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IoT-Based Smart Mobility Systems refer to transportation systems that utilise the Internet of Things (IoT) technologies to enhance urban mobility's efficiency, safety, and sustainability in smart cities. These systems integrate IoT devices, such as sensors, cameras, and GPS trackers, with advanced data analytics and communication networks to create a connected, intelligent transportation infrastructure. The issue of traffic congestion has emerged as a significant global concern due to the expansion of cities in terms of both size and population. Smart mobility encompasses various technological advancements in transportation. This paper evaluates key mobility factors in IoT-based smart mobility systems to establish an efficient and eco-friendly urban ecosystem. The study evaluates various mobility factors such as traffic congestion, pollution level, energy consumption, public transport efficiency, user satisfaction and overall sustainability. The overall sustainability efforts to minimise environmental impact include reducing carbon emissions, energy consumption, and pollution. This research focuses on how IoT can transform urban transportation systems to be more efficient, sustainable, and beneficial to residents, thereby contributing to the broader goal of creating smart, sustainable cities. [ABSTRACT FROM AUTHOR]

Decision-making is crucial in fully autonomous vehicle operations and is expected to greatly influence future transportation systems. Observing the current driving status of autonomous vehicles is vital for its decision-making process. The autonomous connected vehicles on the road send significant data about their movements to the server to maintain continuous training. With the Proof of Authority (PoA) consensus process, blockchain technology provides a valid, decentralised and secure option to improve transactions throughput and minimise delay. The limited computational capacity of vehicles poses a challenge in achieving high accuracy and low latency while training self-driving algorithms. GPT-4V surpassed challenging autonomous systems in scene interpretation and causal thinking. GPT-4V has ability to navigate circumstances without access to database, interpret intentions, and make sound decisions in real-world driving scenarios. The reward function and different driving conditions are organised to allow an optimal search to find the most efficient driving style while ensuring safety. The consequences of the Blockchain-enabled decision-making model (DMM) for Self-Driving Vehicles (SDV) primarily based on GPT-4V and Federated Reinforcement Learning (FRL) would, likely, upgrades in decision-making accuracy, operational performance, statistics integrity, and potentially enhanced learning skills in SDV. Integrating blockchain technology, superior language modelling GPT-4V and FRL may lead to multiplied safety, reliability, and decision-making ability in SDV. This study utilised the Simulation of Urban MObility (SUMO) simulator to assess the ability of SDV to maintain its desired speed consistently and securely in a highway setting using proposed DMM. This study indicates that the suggested DMM, utilising the driving state evaluation approach for SDV, can help these vehicles operate safely and effectively. The performance of the proposed model, such as CPU utilisation, bandwidth and latency, are evaluated through multiple tests. [ABSTRACT FROM AUTHOR]

Recently in the field of vehicular communication, there has been a concentration of research on the integration of a vehicle-to-vehicle (V2V) network. With vehicle-to-vehicle (V2V) communication, users can directly exchange significant information with nearby vehicles. Typically, automobiles tend to travel at higher speeds on highways compared to roads at intersections. As a result, it is necessary to have a reliable system in place that can effectively and securely facilitate communication. In recent times, scientists have developed different methods for distributing information. However, these systems have various issues such as latency, reliability, mobility, and communication cost. Consequently, this results in a lack of dependability for real-time communication. Therefore, this study introduces a novel approach to Federated Learning (FL) by including the Chimp Optimization Algorithm (ChOA). Federated Learning is an approach in the field of machine learning that enables multiple devices or nodes to collaboratively train a model without the need for data exchange. In the area of vehicular communication, utilization of Federated Learning can be employed to develop a predictive model that estimates the trajectory of nearby vehicles by utilizing collected data. The Chimp Optimization Algorithm (ChOA) is designed to improve the model's efficacy. The proposed method aims to enhance the accuracy of the model's predictions regarding the conduct of nearby vehicles, while also reducing the amount of data exchanged between vehicles, by combining Federated Learning and Chimp Optimization termed FLECO. This method has the potential to enhance vehicular communication effectiveness and security, while also improving road safety and traffic management. Federated Learning facilitates the group control of a machine learning (ML) system by vehicles through the adjustment of model parameters. To enhance the energy efficiency of the system, the implementation of resource allocation and an energy-efficient algorithm is employed for Federated Learning, which integrates power and time allocation methods. This paper conducts a comprehensive analysis of the impact of enabling re-routing capabilities on (i) the mobility of vehicles and (ii) Networks for predicting traffic. To achieve this, utilize the SUMO simulator for road traffic to generate vehicle trajectories. Subsequently, we evaluate the vehicular network's connectivity employing established graph metrics. The developed system is simulated using the Python tool and experimentally validated, demonstrating its effective accuracy in vehicular communication. [ABSTRACT FROM AUTHOR]

A self-driving vehicle can navigate autonomously in smart cities without the need for human intervention. The emergence of Autonomous Connected Vehicles (ACVs) poses a substantial threat to public and passenger safety due to the possibility of cyber-attacks, which encompass remote hacking, manipulation of sensor data, and probable disablement or accidents. The sensors collect data to facilitate the network's recognition of local landmarks, such as trees, curbs, pedestrians, signs, and traffic lights. ACVs gather vast amounts of data, encompassing the exact geographical coordinates of the vehicle, captured images, and signals received from various sensors. To create a fully autonomous system, it is imperative to intelligently integrate several technologies, such as sensors, communication, computation, machine learning (ML), data analytics, and other technologies. The primary issues in ACVs involve data privacy and security when instantaneously exchanging substantial volumes of data. This study investigates related data security and privacy research in ACVs using the Blockchain-enabled Federated Reinforcement Learning (BFRL) framework. This paper provides a literature review examining data security and privacy in ACVs and the BFRL framework that can be used to protect ACVs. This study presents the integration of FRL and Blockchain (BC) in the context of smart cities. Furthermore, the challenges and opportunities for future research on ACVs utilising BFRL frameworks are discussed. [ABSTRACT FROM AUTHOR]

Cloud technology is a utility where different hardware and software resources are accessed on pay-per-user ground base. Most of these resources are available in virtualized form and virtual machine (VM) is one of the main elements of visualization. In virtualization, a physical server changes into the virtual machine (VM) and acts as a physical server. Due to the large number of users sometimes the task sent by the user to cloud causes the VM to be under loaded or overloaded. This system state happens due to poor task allocation process in VM and causes the system failure or user tasks delayed. For the improvement of task allocation, several load-balancing techniques are introduced in a cloud but stills the system failure occurs. Therefore we hybrid modified artificial bee colony for improvement in task allocation of VM and minimizing time consummation in cloud datacenter like makespan, total processing time, response time of algorithms, response time of datacenter and degree of imbalance. The consequences of the proposed task-scheduling algorithm are matched with existing heuristic-based scheduling procedures. The experimental consequences demonstrate that our approach is efficient when it is compared with the existing algorithms and reduce 1.7% in time consumption for cloud datacenter. [ABSTRACT FROM AUTHOR]

The rapid explosion of Internet of Things (IoT) devices has resulted in extraordinary data generation, necessitating advanced methods for data processing and privacy preservation. The IoT seeks to enable seamless communication between any object or device and any service provider without any time constraints. On the other hand, IoT devices might expose users to numerous privacy and security risks. The primary goal of privacy-preserving methods is to ensure the confidentiality and integrity of all data transfers. Blockchain technology ensures the authentication and prevention of any duplication of sensor data, hence removing any inaccurate information. Sensors can employ blockchain technology to communicate data autonomously, eliminating reliance on a trusted intermediary. The IoT devices collect and use personal data, giving rise to privacy concerns. Users may need to comprehend the full scope of data collected and its intended utilisation. The user may also be unaware that their information could be revealed to third parties. The Federated reinforcement learning (FRL) framework provides effective methods for guaranteeing the security of data and models in IoT systems. Blockchain ensures data security during data transmission among IoT devices. FRL integrates federated learning (FL) and reinforcement learning (RL) that guarantees the privacy of both the models and the data. FRL addresses issues with agents, safeguards privacy, ensures the confidentiality of agents, and produces global models. Local models are trained on separate devices to fulfil data privacy goals. In this paper, the authors have explored the B-FRL model in IoT systems to provide privacy protection services. [ABSTRACT FROM AUTHOR]

Blockchain is a disseminated open record that is utilized to record the exchange across numerous Personal computers. Blockchain innovation can be applied in any space like banking, medical services, land, travel, food, and store networks. In food products, halal food is served throughout the world. Halal does not only belong to Muslim people it's for everyone globally. Halal means cleanliness of this food product.so, ensure the food products are free from contamination of halal food with other food products. The smart contract was created in the halal food supply chain to ensure consumer trustworthily. Tracking and tracing the halal products in the supply chain from producer to consumer we implement some special tags (RFID/QR Code). These special tags track the food products from farm to consumer and vice versa tracing the halal food from consumer to producer to reduce the contamination of food items with halal food items. [ABSTRACT FROM AUTHOR]

To execute seamless computing along with information sharing, Cloud Computing (CC) plays an important role in measurable resource sharing. But, owing to its open and distributed framework, privacy is a major risk as it is open to attackers. To accomplish their malicious goals, cloud services are abused by intruders. In this paper, using Linear Sigmoid Singleton Deep Neural Network (LS2DNN)with Pearson correlation and Brownian motion induced K-Anonymity (PBKA)centered lightweight privacy preservation technique in the cloud server, an efficient federated learning-based Intrusion Detection System (IDS) is proposed. From the publically available sources, NSL-KDD datasets are downloaded; also, Feature Extraction (FE), null feature removal, and feature mapping are executed. Subsequently, for feature selection, Chebyshev Chaotic Mapping adapted Jaya Optimization Algorithm (C2MJOA) is wielded. Then, for classifying attacked and normal data, the LS2DNNclassifier is utilized.The incoming data from the data owners are privacy preserved via federated learning-centered Pearson correlation and Brownian motion-induced K-Anonymity (PBKA) while testing; moreover, the data is verified for intrusion. Finally, when compared with the prevailing models, the proposed model attained better results. [ABSTRACT FROM AUTHOR]

Numerous gadgets are linked together globally by the Internet of Things (IoT). Health checking, exercise programmers and remote medical assistance are a few examples of emerging areas in the healthcare system. Implementing cloud computing functionality on edge devices is the constant goal of fog computing. The approach is anticipated to surpass the minimum latencies requirement when used with Internet of Things (IoT) medical equipment. IoT devices produce different amounts of healthcare data. Due to the enormous volume of data produced, networks get overloaded, increasing delay. Traditional cloud servers are unable to meet the low latency requirements of IoT medical equipment and consumers. IoT data transfer, it is therefore vital to reduce network latency, computation delay, and energy consumption. Using FC, data can be stored, processed, and analyzed. Cloud computing data is located at a network edge to reduce high latency. Here, a novel resolution to the problem mentioned earlier is proposed. It combines an analytical model with a hybrid fuzzy-based reinforced learning technique in an FC setting. The objective is to reduce energy usage and cloud server latency for health-care IoT. The Internet of Things-FC context is selected and placed by the proposed smart FC analysis technique and algorithm using a fuzzy inference system, optimization techniques, and development approaches. The results showed that our suggested strategy reduced latency by 1.2% in comparison to other techniques. [ABSTRACT FROM AUTHOR]

The convergence from the electric grid to the smart microgrid motivates the incorporation of the intrusion detection system to identify intruders and mitigate the resultant damages to ensure system stability. It is planned to employ the Deep Belief Network (DBN), which is one of the deep learning techniques with some improvement to detect the attacks in a microgrid. To improve the accuracy of the detection, a rule-based detection technique is added to enhance the detection of intruders using DBN. The proposed technique is supported with the layered micro-grid architecture that makes the system flexible and simple toward the implementation. The proposed Enhanced DBN (EDBN) performance is measured in different bus representations for identifying the higher hit rate and rejection rate, lesser miss rate and false-positive rate. Two attacks, such as False Data Injection and Denial of Service attacks, are generated by Greedy Algorithm and are detected by the proposed technique. Compared to the existing detection and control system, the proposed EDBN technique provides accuracy higher than 92%, false alarm rate less than 1%. Thus, the experimental results show that the proposed technique accuracy is higher than the existing intrusion detection techniques in a microgrid. [ABSTRACT FROM AUTHOR]

Healthcare and technology have a long history of interaction but eHealth adoption has been delayed due to a lack of infrastructure, capacity, and political will. It is called a health information technology system (HITS) or smart health system (SHS) when healthcare adopts health technology. Customers should expect improved service in terms of efficiency and cost after the system's modifications have been implemented. Exclusively after the COVID-19 pandemic, it was the test case of eHealth system worldwide, but the eHealth system is in its initial stage and facing different kinds of issues. Technology such as cloud computing and IoT enables HITS services to be delivered via the internet securely and scalable. Pay-as-you-go is the basis for this type of eHealth model, which various healthcare industries use to meet present and future demand by utilizing various frameworks. Cloud computing and the internet of things (IoT) are key components of an eHealth system described in this review paper. We also discuss the various parameters that make up this system's structure. After summarizing several research works based on IoT and cloud computing in eHealth, we describe challenges that still limit the efficiency and indicate future prospects for tackling these problems to further increase the development of eHealth systems in the next few years. [ABSTRACT FROM AUTHOR]

Smart cities utilize Internet of Things (IoT) devices including connected sensors to collect data that may be used to enhance public services, infrastructure, and service delivery. The full promise of smart cities cannot be realized, however, until serious security concerns are handled. The security of any given communication system is heavily dependent on its access controls. To alleviate the strain of access control on low-capacity devices, delegated authorization mechanisms are crucial in communicating the challenges of IoT devices. New user-centric authorization procedures that guarantee user privacy without compromising scalability or efficiency are necessary before IoT devices can be used in smart cities. Blockchain could serve as a technology that enables many different applications within the context of smart cities. Rapid data collection from a plethora of wireless sensors paves the way for big data analytics to quickly take in the foundational visuals required for smart city resources, supply, and infrastructure management. Blockchain and big data are two of the most exciting new technologies that can be used effectively with their integration. Implementing the access control solution for exchange the of data among IoT devices in smart cities is a challenging task. We introduce a novel architecture for managing access to Bigdata using blockchain technology, which could be used in smart cities. Our proposed access control system will allow a resource owner to securely delegate authorization to other parties beyond the smart city's trust boundary. The article describes the architecture and implementation of the proposed blockchain and big data system, as well as its access control and security features. Our testing on private Ethereum has shown that our solution is straightforward to implement in low-latency environments. This proves our proposed solution's applicability to real-world IoT scenarios in the smart city environment. [ABSTRACT FROM AUTHOR]

Blockchain and deep reinforcement learning (DRL) are two separate transaction systems committed to the credibility and usefulness of system functionality. There is rapid growth importance in integrating both technologies into effective and stable information exchange and research solutions. Blockchain is a revolutionary platform for future generational telecommunications networking that will set up the secured and distributed information exchange framework. In combination with DRL, blockchain could significantly improve the efficiency of mobile communications. The rapid growth of networks for the Internet of Things (IoT) necessitates the development of suitable and reliable infrastructure as well as a significant proportion of the information. Blockchain, a distributed and reliable ledger, is often considered a considerably beneficial means of providing scientific confidentiality and security to IoT devices. When interacting with massive IoT records, the Blockchain's decrease becomes a significant issue. Thus, it is necessary to improve transaction performance and deal with massive data transmission situations. As a result, the work presented here explores the DRL fundamental operation of the blockchain-enabled IoT system, where transactions are simultaneously strengthened, and community-based divisibility is ensured. Throughout this paper, the authors first present the decentralised and efficient structure for communication by incorporating DRL and blockchain across wireless services that allow for the scalable and reliable allocation of information. The results show that the proposed method has shorter delays and requires less transmission power. [ABSTRACT FROM AUTHOR]

Protecting data against tampering is a significant concern in modern times. Digital photographs are essential for displaying information. Digital picture forgeries include adding unusual patterns to real pictures, causing visual heterogeneity. CMF is a sort of digital image forgery in which a segment of an image is linked to a similar picture to cover or recreate forgeried components. The forgery seems authentic since the goal area has the same qualities as the original. Despite several ways to detect CMFD, there exist research gaps such as false detection, excessive execution time and low accuracy. Therefore, to address this issue, we present a hybrid optimization technique and classifier for CMFD. Proposed a novel deep learning technique stacked sparse denoising autoencoder (SSDAE) to classify the images as legitimate or fake. Additionally, the weight and bias parameters of the SSDAE model are optimized using the Grasshopper Optimization Algorithm (GOA) and the Spotted Hyena optimizer (SHO). The experiments are conducted on MICC-F220, MICC-F600, MICC-F2000 and CASIA2.0 datasets. Experimental results indicate that the proposed scheme find out image forgery region with Accuracy = 97.45%; Precision = 98.75%; Recall = 98.25% and F1 = 98.55% on MICC-F200 dataset, Accuracy = 98.92%; Precision = 88.45%; Recall = 85.21% and F1 = 91.41% on MICC-F600 dataset, Accuracy = 99.12%; Precision = 99.25%; Recall = 91.14% and F1 = 85.32% on MICC-F2000 dataset and Accuracy = 98.02%; Precision = 96.03%; Recall = 97.74% and F1 = 97.48% on CASIA 2.0 dataset. [Extracted from the article]

This research focuses on the development of paper from lignocellulosic agricultural wastes, viz., corn husk (CH), which is an underexplored material and sugarcane baggase (SB), in varying proportions, through soda pulping and enhancement of their functionalities through chitosan and beeswax–chitosan emulsion coatings. Fiber digestion was done at optimized conditions; followed by blowing, quenching and then refining of both treated CH and SB pulp to a Canadian Standard Freeness (CSF) of 400–450 mL. The handsheets of 80 GSM (grammage) were prepared as per the standard ISO-5269/1 and were tested for their mechanical and barrier properties as per standard methods of ISO. Handsheets developed from the blend of SB and CH (50:50) and SB fibers (100%) were found to have better mechanical strength (in terms of burst, tensile and tear strength) in comparison to CH fibers (100%). The effect of coatings on mechanical, water resistance, micro-structural, and biodegradable properties of the cellulose papers were also assessed. The chitosan coating significantly improved (p < 0.05) the mechanical properties of papers, the barrier properties against water vapor, moisture and air were also enhanced (up to 85%). Papers coated with beeswax–chitosan emulsion had the longest absorbency time, followed by chitosan-coated and uncoated papers. The results advocated for beeswax–chitosan emulsion as the best among the coatings tested, for aforementioned cellulose papers to enhance their barrier properties. [ABSTRACT FROM AUTHOR]

A novel organic acid-modified starch and carboxymethyl cellulose (CMC) based films plasticized with glycerol were prepared from unconventional tikhur starch (Curcuma angustifolia) by solution casting. Wet milling was used in the laboratory to extract starch from the tikhur rhizome. Carboxymethyl cellulose, at a concentration of (0.2 g−1 starch dry basis) was blended with the starch to improve its film-forming properties. Three different treatments with varying organic acids (lactic, citric, and acetic acid) with a concentration of 5% w/w of starch (2 ppm) in a film-forming solution were given. The effect of organic acid incorporation on the antibacterial, morphological, structural, thermal, and crystalline properties of developed films was studied. The minimum inhibitory concentration values of the three organic acids against gram-negative (E. coli) and gram-positive (S. aureus) bacteria were measured using the tube dilution method. The MIC results revealed that lactic acid and citric acid are effective against both gram-negative and gram-positive bacteria, while acetic acid showed more effectiveness against gram-negative bacteria (E. coli). MBC results revealed that organic acids have potent bactericidal activity. Citric acid resulted in higher inhibition for gram-positive bacteria (S. aureus) compared to gram-negative bacteria (E. coli.). While acetic acid showed higher inhibition for E. coli. than S. aureus. Lactic acid displayed similar inhibition against both S. aureus and E. coli. Among different organic acids, lactic acid incorporation resulted in a more homogeneous, transparent, and thermally stable film. As evidenced by the micrographs, the lactic acid incorporation resulted in a compact film structure without any visible cracks. While X-ray diffraction showed an increase in crystalline properties due to organic acid modification. In this study, it was indicated that modification with organic acids (polycarboxylic acids) effectively improved the overall properties of developed films depending on the type of organic acid used. The developed films have the potential to replace harmful synthetic films in food packaging. [ABSTRACT FROM AUTHOR]

Advances in technology always had an impact on our lives. Several emerging technologies, most notably the Internet of Things (IoT) and blockchain, present transformative opportunities. The blockchain is a decentralized, transparent ledger for storing transaction data. By effectively establishing trust between nodes, it has the remarkable potential to design unique architectures for most enterprise applications. When it first appeared as a platform for anonymous cryptocurrency trading, such as Bitcoin, on a public network platform, blockchain piqued the interest of researchers. The chain is completed when each block connects to the previous block. The Internet of Things (IoT) is a network of networked devices that can exchange data and be managed and controlled via unique identifiers. Automation, wireless sensor networks, embedded systems, and control systems are just a few of the well-known technologies that power the IoT. Converging advancements in real-time analytics, machine learning, commodity sensors, and embedded systems demonstrate the rapid expansion of the IoT paradigm. The Internet of Things refers to the global networking of millions of networked smart gadgets that gather and exchange data. Integrating the IoT and blockchain technology would be a significant step toward developing a reliable, secure, and comprehensive method of storing data collected by smart devices. Internet-enabled devices in the IoT can send data to private blockchain networks, creating immutable records of all transaction history. As a result, these networks produce unchangeable logs of all transactions. This research looks at how blockchain technology and the Internet of Things interact to understand better how devices can communicate with one another. The blockchain-enabled Internet of Things architecture proposed in this article is a useful framework for integrating blockchain technology and the Internet of Things using the most cutting-edge tools and methods currently available. This article discusses the principles of blockchain-based IoT, consensus methods, reviews, difficulties, prospects, applications, trends, and communication between IoT nodes in an integrated framework. [ABSTRACT FROM AUTHOR]

Smart cities would depend on digital technologies that have the potential ahead where Blockchains would perform significantly. Blockchains has become popular because it offers adequate security than other solutions for a wide range of purpose. Blockchains can support the growth of smart cities due to their robust features like transparent, reliable, authenticate, and decentralized features. At present, big cities are utilizing sophisticated technologies with knowledge platforms to evaluate the quality of life of their residents. Society participation and dedication grow when humans are even more committed to responding with and being adaptable at some stage in their metropolis authorities and responding in an established way to personal problems. Big data, IoT, and Blockchain would accelerate and extend its government services, capacity, and even reduces costs and provides exciting opportunities like transportation services, exchanging services, healthcare services, etc. This study shows the reviews and importance of Blockchain in futuristic smart cities. [ABSTRACT FROM AUTHOR]

This research presents a crow search algorithm (CSA) for reducing the cellular network cost when reporting the cell planning (RCP) scheme. In cellular systems, the RCP scheme is used to maintain location. We employ CSA as an optimization tool because it is a bio-inspired optimization technique. In this study, CSA uses the cellular network's diversity to optimize the cost of reporting the RCP scheme. The cost of location management is calculated using dynamic awareness probability (DAP) and a CSA for various cellular network sizes. With each iteration of the CSA, the dynamic properties of the DAP are used to change the decision threshold. This provides additional freedom and enhances decision-making abilities. As a result, the set awareness probability allows for a cheaper cost per call arrival. Extensive simulations are used to test and evaluate the suggested method's performance. The experiments are carried out with 4 × 4, 6 × 6, and 8 × 8 cells in current cellular systems. The recommended CSA is used to measure performance in groups of 50, 100, 150, and 200 people. Multiple graphs displaying statistical measurements, convergence rates, and other data are used to present the conclusions. It was determined that scaling up from a smaller to a larger network lowers the cost per call arrival by about 12%. This shows a possible vision of the proposed CSA's vast range of uses and needs more research to improve existing cellular services. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) networks are rapidly expanding, which requires adequate and reliable infrastructure and a large amount of data. The IoT device security and technical confidentiality may benefit from using Blockchain, a decentralised and trustworthy ledger. Increasing transaction throughput and coping with big data transfer situations is critical when dealing with significant volumes of IoT data on the Blockchain. Consequently, this research investigates the Deep Reinforcement Learning (DRL) crucial functioning of the blockchain-enabled IoT structure, wherever transactions are instantaneously expanded and public divisibility is confirmed. It is important to note that DRL and Blockchain are two separate advancements devoted to the reliability and usefulness of system operation. These are both transactional systems. Technology integration into information exchange and research solutions is becoming increasingly critical. As a result of Blockchain, information may be exchanged securely and decentralised. When used in tandem with DRL, it can significantly improve communication efficiency. By combining DRL and Blockchain throughout the IoT, the author first presents a decentralised and efficient communication structure that allows for scalable and trustworthy information allocation and better performance than earlier options. The DRL approach assesses whether to offload and which service to dump to improve performance up to 87.5%. Furthermore, this method focuses on constructing an effective offloading mechanism for Blockchain-based communication systems to boost performance. [ABSTRACT FROM AUTHOR]

Federated learning (FL) is a cutting-edge artificial intelligence approach. It is a decentralized problem-solving technique that allows users to train using massive data. Unprocessed information is stored in advanced technology by a secret confidentiality service, which incorporates machine learning (ML) training while removing data connections. As researchers in the field promote ML configurations containing a large amount of private data, systems and infrastructure must be developed to improve the effectiveness of advanced learning systems. This study examines FL in-depth, focusing on application and system platforms, mechanisms, real-world applications, and process contexts. FL creates robust classifiers without requiring information disclosure, resulting in highly secure privacy policies and access control privileges. The article begins with an overview of FL. Then, we examine technical data in FL, enabling innovation, contracts, and software. Compared with other review articles, our goal is to provide a more comprehensive explanation of the best procedure systems and authentic FL software to enable scientists to create the best privacy preservation solutions for IoT devices. We also provide an overview of similar scientific papers and a detailed analysis of the significant difficulties encountered in recent publications. Furthermore, we investigate the benefits and drawbacks of FL and highlight comprehensive distribution scenarios to demonstrate how specific FL models could be implemented to achieve the desired results. [ABSTRACT FROM AUTHOR]

This work aimed to develop biobased composite active edible film of tapioca starch‐κ‐carrageenan reinforced with Pimpinella anisum essential oil and to evaluate the effect of reinforcement on physico‐mechanical, barrier, optical, and antimicrobial properties. Nineteen different components of oil were identified with distinct chromatogram peaks using Gas Chromatography–Mass Spectroscopy. The Minimum Inhibitory Concentration value of essential oil against standard test bacteria ranged between 0.6% and 1%. The dynamic oscillatory rheological measurements of film‐forming solution revealed solid‐like viscoelastic behavior. Incorporation of oil in the film caused significant (p < 0.05) increase in moisture absorption, b value, total color difference (∆E) haziness, transparency, and significant (p < 0.05) decrease in elongation at break, moisture, whiteness index, and L* value. Antimicrobial functionality by disc diffusion of treated films was found highly significant (p < 0.01). The results of the study provide a promising potential of composite active edible film with enhanced antimicrobial functionality. Novelty impact statement: The new‐fangled bio composite blend is functionalized with Pimpinella anisum essential oil.The study originally demonstrates the effect of incorporation of P. anisum essential oil on the quality and acceptability of unique bio‐composite edible film.The formed composite active edible film can be commercially exploited to provide better quality and safety to various foods with eco‐friendly disposal. [ABSTRACT FROM AUTHOR]

Federated-Learning (FL), a new paradigm in the machine-learning approach, wherein the clients train the global model collaboratively across various computational distributed units. The participants of the FL-networks performs communication with the centralized server without the exchange of sample data. This mechanism permits the users to obtain the richer global model performing training upon the larger data points. In this study, various researches of federated learning in distributed environment have been analysed. The Federated-learning framework model is implemented in centralized, decentralized and heterogeneous approach. Further, the privacy of the data collaborations and maintenance of secured framework in FL is focused. Differential-privacy technique is highly concentrated in various researches as the standardized method for mitigating those privacy risks. In some FL models, such as DRL-Deep reinforcement learning model is evolved for assisting the edge computing in a distributed environment, are highly focused in various studies. FedGRU-algorithm for traffic-flow prediction, non-IID, non-balanced, sparse and distributed attributes of federated-optimization is also analysed. The federated-learning framework contributes to obtain the global model in distributed systems handling heterogeneous resources in certain researches. The latter section of the paper demonstrates the critical analysis of the study, and the parameters relying upon the federated learning model were analysed. [ABSTRACT FROM AUTHOR]

The article titled "Latency aware smart health care system using edge and fog computing" has been retracted by the Editor-in-Chief and the publisher due to concerns identified during an investigation. These concerns include unsupported citations, non-standard phrasing, and image irregularities. The Editor-in-Chief no longer has confidence in the results and conclusions of the article. One of the authors, Arif Ullah, disagrees with the retraction, while the publisher has been unable to contact the other authors, Saman Yasin and Tanweer Alam. Springer Nature, the publisher, remains neutral regarding jurisdictional claims and institutional affiliations. [Extracted from the article]

The COVID-19 is a highly contagious infection that has already had a catastrophic effect on the most severe diseases, including death, worldwide. Blockchain-based healthcare systems are being introduced to help simplify mobile and telehealth services, reducing patient stress and the cost of critical clinical services. Shared the advantages of blockchain for building a cutting-edge authentication infrastructure and detecting COVID-19 suspicious cases. The authors presented a blockchain-based design for developing a real-time cellular health monitoring system for COVID-19 patients in this paper. This study identifies clinical problems and electronic diagnoses for people with COVID-19 infectious diseases and provides a framework for them. Any mobile application can be configured on digital devices such as smartphones. COVID-19 patients may benefit from such applications. Smartphone apps are designed to save time and money while increasing the efficiency of infectious patients. IoT and Blockchain strategies are presented in the four-layer structure. [ABSTRACT FROM AUTHOR]

The tracking system is used to track moving individuals and provide an instant timeline of position information for tracking analysis. People Tracking System in the crowd for Smart Cities is a mobile application that allows parents to monitor the location of their children in a crowded environment. Besides children, there are also elderly and disabled people, so the person responsible for them can use this application to track their location. The parents have the application in which they can track the location and on the other hand, the child or the old person or the disabled person has a device that contains the Global Positioning System chip. The main objective of this study is to design an application with the system that will help the parents to track their children to reduce the cases where the children or the other mentioned categories of people might get lost. The current solution to this problem is that the children first have a wearable wrist where they imprint the phone number of their parents so that if the child is lost, there is an office where the child is taken and taken care of until they contact the parents to come and pick up the child. The problem with the current way that it takes time and there is a risk that child will be lost or kidnapped before ever reaching for any help, so the new way is better to also prevent them from going far away or being lost for hours, so the recovery here will be quick unlike the regularly used way today. This goal will be achieved by systematic objectives, starting from studying the existing systems, planning, and analysing, designing, and implementing, and finally testing the proposed system. [ABSTRACT FROM AUTHOR]

A field experiment on screening of different germplasm of maize with different maturity period against stem borer, Chilo partellus was carried out at research farm, Tirhut College of Agriculture, Dholi during Kharif 2019. The present investigation determined the resistance or susceptibility of different genotypes corn against maize stem borer attack. The present experimental result revealed that fifteen genotypes of early maturity, thirty nine genotypes of medium maturity, eighteen genotypes of late maturity, twenty genotypes of QPM, twenty genotypes of baby corn, eighteen genotypes of sweet corn, seven genotypes of rain-fed medium maturity, and nine genotypes of rain-fed late maturity groups of various genotypes of maize were found a moderate resistant to stem borer damage and also observed that among all maize germplasm of different maturity groups, thirty germplasm of maize were susceptible to stem borer infestation. [ABSTRACT FROM AUTHOR]

Single-use plastic (SUP) being a versatile material, is adopted as an alternate to traditional materials specifically for the use in food packaging due to its inherent characteristics like high durability, inertness, and protecting ability but has become a curse for living being today due to its random usage and unplanned rejection to nature. Mostly plastics used in packaging of beverages, fresh meats, fruits and vegetables are under concern today. Single-use packages result in generation of several billion tons of garbage till date, which pollutes the environment. At the immediate past, it has come to light that micro plastics obtained due to slow degradation of SUP present in oceans, are also being consumed by marine organisms such as fishes and shellfish species which disturbs the marine life extensively. Hence, finding right strategy to mitigate the plastic waste related issues has becoming inevitable today. This review paper briefs various strategies undertaken worldwide to mitigate the pollution due to generation of plastic waste. Various notable impact of adopted strategies and recent innovations to replace the SUP products are also discussed and in view of this a roadmap is also suggested which can be used to achieve the milestone of Zero Plastic Waste. [ABSTRACT FROM AUTHOR]

Linear low-density polyethylene (LLDPE), biaxially oriented polypropylene (BOPP), polyethylene terephthalate (PET) and Nylon 6 films used for food packaging application were subjected to e-beam (EB) irradiation at different doses in the range of 0 to 80 kGy. The mechanical, barrier, optical and thermal properties of the irradiated films were evaluated and compared with the non-irradiated films. A notable decrease in oxygen transmission rate (OTR) was observed for BOPP and Nylon 6 specimens after irradiation, whereas for PET samples, no significant change was noticed. Water vapour transmission rate (WVTR) was found to exhibit a decreasing trend for PET and Nylon 6 samples but remains unchanged for BOPP after EB irradiation. Tensile strength (TS) of Nylon 6 and LLDPE films was found to increase upon irradiation up to 20 kGy, whereas for BOPP, a clear decreasing trend was observed. No significant change in TS and elongation at break was observed for PET film after irradiation. The melting temperature (Tm) and enthalpy of melting (ΔHm) were decreased with EB dose as revealed by differential scanning calorimetry (DSC) studies. [ABSTRACT FROM AUTHOR]

Residues and by-products are the unavoidable outputs of cereal, sugarcane and dairy processing industry which can be resorted economically. Widespread focus on plastic replacement, residue burning and waste reduction had led to characterization and identification of by-products for economic packaging applications. Extensive research work had been done on whey usage in eco-friendly packaging but packaging applications of cereal crop residues, cereal milling by-products and sugarcane bagasse are still in nascent stage. However, research studies on extraction and exploitation of cellulose, hemicellulose and other film forming components from these by-products had been reported for packaging applications but described processes have low techno-commerciality. Current status and innovations in utilization of cereal residue (rice straw, wheat straw and corn cob), cereal milling by-products (husk, hull and bran), sugarcane processing waste (bagasse and sugar beet pulp) and whey based packaging applications as well as application in bio-composites, are discussed briefly in this review. [ABSTRACT FROM AUTHOR]

In this paper, an effort has been made to compile various research investigations done so far on fast response gauges for the short duration measurement in the transient state. The review paper is not only about summarization of all-important research work; rather, a detailed review on the development of the fast response gauges, its application, and various methods of heat estimation from the measured transient temperature history is also presented. Before discussing the development of fast response gauges, various heat flux measurement techniques and newer trends in heat transfer gauges are presented. The focus of this Review is mainly on the thin-film gauge, a robust fast response temperature detector best suited for the short duration measurement. The literature on the application of thin-film gauges for various engineering systems such as turbomachinery and aerodynamic heating systems is broadly categorized as single-layered and multi-layered gauges. The applications for the internal combustion engine and low heat flux measurement are also discussed later. The summary tables with studies arranged in chronological order are also provided. Finally, the estimation of the heat transfer rate from the measured transient temperature data is presented. [ABSTRACT FROM AUTHOR]

Heat transfer analysis is the one of the most important designing aspects for many engineering systems. The design prospect in the preview of heat transfer focuses on the prediction of heat flux with the help of measured transient temperature data. Thin film gauges are one of the most predominant method for the heat flux prediction especially for short duration transient temperature measurement. Thin film gauges are usually exposed to the heated environment for the measurement purpose. However, there are some prominent research areas like ablation phenomenon met to spacecraft thermal shields during re-entry to the atmosphere, for which direct exposure of the thin film gauge to the heated environment causes the functional and working difficulties associated with the gauge. In the present study, it is aimed to investigate the suitability of thin film gauge for the conduction-based short duration measurement. An experimental set up is fabricated, which is used to supply the heat load to the hand-made thin film gauge using platinum as sensing element and quartz as a substrate. The transient temperature data is recorded during experiment is further compared with the simulated temperature histories obtained through finite element analysis. The heat flux estimation for both the analysis is made using measured transient temperature data by convolute integral of one- dimensional heat conduction equation. The estimated heat flux value for the experimental and numerical result is found to be in excellent agreement. [ABSTRACT FROM AUTHOR]

The present study aimed at developing composite edible film from a blend of Ipomoea batatas, κ‐carrageenan, and glycerol. Three different treatments by varying glycerol concentrations (5%‐TS1, 10%‐TS2 & 15%‐TS3) in film‐forming solution were prepared and resultant films were evaluated for rheological, physicomechanical, barrier, and optical properties. All the treatments showed film‐forming ability with varying degrees of distinction. The steady rheology of film‐forming solution containing 10 and 15% glycerol in gel state showed non‐Newtonian fluid behavior. Film solubility improved significantly (p <.01) with glycerol; however water vapor permeability of films had an intermediate value, comparable to other films reported in literature. Mechanical properties of films were excellent in terms of tensile strength, which reached 15 N/mm2. The films presented good optical properties relating to hunter colorimeter. Therefore, the composite blend of I. batatas and κ‐carrageenan can be tailored with glycerol concentration to apply as edible film over different foods. Practical applications: The demerits associated with plastics have eventually led researchers' to explore natural alternatives. Edible films are one of the prospering substitutes, but the films prepared from single polymer films are lacking mechanical strength. The composite blend of Ipomoea batatas and κ‐carrageenan is having promising competence to be mold as packaging film with enhanced physicomechanical properties. Glycerol concentration is having an undeviating impact on physicomechanical properties of the film. Therefore, film processing protocol can be stringently controlled to have product‐specific films for practical commercial applications. [ABSTRACT FROM AUTHOR]

Nowadays genetic algorithm (GA) is greatly used in engineering pedagogy as an adaptive technique to learn and solve complex problems and issues. It is a meta-heuristic approach that is used to solve hybrid computation challenges. GA utilizes selection, crossover, and mutation operators to effectively manage the searching system strategy. This algorithm is derived from natural selection and genetics concepts. GA is an intelligent use of random search supported with historical data to contribute the search in an area of the improved outcome within a coverage framework. Such algorithms are widely used for maintaining high-quality reactions to optimize issues and problems investigation. These techniques are recognized to be somewhat of a statistical investigation process to search for a suitable solution or prevent an accurate strategy for challenges in optimization or searches. These techniques have been produced from natural selection or genetics principles. For random testing, historical information is provided with intelligent enslavement to continue moving the search out from the area of improved features for processing of the outcomes. It is a category of heuristics of evolutionary history using behavioral science-influenced methods like an annuity, gene, preference, or combination (sometimes refers to as hybridization). This method seemed to be a valuable tool to find solutions for problems optimization. In this paper, the author has explored the GAs, its role in engineering pedagogies, and the emerging areas where it is using, and its implementation. [ABSTRACT FROM AUTHOR]

The present study was aimed to develop Manihot esculenta and Carrageenan bio‐based composite active film functionalized with anise, caraway, and nutmeg essential oils (EOs) and to assess the shelf life of chicken nuggets wrapped with the developed film at refrigeration storage. Overall, the Minimum Inhibitory Concentration (MIC) values of the three EOs ranged from 0.4 to 0.8% v/v of which nutmeg EO was found most effective. Incorporation of EOs in the film resulted in significant (P ˂ 0.05) decrease in tensile strength and water activity while elongation at break was significantly (P ˂ 0.05) increased. No significant (P ˃ 0.05) changes in thickness and Water Vapor Transmission Rate (WVTR) were observed. Based on physicomechanical and sensory evaluation, films incorporated with 0.5, 1, and 1% concentration of anise, nutmeg, and caraway EO were selected. Chicken nuggets overwrapped with aforementioned films were stored aerobically at refrigeration temperature (4 ± 1 °C) to evaluate antimicrobial, antioxidant, and sensory characteristics. The result indicated that pH, peroxide, free fatty acid (FFA), and thiobarbituric acide (TBA) value of treatments were significantly (P ˂ 0.05) lower than controls however significantly (P < 0.05) higher DPPH activity was observed in all treatments. The total plate count, psychrophilic count and, yeast and mold count were also significantly (P ˂ 0.01) lower in treatment groups and were within the permissible limits. The treated samples were well acceptable during whole storage period of 15 days. The application of composite, active edible bio‐based film was found proficient in confining product quality attributes throughout storage. Practical Application: The majority of films used for packaging of meat and meat products are derived from synthetic "plastic" materials. The demerits associated with plastics have eventually led to explore natural alternatives such as edible films. The composite‐active bio‐based films have a huge potential to be molded for specific film properties based on requirements of product‐specific packaging conditions. [ABSTRACT FROM AUTHOR]

Freshly harvested capsicum has limited shelf life (5-6 days) under ambient storage conditions. The functional quality of capsicum has been assessed in two sizes (300g±10%) and (900g±10%) of 30µ flex freshTM expanded polyethylene biopolymer pouches at 3 and 10°C and 90-95% RH under modified atmospheric storage. In both small and big size pouches, composition of oxygen and carbon dioxide varied from 18.8-19.9% and 18.8-19.4% and from 2.2-0.7% and 2.0-1.4%, respectively during 28 days of storage at 3°C. Positive and significant correlation was observed between overall acceptability and CO2 composition however, negative correlation was observed with O2 composition. Minimum increase (6.02% and 4.35%) in PLW, minimum decrease in firmness (4.71N-3.50N and 4.71N-2.70N), minimum decrease in chlorophyll (70.3% and 75.5%) and minimum losses (47.6% and 40.3%) of ascorbic acid were obtained after 28 days of storage in small and big size capsicum, respectively at 3°C. The decrease in total phenol and antioxidant activity was 82.4% and 78.6% and 60.1% and 59.4%, respectively after 28 days of the storage at 3°C of small and big size capsicum, respectively. Judges observed most acceptable consistency and OAA score for both sizes of capsicum in pouches up to 21 days of storage at 3°C. [ABSTRACT FROM AUTHOR]

The present investigation was carried out at research farm, Tirhut College of Agriculture, Dholi during kharif 2016 in evaluate the efficacy of tetraniliprole. The results revealed that treatment T4 was found to be the best among all insecticides tested with respect to number of damaged plant, minimum leaf injury, yield and plant height. However, treatment T2, T3, T5 and T6 were found safe to natural enemies among all the insecticides tested. [ABSTRACT FROM AUTHOR]

Eggplant genotypes of white, green and violet are very popular in the specific regions of sub-tropics and tropics. These possess enormous health benefits as having high antioxidant activity. Due to limited shelf life i.e. 3 days at ambient storage, wholesalers and retailers apply many unhygienic practices. They use petroleum based oil to make the fruit surface shiny and attractive. In the present study, Eggplant genotypes, viz. violet, green and white, were assessed after carnauba wax (CW) emulsion containing additives such as 2.5% polyethylene glycol (PEG) and 0.5% sodium alginate (SA) as T1, 0.5% SA as T2, 2.5% PEG as T3 and non-coated eggplant fruits as control fruits (T4) for shelf life extension and retention of nutritional quality attributes at ambient condition (20 ± 2 °C and RH 52–54%) for 7 days. The additives mixed CW coatings were effective in extending the shelf life and retaining the nutritional quality attributes than control genotypes. Sodium alginate (SA) mixed CW coating resulted in maximum shelf life extension of white eggplant genotypes by 7 days as compared to 3–4 days in control eggplant genotypes at ambient storage. However, 6.9%, 1.83% and 6.04% decrease in physiological loss in weight was obtained in 0.5% SA based CW coating after 5 days of ambient storage of violet, green and white eggplant genotypes, respectively in comparison to fully control eggplant genotype. The firmness level of eggplant genotypes in 0.5% SA based CW coating was more (85.1%, 50% and 45%) after 7 days of ambient storage of violet, green and white genotypes, respectively in comparison to fully control eggplant genotype. Similarly, total phenol content was also increased (26.1%, 37.5% and 180.9%) in 0.5% SA based CW coated eggplant genotypes of violet, green and white, respectively after 7 days of ambient storage in comparison to fully control eggplant genotype. Initially there had been increase in antioxidant activity in white genotype up to 5 days of storage which thereafter decreased on 7 days of storage in all the treatments. However, violet and green genotypes had not followed any trend during storage. [ABSTRACT FROM AUTHOR]

Pulp and paper industry is one of the major sector in every country of the globe contributing not only to Gross Domestic Product but surprisingly to environmental pollution and health hazards also. Paper and paperboard based material is the one of the earliest and largest used packaging form for food products like milk and milk based products, beverages, dry powders, confectionary, bakery products etc. owing to its eco-friendly hallmark. Various toxic chemicals like printing inks, phthalates, surfactants, bleaching agents, hydrocarbons etc. are incorporated in the paper during its development process which leaches into the food chain during paper production, food consumption and recycling through water discharges. Recycling is considered the best option for replenishing the loss to environment but paper can be recycled maximum six to seven times and paper industry waste is very diverse in nature and composition. Various paper disposal methods like incineration, landfilling, pyrolysis and composting are available but their process optimization becomes a barrier. This review article aims at discussing in detail the use of paper and paper based packaging materials for food applications and painting a wide picture of various health and environmental issues related to the usage of paper and paper based packaging material in food industry. A brief comparison of the environmental aspects of paper production, recycling and its disposal options (incineration and land filling) had also been discussed. [ABSTRACT FROM AUTHOR]

In many research activities related to the heated environment near the wall or surface, measurement of transient surface temperature and determination of convective heating rate is one of the most important tasks. Since the effective measurement time available for the regime to enumerate is very small, it is essential to use temperature detectors with highly sensitive, precise and instantaneous temperature measurement properties. Thin film gauges with these properties are best suited for such short duration transient surface temperature measurements. This study focuses on the establishment of new calibration technique in convection heat transfer mode. A fabricated calibration set-up is used to supply the heat flux to the thin film gauge, which is in-house built with platinum as sensing element and quartz as an insulating substrate. The heat load applied on the thin film gauge is of impact or step type. The deduction of heat flux value is carried out with recorded transient temperature data. Moreover, the numerical analysis is also carried out to get the heat flux values. The authenticity of the experimental set up and thin film gauge effectiveness is supported by the excellent agreement of the experimental and numerical results. [ABSTRACT FROM AUTHOR]