Five-phase Synchronous Reluctance Machines Equipped with a Novel Type of Fractional Slot Winding

Multi-phase machines are so attractive for electrical machine designers because of their valuable advantages such as high reliability and fault tolerant ability.Meanwhile,fractional slot concentrated windings(FSCW)are well known because of short end winding length,simple structure,field weakening sufficiency,fault tolerant capability and higher slot fill factor.The five-phase machines equipped with FSCW,are very good candidates for the purpose of designing motors for high reliable applications,like electric cars,major transporting buses,high speed trains and massive trucks.But,in comparison to the general distributed windings,the FSCWs contain high magnetomotive force(MMF)space harmonic contents,which cause unwanted effects on the machine ability,such as localized iron saturation and core losses.This manuscript introduces several new five-phase fractional slot winding layouts,by the means of slot shifting concept in order to design the new types of synchronous reluctance motors(SynRels).In order to examine the proposed winding’s performances,three sample machines are designed as case studies,and analytical study and finite element analysis(FEA)is used for validation.

Genuine entanglement under squeezed generalized amplitude damping channels with memory

We study genuine entanglement among three qubits undergoing a noisy process that includes dissipation, squeezing,and decoherence. We obtain a general solution and analyze the asymptotic quantum states. We find that most of these asymptotic states can be genuinely entangled depending upon the parameters of the channel, memory parameter, and the parameters of the initial states. We study Greenberger–Horne–Zeilinger(GHZ) states and W states, mixed with white noise,and determine the conditions for them to be genuinely entangled at infinity. We find that for these mixtures, it is possible to start with a bi-separable state(with a specific mixture of white noise) and end with genuine entangled states. However, the memory parameter μ must be very high. We find that in contrast to the two-qubit case, none of the three-qubit asymptotic states for n → ∞ are genuinely entangled.

Intelligent Fractional-Order Controller for SMES Systems in Renewable Energy-Based Microgrid

An autonomous microgrid that runs on renewable energy sources is presented in this article.It has a supercon-ducting magnetic energy storage(SMES)device,wind energy-producing devices,and an energy storage battery.However,because such microgrids are nonlinear and the energy they create varies with time,controlling and managing the energy inside them is a difficult issue.Fractional-order proportional integral(FOPI)controller is recommended for the current research to enhance a standalone microgrid’s energy management and performance.The suggested dedicated control for the SMES comprises two loops:the outer loop,which uses the FOPI to regulate the DC-link voltage,and the inner loop,responsible for regulating the SMES current,is constructed using the intelligent FOPI(iFOPI).The FOPI+iFOPI parameters are best developed using the dandelion optimizer(DO)approach to achieve the optimum performance.The suggested FOPI+iFOPI controller’s performance is contrasted with a conventional PI controller for variations in wind speed and microgrid load.The optimal FOPI+iFOPI controller manages the voltage and frequency of the load.The behavior of the microgrid as a reaction to step changes in load and wind speed was measured using the proposed controller.MATLAB simulations were used to evaluate the recommended system’s performance.The results of the simulations showed that throughout all interruptions,the recommended microgrid provided the load with AC power with a constant amplitude and frequency.In addition,the required load demand was accurately reduced.Furthermore,the microgrid functioned incredibly well despite SMES and varying wind speeds.Results obtained under identical conditions were compared with and without the best FOPI+iFOPI controller.When utilizing the optimal FOPI+iFOPI controller with SMES,it was found that the microgrid performed better than the microgrid without SMES.

Optimization of Cooperative RelayingMolecular Communications for Nanomedical Applications

Recently,nano-systems based on molecular communications via diffusion(MCvD)have been implemented in a variety of nanomedical applications,most notably in targeted drug delivery system(TDDS)scenarios.Furthermore,because the MCvD is unreliable and there exists molecular noise and inter symbol interference(ISI),cooperative nano-relays can acquire the reliability for drug delivery to targeted diseased cells,especially if the separation distance between the nano transmitter and nano receiver is increased.In this work,we propose an approach for optimizing the performance of the nano system using cooperative molecular communications with a nano relay scheme,while accounting for blood flow effects in terms of drift velocity.The fractions of the molecular drug that should be allocated to the nano transmitter and nano relay positioning are computed using a collaborative optimization problem solved by theModified Central Force Optimization(MCFO)algorithm.Unlike the previous work,the probability of bit error is expressed in a closed-form expression.It is used as an objective function to determine the optimal velocity of the drug molecules and the detection threshold at the nano receiver.The simulation results show that the probability of bit error can be dramatically reduced by optimizing the drift velocity,detection threshold,location of the nano-relay in the proposed nano system,and molecular drug budget.

Production of Spirulina platensis Using Inexpensive Local Resources in Palestine

Spirulina platensis is a special and unique cyanobacteria that is produced worldwide with a varied cost of cultivation media. In this study, five main experiments with different treatments were performed to evaluate the possibility of using cheap aquaculture water for Spirulina production, to test if solutions made by plant ash (PAS) could be used for Spirulina production, to determine if brackish water (BW) and mining water have a good impact on Spirulina production, to create a medium composed of cheap chemicals and fertilizers to be used for Spirulina cultivation, and to test if a mixture made from local components could be used to produce Spirulina. All experiments were performed via growth and dry weight measurements, including determination of chemical and physical characteristics of the samples with a comparison with Zarrouk medium (ZM) as a reference for each experiment, and all experiments were performed for 21 days to determine the best media type that lasts longer for commercial purposes. In all experiments, pH values were between 8 and 11, and EC was between 9.8 and 30 ms/cm, while temperature was at 30°C and 35°C, and light was at 1500 and 5000 Lux for 16 h light and 8 h dark. Spirulina can grow in (FW). It can also grow in FW diluted with BW. Also a 3% PAS can be used as a source to cultivate Spirulina at a very low price compared to ZM. The chemical fertilizer formula was one of the best types among all treatments with a good price. A mixture of these local resources could be a very good cheap alternative source. The main result that was obtained from all experiments in this study is the ability of Spirulina to grow within a wide range of chemical parameters at a lower price.

Enhancing Mild Cognitive Impairment Detection through Efficient Magnetic Resonance Image Analysis

Neuroimaging has emerged over the last few decades as a crucial tool in diagnosing Alzheimer’s disease(AD).Mild cognitive impairment(MCI)is a condition that falls between the spectrum of normal cognitive function and AD.However,previous studies have mainly used handcrafted features to classify MCI,AD,and normal control(NC)individuals.This paper focuses on using gray matter(GM)scans obtained through magnetic resonance imaging(MRI)for the diagnosis of individuals with MCI,AD,and NC.To improve classification performance,we developed two transfer learning strategies with data augmentation(i.e.,shear range,rotation,zoom range,channel shift).The first approach is a deep Siamese network(DSN),and the second approach involves using a cross-domain strategy with customized VGG-16.We performed experiments on the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset to evaluate the performance of our proposed models.Our experimental results demonstrate superior performance in classifying the three binary classification tasks:NC vs.AD,NC vs.MCI,and MCI vs.AD.Specifically,we achieved a classification accuracy of 97.68%,94.25%,and 92.18%for the three cases,respectively.Our study proposes two transfer learning strategies with data augmentation to accurately diagnose MCI,AD,and normal control individuals using GM scans.Our findings provide promising results for future research and clinical applications in the early detection and diagnosis of AD.

MXene Enhanced 3D Needled Waste Denim Felt for High‑Performance Flexible Supercapacitors

MXene,a transition metal carbide/nitride,has been prominent as an ideal electrochemical active material for supercapacitors.However,the low MXene load limits its practical applications.As environmental concerns and sustainable development become more widely recognized,it is necessary to explore a greener and cleaner technology to recycle textile by-products such as cotton.The present study proposes an effective 3D fabrication method that uses MXene to fabricate waste denim felt into ultralight and flexible supercapacitors through needling and carbonization.The 3D structure provided more sites for loading MXene onto Z-directional fiber bundles,resulting in more efficient ion exchange between the electrolyte and electrodes.Furthermore,the carbonization process removed the specific adverse groups in MXenes,further improving the specific capacitance,energy density,power density and electrical conductivity of supercapacitors.The electrodes achieve a maximum specific capacitance of 1748.5 mF cm-2 and demonstrate remarkable cycling stability maintaining more than 94%after 15,000 galvanostatic charge/discharge cycles.Besides,the obtained supercapacitors present a maximum specific capacitance of 577.5 mF cm^(-2),energy density of 80.2μWh cm^(-2)and power density of 3 mW cm^(-2),respectively.The resulting supercapacitors can be used to develop smart wearable power devices such as smartwatches,laying the foundation for a novel strategy of utilizing waste cotton in a high-quality manner.

Determination of the best materials for development and designing product using a multi-criteria decision-making

Purpose–Material selection,driven by wide and often conflicting objectives,is an important,sometimes difficult problem in material engineering.In this context,multi-criteria decision-making(MCDM)methodologies are effective.An approach of MCDM is needed to cater to criteria of material assortment simultaneously.More firms are now concerned about increasing their productivity using mathematical tools.To occupy a gap in the previous literature this research recommends an integrated MCDM and mathematical Bi-objective model for the selection of material.In addition,by using the Technique for Order Preference by Similarity to Ideal Solution(TOPSIS),the inherent ambiguities of decision-makers in paired evaluations are considered in this research.It goes on to construct a mathematical bi-objective model for determining the best item to purchase.Design/methodology/approach–The entropy perspective is implemented in this paper to evaluate the weight parameters,while the TOPSIS technique is used to determine the best and worst intermediate pipe materials for automotive exhaust system.The intermediate pipes are used to join the components of the exhaust systems.The materials usually used to manufacture intermediate pipe are SUS 436LM,SUS 430,SUS 304,SUS 436L,SUH 409 L,SUS 441 L and SUS 439L.These seven materials are evaluated based on tensile strength(TS),hardness(H),elongation(E),yield strength(YS)and cost(C).A hybrid methodology combining entropy-based criteria weighting,with the TOPSIS for alternative ranking,is pursued to identify the optimal design material for an engineered application in this paper.This study aims to help while filling the information gap in selecting the most suitable material for use in the exhaust intermediate pipes.After that,the authors searched for and considered eight materials and evaluated them on the following five criteria:(1)TS,(2)YS,(3)H,(4)E and(5)C.The first two criteria have been chosen because they can have a lot of influence on the behavior of the exhaust intermediate pipes,on their performance and on the cost.In this structure,the weights of the criteria are calculated objectively through the entropy method in order to have an unbiased assessment.This essentially measures the quantity of information each criterion contribution,indicating the relative importance of these criteria better.Subsequently,the materials were ranked using the TOPSIS method in terms of their relative performance by measuring each material from an ideal solution to determine the best alternative.The results show that SUS 309,SUS 432L and SUS 436 LM are the first three materials that the exhaust intermediate pipe optimal design should consider.Findings–The material matrix of the decision presented in Table 3 was normalized through Equation 5,as shown in Table 5,and the matrix was multiplied with weighting criteriaß_j.The obtained weighted normalized matrix V_ij is presented in Table 6.However,the ideal,worst and best value was ascertained by employing Equation 7.This study is based on the selection of material for the development of intermediate pipe using MCDM,and it involves four basic stages,i.e.method of translation criteria,screening process,method of ranking and search for methods.The selection was done through the TOPSIS method,and the criteria weight was obtained by the entropy method.The result showed that the top three materials are SUS 309,SUS 432L and SUS 436 LM,respectively.For the future work,it is suggested to select more alternatives and criteria.The comparison can also be done by using different MCDM techniques like and Choice Expressing Reality(ELECTRE),Decision-Making Trial and Evaluation Laboratory(DEMATEL)and Preference Ranking Organization Method for Enrichment Evaluation(PROMETHEE).Originality/value–The results provide important conclusions for material selection in this targeted application,verifying the employment of mutual entropy-TOPSIS methodology for a series of difficult engineering decisions in material engineering concepts that combine superior capacity with better performance as well as cost-efficiency in various engineering design.

Designing a Protection Scheme in Micro-Grid Systems with DG Using Central Protection Unite and Multiple Setting Group Protection Relays

Distributed generators now is widely used in electrical power networks, in some cases it works seasonally, and some types works at special weather conditions like photo voltaic systems and wind energy, and due to this continuous changes in generation condition, the fault current level in network will be affected, this changes in fault current level will affect in the coordination between protection relays and to keep the coordination at right way, an adaptive protection system is required that can adaptive its setting according to generation changes, the fault current level in each case is evaluated using ETAP software, and the required relay setting in each case is also evaluated using Grey Wolf Optimizer (GWO) algorithm, and to select suitable setting which required in each condition, to select the active setting group of protection relay according to generation capacity, central protection unite can be used, and to improve protection stability and minimizing relays tripping time, a proposed method for selecting suitable backup relay is used, which leads to decrease relays tripping time and increase system stability, output settings for relays in all cases achieved our constrains.

Impacts of Electrical Line Losses Comprising Mul-ti-Distributed Generation in Distribution System

This paper proposes to study the impacts of electrical line losses due to the connection of distributed generators (DG) to 22kV distribution system of Provincial Electricity Authority (PEA). Data of geographic information systems (GIS) including the distance of distribution line and location of load being key parameter of PEA is simulated using digital simulation and electrical network calculation program (DIgSILENT) to analyze power loss of the distribution system. In addition, the capacity and location of DG installed into the distribution system is considered. The results are shown that, when DG is installed close to the substation, the electrical line losses are reduced. However, if DG capacity becomes larger and the distance between DG and load is longer, the electrical line losses tend to increase. The results of this paper can be used to create the suitability and fairness of the fee for both DG and utility.

Electrical Performance and Effect of Frequency Electromagnetic Waves on Subterranean Termites <i>Coptotermes curvignathus</i>Holmgren

The research aimed to propose a non-destructive technology to control subterranean termites Coptotermes curvignathus Holmgren infestation based on electromagnetic waves. A portable apparatus for this technology has been built and its experiment is presented in this paper. Some electrical parameters were measured and analyzed along with their effects to the termites. The experiment using frequency range between 30 Hz - 600 kHz has been done. The average error of the apparatus by comparing the result with the direct measurement using oscilloscope was also measured. The highest error value appeared at 600 kHz with frequency error 6.05 kHz. The highest error of voltage (i.e. 0.186 Volt) appeared at 100 kHz. For safetiness, the highest magnetic field at 300 kHz was 0.1815 μT and at 500 kHz was 0.00725 μT which were safe for human. The average value of termites mortality was higher on irradiation time 120 minutes than 60 minutes respectively in all test frequency: 300 kHz, 400 kHz, 500 kHz and 600 kHz. This paper presents an important information of the electromagnatic-based technology for environmental friendly termites control in spite of using the insecticides.

Comparative Study of Three Phase 3-Leg Active Power Filters Connected to 4-Wire Electrical Systems

This paper presented the simulation results of the three phase electrical systems supplied by four wires with power quality problems, to which the parallel 3-leg APF （active power filters） are connected. The purpose of this study is to analyze the results obtained in these conditions in order to observe the limits of the 3-leg active power filters and to form a foundation for the future studies of the 4-leg active power filters. For a complete analysis, the APF will be controlled by four control methods： synchronous reference system control, indirect control, instantaneous p-q theory control, and positive sequence control. The analysis will watch the power quality indicators： THD （total harmonic distortion factor）, PF （power factor）, Iunb （unbalance factor）.

Photovoltaic Models Parameters Estimation Based on Weighted Mean of Vectors

Renewable energy sources are gaining popularity,particularly photovoltaic energy as a clean energy source.This is evident in the advancement of scientific research aimed at improving solar cell performance.Due to the non-linear nature of the photovoltaic cell,modeling solar cells and extracting their parameters is one of the most important challenges in this discipline.As a result,the use of optimization algorithms to solve this problem is expanding and evolving at a rapid rate.In this paper,a weIghted meaN oF vectOrs algorithm(INFO)that calculates the weighted mean for a set of vectors in the search space has been applied to estimate the parameters of solar cells in an efficient and precise way.In each generation,the INFO utilizes three operations to update the vectors’locations:updating rules,vector merging,and local search.The INFO is applied to estimate the parameters of static models such as single and double diodes,as well as dynamic models such as integral and fractional models.The outcomes of all applications are examined and compared to several recent algorithms.As well as the results are evaluated through statistical analysis.The results analyzed supported the proposed algorithm’s efficiency,accuracy,and durability when compared to recent optimization algorithms.

Model-Free Adaptive Frequency Calibration for Voltage-Controlled Oscillators

The operating frequency accuracy of the local oscillators is critical for the overall system performance in the communication systems.However,the high-precision oscillators could be too expensive for civil applications.In this paper,we propose a model-free adaptive frequency calibration framework for a voltage-controlled crystal oscillator(VCO)equipped with a time to digital converter(TDC),which can significantly improve the frequency accuracy of the VCO thus calibrated.The idea is to utilize a high-precision TDC to directly measure the VCO period which is then passed to a model-free method for working frequency calibration.One advantage of this method is that the working frequency calibration employs the system history of input/output(I/O)data,instead of establishing an accurate VCO voltagecontrolled oscillator model.Another advantage is the lightweight calibration method with low complexity such that it can be implemented on an MCU with limited computation capabilities.Experimental results show that the proposed calibration method can improve the frequency accuracy of a VCO from±20 ppm to±10 ppb,which indicates the promise of the modelfree adaptive frequency calibrator for VCOs.

An ensemble deep learning model for cyber threat hunting in industrial internet of things

By the emergence of the fourth industrial revolution,interconnected devices and sensors generate large-scale,dynamic,and inharmonious data in Industrial Internet of Things(IIoT)platforms.Such vast heterogeneous data increase the challenges of security risks and data analysis procedures.As IIoT grows,cyber-attacks become more diverse and complex,making existing anomaly detection models less effective to operate.In this paper,an ensemble deep learning model that uses the benefits of the Long Short-Term Memory(LSTM)and the AutoEncoder(AE)architecture to identify out-of-norm activities for cyber threat hunting in IIoT is proposed.In this model,the LSTM is applied to create a model on normal time series of data(past and present data)to learn normal data patterns and the important features of data are identified by AE to reduce data dimension.In addition,the imbalanced nature of IIoT datasets has not been considered in most of the previous literature,affecting low accuracy and performance.To solve this problem,the proposed model extracts new balanced data from the imbalanced datasets,and these new balanced data are fed into the deep LSTM AE anomaly detection model.In this paper,the proposed model is evaluated on two real IIoT datasets-Gas Pipeline(GP)and Secure Water Treatment(SWaT)that are imbalanced and consist of long-term and short-term dependency on data.The results are compared with conventional machine learning classifiers,Random Forest(RF),Multi-Layer Perceptron(MLP),Decision Tree(DT),and Super Vector Machines(SVM),in which higher performance in terms of accuracy is obtained,99.3%and 99.7%based on GP and SWaT datasets,respectively.Moreover,the proposed ensemble model is compared with advanced related models,including Stacked Auto-Encoders(SAE),Naive Bayes(NB),Projective Adaptive Resonance Theory(PART),Convolutional Auto-Encoder(C-AE),and Package Signatures(PS)based LSTM(PS-LSTM)model.

A Secure Framework for Blockchain Transactions Protection

One of the most extensively used technologies for improving the security of IoT devices is blockchain technology.It is a new technology that can be utilized to boost the security.It is a decentralized peer-to-peer network with no central authority.Multiple nodes on the network mine or verify the data recorded on the Blockchain.It is a distributed ledger that may be used to keep track of transactions between several parties.No one can tamper with the data on the blockchain since it is unchangeable.Because the blocks are connected by hashes,the transaction data is safe.It is managed by a system that is based on the consensus of network users rather than a central authority.The immutability and tamper-proof nature of blockchain security is based on asymmetric cryptography and hashing.Furthermore,Blockchain has an immutable and tamper-proof smart contract,which is a logic that enforces the Blockchain’s laws.There is a conflict between the privacy protection needs of cyber-security threat intelligent(CTI)sharing and the necessity to establish a comprehensive attack chain during blockchain transactions.This paper presents a blockchain-based data sharing paradigm that protects the privacy of CTI sharing parties while also preventing unlawful sharing and ensuring the benefit of legitimate sharing parties.It builds a full attack chain using encrypted threat intelligence and exploits the blockchain’s backtracking capacity to finish the decryption of the threat source in the attack chain.Smart contracts are also used to send automatic early warning replies to possible attack targets.Simulation tests are used to verify the feasibility and efficacy of the suggested model.

Initiating Binary Metal Oxides Microcubes Electromagnetic Wave Absorber Toward Ultrabroad Absorption Bandwidth Through Interfacial and Defects Modulation

Cobalt nickel bimetallic oxides(NiCo_(2)O_(4))have received numerous attentions in terms of their controllable morphology,high temperature,corrosion resistance and strong electromagnetic wave(EMW)absorption capability.However,broadening the absorption bandwidth is still a huge challenge for NiCo_(2)O_(4)-based absorbers.Herein,the unique NiCo_(2)O_(4)@C core-shell microcubes with hollow structures were fabricated via a facile sacrificial template strategy.The concentration of oxygen vacancies and morphologies of the three-dimensional(3D)cubic hollow core-shell NiCo_(2)O_(4)@C framework were effectively optimized by adjusting the calcination temperature.The specially designed 3D framework structure facilitated the multiple reflections of incident electromagnetic waves and provided rich interfaces between multiple components,generating significant interfacial polarization losses.Dipole polarizations induced by oxygen vacancies could further enhance the attenuation ability for the incident EM waves.The optimized NiCo_(2)O_(4)@C hollow microcubes exhibit superior EMW absorption capability with minimum RL(RLmin)of-84.45 dB at 8.4 GHz for the thickness of 3.0 mm.Moreover,ultrabroad effective absorption bandwidth(EAB)as large as 12.48 GHz(5.52-18 GHz)is obtained.This work is believed to illuminate the path to synthesis of high-performance cobalt nickel bimetallic oxides for EMW absorbers with excellent EMW absorption capability,especially in broadening effective absorption bandwidth.

Optimal Operation of Distributed Generations Considering Demand Response in a Microgrid Using GWO Algorithm

The widespread penetration of distributed energy sources and the use of load response programs,especially in a microgrid,have caused many power system issues,such as control and operation of these networks,to be affected.The control and operation of many small-distributed generation units with different performance characteristics create another challenge for the safe and efficient operation of the microgrid.In this paper,the optimum operation of distributed generation resources and heat and power storage in a microgrid,was performed based on real-time pricing through the proposed gray wolf optimization(GWO)algorithm to reduce the energy supply cost with the microgrid.Distributed generation resources such as solar panels,diesel generators with battery storage,and boiler thermal resources with thermal storage were used in the studied microgrid.Also,a combined heat and power(CHP)unit was used to produce thermal and electrical energy simultaneously.In the simulations,in addition to the gray wolf algorithm,some optimization algorithms have also been used.Then the results of 20 runs for each algorithm confirmed the high accuracy of the proposed GWO algorithm.The results of the simulations indicated that the CHP energy resources must be managed to have a minimum cost of energy supply in the microgrid,considering the demand response program.

N-Doped rGO-Like Carbon Prepared from Coconut Shell:Structure and Specific Capacitance

An rGO−like carbon compound has been synthesized from biomass,i.e.,old coconut shell,by a carbonization process followed by heating at 400°C for 5 h.The nitrogen doping was achieved by adding the urea(CH4N2O)and stirring at 70°C for 14 h.The morphology and structure of the rGO-like carbon were investigated by electron microscopies and Raman spectroscopy.The presence of C-N functional groups was analyzed by Fourier transform infrared and synchrotron X-ray photoemission spectroscopy,while the particle and the specific capacitance were measured by particle sizer and cyclic voltammetry.The highest specific capacitance of 72.78 F/g is achieved by the sample with 20%urea,having the smallest particles size and the largest surface area.The corresponding sample has shown to be constituted by the appropriate amount of C–N pyrrolic and pyridinic defects.

PoIR: A Node Selection Mechanism in Reputation-Based Blockchain Consensus Using Bidirectional LSTM Regression Model

This research presents a reputation-based blockchain consensus mechanism called Proof of Intelligent Reputation(PoIR)as an alternative to traditional Proof of Work(PoW).PoIR addresses the limitations of existing reputationbased consensus mechanisms by proposing a more decentralized and fair node selection process.The proposed PoIR consensus combines Bidirectional Long Short-Term Memory(BiLSTM)with the Network Entity Reputation Database(NERD)to generate reputation scores for network entities and select authoritative nodes.NERD records network entity profiles based on various sources,i.e.,Warden,Blacklists,DShield,AlienVault Open Threat Exchange(OTX),and MISP(Malware Information Sharing Platform).It summarizes these profile records into a reputation score value.The PoIR consensus mechanism utilizes these reputation scores to select authoritative nodes.The evaluation demonstrates that PoIR exhibits higher centralization resistance than PoS and PoW.Authoritative nodes were selected fairly during the 1000-block proposal round,ensuring a more decentralized blockchain ecosystem.In contrast,malicious nodes successfully monopolized 58%and 32%of transaction processes in PoS and PoW,respectively,but failed to do so in PoIR.The findings also indicate that PoIR offers efficient transaction times of 12 s,outperforms reputation-based consensus such as PoW,and is comparable to reputation-based consensus such as PoS.Furthermore,the model evaluation shows that BiLSTM outperforms other Recurrent Neural Network models,i.e.,BiGRU(Bidirectional Gated Recurrent Unit),UniLSTM(Unidirectional Long Short-Term Memory),and UniGRU(Unidirectional Gated Recurrent Unit)with 0.022 Root Mean Squared Error(RMSE).This study concludes that the PoIR consensus mechanism is more resistant to centralization than PoS and PoW.Integrating BiLSTM and NERD enhances the fairness and efficiency of blockchain applications.