Message Authentication Research Articles

Lightweight Optimized Message Authentication Scheme for IEC 61850 Sampled Value Messages

Lightweight Optimized Message Authentication Scheme for IEC 61850 Sampled Value Messages

A laminar spray group combustion experiment (LSGCE) with in situ image diagnostic methods and image analysis algorithms.

Spray combustion is important for different engines. The understanding of spray combustion should be further promoted especially in the non-dilute region, and there is lack of well-defined spray experiments. In this study, an experimental platform was developed. Using this platform, a cylindrical quasi-laminar spray can be formed and ignited by a thin and straight hot wire, making it a simple configuration. Two image diagnostic methods were also developed to capture in situ microscopic droplet images and macroscopic droplet-flame images synchronously. Different image analysis algorithms were developed to obtain droplet statistics (diameter, velocity, and number density) and flame information (size, location, and flame propagation speed) from the raw images. The design, diagnostic methods, and image analysis methods are detailedly presented. This experimental platform can cover a wide range of operating conditions, with Gig in a range of 0.01-0.06 and temperature in a range from room temperature to 1400K. In addition, this platform is small in size and is capable of further implanting into a ground-based microgravity facilitaty. The whole experimental system can be applied in spray ignition and combustion studies and can provide legitimate data for further model development.

Secure and efficient device‐to‐device communication in IoT: The DMBSOA‐enhanced MQTT protocol

AbstractThe Internet of Things (IoT) plays a crucial role in enhancing technology by facilitating data transfer, storage, control, and management across networks. Secure communication within IoT environments remains a significant challenge. This research aims to enhance IoT security by integrating Message Queuing Telemetry Transport (MQTT) and MQTT‐Sensor Network (MQTT‐SN) protocols with the Dual Mutation‐Based Seagull Optimization Algorithm (DMBSOA). MQTT, known for its lightweight and dependable messaging, is widely used in the IoT community but is vulnerable to cyber‐attacks, particularly concerning privacy and authentication. DMBSOA, inspired by seagulls' foraging behavior, optimizes MQTT settings to enhance security, reliability, and performance. The proposed model dynamically adjusts key parameters such as transmission frequency, Quality of service (QoS) levels, and message size to improve energy consumption, throughput, end‐to‐end delay, and packet delivery ratio. A comprehensive system model is presented, comprising publisher, subscriber, and broker nodes, with security mechanisms integrated into the broker to ensure data integrity, authentication, and encryption. MQTT operates over Transmission Control Protocol/Internet Protocol (TCP/IP), while MQTT‐SN uses User Datagram Protocol (UDP), catering to resource‐constrained devices and low‐power modes. The proposed technique attained throughput (68 kB), energy consumption (120 mJ), security level (96%), energy efficiency (98.80%), path loss (22 dB), end‐to‐end delay (35 ms), processing time (230 s) and packet delivery ratio (0.98). The DMBSOA‐optimized MQTT protocol demonstrates superior performance compared to these models, highlighting its potential to meet the evolving demands of IoT security. This research underscores the effectiveness of DMBSOA in enhancing MQTT protocol security and efficiency, providing a promising solution for secure IoT communication.

A Quantum-Resistant Identity Authentication and Key Agreement Scheme for UAV Networks Based on Kyber Algorithm

Unmanned aerial vehicles (UAVs) play a critical role in various fields, including logistics, agriculture, and rescue operations. Effective identity authentication and key agreement schemes are vital for UAV networks to combat threats. Current schemes often employ algorithms like elliptic curve cryptography (ECC) and Rivest–Shamir–Adleman (RSA), which are vulnerable to quantum attacks. To address this issue, we propose LIGKYX, a novel scheme combining the quantum-resistant Kyber algorithm with the hash-based message authentication code (HMAC) for enhanced security and efficiency. This scheme enables the mutual authentication between UAVs and ground stations and supports secure session key establishment protocols. Additionally, it facilitates robust authentication and key agreement among UAVs through control stations, addressing the critical challenge of quantum-resistant security in UAV networks. The proposed LIGKYX scheme operates based on the Kyber algorithm and elliptic curve Diffie–Hellman (ECDH) key exchange protocol, employing the HMAC and pre-computation techniques. Furthermore, a formal verification tool validated the security of LIGKYX under the Dolev–Yao threat model. Comparative analyses on security properties, communication overhead, and computational overhead indicate that LIGKYX not only matches or exceeds existing schemes but also uniquely counters quantum attacks effectively, ensuring the security of UAV communication networks with a lower time overhead for authentication and communication.

Increasing Employee Performance Through Physical Work Environment and Work Motivation

Uncomfortable workstations, inadequate lighting, or improper seating can lead to physical discomfort, less motivation, reduced productivity, and decreased employee performance. This research strives to determine to what extent the impact of the physical work environment in the workplace and encouraging work motivation can improve employee performance. The author's research approach uses quantitative surveys at one of the government agencies in West Bandung. Respondents are 77 state apparatus employees. Data was collected using a structured questionnaire, which was then tested for the validity and reliability of the research instrument. The author's data testing uses path analysis. The research results show that the physical work environment, such as comfortable workstations and adequate lighting, can improve employee performance. Work motivation, such as the desire to develop, impacts employee performance. Research implications indicate that the presence of comfortable workstations and fulfilling employees' desires for development can contribute to increasing employee performance. The research results contribute to a deeper understanding of the role of the physical work environment and employee work motivation to provide positive aspects for the organization to achieve goals. The author is aware that this research only uses survey data. Hence, there are several areas for improvement where the scope of the research could be more extensive and only focus on employees who work in government agencies. Also, this research only discusses the physical work environment in the workplace, encouraging work motivation and employee performance.

Non-Fungible Tokens (NFTs) in Healthcare: A Systematic Review.

Amid global health challenges, resilient health systems require continuous innovation and progress. Stakeholders highlight the critical role of digital technologies in accelerating this progress. However, the digital health field faces significant challenges, including the sensitivity of health data, the absence of evidence-based standards, data governance issues, and a lack of evidence on the impact of digital health strategies. Overcoming these challenges is crucial to unlocking the full potential of digital health innovations in enhancing healthcare delivery and outcomes. Prioritizing security and privacy is essential in developing digital health solutions that are transparent, accessible, and effective. Non-fungible tokens (NFTs) have gained widespread attention, including in healthcare, offering innovative solutions and addressing challenges through blockchain technology. This paper addresses the gap in systematic-level studies on NFT applications in healthcare, aiming to comprehensively analyze use cases and associated research challenges. The search included primary studies published between 2014 and November 2023, searching in a balanced set of databases compiling articles from different fields. A review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework and strictly focusing on research articles related to NFT applications in the healthcare sector. The electronic search retrieved 1902 articles, ultimately resulting in 15 articles for data extraction. These articles span applications of NFTs in medical devices, pathology exams, diagnosis, pharmaceuticals, and other healthcare domains, highlighting their potential to eliminate centralized trust sources in health informatics. The review emphasizes the adaptability and versatility of NFT-based solutions, indicating their broader applicability across various healthcare stages and expansion into diverse industries. Given their role in addressing challenges associated with enhancing data integrity, availability, non-repudiation, and authentication, NFTs remain a promising avenue for future research within digital health solutions.

Backdoor-resistant certificateless-based message-locked integrity auditing for computing power network

With the evolution of 6G technology, the computing power network (CPN), a crucial infrastructure underpinning the future of digital transformation, is being tightly integrated with 6G to jointly foster significant enhancements in network performance. However, the openness of the network and the semi-trusted nature of cloud service providers pose risks to user data, including potential theft and tampering. To address these challenges, this paper introduces a novel scheme called the backdoor-resistant certificateless message-locked integrity audit (BRCLMIA). This scheme incorporates a cryptographic reverse firewall, offering an efficient and secure approach to auditing CPN data integrity. Our thorough analysis demonstrates that the BRCLMIA scheme effectively safeguards against existential forgery under adaptive message attacks, safeguards against algorithm replacement attacks, and ensures the integrity and authenticity of data transmitted within the CPN. Furthermore, the performance analysis reveals that the BRCLMIA scheme exhibits remarkable communication and computational efficiency while defending against various attacks.

Providing Security for Flash Loan System Using Cryptocurrency Wallets Supported by XSalsa20 in a Blockchain Environment

The days of long lines at banks and piles of paperwork are long gone. The lending landscape has been completely transformed by digital loans, which provide a quick, easy, and frequently paperless transactions. A payee can apply for a digital loan anytime, anywhere. The entire lending procedure is accessible and efficient, but the availability of data on the Internet provides many risks and threats, where there are certain difficulties in the world of digital lending and data security, and privacy are major concerns. It is important to address the possibility of predatory lending practices that target weaker payees, especially with flash loans, which are considered a critical type of digital loan, as they add additional pressure to banks in terms of security because they return to the same block of the blockchain, and the possibility of tampering with them is considered great. Therefore, we have developed a security protocol based on the principle of digital cryptocurrency wallets: these digital wallets are protected by our use of the Xsalsa20 algorithm. It has high specifications and is enhanced with the usage of the Crow search algorithm, which guarantees fast and efficient search results. After the analysis of the proposed system in the Tamarin Prover tool, we obtained proof of a lot of security properties like data authenticity, perfect forward secrecy, and many others, which constitute the matter that gives our system the power of security. Also, we obtained 0.6667 ms. as the rate of processing speed, 2990 ms. as the time required by the proposed system to repay loans after the conditions are met, and, finally, our proposed system has the power to present a flexible property by creating 29,700 loans every 3 s; by this, we obtained a system that is secure, authentic, fast, and flexible.

Eksplorasi Potensi Penggunaan Blockchain Dalam Optimalisasi Manajemen Pelabuhan di Indonesia: Tinjauan Literatur

This research explores the potential use of Blockchain technology to optimize port management in Indonesia, which is a crucial element in the global supply chain. The research method employed is a literature review, with analysis and synthesis of information to understand Blockchain's contribution to improving port management. The identified potential uses of Blockchain include real-time logistics tracking and monitoring, data validation and authentication, increased transparency and trust, and port process efficiency. The study also highlights the use of smart technology and smart contracts in port management, as well as challenges in adopting Blockchain technology. A comprehensive conceptual framework is proposed for the application of Blockchain in Indonesia's maritime industry, including a feasibility analysis framework to assess implementation readiness. The results of this literature review are expected to provide deep insights and serve as a foundation for further research and assist stakeholders in making decisions related to the application of Blockchain technology in port management.

Assessment of the effects of the use of preconsumer cotton waste on the quality of rotor yarns

Due to the shortage of raw fiber materials and stricter legislative conditions, it has become necessary to process even the more contaminated fiber wastes that contain only a small amount of useable fibers. Hence, this paper investigates the impact of different cleaning channels on the Rieter R37 rotor spinning machine in controlling spinning strategy and yarn quality using recovered blowroom cotton waste fibers. The study involves spinning 98-tex yarns with different cleaning channels during the spinning process, using recovered blowroom cotton waste in blends with virgin cotton processed into slivers by two methods. Qualitative indicators in the fiber-sliver-yarn line were evaluated and graphically compared. The statistical significance of the influencing factors was determined using the Generalized Anova. Additionally, the quality of the experimental samples was compared to authentic data from global production using the USTER® STATISTIC. The results underline the significant influence of fiber quality, sliver preparation method and implemented cleaning channels on the arrangement of fibers in yarn structures as evidenced by structural and mechanical parameters. In particular, the comparison with USTER® STATISTICS confirms that all yarns meet the required quality standards for selected applications, including those spun exclusively from 100 % cotton waste. Furthermore, the results demonstrate that this innovative technology enables yarn manufacturers to meet customer demands, ensure optimal yarn quality and achieve cost savings by optimizing waste removal without compromising fiber yield.

Identification and functional analysis of circular RNA expression profiles associated with ammonia exposure in chicken lungs

Ammonia acts as a detrimental atmospheric pollutant, posing a sever threat to respiratory tract health and causing lung injury in humans and animals. Circular RNAs (circRNAs) are a distinctive class of non-coding RNA generated by back-splicing of linear RNA, implicated in various biological processes. However, their role in the immune response of chicken lungs to ammonia exposure remains unclear. In this study, we examined the expression profiles of circRNAs in chicken lungs under ammonia stimulation. In total, 61 differentially expressed (DE) circRNAs were identified between the ammonia exposure and control groups, including 17 up-regulated and 44 down-regulated circRNAs. The source genes of these DE circRNAs were predominantly enriched in Influenza A, SNARE interactions in vesicular transport, and Notch signaling pathway. Notably, nine DE circRNAs (circNBAS, circMTIF2, circXPO1, circSNX24, circRAB11A, circARID3B, circUSP54, circPPARA, and circERG) were selected for validation the reliability and authenticity of RNA-seq data. Results showed the back-splicing circular structure, as well as the reliability and accuracy of RNA-seq data in quantifying circRNA expression, as the RT-qPCR results were in agreement with the RNA-seq data. Moreover, we constructed the circRNA-miRNA-mRNA regulatory networks and identified several regulatory networks in chicken lungs under ammonia stimulation, including circRAB11A-gga-miR-191b-3p-BRD2 and circARID3B-gga-miR-1696-CKS2. Taken together, our study delineates the circRNA expression profile and their potential roles in the immune response of chicken lungs to ammonia exposure. These findings offer insights into molecular mechanisms that may mitigate diseases associated with ammonia induced respiratory tract pollution in humans and animals.

A dual hashing-based authentication and secure data transmission scheme for vehicular cloud environment using MECC with optimal resource allocation mechanism

A dual hashing-based authentication and secure data transmission scheme for vehicular cloud environment using MECC with optimal resource allocation mechanism

Challenges and possibilities in compiling Aeronautical English corpora: The case of the Aerocorpus

Authentic materials are an important resource for teaching and assessment, especially when it comes to English for Specific Purposes. To this end, corpus linguistics has developed a supporting role as it provides a wide range of possibilities for extracting and exploring authentic data. With regard to Aeronautical English (AE), which refers to oral communications between pilots and air traffic controllers (ATCOs), corpora offer substantial benefits in the development of teaching and testing materials. However, putting together corpora in the context of AE faces challenges in availability, selection, compilation, and copyright issues as some countries limit access to radiotelephony communications due to their sensitive content. This paper reports the experience of compiling a collaborative corpus called “Aerocorpus” proposed during two webinars held for AE teachers and testers from all over the world. The compilation methodology aims to address the aforementioned challenges and offer alternative solutions. Despite existing limitations, we see considerable gains in using authentic materials and are looking into ways of protecting the “black box” containing sensitive information while continuing to compile the corpus, which currently features 38,135 words from 45 situations, and to make it available to a wider community of AE professionals.

Multilayer Perceptron Network to Detect Fraud in Digital Images

The major challenge of data authenticity is how to check for image fraud, which createsa huge problemforthe credibility of visual media. In this paper, we proposea method to investigatethe performanceof a Multilayer Perceptron (MLP)to extract the fraud images,this network is a classof supervised Artificial Neural Network(ANN. Theproposal modelappliesMLP modelto allocateextracted image features in order to distinguishthembetween realand modifiedcontents. Theexaminedfeatures are includedwithinstatistical matrices, analysis of histogramspace, and possible inequality that may arise during modifications. The proposed MLP wastrained withdataset that contains both realand fraudulent images, thus allowing the modelto extractknowledge from the originalpatterns that differentiate between those two classes. The model's performance wasvalidated with severalmetrics, including accuracy,precision,and computational cost. Furthermore,this paper presentscomparisons against traditional methods that were examined in the procedure. The finding of this work enhances the model withimprovedimage fraud detection by showcasing the capabilities of MLPs within 162.59 seconds to 86% detection, while the base algorithm in 205.92 seconds succeeded in recognizing 82%.

Technological innovations in accounting for food supply chain management

The food supply chain management sector has undergone significant transformations in recent years, largely driven by technological innovations aimed at enhancing efficiency, transparency, and sustainability. This review delves into the burgeoning realm of technological advancements in accounting within the food supply chain management domain. Technological innovations such as blockchain, Internet of Things (IoT), artificial intelligence (AI), and big data analytics have revolutionized traditional accounting practices in the food supply chain. Blockchain technology, for instance, has emerged as a promising solution for enhancing transparency and traceability throughout the supply chain by providing an immutable ledger of transactions. This ensures the authenticity of data related to food products, from origin to consumption, thus mitigating risks associated with fraud and food safety incidents. Moreover, the integration of IoT devices enables real-time monitoring of various parameters such as temperature, humidity, and location, thereby ensuring optimal conditions for food transportation and storage. This not only minimizes wastage but also enhances the overall quality and freshness of food products. Artificial intelligence and machine learning algorithms play a pivotal role in optimizing inventory management and demand forecasting processes. By analyzing vast amounts of historical data and current market trends, these technologies facilitate accurate predictions of consumer demand, thereby reducing stockouts and overstocking scenarios. Furthermore, big data analytics enables stakeholders to derive valuable insights from large datasets generated across the supply chain. These insights aid in identifying inefficiencies, optimizing resource allocation, and making data-driven decisions to enhance profitability and sustainability. However, despite the numerous benefits offered by technological innovations in accounting for food supply chain management, challenges such as data security, interoperability, and the need for skilled personnel remain significant hurdles to widespread adoption. Addressing these challenges will be crucial in harnessing the full potential of technology to create a more resilient, transparent, and sustainable food supply chain ecosystem. In conclusion, technological innovations in accounting have emerged as key enablers in revolutionizing the food supply chain management landscape. By leveraging blockchain, IoT, AI, and big data analytics, stakeholders can unlock new opportunities for enhancing efficiency, transparency, and sustainability throughout the food supply chain. Keywords: Accounting, Food, Supply Chain, Management, Technology, Innovation, Review.

Technological innovations in accounting for food supply chain management

The food supply chain management sector has undergone significant transformations in recent years, largely driven by technological innovations aimed at enhancing efficiency, transparency, and sustainability. This review delves into the burgeoning realm of technological advancements in accounting within the food supply chain management domain. Technological innovations such as blockchain, Internet of Things (IoT), artificial intelligence (AI), and big data analytics have revolutionized traditional accounting practices in the food supply chain. Blockchain technology, for instance, has emerged as a promising solution for enhancing transparency and traceability throughout the supply chain by providing an immutable ledger of transactions. This ensures the authenticity of data related to food products, from origin to consumption, thus mitigating risks associated with fraud and food safety incidents. Moreover, the integration of IoT devices enables real-time monitoring of various parameters such as temperature, humidity, and location, thereby ensuring optimal conditions for food transportation and storage. This not only minimizes wastage but also enhances the overall quality and freshness of food products. Artificial intelligence and machine learning algorithms play a pivotal role in optimizing inventory management and demand forecasting processes. By analyzing vast amounts of historical data and current market trends, these technologies facilitate accurate predictions of consumer demand, thereby reducing stockouts and overstocking scenarios. Furthermore, big data analytics enables stakeholders to derive valuable insights from large datasets generated across the supply chain. These insights aid in identifying inefficiencies, optimizing resource allocation, and making data-driven decisions to enhance profitability and sustainability. However, despite the numerous benefits offered by technological innovations in accounting for food supply chain management, challenges such as data security, interoperability, and the need for skilled personnel remain significant hurdles to widespread adoption. Addressing these challenges will be crucial in harnessing the full potential of technology to create a more resilient, transparent, and sustainable food supply chain ecosystem. In conclusion, technological innovations in accounting have emerged as key enablers in revolutionizing the food supply chain management landscape. By leveraging blockchain, IoT, AI, and big data analytics, stakeholders can unlock new opportunities for enhancing efficiency, transparency, and sustainability throughout the food supply chain. Keywords: Accounting, Food, Supply Chain, Management, Technology, Innovation, Review.

PENENTUAN SYARAT PADA PERTENGAHAN PERJANJIAN DALAM JUAL BELI KREDIT PERALATAN PERTANIAN

Buying and selling as a means of getting goods easily. Buying and selling credit is a sale and purchase transaction carried out by the community using the payment in installments (installments). Such as buying and selling credit carried out by villagers Payo Bay, Banyuasin district. The concept of buying and selling on credit is the element of time, element of risk, element of submission, element of trust, and element of approval. Buy and sell This credit helps the residents of Teluk Payo village. They can buy agricultural equipment by paying it off because they don't have enough money if buying in cash. By buying equipment for farming. This certainly makes it easier for them to farm and the results will be more Good.In writing a thesis using field research (field research) because this research was carried out in the field, namely about the practice of buying and selling credit agricultural equipment in Teluk Payo Village, Banyuasin Regency. As for analyzing the author's data using primary and secondary data methods, ie the method of analysis describes a situation or area of a particular population directly factual. Conclusions that can be drawn from the writing of this thesis is the result of the study, it can be concluded that in the practice of buying and selling credit this agricultural equipment there is a discrepancy that occurs between the parties involved, namely the additional of the amount of money that was not agreed upon. Which one This discrepancy occurs because of the failure of these farmers in farming. That application of intermediate conditions in the financing of agricultural equipment in the village Payo Bay, Banyuasin Regency is not yet fully following the principles of sharia economic law. This can be seen from several conditions that still violate sharia principles, such as adding a certain amount of money to maximize profit.

Smartphone's on, humanness's off: Phubbing breeds dehumanization via subjectivity uncertainty

Increasing scholarly interest in phubbing has helped researchers accrue knowledge regarding its psychological outcomes; however, how phubbing affects person perceptions and interpersonal behaviors in general interpersonal contexts, which is critical to a thorough knowledge of the implications of phubbing, remains inadequately researched. To bridge the gaps, we investigated whether and how phubbing affects dehumanization and the downstream consequence of this effect. Four studies (one preregistered), using authentic behavioral data (69,399 posts) from social media (Study 1), correlational design (Study 2), and experimental methods (Studies 3–4), concurred in supporting the fact that phubbing increases dehumanization. Furthermore, subjectivity uncertainty mediated the effect of phubbing on dehumanization (Studies 2–4), while the alternative mediating role of metadehumanization was ruled out (Studies 2–3). In addition, dehumanization elicited by phubbing further increased aggressive behaviors (Study 4). Our research provides new insights into the consequences of phubbing and the factors contributing to dehumanization. It reminds people to note the possible severe consequences of phubbing, an unwitting act, in interpersonal interactions.

Predictive State-of-Health (SOH) of lithium-ion batteries throughout their lifecycle based on LSTM neural networks

With the rapid development of new energy sources, the demand for energy storage systems in the power industry has surged. While lithium-ion battery energy storage systems have gained widespread market acceptance due to their performance advantages, concerns regarding battery safety and durability have also escalated. Therefore, accurately predicting the health status of batteries has become a crucial aspect in assessing their safety and durability. State of Health (SOH), as a vital indicator of battery health status, has become a focal point for many researchers aiming to enhance the accuracy of its prediction. Today, owing to the rapid advancements in machine learning and neural networks, data-driven approaches have gradually emerged as one of the primary methods for SOH prediction. This paper proposes a method based on Long Short-Term Memory (LSTM) neural networks, which combines theoretical analysis with neural network models, utilizing authentic battery aging data from NASA to predict the health status of lithium-ion batteries throughout their lifecycle. Experimental results demonstrate that the proposed method exhibits high accuracy.

Predictive State-of-Health (SOH) of lithium-ion batteries throughout their lifecycle based on LSTM neural networks

With the rapid development of new energy sources, the demand for energy storage systems in the power industry has surged. While lithium-ion battery energy storage systems have gained widespread market acceptance due to their performance advantages, concerns regarding battery safety and durability have also escalated. Therefore, accurately predicting the health status of batteries has become a crucial aspect in assessing their safety and durability. State of Health (SOH), as a vital indicator of battery health status, has become a focal point for many researchers aiming to enhance the accuracy of its prediction. Today, owing to the rapid advancements in machine learning and neural networks, data-driven approaches have gradually emerged as one of the primary methods for SOH prediction. This paper proposes a method based on Long Short-Term Memory (LSTM) neural networks, which combines theoretical analysis with neural network models, utilizing authentic battery aging data from NASA to predict the health status of lithium-ion batteries throughout their lifecycle. Experimental results demonstrate that the proposed method exhibits high accuracy.