Search results for: energy demand model

Authors: Hajar Hajmohammadi, Paul Pfeffer, Anna De Simoni, Jim Cole, Chris Griffiths, Sally Hull, Benjamin Heydecker

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Background: There is strong interest in the relationship between short-term air pollution exposure and human health. Most studies in this field focus on serious health effects such as death or hospital admission, but air pollution exposure affects many people with less severe impacts, such as exacerbations of respiratory conditions. A lack of quantitative analysis and inconsistent findings suggest improved methodology is needed to understand these effectsmore fully. Method: We developed a time series regression model to quantify the relationship between daily NOₓ concentration and Asthma exacerbations requiring oral steroids from primary care settings. Explanatory variables include daily NOₓ concentration measurements extracted from 8 available background and roadside monitoring stations in east London and daily ambient temperature extracted for London City Airport, located in east London. Lags of NOx concentrations up to 21 days (3 weeks) were used in the model. The dependent variable was the daily number of oral steroid courses prescribed for GP registered patients with asthma in east London. A mixed distribution model was then fitted to the significant lags of the regression model. Result: Results of the time series modelling showed a significant relationship between NOₓconcentrations on each day and the number of oral steroid courses prescribed in the following three weeks. In addition, the model using only roadside stations performs better than the model with a mixture of roadside and background stations.

Keywords: air pollution, time series modeling, public health, road transport

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Authors: Hassan Karimi, Fred Piercy, Ruoxi Chen, Ana L. Jaramillo-Sierra, Wei-Ning Chang, Manjushree Palit, Catherine Martosudarmo, Angelito Antonio

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Background: The two psychotherapy camps, the randomized clinical trials (RCTs) and the common factors model, have competitively claimed specific explanations for therapy effectiveness. Recently, scholars called for empirical evidence to show the role of common factors in therapeutic outcome in marriage and family therapy. Purpose: This cross-national study aims to explore how clinicians, across different nations and theoretical orientations, attribute the contribution of common factors to therapy outcome. Method: A brief common factors questionnaire (CFQ-with a Cronbach’s Alpha, 0.77) was developed and administered in seven nations. A series of statistical analyses (paired-samples t-test, independent sample t-test, ANOVA) were conducted: to compare clinicians perceived contribution of total common factors versus model-specific factors, to compare each pair of common factors’ categories, and to compare clinicians from collectivistic nations versus clinicians from individualistic nation. Results: Clinicians across seven nations attributed 86% to common factors versus 14% to model-specific factors. Clinicians attributed 34% of therapeutic change to client’s factors, 26% to therapist’s factors, 26% to relationship factors, and 14% to model-specific techniques. The ANOVA test indicated each of the three categories of common factors (client 34%, therapist 26%, relationship 26%) showed higher contribution in therapeutic outcome than the category of model specific factors (techniques 14%). Clinicians with psychology degree attributed more contribution to model-specific factors than clinicians with MFT and counseling degrees who attributed more contribution to client factors. Clinicians from collectivistic nations attributed larger contributions to therapist’s factors (M=28.96, SD=12.75) than the US clinicians (M=23.22, SD=7.73). The US clinicians attributed a larger contribution to client’s factors (M=39.02, SD=1504) than clinicians from the collectivistic nations (M=28.71, SD=15.74). Conclusion: The findings indicate clinicians across the globe attributed more than two thirds of therapeutic change to CFs, which emphasize the training of the common factors model in the field. CFs, like model-specific factors, vary in their contribution to therapy outcome in relation to specific client, therapist, problem, treatment model, and sociocultural context. Sociocultural expectations and norms should be considered as a context in which both CFs and model-specific factors function toward therapeutic goals. Clinicians need to foster a cultural competency specifically regarding the divergent ways that CFs can be activated due to specific sociocultural values.

Keywords: common factors, model-specific factors, cross-national survey, therapist cultural competency, enhancing therapist efficacy

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Authors: Yuan-Jye Tseng, Yi-Shiuan Chen

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In this paper, a new concept of closed-loop design model is presented. The closed-loop design model is developed by integrating forward design and reverse design. Based on this new concept, a closed-loop design model for sustainable manufacturing by integrated evaluation of forward design, reverse design, and green manufacturing using a fuzzy analytic network process is developed. In the design stage of a product, with a given product requirement and objective, there can be different ways to design the detailed components and specifications. Therefore, there can be different design cases to achieve the same product requirement and objective. Thus, in the design evaluation stage, it is required to analyze and evaluate the different design cases. The purpose of this research is to develop a model for evaluating the design cases by integrated evaluation of forward design, reverse design, and green manufacturing models. A fuzzy analytic network process model is presented for integrated evaluation of the criteria in the three models. The comparison matrices for evaluating the criteria in the three groups are established. The total relational values among the three groups represent the total relational effects. In application, a super matrix can be created and the total relational values can be used to evaluate the design cases for decision-making to select the final design case. An example product is demonstrated in this presentation. It shows that the model is useful for integrated evaluation of forward design, reverse design, and green manufacturing to achieve a closed-loop design for sustainable manufacturing objective.

Keywords: design evaluation, forward design, reverse design, closed-loop design, supply chain management, closed-loop supply chain, fuzzy analytic network process

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Authors: Ok-Kyun Im, Kyoung Hee Kim

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We live in an era that faces global challenges of climate changes and resource depletion. With the rapid urbanization and growing energy consumption in the built environment, building facades become ever more important in architectural practice and environmental stewardship. Furthermore, building facade undergoes complex dynamics of social, cultural, environmental and technological changes. Kinetic facades have drawn attention of architects, designers, and engineers in the field of adaptable, responsive and interactive architecture since 1980’s. Materials and building technologies have gradually evolved to address the technical implications of kinetic facades. The kinetic façade is becoming an independent system of the building, transforming the design methodology to sustainable building solutions. Accordingly, there is a need for a new design methodology to guide the design of a kinetic façade and evaluate its sustainable performance. The research objectives are two-fold: First, to establish a new design methodology for kinetic facades and second, to develop a micro-oculi façade system and assess its performance using the established design method. The design approach to the micro-oculi facade is comprised of 1) façade geometry optimization and 2) dynamic building energy simulation. The façade geometry optimization utilizes multi-objective optimization process, aiming to balance the quantitative and qualitative performances to address the sustainability of the built environment. The dynamic building energy simulation was carried out using EnergyPlus and Radiance simulation engines with scripted interfaces. The micro-oculi office was compared with an office tower with a glass façade in accordance with ASHRAE 90.1 2013 to understand its energy efficiency. The micro-oculi facade is constructed with an array of circular frames attached to a pair of micro-shades called a micro-oculus. The micro-oculi are encapsulated between two glass panes to protect kinetic mechanisms with longevity. The micro-oculus incorporates rotating gears that transmit the power to adjacent micro-oculi to minimize the number of mechanical parts. The micro-oculus rotates around its center axis with a step size of 15deg depending on the sun’s position while maximizing daylighting potentials and view-outs. A 2 ft by 2ft prototyping was undertaken to identify operational challenges and material implications of the micro-oculi facade. In this research, a systematic design methodology was proposed, that integrates multi-objectives of kinetic façade design criteria and whole building energy performance simulation within a holistic design process. This design methodology is expected to encourage multidisciplinary collaborations between designers and engineers to collaborate issues of the energy efficiency, daylighting performance and user experience during design phases. The preliminary energy simulation indicated that compared to a glass façade, the micro-oculi façade showed energy savings due to its improved thermal properties, daylighting attributes, and dynamic solar performance across the day and seasons. It is expected that the micro oculi façade provides a cost-effective, environmentally-friendly, sustainable, and aesthetically pleasing alternative to glass facades. Recommendations for future studies include lab testing to validate the simulated data of energy and optical properties of the micro-oculi façade. A 1:1 performance mock-up of the micro-oculi façade can suggest in-depth understanding of long-term operability and new development opportunities applicable for urban façade applications.

Keywords: energy efficiency, kinetic facades, sustainable architecture, urban facades

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Authors: Kobamelo Mashaba

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The molten slag has a high substantial temperatures range between 1723-1923, carrying a huge amount of useful energy for reducing energy consumption and CO₂ emissions under the heat recovery process. Therefore in this study, we investigated the performance of the modified crank Nicolson method for a delayed partial differential equation on the heat recovery of molten slag in the metallurgical mining environment. It was proved that the proposed method converges quickly compared to the classic method with the existence of a unique solution. It was inferred from numerical result that the proposed methodology is more viable and profitable for the mining industry.

Keywords: delayed partial differential equation, modified Crank-Nicolson Method, molten slag, heat recovery, parabolic equation

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Authors: Boce Zhang, Yaguang Luo

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Ripening of climacteric fruits, such as bananas and avocados, are usually initiated days prior to the retail marketing. However, upon the onset of irreversible ripening, they undergo rapid spoilage if not consumed within a narrow climacteric time window. Controlled ripening of climacteric fruits is a critical step to provide consumers with high-quality products while reducing postharvest losses and food waste. There is a high demand for technologies that can retard the ripening process or enable accelerated ripening immediately before consumption. In this work, metal−organic framework (MOF) was developed as a solid porous matrix to encapsulate gaseous hormone, including ethylene, for subsequent application. The feasibility of the on-demand stimulated ripening of bananas and avocados is also evaluated. MOF was synthesized and loaded with ethylene gas. The MOF−ethylene was placed inside sealed containers with preclimacteric bananas and avocados and stored at 16 °C. The fruits were treated for 24-48 hours, and evaluated for ripening progress. Results indicate that MOF−ethylene treatment significantly accelerated the ripening-related changes of color and textural properties in treated bananas and avocados. The average ripening period for both avocados and bananas were reduced in half by using this method. No significant differences of quality characteristics at respective ripening stages were observed between produce ripened via MOF-ethylene versus exogenously supplied ethylene gas or endogenously produced ethylene. Solid MOF matrices could have multiple advantages compared to existing systems, including easy to transport and safe to use by minimally trained produce handlers and consumers. We envision that this technology can help tackle food waste challenges at the critical retail and consumer stages in the food supply chain.

Keywords: climacteric produce, controllable ripening, food waste challenge, metal organic framework

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Authors: El Hadi Bouguerra, Nouredine Retiel

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To avoid or at least to attenuate the global warming, it is essential to reduce the energy consumption of the buildings where the biggest potential of savings exists. The impending danger can come from the increase in the needs of air conditioning not only because of the climate warming but also the fast equipping of emerging or developing countries. Passive solutions exist and others are in promising development and therefore, must be applied wherever it is possible. Even if they do not always avoid the resort to an active cooling (mechanical), they allow lowering the load at an acceptable level which can be possibly taken in relay by the renewable energies. These solutions have the advantage to be relatively less expensive and especially adaptable to the existing housing. However, it is the internal convection resistance that controls the heat exchange between the phase change materials (PCM) and the indoor temperature because of the very low heat coefficients of natural convection. Therefore, it is reasonable to link the switch temperature Tm to the temperature of the substrate (walls and ceiling) because conduction heat transfer is dominant. In this case, external conditions (heat sources such as solar irradiation and ambient temperatures) and conductivities of envelope constituents are the most important factors. The walls are not at the same temperature year round; therefore, it is difficult to set a unique switch temperature for the whole season, making the average values a key parameter. With this work, the authors’ aim is to see which parameters influence the optimum switch temperature of a PCM and additionally, if a better selection of PCMs relating to their optimum temperature can enhance their energetic performances.

Keywords: low energy building, energy conservation, phase change materials, PCM

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Authors: Ahmed H. Abdullah, Pavel Kroupa

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Globular clusters (GC) are important objects for tracing the early evolution of a galaxy. We study the correlation between the cluster population and the global properties of the host galaxy. We found that the correlation between cluster population (NGC) and the baryonic mass (Mb) of the host galaxy are best described as 10 −5.6038Mb. In order to understand the origin of the U -shape relation between the GC specific frequency (SN) and Mb (caused by the high value of SN for dwarfs galaxies and giant ellipticals and a minimum SN for intermediate mass galaxies≈ 1010M), we derive a theoretical model for the specific frequency (SNth). The theoretical model for SNth is based on the slope of the power-law embedded cluster mass function (β) and different time scale (Δt) of the forming galaxy. Our results show a good agreement between the observation and the model at a certain β and Δt. The model seems able to reproduce higher value of SNth of β = 1.5 at the midst formation time scale.

Keywords: galaxies: dwarf, globular cluster: specific frequency, number of globular clusters, formation time scale

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Authors: Eman W. Boghdady, A. R. Shehata, M. A. Azem

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Database (DB) security demands permitting authorized users and prohibiting non-authorized users and intruders actions on the DB and the objects inside it. Organizations that are running successfully demand the confidentiality of their DBs. They do not allow the unauthorized access to their data/information. They also demand the assurance that their data is protected against any malicious or accidental modification. DB protection and confidentiality are the security concerns. There are four types of controls to obtain the DB protection, those include: access control, information flow control, inference control, and cryptographic. The cryptographic control is considered as the backbone for DB security, it secures the DB by encryption during storage and communications. Current cryptographic techniques are classified into two types: traditional classical cryptography using standard algorithms (DES, AES, IDEA, etc.) and chaos cryptography using continuous (Chau, Rossler, Lorenz, etc.) or discreet (Logistics, Henon, etc.) algorithms. The important characteristics of chaos are its extreme sensitivity to initial conditions of the system. In this paper, DB-security systems based on chaotic algorithms are described. The Pseudo Random Numbers Generators (PRNGs) from the different chaotic algorithms are implemented using Matlab and their statistical properties are evaluated using NIST and other statistical test-suits. Then, these algorithms are used to secure conventional DB (plaintext), where the statistical properties of the ciphertext are also tested. To increase the complexity of the PRNGs and to let pass all the NIST statistical tests, we propose two hybrid PRNGs: one based on two chaotic Logistic maps and another based on two chaotic Henon maps, where each chaotic algorithm is running side-by-side and starting from random independent initial conditions and parameters (encryption keys). The resulted hybrid PRNGs passed the NIST statistical test suit.

Keywords: algorithms and data structure, DB security, encryption, chaotic algorithms, Matlab, NIST

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Authors: Florian Pfeffel, Valentin Nickolai, Christian Louis Kühner

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Digitalization has disrupted the traditional workplace environment by allowing many employees to work from anywhere at any time. This trend of working from home was further accelerated due to the COVID-19 crisis, which forced companies to rethink their workplace models. While in many companies, this shift happened out of pure necessity; many employees were left more satisfied with their job due to the opportunity to work from home. This study focuses on employees’ job satisfaction in the service sector in dependence on the different work models, which are defined as a “work from home” model, the traditional “work in office” model, and a hybrid model. Using structural equation modeling (SEM), these three work models have been analyzed based on 13 influencing factors on job satisfaction that have been further summarized in the three groups “classic influencing factors”, “influencing factors changed by remote working”, and “new remote working influencing factors”. Based on the influencing factors on job satisfaction, a survey has been conducted with n = 684 employees in the service sector. Cronbach’s alpha of the individual constructs was shown to be suitable. Furthermore, the construct validity of the constructs was confirmed by face validity, content validity, convergent validity (AVE > 0.5: CR > 0.7), and discriminant validity. Additionally, confirmatory factor analysis (CFA) confirmed the model fit for the investigated sample (CMIN/DF: 2.567; CFI: 0.927; RMSEA: 0.048). The SEM-analysis has shown that the most significant influencing factor on job satisfaction is “identification with the work” with β = 0.540, followed by “Appreciation” (β = 0.151), “Compensation” (β = 0.124), “Work-Life-Balance” (β = 0.116), and “Communication and Exchange of Information” (β = 0.105). While the significance of each factor can vary depending on the work model, the SEM-analysis shows that the identification with the work is the most significant factor in all three work models and, in the case of the traditional office work model, it is the only significant influencing factor. The study shows that employees who work entirely remotely or have a hybrid work model are significantly more satisfied with their job, with a job satisfaction score of 5.0 respectively on a scale from 1 (very dissatisfied) to 7 (very satisfied), than employees do not have the option to work from home with a score of 4.6. This comes as a result of the lower identification with the work in the model without any remote working. Furthermore, the responses indicate that it is important to consider the individual preferences of each employee when it comes to the work model to achieve overall higher job satisfaction. Thus, it can be argued that companies can profit off of more motivation and higher productivity by considering the individual work model preferences, therefore, increasing the identification with the respective work.

Keywords: home-office, identification with work, job satisfaction, new work, remote work, structural equation modeling

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Authors: Chien-Jen Liu, Shu Ching Yang

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Using the technology acceptance model (TAM), this study examined the external variables of technological complexity (TC) to acquire a better understanding of the factors that influence the acceptance of computer application courses by learners at Active Aging Universities. After the learners in this study had completed a 27-hour Facebook course, 44 learners responded to a modified TAM survey. Data were collected to examine the path relationships among the variables that influence the acceptance of Facebook-mediated community learning. The partial least squares (PLS) method was used to test the measurement and the structural model. The study results demonstrated that attitudes toward Facebook use directly influence behavioral intentions (BI) with respect to Facebook use, evincing a high prediction rate of 58.3%. In addition to the perceived usefulness (PU) and perceived ease of use (PEOU) measures that are proposed in the TAM, other external variables, such as TC, also indirectly influence BI. These four variables can explain 88% of the variance in BI and demonstrate a high level of predictive ability. Finally, limitations of this investigation and implications for further research are discussed.

Keywords: technology acceptance model (TAM), technological complexity, partial least squares (PLS), perceived usefulness

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Authors: Wann-Ming Wey

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In recent years, in addition to face the external threats such as energy shortages and climate change, traffic congestion and environmental pollution have become anxious problems for many cities. Considering private automobile-oriented urban development had produced many negative environmental and social impacts, the transit-oriented development (TOD) has been considered as a sustainable urban model. TOD encourages public transport combined with friendly walking and cycling environment designs, however, non-motorized modes help improving human health, energy saving, and reducing carbon emissions. Due to environmental changes often affect the planners’ decision-making; this research applies dynamic network process (DNP) which includes the time dependent concept to promoting friendly walking and cycling environmental designs as an advanced planning support system for environment improvements. This research aims to discuss what kinds of design strategies can improve a friendly walking and cycling environment under TOD. First of all, we collate and analyze environment designing factors by reviewing the relevant literatures as well as divide into three aspects of “safety”, “convenience”, and “amenity” from fifteen environment designing factors. Furthermore, we utilize fuzzy Delphi Technique (FDT) expert questionnaire to filter out the more important designing criteria for the study case. Finally, we utilized DNP expert questionnaire to obtain the weights changes at different time points for each design criterion. Based on the changing trends of each criterion weight, we are able to develop appropriate designing strategies as the reference for planners to allocate resources in a dynamic environment. In order to illustrate the approach we propose in this research, Taipei city as one example has been used as an empirical study, and the results are in depth analyzed to explain the application of our proposed approach.

Keywords: environment planning support systems, walking and cycling, transit-oriented development (TOD), dynamic network process (DNP)

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Authors: Joe Hachem, Marianne Cuif-Sjostrand, Thierry Schuhler, Dominique Orhon, Assaad Zoughaib

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The exhaust gas recirculation, EGR, implementation on gas turbines is increasingly gaining the attention of many researchers. This emerging technology presents many advantages, such as lowering the NOx emissions and facilitating post-combustion carbon capture as the carbon dioxide concentration in the cycle increases. As interesting as this technology may seem, the gas turbine, or its thermodynamic equivalent, the Brayton cycle, shows an intrinsic efficiency decrease with increasing EGR rate. In this paper, a thermodynamic model is presented to show the cycle efficiency decrease with EGR, alternative values of design parameters of both the pressure ratio (PR) and the turbine inlet temperature (TIT) are then proposed to optimize the cycle efficiency with different EGR rates. Results show that depending on the given EGR rate, both the design PR & TIT should be increased to compensate for the deficit in efficiency.

Keywords: gas turbines, exhaust gas recirculation, design parameters optimization, thermodynamic approach

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Authors: Bhavesh Dadhich, Fenil Bamnoliya, Akshita Swaminathan

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This project aims to design a system to generate energy from flowing wind due to the motion of a vehicle on the road or from the flow of wind in compact areas to utilize the wasteful energy into a useful one. It is envisaged through a design and aerodynamic performance improvement of a Savonius vertical axis wind turbine rotor and used in an integrated system with a Triboelectric Nanogenerator (TENG) that can generate a good amount of electrical energy. Aerodynamic calculations are performed numerically using Computational Fluid Dynamics software, and TENG's performance is evaluated analytically. The Turbine's coefficient of power is validated with published results for an inlet velocity of 7 m/s with a Tip Speed Ratio of 0.75 and found to reasonably agree with that of experiment results. The baseline design is modified with a new blade arc angle and rotor position angle based on the recommended parameter ranges suggested by previous researchers. Simulations have been performed for different T.S.R. values ranging from 0.25 to 1.5 with an interval of 0.25 with two applicable free stream velocities of 5 m/s and 7m/s. Finally, the newly designed VAWT CFD performance results are used as input for the analytical performance prediction of the triboelectric nanogenerator. The results show that this approach could be feasible and useful for small power source applications.

Keywords: savonius turbine, power, overlap ratio, tip speed ratio, TENG

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Authors: Chungkuk Jin, Moo-Hyun Kim, HeonYong Kang

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A surface riding (SR) wave energy converter (WEC) is designed and its feasibility and performance are numerically simulated by the author-developed floater-mooring-magnet-electromagnetics fully-coupled dynamic analysis computer program. The biggest advantage of the SR-WEC is that the performance is equally effective even in low sea states and its structural robustness is greatly improved by simply riding along the wave surface compared to other existing WECs. By the numerical simulations and actuator testing, it is clearly demonstrated that the concept works and through the optimization process, its efficiency can be improved.

Keywords: computer simulation, electromagnetics fully-coupled dynamics, floater-mooring-magnet, optimization, performance evaluation, surface riding, WEC

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Authors: Azeez Shaik, Yagnik Kalariya, Amit Salvi

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High-performance requirements and emission norms were forcing the automobile industry to opt for lightweight materials which improve the fuel efficiency and absorb energy during crash applications. In such scenario, the woven fabric composites are providing better energy absorption compared to metals. Woven fabric composites have a repetitive unit cell (RUC) and the mechanical properties of these materials are highly dependent on RUC. This work investigates the importance of detailed modelling of the RUC, the size effects associated and the mitigation techniques to avoid them using Finite element analysis approach.

Keywords: repetitive unit cell, representative volume element, size effects, cohesive zone, finite element analysis

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Authors: Mohammadreza Akbari, Pooya Soleimani Besheli, Reza khalili, Sara Akbari, Davood Domiri Ganji

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In this symposium, our aims are accuracy, capabilities and power at solving of the complicate non-linear differential at the reaction chemical in the catalyst reactor (heterogeneous reaction). Our purpose is to enhance the ability of solving the mentioned nonlinear differential equations at chemical engineering and similar issues with a simple and innovative approach which entitled ‘’Akbari-Ganji's Method’’ or ‘’AGM’’. In this paper we solve many examples of nonlinear differential equations of chemical reactions and its investigate. The chemical reactor with the energy changing (non-isotherm) in two reactors of mixed and plug are separately studied and the nonlinear differential equations obtained from the reaction behavior in these systems are solved by a new method. Practically, the reactions with the energy changing (heat or cold) have an important effect on designing and function of the reactors. This means that possibility of reaching the optimal conditions of operation for the maximum conversion depending on nonlinear nature of the reaction velocity toward temperature, results in the complexity of the operation in the reactor. In this case, the differential equation set which governs the reactors can be obtained simultaneous solution of mass equilibrium and energy and temperature changing at concentration.

Keywords: new method (AGM), nonlinear differential equation, tubular and mixed reactors, catalyst bed

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Authors: Devina Aprilia Gunawan, Tasya Aspiranti, Inugrah Ratia Pratiwi

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This research aims to classify the mining sector companies based on Altman’s Z-score model, and providing an analysis based on the Altman’s Z-score model’s financial ratios to provide a picture about the financial condition in mining sector companies in Indonesia and their viability in the future, and to find out the partial and simultaneous impact of each of the financial ratio variables in the Altman’s Z-score model, namely (WC/TA), (RE/TA), (EBIT/TA), (MVE/TL), and (S/TA), toward the financial condition represented by the Z-score itself. Among 38 mining sector companies listed in Indonesia Stock Exchange (IDX), 28 companies are selected as research sample according to the purposive sampling criteria.The results of this research showed that during 3 years research period at 2010-2012, the amount of the companies that was predicted to be healthy in each year was less than half of the total sample companies and not even reach up to 50%. The multiple regression analysis result showed that all of the research hypotheses are accepted, which means that (WC/TA), (RE/TA), (EBIT/TA), (MVE/TL), and (S/TA), both partially and simultaneously had an impact towards company’s financial condition.

Keywords: Altman’s Z-score model, financial condition, mining companies, Indonesia

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Authors: Victor Breux, Jérôme Boutet, Alain Goret, Viviane Cattin

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Early detection of anomalies in data centers is important to reduce downtimes and the costs of periodic maintenance. However, there is little research on this topic and even fewer on the fusion of sensor data for the detection of abnormal events. The goal of this paper is to propose a method for anomaly detection in data centers by combining sensor data (temperature, humidity, power) and deep learning models. The model described in the paper uses one autoencoder per sensor to reconstruct the inputs. The auto-encoders contain Long-Short Term Memory (LSTM) layers and are trained using the normal samples of the relevant sensors selected by correlation analysis. The difference signal between the input and its reconstruction is then used to classify the samples using feature extraction and a random forest classifier. The data measured by the sensors of a data center between January 2019 and May 2020 are used to train the model, while the data between June 2020 and May 2021 are used to assess it. Performances of the model are assessed a posteriori through F1-score by comparing detected anomalies with the data center’s history. The proposed model outperforms the state-of-the-art reconstruction method, which uses only one autoencoder taking multivariate sequences and detects an anomaly with a threshold on the reconstruction error, with an F1-score of 83.60% compared to 24.16%.

Keywords: anomaly detection, autoencoder, data centers, deep learning

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Authors: Javier Fernandez, Mayte Medina, Rafael Fernandez de Canete, Nuria Alcain, Juan Carlos Ramos-Diaz

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At present, the experimentation of anesthetic drugs on patients requires a regulation protocol, and the response of each patient to several doses of entry drug must be well known. Therefore, the development of pharmacological dose control systems is a promising field of research in anesthesiology. In this paper, it has been developed a non-linear compartmental the pharmacokinetic-pharmacodynamical model which describes the anesthesia depth effect in a sufficiently reliable way over a set of patients with the depth effect quantified by the Bi-Spectral Index. Afterwards, an Artificial Neural Network (ANN) predictive controller has been designed based on the depth of anesthesia model so as to keep the patient in the optimum condition while he undergoes surgical treatment. For the purpose of quantifying the efficiency of the neural predictive controller, a classical proportional-integral-derivative controller has also been developed to compare both strategies. Results show the superior performance of predictive neural controller during BiSpectral Index reference tracking.

Keywords: anesthesia, bi-spectral index, neural network control, pharmacokinetic-pharmacodynamical model

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Authors: Pui-Lai To, Chechen Liao, Tzu-Ling Lin

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Virtual communities have created a range of new social spaces in which to meet and interact with one another. Both as a stand-alone model or as a supplement to sustain competitive advantage for normal business models, building virtual communities has been hailed as one of the major strategic innovations of the new economy. However for a virtual community to evolve, the biggest challenge is how to make members actively give information or provide advice. Even in busy virtual communities, usually, only a small fraction of members post information actively. In order to investigate the determinants of information giving willingness of those contributors who usually actively provide their opinions, we proposed a model to understand the reasons for contribution in communities. The study will definitely serve as a basis for the future growth of information giving in virtual communities.

Keywords: information giving, social identity, trust, virtual community

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Authors: Taiki Baba, Tomoaki Hashimoto

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The random dither quantization method enables us to achieve much better performance than the simple uniform quantization method for the design of quantized control systems. Motivated by this fact, the stochastic model predictive control method in which a performance index is minimized subject to probabilistic constraints imposed on the state variables of systems has been proposed for linear feedback control systems with random dither quantization. In other words, a method for solving optimal control problems subject to probabilistic state constraints for linear discrete-time control systems with random dither quantization has been already established. To our best knowledge, however, the feasibility of such a kind of optimal control problems has not yet been studied. Our objective in this paper is to investigate the feasibility of stochastic model predictive control problems for linear discrete-time control systems with random dither quantization. To this end, we provide the results of numerical simulations that verify the feasibility of stochastic model predictive control problems for linear discrete-time control systems with random dither quantization.

Keywords: model predictive control, stochastic systems, probabilistic constraints, random dither quantization

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Authors: Maryam Maleki, Esther Mead, Mohammad Arani, Nitin Agarwal

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This study is conducted to examine how opposing viewpoints related to COVID-19 were diffused on Twitter. To accomplish this, six datasets using two epidemiological models, SIR (Susceptible, Infected, Recovered) and SEIZ (Susceptible, Exposed, Infected, Skeptics), were analyzed. The six datasets were chosen because they represent opposing viewpoints on the COVID-19 pandemic. Three of the datasets contain anti-subject hashtags, while the other three contain pro-subject hashtags. The time frame for all datasets is three years, starting from January 2020 to December 2022. The findings revealed that while both models were effective in evaluating the propagation trends of these polarizing viewpoints, the SEIZ model was more accurate with a relatively lower error rate (6.7%) compared to the SIR model (17.3%). Additionally, the relative error for both models was lower for anti-subject hashtags compared to pro-subject hashtags. By leveraging epidemiological models, insights into the propagation trends of polarizing viewpoints on Twitter were gained. This study paves the way for the development of methods to prevent the spread of ideas that lack scientific evidence while promoting the dissemination of scientifically backed ideas.

Keywords: mathematical modeling, epidemiological model, seiz model, sir model, covid-19, twitter, social network analysis, social contagion

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Authors: E. D. Paul, O. F. Paul, J. E. Toryila, A. J. Salifu, C. E. Gimba

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Adsorption of Cd2+ ions from aqueous solution by Chitosan, a natural polymer, obtained from a mixture of the exoskeletons of Littorina littorea (Periwinkle) and Achatinoidea (Snail) was studied at varying adsorbent dose, contact time, metal ion concentrations, temperature and pH using batch adsorption method. The equilibrium adsorption isotherms were determined between 298 K and 345 K. The adsorption data were adjusted to Langmuir, Freundlich and the pseudo second order kinetic models. It was found that the Langmuir isotherm model most fitted the experimental data, with a maximum monolayer adsorption of 35.1 mgkg⁻¹ at 308 K. The entropy and enthalpy of adsorption were -0.1121 kJmol⁻¹K⁻¹ and -11.43 kJmol⁻¹ respectively. The Freundlich adsorption model, gave Kf and n values consistent with good adsorption. The pseudo-second order reaction model gave a straight line plot with rate constant of 1.291x 10⁻³ kgmg⁻¹ min⁻¹. The qe value was 21.98 mgkg⁻¹, indicating that the adsorption of Cadmium ion by the chitosan composite followed the pseudo-second order kinetic model.

Keywords: adsorption, chitosan, littorina littorea, achatinoidea, natural polymer

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Authors: Nurulhuda A. Manaf, Nor Najihah Zainal Abidin, Nur Amalina Jamaludin

Abstract:

Coordinating service interactions are a vital part of developing distributed applications that are built up as networks of autonomous participants, e.g., software components, web services, online resources, involve a collaboration between a diverse number of participant services on different providers. The complexity in coordinating service interactions reflects how important the techniques and approaches require for designing and coordinating the interaction between participant services to ensure the overall goal of a collaboration between participant services is achieved. The objective of this research is to develop capability of steering a complex service interaction towards a desired outcome. Therefore, an efficient technique for modelling, generating, and verifying the coordination of service interactions is developed. The developed model describes service interactions using service choreographies approach and focusing on a declarative approach, advocating an Object Management Group (OMG) standard, Semantics of Business Vocabulary and Rules (SBVR). This model, namely, SBVR model for service choreographies focuses on a declarative deontic rule expressing both obligation and prohibition, which can be more useful in working with coordinating service interactions. The generated SBVR model is then be formulated and be transformed into Alloy model using Alloy Analyzer for verifying the generated SBVR model. The transformation of SBVR into Alloy allows to automatically generate the corresponding coordination of service interactions (service choreography), hence producing an immediate instance of execution that satisfies the constraints of the specification and verifies whether a specific request can be realised in the given choreography in the generated choreography.

Keywords: service choreography, service coordination, behavioural modelling, complex interactions, declarative specification, verification, model transformation, semantics of business vocabulary and rules, SBVR

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Authors: Dhaladhuli Pranavi, Amirtham Rajagopal

Abstract:

There are various failure modes in ceramic matrix composites; notable ones are fiber breakage, matrix cracking and fiber matrix debonding. Crack nucleation and propagation in microstructure of such composites requires an understanding of interaction of crack with the multiple inclusion heterogeneous system and interfaces. In order to assess structural integrity, the material parameters especially of the interface that governs the crack growth should be determined. In the present work, a nonlocal phase field approach is proposed to model the crack interface interaction in such composites. Nonlocal approaches help in understanding the complex mechanisms of delamination growth and mitigation and operates at a material length scale. The performance of the proposed formulation is illustrated through representative numerical examples. The model proposed is implemented in the framework of the finite element method. Several parametric studies on interface crack interaction are conducted. The proposed model is easy and simple to implement and works very well in modeling fracture in composite systems.

Keywords: composite, interface, nonlocal, phase field

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Authors: Kassahun Birhanu Tadesse, Megersa Olumana Dinka

Abstract:

Reliable future river flow information is basic for planning and management of any river systems. For data scarce river system having only a river flow records like the Waterval River, a univariate time series models are appropriate for river flow forecasting. In this study, a univariate Seasonal Autoregressive Integrated Moving Average (SARIMA) model was applied for forecasting Waterval River flow using GRETL statistical software. Mean monthly river flows from 1960 to 2016 were used for modeling. Different unit root tests and Mann-Kendall trend analysis were performed to test the stationarity of the observed flow time series. The time series was differenced to remove the seasonality. Using the correlogram of seasonally differenced time series, different SARIMA models were identified, their parameters were estimated, and diagnostic check-up of model forecasts was performed using white noise and heteroscedasticity tests. Finally, based on minimum Akaike Information (AIc) and Hannan-Quinn (HQc) criteria, SARIMA (3, 0, 2) x (3, 1, 3)12 was selected as the best model for Waterval River flow forecasting. Therefore, this model can be used to generate future river information for water resources development and management in Waterval River system. SARIMA model can also be used for forecasting other similar univariate time series with seasonality characteristics.

Keywords: heteroscedasticity, stationarity test, trend analysis, validation, white noise

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Authors: Saroj Poudel, Joshua Mancini, Douglas Pike, Jennifer Timm, Alexei Tyryshkin, Vikas Nanda, Paul Falkowski

Abstract:

On the early Earth, protein-metal complexes likely harvested energy from a reduced environment. These complexes would have been precursors to the metabolic enzymes of ancient organisms. Hydrogenase is an essential enzyme in most anaerobic organisms for the reduction and oxidation of hydrogen in the environment and is likely one of the earliest evolved enzymes. To attempt to reinvent a precursor to modern hydrogenase, we computationally designed a short thirteen amino acid peptide that binds the often-required catalytic transition metal Nickel in hydrogenase. This simple complex can achieve hundreds of hydrogen evolution cycles using light energy in a broad range of temperature and pH. Biophysical and structural investigations strongly indicate the peptide forms a di-nickel active site analogous to Acetyl-CoA synthase, an ancient protein central to carbon reduction in the Wood-Ljungdahl pathway and capable of hydrogen evolution. This work demonstrates that prior to the complex evolution of multidomain enzymes, early peptide-metal complexes could have catalyzed energy transfer from the environment on the early Earth and enabled the evolution of modern metabolism

Keywords: hydrogenase, prebiotic enzyme, metalloenzyme, computational design

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Authors: Areej Isam Barham

Abstract:

Developing students’ number sense is an essential element in the learning of mathematics. Number sense is one of the foundational ideas in mathematics where students need to understand numbers, representing them in different ways, and realize the relationships among numbers. Number sense also reflects students’ understanding of the meaning of operations, how they related to one another, how to compute fluently and make reasonable estimates. Developing students’ number sense in the mathematics classroom requires good preparation for mathematics teachers, those who will direct their students towards the real understanding of numbers and its implementation in the learning of mathematics. This study describes the development of elementary prospective mathematics teachers’ number sense through a mathematics teaching methods course at Qatar University. The study examined the effect of using the Lesh model in enhancing mathematics prospective teachers’ number sense. Thirty-nine elementary prospective mathematics teachers involved in the current study. The study followed an experimental research approach, and quantitative research methods were used to answer the research questions. Pre-post number sense test was constructed and implemented before and after teaching by using the Lesh model. Data were analyzed using Statistical Packages for Social Sciences (SPSS). Descriptive data analysis and t-test were used to examine the impact of using the Lesh model in enhancing prospective teachers’ number sense. Finding of the study indicated poor number sense and limited numeracy skills before implementing the use of the Lesh model, which highly demonstrate the importance of the study. The results of the study also revealed a positive impact on the use of the Lesh model in enhancing prospective teachers’ number sense with statistically significant differences. The discussion of the study addresses different features and issues related to the participants’ number sense. In light of the study, the research presents recommendations and suggestions for the future development of mathematics prospective teachers’ number sense.

Keywords: number sense, Lesh model, prospective mathematics teachers, development of number sense

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Authors: Rosa Tsegaye Aga, Xuan Jiang, Pavel Vazquez Faci, Siqing Liu, Simon Rayner, Endalkachew Alemu, Markos Abebe

Abstract:

Tuberculosis (TB) is a major cause of disease globally. In most cases, TB is treatable and curable, but only with the proper treatment. There is a time when drug-resistant TB occurs when bacteria become resistant to the drugs that are used to treat TB. Current strategies to identify drug-resistant TB bacteria are laboratory-based, and it takes a longer time to identify the drug-resistant bacteria and treat the patient accordingly. But machine learning (ML) and data science approaches can offer new approaches to the problem. In this study, we propose to develop an ML-based model to predict the antibiotic resistance phenotypes of TB isolates in minutes and give the right treatment to the patient immediately. The study has been using the whole genome sequence (WGS) of TB isolates as training data that have been extracted from the NCBI repository and contain different countries’ samples to build the ML models. The reason that different countries’ samples have been included is to generalize the large group of TB isolates from different regions in the world. This supports the model to train different behaviors of the TB bacteria and makes the model robust. The model training has been considering three pieces of information that have been extracted from the WGS data to train the model. These are all variants that have been found within the candidate genes (F1), predetermined resistance-associated variants (F2), and only resistance-associated gene information for the particular drug. Two major datasets have been constructed using these three information. F1 and F2 information have been considered as two independent datasets, and the third information is used as a class to label the two datasets. Five machine learning algorithms have been considered to train the model. These are Support Vector Machine (SVM), Random forest (RF), Logistic regression (LR), Gradient Boosting, and Ada boost algorithms. The models have been trained on the datasets F1, F2, and F1F2 that is the F1 and the F2 dataset merged. Additionally, an ensemble approach has been used to train the model. The ensemble approach has been considered to run F1 and F2 datasets on gradient boosting algorithm and use the output as one dataset that is called F1F2 ensemble dataset and train a model using this dataset on the five algorithms. As the experiment shows, the ensemble approach model that has been trained on the Gradient Boosting algorithm outperformed the rest of the models. In conclusion, this study suggests the ensemble approach, that is, the RF + Gradient boosting model, to predict the antibiotic resistance phenotypes of TB isolates by outperforming the rest of the models.

Keywords: machine learning, MTB, WGS, drug resistant TB

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