Search results for: hybrid hierarchical clustering

Authors: Daniel Joseph Whittaker

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This paper examines the broad array of historic sites from which Chicago common brick has emerged, and the methods this brick has been utilized within and around a new hybrid structure recently completed-and periodically opened to the public, as a private art, architecture, design, and social activism gallery space. Various technical aspects regarding the structural and aesthetic reuse methods of salvaged brick within the interior and exterior of this new Tadao Ando-designed building in Lincoln Park, Chicago, are explored. This paper expands specifically upon the multiple possible origins of Chicago common brick, as well as the extant brick currently composing the surrounding alley which is integral to demarcating the southern site boundary of the old apartment building now gallery. Themes encompassing Chicago’s archeological and architectural history, local resource extraction, and labor practices permeate this paper’s investigation into urban, social and architectural history and building construction technology advancements through time.

Keywords: masonry construction, history brickmaking, private museums, Chicago Illinois, Tadao Ando

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Authors: Nighat Zia-ud-Den, Shazia Anwer Bukhari

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In the present study, sunflower seeds were exposed to magnetic bio-stimulation at different milli Tesla, and their effects were studied. The present study addressed to establish the effectiveness of magnetic bio-stimulation on seed germination, growth, and other dynamics of crop growth. The changes in physiological characters, i.e. the growth parameters of seedlings (biomass, root and shoot length, fresh and dry weight of root shoot leaf and fruit, leaf area, the height of plants, number of leaves, and number of fruits per plant) and antioxidant activities were measured. The parameters related to germination and growth were measured under controlled conditions while they changed significantly compared with that of the control. These changes suggested that magnetic seed stimulator enhanced the inner energy of seeds, which contributed to the acceleration of the growth and development of seedlings. Moreover, pretreatment with a magnetic field was found to be a positive impact on sunflower seeds germination, growth, and other biochemical parameters.

Keywords: sunflower seeds, physical priming method, biochemical parameters, antioxidant activities

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Authors: S. P. Jordan, G. Christian, S. P. Jeffs

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Modern composite materials are increasingly being chosen in replacement of heavier metallic material systems within many engineering fields including aerospace and automotive industries. The increasing push towards satisfying environmental targets are fuelling new material technologies and manufacturing processes. This paper will introduce materials and manufacturing processes using metal matrix composites along with manufacturing processes optimized at Alvant Ltd., based in Basingstoke in the UK which offers modern, cost effective, selectively reinforced composites for light-weighting applications within engineering. An overview and introduction into modern optimized manufacturing methods capable of producing viable replacements for heavier metallic and lower temperature capable polymer composites are offered. A review of the capabilities and future applications of this viable material is discussed to highlight the potential involved in further optimization of old manufacturing techniques, to fully realize the potential to lightweight material using cost-effective methods.

Keywords: aluminium matrix composites, light-weighting, hybrid squeeze casting, strategically placed reinforcements

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Authors: Zhaojun Wang, Zongdi Sun, Yongjie Chen, Manman Chen, Linghui Wang

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This paper uses the analytic hierarchy process to study the execution effect of the minimum purchase price of grain in different regions and various grain crops. Firstly, for different regions, five indicators including grain yield, grain sown area, gross agricultural production, grain consumption price index, and disposable income of rural residents were selected to construct an evaluation index system. We collect data of six provinces including Hebei Province, Heilongjiang Province and Shandong Province from 2006 to 2017. Then, the judgment matrix is constructed, and the hierarchical single ordering and consistency test are carried out to determine the scoring standard for the minimum purchase price of grain. The ranking of the execution effect from high to low is: Heilongjiang Province, Shandong Province, Hebei Province, Guizhou Province, Shaanxi Province, and Guangdong Province. Secondly, taking Shandong Province as an example, we collect the relevant data of sown area and yield of cereals, beans, potatoes and other crops from 2006 to 2017. The weight of area and yield index is determined by expert scoring method. And the average sown area and yield of cereals, beans and potatoes in 2006-2017 were calculated, respectively. On this basis, according to the sum of products of weights and mean values, the execution effects of different grain crops are determined. It turns out that among the cereals, the minimum purchase price had the best execution effect on paddy, followed by wheat and finally maize. Moreover, among major categories of crops, cereals perform best, followed by beans and finally potatoes. Lastly, countermeasures are proposed for different regions, various categories of crops, and different crops of the same category.

Keywords: analytic hierarchy process, grain yield, grain sown area, minimum grain purchase price

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Authors: H. Oudira, A. Saifi

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The therapeutic utility of certain Auger emitters such as iodine-125 depends on their position within the cell nucleus . Or diagnostically, and to maintain as low as possible cell damage, it is preferable to have radionuclide localized outside the cell or at least the core. One solution to this problem is to consider markers capable of conveying anticancer drugs to the tumor site regardless of their location within the human body. The objective of this study is to simulate the impact of a complex such as bleomycin on single and double strand breaks in the DNA molecule. Indeed, this simulation consists of the following transactions: - Construction of BLM -Fe- DNA complex. - Simulation of the electron’s transport from the metastable state excitation of Fe 57 by the Monte Carlo method. - Treatment of chemical reactions in the considered environment by the diffusion equation. For physical, physico-chemical and finally chemical steps, the geometry of the complex is considered as a sphere of 50 nm centered on the binding site , and the mathematical method used is called step by step based on Monte Carlo codes.

Keywords: concentration, yield, radical species, bleomycin, excitation, DNA

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Authors: Ghofrane Rehaiem, Hamza Gharsellaoui, Samir Benahmed

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In this study, Artificial Neural Networks (ANNs) were used for modeling the parameters that allow the real-time scheduling of embedded systems under resources constraints designed for real-time applications running. The objective of this work is to implement a neural networks based approach for real-time scheduling of embedded systems in order to handle real-time constraints in execution scenarios. In our proposed approach, many techniques have been proposed for both the planning of tasks and reducing energy consumption. In fact, a combination of Dynamic Voltage Scaling (DVS) and time feedback can be used to scale the frequency dynamically adjusting the operating voltage. Indeed, we present in this paper a hybrid contribution that handles the real-time scheduling of embedded systems, low power consumption depending on the combination of DVS and Neural Feedback Scheduling (NFS) with the energy Priority Earlier Deadline First (PEDF) algorithm. Experimental results illustrate the efficiency of our original proposed approach.

Keywords: optimization, neural networks, real-time scheduling, low-power consumption

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Authors: Kok Hong Tan, Hing Wah Lee, Jhih-Wei Chen, Chang Fu Dee, Chung-Lin Wu, Siang-Piao Chai, Wei Sea Chang

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Semiconductor photocatalyst is known as one of the key roles in developing clean and sustainable energy. However, most of the semiconductor only possesses photoactivity within the UV light region, and hence, decreases the overall photocatalyst efficiency. Generally, the overall effectiveness of the photocatalyst activity is determined by three critical steps: (i) light absorption efficiency and photoexcitation electron-hole pair generation, (ii) separation and migration of charge carriers to the surface of the photocatalyst, and (iii) surface reaction of the carriers with its environment. Much effort has been invested on optimizing hierarchical nanostructures of semiconductors for efficient photoactivity due to the fact that the visible light absorption capability and occurrence of the chemical reactions mostly depend on the dimension of photocatalysts. In this work, we incorporated zero-dimensional (0D) gold nanoparticles (AuNPs) and one dimensional (1D) Zinc Oxide (ZnO) nanorods (NRs) onto strontium titanate (STO) for efficient visible light absorption, charge transfer, and separation. We demonstrate that the electrical and optical properties of the photocatalyst can be tuned by controlling the dimensional structures of AuNPs and ZnO NRs. We found that smaller AuNPs sizes exhibited higher photoactivity because of Fermi level shifting toward the conductive band of STO, STO band gap narrowing and broadening of absorption spectrum to the visible light region. For ZnO NRs, it was found that the average ZnO NRs c-axis length must achieve of certain length to induce multiphoton absorption as a result of light reflection and trapping behavior in the free space between adjacent ZnO NRs hence broadening the absorption spectrum of ZnO from UV to visible light region. This work opens up a new way of broadening the absorption spectrum by incorporating controllable nanostructures of semiconductors, which is important in optimizing the solar water splitting process.

Keywords: gold nanoparticles, photoelectrochemical, PEC, semiconductor photocatalyst, zinc oxide nanorods

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Authors: Maryam Zarabian, Hector Guzman, Pedro Pereira-Almao, Abraham Fapojuwo

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Dry reforming of methane (DRM) has sparked significant industrial and scientific interest not only as a viable alternative for addressing the environmental concerns of two main contributors of the greenhouse effect, i.e., carbon dioxide (CO₂) and methane (CH₄), but also produces syngas, i.e., a mixture of hydrogen (H₂) and carbon monoxide (CO) utilized by a wide range of downstream processes as a feedstock for other chemical productions. In this study, we develop an AI-enable syngas production model to tackle the problem of achieving an equivalent H₂/CO ratio [1:1] with respect to the most efficient conversion. Firstly, the unsupervised density-based spatial clustering of applications with noise (DBSAN) algorithm removes outlier data points from the original experimental dataset. Then, random forest (RF) and deep neural network (DNN) models employ the error-free dataset to predict the DRM results. DNN models inherently would not be able to obtain accurate predictions without a huge dataset. To cope with this limitation, we employ reusing pre-trained layers’ approaches such as transfer learning and greedy layer-wise pretraining. Compared to the other deep models (i.e., pure deep model and transferred deep model), the greedy layer-wise pre-trained deep model provides the most accurate prediction as well as similar accuracy to the RF model with R² values 1.00, 0.999, 0.999, 0.999, 0.999, and 0.999 for the total outlet flow, H₂/CO ratio, H₂ yield, CO yield, CH₄ conversion, and CO₂ conversion outputs, respectively.

Keywords: artificial intelligence, dry reforming of methane, artificial neural network, deep learning, machine learning, transfer learning, greedy layer-wise pretraining

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Authors: S. Neelima, P. S. Subramanyam

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The major tool for distribution planning is load forecasting, which is the anticipation of the load in advance. Artificial neural networks have found wide applications in load forecasting to obtain an efficient strategy for planning and management. In this paper, the application of neural networks to study the design of short term load forecasting (STLF) Systems was explored. Our work presents a pragmatic methodology for short term load forecasting (STLF) using proposed two-stage model of wavelet transform (WT) and artificial neural network (ANN). It is a two-stage prediction system which involves wavelet decomposition of input data at the first stage and the decomposed data with another input is trained using a separate neural network to forecast the load. The forecasted load is obtained by reconstruction of the decomposed data. The hybrid model has been trained and validated using load data from Telangana State Electricity Board.

Keywords: electrical distribution systems, wavelet transform (WT), short term load forecasting (STLF), artificial neural network (ANN)

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Authors: Phuc D. Nguyen, Kristy L. Hansen, Branko Zajamsek

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The existence of special characteristics of wind farm noise such as amplitude modulation (AM) contributes significantly to annoyance, which could ultimately result in sleep disturbance and other adverse health effects for residents living near wind farms. In order to detect and quantify this phenomenon, several methods have been developed which can be separated into three types: time-domain, frequency-domain and hybrid methods. However, due to a lack of systematic validation of these methods, it is still difficult to select the best method for identifying AM. Furthermore, previous comparisons between AM methods have been predominantly qualitative or based on synthesised signals, which are not representative of the actual noise. In this study, a comparison between methods for detecting and quantifying AM has been carried out. The results are based on analysis of real noise data which were measured at a wind farm in South Australia. In order to evaluate the performance of these methods in terms of detecting AM, an approach has been developed to select the most successful method of AM detection. This approach uses a receiver operating characteristic (ROC) curve which is based on detection of AM in audio files by experts.

Keywords: amplitude modulation, wind farm noise, ROC curve

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Authors: W. Hashim, H. Burok, N. Ghazaly, H. Ahmad Nasir, N. Mohamad Anas, A. F. Ismail, K. L. Yau

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Future communication networks require devices that are able to work on a single platform but support heterogeneous operations which lead to service diversity and functional flexibility. This paper proposes two cognitive mechanisms termed cognitive hybrid function which is applied in multiple broadband user terminals in order to maintain reliable connectivity and preventing unnecessary interferences. By employing such mechanisms especially for future hyper-dense network, we can observe their performances in terms of optimized speed and power saving efficiency. Results were obtained from several empirical laboratory studies. It was found that selecting reliable network had shown a better optimized speed performance up to 37% improvement as compared without such function. In terms of power adjustment, our evaluation of this mechanism can reduce the power to 5dB while maintaining the same level of throughput at higher power performance. We also discuss the issues impacting future telecommunication standards whenever such devices get in place.

Keywords: dense network, intelligent network selection, multiple networks, transmit power adjustment

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Authors: M. Boudjerda, B. Touaibia, M. K. Mihoubi

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In Algeria agricultural irrigation is the primary water consuming sector followed by the domestic and industrial sectors. Economic development in the last decade has weighed heavily on water resources which are relatively limited and gradually decreasing to the detriment of agriculture. The research presented in this paper focuses on the optimization of irrigation water demand. Dynamic Programming-Neural Network (DPNN) method is applied to investigate reservoir optimization. The optimal operation rule is formulated to minimize the gap between water release and water irrigation demand. As a case study, Foum El-Gherza dam’s reservoir system in south of Algeria has been selected to examine our proposed optimization model. The application of DPNN method allowed increasing the satisfaction rate (SR) from 12.32% to 55%. In addition, the operation rule generated showed more reliable and resilience operation for the examined case study.

Keywords: water management, agricultural demand, dam and reservoir operation, Foum el-Gherza dam, dynamic programming, artificial neural network

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Authors: E. Rama Devi Jothilingam

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Diabetes mellitus is now one of the most common non communicable diseases globally. India leads the world with largest number of diabetic subjects earning the title "diabetes capital of the world". In order to reduce the mortality rate, a fuzzy expert system is designed to predict the severity of cardiac and renal problems of diabetic patients using fuzzy logic. Since uncertainty is inherent in medicine, fuzzy logic is used in this research work to remove the inherent fuzziness of linguistic concepts and uncertain status in diabetes mellitus which is the prime cause for the cardiac arrest and renal failure. In this work, the controllable risk factors "blood sugar, insulin, ketones, lipids, obesity, blood pressure and protein/creatinine ratio" are considered as input parameters and the "the stages of cardiac" (SOC)" and the stages of renal" (SORD) are considered as the output parameters. The triangular membership functions are used to model the input and output parameters. The rule base is constructed for the proposed expert system based on the knowledge from the medical experts. Mamdani inference engine is used to infer the information based on the rule base to take major decision in diagnosis. Mean of maximum is used to get a non fuzzy control action that best represent possibility distribution of an inferred fuzzy control action. The proposed system also classifies the patients with high risk and low risk using fuzzy c means clustering techniques so that the patients with high risk are treated immediately. The system is validated with Matlab and is used as a tracking system with accuracy and robustness.

Keywords: Diabetes mellitus, fuzzy expert system, Mamdani, MATLAB

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Authors: Cavidan Yakupoglu, Kurt Rohloff

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In this study, we aim to provide a novel parameter selection model for the BFVrns scheme, which is one of the prominent FHE schemes. Parameter selection in lattice-based FHE schemes is a practical challenges for experts or non-experts. Towards a solution to this problem, we introduce a hybrid principles-based approach that combines theoretical with experimental analyses. To begin, we use regression analysis to examine the parameters on the performance and security. The fact that the FHE parameters induce different behaviors on performance, security and Ciphertext Expansion Factor (CEF) that makes the process of parameter selection more challenging. To address this issue, We use a multi-objective optimization algorithm to select the optimum parameter set for performance, CEF and security at the same time. As a result of this optimization, we get an improved parameter set for better performance at a given security level by ensuring correctness and security against lattice attacks by providing at least 128-bit security. Our result enables average ~ 5x smaller CEF and mostly better performance in comparison to the parameter sets given in [1]. This approach can be considered a semiautomated parameter selection. These studies are conducted using the PALISADE homomorphic encryption library, which is a well-known HE library. The abstract goes here.

Keywords: lattice cryptography, fully homomorphic encryption, parameter selection, LWE, RLWE

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Authors: Paolo Boscaro, Vasile Hulea, François Fajula, Francis Luck, Anne Galarneau

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TiO2 based monoliths with hierarchical macropores and mesopores have been synthesized following a novel one pot sol-gel synthesis method. Taking advantage of spinodal separation that occurs between titanium isopropoxide and an acidic solution in presence of polyethylene oxide polymer, monoliths with homogeneous interconnected macropres of 3 μm in diameter and mesopores of ca. 6 nm (surface area 150 m2/g) are obtained. Furthermore, these monoliths present some carbon and nitrogen (as shown by XPS and elemental analysis), which considerably reduce titanium oxide energy gap and enable light to be absorbed up to 700 nm wavelength. XRD shows that anatase is the dominant phase with a small amount of brookite. Enhanced light absorption and high porosity of the monoliths are responsible for a remarkable photocatalytic activity. Wastewater treatment has been performed in closed reactor under sunlight using orange G dye as target molecule. Glass reactors guarantee that most of UV radiations (to almost 300 nm) of solar spectrum are excluded. TiO2 nanoparticles P25 (usually used in photocatalysis under UV) and un-doped TiO2 monoliths with similar porosity were used as comparison. C,N-doped TiO2 monolith allowed a complete colorant degradation in less than 1 hour, whereas 10 h are necessary for 40% colorant degradation with P25 and un-doped monolith. Experiment performed in the dark shows that only 3% of molecules have been adsorbed in the C,N-doped TiO2 monolith within 1 hour. The much higher efficiency of C,N-doped TiO2 monolith in comparison to P25 and un-doped monolith, proves that doping TiO2 is an essential issue and that nitrogen and carbon are effective dopants. Monoliths offer multiples advantages in respect to nanometric powders: sample can be easily removed from batch (no needs to filter or to centrifuge). Moreover flow reactions can be set up with cylindrical or flat monoliths by simple sheathing or by locking them with O-rings.

Keywords: C-N doped, sunlight photocatalytic activity, TiO2 monolith, visible absorbance

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Authors: Deepika Christopher, Garima Anand

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To determine the best model for churn prediction in the telecom industry, this paper compares 11 machine learning algorithms, namely Logistic Regression, Support Vector Machine, Random Forest, Decision Tree, XGBoost, LightGBM, Cat Boost, AdaBoost, Extra Trees, Deep Neural Network, and Hybrid Model (MLPClassifier). It also aims to pinpoint the top three factors that lead to customer churn and conducts customer segmentation to identify vulnerable groups. According to the data, the Logistic Regression model performs the best, with an F1 score of 0.6215, 81.76% accuracy, 68.95% precision, and 56.57% recall. The top three attributes that cause churn are found to be tenure, Internet Service Fiber optic, and Internet Service DSL; conversely, the top three models in this article that perform the best are Logistic Regression, Deep Neural Network, and AdaBoost. The K means algorithm is applied to establish and analyze four different customer clusters. This study has effectively identified customers that are at risk of churn and may be utilized to develop and execute strategies that lower customer attrition.

Keywords: attrition, retention, predictive modeling, customer segmentation, telecommunications

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Authors: Intesar Ibrahim

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Large-scale Libyan emigration is a relatively new phenomenon. Most of the Libyan families in the UK are new immigrants, unlike the other neighbouring countries of Egypt, Tunisia, Algeria and even Sudan. Libyans have no particular history of large-scale migration. On the other hand, many Libyan families live in modest homes located in large Muslim communities of Pakistanis and Yemenis. In the UK as a whole, there are currently 16 Libyan schools most of which are run during the weekend for children of school age. There are three such weekend schools in Sheffield that teach a Libyan school curriculum, and Libyan women and men run these schools. Further, there is also a Masjid (mosque) that is operated by Libyans, beside the other Masjids in the city, which most of the Libyan community attend for prayer and for other activities such as writing marriage contracts. The presence of this Masjid increases the attraction for Libyans to reside in the Sheffield area. This paper studies how Libyan immigrants in the UK make their decisions on their housing and living environment in the UK. Libyan residents in the UK come from different Libyan regions, social classes and lifestyles; this may have an impact on their choices in the interior designs of their houses in the UK. A number of case studies were chosen from Libyan immigrants who came from different types of dwellings in Libya, in order to compare with their homes and their community lifestyle in the UK and those in Libya. This study explores the meaning and the ways of using living rooms in Libyan emigrants’ houses in the UK and compares those with those in their houses back in their home country. For example, the way they set up furniture in rooms acts as an indicator of the hierarchical structure of society. The design of furniture for Libyan sitting rooms for floor-seating is different from that of the traditional English sitting room. The paper explores the identity and cultural differences that affected the style and design of the living rooms for Libyan immigrants in the UK. The study is carried out based on the "production of space" theory that any culture has its needs, style of living and way of thinking. I argue that the study found more than 70% of Libyan immigrants in the UK still furnish the living room in their traditional way (flooring seating).

Keywords: place, identity, culture, immigrants

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Authors: Min L. Stewart, Patrick Johnston

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Predictive coding has been proposed as a general explanatory framework for understanding the neural mechanisms of perception. As such, an underweighting of perceptual priors has been hypothesised to underpin a range of differences in inferential and sensory processing in autism spectrum disorders. However, empirical evidence to support this has not been well established. The present study uses an electroencephalography paradigm involving changes of facial identity and person category (actors etc.) to explore how levels of autistic traits (AT) affect predictive coding at multiple stages in the visual processing hierarchy. The study uses a rapid serial presentation of faces, with hierarchically structured sequences involving both periodic and aperiodic repetitions of different stimulus attributes (i.e., person identity and person category) in order to induce contextual expectations relating to these attributes. It investigates two main predictions: (1) significantly larger and late neural responses to change of expected visual sequences in high-relative to low-AT, and (2) significantly reduced neural responses to violations of contextually induced expectation in high- relative to low-AT. Preliminary frequency analysis data comparing high and low-AT show greater and later event-related-potentials (ERPs) in occipitotemporal areas and prefrontal areas in high-AT than in low-AT for periodic changes of facial identity and person category but smaller ERPs over the same areas in response to aperiodic changes of identity and category. The research advances our understanding of how abnormalities in predictive coding might underpin aberrant perceptual experience in autism spectrum. This is the first stage of a research project that will inform clinical practitioners in developing better diagnostic tests and interventions for people with autism.

Keywords: hierarchical visual processing, face processing, perceptual hierarchy, prediction error, predictive coding

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Authors: Alireza Alizadeh, Hossein Ghadimi, Oveis Abedinia, Noradin Ghadimi

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A Particle Swarm Optimization with Time Varying Acceleration Coefficients (PSO-TVAC) is proposed to determine optimal economic load dispatch (ELD) problem in this paper. The proposed methodology easily takes care of solving non-convex economic load dispatch problems along with different constraints like transmission losses, dynamic operation constraints and prohibited operating zones. The proposed approach has been implemented on the 3-machines 6-bus, IEEE 5-machines 14-bus, IEEE 6-machines 30-bus systems and 13 thermal units power system. The proposed technique is compared to solve the ELD problem with hybrid approach by using the valve-point effect. The comparison results prove the capability of the proposed method giving significant improvements in the generation cost for the economic load dispatch problem.

Keywords: PSO-TVAC, economic load dispatch, non-convex cost function, prohibited operating zone, transmission losses

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Authors: Sahar El-Achkar, Mizan Ahmad

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Poor adaptation of developmental milestones over the lifespan can significantly impact emotional experiences and Satisfaction with Life (SWL) post-retirement. Thus, it is important to understand how adaptive behaviour over the life course can predict emotional experiences. Broadly emotional experiences are either Positive Affect (PA) or Negative Affect (NA). This study sought to explore the impact of successful adaptation of developmental milestones throughout one’s life on emotional experiences and satisfaction with life following retirement. A cross-sectional self-report survey was completed by 132 Australian retirees between the ages 55 and 70 years. Three hierarchical regression models were fitted, controlling for age and gender, to predict PA, NA, and SWL. The full model predicting PA was statistically significant overall, F (8, 121) = 17.97, p < .001, account for 57% of the variability in PA. Industry/Inferiority were significantly predictive of PA. The full model predicting NA was statistically significant overall, F (8, 121) = 12.00, p < .001, accounting for 51% of the variability in NA. Age and Trust/Mistrust were significantly predictive of NA. The full model predicting NA was statistically significant overall, F (8, 121) = 12.00, p < .001, accounting for 51% of the variability in NA. Age and Trust/Mistrust were significantly predictive of NA. The full model predicting SWL, F (8, 121) = 11.05, p < .001, accounting for 45% of the variability in SWL. Trust/Mistrust and Ego Integrity/Despair were significantly predictive of SWL. A sense of industry post-retirement is important in generating PA. These results highlight that individuals presenting with adaptation and identity issues are likely to present with adjustment challenges and unpleasant emotional experiences post-retirement. This supports the importance of identifying and understanding the benefits of successful adaptation and development throughout the lifespan and its significance for the self-concept. Most importantly, the quality of lives of many may be improved, and the future risk of continued poor emotional experiences and SWL post-retirement may be mitigated. Specifically, the clinical implications of these findings are that they support the promotion of successful adaption over the life course and healthy ageing.

Keywords: adaptation, development, negative affect, positive affect, retirement, satisfaction with life

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Authors: Bernard Doherty, Andrew Jelfs, Aveek Dasgupta, Patrick Holden

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Many companies have moved to agile and hybrid agile methodologies where portions of the Software Design Life-cycle (SDLC) and Software Test Life-cycle (STLC) can be time boxed in order to enhance delivery speed, quality and to increase flexibility to changes in software requirements. Despite widespread proliferation of agile practices, implementation often fails due to lack of adequate project management support, decreased motivation or fear of increased interaction. Consequently, few organizations effectively adopt agile processes with tailoring often required to integrate agile methodology in large scale environments. This paper provides an overview of the challenges in implementing an innovative large-scale tailored realization of the agile methodology termed the Agile Factory Model (AFM), with the aim of comparing and contrasting issues of specific importance to organizations undertaking large scale agile development. The conclusions demonstrate that agile practices can be effectively translated to a globally distributed development environment.

Keywords: agile, agile factory model, globally distributed development, large-scale agile

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Authors: Mahan Qwamizadeh, Kun Zhou, Zuoqi Zhang, Yong Wei Zhang

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Typical load-bearing biological materials like bone, mineralized tendon and shell, are biocomposites made from both organic (collagen) and inorganic (biomineral) materials. This amazing class of materials with intrinsic internally designed hierarchical structures show superior mechanical properties with regard to their weak components from which they are formed. Extensive investigations concentrating on static loading conditions have been done to study the biological materials failure. However, most of the damage and failure mechanisms in load-bearing biological materials will occur whenever their structures are exposed to dynamic loading conditions. The main question needed to be answered here is: What is the relation between the layout and architecture of the load-bearing biological materials and their dynamic behavior? In this work, a staggered model has been developed based on the structure of natural materials at nanoscale and Finite Element Analysis (FEA) has been used to study the dynamic behavior of the structure of load-bearing biological materials to answer why the staggered arrangement has been selected by nature to make the nanocomposite structure of most of the biological materials. The results showed that the staggered structures will efficiently attenuate the stress wave rather than the layered structure. Furthermore, such staggered architecture is effectively in charge of utilizing the capacity of the biostructure to resist both normal and shear loads. In this work, the geometrical parameters of the model like the thickness and aspect ratio of the mineral inclusions selected from the typical range of the experimentally observed feature sizes and layout dimensions of the biological materials such as bone and mineralized tendon. Furthermore, the numerical results validated with existing theoretical solutions. Findings of the present work emphasize on the significant effects of dynamic behavior on the natural evolution of load-bearing biological materials and can help scientists to design bioinspired materials in the laboratories.

Keywords: load-bearing biological materials, nanostructure, staggered structure, stress wave decay

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Authors: Nesreddine Djafar Henni, Nassim Djedoui, Rachid Chebili

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Recent decades have witnessed significant efforts being made to optimize different types of structures and components. The concept of cost optimization in reinforced concrete structures, which aims at minimizing financial resources while ensuring maximum building safety, comprises multiple materials, and the objective function for their optimal design is derived from the construction cost of the steel as well as concrete that significantly contribute to the overall weight of reinforced concrete (RC) structures. To achieve this objective, this work has been devoted to optimizing the structural design of 3D RC frame buildings which integrates, for the first time, the Algerian regulations. Three different test examples were investigated to assess the efficiency of our work in optimizing RC frame buildings. The hybrid GWOPSO algorithm is used, and 30000 generations are made. The cost of the building is reduced by iteration each time. Concrete and reinforcement bars are used in the building cost. As a result, the cost of a reinforced concrete structure is reduced by 30% compared with the initial design. This result means that the 3D cost-design optimization of the framed structure is successfully achieved.

Keywords: optimization, automation, API, Malab, RC structures

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Authors: Abdellahi Cheikh

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With the emergence of quantum computers with very powerful capabilities, the security of the exchange of shared keys between two interlocutors poses a big problem in terms of the rapid development of technologies such as computing power and computing speed. Therefore, the Diffie-Hellmann (DH) algorithm is more vulnerable than ever. No mechanism guarantees the security of the key exchange, so if an intermediary manages to intercept it, it is easy to intercept. In this regard, several studies have been conducted to improve the security of key exchange between two interlocutors, which has led to interesting results. The modification made on our model Diffie-Hellman-RSA-AES (DRA), which encrypts the information exchanged between two users using the three-encryption algorithms DH, RSA and AES, by using stenographic photos to hide the contents of the p, g and ClesAES values that are sent in an unencrypted state at the level of DRA model to calculate each user's public key. This work includes a comparative study between the DRA model and all existing solutions, as well as the modification made to this model, with an emphasis on the aspect of reliability in terms of security. This study presents a simulation to demonstrate the effectiveness of the modification made to the DRA model. The obtained results show that our model has a security advantage over the existing solution, so we made these changes to reinforce the security of the DRA model.

Keywords: Diffie-Hellmann, DRA, RSA, advanced encryption standard

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Authors: Rabia Korkmaz Tan, Şebnem Bora

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The major problem encountered when modeling complex systems with agent-based modeling and simulation techniques is the existence of large parameter spaces. A complex system model cannot be expected to reflect the whole of the real system, but by specifying the most appropriate parameters, the actual system can be represented by the model under certain conditions. When the studies conducted in recent years were reviewed, it has been observed that there are few studies for parameter tuning problem in agent based simulations, and these studies have focused on tuning parameters of a single model. In this study, an approach of parameter tuning is proposed by using metaheuristic algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Bee Colonies (ABC), Firefly (FA) algorithms. With this hybrid structured study, the parameter tuning problems of the models in the different fields were solved. The new approach offered was tested in two different models, and its achievements in different problems were compared. The simulations and the results reveal that this proposed study is better than the existing parameter tuning studies.

Keywords: parameter tuning, agent based modeling and simulation, metaheuristic algorithms, complex systems

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Authors: Seyedamir Makinejadsanij

Abstract:

One of the most important factors in the production of quality steel is to know the exact weight of steel in the steelmaking area. In this study, a calculation method is presented to estimate the exact weight of the melt as well as the objects transported by the overhead crane. Iran Alloy Steel Company's steelmaking area has three 90-ton cranes, which are responsible for transferring the ladles and ladle caps between 34 areas in the melt shop. Each crane is equipped with a Disomat Tersus weighing system that calculates and displays real-time weight. The moving object has a variable weight due to swinging, and the weighing system has an error of about +-5%. This means that when the object is moving by a crane, which weighs about 80 tons, the device (Disomat Tersus system) calculates about 4 tons more or 4 tons less, and this is the biggest problem in calculating a real weight. The k-means algorithm is an unsupervised clustering method that was used here. The best result was obtained by considering 3 centers. Compared to the normal average(one) or two, four, five, and six centers, the best answer is with 3 centers, which is logically due to the elimination of noise above and below the real weight. Every day, the standard weight is moved with working cranes to test and calibrate cranes. The results are shown that the accuracy is about 40 kilos per 60 tons (standard weight). As a result, with this method, the accuracy of moving weight is calculated as 99.95%. K-means is used to calculate the exact mean of objects. The stopping criterion of the algorithm is also the number of 1000 repetitions or not moving the points between the clusters. As a result of the implementation of this system, the crane operator does not stop while moving objects and continues his activity regardless of weight calculations. Also, production speed increased, and human error decreased.

Keywords: k-means, overhead crane, melt weight, weight estimation, swing problem

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Authors: Jianhua Zhang, Weiping Chen, Guanjie Chen, Jason Flannick, Emma Fikse, Glenda Smerin, Yanqin Yang, Yulong Li, John A. Hanover, William F. Simonds

Abstract:

Ethnic disparities in many diseases are well recognized and reflect the consequences of genetic, behavior, and environmental factors. However, direct scientific evidence connecting the ethnic genetic variations and the disease disparities has been elusive, which may have led to the ethnic inequalities in large scale genetic studies. Through the genome-wide analysis of data representing 185,934 subjects, including 14,955 from our own studies of the African America Diabetes Mellitus, we discovered sets of genetic variants either unique to or conserved in all ethnicities. We further developed a quantitative gene function-based high-risk variant index (hrVI) of 20,428 genes to establish profiles that strongly correlate with the subjects' self-identified ethnicities. With respect to the ability to detect human essential and pathogenic genes, the hrVI analysis method is both comparable with and complementary to the well-known genetic analysis methods, pLI and VIRlof. Application of the ethnicity-specific hrVI analysis to the type 2 diabetes mellitus (T2DM) national repository, containing 20,791 cases and 24,440 controls, identified 114 candidate T2DM-associated genes, 8.8-fold greater than that of ethnicity-blind analysis. All the genes identified are defined as either pathogenic or likely-pathogenic in ClinVar database, with 33.3% diabetes-associated and 54.4% obesity-associated genes. These results demonstrate the utility of hrVI analysis and provide the first genetic evidence by clustering patterns of how genetic variations among ethnicities may impede the discovery of diabetes and foreseeably other disease-associated genes.

Keywords: diabetes-associated genes, ethnic health disparities, high-risk variant index, hrVI, T2DM

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Authors: Ismail Rahama Adam Hamid

Abstract:

This paper presents the development of a thermal model for a flat, double-sided linear generator designed for use in free-piston engines. The study conducted in this paper examines the influence of temperature on the performance of the permeant magnet linear generator, an integral and pivotal component within the system. This research places particular emphasis on the Neodymium Iron Boron (NdFeB) permanent magnet, which serves as a source of magnetic field for the linear generator. In this study, an internal combustion engine that tends to produce heat is connected to a generator. Considering the temperatures rise from both the combustion process and the thermal contributions of current-carrying conductors and frictional forces. Utilizing Computational Fluid Dynamics (CFD) method, a thermal model of the (NdFeB) magnet within the linear generator is constructed and analyzed. Furthermore, the temperature field is examined to ensure that the linear generator operates under stable conditions without the risk of demagnetization.

Keywords: free piston engine, permanent magnet, linear generator, demagnetization, simulation

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Authors: Brandtner-Hafner Martin

Abstract:

Adhesives are increasingly being used in the construction sector. On the one hand, this concerns dowel reinforcements using chemical anchors. On the other hand, the sealing and repair of cracks in structural concrete components are still on the rise. In the field of bonding, the interface between the joined materials is the most critical area. Therefore, it is of immense importance to characterize and investigate this section sufficiently by fracture analysis. Since standardized mechanical test methods are not sufficiently capable of doing this, recourse is made to an innovative concept based on fracture energy. Therefore, a series of experimental tests were performed using the so-called GF-principle to study the interface bonding safety of adhesively bonded concrete joints. Several different structural adhesive systems based on epoxy, CA/A hybrid, PUR, MS polymer, dispersion, and acrylate were selected for bonding concrete substrates. The results show that stable crack propagation and prevention of uncontrolled failure in bonded concrete joints depend very much on the adhesive system used, and only fracture analytical evaluation methods can provide empirical information on this.

Keywords: interface bonding safety, adhesively bonded concrete joints, GF-principle, fracture analysis

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Authors: Emilie Gout, Mathias Monnot, Olivier Boutin, Pierre Vanloot, Philippe Moulin

Abstract:

Water is a precious resource. Treating industrial wastewater remains a considerable technical challenge of our century. The effluent considered for this study is landfill leachate treated by reverse osmosis (RO). Nowadays, in most developed countries, sanitary landfilling is the main method to deal with municipal solid waste. Rainwater percolates through solid waste, generating leachates mostly comprised of organic and inorganic matter. Whilst leachate ages, its composition varies, becoming more and more bio-refractory. RO is already used for landfill leachates as it generates good quality permeate. However, its mains drawback is the production of highly polluted concentrates that cannot be discharged in the environment or reused, which is an important industrial issue. It is against this background that the study of coupling RO with wet air oxidation (WAO) was set to intensify and optimize processes to meet current regulations for water discharge in the environment. WAO is widely studied for effluents containing bio-refractory compounds. Oxidation consists of a destruction reaction capable of mineralizing the recalcitrant organic fraction of pollution into carbon dioxide and water when complete. WAO process in subcritical conditions requires a high-energy consumption, but it can be autothermic in a certain range of chemical oxygen demand (COD) concentrations (10-100 g.L⁻¹). Appropriate COD concentrations are reached in landfill leachate RO concentrates. Therefore, the purpose of this work is to report the performances of mineralization during WAO on RO concentrates. The coupling of RO/WAO has shown promising results in previous works on both synthetic and real effluents in terms of organic carbon (TOC) reduction by WAO and retention by RO. Non-catalytic WAO with air as oxidizer was performed in a lab-scale stirred autoclave (1 L) on landfill leachates RO concentrates collected in different seasons in a sanitary landfill in southern France. The yield of WAO depends on operating parameters such as total pressure, temperature, and time. Compositions of the effluent are also important aspects for process intensification. An experimental design methodology was used to minimize the number of experiments whilst finding the operating conditions achieving the best pollution reduction. The simulation led to a set of 18 experiments, and the responses to highlight process efficiency are pH, conductivity, turbidity, COD, TOC, and inorganic carbon. A 70% oxygen excess was chosen for all the experiments. First experiments showed that COD and TOC abatements of at least 70% were obtained after 90 min at 300°C and 20 MPa, which attested the possibility to treat RO leachate concentrates with WAO. In order to meet French regulations and validate process intensification with industrial effluents, some continuous experiments in a bubble column are foreseen, and some further analyses will be performed, such as biological oxygen demand and study of gas composition. Meanwhile, other industrial effluents are treated to compare RO-WAO performances. These effluents, coming from pharmaceutical, petrochemical, and tertiary wastewater industries, present different specific pollutants that will provide a better comprehension of the hybrid process and prove the intensification and feasibility of the process at an industrial scale. Acknowledgments: This work has been supported by the French National Research Agency (ANR) for the Project TEMPO under the reference number ANR-19-CE04-0002-01.

Keywords: hybrid process, landfill leachates, process intensification, reverse osmosis, wet air oxidation

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