Search results for: strength prediction models

Authors: Aspasia Koutsokosta, Stefanos Katsavounis

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This paper reviews the model-based qualitative and quantitative Operations Management research in the context of Construction Supply Chain Management (CSCM). Construction industry has been traditionally blamed for low productivity, cost and time overruns, waste, high fragmentation and adversarial relationships. The construction industry has been slower than other industries to employ the Supply Chain Management (SCM) concept and develop models that support the decision-making and planning. However the last decade there is a distinct shift from a project-based to a supply-based approach of construction management. CSCM comes up as a new promising management tool of construction operations and improves the performance of construction projects in terms of cost, time and quality. Modeling the Construction Supply Chain (CSC) offers the means to reap the benefits of SCM, make informed decisions and gain competitive advantage. Different modeling approaches and methodologies have been applied in the multi-disciplinary and heterogeneous research field of CSCM. The literature review reveals that a considerable percentage of CSC modeling accommodates conceptual or process models which discuss general management frameworks and do not relate to acknowledged soft OR methods. We particularly focus on the model-based quantitative research and categorize the CSCM models depending on their scope, mathematical formulation, structure, objectives, solution approach, software used and decision level. Although over the last few years there has been clearly an increase of research papers on quantitative CSC models, we identify that the relevant literature is very fragmented with limited applications of simulation, mathematical programming and simulation-based optimization. Most applications are project-specific or study only parts of the supply system. Thus, some complex interdependencies within construction are neglected and the implementation of the integrated supply chain management is hindered. We conclude this paper by giving future research directions and emphasizing the need to develop robust mathematical optimization models for the CSC. We stress that CSC modeling needs a multi-dimensional, system-wide and long-term perspective. Finally, prior applications of SCM to other industries have to be taken into account in order to model CSCs, but not without the consequential reform of generic concepts to match the unique characteristics of the construction industry.

Keywords: construction supply chain management, modeling, operations research, optimization, simulation

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Authors: Ruben Dario Tovar-Valencia, Eshan Ganju, Fei Han, Monica Prezzi, Rodrigo Salgado

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Particle breakage occurs even in strong silica sand particles. There is compelling evidence that suggests that particle breakage causes changes in several properties such as permeability, peak strength, dilatancy and critical state friction angle. Current pile design methods that are based on soil properties do not account for particle breakage that occurs during driving or jacking of displacement piles. This may lead to significant overestimation of pile capacity in sands dominated by particles susceptible to breakage, such as carbonate sands. The objective of this paper is to study the influence of shear displacement on particle breakage and friction angle of carbonate sands, and to furthermore quantify the change in friction angle observed with different levels of particle breakage. To study the phenomenon of particle breakage, multiple ring shear tests have been performed at different levels of vertical confinement on a thoroughly characterized carbonate sand to find i) the shear displacement necessary to reach stable friction angles and ii) the effect of particle breakage on the mobilized friction angle of the tested sand. The findings of this study can potentially be used to update the current pile design methods by developing a friction angle which is a function of shear displacement and breakage characteristics of the sand instead of being a constant value.

Keywords: breakage, carbonate sand, friction angle, pile design, ring shear test

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Authors: Behrooz Ataee, Mohammad Golzar

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The main purpose of using side by side (SBS) hybrid yarn in pultrusion thermoplastic method is reprisal the effect of high viscosity in melted thermoplastic and reduction of distance between reinforced fiber and melted thermoplastic. SBS hybrid fiber yarn composed of thermoplastic fibers and fiber reinforcement should be produced in the preparation of pultruded thermoplastic composites prepreg to reach better impregnation. An experimental set-up was designed and built to pultrude continues polypropylene and glass fiber to get obtain a suitable impregnated round prepregs. In final stage, the round prepregs come together to produce rectangular profile. Higher fiber volume fraction produces higher void volume fraction, however the second stage of the production process of rectangular profile and the cold die decrease 50% of the void volume fraction. Results show that whit increasing void volume fraction, flexural and shear strength decrease. Also, under certain conditions of parameters the pultruded profiles exhibit better flexural and shear strength. The pulling speed seems to have the greatest influence on the profile quality. In addition, adding cold die strongly increases the surface quality of rectangular profile.

Keywords: thermoplastic pultrusion, hybrid pultrusion, side-by-side fibers, impregnation

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Authors: Sahar Heidary, Ramin Ghasemi Shayan

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The overall goal Monte Carlo N-atom radioactivity transference PC program (MCNP4C) was done for the regeneration of x-ray groups in diagnostic radiology and mammography. The electrons were transported till they slow down and stopover in the target. Both bremsstrahlung and characteristic x-ray creation were measured in this study. In this issue, the x-ray spectra forecast by several computational models recycled in the diagnostic radiology and mammography energy kind have been calculated by appraisal with dignified spectra and their outcome on the scheming of absorbed dose and effective dose (ED) told to the adult ORNL hermaphroditic phantom quantified. This comprises practical models (TASMIP and MASMIP), semi-practical models (X-rayb&m, X-raytbc, XCOMP, IPEM, Tucker et al., and Blough et al.), and Monte Carlo modeling (EGS4, ITS3.0, and MCNP4C). Images got consuming synchrotron radiation (SR) and both screen-film and the CR system were related with images of the similar trials attained with digital mammography equipment. In sight of the worthy feature of the effects gained, the CR system was used in two mammographic inspections with SR. For separately mammography unit, the capability acquiesced bilateral mediolateral oblique (MLO) and craniocaudal(CC) mammograms attained in a woman with fatty breasts and a woman with dense breasts. Referees planned the common groups and definite absences that managed to a choice to miscarry the part that formed the scientific imaginings.

Keywords: mammography, monte carlo, effective dose, radiology

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Authors: S. Gvazava, N. Khidasheli, G. Gordeziani, A. DL. Batako

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The work presented in this paper studied the tribological characteristics (wear resistance, friction coefficient) of austempered ductile iron (ADI) with different combinations of structural composition (upper bainite, lower bainite, retained austenite) in dry sliding friction. A range of structural states of the metal matrix was obtained by changing the regimes of isothermal quenching of high-strength cast iron. The tribological tests were carried out using two sets of isothermal quenched cast irons. After austenitization at 900°С for 60 minutes, the specimens from the first group were isothermally quenched at the 300°С temperature and the specimens from the second set – at 400°С. The investigations showed that the isothermal quenching increases the friction coefficient of high-strength cast irons. The friction coefficient was found to be in the range from 0.4 to 0.55 for cast irons, depending on the structures of the metal matrix. The quenched cast irons having lower bainite demonstrate higher wear resistance in dry friction conditions. The dependence of wear resistance on the amount of retained austenite in isothermal quenched cast irons has a nonlinear characteristic and reaches its maximum value when the content of retained austenite is about 15-22%. The boron micro-additives allowed to reduce the friction coefficient of ADI and increase their wear resistance by 1.5-1.7 times.

Keywords: wear resistance, dry sliding, austempering, ADI, friction coefficient, retained austenite, isothermal quenching

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Authors: Chongyang Ye, Rong Liu

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Elastic compression stockings (ECSs) have been widely applied in prophylaxis and treatment of chronic venous insufficiency of lower extremities. The medical function of ECS is to improve venous return and increase muscular pumping action to facilitate blood circulation, which is largely determined by the complex interaction between the ECS and lower limb tissues. Understanding the mechanical transmission of ECS along the skin surface, deeper tissues, and vascular system is essential to assess the effectiveness of the ECSs. In this study, a three-dimensional (3D) finite element (FE) model of the leg-ECS system integrated with a 3D fluid-solid interaction (FSI) model of the leg-vein system was constructed to analyze the biomechanical properties of veins and soft tissues under different ECS compression. The Magnetic Resonance Imaging (MRI) of the human leg was divided into three regions, including soft tissues, bones (tibia and fibula) and veins (peroneal vein, great saphenous vein, and small saphenous vein). The ECSs with pressure ranges from 15 to 26 mmHg (Classes I and II) were adopted in the developed FE-FSI model. The soft tissue was assumed as a Neo-Hookean hyperelastic model with the fixed bones, and the ECSs were regarded as an orthotropic elastic shell. The interfacial pressure and stress transmission were simulated by the FE model, and venous hemodynamics properties were simulated by the FSI model. The experimental validation indicated that the simulated interfacial pressure distributions were in accordance with the pressure measurement results. The developed model can be used to predict interfacial pressure, stress transmission, and venous hemodynamics exerted by ECSs and optimize the structure and materials properties of ECSs design, thus improving the efficiency of compression therapy.

Keywords: elastic compression stockings, fluid-solid interaction, tissue and vein properties, prediction

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Authors: Rym Sassi, Franck Fayon, Mohend Chaouche, Emmanuel Veron, Valerie Montouillout

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The chemical phenomena occurring during cement hydration are complex and interdependent, and even after almost two centuries of studies, they are still difficult to solve for complex mixtures combining different hydraulic binders. Powder-XRD has been widely used for characterizing the crystalline phases in both anhydrous and hydrated cement, but only limited information is obtained in the case of strongly disordered and amorphous phases. In contrast, local spectroscopies like solid-state NMR can provide a quantitative description of noncrystalline phases. In this work, the structural modifications occurring during hydration of a fast-setting ternary binder based on white Portland cement, white calcium aluminate cement, and calcium sulfate were investigated using advanced solid-state NMR methods. We particularly focused on the early stage of the hydration up to 28 days, working with samples whose hydration was controlled and stopped. ²⁷Al MQ-MAS as well as {¹H}-²⁷Al and {¹H}-²⁹Si Cross- Polarization MAS NMR techniques were combined to distinguish all of the aluminum and silicon species formed during the hydration. The NMR quantification of the different phases was conducted in parallel with the XRD analyses. The consumption of initial products, as well as the precipitation of hydraulic phases (ettringite, monosulfate, strätlingite, CSH, and CASH), were unambiguously quantified. Finally, the drawing of the consumption and formation of phases was correlated with mechanical strength measurements.

Keywords: cement, hydration, hydrates structure, mechanical strength, NMR

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Authors: L. Ramirez, E. Guillén, J. Sánchez

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Some of the main goals about telecare such as monitoring, treatment, telediagnostic are deployed with the integration of applications with specific appliances. In order to achieve a coherent model to integrate software, hardware, and healthcare systems, different telehealth models with Internet of Things (IoT), cloud computing, artificial intelligence, etc. have been implemented, and their advantages are still under analysis. In this paper, we propose an integrated model based on IoT architecture and cloud computing telehealth center. Analytics module is presented as a solution to control an ideal diagnostic about some diseases. Specific features are then compared with the recently deployed conventional models in telemedicine. The main advantage of this model is the availability of controlling the security and privacy about patient information and the optimization on processing and acquiring clinical parameters according to technical characteristics.

Keywords: analytics, telemedicine, internet of things, cloud computing

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Authors: M. L. Burnwal, P. Raychowdhury

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Earthquake leads to adverse effects on buildings resting on soft soils. Mitigating the response of shallow foundations on soft soil with different methods reduces settlement and provides foundation stability. Few methods such as the rocking foundation (used in Performance-based design), deep foundation, prefabricated drain, grouting, and Vibro-compaction are used to control the pore pressure and enhance the strength of the loose soils. One of the problems with these methods is that the settlement is uncontrollable, leading to differential settlement of the footings, further leading to the collapse of buildings. The present study investigates the utility of geosynthetics as a potential improvement of the subsoil to reduce the earthquake-induced settlement of structures. A steel moment-resisting frame building resting on loose liquefiable dry soil, subjected to Uttarkashi 1991 and Chamba 1995 earthquakes, is used for the soil-structure interaction (SSI) analysis. The continuum model can simultaneously simulate structure, soil, interfaces, and geogrids in the OpenSees framework. Soil is modeled with PressureDependentMultiYield (PDMY) material models with Quad element that provides stress-strain at gauss points and is calibrated to predict the behavior of Ganga sand. The model analyzed with a tied degree of freedom contact reveals that the system responses align with the shake table experimental results. An attempt is made to study the responses of footing structure and geosynthetics with unreinforced and reinforced bases with varying parameters. The result shows that geogrid reinforces shallow foundation effectively reduces the settlement by 60%.

Keywords: settlement, shallow foundation, SSI, continuum FEM

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Authors: Fahad Alanazi, Andrew Jones

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Digital forensics seeks to achieve the successful investigation of digital crimes through obtaining acceptable evidence from digital devices that can be presented in a court of law. Thus, the digital forensics investigation is normally performed through a number of phases in order to achieve the required level of accuracy in the investigation processes. Since 1984 there have been a number of models and frameworks developed to support the digital investigation processes. In this paper, we review a number of the investigation processes that have been produced throughout the years and introduce a proposed digital forensic model which is based on the scope of the Saudi Arabia investigation process. The proposed model has been integrated with existing models for the investigation processes and produced a new phase to deal with a situation where there is initially insufficient evidence.

Keywords: digital forensics, process, metadata, Traceback, Sauid Arabia

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Authors: Martin K. Steiger, Lukas Heisler, Hans-Georg Brachtendorf

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Deep neural networks and their variants form the backbone of many AI applications. Based on the so-called residual networks, a continuous formulation of such models as ordinary differential equations (ODEs) has proven advantageous since different techniques may be applied that significantly increase the learning speed and enable controlled trade-offs with the resulting error at the same time. For the evaluation of such models, high-performance numerical differential equation solvers are used, which also provide the gradients required for training. However, whether classical gradient-based methods are even applicable or which one yields the best results has not been discussed yet. This paper aims to redeem this situation by providing empirical results for different applications.

Keywords: deep neural networks, gradient-based learning, image processing, ordinary differential equation networks

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Authors: Alice Schittek, Isabelle Roskam, Moira Mikolajczak

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Background: Perfectionistic strivings (PS) and perfectionistic concerns (PC) are associated with an increase in parental burnout (PB), and PB causally increases violence towards the offspring. Objective: To our best knowledge, no study has ever investigated whether perfectionism (PS and PC) predicts violence towards the offspring and whether PB could explain this link. We hypothesized that an increase in PS and PC would lead to an increase in violence via an increase in PB. Method: 228 participants responded to an online survey, with three measurement occasions spaced two months apart. Results: Contrary to expectations, cross-lagged path models revealed that violence towards the offspring prospectively predicts an increase in PS and PC. Mediation models showed that PB is not a significant mediator. The results of all models did not change when controlling for social desirability. Conclusion: The present study shows that violence towards the offspring increases the risk of PS and PC in parents, which highlights the importance of understanding the effect of child maltreatment on the whole family system and not just on children. Results are discussed in light of the feeling of guilt experienced by parents. Considering the insignificant mediation effect, PB research should slowly shift towards more (quasi) causal designs, allowing to identify which significant correlations translate into causal effects. Implications: Clinicians should focus on preventing child maltreatment as well as treating parental perfectionism. Researchers should unravel the effects of child maltreatment on the family system.

Keywords: maltreatment, parental burnout, perfectionistic strivings, perfectionistic concerns, perfectionism, violence

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Authors: Ana Neves, John Leander, Ignacio Gonzalez, Raid Karoumi

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Systematic monitoring and inspection are needed to assess the present state of a structure and predict its future condition. If an irregularity is noticed, repair actions may take place and the adequate intervention will most probably reduce the future costs with maintenance, minimize downtime and increase safety by avoiding the failure of the structure as a whole or of one of its structural parts. For this to be possible decisions must be made at the right time, which implies using systems that can detect abnormalities in their early stage. In this sense, Structural Health Monitoring (SHM) is seen as an effective tool for improving the safety and reliability of infrastructures. This paper explores the decision-making problem in SHM regarding the maintenance of civil engineering structures. The aim is to assess the present condition of a bridge based exclusively on measurements using the suggested method in this paper, such that action is taken coherently with the information made available by the monitoring system. Artificial Neural Networks are trained and their ability to predict structural behavior is evaluated in the light of a case study where acceleration measurements are acquired from a bridge located in Stockholm, Sweden. This relatively old bridge is presently still in operation despite experiencing obvious problems already reported in previous inspections. The prediction errors provide a measure of the accuracy of the algorithm and are subjected to further investigation, which comprises concepts like clustering analysis and statistical hypothesis testing. These enable to interpret the obtained prediction errors, draw conclusions about the state of the structure and thus support decision making regarding its maintenance.

Keywords: artificial neural networks, clustering analysis, model-free damage detection, statistical hypothesis testing, structural health monitoring

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Authors: Chandu Rathnayake, Isuri Anuradha

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Early detection and accurate diagnosis of lung diseases play a crucial role in improving patient prognosis. However, conventional diagnostic methods heavily rely on subjective symptom assessments and medical imaging, often causing delays in diagnosis and treatment. To overcome this challenge, we propose a novel lung disease prediction system that integrates patient symptoms and X-ray images to provide a comprehensive and reliable diagnosis.In this project, develop a mobile application specifically designed for detecting lung diseases. Our application leverages both patient symptoms and X-ray images to facilitate diagnosis. By combining these two sources of information, our application delivers a more accurate and comprehensive assessment of the patient's condition, minimizing the risk of misdiagnosis. Our primary aim is to create a user-friendly and accessible tool, particularly important given the current circumstances where many patients face limitations in visiting healthcare facilities. To achieve this, we employ several state-of-the-art algorithms. Firstly, the Decision Tree algorithm is utilized for efficient symptom-based classification. It analyzes patient symptoms and creates a tree-like model to predict the presence of specific lung diseases. Secondly, we employ the Random Forest algorithm, which enhances predictive power by aggregating multiple decision trees. This ensemble technique improves the accuracy and robustness of the diagnosis. Furthermore, we incorporate a deep learning model using Convolutional Neural Network (CNN) with the RestNet50 pre-trained model. CNNs are well-suited for image analysis and feature extraction. By training CNN on a large dataset of X-ray images, it learns to identify patterns and features indicative of lung diseases. The RestNet50 architecture, known for its excellent performance in image recognition tasks, enhances the efficiency and accuracy of our deep learning model. By combining the outputs of the decision tree-based algorithms and the deep learning model, our mobile application generates a comprehensive lung disease prediction. The application provides users with an intuitive interface to input their symptoms and upload X-ray images for analysis. The prediction generated by the system offers valuable insights into the likelihood of various lung diseases, enabling individuals to take appropriate actions and seek timely medical attention. Our proposed mobile application has significant potential to address the rising prevalence of lung diseases, particularly among young individuals with smoking addictions. By providing a quick and user-friendly approach to assessing lung health, our application empowers individuals to monitor their well-being conveniently. This solution also offers immense value in the context of limited access to healthcare facilities, enabling timely detection and intervention. In conclusion, our research presents a comprehensive lung disease prediction system that combines patient symptoms and X-ray images using advanced algorithms. By developing a mobile application, we provide an accessible tool for individuals to assess their lung health conveniently. This solution has the potential to make a significant impact on the early detection and management of lung diseases, benefiting both patients and healthcare providers.

Keywords: CNN, random forest, decision tree, machine learning, deep learning

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Authors: Ning Li, Jiacheng Zhang, Zheng Yang, Sylvia Kang

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Introduction: Polycystic ovary syndrome (PCOS) is a common physiological symptom in women of reproductive age. Women with PCOS may have infrequent or prolonged menstrual periods and excess male hormone (androgen) levels. Mira analyzes the cycle profiles and the luteinizing hormone (LH) changes in urine, closely related to the fertility level of healthy women and PCOS women. From the difference between the two groups, Mira helps to understand the physiological state of PCOS women and their hormonal changes in the menstrual cycle. Methods: In this study, data from 1496 cycles and information from 342 women belonging to two groups (181 PCOS and 161 Healthy) were collected and analyzed. Women test their luteinizing hormone (LH) in urine daily with Mira fertility test wand and Mira analyzer, from the day after the menstruation to the starting day of the next menstruation. All the collected data meets Mira’s user agreement and users’ identification was removed. The cycle length, LH peak, and other cycle information of the PCOS group were compared with the Healthy group. Results: The average cycle length of PCOS women is 41 days and of the Healthy women is 33 days. 91.4% of cycle length is within 40 days for the Healthy group, while it decreases to 71.9% for the PCOS group. This means PCOS women have a longer menstrual cycle and more variation during the cycle. With more variation, the ovulation prediction becomes more difficult for the PCOS group. The deviation between the LH surge day and the predicted ovulation day, calculated by the starting day of the next menstruation minus 14 days, is greater in the PCOS group compared with the Healthy group. Also, 46.96% of PCOS women have an irregular cycle, and only 19.25% of healthy women show an irregular cycle. Conclusion: PCOS women have longer menstrual cycles and more variation during the menstrual cycles. The traditional ovulation prediction is not suitable for PCOS women.

Keywords: menstrual cycle, PCOS, urinary luteinizing hormone, Mira

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Authors: Sarah Potter, Michele Laliberte

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As eating disorders are associated with high levels of chronicity, impairment, and distress, it is paramount to evaluate factors that may improve treatment outcomes in this group. Individuals with eating disorders exhibit elevated levels of perfectionism and emotion dysregulation, as well as reduced self-compassion. These variables are related to eating disorder outcomes, including shape/weight concerns and psychosocial impairment. Thus, these factors may be tenable targets for treatment within eating disorder populations. However, the relative contributions of perfectionism, emotion dysregulation, and self-compassion to the severity of shape/weight concerns and psychosocial impairment remain largely unexplored. In the current study, mediation analyses were conducted to clarify how perfectionism, emotion dysregulation, and self-compassion are linked to shape/weight concerns and psychosocial impairment. The sample was comprised of 85 patients from an outpatient eating disorder clinic. The patients completed self-report measures of perfectionism, self-compassion, emotion dysregulation, eating disorder symptoms, and psychosocial impairment. Specifically, emotion dysregulation was assessed as a mediator in the relationships between (1) perfectionism and shape/weight concerns, (2) self-compassion and shape/weight concerns, (3) perfectionism and psychosocial impairment, and (4) self-compassion and psychosocial impairment. It was postulated that emotion dysregulation would significantly mediate relationships in the former two models. An a priori hypothesis was not constructed in reference to the latter models, as these analyses were preliminary and exploratory in nature. The PROCESS macro for SPSS was utilized to perform these analyses. Emotion dysregulation fully mediated the relationships between perfectionism and eating disorder outcomes. In the link between self-compassion and psychosocial impairment, emotion dysregulation partially mediated this relationship. Finally, emotion dysregulation did not significantly mediate the relationship between self-compassion and shape/weight concerns. The results suggest that emotion dysregulation and self-compassion may be suitable targets to decrease the severity of psychosocial impairment and shape/weight concerns in individuals with eating disorders. Further research is required to determine the stability of these models over time, between diagnostic groups, and in nonclinical samples.

Keywords: eating disorders, emotion dysregulation, perfectionism, self-compassion

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Authors: Sabrina Neururer, Marco Schweitzer, Werner Hackl, Bernhard Tilg, Patrick Raudaschl, Andreas Huber, Bernhard Pfeifer

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History and the current outbreak of Covid-19 have shown the deadly potential of infectious diseases. However, infectious diseases also have a serious impact on areas other than health and healthcare, such as the economy or social life. These areas are strongly codependent. Therefore, disease control measures, such as social distancing, quarantines, curfews, or lockdowns, have to be adopted in a very considerate manner. Infectious disease modeling can support policy and decision-makers with adequate information regarding the dynamics of the pandemic and therefore assist in planning and enforcing appropriate measures that will prevent the healthcare system from collapsing. In this work, an agent-based simulation package named “survival” for simulating infectious diseases is presented. A special focus is put on SARS-Cov-2. The presented simulation package was used in Austria to model the SARS-Cov-2 outbreak from the beginning of 2020. Agent-based modeling is a relatively recent modeling approach. Since our world is getting more and more complex, the complexity of the underlying systems is also increasing. The development of tools and frameworks and increasing computational power advance the application of agent-based models. For parametrizing the presented model, different data sources, such as known infections, wastewater virus load, blood donor antibodies, circulating virus variants and the used capacity for hospitalization, as well as the availability of medical materials like ventilators, were integrated with a database system and used. The simulation result of the model was used for predicting the dynamics and the possible outcomes and was used by the health authorities to decide on the measures to be taken in order to control the pandemic situation. The survival package was implemented in the programming language Java and the analytics were performed with R Studio. During the first run in March 2020, the simulation showed that without measures other than individual personal behavior and appropriate medication, the death toll would have been about 27 million people worldwide within the first year. The model predicted the hospitalization rates (standard and intensive care) for Tyrol and South Tyrol with an accuracy of about 1.5% average error. They were calculated to provide 10-days forecasts. The state government and the hospitals were provided with the 10-days models to support their decision-making. This ensured that standard care was maintained for as long as possible without restrictions. Furthermore, various measures were estimated and thereafter enforced. Among other things, communities were quarantined based on the calculations while, in accordance with the calculations, the curfews for the entire population were reduced. With this framework, which is used in the national crisis team of the Austrian province of Tyrol, a very accurate model could be created on the federal state level as well as on the district and municipal level, which was able to provide decision-makers with a solid information basis. This framework can be transferred to various infectious diseases and thus can be used as a basis for future monitoring.

Keywords: modelling, simulation, agent-based, SARS-Cov-2, COVID-19

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Authors: Rifqi Irvansyah, Abdullah Abdullah, Yunita Idris, Bunga Raihanda

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Banda Aceh is particularly susceptible to heightened vulnerability during natural disasters due to its concentrated exposure to multi-hazard risks. Despite urgent priorities during the aftermath of natural disasters, such as the 2004 Indian Ocean earthquake and tsunami, several public facilities, including school buildings, sustained damage yet continued operations without adequate repairs, even though they were submerged by the tsunami. This research aims to evaluate the consequences of column damage induced by tsunami inundation on the structural integrity of buildings. The investigation employs interaction diagrams for columns to assess their capacity, taking into account factors such as rebar deterioration and corrosion. The analysis result shows that one-fourth of the K1 columns on the first floor fall outside of the column interaction diagram, indicating that the column structure cannot handle the load above it, as evidenced by the presence of Pu and Mu, which are greater than the column's design strength. This suggests that the five columns of K1 should be cause for concern, as the column's capacity is decreasing. These results indicate that the structure of the building cannot sustain the applied load because the column cross-section has deteriorated. In contrast, all K2 columns meet the design strength, indicating that the column structure can withstand the structural loads.

Keywords: tsunami inundation, column damage, column interaction diagram, mitigation effort

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Authors: Nirav Jadav, M. G.Vanza

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Black cotton soils mainly exhibit the property of swelling/shrinkage when they react to moisture variations. This property causes development of cracks in the structures resting on these soils, which poses instability to the structures. Soil stabilization is a technique to enhance the geotechnical characteristics of Black cotton soils by changing their properties. Due to rapid growth in construction industry, a lot of waste material is being generated every day, which poses the problem of its disposal. If the waste material can be utilized for soil stabilization, it will mitigate the problems of its disposal. The tests results evaluate that the strength of the Black cotton soils increased by the use of C&D waste material. This study determines various Index and engineering properties of soil and compare for different proportions of soil and C&D Waste. For finding properties of soil and C&D Waste, various test is carried out like sieve analysis, hydrometer test, specific gravity test, Atterberg’s limit test, Standard proctor test and soil Triaxial unconsolidated undrained test. It also takes into account the characteristics alteration due to addition of C&D Waste in active and passive pressure. This study presents the efficacy for use of C&D Waste as a stabilizing material to be mixed with backfill soil in retaining walls. Standard proctor test was conducted at proportions S1W0 (soil = 100%, Waste = 0%), S7W1 (soil = 87.5%, waste = 12.5%), S3W1, S5W3 and S1W1. From these, S5W3 showed optimum results, so this proportion was considered for Soil Triaxial UU-Test. Also, S1W0 was considered too. When 37.5% of soil is replaced by C&D Waste, the Optimum moisture content (OMC) decrease by 11.48%, further, increase C&D Waste in soil OMC remains constant, and maximum dry density (MDD) were observed to be increased by 9.27%, further increased C&D Waste in soil MDD reduces. Carried out strength test, which shows cohesion decreased by 162% and the internal friction angle increased by 49.4% with compare to virgin soil. The study focuses on the potential use of C&D Waste as a stabilizing material in the retaining wall backfill. The active earth pressure decreases, and the passive earth pressure increases in the S5W3 mixture compared to the S1W0 mixture at the same depth.

Keywords: black cotton soil, construction demolition waste, compaction test, strength test

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Authors: Arunas Burinskas, Manuela Tvaronavičienė

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Although the new trade theories, unlike the theories of an industrial organisation, see the structure of the market and competition between enterprises through their heterogeneity according to various parameters, they do not pay any particular attention to the analysis of the market structure and its development. In this article, although we relied mainly on models developed by the scholars of new trade theory, we proposed a different approach. In our simulation model, we model market demand according to normal distribution function, while on the supply side (as it is in the new trade theory models), productivity is modeled with the Pareto distribution function. The results of the simulation show that companies with higher productivity (lower marginal costs) do not pass on all the benefits of such economies to buyers. However, even with higher marginal costs, firms can choose to offer higher value-added goods to stay in the market. In general, the structure of the market is formed quickly enough and depends on the skills available to firms.

Keywords: market, structure, simulation, heterogenous firms

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Authors: D. S. Fardhyanti, Megawati, W. B. Sediawan

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Coal tar is a liquid by-product of the process of coal gasification and carbonation. This liquid oil mixture contains various kinds of useful compounds such as aromatic compounds and phenolic compounds. These compounds are widely used as raw material for insecticides, dyes, medicines, perfumes, coloring matters, and many others. This research investigates thermodynamic modelling of liquid-liquid equilibria (LLE) in the separation of phenol from the coal tar by solvent extraction. The equilibria are modeled by ternary components of Wohl, Van Laar, and Three-Suffix Margules models. The values of the parameters involved are obtained by curve-fitting to the experimental data. Based on the comparison between calculated and experimental data, it turns out that among the three models studied, the Three-Suffix Margules seems to be the best to predict the LLE of p-Cresol mixtures for those system.

Keywords: coal tar, phenol, Wohl, Van Laar, Three-Suffix Margules

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Authors: Massimo Miccoli, Luca Marangoni, Alberto Aniello Scaringi, Alessandro Marceddu, Alessandro Amicone

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The susceptibility of deep neural networks (DNNs) to adversarial manipulations is a recognized challenge within the computer vision domain. Adversarial examples, crafted by adding subtle yet malicious alterations to benign images, exploit this vulnerability. Various defense strategies have been proposed to safeguard DNNs against such attacks, stemming from diverse research hypotheses. Building upon prior work, our approach involves the utilization of autoencoder models. Autoencoders, a type of neural network, are trained to learn representations of training data and reconstruct inputs from these representations, typically minimizing reconstruction errors like mean squared error (MSE). Our autoencoder was trained on a dataset of benign examples; learning features specific to them. Consequently, when presented with significantly perturbed adversarial examples, the autoencoder exhibited high reconstruction errors. The architecture of the autoencoder was tailored to the dimensions of the images under evaluation. We considered various image sizes, constructing models differently for 256x256 and 512x512 images. Moreover, the choice of the computer vision model is crucial, as most adversarial attacks are designed with specific AI structures in mind. To mitigate this, we proposed a method to replace image-specific dimensions with a structure independent of both dimensions and neural network models, thereby enhancing robustness. Our multi-modal autoencoder reconstructs the spectral representation of images across the red-green-blue (RGB) color channels. To validate our approach, we conducted experiments using diverse datasets and subjected them to adversarial attacks using models such as ResNet50 and ViT_L_16 from the torch vision library. The autoencoder extracted features used in a classification model, resulting in an MSE (RGB) of 0.014, a classification accuracy of 97.33%, and a precision of 99%.

Keywords: adversarial attacks, malicious images detector, binary classifier, multimodal transformer autoencoder

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Authors: Ahmad Aslizadeh, Farid Ghaderi

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Mapping organizational knowledge is an innovative concept and useful instrument of representation, capturing and visualization of implicit and explicit knowledge. There are a diversity of methods, instruments and techniques presented by different researchers following mapping organizational knowledge to reach determined goals. Implicating of these methods, it is necessary to know their exigencies and conditions in which those can be used. Integrating identified methods of knowledge mapping and comparing them would help knowledge managers to select the appropriate methods. This research conducted to presenting a model and framework to map organizational knowledge. At first, knowledge maps, their applications and necessity are introduced because of extracting comparative framework and detection of their structure. At the next step techniques of researchers such as Eppler, Kim, Egbu, Tandukar and Ebner as knowledge mapping models are presented and surveyed. Finally, they compare and a superior model would be introduced.

Keywords: knowledge mapping, knowledge management, comparative study, business and management

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Authors: Simone Leonardi, Luca Ardito

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Micro-influencers are effective elements in the marketing strategies of companies and institutions because of their capability to create an hyper-engaged audience around a specific topic of interest. In recent years, many scientific approaches and commercial tools have handled the task of detecting this type of social media users. These strategies adopt solutions ranging from rule based machine learning models to deep neural networks and graph analysis on text, images, and account information. This work compares the existing solutions and proposes an ensemble method to generalize them with different input data and social media platforms. The deployed solution combines deep learning models on unstructured data with statistical machine learning models on structured data. We retrieve both social media accounts information and multimedia posts on Twitter and Instagram. These data are mapped into feature vectors for an eXtreme Gradient Boosting (XGBoost) classifier. Sixty different topics have been analyzed to build a rule based gold standard dataset and to compare the performances of our approach against baseline classifiers. We prove the effectiveness of our work by comparing the accuracy, precision, recall, and f1 score of our model with different configurations and architectures. We obtained an accuracy of 0.91 with our best performing model.

Keywords: deep learning, gradient boosting, image processing, micro-influencers, NLP, social media

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Authors: M. Varin, A. M. Dubois, R. Gadbois-Langevin, B. Chalghaf

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Good knowledge of the physical environment is essential for an integrated forest planning. This information enables better forecasting of operating costs, determination of cutting volumes, and preservation of ecologically sensitive areas. The use of satellite images in stereoscopic pairs gives the capacity to generate high precision 3D models, which are scale-adapted for harvesting operations. These models could represent an alternative to 3D LiDAR data, thanks to their advantageous cost of acquisition. The objective of the study was to assess the quality of stereo-derived canopy height models (CHM) in comparison to a traditional LiDAR CHM and ground tree-height samples. Two study sites harboring two different forest stand types (broadleaf and conifer) were analyzed using stereo pairs and tri-stereo images from the WorldView-3 satellite to calculate CHM. Acquisition of multispectral images from an Unmanned Aerial Vehicle (UAV) was also realized on a smaller part of the broadleaf study site. Different algorithms using two softwares (PCI Geomatica and Correlator3D) with various spatial resolutions and band selections were tested to select the 3D modeling technique, which offered the best performance when compared with LiDAR. In the conifer study site, the CHM produced with Corelator3D using only the 50-cm resolution panchromatic band was the one with the smallest Root-mean-square deviation (RMSE: 1.31 m). In the broadleaf study site, the tri-stereo model provided slightly better performance, with an RMSE of 1.2 m. The tri-stereo model was also compared to the UAV, which resulted in an RMSE of 1.3 m. At individual tree level, when ground samples were compared to satellite, lidar, and UAV CHM, RMSE were 2.8, 2.0, and 2.0 m, respectively. Advanced analysis was done for all of these cases, and it has been noted that RMSE is reduced when the canopy cover is higher when shadow and slopes are lower and when clouds are distant from the analyzed site.

Keywords: very high spatial resolution, satellite imagery, WorlView-3, canopy height models, CHM, LiDAR, unmanned aerial vehicle, UAV

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Authors: Ofosuhene O. Apenteng, Noor Azina Ismail

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The human immunodeficiency virus (HIV) that causes acquired immune deficiency syndrome (AIDS) remains a global cause of morbidity and mortality. It has caused panic since its emergence. Relationships between migration and HIV/AIDS have become complex. In the absence of prospectively designed studies, dynamic mathematical models that take into account the migration movement which will give very useful information. We have explored the utility of mathematical models in understanding transmission dynamics of HIV and AIDS and in assessing the magnitude of how migration has impact on the disease. The model was calibrated to HIV and AIDS incidence data from Malaysia Ministry of Health from the period of 1986 to 2011 using Bayesian analysis with combination of Markov chain Monte Carlo method (MCMC) approach to estimate the model parameters. From the estimated parameters, the estimated basic reproduction number was 22.5812. The rate at which the susceptible individual moved to HIV compartment has the highest sensitivity value which is more significant as compared to the remaining parameters. Thus, the disease becomes unstable. This is a big concern and not good indicator from the public health point of view since the aim is to stabilize the epidemic at the disease-free equilibrium. However, these results suggest that the government as a policy maker should make further efforts to curb illegal activities performed by migrants. It is shown that our models reflect considerably the dynamic behavior of the HIV/AIDS epidemic in Malaysia and eventually could be used strategically for other countries.

Keywords: epidemic model, reproduction number, HIV, MCMC, parameter estimation

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Authors: Onabajo Paul

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The testing and assessment of physical fitness of school-aged adolescents in Nigeria has been going on for several decades. Originally, these tests strictly focused on identifying health and physical fitness status and comparing the results of adolescents with others. There is a considerable interest in health and performance fitness of adolescents in which results attained are compared with criteria representing positive health rather than simply on score comparisons with others. Despite the fact that physical education program is being studied in secondary schools and physical activities are encouraged, it is observed that regular assessment of students’ fitness level and health status seems to be scarce or not being done in these schools. The purpose of the study was to assess the heath and performance fitness of adolescents in middle-income schools in Lagos State. A total number of 150 students were selected using the simple random sampling technique. Participants were measured on hand grip strength, sit-up, pacer 20 meter shuttle run, standing long jump, weight and height. The data collected were analyzed with descriptive statistics of means, standard deviations, and range and compared with fitness norms. It was concluded that majority 111(74.0%) of the adolescents achieved the healthy fitness zone, 33(22.0%) were very lean, and 6(4.0%) needed improvement according to the normative standard of Body Mass Index test. For muscular strength, majority 78(52.0%) were weak, 66(44.0%) were normal, and 6(4.0%) were strong according to the normative standard of hand-grip strength test. For aerobic capacity fitness, majority 93(62.0%) needed improvement and were at health risk, 36(24.0%) achieved healthy fitness zone, and 21(14.0%) needed improvement according to the normative standard of PACER test. Majority 48(32.0%) of the participants had good hip flexibility, 38(25.3%) had fair status, 27(18.0%) needed improvement, 24(16.0%) had very good hip flexibility status, and 13(8.7%) of the participants had excellent status. Majority 61(40.7%) had average muscular endurance status, 30(20.0%) had poor status, 29(18.3%) had good status, 28(18.7%) had fair muscular endurance status, and 2(1.3%) of the participants had excellent status according to the normative standard of sit-up test. Majority 52(34.7%) had low jump ability fitness, 47(31.3%) had marginal fitness, 31(20.7%) had good fitness, and 20(13.3%) had high performance fitness according to the normative standard of standing long jump test. Based on the findings, it was concluded that majority of the adolescents had better Body Mass Index status, and performed well in both hip flexibility and muscular endurance tests. Whereas majority of the adolescents performed poorly in aerobic capacity test, muscular strength and jump ability test. It was recommended that to enhance wellness, adolescents should be involved in physical activities and recreation lasting 30 minutes three times a week. Schools should engage in fitness program for students on regular basis at both senior and junior classes so as to develop good cardio-respiratory, muscular fitness and improve overall health of the students.

Keywords: adolescents, health-related fitness, performance-related fitness, physical fitness

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Authors: Khandoker Akib Mohammad

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While analyzing time-to-event data, it is possible that a certain fraction of subjects will never experience the event of interest, and they are said to be cured. When this feature of survival models is taken into account, the models are commonly referred to as cure models. In the presence of covariates, the conditional survival function of the population can be modelled by using the cure model, which depends on the probability of being uncured (incidence) and the conditional survival function of the uncured subjects (latency), and a combination of logistic regression and Cox proportional hazards (PH) regression is used to model the incidence and latency respectively. In this paper, we have shown the asymptotic normality of the profile likelihood estimator via asymptotic expansion of the profile likelihood and obtain the explicit form of the variance estimator with an implicit function in the profile likelihood. We have also shown the efficient score function based on projection theory and the profile likelihood score function are equal. Our contribution in this paper is that we have expressed the efficient information matrix as the variance of the profile likelihood score function. A simulation study suggests that the estimated standard errors from bootstrap samples (SMCURE package) and the profile likelihood score function (our approach) are providing similar and comparable results. The numerical result of our proposed method is also shown by using the melanoma data from SMCURE R-package, and we compare the results with the output obtained from the SMCURE package.

Keywords: Cox PH model, cure model, efficient score function, EM algorithm, implicit function, profile likelihood

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Authors: Mustafa Abu Ghalia, Yaser Dahman

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A new class of polyurethane (PU) reinforced with green bacterial cellulose nanofibers (BC) were prepared using a solvent casting method, with the goal of fabricating green nanocomposites. Four series classes of BC (1, 2.5, 5, and 10 wt%) were reinforced into PU matrices via BC surface modification and subsequently BC-grafted into PU throughout silane coupling agent to improve BC dispersion and its interfacial interaction. The experiment results from the tensile tester were evaluated according to the response surface method (RSM) for optimizing the impacts of variable parameters, pore size, porosity, and BC contents on the mechanical properties. The compressive strength for PU-5 BC wt% was about 9.8 MPa, and decrease when being generated prosperity to recorded at 4.9 MPa. Nielson model was applied to investigate the BC stress concentration on the PU matrices. Likewise, krenche and Hapli-Tasi model were employed to evaluate the BC nanofiber reinforcement potential and BC orientation into PU matrices. The analysis of variance (ANOVA) demonstrated that only BC loading has a significant effect in increases tensile strength, young’s modulus, and a flexural modulus of the PU-BC nanocomposites. The optimal factors of the variables experiment confirmed to be 5 wt% for BC, 230 for pore size, and 80 % for porosity. Scanning electron microscopy (SEM) micrographs showed that the uniform distribution of nanofibers in the PU matrices with the addition of BC 5 wt %. Hydrolytic degradation revealed that the weight loss in PU-BC scaffold is higher than PU-BC wt %.

Keywords: polyurethane scaffold, mechanical properties, tissue engineering, polyurethane

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Authors: Endang Jati Mat Sahid, Hussain Ali Bekhet

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Ability to supply reliable and secure electricity has been one of the crucial components of economic development for any country. Forecasting of electricity production is therefore very important for accurate investment planning of generation power plants. In this study, we aim to examine and analyze the factors that affect electricity generation. Multiple regression models were used to find the relationship between various variables and electricity production. The models will simultaneously determine the effects of the variables on electricity generation. Many variables influencing electricity generation, i.e. natural gas (NG), coal (CO), fuel oil (FO), renewable energy (RE), gross domestic product (GDP) and fuel prices (FP), were examined for Malaysia. The results demonstrate that NG, CO, and FO were the main factors influencing electricity generation growth. This study then identified a number of policy implications resulting from the empirical results.

Keywords: energy policy, energy security, electricity production, Malaysia, the regression model

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