Search results for: reduced order models

Authors: Kathy Stein, Mariola Lysson, Anja Schmidt, Beatrix Schumak, Sabine Specht, Hicham Bouabe, Jürgen Heesemann, Axel Roers, Joerg C. Kalff, Sven Wehner

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Objective: Postoperative ileus (POI) is a common complication of abdominal surgery. Monocyte infiltration is a hallmark of POI. The polarization of macrophages/monocytes in this process is not well understood. We aimed to investigate if and how M2 macrophage/monocyte differentiation is involved in POI pathogenesis. Design: POI was induced by intestinal manipulation (IM). C57Bl/6, CCR2-/-, IL-10 reporter (ITIB), IL-10-/- and LysMcre/IL-10fl/fl mice underwent IM. At various points in time leukocyte influx, gene and protein expression of cytokines, chemokines and M2 differentiation markers and intestinal motility were analyzed. Results: IM induced the postoperative expression of the M2 markers Arginase-1 and YM-1, predominantly in F4/80+Ly6C+ monocytes. Gene expression analyses indicated an IL-10-dependent, IL-4-independent M2 polarization of these monocytes. IL-10 dependency of M2 differentiation was confirmed in IL-10 deficient mice. Leukocytes, in the order of infiltrating monocytes, neutrophils, and resident macrophages were the main IL-10 producers during POI. IL-10 producing monocytes as well as M2 marker expression were almost absent in CCR2-deficient mice. However, postoperative IL-10 expression was not altered in CCR2-/- mice. The loss of M2 polarized monocytes neither protected CCR2-/- mice from nor affected resolution of POI. In contrast, IL-10 deficiency reduced postoperative neutrophil numbers and ameliorated POI. IL-10Ra expression was strongly induced in neutrophils but not in monocytes. Conclusion: We conclude that IL-10 counteracts POI resolution by activating IL-10Ra-expressing neutrophils in the late phase of disease while IL-10-dependent M2 differentiation is not pivotal to POI manifestation and resolution.

Keywords: interleukin-10, macrophages, neutrophils, postoperative ileus

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Authors: Amina Ottozbeer, Nazanin Derakhshan

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Emotion regulation, a transdiagnostic process, is often impaired in individuals with high trait anxiety due to compromised executive functioning and attentional control. Recent research underscores the importance of studying individual differences and contextual factors in understanding the adaptability of emotion regulation processes, particularly in those with high trait anxiety. Prior studies have emphasized the role of self-efficacy in promoting positive cognitive flexibility outcomes and mitigating executive function impairments in highly anxious individuals. Accordingly, the objective of this study was to examine the moderating influence of attentional control, cognitive flexibility, and self-efficacy on the relationship between trait anxiety and emotion regulation. Using a correlational design, an online study was conducted with a sample of 82 participants (mean age: 22 years). Self-report questionnaires measured individual difference variables. The Classic Stroop Task assessed attentional control as an objective measure of cognitive flexibility . The findings revealed three significant interactions. Firstly, high cognitive flexibility and self-efficacy were linked to reduced expressive suppression in individuals with low trait anxiety. Secondly, elevations in cognitive flexibility and self-efficacy were associated with increased suppression in those with high trait anxiety. Thirdly, high trait anxiety was associated with reduced attentional control. The results suggest that typically adaptive processes can yield different outcomes in highly anxious populations, highlighting the need to explore additional variables that could alter the impact of cognitive flexibility and self-efficacy on emotion regulation in individuals with high anxiety. These findings have significant clinical implications, emphasizing the need to consider individual differences in emotion regulation and trait anxiety to inform more effective psychological treatments.

Keywords: attentional control, trait anxiety, emotional dysregulation, transdiagnostic, individual differences

Procedia PDF Downloads 37

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: Alvaro Javier Ortega

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A large number of companies face the challenge of matching highly-skilled professionals to high-end positions by human resource deployment professionals. However, when the professional list and tasks to be matched are larger than a few dozens, this process result is far from optimal and takes a long time to be made. Therefore, an automated assignment algorithm for this workforce management problem is needed. The majority of companies are divided into several sectors or departments, where trained employees with different experience levels deal with a large number of tasks daily. Also, the execution order of all tasks is of mater consequence, due to some of these tasks just can be run it if the result of another task is provided. Thus, a wrong execution order leads to large waiting times between consecutive tasks. The desired goal is, therefore, creating accurate matches and a near-optimal execution order that maximizes the number of tasks performed and minimizes the idle time of the expensive skilled employees. The problem described before can be model as a mixed-integer non-linear programming (MINLP) as it will be shown in detail through this paper. A large number of MINLP algorithms have been proposed in the literature. Here, genetic algorithm solutions are considered and a comparison between two different mutation approaches is presented. The simulated results considering different complexity levels of assignment decisions show the appropriateness of the proposed model.

Keywords: employees, genetic algorithm, industry management, workforce

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Authors: Muhammad Umair Arshad

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Background and Objectives: Different vegetables have been reported to possess diabetic potential in in-vitro studies; however, the same role of these vegetables has not been much explored through human intervention. Therefore, the present study was conducted to examine the comparative effects of muffins supplemented with bitter gourd (BGM), and other vegetables like spinach (SPM) and eggplant (EPM) on subjective appetite, blood glucose (BG), gut hormones and food intake in healthy young males through a randomized, cross over experiment. Methods and Study Design: After 12 hours fasting, twenty-four healthy young males (18-30 Y) were fed 250ml of plain muffins (control) or supplemented with bitter gourd powder, BGM (10g/100g flour), or spinach powder, SPM (10g/100g flour), or eggplant powder, EPM (10g/100g flour). An ad libitum pizza meal was served at 120min to measure the food intake. Subjective appetite, blood glucose, and gut hormones (insulin, GLP-1, active ghrelin) were measured at intervals from baseline to 120min. Results: Post-treatment (0-120min) glucose, but not insulin, decreased following all the vegetables supplemented muffins compared to the control (p < 0.0001) with a more pronounced effect of BGM. However, post-treatment avg. subjective appetite (p=0.0017) and food intake (p=0.0021) were reduced following BGM but not SPM and EPM. BGM further improved GLP-1 concentration (p < 0.0001), and reduced active ghrelin (p=0.0022), compared with control. Conclusions: The bitter gourd supplemented baked foods possess potential more than other vegetables to regulate postprandial appetite and glycemic responses, without a disproportionate increase in insulin concentration.

Keywords: vegetables, muffins, glucose homeostasis, subjective appetite, food intake

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Authors: Y. N. Reddy

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The class of differential-difference equations which have characteristics of both classes, i.e., delay/advance and singularly perturbed behaviour is known as singularly perturbed differential-difference equations. The expression ‘positive shift’ and ‘negative shift’ are also used for ‘advance’ and ‘delay’ respectively. In general, an ordinary differential equation in which the highest order derivative is multiplied by a small positive parameter and containing at least one delay/advance is known as singularly perturbed differential-difference equation. Singularly perturbed differential-difference equations arise in the modelling of various practical phenomena in bioscience, engineering, control theory, specifically in variational problems, in describing the human pupil-light reflex, in a variety of models for physiological processes or diseases and first exit time problems in the modelling of the determination of expected time for the generation of action potential in nerve cells by random synaptic inputs in dendrites. In this paper, we envisage the use of Liouville Green Transformation to find the solution of singularly perturbed differential difference equations. First, using Taylor series, the given singularly perturbed differential difference equation is approximated by an asymptotically equivalent singularly perturbation problem. Then the Liouville Green Transformation is applied to get the solution. Several model examples are solved, and the results are compared with other methods. It is observed that the present method gives better approximate solutions.

Keywords: difference equations, differential equations, singular perturbations, boundary layer

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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: M. S. Hasni, J. Guan, K. Yakimchuk, M. Berglund, B. Sander, G. Enblad, R. M. Amini, S. Okret

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Lymphomas are generally not considered as endocrine-related cancers. However, most lymphoid malignancies show gender differences in incidence and show prognosis with males being more affected. Furthermore, some epidemiological data indicate a protective role of estrogens against Non-Hodgkin lymphomas. Recent studies have demonstrated estrogen receptor β (ERβ) to be the major ER expressed in normal and malignant cells of lymphoid origin. We have analyzed the effects of estradiol and selective ERα and ERβ agonists on lymphoma growth in culture and in vivo. Treating lymphoma cells with estradiol or ERα selective agonist had minor or no effect on cell growth while selective ERβ agonist treatment showed an antiproliferative effect. When grafting mice with murine T lymphoma cells, male mice developed larger tumors compared to female mice, a difference that was abolished following ovariectomy, demonstrating estrogen-dependent growth in vivo. When subcutaneously grafting lymphoma cells to mice, so far growth of all tested human B lymphoma tumors (Raji and Ramos Burkitt lymphoma, SU.DHL4 (GC) and U2932 (ABC) DLBCL, Granta-519, Maver1 and Z138 MCL cells), were reduced following treatment with ERβ selective agonist (ref. 2 and unpublished). Moreover, the number and size of liver foci of disseminating Raji cells was reduced. We have identified target genes and mechanism that could explain the above effects of ERβ agonists. This included effects on angio and lymphangiogenesis. Now we have further analyzed effects of ERβ agonists on Ibrutinib-sensitive and -insensitive MCL cells in xenograft experiments as well as ERβ expression in primary lymphoma material (DLBCL). Preliminary statistical analysis has been done correlating ERβ expression to other biomarkers and clinical data.

Keywords: lymphomas, estrogen receptors, cancer, liver foci

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Authors: Fentahun Ayu Muche

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This review investigates the application of digital twin technology in 3D-printed concrete (3DPC) structures, with a primary focus on the early-age behavior of concrete. The integration of digital twins into 3DPC construction holds significant promise for monitoring, predicting, and optimizing structural performance during critical early stages, such as curing and setting. Digital twin technology integrates a range of advanced systems, including 3D modeling, machine learning, physical modeling, simulation technologies, communication technologies, and IOT. 3DPC, an additive manufacturing (AM) technology, constructs concrete structures layer by layer without deformation. The process begins with the mixer tank, pump, and nozzle system. Compared to traditional concrete construction methods, 3DPC offers numerous advantages, such as enhanced design flexibility, material efficiency, faster construction speed, reduced costs, higher quality, minimal material wastage, and elimination of formwork, reduced errors, and decreased labor requirements. Concrete, the most widely used construction material globally, is composed of materials such as cement, aggregates, admixtures, additives, supplementary cementitious materials (SCMs), fibers, and geopolymer components. At early ages, properties such as fresh state behavior, mechanical characteristics, hydration processes, microstructure, and chemical properties are particularly critical. This paper synthesizes current research, identifies key challenges, and highlights future directions in the evolving field of digital twins in 3DPC, emphasizing their transformative potential for advancing construction technologies.

Keywords: digital twin, 3DCP, concrete, early age properties, material composition

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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: Rana S. Alghamdi

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Play in early childhood education has been stifled across the world due to an overemphasis on academic achievement and a reduced focus on physical play and motor development. In Saudi Arabia, teachers reticent to allocate more time to play for fear of retribution from parents and administrators that children are losing academic seat time. This practice has proven to be detrimental to the social, emotional, physical, and cognitive development of children. Teachers are pressured to prioritize Arabic, math, and science while providing minimal time for physical activities. Administrators tend to push for an ever-increasing emphasis on academia in order to achieve higher test scores. However, young children often find it difficult to concentrate if they are not able to get out energy through physical play. Furthermore, many youth educators are not qualified to oversee physical activities, and many facilities are unprepared for safe, outdoor play. This results in children getting little to no outdoor activity. They are stuck in a strict academic regimen that may dampen the creativity and imagination easily fostered through cooperative play. For a stronger educational system and more well-rounded students, Saudi schools should enact policies that extend the number of required hours dedicated to outdoor and physical play. They should also offer training for unqualified teachers. This training should focus on the benefits of physical play and instruct them on how to facilitate these activities safely and effectively. School administrators must focus on providing adequate equipment and safe environments for the purpose of outdoor play and education. In doing so, they will be setting their students up for a successful future and improving their abilities in all aspects of education.

Keywords: early childhood education, play, outdoor, Saudi Arabia

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Authors: Ehsan Mohseni

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The aim of this thesis is to provide numerical analyses of reinforced concrete beams-column joints with/without CFRP (Carbon Fiber Reinforced Polymer) in order to achieve a better understanding of the behaviour of strengthened beamcolumn joints. A comprehensive literature survey prior to this study revealed that published studies are limited to a handful only; the results are inconclusive and some are even contradictory. Therefore in order to improve on this situation, following that review, a numerical study was designed and performed as presented in this thesis. For the numerical study, dimensions, end supports, and characteristics of the beam and column models were the same as those chosen in an experimental investigation performed previously where ten beamcolumn joint were tested tofailure. Finite element analysis is a useful tool in cases where analytical methods are not capable of solving the problem due to the complexities associated with the problem. The cyclic behaviour of FRP strengthened reinforced concrete beam-columns joints is such a case. Interaction of steel (longitudinal and stirrups), concrete and FRP, yielding of steel bars and stirrups, cracking of concrete, the redistribution of stresses as some elements unload due to crushing or yielding and the confinement of concrete due to the presence of FRP are some of the issues that introduce the complexities into the problem.Numerical solutions, however, can provide further in formation about the behaviour in lieu of the costly experiments or complex closed form solutions. This thesis presents the results of a numerical study on beam-column joints subjected to cyclic loads that are strengthened with CFRP wraps or strrips in a variety of configurations. The analyses are performed by Abaqus finite element program and are calibrated with the experiments. A range of issues in beam-column joints including the cracking load, the ultimate load, lateral load-displacement curves of joints, are investigated.The numerical results for different configurations of strengthening are compared. Finally, the computed numerical results are compared with those obtained from experiments. the cracking load, the ultimate load, lateral load-displacement curves obtained from numerical analysis for all joints were in very good agreement with the corresponding experimental ones.The results obtained from the numerical analysis in most cases implies that this method is conservative and therefore can be used in design applications with confidence.

Keywords: numerical analysis, strengthening, CFRP, reinforced concrete joints

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Authors: Jean Yves Uwamungu, Fiston Bizimana, Chunsheng Hu

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Although pesticides are used in crop productivity, their use is highly harming the soil environment, and measures must be taken in the future to eradicate soil and groundwater pollution. The primary aim was to determine the effect of biochar addition on the imidacloprid adsorption on soil prior to no-tillage (NT) and rotational tillage (RT) conditions. In the laboratory, batch tests were conducted to determine the imidacloprid adsorption on soil using equilibrium and kinetic modelling with the addition of biochar. The clay level of the soil was found to be more significant when no-tillage was applied (22.42) than when rotational tillage was applied (14.27). The imidacloprid adsorption equilibrium was significantly shortened to 25 min after biochar addition. The isotherms and kinetic findings confirmed that the adsorption occurred according to Freundlich and pseudo-second-order kinetic models, respectively. The adsorption capacity of imidacloprid (40<35<25 °C) increased with decreasing temperature, indicating an exothermic adsorption behaviour, whereas negative Gibbs free energy (G) values of -6980.5 and 5983.93 Jmol-1, respectively, for soil prior to NT and RT at 25 °C, asserted spontaneous adsorption. The negative values of entropy (ΔS); -22.83 and -38.15 Jmol-1K-1, prior to NT and RT applications, respectively, described a lowered randomness process. The enthalpy was greater when RT was applied (-17533 J mol-1) than when NT was applied (-450 J mol-1). Lastly, it was shown that NTtreatment enhanced imidacloprid adsorption capacity more than RT treatment and that biochar addition enhanced pesticide adsorption in both treatments.

Keywords: adsorption, biochar, imidacloprid, soil, tillage

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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: Wei Kunkun, Cao Wanyang, Xu Yujie, Qiao Yuzhi, Liu Yingning

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The reasonable design of bus parking spaces can improve the traffic capacity of the station and reduce traffic congestion. In order to reasonably determine the number of berths at BRT (Bus Rapid Transit) stops, it is based on the actual bus rapid transit station observation data, scheduling data, and passenger flow data. Optimize the number of station berths from the perspective of optimizing the balance of supply and demand at the site. Combined with the classical capacity calculation model, this paper first analyzes the important factors affecting the traffic capacity of BRT stops by using SPSS PRO and MATLAB programming software, namely the distribution of BRT stops and the distribution of BRT stop time. Secondly, the method of calculating the number of the classic human capital management (HCM) model is optimized based on the actual passenger demand of the station, and the method applicable to the actual number of station berths is proposed. Taking Gangding Station of Zhongshan Avenue Bus Rapid Transit Corridor in Guangzhou as an example, based on the calculation method proposed in this paper, the number of berths of sub-station 1, sub-station 2 and sub-station 3 is 2, which reduces the road space of the station by 33.3% compared with the previous berth 3 of each sub-station, and returns to social vehicles. Therefore, under the condition of ensuring the passenger flow demand of BRT stations, the road space of the station is reduced, and the road is returned to social vehicles, the traffic capacity of social vehicles is improved, and the traffic capacity and efficiency of the BRT corridor system are improved as a whole.

Keywords: urban transportation, bus rapid transit station, HCM model, capacity, number of berths

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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: Mehrnoosh Askarizadeh

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Agile manufacturing concept was first coined by Iacocca institute in 1991 as a new manufacturing paradigm in order to provide and ensure competitiveness in the emerging global manufacturing order. Afterward, a considerable number of studies have been conducted in this area. Reviewing these studies reveals that they mostly focus on agile manufacturing drivers, definition and characteristics but few of them propose practical solutions to achieve it. Agile manufacturing is recommended as a successful paradigm after lean for the 21st manufacturing firms. This competitive concept has been developed in response to the continuously changes and uncertainties in today’s business environment. In order to become an agile competitor, a manufacturing firm should focus on enriching its agility capabilities. These agility capabilities can be categorized into seven groups: proactiveness, customer focus, responsiveness, quickness, flexibility, basic competence and partnership. A manufacturing firm which is aiming at achieving agility should first develop its own appropriate agility strategy. This strategy prioritizes required agility capabilities.

Keywords: agile manufacturing, product architecture, customer focus, responsiveness, quickness, flexibility, basic competence

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Authors: Seung Chul Lee, Jooyeong Kim, Ki Ok Ahn, Juok Park

Abstract:

Background: Injuries caused by motorcycle crashes are one of the major public health burdens leading to high mortality, functional disability. The risk of death among motorcyclists is 30 times greater than that among car drivers, with head injuries the leading cause of death. The motorcycle helmet is crucial protective equipment for motorcyclists. Aims: This study aimed to measure the protective effect of motorcycle helmet use on intracranial injury and mortality and to compare the preventive effect in drivers and passengers. Methods: This is a cross-sessional study based on the Emergency Department (ED)–based Injury In-depth Surveillance (EDIIS) database from 23 EDs in Korea. All of the trauma patients injured in motorcycle crashes between January 1, 2013 and December 31, 2016 were eligible, excluding cases with unknown helmet use and outcomes. The primary and secondary outcomes were intracranial injury and in-hospital mortality. We calculated adjusted odds ratios (AORs) of helmet use for study outcomes after adjusting for potential confounders. Using interaction models, we compared the protective effect of helmet use on outcomes across driving status (driver and passenger). Results: Among 17,791 eligible patients, 10,668 (60.0%) patients were wearing helmets at the time of the crash, 2,128 (12.0%) patients had intracranial injuries and 331 (1.9%) patients had in-hospital death. 16,381 (92.1%) patients were drivers and 1410 (7.9%) patients were passengers. 62.6% of drivers and 29.1% of passengers were wearing helmets at the time of the crash. Compared to un-helmeted group, the helmeted group was less likely to have an intracranial injury(8.0% vs. 17.9%, AOR: 0.43 (0.39-0.48)) and in-hospital mortality (1.0% vs. 3.2%, AOR: 0.29 (0.22-0.37)).In the interaction model, AORs (95% CIs) of helmet use for intracranial injury were 0.42 (0.38-0.47) in drivers and 0.61(0.41-0.90) in passengers, respectively. There was a significant preventive effect of helmet use on in-hospital mortality in drivers (AOR: 0.26(0.21–0.34)). Discussion and conclusions: Wearing helmets in motorcycle crashes reduced intracranial injuries and in-hospital mortality. The preventive effect of motorcycle helmet use on intracranial injury was stronger in drivers than in passengers. There was a significant preventive effect of helmet use on in-hospital mortality in driver but not in passengers. Public health efforts to increase motorcycle helmet use are needed to reduce health burden from injuries caused by motorcycle crashes.

Keywords: intracranial injury, helmet, mortality, motorcycle crashes

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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: Pompa Marcello, Mauro Capocelli, Vincenzo Piemonte

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This work focuses on a mathematical model able to describe the gastro-intestinal physiology and providing a rational tool for the design of an artificial gastro-intestinal system. This latter is mainly devoted to analyse the absorption and bioavailability of drugs and nutrients through in vitro tests in order to overcome (or, at least, to partially replace) in vivo trials. The provided model realizes a conjunction ring (with extended prediction capability) between in vivo tests and mechanical-laboratory models emulating the human body. On this basis, no empirical equations controlling the gastric emptying are implemented in this model as frequent in the cited literature and all the sub-unit and the related system of equations are physiologically based. More in detail, the model structure consists of six compartments (stomach, duodenum, jejunum, ileum, colon and blood) interconnected through pipes and valves. Paracetamol, Ketoprofen, Irbesartan and Ketoconazole are considered and analysed in this work as reference drugs. The mathematical model has been validated against in vivo literature data. Results obtained show a very good model reliability and highlight the possibility to realize tailored simulations for different couples patient-drug, including food adsorption dynamics.

Keywords: gastro-intestinal model, drugs bioavailability, paracetamol, ketoprofen

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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: 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: Chun Hsien Wu, Guan Xuan Wu, Hsia Ying Cheng, Shyh Ming Kuo

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Articular cartilage is composed of chondrocytes and extracellular matrix, such as collagen fibers, glycosaminoglycans, etc., which play an important role in lubricating and cushion joint activities. The phenotypic expression and metabolic activity of chondrocytes are extremely important in maintaining the functions of articular cartilage. In in vitro passaged culture of chondrocytes, chondrocytes gradually lose their original cell phenotype and morphology, which is called dedifferentiation. After continuous passaged culture of chondrocytes or induction by inflammatory factor IL-1, chondrocytes changed their phenotype and morphology. Also, the extracellular matrix type II collagen and GAG secretion were significantly reduced, while type I and X collagen were synthesized. Farnesol is an anti-inflammatory and antioxidant sesquiterpene compound that has the specific property of promoting collagen production. The purpose of this study was to investigate whether farnesol could restore the original type II collagen synthesis and, furthermore, the mechanisms of farnesol on the synthesis of type II collagen from the de-differentiated chondrocytes. The obtained results showed that the de-differentiated chondrocytes significantly restored to secret type II collagen and GAG (2.5-folds increases), and the secretion of collagen I and X and PGE2 synthesis were also significantly reduced after being treated with farnesol, indicating that farnesol had a restoration/re-differentiation effect on de-differentiated chondrocytes. The de-differentiated chondrocytes exhibited decreased expression of PPAR-γ and upregulated TGF-β expression to increase the MMP-13 expression. Higher expression of MMP-13 caused chondrocytes to secret type X collagen. On the contrary, increasing the expression of PPAR-γ would benefit the production of type II collagen. As shown, the PPAR-γ expression increased, and MMP-13 expression decreased after being treated with farnesol, indicating a possible signal pathway of farnesol to restore the production of type II collagen. However, more detailed mechanisms still need to evaluate.

Keywords: chondrocytes, de-differentiation, farnesol, re-differentiation

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Authors: Ling Qi, Matloob Khushi, Josiah Poon

Abstract:

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

Procedia PDF Downloads 131