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The inverter module, as a critical device in arc plasma power supplies for converting DC to AC, often encounters issues such as stability errors and system disturbances due to the impact of shock loads when tracking rated voltage and frequency signals. This paper proposes an improved Particle Swarm Optimization (PSO) strategy for Fractional-Order PID (FOPID) control with limited parameter selection, aiming to address the poor performance of inverter modules in arc plasma power supply systems. The proposed strategy incorporates the spiral position updating mode from the Whale Optimization Algorithm into the position updating process of PSO, which improves particle diversity, global search capability and optimization stability. The effectiveness of the proposed method is verified by comparing it with the Ziegler–Nichols tuning method and the standard PSO algorithm in the MATLAB/Simulink simulation environment. A model of an inverter system is established for validation purposes. The results indicate that applying the improved PSO-based FOPID control strategy to the inverter process of the arc plasma power supply system significantly enhances the output waveform quality, reduces harmonic distortion, and achieves excellent control performance. [ABSTRACT FROM AUTHOR]

Traffic engineering is critical for optimizing network performance and resource utilization. Software-Defined Networking (SDN) offers a flexible architecture that allows dynamic traffic management. However, traditional traffic engineering methods can struggle as SDN networks grow in size and complexity, making congestion management a key challenge. This paper presents a novel traffic engineering solution for SDN that utilizes Particle Swarm Optimization (PSO) within a scalable hierarchical framework. The approach addresses the difficulties of dynamic traffic patterns and resource allocation in large-scale SDN deployments by breaking down traffic management tasks into manageable domains. By leveraging PSO's ability to explore large solution spaces, the algorithm optimizes routing paths and bandwidth allocation based on a fitness function designed to minimize congestion, latency, and other key performance metrics. The proposed method, called Particle Swarm Optimization for Congestion Management (PSOCB), focuses on optimizing cluster switches using meta-heuristic techniques. PSOCB achieves a 13% increase in throughput and an 8% improvement in link performance, while reducing delays compared to traditional methods. Although PSOCB occasionally showed higher congestion than baseline approaches, it quickly restored normal network conditions through its congestion removal strategies. Simulations on realistic network topologies demonstrate substantial performance improvements over conventional static routing, highlighting the effectiveness and scalability of our PSO-based hierarchical traffic engineering solution. [ABSTRACT FROM AUTHOR]

Fuzzy logic controller (FLC) is renowned for its adaptability and intuitive decision-making capabilities in active suspension systems, which face challenges stemming from unpredictable disturbances and complex vehicle dynamics. In this study, we introduce a novel optimization approach termed WW-PSO, which merges particle swarm optimization (PSO) with water wave optimization (WWO), aiming to elevate the performance of an FLC-based active suspension system. WWO efficiently solves optimization problems by simulating natural water wave behaviors. The hybridization of PSO and WWO leverages their complementary exploration and exploitation capabilities, resulting in improved performance and robustness of the optimized controller. The performance of the proposed controller, which is augmented with a linear quadratic controller (LQR), is evaluated across three scenarios featuring different road profiles and compared against other recent optimization methods which include genetic algorithm, tent sparrow search algorithm (Tent-SSA), and ST-PS-SO which is a combination of PSO, sewing trainee-based optimization, and symbiotic organism search. Simulation results show that the proposed WW-PSO significantly improves integral time absolute error (ITAE) for both body and wheel displacements, overshoot/undershoot (OS/US), and settling time. Specifically, the proposed method achieves a 53.37% improvement in ITAE, a 56.44% reduction in OS/US, and a 13.09% decrease in settling time for body displacements. For wheel displacements, it achieves a 52.90% improvement in ITAE, a 48.72% reduction in OS/US, and a 14.15% decrease in settling time. These enhancements demonstrate the hybrid method's effectiveness in improving vehicle stability and passenger comfort across a range of road conditions. [ABSTRACT FROM AUTHOR]

Traditional clustering algorithms such as K-means need to input the number of clusters before the start of the algorithm. Affinity propagation (AP) clustering algorithm solves this problem by considering each data point as a prospective cluster head (exemplar) and finding a set of appropriate exemplars by message passing. However, the AP clustering algorithm requires two parameters: preference and damping factor. Providing the parameters in advance poses the same issue faced in the traditional clustering algorithm. Moreover, all data points are not equally relevant for becoming cluster heads. To overcome these problems, we propose two parameter-free particle swarm optimization-based algorithms, PSO-APver1 and PSO-APver2. Furthermore, we introduce a novel version of mutant PSO where two cluster validity indices are used to judge the quality of the clustering solution. In PSO-APver2, we consider the internal data distribution using the square wave function to determine the initial preference value of data points. We conducted experiments on 8 real-world datasets to show the efficacy of our proposed algorithms over classic algorithms and two AP-based algorithms. We conducted the Friedman test followed by post hoc analysis to exhibit the significance of our work. [ABSTRACT FROM AUTHOR]

The cable-driven redundant space manipulator (CDRSM) has a wide range of potential applications in on-orbit servicing due to its light weight, low inertia, and intrinsic compliance. Especially for capturing and detumbling large-momentum tumbling targets in space, it requires agile approaching and synchronizing, compliant contact and dexterous post-capture manipulation. Due to the limited energy resources of the space robot, we propose a coordinated configuration and end-effector (EE) trajectory optimization method to minimize base disturbance, as well as maximize EE stiffness for guaranteeing the accuracy of post-capture manipulation. The dynamics coupling between the free-floating base and the CDRSM is derived, along with the kinematic modeling and stiffness analysis of the cable-driven joints. Considering the equiangular constraint relationship and the kinematic coupling characteristics of the cable-driven joints, the configuration of the CDRSM and the EE trajectory are parametrically represented using the arm angle and the rational Bézier curves, respectively. Further, based on these parameter variables, the particle swarm optimization and multiple-objective particle swarm optimization algorithms are used to minimize the base disturbance and maximize EE stiffness during post-capture manipulation. Finally, several simulation results demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this investigation, an optimized heat treatment following rolling was applied to an A390/6Ti₂AlC composite, and its influence on the microstructure and properties was studied. These composites, known for their enhanced mechanical properties, are used in critical applications like face plate assemblies and brake rotors. Analysis using field emission scanning electron microscopy (FESEM), energy-dispersive X-ray spectroscopy (EDX) and X-ray diffraction (XRD) techniques indicated a uniform distribution of particulates. Notably, composites with a 6% Ti₂AlC weight fraction exhibited a substantial improvement, with a 16% increase in tensile strength and a 4% increase in yield strength compared to the base alloy. To further enhance the material's mechanical properties for superior durability and performance, an optimized regimen of rolling followed by heat treatment was applied, refining the microstructure and improving the composite material's balance of strength and flexibility for specific applications. [ABSTRACT FROM AUTHOR]

This article presents an innovative asymmetric multilevel inverter (MLI) topology that outperforms conventional counterparts. The introduced topology presents a breakthrough in implementing power electronics control by maximizing specific levels while minimizing switching components. A cutting-edge control scheme for optimal operation of the cascaded half-bridge MLI is presented. The ant lion optimization (ALO) algorithm was implemented to optimize the switching control to reduce the total harmonic distortion (THD) and improve power quality. For verification, the performance and effectiveness of the ALO technique are assessed by comparing its results to those obtained using the simplified sinusoidal pulse width modulation (SSPWM) technique, genetic algorithm (GA), and particle swarm optimization (PSO) in existing literature. Simulation results verified the efficacy of ALO in finding the optimal parameters. The suggested method showcases a remarkable reduction in the THD compared to SSPWM. The quality of the resulting waveform was enhanced, and both filter size and cost were significantly reduced. To meet stringent IEEE standards, an LC filter has been designed with minimal size and proper requirements. Experimental results validation of the suggested scheme, using a dSPACE R&D controller board unequivocally, confirmed its robustness and effectiveness. This groundbreaking study not only introduces a superior asymmetric MLI topology but also validates its exceptional performance through comprehensive analysis and experimentation. The experimental waveforms showed good matching with the simulation outcomes. The findings hold immense promise for advancing the field of power system control and revolutionizing the designing and implementation of efficient and cost-effective inverter systems. [ABSTRACT FROM AUTHOR]

In this study, a linked simulation optimization (SO) model is presented for identification of groundwater contaminant sources. The SO model consists of two steps namely, simulation and optimization. The simulation step entails developing a groundwater contaminant transport model in which the advection–dispersion-reaction equation (ADRE) is solved for predicting the concentration of the contaminant. The system parameters (hydraulic conductivity, dispersivity, etc.) and control variables (pumping, recharge, etc.,) are given as model inputs. A meshless technique called the meshless Local Radial Point Interpolation Method (LRPIM) is employed to solve the contaminant transport equation. The simulation model is linked with three different swarm intelligence-based optimization models namely, teaching–learning based optimization (TLBO), grey wolf optimization (GWO) and particle swarm optimization (PSO) to form three SO models namely LRPIM-PSO, LRPIM-GWO and LRPIM-TLBO. The SO model minimizes the difference between the predicted and observed concentrations to determine the unknown source locations and release histories. The applicability of the developed SO models for source identification (SI) is demonstrated with a hypothetical and real aquifer problems to identify the groundwater contaminant sources. All the 3 models are able to locate the sources and release histories satisfactorily. However, the LRPIM-TLBO has been found to be more accurate followed by LRPIM-PSO and LRPIM-GWO. [ABSTRACT FROM AUTHOR]

As the world endeavors to shift toward sustainable energy solutions, the pivotal role of solar energy, specifically photovoltaics, becomes increasingly evident. This study investigates the critical task of accurately predicting photovoltaics power output, a fundamental aspect of maximizing economic benefits and ensuring stability in modern electric power systems. Three categories of models, including deterministic, statistical, and hybrid, are explored, with a focus on machine learning (ML) models such as AdaBoost and HGBoost. The results indicate that AdaBoost generally outperforms HGBoost in terms of accuracy metrics, such as R2, RMSE, and VAF, demonstrating its effectiveness in photovoltaics power prediction. However, the difference in performance, while notable, may not be substantial across all metrics, suggesting that the choice of the best model could depend on specific use cases and trade-offs between accuracy and computational efficiency. In addition, this study introduces hybrid models incorporating optimization algorithms such as fruit-fly optimizer, satin bowerbird optimizer, and particle swarm optimizer, with the integration of HGBoost and satin bowerbird optimizer emerging as the top-performing hybrid model (R2 = 0.9907), depicting enhanced accuracy and reduced error rates. The study concludes that combining ML with optimization algorithms significantly enhances PV power prediction accuracy, offering valuable insights for integrating renewable energy into modern energy systems. [ABSTRACT FROM AUTHOR]

In recent years, the high cost of searching and processing raw materials and the fact that raw material resources have come to the point of depletion force industry and science to seek different solutions. In addition, due to the necessity of increasing the quality, the tendency to composite production has increased. However, it is necessary to make improvements in parameters such as quality, time, and cost in composite production. The use of artificial intelligence technology in the production of composites is becoming more and more common. On the other hand, studies on the determination of raw material mixture ratios are not common. Such studies are usually carried out with experimental productions, which increases the production cost and prolongs the production process. In this study, it was studied on the proportional optimization of the mixture components of thermoplastic hybrid composite materials by using the particle swarm optimization algorithm. First, a dataset was created with the real values obtained from the experimental productions, and the raw material mixture ratios to be included in the mixture were determined by making simulation studies on this dataset. With these obtained ratios, new productions were made, and tests were applied on these productions. Test results have shown that the resulting products are more successful by over 95%. Thus, it has been proven that especially the use of artificial intelligence technology in the field of composites in pre-production stages such as raw material and mixture processes can both reduce the cost and increase the quality. [ABSTRACT FROM AUTHOR]

The half-adder (HA) is one of the most useful combinational logic functions for data processing. We report on demonstrating an all-optical HA comprising AND and XOR all-optical logic gates (AO-LGs) without nonlinearity and thus with arbitrarily low intensity, ultrafast light-by-light control. The proposed method engages the coherent interaction of optical waves on a metasurface nanostructure, known as coherent perfect absorption (CPA), which is only 50 nm thick, to efficiently control the absorption of near-infrared (700–800 nm) pulses. Particle Swarm Optimization (PSO) determines the most efficient dimensions of metasurface unit cells. Another significant innovation of this article is the utilization of an adaptive neuro-fuzzy inference system (ANFIS) instead of a simple threshold. The flexibility of the designed fuzzy system allows it to be used for any CPA system, applied in the implementation of logic gates. Achieved figures of merit of AO-LGs, including contrast ratio (CR = 89.77 dB) and extinction ratio (ER = 87.53 dB), prove the impressive effects of ANFIS on the gates' efficiency. Smaller dimensions, lower power consumption, and a reduced logic state range compared to other HAs are additional specifications of the designed HA, achieved through optimized metasurface-based CPA and ANFIS. [ABSTRACT FROM AUTHOR]

Purpose: An artificial respiratory system provides support to critically ill patients. Optimizing the control of airway pressure in an artificial respiratory system is difficult due to its non-linear characteristics. A PID controller is most widely used for artificial respiratory systems. This paper presents the optimization of a PID controller for controlling airway pressure in an artificial respiratory system based on a Teaching Learning-Based Optimization Algorithm. Methods: The artificial respiratory system is modeled mathematically, and a transfer function is derived to design the proposed controller. A comparative study of the proposed TLBO-based controller is done with a Particle Swarm Optimization, PSO-based controller, and a conventional Zeigler-Nichols tuned controller in terms of performance indices. The proposed controller was also tested for robustness by varying lung compliance and leakage resistance from - 20% to + 20% of the nominal value. Results: It is observed that the proposed controllers based on the TLBO Algorithm showed better response, in terms of rise time, overshoot, and settling time. The overshoot was reduced to 0% in the case of the TLBO-tuned PID controller, as compared to 10.56% in the PSO-tuned PID controller. Also, in the case of the proposed TLBO-tuned controller rise time and settling time were reduced to 2.30 and 2.98 seconds, from 4.65 and 5.26 seconds in the conventionally tuned Zeigler Nichols-tuned PID controller. Conclusions: In this study, simulation results of Particle swarm optimization tuned and teaching learning optimization-based PID controllers on an artificial respiratory system are presented. The results showed that the poor performance of the conventionally tuned PID controller can be improved significantly by the application of TLBO algorithm-tuned controllers. [ABSTRACT FROM AUTHOR]

With the rapid advancement of the new energy transformation process, the stability of photovoltaic microgrid output is particularly important. However, current photovoltaic microgrids suffer from unstable output and power fluctuations. To improve the stability and system controllability of photovoltaic microgrid output, this study constructs an optimized grey wolf optimization algorithm. Using the idea of small step perturbation, it is applied to the maximum power point tracking solar controller to construct a maximum power point controller algorithm based on the improved algorithm. Secondly, the algorithm is combined with photovoltaic arrays to construct a maximum tracking point control system for photovoltaic arrays based on the algorithm. Finally, the system is combined with low-pass filtering power allocation and secondary power allocation strategies, as well as a hybrid storage system, to construct a photovoltaic microgrid control model. In the performance comparison analysis of the research algorithm, the average accuracy and average loss value of the algorithm were 98.2% and 0.15, respectively, which were significantly better than the compared algorithms. The performance analysis of the photovoltaic microgrid control model showed that the model could effectively regulate and control the output power of the microgrid under two operating conditions, demonstrating its effectiveness. The above results indicate that The proposed algorithm and the improved algorithm of the PV microgrid control model can not only improve the steady-state tracking accuracy, but also have better dynamic performance and improve the tracking speed. The control strategy can maintain the operational stability of the microgrid system and realize the smooth switching control of each mode, meeting the stability and flexibility requirements of the PV microgrid system. The novelty of this study is that the improved Grey Wolf optimization algorithm enhances the global search ability by introducing the random jump mechanism of Levy flight algorithm and the combination of particle swarm optimization algorithm and Grey Wolf optimization algorithm to avoid falling into the local optimal. The randomness and ergodicity of Levy flight algorithm enable the hybrid algorithm to quickly adapt to the changes of light intensity and environmental conditions, and maintain the efficient operation of MPPT. Moreover, particle swarm optimization has strong local search ability, and gray Wolf optimization improves local search accuracy. The combination of the two improves local search accuracy. By combining the characteristics of Levy flight algorithm, the parameters of PSO and GWO algorithm, such as inertia weight and convergence factor, are dynamically adjusted to adapt to different working conditions of MPPT. The optimal solution is output as the optimal strategy of MPPT through collaboration. The potential practical impact is that the improved MPPT control strategy can track the maximum power point more effectively, improve the efficiency and stability of the photovoltaic power generation system, reduce energy waste by improving the tracking accuracy and convergence speed of the photovoltaic system, and improve the robustness of the system. [ABSTRACT FROM AUTHOR]

Due to the necessity of virtualization in a fog environment with limited resources, service providers are challenged to reduce the energy consumption of hosts. The consolidation of virtual machines (VMs) has led to a significant amount of research into the effective management of energy usage. Due to their high computational overhead, the existing virtualization techniques may not be suited to minimize the energy consumption of fog devices. As containers have recently gained popularity for encapsulating fog services, they are an ideal candidate for addressing this issue, particularly for fog devices. In the proposed work, an ensemble model is used for load prediction on hosts to classify them as overloaded, underloaded, or balanced. A container selection algorithm identifies containers for migration when a host becomes overloaded. Additionally, an energy-efficient container migration strategy facilitated by a dynamic inertia weight-based particle swarm optimization (DIWPSO) algorithm is introduced to meet resource demands. This approach entails migrating containers from overloaded hosts to others in order to balance the load and reduce the energy consumption of hosts located on fog nodes. Experimental results demonstrate that load balancing can be achieved at a lower migration cost. The proposed DIWPSO algorithm significantly reduces energy consumption by 10.89% through container migration. Moreover, compared to meta-heuristic solutions such as PSO, ABC (Artificial Bee Colony), and E-ABC (Enhanced Artificial Bee Colony), the proposed DIWPSO algorithm shows superior performance across various evaluation parameters. [ABSTRACT FROM AUTHOR]

The node localization problem of wireless sensor networks (WSNs) is addressed in this article with a node localization algorithm designed using fuzzy logic and a nature-inspired algorithm. The coordinates of target nodes are obtained using fuzzy logic reasoning and nature-inspired algorithms. The fuzzy logic concept is used to remove the nonlinearities that arise due to signal strength measurement in the process of range estimation. The triangular and trapezoidal membership functions are used with the Mamdani fuzzy inference system for distance improvement between sensor nodes. Further, particle swarm optimization (PSO) and the Jaya algorithm (JA) determine the target nodes' location coordinates. The comparison of the proposed fuzzy logic-based PSO (FL-PSO) and fuzzy logic-based JA (FL-JA) algorithms is made with PSO and Jaya algorithm-based node localization algorithms for localization error. The influence of anchor nodes and degree of irregularity is verified during localization analysis on the FL-PSO and FL-JA node localization algorithms. The proposed FL-PSO and FL-JA node localization algorithms are evaluated for scalability, computation time, mean absolute deviation, and complexity to determine their efficacy. The simulations are carried out on MATLAB software in addition to the fuzzy logic toolbox. [ABSTRACT FROM AUTHOR]

Hybrid wireless networks are a combination of traditional fixed-network systems and mobile ad hoc networks. They are used in various applications such as online classes, video conference meetings, rescue operations during natural catastrophes, military engagements, and battlefield operations. One of the most challenging problems in a dynamic connectionless environment such as MANET is ensuring secure transmission. Another important challenge is maximising network lifespan by optimising energy consumption. In order to address the aforementioned concerns, we propose the utilisation of a clustering routing system for hybrid wireless networks that is both energy efficient and based on trust. This protocol incorporates particle swarm optimisation and Fuzzy logic for its implementation. The system constructs a cluster by selecting and designating the cluster head and super cluster head nodes. Secure transmission involves evaluating trust factors for each node and calculating a trust value. Multiple secure paths are then generated from the source to the destination. The path with the highest trust value and the greatest bandwidth is chosen for the transmission. The experimental investigation of the proposed BT-CPHM protocol demonstrates a decrease in delay, power consumption, packet drop, and an improvement in throughput and network longevity. [ABSTRACT FROM AUTHOR]

The Internet of Things represents a rapidly evolving networking paradigm that brings numerous benefits through its diverse applications. Advances in embedded system architectures and the compression of IPv6 have facilitated embedding IP stack functionalities within resource-constrained low power and lossy networks (LLNs). However, the widespread deployment of LLN networks within the IoT infrastructure has made them a target for cyber attackers. Machine and Deep Learning methods show promise in detecting anomalies within the Routing Protocol for LLNs. However, they face challenges such as handling imbalanced data, identifying relevant attack features, selecting the most suitable model parameters for detection, and accurately pinpointing attacker nodes across various locations in LLN. This study proposes an approach for detecting Selective Forwarding attacks in the LLN, consisting of four main stages: data preparation, data balancing using an optimized synthetic minority over-sampling technique, feature selection with binary particle swarm optimization algorithm, and attack detection via an optimized Random Forest with GridSearchCV. The proposed approach is evaluated using a grid-based dataset across three scenarios that differ in attacker node positioning relative to the LLN's root node. The findings indicate that the proposed approach achieved the highest accuracy and F-measure score of 99.82% in the second scenario. It also recorded precision scores of 100% in both the first and second scenarios and a recall score of 100% in the third scenario. Ultimately, the proposed approach outperforms existing methods, demonstrating its superiority in detecting SF attacks within LLNs. [ABSTRACT FROM AUTHOR]

The economic load dispatch problem owns features like multimodality, non-convexity, and hence, the traditional programming models fail to offer a solution to it. We have shown our research interest in PSO to solve the problem due to the umpteen numbers of applications it supports along with their improved swarming intelligence. Our previous paper has been dealt with an improved form of PSO, wherein adaptive acceleration constants were exploited. The adaptive nature comes from the dynamic changing of the acceleration constants with regard to the particle’s position as well as the number of function assessments. In that algorithm, the particles were enabled to search in a systematic distributed environment and the algorithm was known as PSO with Distributed Acceleration Constant (PSODAC). This paper makes an extension of PSODAC through hybridizing it with the notion behind Differential Evolution (DE). The hybridization has been performed in a sequential manner. Hence, it achieves the name as Sequentially Hybridized DE and PSODAC (SH-DEPSODAC), irrespective of the fact that the PSODAC has been employed. A total of three test systems have been utilized to test and examine the superiority of PSODAC for exhibiting higher particle dynamics. [ABSTRACT FROM AUTHOR]

The idea of network function virtualization has emerged recently as a means of accelerating the deployment of middleboxes and network operations while also lowering deployment costs. Service function chaining provides network connectivity and steers traffic between the deployed VNF instances to provide a network service. However, integrating NFV within SFC scheduling introduces complexities, particularly in efficiently allocating resources to VNFs amidst dynamic network traffic and service demands. Optimization of VNF placement and scheduling is essential to minimize execution costs while meeting stringent Service Level Agreements (SLAs) and ensuring quality-guaranteed services. Moreover, the convergence of NFV and SFC scheduling brings for security challenges, including unauthorized access, data interception, and service disruption. Balancing optimization objectives with stringent security requirements poses a non-trivial task, emphasizing the need for prioritizing security in resource allocation and scheduling decisions. Meeting SFC deadlines is challenging due to dynamic network conditions, service demands, and resource allocation complexity. Failure to meet deadlines can lead to service quality degradation, SLA violations, and financial penalties. To address these challenges, the security and cost-aware SFC scheduling problem is formulated as an optimization problem. Moreover, a three-level security model is designed for both the VNFs and the physical machines in the NFV-enabled networks. Since the problem is NP-hard, we propose two heuristics named Particle Swarm Optimization-Based SFC Scheduling approach and Group Learning Particle Swarm Optimization-Based SFC Scheduling model that focus on optimizing the execution cost of SFC while meeting security and deadline requirements. The proposed models are compared with the existing SFC scheduling models. The effectiveness of the proposed scheduling approaches is evaluated through extensive simulations and it is shown that proposed scheduling approaches outperform the existing models in terms of average execution cost, security violation ratio, deadline violation ratio, service level agreement violation ratio, and average delay. [ABSTRACT FROM AUTHOR]

In the modern era of information technology, the volume of multimedia data is increasing drastically. The copyright protection of this online content has been considerably challenged as various intruders modified these data by applying malicious attacks to forge it. Watermarking schemes can provide anti-interference capability and protect the copyright of digital multimedia content by storing, transmitting, and processing data. Essential attributes of image watermarking includes imperceptibility, robustness, and security. In this proposed work, Integer wavlet transform (IWT) is applied to the cover image, and the LL sub-band is subjected to Schur decomposition (SD) to get upper triangular and unitary matrices. The diagonal coefficients of the previously obtained upper triangular matrix are modified to embed the watermark. This paper also presents an analysis of the proposed scheme for False positive problem (FPP) detection. In this technique, the trade-off between robustness and imperceptibility is adjusted by optimizing the embedding gain factor through the application of Particle swarm optimization (PSO) and Manta ray foraging optimization (MRFO). The comparison of the performance of the suggested scheme against different attacks is made for both the optimization strategies. The suggested method offers nearly 40 dB PSNR and more than 0.9900 NCC values in most of the instances that ensures the watermark is correctly recovered and undetected. The average percentage improvement in NCC is 11.39% and in PSNR is 24.01% for MRFO. [ABSTRACT FROM AUTHOR]

In this study, we examine the assessment of surface roughness on turned surfaces of Ti 6Al 4V using a computer vision system. We utilize the Dual-Tree Complex Wavelet Transform (DTCWT) to break down the images of the turned surface into sub-images oriented in directions. Three different methods of feature generation have been compared, i.e., the use of Gray-Level Co-Occurrence Matrix (GLCM) and DTCWT-based extraction of second-order statistical features, DTCWT Image fusion, and the use of GLCM for feature extraction, and DTCWT image fusion using Particle Swarm Optimization (PSO) based GLCM features. Principal Component Analysis (PCA) was utilized to identify and select features. The model was developed using a Radial Basis Function Neural Network (RBFNN). Accordingly, six models were designed based on the three feature generation methods, considering all features and features selected using PCA. The RBFNN model, which incorporates DTCWT Image fusion and utilizes PSO with PCA features, achieved a training data prediction accuracy of 100% and a test data prediction accuracy of 99.13%. [ABSTRACT FROM AUTHOR]

To tackle the challenge of low accuracy in stock prediction within high-noise environments, this paper innovatively introduces the CED-PSO-StockNet time series model. Initially, the model decomposes raw stock data using the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) technique and reconstructs the components by estimating their frequencies via the extreme point method. This process enhances component stability and mitigates noise interference. Subsequently, an Encoder-Decoder framework equipped with an attention mechanism is employed for precise prediction of the reconstructed components, facilitating more effective extraction and utilization of data features. Furthermore, this paper utilizes an Improved Particle Swarm Optimization (IPSO) algorithm to optimize the model parameters. On the Pudong Bank dataset, through ablation experiments and comparisons with baseline models, various optimization strategies incorporated into the proposed CED-PSO-StockNet model were effectively validated. Compared to the standalone LSTM model, CED-PSO-StockNet achieved a remarkable 45.59% improvement in the R2 metric. To further assess the model's generalization capability, this paper also conducted comparative experiments on the Ping An Bank dataset, and the results underscored the significant advantages of CED-PSO-StockNet in the domain of stock prediction. [ABSTRACT FROM AUTHOR]

This study aims to enhance understanding of material wear behavior by investigating the microstructure and wear characteristics of AA7178 matrix alloy reinforced with varying wt.% (0, 1, 2, and 3%) of nano-SiC particles, fabricated through stir-casting. The research addresses deficiencies in current materials used in aerospace components, where wear resistance is crucial. These materials often suffer from inadequate wear resistance, surface damage susceptibility, and rapid degradation under abrasive conditions, resulting in increased maintenance costs, reduced component lifespans, and diminished system efficiency. To address these issues, the hypothesis posits that integrating nano-SiC particles into the AA7178 matrix alloy will enhance wear resistance and advance high-performance metal matrix composites. The dry sliding wear behavior of the nanocomposites was examined using a pin-on-disc wear test apparatus with EN31 steel as the counterbody material. Systematic variations in sliding velocity (1, 2, 3, and 4 m/s), distance (500, 1000, 1500, and 2000 m), and load (10, 20, 30, and 40 N) were conducted. Employing the L16 Taguchi method, experimental research and wear analysis revealed that the most influential factor affecting wear rate was the applied load (67.39%), followed by the SiC wt.% (30.85%), with an R2 value of 99.80%. Optimization of wear process parameters was achieved through metaheuristic techniques, encompassing the Rao-1, Particle Swarm Optimization (PSO), and Artificial Bee Colony (ABC) algorithms. The Rao-1 technique demonstrated robustness at approximately 98%, displaying higher efficiency in parameter optimization. Optical microscope analysis identified micro-cutting and micro-ploughing as the primary wear mechanisms. Under specific conditions—9.81 N applied load, 4 m/s sliding speed, 500 m wear distance, and a 3% SiC wt.%—the AA7178/SiC composites exhibited optimal wear resistance, measuring 1.7088*10−3 mm3/m. These findings offer critical guidance for engineering high-performance metal matrix composites, effectively addressing the inherent deficiencies in aerospace component materials. [ABSTRACT FROM AUTHOR]

This research suggests a robust integration of artificial neural networks (ANN) for predicting swell pressure and the unconfined compression strength of expansive soils (PsUCS-ES). Four novel ANN-based models, namely ANN-PSO (i.e., particle swarm optimization), ANN-GWO (i.e., grey wolf optimization), ANN-SMA (i.e., slime mould algorithm) alongside ANN-MPA (i.e., marine predators' algorithm) were deployed to assess the PsUCS-ES. The models were trained using the nine most influential parameters affecting PsUCS-ES, collected from a broader range of 145 published papers. The observed results were compared with the predictions made by the ANN-based metaheuristics models. The efficacy of all these formulated models was evaluated by utilizing mean absolute error (MAE), Nash–Sutcliffe (NS) efficiency, performance index ρ, regression coefficient (R2), root mean square error (RMSE), ratio of RMSE to standard deviation of actual observations (RSR), variance account for (VAF), Willmott's index of agreement (WI), and weighted mean absolute percentage error (WMAPE). All the developed models for Ps-ES had an R significantly > 0.8 for the overall dataset. However, ANN-MPA excelled in yielding high R values for training dataset (TrD), testing dataset (TsD), and validation dataset (VdD). This model also exhibited the lowest MAE of 5.63%, 5.68%, and 5.48% for TrD, TsD, and VdD, respectively. The results of the UCS model's performance revealed that R exceeded 0.9 in the TrD. However, R decreased for TsD and VdD. Also, the ANN-MPA model yielded higher R values (0.89, 0.93, and 0.94) and comparatively low MAE values (5.11%, 5.67, and 3.61%) in the case of PSO, GWO, and SMA, respectively. The UCS models witnessed an overfitting problem because the aforementioned R values of the metaheuristics were 0.62, 0.56, and 0.58 (TsD), respectively. On the contrary, no significant observation was recorded in the VdD of UCS models. All the ANN-base models were also tested using the a-20 index. For all the formulated models, maximum points were recorded to lie within ± 20% error. The results of sensitivity as well as monotonicity analyses depicted trending results that corroborate the existing literature. Therefore, it can be inferred that the recently built swarm-based ANN models, particularly ANN-MPA, can solve the complexities of tuning the hyperparameters of the ANN-predicted PsUCS-ES that can be replicated in practical scenarios of geoenvironmental engineering. [ABSTRACT FROM AUTHOR]

In sophisticated engineering machining, ceramic materials are in great demand in today's precision industries because they have a wide range of potential applications, including automobiles, aircraft, and biomedical engineering. Silicon nitride ceramics (Si3N4) are difficult to manufacture using conventional machining processes. Modern technology allows Ultrasonic Micro-machining (USMM) to create almost any kind of material. Abrasive particles made of silicon carbide are used in this research work to look into the Si3N4 USMM process parameters. The physical structure and chemical composition have been examined in a scanning electron microscope with an integrated energy-dispersive X-ray analyzer. Particle swam optimization and Response Surface Methodology (RSM) desirability were the two types of optimizations used to find the best USMM process parameters. It has been shown that both techniques can be used together to get the best Material Removal Rate (MRR). The best settings were Slurry Concentration: 50 (g/l), Power Rating: 329 (W), and Tool Feed Rate: 1.06 (mm/min). Similarly, to minimize Overcut (OC) and Taper Angle (TA), the ideal method is to use a slurry concentration of 50 (g/l), a power rating of 400 (W), and a tool feed rate of 1.2 (mm/min). Finally, using the RSM, the USMM process was optimized in a way that maximized the MRR while minimizing OC and TA. It was found that the slurry concentration of 42.7 g/l, the power rating of 357.6 W, and the tool feed rate of 1.2 mm/min were the ideal settings for the USMM process. [ABSTRACT FROM AUTHOR]

The Particle Swarm Optimization (PSO) algorithm faces several inherent challenges when applied to dynamic and large-scale optimization problems. These challenges encompass the issues of outdated particle memory, inadequate scalability in high-dimensional search spaces, the incapability to detect environmental changes, a continual trade-off between exploration and exploitation, and the potential loss of population diversity within the problem space. To address these challenges, we propose a novel hybrid PSO algorithm, denoted as Parent–Child Multi-Swarm Clustered Memory (PCSCM). PCSCM is explicitly designed to leverage an enhanced memory system, capable of mitigating the issue of outdated particle memory after convergence, and efficiently adapting to changing environmental conditions. This innovative memory system retains and retrieves promising solutions from the past when environmental alterations occur. Additionally, PCSCM introduces clustering mechanisms for particles within each swarm, aimed at augmenting diversity within the problem space. This clustering strategy substantially bolsters the algorithm's performance in tracking evolving optimal solutions and positively contributes to its scalability. Crucially, the clustering approach is implemented not only for the main population but also for stored solutions in memory, which collectively strike a balance between exploration and exploitation. In the proposed method, particle swarms are divided into parent and child swarms, with parent swarms dedicated to preserving diversity; while, child swarms focus on identifying local solutions. These clustering and memory strategies are consistently applied within each sub-swarm to effectively address the challenges posed by high-dimensional search spaces. In addition to addressing challenges related to dynamic optimization, our proposed Parent–Child Multi-Swarm Clustered Memory (PCSCM) algorithm introduces an innovative mechanism for detecting environmental changes. This novel approach enhances the algorithm's adaptability by efficiently identifying moments when the optimization environment undergoes significant shifts. The detection of such changes is a crucial aspect of the PCSCM algorithm, contributing to its robust performance in dynamic scenarios. The effectiveness and robustness of the PCSCM algorithm are substantiated through extensive simulation experiments. These experiments provide insights into PCSCM's behavior in dynamic environments and showcase its ability to scale proficiently in high-dimensional settings. Particularly noteworthy are the results obtained when benchmarked against the Moving Peaks Benchmark and Generalized Moving Peaks Benchmark. These results not only underscore the algorithm's efficiency but also demonstrate its superiority when compared to several existing state-of-the-art optimization methods, including Multi-Swarm PSO, AmQSO, CPSO, Cellular PSO, FMSO, mQSO10 (5 + 5q), and DPSABC. [ABSTRACT FROM AUTHOR]

The process of machining CFRP composites presents unique challenges, particularly in the context of WEDM. The inherent properties of CFRP composites, such as their low electrical conductivity, anisotropic nature, and heterogeneous composition, require further research to enhance their machinability through WEDM techniques. This study examines the enhancement of kerf characteristics such as kerf width (Wk), delamination factor (DFK), and cutting speed (CSK) in thin woven 0°/90° CFRP composites using high-speed WEDM. A Taguchi L16 experimental analysis was employed to analyze the impact of key process parameters, including pulse-on (Pon), pulse-off (Poff), and input current (I), in conjunction with CFRP parameters such as the CFRP thickness (T) and cutting direction on the kerf characteristics. The CFRP thickness ranged from 0.5 to 2.0 mm, and the cutting directions studied were horizontal and inclined 30° cuts. A multiple-response optimization strategy using the CRITIC-WASPAS approach coupled with a particle swarm optimization (PSO) algorithm were applied to identify the ideal process combination for various CFRP thicknesses. The findings indicated that the CFRP thickness, pulse-off time, and input current are the most statistically significant factors influencing the overall kerf characteristics. The cutting direction has a negligible effect on the kerf width but has conflicting effects on the delamination factor and cutting speed. Specifically, a horizontal cut decreases delamination, whereas an inclined 30° cut is preferable for achieving higher cutting speeds. For precise kerf cutting, optimal process combinations were determined: Pon (30 µs), Poff (30 µs), and I (ranging from 4 to 5 A) for 0.5 mm CFRP thickness, and Pon (30 µs), Poff (15 µs), and varying input currents of 4 A, ranging from 4 to 3 A, and 3 A for CFRP thicknesses of 1.0, 1.5, and 2.0 mm, respectively.Article Highlights: HS-WEDM delivers excellent results in attaining precise kerf quality for thin CFRP composites. The thickness of the CFRP composite significantly influences the overall kerf characteristics when using HS-WEDM. The application of CRITIC-WASPAS and PSO enhances the kerf quality by 8% to 22% across various CFRP thicknesses. [ABSTRACT FROM AUTHOR]

The bearing capacity of strip footings is significantly influenced by uncertainties related to the footing, soil conditions, and load inclination. Given the inherent unpredictability in footing design, the reliability-based design of geotechnical structures has garnered considerable interest in the research community. This paper presents a state-of-the-art probabilistic design for footings under inclined loading using the first-order reliability method (FORM) combined with a hybrid least squares support vector machine (LSSVM) learning approach. A comprehensive dataset comprising 920 samples from the literature, with the reduction factor (RF) as the output parameter, was utilized to simulate hybrid LSSVM models based on particle swarm optimization (PSO) and Harris hawks optimization (HHO). The input variables for predicting the bearing capacity include the load eccentricity-to-width ratio, embedment ratio, load inclination-to-friction angle, and load arrangement. The performance metrics indicate that among the three proposed machine learning models, the LSSVM-PSO model achieves the best predictive performance, with an R2 of 0.991 and an RMSE of 0.051 during training and an R2 of 0.962 and an RMSE of 0.109 during testing. The model's performance was further evaluated via rank analysis, reliability analysis, regression plots, and uncertainty analysis. The reliability index (β) and corresponding probability of failure (POF) computed using FORM were compared with the actual values for both phases. The study concluded that the LSSVM-PSO is the most reliable method for reliability-based design, demonstrating superior performance and reliability. This hybrid approach offers a robust framework for addressing uncertainties in geotechnical engineering, enhancing the reliability and accuracy of footing design under inclined loading conditions. [ABSTRACT FROM AUTHOR]

As a high energy consuming and high emission industry, the carbon emissions of the manufacturing industry have attracted much attention. Traditional carbon emission methods often struggle to achieve real-time optimization and have limited applicability under complex process conditions. Therefore, this study proposes a multi-objective optimization method for mechanical processing based on a particle swarm optimization algorithm and a carbon emission quantification algorithm. This method first establishes a carbon emission model for the mechanical processing process, analyzes the carbon emission factors at different stages, and optimizes process parameters and routes through an improved particle swarm optimization algorithm. The results indicated that as the number of processed products increased, the carbon emissions corresponding to each model also increased. When the number of processed products reached around 500, the carbon emissions of each model tended to stabilize, with a carbon emission of 0.43 kg CO2. However, when the number reached around 400, the processing time was 0.52 h. Research has shown that the proposed optimization algorithm and carbon emission quantification algorithm for multi-objective optimization of mechanical processing can effectively reduce carbon emissions. This provides a scientific basis for the low-carbon transformation of the manufacturing industry. [ABSTRACT FROM AUTHOR]

The Industrial Internet of Things (IIoT) attributes to intelligent sensors and actuators for better manufacturing and industrial operations. At the same time, IIoT devices must be secured from the potentially catastrophic effects of eventual attacks, and this necessitates real-time prediction and preventive strategies for cyber-attack vectors. Due to this, the objective of this investigation is to obtain a high-accuracy intrusion detection technique with a minimum payload. As the experimental process, we have utilized the IIoT network security dataset, namely WUSTL-IIOT-2021. The feature selection technique Particle Swarm Optimization (PSO) and feature reduction techniques such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and t-distributed stochastic neighbor embedding (t-SNE) are applied. Additionally, the Generalized Additive Model (GAM) and Multivariate Adaptive Regression Splines (MARS) are used to detect payloads that can interfere with the normal operation of an application. Both PSO and PCA combined with MARS have produced predictive results with an exceptional accuracy of 100%. Yet, the trained Machine Learning (ML) model is quantized with 4-bit and 8-bit, and it is deployed on Azure IoT Edge to simulate edge devices. Experimental results show that the latency of the model was reduced by 25% on quantization. [ABSTRACT FROM AUTHOR]

This work proposes a novel robotic exoskeleton for rehabilitation of the index finger. Though all motions of index finger are essential, the major range of motion is covered by flexion/extension motion. Hence, a Stephenson III six-bar mechanism has been synthesized for the robotic exoskeleton device for a pre-defined trajectory to address post stroke rehabilitation of patients. The flexion/extension trajectory was obtained experimentally using image processing. Based on the trajectory, a mathematical model was formulated which was used as the objective function for the optimization problem. To eliminate any defects that may be encountered during the synthesis, "loop-by-loop defect rectification" procedure was implemented along with well-established optimization algorithms such as TLBO, BWP, GWO and PSO for synthesis of the desired mechanism. It has been found that TLBO outperformed all the others as it could reduce the objective function value to 0.69849. whereas, BWP reduced it to 8.9952, GWO reduced it to 13.1388, and PSO could only reduce it to 6 × 105. Therefore, the design obtained using TLBO was considered for developing the prototype of the device. The device was validated experimentally using image processing, and it is found to cover the prescribed range of motion. Thus, the proposed exoskeleton is deemed to be a viable solution for post stroke index-finger rehabilitation. [ABSTRACT FROM AUTHOR]

Diabetes is a chronic medical condition that disrupts the body's normal blood sugar levels. It is essential to detect this disease at an early stage in order to prevent organ and tissue injury. This study focuses on diagnosing diabetes by leveraging ensemble learning methods, which involve combining various machine learning techniques. The goal is to create an ensemble learning model that achieves the best classification performance by employing different classifiers and combining techniques. The study explores boosting, bagging, voting, and stacking ensemble learning methods, while also introducing an approach called PSO-GWO (Particle Swarm Optimization and Grey Wolf Optimization) hybrid method for optimizing the model's hyperparameters. The model consisting of combining various classifiers in the stacking ensemble learning method provided the highest classification performance in diagnosing diabetes. The 5-fold cross-validation method is used in the study. Within the scope of the study, the highest accuracy with (98.10%) is obtained with the random forest classifier. The results of the study are presented in comparison with other studies in the literature. These findings contribute to the field of diabetes diagnosis and highlight the potential for developing more accurate and reliable diagnostic systems in the future. [ABSTRACT FROM AUTHOR]

A power system is a complex, nonlinear, and dynamic system, with operating parameters that change over time. Because of the complexity of big load lines and faults, high-voltage transmission power systems are usually vulnerable to transient instability concerns, resulting in power losses and increased voltage variation. This issue has the potential to cause catastrophic events such as cascade failure or widespread blackouts. This problem affects Ethiopia's high-voltage transmission power grid in the northwest area. Because of the increased demand for electrical energy, transmission lines' maximum carrying capacity should be enhanced to maintain a secure and uninterrupted power supply to consumers. To address the problem, ANFIS-based UPFC is used for high-voltage transmission lines. This device was chosen as the ideal alternative due to its capacity to rapidly correct reactive power on high-voltage transmission networks. The PSO algorithm was used to determine the best location for the UPFC. The ANFIS controller receives voltage error and rate of change of voltage error as inputs. Seventy percentage of the retrieved data are used for ANFIS training and 30% for ANFIS testing. To show the performance of the proposed controller, three-phase ground faults are used from a severity perspective. When a three-phase ground fault occurs at the midpoint of the Beles to Bahir Dar transmission line, for instance, the settling time of rotor angle deviation, rotor speed, rotor speed deviation, and output active power of synchronous generators using ANFIS-based UPFC is reduced by 70.58%, 37.75%, 37.75%, and 43.75%, respectively, compared to a system without UPFC. At peak load, PI-based UPFC and ANFIS-based UPFC reduce active power loss by 51.25% and 71.50%, respectively, compared to a system without UPFC on the Beles to Bahir Dar transmission line. In terms of percentage overshoot and settling time, ANFIS-based UPFC outperforms PI-based UPFC for transient stability enhancement. [ABSTRACT FROM AUTHOR]

Diabetes mellitus, a pervasive and intricate metabolic disorder, disrupts hormonal equilibrium, resulting in elevated glucose levels and widespread organ impact. Timely and precise diagnosis is pivotal for effective disease control. This research introduces an advanced Deep 1D-Convolutional Neural Network (1DCNN) tailored for diabetes classification, specifically addressing challenges posed by imbalanced datasets and missing values. Through three experiments, encompassing a baseline 1DCNN, hyperparameter optimization(HO) using a Genetic Algorithm (GA), and a Particle Swarm Optimization (PSO) comparison, we aim to identify the most effective model configuration. Our study yields improved results compared to the state-of-the-art. Achieving high precision, recall, and accuracy, along with superior AUC and PR curves, our research significantly contributes to the refinement of diabetes prediction models, fostering enhanced disease comprehension and control. [ABSTRACT FROM AUTHOR]

WingAnalogy is a computer tool for automated insect wing morphology and asymmetry analysis. It facilitates project management, enabling users to import pairs of wing images obtained from individual insects, such as left and right, fore- and hindwings. WingAnalogy employs image processing and computer vision to segment wing structures and extract cell boundaries, and junctions. It quantifies essential metrics encompassing cell and wing characteristics, including area, length, width, circularity, and centroid positions. It enables users to scale and superimpose wing images utilizing Particle Swarm Optimization (PSO). WingAnalogy computes regression, Normalized Root Mean Square Error (NRMSE), various cell-based parameters, and distances between cell centroids and junctions. The software generates informative visualizations, aiding researchers in comprehending and interpreting asymmetry patterns. WingAnalogy allows for dividing wings into up to five distinct wing cell sets, facilitating localized comparisons. The software excels in report generation, providing detailed asymmetry measurements in PDF, CSV, and TXT formats. [ABSTRACT FROM AUTHOR]

Water covers about 71% of the Earth's surface, which is crucial for transportation, climate regulation, and the production of pharmaceuticals. The Internet of Underwater Things (IoUT) can detect valuable items such as minerals, metals, corals, and coral reefs. One of the crucial purposes is preventing damage from natural disasters. IoT principles helped advance plans for a new underwater network. Underwater networks suffer from some issues, including a lack of dependability, constrained capacity, long propagation delays, high processing requirements, high energy costs, and node detection with encrypted communication. Along with node identification and dynamic network setup, real-time, secure data exchange is one of our main research interests. These problems provide significant challenges for IoUT. Our proposed scheme involves a dynamic graph for network design, an Artificial fish swarm algorithm (AFSA) based on AI for node recognition, and Elliptic Curve Cryptography (ECC) for a secure communication mechanism. For underwater objects, this proposed approach is more trustworthy, safe, and resilient, preventing damage from natural disasters and being helpful for design and secure communication in maritime engineering. [ABSTRACT FROM AUTHOR]

Predicting air pollution in cities has become an important tool for preventing its negative impacts. Therefore, citizens should be aware of air quality level, especially for individuals suffering from diseases caused by air pollutants. Collective efforts from researchers, environmental institutions, governments, industrial companies, and policy makers are shaping the future of the Air Quality Index (AQI) to effectively address severe air pollution in urban areas. Many air quality prediction models have been introduced in the literature; modern advances in deep learning techniques are promising more precise prediction results and data integration. The aim of this paper is to review methods, contributions, findings, limitations, and gaps in predicting air quality index and PM2.5 concentrations using a hybrid deep learning approach. A literature review led researchers to propose a Hybrid Deep Learning model with Particle Swarm Optimization (HDLP) which combines CNN, LSTM, and PSO. Algorithm, first Discrete Wavelet Transform (DWT) is used to solve the air pollution signal, then it is fed to CNN-LSTM neural network for PSO optimization to get the final prediction result. Optimized parameters input models are trained on the original data. In addition to the beneficial assessment cycle, to outperform current models. [ABSTRACT FROM AUTHOR]

In this work, intelligent numerical models for the prediction of debris flow susceptibility using slope stability failure factor of safety (FOS) machine learning predictions have been developed. These machine learning techniques were trained using novel metaheuristic methods. The application of these training mechanisms was necessitated by the need to enhance the robustness and performance of the three main machine learning methods. It was necessary to develop intelligent models for the prediction of the FOS of debris flow down a slope with measured geometry due to the sophisticated equipment required for regular field studies on slopes prone to debris flow and the associated high project budgets and contingencies. With the development of smart models, the design and monitoring of the behavior of the slopes can be achieved at a reduced cost and time. Furthermore, multiple performance evaluation indices were utilized to ensure the model's accuracy was maintained. The adaptive neuro-fuzzy inference system, combined with the particle swarm optimization algorithm, outperformed other techniques. It achieved an FOS of debris flow down a slope performance of over 85%, consistently surpassing other methods. [ABSTRACT FROM AUTHOR]

WSNs (Wireless sensor networks) are used in multiple applications including IoT (Internet of Things) applications like intelligent control, prediction, tracking, and other communication network services (Internet-of-Things). Due to their limited design for two-dimensional space, high computing costs, or sensitivity to measurement errors, the typical localization frameworks might not perform well in real-world settings. Location information of deployed sensor nodes in their surrounding environments is important for algorithmic three-dimensional localizations. But there are drawbacks to current 3D localization algorithms in many parameters including complexity, positional precisions, and excessive energy consumption. Hence, this work proposes Enhanced 3D-DV-Hop (3D-Distance Vector Hop) localizations based on PSO (Particle Swarm Optimization) and GAs (Genetic algorithms) for the aforementioned issues. To further increase the diversity and accuracy of the DV-Hop outputs, a learning technique is employed. The learning technique leads to an improvement in search effectiveness, convergence speed, and result in quality. The simulation results demonstrate that the suggested strategy can improve positioning coverage while maintaining positioning accuracy. [ABSTRACT FROM AUTHOR]

Purpose: Pelvic support osteotomy (PSO) is regarded to provide pelvic stability and improve abductor function to delay or even avoid total hip arthroplasty (THA) in young patients with high-riding hip dysplasia. However, some of these patients eventually have to undergo THA. Because of the double-angulation deformity of the femur after PSO, subsequent THA is challenging. This study aimed to analyze whether PSO surgery is suitable for high-riding hip dysplasia and summarize orthopaedic strategy during THA for patients with previous PSO. Methods: This case–control study included eight cases of THA for high-riding hip dysplasia patients with previous PSO (study group) and 24 cases of high-riding hip dysplasia patients without any hip surgical therapy (control group) by a 1:3 match (from May 2018 to January 2022). We compared demographics and joint function before and after THA between two groups and recorded all patients' preoperative imaging data, surgical procedures, postoperative imaging data, and complications. The surgical techniques for patients with previous PSO were highlighted. Results: There was no statistical difference between the two groups in demographic (p > 0.05). The study group had worse hip Harris score (HHS), range of motion (ROM), visual analogue scale (VAS), and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) (p < 0.05) compared with the control group before THA. All patients had concurrent THA and osteotomy at the proximal femur, but the study group experienced longer operation time (p = 0.047) with more blood loss (p = 0.027) and higher complication rate compared with the control group (p = 0.009). At the last follow-up, the study group's HHS, ROM, VAS, and WOMAC were still worse than those in the control group. Conclusions: PSO did not improve the joint function of high-riding hip dysplasia patients but brought challenges to subsequent THA and affected the surgical outcomes. In short, we suggested that PSO is unsuitable for routine high-riding hip dysplasia patients. [ABSTRACT FROM AUTHOR]

This paper presents a comparative study between four techniques recently used to improve the wind energy conversion system (WECS) to water pumping systems. The WECS is a renewable energy source which has developed rapidly in recent years. The use of the WECS in the water pumping field is a free solution (economically) compared to the use of the electricity grid supply. The control of WECS, equipped with a permanent magnet synchronous generator, has the objective of carefully maximising power generation. A comparative study between the proposed Fuzzy Logic Control, optimised using a genetic algorithm and particle swarm optimisation algorithm, and the conventional Perturb and Observe MPPT method using Matlab/Simulink, is presented. The performance of the proposed system has been verified against the generated output voltage, current and power waveforms, intermediate circuit voltage waveform, and generator speed. The presented results demonstrate the effectiveness of the control strategy applied in this work. [ABSTRACT FROM AUTHOR]

The precise estimation of the shear strength of reinforced concrete walls is critical for structural engineers. This projection, nevertheless, is exceedingly complicated because of the varied structural geometries, plethora of load cases, and highly nonlinear relationships between the design requirements and the shear strength. Recent related design code regulations mostly depend on experimental formulations, which have a variety of constraints and establish low prediction accuracy. Hence, different soft computing techniques are used in this study to evaluate the shear capacity of reinforced concrete walls. In particular, developed models for estimating the shear capacity of concrete walls have been investigated, based on experimental test data accessible in the relevant literature. Adaptive neuro-fuzzy inference system, the integrated genetic algorithms, and the integrated particle swarm optimization methods were used to optimize the fuzzy model's membership function range and the results were compared to the outcomes of random forests (RF) model. To determine the accuracy of the models, the results were assessed using several indices. Outliers in the anticipated data were identified and replaced with appropriate values to ensure prediction accuracy. The comparison of the resulting findings with the relevant experimental data demonstrates the potential of hybrid models to determine the shear capacity of reinforced concrete walls reliably and effectively. The findings revealed that the RF model with RMSE = 151.89, MAE = 111.52, and R 2 = 0.9351 has the best prediction accuracy. Integrated GAFIS and PSOFIS performed virtually identically and had fewer errors than ANFIS. The sensitivity analysis shows that the thickness of the wall (b w) and concrete compressive strength (f c) have the most and the least effects on shear strength, respectively. [ABSTRACT FROM AUTHOR]

Purpose: Pelvic support osteotomy (PSO) is regarded to provide pelvic stability and improve abductor function to delay or even avoid total hip arthroplasty (THA) in young patients with high-riding hip dysplasia. However, some of these patients eventually have to undergo THA. Because of the double-angulation deformity of the femur after PSO, subsequent THA is challenging. This study aimed to analyze whether PSO surgery is suitable for high-riding hip dysplasia and summarize orthopaedic strategy during THA for patients with previous PSO. Methods: This case–control study included eight cases of THA for high-riding hip dysplasia patients with previous PSO (study group) and 24 cases of high-riding hip dysplasia patients without any hip surgical therapy (control group) by a 1:3 match (from May 2018 to January 2022). We compared demographics and joint function before and after THA between two groups and recorded all patients' preoperative imaging data, surgical procedures, postoperative imaging data, and complications. The surgical techniques for patients with previous PSO were highlighted. Results: There was no statistical difference between the two groups in demographic (p > 0.05). The study group had worse hip Harris score (HHS), range of motion (ROM), visual analogue scale (VAS), and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) (p < 0.05) compared with the control group before THA. All patients had concurrent THA and osteotomy at the proximal femur, but the study group experienced longer operation time (p = 0.047) with more blood loss (p = 0.027) and higher complication rate compared with the control group (p = 0.009). At the last follow-up, the study group's HHS, ROM, VAS, and WOMAC were still worse than those in the control group. Conclusions: PSO did not improve the joint function of high-riding hip dysplasia patients but brought challenges to subsequent THA and affected the surgical outcomes. In short, we suggested that PSO is unsuitable for routine high-riding hip dysplasia patients. [ABSTRACT FROM AUTHOR]

In this study, effects of high-order interactions on synchronization of the fractional-order Hindmarsh–Rose neuron models have been examined deeply. Three different network situations in which first-order coupling, high-order couplings and first-plus second-order couplings included in the neuron models, have been considered, respectively. In order to find the optimal values of the first- and high-order coupling parameters by minimizing the cost function resulted from pairwise and triple interactions, the particle swarm optimization algorithm is employed. It has been deduced from the numerical simulation results that the first-plus second-order couplings induce the synchronization with both reduced first-order coupling strength and total cost compared to the first-order coupled case solely. When the only first-order coupled case is compared with the only second-order coupled case, it is determined that the neural network with only second-order couplings involved could achieve synchronization with lower coupling strength and, as a natural result, lower cost. On the other hand, solely second- and first-plus second-order coupled networks give very similar results each other. Therefore, high-order interactions have a positive effect on the synchronization. Additionally, increasing the network size decreases the values of the both first- and high-order coupling strengths to reach synchronization. However, in this case, total cost should be kept in the mind. Decreasing the fractional order parameter causes slower synchronization due to the decreased frequency of the neural response. On the other hand, more synchronous network is possible with increasing the fractional order parameter. Thus, the neural network with higher fractional order as well as high-order coupled is a good candidate in terms of the neural synchronization. [ABSTRACT FROM AUTHOR]

One of the challenges that Singular Value Decomposition (SVD)-based algorithms in watermarking face is the false positive problem while maintaining important parameters in watermarking, especially in different attacks. False positives occur when the watermark is placed on the S component of the SVD transformation of the host image. The proposed solutions have problems such as the low-quality watermarked image and its lack of stability against various attacks. To solve such problems, low-frequency bands of the host image, which are obtained by transforming the Contourlet obtained by taking the SVD transform and separating the S component are embedded by multiplying a parameter called delta obtained from the PSO (Particle swarm optimization) algorithm in the watermark image, and the embedding stage ends at this point. The result of this method is a relatively improved NC value, especially for the salt and pepper attack, which is compared with different articles in the results section. In the extraction section, the contourlet transform is taken from both the host image and the watermark, and the results are expressed with and without the denoising convolutional neural network algorithm (DnCNN). The values obtained for the false positive problem in the results section show that this scheme, while not having such a problem, is also resistant to various attacks. [ABSTRACT FROM AUTHOR]

This paper presents an optimum design of a coaxial magnetic gear (CMG) Integrated with a Permanent Magnet Synchronous Generator (PMSG) of a wind turbine using a pelican optimization algorithm (POA) launched in 2022. In order to evaluate the proposed system design based on POA, the well-known Particle Swarm Optimization (PSO) is implemented for the same CMG under the same conditions and constraints. Also, a Taguchi-method-based sensitivity analysis for the key design variables is employed to determine their impact on the system’s performance. The optimization process for the CMG design based on POA and PSO is executed based on the 2-D Finite element analysis (FEA) using the Finite Element Method Magnetics (FEMM) program. The results obtained from the POA and PSO are compared and analyzed. The results underline that the optimum design for the CMG/PMSG integrated system based on POA achieves more torque/weight ratio and then more generated power and currents as compared to PSO. It is concluded that the proposed design based on POA gives a superior and efficient design with substantial stability and rapid convergence.Article Highlights: The article designs a coaxial magnetic gear (CMG) based on a pelican optimization algorithm (POA) presented in 2022. A 2-D finite element analysis is employed to calculate the optimal torque produced by the POA. A comparative study is presented between the results of the POA, and a well- known optimization called the Particle swarm Optimization (PSO) for the same CMG. [ABSTRACT FROM AUTHOR]

The Near-Surface Mounted (NSM) strengthening technique has emerged as a promising alternative to traditional strengthening methods in recent years. Over the past two decades, researchers have extensively studied its potential, advantages, and applications, as well as related parameters, aiming at optimization of construction systems. However, there is still a need to explore further, both from a static perspective, which involves accounting for the non-conservation of the contact section resulting from the bond-slip effect between fiber-reinforced polymer (FRP) rods and resin and is typically neglected by existing analytical models, as well as from a dynamic standpoint, which involves studying the trends of vibration frequencies to understand the effects of various forms of damage and the efficiency of reinforcement. To address this gap in knowledge, this research involves static and dynamic tests on simply supported reinforced concrete (RC) beams using rods of NSM carbon fiber reinforced polymer (CFRP) and glass fiber reinforced polymer (GFRP). The main objective is to examine the effects of various strengthening methods. This research conducts bending tests with loading cycles until failure, and it helps to define the behavior of beam specimens under various damage degrees, including concrete cracking. Dynamic analysis by free vibration testing enables tracking of the effectiveness of the reinforcement at various damage levels at each stage of the loading process. In addition, application of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) is proposed to optimize Gradient Boosting (GB) training performance for concrete strain prediction in NSM-FRP RC. The GB using Particle Swarm Optimization (GBPSO) and GB using Genetic Algorithm (GBGA) systems were trained using an experimental data set, where the input data was a static applied load and the output data was the consequent strain. Hybrid models of GBPSO and GBGA have been shown to provide highly accurate results for predicting strain. These models combine the strengths of both optimization techniques to create a powerful and efficient predictive tool. [ABSTRACT FROM AUTHOR]

The cloud storage service, known for its flexible and expandable nature, often has difficulties managing operating costs while ensuring dependable service and quick response times. This investigation presents a novel approach to optimizing cost efficiency in cloud storage systems by applying particle swarm optimization of the Markovian retrial queueing model in a generic setup by incorporating the working vacation and users' discouragement behavior. Some users may opt not to enter the system or join the retry pool to wait for their turn if the server is occupied. After returning from working vacation, if there is one user available for service, the server can interrupt the vacation period. The server is subject to breakdown and can be recovered after getting the repair. In the proposed model, the server is prone to catastrophes and can fail at any time, leading to the entire system breaking down, and no users being able to access it during this period. Chapman–Kolmogorov (CK) steady-state equations associated with the quasi-birth-death (QBD) process are constructed to make a mathematical design. The governing equations framed to derive the queue length distributions and various performance indices are solved using the recursive method and difference equation theory. The fuzzified parameters are used to develop the FM/FM/1/WV model, which is analyzed using a parametric nonlinear programming approach. To determine the optimal design parameters, the cost minimization problem has been done using the quasi-Newton method and particle swarm optimization. This model incorporates features such as server failures, retrials, and catastrophes, thereby reflecting the complex nature of cloud storage operations. A suitable illustration of cloud storage is taken for both classical and fuzzified models to facilitate the numerical results of performance indices and optimal decision descriptors. [ABSTRACT FROM AUTHOR]

The development of various communication technologies has made it possible to connect any intelligent device with each other and to the Internet. However, with the proliferation of such devices, the need for dynamic networks that require less efforts in managing and optimizing end users experience is mounting at a fast pace. Wireless Mesh Networks (WMNs) are a good choice not only to meeting users demands but also to reducing maintenance and upfront costs. Nevertheless, deploying a reliable highly connected WMN at low cost requires the utilization of the least possible mesh routers, yet fully interconnected and able to cover all mesh clients. To cope with these challenges, we have previously implemented a hybrid intelligent system, called WMN-PSODGA, which combines two intelligent algorithms: Particle Swarm Optimization and Distributed Genetic Algorithm. In this paper, we consider Boulevard and Stadium distributions of mesh clients and evaluate the performance of the WMN for different router replacement methods. By comparing the simulation results for each distribution and router replacement method, we conclude that the best scenario in terms of client coverage, router connectivity and load balancing is Stadium distribution with Rational Decrement of Vmax Method as a router replacement method. [ABSTRACT FROM AUTHOR]

Detecting cardiac abnormalities promptly is critical for preventing unexpected and premature fatalities. In this research, four types of cardiac arrhythmias such as Ventricular Tachycardia, Premature Ventricular Contraction, Normal Sinus Rhythm and Supraventricular Tachycardia are detected from the amassed Physiobank MIT-BIH cardiac arrhythmia database. Dimensionality reduction techniques like Stochastic Neighbour Embedding (SNE), Neighbourhood Preserving Embedding, Linear Local Tangent Space Alignment and Gaussian Process Latent Variable Model are used to reduce the dimension of the ECG signals. The appropriate features of dimensionally reduced ECG signals are selected by the Elephant Search Optimization (ESO) technique. Finally, classification is performed using the relevant classifiers, such as Support Vector Machine, Adaboost, Modest Adaboost based on Ridge Regression (Modest Adaboost.RR), Extreme Gradient Boost (XGboost) and Naïve Bayes (NBC) classifiers. Multiple classifiers without and with ESO feature selection for different cardiac cases have an average classification accuracy of 62.23% and 73.61%, respectively. These multiple classifiers are defined by a set of control parameters known as hyper-parameters, which must be tuned in order to achieve optimal results. Experts have developed many approaches for detecting cardiac arrhythmias, but these multiple classifiers do not always perform well when the usual parameters for machine learning classification models are employed. In this paper, various classifiers are used in conjunction with the Stochastic Gradient Descent (SGD), Particle Swarm Optimization (PSO) and Bayesian Tree-structured Parzen Estimator (BTPE) to enhance the cardiac arrhythmia classification accuracy via hyper-parameter tuning. Multiple classifiers with SGD, PSO and BTPE hyper-parameters tuning techniques for various cardiac cases have an average classification accuracy of 80.13%, 90.67% and 94.96%, respectively. The Classifier's performance is analysed based on metrics like Classification Accuracy, F1 score, Error Rate, Matthew's correlation coefficient, Jaccard Index and Cohen's Kappa Coefficient with and without ESO features selection method and hyper-parameters tuning techniques. The analysis utilizes the MATLAB R2014a software for result evaluation. The results show that the SNE-ESO approach, along with the XGboost-BTPE, achieved the highest classification accuracy of 99.89% for detecting {Ventricular Tachycardia}-{Normal Sinus Rhythm} cases. In terms of classification benchmarks, the results exhibit that the BTPE hyper-parameter tuning technique surpasses the SGD and PSO techniques. [ABSTRACT FROM AUTHOR]