Search results for: adaptive fuzzy PI controller

Authors: Kriti Upadhyay, Ashraf Ali, Puja Sohal, Randeep Guleria

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Background: In Chronic Obstructive Pulmonary diseases (COPD) pathogenesis oxidative stress plays an important role. Hypoxia-Inducible factor (HIF-1α) is a dimeric protein complex which Functions as a master transcriptional regulator of the adaptive response to hypoxiaand is a risk factor that increases when oxidative stress triggers. The role ofHIF-1αin COPD due to smoking is lacking. Aim: This study aims to evaluate the role of HIF-1α in smoker COPD patients comparing its association with diseases severity. Method: In this cross-sectional study, we recruited 87 subjects, 57 were smokers with COPD,15 were smokers without COPD and other 15 were non-smoker healthy controls. The mean age was 54.6± 9.32 (cases 57.08±8.15; controls 50.0± 9.8). There were 62%smokers, 25% non-smokers,7% tobacco chewers and 6% ex-smokers. Enzyme-linked immune sorbent assay (ELISA) method was used for analyzing serum samples wherein HIF-1α was analyzed by Sandwich-ELISA. Results: In smoker COPD patients, a significantly higher HIF-1α level showed positive association with hypoxia, smoking status and severity of disease (p=0.03). The mean value of HIF-1α was not significantly different in smokers without COPD and healthy controls. Conclusion: It is found that HIF-1α level was increased in smoker COPD, but not in smokers without COPD. This suggests that development of COPD drive the HIF-1α pathway and it correlates with the severity of diseases.

Keywords: COPD, chronic obstructive pulmonary diseases, smokers, nonsmokers, hypoxia

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Authors: A. Buruzs, M. F. Hatwágner, A. Torma, L. T. Kóczy

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The development, operation and maintenance of Integrated Waste Management Systems (IWMS) affects essentially the sustainable concern of every region. The features of such systems have great influence on all of the components of sustainability. In order to reach the optimal way of processes, a comprehensive mapping of the variables affecting the future efficiency of the system is needed such as analysis of the interconnections among the components and modelling of their interactions. The planning of a IWMS is based fundamentally on technical and economical opportunities and the legal framework. Modelling the sustainability and operation effectiveness of a certain IWMS is not in the scope of the present research. The complexity of the systems and the large number of the variables require the utilization of a complex approach to model the outcomes and future risks. This complex method should be able to evaluate the logical framework of the factors composing the system and the interconnections between them. The authors of this paper studied the usability of the Fuzzy Cognitive Map (FCM) approach modelling the future operation of IWMS’s. The approach requires two input data set. One is the connection matrix containing all the factors affecting the system in focus with all the interconnections. The other input data set is the time series, a retrospective reconstruction of the weights and roles of the factors. This paper introduces a novel method to develop time series by content analysis.

Keywords: content analysis, factors, integrated waste management system, time series

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Authors: Jongbae Lee, Seongsoo Lee

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Modern sensors often embed small-size digital controller for sensor control, value calibration, and signal processing. These sensors require digital data communication with host microprocessors, but conventional digital communication protocols are too heavy for price reduction. SAE J2716 SENT (single edge nibble transmission) protocol transmits direct digital waveforms instead of complicated analog modulated signals. In this paper, a SENT interface is designed in Verilog HDL (hardware description language) and implemented in FPGA (field-programmable gate array) evaluation board. The designed SENT interface consists of frame encoder/decoder, configuration register, tick period generator, CRC (cyclic redundancy code) generator/checker, and TX/RX (transmission/reception) buffer. Frame encoder/decoder is implemented as a finite state machine, and it controls whole SENT interface. Configuration register contains various parameters such as operation mode, tick length, CRC option, pause pulse option, and number of nibble data. Tick period generator generates tick signals from input clock. CRC generator/checker generates or checks CRC in the SENT data frame. TX/RX buffer stores transmission/received data. The designed SENT interface can send or receives digital data in 25~65 kbps at 3 us tick. Synthesized in 0.18 um fabrication technologies, it is implemented about 2,500 gates.

Keywords: digital sensor interface, SAE J2716, SENT, verilog HDL

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Authors: H. Sakamoto, J. Tochimoto, S. Kurosawa, M. Suzuki, S. Oguri

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Salt stress adversely affects plant growth at various stages of development including seed germination, seedling establishment, vegetative growth and finally reproduction. Because of their immobile nature, plants have evolved mechanisms to sense and respond to salt stress. Seed dormancy is an adaptive trait that enables seed germination to coincide with favorable environmental conditions. We identified a novel locus of Arabidopsis, designated SHG1 (salt hypersensitive germination 1), whose disruption leads to reduced germination rate under moderate salt stress conditions. SHG1 encodes a transmembrane protein with an ankyrin repeat motif that has been implicated in diverse cellular processes such as signal transduction. The SGH1-disrupted Arabidopsis mutant died at the cotyledon stage when sown on salt-containing medium, although wild type plants could form true leaves under the same conditions. On the other hand, this mutant showed similar phenotypes to wild type plants when sown on medium without salt and transferred to salt-containing medium at the vegetative stage. These results suggested that SHG1 played indispensable role in the seed germination and seedling establishment under moderate salt stress conditions. SHG1 may be involved in the release of seed dormancy.

Keywords: germination, ankyrin repeat, arabidopsis, salt tolerance

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Authors: Masoud Rabbani, Majede Smizadeh, Hamed Farrokhi-Asl

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We study jointly determining prices and production in a multiple period horizon under a general non-stationary stochastic demand with continuous prices. In some periods we need to increase capacity of production to satisfy demand. This paper presents a model to aid multi-period production capacity planning by quantifying the trade-off between product quality and production cost. The product quality is estimated as the statistical variation from the target performances obtained from the output tolerances of the production machines that manufacture the components. We consider different tolerance for different machines that use to increase capacity. The production cost is estimated as the total cost of owning and operating a production facility during the planning horizon.so capacity planning has cost that impact on price. Pricing products often turns out to be difficult to measure them because customers have a reservation price to pay that impact on price and demand. We decide to determine prices and production for periods after enhance capacity and consider reservation price to determine price. First we use an algorithm base on fuzzy set of the optimal objective function values to determine capacity planning by determine maximize interval from upper bound in minimum objectives and define weight for objectives. Then we try to determine inventory and pricing policies. We can use a lemma to solve a problem in MATLAB and find exact answer.

Keywords: price policy, inventory policy, capacity planning, product quality, epsilon -constraint

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Authors: Olatunbosun Samuel Adekogbe

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This paper addresses the question of musical signification, revolving around nature and its natural divides; the paper tends to examine the roles of the dispositional apparatus of listeners to react to sounding environments through music as coordinated sound that focuses on the powerful strain between vibrational occurrences of sound and potentials of being structured. This paper sets out to examine music as a simple conventional design that does not allude to something beyond music and sound as a vehicle to communicate through production, perception, translation, and reaction with regard to melodic and semiotic functions of sounds. This paper adopts the application of questionnaire and evolutionary approach methods to probe musical adaptation, reproduction, and natural selection as the basis for explaining specific human behavioural responses to musical sense-making beyond the above-sketched dichotomies, with a major focus on the transition from acoustic-emotional sensibilities to musical meaning in the selected soundscapes. It was observed that music has emancipated itself from the level of mere acoustic processing of sounds to a functional description in terms of allowing music users to share experiences and interact with the soundscaping environment. The paper, therefore, concludes that the audience as music participants and listeners in the selected soundscapes have been conceived as adaptive devices in the paradigm shift, which can build up new semiotic linkages with the sounding environments in southwestern Nigeria.

Keywords: semiotics, sound, music, soundscape, environment

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Authors: Seyed Esmail Seyedi Bariran, Khairul Salleh Mohamed Sahari

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In multi-echelon supply chain networks, optimal supplier selection significantly depends on the accuracy of suppliers’ performance prediction. Different methods of multi criteria decision making such as ANN, GA, Fuzzy, AHP, etc have been previously used to predict the supplier performance but the “black-box” characteristic of these methods is yet a major concern to be resolved. Therefore, the primary objective in this paper is to implement an artificial intelligence-based gene expression programming (GEP) model to compare the prediction accuracy with that of ANN. A full factorial design with %95 confidence interval is initially applied to determine the appropriate set of criteria for supplier performance evaluation. A test-train approach is then utilized for the ANN and GEP exclusively. The training results are used to find the optimal network architecture and the testing data will determine the prediction accuracy of each method based on measures of root mean square error (RMSE) and correlation coefficient (R2). The results of a case study conducted in Supplying Automotive Parts Co. (SAPCO) with more than 100 local and foreign supply chain members revealed that, in comparison with ANN, gene expression programming has a significant preference in predicting supplier performance by referring to the respective RMSE and R-squared values. Moreover, using GEP, a mathematical function was also derived to solve the issue of ANN black-box structure in modeling the performance prediction.

Keywords: Supplier Performance Prediction, ANN, GEP, Automotive, SAPCO

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Authors: Michael Adedotun Oke

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This paper, therefore, under study the various multiplier usages of the different Animal Dungs, from the animals such as Rabbits, Cows, Fishes, Sheep, and Poultry manure in the areas council of the Federal Capital Territory Abuja, Nigeria. Thus the various observations, with the pictorial representation, that was taken with the field survey from the different farms in Gwagawalada. Shows that the rabbits dungs are being used in some of the vegetables and crop farms, which serves as the nutrients, reduces the cost of production, ensure profitability, which also increases the different vegetative growth, early maturity, and the development of the crop and this is also applicable to some crops like maize, sweet potatoes. While the manure of the poultry products are being incorporated to fish ponds and the cows dungs are being used to serve as some manure to some certain crops, e.g. Okro, Maize, Pepper. Which provides the necessary nutritious values, but the various number of quantity of different bags of the various application are lacking, and the time of usage, it is also a life germane questions, which there are needs for further adaptive research, that will be involved and the reintroduction of new technology, that will be used in terms of the different methodology such as broadcasting and ring applications, of the dungs at large, while the seasons of the various applications. Thus the paper, therefore, suggested a training programs and production of manuals that will guide the various applications and usage and the effective dissemination of the various used of the simple technology, that will advances and teaching of a new mode of and the time of applications and the various quantity to used, during the applications.

Keywords: animals, usage, livestock, dungs, feaces, gwagawalada

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Authors: Shital Suresh Borse, Vijayalaxmi Kadroli

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E-commerce industries nowadays implement the latest AI, ML Techniques to improve their own performance and prediction accuracy. This helps to gain a huge profit from the online market. Ant Colony Optimization, Genetic algorithm, Particle Swarm Optimization, Neural Network & GWO help many e-commerce industries for up-gradation of their predictive performance. These algorithms are providing optimum results in various applications, such as stock price prediction, prediction of drug-target interaction & user ratings of similar products in e-commerce sites, etc. In this study, customer reviews will play an important role in prediction analysis. People showing much interest in buying a lot of services& products suggested by other customers. This ultimately increases net profit. In this work, a convolution neural network (CNN) is proposed which further is useful to optimize the prediction accuracy of an e-commerce website. This method shows that CNN is used to optimize hyperparameters of GWO algorithm using an appropriate coding scheme. Accurate model results are verified by comparing them to PSO results whose hyperparameters have been optimized by CNN in Amazon's customer review dataset. Here, experimental outcome proves that this proposed system using the GWO algorithm achieves superior execution in terms of accuracy, precision, recovery, etc. in prediction analysis compared to the existing systems.

Keywords: prediction analysis, e-commerce, machine learning, grey wolf optimization, particle swarm optimization, CNN

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Authors: Shackira Am, Jos T. Puthur

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The phytostabilization potential of a halophyte Acanthus ilicifolius L. has been evaluated with special attention to the nutritional as well as anatomical adaptations developed by the plant. Distribution of essential elements influenced by the excess Zn²⁺ ions in the root tissue was studied by FEG-SEM EDX microanalysis. Significant variations were observed in the uptake and allocation of mineral elements like Mg, P, K, S, Na, Si and Al in the root of A. ilicifolius. The increase in S is in correlation with the increased synthesis of glutathione which might be involved in the biosynthesis of phytochelatins. This in turn might be aiding the plant to tolerate the adverse environmental conditions by stabilizing the excess Zn in the root tissue itself. Moreover it is revealed that most of the Zn were accumulated towards the central region near the vascular tissue. Treatment with ZnSO₄ in A. ilicifolius caused significant increase in the number of glandular trichomes on the adaxial leaf surface as compared to the leaves of control plants. In addition to this, A. ilicifolius when treated with ZnSO₄, exhibited a deeply stained layer of cells immediate to the endodermis, forming more or less a ring like structure around the xylem vessels. Phloem cells in these plants were crushed/reduced in numbers. There were no such deeply stained cells forming a ring around the xylem vessels in the control plants. These adaptive responses make the plant a suitable candidate for the phytostabilization of Zn. In addition the nutritional adjustment of the plant equips them for a better survival under increased concentration of Zn²⁺.

Keywords: Acanthus ilicifolius, mineral elements, phytostabilization, zinc

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Authors: Kun-Ping Cheng, Dong-Shang Chang, Rou-Wen Wang

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Pre-treatment of automobile paint-shop procedures are the preparation of warm water rinsing tank, hot water rinsing tank, degreasing tank, phosphate tank. The conventional boiler steam fuel is natural gas, producing steam to supply the heat exchange of each tank sink. In this study, the high-frequency soldering economizer is developed for recovering waste heat in the automotive paint-shop (RTO, Regenerative Thermal Oxidation). The heat recovery rate of the new economizer is 20% to 30% higher than the conventional embedded heat pipe. The adaptive control system responded to both RTO furnace exhaust gas and heat demands. In order to maintain the temperature range of the tanks, pre-treatment tanks are directly heated by waste heat recovery device (gas-to-water heat exchanger) through the hot water cycle of heat transfer. The performance of developed waste heat recovery system shows the annual recovery achieved to 1,226,411,483 Kcal of heat (137.8 thousand cubic meters of natural gas). Boiler can reduce fuel consumption by 20 to 30 percent compared to without waste heat recovery. In order to alleviate environmental impacts, the temperature at the end of the flue is further reduced from 160 to 110°C. The innovative waste heat recovery is helpful to energy savings and sustainable environment.

Keywords: waste heat recovery system, sustainability, RTO (Regenerative Thermal Oxidation), economizer, automotive industry

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Authors: M. Dhana Lakshmi, S. Sakthivel Murugan

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As light passes from source to observer in the water medium, it is scattered by the suspended particulate matter. This scattering effect will plague the captured images with non-uniform illumination, blurring details, halo artefacts, weak edges, etc. To overcome this, pixel normalization with an Amended Unsharp Mask (AUM) filter is proposed to enhance the degraded image. To validate the robustness of the proposed technique irrespective of atmospheric light, the considered datasets are collected on dual locations. For those images, the maxima and minima pixel intensity value is computed and normalized; then the AUM filter is applied to strengthen the blurred edges. Finally, the enhanced image is obtained with good illumination and contrast. Thus, the proposed technique removes the effect of scattering called de-hazing and restores the perceptual information with enhanced edge detail. Both qualitative and quantitative analyses are done on considering the standard non-reference metric called underwater image sharpness measure (UISM), and underwater image quality measure (UIQM) is used to measure color, sharpness, and contrast for both of the location images. It is observed that the proposed technique has shown overwhelming performance compared to other deep-based enhancement networks and traditional techniques in an adaptive manner.

Keywords: underwater drone imagery, pixel normalization, thresholding, masking, unsharp mask filter

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Authors: Husham Bayazed

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A growing number of studies indicate that atherosclerotic coronary artery disease (CAD) has a complex pathogenesis that extends beyond cholesterol intimal infiltration. The atherosclerosis process may involve an immune micro-environmental condition driven by local activation of the adaptive and innate immunity arrays, resulting in the formation of atherosclerotic plaques. Therefore, despite the wide usage of lipid-lowering agents, these devastating coronary diseases are not averted either at primary or secondary prevention levels. Many trials have recently shown an interest in the immune targeting of the inflammatory process of atherosclerotic plaques, with the promised improvement in atherosclerotic cardiovascular disease outcomes. This recently includes the immune-modulatory drug “Canakinumab” as an anti-interleukin-1 beta monoclonal antibody in addition to "Colchicine,” which's established as a broad-effect drug in the management of other inflammatory conditions. Recent trials and studies highlight the importance of inflammation and immune reactions in the pathogenesis of atherosclerosis and plaque formation. This provides an insight to discuss and extend the therapies from old lipid-lowering drugs (statins) to anti-inflammatory drugs (colchicine) and new targeted immune-modulatory therapies like inhibitors of IL-1 beta (canakinumab) currently under investigation.

Keywords: atherosclerotic plagues, immune microenvironment, lipid-lowering agents, and immunomodulatory drugs

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Authors: Amy L. Irvine, Gabriel Reygondeau, Derek P. Tittensor

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Marine protected areas (MPAs) within Canada’s exclusive economic zone help ensure the conservation and sustainability of marine ecosystems and the continued provision of ecosystem services to society (e.g., food, carbon sequestration). With ongoing and accelerating climate change, however, MPAs may become undermined in terms of their effectiveness at fulfilling these outcomes. Many populations of species, especially those at their thermal range limits, may shift to cooler waters or become extirpated due to climate change, resulting in new species compositions and ecological interactions within static MPA boundaries. While Canadian MPA management follows international guidelines for marine conservation, no consistent approach exists for adapting MPA networks to climate change and the resulting altered ecosystem conditions. To fill this gap, projected climate-driven shifts in species distributions on Canada’s east coast were analyzed to identify when native species emigrate and novel species immigrate within the network and how high mitigation and carbon emission scenarios influence these timelines. Indicators of the ecological changes caused by these species' shifts in the biological community were also developed. Overall, our research provides projections of climate change impacts and helps to guide adaptive management responses within the Canadian east coast MPA network.

Keywords: climate change, ecosystem modeling, marine protected areas, management

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Authors: Sultan Ghaleb Aldaihani

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- The environment in which educational leadership takes place is becoming increasingly complex due to factors like globalization and rapid technological change. - This is creating a "leadership gap" where the complexity of the environment outpaces the ability of leaders to effectively respond. - Educational leadership involves guiding teachers and the broader school system towards improved student learning and achievement. 2. Implications of Artificial Intelligence (AI) in Educational Leadership: - AI has great potential to enhance education, such as through intelligent tutoring systems and automating routine tasks to free up teachers. - AI can also have significant implications for educational leadership by providing better information and data-driven decision-making capabilities. - Computer-adaptive testing can provide detailed, individualized data on student learning that leaders can use for instructional decisions and accountability. 3. Enhancing Decision-Making Processes: - Statistical models and data mining techniques can help identify at-risk students earlier, allowing for targeted interventions. - Probability-based models can diagnose students likely to drop out, enabling proactive support. - These data-driven approaches can make resource allocation and decision-making more effective. 4. Improving Efficiency and Productivity: - AI systems can automate tasks and change processes to improve the efficiency of educational leadership and administration. - Integrating AI can free up leaders to focus more on their role's human, interactive elements.

Keywords: Education, Leadership, Technology, Artificial Intelligence

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Authors: Azizah Ismail, Zainab Khalifah, Abbas Mardani

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Sustainable community-based tourism through homestay programme is a growing niche market that has impacted destinations in many countries including Malaysia. With demand predicted to continue increasing, the importance of the homestay product will grow in the tourism industry. This research examines the sustainability criteria for homestay programme in Malaysia covering economic, socio-cultural and environmental dimensions. This research applied a two-stage methodology for data analysis. Specifically, the researcher implements a hybrid method which combines two multi-criteria decision making approaches. In the first stage of the methodology, the Decision Making Trial and Evaluation Laboratory (DEMATEL) technique is applied. Then, Analytical Network Process (ANP) is employed for the achievement of the objective of the current research. After factors identification and problem formulation, DEMATEL is used to detect complex relationships and to build a Network Relation Map (NRM). Then ANP is used to prioritize and find the weights of the criteria and sub-criteria of the decision model. The research verifies the framework of multi-criteria for sustainable community-based tourism from the perspective of stakeholders. The result also provides a different perspective on the importance of sustainable criteria from the view of multi-stakeholders. Practically, this research gives the framework model and helps stakeholders to improve and innovate the homestay programme and also promote community-based tourism.

Keywords: community-based tourism, homestay programme, sustainable tourism criteria, sustainable tourism development

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Authors: Panpan Fan, Bo Ming, Niels Anten, Jochem Evers, Yaoyao Li, Shaokun Li, Ruizhi xie

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Nitrogen (N) utilization for crop production under N deficiency conditions is subject to a trade-off between maintaining specific leaf N content (SLN), important for radiation-use efficiency (RUE), versus maintaining leaf area (LA) development, important for light capture. This paper aims to explore how maize deals with this trade-off through responses in SLN, LA and their underlying traits during the vegetative and reproductive growth stages. In a ten-year N fertilization trial in Jilin province, Northeast China, three N fertilizer levels have been maintained: N-deficiency (N0), low N supply (N1), and high N supply (N2). We analyzed data from years 8 and 10 of this experiment for two common hybrids. Under N deficiency, maize plants maintained LA and decreased SLN during vegetative stages, while both LA and SLN decreased comparably during reproductive stages. Canopy-average specific leaf area (SLA) decreased sharply during vegetative stages and slightly during reproductive stages, mainly because senesced leaves in the lower canopy had a higher SLA. In the vegetative stage, maize maintained leaf area at low N by maintaining leaf biomass (albeit hence having N content/mass) and slightly increasing SLA. These responses to N deficiency were stronger in maize hybrid XY335 than in ZD958. We conclude the main strategy of maize to cope with low N is to maintain plant growth, mainly by increasing SLA throughout the plant during early growth. N was too limiting for either strategy to be followed during later growth stages.

Keywords: leaf N content per unit leaf area, N deficiency, specific leaf area, maize strateg

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Authors: Masahiro Miyaji

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When a driver happens to be involved in some traffic congestion or after traffic incidents, the driver may fall in a state of anger. State of anger may encounter decisive risk resulting in severer traffic accidents. Preventive safety function using driver’s psychosomatic state with regard to anger may be one of solutions which would avoid that kind of risks. Identifying driver’s anger state is important to create countermeasures to prevent the risk of traffic accidents. As a first step, this research figured out root cause of traffic incidents by means of using Internet survey. From statistical analysis of the survey, dominant psychosomatic states immediately before traffic incidents were haste, distraction, drowsiness and anger. Then, we replicated anger state of a driver while driving, and then, replicated it by means of using driving simulator on bench test basis. Six types of facial expressions including anger were introduced as alternative characteristics. Kohonen neural network was adopted to classify anger state. Then, we created a methodology to detect anger state of a driver in high accuracy. We presented a driving support safety function. The function adapts driver’s anger state in cooperation with an autonomous driving unit to reduce the number of traffic accidents. Consequently, e evaluated reduction rate of driver’s anger in the traffic accident. To validate the estimation results, we referred the reduction rate of Advanced Safety Vehicle (ASV) as well as Intelligent Transportation Systems (ITS).

Keywords: Kohonen neural network, driver’s anger state, reduction of traffic accidents, driver’s state adaptive driving support safety

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Authors: Purna Prasad Acharya, Madhav Karki, Sunta B. Tamang, Uttam Basnet, Chhatra Katwal

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The paper outlines the idea behind achieving sustainable lifestyles based on the spiritual values and teachings of Lord Buddha. This objective is to be achieved by spreading the tenets and teachings of Buddhism throughout the Asia Pacific region and the world from the sacred birth place of Buddha - Lumbini, Nepal. There is an urgent need to advance the relevance of Buddhist philosophy in tackling the triple planetary crisis of climate change, nature’s decline, and pollution. Today, the world is facing an existential crisis due to the above crises, exasperated by hunger, poverty and armed conflict. To address multi-dimensional impacts, the global communities have to adopt simple life styles that respect nature and universal human values. These were the basic teachings of Gautam Buddha. Lumbini, Nepal has the moral obligation to widely disseminate Buddha’s teaching to the world and receive constant feedback and learning to develop human and ecosystem resilience by molding the lifestyles of current and future generations through adaptive learning and simplicity across the geography and nationality based on spirituality and environmental stewardship. By promoting Buddhism, Nepal has developed a pro-nature tourism industry that focuses on both its spiritual and bio-cultural heritage. Nepal is a country rich in ancient wisdom, where sages have sought knowledge, practiced meditation, and followed spiritual paths for thousands of years. It can spread the teachings of Buddha in a way people can search for and adopt ways to live, creating harmony with nature. Using tools of natural sciences and social sciences, the team will package knowledge and share the idea of community well-being within the framework of environmental sustainability, social harmony and universal respect for nature and people in a more holistic manner. This notion takes into account key elements of sustainable development such as food-energy-water-biodiversity interconnections, environmental conservation, ecological integrity, ecosystem health, community resiliency, adaptation capacity, and indigenous culture, knowledge and values. This inclusive concept has garnered a strong network of supporters locally, regionally, and internationally. The key objectives behind this concept are: a) to leverage expertise and passion of a network of global collaborators to advance research, education, and policy outreach in the areas of human sustainability based on lifestyle change using the power of spirituality and Buddha’s teaching, resilient lifestyles, and adaptive living; b) help develop creative short courses for multi-disciplinary teaching in educational institutions worldwide in collaboration with Lumbini Buddha University and other relevant partners in Nepal; c) help build local and regional intellectual and cultural teaching and learning capacity by improving professional collaborations to promote nature based and Buddhist value-based lifestyles by connecting Lumbini to Nepal’s rich nature; d) promote research avenues to provide policy relevant knowledge that is creative, innovative, as well as practical and locally viable; and e) connect local research and outreach work with academic and cultural partners in South Korea so as to open up Lumbini based Buddhist heritage and Nepal’s Karnali River basin’s unique natural landscape to Korean scholars and students to promote sustainable lifestyles leading to human living in harmony with nature.

Keywords: triple planetary crisis, spirituality, sustainable lifestyles, living in harmony with nature, resilience

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Authors: Sabahat Nadeem, Farman Ali Khan

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Emotions are considered as a vital factor affecting the process of information handling, level of attention, memory capacity and decision making. Latest e-Learning systems are therefore taking into consideration the effective state of learners to make the learning process more effective and enjoyable. One such use of user’s affective information is in the systems that tend to regulate users’ emotions to a state optimally desirable for learning. So for, this objective has been tried to be achieved with the help of teaching strategies, background music, guided imagery, video clips and odors. Nevertheless, we know that colors can affect human emotions. Relationship between color and emotions has a strong influence on how we perceive our environment. Similarly, the colors of the interface can also affect the user positively as well as negatively. This affective behavior of color and its use as emotion regulation agent is not yet exploited. Therefore, this research proposes a Color-based Emotion Regulation Model (CERM), a new framework that can automatically adapt its colors according to user’s emotional state and her personality type and can help in producing a desirable emotional effect, aiming at providing an unobtrusive emotional support to the users of e-learning environment. The evaluation of CERM is carried out by comparing it with classical non-adaptive, static colored learning management system. Results indicate that colors of the interface, when carefully selected has significant positive impact on learner’s emotions.

Keywords: effective learning, e-learning, emotion regulation, emotional design

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Authors: Yeliz Karaca, Rana Karabudak

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Multifractal Denoising techniques are used in the identification of significant attributes by removing the noise of the dataset. Magnetic resonance (MR) image technique is the most sensitive method so as to identify chronic disorders of the nervous system such as Multiple Sclerosis. MRI and Expanded Disability Status Scale (EDSS) data belonging to 120 individuals who have one of the subgroups of MS (Relapsing Remitting MS (RRMS), Secondary Progressive MS (SPMS), Primary Progressive MS (PPMS)) as well as 19 healthy individuals in the control group have been used in this study. The study is comprised of the following stages: (i) L2 Norm Multifractal Denoising technique, one of the multifractal technique, has been used with the application on the MS data (MRI and EDSS). In this way, the new dataset has been obtained. (ii) The new MS dataset obtained from the MS dataset and L2 Multifractal Denoising technique has been applied to the K-Means and Fuzzy C Means clustering algorithms which are among the unsupervised methods. Thus, the clustering performances have been compared. (iii) In the identification of significant attributes in the MS dataset through the Multifractal denoising (L2 Norm) technique using K-Means and FCM algorithms on the MS subgroups and control group of healthy individuals, excellent performance outcome has been yielded. According to the clustering results based on the MS subgroups obtained in the study, successful clustering results have been obtained in the K-Means and FCM algorithms by applying the L2 norm of multifractal denoising technique for the MS dataset. Clustering performance has been more successful with the MS Dataset (L2_Norm MS Data Set) K-Means and FCM in which significant attributes are obtained by applying L2 Norm Denoising technique.

Keywords: clinical decision support, clustering algorithms, multiple sclerosis, multifractal techniques

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Authors: Zeyu Zhou, Xiaojun Bi

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Due to the limited environmental parameters and the limited resolution of the sensor, the universal existence of the mixed pixels in the process of remote sensing images restricts the spatial resolution of the remote sensing images. Sub-pixel mapping technology can effectively improve the spatial resolution. As the bilinear interpolation algorithm inevitably produces the edge blur effect, which leads to the inaccurate sub-pixel mapping results. In order to avoid the edge blur effect that affects the sub-pixel mapping results in the interpolation process, this paper presents a new edge-directed interpolation algorithm which uses the covariance adaptive interpolation algorithm on the edge of the low-resolution image and uses bilinear interpolation algorithm in the low-resolution image smooth area. By using the edge-directed interpolation algorithm, the super-resolution of the image with low resolution is obtained, and we get the percentage of each sub-pixel under a certain type of high-resolution image. Then we rely on the probability value as a soft attribute estimate and carry out sub-pixel scale under the ‘hard classification’. Finally, we get the result of sub-pixel mapping. Through the experiment, we compare the algorithm and the bilinear algorithm given in this paper to the results of the sub-pixel mapping method. It is found that the sub-pixel mapping method based on the edge-directed interpolation algorithm has better edge effect and higher mapping accuracy. The results of the paper meet our original intention of the question. At the same time, the method does not require iterative computation and training of samples, making it easier to implement.

Keywords: remote sensing images, sub-pixel mapping, bilinear interpolation, edge-directed interpolation

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Authors: Helene Thieblemont, Fariborz Haghighat

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Energy storage systems play a crucial role in decreasing building energy consumption during peak periods and expand the use of renewable energies in buildings. To provide a high building thermal performance, the energy storage system has to be properly controlled to insure a good energy performance while maintaining a satisfactory thermal comfort for building’s occupant. In the case of passive discharge storages, defining in advance the required amount of energy is required to avoid overheating in the building. Consequently, anticipatory supervisory control strategies have been developed forecasting future energy demand and production to coordinate systems. Anticipatory supervisory control strategies are based on some predictions, mainly of the weather forecast. However, if the forecasted hourly outdoor temperature may be found online with a high accuracy, solar radiations predictions are most of the time not available online. To estimate them, this paper proposes an advanced approach based on the forecast of weather conditions. Several methods to correlate hourly weather conditions forecast to real hourly solar radiations are compared. Results show that using weather conditions forecast allows estimating with an acceptable accuracy solar radiations of the next day. Moreover, this technique allows obtaining hourly data that may be used for building models. As a result, this solar radiation prediction model may help to implement model-based controller as Model Predictive Control.

Keywords: anticipatory control, model predictive control, solar radiation forecast, thermal storage

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Authors: Mirvat Shamseddine, Issam Lakkis

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We present an efficient, three-dimensional parallel multi-scale Direct Simulation Monte Carlo (DSMC) algorithm for the simulation of unsteady rarefied gas flows in micro/nanosystems. The algorithm employs a novel spatiotemporal adaptivity scheme. The scheme performs a fully dynamic multi-level grid adaption based on the gradients of flow macro-parameters and an automatic temporal adaptation. The computational domain consists of a hierarchical octree-based Cartesian grid representation of the flow domain and a triangular mesh for the solid object surfaces. The hybrid mesh, combined with the spatiotemporal adaptivity scheme, allows for increased flexibility and efficient data management, rendering the framework suitable for efficient particle-tracing and dynamic grid refinement and coarsening. The parallel algorithm is optimized to run DSMC simulations of strongly unsteady, non-equilibrium flows over multiple cores. The presented method is validated by comparing with benchmark studies and then employed to improve the design of micro-scale hotwire thermal sensors in rarefied gas flows.

Keywords: DSMC, oct-tree hierarchical grid, ray tracing, spatial-temporal adaptivity scheme, unsteady rarefied gas flows

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Authors: Fatemeh Keshavarz

Abstract:

Background: Schizophrenia is a disease of the nervous system, and immune system disorders can affect its pathogenesis. Activation of microglia, proinflammatory cytokines, disruption of the blood-brain barrier (BBB) due to inflammation, activation of autoreactive B cells, and consequently the production of autoantibodies against system antigens are among the immune processes involved in neurological diseases. interleukin 32 (IL-32) a proinflammatory cytokine that important player in the activation of the innate and adaptive immune responses. This study aimed to measure the serum level of IL-32 as well as the frequency of autoantibody positivity against several nervous system antigens in patients with schizophrenia. Material and Methods: This study was conducted on 40 patients with schizophrenia and 40 healthy individuals in the control group. Serum IL-32 levels were measured by ELISA. The frequency of autoantibodies against Hu, Ri, Yo, Tr, CV2, Amphiphysin, SOX1, Zic4, ITPR1, CARP, GAD, Recoverin, Titin, and Ganglioside antigens were measured by indirect immunofluorescence method. Results: Serum IL-32 levels in patients with schizophrenia were significantly higher compared to the control group. Autoantibodies were positive in 8 patients for GAD antigen and 5 patients for Ri antigen, which showed a significant relationship compared to the control group. Autoantibodies were also positive in 2 patients for CV2, in 1 patient for Hu, and in 1 patient for CARP. Negative results were reported for other antigens. Conclusion: Our findings suggest that elevated the serum IL-32 level and autoantibody positivity against several nervous system antigens may be involved in the pathogenesis of schizophrenia.

Keywords: schizophrenia, microglia, autoantibodies, IL-32

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Authors: Jonas Colin

Abstract:

Standing at the vanguard of AI development, Alpha represents an unprecedented synthesis of logical rigor and human abstraction, meticulously crafted to mirror the user's unique persona and personality, a feat previously unattainable in AI development. Alpha, an avant-garde artefact in the realm of artificial intelligence, epitomizes a paradigmatic shift in personalized digital interaction, amalgamating user-specific dialogic patterns with the sophisticated algorithmic prowess of OpenAI's GPT-3.5 to engender a platform for enhanced metacognitive engagement and individualized user experience. Underpinned by a sophisticated algorithmic framework, Alpha integrates vast datasets through a complex interplay of neural network models and symbolic AI, facilitating a dynamic, adaptive learning process. This integration enables the system to construct a detailed user profile, encompassing linguistic preferences, emotional tendencies, and cognitive styles, tailoring interactions to align with individual characteristics and conversational contexts. Furthermore, Alpha incorporates advanced metacognitive elements, enabling real-time reflection and adaptation in communication strategies. This self-reflective capability ensures continuous refinement of its interaction model, positioning Alpha not just as a technological marvel but as a harbinger of a new era in human-computer interaction, where machines engage with us on a deeply personal and cognitive level, transforming our interaction with the digital world.

Keywords: chatbot, GPT 3.5, metacognition, symbiose

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Authors: Moaine Jebara, Lionel Boillereaux, Sofiane Belhabib, Michel Havet, Alain Sarda, Pierre Mousseau, Rémi Deterre

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Energy consumption efficiency is a major concern for the material processing industry such as thermoforming process and molding. Indeed, these systems should deliver the right amount of energy at the right time to the processed material. Recent technical development, as well as the particularities of the heating system dynamics, made the Model Predictive Control (MPC) one of the best candidates for thermal control of several production processes like molding and composite thermoforming to name a few. The main principle of this technique is to use a dynamic model of the process inside the controller in real time in order to anticipate the future behavior of the process which allows the current timeslot to be optimized while taking future timeslots into account. This study presents a procedure based on a predictive control that brings balance between optimality, simplicity, and flexibility of its implementation. The development of this approach is progressive starting from the case of a single zone before its extension to the multizone and/or multisource case, taking thus into account the thermal couplings between the adjacent zones. After a quadratic formulation of the MPC criterion to ensure the thermal control, the linear expression is retained in order to reduce calculation time thanks to the use of the ARMAX linear decomposition methods. The effectiveness of this approach is illustrated by experiment and simulation.

Keywords: energy efficiency, linear decomposition methods, model predictive control, mold heating systems

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Authors: Simon Keller, Rainer Mueller

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Continuous range queries are a common means to handle mobile clients in high-density areas. Most existing approaches focus on settings in which the range queries for location-based services are more or less static, whereas the mobile clients in the ranges move. We focus on a category called dynamic real-time range queries (DRRQ), assuming that both, clients requested by the query and the inquirers, are mobile. In consequence, the query parameters and the query results continuously change. This leads to two requirements: the ability to deal with an arbitrarily high number of mobile nodes (scalability) and the real-time delivery of range query results. In this paper, we present the highly decentralized solution adaptive quad streaming (AQS) for the requirements of DRRQs. AQS approximates the query results in favor of a controlled real-time delivery and guaranteed scalability. While prior works commonly optimize data structures on the involved servers, we use AQS to focus on a highly distributed cell structure without data structures automatically adapting to changing client distributions. Instead of the commonly used request-response approach, we apply a lightweight streaming method in which no bidirectional communication and no storage or maintenance of queries are required at all.

Keywords: approximation of client distributions, continuous spatial range queries, mobile objects, streaming-based decentralization in spatial mobile environments

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Authors: Nadine Sammy, Mark Wilson, Samuel Vine

Abstract:

The Theory of Challenge and Threat States in Athletes (TCTSA) states that self-efficacy is an antecedent of challenge and threat. These states result from conscious and unconscious evaluations of situational demands and personal resources and are represented by both cognitive and physiological markers. Challenge is considered a more adaptive stress response as it is associated with a more efficient cardiovascular profile, as well as better performance and attention effects compared with threat. Self-efficacy is proposed to influence challenge/threat because an individual’s belief that they have the skills necessary to execute the courses of action required to succeed contributes to a perception that they can cope with the demands of the situation. This study experimentally examined the effects of self-efficacy on cardiovascular responses (challenge and threat), demand and resource evaluations, performance and attention under pressurised conditions. Forty-five university students were randomly assigned to either a control (n=15), low self-efficacy (n=15) or high self-efficacy (n=15) group and completed baseline and pressurised golf putting tasks. Self-efficacy was manipulated using false feedback adapted from previous studies. Measures of self-efficacy, cardiovascular reactivity, demand and resource evaluations, task performance and attention were recorded. The high self-efficacy group displayed more favourable cardiovascular reactivity, indicative of a challenge state, compared with the low self-efficacy group. The former group also reported high resource evaluations, but no task performance or attention effects were detected. These findings demonstrate that levels of self-efficacy influence cardiovascular reactivity and perceptions of resources under pressurised conditions.

Keywords: cardiovascular, challenge, performance, threat

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Authors: Benjamin Ankomah-Asare, Richard Adade

Abstract:

Sea level rise (SLR) poses a significant threat to coastal communities globally. Ghana has over the years implemented protective measures such as the construction of groynes and revetment to serve as barriers to sea waves in major cities and towns to prevent sea erosion and flooding. For vulnerable rural coastal communities, the planned retreat is often proposed; however, relocation costs are often underestimated as losses of future social and cultural value are not always adequately taken into account. Through a mixed-methods approach combining qualitative interviews, surveys, and spatial analysis, the study examined the experiences of coastal rural communities in Ghana and assess the effectiveness of relocation strategies in addressing the socio-economic and environmental challenges posed by sea level rise. The study revealed the devastating consequences of sea level rise on these communities, including increased flooding, erosion, and saltwater intrusion into freshwater sources. Moreover, it highlights the adaptive capacities within these communities and how factors such as infrastructure, economic activities, cultural heritage, and governance structures shape their resilience in the face of environmental change. While relocation can be an effective strategy in reducing the risks associated with sea level rise, the study recommends that proper implementation of this adaptation strategy can be achieved when coupled with community-led planning, participatory decision-making, and targeted support for vulnerable groups.

Keywords: sea level rise, relocation, socio-ecological impacts, rural communities

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