Wolf Research Articles

Adaptive tunicate swarm optimization with partial transmit sequence for phase optimization in MIMO-OFDM

<span>Multiple-input multiple-output (MIMO) and orthogonal frequency division multiplexing (OFDM) are widely utilized in wireless systems and maximum data rate communications. The MIMO-OFDM technology increases the efficiency of spectrum utilization. The peak-to-average-power ratio (PAPR) minimization in MIMO-OFDM is a complex task in wireless communications systems. In this research, an adaptive tunicate swarm optimization with partial transmit sequence (ATSO-PTS) algorithm is proposed for a reduction of PAPR in MIMO-OFDM. The nonsquare-matrix-based differential space time coding (N-DSTC) scheme is used for the encoding and decoding process of MIMO-OFDM. The N-DSTC encoding and decoding are linear error-correcting codes that are utilized for message transmission over noisy channels. The pre-specified quadrate phase shift keying (QPSK) symbol is deployed for the modulation and demodulation scheme. On the receiver side, the serial to parallel (S/P) conversion, and fast Fourier transform (FFT) are accomplished, alongside the received data bits being demodulated to obtain the output bits. The proposed ATSO-PTS method achieves better results according to performance metrices PAPR, bit error rate (BER) and signal-to-noise-ratio (SNR), with values of about 2.9, 0.01 and 0.025, respectively. This ensures superior results when compared to the existing methods of twin symbol hybrid optimization applied to partial transmit sequence (TSHO-PTS), selective level mapping and PTS (SLM-PTS), and particle swarm and grey wolf (PS-GW) with PTS, respectively.</span>

Identifying Zoonotic Parasites in Domiciled and Non-Domiciled Dogs (Canis lupus familiaris) Within an Urban Zone of the Eastern State of Mexico.

There are over 42 million dogs in Mexico, with a significant population living on the streets, especially in the State of Mexico. These dogs can act as carriers of zoonotic pathogens, placing children and individuals with chronic diseases or immunodeficiencies at risk. To evaluate the prevalence of zoonotic parasites in feral and domestic dogs in the urban area of the eastern State of Mexico and assess their potential impact on public health. The study was conducted from July 2022 to March 2023 in the urban area located in the eastern region of the State of Mexico. A total of 134 samples of dog faces were collected through convenience sampling, from both domiciled and non-domiciled dogs. Fifty-one dogs were identified with Ancylostomatidae family (38.1%, 95% CI: 27.0%-52.1%), 10 with Toxocara spp. (7.5%, 95% CI: 3.6%-13.3%), 7 with Dipylidium caninum (5.5%, 95% CI: 2.1%-10.5%), 8 with Cystoisospora spp. (6.0%, 95% CI: 2.6%-11.4%), 6 with Giardia spp. (4.5%, 95% CI: 1.7-9.5%) and 2 positive cases for Hymenolepis spp. (1.5%, 95% CI: 0.2%-5.3%) were identified. This study highlights a public health concern related to non-domiciled dogs, which can serve as carriers of zoonotic parasites. Interactions among non-domiciled dogs, domiciled dogs and humans heighten the risk of transmission. Implementing prevention, control and awareness strategies is crucial to reduce the spread of these parasites.

Energy-efficient clustering and routing using fuzzy k-medoids and adaptive ranking-based wireless sensor network

<p>The wireless sensor network (WSN) is a vital component of infrastructure that is seeing tremendous demand and quick expansion in a variety of industries, including forestry, airports, healthcare, and the military. Increasing network lifetime and reducing power consumption (PC) are now major goals in WSN research. This research proposes a unique energy-efficient cross-layer WSN design that aims to maximize network lifetime while maintaining quality of service (QoS) criteria to address these challenges. The research initially utilizes the fuzzy k-medoids (FKMeds) clustering technique to group sensor nodes (SN) to improve resilience, scalability, and minimize network traffic. Following that, the hybrid improved grey wolf and ant colony (HIGWAC) optimization approach is applied to choose cluster heads (CH), minimizing distances, reducing latency, and optimizing energy stability. Finally, data is transmitted through the shortest pathways using the adaptive ranking-based energy-efficient opportunistic routing (ARanEOR) protocol, which ensures effective and energy-conserving routing in WSN while dynamically lowering network overhead. Compared to existing approaches, the proposed method in this study outperforms them in terms of energy efficiency, latency, and network longevity.</p>

GWO-ANFIS-RD3PG: A reinforcement learning approach with dynamic adjustment and dual replay mechanism for building energy forecasting

Reliable prediction of building energy consumption is essential for effective and sustainable energy management. Traditional forecasting methods, however, face challenges when dealing with sudden changes in energy consumption. To address the above-mentioned issue, we introduce the GWO-ANFIS-RD3PG prediction model in this paper. This model aims to ensure global forecasting accuracy while minimizing prediction errors at sudden change points. A Grey Wolf Optimization (GWO) algorithm with the Adaptive Neuro-Fuzzy Inference System (ANFIS) is proposed to predict the energy consumption type and its fluctuation magnitude for the next time step. Within the Recurrent Dual Experience Replay Buffer DDPG (RD3PG) prediction module, we employ Long Short-Term Memory (LSTM), as the actor network in the Deep Deterministic Policy Gradient (DDPG), to account for long-term dependencies in time series data. Notably, we introduce an adaptive action modification mechanism that allows the agent to optimize actions based on the output of the aforementioned ANFIS, enabling real-time adjustments during sudden change. To further enhance the model’s performance, we adopt a dual experience replay mechanism to store data samples from sudden change points, capturing insight information during these anomalies. Experimental results on two real-world datasets demonstrate that this method reduces MAE by 5.81% and 13.47%, and RMSE by 5.69% and 15.21% compared to the state-of-art models, showing the significance of the proposed method for energy efficiency improvement in real-world building operations.

Fuel constrained sustainable water-gas-heat-power generation expansion planning of isolated microgrid

On account of slowly reducing of fossil-fuel, profitable utilization of available fossil-fuel to produce electric power is a major concern for electric power producers. Here, short-period fuel constrained power, heat, natural gas and clean water augmentation planning (FCPHGWAP) of isolated microgrid considering carbon capture is presented. FCPHGWAP finds out the most economical and consistent augmentation plan to meet the prophesied power, heat, natural gas and clean water demand over a short-term time horizon whilst satisfying several technical as well as societal constraints. An archetypical system having extant diesel generators (DGs), PVT panel, wind turbine generator (WG), micro-cogeneration unit (MCU), battery energy storage system (BESS), plug-in electric vehicles (PEVs), thermal energy storage system, natural gas storage unit, carbon capture unit (CCU), electrolyzer, hydrogen storage unit and aspirant PVT panel, WGs, MCUs and PEVs is taken into consideration. Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients and grey wolf optimization are used to solve this FCPHGWAP problem. It is observed that net present value having fuel constraints is less than that of without fuel constraints.

A hybrid model based on novel SVM-SVR and weighted combination strategy for build-up rate prediction

Abstract The build-up rate prediction is of great importance for trajectory control in the field of drilling. However, it is very difficult to achieve accurate prediction due to the complexity, nonlinearity, and multiple uncertainties of the drilling system. As a consequence, a novel hybrid prediction model is proposed, which uses multiple feature selection methods, the model combination strategy based on machine learning, and three prediction models to improve the prediction accuracy of the build-up rate. More precisely, correlation analysis, importance analysis, and statistical analysis are employed to ensure the effectiveness of feature selection. Then, a novel classification prediction model called support vector machine-support vector regression (SVM-SVR) is proposed to improve the accuracy of samples with the higher build-up rate. Subsequently, the SVR optimized by grey wolf optimizer (GWO-SVR) and back propagation (BP) neural network are constructed. Finally, the three models are integrated by a weighted combination method based on SVR to realize the accurate prediction of the build-up rate. To verify the performance of the hybrid model, the data of the Z48 well in Sichuan province is used, and the results show that the hybrid model can reduce by 22.7% in mean absolute error and 32% in mean square error when compared with the existing models.

Impact of Balanced Exploration and Exploitation on Highdimensional Feature Selection with Hierarchical Whale Optimisation Algorithm

High-dimensional datasets (HDDs) pose significant challenges in feature selection due to their complex nature. While metaheuristic algorithms like the Whale Optimisation Algorithm (WOA) have shown promise in addressing these challenges, stability issues are least addressed. Through an extensive literature review, it was identified that stability is manifested in the equilibrium of exploration and exploitation. Hence, this study introduced the Hierarchical Whale Optimisation Algorithm (HiWOA), an approach designed to enhance the WOA’s stability and performance in HDD feature selection tasks. The HiWOA incorporates a two-phase strategy comprising a nonlinear control parameter based on the arcsine function and a hierarchical position-update mechanism adapted from the Grey Wolf Optimiser. The proposed HiWOA was evaluated through 23 benchmark optimisation functions and feature selection experiments on 11 medical HDDs. The results indicate that the HiWOA outperformed the WOA and a modified variant (mWOA) in terms of a better fitness value, a more balanced exploration-exploitation ratio, and improved classification accuracy with fewer selected features. These findings demonstrate the HiWOA’s effectiveness in enhancing stability, making it a robust solution for high-dimensional optimisation and feature selection.

Transformer fault identification based on GWO-optimized Dual-channel M-A method.

In order to improve the accuracy of the transformer fault identification using nature-inspired algorithms, an identification method based on the GWO (Grey Wolf Optimizer)-optimized Dual-channel MLP (Multilayer Perceptron)-Attention is proposed. First, a Dual-channel model is constructed by combining the AM (Attention Mechanism) and MLP. Subsequently, the GWO algorithm is used to optimize the number and the nodes of the hidden layer in the Dual-channel MLP-Attention model. Typical transformer faults are simulated using DDRTS (Digital Dynamic Real-Time Simulator) system. Experiments showed that the GWO- optimized method has an accuracy rate of 95.3%-96.7% in identifying the transformer faults. Compared with BP, SVM, MLP, and single-channel M-A models, the proposed method improved the accuracy by14.1%, 9.6%, 9.3%, and 3.3% respectively. This result indicates the rationality and effectiveness of the proposed method in transformer fault identification.

Third‐Party Affiliation in Domestic Dogs During and After a Human Conflict

ABSTRACTSeveral behaviors occur in the aftermath of within‐group conflicts. These include spontaneous affiliation toward the victim from an uninvolved third party. When third‐party affiliations reduce the stress of the victim, this behavior has been defined as consolation. Given the absence of previous reports, the objective of the present study was to evaluate the presence of third‐party post‐conflict affiliation when dogs (Canis lupus familiaris) observe their owners arguing. We carried out two studies varying the intensity and the duration of the dispute. Affiliative behaviors toward each of the owners were registered, as well as stress‐related behaviors. Our findings support the existence of third‐party affiliation from dogs toward their owners during and after a conflict between them, evidenced as higher rates of victim‐directed affiliative behaviors in the experimental condition versus the control, in both studies. Moreover, dogs exhibited more stress‐related behaviors in the experimental condition compared to the control, but only in the second study, which suggests these stimuli were experienced as aversive, even though they were not aimed at the dogs. In addition, in the second study dogs displayed aggressor‐directed behaviors that could be interpreted as appeasement. Finally, there was no evidence that the level of the bond between the dog and each owner acts as a modulator of affiliative behavior. Further studies are required to expand our understanding of these abilities of dogs and its effects on the emotional state of the victim.

Frequency stabilization of interconnected diverse power systems with integration of renewable energies and energy storage systems

Recent improvements in renewable energy sources (RESs) and their extensive use in the power industry have created significant issues for their operation, security, and management. Due to the ongoing reduction of power system inertia, maintaining operational frequency at its nominal value and minimizing tie-line power variations constitute essential variables for these improvements. A novel improved frequency stabilization approach based on modified fractional order tilt controller is presented for interconnected diverse power systems with integration of sea wave energy, photovoltaic, wind, energy storage system and biodiesel generator. The fractional order tilt integral double derivative (FOTIDD2) is a novel controller that is developed to address the frequency stabilization problems of a hybrid power structure that is integrated with sources of clean energy and devices for storing energy. A recent optimization algorithm inspired by human behavior called mother optimization algorithm (MOA) is applied to adjust the coefficient of the proposed cascaded controller. The FOTIDD2 controller was compared with other control methods in terms of its transient performance, in order to determine its effectiveness. Further, the mother optimization algorithm results are compared with those of sine cosine algorithm (SCA), fox optimization algorithm (FOA), and grey wolf optimization (GWO). FOTIDD2 controller was found to be more effective than PID controller in respect of reducing overshoot (Osh) by 79.31%, 83.78% and 67.99%, improving time settlement by 33.22%, 29.87%, and 22.45% and reducing undershoot (Ush) by 79.13%, 89.34%, and 86.90% for the (∆F1), (∆F2), and (∆Ptie), respectively.

Detection of PIK3CA hotspot mutations in canine mammary tumors using droplet digital PCR: tissue validation and liquid biopsy feasibility

Domestic dogs (Canis lupus familiaris) serve as valuable translational models for human cancer research due to their biological similarities. Canine mammary tumors (CMTs), frequently diagnosed in female dogs, share various characteristics with human breast cancers. This study investigates the PIK3CA (H1047R) mutation in CMTs using droplet digital PCR (ddPCR) and explores the potential of liquid biopsy for non-invasive detection. We analyzed 80 formalin-fixed, paraffin-embedded (FFPE) CMT tissue samples and compared ddPCR results with next-generation sequencing (NGS) data, achieving high concordance. Plasma and serum samples were also assessed for mutation concordance with tissue results. Our findings indicate a higher frequency of the PIK3CA (H1047R) mutations in benign and grade I malignant CMTs compared to more aggressive malignancies. The ddPCR assay demonstrated high sensitivity and specificity, with plasma testing showing 78.6% sensitivity and 87.5% specificity, and serum testing showing 66.7% sensitivity and 90.0% specificity. These results highlight the viability of liquid biopsy as a minimally invasive method for monitoring PIK3CA mutations in canine patients. The study suggests that liquid biopsy techniques hold significant promise for improving the early detection and monitoring of canine cancers, warranting further research to refine these methods and explore their applications in canine cancer diagnostics and treatment.

A New Algorithm for Predicting Dam Deformation Using Grey Wolf-Optimized Variational Mode Long Short-Term Neural Network

To solve the problems of difficult-to-model parameter selections, useful-signal extraction and improper-signal decomposition in nonlinear, non-stationary dam displacement time series prediction methods, we propose a new predictive model for grey wolf optimization and variational mode decomposition and long short-term memory (GVLSTM). Firstly, we used the grey wolf optimization (GWO) algorithm to optimize the parameters of variable mode decomposition (VMD), obtaining the optimal parameter combination. Secondly, we used multiscale permutation entropy (MPE) as a standard to select signal screening, determining and recon-structing the effective modal components. Finally, the long short-term memory neural network (LSTM) was used to learn the dam deformation characteristics. The result shows that the GVLSTM model can effectively reduce the estimation deviation of the prediction model. Compared with VMDGRU and VMDANN, the average RMSE and MAE value of each station is increased by 19.11%~28.58% and 27.66%~29.63%, respectively. We used determination (R2) coefficient to judge the performance of the prediction model, and the value of R2 was 0.95~0.97, indicating that our method has good performance in predicting dam deformation. The proposed method has outstanding advantages of high accuracy, reliability, and stability for dam deformation prediction.

Bat-associated ticks as a potential link for vector-borne pathogen transmission between bats and other animals.

Potentially zoonotic pathogens have been previously detected in bat-associated ticks, but their role in disease transmission and their frequency of feeding on non-bat hosts is poorly known. We used molecular blood meal analysis to reveal feeding patterns of the bat-associated tick species Ixodes ariadnae, I. simplex, and I. vespertilionis collected from cave and mine walls in Central and Southeastern Europe. Vertebrate DNA, predominantly from bats, was detected in 43.5% of the samples (70 of 161 ticks) but in these ticks we also detected the DNA of non-chiropteran hosts, such as dog, Canis lupus familiaris, wild boar, Sus scrofa, and horse, Equus caballus, suggesting that bat-associated ticks may exhibit a much broader host range than previously thought, including domestic and wild mammals. Furthermore, we detected the zoonotic bacteria Neoehrlichia mikurensis in bat ticks for the first time, and other bacteria, such as Bartonella and Wolbachia. In the light of these findings, the role of bat ticks as disease vectors should be urgently re-evaluated in more diverse host systems, as they may contribute to pathogen transmission between bats and non-chiropteran hosts.

A smarter approach to liquefaction risk: harnessing dynamic cone penetration test data and machine learning for safer infrastructure

This paper presents a novel approach for assessing liquefaction potential by integrating Dynamic Cone Penetration Test (DCPT) data with advanced machine learning (ML) techniques. DCPT offers a cost-effective, rapid, and adaptable method for evaluating soil resistance, making it suitable for liquefaction assessment across diverse soil conditions. This study establishes a threshold criterion based on the ratio of the penetration rate to the dynamic resistance (e/qd), where values exceeding four indicate high liquefaction susceptibility. ML models, including Support Vector Machine (SVM) optimized with Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Genetic Algorithm (GA), and Firefly Algorithm (FA), were employed to predict the e/qd ratio using key geotechnical parameters, such as fine content, peak ground acceleration, reduction factor, and penetration rate. The SVM-PSO model demonstrated superior performance, with high R2 values of 0.999 and 0.989 in the training and testing phases, respectively. The proposed methodology offers a sustainable and accurate approach for liquefaction assessment, reducing the environmental impact of geotechnical investigations, while ensuring reliable predictions. This study bridges the gap between field testing and advanced computational techniques, providing a powerful tool for geotechnical engineers to assess liquefaction risks and design resilient infrastructures.

Optimizing reconfigurable manufacturing system configuration selection with multi-objective grey wolf optimization

Optimizing reconfigurable manufacturing system configuration selection with multi-objective grey wolf optimization

Jamming Attacks Detection Based on IGWO for Optimization of Fast Correlation-Based Feature extraction in Wireless Communication

Background: Wireless networks are essential communication technologies that prevent cable installation prices and burdens. Because of this technology's pervasive usage, wireless network safety is a significant problem. Owing to distributed and open wireless medium aspects, attackers might use different jamming methods to exploit physical and MAC layer protocol vulnerabilities. In addition, jamming attacks require to be accurately grouped so that suitable countermeasures can be considered. Given the potential severity of such attacks, precisely identifying and classifying them is critical for implementing effective responses. The motivation for this paper is the need to improve the detection and categorization of jamming signals using modern machine learning algorithms, consequently enhancing wireless network security and reliability. Objective: In this paper, we compare some machine learning models' efficiency for diagnosing jamming signals. Methods: Such algorithms refer to support vector machine (SVM) and k-nearest neighbors (KNN). We checked the signal features that recognize jamming signals. After the jamming attack model, the developed grey wolf optimizer version known as IGWO (improved grey wolf optimizer) has been discussed for feature extraction of software usability. Four separate metrics were employed as features to detect jamming attacks in order to evaluate the machine learning models. This novel feature extraction method is crucial for improving the accuracy of jamming detection. Results: The measurements of these parameters were gathered through a simulation of a real setting. And generated a large dataset using these parameters. Conclusion: The simulation results illustrate that the KNN algorithm based on jamming detection could diagnose jammers having a minimal likelihood of false alarms and a high level of accuracy.

A hybrid decision support system in medical emergencies using artificial neural network and hyperbolic secant grey wolf optimization techniques

A hybrid decision support system in medical emergencies using artificial neural network and hyperbolic secant grey wolf optimization techniques

Multi-objective optimization of automotive seat frames using machine learning

The optimal design of automobile seats plays an important role in passenger safety in high-speed accidents. In order to enhance the accuracy of the prediction of the input variables and output response of the seat, a hybrid machine learning prediction model that combines the improved gray wolf optimizer (IGWO) and back propagation neural network (BPNN) has been proposed, and the prediction effect of the model was validated using the seat simulation data. Initially, based on the experimental data, finite element models were developed for eight typical working conditions of automobile seats and their accuracy was validated. Subsequently, the energy absorption to mass ratio method was employed to screen the design variables, resulting in the selection of 17 thickness variables and 15 material variables. Thereafter, the gray wolf optimizer (GWO) algorithm underwent enhancement through the incorporation of the dynamic leadership hierarchy (DLH) mechanism and the revision of the positional formula, yielding the IGWO algorithm. Following this, the IGWO algorithm was applied to optimize the hyperparameters of BPNN, culminating in the establishment of the IGWO-BPNN model. Ultimately, the seat multi-objective optimization design process was addressed using multi-objective gray wolf optimizer (MOGWO) to achieve the Pareto frontier, while the decision-making was conducted using the combined compromise solution (CoCoSo) method to determine the best trade-off solution. Furthermore, the effectiveness of the proposed optimal design method is evidenced by comparing the baseline design, simulation analysis, and optimal design methods. The results indicate that the optimized automotive seat frame achieves a reduction in cost by 20.7 % and mass by 22.9 %, simultaneously maintaining safety performance. Consequently, the proposed optimization design methodology is demonstrated to be highly effective for the multi-objective optimization design of automotive seat frames.

Deep learning-based improved transformer model on android malware detection and classification in internet of vehicles

With the growing popularity of autonomous vehicles (AVs), confirming their safety has become a significant concern. Vehicle manufacturers have combined the Android operating system into AVs to improve consumer comfort. However, the diversity and weaknesses of the Android operating system pose substantial safety risks to AVs, as these factors can expose them to threats, namely Android malware. The advanced behaviour of multi-data source fusion in autonomous driving models has mitigated recognition accuracy and effectualness for Android malware. To efficiently counter new malware variants, novel techniques distinct from conventional methods must be utilized. Machine learning (ML) techniques cannot detect every new and complex malware variant. The deep learning (DL) model is an efficient tool for detecting various malware variants. This manuscript proposes a Deep Learning-Based Improved Transformer Model on Android Malware Detection (DLBITM-AMD) technique for Internet vehicles (IoVs). The main aim of the presented DLBITM-AMD approach is to detect Android malware effectually and accurately. The DLBITM-AMD method performs a Z-score normalization process to convert the raw data into a standard form. Then, the DLBITM-AMD approach utilizes the binary grey wolf optimization (BGWO) model to select optimum feature subsets. An improved transformer is integrated with the RNN model and softmax to enhance classification for Android malware recognition. Finally, the snake optimizer algorithm (SOA) method is employed to select the optimum parameter for the classification method. An extensive experiment of the DLBITM-AMD method is accomplished on a benchmark dataset. The performance validation of the DLBITM-AMD technique portrayed a superior accuracy value of 99.26% over existing Android malware recognition models.

Prediction of Fading for Painted Cultural Relics Using the Optimized Gray Wolf Optimization-Long Short-Term Memory Model

Prediction of Fading for Painted Cultural Relics Using the Optimized Gray Wolf Optimization-Long Short-Term Memory Model