Black Widow Research Articles

Improved Black Widow Optimization: An investigation into enhancing cloud task scheduling efficiency

The Black Widow Optimization (BWO) algorithm has garnered significant attention within the realm of metaheuristic algorithms due to its potential to address diverse problems across various domains. However, a noteworthy weakness of BWO is its utilization of a random selection technique, which can lead to reduced diversity, expedited convergence, and potential entrapment in local optima. This research introduces a novel approach to augment the BWO algorithm by integrating alternative selection schemes, thereby surpassing the limitations of the current selection methodology. To assess the effectiveness of these proposed variants, we employ the CEC 2019 benchmark functions as the standard evaluation metric. Subsequently, we utilize the best-performing BWO version, PIBWO, to address cloud scheduling challenges. In a series of comparative experiments, PIBWO demonstrates superior performance compared to existing algorithms, showcasing remarkable enhancements in makespan reduction, energy consumption minimization, and cost efficiency. These findings underscore PIBWO’s potential as a robust solution for addressing cloud task scheduling challenges, offering promising avenues for developing more sustainable and cost-effective cloud computing systems.

An effective networks intrusion detection approach based on hybrid Harris Hawks and multi-layer perceptron

This paper proposes an Intrusion Detection System (IDS) employing the Harris Hawks Optimization algorithm (HHO) to optimize Multilayer Perceptron learning by optimizing bias and weight parameters. HHO-MLP aims to select optimal parameters in its learning process to minimize intrusion detection errors in networks. HHO-MLP has been implemented using EvoloPy NN framework, an open-source Python tool specialized for training MLPs using evolutionary algorithms. For purposes of comparing the HHO model against other evolutionary methodologies currently available, specificity and sensitivity measures, accuracy measures, and mse and rmse measures have been calculated using KDD datasets. Experiments have demonstrated the HHO MLP method is effective at identifying malicious patterns. HHO-MLP has been tested against evolutionary algorithms like Butterfly Optimization Algorithm (BOA), Grasshopper Optimization Algorithms (GOA), and Black Widow Optimizations (BOW), with validation by Random Forest (RF), XGBoost. HHO-MLP showed superior performance by attaining top scores with accuracy rate of 93.17%, sensitivity level of 89.25%, and specificity percentage of 95.41%.

Improving the prediction of wildfire susceptibility on Hawaiʻi Island, Hawaiʻi, using explainable hybrid machine learning models

This study presents a comparative analysis of four Machine Learning (ML) models used to map wildfire susceptibility on Hawaiʻi Island, Hawaiʻi. Extreme Gradient Boosting (XGBoost) combined with three meta-heuristic algorithms – Whale Optimization (WOA), Black Widow Optimization (BWO), and Butterfly Optimization (BOA) – were employed to map areas susceptible to wildfire. To generate a wildfire inventory, 1408 wildfire points were identified within the study area from 2004 to 2022. The four ML models (XGBoost, WOA-XGBoost, BWO-XGBoost, and BOA-XGBoost) were run using 14 wildfire-conditioning factors categorized into four main groups: topographical, meteorological, vegetation, and anthropogenic. Six performance metrics – sensitivity, specificity, positive predictive values, negative predictive values, the Area Under the receiver operating characteristic Curve (AUC), and the average precision (AP) of Precision–Recall Curves (PRCs) – were used to compare the predictive performance of the ML models. The SHapley Additive exPlanations (SHAP) framework was also used to interpret the importance values of the 14 influential variables for the modeling of wildfire on Hawaiʻi Island using the four models. The results of the wildfire modeling indicated that all four models performed well, with the BWO-XGBoost model exhibiting a slightly higher prediction performance (AUC = 0.9269), followed by WOA-XGBoost (AUC = 0.9253), BOA-XGBoost (AUC = 0.9232), and XGBoost (AUC = 0.9164). SHAP analysis revealed that the distance from a road, annual temperature, and elevation were the most influential factors. The wildfire susceptibility maps generated in this study can be used by local authorities for wildfire management and fire suppression activity.

Discovery and Timing of Millisecond Pulsars in the Globular Cluster M5 with FAST and Arecibo

We report on a comprehensive multiwavelength study of the pulsars in the globular cluster (GC) M5, including the discovery of M5G, a new compact noneclipsing “black widow” pulsar. Thanks to the analysis of 34 yr of radio data taken with the Five-hundred-meter Aperture Spherical radio Telescope and Arecibo telescopes, we obtained new phase-connected timing solutions for four pulsars and improved those of the other three. These have resulted in, among other things, (a) much improved proper motions for five pulsars, with transverse velocities (relative to the cluster) that are smaller than their respective escape velocities; (b) 3σ and 1.5σ detections of Shapiro delays in M5F and M5D, respectively; and (c) greatly improved measurement of the periastron advance in M5B, whose value of implies that M5B is still likely to be a heavy () neutron star. The binary pulsars M5D, M5E, and M5F are confirmed to be in low-eccentricity binary systems, the low-mass companions of which are newly identified to be He white dwarfs using Hubble Space Telescope data. Four pulsars are also found to be associated with X-ray sources. Similarly to the eclipsing pulsar M5C, M5G shows little or no nonthermal X-ray emission, indicative of weak synchrotron radiation produced by intrabinary shocks. All seven pulsars known in M5 have short spin periods (<8 ms), and five are in binary systems with low orbital eccentricities. These characteristics differ from the overall GC pulsar population but confirm the expectations for the pulsar population in a cluster with a small rate of stellar encounters per binary system.

Polarization of Intrabinary Shock Emission in Spider Pulsars

In so-called spider pulsars, the X-ray band is dominated by intrabinary shock (IBS) synchrotron emission. While the double-peaked X-ray light curves from these shocks have been well characterized in several spider systems (both black widows and redbacks), the polarization of this emission is yet to be studied. Motivated by the new polarization capability of the Imaging X-ray Polarization Explorer and the confirmation of highly ordered magnetic fields in pulsar wind nebulae, we model the IBS polarization by employing two potential magnetic field configurations: toroidal magnetic fields imposed by the preshock pulsar wind, and tangential shock-generated fields that follow the postshock flow. We find that if IBSs host ordered magnetic fields, the synchrotron X-rays from spider binaries can display a high degree of polarization (≳50%), while the variation in polarization angle provides a good probe of the binary geometry and the magnetic field structure. Our results encourage observational polarization studies of spider pulsars that can distinguish the proposed magnetic models and that constrain the unique properties of these systems better.

Intelligent Traffic Congestion Control Using Black Widow Optimization with Hybrid Deep Learning on Smart City Environment

Abstract Intelligent traffic congestion control is an integral aspect of making sustainable and efficient smart cities. With the increasing count of vehicles on the road and rapid urbanization, traffic congestion is a main concern nowadays that hinders the growth of the economy and affects the quality of life. In smart cities, an intelligent transportation solution (ITS) is enhance traffic flow by adjusting traffic signal timing and observing traffic patterns. Currently, one of the vital dilemmas in terms of transportation systems was traffic congestion which needs to be resolved for minimizing driver frustration, traffic jams, fuel waste, and accidents. Due to the high count of vehicles, most of the traffic interruptions in metropolitan cities arise. With the advancements in Artificial Intelligence (AI) and Machine Learning (ML), smart environments monitored in smart cities observe the influencing issues of the environment correctly, with the best control of traffic congestion, pollution, and other negative effects. Therefore, this study presents an intelligent traffic congestion control using Black Widow Optimization with Hybrid Deep Learning (ITC-BWOHDL) technique in Smart City Environment. The main aim of the ITC-BWOHDL technique is to utilize feature subset selection with parameter-tuning strategies for effective traffic congestion management. To obtain this, the ITC-BWOHDL technique primarily designs the emperor penguin optimizer-based feature selection (EPO-FS) approach for selecting a useful set of features. For the detection of traffic congestion, the ITCBWOHDL technique makes use of the HDL model which incorporates convolutional neural network (CNN) with gated recurrent unit (GRU) approach. To improve the classification results of the HDL model, the BWO-based hyperparameter tuning process gets executed. For exhibiting the improved classification outcome of the ITC-BWOHDL system, a comprehensive range of experiments was executed. The obtained outcome described the betterment of ITC-BWOHDL method over other existing techniques.

Constraining the Orbital Inclination and Companion Properties of Three Black Widow Pulsars Detected by FAST

Black widows (BWs) are millisecond pulsars ablating their companion stars. The out-flowing material from the companion can block the radio emission of the pulsar, resulting in eclipses. In this paper, we construct a model for the radio eclipse by calculating the geometry of the bow shock between the winds of the pulsar and companion, where the shock shapes the eclipsing medium but had not been described in detail in previous works. The model is further used to explain the variations of the flux density and dispersion measure of three BW pulsars (i.e., PSR B1957+20, J2055+3829, and J2051−0827) detected by the Five-hundred-meter Aperture Spherical radio Telescope. Consequently, we constrained the parameters of the three BW systems such as the inclination angles and true anomalies of the observer as well as the mass-loss rates and wind velocity of the companion stars. With the help of these constraints, it is expected that magnetic fields of companion stars and even masses of pulsars could further be determined as some extra observation can be achieved in the future.

The He star donor channel towards the black widow PSR J1953+1844

ABSTRACT Black widows (BWs) are a type of eclipsing millisecond pulsars (MSPs) with low companion masses ($\lesssim 0.05\, \rm M_\odot$) and tight orbits (&amp;lt;1 d). PSR J1953+1844 (i.e. M71E) is a black window (BW) with the shortest orbital period (∼53 min) ever discovered, which was recently detected by Five-hundred-meter Aperture Spherical radio Telescope. Its companion mass is $\sim 0.01\, \rm M_\odot$ according to its mass function, indicating that the companion may be a hydrogen-deficient star. However, the origin of PSR J1953+1844 is highly unclear. In this paper, we explored the origin of PSR J1953+1844 through the neutron star+He star channel, in which the system can experience ultracompact X-ray binary phase. We found that the He star donor channel can reproduce the characteristics of PSR J1953+1844, indicating that this work provides an alternative formation channel for this source. Meanwhile, the minimum orbital period of BWs formed by this channel is ∼28 min, corresponding to the companion mass of $0.058\, \rm M_\odot$. In addition, we note that even though PSR J1953+1844 has a short orbital period, it cannot be detected by the gravitational wave (GW) observatories like Laser Interferometer Space Antenna, TaiJi, and TianQin. However, we still expect that the BWs with extremely tight orbit produced by this channel are the potential sources of future space-based GW observatories. Moreover, our simulations show that PSR J1953+1844 may eventually evolve into an isolated MSP.

Solving probabilistic optimal power flow with renewable energy sources in distribution networks using fire hawk optimizer

The main contribution in this article is proposing a modern meta-heuristic algorithm for solving nonlinear, nonconvex, and complex OPF problems effectively in distribution networks, considering RES. The Fire Hawk Optimizer (FHO) algorithm is proposed in this work. The considered objective function (OF) is the combined minimization of generating cost, active power loss (Plosses), and load bus voltage deviation (VD). The proposed strategy is validated using the standard IEEE 69-bus benchmark. The obtained results are compared with various recent meta-heuristic techniques such as Capuchin Search Algorithm (CSA), Bear Smell Search Algorithm (BSSA), Black Widow Optimization (BWO) Algorithm, Walrus Optimization Algorithm (WaOA), and Prairie Dog Optimization (PDO) Algorithm. One-way ANOVA and the Tukey test, two common statistical tests, are used to compare algorithms effectively. The FHO shows the best and superior performance with minimum mean cost and standard deviation values of 51.2711 and 5.1131 respectively. This demonstrates that FHO results are more consistent and less diverse. The FHO shows a lower computational time of (25.3241 s) as compared with other strategies. The statistical results confirm the proposed approach's reliability, stability, and consistency. The proposed method provided reliable, high-quality results with faster computing times. Additionally, it has a significant convergence that makes it superior to solving OPF problems in distribution networks with variable RES and NRES.

Epidemiological Review of Black Widow Human Exposures as Reported to the National Poison Data System

Epidemiological Review of Black Widow Human Exposures as Reported to the National Poison Data System

Dynamic Voltage Restorer to Mitigate Voltage Sag/Swell using Black Widow Optimization Technique with FOPID Controller

The efficiency with which electrical equipment use electricity is essential for several reasons. First, superior power quality (PQ) improves efficiency and facilitates peak performance in electronic equipment. This article's goal is to make the advantages of installing a Dynamic Voltage Restorer (DVR) to enhance PQ for energy users clearer. To improve DVR dependability and user friendliness, the suggested technique uses a hysteresis voltage control system that works with variable switching frequency. Simulation findings show that voltage compensation is successful under disturbances when a black widow optimization (BWO) based Factional order proportional derivative (FOPID) controller is used. This paper proposed is to improve PQ using a BWO- FOPID controller and compare the outcomes to those achieved from a previously developed PI controller in a distribution power system using MATLAB.

Optimal Allocation of Distribution Static Synchronous Compensators in Distribution Networks Considering Various Load Models Using the Black Widow Optimization Algorithm

Incorporating Distribution Static Synchronous Compensator (DSTATCOM) units into the radial distribution network (RDN) represents a practical approach to providing reactive compensation, minimizing power loss, and enhancing voltage profile and stability. This research introduces a unique optimization technique called the Black Widow Optimization (BWO) algorithm for strategically placing DSTATCOM units within the RDN. The primary objective is to minimize power loss while simultaneously evaluating various techno-economic parameters such as the voltage profile index (VPI), voltage stability index (VSI), and annual cost savings. The analysis of optimal DSTATCOM allocation, employing the proposed BWO algorithm, encompasses different load models, including constant impedance (CZ), constant current (CI), constant power (CP), and composite (ZIP) models. These analyses consider three distinct scenarios: single and multiple DSTATCOM integration. To gauge the effectiveness of the proposed BWO technique, it is applied to the IEEE 33-bus and 69-bus RDNs as test cases. Simulation results confirm the efficiency of the proposed approach across all four load models. Notably, in the case of the constant power model, the percentage reduction in power loss is substantial, with a reduction of 34.79% for the IEEE 33-bus RDN and 36.09% for the IEEE 69-bus RDN compared to their respective base cases.

The black widow pulsar J1641+8049 in the optical, radio, and X-rays

ABSTRACT PSR J1641+8049 is a 2 ms black widow pulsar with the 2.2 h orbital period detected in the radio and γ-rays. We performed new phase-resolved multiband photometry of PSR J1641+8049 using the OSIRIS instrument at the Gran Telescopio Canarias. The obtained data were analysed together with the new radio-timing observations from the Canadian Hydrogen Intensity Mapping Experiment (CHIME), the X-ray data from the Spectrum-RG/eROSITA all-sky survey, and all available optical photometric observations. An updated timing solution based on CHIME data is presented, which accounts for secular and periodic modulations in pulse dispersion. The system parameters obtained through the light-curve analysis, including the distance to the source 4.6–4.8 kpc and the orbital inclination 56–59 deg, are found to be consistent with previous studies. However, the optical flux of the source at the maximum brightness phase faded by a factor of ∼2 as compared to previous observations. Nevertheless, the face of the J1641+8049 companion remains one of the most heated (8000–9500 K) by a pulsar among the known black widow pulsars. We also report a new estimation on the pulsar proper motion of ≈2 mas yr−1, which yields a spin-down luminosity of ≈4.87 × 1034 erg s−1 and a corresponding heating efficiency of the companion by the pulsar of 0.3–0.7. The pulsar was not detected in X-rays implying its X-ray-luminosity was $\lesssim$3 × 1031 erg s−1 at the date of observations.

Black Widow Optimization for Power System Load Frequency Control: A Comparative Study

This research paper mainly engrossed on developing a suitable novel tuning methodology named Black Widow Optimization (BWO) Algorithm for power system optimization problems. Load Frequency Control (LFC) and Automatic Voltage Regulator (AVR), two of the most important control systems in the power system arena, are employed as test systems to assess the efficiency of the suggested BWO approach. Various analyses, such as transient analysis, are employed to evaluate the efficiency of the suggested BWO approach in LFC and AVR systems. robustness analysis and convergence analysis. The significant performance measures of test system such as output power of each generating unit, maximum peak, settling time, steady state error, rise time and voltage/frequency deviations of the frequency and voltage responses are considered for the comparison purposes. The comparative analysis clearly demonstrates that the proposed novel tuning methodology provides better transient performances subjected to Step Load Perturbations (SLP), Variable Load Perturbations (VLP), and robustness performances under a wide range of load and parameter changes ranging from -50% to +50%. The convergence analysis of BWO algorithm also performed in LFC and AVR test systems and results better convergence characteristics with minimal convergence time as well as least fitness value is obtained with the BWO algorithm.

An efficient evacuation path optimization for passengers in subway stations under floods

The optimization of passenger evacuation paths in subway stations under flood scenarios plays an important role in improving evacuation efficiency and ensuring escape safety. Considering the impact of evacuation network failures such as gates on path planning, a two-stage passenger evacuation path optimization method under flood scenarios of subway stations is established with the objectives of minimizing total evacuation time, risk, and congestion. The black widow algorithm is proposed to optimize the BP neural network model to predict the travel time of passengers at nodes, which could improve the prediction accuracy of calculating the total evacuation time. The path optimization model is solved by the NSGA-II algorithm, and the optimal Pareto solution is determined based on the minimum total cost. Taking a subway station in Qingdao, China as an example, a passenger evacuation simulation system under flood scenarios is built using PathFinder software. The effect of the path optimization strategy is comprehensively evaluated through comparative experiments. It is found that the overall evacuation optimization degree could be increased by 18.75%, by comparing the evacuation time, congestion, and risk objectives under the situations with and without path optimization strategies.

YOLO-SCL: a lightweight detection model for citrus psyllid based on spatial channel interaction.

Efficient and accurate detection and providing early warning for citrus psyllids is crucial as they are the primary vector of citrus huanglongbing. In this study, we created a dataset comprising images of citrus psyllids in natural environments and proposed a lightweight detection model based on the spatial channel interaction. First, the YOLO-SCL model was based on the YOLOv5s architecture, which uses an efficient channel attention module to perform local channel attention on the inputs in the recursive gated convolutional modules to achieve a combination of global spatial and local channel interactions, improving the model's ability to express the features of the critical regions of small targets. Second, the lightweight design of the 21st layer C3 module in the neck network of the YOLO-SCL model and the small target feature information were retained to the maximum extent by deleting the two convolutional layers, whereas the number of parameters was reduced to improve the detection accuracy of the model. Third, with the detection accuracy of the YOLO-SCL model as the objective function, the black widow optimization algorithm was used to optimize the hyperparameters of the YOLO-SCL model, and the iterative mechanism of swarm intelligence was used to further improve the model performance. The experimental results showed that the YOLO-SCL model achieved a mAP@0.5 of 97.07% for citrus psyllids, which was 1.18% higher than that achieved using conventional YOLOv5s model. Meanwhile, the number of parameters and computation amount of the YOLO-SCL model are 6.92 M and 15.5 GFlops, respectively, which are 14.25% and 2.52% lower than those of the conventional YOLOv5s model. In addition, after using the black widow optimization algorithm to optimize the hyperparameters, the mAP@0.5 of the YOLO-SCL model for citrus psyllid improved to 97.18%, making it more suitable for the natural environments in which citrus psyllids are to be detected. The experimental results showed that the YOLO-SCL model has good detection accuracy for citrus psyllids, and the model was ported to the Jetson AGX Xavier edge computing platform, with an average processing time of 38.8 ms for a single-frame image and a power consumption of 16.85 W. This study provides a new technological solution for the safety of citrus production.

The Optimization of a Pipeline Temperature Monitoring Method Based on Non-Local Means with the Black Widow Optimization Algorithm

The accuracy of pipeline temperature monitoring using the Brillouin Optical Time Domain Analysis system depends on the Brillouin Gain Spectrum in the Brillouin Optical Time Domain Analysis system. The Non-Local Means noise reduction algorithm, due to its ability to use the data patterns available within the two-dimensional measurement data space, has been used to improve the Brillouin Gain Spectrum in the Brillouin Optical Time Domain Analysis system. This paper studies a new Non-Local Means algorithm optimized through the Black Widow Optimization Algorithm, in view of the unreasonable selection of smoothing parameters in other Non-Local Means algorithms. The field test demonstrates that, the new algorithm, when compared to other Non-Local Means methods, excels in preserving the detailed information within the Brillouin Gain Spectrum. It successfully restores the fundamental shape and essential characteristics of the Brillouin Gain Spectrum. Notably, at the 25 km fiber end, it achieves a 3 dB higher Signal-to-Noise Ratio compared to other Non-Local Means noise reduction algorithms. Furthermore, the Brillouin Gain Spectrum values exhibit increases of 9.4% in Root Mean Square Error, 12.5% in Sum of Squares Error, and 10% in Full Width at Half Maximum. The improved method has a better denoising effect and broad application prospects in pipeline safety.

The Representation of Radical Feminism in Black Widow Movie Directed by Cate Shortland

&lt;div class="WordSection1"&gt;&lt;p&gt;&lt;span lang="EN-US"&gt;The goal of feminism is to fully establish gender equality in law and practice while combating sexism, sexist exploitation, and oppression. Cate Shortland is the director of the superhero science fiction film &lt;a name="_Hlk145877994"&gt;&lt;/a&gt;Black Widow. In the film, black widowed women are controlled by the Red Room Academy, a clandestine criminal organization. The primary character and former Black Widow Natasha Romanoff is depicted destroying the Red Room Academy and its male head, Dreykov. The theory of feminism and film analysis are used by the researcher to answer these research questions. There are three objectives in this research. The first objective is to describe the character value of Natasha in the movie. Next is to analyze the radical feminism shown by Natasha in the movie. Last, to analyze the moral value that the director wants to convey. This research was qualitative descriptive method by watching the movie and taking several scenes that were related to the theory. The results of this research reveal that: (1) There are three-character value from Natasha as the main character of the movie; independent, social care and perseverance. (2) The analysis reveals the radical feminism inclinations portrayed by Natasha Romanoff is the act of self-sacrifice. (3) The moral value found in Black Widow movie is about caring each other and women’s right.&lt;/span&gt;&lt;/p&gt;&lt;/div&gt;

A differential evolution optimization-based black widow spider method in PV systems under shading conditions

The black widow spider optimization algorithm is a new population optimization method, which shows strong advantages in solving complex problems and optimization. PV systems are multi-peaked in shading conditions, and traditional MPPT technology cannot successfully capture the global maximum power point. Therefore, this study proposes an MPPT control method based on IBWO. In this context, a GMPP tracking control strategy based on IBWO is proposed in this research. Considering the complexity of PV shading, six shading conditions are designed in this study to compare the performance of PSO, BWO, SSA and the proposed method. Taking into account the real climatic conditions of different seasons. The simulation results show that the proposed control method has the ability to track the global maximum power point, the efficiency is almost 100%, and the tracking time is about 0.2s. The verification results of the test platform show that the tracking efficiency of the proposed method reaches 98.75%. Experimental results show that the IBWO method can be used as a solution to the global optimization problem of MPPT, which can improve the tracking efficiency of PV system while accompanied with fast response time.

THD Reduction of Improved Single Source MLI Using Upgraded Black Widow Optimization Algorithm

An upgraded Black Widow Optimization algorithm for obtaining the ideal switching angles of a reduced structure multilevel inverter is proposed in this paper with minimum voltage THD. The conventional BWO algorithm has been modified and proposed to solve the THD minimization problem with faster convergence rate, lesser tuning variables, and lesser number of equations than the other existing nature inspired algorithms. The reduced structure MLI is designed with three asymmetrical DC voltage sources of ratio 1 : 2 : 4 to generate 15-level output voltage which requires reduced number of switching devices in comparison with the existing topologies of equivalent voltage level. The asymmetrical DC voltage sources to the MLI are derived from a single DC voltage source using a multisecondary winding DC-DC converter in order to provide the isolation between the load side and source side. The proposed modified BWO algorithm reduces the complexity of solving the nonlinear equations to achieve the best result by avoiding the local optima. To verify the effectiveness of the upgraded BWO algorithm, the optimization problem for three phase fifteen level MLI has been solved using the genetic algorithm, BFO algorithm, and PSO algorithm. The results for the above three applied algorithms are compared with the upgraded BWO and presented. The proposed algorithm has minimized the output voltage THD to 1.83% at 0.88 modulation index. The developed scheme has been corroborated with the help of MATLAB simulation and the results have been verified through a hardware model developed in laboratory.