Search results for: co-authorship network analysis

Authors: Reena Murali, David Peter S.

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The small interfering RNA (siRNA) alters the regulatory role of mRNA during gene expression by translational inhibition. Recent studies shows that up regulation of mRNA cause serious diseases like Cancer. So designing effective siRNA with good knockdown effects play an important role in gene silencing. Various siRNA design tools had been developed earlier. In this work, we are trying to analyze the existing good scoring second generation siRNA predicting tools and to optimize the efficiency of siRNA prediction by designing a computational model using Artificial Neural Network and whole stacking energy (ΔG), which may help in gene silencing and drug design in cancer therapy. Our model is trained and tested against a large data set of siRNA sequences. Validation of our results is done by finding correlation coefficient of experimental versus observed inhibition efficacy of siRNA. We achieved a correlation coefficient of 0.727 in our previous computational model and we could improve the correlation coefficient up to 0.753 when the threshold of whole tacking energy is greater than or equal to -32.5 kcal/mol.

Keywords: artificial neural network, double stranded RNA, RNA interference, short interfering RNA

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Authors: Tharntip Tawnie Chutikulrungsee, Oliver Kisalay Burmeister, Maumita Bhattacharya, Dragana Calic

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Sharing on social network sites (SNSs) has rapidly emerged as a new social norm and has become a global phenomenon. Billions of users reveal not only their own information (self disclosure) but also information about others (other-generated disclosure), resulting in a risk and a serious threat to either personal or informational privacy. Self-disclosure (SD) has been extensively researched in the literature, particularly regarding control of individual and existing privacy management. However, far too little attention has been paid to other-generated disclosure (OGD), especially by insiders. OGD has a strong influence on self-presentation, self-image, and electronic word of mouth (eWOM). Moreover, OGD is more credible and less likely manipulated than SD, but lacks privacy control and legal protection to some extent. This article examines OGD in depth, ranging from motivation to both online and offline impacts, based upon lived experiences from both ‘the disclosed’ and ‘the discloser’. Using purposive sampling, this phenomenological study involves an online survey and in-depth interviews. The findings report the influence of peer disclosure as well as users’ strategies to mitigate privacy issues. This article also calls attention to the challenge of OGD privacy and inadequacies in the law related to privacy protection in the digital domain.

Keywords: facebook, online privacy, other-generated disclosure, social networks sites (SNSs)

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Authors: Hanieh Tarbiat Khosrowshahi, Mojtaba Shakeri

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Wireless Sensor Networks (WSNs) consist of a set of sensor nodes with limited capability. WSNs may suffer from multiple node failures when they are exposed to harsh environments such as military zones or disaster locations and lose connectivity by getting partitioned into disjoint segments. Relay nodes (RNs) are alternatively introduced to restore connectivity. They cost more than sensors as they benefit from mobility, more power and more transmission range, enforcing a minimum number of them to be used. This paper addresses the problem of RN placement in a multiple disjoint network by developing a genetic algorithm (GA). The problem is reintroduced as the Steiner tree problem (which is known to be an NP-hard problem) by the aim of finding the minimum number of Steiner points where RNs are to be placed for restoring connectivity. An upper bound to the number of RNs is first computed to set up the length of initial chromosomes. The GA algorithm then iteratively reduces the number of RNs and determines their location at the same time. Experimental results indicate that the proposed GA is capable of establishing network connectivity using a reasonable number of RNs compared to the best existing work.

Keywords: connectivity restoration, genetic algorithms, multiple-node failure, relay nodes, wireless sensor networks

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Authors: Tareq Oshan

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Facility location and size decisions are important to any company because they affect profitability and success. However, warehouses are exposed to various risks of failure that affect their activity. This paper presents a mixed-integer non-linear mathematical model that can be used to determine optimal warehouse locations and sizes, which warehouses to fortify, and which branches should be assigned to specific warehouses when there is a risk of warehouse failure. Every branch is assigned to a fortified primary warehouse or a nonfortified primary warehouse and a fortified backup warehouse. The standard method and an introduced method, based on the average probabilities, for linearizing this mathematical model were used. A Canadian case study was used to demonstrate the developed mathematical model, followed by some sensitivity analysis.

Keywords: supply chain network design, fortified warehouse, mixed-integer mathematical model, warehouse failure risk

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Authors: Omer Cahana, Ofer Levi, Maya Herman

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Magnetic Resonance Imaging (MRI) is a lengthy medical scan that stems from a long acquisition time. Its length is mainly due to the traditional sampling theorem, which defines a lower boundary for sampling. However, it is still possible to accelerate the scan by using a different approach such as Compress Sensing (CS) or Parallel Imaging (PI). These two complementary methods can be combined to achieve a faster scan with high-fidelity imaging. To achieve that, two conditions must be satisfied: i) the signal must be sparse under a known transform domain, and ii) the sampling method must be incoherent. In addition, a nonlinear reconstruction algorithm must be applied to recover the signal. While the rapid advances in Deep Learning (DL) have had tremendous successes in various computer vision tasks, the field of MRI reconstruction is still in its early stages. In this paper, we present an end-to-end method for MRI reconstruction from k-space to image. Our method contains two parts. The first is sensitivity map estimation (SME), which is a small yet effective network that can easily be extended to a variable number of coils. The second is reconstruction, which is a top-down architecture with lateral connections developed for building high-level refinement at all scales. Our method holds the state-of-art fastMRI benchmark, which is the largest, most diverse benchmark for MRI reconstruction.

Keywords: magnetic resonance imaging, image reconstruction, pyramid network, deep learning

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Authors: Zhuoran Shan, Li Wan, Xianchun Zhang

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With an increasing trend of regionalization and polycentricity in Chinese contemporary big cities, “sub-region life circle” turns to be an effective method on rational organization of urban function and spatial structure. By the method of questionnaire, network big data, route inversion on internet map, GIS spatial analysis and logistic regression, this article makes research on characteristic and formation mechanism of “sub-region life circle” based on human high-probability daily activity in Chinese big cities. Firstly, it shows that “sub-region life circle” has been a new general spatial sphere of residents' high-probability daily activity and mobility in China. Unlike the former analysis of the whole metropolitan or the micro community, “sub-region life circle” has its own characteristic on geographical sphere, functional element, spatial morphology and land distribution. Secondly, according to the analysis result with Binary Logistic Regression Model, the research also shows that seven factors including land-use mixed degree and bus station density impact the formation of “sub-region life circle” most, and then analyzes the index critical value of each factor. Finally, to establish a smarter “sub-region life circle”, this paper indicates that several strategies including jobs-housing fit, service cohesion and space reconstruction are the keys for its spatial organization optimization. This study expands the further understanding of cities' inner sub-region spatial structure based on human daily activity, and contributes to the theory of “life circle” in urban's meso-scale.

Keywords: sub-region life circle, characteristic, formation mechanism, human activity, spatial structure

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Authors: Hasti Ebrahimi

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This study sheds light on the challenges and difficulties caused by the Iranian clerical system’s intervention in the country’s school education during the COVID-19 pandemic, when schools remained closed for almost two years. The pandemic brought Iranian elementary school education to a standstill for almost 6 months before the country developed a nationwide learning platform – a customized television network. While the initiative seemed to have been welcomed by the majority of Iranian parents, it resented some of the more traditional strata of the society, including the influential Friday Prayer Leaders who found the televised version of the elementary education ‘less spiritual’ and ‘more ‘material’ or science-based. That prompted the Iranian Channel of Education, the specialized television network that had been chosen to serve as a nationally televised school during the pandemic, to try to redefine much of its online elementary school educational content within the religious ideology of the Islamic Republic of Iran. As a result, young clergies appeared on the television screen as preachers of Islamic morality, religious themes and even sociology, history, and arts. The present research delves into the consequences of such an intervention, how it might have impacted the infrastructure of Iranian elementary education and whether or not the new ideology-infused curricula would withstand the opposition of students and mainstream teachers. The main methodology used in this study is Critical Discourse Analysis with a cognitive approach. It systematically finds and analyzes the alternative ideological structures of discourse in the Iranian Channel of Education from September 2021 to July 2022, when the clergy ‘teachers’ replaced ‘regular’ history and arts teachers on the television screen for the first time. It has aimed to assess how the various uses of the alternative ideological discourse in elementary school content have influenced the processes of learning: the acquisition of knowledge, beliefs, opinions, attitudes, abilities, and other cognitive and emotional changes, which are the goals of institutional education. This study has been an effort aimed at understanding and perhaps clarifying the relationships between the traditional textual structures and processing on the one hand and socio-cultural contexts created by the clergy teachers on the other. This analysis shows how the clerical portion of elementary education on the Channel of Education that seemed to have dominated the entire televised teaching and learning process faded away as the pandemic was contained and mainstream classes were restored. It nevertheless reflects the deep ideological rifts between the clerical approach to school education and the mainstream teaching process in Iranian schools. The semantic macrostructures of social content in the current Iranian elementary school education, this study suggests, have remained intact despite the temporary ideological intervention of the ruling clerical elite in their formulation and presentation. Finally, using thematic and schematic frameworks, the essay suggests that the ‘clerical’ social content taught on the Channel of Education during the pandemic cannot have been accepted cognitively by the channel’s target audience, including students and mainstream teachers.

Keywords: televised elementary school learning, Covid 19, critical discourse analysis, Iranian clerical ideology

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Authors: Fidel Lòpez Saca, Carlos Avilés-Cruz, Miguel Magos-Rivera, José Antonio Lara-Chávez

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Automated object recognition and identification systems are widely used throughout the world, particularly in assembly lines, where they perform quality control and automatic part selection tasks. This article presents the design and implementation of an object recognition system in an assembly line. The proposed shapes-color recognition system is based on deep learning theory in a specially designed convolutional network architecture. The used methodology involve stages such as: image capturing, color filtering, location of object mass centers, horizontal and vertical object boundaries, and object clipping. Once the objects are cut out, they are sent to a convolutional neural network, which automatically identifies the type of figure. The identification system works in real-time. The implementation was done on a Raspberry Pi 3 system and on a Jetson-Nano device. The proposal is used in an assembly course of bachelor’s degree in industrial engineering. The results presented include studying the efficiency of the recognition and processing time.

Keywords: deep-learning, image classification, image identification, industrial engineering.

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Authors: Ian O'Loughlin

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Memory networks aim to integrate some of the recent successes in machine learning with a dynamic memory base that can be updated and deployed in artificial reasoning tasks. These models involve training networks to identify, update, and operate over stored elements in a large memory array in order, for example, to ably perform question and answer tasks parsing real-world and simulated discourses. This family of approaches still faces numerous challenges: the performance of these network models in simulated domains remains considerably better than in open, real-world domains, wide-context cues remain elusive in parsing words and sentences, and even moderately complex sentence structures remain problematic. This innovation, employing an array of stored and updatable ‘memory’ elements over which the system operates as it parses text input and develops responses to questions, is a compelling one for at least two reasons: first, it addresses one of the difficulties that standard machine learning techniques face, by providing a way to store a large bank of facts, offering a way forward for the kinds of long-term reasoning that, for example, recurrent neural networks trained on a corpus have difficulty performing. Second, the addition of a stored long-term memory component in artificial reasoning seems psychologically plausible; human reasoning appears replete with invocations of long-term memory, and the stored but dynamic elements in the arrays of memory networks are deeply reminiscent of the way that human memory is readily and often characterized. However, this apparent psychological plausibility is belied by a recent turn in the study of human memory in cognitive science. In recent years, the very notion that there is a stored element which enables remembering, however dynamic or reconstructive it may be, has come under deep suspicion. In the wake of constructive memory studies, amnesia and impairment studies, and studies of implicit memory—as well as following considerations from the cognitive neuroscience of memory and conceptual analyses from the philosophy of mind and cognitive science—researchers are now rejecting storage and retrieval, even in principle, and instead seeking and developing models of human memory wherein plasticity and dynamics are the rule rather than the exception. In these models, storage is entirely avoided by modeling memory using a recurrent neural network designed to fit a preconceived energy function that attains zero values only for desired memory patterns, so that these patterns are the sole stable equilibrium points in the attractor network. So although the array of long-term memory elements in memory networks seem psychologically appropriate for reasoning systems, they may actually be incurring difficulties that are theoretically analogous to those that older, storage-based models of human memory have demonstrated. The kind of emergent stability found in the attractor network models more closely fits our best understanding of human long-term memory than do the memory network arrays, despite appearances to the contrary.

Keywords: artificial reasoning, human memory, machine learning, neural networks

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Authors: Issam Alnader, Aboubaker Lasebae, Rand Raheem

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Wireless sensor networks (WSNs) are distributed network systems used in a wide range of applications, including safety-critical systems. The latter provide critical services, often concerned with human life or assets. Therefore, ensuring the dependability requirements of Safety critical systems is of paramount importance. The purpose of this paper is to utilize the Hidden Markov Model (HMM) to elongate the service availability of WSNs by increasing the time it takes a node to become obsolete via optimal load balancing. We propose an HMM algorithm that, given a WSN, analyses and predicts undesirable situations, notably, nodes dying unexpectedly or prematurely. We apply this technique to improve on C. Lius’ algorithm, a scheduling-based algorithm which has served to improve the lifetime of WSNs. Our experiments show that our HMM technique improves the lifetime of the network, achieved by detecting nodes that die early and rebalancing their load. Our technique can also be used for diagnosis and provide maintenance warnings to WSN system administrators. Finally, our technique can be used to improve algorithms other than C. Liu’s.

Keywords: wireless sensor networks, IoT, dependability of safety WSNs, energy conservation, sleep awake schedule

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Authors: Sarah E. Nelson, Casey Weiner, Alexander Sigmon, Jun Hua, Haris I. Sair, Jose I. Suarez, Robert D. Stevens

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Graph-theoretical information from structural connectomes indicated significant connectivity changes and improved acute prognostication in a Random Forest (RF) model in aneurysmal subarachnoid hemorrhage (aSAH), which can lead to significant morbidity and mortality and has traditionally been fraught by poor methods to predict outcome. This study’s hypothesis was that structural connectivity changes occur in canonical brain networks of acute aSAH patients, and that these changes are associated with functional outcome at six months. In a prospective cohort of patients admitted to a single institution for management of acute aSAH, patients underwent diffusion tensor imaging (DTI) as part of a multimodal MRI scan. A weighted undirected structural connectome was created of each patient’s images using Constant Solid Angle (CSA) tractography, with 176 regions of interest (ROIs) defined by the Johns Hopkins Eve atlas. ROIs were sorted into four networks: Default Mode Network, Executive Control Network, Salience Network, and Whole Brain. The resulting nodes and edges were characterized using graph-theoretic features, including Node Strength (NS), Betweenness Centrality (BC), Network Degree (ND), and Connectedness (C). Clinical (including demographics and World Federation of Neurologic Surgeons scale) and graph features were used separately and in combination to train RF and Logistic Regression classifiers to predict two outcomes: dichotomized modified Rankin Score (mRS) at discharge and at six months after discharge (favorable outcome mRS 0-2, unfavorable outcome mRS 3-6). A total of 56 aSAH patients underwent DTI a median (IQR) of 7 (IQR=8.5) days after admission. The best performing model (RF) combining clinical and DTI graph features had a mean Area Under the Receiver Operator Characteristic Curve (AUROC) of 0.88 ± 0.00 and Area Under the Precision Recall Curve (AUPRC) of 0.95 ± 0.00 over 500 trials. The combined model performed better than the clinical model alone (AUROC 0.81 ± 0.01, AUPRC 0.91 ± 0.00). The highest-ranked graph features for prediction were NS, BC, and ND. These results indicate reorganization of the connectome early after aSAH. The performance of clinical prognostic models was increased significantly by the inclusion of DTI-derived graph connectivity metrics. This methodology could significantly improve prognostication of aSAH.

Keywords: connectomics, diffusion tensor imaging, graph theory, machine learning, subarachnoid hemorrhage

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Authors: Sannikumar Patel, Brian Nolan, Markus Hofmann, Philip Owende, Kunjan Patel

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Sentiment analysis and opinion mining have become emerging topics of research in recent years but most of the work is focused on data in the English language. A comprehensive research and analysis are essential which considers multiple languages, machine translation techniques, and different classifiers. This paper presents, a comparative analysis of different approaches for multilingual sentiment analysis. These approaches are divided into two parts: one using classification of text without language translation and second using the translation of testing data to a target language, such as English, before classification. The presented research and results are useful for understanding whether machine translation should be used for multilingual sentiment analysis or building language specific sentiment classification systems is a better approach. The effects of language translation techniques, features, and accuracy of various classifiers for multilingual sentiment analysis is also discussed in this study.

Keywords: cross-language analysis, machine learning, machine translation, sentiment analysis

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Authors: Jehan Fahim M. Alsulami

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Academic research about sentiment analysis in sentiment analysis has obtained significant advancement over recent years and is flourishing from the collection of knowledge provided by various academic disciplines. In the current study, the status and development trend of the field of sentiment analysis in social networks is evaluated through a bibliometric analysis of academic publications. In particular, the distributions of publications and citations, the distribution of subject, predominant journals, authors, countries are analyzed. The collaboration degree is applied to measure scientific connections from different aspects. Moreover, the keyword co-occurrence analysis is used to find out the major research topics and their evolutions throughout the time span. The area of sentiment analysis in social networks has gained growing attention in academia, with computer science and engineering as the top main research subjects. China and the USA provide the most to the area development. Authors prefer to collaborate more with those within the same nation. Among the research topics, newly risen topics such as COVID-19, customer satisfaction are discovered.

Keywords: bibliometric analysis, sentiment analysis, social networks, social media

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Authors: Shola Haruna Adeosun, F. Adebiyi Ajoke, Odedeji Adeoye

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Video billboard advertising by networks and brand switching was conceived out of inquisition at the huge billboard advertising expenditures made by the three major GSM network operators in Nigeria. The study was anchored on Lagos State Metropolis with a current census population over 1,000,000. From this population, a purposive sample of 400 was adopted, and the questionnaire designed for the survey was carefully allocated to members of this ample in the five geographical zones of the city so that each rung of the society was well represented. The data obtained were analyzed using tables and simple percentages. The results obtained showed that subscribers of these networks were hardly influenced by the video billboard advertisements. They overwhelmingly showed that rather than the slogans of the GSM networks carried on the video billboards, it was the incentives to subscribers as well as the promotional strategies of these organizations that moved them to switch from one network to another. These switching lasted only as long as the incentives and promotions were in effect. The results of the study also seemed to rekindle the age-old debate on media effects, by the unyielding schools of the theory of ‘all-powerful media’, ‘the limited effects media’, ‘the controlled effects media’ and ‘the negotiated media influence’.

Keywords: evaluation, impact, video billboard, marketing, services

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Authors: Ting Kai Chia, Ruifeng Yan, Feifei Bai, Tapan Saha

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This research presents a real-world power system oscillation incident in 2022 originated by a hybrid solar photovoltaic (PV) and wind renewable energy farm with a rated capacity of approximately 300MW in Australia. The voltage and reactive power outputs recorded at the point of common coupling (PCC) oscillated at a sub-synchronous frequency region, which sustained for approximately five hours in the network. The reactive power oscillation gradually increased over time and reached a recorded maximum of approximately 250MVar peak-to-peak (from inductive to capacitive). The network service provider was not able to quickly identify the location of the oscillation source because the issue was widespread across the network. After the incident, the original equipment manufacturer (OEM) concluded that the oscillation problem was caused by the incorrect setting recovery of the hybrid power plant controller (HPPC) in the voltage and reactive power control loop after a loss of communication event. The voltage controller normally outputs a reactive (Q) reference value to the Q controller which controls the Q dispatch setpoint of PV and wind plants in the hybrid farm. Meanwhile, a feed-forward (FF) configuration is used to bypass the Q controller in case there is a loss of communication. Further study found that the FF control mode was still engaged when communication was re-established, which ultimately resulted in the oscillation event. However, there was no detailed explanation of why the FF control mode can cause instability in the hybrid farm. Also, there was no duplication of the event in the simulation to analyze the root cause of the oscillation. Therefore, this research aims to model and replicate the oscillation event in a simulation environment and investigate the underlying behavior of the HPPC and the consequent oscillation mechanism during the incident. The outcome of this research will provide significant benefits to the safe operation of large-scale renewable energy generators and power networks.

Keywords: PV, oscillation, modelling, wind

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Authors: Omer Cahana, Maya Herman, Ofer Levi

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Magnetic resonance imaging (MRI) is a long medical scan that stems from a long acquisition time. This length is mainly due to the traditional sampling theorem, which defines a lower boundary for sampling. However, it is still possible to accelerate the scan by using a different approach, such as compress sensing (CS) or parallel imaging (PI). These two complementary methods can be combined to achieve a faster scan with high-fidelity imaging. In order to achieve that, two properties have to exist: i) the signal must be sparse under a known transform domain, ii) the sampling method must be incoherent. In addition, a nonlinear reconstruction algorithm needs to be applied to recover the signal. While the rapid advance in the deep learning (DL) field, which has demonstrated tremendous successes in various computer vision task’s, the field of MRI reconstruction is still in an early stage. In this paper, we present an extension of the state-of-the-art model in MRI reconstruction -VarNet. We utilize VarNet by using dilated convolution in different scales, which extends the receptive field to capture more contextual information. Moreover, we simplified the sensitivity map estimation (SME), for it holds many unnecessary layers for this task. Those improvements have shown significant decreases in computation costs as well as higher accuracy.

Keywords: MRI, deep learning, variational network, computer vision, compress sensing

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Authors: Xiao Zhou, Jianlin Cheng

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A single amino acid mutation can have a significant impact on the stability of protein structure. Thus, the prediction of protein stability change induced by single site mutations is critical and useful for studying protein function and structure. Here, we presented a deep learning network with the dropout technique for predicting protein stability changes upon single amino acid substitution. While using only protein sequence as input, the overall prediction accuracy of the method on a standard benchmark is >85%, which is higher than existing sequence-based methods and is comparable to the methods that use not only protein sequence but also tertiary structure, pH value and temperature. The results demonstrate that deep learning is a promising technique for protein stability prediction. The good performance of this sequence-based method makes it a valuable tool for predicting the impact of mutations on most proteins whose experimental structures are not available. Both the downloadable software package and the user-friendly web server (DNpro) that implement the method for predicting protein stability changes induced by amino acid mutations are freely available for the community to use.

Keywords: bioinformatics, deep learning, protein stability prediction, biological data mining

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Authors: S. Fröhlich, M. Herold, M. Allmer

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Early stage quantitative analysis of host cell protein (HCP) variations is challenging yet necessary for comprehensive bioprocess development. High resolution mass spectrometry (HRMS) provides a high-end technology for accurate identification alongside with quantitative information. Hereby we describe a flexible HRMS assay platform to quantify HCPs relevant in microbial expression systems such as E. Coli in both up and downstream development by means of MVDA tools. Cell pellets were lysed and proteins extracted, purified samples not further treated before applying the SMART tryptic digest kit. Peptides separation was optimized using an RP-UHPLC separation platform. HRMS-MSMS analysis was conducted on an Orbitrap Velos Elite applying CID. Quantification was performed label-free taking into account ionization properties and physicochemical peptide similarities. Results were analyzed using SIEVE 2.0 (Thermo Fisher Scientific) and SIMCA (Umetrics AG). The developed HRMS platform was applied to an E. Coli expression set with varying productivity and the corresponding downstream process. Selected HCPs were successfully quantified within the fmol range. Analysing HCP networks based on pattern analysis facilitated low level quantification and enhanced validity. This approach is of high relevance for high-throughput screening experiments during upstream development, e.g. for titer determination, dynamic HCP network analysis or product characterization. Considering the downstream purification process, physicochemical clustering of identified HCPs is of relevance to adjust buffer conditions accordingly. However, the technology provides an innovative approach for label-free MS based quantification relying on statistical pattern analysis and comparison. Absolute quantification based on physicochemical properties and peptide similarity score provides a technological approach without the need of sophisticated sample preparation strategies and is therefore proven to be straightforward, sensitive and highly reproducible in terms of product characterization.

Keywords: process analytical technology, mass spectrometry, process characterization, MVDA, pattern recognition

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Authors: Shengnan Chen, Shuhua Wang

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Successful production of hydrocarbon from unconventional tight oil reserves has changed the energy landscape in North America. The oil contained within these reservoirs typically will not flow to the wellbore at economic rates without assistance from advanced horizontal well and multi-stage hydraulic fracturing. Efficient and economic development of these reserves is a priority of society, government, and industry, especially under the current low oil prices. Meanwhile, society needs technological and process innovations to enhance oil recovery while concurrently reducing environmental impacts. Recently, big data analysis and artificial intelligence become very popular, developing data-driven insights for better designs and decisions in various engineering disciplines. However, the application of data mining in petroleum engineering is still in its infancy. The objective of this research aims to apply intelligent data analysis and data-driven models to exploit unconventional oil reserves both efficiently and economically. More specifically, a comprehensive database including the reservoir geological data, reservoir geophysical data, well completion data and production data for thousands of wells is firstly established to discover the valuable insights and knowledge related to tight oil reserves development. Several data analysis methods are introduced to analysis such a huge dataset. For example, K-means clustering is used to partition all observations into clusters; principle component analysis is applied to emphasize the variation and bring out strong patterns in the dataset, making the big data easy to explore and visualize; exploratory factor analysis (EFA) is used to identify the complex interrelationships between well completion data and well production data. Different data mining techniques, such as artificial neural network, fuzzy logic, and machine learning technique are then summarized, and appropriate ones are selected to analyze the database based on the prediction accuracy, model robustness, and reproducibility. Advanced knowledge and patterned are finally recognized and integrated into a modified self-adaptive differential evolution optimization workflow to enhance the oil recovery and maximize the net present value (NPV) of the unconventional oil resources. This research will advance the knowledge in the development of unconventional oil reserves and bridge the gap between the big data and performance optimizations in these formations. The newly developed data-driven optimization workflow is a powerful approach to guide field operation, which leads to better designs, higher oil recovery and economic return of future wells in the unconventional oil reserves.

Keywords: big data, artificial intelligence, enhance oil recovery, unconventional oil reserves

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Authors: Ailing Yang, Penghan Song, Aihua Cai

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The nonlinear maneuvering target tracking problem is mainly a state estimation problem when the target motion model is uncertain. Traditional solutions include Kalman filtering based on Bayesian filtering framework and extended Kalman filtering. However, these methods need prior knowledge such as kinematics model and state system distribution, and their performance is poor in state estimation of nonprior complex dynamic systems. Therefore, in view of the problems existing in traditional algorithms, a convolution LSTM target state estimation (SAConvLSTM-SE) algorithm based on Self-Attention memory (SAM) is proposed to learn the historical motion state of the target and the error distribution information measured at the current time. The measured track point data of airborne radar are processed into data sets. After supervised training, the data-driven deep neural network based on SAConvLSTM can directly obtain the target state at the next moment. Through experiments on two different maneuvering targets, we find that the network has stronger robustness and better tracking accuracy than the existing tracking methods.

Keywords: maneuvering target, state estimation, Kalman filter, LSTM, self-attention

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Authors: Vethathirri Ramanujam Srinivasan, S. M. Shiva Nagendra

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Air quality management in urban areas is a serious concern in both developed and developing countries. In this regard, more number of air quality monitoring stations are planned to mitigate air pollution in urban areas. In India, Central Pollution Control Board has set up 574 air quality monitoring stations across the country and proposed to set up another 500 stations in the next few years. The number of monitoring stations for each city has been decided based on population data. The setting up of ambient air quality monitoring stations and their operation and maintenance are highly expensive. Therefore, there is a need to optimize monitoring networks for air quality management. The present paper discusses the various methods such as Indian Standards (IS) method, US EPA method and European Union (EU) method to arrive at the minimum number of air quality monitoring stations. In addition, optimization of rain-gauge method and Inverse Distance Weighted (IDW) method using Geographical Information System (GIS) are also explored in the present work for the design of air quality network in Chennai city. In summary, additionally 18 stations are required for Chennai city, and the potential monitoring locations with their corresponding land use patterns are ranked and identified from the 1km x 1km sized grids.

Keywords: air quality monitoring network, inverse distance weighted method, population based method, spatial variation

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Authors: Felipe A. Guth, Shane Ward, Kevin McDonnell

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In the precision agriculture area, one of the main tasks is the automated detection of diseases in crops. Machine Learning algorithms have been studied in recent decades for such tasks in view of their potential for improving economic outcomes that automated disease detection may attain over crop fields. The latest generation of deep learning convolution neural networks has presented significant results in the area of image classification. In this way, this work has tested the implementation of an architecture of deep learning convolution neural network for the detection of diseases in different types of crops. A data augmentation strategy was used to meet the requirements of the algorithm implemented with a deep learning framework. Two test scenarios were deployed. The first scenario implemented a neural network under images extracted from a controlled environment while the second one took images both from the field and the controlled environment. The results evaluated the generalisation capacity of the neural networks in relation to the two types of images presented. Results yielded a general classification accuracy of 59% in scenario 1 and 96% in scenario 2.

Keywords: convolutional neural networks, deep learning, disease detection, precision agriculture

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Authors: Mesbaholdin Salami, Farzad Movahedi Sobhani, Mohammad Sadegh Ghazizadeh

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Evaluation of organizational performance is among the most important measures that help organizations and entities continuously improve their efficiency. Organizations can use the existing data and results from the comparison of units under investigation to obtain an estimation of their performance. The Malmquist Productivity Index (MPI) is an important index in the evaluation of overall productivity, which considers technological developments and technical efficiency at the same time. This article proposed a model based on the multistage MPI, considering limited data (Grey’s theory). This model can evaluate the performance of units using limited and uncertain data in a multistage process. It was applied by the electricity market manager to Iran’s electric power supply chain (EPSC), which contains uncertain data, to evaluate the performance of its actors. Results from solving the model showed an improvement in the accuracy of future performance of the units under investigation, using the Grey’s system theory. This model can be used in all case studies, in which MPI is used and there are limited or uncertain data.

Keywords: Malmquist Index, Grey's Theory, CCR Model, network data envelopment analysis, Iran electricity power chain

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Authors: Abebe Mulugojam Negash, Yongbin Yu, Ekong Favour, Bekalu Nigus Dawit, Molla Woretaw Teshome, Aynalem Birtukan Yirga

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Breast cancer remains a significant health concern, necessitating advancements in diagnostic methodologies. Addressing this, our paper confronts the notable challenges in breast cancer classification, particularly the imbalance in datasets and the constraints in the accuracy and interpretability of prevailing deep learning approaches. We proposed an attention-based residual neural network (ResNet), which effectively combines the robust features of ResNet with an advanced attention mechanism. Enhanced through strategic data augmentation and positive weight adjustments, this approach specifically targets the issue of data imbalance. The proposed model is tested on the BreakHis dataset and achieved accuracies of 99.00%, 99.04%, 98.67%, and 98.08% in different magnifications (40X, 100X, 200X, and 400X), respectively. We evaluated the performance by using different evaluation metrics such as precision, recall, and F1-Score and made comparisons with other state-of-the-art methods. Our experiments demonstrate that the proposed model outperforms existing approaches, achieving higher accuracy in breast cancer classification.

Keywords: residual neural network, attention mechanism, positive weight, data augmentation

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Authors: Tomas Berhanu Bekele

Abstract:

Traffic congestion is one of the main problems related to transportation in developed as well as developing countries. Traffic control systems are based on the idea of avoiding traffic instabilities and homogenizing traffic flow in such a way that the risk of accidents is minimized and traffic flow is maximized. Lately, Intelligent Transport Systems (ITS) has become an important area of research to solve such road traffic-related issues for making smart decisions. It links people, roads and vehicles together using communication technologies to increase safety and mobility. Moreover, accurate prediction of road traffic is important to manage traffic congestion. The aim of this study is to develop an ANN model for the prediction of traffic flow and to compare the ANN model with the linear regression model of traffic flow predictions. Data extraction was carried out in intervals of 15 minutes from the video player. Video of mixed traffic flow was taken and then counted during office work in order to determine the traffic volume. Vehicles were classified into six categories, namely Car, Motorcycle, Minibus, mid-bus, Bus, and Truck vehicles. The average time taken by each vehicle type to travel the trap length was measured by time displayed on a video screen.

Keywords: intelligent transport system (ITS), traffic flow prediction, artificial neural network (ANN), linear regression

Procedia PDF Downloads 69

Authors: S. Jayasinghe, R. B. N. Dissanayake

Abstract:

Optimization modelling together with the Network models and Linear Programming techniques is a powerful tool in problem solving and decision making in real world applications. This study developed a mathematical model to optimize the net profit by minimizing the transportation cost. This model focuses the transportation among decentralized production plants to a centralized distribution centre and then the distribution among island wide agencies considering the customer satisfaction as a requirement. This company produces basically 9 types of food items with 82 different varieties and 4 types of non-food items with 34 different varieties. Among 6 production plants, 4 were located near the city of Mawanella and the other 2 were located in Galewala and Anuradhapura cities which are 80 km and 150 km away from Mawanella respectively. The warehouse located in the Mawanella was the main production plant and also the only distribution plant. This plant distributes manufactured products to 39 agencies island-wide. The average values and average amount of the goods for 6 consecutive months from May 2013 to October 2013 were collected and then average demand values were calculated. The following constraints are used as the necessary requirement to satisfy the optimum condition of the model; there was one source, 39 destinations and supply and demand for all the agencies are equal. Using transport cost for a kilometer, total transport cost was calculated. Then the model was formulated using distance and flow of the distribution. Network optimization and linear programming techniques were used to originate the model while excel solver is used in solving. Results showed that company requires total transport cost of Rs. 146, 943, 034.50 to fulfil the customers’ requirement for a month. This is very much less when compared with data without using the model. Model also proved that company can reduce their transportation cost by 6% when distributing to island-wide customers. Company generally satisfies their customers’ requirements by 85%. This satisfaction can be increased up to 97% by using this model. Therefore this model can be used by other similar companies in order to reduce the transportation cost.

Keywords: mathematical model, network optimization, linear programming

Procedia PDF Downloads 347

Authors: Nikola Toshikj, Michel Ramonda, Sylvain Catrouillet, Jean-Jacques Robin, Sebastien Blanquer

Abstract:

Biodegradable and biocompatible block copolymers have risen as the golden materials in both medical and environmental applications. Moreover, if their architecture is of controlled manner, higher applications can be foreseen. In the meantime, organocatalytic ROP has been promoted as more rapid and immaculate route, compared to the traditional organometallic catalysis, towards efficient synthesis of block copolymer architectures. Therefore, herein we report novel organocatalytic pathway with guanidine molecules (TBD) for supported synthesis of trimethylene carbonate initiated by poly(caprolactone) as pre-polymer. Pristine PTMC-b-PCL-b-PTMC block copolymer structure, without any residual products and clear desired block proportions, was achieved under 1.5 hours at room temperature and verified by NMR spectroscopies and size-exclusion chromatography. Besides, when elaborating block copolymer films, further stability and amelioration of mechanical properties can be achieved via additional reticulation step of precedently methacrylated block copolymers. Subsequently, stimulated by the insufficient studies on the phase-separation/crystallinity relationship in these semi-crystalline block copolymer systems, their intrinsic thermal and morphology properties were investigated by differential scanning calorimetry and atomic force microscopy. Firstly, by DSC measurements, the block copolymers with χABN values superior to 20 presented two distinct glass transition temperatures, close to the ones of the respecting homopolymers, demonstrating an initial indication of a phase-separated system. In the interim, the existence of the crystalline phase was supported by the presence of melting temperature. As expected, the crystallinity driven phase-separated morphology predominated in the AFM analysis of the block copolymers. Neither crosslinking at melted state, hence creation of a dense polymer network, disturbed the crystallinity phenomena. However, the later revealed as sensible to rapid liquid nitrogen quenching directly from the melted state. Therefore, AFM analysis of liquid nitrogen quenched and crosslinked block copolymer films demonstrated a thermodynamically driven phase-separation clearly predominating over the originally crystalline one. These AFM films remained stable with their morphology unchanged even after 4 months at room temperature. However, as demonstrated by DSC analysis once rising the temperature above the melting temperature of the PCL block, neither the crosslinking nor the liquid nitrogen quenching shattered the semi-crystalline network, while the access to thermodynamical phase-separated structures was possible for temperatures under the poly (caprolactone) melting point. Precisely this coexistence of dual crosslinked/crystalline networks in the same copolymer structure allowed us to establish, for the first time, the shape-memory properties in such materials, as verified by thermomechanical analysis. Moreover, the response temperature to the material original shape depended on the block copolymer emplacement, hence PTMC or PCL as end-block. Therefore, it has been possible to reach a block copolymer with transition temperature around 40°C thus opening potential real-life medical applications. In conclusion, the initial study of phase-separation/crystallinity relationship in PTMC-b-PCL-b-PTMC block copolymers lead to the discovery of novel shape memory materials with superior properties, widely demanded in modern-life applications.

Keywords: biodegradable block copolymers, organocatalytic ROP, self-assembly, shape-memory

Procedia PDF Downloads 129

Authors: T. Praveenkumar, Kulpreet Singh, Divy Bhanpuriya, M. Saimurugan

Abstract:

This study analysed the classification accuracy for gearbox faults using Machine Learning Techniques. Gearboxes are widely used for mechanical power transmission in rotating machines. Its rotating components such as bearings, gears, and shafts tend to wear due to prolonged usage, causing fluctuating vibrations. Increasing the dependability of mechanical components like a gearbox is hampered by their sealed design, which makes visual inspection difficult. One way of detecting impending failure is to detect a change in the vibration signature. The current study proposes various machine learning algorithms, with aid of these vibration signals for obtaining the fault classification accuracy of an automotive 4-Speed synchromesh gearbox. Experimental data in the form of vibration signals were acquired from a 4-Speed synchromesh gearbox using Data Acquisition System (DAQs). Statistical features were extracted from the acquired vibration signal under various operating conditions. Then the extracted features were given as input to the algorithms for fault classification. Supervised Machine Learning algorithms such as Support Vector Machines (SVM) and unsupervised algorithms such as Deep Feed Forward Neural Network (DFFNN), Deep Belief Networks (DBN) algorithms are used for fault classification. The fusion of DBN & DFFNN classifiers were architected to further enhance the classification accuracy and to reduce the computational complexity. The fault classification accuracy for each algorithm was thoroughly studied, tabulated, and graphically analysed for fused and individual algorithms. In conclusion, the fusion of DBN and DFFNN algorithm yielded the better classification accuracy and was selected for fault detection due to its faster computational processing and greater efficiency.

Keywords: deep belief networks, DBN, deep feed forward neural network, DFFNN, fault diagnosis, fusion of algorithm, vibration signal

Procedia PDF Downloads 116

Authors: A. Moualdia, D. J. Boudana, O. Bouchhida, A. Medjber

Abstract:

Wind energy has many advantages, it does not pollute and it is an inexhaustible source. However, the cost of this energy is still too high to compete with traditional fossil fuels, especially on sites less windy. The performance of a wind turbine depends on three parameters: the power of wind, the power curve of the turbine and the generator's ability to respond to wind fluctuations. This paper presents a control chain conversion based on a double-fed asynchronous machine and flow-oriented. The supply system comprises of two identical converters, one connected to the rotor and the other one connected to the network via a filter. The architecture of the device is up by three commands are necessary for the operation of the turbine control extraction of maximum power of the wind to control itself (MPPT) control of the rotor side converter controlling the electromagnetic torque and stator reactive power and control of the grid side converter by controlling the DC bus voltage and active power and reactive power exchanged with the network. The proposed control has been validated in both modes of operation of the three-bladed wind 7.5 kW, using Matlab/Simulink. The results of simulation control technology study provide good dynamic performance and static.

Keywords: D.F.I.G, variable wind speed, hypersynchrone, energy quality, hyposynchrone

Procedia PDF Downloads 368

Authors: A. G. Kovalenko, Y. N. Amirgaliyev, A. U. Kalizhanova, L. S. Balgabayeva, A. H. Kozbakova, Z. S. Aitkulov

Abstract:

It is known that statistical information on operation of the compound multisite system is often far from the description of actual state of the system and does not allow drawing any conclusions about the correctness of its operation. For example, from the world practice of operation of systems of water supply, water disposal, it is known that total measurements at consumers and at suppliers differ between 40-60%. It is connected with mathematical measure of inaccuracy as well as ineffective running of corresponding systems. Analysis of widely-distributed systems is more difficult, in which subjects, which are self-maintained in decision-making, carry out economic interaction in production, act of purchase and sale, resale and consumption. This work analyzed mathematical models of sellers, consumers, arbitragers and the models of their interaction in the provision of dispersed single-product market of perfect competition. On the basis of these models, the methods, allowing estimation of every subject’s operating options and systems as a whole are given.

Keywords: dispersed systems, models, hydraulic network, algorithms

Procedia PDF Downloads 286