Search results for: preposition error detection

Authors: Sanja Podunavac-Kuzmanović, Strahinja Kovačević, Lidija Jevrić, Evgenija Djurendić, Jovana Ajduković

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There are several methods for determination of the lipophilicity of biologically active compounds, however chromatography has been shown as a very suitable method for this purpose. Chromatographic (C18-RP-HPLC) analysis of a series of 24 17-picolyl and 17-picolinylidene androstane derivatives was carried out. The obtained retention indices (logk, methanol (90%) / water (10%)) were correlated with calculated physicochemical and lipophilicity descriptors. The QSRR analysis was carried out applying principal component regression (PCR) and partial least squares regression (PLS). The PCR and PLS model were selected on the basis of the highest variance and the lowest root mean square error of cross-validation. The obtained PCR and PLS model successfully correlate the calculated molecular descriptors with logk parameter indicating the significance of the lipophilicity of compounds in chromatographic process. On the basis of the obtained results it can be concluded that the obtained logk parameters of the analyzed androstane derivatives can be considered as their chromatographic lipophilicity. These results are the part of the project No. 114-451-347/2015-02, financially supported by the Provincial Secretariat for Science and Technological Development of Vojvodina and CMST COST Action CM1105.

Keywords: androstane derivatives, chromatography, molecular structure, principal component regression, partial least squares regression

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Authors: S. Aisyah Azizan, M. Syafiq Azmi, Yuki Harada, Yoshinobu Maeda, Takaomi Matsutani

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We introduced an all-optical multi-casting characteristics with wavelength conversion based on a novel all-optical triode using negative feedback semiconductor optical amplifier. This study was demonstrated with a transfer speed of 10 Gb/s to a non-return zero 231-1 pseudorandom bit sequence system. This multi-wavelength converter device can simultaneously provide three channels of output signal with the support of non-inverted and inverted conversion. We studied that an all-optical multi-casting and wavelength conversion accomplishing cross gain modulation is effective in a semiconductor optical amplifier which is effective to provide an inverted conversion thus negative feedback. The relationship of received power of back to back signal and output signals with wavelength 1535 nm, 1540 nm, 1545 nm, 1550 nm, and 1555 nm with bit error rate was investigated. It was reported that the output signal wavelengths were successfully converted and modulated with a power penalty of less than 8.7 dB, which the highest is 8.6 dB while the lowest is 4.4 dB. It was proved that all-optical multi-casting and wavelength conversion using an optical triode with a negative feedback by three channels at the same time at a speed of 10 Gb/s is a promising device for the new wavelength conversion technology.

Keywords: cross gain modulation, multicasting, negative feedback optical amplifier, semiconductor optical amplifier

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Authors: Y. Laib Dit Leksir, S. Bouhouche

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Analysis and processing of data bases resulting from infrared thermal measurements made on the electrical installation requires the development of new tools in order to obtain correct and additional information to the visual inspections. Consequently, the methods based on the capture of infrared digital images show a great potential and are employed increasingly in various fields. Although, there is an enormous need for the development of effective techniques to analyse these data base in order to extract relevant information relating to the state of the equipments. Our goal consists in introducing recent techniques of modeling based on new methods, image and signal processing to develop mathematical models in this field. The aim of this work is to capture the anomalies existing in electrical equipments during an inspection of some machines using A40 Flir camera. After, we use binarisation techniques in order to select the region of interest and we make comparison between these methods of thermal images obtained to choose the best one.

Keywords: infrared thermography, defect detection, troubleshooting, electrical equipment

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Authors: Mohammad Namazi, Mohammad Sadeghzadeh Maharluie

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Predicting earnings management is vital for the capital market participants, financial analysts and managers. The aim of this research is attempting to respond to this query: Is there a significant difference between the regression model and neural networks’ models in predicting earnings management, and which one leads to a superior prediction of it? In approaching this question, a Linear Regression (LR) model was compared with two neural networks including Multi-Layer Perceptron (MLP), and Generalized Regression Neural Network (GRNN). The population of this study includes 94 listed companies in Tehran Stock Exchange (TSE) market from 2003 to 2011. After the results of all models were acquired, ANOVA was exerted to test the hypotheses. In general, the summary of statistical results showed that the precision of GRNN did not exhibit a significant difference in comparison with MLP. In addition, the mean square error of the MLP and GRNN showed a significant difference with the multi variable LR model. These findings support the notion of nonlinear behavior of the earnings management. Therefore, it is more appropriate for capital market participants to analyze earnings management based upon neural networks techniques, and not to adopt linear regression models.

Keywords: earnings management, generalized linear regression, neural networks multi-layer perceptron, Tehran stock exchange

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Authors: Mumtaz Begum Mustafa, Siti Salwah Salim, Feizal Dani Rahman

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Recently, Automatic Speech Recognition (ASR) systems were used to assist children in language acquisition as it has the ability to detect human speech signal. Despite the benefits offered by the ASR system, there is a lack of ASR systems for Malay-speaking children. One of the contributing factors for this is the lack of continuous speech database for the target users. Though cross-lingual adaptation is a common solution for developing ASR systems for under-resourced language, it is not viable for children as there are very limited speech databases as a source model. In this research, we propose a two-stage adaptation for the development of ASR system for Malay-speaking children using a very limited database. The two stage adaptation comprises the cross-lingual adaptation (first stage) and cross-age adaptation. For the first stage, a well-known speech database that is phonetically rich and balanced, is adapted to the medium-sized Malay adults using supervised MLLR. The second stage adaptation uses the speech acoustic model generated from the first adaptation, and the target database is a small-sized database of the target users. We have measured the performance of the proposed technique using word error rate, and then compare them with the conventional benchmark adaptation. The two stage adaptation proposed in this research has better recognition accuracy as compared to the benchmark adaptation in recognizing children’s speech.

Keywords: Automatic Speech Recognition System, children speech, adaptation, Malay

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Authors: Zehai Yu, Hui Zhu, Linglong Lin, Huawei Liang, Biao Yu, Weixin Huang

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With the development of intelligent vehicle systems, a high-precision road map is increasingly needed in many aspects. The automatic lane lines extraction and modeling are the most essential steps for the generation of a precise lane-level road map. In this paper, an automatic lane-level road map generation system is proposed. To extract the road markings on the ground, the multi-region Otsu thresholding method is applied, which calculates the intensity value of laser data that maximizes the variance between background and road markings. The extracted road marking points are then projected to the raster image and clustered using a two-stage clustering algorithm. Lane lines are subsequently recognized from these clusters by the shape features of their minimum bounding rectangle. To ensure the storage efficiency of the map, the lane lines are approximated to cubic polynomial curves using a Bayesian estimation approach. The proposed lane-level road map generation system has been tested on urban and expressway conditions in Hefei, China. The experimental results on the datasets show that our method can achieve excellent extraction and clustering effect, and the fitted lines can reach a high position accuracy with an error of less than 10 cm.

Keywords: curve fitting, lane-level road map, line recognition, multi-thresholding, two-stage clustering

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Authors: M. A. Meslem

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For precise geoid determination, we use a reference field to subtract long and medium wavelength of the gravity field from observations data when we use the remove-compute-restore technique. Therefore, a comparison study between considered models should be made in order to select the optimal reference gravity field to be used. In this context, two recent global geopotential models have been selected to perform this comparison study over Northern Algeria. The Earth Gravitational Model (EGM2008) and the Global Gravity Model (GECO) conceived with a combination of the first model with anomalous potential derived from a GOCE satellite-only global model. Free air gravity anomalies in the area under study have been used to compute residual data using both gravity field models and a Digital Terrain Model (DTM) to subtract the residual terrain effect from the gravity observations. Residual data were used to generate local empirical covariance functions and their fitting to the closed form in order to compare their statistical behaviors according to both cases. Finally, height anomalies were computed from both geopotential models and compared to a set of GPS levelled points on benchmarks using least squares adjustment. The result described in details in this paper regarding these two models has pointed out a slight advantage of GECO global model globally through error degree variances comparison and ground-truth evaluation.

Keywords: quasigeoid, gravity aomalies, covariance, GGM

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Authors: Tian-Fang Kang, Chao-Nan Ge, Rui Li

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An electrochemical biosensor for the determination of organophosphorus pesticides was developed based on electrochemical co-deposition of Au and Pd nanoparticles on glassy carbon electrode (GCE). Energy disperse spectroscopy (EDS) analysis was used for characterization of the surface structure. Scanning electron micrograph (SEM) demonstrates that the films are uniform and the nanoclusters are homogeneously distributed on the GCE surface. Acetylcholinesterase (AChE) was immobilized on the Au and Pd nanoparticle modified electrode (Au-Pd/GCE) by cross-linking with glutaraldehyde. The electrochemical behavior of thiocholine at the biosensor (AChE/Au-Pd/GCE) was studied. The biosensors exhibited substantial electrocatalytic effect on the oxidation of thiocholine. The peak current of linear scan voltammetry (LSV) of thiocholine at the biosensor is proportional to the concentration of acetylthiocholine chloride (ATCl) over the range of 2.5 × 10-6 to 2.5 × 10-4 M in 0.1 M phosphate buffer solution (pH 7.0). The percent inhibition of acetylcholinesterase was proportional to the logarithm of parathion concentration in the range of 4.0 × 10-9 to 1.0 × 10-6 M. The detection limit of parathion was 2.6 × 10-9 M. The proposed method exhibited high sensitivity and good reproducibility.

Keywords: acetylcholinesterase, Au-Pd nanoparticles, electrochemical biosensors, parathion

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Authors: Mahdi Salarian, Xi Xu, Rashid Ansari

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Accurate localization of mobile devices based on camera-acquired visual media information usually requires a search over a very large GPS-referenced image database. This paper proposes an efficient method for limiting the search space for image retrieval engine by extracting and leveraging additional media information about Estimated Positional Error (EP E) to address complexity and accuracy issues in the search, especially to be used for compensating GPS location inaccuracy in dense urban areas. The improved performance is achieved by up to a hundred-fold reduction in the search area used in available reference methods while providing improved accuracy. To test our procedure we created a database by acquiring Google Street View (GSV) images for down town of Chicago. Other available databases are not suitable for our approach due to lack of EP E for the query images. We tested the procedure using more than 200 query images along with EP E acquired mostly in the densest areas of Chicago with different phones and in different conditions such as low illumination and from under rail tracks. The effectiveness of our approach and the effect of size and sector angle of the search area are discussed and experimental results demonstrate how our proposed method can improve performance just by utilizing a data that is available for mobile systems such as smart phones.

Keywords: localization, retrieval, GPS uncertainty, bag of word

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Authors: Fatih Tarlak

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Predictive microbiology can be considered as an important field in food microbiology in which it uses predictive models to describe the microbial growth in different food products. Predictive models estimate the growth of microorganisms quickly, efficiently, and in a cost-effective way as compared to traditional methods of enumeration, which are long-lasting, expensive, and time-consuming. The mathematical models used in predictive microbiology are mainly categorised as primary and secondary models. The primary models are the mathematical equations that define the growth data as a function of time under a constant environmental condition. The secondary models describe the effects of environmental factors, such as temperature, pH, and water activity (aw) on the parameters of the primary models, including the maximum specific growth rate and lag phase duration, which are the most critical growth kinetic parameters. The combination of primary and secondary models provides valuable information to set limits for the quantitative detection of the microbial spoilage and assess product shelf-life.

Keywords: shelf-life, growth model, predictive microbiology, simulation

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Authors: Degale Desta, Cheng Jian

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The most important factor in identifying a person is their face. Even identical twins have their own distinct faces. As a result, identification and face recognition are needed to tell one person from another. A face recognition system is a verification tool used to establish a person's identity using biometrics. Nowadays, face recognition is a common technique used in a variety of applications, including home security systems, criminal identification, and phone unlock systems. This system is more secure because it only requires a facial image instead of other dependencies like a key or card. Face detection and face identification are the two phases that typically make up a human recognition system.The idea behind designing and creating a face recognition system using deep learning with Azure ML Python's OpenCV is explained in this paper. Face recognition is a task that can be accomplished using deep learning, and given the accuracy of this method, it appears to be a suitable approach. To show how accurate the suggested face recognition system is, experimental results are given in 98.46% accuracy using Fast-RCNN Performance of algorithms under different training conditions.

Keywords: deep learning, face recognition, identification, fast-RCNN

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Authors: Benson Ade Eniola Afere

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This paper introduces a family of fourth-order hybrid beta polynomial kernels developed for statistical analysis. The assessment of these kernels' performance centers on two critical metrics: asymptotic mean integrated squared error (AMISE) and kernel efficiency. Through the utilization of both simulated and real-world datasets, a comprehensive evaluation was conducted, facilitating a thorough comparison with conventional fourth-order polynomial kernels. The evaluation procedure encompassed the computation of AMISE and efficiency values for both the proposed hybrid kernels and the established classical kernels. The consistently observed trend was the superior performance of the hybrid kernels when compared to their classical counterparts. This trend persisted across diverse datasets, underscoring the resilience and efficacy of the hybrid approach. By leveraging these performance metrics and conducting evaluations on both simulated and real-world data, this study furnishes compelling evidence in favour of the superiority of the proposed hybrid beta polynomial kernels. The discernible enhancement in performance, as indicated by lower AMISE values and higher efficiency scores, strongly suggests that the proposed kernels offer heightened suitability for statistical analysis tasks when compared to traditional kernels.

Keywords: AMISE, efficiency, fourth-order Kernels, hybrid Kernels, Kernel density estimation

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Authors: Shih-Heng Tung, Ming-Hsiang Shih, Wen-Pei Sung

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Because of the rapid development of digital camera and computer, digital image correlation method has drawn lots of attention recently and has been applied to a variety of fields. However, the image distortion is inevitable when the image is captured through a lens. This image distortion problem can result in an innegligible error while using digital image correlation method. There are already many different ways to correct the image distortion, and most of them require specific image patterns or precise control points. A new distortion correction method is proposed in this study. The proposed method is based on the fact that a flat surface should keep flat when it is measured using three-dimensional (3D) digital image measurement technique. Lens distortion can be divided into radial distortion, decentering distortion and thin prism distortion. Because radial distortion has a more noticeable influence than the other types of distortions, this method deals only with radial distortion. The simplified 3D digital image measurement technique is adopted to measure the surface coordinates of a flat specimen. Then the gradient method is applied to find the best correction parameters. A few experiments are carried out in this study to verify the correctness of this method. The results show that this method can achieve a good accuracy and it is suitable for both large and small distortion conditions. The most important advantage is that it requires neither mark with specific pattern nor precise control points.

Keywords: 3D DIC, radial distortion, distortion correction, planarity

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Authors: Pengwei Qiao, Sucai Yang, Wenxia Wei

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Soil pollution has become an important issue in China. Accurate spatial distribution prediction of pollutants with interpolation methods is the basis for soil remediation in the site. However, a relatively strong variability of pollutants would decrease the prediction accuracy. Theoretically, partition interpolation can result in accurate prediction results. In order to verify the applicability of partition interpolation for a site, benzo (b) fluoranthene (BbF) in four soil layers was adopted as the research object in this paper. IDW (inverse distance weighting)-, RBF (radial basis function)-and OK (ordinary kriging)-based partition interpolation accuracies were evaluated, and their influential factors were analyzed; then, the uncertainty and applicability of partition interpolation were determined. Three conclusions were drawn. (1) The prediction error of partitioned interpolation decreased by 70% compared to unpartitioned interpolation. (2) Partition interpolation reduced the impact of high CV (coefficient of variation) and high concentration value on the prediction accuracy. (3) The prediction accuracy of IDW-based partition interpolation was higher than that of RBF- and OK-based partition interpolation, and it was suitable for the identification of highly polluted areas at a contaminated site. These results provide a useful method to obtain relatively accurate spatial distribution information of pollutants and to identify highly polluted areas, which is important for soil pollution remediation in the site.

Keywords: accuracy, applicability, partition interpolation, site, soil pollution, uncertainty

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Authors: Qi Xiu, Hiromu Hota, Yohsuke Ishii, Takuya Oda

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Recently, collectable manufacturing data are rapidly increasing. On the other hand, mega recall is getting serious as a social problem. Under such circumstances, there are increasing needs for preventing mega recalls by defect analysis such as root cause analysis and abnormal detection utilizing manufacturing data. However, the time to classify strings in manufacturing data by traditional method is too long to meet requirement of quick defect analysis. Therefore, we present String Length Distribution Classification method (SLDC) to correctly classify strings in a short time. This method learns character features, especially string length distribution from Product ID, Machine ID in BOM and asset list. By applying the proposal to strings in actual manufacturing data, we verified that the classification time of strings can be reduced by 80%. As a result, it can be estimated that the requirement of quick defect analysis can be fulfilled.

Keywords: string classification, data quality, feature selection, probability distribution, string length

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Authors: Abhijeet Ostawal, Parmjit Lall

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The run times for a large strategic model that we were managing had become too long leading to delays in project delivery, increased costs and loss in productivity. Software developers are continuously working towards developing more efficient tools by changing their algorithms and processes. The issue faced by our team was how do you apply the latest technologies on validated existing models which are based on much older versions of software that do not have the latest software capabilities. The multi-model transport model that we had could only be run in sequential assignment order. Recent upgrades to the software now allowed the assignment to be run in parallel, a concept called parallelization. Parallelization is a Python script working only within the latest version of the software. A full model transfer to the latest version was not possible due to time, budget and the potential changes in trip assignment. This article is to show the method to adapt and update the Python script in such a way that it can be used in older software versions by calling the latest version and then recalling the old version for assignment model without affecting the results. Through a process of trial-and-error run time savings of up to 30-40% have been achieved. Assignment results were maintained within the older version and through this learning process we’ve applied this methodology to other even older versions of the software resulting in huge time savings, more productivity and efficiency for both client and consultant.

Keywords: model run time, demand model, parallelisation, python scripting

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Authors: Can Hu, Huixia Shi, Hongcheng Mei, Jun Zhu, Hongling Guo

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Textile fibers are important trace evidence and frequently encountered in criminal investigations. A significant aspect of fiber evidence examination is the determination of fiber dyes. Although several instrumental methods have been developed for dyes detection, the analysis speed is not fast enough yet. A rapid dye analysis method is still needed to further improve the efficiency of case handling. Capillary electrophoresis has the advantages of high separation speed and high separation efficiency and is an ideal method for the rapid analysis of fiber dyes. In this paper, acid dyes used for protein fiber dyeing were determined by a developed short-end injection capillary electrophoresis technique. Five acid red dyes with similar structures were successfully baseline separated within 5 min. The separation reproducibility is fairly good for the relative standard deviation of retention time is 0.51%. The established method is rapid and accurate which has great potential to be applied in forensic setting.

Keywords: acid dyes, capillary electrophoresis, fiber evidence, rapid determination

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Authors: Alberto Alcalá-Alvarez, Pablo Padilla-Longoria

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The present paper describes a framework for constructing mathematical networks encoding relevant musical information from a music score for structural analysis. These graphs englobe statistical information about music elements such as notes, chords, rhythms, intervals, etc., and the relations among them, and so become helpful in visualizing and understanding important stylistic features of a music fragment. In order to build such networks, musical data is parsed out of a digital symbolic music file. This data undergoes different analytical procedures from Graph Theory, such as measuring the centrality of nodes, community detection, and entropy calculation. The resulting networks reflect important structural characteristics of the fragment in question: predominant elements, connectivity between them, and complexity of the information contained in it. Music pieces in different styles are analyzed, and the results are contrasted with the traditional analysis outcome in order to show the consistency and potential utility of this method for music analysis.

Keywords: computational musicology, mathematical music modelling, music analysis, style classification

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Authors: R. S. Remya, U. S. Sethulekshmi

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Detecting the authenticity of a video is an important issue in digital forensics as Video is used as a silent evidence in court such as in child pornography, movie piracy cases, insurance claims, cases involving scientific fraud, traffic monitoring etc. The biggest threat to video data is the availability of modern open video editing tools which enable easy editing of videos without leaving any trace of tampering. In this paper, we propose an efficient passive method for inter-frame video tampering detection, its type and location by estimating the optical flow of wavelet features of adjacent frames and thresholding the variation in the estimated feature. The performance of the algorithm is compared with the z-score thresholding and achieved an efficiency above 95% on all the tested databases. The proposed method works well for videos with dynamic (forensics) as well as static (surveillance) background.

Keywords: discrete wavelet transform, optical flow, optical flow variation, video tampering

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Authors: Osama Abdallah Mohammed Enan, Amin Babiker A/Nabi Mustafa

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The aim of this study is to evaluate and analyze different parameters of WCDMA (wideband code division multiplication). Moreover, this study also incorporates brief yet throughout analysis of WCDMA’s components as well as its internal architecture. This study also examines different power controls. These power controls may include open loop power control, closed or inner group loop power control and outer loop power control. Different handover techniques or methods of WCDMA are also illustrated in this study. These handovers may include hard handover, inter system handover and soft and softer handover. Different duplexing techniques are also described in the paper. This study has also presented an idea about different parameters of WCDMA that leads the system towards QoS issues. This may help the operator in designing and developing adequate network configuration. In addition to this, the study has also investigated various parameters including Bit Energy per Noise Spectral Density (Eb/No), Noise rise, and Bit Error Rate (BER). After simulating these parameters, using MATLAB environment, it was investigated that, for a given Eb/No value the system capacity increase by increasing the reuse factor. Besides that, it was also analyzed that, noise rise is decreasing for lower data rates and for lower interference levels. Finally, it was examined that, BER increase by using one type of modulation technique than using other type of modulation technique.

Keywords: duplexing, handover, loop power control, WCDMA

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Authors: Abdelgawad, Salah El-Tahawy

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This paper aimed to establish econometrical equation models for the Nile delta region in Egypt, which will represent a basement for future predictions of Lumpy skin disease outbreaks and its pathway in relation to climate change. Data of lumpy skin disease (LSD) outbreaks were collected from the cattle farms located in the provinces representing the Nile delta region during 1 January, 2015 to December, 2015. The obtained results indicated that there was a significant association between the degree of the LSD outbreaks and the investigated climate factors (temperature, wind speed, and humidity) and the outbreaks peaked during the months of June, July, and August and gradually decreased to the lowest rate in January, February, and December. The model obtained depicted that the increment of these climate factors were associated with evidently increment on LSD outbreaks on the Nile Delta of Egypt. The model validation process was done by the root mean square error (RMSE) and means bias (MB) which compared the number of LSD outbreaks expected with the number of observed outbreaks and estimated the confidence level of the model. The value of RMSE was 1.38% and MB was 99.50% confirming that this established model described the current association between the LSD outbreaks and the change on climate factors and also can be used as a base for predicting the of LSD outbreaks depending on the climatic change on the future.

Keywords: LSD, climate factors, Nile delta, modeling

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Authors: Yusuf Seedat

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The necessity to understand global financial markets has increased following the unfortunate spread of the recent financial crisis around the world. Financial markets are considered to be complex systems consisting of highly volatile move-ments whose indexes fluctuate without any clear pattern. Analytic methods of stock prices have been proposed in which financial markets are modeled using common network analysis tools and methods. It has been found that two key components of social network analysis are relevant to modeling financial markets, allowing us to forecast accurate predictions of stock prices within the financial market. Financial markets have a number of interacting components, leading to complex behavioral patterns. This paper describes a social network approach to analyzing financial markets as a viable approach to studying the way complex stock markets function. We also look at how social network analysis techniques and metrics are used to gauge an understanding of the evolution of financial markets as well as how community detection can be used to qualify and quantify in-fluence within a network.

Keywords: network analysis, social networks, financial markets, stocks, nodes, edges, complex networks

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Authors: Dua Hişam, Serhat İkizoğlu

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Identifying the problem behind balance disorder is one of the most interesting topics in the medical literature. This study has considerably enhanced the development of artificial intelligence (AI) algorithms applying multiple machine learning (ML) models to sensory data on gait collected from humans to classify between normal people and those suffering from Vestibular System (VS) problems. Although AI is widely utilized as a diagnostic tool in medicine, AI models have not been used to perform feature extraction and identify VS disorders through training on raw data. In this study, three machine learning (ML) models, the Random Forest Classifier (RF), Extreme Gradient Boosting (XGB), and K-Nearest Neighbor (KNN), have been trained to detect VS disorder, and the performance comparison of the algorithms has been made using accuracy, recall, precision, and f1-score. With an accuracy of 95.28 %, Random Forest Classifier (RF) was the most accurate model.

Keywords: vestibular disorder, machine learning, random forest classifier, k-nearest neighbor, extreme gradient boosting

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Authors: Maniyarasan Rajendran

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Every design has a process, and every architect deals in the ways best known to them. The design translation from the concept to completion change in accordance with their design philosophies, their tools, availability of resources, and at times the clients and the context of the design as well. The approach to understanding the design process requires formalisation of the design intents. The design process is characterised by change, with the time and the technology. The design flow is just indicative and never exhaustive. The knowledge and experience of stakeholders remain limited to the part they played in the project, and their ability to remember, and is through the Photographs. These artefacts, when circulated can hardly tell what the project is. They can never tell the narrative behind. In due course, the design processes are lost. The Design junctions are lost in the journey. Photographs acted as major source materials, along with its importance in architectural revivalism in the 19th century. From the history, we understand that it has been photographs, that act as the dominant source of evidence. The idea of recording is also followed with the idea of getting inspired from the records and documents. The design concept, the architectural firms’ philosophies, the materials used, the special needs, the numerous ‘Trial-and-error’ methods, design methodology, experience of failures and success levels, and the knowledge acquired, etc., and the various other aspects and methods go through in every project, and they deserve/ought to be recorded. The knowledge can be preserved and passed through generations, by documenting the design processes involved. This paper explores the idea of a process documentation as a tool of self-reflection, creation of architectural firm’ repository, and these implications proceed with the design evolution of the team.

Keywords: architecture, design, documentation, records

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Authors: Sungroul Kim, Yeonjin Kim

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This study was conducted by comparing the concentrations of heavy metals analyzed from the same samples with three X-Ray fluorescence (XRF) spectrometer in three different global research institutions, including PAN (A Branch of Malvern Panalytical, Seoul, South Korea), RTI (Research Triangle Institute, NC, U.S.A), and aerosol laboratory in Harvard University, Boston, U.S.A. To achieve our research objectives, the indoor air filter samples were collected at homes (n=24) of adults or child asthmatics then analyzed in PAN followed by Harvard University and RTI consecutively. Descriptive statistics were conducted for data comparison as well as correlation and simple regression analysis using R version 4.0.3. As a result, detection rates of most heavy metals analyzed in three institutions were about 90%. Of the 25 elements commonly analyzed among those institutions, 16 elements showed an R² (coefficient of determination) of 0.7 or higher (10 components were 0.9 or higher). The findings of this study demonstrated that XRF was a useful device ensuring reproducibility and compatibility for measuring heavy metals in PM2.5 collected from indoor air of asthmatics’ home.

Keywords: heavy metals, indoor air quality, PM2.5, X-ray fluorescence

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Authors: Jude Inwumoh, Adam P. R. Taylor, Kosala Gunawardane

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Modular multilevel converters (MMC) exhibit a highly scalable and modular characteristic with good voltage/power expansion, fault tolerance capability, low output harmonic content, good redundancy, and a flexible front-end configuration. Fault detection, location, and isolation, as well as maintaining fault ride-through (FRT), are major challenges to MMC reliability and power supply sustainability. Different papers have been reviewed to seek the best MMC configuration with fault capability. DC faults are the most common fault, while the probability that AC fault occurs in a modular multilevel converter (MCC) is low; though, AC faults consequence are severe. This paper reviews several MMC topologies and modulation techniques in tackling faults. These fault control strategies are compared based on cost, complexity, controllability, and power loss. A meshed network of half-bridge (HB) MMC topology was optimal in rendering fault ride through than any other MMC topologies but only when combined with DC circuit breakers (CBS), AC CBS, and fault current limiters (FCL).

Keywords: MMC-HVDC, DC faults, fault current limiters, control scheme

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Authors: Leo Nnamdi Ozurumba-Dwight

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Messenger ribooxynucleic acid (mRNA) molecules are compositional, protein-based. These proteins, encoding mRNA molecules (which collectively connote the transcriptome), when analyzed by RNA sequencing (RNAseq), unveils the nature of gene expression in the RNA. The obtained gene expression provides clues of cellular traits and their dynamics in presentations. These can be studied in relation to function and responses. RNAseq is a practical concept in Genomics as it enables detection and quantitative analysis of mRNA molecules. Single cell and spatial transcriptomics both present varying avenues for expositions in genomic characteristics of single cells and pooled cells in disease conditions such as cancer, auto-immune diseases, hematopoietic based diseases, among others, from investigated biological tissue samples. Single cell transcriptomics helps conduct a direct assessment of each building unit of tissues (the cell) during diagnosis and molecular gene expressional studies. A typical technique to achieve this is through the use of a single-cell RNA sequencer (scRNAseq), which helps in conducting high throughput genomic expressional studies. However, this technique generates expressional gene data for several cells which lack presentations on the cells’ positional coordinates within the tissue. As science is developmental, the use of complimentary pre-established tissue reference maps using molecular and bioinformatics techniques has innovatively sprung-forth and is now used to resolve this set back to produce both levels of data in one shot of scRNAseq analysis. This is an emerging conceptual approach in methodology for integrative and progressively dependable transcriptomics analysis. This can support in-situ fashioned analysis for better understanding of tissue functional organization, unveil new biomarkers for early-stage detection of diseases, biomarkers for therapeutic targets in drug development, and exposit nature of cell-to-cell interactions. Also, these are vital genomic signatures and characterizations of clinical applications. Over the past decades, RNAseq has generated a wide array of information that is igniting bespoke breakthroughs and innovations in Biomedicine. On the other side, spatial transcriptomics is tissue level based and utilized to study biological specimens having heterogeneous features. It exposits the gross identity of investigated mammalian tissues, which can then be used to study cell differentiation, track cell line trajectory patterns and behavior, and regulatory homeostasis in disease states. Also, it requires referenced positional analysis to make up of genomic signatures that will be sassed from the single cells in the tissue sample. Given these two presented approaches to RNA transcriptomics study in varying quantities of cell lines, with avenues for appropriate resolutions, both approaches have made the study of gene expression from mRNA molecules interesting, progressive, developmental, and helping to tackle health challenges head-on.

Keywords: transcriptomics, RNA sequencing, single cell, spatial, gene expression.

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Authors: Mohammad Hossein Modarressi, Mozhgan Mondeali, Khabat Barkhordari, Ali Etemadi

Abstract:

The COVID-19 pandemic, caused by SARS-CoV-2, has led to significant concerns worldwide due to its catastrophic effects on public health. The SARS-CoV-2 infection is initiated with the binding of the receptor-binding domain (RBD) in its spike protein to the ACE2 receptor in the host cell membrane. Due to the error-prone entity of the viral RNA-dependent polymerase complex, the virus genome, including the coding region for the RBD, acquires new mutations, leading to the appearance of multiple variants. These variants can potentially impact transmission, virulence, antigenicity and evasive immune properties. S477N mutation located in the RBD has been observed in the SARS-CoV-2 omicron (B.1.1. 529) variant. In this study, we investigated the consequences of S477N mutation at the molecular level using computational approaches such as molecular dynamics simulation, protein-protein interaction analysis, immunoinformatics and free energy computation. We showed that displacement of Ser with Asn increases the stability of the spike protein and its affinity to ACE2 and thus increases the transmission potential of the virus. This mutation changes the folding and secondary structure of the spike protein. Also, it reduces antibody neutralization, raising concern about re-infection, vaccine breakthrough and therapeutic values.

Keywords: S477N, COVID-19, molecular dynamic, SARS-COV2 mutations

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Authors: Leah Wright, Trevor Townsend

Abstract:

Mode choice models are crucial instruments in the analysis of travel behaviour. These models show the relationship between an individual’s choice of transportation mode for a given O-D pair and the individual’s socioeconomic characteristics such as household size and income level, age and/or gender, and the features of the transportation system. The most popular functional forms of these models are based on Utility-Based Choice Theory, which addresses the uncertainty in the decision-making process with the use of an error term. However, with the development of artificial intelligence, many researchers have started to take a different approach to travel demand modelling. In recent times, researchers have looked at using neural networks, fuzzy logic and rough set theory to develop improved mode choice formulas. The concept of cloud theory has recently been introduced to model decision-making under uncertainty. Unlike the previously mentioned theories, cloud theory recognises a relationship between randomness and fuzziness, two of the most common types of uncertainty. This research aims to investigate the use of cloud theory in mode choice models. This paper highlights the conceptual framework of the mode choice model using cloud theory. Merging decision-making under uncertainty and mode choice models is state of the art. The cloud theory model is expected to address the issues and concerns with the nested logit and improve the design of mode choice models and their use in travel demand.

Keywords: Cloud theory, decision-making, mode choice models, travel behaviour, uncertainty

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Authors: Khalil Ibrahim, Ahmed Ramadan, Mohamed Fanni, Yo Kobayashi, Ahmed Abo-Ismail, Masakatus G. Fujie

Abstract:

This paper deals with the design process and the dynamic control simulation of a new type of 4-DOFs parallel mechanism that can be used as an endoscopic surgical manipulator. The proposed mechanism, 2-PUU_2-PUS, is designed based on the screw theory and the parallel virtual chain type synthesis method. Based on the structure analysis of the 4-DOF parallel mechanism, the inverse position equation is studied using the inverse analysis theory of kinematics. The design and the stress analysis of the mechanism are investigated using SolidWorks software. The virtual prototype of the parallel mechanism is constructed, and the dynamic simulation is performed using ADAMS TM software. The system model utilizing PID and PI controllers has been built using MATLAB software. A more realistic simulation in accordance with a given bending angle and point to point control is implemented by the use of both ADAMS/MATLAB software. The simulation results showed that this control method has solved the coordinate control for the 4-DOF parallel manipulator so that each output is feedback to the four driving rods. From the results, the tracking performance is achieved. Other control techniques, such as intelligent ones, are recommended to improve the tracking performance and reduce the numerical truncation error.

Keywords: parallel mechanisms, medical robotics, tracjectory control, virtual chain type synthesis method

Procedia PDF Downloads 471