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In recent times, electronic portfolios (e-portfolios) are being increasingly used by students and lifelong learners as digital online multimedia résumés that showcase their skill sets and achievements. E-portfolios require secure, reliable, and privacy-preserving credential issuance and verification mechanisms to prove learning achievements. However, existing systems provide private institution-wide centralized solutions that primarily rely on trusted third parties to issue and verify credentials. Furthermore, they do not enable learners to own, control, and share their e-portfolio information across organizations, which increases the risk of forged and fraudulent credentials. Therefore, we propose a consortium blockchain-based e-portfolio management scheme that is decentralized, secure, and trustworthy. Smart contracts are leveraged to enable learners to completely own, publish, and manage their e-portfolios, and also enable potential employers to verify e-portfolio credentials and artifacts without relying on trusted third parties. Blockchain is used as an immutable distributed ledger that records all transactions and logs for tamper-proof trusted data provenance, accountability, and traceability. This system guarantees the authenticity and integrity of user credentials and e-portfolio data. Decentralized identifiers and verifiable credentials are used for user profile identification, authentication, and authorization, whereas verifiable claims are used for e-portfolio credential proof authentication and verification. We have designed and implemented a prototype of the proposed scheme using a Quorum consortium blockchain network. Based on the evaluations, our solution is feasible, secure, and privacy-preserving. It offers excellent performance.

The electric vehicle (EV) is considered a premium solution to global warming and various types of pollution. Nonetheless, a key concern is the recharging of EV batteries. Therefore, this study proposes a novel approach that considers the costs of transportation loss, buildup, and substation energy loss and that incorporates harmonic power loss into optimal rapid charging station (RCS) planning. A novel optimization technique, called binary lightning search algorithm (BLSA), is proposed to solve the optimization problem. BLSA is also applied to a conventional RCS planning method. A comprehensive analysis is conducted to assess the performance of the two RCS planning methods by using the IEEE 34-bus test system as the power grid. The comparative studies show that the proposed BLSA is better than other optimization techniques. The daily total cost in RCS planning of the proposed method, including harmonic power loss, decreases by 10% compared with that of the conventional method.

With the swift evolution of wireless technologies, the demand for the Internet of Things (IoT) security is rising immensely. Elliptic curve cryptography (ECC) provides an attractive solution to fulfill this demand. In recent years, Edwards curves have gained widespread acceptance in digital signatures and ECC due to their faster group operations and higher resistance against side-channel attacks (SCAs) than that of the Weierstrass form of elliptic curves. In this paper, we propose a high-speed, low-area, simple power analysis (SPA)-resistant field-programmable gate array (FPGA) implementation of ECC processor with unified point addition on a twisted Edwards curve, namely Edwards25519. Efficient hardware architectures for modular multiplication, modular inversion, unified point addition, and elliptic curve point multiplication (ECPM) are proposed. To reduce the computational complexity of ECPM, the ECPM scheme is designed in projective coordinates instead of affine coordinates. The proposed ECC processor performs 256-bit point multiplication over a prime field in 198,715 clock cycles and takes 1.9 ms with a throughput of 134.5 kbps, occupying only 6543 slices on Xilinx Virtex-7 FPGA platform. It supports high-speed public-key generation using fewer hardware resources without compromising the security level, which is a challenging requirement for IoT security.

Natural convection heat and mass transport in porous media is a fundamental phenomenon that has many uses in material processing, energy systems, and environmental engineering. Increasing the effectiveness of heat exchangers, geothermal energy extraction, and soil moisture retention, among other processes, requires an understanding of the behavior of fluid flow in doubly stratified high porosity media. The majority of current research concentrates on stable natural convection, whereas some studies ignore how magnetic fields, double stratification, and Darcy factors affect unsteady natural convection. This study aims to develop a novel double‐stratification convection model that incorporates both thermal and mass stratification, including the Darcy number (D) and magnetic parameter (M) in a unified framework to effectively depict convection in stratified systems. In the governing boundary layer equation, the system of non‐linear coupled partial differential equations is transformed by the standard transformation into a non‐dimensional system of coupled non‐linear partial differential equations. To solve this system numerically, the explicit finite difference approach will be applied. The numerical solutions were derived using Compact Visual FORTRAN 6.6a and MATLAB R2015. To completely understand the impact of physical characteristics, a thorough analysis of the stability and convergence criteria was conducted. Graphics will be used to illustrate the doubly stratified effects on temperature, velocity, concentration, shear stress, and Nusselt number. The mesh sensitivity and validation tests have been performed and presented, while the time‐sensitivity test yields the dimensionless time. The results of this study will be analyzed for different values of the specified parameters and shown graphically. The effects of important parameters were investigated, such as the thermal stratification number (), mass stratification number (), Darcy number (D), and Prandtl number (Pr). The findings demonstrate that while concentration rises, velocity and temperature fall as the Prandtl number rises. Higher Darcy numbers also cause temperature and concentration to drop while velocity increases. Higher thermal stratification decreases local shear stress but raises the Nusselt number, based on the results of an investigation into the effects of these parameters on shear stress and Nusselt number. It performs 15% better than the previous work of Ganesan, under a specified threshold value. These results offer a deeper comprehension of the physical behavior of the model under various circumstances. These findings also provide quantitative insight into magnetohydrodynamic transport mechanisms in doubly stratified porous media relevant to geothermal energy systems, subsurface fluid transport, and filtration processes.

Enhancing the resilience and reliability of power grids is crucial amid rising cyber threats and system complexities. To address these challenges, this paper proposes an energy‐efficient, consortium blockchain‐based global alarm system for power grid management. Using smart contracts and the proof of‐authority consensus algorithm, the alarm system triggers global alarms upon detecting local anomalies, ensuring a prompt response to partition the power grid and mitigate failures. The effectiveness is validated by simulating the Iberian power system with 15 providers from various regions. Key metrics, such as load shedding, damage reduction, energy consumption, latency, and transaction costs, are used to assess the performance. Through simulations, we show that the blockchain‐based system effectively limits the damage propagation and the load shedding during cascading failures by delaying the onset of instability and maintaining lower damage levels compared to non‐blockchain scenarios. Our investigations reveal that the proposed global alarm mechanism reduces the damage and load shedding by up to 29% and 87%, respectively, showcasing its potential for preventing widespread outages. The proposed global alarm system is designed to enhance the resilience and security of interconnected power grids through a robust, decentralised framework. The overall system architecture comprises (a) local alarm units, (b) a consortium blockchain network, and (c) a smart contract. This setup ensures that alerts generated by local alarm units are securely transmitted and recorded on the blockchain, where the smart contract validates and aggregates these alarms to issue global alarms if necessary.

The global production of macadamia nuts has witnessed a significant increase, resulting in the accumulation of large quantities of discarded nutshells. These nutshells possess the properties of remarkable hardness and toughness, which are comparable to those of aluminum. Incorporating natural fillers to enhance the properties of composite materials for various applications, including light duty, structural, and semi-structural purposes, is a common practice. Given their inherent hardness and toughness, macadamia nutshells present an intriguing choice as fillers, provided that the manufacturing conditions are economically viable. With the urgent need to shift toward natural fillers and reduce reliance on synthetics, exploring macadamia nutshells as components of natural fiber composites becomes imperative. This review aims to comprehensively examine the existing body of knowledge on macadamia nutshells and their bio-synthetic polymer composites, highlighting key research findings, achievements, and identifying knowledge gaps. Furthermore, the article will outline prospective areas of focus for future research endeavors in this domain, aligning with the universal goal of minimizing synthetic materials.

Bitcoin, which has been used for 13 years, has a role in transactions and investments as a major cryptocurrency. However, as the number of users increases, Bitcoin faces difficulties, such as scalability for transaction throughput and energy-consumption problems due to the concentration of the mining pool. When Bitcoin first started to come out, it began to develop gradually through the mining of individuals. Nevertheless, as the price of the cryptocurrency gradually climbed, large mining corporation groups entered the mining competition with integrated circuit (IC) chips. Consequently, the substantial increase in power consumption is raising concerns regarding energy expenditure. This paper confirms that the verifiable random selection consensus protocol based on proof of work facilitates a fair and efficient system, enabling the participation of numerous individual miners in the mining competition while counteracting the monopolization of the hash rate by large mining corporations, thereby preserving the decentralization of mining. The protocol demonstrates the potential to mitigate substantial energy consumption. Moreover, it embodies features that create barriers to the adoption of high-energy-consuming application-specific integrated circuit equipment, significantly diminishing the principal factors contributing to extensive power utilization.

Obesity is a chronic multifactorial disease characterized by the accumulation of body fat and serves as a gateway to a number of metabolic-related diseases. Epidemiologic data indicate that Obesity is acting as a risk factor for neuro-psychiatric disorders such as schizophrenia, major depression disorder and vice versa. However, how obesity may biologically interact with neurodevelopmental or neurological psychiatric conditions influenced by hereditary, environmental, and other factors is entirely unknown. To address this issue, we have developed a pipeline that integrates bioinformatics and statistical approaches such as transcriptomic analysis to identify differentially expressed genes (DEGs) and molecular mechanisms in patients with psychiatric disorders that are also common in obese patients. Biomarker genes expressed in schizophrenia, major depression, and obesity have been used to demonstrate such relationships depending on the previous research studies. The highly expressed genes identify commonly altered signalling pathways, gene ontology pathways, and gene-disease associations across disorders. The proposed method identified 163 significant genes and 134 significant pathways shared between obesity and schizophrenia. Similarly, there are 247 significant genes and 65 significant pathways that are shared by obesity and major depressive disorder. These genes and pathways increase the likelihood that psychiatric disorders and obesity are pathogenic. Thus, this study may help in the development of a restorative approach that will ameliorate the bidirectional relation between obesity and psychiatric disorder. Finally, we also validated our findings using genome-wide association study (GWAS) and whole-genome sequence (WGS) data from SCZ, MDD, and OBE. We confirmed the likely involvement of four significant genes both in transcriptomic and GWAS/WGS data. Moreover, we have performed co-expression cluster analysis of the transcriptomic data and compared it with the results of transcriptomic differential expression analysis and GWAS/WGS.

Lightning search algorithm (LSA) is a novel nature-inspired optimization algorithm based on the phenomenon of lighting. This optimization algorithm is generalized from the mechanism of step leader propagation. In this study, a variant of LSA for solving binary optimization problems called as binary LSA (BLSA) is presented. It is done by introducing some modification and introducing tangent hyperbolic sigmoid activation function in updating process of the original version of LSA. To evaluate the quality, convergence rate and robustness of the algorithm, the BLSA is tested with a set of well-utilized 24 benchmark functions. Furthermore, a comparative study with other four well-known binary optimization methods is given for validation of the BLSA performance. The results affirm that the proposed BLSA outperforms the other binary optimization algorithms in multidimensional search space in terms of search accuracy and convergence.

Recycling plastic is an excellent way to reduce the environmental impact of its production and use. In a circular economy, recycling of recycled plastic is necessary. Most plastic bags are made of thermoplastic, like high-density polyethylene (HDPE) with a melting point of 130°C, and low-density polyethylene (LDPE). In contrast, recycled plastic bags are made up of many different unknown substances. In this study, the melting of used plastic bags containing 80% unknown recycled material was investigated. FTIR analysis showed that the bags consisted mainly of HDPE. The bags were melted at 160°C, 200°C, and 250°C for 30 min. GC-FID and HP-SPME GCMS analyses showed that the bags released flammable gases (methane, ethylene, and alkane/alkene hydrocarbons) but little acetylene. Aromatic and aliphatic hydrocarbons eluded from the bags at 10% of the gas volume at 250°C. Long-chain alkanes, mostly hexadecane, were the dominant compound, amounting to 28% at 160°C and increasing to 43% at 250°C. On the other hand, alkenes decreased with a rising temperature (23% at 160°C to 3% at 250°C), as they were transformed into alkanes. Methylated compounds, for example, methylated alkanes at 10%, were present at all temperatures. Methane and methylated compounds are released from plastic and contamination of the bags with organic matter. The bags released small amounts of toxic phthalates. The results show that melting recycled plastic bags for remoulding is promising if safety precautions that ensure sufficient ventilation are utilised.