Search results for: prediction models

Authors: B. Godefroy, D. Beladjine, K. Beddiar

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In France, construction BIM actors commonly evoke the BIM gains for exploitation by integrating of the life cycle of a building. The standardization of level 7 of development would achieve this stage of the digital model. The householders include local public authorities, social landlords, public institutions (health and education), enterprises, facilities management companies. They have a dual role: owner and manager of their housing complex. In a context of financial constraint, the BIM of exploitation aims to control costs, make long-term investment choices, renew the portfolio and enable environmental standards to be met. It assumes a knowledge of the existing buildings, marked by its size and complexity. The information sought must be synthetic and structured, it concerns, in general, a real estate complex. We conducted a study with professionals about their concerns and ways to use it to see how householders could benefit from this development. To obtain results, we had in mind the recurring interrogation of the project management, on the needs of the operators, we tested the following stages: 1) Inculcate a minimal culture of BIM with multidisciplinary teams of the operator then by business, 2) Learn by BIM tools, the adaptation of their trade in operations, 3) Understand the place and creation of a graphic and technical database management system, determine the components of its library so their needs, 4) Identify the cross-functional interventions of its managers by business (operations, technical, information system, purchasing and legal aspects), 5) Set an internal protocol and define the BIM impact in their digital strategy. In addition, continuity of management by the integration of construction models in the operation phase raises the question of interoperability in the control of the production of IFC files in the operator’s proprietary format and the export and import processes, a solution rivaled by the traditional method of vectorization of paper plans. Companies that digitize housing complex and those in FM produce a file IFC, directly, according to their needs without recourse to the model of construction, they produce models business for the exploitation. They standardize components, equipment that are useful for coding. We observed the consequences resulting from the use of the BIM in the property industry and, made the following observations: a) The value of data prevail over the graphics, 3D is little used b) The owner must, through his organization, promote the feedback of technical management information during the design phase c) The operator's reflection on outsourcing concerns the acquisition of its information system and these services, observing the risks and costs related to their internal or external developments. This study allows us to highlight: i) The need for an internal organization of operators prior to a response to the construction management ii) The evolution towards automated methods for creating models dedicated to the exploitation, a specialization would be required iii) A review of the communication of the project management, management continuity not articulating around his building model, it must take into account the environment of the operator and reflect on its scope of action.

Keywords: information system, interoperability, models for exploitation, property industry

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Authors: Kldiashvili Ekaterina, Burduli Archil, Ghortlishvili Gocha

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Introduction – Medical information system (MIS) is at the heart of information technology (IT) implementation policies in healthcare systems around the world. Different architecture and application models of MIS are developed. Despite of obvious advantages and benefits, application of MIS in everyday practice is slow. Objective - On the background of analysis of the existing models of MIS in Georgia has been created a multi-user web-based approach. This presentation will present the architecture of the system and its application for image based second opinion consultations. Methods – The MIS has been created with .Net technology and SQL database architecture. It realizes local (intranet) and remote (internet) access to the system and management of databases. The MIS is fully operational approach, which is successfully used for medical data registration and management as well as for creation, editing and maintenance of the electronic medical records (EMR). Five hundred Georgian language electronic medical records from the cervical screening activity illustrated by images were selected for second opinion consultations. Results – The primary goal of the MIS is patient management. However, the system can be successfully applied for image based second opinion consultations. Discussion – The ideal of healthcare in the information age must be to create a situation where healthcare professionals spend more time creating knowledge from medical information and less time managing medical information. The application of easily available and adaptable technology and improvement of the infrastructure conditions is the basis for eHealth applications. Conclusion - The MIS is perspective and actual technology solution. It can be successfully and effectively used for image based second opinion consultations.

Keywords: digital images, medical information system, second opinion consultations, electronic medical record

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Authors: Djarot B. Darmadi

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The wider growing Finite Element Method (FEM) application is caused by its benefits of cost saving and environment friendly. Also, by using FEM a deep understanding of certain phenomenon can be achieved. This paper observed the role of material properties and volumetric change when Solid State Phase Transformation (SSPT) takes place in residual stress formation due to a welding process of ferritic steels through coupled Thermo-Metallurgy-Mechanical (TMM) analysis. The correctness of FEM residual stress prediction was validated by experiment. From parametric study of the FEM model, it can be concluded that the material properties change tend to over-predicts residual stress in the weld center whilst volumetric change tend to underestimates it. The best final result is the compromise of both by incorporates them in the model which has a better result compared to a model without SSPT.

Keywords: residual stress, ferritic steels, SSPT, coupled-TMM

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Authors: Maryam Fallahpoor, Biswajeet Pradhan

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Background: Artificial intelligence (AI) serves as a valuable tool in mitigating the scarcity of human resources required for the evaluation and categorization of vast quantities of medical imaging data. When AI operates with optimal precision, it minimizes the demand for human interpretations and, thereby, reduces the burden on radiologists. Among various AI approaches, deep learning (DL) stands out as it obviates the need for feature extraction, a process that can impede classification, especially with intricate datasets. The advent of DL models has ushered in a new era in medical imaging, particularly in the context of COVID-19 detection. Traditional 2D imaging techniques exhibit limitations when applied to volumetric data, such as Computed Tomography (CT) scans. Medical images predominantly exist in one of two formats: neuroimaging informatics technology initiative (NIfTI) and digital imaging and communications in medicine (DICOM). Purpose: This study aims to employ DL for the classification of COVID-19-infected pulmonary patients and normal cases based on 3D CT scans while investigating the impact of image format. Material and Methods: The dataset used for model training and testing consisted of 1245 patients from IranMehr Hospital. All scans shared a matrix size of 512 × 512, although they exhibited varying slice numbers. Consequently, after loading the DICOM CT scans, image resampling and interpolation were performed to standardize the slice count. All images underwent cropping and resampling, resulting in uniform dimensions of 128 × 128 × 60. Resolution uniformity was achieved through resampling to 1 mm × 1 mm × 1 mm, and image intensities were confined to the range of (−1000, 400) Hounsfield units (HU). For classification purposes, positive pulmonary COVID-19 involvement was designated as 1, while normal images were assigned a value of 0. Subsequently, a U-net-based lung segmentation module was applied to obtain 3D segmented lung regions. The pre-processing stage included normalization, zero-centering, and shuffling. Four distinct 3D CNN models (ResNet152, ResNet50, DensNet169, and DensNet201) were employed in this study. Results: The findings revealed that the segmentation technique yielded superior results for DICOM images, which could be attributed to the potential loss of information during the conversion of original DICOM images to NIFTI format. Notably, ResNet152 and ResNet50 exhibited the highest accuracy at 90.0%, and the same models achieved the best F1 score at 87%. ResNet152 also secured the highest Area under the Curve (AUC) at 0.932. Regarding sensitivity and specificity, DensNet201 achieved the highest values at 93% and 96%, respectively. Conclusion: This study underscores the capacity of deep learning to classify COVID-19 pulmonary involvement using real 3D hospital data. The results underscore the significance of employing DICOM format 3D CT images alongside appropriate pre-processing techniques when training DL models for COVID-19 detection. This approach enhances the accuracy and reliability of diagnostic systems for COVID-19 detection.

Keywords: deep learning, COVID-19 detection, NIFTI format, DICOM format

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Authors: Gleda Kutrolli, Maksi Kutrolli, Etjon Meco

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SARS-CoV-2 virus is currently one of the most infectious pathogens for humans. It started in China at the end of 2019 and now it is spread in all over the world. The origin and diffusion of the SARS-CoV-2 epidemic, is analysed based on the discussion of viral phylogeny theory. With the aim of understanding the spread of infection in the affected countries, it is crucial to modelize the spread of the virus and simulate its activity. In this paper, the prediction of coronavirus outbreak is done by using SIR model without vital dynamics, applying different numerical technique solving ordinary differential equations (ODEs). We find out that ABM and MRT methods perform better than other techniques and that the activity of the virus will decrease in April but it never cease (for some time the activity will remain low) and the next cycle will start in the middle July 2020 for Norway and Denmark, and October 2020 for Sweden, and September for Finland.

Keywords: forecasting, ordinary differential equations, SARS-COV-2 epidemic, SIR model

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Authors: Ntandazo Dlatu, Benjamin Longo-Mbenza, Andre Renzaho, Ruffin Appalata, Yolande Yvonne Valeria Matoumona Mavoungou, Mbenza Ben Longo, Kenneth Ekoru, Blaise Makoso, Gedeon Longo Longo

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Background: The gap between complexities related to the integration of Tuberculosis /HIV control and evidence-based knowledge motivated the initiation of the study. Therefore, the objective of this study was to explore correlations between national TB management guidelines, multiple deprivation indexes, quantiles, components and levels of Tuberculosis control programme using mathematical modeling in rural O.R. Tambo District Health Facilities, South Africa. Methods: The study design used mixed secondary data analysis and cross-sectional analysis between 2009 and 2013 across O.R Tambo District, Eastern Cape, South Africa using univariate/ bivariate analysis, linear multiple regression models, and multivariate discriminant analysis. Health inequalities indicators and component of an impediment to the tuberculosis control programme were evaluated. Results: In total, 62 400 records for TB notification were analyzed for the period 2009-2013. There was a significant but negative between Financial Year Expenditure (r= -0.894; P= 0.041) Seropositive HIV status(r= -0.979; P= 0.004), Population Density (r = -0.881; P= 0.048) and the number of TB defaulter in all TB cases. It was shown unsuccessful control of TB management program through correlations between numbers of new PTB smear positive, TB defaulter new smear-positive, TB failure all TB, Pulmonary Tuberculosis case finding index and deprivation-concentration-dispersion index. It was shown successful TB program control through significant and negative associations between declining numbers of death in co-infection of HIV and TB, TB deaths all TB and SMIAD gradient/ deprivation-concentration-dispersion index. The multivariate linear model was summarized by unadjusted r of 96%, adjusted R2 of 95 %, Standard Error of estimate of 0.110, R2 changed of 0.959 and significance for variance change for P=0.004 to explain the prediction of TB defaulter in all TB with equation y= 8.558-0.979 x number of HIV seropositive. After adjusting for confounding factors (PTB case finding the index, TB defaulter new smear-positive, TB death in all TB, TB defaulter all TB, and TB failure in all TB). The HIV and TB death, as well as new PTB smear positive, were identified as the most important, significant, and independent indicator to discriminate most deprived deprivation index far from other deprivation quintiles 2-5 using discriminant analysis. Conclusion: Elimination of poverty such as overcrowding, lack of sanitation and environment of highest burden of HIV might end the TB threat in O.R Tambo District, Eastern Cape, South Africa. Furthermore, ongoing adequate budget comprehensive, holistic and collaborative initiative towards Sustainable Developmental Goals (SDGs) is necessary for complete elimination of TB in poor O.R Tambo District.

Keywords: tuberculosis, HIV/AIDS, success, failure, control program, health inequalities, South Africa

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Authors: Jun-Ki Choi

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The usage of valuable resources and the potential for waste generation at the end of the life cycle of photovoltaic (PV) technologies necessitate a proactive planning for a PV recycling infrastructure. To ensure the sustainability of PV in large scales of deployment, it is vital to develop and institute low-cost recycling technologies and infrastructure for the emerging PV industry in parallel with the rapid commercialization of these new technologies. There are various issues involved in the economics of PV recycling and this research examine those at macro and micro levels, developing a holistic interpretation of the economic viability of the PV recycling systems. This study developed mathematical models to analyze the profitability of recycling technologies and to guide tactical decisions for allocating optimal location of PV take-back centers (PVTBC), necessary for the collection of end of life products. The economic decision is usually based on the level of the marginal capital cost of each PVTBC, cost of reverse logistics, distance traveled, and the amount of PV waste collected from various locations. Results illustrated that the reverse logistics costs comprise a major portion of the cost of PVTBC; PV recycling centers can be constructed in the optimally selected locations to minimize the total reverse logistics cost for transporting the PV wastes from various collection facilities to the recycling center. In the micro- process level, automated recycling processes should be developed to handle the large amount of growing PV wastes economically. The market price of the reclaimed materials are important factors for deciding the profitability of the recycling process and this illustrates the importance of the recovering the glass and expensive metals from PV modules.

Keywords: photovoltaic, recycling, mathematical models, sustainability

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Authors: Redouane Krini, Lutz Nuhn, Hicham El Mard Cheol Woo Ha, Yoondeok Han, Kwang-Sup Lee, Dong-Yol Yang, Jinsoo Joo, Rudolf Zentel

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To use Quantum Dots (QDs) in the two photon initiated polymerization technique (TPIP) for 3D patternings, QDs were modified on the surface with photosensitive end groups which are able to undergo a photopolymerization. We were able to fabricate fluorescent 3D lattice structures using photopatternable QDs by TPIP for photonic devices such as photonic crystals and metamaterials. The QDs in different diameter have different emission colors and through mixing of RGB QDs white light fluorescent from the polymeric structures has been created. Metamaterials are capable for unique interaction with the electrical and magnetic components of the electromagnetic radiation and for manipulating light it is crucial to have a negative refractive index. In combination with QDs via TPIP technique polymeric structures can be designed with properties which cannot be found in nature. This makes these artificial materials gaining a huge importance for real-life applications in photonic and optoelectronic. Understanding of interactions between nanoparticles and biological systems is of a huge interest in the biomedical research field. We developed a synthetic strategy of polymer functionalized nanoparticles for biomedical studies to obtain hybrid systems of QDs and copolymers with a strong binding network in an inner shell and which can be modified in the end through their poly(ethylene glycol) functionalized outer shell. These hybrid systems can be used as models for investigation of cell penetration and drug delivery by using measurements combination between CryoTEM and fluorescence studies.

Keywords: biomedical study models, lithography, photo induced polymerization, quantum dots

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Authors: Slim Chokri

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Load forecasting is one of the central functions in power systems operations. Electricity cannot be stored, which means that for electric utility, the estimate of the future demand is necessary in managing the production and purchasing in an economically reasonable way. A majority of the recently reported approaches are based on neural network. The attraction of the methods lies in the assumption that neural networks are able to learn properties of the load. However, the development of the methods is not finished, and the lack of comparative results on different model variations is a problem. This paper presents a new approach in order to predict the Tunisia daily peak load. The proposed method employs a computational intelligence scheme based on the Fuzzy neural network (FNN) and support vector regression (SVR). Experimental results obtained indicate that our proposed FNN-SVR technique gives significantly good prediction accuracy compared to some classical techniques.

Keywords: neural network, load forecasting, fuzzy inference, machine learning, fuzzy modeling and rule extraction, support vector regression

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Authors: Yaseri Dahlia Apritasari

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Visual comfort is important for the building occupants to need. Visual comfort can be fulfilled through natural lighting (daylighting) and artificial lighting. One strategy to optimize natural lighting can be achieved through the facade second skin design. This strategy can reduce glare, and fulfill visual comfort need. However, the design strategy cannot achieve light intensity for visual comfort. Because the materials, design and opening percentage of the facade of second skin blocked sunlight. This paper discusses aluminum material for the facade second skin design that can fulfill the optimal visual comfort with the case studies Multi Media Tower building. The methodology of the research is combination quantitative and qualitative through field study observed, lighting measurement and visual comfort questionnaire. Then it used too simulation modeling (DIALUX 4.13, 2016) for three facades second skin design model. Through following steps; (1) Measuring visual comfort factor: light intensity indoor and outdoor; (2) Taking visual comfort data from building occupants; (3) Making models with different facade second skin design; (3) Simulating and analyzing the light intensity value for each models that meet occupants visual comfort standard: 350 lux (Indonesia National Standard, 2010). The result shows that optimization of aluminum material for the facade second skin design can meet optimal visual comfort for building occupants. The result can give recommendation aluminum opening percentage of the facade second skin can meet optimal visual comfort for building occupants.

Keywords: aluminium material, Facade, second skin, visual comfort

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Authors: Saif Alomari

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The paper aims at giving physical and mathematical descriptions of how the structural parameters of a resonant tunnelling diode (RTD) affect its output characteristics. Specifically, the value of the peak voltage, peak current, peak to valley current ratio (PVCR), and the difference between peak and valley voltages and currents ΔV and ΔI. A simulation-based approach using the Non-Equilibrium Green Function (NEGF) formalism based on the Silvaco ATLAS simulator is employed to conduct a series of designed experiments. These experiments show how the doping concentration in the emitter and collector layers, their thicknesses, and the width of the barriers and the quantum well influence the above-mentioned output characteristics. Each of these parameters was systematically changed while holding others fixed in each set of experiments. Factorial experiments are outside the scope of this work and will be investigated in future. The physics involved in the operation of the device is thoroughly explained and mathematical models based on curve fitting and underlaying physical principles are deduced. The models can be used to design devices with predictable output characteristics. These models were found absent in the literature that the author acanned. Results show that the doping concentration in each region has an effect on the value of the peak voltage. It is found that increasing the carrier concentration in the collector region shifts the peak to lower values, whereas increasing it in the emitter shifts the peak to higher values. In the collector’s case, the shift is either controlled by the built-in potential resulting from the concentration gradient or the conductivity enhancement in the collector. The shift to higher voltages is found to be also related to the location of the Fermi-level. The thicknesses of these layers play a role in the location of the peak as well. It was found that increasing the thickness of each region shifts the peak to higher values until a specific characteristic length, afterwards the peak becomes independent of the thickness. Finally, it is shown that the thickness of the barriers can be optimized for a particular well width to produce the highest PVCR or the highest ΔV and ΔI. The location of the peak voltage is important in optoelectronic applications of RTDs where the operating point of the device is usually the peak voltage point. Furthermore, the PVCR, ΔV, and ΔI are of great importance for building RTD-based oscillators as they affect the frequency response and output power of the oscillator.

Keywords: peak to valley ratio, peak voltage shift, resonant tunneling diodes, structural parameters

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Authors: Laidi Maamar, Hanini Salah

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The present work investigates the potential of artificial neural network (ANN) model to predict the horizontal global solar radiation (HGSR). The ANN is developed and optimized using three years meteorological database from 2011 to 2013 available at the meteorological station of Blida (Blida 1 university, Algeria, Latitude 36.5°, Longitude 2.81° and 163 m above mean sea level). Optimal configuration of the ANN model has been determined by minimizing the Root Means Square Error (RMSE) and maximizing the correlation coefficient (R2) between observed and predicted data with the ANN model. To select the best ANN architecture, we have conducted several tests by using different combinations of parameters. A two-layer ANN model with six hidden neurons has been found as an optimal topology with (RMSE=4.036 W/m²) and (R²=0.999). A graphical user interface (GUI), was designed based on the best network structure and training algorithm, to enhance the users’ friendliness application of the model.

Keywords: artificial neural network, global solar radiation, solar energy, prediction, Algeria

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Authors: Omonov Jahongir Mahmatkulovic

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Purpose: To study the significance of D-dimer (DD), prothrombin time (PT), activated partial thromboplastin time (APTT), thrombin time (TT), and fibrinogen coagulation parameters (Fg) in predicting the course, severity and prognosis of COVID-19. Source and method of research: From September 15, 2021, to November 5, 2021, 93 patients aged 25 to 60 with suspected COVID-19, who are under inpatient treatment at the multidisciplinary clinic of the Tashkent Medical Academy, were retrospectively examined. DD, PT, APTT, and Fg were studied in dynamics and studied changes. Results: Coagulation disorders occurred in the early stages of COVID-19 infection with an increase in DD in 54 (58%) patients and an increase in Fg in 93 (100%) patients. DD and Fg levels are associated with the clinical classification. Of the 33 patients who died, 21 had an increase in DD in the first laboratory study, 27 had an increase in DD in the second and third laboratory studies, and 15 had an increase in PT in the third test. The results of the ROC analysis of mortality showed that the AUC DD was three times 0.721, 0.801, and 0.844, respectively; PT was 0.703, 0.845, and 0.972. (P<0:01). Conclusion”: Coagulation dysfunction is more common in patients with severe and critical conditions. DD and PT can be used as important predictors of mortality from COVID-19.

Keywords: Covid19, DD, PT, Coagulogram analysis, APTT

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Authors: Pau Rodriguez, Jordi Gonzalez, Josep M. Gonfaus, Xavier Roca

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Image sharing in social networks has increased exponentially in the past years. Officially, there are 600 million Instagrammers uploading around 100 million photos and videos per day. Consequently, there is a need for developing new tools to understand the content expressed in shared images, which will greatly benefit social media communication and will enable broad and promising applications in education, advertisement, entertainment, and also psychology. Following these trends, our work aims to take advantage of the existing relationship between text and personality, already demonstrated by multiple researchers, so that we can prove that there exists a relationship between images and personality as well. To achieve this goal, we consider that images posted on social networks are typically conditioned on specific words, or hashtags, therefore any relationship between text and personality can also be observed with those posted images. Our proposal makes use of the most recent image understanding models based on neural networks to process the vast amount of data generated by social users to determine those images most correlated with personality traits. The final aim is to train a weakly-supervised image-based model for personality assessment that can be used even when textual data is not available, which is an increasing trend. The procedure is described next: we explore the images directly publicly shared by users based on those accompanying texts or hashtags most strongly related to personality traits as described by the OCEAN model. These images will be used for personality prediction since they have the potential to convey more complex ideas, concepts, and emotions. As a result, the use of images in personality questionnaires will provide a deeper understanding of respondents than through words alone. In other words, from the images posted with specific tags, we train a deep learning model based on neural networks, that learns to extract a personality representation from a picture and use it to automatically find the personality that best explains such a picture. Subsequently, a deep neural network model is learned from thousands of images associated with hashtags correlated to OCEAN traits. We then analyze the network activations to identify those pictures that maximally activate the neurons: the most characteristic visual features per personality trait will thus emerge since the filters of the convolutional layers of the neural model are learned to be optimally activated depending on each personality trait. For example, among the pictures that maximally activate the high Openness trait, we can see pictures of books, the moon, and the sky. For high Conscientiousness, most of the images are photographs of food, especially healthy food. The high Extraversion output is mostly activated by pictures of a lot of people. In high Agreeableness images, we mostly see flower pictures. Lastly, in the Neuroticism trait, we observe that the high score is maximally activated by animal pets like cats or dogs. In summary, despite the huge intra-class and inter-class variabilities of the images associated to each OCEAN traits, we found that there are consistencies between visual patterns of those images whose hashtags are most correlated to each trait.

Keywords: emotions and effects of mood, social impact theory in social psychology, social influence, social structure and social networks

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Authors: Ju-Young Hwang, Hyo-Gyoung Kwak

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This paper introduces a numerical analysis method for reinforced-concrete (RC) structures exposed to fire and compares the result with experimental results. The proposed analysis method for RC structure under the high temperature consists of two procedures. First step is to decide the temperature distribution across the section through the heat transfer analysis by using the time-temperature curve. After determination of the temperature distribution, the nonlinear analysis is followed. By considering material and geometrical nonlinearity with the temperature distribution, nonlinear analysis predicts the behavior of RC structure under the fire by the exposed time. The proposed method is validated by the comparison with the experimental results. Finally, prediction model to describe the status of after-cooling concrete can also be introduced based on the results of additional experiment. The product of this study is expected to be embedded for smart structure monitoring system against fire in u-City.

Keywords: RC, high temperature, after-cooling analysis, nonlinear analysis

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Authors: Iman Farasat, Howard M. Salis

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Engineered genetic circuits reprogram cellular behavior to act as living computers with applications in detecting cancer, creating self-controlling artificial tissues, and dynamically regulating metabolic pathways. Phenemenological models are often used to simulate and design genetic circuit behavior towards a desired behavior. While such models assume that each circuit component’s function is modular and independent, even small changes in a circuit (e.g. a new promoter, a change in transcription factor expression level, or even a new media) can have significant effects on the circuit’s function. Here, we use statistical thermodynamics to account for the several factors that control transcriptional regulation in bacteria, and experimentally demonstrate the model’s accuracy across 825 measurements in several genetic contexts and hosts. We then employ our first principles model to design, experimentally construct, and characterize a family of signal amplifying genetic circuits (genetic OpAmps) that expand the dynamic range of cell sensors. To develop these models, we needed a new approach to measuring the in vivo binding free energies of transcription factors (TFs), a key ingredient of statistical thermodynamic models of gene regulation. We developed a new high-throughput assay to measure RNA polymerase and TF binding free energies, requiring the construction and characterization of only a few constructs and data analysis (Figure 1A). We experimentally verified the assay on 6 TetR-homolog repressors and a CRISPR/dCas9 guide RNA. We found that our binding free energy measurements quantitatively explains why changing TF expression levels alters circuit function. Altogether, by combining these measurements with our biophysical model of translation (the RBS Calculator) as well as other measurements (Figure 1B), our model can account for changes in TF binding sites, TF expression levels, circuit copy number, host genome size, and host growth rate (Figure 1C). Model predictions correctly accounted for how these 8 factors control a promoter’s transcription rate (Figure 1D). Using the model, we developed a design framework for engineering multi-promoter genetic circuits that greatly reduces the number of degrees of freedom (8 factors per promoter) to a single dimensionless unit. We propose the Ptashne (Pt) number to encapsulate the 8 co-dependent factors that control transcriptional regulation into a single number. Therefore, a single number controls a promoter’s output rather than these 8 co-dependent factors, and designing a genetic circuit with N promoters requires specification of only N Pt numbers. We demonstrate how to design genetic circuits in Pt number space by constructing and characterizing 15 2-repressor OpAmp circuits that act as signal amplifiers when within an optimal Pt region. We experimentally show that OpAmp circuits using different TFs and TF expression levels will only amplify the dynamic range of input signals when their corresponding Pt numbers are within the optimal region. Thus, the use of the Pt number greatly simplifies the genetic circuit design, particularly important as circuits employ more TFs to perform increasingly complex functions.

Keywords: transcription factor, synthetic biology, genetic circuit, biophysical model, binding energy measurement

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Authors: Md. Mahadi Hasan Sany, Mumenunnessa Keya, Sharun Khushbu, Sheikh Abujar

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Since the beginning of China's economic reform, trade between the U.S. and China has grown rapidly, and has increased since China's accession to the World Trade Organization in 2001. The US imports more than it exports from China, reducing the trade war between China and the U.S. for the 2019 trade deficit, but in 2020, the opposite happens. In international and U.S. trade, Washington launched a full-scale trade war against China in March 2016, which occurred a catastrophic epidemic. The main goal of our study is to measure and predict trade relations between China and the U.S., before and after the arrival of the COVID epidemic. The ML model uses different data as input but has no time dimension that is present in the time series models and is only able to predict the future from previously observed data. The LSTM (a well-known Recurrent Neural Network) model is applied as the best time series model for trading forecasting. We have been able to create a sustainable forecasting system in trade between China and the US by closely monitoring a dataset published by the State Website NZ Tatauranga Aotearoa from January 1, 2015, to April 30, 2021. Throughout the survey, we provided a 180-day forecast that outlined what would happen to trade between China and the US during COVID-19. In addition, we have illustrated that the LSTM model provides outstanding outcome in time series data analysis rather than RFR and SVR (e.g., both ML models). The study looks at how the current Covid outbreak affects China-US trade. As a comparative study, RMSE transmission rate is calculated for LSTM, RFR and SVR. From our time series analysis, it can be said that the LSTM model has given very favorable thoughts in terms of China-US trade on the future export situation.

Keywords: RFR, China-U.S. trade war, SVR, LSTM, deep learning, Covid-19, export value, forecasting, time series analysis

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Authors: William Anderson, Kyle Kobold, Oleg Yakimenko

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Green microgrids using mostly renewable energy (RE) for generation, are complex systems with inherent nonlinear dynamics. Among a variety of different optimization tools there are only a few ones that adequately consider this complexity. This paper evaluates applicability of two somewhat similar optimization tools tailored for standalone RE microgrids and also assesses a machine learning tool for performance prediction that can enhance the reliability of any chosen optimization tool. It shows that one of these microgrid optimization tools has certain advantages over another and presents a detailed routine of preparing input data to simulate RE microgrid behavior. The paper also shows how neural-network-based predictive modeling can be used to validate and forecast solar power generation based on weather time series data, which improves the overall quality of standalone RE microgrid analysis.

Keywords: microgrid, renewable energy, complex systems, optimization, predictive modeling, neural networks

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Authors: Derejaw Lake Melie, Alemu Kumlachew Tegegne

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The increasing prevalence of social media in Ethiopia has exacerbated societal challenges by fostering the proliferation of negative emotional posts and comments. Illicit use of social media has further exacerbated divisions among the population. Addressing these issues through manual identification and aggregation of emotions from millions of users for swift decision-making poses significant challenges, particularly given the rapid growth of Amharic language usage on social platforms. Consequently, there is a critical need to develop an intelligent system capable of automatically detecting and categorizing negative emotional content into social, religious, and political categories while also filtering out toxic online content. This paper aims to leverage sentiment analysis techniques to achieve automatic detection and filtering of negative emotional content from Amharic social media texts, employing a comparative study of deep learning algorithms. The study utilized a dataset comprising 29,962 comments collected from social media platforms using comment exporter software. Data pre-processing techniques were applied to enhance data quality, followed by the implementation of deep learning methods for training, testing, and evaluation. The results showed that CNN, GRU, LSTM, and Bi-LSTM classification models achieved accuracies of 83%, 50%, 84%, and 86%, respectively. Among these models, Bi-LSTM demonstrated the highest accuracy of 86% in the experiment.

Keywords: negative emotion, emotion detection, social media filtering sentiment analysis, deep learning.

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Authors: Xi Liu, Fan Yuan

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In the evolving landscape of physics-based machine learning (PBML), particularly within fluid dynamics and its applications in electromechanical engineering, robot vision, and robot learning, achieving precision and alignment with researchers' specific needs presents a formidable challenge. In response, this work proposes a methodology that integrates neural transformation with a modified smoothed particle hydrodynamics model for generating transformed 3D fluid simulations. This approach is useful for nanoscale science, where the unique and complex behaviors of viscoelastic medium demand accurate neurally-transformed simulations for materials understanding and manipulation. In electromechanical engineering, the method enhances the design and functionality of fluid-operated systems, particularly microfluidic devices, contributing to advancements in nanomaterial design, drug delivery systems, and more. The proposed approach also aligns with the principles of PBML, offering advantages such as multi-fluid stylization and consistent particle attribute transfer. This capability is valuable in various fields where the interaction of multiple fluid components is significant. Moreover, the application of neurally-transformed hydrodynamical models extends to manufacturing processes, such as the production of microelectromechanical systems, enhancing efficiency and cost-effectiveness. The system's ability to perform neural transfer on 3D fluid scenes using a deep learning algorithm alongside physical models further adds a layer of flexibility, allowing researchers to tailor simulations to specific needs across scientific and engineering disciplines.

Keywords: physics-based machine learning, robot vision, robot learning, hydrodynamics

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Authors: Jesus Ruano, Francesc Xavier Trias, Asensi Oliva

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A three-dimensional incompressible Large Eddy Simulation (LES) is performed to compute the hydrodynamic field around a pair of tandem cylinders. Symmetry-preserving schemes will be used during this simulation in conjunction with Finite Volume Method (FVM) to obtain the hydrodynamic field around the selected geometry. A set of results consisting of pressure and velocity and the combination of them will be stored at different surfaces near the cylinders as the initial input for the second part of the study. A post-processing of the obtained results based on Ffowcs-Williams and Hawkings (FWH) equation with a Fourier Transform of the acoustic sources will be used to compute noise at several probes located far away from the region where the hydrodynamics are computed. Directivities as well as spectral profile of the obtained acoustic field will be analyzed.

Keywords: far-field noise, Ffowcs-Williams and Hawkings, finite volume method, large eddy simulation, long-span bodies

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Authors: Katarzyna Wyka, Dana Sylvan, JoAnn Difede

Abstract:

Background and significance: The following disaster, population-based screening programs are routinely established to assess physical and psychological consequences of exposure. These data sets are highly skewed as only a small percentage of trauma-exposed individuals develop health issues. Commonly used statistical methodology in post-disaster mental health generally involves population-averaged models. Such models aim to capture the overall response to the disaster and its aftermath; however, they may not be sensitive enough to accommodate population heterogeneity in symptomatology, such as post-traumatic stress or depressive symptoms. Methods: We use an archival longitudinal data set from Weill-Cornell 9/11 Mental Health Screening Program established following the World Trade Center (WTC) terrorist attacks in New York in 2001. Participants are rescue and recovery workers who participated in the site cleanup and restoration (n=2960). The main outcome is the post-traumatic stress symptoms (PTSD) severity score assessed via clinician interviews (CAPS). For a detailed understanding of response to the disaster and its aftermath, we are adapting quantile regression methodology with particular focus on predictors of extreme distress and resilience to trauma. Results: The response variable was defined as the quantile of the CAPS score for each individual under two different scenarios specifying the unconditional quantiles based on: 1) clinically meaningful CAPS cutoff values and 2) CAPS distribution in the population. We present graphical summaries of the differential effects. For instance, we found that the effect of the WTC exposures, namely seeing bodies and feeling that life was in danger during rescue/recovery work was associated with very high PTSD symptoms. A similar effect was apparent in individuals with prior psychiatric history. Differential effects were also present for age and education level of the individuals. Conclusion: We evaluate the utility of quantile regression in disaster research in contrast to the commonly used population-averaged models. We focused on assessing the distribution of risk factors for post-traumatic stress symptoms across quantiles. This innovative approach provides a comprehensive understanding of the relationship between dependent and independent variables and could be used for developing tailored training programs and response plans for different vulnerability groups.

Keywords: disaster workers, post traumatic stress, PTSD, quantile regression

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Authors: N. P. Yadav, Deepti Verma

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This paper focuses on the mathematical modeling for solidification of Al alloy in a cube mould cavity to study the solidification behavior of casting process. The parametric investigation of solidification process inside the cavity was performed by using computational solidification/melting model coupled with Volume of fluid (VOF) model. The implicit filling algorithm is used in this study to understand the overall process from the filling stage to solidification in a model metal casting process. The model is validated with past studied at same conditions. The solidification process are analyzed by including the effect of pouring velocity and temperature of liquid metal, effect of wall temperature as well natural convection from the wall and geometry of the cavity. These studies show the possibility of various defects during solidification process.

Keywords: buoyancy driven flow, natural convection driven flow, residual flow, secondary flow, volume of fluid

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Authors: Mouhamadou Diop, Mohamed I. Hassan

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Molten metallic flow in metallurgical plant is highly turbulent and presents a complex coupling with heat transfer, phase transfer, chemical reaction, momentum transport, etc. Molten silicon flow has significant effect in directional solidification of multicrystalline silicon by affecting the temperature field and the emerging crystallization interface as well as the transport of species and impurities during casting process. Owing to the complexity and limits of reliable measuring techniques, computational models of fluid flow are useful tools to study and quantify these problems. The overall objective of this study is to investigate the potential of a traveling magnetic field for an efficient operating control of the molten metal flow. A multidimensional numerical model will be developed for the calculations of Lorentz force, molten metal flow, and the related phenomenon. The numerical model is implemented in a laboratory-scale silicon crystallization furnace. This study presents the potential of traveling magnetic field approach for an efficient operating control of the molten flow. A numerical model will be used to study the effects of magnetic force applied on the molten flow, and their interdependencies. In this paper, coupled and decoupled, steady and unsteady models of molten flow and crystallization interface will be compared. This study will allow us to retrieve the optimal traveling magnetic field parameter range for crystallization furnaces and the optimal numerical simulations strategy for industrial application.

Keywords: multidimensional, numerical simulation, solidification, multicrystalline, traveling magnetic field

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Authors: Mahmoud S. El-Sebaey, Asko Ellman, Ahmed Hegazy, Tarek Ghonim

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Due to population growth, the need for drinkable healthy water is highly increased. Consequently, and since the conventional sources of water are limited, researchers devoted their efforts to oceans and seas for obtaining fresh drinkable water by thermal distillation. The current work is dedicated to the design and fabrication of modified solar still model, as well as conventional solar still for the sake of comparison. The modified still is single slope double basin solar still. The still consists of a lower basin with a dimension of 1000 mm x 1000 mm which contains the sea water, as well as the top basin that made with 4 mm acrylic, was temporarily kept on the supporting strips permanently fixed with the side walls. Equally ten spaced vertical glass strips of 50 mm height and 3 mm thickness were provided at the upper basin for the stagnancy of the water. Window glass of 3 mm was used as the transparent cover with 23° inclination at the top of the still. Furthermore, the performance evaluation and comparison of these two models in converting salty seawater into drinkable freshwater are introduced, analyzed and discussed. The experiments were performed during the period from June to July 2018 at seawater depths of 2, 3, 4 and 5 cm. Additionally, the solar still models were operated simultaneously in the same climatic conditions to analyze the influence of the modifications on the freshwater output. It can be concluded that the modified design of double basin single slope solar still shows the maximum freshwater output at all water depths tested. The results showed that the daily productivity for modified and conventional solar still was 2.9 and 1.8 dm³/m² day, indicating an increase of 60% in fresh water production.

Keywords: freshwater output, solar still, solar energy, thermal desalination

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Authors: Jai Prakash Prasad, Suresh Chandra Mohan

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Modern wireless sensor networks (WSN) consist of small size, low cost devices which are networked through tight wireless communications. WSN fundamentally offers cooperation, coordination among sensor networks. Potential applications of wireless sensor networks are in healthcare, natural disaster prediction, data security, environmental monitoring, home appliances, entertainment etc. The design, development and deployment of WSN based on application requirements. The WSN design performance is optimized to improve network lifetime. The sensor node resources constrain such as energy and bandwidth imposes the limitation on efficient resource utilization and sensor node management. The proposed N-Tier GRID routing protocol focuses on the design of energy efficient large scale wireless sensor network for improved performance than the existing protocol.

Keywords: energy efficient, network lifetime, sensor networks, wireless communication

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Authors: Pegah Eshraghi, Zahra Sadat Zomorodian, Mohammad Tahsildoost

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Over the past decade, energy consumption in educational buildings has steadily increased. The purpose of this research is to provide a method to quickly predict the energy consumption of buildings using separate evaluation of zones and decomposing the building to eliminate the complexity of geometry at the early design stage. To produce this framework, machine learning algorithms such as Support vector regression (SVR) and Artificial neural network (ANN) are used to predict energy consumption and thermal comfort metrics in a school as a case. The database consists of more than 55000 samples in three climates of Iran. Cross-validation evaluation and unseen data have been used for validation. In a specific label, cooling energy, it can be said the accuracy of prediction is at least 84% and 89% in SVR and ANN, respectively. The results show that the SVR performed much better than the ANN.

Keywords: early stage of design, energy, thermal comfort, validation, machine learning

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Authors: Sophia Shi

Abstract:

Acute kidney injury (AKI) is the sudden onset of kidney damage in which the kidneys cannot filter waste from the blood, requiring emergency hospitalization. AKI patient mortality rate is high in the ICU and is virtually impossible for doctors to predict because it is so unexpected. Currently, there is no hybrid model predicting AKI that takes advantage of two types of data. De-identified patient data from the MIMIC-III database and de-identified kidney images and corresponding patient records from the Beijing Hospital of the Ministry of Health were collected. Using data features including serum creatinine among others, two numeric models using MIMIC and Beijing Hospital data were built, and with the hospital ultrasounds, an image-only model was built. Convolutional neural networks (CNN) were used, VGG and Resnet for numeric data and Resnet for image data, and they were combined into a hybrid model by concatenating feature maps of both types of models to create a new input. This input enters another CNN block and then two fully connected layers, ending in a binary output after running through Softmax and additional code. The hybrid model successfully predicted AKI and the highest AUROC of the model was 0.953, achieving an accuracy of 90% and F1-score of 0.91. This model can be implemented into urgent clinical settings such as the ICU and aid doctors by assessing the risk of AKI shortly after the patient’s admission to the ICU, so that doctors can take preventative measures and diminish mortality risks and severe kidney damage.

Keywords: Acute kidney injury, Convolutional neural network, Hybrid deep learning, Patient record data, ResNet, Ultrasound kidney images, VGG

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Authors: S. A. Naeini, A. Khalili

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Nowadays, tunnels with different applications are developed, and most of them are related to subway tunnels. The excavation of shallow tunnels that pass under municipal utilities is very important, and the surface settlement control is an important factor in the design. The study sought to analyze the settlement and also to find an appropriate model in order to predict the behavior of the tunnel in Tehran subway line-3. The displacement in these sections is also determined by using numerical analyses and numerical modeling. In addition, the Adaptive Neuro-Fuzzy Inference System (ANFIS) method is utilized by Hybrid training algorithm. The database pertinent to the optimum network was obtained from 46 subway tunnels in Iran and Turkey which have been constructed by the new Austrian tunneling method (NATM) with similar parameters based on type of their soil. The surface settlement was measured, and the acquired results were compared to the predicted values. The results disclosed that computing intelligence is a good substitute for numerical modeling.

Keywords: settlement, Subway Line, FLAC3D, ANFIS Method

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Authors: Martina Marinelli, Samanta Vellante, Francesco Pilotti, Daniele Di Valerio, Gaetanino Paolone

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Any development in web marketing technology requires changes in information engineering to identify instruments and techniques suitable for the production of software applications for informative marketing. Moreover, for large web solutions, designing an interface that enables human interactions is a complex process that must bridge between informative marketing requirements and the developed solution. A user-friendly interface in web marketing applications is crucial for a successful business. The paper introduces mkInfo - a software platform that implements informative marketing. Informative marketing is a new interpretation of marketing which places the information at the center of every marketing action. The creative team includes software engineering researchers who have recently authored an article on automatic code generation. The authors have created the mkInfo software platform to generate informative marketing web applications. For each web application, it is possible to automatically implement an opt in page, a landing page, a sales page, and a thank you page: one only needs to insert the content. mkInfo implements an autoresponder to send mail according to a predetermined schedule. The mkInfo platform also includes e-commerce for a product or service. The stakeholder can access any opt-in page and get basic information about a product or service. If he wants to know more, he will need to provide an e-mail address to access a landing page that will generate an e-mail sequence. It will provide him with complete information about the product or the service. From this point on, the stakeholder becomes a user and is now able to purchase the product or related services through the mkInfo platform. This paper suggests a possible definition for Informative Marketing, illustrates its basic principles, and finally details the mkInfo platform that implements it. This paper also offers some Informative Marketing models, which are implemented in the mkInfo platform. Informative marketing can be applied to products or services. It is necessary to realize a web application for each product or service. The mkInfo platform enables the product or the service producer to send information concerning a specific product or service to all stakeholders. In conclusion, the technical contributions of this paper are: a different interpretation of marketing based on information; a modular architecture for web applications, particularly for one with standard features such as information storage, exchange, and delivery; multiple models to implement informative marketing; a software platform enabling the implementation of such models in a web application. Future research aims to enable stakeholders to provide information about a product or a service so that the information gathered about a product or a service includes both the producer’s and the stakeholders' point of view. The purpose is to create an all-inclusive management system of the knowledge regarding a specific product or service: a system that includes everything about the product or service and is able to address even unexpected questions.

Keywords: informative marketing, opt in page, software platform, web application

Procedia PDF Downloads 124