). Since there is no phase information within DC systems this method cannot be transferred to DC PD diagnostic. Furthermore, the behaviour of e.g. free-moving particles differs significantly at DC: Under the influence of a constant direct electric field, charge carriers can accumulate on particles’ surfaces. As a result, a particle can lift-off, oscillate between the inner conductor and the enclosure or rapidly bounces at just one electrode, which is known as firefly motion. Depending on the motion and the relative position of the particle to the electrodes, broadband electromagnetic PD pulses are emitted, which can be recorded by ultra-high frequency (UHF) measuring methods. PDs are often accompanied by light emissions at the particle’s tip which enables optical detection. This contribution investigates PD characteristics of free moving metallic particles in a commercially available 300 kV SF6-insulated HVDC-GIS. The influences of various defect parameters on the particle motion and the PD characteristic are evaluated experimentally. Several particle geometries, such as cylinder, lamella, spiral and sphere with different length, diameter and weight are determined. The applied DC voltage is increased stepwise from inception voltage up to UDC = ± 400 kV. Different physical detection methods are used simultaneously in a time-synchronized setup. Firstly, the electromagnetic waves emitted by the particle are recorded by an UHF measuring system. Secondly, a photomultiplier tube (PMT) detects light emission with a wavelength in the range of λ = 185…870 nm. Thirdly, a high-speed camera (HSC) tracks the particle’s motion trajectory with high accuracy. Furthermore, an electrically insulated electrode is attached to the grounded enclosure and connected to a current shunt in order to detect low frequency ion currents: The shunt measuring system’s sensitivity is in the range of 10 nA at a measuring bandwidth of bw = DC…1 MHz. Currents of charge carriers, which are generated at the particle’s tip migrate through the gas gap to the electrode and can be recorded by the current shunt. All recorded PD signals are analyzed in order to identify characteristic properties of different particles. This includes e.g. repetition rates and amplitudes of successive pulses, characteristic frequency ranges and detected signal energy of single PD pulses. Concluding, an advanced understanding of underlying physical phenomena particle motion in direct electric field can be derived.

Keywords: current shunt, free moving particles, high-speed imaging, HVDC-GIS, UHF

Procedia PDF Downloads 132

Authors: Christelle Rabbat, Carole Vouebou, Sary Awad, Alan Jean-Marie

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Hydrogen has been proven to be a viable alternative to standard fuels as it is easy to produce and only generates water vapour and zero carbon emissions. However, despite the hydrogen benefits, the widespread adoption of hydrogen fuel cell vehicles and internal combustion engine vehicles is impeded by several challenges. The lack of refueling infrastructures remains one of the main hindering factors due to the high costs associated with their design, construction, and operation. Besides, the lack of hydrogen vehicles on the road diminishes the economic viability of investing in refueling infrastructure. Simultaneously, the absence of accessible refueling stations discourages consumers from adopting hydrogen vehicles, perpetuating a cycle of limited market uptake. To address these challenges, the implementation of adequate policies incentivizing the use of hydrogen vehicles and the reduction of the investment and operation costs of hydrogen refueling stations (HRS) are essential to put both investors and customers at ease. Even though the transition to hydrogen cars has been rather slow, public transportation companies have shown a keen interest in this highly promising fuel. Besides, their hydrogen demand is easier to predict and regulate than personal vehicles. Due to the reduced complexity of designing a suitable hydrogen supply chain for public vehicles, this sub-sector could be a great starting point to facilitate the adoption of hydrogen vehicles. Consequently, this study will focus on designing a chain of on-site green HRS for the public transportation network in Nantes Metropole leveraging the latest relevant technological advances aiming to reduce the costs while ensuring reliability, safety, and ease of access. To reduce the cost of HRS and encourage their widespread adoption, a network of 7 H35-T40 HRS has been designed, replacing the conventional J-T valves with turbo-expanders. Each station in the network has a daily capacity of 1,920 kg. Thus, the HRS network can produce up to 12.5 tH2 per day. The detailed cost analysis has revealed a CAPEX per station of 16.6 M euros leading to a network CAPEX of 116.2 M euros. The proposed station siting prioritized Nantes metropole’s 5 bus depots and included 2 city-centre locations. Thanks to the turbo-expander technology, the cooling capacity of the proposed HRS is 19% lower than that of a conventional station equipped with J-T valves, resulting in significant CAPEX savings estimated at 708,560 € per station, thus nearly 5 million euros for the whole HRS network. Besides, the turbo-expander power generation ranges from 7.7 to 112 kW. Thus, the power produced can be used within the station or sold as electricity to the main grid, which would, in turn, maximize the station’s profit. Despite the substantial initial investment required, the environmental benefits, cost savings, and energy efficiencies realized through the transition to hydrogen fuel cell buses and the deployment of HRS equipped with turbo-expanders offer considerable advantages for both TAN and Nantes Metropole. These initiatives underscore their enduring commitment to fostering green mobility and combatting climate change in the long term.

Keywords: green hydrogen, refueling stations, turbo-expander, heavy-duty vehicles

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Authors: May Bazzi, Shafik Tokmaj, Younes Saberi, Mats Geijer, Tony Jurkiewicz, Patrik Sund, Anna Bjällmark

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Mobile radiography, employing portable X-ray equipment, has become a routine procedure within hospital settings, with chest X-rays in intensive care units standing out as the most prevalent mobile X-ray examinations. This approach is not limited to hospitals alone, as it extends its benefits to imaging patients in various settings, particularly those too frail to be transported, such as elderly care residents in nursing homes. Moreover, the utility of mobile X-ray isn't confined solely to traditional healthcare recipients; it has proven to be a valuable resource for vulnerable populations, including the homeless, drug users, asylum seekers, and patients with multiple co-morbidities. Mobile X-rays reduce patient stress, minimize costly hospitalizations, and offer cost-effective imaging. While studies confirm its reliability, further research is needed, especially regarding image quality. Recent advancements in lightweight equipment with enhanced battery and detector technology provide the potential for nearly handheld radiography. The main aim of this study was to evaluate a new lightweight mobile X-ray system with two different detectors and compare the image quality with a modern stationary system. Methods: A total of 74 images of the chest (chest anterior-posterior (AP) views and chest lateral views) and pelvic/hip region (AP pelvis views, hip AP views, and hip cross-table lateral views) were acquired on a whole-body phantom (Kyotokagaku, Japan), utilizing varying image parameters. These images were obtained using a stationary system - 18 images (Mediel, Sweden), a mobile X-ray system with a second-generation detector - 28 images (FDR D-EVO II; Fujifilm, Japan) and a mobile X-ray system with a third-generation detector - 28 images (FDR D-EVO III; Fujifilm, Japan). Image quality was assessed by visual grading analysis (VGA), which is a method to measure image quality by assessing the visibility and accurate reproduction of anatomical structures within the images. A total of 33 image criteria were used in the analysis. A panel of two experienced radiologists, two experienced radiographers, and two final-term radiographer students evaluated the image quality on a 5-grade ordinal scale using the software Viewdex 3.0 (Viewer for Digital Evaluation of X-ray images, Sweden). Data were analyzed using visual grading characteristics analysis. The dose was measured by the dose-area product (DAP) reported by the respective systems. Results: The mobile X-ray equipment (both detectors) showed significantly better image quality than the stationary equipment for the pelvis, hip AP and hip cross-table lateral images with AUCVGA-values ranging from 0.64-0.92, while chest images showed mixed results. The number of images rated as having sufficient quality for diagnostic use was significantly higher for mobile X-ray generation 2 and 3 compared with the stationary X-ray system. The DAP values were higher for the stationary compared to the mobile system. Conclusions: The new lightweight radiographic equipment had an image quality at least as good as a fixed system at a lower radiation dose. Future studies should focus on clinical images and consider radiographers' viewpoints for a comprehensive assessment.

Keywords: mobile x-ray, visual grading analysis, radiographer, radiation dose

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Authors: Mary T. McKinley

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As socio-economic globalization impacts education and turns knowledge into a commodity, institutions of higher education are becoming more intentional about infusing a global and intercultural perspective into education via the recruitment of international students. Coming from dissimilar cultures, many of these students are evaluated and held accountable to Euro-American values of independence, self-reliance, and autonomy. Not surprisingly, these students often experience culture shock with deleterious effects on their mental health and academic functioning. Thus, it is critical to understand the experiences of international students with the hope that such knowledge will keep the field of psychology from promulgating Eurocentric ideals and values and prevent the training of these students as good-enough White therapists. Using a critical narrative inquiry framework, this study elicits stories about the challenges encountered by international students as they navigate their clinical training in the presence of acculturative stress and potentially different worldviews. With its emphasis on story-telling as meaning making, narrative research design is hinged on the assumption that people are interpretive beings who make meaning of themselves and their world through the language of stories. Also, dominant socially-constructed narratives play a central role in creating and maintaining hegemonic structures that privilege certain individuals and ideologies at the expense of others. On this premise, narrative inquiry begins with an exploration of the experiences of participants in their lived stories. Bounded narrative segments were read, interpreted, and analyzed using a critical events approach. Throughout the process, issues of reliability and researcher bias were addressed by keeping a reflective analytic memo, as well as triangulating the data using peer-reviewers and check-ins with participants. The findings situate culture at the epicenter of international students’ acculturation challenges as well as their resiliency in psychology doctoral programs. It was not uncommon for these international students to experience ethical dilemmas inherent in learning content that conflicted with their cultural beliefs and values. Issues of cultural incongruence appear to be further exacerbated by visible markers for differences like speech accent and clothing attire. These stories also link the acculturative stress reported by international students to the experiences of perceived racial discrimination and lack of support from the faculty, administration, peers, and the society at large. Beyond the impact on the international students themselves, there are implications for internationalization in psychology with the goal of equipping doctoral programs to be better prepared to meet the needs of their international students. More than ever before, programs need to liaise with international students’ services and work in tandem to meet the unique needs of this population of students. Also, there exists a need for multiculturally competent supervisors working with international students with varying degrees of acculturation. In addition to making social justice and advocacy salient in students’ multicultural training, it may be helpful for psychology doctoral programs to be more intentional about infusing cross-cultural theories, indigenous psychotherapies, and/or when practical, the possibility for geographically cross-cultural practicum experiences in the home countries of international students while taking into consideration the ethical issues for virtual supervision.

Keywords: decolonizing pedagogies, international students, multiculturalism, psychology doctoral programs

Procedia PDF Downloads 91

Authors: Nurlan Smagulov, Aiman Konkabayeva, Akerke Sadykova, Arailym Serik

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Introduction. Adverse environmental factors do not immediately lead to pathological changes in the body. They can exert the growth of pre-pathology characterized by shifts in physiological, biochemical, immunological and other indicators of the body state. These disorders are unstable, reversible and indicative of body reactions. There is an opportunity to objectively judge the internal structure of the adaptive body reactions at the level of individual organs and systems. In order to obtain a stable response of the body to the chronic effects of unfavorable environmental factors of low intensity (compared to production environment factors), a time called the «lag time» is needed. The obtained results without considering this factor distort reality and, for the most part, cannot be a reliable statement of the main conclusions in any work. A technique is needed to reduce methodological errors and combine mathematical logic using statistical methods and a medical point of view, which ultimately will affect the obtained results and avoid a false correlation. Objective. Development of a methodology for assessing and predicting the environmental factors impact on the population health considering the «lag time.» Methods. Research objects: environmental and population morbidity indicators. The database on the environmental state was compiled from the monthly newsletters of Kazhydromet. Data on population morbidity were obtained from regional statistical yearbooks. When processing static data, a time interval (lag) was determined for each «argument-function» pair. That is the required interval, after which the harmful factor effect (argument) will fully manifest itself in the indicators of the organism's state (function). The lag value was determined by cross-correlation functions of arguments (environmental indicators) with functions (morbidity). Correlation coefficients (r) and their reliability (t), Fisher's criterion (F) and the influence share (R2) of the main factor (argument) per indicator (function) were calculated as a percentage. Results. The ecological situation of an industrially developed region has an impact on health indicators, but it has some nuances. Fundamentally opposite results were obtained in the mathematical data processing, considering the «lag time». Namely, an expressed correlation was revealed after two databases (ecology-morbidity) shifted. For example, the lag period was 4 years for dust concentration, general morbidity, and 3 years – for childhood morbidity. These periods accounted for the maximum values of the correlation coefficients and the largest percentage of the influencing factor. Similar results were observed in relation to the concentration of soot, dioxide, etc. The comprehensive statistical processing using multiple correlation-regression variance analysis confirms the correctness of the above statement. This method provided the integrated approach to predicting the degree of pollution of the main environmental components to identify the most dangerous combinations of concentrations of leading negative environmental factors. Conclusion. The method of assessing the «environment-public health» system (considering the «lag time») is qualitatively different from the traditional (without considering the «lag time»). The results significantly differ and are more amenable to a logical explanation of the obtained dependencies. The method allows presenting the quantitative and qualitative dependence in a different way within the «environment-public health» system.

Keywords: ecology, morbidity, population, lag time

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Authors: Solanki Ravirajsinh, Kudo Kuniaki, Sharma Ankit, Devi Sherine, Kuboshima Misaki, Tachi Shuntaro

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In software development life cycles, the absence of scalable CI/CD significantly impacts organizations, leading to increased overall maintenance costs, prolonged release delivery times, heightened manual efforts, and difficulties in meeting tight deadlines. Implementing CI/CD with standard serverless technologies using cloud services overcomes all the above-mentioned issues and helps organizations improve efficiency and faster delivery without the need to manage server maintenance and capacity. By integrating scalable CI/CD with scalable automation testing, productivity, quality, and agility are enhanced while reducing the need for repetitive work and manual efforts. Implementing scalable CI/CD for development using cloud services like ECS (Container Management Service), AWS Fargate, ECR (to store Docker images with all dependencies), Serverless Computing (serverless virtual machines), Cloud Log (for monitoring errors and logs), Security Groups (for inside/outside access to the application), Docker Containerization (Docker-based images and container techniques), Jenkins (CI/CD build management tool), and code management tools (GitHub, Bitbucket, AWS CodeCommit) can efficiently handle the demands of diverse development environments and are capable of accommodating dynamic workloads, increasing efficiency for faster delivery with good quality. CI/CD pipelines encourage collaboration among development, operations, and quality assurance teams by providing a centralized platform for automated testing, deployment, and monitoring. Scalable CI/CD streamlines the development process by automatically fetching the latest code from the repository every time the process starts, building the application based on the branches, testing the application using a scalable automation testing framework, and deploying the builds. Developers can focus more on writing code and less on managing infrastructure as it scales based on the need. Serverless CI/CD eliminates the need to manage and maintain traditional CI/CD infrastructure, such as servers and build agents, reducing operational overhead and allowing teams to allocate resources more efficiently. Scalable CI/CD adjusts the application's scale according to usage, thereby alleviating concerns about scalability, maintenance costs, and resource needs. Creating scalable automation testing using cloud services (ECR, ECS Fargate, Docker, EFS, Serverless Computing) helps organizations run more than 500 test cases in parallel, aiding in the detection of race conditions, performance issues, and reducing execution time. Scalable CI/CD offers flexibility, dynamically adjusting to varying workloads and demands, allowing teams to scale resources up or down as needed. It optimizes costs by only paying for the resources as they are used and increases reliability. Scalable CI/CD pipelines employ automated testing and validation processes to detect and prevent errors early in the development cycle.

Keywords: achieve parallel execution, cloud services, scalable automation testing, scalable continuous integration and deployment

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Authors: Stephanie Giordano, Rosa Plasencia

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In the USA, in 2013, public service systems such as Medicaid, aging, and disability systems undertook an effort to measure the quality of service delivery by examining the experiences and outcomes of those receiving public services. The goal of this effort was to develop a survey to measure the experiences and outcomes of those receiving public services, with the goal of measuring system performance for quality improvement. The performance indicators were developed through with input from directors of state aging and disability service systems, along with experts and stakeholders in the field across the United States. This effort, National Core Indicators –Aging and Disabilities (NCI-AD), grew out of National Core Indicators –Intellectual and Developmental Disabilities, an effort to measure developmental disability (DD) systems across the States. The survey tool and administration protocol underwent multiple rounds of testing and revision between 2013 and 2015. The measures in the final tool – called the Adult Consumer Survey (ACS) – emphasize not just important indicators of healthcare access and personal safety but also includes indicators of system quality based on person-centered outcomes. These measures indicate whether service systems support older adults and people with disabilities to live where they want, maintain relationships and engage in their communities and have choice and control in their everyday lives. Launched in 2015, the NCI-AD Adult Consumer Survey is now used in 23 states in the US. Surveys are conducted by NCI-AD trained surveyors via direct conversation with a person receiving public long-term services and supports (LTSS). Until 2020, surveys were only conducted in person. However, after a pilot to test the reliability of videoconference and telephone survey modes, these modes were adopted as an acceptable practice. The nature of the survey is that of a “guided conversation” survey administration allows for surveyor to use wording and terminology that is best understand by the person surveyed. The survey includes a subset of questions that may be answered by a proxy respondent who knows the person well if the person is receiving services in unable to provide valid responses on their own. Surveyors undergo a standardized training on survey administration to ensure the fidelity of survey administration. In addition to the main survey section, a Background Information section collects data on personal and service-related characteristics of the person receiving services; these data are typically collected through state administrative record. This information is helps provide greater context around the characteristics of people receiving services. It has also been used in conjunction with outcomes measures to look at disparity (including by race and ethnicity, gender, disability, and living arrangements). These measures of quality are critical for public service delivery systems to understand the unique needs of the population of older adults and improving the lives of older adults as well as people with disabilities. Participating states may use these data to identify areas for quality improvement within their service delivery systems, to advocate for specific policy change, and to better understand the experiences of specific populations of people served.

Keywords: quality of life, long term services and supports, person-centered practices, aging and disability research, survey methodology

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Authors: Maria Karnachoriti, Ellas Spyratou, Dimitrios Lykidis, Maria Lambropoulou, Yiannis S. Raptis, Ioannis Seimenis, Efstathios P. Efstathopoulos, Athanassios G. Kontos

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Colorectal cancer is the third most common cancer diagnosed in Europe, according to the latest incidence data provided by the World Health Organization (WHO), and early diagnosis has proved to be the key in reducing cancer-related mortality. In cases where surgical interventions are required for cancer treatment, the accurate discrimination between healthy and cancerous tissues is critical for the postoperative care of the patient. The current study focuses on the ex vivo handling of surgically excised colorectal specimens and the acquisition of their spectral fingerprints using Raman spectroscopy. Acquired data were analyzed in an effort to discriminate, in microscopic scale, between healthy and malignant margins. Raman spectroscopy is a spectroscopic technique with high detection sensitivity and spatial resolution of few micrometers. The spectral fingerprint which is produced during laser-tissue interaction is unique and characterizes the biostructure and its inflammatory or cancer state. Numerous published studies have demonstrated the potential of the technique as a tool for the discrimination between healthy and malignant tissues/cells either ex vivo or in vivo. However, the handling of the excised human specimens and the Raman measurement conditions remain challenging, unavoidably affecting measurement reliability and repeatability, as well as the technique’s overall accuracy and sensitivity. Therefore, tissue handling has to be optimized and standardized to ensure preservation of cell integrity and hydration level. Various strategies have been implemented in the past, including the use of balanced salt solutions, small humidifiers or pump-reservoir-pipette systems. In the current study, human colorectal specimens of 10X5 mm were collected from 5 patients up to now who underwent open surgery for colorectal cancer. A novel, non-toxic zinc-based fixative (Z7) was used for tissue preservation. Z7 demonstrates excellent protein preservation and protection against tissue autolysis. Micro-Raman spectra were recorded with a Renishaw Invia spectrometer from successive random 2 micrometers spots upon excitation at 785 nm to decrease fluorescent background and secure avoidance of tissue photodegradation. A temperature-controlled approach was adopted to stabilize the tissue at 2 °C, thus minimizing dehydration effects and consequent focus drift during measurement. A broad spectral range, 500-3200 cm-1,was covered with five consecutive full scans that lasted for 20 minutes in total. The average spectra were used for least square fitting analysis of the Raman modes.Subtle Raman differences were observed between normal and cancerous colorectal tissues mainly in the intensities of the 1556 cm-1 and 1628 cm-1 Raman modes which correspond to v(C=C) vibrations in porphyrins, as well as in the range of 2800-3000 cm-1 due to CH2 stretching of lipids and CH3 stretching of proteins. Raman spectra evaluation was supported by histological findings from twin specimens. This study demonstrates that Raman spectroscopy may constitute a promising tool for real-time verification of clear margins in colorectal cancer open surgery.

Keywords: colorectal cancer, Raman spectroscopy, malignant margins, spectral fingerprints

Procedia PDF Downloads 68

Authors: Oscar Bareiro, Johannes Sackmann, Thomas Gries

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Carbon fiber reinforced polymer composites (CFRP) are used in a wide variety of applications due to its advantageous properties and design versatility. The braiding process enables the manufacture of components with good toughness and fatigue strength. However, failure mechanisms of CFRPs are complex and still present challenges associated with their maintenance and repair. Within the broad scope of structural health monitoring (SHM), strain monitoring can be applied to composite materials to improve reliability, reduce maintenance costs and safely exhaust service life. Traditional SHM systems employ e.g. fiber optics, piezoelectrics as sensors, which are often expensive, time consuming and complicated to implement. A cost-efficient alternative can be the exploitation of the conductive properties of fiber-based sensors such as carbon, copper, or constantan - a copper-nickel alloy – that can be utilized as sensors within composite structures to achieve strain monitoring. This allows the structure to provide feedback via electrical signals to a user which are essential for evaluating the structural condition of the structure. This work presents a strategy for the in-process integration of resistance-based sensors (Elektrisola Feindraht AG, CuNi23Mn, Ø = 0.05 mm) into textile preforms during its manufacture via the braiding process (Herzog RF-64/120) to achieve strain monitoring of braided composites. For this, flat samples of instrumented composite laminates of carbon fibers (Toho Tenax HTS40 F13 24K, 1600 tex) and epoxy resin (Epikote RIMR 426) were manufactured via vacuum-assisted resin infusion. These flat samples were later cut out into test specimens and the integrated sensors were wired to the measurement equipment (National Instruments, VB-8012) for data acquisition during the execution of mechanical tests. Quasi-static tests were performed (tensile, 3-point bending tests) following standard protocols (DIN EN ISO 527-1 & 4, DIN EN ISO 14132); additionally, dynamic tensile tests were executed. These tests were executed to assess the sensor response under different loading conditions and to evaluate the influence of the sensor presence on the mechanical properties of the material. Several orientations of the sensor with regards to the applied loading and sensor placements inside the laminate were tested. Strain measurements from the integrated sensors were made by programming a data acquisition code (LabView) written for the measurement equipment. Strain measurements from the integrated sensors were then correlated to the strain/stress state for the tested samples. From the assessment of the sensor integration approach it can be concluded that it allows for a seamless sensor integration into the textile preform. No damage to the sensor or negative effect on its electrical properties was detected during inspection after integration. From the assessment of the mechanical tests of instrumented samples it can be concluded that the presence of the sensors does not alter significantly the mechanical properties of the material. It was found that there is a good correlation between resistance measurements from the integrated sensors and the applied strain. It can be concluded that the correlation is of sufficient accuracy to determinate the strain state of a composite laminate based solely on the resistance measurements from the integrated sensors.

Keywords: braiding process, in-process sensor integration, instrumented composite material, resistance-based sensor, strain monitoring

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Authors: Catherine Vasnetsov, Victor Vasnetsov

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Context: Thermo-responsive polymers are materials that undergo significant changes in their physical properties in response to temperature changes. These polymers have gained significant attention in research due to their potential applications in various industries and medicine. However, the molecular mechanisms underlying their behavior are not well understood, particularly in relation to cosolvency, which is crucial for practical applications. Research Aim: This study aimed to theoretically investigate the phenomenon of cosolvency in long-chain polymers using the Flory-Huggins statistical-mechanical framework. The main objective was to understand the interactions between the polymer, solvent, and cosolvent under different conditions. Methodology: The research employed a combination of Monte Carlo computer simulations and advanced machine-learning methods. The Flory-Huggins mean field theory was used as the basis for the simulations. Spinodal graphs and ternary plots were utilized to develop an initial computer model for predicting polymer behavior. Molecular dynamic simulations were conducted to mimic real-life polymer systems. Machine learning techniques were incorporated to enhance the accuracy and reliability of the simulations. Findings: The simulations revealed that the addition of very low or very high volumes of cosolvent molecules resulted in smaller radii of gyration for the polymer, indicating poor miscibility. However, intermediate volume fractions of cosolvent led to higher radii of gyration, suggesting improved miscibility. These findings provide a possible microscopic explanation for the cosolvency phenomenon in polymer systems. Theoretical Importance: This research contributes to a better understanding of the behavior of thermo-responsive polymers and the role of cosolvency. The findings provide insights into the molecular mechanisms underlying cosolvency and offer specific predictions for future experimental investigations. The study also presents a more rigorous analysis of the Flory-Huggins free energy theory in the context of polymer systems. Data Collection and Analysis Procedures: The data for this study was collected through Monte Carlo computer simulations and molecular dynamic simulations. The interactions between the polymer, solvent, and cosolvent were analyzed using the Flory-Huggins mean field theory. Machine learning techniques were employed to enhance the accuracy of the simulations. The collected data was then analyzed to determine the impact of cosolvent volume fractions on the radii of gyration of the polymer. Question Addressed: The research addressed the question of how cosolvency affects the behavior of long-chain polymers. Specifically, the study aimed to investigate the interactions between the polymer, solvent, and cosolvent under different volume fractions and understand the resulting changes in the radii of gyration. Conclusion: In conclusion, this study utilized theoretical modeling and computer simulations to investigate the phenomenon of cosolvency in long-chain polymers. The findings suggest that moderate cosolvent volume fractions can lead to improved miscibility, as indicated by higher radii of gyration. These insights contribute to a better understanding of the molecular mechanisms underlying cosolvency in polymer systems and provide predictions for future experimental studies. The research also enhances the theoretical analysis of the Flory-Huggins free energy theory.

Keywords: molecular modelling, flory-huggins, cosolvency, stimuli-responsive polymers

Procedia PDF Downloads 43

Authors: Chooi Fong Lee

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Background: Maternal psychological well-being has been investigated from various aspects, such as social support, employment status. However, a perspective from self-discrepancy theory has not been employed. Moreover, most were focused on young mothers. Less is understanding the middle-aged mother’s psychological well-being. Objective: To examine the influence of maternal self-discrepancy between actual and ideal self on maternal role achievement, state anxiety, trait anxiety, and subjective well-being among Japanese middle-aged mothers across their employment status. Method: A pilot study was conducted with 20 mother participants (aged 40-55, 9 regular-employed, 8 non-regular-employed, and 3 homemaker mothers) to assess the viability of survey questionnaires (Maternal Role Achievement Scale, State-Trait Anxiety Inventory, Subjective Well-being Scale, and a self-report). Participants were randomly selected voluntarily from the college students’ mothers. Participants accessed the survey via a designated URL. The self-report questionnaire prompted participants to list up to 3 ideal selves they aspired to be and rate the extent to which their actual selves deviated from their ideal selves on a 7-point scale (1= not at all; 4 = medium; 7 = extremely). The findings confirmed the validity of the survey questionnaires, indicating their appropriateness for use in subsequent research. Self-discrepancy scores were calculated by subtracting participants’ degree ratings from a 7-point scale, summing them up, and then dividing the total by 3. Setting: We ensured participants were randomly selected from the research firm to mitigate bias. The self-report questionnaire was adapted from a validated instrument and underwent rigorous modification and testing in the pilot study. The final sample consisted of 241 participants, 97 regular-employed, 87 non-regular employed, and 57 homemaker mothers. Result: The reliability coefficient for the discrepancy score is α=.75. The findings indicate that regular-employed mothers tend to exhibit lower self-discrepancy scores compared to non-regular employed and homemaker mothers. This discrepancy negatively impacts maternal role, state anxiety, and subjective well-being while positively affecting trait anxiety. Trait anxiety arises when one feels they did not meet their ideal self, as evidenced by higher levels in homemaker mothers, who experience lower state anxiety. Conversely, regular-employed mothers exhibit higher state anxiety but lower trait anxiety, suggesting satisfaction in their professional pursuits despite balancing work and family responsibilities. Full-time maternal roles contribute to lower state anxiety but higher trait anxiety among homemaker mothers due to a lack of personal identity achievement. Non-regular employed mothers show similarities to homemaker mothers. In self-reports, regular-employed mothers highlight support and devotion to their children’s development, while non-regular-employed mothers seek life fulfillment through part-time work alongside child-rearing duties. Homemaker mothers emphasize qualities like sociability, and communication skills, potentially influencing their self-discrepancy scores. Furthermore, the hierarchical multiple regression analysis revealed that the discrepancy scores significantly predict subjective well-being. Conclusion: There may be the need for broader generalizability beyond our sample of Japanese mothers; however, the findings offer valuable insights into the impact of maternal self-discrepancy on psychological well-being among middle-aged mothers across different employment statuses. Understanding these dynamics becomes crucial as contemporary women increasingly pursue higher education and depart from traditional motherhood norms.

Keywords: maternal employment, maternal role, self-discrepancy, state-trait anxiety, subjective well-being

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Authors: Samuel Olajide Babawale, Jonathan Bridge

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Natural Flood Management (NFM) is promoted as part of sustainable flood management (SFM) in response to climate change adaptation. Stakeholder engagement is central to this approach, and current trends are progressively moving towards a collaborative learning approach where stakeholder participation is perceived as one of the indicators of sustainable development. Within this methodology, participation embraces a diversity of knowledge and values underpinned by a philosophy of empowerment, equity, trust, and learning. To identify barriers to NFM uptake, there is a need for a new understanding of how stakeholder participation could be enhanced to benefit individual and community resilience within SFM. This is crucial in light of climate change threats and scientific reliability concerns. In contributing to this new understanding, this research evaluated the proposed interventions on six (6) UK NFM in a catchment known as the Sheffield Lakeland Partnership Area with reference to the Environment Agency Working with Natural Processes (WWNP) Potentials/Opportunities. Three of the opportunities, namely Run-off Attenuation Potential of 1%, Run-off Attenuation Potential of 3.3% and Riparian Woodland Potential, were modeled. In all the models, the interventions, though they have been proposed or already in place, are not in agreement with the data presented by EA WWNP. Findings show some institutional weaknesses, which are seen to inhibit the development of adequate flood management solutions locally with damaging implications for vulnerable communities. The gap in communication from practitioners poses a challenge to the implementation of real flood mitigating measures that align with the lead agency’s nationally accepted measures which are identified as not feasible by the farm management officers within this context. Findings highlight a dominant top-bottom approach to management with very minimal indication of local interactions. Current WWNP opportunities have been termed as not realistic by the people directly involved in the daily management of the farms, with less emphasis on prevention and mitigation. The targeted approach suggested by the EA WWNP is set against adaptive flood management and community development. The study explores dimensions of participation using the self-reliance and self-help approach to develop a methodology that facilitates reflections of currently institutionalized practices and the need to reshape spaces of interactions to enable empowered and meaningful participation. Stakeholder engagement and resilience planning underpin this research. The findings of the study suggest different agencies have different perspectives on “community participation”. It also shows communities in the case study area appear to be least influential, denied a real chance of discussing their situations and influencing the decisions. This is against the background that the communities are in the most productive regions, contributing massively to national food supplies. The results are discussed concerning practical implications for addressing interagency partnerships and conducting grassroots collaborations that empower local communities and seek solutions to sustainable development challenges. This study takes a critical look into the challenges and progress made locally in sustainable flood risk management and adaptation to climate change by the United Kingdom towards achieving the global 2030 agenda for sustainable development.

Keywords: natural flood management, sustainable flood management, sustainable development, working with natural processes, environment agency, run-off attenuation potential, climate change

Procedia PDF Downloads 49

Authors: Komal K., Cleary F., Wells J S.G., Bennett L

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Smart fabric garments enable various monitoring applications across sectors such as healthcare, sports and fitness, and the military. Healthcare smart garments monitoring EEG, EMG, and ECG rely on the use of electrodes (dry or wet). However, such electrodes, when used for long-term monitoring, can cause discomfort and skin irritation for the wearer because of their inflexible structure and weight. Ongoing research has been investigating textile-based electrodes (textrodes) in order to provide more comfortable and usable fabric-based electrodes capable of providing intuitive biopotential monitoring. Progress has been made in this space, but they still face a critical design challenge in maintaining consistent skin contact, which directly impacts signal quality. Furthermore, there is a lack of an ISO-based testing framework to validate the electrode design and assess its ability to achieve enhanced performance, strength, usability, and durability. This study proposes the development and evaluation of an ISO-compliant testing framework for standard 2D and advanced 3D embroidered textrodes designs that have a unique structure in order to establish enhanced skin contact for the wearer. This testing framework leverages ISO standards: ISO 13934-1:2013 for tensile and zone-wise strength tests; ISO 13937-2 for tear tests; and ISO 6330 for washing, validating the textrode's performance, a necessity for wearables health parameter monitoring applications. Five textrodes (C1-C5) were designed using EPC win digitization software. Varying patterns such as running stitches, lock stitches, back-to-back stitches, and moss stitches were used to create various embroidered tetrodes samples using Madeira HC12 conductive thread with a resistivity of 100 ohm/m. The textrode designs were then fabricated using a ZSK technical embroidery machine. A comparative analysis was conducted based on a series of laboratory tests adhering to ISO compliance requirements. Tests focusing on the application of strain were applied to the textrodes, and these included: (1) analysis of the electrode's overall surface area strength; (2) assessment of the robustness of the textrodes boundaries; and (3) the assignment of fault test zones to each textrode, where vertical and horizontal slits of 3mm were applied to evaluate the performance of textrodes and its durability. Specific ISO-compliant tests linked to washing were conducted multiple times on each textrode sample to assess both mechanical and chemical damage. Additionally, abrasion and pilling tests were performed to evaluate mechanical damage on the surface of the textrodes and to compare it with the washing test. Finally, the textrodes were assessed based on morphological and surface resistance changes. Results demonstrate that textrode C4, featuring a 3-D layered structure consisting of foam, fabric, and conductive thread layers, significantly enhances skin-electrode contact for biopotential recording. The inclusion of a 3D foam layer was particularly effective in maintaining the shape of the electrode during strain tests, making it the top-performing textrode sample. Therefore, the layered 3D design structure of textrode C4 ranks highest when tested for durability, reusability, and washability. The ISO testing framework established in this study will support future research, validating the durability and reliability of textrodes for a wide range of applications.

Keywords: smart fabric, textrodes, testing framework, ISO compliant

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Authors: F. Pedro, S. Dias, A. Tadeu, J. António, Ó. López, A. Coelho

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Railway transportation is considered the most economical and sustainable way to travel. However, future mobility brings important challenges to railway operators. The main target is to develop solutions that stimulate sustainable mobility. The research and innovation goals for this domain are efficient solutions, ensuring an increased level of safety and reliability, improved resource efficiency, high availability of the means (train), and satisfied passengers with the travel comfort level. These requirements are in line with the European Strategic Agenda for the 2020 rail sector, promoted by the European Rail Research Advisory Council (ERRAC). All these aspects involve redesigning current equipment and, in particular, the interior of the carriages. Recent studies have shown that two of the most important requirements for passengers are reasonable ticket prices and comfortable interiors. Passengers tend to use their travel time to rest or to work, so train interiors and their systems need to incorporate features that meet these requirements. Among the various systems that integrate train interiors, the flooring system is one of the systems with the greatest impact on passenger safety and comfort. It is also one of the systems that takes more time to install on the train, and which contributes seriously to the weight (mass) of all interior systems. Additionally, it presents a strong impact on manufacturing costs. The design of railway floor, in the development phase, is usually made relying on a design software that allows to draw and calculate several solutions in a short period of time. After obtaining the best solution, considering the goals previously defined, experimental data is always necessary and required. This experimental phase has such great significance, that its outcome can provoke the revision of the designed solution. This paper presents the methodology and some of the results of an experimental characterisation of composite panels for railway application. The mechanical tests were made for unaged specimens and for specimens that suffered some type of aging, i.e. heat, cold and humidity cycles or freezing/thawing cycles. These conditionings aim to simulate not only the time effect, but also the impact of severe environmental conditions. Both full solutions and separated components/materials were tested. For the full solution, (panel) these were: four-point bending tests, tensile shear strength, tensile strength perpendicular to the plane, determination of the spreading of water, and impact tests. For individual characterisation of the components, more specifically for the covering, the following tests were made: determination of the tensile stress-strain properties, determination of flexibility, determination of tear strength, peel test, tensile shear strength test, adhesion resistance test and dimensional stability. The main conclusions were that experimental characterisation brings a huge contribution to understand the behaviour of the materials both individually and assembled. This knowledge contributes to the increase the quality and improvements of premium solutions. This research work was framed within the POCI-01-0247-FEDER-003474 (coMMUTe) Project funded by Portugal 2020 through the COMPETE 2020.

Keywords: durability, experimental characterization, mechanical tests, railway flooring system

Procedia PDF Downloads 124

Authors: Danah Al-Selahi, Sulaiman Al-Ghunaim, Bashayer Sadiq, Fatma Al-Otaibi, Ali Ameen

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In light of recent changes affecting the Oil & Gas Industry, optimization measures have become imperative for all companies globally, including Kuwait Oil Company (KOC). To keep abreast of the dynamic market, a detailed Integrated Asset Plan (IAP) was developed to drive optimization across the organization, which was facilitated through the in-house developed software “Well Inventory Data Entry” (WIDE). This comprehensive and integrated approach enabled centralization of all planned asset components for better well planning, enhancement of performance, and to facilitate continuous improvement through performance tracking and midterm forecasting. Traditionally, this was hard to achieve as, in the past, various legacy methods were used. This paper briefly describes the methods successfully adopted to meet the company’s objective. IAPs were initially designed using computerized spreadsheets. However, as data captured became more complex and the number of stakeholders requiring and updating this information grew, the need to automate the conventional spreadsheets became apparent. WIDE, existing in other aspects of the company (namely, the Workover Optimization project), was utilized to meet the dynamic requirements of the IAP cycle. With the growth of extensive features to enhance the planning process, the tool evolved into a centralized data-hub for all asset-groups and technical support functions to analyze and infer from, leading WIDE to become the reference two-year operational plan for the entire company. To achieve WIDE’s goal of operational efficiency, asset-groups continuously add their parameters in a series of predefined workflows that enable the creation of a structured process which allows risk factors to be flagged and helps mitigation of the same. This tool dictates assigned responsibilities for all stakeholders in a method that enables continuous updates for daily performance measures and operational use. The reliable availability of WIDE, combined with its user-friendliness and easy accessibility, created a platform of cross-functionality amongst all asset-groups and technical support groups to update contents of their respective planning parameters. The home-grown entity was implemented across the entire company and tailored to feed in internal processes of several stakeholders across the company. Furthermore, the implementation of change management and root cause analysis techniques captured the dysfunctionality of previous plans, which in turn resulted in the improvement of already existing mechanisms of planning within the IAP. The detailed elucidation of the 2 year plan flagged any upcoming risks and shortfalls foreseen in the plan. All results were translated into a series of developments that propelled the tool’s capabilities beyond planning and into operations (such as Asset Production Forecasts, setting KPIs, and estimating operational needs). This process exemplifies the ability and reach of applying advanced development techniques to seamlessly integrated the planning parameters of various assets and technical support groups. These techniques enables the enhancement of integrating planning data workflows that ultimately lay the founding plans towards an epoch of accuracy and reliability. As such, benchmarks of establishing a set of standard goals are created to ensure the constant improvement of the efficiency of the entire planning and operational structure.

Keywords: automation, integration, value, communication

Procedia PDF Downloads 115

Authors: Stefan K. Behfar

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The rapid growth of the Ethereum network has brought forth the urgent need for scalable analysis methods to handle the increasing volume of blockchain data. In this research, we propose efficient methodologies for making Ethereum network analysis scalable. Our approach leverages a combination of graph-based data representation, probabilistic sampling, and parallel processing techniques to achieve unprecedented scalability while preserving critical network insights. Data Representation: We develop a graph-based data representation that captures the underlying structure of the Ethereum network. Each block transaction is represented as a node in the graph, while the edges signify temporal relationships. This representation ensures efficient querying and traversal of the blockchain data. Probabilistic Sampling: To cope with the vastness of the Ethereum blockchain, we introduce a probabilistic sampling technique. This method strategically selects a representative subset of transactions and blocks, allowing for concise yet statistically significant analysis. The sampling approach maintains the integrity of the network properties while significantly reducing the computational burden. Graph Convolutional Networks (GCNs): We incorporate GCNs to process the graph-based data representation efficiently. The GCN architecture enables the extraction of complex spatial and temporal patterns from the sampled data. This combination of graph representation and GCNs facilitates parallel processing and scalable analysis. Distributed Computing: To further enhance scalability, we adopt distributed computing frameworks such as Apache Hadoop and Apache Spark. By distributing computation across multiple nodes, we achieve a significant reduction in processing time and enhanced memory utilization. Our methodology harnesses the power of parallelism, making it well-suited for large-scale Ethereum network analysis. Evaluation and Results: We extensively evaluate our methodology on real-world Ethereum datasets covering diverse time periods and transaction volumes. The results demonstrate its superior scalability, outperforming traditional analysis methods. Our approach successfully handles the ever-growing Ethereum data, empowering researchers and developers with actionable insights from the blockchain. Case Studies: We apply our methodology to real-world Ethereum use cases, including detecting transaction patterns, analyzing smart contract interactions, and predicting network congestion. The results showcase the accuracy and efficiency of our approach, emphasizing its practical applicability in real-world scenarios. Security and Robustness: To ensure the reliability of our methodology, we conduct thorough security and robustness evaluations. Our approach demonstrates high resilience against adversarial attacks and perturbations, reaffirming its suitability for security-critical blockchain applications. Conclusion: By integrating graph-based data representation, GCNs, probabilistic sampling, and distributed computing, we achieve network scalability without compromising analytical precision. This approach addresses the pressing challenges posed by the expanding Ethereum network, opening new avenues for research and enabling real-time insights into decentralized ecosystems. Our work contributes to the development of scalable blockchain analytics, laying the foundation for sustainable growth and advancement in the domain of blockchain research and application.

Keywords: Ethereum, scalable network, GCN, probabilistic sampling, distributed computing

Procedia PDF Downloads 39

Authors: O. Lopez, F. Pedro, A. Tadeu, J. Antonio, A. Coelho

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The future of the railway industry lies in the innovation of lighter, more efficient and more sustainable trains. Weight optimizations in railway vehicles allow reducing power consumption and CO₂ emissions, increasing the efficiency of the engines and the maximum speed reached. Additionally, they reduce wear of wheels and rails, increase the space available for passengers, etc. Among the various systems that integrate railway interiors, the flooring system is one which has greater impact both on passenger safety and comfort, as well as on the weight of the interior systems. Due to the high weight saving potential, relative high mechanical resistance, good acoustic and thermal performance, ease of modular design, cost-effectiveness and long life, the use of new sustainable composite materials and panels provide the latest innovations for competitive solutions in the development of flooring systems. However, one of the main drawbacks of the flooring systems is their relatively poor resistance to point loads. Point loads in railway interiors can be caused by passengers or by components fixed to the flooring system, such as seats and restraint systems, handrails, etc. In this way, they can originate higher fatigue solicitations under service loads or zones with high stress concentrations under exceptional loads (higher longitudinal, transverse and vertical accelerations), thus reducing its useful life. Therefore, to verify all the mechanical and functional requirements of the flooring systems, many physical prototypes would be created during the design phase, with all of the high costs associated with it. Nowadays, the use of virtual prototyping methods by computer-aided design (CAD) and computer-aided engineering (CAE) softwares allow validating a product before committing to making physical test prototypes. The scope of this work was to current computer tools and integrate the processes of innovation, development, and manufacturing to reduce the time from design to finished product and optimise the development of the product for higher levels of performance and reliability. In this case, the mechanical response of several sandwich panels with different cores, polystyrene foams, and composite corks, were assessed, to optimise the weight and the mechanical performance of a flooring solution for railways. Sandwich panels with aluminum face sheets were tested to characterise its mechanical performance and determine the polystyrene foam and cork properties when used as inner cores. Then, a railway flooring solution was fully modelled (including the elastomer pads to provide the required vibration isolation from the car body) and perform structural simulations using FEM analysis to comply all the technical product specifications for the supply of a flooring system. Zones with high stress concentrations are studied and tested. The influence of vibration modes on the comfort level and stability is discussed. The information obtained with the computer tools was then completed with several mechanical tests performed on some solutions, and on specific components. The results of the numerical simulations and experimental campaign carried out are presented in this paper. This research work was performed as part of the POCI-01-0247-FEDER-003474 (coMMUTe) Project funded by Portugal 2020 through COMPETE 2020.

Keywords: cork agglomerate core, mechanical performance, numerical simulation, railway flooring system

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Authors: Meena Agrawal, Chaitanya P. Agrawal

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The power systems are transforming and becoming smarter with innovations in technologies to enable embark simultaneously upon the sustainable energy needs, rising environmental concerns, economic benefits and quality requirements. The advantages provided by inter-connection of renewable energy resources are becoming more viable and dependable with the smart controlling technologies. The limitation of most renewable resources have their diversity and intermittency causing problems in power quality, grid stability, reliability, security etc. is being cured by these efforts. A necessitate of optimal energy management by intelligent Micro-Grids at the distribution end of the power system has been accredited to accommodate sustainable renewable Distributed Energy Resources on large scale across the power grid. All over the world Smart Grids are emerging now as foremost concern infrastructure upgrade programs. The hardware setup includes NI cRIO 9022, Compact Reconfigurable Input Output microcontroller board connected to the PC on a LAN router with three hardware modules. The Real-Time Embedded Controller is reconfigurable controller device consisting of an embedded real-time processor controller for communication and processing, a reconfigurable chassis housing the user-programmable FPGA, Eight hot-swappable I/O modules, and graphical LabVIEW system design software. It has been employed for signal analysis, controls and acquisition and logging of the renewable sources with the LabVIEW Real-Time applications. The employed cRIO chassis controls the timing for the module and handles communication with the PC over the USB, Ethernet, or 802.11 Wi-Fi buses. It combines modular I/O, real-time processing, and NI LabVIEW programmable. In the presented setup, the Analog Input Module NI 9205 five channels have been used for input analog voltage signals from renewable energy sources and NI 9227 four channels have been used for input analog current signals of the renewable sources. For switching actions based on the programming logic developed in software, a module having Electromechanical Relays (single-pole single throw) with 4-Channels, electrically isolated and LED indicating the state of that channel have been used for isolating the renewable Sources on fault occurrence, which is decided by the logic in the program. The module for Ethernet based Data Acquisition Interface ENET 9163 Ethernet Carrier, which is connected on the LAN Router for data acquisition from a remote source over Ethernet also has the module NI 9229 installed. The LabVIEW platform has been employed for efficient data acquisition, monitoring and control. Control logic utilized in program for operation of the hardware switching Related to Fault Relays has been portrayed as a flowchart. A communication system has been successfully developed amongst the sources and loads connected on different computers using Hypertext transfer protocol, HTTP or Ethernet Local Stacked area Network TCP/IP protocol. There are two main I/O interfacing clients controlling the operation of the switching control of the renewable energy sources over internet or intranet. The paper presents experimental results of the briefed setup for intelligent control of the micro-grid for renewable energy sources, besides the control of Micro-Grid with data acquisition and control hardware based on a microcontroller with visual program developed in LabVIEW.

Keywords: data acquisition and control, LabVIEW, microcontroller cRIO, Smart Micro-Grid

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Authors: Monique M. Keller, Roline Barnes, Corlia Brandt

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Introduction Scientific evidence on the hand forces and the types of grasps measurement during daily tasks are lacking, leaving a gap in the field of hand rehabilitation and robotics. Measuring the grasp forces and types produced by the individual fingers during daily functional tasks is valuable to inform and grade rehabilitation practices for second to fifth metacarpal fractures with robust scientific evidence. Feix et al, 2016 identified the most extensive and complete grasp study that resulted in the GRASP taxonomy. Covid-19 virus changed data collection across the globe and safety precautions in research are essential to ensure the health of participants and researchers. Methodology A cross-sectional study investigated six healthy adults aged 20 to 59 years, pilot participants’ hand forces during 105 tasks. The tasks were categorized into five sections namely, personal care, transport and moving around, home environment and inside, gardening and outside, and office. The predominant grasp of each task was identified guided by the GRASP Taxonomy. Grasp forces were measured with 13mm force-sensing resistors glued onto a glove attached to each of the dominant and non-dominant hand’s individual fingers. Testing equipment included Flexiforce 13millimetres FSR .5" circle, calibrated prior to testing, 10k 1/4w resistors, Arduino pro mini 5.0v – compatible, Esp-01-kit, Arduino uno r3 – compatible board, USB ab cable - 1m, Ftdi ft232 mini USB to serial, Sil 40 inline connectors, ribbon cable combo male header pins, female to female, male to female, two gloves, glue to attach the FSR to glove, Arduino software programme downloaded on a laptop. Grip strength measurements with Jamar dynamometer prior to testing and after every 25 daily tasks were taken to will avoid fatigue and ensure reliability in testing. Covid-19 precautions included wearing face masks at all times, screening questionnaires, temperatures taken, wearing surgical gloves before putting on the testing gloves 1.5 metres long wires attaching the FSR to the Arduino to maintain social distance. Findings Predominant grasps observed during 105 tasks included, adducted thumb (17), lateral tripod (10), prismatic three fingers (12), small diameter (9), prismatic two fingers (9), medium wrap (7), fixed hook (5), sphere four fingers (4), palmar (4), parallel extension (4), index finger extension (3), distal (3), power sphere (2), tripod (2), quadpod (2), prismatic four fingers (2), lateral (2), large-diameter (2), ventral (2), precision sphere (1), palmar pinch (1), light tool (1), inferior pincher (1), and writing tripod (1). Range of forces applied per category, personal care (1-25N), transport and moving around (1-9 N), home environment and inside (1-41N), gardening and outside (1-26.5N), and office (1-20N). Conclusion Scientifically measurements of finger forces with careful consideration to types of grasps used in daily tasks should guide rehabilitation practices and robotic design to ensure a return to the full participation of the individual into the community.

Keywords: activities of daily living (ADL), Covid-19, force-sensing resistors, grasps, hand forces

Procedia PDF Downloads 165

Authors: Benjamin Arazi

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Blockchain is the technology behind cryptocurrencies like Bitcoin. It revolutionizes the meaning of trust in the sense of offering total reliability without relying on any central entity that controls or supervises the system. The Wall Street Journal states: “Blockchain Marks the Next Step in the Internet’s Evolution”. Blockchain was listed as #1 in Linkedin – The Learning Blog “most in-demand hard skills needed in 2020”. As stated there: “Blockchain’s novel way to store, validate, authorize, and move data across the internet has evolved to securely store and send any digital asset”. GSMA, a leading Telco organization of mobile communications operators, declared that “Blockchain has the potential to be for value what the Internet has been for information”. Motivated by these seminal observations, this paper presents the foundations of a Blockchain-based “Internet of Assets” academic program that joins under one roof leading application areas that are characterized by the transfer of assets over communication lines. Two such areas, which are pillars of our economy, are Fintech – Financial Technology and mobile communications services. The next application in line is Healthcare. These challenges are met based on available extensive professional literature. Blockchain-based assets communication is based on extending the principle of Bitcoin, starting with the basic question: If digital money that travels across the universe can ‘prove its own validity’, can this principle be applied to digital content. A groundbreaking positive answer here led to the concept of “smart contract” and consequently to DLT - Distributed Ledger Technology, where the word ‘distributed’ relates to the non-existence of reliable central entities or trusted third parties. The terms Blockchain and DLT are frequently used interchangeably in various application areas. The World Bank Group compiled comprehensive reports, analyzing the contribution of DLT/Blockchain to Fintech. The European Central Bank and Bank of Japan are engaged in Project Stella, “Balancing confidentiality and auditability in a distributed ledger environment”. 130 DLT/Blockchain focused Fintech startups are now operating in Switzerland. Blockchain impact on mobile communications services is treated in detail by leading organizations. The TM Forum is a global industry association in the telecom industry, with over 850 member companies, mainly mobile operators, that generate US$2 trillion in revenue and serve five billion customers across 180 countries. From their perspective: “Blockchain is considered one of the digital economy’s most disruptive technologies”. Samples of Blockchain contributions to Fintech (taken from a World Bank document): Decentralization and disintermediation; Greater transparency and easier auditability; Automation & programmability; Immutability & verifiability; Gains in speed and efficiency; Cost reductions; Enhanced cyber security resilience. Samples of Blockchain contributions to the Telco industry. Establishing identity verification; Record of transactions for easy cost settlement; Automatic triggering of roaming contract which enables near-instantaneous charging and reduction in roaming fraud; Decentralized roaming agreements; Settling accounts per costs incurred in accordance with agreement tariffs. This clearly demonstrates an academic education structure where fundamental technologies are studied in classes together with these two application areas. Advanced courses, treating specific implementations then follow separately. All are under the roof of “Internet of Assets”.

Keywords: blockchain, education, financial technology, mobile telecommunications services

Procedia PDF Downloads 150

Authors: Ho-Ting Wu, Kai-Wei Ke, Bo-Yu Huang, Liang-Lin Yan, Chun-Ting Lin

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With the advances in advanced technology, wireless sensor network (WSN) has become one of the most promising candidates to implement the wireless industrial internet of things (IIOT) architecture. However, the legacy IEEE 802.15.4 based WSN technology such as Zigbee system cannot meet the stringent QoS requirement of low powered, real-time, and highly reliable transmission imposed by the IIOT environment. Recently, the IEEE society developed IEEE 802.15.4e Time Slotted Channel Hopping (TSCH) access mode to serve this purpose. Furthermore, the IETF 6TiSCH working group has proposed standards to integrate IEEE 802.15.4e with IPv6 protocol smoothly to form a complete protocol stack for IIOT. In this work, we develop key network technologies for IEEE 802.15.4e based wireless IIoT architecture, focusing on practical design and system implementation. We realize the OpenWSN-based wireless IIOT system. The system architecture is divided into three main parts: web server, network manager, and sensor nodes. The web server provides user interface, allowing the user to view the status of sensor nodes and instruct sensor nodes to follow commands via user-friendly browser. The network manager is responsible for the establishment, maintenance, and management of scheduling and topology information. It executes centralized scheduling algorithm, sends the scheduling table to each node, as well as manages the sensing tasks of each device. Sensor nodes complete the assigned tasks and sends the sensed data. Furthermore, to prevent scheduling error due to packet loss, a schedule inspection mechanism is implemented to verify the correctness of the schedule table. In addition, when network topology changes, the system will act to generate a new schedule table based on the changed topology for ensuring the proper operation of the system. To enhance the system performance of such system, we further propose dynamic bandwidth allocation and distributed scheduling mechanisms. The developed distributed scheduling mechanism enables each individual sensor node to build, maintain and manage the dedicated link bandwidth with its parent and children nodes based on locally observed information by exchanging the Add/Delete commands via two processes. The first process, termed as the schedule initialization process, allows each sensor node pair to identify the available idle slots to allocate the basic dedicated transmission bandwidth. The second process, termed as the schedule adjustment process, enables each sensor node pair to adjust their allocated bandwidth dynamically according to the measured traffic loading. Such technology can sufficiently satisfy the dynamic bandwidth requirement in the frequently changing environments. Last but not least, we propose a packet retransmission scheme to enhance the system performance of the centralized scheduling algorithm when the packet delivery rate (PDR) is low. We propose a multi-frame retransmission mechanism to allow every single network node to resend each packet for at least the predefined number of times. The multi frame architecture is built according to the number of layers of the network topology. Performance results via simulation reveal that such retransmission scheme is able to provide sufficient high transmission reliability while maintaining low packet transmission latency. Therefore, the QoS requirement of IIoT can be achieved.

Keywords: IEEE 802.15.4e, industrial internet of things (IIOT), scheduling mechanisms, wireless sensor networks (WSN)

Procedia PDF Downloads 131

Authors: Anjalika Fernando, Banuka Athuraliya

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This paper presents a comprehensive approach for converting Sinhala Sign Language (SSL) into grammatically correct sentences using Natural Language Processing (NLP) techniques in real-time. While previous studies have explored various aspects of SSL translation, the research gap lies in the absence of grammar checking for SSL. This work aims to bridge this gap by proposing a two-stage methodology that leverages deep learning models to detect signs and translate them into coherent sentences, ensuring grammatical accuracy. The first stage of the approach involves the utilization of a Long Short-Term Memory (LSTM) deep learning model to recognize and interpret SSL signs. By training the LSTM model on a dataset of SSL gestures, it learns to accurately classify and translate these signs into textual representations. The LSTM model achieves a commendable accuracy rate of 94%, demonstrating its effectiveness in accurately recognizing and translating SSL gestures. Building upon the successful recognition and translation of SSL signs, the second stage of the methodology focuses on improving the grammatical correctness of the translated sentences. The project employs a Neural Machine Translation (NMT) architecture, consisting of an encoder and decoder with LSTM components, to enhance the syntactical structure of the generated sentences. By training the NMT model on a parallel corpus of Sinhala wrong sentences and their corresponding grammatically correct translations, it learns to generate coherent and grammatically accurate sentences. The NMT model achieves an impressive accuracy rate of 98%, affirming its capability to produce linguistically sound translations. The proposed approach offers significant contributions to the field of SSL translation and grammar correction. Addressing the critical issue of grammar checking, it enhances the usability and reliability of SSL translation systems, facilitating effective communication between hearing-impaired and non-sign language users. Furthermore, the integration of deep learning techniques, such as LSTM and NMT, ensures the accuracy and robustness of the translation process. This research holds great potential for practical applications, including educational platforms, accessibility tools, and communication aids for the hearing-impaired. Furthermore, it lays the foundation for future advancements in SSL translation systems, fostering inclusive and equal opportunities for the deaf community. Future work includes expanding the existing datasets to further improve the accuracy and generalization of the SSL translation system. Additionally, the development of a dedicated mobile application would enhance the accessibility and convenience of SSL translation on handheld devices. Furthermore, efforts will be made to enhance the current application for educational purposes, enabling individuals to learn and practice SSL more effectively. Another area of future exploration involves enabling two-way communication, allowing seamless interaction between sign-language users and non-sign-language users.In conclusion, this paper presents a novel approach for converting Sinhala Sign Language gestures into grammatically correct sentences using NLP techniques in real time. The two-stage methodology, comprising an LSTM model for sign detection and translation and an NMT model for grammar correction, achieves high accuracy rates of 94% and 98%, respectively. By addressing the lack of grammar checking in existing SSL translation research, this work contributes significantly to the development of more accurate and reliable SSL translation systems, thereby fostering effective communication and inclusivity for the hearing-impaired community

Keywords: Sinhala sign language, sign Language, NLP, LSTM, NMT

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Authors: Muhammad Bilal Shaikh

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Mineral processing plants play a pivotal role in extracting valuable minerals from raw ores, contributing significantly to various industries. However, the optimization of plant throughput remains a complex challenge, necessitating innovative approaches for increased efficiency and productivity. This research paper investigates the application of Multimodal Artificial Intelligence (MAI) techniques to address this challenge, aiming to improve overall plant throughput in mineral processing operations. The integration of multimodal AI leverages a combination of diverse data sources, including sensor data, images, and textual information, to provide a holistic understanding of the complex processes involved in mineral extraction. The paper explores the synergies between various AI modalities, such as machine learning, computer vision, and natural language processing, to create a comprehensive and adaptive system for optimizing mineral processing plants. The primary focus of the research is on developing advanced predictive models that can accurately forecast various parameters affecting plant throughput. Utilizing historical process data, machine learning algorithms are trained to identify patterns, correlations, and dependencies within the intricate network of mineral processing operations. This enables real-time decision-making and process optimization, ultimately leading to enhanced plant throughput. Incorporating computer vision into the multimodal AI framework allows for the analysis of visual data from sensors and cameras positioned throughout the plant. This visual input aids in monitoring equipment conditions, identifying anomalies, and optimizing the flow of raw materials. The combination of machine learning and computer vision enables the creation of predictive maintenance strategies, reducing downtime and improving the overall reliability of mineral processing plants. Furthermore, the integration of natural language processing facilitates the extraction of valuable insights from unstructured textual data, such as maintenance logs, research papers, and operator reports. By understanding and analyzing this textual information, the multimodal AI system can identify trends, potential bottlenecks, and areas for improvement in plant operations. This comprehensive approach enables a more nuanced understanding of the factors influencing throughput and allows for targeted interventions. The research also explores the challenges associated with implementing multimodal AI in mineral processing plants, including data integration, model interpretability, and scalability. Addressing these challenges is crucial for the successful deployment of AI solutions in real-world industrial settings. To validate the effectiveness of the proposed multimodal AI framework, the research conducts case studies in collaboration with mineral processing plants. The results demonstrate tangible improvements in plant throughput, efficiency, and cost-effectiveness. The paper concludes with insights into the broader implications of implementing multimodal AI in mineral processing and its potential to revolutionize the industry by providing a robust, adaptive, and data-driven approach to optimizing plant operations. In summary, this research contributes to the evolving field of mineral processing by showcasing the transformative potential of multimodal artificial intelligence in enhancing plant throughput. The proposed framework offers a holistic solution that integrates machine learning, computer vision, and natural language processing to address the intricacies of mineral extraction processes, paving the way for a more efficient and sustainable future in the mineral processing industry.

Keywords: multimodal AI, computer vision, NLP, mineral processing, mining

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Authors: Nursuhaili Najwa Masrol, Nur Hafizah Mohammed, Nur Nadhirah Rusyda Rosnan, Vijaya Subramaniam, Sim Choon Cheak

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Replanting in oil palm cultivation is vital to enable the introduction of planting materials and provides an opportunity to improve the road, drainage, terrace design, and planting density. Oil palm replanting is fundamentally necessary every 25 years. The adoption of the digital replanting blueprint is imperative as it can assist the Malaysia Oil Palm industry in addressing challenges such as labour shortages and limited expertise related to replanting tasks. Effective replanting planning should commence at least 6 months prior to the actual replanting process. Therefore, this study will help to plan and design the replanting blueprint with high-precision translation on the ground. With the advancement of geospatial technology, it is now feasible to engage in thoroughly researched planning, which can help maximize the potential yield. A blueprint designed before replanting is to enhance management’s ability to optimize the planting program, address manpower issues, or even increase productivity. In terrace planting blueprints, geographic tools have been utilized to design the roads, drainages, terraces, and planting points based on the ARM standards. These designs are mapped with location information and undergo statistical analysis. The geospatial approach is essential in precision agriculture and ensuring an accurate translation of design to the ground by implementing high-accuracy technologies. In this study, geospatial and remote sensing technologies played a vital role. LiDAR data was employed to determine the Digital Elevation Model (DEM), enabling the precise selection of terraces, while ortho imagery was used for validation purposes. Throughout the designing process, Geographical Information System (GIS) tools were extensively utilized. To assess the design’s reliability on the ground compared with the current conventional method, high-precision GPS instruments like EOS Arrow Gold and HIPER VR GNSS were used, with both offering accuracy levels between 0.3 cm and 0.5cm. Nearest Distance Analysis was generated to compare the design with actual planting on the ground. The analysis revealed that it could not be applied to the roads due to discrepancies between actual roads and the blueprint design, which resulted in minimal variance. In contrast, the terraces closely adhered to the GPS markings, with the most variance distance being less than 0.5 meters compared to actual terraces constructed. Considering the required slope degrees for terrace planting, which must be greater than 6 degrees, the study found that approximately 65% of the terracing was constructed at a 12-degree slope, while over 50% of the terracing was constructed at slopes exceeding the minimum degrees. Utilizing blueprint replanting promising strategies for optimizing land utilization in agriculture. This approach harnesses technology and meticulous planning to yield advantages, including increased efficiency, enhanced sustainability, and cost reduction. From this study, practical implementation of this technique can lead to tangible and significant improvements in agricultural sectors. In boosting further efficiencies, future initiatives will require more sophisticated techniques and the incorporation of precision GPS devices for upcoming blueprint replanting projects besides strategic progression aims to guarantee the precision of both blueprint design stages and its subsequent implementation on the field. Looking ahead, automating digital blueprints are necessary to reduce time, workforce, and costs in commercial production.

Keywords: replanting, geospatial, precision agriculture, blueprint

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Authors: V. Howard, R. Maguire, S. Corrigan

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Background: Type 1 Diabetes (T1D) is a chronic autoimmune disease, typically diagnosed in childhood. T1D puts an enormous strain on families; controlling blood-glucose in children is difficult and the consequences of poor control for patient health are significant. Successful illness management and better health outcomes can be dependent on quality of caregiving. On diagnosis, parent-caregivers face a steep learning curve as T1D care requires a significant level of knowledge to inform complex decision making throughout the day. The majority of illness management is carried out in the home setting, independent of clinical health providers. Parent-caregivers vary in their level of knowledge and their level of engagement in applying this knowledge in the practice of illness management. Enabling researchers to quantify these aspects of the caregiver experience is key to identifying targets for psychosocial support interventions, which are desirable for reducing stress and anxiety in this highly burdened cohort, and supporting better health outcomes in children. Currently, there are limited tools available that are designed to capture this information. Where tools do exist, they are not comprehensive and do not adequately capture the lived experience. Objectives: Development of quantitative tools, informed by lived experience, to enable researchers gather data on parent-caregiver knowledge and engagement, which accurately represents the experience/cohort and enables exploration of questions that are of real-world value to the cohort themselves. Methods: This research employed an engaged approach to address the problem of quantifying two key aspects of caregiver diabetes management: Knowledge and engagement. The research process was multi-staged and iterative. Stage 1: Working from a constructivist standpoint, literature was reviewed to identify relevant questionnaires, scales and single-item measures of T1D caregiver knowledge and engagement, and harvest candidate questionnaire items. Stage 2: Aggregated findings from the review were circulated among a PPI (patient and public involvement) expert panel of caregivers (n=6), for discussion and feedback. Stage 3: In collaboration with the expert panel, data were interpreted through the lens of lived experience to create a long-list of candidate items for novel questionnaires. Items were categorized as either ‘knowledge’ or ‘engagement’. Stage 4: A Delphi-method process (iterative surveys) was used to prioritize question items and generate novel questions that further captured the lived experience. Stage 5: Both questionnaires were piloted to refine wording of text to increase accessibility and limit socially desirable responding. Stage 6: Tools were piloted using an online survey that was deployed using an online peer-support group for caregivers for Juveniles with T1D. Ongoing Research: 123 parent-caregivers completed the survey. Data analysis is ongoing to establish face and content validity qualitatively and through exploratory factor analysis. Reliability will be established using an alternative-form method and Cronbach’s alpha will assess internal consistency. Work will be completed by early 2024. Conclusion: These tools will enable researchers to gain deeper insights into caregiving practices among parents of juveniles with T1D. Development was driven by lived experience, illustrating the value of engaged research at all levels of the research process.

Keywords: caregiving, engaged research, juvenile type 1 diabetes, quantified engagement and knowledge

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