Search results for: algorithm techniques

Authors: Ying Han, Weirong Chen, Qi Li

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An energy management method based on equivalent hydrogen consumption minimum strategy is proposed in this paper aiming at the direct-current (DC) microgrid consisting of photovoltaic cells, fuel cells, energy storage devices, converters and DC loads. The rational allocation of fuel cells and battery devices is achieved by adopting equivalent minimum hydrogen consumption strategy with the full use of power generated by photovoltaic cells. Considering the balance of the battery’s state of charge (SOC), the optimal power of the battery under different SOC conditions is obtained and the reference output power of the fuel cell is calculated. And then a droop control method based on time-varying droop coefficient is proposed to realize the automatic charge and discharge control of the battery, balance the system power and maintain the bus voltage. The proposed control strategy is verified by RT-LAB hardware-in-the-loop simulation platform. The simulation results show that the designed control algorithm can realize the rational allocation of DC micro-grid energy and improve the stability of system.

Keywords: DC microgrid, equivalent minimum hydrogen consumption strategy, energy management, time-varying droop coefficient, droop control

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Authors: Reza Alikhani

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This research focuses on the establishment of horizontal cooperation among companies to enhance their operational efficiency and competitiveness. The study proposes an approach to horizontal collaboration, called coalition configuration, which involves partnering companies sharing distribution centers in a network design problem. The paper investigates which coalition should be formed in each distribution center to minimize the total cost of the network. Moreover, potential uncertainties, such as operational and disruption risks, are considered during the collaborative design phase. To address this problem, a two-stage robust optimization model for collaborative distribution network design under surging demand and facility disruptions is presented, along with a column-and-constraint generation algorithm to obtain exact solutions tailored to the proposed formulation. Extensive numerical experiments are conducted to analyze solutions obtained by the model in various scenarios, including decisions ranging from fully centralized to fully decentralized settings, collaborative versus non-collaborative approaches, and different amounts of uncertainty budgets. The results show that the coalition formation mechanism proposes some solutions that are competitive with the savings of the grand coalition. The research also highlights that collaboration increases network flexibility and resilience while reducing costs associated with demand and capacity uncertainties.

Keywords: logistics, warehouse sharing, robust facility location, collaboration for resilience

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Authors: Loai AbdAllah, Mahmoud Kaiyal

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Missing values in real-world datasets are a common problem. Many algorithms were developed to deal with this problem, most of them replace the missing values with a fixed value that was computed based on the observed values. In our work, we used a distance function based on Bhattacharyya distance to measure the distance between objects with missing values. Bhattacharyya distance, which measures the similarity of two probability distributions. The proposed distance distinguishes between known and unknown values. Where the distance between two known values is the Mahalanobis distance. When, on the other hand, one of them is missing the distance is computed based on the distribution of the known values, for the coordinate that contains the missing value. This method was integrated with Wikaya, a digital health company developing a platform that helps to improve prevention of chronic diseases such as diabetes and cancer. In order for Wikaya’s recommendation system to work distance between users need to be measured. Since there are missing values in the collected data, there is a need to develop a distance function distances between incomplete users profiles. To evaluate the accuracy of the proposed distance function in reflecting the actual similarity between different objects, when some of them contain missing values, we integrated it within the framework of k nearest neighbors (kNN) classifier, since its computation is based only on the similarity between objects. To validate this, we ran the algorithm over diabetes and breast cancer datasets, standard benchmark datasets from the UCI repository. Our experiments show that kNN classifier using our proposed distance function outperforms the kNN using other existing methods.

Keywords: missing values, incomplete data, distance, incomplete diabetes data

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Authors: R. Denkova

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Nowadays, aesthetic and psychological researches in some countries show that the aesthetic ideal for women is built by the same pattern of big volumes – lips, cheek, facial disproportions. They all look like made of a matrix. And they lose their unique and emotional aspects of beauty. How to escape this matrix and find the balance? The secret to being a unique injector is good assessment, creating a treatment plan and flawless injection strategy. The newest concepts in this new injection era which meet the requirements of a modern society and deliver balanced and natural looking results are based on the concept of injecting not the consequence, but the reason. Three case studies are presented with full face assessment, treatment plan and before/after pictures. Using different approaches and techniques of the MD codes concept, lights and shadows concept in order to preserve the emotional beauty and identity of the women. In conclusion, the cases demonstrate that beauty exists even beyond the matrix and it is the injector’s mission and responsibility is to preserve and highlight the natural beauty and unique identity of every different patient.

Keywords: beyond the matrix, emotional beauty, face assessment, injector, treatment plan

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Authors: Muhtasim Shafi Kader, Fizar Ahmed, Annesha Acharjee

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Machine learning and data mining are crucial in health care, as well as medical information and detection. Machine learning approaches are now being utilized to improve awareness of a variety of critical health issues, including diabetes detection, neuron cell tumor diagnosis, COVID 19 identification, and so on. Parkinson’s disease is basically a disease for our senior citizens in Bangladesh. Parkinson's Disease indications often seem progressive and get worst with time. People got affected trouble walking and communicating with the condition advances. Patients can also have psychological and social vagaries, nap problems, hopelessness, reminiscence loss, and weariness. Parkinson's disease can happen in both men and women. Though men are affected by the illness at a proportion that is around partial of them are women. In this research, we have to get out the accurate ML algorithm to find out the disease with a predictable dataset and the model of the following machine learning classifiers. Therefore, nine ML classifiers are secondhand to portion study to use machine learning approaches like as follows, Naive Bayes, Adaptive Boosting, Bagging Classifier, Decision Tree Classifier, Random Forest classifier, XBG Classifier, K Nearest Neighbor Classifier, Support Vector Machine Classifier, and Gradient Boosting Classifier are used.

Keywords: naive bayes, adaptive boosting, bagging classifier, decision tree classifier, random forest classifier, XBG classifier, k nearest neighbor classifier, support vector classifier, gradient boosting classifier

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Authors: Felix Aberu

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The high rate of greenhouse gas (CO₂) emission and increased concentration of Carbon Dioxide in the atmosphere are not unconnected to both human and natural activities. This has caused climate change and global warming in the world. The adverse effect of these climatic changes has no doubt threatened human existence. Hence, this study examined the effects of environmental and climate influence on mortality and morbidity rates, with particular reference to the world’s leading CO₂ emission countries, using both the pre-estimation, estimation, and post-estimation techniques for more dependable outcomes. Hence, the System Generalized Method of Moments (SGMM) was adopted as the main estimation technique for the data analysis from 1996 to 2023. The coefficient of carbon emissions confirmed a positive and significant relationship among CO₂ emission, mortality, and morbidity rates in the world’s leading CO₂ emissions countries, which implies that carbon emission has contributed to mortality and morbidity rates in the world. Therefore, significant action should be taken to facilitate the expansion of environmental protection and sustainability initiatives in any CO₂ emissions nations of the world.

Keywords: environmental, mortality, morbidity, health outcomes, carbon emissions

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Authors: Eisa M. Al-Balhan, Mamdouh M. Soliman

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This study introduced learning styles techniques into mathematics teaching to improve mathematics achievement and motivation among Kuwaiti fourth- and fifth-grade low achievers. The study consisted of two groups. The control group (N = 212) received traditional math tutoring based on a textbook and the tutor’s knowledge of math. The experimental group (N = 209) received math tutoring from instructors trained in the Learning Style™ approach. Three instruments were used: Motivation Scale towards Mathematics; Achievement in Mathematics Test; and the manual of learning style approach indicating the individual’s preferred learning style: AKV, AVK, KAV, KVA, VAK, or VKA. The participating teachers taught to the detected learning style of each student or group. The findings show significant improvement in achievement and motivation towards mathematics in the experimental group. The outcome offers information to variables affecting achievement and motivation towards mathematics and demonstrates the leading role of Kuwait in education within the region.

Keywords: elementary school, learning style, math low achievers, SmartWired™, math instruction, motivation

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Authors: Zhiqiang Xu, Hongfang Ma, Haitao Zhang, Weixin Qian, Weiyong Ying

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SSZ-13 materials with different Si/Al ratio were prepared by varying the composition of aluminosilicate precursor solutions upon hydrothermal treatment at 150 °C. The Si/Al ratio of the initial system was systematically changed from 12.5 to infinity in order to study the limits of Al composition in precursor solutions for constructing CHA structure. The intermediates and final products were investigated by complementary techniques such as XRD, HRTEM, FESEM, and chemical analysis. NH₃-TPD was used to study the Brønsted acidity of SSZ-13 samples with different Si/Al ratios. The effect of the Si/Al ratio on the precursor species, ultimate crystal size, morphology and yield was investigated. The results revealed that Al species determine the nucleation rate and the number of nuclei, which is tied to the morphology and yield of SSZ-13. The size of SSZ-13 increased and the yield decreased as the Si/Al ratio was improved. Varying Si/Al ratio of the initial system is a facile, commercially viable method of tailoring SSZ-13 crystal size and morphology. Furthermore, SSZ-13 materials with different Si/Al ratio were tested as catalysts for the methanol to olefins (MTO) reaction at 350 °C. SSZ-13 with the Si/Al ratio of 35 shows the best MTO catalytic performance.

Keywords: crystallization, MTO, Si/Al ratio, SSZ-13

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Authors: A. Jamsheera, F. Arjmand, D. K. Mohapatra

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Small molecules binding to specific sites along DNA molecule are considered as potential chemotherapeutic agents. Their role as mediators of key biological functions and their unique intrinsic properties make them particularly attractive therapeutic agents. Keeping in view, novel dipeptide complexes Cu(II)-Val-Pro (1), Zn(II)-Val-Pro (2), Cu(II)-Ala-Pro (3) and Zn(II)-Ala-Pro (4) were synthesized and thoroughly characterized using different spectroscopic techniques including elemental analyses, IR, NMR, ESI–MS and molar conductance measurements. The solution stability study carried out by UV–vis absorption titration over a broad range of pH proved the stability of the complexes in solution. In vitro DNA binding studies of complexes 1–4 carried out employing absorption, fluorescence, circular dichroism and viscometric studies revealed the binding of complexes to DNA via groove binding. UV–vis titrations of 1–4 with mononucleotides of interest viz., 5´-GMP and 5´-TMP were also carried out. The DNA cleavage activity of the complexes 1 and 2 were ascertained by gel electrophoresis assay which revealed that the complexes are good DNA cleavage agents and the cleavage mechanism involved a hydrolytic pathway. Furthermore, in vitro antitumor activity of complex 1 was screened against human cancer cell lines of different histological origin.

Keywords: dipeptide Cu(II) and Zn(II) complexes, DNA binding profile, pBR322 DNA cleavage, in vitro anticancer activity

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Authors: Shi Jiaxin

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The Government Work Report is an essential form of document in the government of the People’s Republic of China. It covers all aspects of Chinese society and reflects China’s development strategy and trend. There are countless special terms in Government Work Report. Only by understanding Chinese-specific terms can we understand the content of the Government Work Report. Only by accurately translating the Chinese-specific terms can people come from all across the world know the Chinese government work report and understand China. Relevance theory is a popular theory of cognitive pragmatics. Relevance Translation Theory, which is closely related to Relevance Theory, has crucial and major guiding significance for the translation of Chinese-specific. Through studying Relevance Theory and researching the translation techniques, strategies and applications in the process of translating Chinese-specific terms from the perspective of Relevance Theory, we can understand the meaning and connotation of Chinese-specific terms, then solve various problems in the process of C-E translation, and strengthen our translation ability.

Keywords: government work report, Chinese-specific terms, relevance theory, translation

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Authors: Panupong Makvichian

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Global Navigation Satellite System (GNSS) is nowadays a common technology that improves navigation functions in our life. Additionally, GNSS is also being employed on behalf of an accurate atmospheric sensor these times. Meteorology is a practical application of GNSS, which is unnoticeable in the background of people’s life. GNSS Precise Point Positioning (PPP) is a positioning method that requires data from a single dual-frequency receiver and precise information about satellite positions and satellite clocks. In addition, careful attention to mitigate various error sources is required. All the above data are combined in a sophisticated mathematical algorithm. At this point, the research is going to demonstrate how GNSS and PPP method is capable to provide high-precision estimates, such as 3D positions or Zenith tropospheric delays (ZTDs). ZTDs combined with pressure and temperature information allows us to estimate the water vapor in the atmosphere as precipitable water vapor (PWV). If the process is replicated for a network of GNSS sensors, we can create thematic maps that allow extract water content information in any location within the network area. All of the above are possible thanks to the advances in GNSS data processing. Therefore, we are able to use GNSS data for climatic trend analysis and acquisition of the further knowledge about the atmospheric water content.

Keywords: GNSS, precise point positioning, Zenith tropospheric delays, precipitable water vapor

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Authors: Juhee Lee, Yongjun Lee

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In designing a low-energy-consuming buildings, the heat transfer through a large glass or wall becomes critical. Multiple layers of the window glasses and walls are employed for the high insulation. The gravity driven air flow between window glasses or wall layers is a natural heat convection phenomenon being a key of the heat transfer. For the first step of the natural heat transfer analysis, in this study the development and application of a finite volume method for the numerical computation of viscous incompressible flows is presented. It will become a part of the natural convection analysis with high-order scheme, multi-grid method, and dual-time step in the future. A finite volume method based on a fully-implicit second-order is used to discretize and solve the fluid flow on unstructured grids composed of arbitrary-shaped cells. The integrations of the governing equation are discretised in the finite volume manner using a collocated arrangement of variables. The convergence of the SIMPLE segregated algorithm for the solution of the coupled nonlinear algebraic equations is accelerated by using a sparse matrix solver such as BiCGSTAB. The method used in the present study is verified by applying it to some flows for which either the numerical solution is known or the solution can be obtained using another numerical technique available in the other researches. The accuracy of the method is assessed through the grid refinement.

Keywords: finite volume method, fluid flow, laminar flow, unstructured grid

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Authors: R. C. Tyagi

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Quasi-periodicity of frequency intervals observed in Shruti based Absolute Scale of Music has been used to graphically identify the Anchor notes ‘Vadi’ and ‘Samvadi’ which are nodal points for expansion, elaboration and iteration of the emotional theme represented by the characteristic tonic arrangement in Raga compositions. This analysis leads to defining the Tonic parameters in the octave including the key-note frequency, tonic intervals’ anchor notes and the on-set and range of quasi-periodicities as exponents of 2. Such uniformity of representation of characteristic data would facilitate computational analysis and synthesis of music compositions and also help develop noise suppression techniques. Criteria for tuning of strings for compatibility with placement of frets on finger boards is discussed. Natural Rhythmic cycles in music compositions are analytically shown to lie between 3 and 126 beats.

Keywords: absolute scale, anchor notes, computational analysis, frets, innovation, noise suppression, Quasi-periodicity, rhythmic cycle, tonic interval, Shruti

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Authors: Hao Wang, Shengchun Wang, Weidong Wang

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On the influence of train vibration and environmental noise on the measurement of track wear, we proposed a method for automatic extraction of circular arc on the inner or outer side of the rail waist and achieved the high-precision registration of rail profile. Firstly, a polynomial fitting method based on truncated residual histogram was proposed to find the optimal fitting curve of the profile and reduce the influence of noise on profile curve fitting. Then, based on the curvature distribution characteristics of the fitting curve, the interval search algorithm based on dynamic window’s maximum curvature entropy was proposed to realize the automatic segmentation of small circular arc. At last, we fit two circle centers as matching reference points based on small circular arcs on both sides and realized the alignment from the measured profile to the standard designed profile. The static experimental results show that the mean and standard deviation of the method are controlled within 0.01mm with small measurement errors and high repeatability. The dynamic test also verified the repeatability of the method in the train-running environment, and the dynamic measurement deviation of rail wear is within 0.2mm with high repeatability.

Keywords: curvature entropy, profile registration, rail wear, structured light, train-running

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Authors: Naushaba Naseem Ahmed

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Economic development does not merely depend upon the level of development but also on its distributive aspect. As it is a serious issue, the fruit of development is not equally distributed among the different section of peoples and different part of the country this cause the regional disparities in the levels of social economic development. Different part of the country has different resource endowments in term of natural, human and capital. If there is the uniform condition to grow, these areas that have better resources, are favourably placed grow comparatively faster as other areas. Thus with the very stage of development, gap between resourceful and less resourceful area goes on widening. This paper is an attempt to highlight the levels of disparities in term of economic development with the help of selected variables. Principal component analysis, correlation, and coefficient of variation are the techniques which were used in paper and employed published data for analysis. The result shows that Western region of Uttar Pradesh is more developed followed by Central Region. There will be urgent need in investment and developmental policies for the backward region like Bundelkhand region of Uttar Pradesh.

Keywords: coefficient of variation, correlation, economic development, principal component analysis

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Authors: Sandeep Singh Sandhua, Karanvir Singh Ghuman, Arun Kumar

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Stellite 21 is cobalt based super alloy used in improving the wear performance of stainless steel engineering components subjected to harsh environmental conditions. This piece of research focuses on the wear analysis of satellite 21 cladded on AISI 304 L substrate using SMAW process. Bead on plate experiments were carried out by varying current and electrode manipulation techniques to optimize the dilution and microhardness. 80 Amp current and weaving technique was found to be optimum set of parameters for overlaying which were further used for multipass multilayer cladding of AISI 304 L substrate. The wear performance was examined on pin on dics wear testing machine under room temperature conditions. The results from this study show that Stellite 21 overlays show a significant improvement in the frictional wear resistance after TIG remelting. It is also established that low dilution procedures are important in controlling the metallurgical composition of these overlays which has a consequent effect in enhancing hardness and wear resistance of these overlays.

Keywords: surfacing, stellite 21, dilution, SMAW, frictional wear, micro-hardness

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Authors: Jaroslav Fiala, Jaroslav Kaiser, Pavel Zlabek, Vaclav Mentl

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The X-ray diffraction technique was recognized as a useful tool for the assessment of material degradation degree after a long-time service. In many industrial applications materials are subjected to degradation of mechanical properties as a result of real service conditions. The assessment of the remnant lifetime of components and structures is commonly based on correlated procedures including numerous destructive, non-destructive and mathematical techniques that should guarantee reasonable precise assessment of the current damage extent of materials in question and the remnant lifetime assessment. This paper summarizes results of an experimental programme concentrated on mechanical properties degradation of welded components. Steel an Al-alloy test specimens of base metal, containing welds and simple weldments were fatigue loaded at room temperature to obtain Woehler S-N curve. X-ray diffraction technique was applied to assess the degradation degree of material as a result of cyclic loading.

Keywords: fatigue loading, material degradation, steels, AL-alloys, X-ray diffraction

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Authors: Muhammad Ali

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Cyber-attacks and anomaly detection on the Internet of Things (IoT) infrastructure is emerging concern in the domain of data-driven intrusion. Rapidly increasing IoT risk is now making headlines around the world. denial of service, malicious control, data type probing, malicious operation, DDos, scan, spying, and wrong setup are attacks and anomalies that can affect an IoT system failure. Everyone talks about cyber security, connectivity, smart devices, and real-time data extraction. IoT devices expose a wide variety of new cyber security attack vectors in network traffic. For further than IoT development, and mainly for smart and IoT applications, there is a necessity for intelligent processing and analysis of data. So, our approach is too secure. We train several machine learning models that have been compared to accurately predicting attacks and anomalies on IoT systems, considering IoT applications, with ANOVA-based feature selection with fewer prediction models to evaluate network traffic to help prevent IoT devices. The machine learning (ML) algorithms that have been used here are KNN, SVM, NB, D.T., and R.F., with the most satisfactory test accuracy with fast detection. The evaluation of ML metrics includes precision, recall, F1 score, FPR, NPV, G.M., MCC, and AUC & ROC. The Random Forest algorithm achieved the best results with less prediction time, with an accuracy of 99.98%.

Keywords: machine learning, analysis of variance, Internet of Thing, network security, intrusion detection

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Authors: B. M. Patil, S. R. Dharwadkar

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Zirconolite (CaZrTi2O7) is one of the three major phases in the synthetic ceramic 'SYNROC' which is used for immobilization of high-level nuclear waste and also acts as photocatalytic and photophysical properties. In the present work the nanocrystalline CaZrTi2O7 was synthesized from Calcium Zirconyl Titanate tartrate precursor (CZTT) employing two different heating techniques such as Conventional heating (Muffle furnace) and Microwave heating (Microwave Oven). Thermal decomposition of the CZTT precursors in air yielded nanocrystalline CaZrTi2O7 powder as the end product. The products obtained by annealing the CZTT precursor using both heating method were characterized using simultaneous TG-DTA, FTIR, XRD, SEM, TEM, NTA and thermodilatometric study. The physical characteristics such as crystallinity, morphology and particle size of the product obtained by heating the CZTT precursor at the different temperatures in a Muffle furnace and Microwave oven were found to be significantly different. The microwave heating technique considerably lowered the synthesis temperature of CaZrTi2O7. The influence of microwave heating was more pronounced as compared to Muffle furnace heating. The details of the synthesis of CaZrTi2O7 from CZTT precursor are discussed.

Keywords: CZTT, CaZrTi2O7, microwave, SYNROC, zirconolite

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Authors: Sagri Sharma

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Analysis of diseases integrating multi-factors increases the complexity of the problem and therefore, development of frameworks for the analysis of diseases is an issue that is currently a topic of intense research. Due to the inter-dependence of the various parameters, the use of traditional methodologies has not been very effective. Consequently, newer methodologies are being sought to deal with the problem. Supervised Learning Algorithms are commonly used for performing the prediction on previously unseen data. These algorithms are commonly used for applications in fields ranging from image analysis to protein structure and function prediction and they get trained using a known dataset to come up with a predictor model that generates reasonable predictions for the response to new data. Gene expression profiles generated by DNA analysis experiments can be quite complex since these experiments can involve hypotheses involving entire genomes. The application of well-known machine learning algorithm - Support Vector Machine - to analyze the expression levels of thousands of genes simultaneously in a timely, automated and cost effective way is thus used. The objectives to undertake the presented work are development of a methodology to identify genes relevant to Hepatocellular Carcinoma (HCC) from gene expression dataset utilizing supervised learning algorithms and statistical evaluations along with development of a predictive framework that can perform classification tasks on new, unseen data.

Keywords: artificial intelligence, biomarker, gene expression datasets, hepatocellular carcinoma, machine learning, supervised learning algorithms, support vector machine

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Authors: James S. Kwame, E. Yakushina, P. Blackwell

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The challenges in forming titanium alloys at room temperature are well researched and are linked both to the limitations imposed by the basic crystal structure and their ability to form texture during plastic deformation. One major issue of concern for the sheet forming of titanium alloys is their high sensitivity to surface inhomogeneity. Various machining processes are utilised in preparing sheet hole edges for edge flanging applications. However, the response of edge forming tendencies of titanium to different edge surface finishes is not well investigated. The hole expansion test is used in this project to elucidate the impact of abrasive water jet (AWJ) and electro-discharge machining (EDM) cutting techniques on the edge formability of CP-Ti (Grade 2) and Ti-3Al-2.5V alloys at room temperature. The results show that the quality of the edge surface finish has a major effect on the edge formability of the materials. The work also found that the variations in the edge forming performance are mainly the result of the influence of machining induced edge surface defects.

Keywords: titanium alloys, hole expansion test, edge formability, non-conventional machining

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Authors: Mozadded Hossen

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This study investigates the varied effects of internet addiction on college students, specifically examining the behavioral and academic outcomes. The widespread use of the Internet in academic settings has substantially impacted students' mental well-being and academic achievements. The study investigates the correlation between excessive internet usage and addiction, which manifests through symptoms including social isolation, anxiety, despair, and sleep disruptions. Additionally, the study examines the relationship between internet addiction and academic results, finding that kids with more severe addiction levels generally have lower academic performance, experience diminished focus, and show reduced involvement in academic tasks. The study intends to analyze the many consequences of internet addiction to gain insights into its ramifications. It also urges educational institutions to develop techniques that can reduce the negative impact of internet addiction and encourage healthier internet use among students. The results emphasize the necessity of implementing comprehensive measures to tackle the behavioral and academic difficulties caused by internet addiction among college students.

Keywords: internet addiction, behavioral consequences, college students, social isolation

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Authors: Hamid Aslani, Ahmed Radwan

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In this paper, a single element microstrip antenna is presented for UWB applications by using techniques as partial ground plane and modified the shape of the patch. The antenna is properly designed to have a compact size and constant gain against frequency. The simulated results have done using two EM software and show good agreement with the measured results for the fabricated antenna. Then a designing of two elements patch antenna array for UWB in the frequency band of 3.1-10 GHz is presented in this paper. The array is constructed by means of feeding two omni-directional modified circular patch elements with a modified power divider. Experimental results show that the array has a stable radiation pattern and low return loss over a broad bandwidth of 64% (3.1–10 GHz). Due to its planar profile, physically compact size, wide impedance bandwidth, directive performance over a wide bandwidth proposed antenna is a good candidate for portable UWB applications and other UWB integrated circuits.

Keywords: ultra wide band, radiation performance, microstrip antenna, size miniaturized antenna

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Authors: Mahdi Almaky

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Conducting polymer of N-(3-pyrrol-1-yl-propyl)-2,2`-bipyridinium hexafluoro-phosphate (PPBH) was prepared via chemical and electrochemical polymerization methods. The bulk polymer showed conductivity in the order of 10-12 S cm-1. DNA-templated polymer nano wires of PPBH (PolyPPBH-DNA) have been chemically prepared then used as templates to direct the formation of metal nanowires (Cu) in order to enhance the electrical properties of the polymer/DNA wires. The chemical structures, morphology and the electrical characterisation of the as obtained structures have been characterized through spectroscopic (FTIR, UV-vis and XPS), single-crystal X-ray diffraction and microscopic (AFM, EFM and c-AFM) techniques. The morphology of the nanomaterials has been observed by AFM; showing the nanowires are uniform and continuous. The polymer conductivity was slightly improved after metallization. The conductivity of Cu-PolyPPBH-DNA nanowires was estimated to be 7.1x10-2 S cm-1. This conductivity is slightly higher than the conductivity of PolyPPBH-DNA nano wires (2.0 x 10-2 S cm-1), but it is lower than the measurements for PPy/DNA nano wires (2.1 x 10-1 S cm-1) prepared and measured by using c-AFM probe. These results reflect the large effect of the chemical structure (N-substitution) on the electrical properties of these polymers by reducing the extended conjugation.

Keywords: DNA, template, nano wires, N-Alkylatedpyrrole, copper

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Authors: Najib K. Dankadai, Ahmad 'Athif Mohd Faudzi, Khairuddin Osman, Muhammad Rusydi Muhammad Razif, IIi Najaa Aimi Mohd Nordin

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Researches in soft actuators are now growing rapidly because of their adequacy to be applied in sectors like medical, agriculture, biological and welfare. This paper presents system identification (SI) and control of the force generated by a two chambers pneumatic soft actuator (PSA). A force mathematical model for the actuator was identified experimentally using data acquisition card and MATLAB SI toolbox. Two control techniques; a predictive functional control (PFC) and conventional proportional integral and derivative (PID) schemes are proposed and compared based on the identified model for the soft actuator flexible mechanism. Results of this study showed that both of the proposed controllers ensure accurate tracking when the closed loop system was tested with the step, sinusoidal and multi step reference input through MATLAB simulation although the PFC provides a better response than the PID.

Keywords: predictive functional control (PFC), proportional integral and derivative (PID), soft actuator, system identification

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Authors: Sofia M. Karadimitriou, Kostas Triantafyllopoulos, Timothy Heaton

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Reduced dimension Dynamic Spatio-Temporal Models (DSTMs) jointly describe the spatial and temporal evolution of a function observed subject to noise. A basic state space model is adopted for the discrete temporal variation, while a continuous autoregressive structure describes the continuous spatial evolution. Application of such a DSTM relies upon the pre-selection of a suitable reduced set of basic functions and this can present a challenge in practice. In this talk, we propose an online estimation method for high dimensional spatio-temporal data based upon DSTM and we attempt to resolve this issue by allowing the basis to adapt to the observed data. Specifically, we present a wavelet decomposition in order to obtain a parsimonious approximation of the spatial continuous process. This parsimony can be achieved by placing a Laplace prior distribution on the wavelet coefficients. The aim of using the Laplace prior, is to filter wavelet coefficients with low contribution, and thus achieve the dimension reduction with significant computation savings. We then propose a Hierarchical Bayesian State Space model, for the estimation of which we offer an appropriate particle filter algorithm. The proposed methodology is illustrated using real environmental data.

Keywords: multidimensional Laplace prior, particle filtering, spatio-temporal modelling, wavelets

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Authors: Firdaous Zair, Zoubir Elfelsoufi, Mohammed Fourka

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The allocation of resources is an issue that is needed on the tactical and operational strategic plan. This work considers the allocation of resources in the case of pure players, manufacturers and Click & Mortars that have launched online sales. The aim is to improve the level of customer satisfaction and maintaining the benefits of e-retailer and of its cooperators and reducing costs and risks. Our contribution is a decision support system and tool for improving the allocation of resources in logistics chains e-commerce B2C context. We first modeled the B2C chain with all operations that integrates and possible scenarios since online retailers offer a wide selection of personalized service. The personalized services that online shopping companies offer to the clients can be embodied in many aspects, such as the customizations of payment, the distribution methods, and after-sales service choices. In addition, every aspect of customized service has several modes. At that time, we analyzed the optimization problems of supply chain resource allocation in customized online shopping service mode, which is different from the supply chain resource allocation under traditional manufacturing or service circumstances. Then we realized an optimization model and algorithm for the development based on the analysis of the allocation of the B2C supply chain resources. It is a multi-objective optimization that considers the collaboration of resources in operations, time and costs but also the risks and the quality of services as well as dynamic and uncertain characters related to the request.

Keywords: e-commerce, supply chain, B2C, optimisation, resource allocation

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Authors: Bingchun Liu, Pei-Chann Chang, Natasha Huang, Dun Li

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Machine Learning and Data Mining are the two important tools for extracting useful information and knowledge from large datasets. In machine learning, classification is a wildly used technique to predict qualitative variables and is generally preferred over regression from an operational point of view. Due to the enormous increase in air pollution in various countries especially China, Air Quality Classification has become one of the most important topics in air quality research and modelling. This study aims at introducing a hybrid classification model based on information theory and Support Vector Machine (SVM) using the air quality data of four cities in China namely Beijing, Guangzhou, Shanghai and Tianjin from Jan 1, 2014 to April 30, 2016. China's Ministry of Environmental Protection has classified the daily air quality into 6 levels namely Serious Pollution, Severe Pollution, Moderate Pollution, Light Pollution, Good and Excellent based on their respective Air Quality Index (AQI) values. Using the information theory, information gain (IG) is calculated and feature selection is done for both categorical features and continuous numeric features. Then SVM Machine Learning algorithm is implemented on the selected features with cross-validation. The final evaluation reveals that the IG and SVM hybrid model performs better than SVM (alone), Artificial Neural Network (ANN) and K-Nearest Neighbours (KNN) models in terms of accuracy as well as complexity.

Keywords: machine learning, air quality classification, air quality index, information gain, support vector machine, cross-validation

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Authors: Duygu Merve Bulut, Fehime Yesim Gurani

Abstract:

The Japanese architect Shigeru Ban has succeeded in bringing a different understanding to the modern architectural design approach with both the material selection and the techniques he used while combining the material with the design. Ban, who reflects his respect to people and nature with his designs, has encouraged that design should be done with economic materials, easily accessible and understandable for everyone. Because of this, Ban has attracted attention and appreciated in the architectural world with his environmentally-sensitive design ideology and humanitarian projects. In order to understand Ban’s environmentally-sensitive design approach, with this article, Ban’s projects which have used natural materials; the projects of Ban’s Japenese Pavilion in Germany, Papertainer Museum in South Korea, Centre Pompidou-Metz in France and Cardboard Cathedral in New Zealand were examined and analyzed. In the following parts, 'paper tube' technology that creates awareness in architectural area, which developed and applied by Ban; has been examined in terms of building material and structure of sustainable space design. As a result of this review, Ban’s approach is evaluated in terms of its contribution to the understanding of sustainable design.

Keywords: ecological design, environmentally-sensitive design, paper tube, Shigeru Ban, sustainability

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Authors: Mohamed Al-Badrashiny, Abdelghani Bellaachia

Abstract:

This paper presents a classifier ensemble approach for predicting the survivability of the breast cancer patients using the latest database version of the Surveillance, Epidemiology, and End Results (SEER) Program of the National Cancer Institute. The system consists of two main components; features selection and classifier ensemble components. The features selection component divides the features in SEER database into four groups. After that it tries to find the most important features among the four groups that maximizes the weighted average F-score of a certain classification algorithm. The ensemble component uses three different classifiers, each of which models different set of features from SEER through the features selection module. On top of them, another classifier is used to give the final decision based on the output decisions and confidence scores from each of the underlying classifiers. Different classification algorithms have been examined; the best setup found is by using the decision tree, Bayesian network, and Na¨ıve Bayes algorithms for the underlying classifiers and Na¨ıve Bayes for the classifier ensemble step. The system outperforms all published systems to date when evaluated against the exact same data of SEER (period of 1973-2002). It gives 87.39% weighted average F-score compared to 85.82% and 81.34% of the other published systems. By increasing the data size to cover the whole database (period of 1973-2014), the overall weighted average F-score jumps to 92.4% on the held out unseen test set.

Keywords: classifier ensemble, breast cancer survivability, data mining, SEER

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