Search Results (66)

An orthogonally polarized green dual-wavelength (OPGDW) laser output in a Pr³⁺:LiLuF₄ (Pr:LLF) crystal with the power ratio of 1:1 was realized for the first time. We calculated the condition for obtaining the identical power of the two output wavelengths and achieved the OPGDW laser by adjusting the tilt angle of the intracavity etalon and optimizing the output coupling transmittance. Using a frequency-doubled (2ω) optically pumped semiconductor (OPS) laser of 10 W at 479 nm, a continuous wave (CW) OPGDW laser output at 523 nm (π-polarized) and 538 nm (σ-polarized) was achieved with a combined power of 1.83 W. In addition, by type-II critical phase-matched (CPM) β-BaB₂O₄ (BBO) nonlinear crystal, a 57 mW, 265 nm CW UV laser was also realized by sum-frequency generation (SFG) of 523 nm and 538 nm wavelengths. CW OPGDW lasers with identical power output were ideal for both medical detection and generating UV lasers. Full article

Pakistan’s marine fishing industry is crucial to the country’s economy, generating employment opportunities and foreign revenue. It produces 80% of the country’s total fish production. Otolithes ruber is a commercially important fish on the Makran coast of Pakistan, contributing significantly to the region’s croaker fisheries. This study is the first to apply three length-based approaches for assessing the stock status of O. ruber in the Makran coast: (1) TropFishR to estimate the mortality, growth parameters, and current exploitation status, reference points based on the yield per recruitment model, (2) the length-based Bayesian biomass method (LBB) to calculate stock biomass, and (3) the length-based spawning potential ratio (LBSPR) to estimate the spawning potential ratio. The length–weight relationship of Otolithes ruber was a positive allometric pattern (b = 3.28; R² = 0.94). Growth parameters for Otolithes ruber were L_∞ = 55.47 cm, K = 0.50 year⁻¹. The calculated total mortality rate (Z), natural mortality (M), and fishing mortality (F) were 2.27 year⁻¹, 0.67 year⁻¹, and 1.6 year⁻¹, respectively. The exploitation rate (E) was 0.70, indicating severe overexploitation. The current length at first capture (Lc₅₀) = 27.37 cm was lower than that at first maturity (Lm₅₀) = 30.75 cm, indicating growth overfishing. The current spawning potential ratio (8%) was lower than the optimal value (40%), indicating recruitment overfishing. The current biomass, concerning virgin biomass B/B_o, was also 8%, resulting in a 92% stock decline. We recommend reducing the exploitation pressure by limiting the commercial catch to an optimum length range of 34.5–42.2 cm and reducing fishing pressure by 40% to ensure sustainable fishery management. Full article

Brent oil holds a significant position in the global energy market, as oil prices in many regions are indexed to it. Therefore, forecasting the future price of Brent oil is of great importance. In recent years, artificial intelligence techniques have been widely applied in modeling and prediction tasks. In this study, an Adaptive Neuro-Fuzzy Inference System (ANFIS), a well-established AI approach, was employed for the time-series forecasting of Brent oil prices. To ensure effective learning and improve prediction accuracy, ANFIS was trained using nine different metaheuristic algorithms: Artificial Bee Colony (ABC), Selfish Herd Optimizer (SHO), Biogeography-Based Optimization (BBO), Multi-Verse Optimizer (MVO), Teaching–Learning-Based Optimization (TLBO), Cuckoo Search (CS), Moth Flame Optimization (MFO), Marine Predator Algorithm (MPA), and Flower Pollination Algorithm (FPA). Symmetric training procedures were applied across all algorithms to ensure fair and consistent evaluation. The analyses were conducted on the lowest and highest daily, weekly, and monthly Brent oil prices. Mean squared error (MSE) was used as the primary performance metric. The results showed that all algorithms achieved effective prediction performance. Among them, BBO and TLBO demonstrated superior accuracy and stability, particularly in handling the complexities of Brent oil forecasting. This study contributes to the literature by combining ANFIS and metaheuristics within a symmetric framework of experimentation and evaluation. Full article

A dual-wavelength (DW) Pr³⁺:LiLuF₄ (Pr:LLF) laser with the same threshold and slope efficiency was achieved for the first time. We theoretically deduced the conditions for obtaining the same threshold and slope efficiency of the DW operation, and experimentally demonstrated the orange-red DW Pr:LLF laser by optimizing the output coupling transmittance and adjusting the rotation angle of the intracavity Lyot filter. A CW orange-red DW laser, pumped by a 10 W 479 nm frequency-doubled optically pumped semiconductor laser (2ω-OPSL), delivers combined outputs of 607 nm and 640 nm with a total power of 2.69 W. The orange and red wavelengths maintained balanced power output under each pump level. Furthermore, by a type-I critical phase-matched (CPM) β-BaB₂O₄ (BBO) crystal, a CW ultraviolet (UV) second harmonic generation (SHG) at 312 nm was also obtained through intracavity sum-frequency mixing (SFM) of the 607 nm and 640 nm fundamental beams, achieving a maximum power output of 812 mW. Full article

The application of high-pressure jet-assisted (HPJA) machining can increase tool life during machining, as the cutting fluid penetrates better into the interfaces between the tool and the workpiece. In this work, tool life in semi-finish turning of Inconel 718 with coated carbide tools and a high-pressure coolant supply is investigated. In a preliminary experiment, tool life was compared between conventional flooding and HPJA machining. The results show tool life that is more than twice as long with HPJA at higher cutting speeds. In the main experiment, tool life was investigated as a function of various high-pressure-jet parameters (nozzle diameter, distance between the point of impact of the jet and the cutting edge and pressure of the jet) and basic cutting parameters (cutting speed and feed rate). The relationship between the above-mentioned process parameters and tool life was analyzed and modeled using response surface methodology (RSM). Analysis of variance (ANOVA) was performed to evaluate the statistical significance of each process parameter for the response. The results revealed that cutting speed is the most important factor for maximizing tool life, followed by pressure of the jet and feed rate. In addition, optimization using the biogeographic optimization (BBO) algorithm was performed and validated in this study. The results of the confirmation experiments show that the proposed optimization method is very effective and results in approximately 8.4% longer tool life compared to the best trial results. Full article

The science of biogeography was described through mathematical equations in 1967 by Robert MacArthur and Edward Wilson. In 2008, Dan Simon presented an algorithm called biogeography-based optimization, or BBO, which used some of the principles and definitions described in MacArthur and Wilson’s book. The objectives of this work were to study the behavior of the BBO method when it is hybridized with other evolutionary search methods and to analyze the effect of its application to some examples of mechanical engineering systems. The operators considered in the hybridization study are genetic recombination (crossover) and local search, aiming to overcome the limitations and difficulties that arise when using the original BBO. The results of the original BBO were promising in the context of a global search. However, there is a diversity problem that does not allow for good quality increments in the final phase of the evolutionary process. The additional modifications included, such as the concept of blending in migration, the cycle of mutations and the replacement of the worst solutions by injection of new ones, all show positive effects on the method’s performance. However, the biggest increase happened with the implementation of the hybridization processes. Crossover improved the speed and diversity of the population in some cases, while local search helped the algorithm in later generations, allowing it to quickly reach the optimum point. With this mentioned, it is important to note that the best results were all obtained with the fully modified algorithm. Statistical tests were implemented to validate the significance of changes due to modifications included in the original proposal of BBO. Full article

In this study, a traveling wave Mach-Zehnder intensity modulator (TW-MZM) was designed and optimized for six different electro-optical (EO) crystals: lithium niobate (LNB), potassium niobate (KNB), lithium titanate (LTO), beta barium borate (BBO), cadmium telluride (CdTe), and indium phosphide (InP). The performance of each EO crystal, including optical and radio frequency (RF) loss, applied voltage, and modulation bandwidth, was estimated and compared. The results suggest that, in theory, KNB, LTO, BBO, and CdTe have the potential to outperform LNB. However, it should be noted that the loss associated with KNB and LTO is comparable to that of LNB. The findings demonstrated that BBO and CdTe exhibit a modulation bandwidth exceeding 100 GHz and demonstrate the lowest loss among the considered crystals based on the assumed geometry. Full article

In this study, we developed a green and multifunctional bioactive nanoemulsion (BBG-NEs) of Blumea balsamifera oil using Bletilla striata polysaccharide (BSP) and glycyrrhizic acid (GA) as natural emulsifiers. The process parameters were optimized using particle size, PDI, and zeta potential as evaluation parameters. The physicochemical properties, stability, transdermal properties, and bioactivities of the BBG-NEs under optimal operating conditions were investigated. Finally, network pharmacology and molecular docking were used to elucidate the potential molecular mechanism underlying its wound-healing properties. After parameter optimization, BBG-NEs exhibited excellent stability and demonstrated favorable in vitro transdermal properties. Furthermore, it displayed enhanced antioxidant and wound-healing effects. SD rats wound-healing experiments demonstrated improved scab formation and accelerated healing in the BBG-NE treatment relative to BBO and emulsifier groups. Pharmacological network analyses showed that AKT1, CXCL8, and EGFR may be key targets of BBG-NEs in wound repair. The results of a scratch assay and Western blotting assay also demonstrated that BBG-NEs could effectively promote cell migration and inhibit inflammatory responses. These results indicate the potential of the developed BBG-NEs for antioxidant and skin wound applications, expanding the utility of natural emulsifiers. Meanwhile, this study provided a preliminary explanation of the potential mechanism of BBG-NEs to promote wound healing through network pharmacology and molecular docking, which provided a basis for the mechanistic study of green multifunctional nanoemulsions. Full article

The biogeography-based optimization (BBO) algorithm is known for its simplicity and low computational overhead, but it often struggles with falling into local optima and slow convergence speed. Against this background, this work presents a multi-strategy enhanced BBO variant, named MSBBO. Firstly, the example chasing strategy is proposed to eliminate the destruction of the inferior solutions to superior solutions. Secondly, the heuristic crossover strategy is designed to enhance the search ability of the population. Finally, the prey search–attack strategy is used to balance the exploration and exploitation. To verify the performance of MSBBO, we compare it with standard BBO, seven BBO variants (PRBBO, BBOSB, HGBBO, FABBO, BLEHO, MPBBO and BBOIMAM) and seven meta-heuristic algorithms (GWO, WOA, SSA, ChOA, MPA, GJO and BWO) on multiple dimensions of 24 benchmark functions. It concludes that MSBBO significantly outperforms all competitors both on convergence accuracy, speed and stability, and MSBBO basically converges to the same results on 10,000 dimensions as on 1000 dimensions. Further, MSBBO is applied to six real-world engineering design problems. The experimental results show that our work is still more competitive than other latest optimization techniques (COA, EDO, OMA, SHO and SCSO) on constrained optimization problems. Full article

Gantry-type dual-axis platforms can be used to move heavy loads or perform precision CNC work. Such gantry systems drive a single axis with two linear motors, and under heavy loads, a high driving force is required. This can generate a pulling force between the drive shafts in the coupling mechanism. In these situations, when a synchronization error becomes too large, mechanisms can become deformed or damaged, leading to damaged equipment, or in industrial settings, an additional power consumption. Effectively and accurately acquiring the synchronized movement of the platform is important to reduce energy consumption and optimize the system. In this study, a fractional-order fuzzy PID controller (FOFPID) using Oustaloup’s recursive filter is used to control a synchronous X–Y gantry-type platform. The optimized controller parameters are obtained by the measurement of control errors in a simulated environment. Four optimization methods are tested and compared: particle swarm optimization, invasive weed optimization, a gray wolf optimizer, and biogeography-based optimization. The systems were tested and compared in order to optimize the control parameters. Each of the four algorithms is simulated on four contour shapes: a circle, bow, heart, and star. The simulations and control scheme of the experiments are implemented using MATLAB, and the reference paths were planned using non-uniform rational B-splines (NURBS). After running the simulations to determine the optimal control parameters, each set of acquired control parameters is also tested and compared in the experiments and the results are recorded. Both the simulations and experiments show good results, and the tracking of the X–Y platform showed improved performance. Two performance indices are used to determine and validate the relative performance of the models and results. Full article

The automatic identification of emotions from speech holds significance in facilitating interactions between humans and machines. To improve the recognition accuracy of speech emotion, we extract mel-frequency cepstral coefficients (MFCCs) and pitch features from raw signals, and an improved differential evolution (DE) algorithm is utilized for feature selection based on K-nearest neighbor (KNN) and random forest (RF) classifiers. The proposed multivariate DE (MDE) adopts three mutation strategies to solve the slow convergence of the classical DE and maintain population diversity, and employs a jumping method to avoid falling into local traps. The simulations are conducted on four public English speech emotion datasets: eNTERFACE05, Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS), Surrey Audio-Visual Expressed Emotion (SAEE), and Toronto Emotional Speech Set (TESS), and they cover a diverse range of emotions. The MDE algorithm is compared with PSO-assisted biogeography-based optimization (BBO_PSO), DE, and the sine cosine algorithm (SCA) on emotion recognition error, number of selected features, and running time. From the results obtained, MDE obtains the errors of 0.5270, 0.5044, 0.4490, and 0.0420 in eNTERFACE05, RAVDESS, SAVEE, and TESS based on the KNN classifier, and the errors of 0.4721, 0.4264, 0.3283 and 0.0114 based on the RF classifier. The proposed algorithm demonstrates excellent performance in emotion recognition accuracy, and it finds meaningful acoustic features from MFCCs and pitch. Full article

In order to analyze the performance of a light detection and ranging system based on polarization modulation, it is necessary to theoretically analyze and model the polarization scattering characteristics of common target materials. In this paper, the shortcomings of the classical Hyde pBRDF (polarization bidirectional reflectance distribution function) model are analyzed. Based on the research results of many researchers in recent years, a new six-parameter pBRDF model is proposed. To verify the accuracy of the proposed model, this paper builds a measurement system for the polarization scattering characteristics of the target surface in the laser active imaging scene, and the polarization scattering characteristics of two common materials, namely a white paint coating and an aluminum plate, are measured. Based on the measurement results of the DOP (degree of polarization) of the scattered light of the target material and the BBO-FA (biogeography-based optimization-Firefly algorithm) algorithm, we performed inversion calculations on the key parameters of the target material. Using the parameters of the target material obtained via inversion, we use the model to simulate the Stokes vectors of the target and compare the simulated values of Stokes vectors with the measured values to verify the accuracy of the model. The verification results show that the simulation results of Stokes vectors are in good agreement with the measurement results for these two materials, and the introduction of various improvements to the model can effectively improve the accuracy of the model, which provides a tool for studying the performance parameters of a laser three-dimensional imaging system based on polarization modulation. Full article

Predicting particulate matter with a diameter of 10 μm (PM10) is crucial due to its impact on human health and the environment. Today, aerosol optical depth (AOD) offers high resolution and wide coverage, making it a viable way to estimate PM concentrations. Recent years have also witnessed in-creasing promise in refining air quality predictions via deep neural network (DNN) models, out-performing other techniques. However, learning the weights and biases of the DNN is a task classified as an NP-hard problem. Current approaches such as gradient-based methods exhibit significant limitations, such as the risk of becoming ensnared in local minimal within multi-objective loss functions, substantial computational requirements, and the requirement for continuous objective functions. To tackle these challenges, this paper introduces a novel approach that combines the binary gray wolf optimizer (BGWO) with DNN to improve the optimization of models for air pollution prediction. The BGWO algorithm, inspired by the behavior of gray wolves, is used to optimize both the weight and bias of the DNN. In the proposed BGWO, a novel sigmoid function is proposed as a transfer function to adjust the position of the wolves. This study gathers meteorological data, topographic information, PM10 pollution data, and satellite images. Data preparation includes tasks such as noise removal and handling missing data. The proposed approach is evaluated through cross-validation using metrics such as correlation rate, R square, root-mean-square error (RMSE), and accuracy. The effectiveness of the BGWO-DNN framework is compared to seven other machine learning (ML) models. The experimental evaluation of the BGWO-DNN method using air pollution data shows its superior performance compared with traditional ML techniques. The BGWO-DNN, CapSA-DNN, and BBO-DNN models achieved the lowest RMSE values of 16.28, 19.26, and 20.74, respectively. Conversely, the SVM-Linear and GBM algorithms displayed the highest levels of error, yielding RMSE values of 36.82 and 32.50, respectively. The BGWO-DNN algorithm secured the highest R² (88.21%) and accuracy (93.17%) values, signifying its superior performance compared with other models. Additionally, the correlation between predicted and actual values shows that the proposed model surpasses the performance of other ML techniques. This paper also observes relatively stable pollution levels during spring and summer, contrasting with significant fluctuations during autumn and winter. Full article

In order to enhance the accuracy of the traditional extended Kalman filter (EKF) algorithm in the estimation of the state of charge (SoC) of power batteries, we first derived the state space equation and measurement equation of lithium power batteries based on the Thevenin battery model and the modified Ampere-Hour integral algorithm. Then, the basic principles of EKF, backpropagation neural networks (BPNNs), and a biogeography-based optimization (BBO) algorithm were analyzed, and the arc curve mobility model was used to improve the global search ability of the BBO algorithm. By combining these three algorithms, this paper proposes a BP neural network method based on the BBO algorithm. This method uses the BBO algorithm to optimize the incipient weight and threshold of the BP neural network and uses this improved neural network to modify the estimated value of the extended Kalman filter algorithm (BBOBP-EKF). Finally, the BBOBP-EKF algorithm, the extended Kalman filter algorithm based on the BP neural network (BP-EKF), and the EKF algorithm are used to estimate the error value of the SOC of a power battery, and according to the experimental data, it was confirmed that the proposed BBOBP-EKF algorithm has been improved compared to other algorithms with respect to each error index term, in which the maximum error is 1% less than that of the BP-EKF algorithm and 2.4% less than that of the EKF algorithm, the minimum error is also the smallest, and the estimation accuracy is improved compared to the traditional algorithms. Full article

Existing swarm intelligence (SI) optimization algorithms applied to node localization optimization (NLO) and node coverage optimization (NCO) problems have low accuracy. In this study, a novel balanced butterfly optimizer (BBO) is proposed which comprehensively considers that butterflies in nature have both smell-sensitive and light-sensitive characteristics. These smell-sensitive and light-sensitive characteristics are used for the global and local search strategies of the proposed algorithm, respectively. Notably, the value of individuals’ smell-sensitive characteristic is generally positive, which is a point that cannot be ignored. The performance of the proposed BBO is verified by twenty-three benchmark functions and compared to other state-of-the-art (SOTA) SI algorithms, including particle swarm optimization (PSO), differential evolution (DE), grey wolf optimizer (GWO), artificial butterfly optimization (ABO), butterfly optimization algorithm (BOA), Harris hawk optimization (HHO), and aquila optimizer (AO). The results demonstrate that the proposed BBO has better performance with the global search ability and strong stability. In addition, the BBO algorithm is used to address NLO and NCO problems in wireless sensor networks (WSNs) used in environmental monitoring, obtaining good results. Full article