Search results for: shared frailty survival models

Authors: Rupali Satsangi

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14 years ago, leaders from every country agreed on a vision for the future – a world with less poverty, hunger and disease, greater survival prospects for mothers and their infants, better educated children, equal opportunities for women, and a healthier environment; a world in which developed and developing countries work in partnership for the betterment of all. This vision took the shape of eight Millennium Development Goals, which provide countries around the world a framework for development and time-bound targets by which progress can be measured. However, India has found 35 of the indicators as relevant to India. India’s MDG-framework has been contextualized through a concordance with the existing official indicators of corresponding dimensions in the national statistical system. The present study based on secondary data analyzed the status of India towards achieving the MDGs after reviewing the data study find out that India can miss the MDGs Bus in women health, sanitation and global partnership. These goals were less addressed by India in his policies and takeoffs.

Keywords: millennium development goals, national statistical system, global partnership, healthier environment

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Authors: Akanksha Bhatnagar, Sandhya Kortegare, Felice Elefant

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Context: Alzheimer's disease (AD) is a neurodegenerative disorder that is characterized by progressive cognitive decline and memory loss. The cause of AD is not fully understood, but it is thought to be caused by a combination of genetic, environmental, and lifestyle factors. One of the hallmarks of AD is the loss of neurons in the hippocampus, a brain region that is important for memory and learning. This loss of neurons is thought to be caused by a decrease in histone acetylation, which is a process that regulates gene expression. Research Aim: The research aim of the study was to develop mall molecule compounds that can enhance the activity of Tip60, a histone acetyltransferase that is important for memory and learning. Methodology/Analysis: The researchers used in silico structural modeling and a pharmacophore-based virtual screening approach to design and synthesize small molecule compounds strongly predicted to target and enhance Tip60’s HAT activity. The compounds were then tested in vitro and in vivo to assess their ability to enhance Tip60 activity and rescue cognitive deficits in AD models. Findings: The researchers found that several of the compounds were able to enhance Tip60 activity and rescue cognitive deficits in AD models. The compounds were also developed to cross the blood-brain barrier, which is an important factor for the development of potential AD therapeutics. Theoretical Importance: The findings of this study suggest that Tip60 HAT activators have the potential to be developed as therapeutic agents for AD. The compounds are specific to Tip60, which suggests that they may have fewer side effects than other HDAC inhibitors. Additionally, the compounds are able to cross the blood-brain barrier, which is a major hurdle for the development of AD therapeutics. Data Collection: The study collected data from a variety of sources, including in vitro assays and animal models. The in vitro assays assessed the ability of compounds to enhance Tip60 activity using histone acetyltransferase (HAT) enzyme assays and chromatin immunoprecipitation assays. Animal models were used to assess the ability of the compounds to rescue cognitive deficits in AD models using a variety of behavioral tests, including locomotor ability, sensory learning, and recognition tasks. The human clinical trials will be used to assess the safety and efficacy of the compounds in humans. Questions: The question addressed by this study was whether Tip60 HAT activators could be developed as therapeutic agents for AD. Conclusions: The findings of this study suggest that Tip60 HAT activators have the potential to be developed as therapeutic agents for AD. The compounds are specific to Tip60, which suggests that they may have fewer side effects than other HDAC inhibitors. Additionally, the compounds are able to cross the blood-brain barrier, which is a major hurdle for the development of AD therapeutics. Further research is needed to confirm the safety and efficacy of these compounds in humans.

Keywords: Alzheimer's disease, cognition, neuroepigenetics, drug discovery

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Authors: Y. Lacroix, J.-F. Malbranque

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We present a chronological evolution for naval telecommunication networks. We distinguish periods: with or without multiplexers, with switch systems, with federative systems, with medium switching, and with medium switching with wireless networks. This highlights the introduction of new layers and technology in the architecture. These architectures are presented using layer models of transmission, in a unified way, which enables us to integrate pre-existing models. A ship of a naval fleet has internal communications (i.e. applications' networks of the edge) and external communications (i.e. the use of the means of transmission between edges). We propose architectures, deduced from the layer model, which are the point of convergence between the networks on board and the HF, UHF radio, and satellite resources. This modelling allows to consider end-to-end naval communications, and in a more global way, that is from the user on board towards the user on shore, including transmission and networks on the shore side. The new architectures need take care of quality of services for end-to-end communications, the more remote control develops a lot and will do so in the future. Naval telecommunications will be more and more complex and will use more and more advanced technologies, it will thus be necessary to establish clear global communication schemes to grant consistency of the architectures. Our latest model has been implemented in a military naval situation, and serves as the basic architecture for the RIFAN2 network.

Keywords: equilibrium beach profile, eastern tombolo of Giens, potential function, erosion

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Authors: S. N. Susenburger, D. Boecker

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In the age of Volatility, Uncertainty, Complexity, and Ambiguity (VUCA), where digital transformation is challenging long standing traditional hardware and manufacturing companies, innovation needs a different methodology, strategy, mindset, and culture. What used to be a mindset of scaling per quantity is now shifting to orchestrating ecosystems, platform business models and service bundles. While large corporations are trying to mimic the nimbleness and versatile mindset of startups in the core of their digital strategies, they’re at the frontier of facing one of the largest organizational and cultural changes in history. This paper elaborates on how a manufacturing giant transformed its Corporate Information Technology (IT) to enable digital and Internet of Things (IoT) business while establishing the mindset and the approaches of the Innovation Inside-Out/Outside-In Strategy. It gives insights into the core elements of an innovation culture and the tactics and methodologies leveraged to support the cultural shift and transformation into an IoT company. This paper also outlines the core elements for an innovation culture and how the persona 'Connected Engineer' thrives in the digital innovation environment. Further, it explores how tapping domain-focused ecosystems in vibrant innovative cities can be used as a part of the strategy to facilitate partner co-innovation. Therefore, findings from several use cases, observations and surveys led to conclusion for the strategy tandem of Innovation Inside-Out/Outside-In. The findings indicate that it's crucial in which phases and maturity level the Innovation Inside-Out/Outside-In Strategy is activated: cultural aspects of the business and the regional ecosystem need to be considered, as well as cultural readiness from management and active contributors. The 'not invented here syndrome' is a barrier of large corporations that need to be addressed and managed to successfully drive partnerships, as well as embracing co-innovation and a mindset shifting away from physical products toward new business models, services, and IoT platforms. This paper elaborates on various methodologies and approaches tested in different countries and cultures, including the U.S., Brazil, Mexico, and Germany.

Keywords: innovation management, innovation culture, innovation methodologies, digital transformation

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Authors: T. Jayanthi, K. Velusamy, H. Seetha, S. A. V. Satya Murty

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Verification and Validation of Simulated Process Model is the most important phase of the simulator life cycle. Evaluation of simulated process models based on Verification and Validation techniques checks the closeness of each component model (in a simulated network) with the real system/process with respect to dynamic behaviour under steady state and transient conditions. The process of Verification and validation helps in qualifying the process simulator for the intended purpose whether it is for providing comprehensive training or design verification. In general, model verification is carried out by comparison of simulated component characteristics with the original requirement to ensure that each step in the model development process completely incorporates all the design requirements. Validation testing is performed by comparing the simulated process parameters to the actual plant process parameters either in standalone mode or integrated mode. A Full Scope Replica Operator Training Simulator for PFBR - Prototype Fast Breeder Reactor has been developed at IGCAR, Kalpakkam, INDIA named KALBR-SIM (Kalpakkam Breeder Reactor Simulator) wherein the main participants are engineers/experts belonging to Modeling Team, Process Design and Instrumentation and Control design team. This paper discusses the Verification and Validation process in general, the evaluation procedure adopted for PFBR operator training Simulator, the methodology followed for verifying the models, the reference documents and standards used etc. It details out the importance of internal validation by design experts, subsequent validation by external agency consisting of experts from various fields, model improvement by tuning based on expert’s comments, final qualification of the simulator for the intended purpose and the difficulties faced while co-coordinating various activities.

Keywords: Verification and Validation (V&V), Prototype Fast Breeder Reactor (PFBR), Kalpakkam Breeder Reactor Simulator (KALBR-SIM), steady state, transient state

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Authors: Anne Eugenia Wright

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This study examines issues in accessibility to maritime cultural heritage. Using the Pillar Dollar Wreck in Biscayne National Park, Florida, this study presents an approach to public outreach based on the concept of Design for All. Design for All advocates creating products that are accessible and functional for all users, including those with visual, hearing, learning, mobility, or economic impairments. As a part of this study, a small exhibit was created that uses 3D products as a way to bring maritime cultural heritage to the public. It was presented to the public at East Carolina University’s Joyner Library. Additionally, this study presents a methodology for 3D printing scaled photogrammetry models of archaeological sites in full color. This methodology can be used to present a realistic depiction of underwater archaeological sites to those who are incapable of accessing them in the water. Additionally, this methodology can be used to present underwater archaeological sites that are inaccessible to the public due to conditions such as visibility, depth, or protected status. This study presents a practical use for 3D photogrammetry models, as well as an accessibility strategy to expand the outreach potential for maritime archaeology.

Keywords: Underwater Archaeology, 3D Printing, Photogrammetry, Design for All

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Authors: Po-Yuan Tung, Jen-Chueh Kuo, Chia-Ray Chen, Chien-Hsing Li, Kuo-Liang Pan

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The main-body structure of satellite is mainly constructed by lightweight material, it should be able to withstand certain vibration load during launches. Since various kinds of change possibility in the space, it is an extremely important work to study the random vibration response of satellite structure. This paper based on the reciprocity relationship between sound and structure response and it will try to evaluate the dynamic response of satellite main body under random acoustic load excitation. This paper will study the technical process and verify the feasibility of sonic-borne vibration analysis. One simple plate exposed to the uniform acoustic field is utilized to take some important parameters and to validate the acoustics field model of the reverberation chamber. Then import both structure and acoustic field chamber models into the vibro-acoustic coupling analysis software to predict the structure response. During the modeling process, experiment verification is performed to make sure the quality of numerical models. Finally, the surface vibration level can be calculated through the modal participation factor, and the analysis results are presented in PSD spectrum.

Keywords: vibration, acoustic, modal, honeycomb panel

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Authors: Adeyemi, Samuel Olusegun

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Biometrics and dietary habit of Citharinus citharus in the lower Niger River area of kogi state were studied between October and December, 2010. A total of 120 fish sampled were used for the study. The total length, standard length and weight were taken for each fish sample for the estimations of length-weight relationship using the formula W = aLb and transformed to Log W = Log a + b Log L. Stomach contents were analyzed by frequency of occurrence method. The standard length of males, females and combined sexes ranged between 6.8 - 16.5, 7.3 – 14.3 cm, 6.8 – 74.2 (cm) respectively, with b – values of 3.0963, 3.174 and 3.1382. The condition factor ranged from 2.04 – 2.80, 1.88 – 2.86 and 1.88 – 2.86 respectively. The food and feeding habits shows that the fish feeds mainly sand grain (25.83%), mud (24.16%), plant parts (12.50%), insect part (2.50%), algae (12.50%) and unidentified items (5.00%). C. citharus in the lower Niger area of kogi state could be termed to an omnivore. River Niger could be said to be suitable for growth and survival of the fish species C. citharus.

Keywords: length-weight, sexes, stomach content, feeding habits, plant materials

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Authors: Marta Błażkiewicz, Adam Konefał

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Currently, boron-neutron therapy is carried out mainly with the use of a neutron beam generated in research nuclear reactors. This fact limits the possibility of realization of a BNCT in centers distant from the above-mentioned reactors. Moreover, the number of active nuclear reactors in operation in the world is decreasing due to the limited lifetime of their operation and the lack of new installations. Therefore, the possibilities of carrying out boron-neutron therapy based on the neutron beam from the experimental reactor are shrinking. However, the use of nuclear power reactors for BNCT purposes is impossible due to the infrastructure not intended for radiotherapy. Therefore, a serious challenge is to find ways to perform boron-neutron therapy based on neutrons generated outside the research nuclear reactor. This work meets this challenge. Its goal is to develop a BNCT technique based on commonly available neutron sources such as Cf-252 and PuBe, which will enable the above-mentioned therapy in medical centers unrelated to nuclear research reactors. Advances in the field of neutron source fabrication make it possible to achieve strong neutron fluxes. The current stage of research focuses on the development of virtual models of the above-mentioned sources using the Monte Carlo simulation method. In this study, the GEANT4 tool was used, including the model for simulating neutron-matter interactions - High Precision Neutron. Models of neutron sources were developed on the basis of experimental verification based on the activation detectors method with the use of indium foil and the cadmium differentiation method allowing to separate the indium activation contribution from thermal and resonance neutrons. Due to the large number of factors affecting the result of the verification experiment, the 10% discrepancy between the simulation and experiment results was accepted.

Keywords: BNCT, virtual models, neutron sources, monte carlo, GEANT4, neutron activation detectors, gamma spectroscopy

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Authors: M. Khairudin

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This paper investigates the system identification and design quantitative feedback theory (QFT) for the robust control of a lathe spindle. The dynamic of the lathe spindle is uncertain and time variation due to the deepness variation on cutting process. System identification was used to obtain the dynamics model of the lathe spindle. In this work, real time system identification is used to construct a linear model of the system from the nonlinear system. These linear models and its uncertainty bound can then be used for controller synthesis. The real time nonlinear system identification process to obtain a set of linear models of the lathe spindle that represents the operating ranges of the dynamic system. With a selected input signal, the data of output and response is acquired and nonlinear system identification is performed using Matlab to obtain a linear model of the system. Practical design steps are presented in which the QFT-based conditions are formulated to obtain a compensator and pre-filter to control the lathe spindle. The performances of the proposed controller are evaluated in terms of velocity responses of the the lathe machine spindle in corporating deepness on cutting process.

Keywords: lathe spindle, QFT, robust control, system identification

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Authors: Yin Yuanling

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A Deep Feedback Recurrent Neural Network (DFRNN) and Bidirectional Long Short-Term Memory (BiLSTM) are designed to address the problem of low accuracy of traditional relationship extraction models. This method combines a deep feedback-based recurrent neural network (DFRNN) with a bi-directional long short-term memory (BiLSTM) approach. The method combines DFRNN, which extracts local features of text based on deep feedback recurrent mechanism, BiLSTM, which better extracts global features of text, and Self-Attention, which extracts semantic information. Experiments show that the method achieves an F1 value of 76.69% on the CEC dataset, which is 0.0652 better than the BiLSTM+Self-ATT model, thus optimizing the performance of the deep learning method in the event relationship extraction task.

Keywords: event relations, deep learning, DFRNN models, bi-directional long and short-term memory networks

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Authors: Zahira Anane, Abdelhafid Bayadi

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Corona in the HV overhead transmission lines is an important source of attenuation and distortion of overvoltage surges. This phenomenon of distortion, which is superimposed on the distortion by skin effect, is due to the dissipation of energy by injection of space charges around the conductor, this process with place as soon as the instantaneous voltage exceeds the threshold voltage of the corona effect conductors. This paper presents a mathematical model to determine the corona inception voltage, the critical electric field and the corona radius, to predict the capacitive changes at conductor of transmission line due to corona. This model has been incorporated into the Alternative Transients Program version of the Electromagnetic Transients Program (ATP/EMTP) as a user defined component, using the MODELS interface with NORTON TYPE94 of this program and using the foreign subroutine. For obtained the displacement of corona charge hell, dichotomy mathematical method is used for this computation. The present corona model can be used for computing of distortion and attenuation of transient overvoltage waves being propagated in a transmission line of the very high voltage electric power.

Keywords: high voltage, corona, Type94 NORTON, dichotomy, ATP/EMTP, MODELS, distortion, foreign model

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Authors: Ignitia Motjolopane

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Small and medium enterprises (SMEs) play an important role in economies by contributing to economic growth and employment. In the fourth industrial revolution, the convergence of technologies and the changing nature of work created pressures on economies globally. Generative artificial intelligence (AI) may support SMEs in exploring, exploiting, and transforming business models to align with their growth aspirations. SMEs' growth aspirations fall into four categories: subsistence, income, growth, and speculative. Subsistence-oriented firms focus on meeting basic financial obligations and show less motivation for business model innovation. SMEs focused on income, growth, and speculation are more likely to pursue business model innovation to support growth strategies. SMEs' strategic goals link to distinct business model innovation paths depending on whether SMEs are starting a new business, pursuing growth, or seeking profitability. Integrating generative artificial intelligence in start-up SME business model innovation enhances value creation, user-oriented innovation, and SMEs' ability to adapt to dynamic changes in the business environment. The existing literature may lack comprehensive frameworks and guidelines for effectively integrating generative AI in start-up reiterative business model innovation paths. This paper examines start-up business model innovation path with generative artificial intelligence. A theoretical approach is used to examine start-up-focused SME reiterative business model innovation path with generative AI. Articulating how generative AI may be used to support SMEs to systematically and cyclically build the business model covering most or all business model components and analyse and test the BM's viability throughout the process. As such, the paper explores generative AI usage in market exploration. Moreover, market exploration poses unique challenges for start-ups compared to established companies due to a lack of extensive customer data, sales history, and market knowledge. Furthermore, the paper examines the use of generative AI in developing and testing viable value propositions and business models. In addition, the paper looks into identifying and selecting partners with generative AI support. Selecting the right partners is crucial for start-ups and may significantly impact success. The paper will examine generative AI usage in choosing the right information technology, funding process, revenue model determination, and stress testing business models. Stress testing business models validate strong and weak points by applying scenarios and evaluating the robustness of individual business model components and the interrelation between components. Thus, the stress testing business model may address these uncertainties, as misalignment between an organisation and its environment has been recognised as the leading cause of company failure. Generative AI may be used to generate business model stress-testing scenarios. The paper is expected to make a theoretical and practical contribution to theory and approaches in crafting a robust business model innovation path with generative artificial intelligence in start-up SMEs.

Keywords: business models, innovation, generative AI, small medium enterprises

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Authors: Shujie Liu

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This study aimed to investigate the relationship between teachers’ perceived transformational leadership behaviors and their job satisfaction in China after controlling for teacher self-efficacy. Hierarchical regression analysis (HRA) technique was employed to examine factors’ contributions to teacher job satisfaction with a sample of Chinese high school teachers. The finding of this study provided evidence that teachers’ perceived transformational leadership behaviors accounted for a large percentage (44.9%) of the variance in Chinese teachers’ job satisfaction. Uniquely, school principals’ sense of power was a negative significant predictor of teacher job satisfaction, meaning that the more teachers perceived their principals’ sense of power, the lower of their job satisfaction. Furthermore, this study provided evidence that teacher self-efficacy significantly contributes to teacher job satisfaction. Specifically, teachers’ self-efficacy on student engagement was found to be a significant predictor of teacher job satisfaction. The conclusions were discussed in terms of Chinese cultures. The authors pointed out that how to make teachers involved in school policy making is a challenge for China and that more shared leadership is needed in Chinese schools.

Keywords: Chinese teachers, teacher job satisfaction, teacher self-efficacy, transformational leadership

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Authors: Vinh Duy Cao, Carlos Salas-Bringas, Anna M. Szczotok, Marianne Hiorth, Anna-Lena Kjøniksen

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The increasing cost of energy supply for the purposes of heating and cooling creates a demand for more energy efficient buildings. Improved construction techniques and enhanced material technology can greatly reduce the energy consumption needed for the buildings. Microencapsulated phase change materials (MPCM) suspensions utilized as heat transfer fluids for energy storage and heat transfer applications provide promising potential solutions. A full understanding of the flow and thermal characteristics of microcapsule suspensions is needed to optimize the design of energy storage systems, in order to reduce the capital cost, system size, and energy consumption. The MPCM suspensions exhibited pseudoplastic and thixotropic behaviour, and significantly improved the thermal performance of the suspensions. Three different models were used to characterize the thixotropic behaviour of the MPCM suspensions: the second-order structural, kinetic model was found to give a better fit to the experimental data than the Weltman and Figoni-Shoemaker models. For all samples, the initial shear stress increased, and the breakdown rate accelerated significantly with increasing concentration. The thermal performance and rheological properties, especially the selection of rheological models, will be useful for developing the applications of microcapsules as heat transfer fluids in thermal energy storage system such as calculation of an optimum MPCM concentration, pumping power requirement, and specific power consumption. The effect of temperature on the shear thinning properties of the samples suggests that some of the phase change material is located outside the capsules, and contributes to agglomeration of the samples.

Keywords: latent heat, microencapsulated phase change materials, pseudoplastic, suspension, thixotropic behaviour

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Authors: Luis Amador Jimenez, Md. Shohel Amin

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Pavement performance models are based on projections of observed traffic loads, which makes uncertain to study funding strategies in the long run if history does not repeat. Neural networks can be used to estimate deterioration rates but the learning rate and momentum have not been properly investigated, in addition, economic evolvement could change traffic flows. This study addresses both issues through a case study for roads of Montreal that simulates traffic for a period of 50 years and deals with the measurement error of the pavement deterioration model. Travel demand models are applied to simulate annual average daily traffic (AADT) every 5 years. Accumulated equivalent single axle loads (ESALs) are calculated from the predicted AADT and locally observed truck distributions combined with truck factors. A back propagation Neural Network (BPN) method with a Generalized Delta Rule (GDR) learning algorithm is applied to estimate pavement deterioration models capable of overcoming measurement errors. Linear programming of lifecycle optimization is applied to identify M&R strategies that ensure good pavement condition while minimizing the budget. It was found that CAD 150 million is the minimum annual budget to good condition for arterial and local roads in Montreal. Montreal drivers prefer the use of public transportation for work and education purposes. Vehicle traffic is expected to double within 50 years, ESALS are expected to double the number of ESALs every 15 years. Roads in the island of Montreal need to undergo a stabilization period for about 25 years, a steady state seems to be reached after.

Keywords: pavement management system, traffic simulation, backpropagation neural network, performance modeling, measurement errors, linear programming, lifecycle optimization

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Authors: M. S. Mahmoud

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This study was conducted to evaluate the manganese removal from aqueous solution using Banana peels activated carbon (BPAC). Batch experiments have been carried out to determine the influence of parameters such as pH, biosorbent dose, initial metal ion concentrations and contact times on the biosorption process. From these investigations, a significant increase in percentage removal of manganese 97.4 % is observed at pH value 5.0, biosorbent dose 0.8 g, initial concentration 20 ppm, temperature 25 ± 2 °C, stirring rate 200 rpm and contact time 2 h. The equilibrium concentration and the adsorption capacity at equilibrium of the experimental results were fitted to the Langmuir and Freundlich isotherm models; the Langmuir isotherm was found to well represent the measured adsorption data implying BPAC had heterogeneous surface. A raw groundwater samples were collected from Baharmos groundwater treatment plant network at Embaba and Manshiet Elkanater City/District-Giza, Egypt, for treatment at the best conditions that reached at first phase by BPAC. The treatment with BPAC could reduce iron and manganese value of raw groundwater by 91.4 % and 97.1 %, respectively and the effect of the treatment process on the microbiological properties of groundwater sample showed decrease of total bacterial count either at 22°C or at 37°C to 85.7 % and 82.4 %, respectively. Also, BPAC was characterized using SEM and FTIR spectroscopy.

Keywords: biosorption, banana peels, isothermal models, manganese

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Authors: Radhey Sham Rajoria

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Pressure to increase the competitiveness in the manufacturing sector and for the survival in the market has led to the development of machining centres, which enhance productivity, improve quality, shorten the lead time, and reduce the manufacturing cost. With the innovation of machining centres in the manufacturing sector the production lines have been replaced by these machining centers, having the ability to machine various processes and multiple tooling with automatic tool changer (ATC) for the same part. Also the process plans can be easily generated for complex components. Some means are required to utilize the machining center at its best. The present work is concentrated on the automated part program generation, and in turn automated process plan generation for the turned components on Denford “MIRAC” 8 stations ATC lathe machining centre. A package in C++ on DOS platform is developed which generates the complete CNC part program, process plan and process sequence for the turned components. The input to this system is in the form of a blueprint in graphical format with machining parameters and variables, and the output is the CNC part program which is stored in a .mir file, ready for execution on the machining centre.

Keywords: CNC, MIRAC, ATC, process planning

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Authors: Aidan J. Bowes, Reaz Hasan

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The automotive industry is continually aiming to develop the aerodynamics of car body design. This may be for a variety of beneficial reasons such as to increase speed or fuel efficiency by reducing drag. However recently there has been a greater amount of focus on wind noise produced while driving. Designers in this industry seek a combination of both simplicity of approach and overall effectiveness. This combined with the growing availability of commercial CFD (Computational Fluid Dynamics) packages is likely to lead to an increase in the use of RANS (Reynolds Averaged Navier-Stokes) based CFD methods. This is due to these methods often being simpler than other CFD methods, having a lower demand on time and computing power. In this investigation the effectiveness of turbulent flow and acoustic noise prediction using RANS based methods has been assessed for different wing mirror geometries. Three different RANS based models were used, standard k-ε, realizable k-ε and k-ω SST. The merits and limitations of these methods are then discussed, by comparing with both experimental and numerical results found in literature. In general, flow prediction is fairly comparable to more complex LES (Large Eddy Simulation) based methods; in particular for the k-ω SST model. However acoustic noise prediction still leaves opportunities for more improvement using RANS based methods.

Keywords: acoustics, aerodynamics, RANS models, turbulent flow

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Authors: Utit Sungkharat

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Purpose: To investigate an invented tradition of Thai Buddhists and Thai Muslims for peace. Methods: The data for this qualitative research were collected from related documents and research reports, field data, and in-depth interviews with Buddhist and Muslim religious leaders and people in the community. Results: The results of the research revealed that Thai Buddhists and Thai Muslims in Tamod Community in the Southern part of Thailand who have lived in the same community and shared the same history of the community jointly invented the Songkran tradition holding on to the reason that they have lived in the same community founded by the same person. The reason for inventing this tradition is that Songkran is a tradition for paying respect to ancestors who passed away and people in Tamod have the same ancestor even though they believe in different religions. Therefore, paying respect to the ancestors can be performed together by people of the two religions. The invented tradition has not only united them and empowered them to drive their community to development but also brought peace and happiness to this community.

Keywords: invented tradition, Thai Buddhists, Thai Muslims, peace

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Authors: Pinar Ozdemir Ayber, Zeina Hojeij

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Blended Learning (BL) is a rapidly growing teaching and learning approach, which brings together the best of both face-to-face and online learning to expand learning opportunities for students. However, there is limited research on the practices, opportunities and quality of instruction in Blended Classrooms, and on the role of the teaching faculty as well as the learners in these types of classes. This paper will highlight the researchers’ experiences and reflections on blending their classes. It will focus on the importance of designing effective lesson plans that emphasize learner engagement and motivation in alignment with course learning outcomes. In addition, it will identify the changing roles of the teacher and the learners and suggest appropriate variations to the traditional classroom setting taking into consideration the benefits and the challenges of the Blended Classroom. It is hoped that this paper would provide sufficient input for participants to reflect on ways they can blend their own lessons to promote ubiquitous learning and student autonomy. Practical tips and ideas will be shared with the participants on various strategies and technologies that were used in the researchers’ classes.

Keywords: blended learning, learner autonomy, learner engagement, learner motivation, mobile learning tools

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Authors: Anna Maria Re

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Taking advantage of the ecolinguistic theoretical and practical analysis, the work intends the prophetic, punctual, and at times disturbing language used by David Quammen in Spillover, questioning it from an ecological perspective and contributing to the search for new stories. In the famous volume, the author illustrates a literary history of the great epidemics and pandemics, demonstrating that viruses are nature's inevitable response to man's assault on ecosystems. In doing so, he introduces new words, which have tamed our anxieties in recent years since writing as a human artistic expression can mirror the human conscience. Writing in the Anthropocene, coining a new reference lexicon with respect to what is happening, means offering a form to the idea of survival of the planet, imagining the human being grappling with an environment whose conformation he himself has helped to change with a language that is no longer effective in describing the world as we have known it and that quickly needs a radical overhaul. Following the methodology proposed in Ecolinguistics: language, ecology and the stories we live by, the analysis in the paper will enhance the language that encodes new stories based on: ideologies, framings, metaphors, evaluations, identities, convictions, and salience.

Keywords: Anthropocene, pandemic, spillover, virus, zoonosis

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Authors: Smitha Haridasan, Zelalem Demissie, Atri Dutta, Ajita Rattani

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Aided by the wind, all it takes is one ember and a few minutes to create a wildfire. Wildfires are growing in frequency and size due to climate change. Wildfires and its consequences are one of the major environmental concerns. Every year, millions of hectares of forests are destroyed over the world, causing mass destruction and human casualties. Thus early detection of wildfire becomes a critical component to mitigate this threat. Many computer vision-based techniques have been proposed for the early detection of forest fire using video surveillance. Several computer vision-based methods have been proposed to predict and detect forest fires at various spectrums, namely, RGB, HSV, and YCbCr. The aim of this paper is to propose a multi-spectral deep learning model that combines information from different spectrums at intermediate layers for accurate fire detection. A heterogeneous dataset assembled from publicly available datasets is used for model training and evaluation in this study. The experimental results show that multi-spectral deep learning models could obtain an improvement of about 4.68 % over those based on a single spectrum for fire detection.

Keywords: deep learning, forest fire detection, multi-spectral learning, natural hazard detection

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Authors: John N. Idenyi, Hadimundeen Abdallah, Abigeal D. Adeyemi, Jonathan C. Eya

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Gut microbiomes play a significant role in the growth, metabolism, and health of fish. However, we know very little about the interactive effects of variations in dietary composition and temperature on rainbow trout gut microbiota. Exactly 288 rainbow trout weighing 45.6g ± 0.05 (average ± SD) were fed four isocaloric, isolipidic, and isonitrogenous diets comprising 40% crude protein and 20% crude lipid and formulated as 100 % animal-based protein (AP) and a blend of 50 fish oil (FO)/50 camelina oil (CO), 100 % AP and100 % CO, 100 % plant-based protein (PP) and a blend of 50FO/50CO or 100 % PP and 100 % CO in 14 or 18°C for 150 days. Gut content was analyzed using 16S rRNA gene and shotgun sequencing. The most abundant phyla identified regardless of diet were Tenericutes, Firmicutes, Proteobacteria, Spirochaetes, Bacteroidetes, and Actinobacteria, while Aeromonadaceae and Enterobacteriaceae were dominant families in 18°C. Moreover, gut microbes were dominated by genes relating to an amino acid, carbohydrate, fat, and energy metabolisms and influenced by temperature. The shared functional profiles for all the diets suggest that plant protein sources in combination with CO could be as good as the fish meal with 50/50 FO & CO in rainbow trout farming.

Keywords: aquafeed, aquaculture, microbiome, rainbow trout

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Authors: Ling Qi, Matloob Khushi, Josiah Poon

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This paper introduces a pre-trained Masked Candlestick Model (MCM) for trading time-series data. The pre-trained model is based on three core designs. First, we convert trading price data at each data point as a set of normalized elements and produce embeddings of each element. Second, we generate a masked sequence of such embedded elements as inputs for self-supervised learning. Third, we use the encoder mechanism from the transformer to train the inputs. The masked model learns the contextual relations among the sequence of embedded elements, which can aid downstream classification tasks. To evaluate the performance of the pre-trained model, we fine-tune MCM for three different downstream classification tasks to predict future price trends. The fine-tuned models achieved better accuracy rates for all three tasks than the baseline models. To better analyze the effectiveness of MCM, we test the same architecture for three currency pairs, namely EUR/GBP, AUD/USD, and EUR/JPY. The experimentation results demonstrate MCM’s effectiveness on all three currency pairs and indicate the MCM’s capability for signal extraction from trading data.

Keywords: masked language model, transformer, time series prediction, trading prediction, embedding, transfer learning, self-supervised learning

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Authors: Mohammed Hasan Salahat, Stave Wade

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This paper presents an application of a “Systematic Soft Domain Driven Design Framework” as a soft systems approach to domain-driven design of information systems development. The framework combining techniques from Soft Systems Methodology (SSM), the Unified Modeling Language (UML), and an implementation pattern knows as ‘Naked Objects’. This framework have been used in action research projects that have involved the investigation and modeling of business processes using object-oriented domain models and the implementation of software systems based on those domain models. Within this framework, Soft Systems Methodology (SSM) is used as a guiding methodology to explore the problem situation and to develop the domain model using UML for the given business domain. The framework is proposed and evaluated in our previous works, and a real case study ‘Information Retrieval System for Academic Research’ is used, in this paper, to show further practice and evaluation of the framework in different business domain. We argue that there are advantages from combining and using techniques from different methodologies in this way for business domain modeling. The framework is overviewed and justified as multi-methodology using Mingers Multi-Methodology ideas.

Keywords: SSM, UML, domain-driven design, soft domain-driven design, naked objects, soft language, information retrieval, multimethodology

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Authors: Laura Micheli, Majd Hijazi, Mahmoud Faytarouni

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

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

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Authors: Obulesh Avuku, Satwik Sunnam, Sri Charan Mohan Janthuka, Keerthi Yalamaddi

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In the United States alone, there are over 1.5 million spine fractures per year, resulting in about 17,730 spinal cord injuries. The cervical spine is where fractures in the spine most frequently occur. The prevalence of spinal fractures in the elderly has increased, and in this population, fractures may be harder to see on imaging because of coexisting degenerative illness and osteoporosis. Nowadays, computed tomography (CT) is almost completely used instead of radiography for the imaging diagnosis of adult spine fractures (x-rays). To stop neurologic degeneration and paralysis following trauma, it is vital to trace any vertebral fractures at the earliest. Many approaches have been proposed for the classification of the cervical spine [2d models]. We are here in this paper trying to break the bounds and use the vision transformers, a State-Of-The-Art- Model in image classification, by making minimal changes possible to the architecture of ViT and making it 3D-enabled architecture and this is evaluated using a weighted multi-label logarithmic loss. We have taken this problem statement from a previously held Kaggle competition, i.e., RSNA 2022 Cervical Spine Fracture Detection.

Keywords: cervical spine, spinal fractures, osteoporosis, computed tomography, 2d-models, ViT, multi-label logarithmic loss, Kaggle, public score, private score

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Authors: Yiyuan Tao

Abstract:

Under the impact of global warming together with the intensification of human activities, the hydrological regimes may be altered, and the traditional stationary assumption was no longer satisfied. However, most of the current design standards of water infrastructures were still based on the hypothesis of stationarity, which may inevitably result in severe biases. Many critical impacts of climate on ecosystems, society, and the economy are controlled by extreme events rather than mean values. Therefore, it is of great significance to identify the non-stationarity of precipitation extremes and model the precipitation extremes in a nonstationary framework. The Wei River Basin (WRB), located in a continental monsoon climate zone in China, is selected as a case study in this study. Six extreme precipitation indices were employed to investigate the changing patterns and stationarity of precipitation extremes in the WRB. To identify if precipitation extremes are stationary, the Mann-Kendall trend test and the Pettitt test, which is used to examine the occurrence of abrupt changes are adopted in this study. Extreme precipitation indices series are fitted with non-stationary distributions that selected from six widely used distribution functions: Gumbel, lognormal, Weibull, gamma, generalized gamma and exponential distributions by means of the time-varying moments model generalized additive models for location, scale and shape (GAMLSS), where the distribution parameters are defined as a function of time. The results indicate that: (1) the trends were not significant for the whole WRB, but significant positive/negative trends were still observed in some stations, abrupt changes for consecutive wet days (CWD) mainly occurred in 1985, and the assumption of stationarity is invalid for some stations; (2) for these nonstationary extreme precipitation indices series with significant positive/negative trends, the GAMLSS models are able to capture well the temporal variations of the indices, and perform better than the stationary model. Finally, the differences between the quantiles of nonstationary and stationary models are analyzed, which highlight the importance of nonstationary modeling of precipitation extremes in the WRB.

Keywords: extreme precipitation, GAMLSSS, non-stationary, Wei River Basin

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Authors: Mequanent Wale Mekonen, Edoardo Otranto, Angela Alibrandi

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Diabetic kidney disease is one of the main microvascular complications caused by diabetes. Several clinical and biochemical variables are reported to be associated with diabetic kidney disease in people with type 2 diabetes. However, their interrelations could distort the effect estimation of these variables for the disease's progression. The objective of the study is to determine how the biochemical and clinical variables in people with type 2 diabetes are interrelated with each other and their effects on kidney disease progression through advanced statistical methods. First, principal component analysis was used to explore how the biochemical and clinical variables intercorrelate with each other, which helped us reduce a set of correlated biochemical variables to a smaller number of uncorrelated variables. Then, ordered logit regression models (cumulative, stage, and adjacent) were employed to assess the effect of biochemical and clinical variables on the order-level response variable (progression of kidney function) by considering the proportionality assumption for more robust effect estimation. This retrospective cross-sectional study retrieved data from a type 2 diabetic cohort in a polyclinic hospital at the University of Messina, Italy. The principal component analysis yielded three uncorrelated components. These are principal component 1, with negative loading of glycosylated haemoglobin, glycemia, and creatinine; principal component 2, with negative loading of total cholesterol and low-density lipoprotein; and principal component 3, with negative loading of high-density lipoprotein and a positive load of triglycerides. The ordered logit models (cumulative, stage, and adjacent) showed that the first component (glycosylated haemoglobin, glycemia, and creatinine) had a significant effect on the progression of kidney disease. For instance, the cumulative odds model indicated that the first principal component (linear combination of glycosylated haemoglobin, glycemia, and creatinine) had a strong and significant effect on the progression of kidney disease, with an effect or odds ratio of 0.423 (P value = 0.000). However, this effect was inconsistent across levels of kidney disease because the first principal component did not meet the proportionality assumption. To address the proportionality problem and provide robust effect estimates, alternative ordered logit models, such as the partial cumulative odds model, the partial adjacent category model, and the partial continuation ratio model, were used. These models suggested that clinical variables such as age, sex, body mass index, medication (metformin), and biochemical variables such as glycosylated haemoglobin, glycemia, and creatinine have a significant effect on the progression of kidney disease.

Keywords: diabetic kidney disease, ordered logit model, principal component analysis, type 2 diabetes

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