Three-dimensional transitional separated flow over low-aspect-ratio NACA0012 wings (semi-aspect ratios of sAR = 1 and 2) is numerically investigated at Reynolds numbers of 1 & times; 10(4) and 4 & times; 10(4) , across a range of angles of attack from 10 degrees to 20 degrees. The numerical simulations are performed by solving the unsteady Reynolds-averaged Navier-Stokes (URANS) equations, employing the transitional turbulence model of shear stress transport (SST) gamma - Re-theta to simulate the turbulent flow and predict the laminar-to-turbulent transition. It is observed that the lower aspect ratio wing ( sAR = 1 ) produces lower lift and drag coefficients than the higher aspect ratio wing ( sAR = 2 ), regardless of the Reynolds number. For sAR = 1 , the lift coefficient increases monotonically with angle of attack, whereas sAR = 2 exhibits the opposite trend at Re = 1 & times;10(4) . At lower aspect ratios, the downwash induced by the wingtip vortices affects a broader spanwise region. The spike in the sectional lift coefficient is found closer to the root for sAR = 1 . Moreover, the separation and transition points are observed earlier for the higher aspect ratio wing. For sAR = 1 , the wake remains steady under all tested conditions due to the strong downwash effect. This steady wake behavior may be influenced by the turbulence modeling, which can suppress low-frequency vortex shedding compared to LES or DNS simulations. In contrast, the unsteady flow and Karman vortex shedding appear across the midspan when the downwash weakens for sAR = 2 .

This paper examines the switch from model-based to data-driven control systems, providing an overview of the challenges encountered and the potential solutions. It reviews the history, current progress, and future perspectives of data-driven control technologies. Additionally, it outlines the differences between model-based and data-driven control, compares various data-driven control approaches, and addresses essential issues in data-driven optimization. Finally, the paper delves into the data-driven control process and related research areas.

In recent decades, various pipeline industries, such as oil, gas, and water, have increasingly focused on fiber-reinforced polymer (FRP) pipes. This growing interest in FRP pipes offers multiple advantages over traditional pipelines made of steel and concrete, including exceptional corrosion resistance, a favorable weight-to-strength ratio, reduced maintenance costs due to its durability, and customer-specific customization in sizes and strength. However, the intricate manufacturing processes and its specialized handling and installation requirements make it susceptible to defects. The traditional non-destructive testing (NDT) methods, primarily developed for metals, are inadequate when applied to FRP. It is mainly because fiberglass composites are inherently non-homogeneous and anisotropic in contrast to their metallic counterparts, introducing a unique set of challenges. As a result, the fields of Non-destructive Testing & Evaluation (NDT&E) and Structural Health Monitoring (SHM) for FRP piping systems are currently vibrant areas of research and development. The objectives of this paper are (i) to identify potential damage types in composite pipelines, (ii) to compile a comprehensive list of defects currently examined in the literature, and (iii) to present the latest progress in NDT&E techniques for composite pipelines, specifically addressing the operational constraints and practical challenges involved. Consequently, it is tailored specifically to address the needs and challenges of the pipeline sector. It is found that the state of NDT for composite pipelines is still nascent, with extensive research required to reach maturity. Critical areas for development include broadening inspection ranges, validating performance in real-field conditions, detecting, and characterizing natural defects, and improving imaging techniques. Moreover, there is a need to transition from reactive to proactive strategies in pipeline monitoring.

Industries are rapidly moving toward mitigating errors and manual interventions by automating their process. The same motivation is carried out in this research which targets to study a conveyor system installed in soda ash manufacturing plants. Our aim is to automate the determination of optimal parameters, which are chosen by identifying the flow rate of the materials available on the conveyor belt for maintaining the ratio between raw materials being carried. The ratio is essential to produce 40% pure carbon dioxide gas needed for soda ash production. A visual sensor mounted on the conveyor belt is used to estimate the flow rate of the raw materials. After selecting the region of interest, a segmentation algorithm is defined based on a voting-based technique to segment the most confident region. Moments and contour features are extracted and passed to machine learning algorithms to estimate the flow rate of different experiments. An in-depth analysis is completed on various techniques and convincing results are achieved on the final data split with the best parameters using the Bagging regressor. Each step of the process is made resilient enough to work in a challenging environment even if the belt is placed in an outdoor environment. The proposed solution caters to the current challenges and serves as a practical solution for estimating material flow without manual intervention.

Fast, open, free, and accessible, online social networks are massively used to share news and various information. Unfortunately, their explosive growth amplifies the dissemination of misinformation, posing a severe threat to our societies. Nowadays, it is also a home ground for wrongdoers to spread fake news, rumors, conspiracies, hoaxes, and other forms of deception. Therefore, there is an urgent need to deploy efficient algorithms to tackle this infodemic. Current research focuses mainly on the news content or context to tell the difference between what is credible and what is not. Content-based methods concentrate on detecting the false knowledge the message carries in its writing style. Context-based methods rely on their propagation patterns or the credibility of their source. Finally, some hybrid methods combine these various types. This paper proposes a source-based method in a machine learning framework. It focuses on the profile and the interactions of the news spreaders. Thus, On Twitter, we associate each news article with the interaction network formed by the authors of tweets and their first-degree ego network. Experiments are conducted on two real-world datasets publicly available, covering different domains (Politifact, GossipCop). We select the most diversified news to assess the performance of various machine learning approaches. We conduct an extensive investigation to choose the best network and user-profile features and the most effective machine learning model that can classify news with the highest accuracy. The most effective set of features is well-balanced. It includes four network features and four user-profile parameters. Results show that the “XG Boost” model outperforms its alternatives. (Random Forest, Decision Tree, Multi-Layer Perceptron, K- Nearest Neighbor, Support Vector Machine). It achieves 92% and 91% accuracy on the Politifact and GossipCop datasets. Comparisons with baselines covering a large spectrum of solutions demonstrate its superiority. The proposed solution is promising. Indeed, it requires limited information with few news articles for training. Furthermore, it can detect deception in its initial stage.

This paper is devoted to develop interest of power system engineers in learning basic concepts of image processing and consequently using deep networks to solve problems of complex power system networks. To this end, we study fault classification in a power system through automation of equal area (EAC) criterion. By considering EAC graphs as images and using classical image processing techniques, we successfully distinguish between different transient conditions including sudden change of input power as well as short circuit at the sending end and middle points of a single and double circuit transmission lines. In addition to classification, some parameters are also determined from EAC images such as initial rotor angle, clearing angle, and maximum rotor angle. Further, the use of deep networks is introduced to perform the same task of fault classification and a comparison is drawn with multilayer perceptron neural networks. Developed algorithms are tested in MATLAB as well as Pytorch environments.

Social media microblogs are extensively used to get news and other information. It brings the real challenge to distinguish that what particular information is credible. Especially when user authenticity is hidden, due to the microblog’s anonymity feature. Low credibility content creates an imbalance in society. Therefore many research studies are conducted to assess automatic microblog’s credibility but the majority of them offer different concepts of credibility and the problem seems unresolved. Credibility is multi-disciplinary, hence there is no generalized or accepted credibility concept with all its necessary and detailed constructs/components. Therefore, it is necessary to understand the complete anatomy of information credibility from different disciplines. It is accomplished here through an in-depth and organized study of all the problem dimensions for the identification of comprehensive and necessary credibility constructs. The framework is also proposed based on the identified constructs. It adheres to these constructs and presents their inter-relationships. It is believed that the framework would provide the necessary building blocks for implementing an effective automatic credibility assessment system. The framework is generic to social media and specifically implemented for microblogs. It is completely transformed up to features level, in the context of microblogs. Regarding automatic credibility assessment, it is proposed after detailed analysis that the attempt should be made for hybrid models combining feature-based and graph-based approaches. It is observed that quite a few surveys in the literature focus on some limited aspects of microblogs credibility but no literature survey and fundamental study exists that consolidates the work done. To understand the broader domain of credibility and consolidate the work in this area that can lead us to a suitable framework, we explored the existing literature from different disciplines for the said objectives. We categorized them along various dimensions, developed taxonomy, identified gaps and challenges, proposed a solution, developed a theory-driven framework with its transformation to microblogs, and suggested key areas of research.

With Pakistan being ranked as the 46th largest revenue generator in terms of the E-commerce industry, online frauds have increased proportionally. The process of online shopping has changed drastically as the seller and buyer can now communicate directly through social media applications without needing a specific platform. It implies that all fraud prevention techniques, already in place, fail in such scenarios as they are only applicable to their platform. So, for an easily attainable input to the fraud prevention pipeline, our research focuses on analyzing the fraudulent activities in this market by using commonly available customer, product, and seller traits, as features. For this research, a product-based fraud detection dataset was collected through a survey and various feature selection techniques and ML models were applied to it. In prospect, our approach can be used to develop a utility that automatically extracts relevant features, calculates risk scores, and facilitates customers in purchase decisions, given a threshold on the risk score.

In COVID-19 related infodemic, social media becomes a medium for wrongdoers to spread rumors, fake news, hoaxes, conspiracies, astroturf memes, clickbait, satire, smear campaigns, and other forms of deception.It puts a tremendous strain on society by damaging reputation, public trust, freedom of expression, journalism, justice, truth, and democracy.Therefore, it is of paramount importance to detect and contain unreliable information.Multiple techniques have been proposed to detect fake news propagation in tweets based on tweets content, propagation on the network of users, and the profile of the news generators.Generating human-like content allows deceiving content-based methods.Network-based methods rely on the complete graph to detect fake news, resulting in late detection.User profile-based techniques are effective for bots or fake accounts detection.However, they are not suited to detect fake news from original accounts.To deal with the shortcomings in existing methods, we introduce a source-based method focusing on the news propagators' community, including posters and re-tweeters to detect such contents.Propagators are connected using follower-following relations.A feature set combining the connectivity patterns of news propagators with their profile features is used in a machine learning framework to perform binary classification of tweets.Complex network measures and user profile features are also examined separately.We perform an extensive comparative analysis of the proposed methodology on a real-world COVID-19 dataset, exploiting various machine learning and deep learning models at the community and node levels.Results show that hybrid features perform better than network features and user features alone.Further optimization demonstrates that Ensemble's boosting model CATBoost and deep learning model RNN are the most effective, with an AUC score of 98%.Furthermore, preliminary results show that the proposed solution can also handle fake news in the political and entertainment domain using a small training set.

One of the disciplines behind the science of science is the study of scientific networks.This work focuses on scientific networks as a social network having different nodes and connections.Nodes can be represented by authors, articles or journals while connections by citation, co-citation or co-authorship.One of the challenges in creating scientific networks is the lack of publicly available comprehensive data set.It limits the variety of analyses on the same set of nodes of different scientific networks.To supplement such analyses we have worked on publicly available citation metadata from Crossref and OpenCitatons.Using this data a workflow is developed to create scientific networks.Analysis of these networks gives insights into academic research and scholarship.Different techniques of social network analysis have been applied in the literature to study these networks.It includes centrality analysis, community detection, and clustering coefficient.We have used metadata of Scientometrics journal, as a case study, to present our workflow.We did a sample run of the proposed workflow to identify prominent authors using centrality analysis.This work is not a bibliometric study of any field rather it presents replicable Python scripts to perform network analysis.With an increase in the popularity of open access and open metadata, we hypothesise that this workflow shall provide an avenue for understanding scientific scholarship in multiple dimensions.

One of the disciplines behind the science of science is the study of scientific networks. This work focuses on scientific networks as a social network having different nodes and connections. Nodes can be represented by authors, articles or journals while connections by citation, co-citation or co-authorship. One of the challenges in creating scientific networks is the lack of publicly available comprehensive data set. It limits the variety of analyses on the same set of nodes of different scientific networks. To supplement such analyses we have worked on publicly available citation metadata from Crossref and OpenCitatons. Using this data a workflow is developed to create scientific networks. Analysis of these networks gives insights into academic research and scholarship. Different techniques of social network analysis have been applied in the literature to study these networks. It includes centrality analysis, community detection, and clustering coefficient. We have used metadata of Scientometrics journal, as a case study, to present our workflow. We did a sample run of the proposed workflow to identify prominent authors using centrality analysis. This work is not a bibliometric study of any field rather it presents replicable Python scripts to perform network analysis. With an increase in the popularity of open access and open metadata, we hypothesise that this workflow shall provide an avenue for understanding scientific scholarship in multiple dimensions.

There is an enormous growth of social media which fully promotes freedom of expression through its anonymity feature. Freedom of expression is a human right but hate speech towards a person or group based on race, caste, religion, ethnic or national origin, sex, disability, gender identity, etc. is an abuse of this sovereignty. It seriously promotes violence or hate crimes and creates an imbalance in society by damaging peace, credibility, and human rights, etc. Detecting hate speech in social media discourse is quite essential but a complex task. There are different challenges related to appropriate and social media-specific dataset availability and its high-performing supervised classifier for text-based hate speech detection. These issues are addressed in this study, which includes the availability of social media-specific broad and balanced dataset, with multi-class labels and its respective automatic classifier, a dataset with language subtleties, dataset labeled under a comprehensive definition and well-defined rules, dataset labeled with the strong agreement of annotators, etc. Addressing different categories of hate separately, this paper aims to accurately predict their different forms, by exploring a group of text mining features. Two distinct groups of features are explored for problem suitability. These are baseline features and self-discovered/new features. Baseline features include the most commonly used effective features of related studies. Exploration found a few of them, like character and word n-grams, dependency tuples, sentiment scores, and count of 1st, 2nd person pronouns are more efficient than others. Due to the application of latent semantic analysis (LSA) for dimensionality reduction, this problem is benefited from the utilization of many complex and non-linear models and CAT Boost performed best. The proposed model is compared with related studies in addition to system baseline models. The results produced by the proposed model were much appreciating.

This paper addresses the problem of output-feedback communication and control with event-triggered framework in the context of distributed networked control systems. The design problem of the event-triggered output-feedback control is proposed as a linear matrix inequality (LMI) feasibility problem. The scheme is developed for the distributed system where only partial states are available. In this scheme, a subsystem uses local observers and share its information to its neighbors only when the subsystem׳s local error exceeds a specified threshold. The developed method is illustrated by using a coupled cart example from the literature.

We propose a novel energy-aware approach to detect a leak and estimate its size and location in a noisy water pipeline using least-squares and various pressure measurements in the pipeline network. The novelty in our work hinges on the fusion of the duty-cycling (DC) and data-driven (DD) strategies, both well-known techniques for energy reduction in a wireless sensor network (WSN). To maximize the information gain and minimize the energy consumed by the WSN, we first study the effects of (a) various levels of sensor measurement uncertainty and (b) the use of the smallest possible number of pressure sensors on the overall accuracy of our approach. Using the DD strategy only, a noisy environment, and a small number of sensors, the performance of our scheme shows that, for small leak sizes, the estimation error in both leak location and size becomes unacceptably high. Next, using as few sensors as possible for an acceptable accuracy, we fused the DD strategy with the DC one to minimize the sensing, processing, and communication energies. The fusion approach yielded a better performance with significant energy saving, even in noisy environments. EPANET was used to model the pipeline network and leak and MATLAB to implement, analyze, and evaluate our fusion approach.

OPC, originally the Object Linking and Embedding (OLE) for Process Control, brings a broad communication opportunity between different kinds of control systems. This paper investigates the use of OPC technology for the study of distributed control systems (DCS) as a cost effective and flexible research tool for the development and testing of advanced process control (APC) techniques in university research centers. Co-Simulation environment based on Matlab, LabVIEW and TCP/IP network is presented here. Several implementation issues and OPC based client/server control application have been addressed for TCP/IP network. A nonlinear boiler model is simulated as OPC server and OPC client is used for closed loop model identification, and to design a Model Predictive Controller. The MPC is able to control the NOx emissions in addition to drum water level and steam pressure.

In this paper, we study the impact of triggered control strategies for a class of uncertain linear systems. The event condition is proposed based on the relative error between the current state and the state at last sample time. We introduce robust H ∞ theory into self-triggered sample strategy, and achieve both resource utilization and disturbance attenuation properties. We investigate the full-information feedback H ∞ control for the perturbed linear system and develop a linear matrix inequality (LMI)-based sufficient condition guaranteeing the robust asymptotic stability of the closed-loop system. Employing a self-triggered control, we provide a method of designing the longest sampling period for the relevant H ∞ controller when the system is stabilized and robust. A cart and pendulum example is given to show the efficiency of the theoretical result.

A physical system can be studied as either continuous time or discrete-time system depending upon the control objectives. Discrete-time control systems can be further classified into two categories based on the sampling: (1) time-triggered control systems and (2) event-triggered control systems. Time-triggered systems sample states and calculate controls at every sampling instant in a periodic fashion, even in cases when states and calculated control do not change much. This indicates unnecessary and useless data transmission and computation efforts of a time-triggered system, thus inefficiency. For networked systems, the transmission of measurement and control signals, thus, cause unnecessary network traffic. Event-triggered systems, on the other hand, have potential to reduce the communication burden in addition to reducing the computation of control signals. This paper provides an up-to-date survey on the event-triggered methods for control systems and highlights the potential research directions.

This paper presents an approach for detecting, locating and estimating the size of leak in a pipeline using pressure sensors, differential pressure sensors and flow-rate sensors. To overcome the problem with existing approaches we use differential pressure sensors that detect small change in pressure in order to detect small change in leak size. The pipeline system is modeled and simulated in EPANET software, and the input-output data acquired from it (i.e. sensor measurements and the leak locations and sizes) are used in MATLAB and DTREG software to develop Artificial Neural Network (ANN) and Support Vector Machines (SVM) models. Comparison of results shows that SVM is less sensitive and more stable to noise increment than ANN. However the performance of ANN is better with very small noises.