bluuebunny/crossref_metadata_2025 huggingface dataset:
Ossetian myg / mugæ ‘Sperm’ and Some Aspects of Common Iranian and Indo-European Reconstruction
Authors: Dzitstsoity Yuriy A.
Published: November 2022

The article offers a new approach for the analysis of Ossetian myg / mugæ ‘sperm’ and comments on the pre-history of its derivative myggag / muggag ‘semen’, ‘genus’, etc. The undertaken study of various versions of the fragment of the Nart epic where the word myg is uniquely used allowed the authors to consider various strategies of its euphemisation, which was fruitful for defining its original meaning. Ossetian mygcan be consistently traced back to the Common Iranian etymon going back to PIE *meuk- ‘to release’, etc., cf. Lat. mucus ‘snot’, Greek μύξα ‘slime, mucus’. It is possible also to consider it among other words going back to Iranian *mau- : mū- ‘to be wet, moisten’ and ‘move’: the paper does not differentiate between these two meanings, for which an explanation is provided. It is argued that Osset. myræg ‘first filtrate obtained by brewing the beer’ is the derivative in *-ra- from this Iranian verb, and some other Ossetian and Iranian lexemes which belong (or might belong) here are also considered. The study questions the commonly accepted reconstruction of Scythian *muka ‘sperm’, ‘semen’, ‘genus’ on the basis of the personal name Μουγισαγος, while traditional utilization of Ossetic lexemes for interpretation of Thracian anthroponyms with muca- is considered misleading.
Computer Aided Design of Self-Learning Robotic System Using Imitation Learning
Authors: Jadeja Yagna , Shafik Mahmoud , Wood Paul
Published: November 2022

Artificial intelligence (AI), imitation learning, big data, cloud and distributed computing, robotics cells, and information communication technology, are some of the key tools and elements of the future digital and smart manufacturing facility. There are a number of challenges that digital and smart manufacturing is facing, especially with the complication of AI (i.e., machine, deep and cognitive learning) algorithms, great amount of data to process, and essential complex coding required, which makes immediate changes needed in manufacturing facilities not straightforward. This is notable in small manufacturing cells which is an integrated part of future smart factories such manufacturing facilities are usually needed some annual and regular updates to meet the update in the design specifications of next generation of products. Imitation learning is offering a great opportunity to overcome these challenges and simplify such complications, where human skills, ability to perform specific tasks, knowledge, and talent could be transferred. This is conveying the knowledge, and skills transfer using imitation learning. However, smart manufacturing and industrial revolution needs robotics cells that has skills beyond this, especially when it comes to process optimisation. Therefore, deep imitation learning could come in to help in the development of self-learning robotic systems and cells. Of course with the powerful tools such as distributed computing, blockchain, cloud computing, edge computing, and 5G the collaboration between such self-learning robotic cells will be possible. This will certainly not eliminate human existence but will enhance the manufacturing environment. This paper is focused on presenting the outcomes of CAD simulation and modelling phase of the ongoing research programme that focused on developing a self-learning robotic system using imitation learning. CAD tools have been used and some initial results is presented. Further work is still undertaken, and this will focus on learning from more than one expert, optimisation, impact of dynamic manufacturing environment.
Generative Design and Topology Optimisation of Products for Additive Manufacturing
Authors: Coulthard James , Wang Chang Jiang
Published: November 2022

The aim of this paper is to assess the impacts of topology optimisation and generative design on design optimisation processes when paired with additive manufacturing. This is being done as topology optimisation is a common practice in industry, but the advent of generative design may allow for further critical refinement of parts while maintaining functionality. Additionally, when paired with additive manufacturing the benefits of generative design compound with material savings and the ability to maximise the effectiveness of generative designs complex, organic geometries. A car suspension upright was manually created as an original design. The process of topology optimisation was undertaken on the original part, and a volume reduction to the original part of 65.17% was achieved. Using a case of loading of turning braking, a generative design was produced. This achieved a volume reduction of 88.13% when compared to the original design and 65.91% when compared to the topology optimisation result. Prototype parts were then produced in ABS via the FDM additive manufacturing method as a proof of concept. The optimisation processes on the design were both successful with the topology approach providing a stronger part while the generative design provided a bespoke, refined design using much less material of organic nature. It is found that the generative methodology also provides significant positive sustainability impacts.
Stiffness Measurement of Parallel Kinematic Machines Considering Gravity Effect
Authors: Bandara Shakya , Jin Yan , Van Mien , Sun Dan , Fu Rao , Curley Patrick , Higgins Colm
Published: November 2022

Parallel Kinematic Machine (PKM) is a new type of machine tool, which has the potential to fill in the gap between traditional CNC machines and industrial robots, due to its flexibility and superior motion dynamics. Stiffness is an essential property of a machine tool, as it will affect the machining capability. Although much research has been conducted on stiffness modelling and analysis, most of them employ simplified models and gravity effects have not been well considered or characterized for PKMs. To fill in the knowledge gap, this paper introduces a new experimental stiffness measurement method considering the effect of gravity on the machine tool. An experimental procedure was developed in order to separate the gravity effects on stiffness from the machine structure.
Inventory Optimisation Adoption Amongst SMEs
Authors: Drakeley Peter , Perera Terrence
Published: November 2022

It is acknowledged that UK SMEs make a significant contribution to its economy. However, SMEs are generally slow to adopt new technologies due to intrinsic constraints such as lack of in-house expertise and investment limitations. Inventory is a key asset in any organisation. Most SMEs use of some form of IT platforms to manage inventory but it appears that inventory optimisation is not a prime goal. If deployed correctly, inventory optimisation can significantly reduce costs and improve operational effectiveness. This paper presents the underlying causes behind the lack of commitment to inventory optimisation in UK SMEs based on the evidence gathered from industry and literature. It also outlines potential actions that may be taken to motivate adoption of inventory optimisation in SMEs.
Utilising Digital Twins for Increasing Military Supply Chain Visibility
Authors: Sani Shehu , Schaefer Dirk , Milisavljevic-Syed Jelena
Published: November 2022

The dynamic and complex nature of the military supply chain (MSC) requires effective monitoring of supplies and timely decisions to ensure success in combat operations, particularly in the face of unexpected disruptions such as inclement weather conditions and terrorist attacks. Although, existing technologies such as RFIDs, Next-Generation Wireless Communication (NGWC) and RuBees are applied MSC, they are not sufficient in ensuring end-to-end visibility across the entire SC. However, digital technologies such as the internet of things, cloud computing, Application Programming Interface (APIs), machine learning, augmented and virtual reality, big data, analytics and pervasive computing play a critical role in improving visibility to ensure effective SC management. Hence, the introduction of the concept of digital twin enabled by these technologies creates a digital replica of the whole supply chain operations, ensuring adequate end-to-end visibility through real-time data that ensures effective tracking, monitoring and reporting of all SC activities. The focus of this paper is to explore the enabling technologies of digital twin and its implementation to achieve end-to-end visibility in the military supply chain. In this paper, the authors identify the digital twin’s tracking and monitoring capability, which will improve supply chain management by enabling a cycle of continuous adjustment of the SC against unexpected disruptions.
Identifying and Predicting Cybersecurity Threats in Industry 4.0 Based on the Motivations Towards a Critical Infrastructure
Authors: Alqudhaibi Adel , Aloseel Abdulmohsan , Jagtap Sandeep , Salonitis Konstantinos
Published: November 2022

Industry 4.0 (I4.0) is an emerging concept describing the business setting application of a broad set of digitalisation technologies, connectivity, and automation. The most common critical infrastructure (CI) uses Industrial Control Systems (ICS) for operation and supervisory control. However, the Supervisory Control and Data Acquisition (SCADA) and Internet of things (IoT) systems are examples of ICSs applications. These systems, like any other systems exposed to many security risks and are vulnerable to many threats. This is mainly due to the lack of objective standards and proactive security countermeasures that companies unintentionally neglected in the early stages of designing these systems. It is also due to the absence of managerial and technical skills necessary to implement them. Therefore, identifying and preventing potential security threats against CIs is the focus of this paper. A novel security approach concept that can predict cybersecurity threats based on the CI nature and take into consideration the attack motivations accordingly has been delivered in this paper. The proposed concept of this approach will also facilitate the detection of potential attack types and the required countermeasures in particular infrastructures.
A Survey of Machine Learning Approaches for Visual Inspection on the DAGM Dataset
Authors: Carvalho Philippe , Durupt Alexandre , Grandvalet Yves
Published: November 2022

The task of automatic visual inspection has been tackled by numerous machine learning algorithms. However, there are no global comparative studies of the performance of these algorithms that use metrics directly relevant to their use in an industrial setting. We survey the performance of machine learning algorithms applied to the DAGM dataset, a reference dataset for industrial visual inspection. Our study reports the performance of 17 algorithms for which the learning and evaluation protocols are clear enough to be reproducible. However, not all of these algorithms are comparable, in the sense that they rely on different labelling or even different data. We group the comparable results, and conclude that the DAGM dataset no longer presents a major difficulty for the algorithms based on knowledge of the location of defects in the images. On the other hand, algorithms using only unlabelled images, which are the easiest to implement in practice, do not yet achieve industrially acceptable performance.
Research into Early Stage Identification of Entrepreneurs and Artificial Intelligence: Towards Self-Sustained Tools to Identifying Young Entrepreneurs
Authors: Pearson Stephen Robert , Shafik Mahmoud
Published: November 2022

Youth unemployment is increasing and some countries are exhibiting unprecedented levels of youth unemployment, which according to research from Price Waterhouse Cooper (PwC), is costing the British economy £45bn per year, as well as blighting the careers of workers who miss out on a job in their teens and twenties. Unemployment exists because jobs do not, therefore one way to act to reduce it is to create jobs. It isn’t the governments remit to create jobs, not in the private sector therefore this responsibility is falling more and more on entrepreneurs. This paper seeks to examine the potential use for Artificial Intelligence (AI) in the early-stage identification of young entrepreneurs, in order to provide a more accurate comprehensive survey tool, able to adapt to the questions and answers given during the identification process.
Capturing Manufacturing Knowledge Using Augmented Reality Technologies
Authors: Sarhan Ahmed , Martin Lancelot , El Souri Mohammed , Gao James
Published: November 2022

Manufacturing is a knowledge-intensive business in which companies are distinguished for their ability to deliver products and services using numerous forms of information and knowledge. Various information and communication technologies and systems have been created throughout the years to effectively manage information and knowledge in industry. However, capturing knowledge, particularly tacit knowledge, remains a challenge. Virtual Reality (VR) and Augmented Reality (AR) technologies for industrial tasks have been developed by the research and practitioner communities. For example, in manufacturing, VR/AR have been used to aid in different machining and assembly tasks. This paper presents a proposed an AR equipment ‘Head Mounted Device (HMD)’ under development, as a tool for capturing manufacturing expertise’s knowledge. The technology intends to capture tacit knowledge using a 3D camera installed on the operator’s HMD. The proposed approach also supplies the operator with information on production and ongoing processes.
Research into Early Stage Identification of Entrepreneurs: Educational Tools Using Virtual and Augmented Reality
Authors: Pearson Stephen Robert , Shafik Mahmoud
Published: November 2022

One of the big challenges facing youth over all the world is unemployment. There are many reasons that unemployment exists specially within this community. It is mainly due to lack of jobs, knowledge, professional skills, and inability of the youth to recognise their potential as entrepreneurs. This is off course impact on national economy and there is a need to help this community to identify their individual potential at early stage and develop the necessary educational and training tools that can help them to overcome such unemployment challenge. This ongoing research programme is mainly focused on researching into early-stage identification of young entrepreneurs and aiming to develop the tools that can support them in short and long development. This paper presents the initial outcomes of whether virtual reality and augmented reality tools can offer advanced educational and training tools, better suited to entrepreneurs than conventional classroom-based training. It is important to mention that key part of this research programme is also focused on integrating Artificial Intelligences tools. This is to enhance the tools learning experience and meet individual needs. The outcomes of this studies are reported in the other paper published in this conference.
Cyber Security Biometric Solution Using Automatic Matching and Deep Learning
Authors: Alshebli Sulaiman , Shafik Mahmoud , Kurugollu Fatih
Published: November 2022

Biometrics is an alternative solution to the old means of identity verification, such as access cards. However, monomodal biometric systems suffer from multiple limitations, such as the noise introduced by the sensor and non-universality. Multi-biometrics allows us to overcome these problems and thus obtain better performance. Multimodal biometrics, using deep learning, has recently gained interest over single biometric modalities. In this work, we propose a deep learning model for persons identification/verification using Face and Iris traits. The features of the iris and the face are extracted utilizing the DenseNet121 and FaceNet models, and these features are merged using a feature-level fusion scheme. We also proposed a new automatic matching technique to verify the person’s identity. The results presented in this paper show interest in deep learning approaches for face and iris recognition, especially when models are pre-trained. The results also highlighted the interest in the proposed DenseNet121-FaceNet model and the automatic matching method compared to the standard threshold selection according to the Equal Error Rate point.
Emotions in Mental Healthcare and Psychological Interventions: Towards an Inventive Emotions Recognition Framework Using AI
Authors: Atlam Hany F. , Shafik Mahmoud , Kurugollu Fatih , Elkelany Yasmin
Published: November 2022

One of the major impacts of COVID-19 in the nations is mental health issues. Constant mental health issues can cause disorders, as well as mortality. The growing demand for mental healthcare treatment and limited healthcare resources across the world has shown the need for an inventive framework solution. Artificial Intelligence (AI), Big Data Science, 5G, and Information Communication Technology (ICT) have proven to be able to bring many great improvements and could be the potential way forward to develop such a framework. AI could be a very effective tool to help the healthcare sector to provide more efficient services to patients with mental health issues through their emotions. This paper presents the initial overview and outcomes of the ongoing research programme to develop a proactive multimodal emotion AI recognition framework that detects emotion from various input data sources for early detection of mental health illnesses, as well as provides the required psychological interventions effectively and promptly when required. The data will be collected from various smart wearables and ad-hoc devices, facial expressions, and speech signals. Then, these data will be interpreted using AI into emotions. These emotions will be utilised using AI-based psychological system, which will provide immediate and customized interventions, as well as transmit critical data to the healthcare provider’s central database system for monitoring and supplying the required treatments.
Linking Simulation-Based LCA to Manufacturing Decision Support: An Iron Foundry Case Study
Authors: Liu Yu , Syberfeldt Anna
Published: November 2022

In recent years, an increasing number of legislations have mandated environmental impact evaluations of products from a life cycle perspective. This study applies a discrete-event simulation-based life cycle assessment to study the environmental consequences that respond to system configuration changes in production processes. The proposed method allows capturing the dynamic links in production processes, which is lacking in conventional static LCA modelling. This approach is demonstrated via a real-world case study of a Swedish foundry production line, where its environmental impacts’ hotspots are identified. These environmental consequences are further analyzed to link to the respective production decision domains for providing suggestions on potential improvements. This study demonstrates the value of combining DES and LCA for revealing the hidden environmental consequences of production processes that are difficult to uncover with traditional LCA studies. Moreover, the strengths and difficulties of the proposed method are also discussed.
Development of a Maintenance Strategy to Optimise Maintenance in a World Scale Bioethanol Production Facility
Authors: Saad Sameh M. , Murray Clarke
Published: November 2022

Effective maintenance management systems can provide the right maintenance on the right assets at the right time [1]. Maintenance management at a bioethanol production facility can prove to be difficult. The combined nature of chemical plant and solids handling creates issues with varying levels of maintenance required. The aim of this paper is to determine the correct maintenance, at the correct time for the bioethanol facility by developing a new maintenance structure and the use of mathematical models to enhance decision making in maintenance. It is not uncommon for chemical plants to run six yearly campaigns, but a solid handling plant needs regular interventions to maintain operations. In order to gain a strategic advantage in the marketplace, ensuring the best maintenance and reliability practices are key to the long-term future of the factory and by keeping a focus on maintenance management improvements can be made to the position of the business both operationally and financially. The financial gains will be seen in the reduction of plant downtime for repairs and maintenance that is delivered on the correct frequency and thus lowering the costs of replacement machinery or parts and as the cost reduction is seen, the operation gains will be realised via the previous too. World scale refers to the factory being second largest producer in the EU27 and UK of bioethanol and the UK’s largest producer of animal feed.
Analysis of the Use of VR in Medical and Science and Technology
Authors: Sathi Reddy Padala , Venkatesh Nukella , Kumar S.
Published: November 2022

Using virtual reality as an asset and a part of our future is discussed in this article. You can experience, feel, and even touch the past, as well as the present, and future, through it. As a means of creating our own reality, it serves as a medium for us to do so. A virtual reality experience could include everything from developing a video game to a virtual walk across the world, or even a virtual walk through our own ideal house. The most terrifying and gruelling experiences can be experienced in a safe and educational manner with virtual reality. As a result of our research, we’ve compiled this report on everything we’ve learned about virtual reality so far. It covers everything from levels of immersion to the components that go into creating a virtual environment, as well as the history, present, and future of the technology. As a result, both the technologies themselves and the prices and logistics required to implement them have a variety of restrictions. Nevertheless, these new technologies have their own set of problems, which is why we conclude this study with a number of fresh ways and possibilities for future researchers who want to apply these new technologies to education.
Image Caption Generator in Hindi Using Attention
Authors: Sethi Abhishek , Jain Aditya , Dhiman Chhavi
Published: November 2022

Image Captioning has gained tremendous spotlight in recent years. However, the captioning models generate captions in English language. In this paper, we present an image caption generator for our regional language that is Hindi using Resnet50 and LSTM with attention module. An experimental study is shown highlighting the effect of attention-based learning on generated Hindi captions. Flickr8k dataset in Hindi language is used to validate the performance of proposed work in terms of BLEU score.
A Numerical Study on Conventional Tube Flaring Process
Authors: Singh Shivendra , Brar Gurinder Singh , Kumar Ashu
Published: November 2022

Tube flaring process involves a conical tool of a certain length which is displaced to get an end flared tube, which is utilized to form a tight seal between pipes or tubes. Tube flaring refers to a kind of forging which is often a cold working operation. The tube forming process is widely used in several industries to form condenser pipes, car seat structures, exhaust piping, etc. Thin-walled tubes were used in automobiles to reduce the total weight for better performance. A conical tool is used for tube end forming in which the tool is moved into the tube known as conventional tube flaring. In this work, a numerical study was directed using FEA software ANSYS/Implicit which is used to analyze the stress conditions involved in tube flaring. Here, the tool is considered rigid and displaced by using displacement control by 25 mm, and other parameters were studied at this displacement. Effects of different semi-cone angles were also considered for the study.
Critical Success Factors for Industry 4.0 Readiness and Adoption: A Conceptual Framework for Indian Manufacturing Industries
Authors: Birajdar Dhaval , Vasudevan Hari
Published: November 2022

Technological and managerial advances have benefited modern industrial organisations over the last two decades. They strive to become more competitive, cut production costs and produce products that meet quality standards, while also performing at a higher level. Among the most critical factors in these industries is the use of automation technologies that rely heavily on intelligent information processing techniques, dubbed industry 4.0. It is imperative to adopt industry 4.0 technologies to achieve overall manufacturing growth in today’s world. The Indian manufacturing sector’s readiness to adopt Industry 4.0 technologies is a challenge in the current environment. In this context, a literature review, covering Industry 4.0, critical success factors, industry 4.0 readiness and the intention to adopt Industry 4.0 was carried out to build a theoretical framework. The research aimed to identify the critical success factors required to implement industry 4.0 technologies successfully. This study also sheds light on contextual factors, such as Desorptive Capacity, which can be used to investigate the link between Critical Success Factors and industry 4.0 readiness, leading to the intention to adopt industry 4.0 in Indian manufacturing industries.
Application of Predictive Reinforcement Learning on Extendable Wheeled Systems
Authors: Kanishk  , Kumar Rushil , Rastogi Vikas , Kumar Ajeet
Published: November 2022

Robots and controls systems are increasingly adopting Intelligent non-linear-based control scheme approaches. Such non-linear systems become a complex computational problem for linear control schemes due to the rigorous calculation. Since most systems are inherently non-linear, following through with linear control schemes becomes an exhaustive process. In this research work, we wish to present reinforcement learning-based control with PPO (Proximal Policy Optimization) and MPC (Model Predictive Control) control schemes on our chosen assembly, i.e., the wheeled robot. We will be comparing these control schemes against each other based on the efficiency and performance of these systems for a specified task, including point-to-point locomotion in 3D space.
Simulation of Silicon on Insulator Tunnel FET (SOI TFET) Dosimeter
Authors: Singh Shreyansh , Dev Sushant , Gupta Shashwat , Rewari Sonam
Published: November 2022

This work examines the consequence of γ radioactivity on SOI-based Tunnel Field Effect Transistors & their use as radiation dosimeters. Extensive simulations have been carried out throughout the study in order to examine the creation of e--h+ pairs in the oxide field & to predict the device’s features from the region where the transistor operation around the pinch-off voltage to the significant agglomeration area. It was found that in the presence of a radiation field, the radioactivity-persuaded reduction in pinch-off voltage shift & boundary trapped electrons could not be unnoticed. Furthermore, the radiation properties of TFET were compared to those of MOSFET with the intention of ascertaining its competency as a dosimeter. The Γ radiation model of Silvaco-TCAD was utilised to determine the radiation efficiency of the SOI TFET.
Control of Automatic Voltage Regulator Using Hybrid Optimization Techniques
Authors: Vamsi Vasamsetti , Lavanya V. U. P. , Kumar S.
Published: November 2022

The present work studies the modelling and PID controller tuning aspects of an automatic voltage regulator. The PID controller is tuned using a hybrid optimization algorithm. This paper tunes the controller using the BF-PSO hybrid optimization algorithm. Simulation results and comparative analysis have been provided to validate the algorithm. Time-domain as well as frequency domain analysis has been carried out. In order to keep the voltage at the terminals, the VAR has a fixed set of operational parameters. Different electrical devices will degrade significantly if the terminal voltage is too low or too high. AVRs, which are controlled by a computer, are a key component in power system operation and control. A controller is used by AVR to regulate the terminal voltage. AVR is controlled using a proportional-integral-derivative (PID) controller, one of the most common control systems. The AVR’s PID controller must be properly tuned in order for it to function properly. PID controller tuning approaches have been compared in a comparative analysis.
Introduction of Agile, Green, Lean, and Resilient Elements to Flexible Systems: State of the New Age Trends of Manufacturing in India
Authors: Jassi Jitesh , Pal Parth , Chugh Mayank , Madan A.K.
Published: November 2022

The global manufacturing industry is entering a new age. Brought along are the challenges of complementing the supply and demand needs in the markets. Another hurdle faced by the manufacturing industry is the challenge of rapidly changing climate and environment conditions that call for corrective measures. The global concern is equally shared by India. In this regard, the flexible systems have contributed to resolve the challenges to a certain extent. The paper intends to research the advent of emerging technology that deals with these challenges. The focus remains on the Indian manufacturing industry and the advancements in it. The work presented also gives a perception of the state of research and identifies the potential of relatively new introduced elements to manufacturing.
Design and Development of Smart Machine Health Monitoring (SMHM) Device in an Automotive Manufacturing Organization
Authors: Singh Jagmeet , Ahuja I.P.S. , Singh Harwinder
Published: November 2022

The purpose of this research was to design and develop a machine Smart Machine Health Monitoring device (SMHM) for traditional manufacturing units based on the industry 4.0 (I4.0) concept. Machine tool performance has been demonstrated using metrics such as overall equipment effectiveness (OEE), mean time between failure (MTBF), and mean time to failure (MTTF). The rejection rate and unscheduled downtime were reduced by 68.04 percent and 46.47 percent, respectively, when using SMHM. The breakdown rate in the H-Shackle manufacturing section was reduced by 86.63%. The breakdown in a month with the development of SMHM has been reduced by 81.03 percent. The SMHM system improves MTTF and MTBF by 88.78% and 88.8%, respectively, and OEE by more than 80%. Overall H-shackle production has increased by 59.36%. As a result, the developed SMHM device was discovered to be consistent in increasing the efficiency of the manufacturing section. In the case organization, this system was implemented in the traditional machine tools unit.
Application of Public Transport Accessibility Level (PTAL) Study for Accessing Service Gap in Upcoming Metro Services: A Case Study of Jammu City
Authors: Nandan Rishi , Pandey Rishabh , Kumar Sagar , Kumar Susheel
Published: November 2022

Public transport is an essential service. It plays an important role in providing employment, education, and health care services, especially for people of low-income groups. Despite the availability of sufficient public transport, many times commuters face difficulties in accessing public transport, so efforts are being made by the government to improve access to it. The Union Territory of Jammu & Kashmir is planning to build the Jammu Light Metro system as part of a smart city mission to make good public transport available to commuters. But generally, there is a gap between the transit service and those who intend to avail of it. This paper is regarding a research study to access the futuristic service gap which will exist between Commuter and the Metro lite system in Jammu City using Public Transport Accessibility Level methodology. The methodology uses parameters like peak hour, bus frequency, reliability factor, walk distance, and walk speed to determine the public transport accessibility index. The obtained accessibility values have been linked to the base map to create heat maps showing accessibility levels throughout the municipal area in the form of colour codes representing different ranges of accessibility. Further heat maps have been analysed and recommendations for transportation planning have been made for policymakers.
Improvement in Heat Transfer in Microchannel Using Recharging
Authors: Verma Tanishq , Sharma Utkarsh , Kumar Vaibhav , Padhi Sumeet , Arora Akhilesh
Published: November 2022

The use of microchannel has increased in various fields such as microelectronic chips, which are cooled using a heat exchanger and other different purposes. The project is impacting on Nusselt’s estimated value by introducing a new charge through the second charging station and the flow of the main channel to increase the heat transfer rate in the integrated wall by performing mathematical simulations. First, the performance of the recharge concept is assessed by studying the representation of the 2-D model (i.e., the flow between the two plates) and then comparisons are made between the simple and charged case. Numerical analysis was performed for the 3-D case on three different microchannel geometries with two Reynold numbers (Re = 500,1000) and 11 different objects with different thermal conductivity values. In this project laminar flow is considered and the walls are considered adiabatic and continuous heat is supplied to the lower wall. It is noted that the heat transfer rate is increasing as Nusselt’s average value increases due to the introduction of a new charge. Therefore, the efficiency of the microchannel increases with the development of the concept of recharge. In line with this we look at the effects of axial conduction on the local Nusselt number. By varying the ksf (i.e., using different materials) it is found that high value of conductivity results in axial back conduction and which ultimately decrease the Nusselt number. Similarly, very low thermal conductivity also results in a low Nusselt number due to the fact that the heat distribution is converted to low ksf. Thus, we consider the largest ksf where we obtain the highest value of the Nusselt number by placing the graph between the two excess values. The effects of various geometry on the distribution of temperature and flexibility have also been analysed in this report. It therefore appears that conductivity is the main value as it affects the Nusselt number due to axial back conduction and the Nusselt number increases with re-charging.
Analysis of Mechanical Characteristics of Non-Pneumatic Tyres
Authors: Senadhi A. M. G. Binuri , Bhattacharjee Bodhita , Vipin
Published: November 2022

This research paper provides an analysis for the deformation and vertical stiffness of non-pneumatic tyres (NPT) due to different vertical loads. The non-pneumatic tyres are developed for replacing traditional pneumatic tyres. These tyres do not use air to support the load and subjected to hard conditions and rough terrain. The non-pneumatic tyres are used for heavy load-carrying capacity, vertical stiffness, contact pressure, and rolling. The mechanical characteristics of NPT are studied by using a 3D modelling software – Fusion 360 and analysed by using Finite Element Analysis (FEA) on the simulation software – Ansys.
Investigation of Effect of Geometry on the Linear Behavior of Helical Compression Spring for Valve Applications
Authors: Mishra Aadarsh , Barpande Girish S.
Published: November 2022

Helical compression springs are widely used in various assemblies to store energy. In valves, it is prominently used to hold the valve on its seat against the fluid pressure. Function of a relief valve depends mainly on the sealing force offered by the valve springs. The operational accuracy of a relief valve depends on the sensitivity and linearity of response of the spring against the fluid pressure. In the presented work sensitivity analysis of the spring used for the relief valve is attempted. Two springs of various coil diameters and wire diameter are designed using a MATLAB code. They are manufactured with Spring steel IS 4454 Gr. II material and tested for their accuracy to operate against set pressure. Both designs are tested by varying force and measuring corresponding deflection values. The degree of nonlinearity and sensitivity for both designs is quantified.
Experimental Study on Eco-Bricks
Authors: Goyal P.K. , Tarkar Saurav , Singh Rayan Shivam , Sharma Shrey
Published: November 2022

PET plastic bottles are a major contributor to plastic in all big cities, in this Research, we assess Eco-Bricks (a possible solution to plastic disposal) from an experimental point of view. This Research paper aims to assess the feasibility of using PET bottles as Eco Bricks, in-wall module material. This study suggests using Eco-blocks that are filled with single-use plastic waste as a posed solution for plastic disposal, with the liberty of reoccupying the filler material when advanced recycling choices become available. Using single-use plastic as filler material, this research presents an experimental characterization of density, thermal shrinkage, and compressive strength test, Because the suggested Eco-bricks’ density and elastic modulus are very close to those of EPS used in smaller structures, we believe, these bricks can be a better alternative to such construction materials. In the first experiment density of such Eco-Bricks was figured out using Archimedes Principle, further, we determine the thermal shrinkage of this newly posed construction material, Thermal Shrinkage helps us in gauging the performance of such bricks at higher temperatures, Lastly, the Compressive Strength Test was performed on Eco Bricks.
A Comparative Study of Two Different Wheel Load Models Used for the Estimation of Dynamic Responses of Bridges
Authors: Sudheesh Kumar C P  , Sujatha C  , Shankar K
Published: November 2022

Dynamic analysis of bridges under moving point loads is carried out in this paper. Two different wheel load models, model 1 with uniform space intervals and model 2 with non-uniform space intervals, in this study. Bridge responses due to single and multiple moving point loads are compared. Responses for both resonance and cancellation speeds are also estimated. Effects of damping, number of moving loads and their speed on the dynamic responses of the bridge using these two models are investigated. The objective is to make a comparative study of the two different wheel load models in terms of accuracy of the responses and computational easiness.
A Review on Machine Learning Techniques on IOT Based Privacy Preserving Network
Authors: Aggarwal Ankit , Arora Sneha
Published: November 2022

Expanded data collection and processing at greater rates have come from a growth in the number of devices connected to the internet, which is especially important now that the requirement for real-time action has increased significantly in recent years. It is becoming more difficult to finish data processing within an acceptable time frame as the variety and validity of data continues to grow in volume and quality. Because the data generated is supplied to a variety of different cloud data centres located all over the world, the current cloud architecture is a sub-optimal choice in these situations, according to the researchers. In this approach, implementing machine learning technologies into the present cloud environment may improve the overall efficiency and effectiveness of the cloud environment. In this article, works on machine learning and security in the Internet of Things platform are evaluated.
Histogram Computation with a Reconfigurable Memory Based Fast VLSI Architecture
Authors: Sai Ramesh M.N.N.L. , Anil Kumar R
Published: November 2022

Computation of histogram is the primary work that used in digital image processing systems. Mutual Information is the best measurement that also used for image registration. Calculation of the mutual information requires obtaining separate and combining histogram of the two images. By the increase of the histogram size, the demand for the hardware resource for the computation of the histogram is increased. This paper about the computation of the memory-based VLSI architecture has been shown. The architecture of the computation of the histogram is existed by using the plotting in the field programmable field array. Parallelization of histogram functions is a major problem due to memory collisions. So, to avoid these collisions there is a new technique called parallel histogram computation used. To implement the parallel histogram, it is requiring dual–ported memory. By making the computation of the histogram fast, there are several benefits obtained. Some of the examples are texture classification, image compression, etc shown in this manuscript.
Study of Speed Control Aspects of DC Motor
Authors: Lay Krishna Palem , Varalakshmi K. , Murthy K. V. S. Ramachandra
Published: November 2022

This paper provides different aspects of speed control of DC drive. Different control schemes of DC motor with proper explanation has been provided. Multi-pulse converter and firing angle control scheme has been discussed. Simulation results have been provided for the speed control aspect. It is possible to measure the speed of the motor with a tachometer or incremental encoder. These tachometers and incremental encoders produce erroneous results as a result of a variety of factors. the authors provide a strategy for compensating against imperfections in incremental encoders. In this paper, a study of several speed control systems for direct current motors is discussed in detail. This paper discusses many strategies for controlling the speed of a DC motor, including various speed control schemes. This study discusses and simulates multi-pulse converter-based DC motor speed control. The results of the simulation reveal that the motor can be controlled accurately in terms of speed. The speed of the motor can be measured with a tachometer or incremental encoder.
Expression and clinical significance of TYRP1, ABCB5, and MMP17 in sinonasal mucosal melanoma
Authors: Tu Junhao , Wang Jun , Tang Binxiang , Zhang Zhiqiang , Han Mei , Li Mengyue , Yu Jieqing , Shen Li , Zhang Meiping , Ye Jing
Published: November 2022

BACKGROUND: Sinonasal mucosal melanoma (SNMM) is a lethal malignancy with poor prognosis. Treatment outcomes of SNMM are poor. Novel prognostic or progression markers are needed to help adjust therapy. METHODS: RNA-seq was used to analyze the mRNA expression of tumor tissues and normal nasal mucosa from primary SNMM patients (n= 3). Real-time fluorescent quantitative PCR (qRT-PCR) was used to validate the results of RNA-seq (n= 3), while protein expression was analyzed by immunohistochemistry (IHC, n= 31) and western blotting (n= 3). Retrospective studies were designed to determine the clinical parameters and the total survival rate, and correlation between the protein expression levels of the most significant key genes and prognosis was analyzed. RESULTS: In total, 668 genes were upregulated and 869 genes were downregulated in SNMM (fold change ⩾ 2, adjusted p value < 0.01). Both mRNA and protein expression levels of the key genes in SNMM tumor tissues were higher than those in the normal control nasal mucosal tissues. The expression rates of TYRP1, ABCB5, and MMP17 in 31 primary SNMM cases were 90.32%, 80.65%, and 64.52%, respectively. In addition, age, typical symptoms, and AJCC stage were related to overall survival rate of patients with SNMM (p< 0.05). Furthermore, the expression of ABCB5 was age-related (p= 0.002). Compared with individuals with negative ABCB5 expression, those with positive expression exhibited significantly poor overall survival (p= 0.02). CONCLUSION: The expression levels of TYRP1, ABCB5, and MMP17 were significantly upregulated in SNMM tissues, and the expression of ABCB5 was related to poor prognosis in SNMM. Thus, ABCB5 may serve as a progression marker and can predict unfavorable prognosis in patients with SNMM.
Deep learning radiomics of ultrasonography for differentiating sclerosing adenosis from breast cancer
Authors: Li Chunxiao , Zhang Huili , Chen Jing , Shao Sihui , Li Xin , Yao Minghua , Zheng Yi , Wu Rong , Shi Jun
Published: November 2022

OBJECTIVES: The purpose of our study is to present a method combining radiomics with deep learning and clinical data for improved differential diagnosis of sclerosing adenosis (SA)and breast cancer (BC). METHODS: A total of 97 patients with SA and 100 patients with BC were included in this study. The best model for classification was selected from among four different convolutional neural network (CNN) models, including Vgg16, Resnet18, Resnet50, and Desenet121. The intra-/inter-class correlation coefficient and least absolute shrinkage and selection operator method were used for radiomics feature selection. The clinical features selected were patient age and nodule size. The overall accuracy, sensitivity, specificity, Youden index, positive predictive value, negative predictive value, and area under curve (AUC) value were calculated for comparison of diagnostic efficacy. RESULTS: All the CNN models combined with radiomics and clinical data were significantly superior to CNN models only. The Desenet121+radiomics+clinical data model showed the best classification performance with an accuracy of 86.80%, sensitivity of 87.60%, specificity of 86.20% and AUC of 0.915, which was better than that of the CNN model only, which had an accuracy of 85.23%, sensitivity of 85.48%, specificity of 85.02%, and AUC of 0.870. In comparison, the diagnostic accuracy, sensitivity, specificity, and AUC value for breast radiologists were 72.08%, 100%, 43.30%, and 0.716, respectively. CONCLUSIONS: A combination of the CNN-radiomics model and clinical data could be a helpful auxiliary diagnostic tool for distinguishing between SA and BC.
Neural network-based multisensor image fusion algorithm
Authors: Wang Liang
Published: November 2022

Aiming at the problems of high mean square error and low fusion efficiency of existing fusion algorithms, a neural network-based multi-sensor image fusion algorithm is proposed. The fusion algorithm based on depth-separable convolution neural network (CNN) is determined by analyzing the quality evaluation and fusion methods of multi-sensor images, and summarizing the fusion rules. It is found that the integrity of image information acquisition is 97%, the mean square error is 4, and the fusion time is 2 s. Therefore, the algorithm has a good image fusion effect.
A vector conversion method of building indoor space data based on attribute classification
Authors: Luo Zijiao , Xu Ying , Xue Song
Published: November 2022

Aiming at the problems of low data conversion efficiency, low accuracy and low data utilization rate after conversion in traditional methods, this paper proposes a vector conversion method for building indoor space data based on attribute classification. Firstly, the transformation process of data vectors is analyzed. Secondly, block matching detection and fusion recognition were carried out on the building interior space images, and fuzzy feature extraction method was used to optimize the collection and feature recognition of the building interior space data. Then, the attribute classification method is used to obtain the condition attribute and decision attribute of the data, and realize the building interior space data mining. Then, the K-means algorithm is used to cluster the indoor spatial data samples, and the wavelet transform method is used to de-noise the noisy data in advance. Finally, the obtained data is processed by vector transformation. The experimental results show that the data conversion efficiency of this method is high, and the data conversion accuracy and data utilization rate have been improved.