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(Volume: 3, Issue: 5)
Automatic Detection Of Fake News From Social Media Mandated...
We come across plenty of news in the social media every day and at times, we do share them without knowing them to be true or fake. It is quite normal that propagating a true news or fact has only a beneficial impact on an individual or society. But the propagation of fake news is not the same and seeks utmost attention, since it can deceive and mislead the society with incorrect knowledge being imposed on a specific factual news, which might pertain to public, politics or any other global happening. As the cyberspace can be deployed by anyone without much struggle, the websites are generously dumped with both trustable and non-trustable news. Hence, the detection of fake news from the social media contents is highly essential to prevent and manage any illicit activity or conflict, which could otherwise arise from the spread of fake news. Usually, the authenticity of a news can be declared by fact checking with journalism experts, industrial whistleblowers or regular news-reading community. However, it is not an easy task to manually fact-check and detect the phoney news from the massive news volume that gets updated every single second in the web!!! So, it is actually here the Machine Learning (ML) approaches, in combination with Natural Language Processing (NLP) and Information Retrieval (IR), render hands to automatically detect the fake news. But, how? By creating a knowledge base of known facts with NLP, retrieving the facts associated with the news from the knowledge base and classifying the news as fake or factual through the ML approaches. However, the detection accuracy still requires improvement by analyzing the source, emotions or the textual relationship in the news content, especially when they come from the widespread and rapidly-spreading media like Twitter. Vinita Nair, Dr. Jyoti Pareek and Sanskruti Bhatt, researchers from Gujarat University, have worked on automatically detecting the fake news from Twitter using a knowledge-based deep learning approach. They have created the knowledge base with four facts, namely, Subject-Predicate-Object (SPO) triplet, SPO polarity, SPO frequency and topic modeling using graph theory for fact-checking. These facts are set to respectively capture the textual relationships, the emotional tone, the common textual patterns and group the text under a specific topic, simplifying and enhancing fake news detection from the information bulk. In their article in Procedia Computer Science, Elsevier, the researchers have also employed and compared their methodology with deep learning approaches like, RNN, LSTM and pre-trained models from Google as well as OpenAI. Additionally, the researchers envisage an improvement in automatic fake news detection with the incorporation of Graph-based Neural Networks or Convolutional Neural Networks, directing the upcoming researchers to ensure information reliability in the current internet era. Image courtesy: www.vecteezy.com
Analyze Piezoelectric Patch-Embedded Confined Granular Fills For Maximum Energy Harvest
The growing electricity demand, together with the need for net-zero emission-based electricity generation, have caused the researchers to pool their ideas towards discovering alternative ways of electricity generation. One such idea is the harvest of electricity from the stability-providing, structural health monitoring materials, which are embedded with the confined granular fills of the transportation infrastructures. Usually, the confined granular fills are embedded with electromagnetic, electrostatic and piezoelectric materials, which react inductively, capacitively and mechanically to the dynamic loads imposed on them. In fact, they signal the changes in the environment or granular fill properties, forewarning about the maintenance required in the transportation infrastructures. Of these materials used in the confined granular fills, the piezoelectric material can be deemed as an ideal energy harvester for two main reasons: (1) It does not react with rest of the other conductive materials in the granular fills and (ii) It produces electricity in proportion to the stress-strain characteristics, which it exhibits because of the dynamically-operated loads. However, not all the mechanical energy produced by the piezoelectric material in the confined granular fills gets converted into useful electrical energy to drive the electrical devices, which operate at varying power. Moreover, the consistency in energy production and storage for use in different electrical applications also requires assurance. Hence, the analysis on the factors affecting the energy produced by the piezoelectric material becomes a major area of study. Nisha Kumari and Ashutosh Trivedi from the Delhi Technological University, India, have investigated the output voltage, power and charge density obtained, when the PZT (lead zirconate titanate) patches of varying thickness were embedded in the dynamically-loaded conƒined granular ƒill in different alignments. The material properties of the granular fill were also studied in connection to it. In their article in Advances in Materials Science and Engineering, the researchers have quantitatively affirmed that the output voltage gets affected by the aforesaid factors. In addition, the researchers suggest the confined granular fills to be upscaled with more PZT patches, when subjected to incessant dynamic loads, so as to harvest excessive power without deteriorating the structural health. However, the number of PZT patches required, their position and their connection with granular fill properties again triggers further research to accomplish a sustainable transportation infrastructure. Image courtesy: www.vecteezy.com
Non-Uniform Heating In Nanofluids Needs Investigation
It is quite usual that almost all the engineering processes give away heat as radiation, conduction or convection. Letting fluids like, water or oil to flow in a controlled manner in small tube-like chambers might help reduce the heat risk associated with these processes. However, as different processes exhibit heat of varying ranges, the aforesaid base fluids alone cannot promote safer and optimal operation. This has led to the advent of “Nanofluids”, which are revolutionizing the modern-day cooling systems, heat exchangers and energy systems in automotive, aerospace, manufacturing, construction, medical and similar other engineering applications. Nanofluid is just a fluid, wherein a base fluid is engineered with nano-sized oxides, metals or any other suitable nanomaterials to enhance its thermal conductivity and heat exchange properties. However, at times, the nanofluids are also subjective to non-uniform heating that is produced across devices or apparatus, especially due to convection. This is because convection is a process by which a fluid transfers heat by its own movement. Thus, the nanofluid that is employed to impart better heat transfer and thermal conductivity properties can even cause detrimental effects, as they glide through the coolant systems!!! Hence, research is mandated on the following disciplines: (i) The choice on the nanomaterials, (ii) The mixing proportion with the base fluid (iii) The optimal size or shape of the coolant chamber through which the nanofluid is to be glided and (iv) The electric or magnetic fields affecting the nanofluid passage. Many researchers have worked on one or the other aforesaid phenomena. Shantanu Dutta from Sanaka Education Trusts Group of Institutions, Malandighi, West Bengal, has analyzed whether the copper-water nanofluid in square or rhombus-shaped enclosures produces non-uniform heating, especially due to magnetohydrodynamic natural convection. In an article in The European Physical Journal Special Topics, Springer, the researcher and Thanaa Elnaqeeb of Zagazig University, Egypt, have investigated whether the inclination of the rhombic enclosure that contain the copper-water nanofluid could affect the heat transfer characteristics using numerical simulations. Making comparisons with three other nanofluids, the authors have desired to design an optimal thermal system with their numerically-simulated findings. As the nanofluids provide extreme benefits on thermal management in the massive machineries of various applicable sectors, researching on them has a higher scope in the future.
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Autonomous Robots Are To Be Taught With Social Norms
The use of robots to ease up human activities or tasks is experiencing a rampant growth every year. This growth can be witnessed with various robotic applications, which usually involve a robot or a group of it to manage the warehouse operations, perform a rescue operation, aid in the healthcare actions, monitor the traffic or environment and many more. However, autonomous navigation with increased collaboration and coordination between the robots only decide whether the robot is beneficially-employed or not. A group of robots working together can be assumed as a Multi-Agent System (MAS). Each robot in MAS can be treated as an individual agent that can make its own decision to navigate and operate, without causing conflicts with the rest of the robots. However, it is not an effortless task for a robot to autonomously navigate, as its paths have numerous obstacles that might also include the remaining working robots or even humans. So, how does a robot tend to navigate autonomously? Only after performing various operations that include: (i) Mapping the surrounding environment with the data got from cameras, Global Positioning System (GPS) or various sensors like, radar or lidar; (ii) Localizing its position from the mapping through distance and velocity calculations; (iii) Planning its path with careful obstacle avoidance and (iv) Deciding the best or optimal path to move. Numerous researchers have tried to prevent obstacle avoidance and optimal planning of path using Neural Networks, so that a robot can navigate without conflicts and collisions. However, they are found to be not effectual in case of robots acting as MAS. In fact, these autonomous robots acting as MAS usually move amidst human population, whose behavior or features are unpredictable and change from person to person. Hence, the robots are to be taught with the social norms and sensor data to navigate freely in an environment with humans, robots and obstacles. Ganesh Khekare and Shahrukh Sheikh have dealt with a socially-aware autonomous navigation system for the robots, operating as MAS. In their article in International Journal of Artificial Intelligence and Machine Learning, vol. 11(2), the researchers have employed a value-based deep reinforcement learning for planning the robotic trajectory in the Robot Operating System (ROS) platform. The researchers have also suggested to use sophisticated deep learning models and contemporary cloud architectures in future, foreseeing any physical or virtual task to be achieved with autonomous robots . Image courtesy: www.freepik.com
Cement Nanocomposites For Thermal-Prone Civil Infrastructures
Building and construction works heavily rely on cement composites for them to be long-lasting. Addition of suitable composite materials like, steel or glass fibre provide improved strength, toughness and durability to the cement, allowing the constructions to withstand changes in humidity, moisture or temperature. Nowadays, cement nanocomposites are grabbing attention in almost all the civil infrastructure constructions. Engineering cement with nanoparticles (like, carbon tubes or silica) tend to enhance its microstructure and its mechanical strength, avoiding void creation as well as concrete spalling. However, the durability of these cement nanocomposites at high temperature always remains an issue. Most aerospace, energy, automotive and industrial sectors involve concrete infrastructures, which are subjected to extreme thermal settings. In fact, spalling and other forms of degradation in such infrastructures cause profound destructions. Hence, recent researches in civil or structural engineering focus more on the invention and the utilization of ceramic-based or metal matrix nanocomposites with high thermal stability and resistance. Zirconium diboride (ZrB2), exhibiting properties of both ceramic as well as metal, is one such refractory material that can withstand ultra-high temperatures. Hence, adding ZrB2 in nano proportions to cement is certainly going to improve its tensile and mechanical properties. But, what proportions of ZrB2 has to be added to the cement products to alleviate its degradation at high temperatures? To know this fact, Morteza Savaripour and Younes Komachi have experimentally- investigated the nano-filling effect of ZrB2 in about five proportions to the cement matrix. As per their research in Journal of Thermal Analysis and Calorimetry, Vol. 148, Springer, the cement on added up with 5% mass of ZrB2 had its compressive strength increased by 45% and its spalling effect was unnoticed as well, when subjected to elevated temperatures. Though the authors have let the upcoming researchers to know about an impending cement nanocomposite, identifying pollution-free cement nanocomposites with increased thermal stability is of increased concern to have a sustainable future. Image courtesy: www.freepik.com
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