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Transposable elements (TE) play critical roles in shaping genome evolution. Highly repetitive TE sequences are also a major source of assembly gaps making it difficult to fully understand the impact of these elements on host genomes. The increased capacity of long-read sequencing technologies to span highly repetitive regions promises to provide new insights into patterns of TE activity across diverse taxa. Here we report the generation of highly contiguous reference genomes using PacBio long-read and Omni-C technologies for three species of Passerellidae sparrow. We compared these assemblies to three chromosome-level sparrow assemblies and nine other sparrow assemblies generated using a variety of short- and long-read technologies. All long-read based assemblies were longer (range: 1.12 to 1.41 Gb) than short-read assemblies (0.91 to 1.08 Gb) and assembly length was strongly correlated with the amount of repeat content. Repeat content for Bell's sparrow (31.2% of genome) was the highest level ever reported within the order Passeriformes, which comprises over half of avian diversity. The highest levels of repeat content (79.2% to 93.7%) were found on the W chromosome relative to other regions of the genome. Finally, we show that proliferation of different TE classes varied even among species with similar levels of repeat content. These patterns support a dynamic model of TE expansion and contraction even in a clade where TEs were once thought to be fairly depauperate and static. Our work highlights how the resolution of difficult-to-assemble regions of the genome with new sequencing technologies promises to transform our understanding of avian genome evolution.

Summary: Photovoltaic (PV) systems benefit from the cascaded H‐bridge multilevel inverter (CHMLI) topology due to its high flexibility and efficiency. However, PV mismatches in three‐phase grid‐connected systems result in an unbalanced power supply, which leads to an unbalanced grid current. Proportional integral derivative (PID) controllers can be used to control H‐bridge inverters because of their simplicity of tuning as well as their basic structure. However, due to the nonlinearity and high sensitivity of the PID controller, its performance declines as a result of high overshoot, high settling time, and high rise time during significant external disturbances. Moreover, the switching angle of the inverter must be adjusted to provide the required fundamental voltage while reducing harmonic content. In order to address this problem, a control technique with modulation compensation is proposed. The present work offers a two‐degree‐of‐freedom fractional‐order PID controller with an integrated filter (2DOF–FOPID–IF). Moreover, the optimum switching angles are determined for the CHMLI by employing an improved sparrow optimization (ISO) algorithm. To prove the practicality of the suggested technique, simulation, and experimental data are compared with the existing techniques. The proposed technique shows better performance in terms of settling time, overshoot, rise time, and low harmonics. [ABSTRACT FROM AUTHOR]

[Objective] The aims of this study are to compare and analyze the applicability of modified Gash model and neural network model in simulating and predicting canopy interception of artificial forest to reveal the canopy interception and its response process of Robinia pseudoacacia in arid area, and to provide scientific basis for further understanding of forest eco-hydrological process and its regulation mechanism. [Method] Robinia pseudoacacia plantation in the east of the Yellow River of Ningxia was taken as the research object, the stemflow and throughfall were observed and the canopy interception was calculated. The modified Gash model and neural network model were used to simulate the canopy interception of Robinia pseudoacacia forest. [Result] (1) The throughfall, stemflow and canopy interception of Robinia pseudoacacia plantation in the study area were 154.19 mm, 5.61 mm and 16.5 mm, respectively, and the thresholds for throughfall and stemflow were 1.37 mm and 2.17 mm, respectively. (2) Both the Gash modified model and the optimized neural network model could better simulate the canopy interception of Robinia pseudoacacia. The absolute error, mean square error, root mean square error and mean absolute percentage error of the Gash modified model were 0.20%, 0.06%, 0.24% and 52.43%, respectively. The fitting accuracy of the simulation results reached 83%. Compared with the Gash modified model, the mean square error of the BP neural network algorithm model (SSA-BP) optimized by the sparrow search algorithm was reduced by 61.48%, the mean absolute error was reduced by 40.39%, the root mean square error was reduced by 37.93%, the mean absolute percentage error was reduced by 50.52%, and the coefficient of determination was increased by 1.2%. [Conclusion] In the simulation study of canopy interception, the BP neural network model with sparrow search algorithm has a good reliability, which can effectively reduce the simulation error and improve the prediction accuracy of the model. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV) is highly flexible and versatile, ranging from monitoring and surveying to rescue and military applications, but finding the best path requires a large amount of computing resources. Through intelligent path planning algorithms, UAV can find the best path according to task requirements and environmental conditions, avoid obstacles, and bypass dangerous areas, thereby effectively reducing the risks and errors of task execution. However, ordinary heuristic algorithms often do not achieve satisfactory results. To address this problem, A multi-mechanism balanced advanced sparrow search algorithm (BALSSA) is proposed. To achieve a balance between exploration and exploitation in the algorithm, we introduce two innovative techniques: an adaptive weight jumping mechanism and a suicide mutation perturbation. Then a balanced advanced learning is proposed. this approach enhances the evolutionary learning capabilities of SSA and aids the algorithm in timely escaping from local optima, and then propose a spiral factor improved progressive learning to improve the mutual learning performance between individuals, tested on 23 benchmark test functions, CEC2017 and CEC2022 The set is compared with algorithms in recent years and proposed algorithm variants, and the results show that BALSSA has better optimization and robustness. Finally, the proposed BALSSA is applied to UAV path planning. Compared with the variant SSA in recent years, BALSSA shows more stable and accurate optimization performance to verify the practicability of the improved algorithm. [ABSTRACT FROM AUTHOR]

A novel coupled model integrating Elman-AdaBoost with adaptive mutation sparrow search algorithm (AM-SSA), called AMSSAElman-AdaBoost, is proposed for predicting the existing metro tunnel deformation induced by adjacent deep excavations in soft ground. The novelty is that the modified SSA proposes adaptive adjustment strategy to create a balance between the capacity of exploitation and exploration. In AM-SSA, firstly, the population is initialized by cat mapping chaotic sequences to improve the ergodicity and randomness of the individual sparrow, enhancing the global search ability. Then the individuals are adjusted by Tent chaotic disturbance and Cauchy mutation to avoid the population being too concentrated or scattered, expanding the local search ability. Finally, the adaptive producer-scrounger number adjustment formula is introduced to balance the ability to seek the global and local optimal. In addition, it leads to the improved algorithm achieving a better accuracy level and convergence speed compared with the original SSA. To demonstrate the effectiveness and reliability of AM-SSA, 23 classical benchmark functions and 25 IEEE Congress on Evolutionary Computation benchmark test functions (CEC2005), are employed as the numerical examples and investigated in comparison with some wellknown optimization algorithms. The statistical results indicate the promising performance of AM-SSA in a variety of optimization with constrained and unknown search spaces. By utilizing the AdaBoost algorithm, multiple sets of weak AMSSA-Elman predictor functions are restructured into one strong predictor by successive iterations for the tunnel deformation prediction output. Additionally, the on-site monitoring data acquired from a deep excavation project in Ningbo, China, were selected as the training and testing sample. Meanwhile, the predictive outcomes are compared with those of other different optimization and machine learning techniques. In the end, the obtained results in this real-world geotechnical engineering field reveal the feasibility of the proposed hybrid algorithm model, illustrating its power and superiority in terms of computational efficiency, accuracy, stability, and robustness. More critically, by observing data in real time on daily basis, the structural safety associated with metro tunnels could be supervised, which enables decision-makers to take concrete control and protection measures. [ABSTRACT FROM AUTHOR]

In this paper, a novel optimization method is proposed based on the sparrow search algorithm, namely, multi-strategy-guided sparrow search algorithm (MGSSA). It is well-known that the basic SSA has limitations such as the slow convergence speed and vulnerability to local optimality. Surrounding these two issues, some strategies are presented in the MGSSA. Firstly, the newly introduced ring topology search strategy not only maintains the diversity of the entire population but also enhances the exploration ability of the SSA. Secondly, the proposed leader-based search strategy can improve exploitation ability of the SSA to prevent falling into local optimum as much as possible. Moreover, the coordinated learning strategy is put forward to better balance between the exploration and exploitation abilities. Finally, the MGSSA is compared with seventeen advanced algorithms on two well-known benchmark suites (i.e., CEC-2017 and CEC-2020). Meanwhile, the MGSSA-based forecasting approach is applied to predict the remaining useful life for lithium-ion batteries. The statistical results indicate that the MGSSA is a high-performance optimizer, which can not only solve the defects of the original SSA, but also obtain satisfactory solutions in both complex numerical optimization and real-world application problems. [ABSTRACT FROM AUTHOR]

In light of the issue that the vibration signal from an axle-box bearing collected during the operation of an electric multiple unit (EMU) is seriously polluted by background noise, which leads to difficulty in identifying fault characteristic frequency, this paper proposes a resonance-based sparse signal decomposition (RSSD) and variational mode decomposition (VMD) method based on sparrow search algorithm (SSA) optimization to extract the fault characteristic frequency of the bearing. Firstly, the RSSD method is utilized to decompose the signal based on the obtained optimal combination of quality factors, resulting in the optimal low-resonance component with periodic fault information. Then, the VMD method is performed on this low-resonance component. The parameter combinations for both methods are optimized utilizing the SSA method. Subsequently, envelope demodulation is applied to the intrinsic mode function (IMF) with maximum kurtosis, and fault diagnosis is achieved by comparing it with the theoretical fault characteristic frequency. Finally, experimental validation and comparison are conducted by utilizing simulated signals and example signals. The results demonstrate that the proposed method extracts more obvious periodic fault impact components. It effectively filters out the interference of complex noise and reduces the blindness of setting weights on parameters due to human experience, indicating excellent adaptability and robustness. [ABSTRACT FROM AUTHOR]

Addressing the issues of the sand cat swarm optimization algorithm (SCSO), such as its weak global search ability and tendency to fall into local optima, this paper proposes an improved strategy called the multi-strategy integrated sand cat swarm optimization algorithm (MSCSO). The MSCSO algorithm improves upon the SCSO in several ways. Firstly, it employs the good point set strategy instead of a random strategy for population initialization, effectively enhancing the uniformity and diversity of the population distribution. Secondly, a nonlinear adjustment strategy is introduced to dynamically adjust the search range of the sand cats during the exploration and exploitation phases, significantly increasing the likelihood of finding more high-quality solutions. Lastly, the algorithm integrates the early warning mechanism of the sparrow search algorithm, enabling the sand cats to escape from their original positions and rapidly move towards the optimal solution, thus avoiding local optima. Using 29 benchmark functions of 30, 50, and 100 dimensions from CEC 2017 as experimental subjects, this paper further evaluates the MSCSO algorithm through Wilcoxon rank-sum tests and Friedman's test, verifying its global solid search ability and convergence performance. In practical engineering problems such as reducer and welded beam design, MSCSO also demonstrates superior performance compared to five other intelligent algorithms, showing a remarkable ability to approach the optimal solutions for these engineering problems. [ABSTRACT FROM AUTHOR]

Increased urbanisation influences the morphometric traits of various species, often resulting in urban individuals being smaller than their non-urban counterparts. Urbanisation can affect fundamental eco-evolutionary patterns and impact species' ability to adapt to and occupy rapidly changing environments through morphological changes. We investigated the morphometric responses of two passerine species, the non-native house sparrow (Passer domesticus) and its native congener, the Cape sparrow (Passer melanurus), along gradients of spatial and temporal urbanisation in South Africa over a 52-year period. The house sparrow was significantly heavier, larger and in better condition with increasing urban infrastructure and lower urban vegetation cover, while the Cape sparrow showed opposing trends along these gradients. Temporally, the house sparrow's body mass increased consistently over the 52-year study period, suggesting changes in morphology were concomitant with increasing urbanisation over time. This study demonstrates distinct differences in the morphological responses of the non-native house sparrow and the native Cape sparrow to increasing urban development. These morphological responses may also underpin community-level changes caused by urbanisation, enhancing the capabilities of non-native species to thrive over their native counterparts in these environments. [ABSTRACT FROM AUTHOR]

To improve the performance of the sparrow search algorithm in solving complex optimization problems, this study proposes a novel variant called the Improved Beetle Antennae Search-Based Sparrow Search Algorithm (IBSSA). A new elite dynamic opposite learning strategy is proposed in the population initialization stage to enhance population diversity. In the update stage of the discoverer, a staged inertia weight guidance mechanism is used to improve the update formula of the discoverer, promote the information exchange between individuals, and improve the algorithm's ability to optimize on a global level. After the follower's position is updated, the logarithmic spiral opposition-based learning strategy is introduced to disturb the initial position of the individual in the beetle antennae search algorithm to obtain a more purposeful solution. To address the issue of decreased diversity and susceptibility to local optima in the sparrow population during later stages, the improved beetle antennae search algorithm and sparrow search algorithm are combined using a greedy strategy. This integration aims to improve convergence accuracy. On 20 benchmark test functions and the CEC2017 Test suite, IBSSA performed better than other advanced algorithms. Moreover, six engineering optimization problems were used to demonstrate the improved algorithm's effectiveness and feasibility. [ABSTRACT FROM AUTHOR]

Only a few decades after its introduction to the United States in the mid-nineteenth century, the house sparrow was considered a pest that drove away native birds. Its downfall is representative of a story familiar to scholars of animals and technology who have studied the methods used to control or exclude unwanted species from both rural and urban areas. The case of the house sparrow, however, differs in a crucial respect: the birds made their homes in bird boxes, built technologies designed to attract avian species and bring them closer to humans. This article documents how bird boxes were used as tools to regulate avian life in the late nineteenth and early twentieth centuries in the United States and argues that they should be seen as a technology that mediates and regulates our relationship with nature by promoting or controlling certain aspects of living organisms. [ABSTRACT FROM AUTHOR]

The cover image for this issue of Technology and Culture depicts how bird boxes became contested technologies in the United States. The early twentieth-century image shows a pair of house sparrows (Passer domesticus)— an introduced species—taking over a bird box intended for native birds. But the claim that sparrows seized bird boxes and other nesting places to the detriment of American birds was controversial. Since their mid-nineteenth-century introduction to the United States, sparrows have had both supporters and detractors who used bird boxes as tools to aid or suppress the birds. This essay argues that this image and others like it constitute a form of "biological propaganda" that supported the professionalization of ornithology in the United States. Natural history illustrations are not value neutral but show animals and technology in such a way as to support their creator's specific view of nature and what "belongs" in it. [ABSTRACT FROM AUTHOR]

In order to reduce the energy consumption of the welding robot and ensure the cooperative movement of the robot joints, a trajectory planning method with optimal energy consumption based on improved sparrow search algorithm is proposed. Firstly, the trajectory planning model with optimal energy consumption is established based on the joint torque and angular velocity of the robot. To make the velocity, acceleration and jerk of each joint of the robot be bounded and continuous, the joint space trajectory is constructed with seventh degree B-spline curve. The total energy consumption of the robot is calculated by combining kinematic and dynamic parameters. On the basis of improved sparrow search algorithm, the time series corresponding to the optimal energy consumption is solved by using elite reverse learning, non-dominated sorting and Gaussian-Cauchy variation strategy, and then the optimal continuous motion trajectory of energy consumption is planned. The simulation results show that the proposed method can not only achieve continuous smooth control objective, but also effectively reduce energy consumption. [ABSTRACT FROM AUTHOR]

In vehicular edge computing, both edge and cloud can provide computing services (i.e., tasks). The edge can reduce vehicular task delay by processing data nearby, but is resource‐constrained and cannot handle too many tasks simultaneously. The resource‐rich cloud can handle massive tasks, but is far from users and has low quality of service and energy efficiency. Currently, some work has been done on task offloading for edge‐cloud collaboration. However, either the collaboration among multiple devices is not considered and the load is easily imbalanced; or edge‐cloud collaboration is required for offloading decisions with too many parameters, affecting tasks to be processed efficiently. To address these issues, we learn from the swarm intelligence evolution of sparrow foraging, improve a sparrow search algorithm by integrating three strategies of flyer refine producer update, sin/cos perturbation follower update, and adaptive adjustment agitator update, to optimize the vehicular task offloading location for edge‐cloud collaboration. Furthermore, we combine delay relaxation variables and delay‐energy penalty operator to design a lightweight heuristic task offloading algorithm, and greedily compare the task preoffload location sets with different delay constraints based on the improved sparrow search algorithm, to obtain the optimal task offloading with integrated delay and energy. Simulation experiments verify that our approach can improve the algorithm's optimization accuracy, convergence speed, and robustness. Moreover, the average task delay, total task energy consumption, and load balance degree of our approach outperform the five benchmark algorithms, and can comprehensively optimize task delay and energy consumption, and achieve edge devices load balancing. [ABSTRACT FROM AUTHOR]

Semi-empirical quantum chemical approaches are known to compromise accuracy for the feasibility of calculations on huge molecules. However, the need for ultrafast calculations in interactive quantum mechanical studies, high-throughput virtual screening, and data-driven machine learning has shifted the emphasis toward calculation runtimes recently. This comes with new constraints for the software implementation as many fast calculations would suffer from a large overhead of the manual setup and other procedures that are comparatively fast when studying a single molecular structure, but which become prohibitively slow for high-throughput demands. In this work, we discuss the effect of various well-established semi-empirical approximations on calculation speed and relate this to data transfer rates from the raw-data source computer to the results of the visualization front end. For the former, we consider desktop computers, local high performance computing, and remote cloud services in order to elucidate the effect on interactive calculations, for web and cloud interfaces in local applications, and in world-wide interactive virtual sessions. The models discussed in this work have been implemented into our open-source software SCINE Sparrow. [ABSTRACT FROM AUTHOR]

Summary: Metaheuristic algorithms have special effects in solving optimization problems in real life and have become the focus of researchers. The sparrow search algorithm (SSA) is a newly proposed swarm‐based metaheuristic algorithm that has shown excellent optimization performance. Although compared with other algorithms, SSA shows good performance, the original SSA algorithm still has problems such as weak optimization ability, leading to falling into the local optimum, and being unable to balance exploration and exploitation well. Therefore, this paper proposes an improved SSA using chaotic opposition‐based learning and hybrid updating rules (CHSSA). First, chaotic opposition‐based learning is proposed to improve the diversity of the population. Second, two strategies, including adaptive weights and spiral search, are adopted to update the position. Finally, to evaluate the performance of the proposed CHSSA, this paper uses 23 benchmark functions, IEEE CEC 2017 functions and 4 practical engineering optimization problems to evaluate the algorithm performance. The experimental results show that compared with other advanced optimization algorithms, CHSSA has the characteristics of fast convergence speed, high search accuracy, and strong robustness. [ABSTRACT FROM AUTHOR]

The output power of PV systems is influenced by various factors, resulting in strong volatility and randomness, which makes it difficult to forecast. Therefore, this paper proposes an Informer prediction model based on optimised VMD for predicting short-term PV power. Firstly, the temporal coding of the Informer model is improved and, secondly, the original sequence is decomposed into multiple modal components using VMD, and then optimisation of the results of VMD in conjunction with the optimisation strategy of SSA improves the characteristics of the time series data. Finally, the refined data are fed into the Informer framework for modelling and prediction, utilising the self-attention mechanism and multiscale feature fusion of Informer to precisely forecast PV power. The power of PV prediction data from the SSA-VMD-Informer model and four other commonly used models is compared. Experimental results indicate that the SSA-VMD-Informer model performs exceptionally well in short-term PV power prediction, achieving higher accuracy than traditional methods. As an example, the results of predicting the PV power on 24 April in a region of Xinjiang are 1.3882 for RMSE, 0.8310 for MSE, 1.14 for SDE, and 0.9944 for R2. [ABSTRACT FROM AUTHOR]

To address the anti-swing issue of the payload in bridge cranes, Proportional–Integral–Derivative (PID) control is a commonly used method. However, parameter tuning of the PID controller relies on empirical knowledge and often leads to system overshoot. This paper proposes an Improved Sparrow Search Algorithm (ISSA) to optimize the gains of PID controllers, alleviating adverse effects on payload oscillation and trolley positioning during the operation of overhead cranes. First, tent map chaos mapping is introduced to initialize the sparrow population, enhancing the algorithm's global search capability. Then, by integrating sine and cosine concepts along with nonlinear learning factors, the updating mechanism of discoverer positions is dynamically adjusted, expediting the solving process. Finally, the Lévy flight strategy is employed to update follower positions, thereby enhancing the algorithm's local escape capability. Additionally, a fitness function containing overshoot penalties is proposed to address overshoot issues. Simulation results indicate that the overshoot rates of all algorithms remain less than 3%. Moreover, compared with the Sparrow Search Algorithm (SSA), Particle Swarm Optimization (PSO), Simulated Annealing (SA), and Whale optimization Algorithm (WOA), the optimized PID control system with the ISSA algorithm exhibits superior control performance and possesses certain robustness and adaptability. [ABSTRACT FROM AUTHOR]

Determining space use for species is fundamental to understanding their ecology, and tracking animals can reveal insights into their spatial ecology on home ranges and territories. Recent technological advances have led to GPS-tracking devices light enough for birds as small as ~30 g, creating novel opportunities to remotely monitor fine-scale movements and space use for these smaller species. We tested whether miniaturized GPS tags can allow us to understand space use of migratory birds away from their capture sites and sought to understand both pre-breeding space use as well as territory and habitat use on the breeding grounds. We used GPS tags to characterize home ranges on the breeding grounds for a migratory songbird with limited available breeding information, the Golden-crowned Sparrow (Zonotrichia atricapilla). Using GPS points from 23 individuals across 26 tags (three birds tagged twice), we found home ranges in Alaska and British Columbia were on average 44.1 ha (95% kernel density estimate). In addition, estimates of territory sizes based on field observations (mean 2.1 ha, 95% minimum convex polygon [MCP]) were three times smaller than 95% MCPs created using GPS tags (mean 6.5 ha). Home ranges included a variety of land cover classes, with shrubland particularly dominant (64–100% of home range cover for all but one bird). Three birds tracked twice returned to the same breeding area each year, supporting high breeding site fidelity for this species. We found reverse spring migration for five birds that flew up to 154 km past breeding destinations before returning. GPS-tracking technology allowed for critical ecological insights into this migratory species that breeds in very remote locations. [ABSTRACT FROM AUTHOR]

To solve the difficulty in correctly identifying a compound fault of rolling bearing, a method combining variational mode decomposition (VMD) and harmonic fusion vector bispectrum (HFVB) is proposed. Firstly, to achieve adaptive decomposition of signals, the characteristic ability of envelope entropy to represent signal sparsity is utilized. By employing the minimum envelope entropy as the fitness function for the sparrow search algorithm (SSA), the decomposition levels and penalty factors of VMD are adaptively determined. Secondly, root-mean-square value is treated as fault feature index to self-adaptively choose from intrinsic mode function (IMF) which can embody fault features of bearings. Thirdly, to further highlight fault features, HFVB is used to blend chosen IMFs. Finally, faults of bearings were recognized with spectrum of signals. To verify the effectiveness of proposed method, an analysis was given to vibration signals in different situations, and SSA-VMD-HFVB was compared with classical method. The results demonstrate that the proposed SSA-VMD-HFVB method facilitates the adaptive decomposition of VMD, enabling the selection and effective integration of sensitive fault component signals. This approach enhances the accuracy of complex fault diagnosing in rolling bearings. [ABSTRACT FROM AUTHOR]

Path planning is an important research direction in the field of robotics; however, with the advancement of modern science and technology, the study of efficient, stable, and safe path-planning technology has become a realistic need in the field of robotics research. This paper introduces an improved sparrow search algorithm (ISSA) with a fusion strategy to further improve the ability to solve challenging tasks. First, the sparrow population is initialized using circle chaotic mapping to enhance diversity. Second, the location update formula of the northern goshawk is used in the exploration phase to replace the sparrow search algorithm's location update formula in the security situation. This improves the discoverer model's search breadth in the solution space and optimizes the problem-solving efficiency. Third, the algorithm adopts the Lévy flight strategy to improve the global optimization ability, so that the sparrow jumps out of the local optimum in the later stage of iteration. Finally, the adaptive T-distribution mutation strategy enhances the local exploration ability in late iterations, thus improving the sparrow search algorithm's convergence speed. This was applied to the CEC2021 function set and compared with other standard intelligent optimization algorithms to test its performance. In addition, the ISSA was implemented in the path-planning problem of mobile robots. The comparative study shows that the proposed algorithm is superior to the SSA in terms of path length, running time, path optimality, and stability. The results show that the proposed method is more effective, robust, and feasible in mobile robot path planning. [ABSTRACT FROM AUTHOR]

Global warming has become an undeniable fact, and the frequent occurrence of extremely hot weather is a serious challenge to the survival of animals. As one of the most active components of the ecosystem, birds are sensitive to the constant changes in the climate and environment. To investigate the physiological responses of Eurasian tree sparrows (Passer montanus) to acute heat stress, various thermogenic parameters of the birds, which included resting metabolic rate (RMR), body mass, body temperature, water intake, and liver cellular metabolic capacity, were measured after exposure to 42°C for different times. The antioxidant response of Eurasian tree sparrows to heat stress was investigated by measuring the lipid oxidation indexes and related antioxidant oxidase activities in the serum and liver. Body temperature and water intake were significantly increased after 2 h of heat stress and lasted until the end of the heat stress (12 h). In contrast, RMR was significantly decreased after 2 h of heat stress. At the cellular level, liver COX activity was significantly increased during heat stress from 2 h to 6 h. Additionally, there was a significant increase in MDA content and superoxide dismutase (SOD) and catalase (CAT) activity levels in the serum. The glutathione peroxidase (GSH-PX) activity in the liver was significantly decreased after 2 h of heat stress. Our results indicated that heat production decreased while heat dissipation increased in Eurasian tree sparrows exposed to acute heat stress. At the same time, the internal antioxidant defense system of the birds was activated to counteract the damage caused by high temperatures. Therefore, it was reasonable to speculate that Eurasian tree sparrows might be resistant to high temperatures to the extent that they can maintain normal life activities through the regulation of thermogenic and antioxidant systems under acute heat stress. [ABSTRACT FROM AUTHOR]

The economic friction and political conflicts between some countries and regions have made multinational corporations increasingly focus on the reliability and credibility of manufacturing supply chains. In view of the impact of poor manufacturing entity reliability and service reputation on the new-era manufacturing industry, the time-varying reliability and time-varying credibility of cloud manufacturing (CMfg) services were studied from the perspective of combining nature and society. Taking time-varying reliability, time-varying credibility, composition complexity, composition synergy, execution time, and execution cost as objective functions, a new six-dimension comprehensive evaluation model of service quality was constructed. To solve the optimization problem, this study proposes an improved chaos sparrow search algorithm (ICSSA), where the Bernoulli chaotic mapping formula was introduced to improve the basic sparrow search algorithm (BSSA), and the position calculation formulas of the explorer sparrow and the scouter sparrow were enhanced. The Bernoulli chaotic operator changed the symmetry of the BSSA, increased the uncertainty and randomness of the explorer sparrow position in the new algorithm, and affected the position update and movement strategies of the follower and scouter sparrows. The asymmetric chaotic characteristic brought better global search ability and optimization performance to the ICSSA. The comprehensive performance of the service composition (SvcComp) scheme was evaluated by calculating weighted relative deviation based on six evaluation elements. The WFG and DTLZ series test functions were selected, and the inverse generation distance (IGD) index and hyper volume (HV) index were used to compare and evaluate the convergence and diversity of the ICSSA, BSSA, PSO, SGA, and NSGA-III algorithms through simulation analysis experiments. The test results indicated that the ICSSA outperforms the BSSA, PSO, SGA, and NSGA-III in the vast majority of testing issues. Finally, taking disinfection robot manufacturing tasks as an example, the effectiveness of the proposed CMfg SvcComp optimization model and the ICSSA were verified. The case study results showed that the proposed ICSSA had faster convergence speed and better comprehensive performance for the CMfg SvcComp optimization problem compared with the BSSA, PSO, SGA, and NSGA-III. [ABSTRACT FROM AUTHOR]

Recent research has shown that songbirds that reside at low altitudes can ascend to ∼6000 m above sea level during migratory flight. Since migratory flight is aerobically demanding, whether migratory songbirds exhibit plasticity in breathing to maintain oxygen uptake in low-oxygen environments is unknown. This study investigated whether the hypoxic ventilatory response of sparrows was altered between resident house sparrows (Passer domesticus (Linneaus, 1758)) and migratory song sparrows (Melospiza melodia (A. Wilson, 1810)), and Lincoln's sparrows (Melospiza lincolnii (Audubon, 1834)) or seasonally (long daylight versus short daylight length) within a species. Breathing responses were assessed by stepwise reductions in inspired O2 tension, 21, 16, 12, 9, 7, and 5 kPa during long and short days. Ventilation increased in hypoxia in all species, although song sparrows and Lincoln's sparrows exhibited greater increases in ventilation in severe hypoxia compared to house sparrows. All species became more sensitive to hypoxia during short days compared to long days (increased breathing frequency and total ventilation), with reduced pulmonary oxygen extraction. Although all sparrows had similar ventilatory responses in moderate hypoxia, our findings suggest that migratory sparrows breathe more effectively in severe hypoxia compared to house sparrows, which would be important for maintaining oxygen uptake during migratory flights. [ABSTRACT FROM AUTHOR]

Reasonable workshop layout can reduce the transportation distance, reduce the production cost and improve the production efficiency. Aiming at the problem of unreasonable workshop layout, this paper takes an actual case as the research object and studies its workshop layout. Firstly, the mathematical model with the objectives of minimizing logistics transportation cost and maximizing non-logistics relationship is established. Then, the sparrow search algorithm (SSA) is used to find the optimal layout that satisfies the objective function. Finally, Flexsim software is used to simulate the layout scheme. The results show that compared with the genetic algorithm and the original layout scheme, the layout scheme obtained by the SSA algorithm reduces the total transportation distance by 26 m and 325 m, and improves the non-logistic relationship by 1.6% and 24.84%, respectively. Optimizing the layout using SSA algorithms increases product productivity, which is a good improvement over the original workshop layout. [ABSTRACT FROM AUTHOR]

The harlequin ladybird (Harmonia axyridis) shows a high level of colour polymorphism. Particular forms differ in their colour combination, pattern and abundance.Two species of wild‐caught passerines native to Central Europe were offered various forms of ladybirds, differing in their colour pattern and abundance in nature. We predicted that those forms that are more abundant in the wild are better protected, as they are more familiar to predators.Forms novemdecimsignata and spectabilis, which represent 97% of individuals in the wild population, significantly differ in their visual appearance (mostly orange vs. mostly black). The form axyridis is very rare in the wild, and suturalis and aninkae do not occur in the wild and were derived from laboratory breedingsAs predators, we used great tits (Parus major), which are very aversive towards ladybirds, and tree sparrows (Passer montanus), which are quite willing to attack and even eat ladybirds.We compared bird responses to particular ladybird colour forms and included a brown‐painted control to test the effect of conspicuous colours.We showed that both species of birds attacked all conspicuous forms of the ladybird equally and usually very rarely. The brown‐painted novemdecimsignata form was attacked more frequently compared with the conspicuous forms, showing that the visual appearance prevents birds from attacking any conspicuous colour combination. Sparrows tended to eat the attacked ladybirds of forms novemdecimsignata, spectabilis and brown painted. The rare axyridis form and laboratory forms were very well protected from the attack, very likely due to neophobia.We may conclude that despite the outstanding polymorphism of H. axyridis, its protection against avian predators is very effective as long as the red‐and‐black pattern is preserved. [ABSTRACT FROM AUTHOR]

Animals' learned vocalizations often show high levels of intraspecific variation, and different variants may play different roles in attracting mates and settling territorial disputes. In any animal population with variable vocalizations, certain acoustic variants may be common and others rare. The responses of animals to local versus nonlocal sounds have been well characterized in prior investigations, but few investigations have explored the reactions of animals to songs that are present at different frequencies of occurrence within a population. Using an experimental approach to investigate a breeding population of Savannah sparrows, Passerculus sandwichensis , we tested the responses of territorial male songbirds to playback of locally common songs, locally rare songs and heterospecific songs. Male sparrows showed strong responses to conspecific song but weak responses to heterospecific song. Males showed their strongest responses to locally common songs and weaker responses to locally rare songs. Birds did not appear to discriminate between playback songs based on whether they were of the same song type as their own. We suggest that male Savannah sparrows perceive locally rare songs as weaker threats than locally common songs, similar to birds' reactions to foreign dialects. This could be due to low familiarity with locally rare songs, reduced female preference for locally rare songs, decreased success with territory defence for males singing locally rare songs or other perceived differences between locally rare and locally common songs. Overall, our results suggest that different song types elicit different responses from territorial songbirds, even songs that are found within the same population with different frequencies of occurrence. • Many animals show small-scale geographical variation in vocalizations. • Responses to locally common or locally rare vocalizations are seldom explored. • We used playback to test birds' responses to locally common and locally rare songs. • Savannah sparrows responded more strongly to locally common songs. • We suggest locally common songs are perceived as a stronger threat. [ABSTRACT FROM AUTHOR]

Many biological studies rely on accurate sex determination of individuals to investigate sex-specific differences in wild populations. Numerous species can be reliably sexed in the field using external characteristics such as plumage color and ornamentation or in the hand using breeding characteristics such as a brood patch (female) or cloacal protuberance (male). However, plumage differences may not be present year-round and breeding characteristics are not present outside of the breeding season. This is a problem for researchers as they may not be able to reliably sex individuals in the field outside of the breeding season. However, monochromatic species often exhibit sex differences in morphometrics including wing chord length and tarsus length. Recent studies have found that the Eastern (leucophrys) and Puget Sound (pugetensis) subspecies of the White-crowned Sparrow (Zonotrichia leucophrys) can be reliably sexed using wing chord length. The Gambel's (gambelii) subspecies was last studied in 1985 and the Nuttall's (nuttalli) subspecies has not been tested. We combined wing chord length, tarsus length, and sex data from adult Gambel's and Nuttall's White-crowned Sparrows to determine if these subspecies could be reliably sexed using wing chord length and/or tarsus length. We also determined if the migratory Gambel's subspecies and resident Nuttall's subspecies varied morphologically. We found that the Gambel's and Nuttall's subspecies could be reliably sexed using wing chord length outside overlapping ranges of 74–78 and 69–77 mm, respectively. We also found that the Gambel's subspecies had longer wing chord lengths and shorter tarsus lengths than the Nuttall's subspecies. Our findings provide a useful tool for sex determination in Gambel's, but not Nuttall's, White-crowned Sparrows. Further, our findings show that migratory and resident individuals within the same species vary morphologically, which may relate to the different selective pressures they face. [ABSTRACT FROM AUTHOR]

This study investigated cadmium in urban sparrows from various Meknes zones with differing industrial and traffic intensities, compared to a rural site. Forty sparrows from industrial regions and ten from rural were analysed post-capture. Cadmium extraction used acid mineralisation, quantified via ICP. Findings revealed higher cadmium in sparrows' kidneys from industrial zones than the rural site, with an average of 1.25 µg/g wet weight, indicating urban and industrial impacts on metal accumulation. The kidneys showed notable vulnerability to cadmium. Statistical data highlighted significant concentration differences between zones. This research emphasises the need for environmental monitoring and pollution strategies in urban areas for safeguarding health. [ABSTRACT FROM AUTHOR]

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Animal culture evolves alongside genomes, and the two modes of inheritance—culture and genes—interact in myriad ways. For example, stable geographic variation in culture can act as a reproductive barrier, thereby facilitating genetic divergence between "cultural populations." White-crowned sparrows (Zonotrichia leucophrys) are a well-established model species for bird song learning and cultural evolution, as they have distinct, geographically discrete, and culturally transmitted song types (i.e., song dialects). In this study, we tested the hypothesis that divergence between culturally transmitted songs drives genetic divergence within Nuttall's white-crowned sparrows (Z. l. nuttalli). In accordance with sexual selection theory, we hypothesized that cultural divergence between mating signals both preceded and generated genetic divergence. We characterized the population structure and song variation in the subspecies and found two genetically differentiated populations whose boundary coincides with a major song boundary at Monterey Bay, California. We then conducted a song playback experiment that demonstrated males discriminate between songs based on their degree of divergence from their local dialect. These results support the idea that discrimination against non-local songs is driving genetic divergence between the northern and southern populations. Altogether, this study provides evidence that culturally transmitted bird songs can act as the foundation for speciation by sexual selection. [ABSTRACT FROM AUTHOR]

Deep learning models struggle to effectively capture data features and make accurate predictions because of the strong non-linear characteristics of arbitrage data. Therefore, to fully exploit the model performance, researchers have focused on network structure and hyperparameter selection using various swarm intelligence algorithms for optimization. Sparrow Search Algorithm (SSA), a classic heuristic method that simulates the sparrows' foraging and anti-predatory behavior, has demonstrated excellent performance in various optimization problems. Hence, in this study, the Multi-Strategy Modified Sparrow Search Algorithm (MSMSSA) is applied to the Long Short-Term Memory (LSTM) network to construct an arbitrage spread prediction model (MSMSSA-LSTM). In the modified algorithm, the good point set theory, the proportion-adaptive strategy, and the improved location update method are introduced to further enhance the spatial exploration capability of the sparrow. The proposed model was evaluated using the real spread data of rebar and hot coil futures in the Chinese futures market. The obtained results showed that the mean absolute percentage error, root mean square error, and mean absolute error of the proposed model had decreased by a maximum of 58.5%, 65.2%, and 67.6% compared to several classical models. The model has high accuracy in predicting arbitrage spreads, which can provide some reference for investors. [ABSTRACT FROM AUTHOR]

Green storage plays a key role in modern logistics and is committed to minimizing the environmental impact. To promote the transformation of traditional storage to green storage, research on the capacity allocation of wind-solar-storage microgrids for green storage is proposed. Firstly, this paper proposes a microgrid capacity configuration model, and secondly takes the shortest payback period as the objective function, and uses the improved sparrow search algorithm (ISSA) for optimization. Firstly, the Logistic-Tent compound chaotic mapping method is added to the population initialization of the sparrow search algorithm (SSA). Secondly, the adaptive t-distribution mutation is used to improve the discoverer, and the overall optimization ability of the algorithm is improved. Finally, the hybrid decreasing strategy is adopted in the process of vigilance position update. The ISSA can improve the search efficiency of the algorithm, avoid premature convergence and enhance the robustness of the algorithm, which is helpful to better apply to the optimal configuration of wind-solar-storage microgrid capacity in green storage. By analyzing the optimal capacity allocation results of two typical days, the system can better adapt to the dynamic storage requirements and improve the flexibility and sustainability of the supply chain. [ABSTRACT FROM AUTHOR]

With the expansion of the scale of electric power, high-quality electrical energy remains a crucial aspect of power system management and operation. The generation of reactive power is the primary cause of the decline in electrical energy quality. Therefore, optimization of reactive power in the power system becomes particularly important. The primary objective of this article is to create a multi-objective reactive power optimization (MORPO) model for distribution networks. The model aims to minimize reactive power loss, reduce the overall compensation required for reactive power devices, and minimize the total sum of node voltage deviations. To tackle the MORPO problems for distribution networks, the improved sparrow search algorithm–particle swarm optimization (ISSA-PSO) algorithm is proposed. Specifically, two improvements are proposed in this paper. The first is to introduce a chaotic mapping mechanism to enhance the diversity of the population during initialization. The second is to introduce a three-stage differential evolution mechanism to improve the global exploration capability of the algorithm. The proposed algorithm is tested on the IEEE 33-node system and the practical 22-node system. The results indicate a reduction of 32.71% in network losses for the IEEE 33-node system after optimization, and the average voltage of the circuit increases from 0.9485 p.u. to 0.9748 p.u. At the same time, optimization results in a reduction of 44.07% in network losses for the practical 22-node system, and the average voltage of the circuit increases from 0.9838 p.u. to 0.9921 p.u. Therefore, the proposed method exhibits better performance for reducing network losses and enhancing voltage levels. [ABSTRACT FROM AUTHOR]

In analog circuits, component tolerances and circuit nonlinearity pose obstacles to fault diagnosis. To solve this problem, a soft fault diagnosis method based on Sparrow Search Algorithm (SSA) and Support Vector Machine (SVM) is used. In this study, ISSA is obtained by optimization using four strategies for SSA deficiency. Twenty-three benchmark functions are used for optimization experiments, and ISSA converges faster, more accurately, and with better robustness than other swarm intelligence algorithms. Finally, ISSA is used to optimize the SVM parameters and establish the ISSA-SVM fault diagnosis model. In the Sallen-key test circuit diagnosis experiments, the correct fault diagnosis rates of SSA-SVM and ISSA-SVM are 97.41% and 98.15%, respectively. The results show that the optimized ISSA-SVM model has a good analog circuit fault diagnosis with an increase in diagnostic accuracy. [ABSTRACT FROM AUTHOR]

In this paper, a PCA-SSA-DBIGRU-Attention multi-factor short-term power load forecasting model is proposed. Taking a complete account of the influence of meteorological factors, principal components analysis (PCA) is used to analyze the meteorological factors of daily minimum, maximum, daily average temperature, relative humidity, daily precipitation and power load data at the same time. The realization of original load data is dimensioned down. The complexity of power load forecasting models is reduced. Then, the Attention Double Bidirectional Gating Recurrent Unit (DBIGRU) model is constructed to calculate the different weights of the hidden layer states of the two-layer BIGRU. The hidden layer states are assigned different weights. The Sparrow Search Algorithm (SSA) is incorporated into the DBIGRU-Attention. The SSA-DBIGRU-Attention network model is constructed to optimize the learning rate, the number of iterations and the four hyperparameters of the first and second hidden layer neurons. The extracted principal components are input into SSA-DBIGRU-Attention to realize multi-factor short-term power load forecasting. Experimental results show that the prediction accuracy of the proposed model is improved, and the prediction time is reduced. Compared to the VMD-BILSTM, PCA-DBILSTM, CNN-GRU-Attention and CNN-BIGRU-Attention model, the four aspects of MAPE, MAE, RMSE and time are reduced by 29.55 %, 36.42 %, 32.34% and 12.22 %, respectively, the R2 is improved by 3.09 %. [ABSTRACT FROM AUTHOR]

Blast furnace (BF) ironmaking is a key process in iron and steel production. Because BF ironmaking is a dynamic time series process, it is more appropriate to use a recurrent neural network for modeling. The long short-term memory (LSTM) network is commonly used to model time series data. However, its model performance and generalization ability heavily depend on the parameter configuration. Therefore, it is necessary to study parameter optimization for the LSTM model. The sparrow search algorithm (SSA) holds advantages over traditional optimization algorithms in several aspects, such as no need for prior knowledge, fewer parameters, fast convergence, and high scalability. However, the algorithm still faces some challenges, such as the tendency to become trapped in the local optimum and the imbalance between global search ability and local search ability. Therefore, on the basis of SSA, this study examined the Levy flight strategy, sine search strategy, and step size factor adjustment strategy to improve it. This algorithm, improved by three strategies, is called the improved sparrow search algorithm (ISSA). Then, the ISSA-LSTM model was established. Furthermore, considering the limitations of SSA in dealing with multi-objective problems, the fast non-dominated sorting genetic algorithm (NSGAII) was introduced, and the ISSA-NSGAII model was established. Finally, experimental validation was performed using real blast furnace operation data, which demonstrated the proposed algorithm's superiority in parameter optimization for the LSTM model and prediction for real industrial data. [ABSTRACT FROM AUTHOR]

Aiming at the problem of the limited application range and low accuracy of existing radar calibration methods, this paper studies the radar calibration method based on cooperative targets, and establishes the integrated radar measurement error model. Then, the improved sparrow search algorithm (ISSA) is used to estimate the systematic error, so as to avoid the loss of partial accuracy caused by the process of approximating the nonlinear equation to the linear equation, thus improving the radar calibration effect. The sparrow search algorithm (SSA) is improved through integrating various strategies, and the convergence speed and stability of the algorithm are also improved. The simulation results show that the ISSA can solve radar systematic errors more accurately than the generalized least square method, Kalman filter, and SSA. It takes less time the than SSA and has a certain stability and real-time performance. The radar measurement error after correction is obviously smaller than that before correction, indicating that the proposed method is feasible and effective. [ABSTRACT FROM AUTHOR]

Reducing casualties and property losses through effective evacuation route planning has been a key focus for researchers in recent years. As part of this effort, an enhanced sparrow search algorithm (MSSA) was proposed. Firstly, the Golden Sine algorithm and a nonlinear weight factor optimization strategy were added in the discoverer position update stage of the SSA algorithm. Secondly, the Cauchy-Gaussian perturbation was applied to the optimal position of the SSA algorithm to improve its ability to jump out of local optima. Finally, the local search mechanism based on the mountain climbing method was incorporated into the local search stage of the SSA algorithm, improving its local search ability. To evaluate the effectiveness of the proposed algorithm, the Whale Algorithm, Gray Wolf Algorithm, Improved Gray Wolf Algorithm, Sparrow Search Algorithm, and MSSA Algorithm were employed to solve various test functions. The accuracy and convergence speed of each algorithm were then compared and analyzed. The results indicate that the MSSA algorithm has superior solving ability and stability compared to other algorithms. To further validate the enhanced algorithm's capabilities for path planning, evacuation experiments were conducted using different maps featuring various obstacle types. Additionally, a multi-exit evacuation scenario was constructed according to the actual building environment of a teaching building. Both the sparrow search algorithm and MSSA algorithm were employed in the simulation experiment for multi-exit evacuation path planning. The findings demonstrate that the MSSA algorithm outperforms the comparison algorithm, showcasing its greater advantages and higher application potential. [ABSTRACT FROM AUTHOR]

Skin cancer is the most prevalent kind of cancer in people. It is estimated that more than 1 million people get skin cancer every year in the world. The effectiveness of the disease's therapy is significantly impacted by early identification of this illness. Preprocessing is the initial detecting stage in enhancing the quality of skin images by removing undesired background noise and objects. This study aims is to compile preprocessing techniques for skin cancer imaging that are currently accessible. Researchers looking into automated skin cancer diagnosis might use this article as an excellent place to start. The fully convolutional encoder–decoder network and Sparrow search algorithm (FCEDN-SpaSA) are proposed in this study for the segmentation of dermoscopic images. The individual wolf method and the ensemble ghosting technique are integrated to generate a neighbour-based search strategy in SpaSA for stressing the correct balance between navigation and exploitation. The classification procedure is accomplished by using an adaptive CNN technique to discriminate between normal skin and malignant skin lesions suggestive of disease. Our method provides classification accuracies comparable to commonly used incremental learning techniques while using less energy, storage space, memory access, and training time (only network updates with new training samples, no network sharing). In a simulation, the segmentation performance of the proposed technique on the ISBI 2017, ISIC 2018, and PH2 datasets reached accuracies of 95.28%, 95.89%, 92.70%, and 98.78%, respectively, on the same dataset and assessed the classification performance. It is accurate 91.67% of the time. The efficiency of the suggested strategy is demonstrated through comparisons with cutting-edge methodologies. [ABSTRACT FROM AUTHOR]

Eco‐phylogeographic approaches to comparative population genetic analyses allow for the inclusion of intrinsic influences as drivers of intraspecific genetic structure. This insight into microevolutionary processes, including changes within a species or lineage, provides better mechanistic understanding of species‐specific interactions and enables predictions of evolutionary responses to environmental change. In this study, we used single nucleotide polymorphisms (SNPs) identified from reduced representation sequencing to compare neutral population structure, isolation by distance (IBD), genetic diversity and effective population size (Ne) across three closely related and co‐distributed saltmarsh sparrow species differing along a specialization gradient—Nelson's (Ammospiza nelsoni subvirgata), saltmarsh (A. caudacuta) and seaside sparrows (A. maritima maritima). Using an eco‐phylogeographic lens within a conservation management context, we tested predictions about species' degree of evolutionary history and ecological specialization to tidal marshes, habitat, current distribution and population status on population genetic metrics. Population structure differed among the species consistent with their current distribution and habitat factors, rather than degree of ecological specialization: seaside sparrows were panmictic, saltmarsh sparrows showed hierarchical structure and Nelson's sparrows were differentiated into multiple, genetically distinct populations. Neutral population genetic theory and demographic/evolutionary history predicted patterns of genetic diversity and Ne rather than degree of ecological specialization. Patterns of population variation and evolutionary distinctiveness (Shapely metric) suggest different conservation measures for long‐term persistence and evolutionary potential in each species. Our findings contribute to a broader understanding of the complex factors influencing genetic variation, beyond specialist‐generalist status and support the role of an eco‐phylogeographic approach in population and conservation genetics. [ABSTRACT FROM AUTHOR]

Simple Summary: The Worthen sparrow (WS) is an endemic species of the Mexican rangelands that is considered endangered. The objective of this study was to document the building materials for nest building of this obligate grassland bird species. After completing the breeding season from 2013–2016, 207 empty nests were collected to analyze the construction materials used in their assembly. The findings revealed that Muhlembergia torrey was a key component of the WS's nest. The study was conducted within a well-managed beef cattle operation in northeastern Mexico. Each nest was weighed and dissected to obtain the plant and animal material used to build the nests. The number of materials present per nest and relative frequency were determined. Twenty-one building materials were used. Over the years, Muhlenbergia torreyi represented 85.5% of the total biomass of the nests, and Aristida longiseta, Bouteloua gracilis, Brickellia canescens, Purshia mexicana and Cirsium ehrenbergii constituted 2.45, 2.80, 2.44, 1.34 and 1.11% of the total biomass, respectively. The above-mentioned grasses represented 95.62% of the total biomass. Material of animal origin was horse and cow hair, which represented 0.84 and 0.58% of the total biomass, respectively. It was concluded that, at the study site, Muhlenbergia torreyi was a key nest-building material for the Worthen sparrow nest. [ABSTRACT FROM AUTHOR]

Association rule mining utilizing metaheuristic algorithms is a prominent area of study in the field of data mining. However, when working with extensive data, conventional metaheuristic algorithms exhibit limited search efficiency and face challenges in deriving high-quality rules in multi-objective association rule mining. In order to tackle this issue, a novel approach called the adaptive Weibull distribution sparrow search algorithm is introduced. This algorithm leverages the adaptive Weibull distribution to improve the traditional sparrow search algorithm's capability to escape local optima and enhance convergence during different iterations. Secondly, an enhancement search strategy and a multidirectional learning strategy are introduced to expand the search range of the population. This paper empirically evaluates the proposed method under real datasets and compares it with other leading methods by using three association rule metrics, namely, support, confidence, and lift, as the fitness function. The experimental results show that the quality of the obtained association rules is significantly improved when dealing with datasets of different sizes. [ABSTRACT FROM AUTHOR]

Wafer dicing is a key step in the manufacturing process of semiconductor integrated circuits, and the quality of dicing directly affects the dimensional accuracy and economic benefits of the final product. However, the current inspection of dicing quality is usually carried out after the dicing is completed, which will lead to a decrease in dicing efficiency and an increase in costs. In order to solve these problems, we propose a deep learning-based method, which uses the improved sparrow search algorithm (SSA) to optimize the bidirectional long short-term memory (BILSTM) network to predict the dicing quality. Firstly, the importance of each feature is analysed using kernel principal component analysis for the data collected in the field, and the main feature components are selected as the feature input according to the contribution rate. Secondly, the SSA is improved using the idea of chaotic mapping and Levy flight, and the improved algorithm is used to optimize the hyperparameters of the BILSTM network. Finally, we evaluated the proposed method using the actual dicing dataset. Experimental results show that this method is superior to other prediction algorithms. The average prediction accuracy is 93.44%. [ABSTRACT FROM AUTHOR]

• A MISSA algorithm is proposed to solve the central air conditioning energy-saving problem. • Cooling load prediction provides a foundation for energy-saving optimization. • The three improved strategies are introduced to obtain better operating results. • The experimental results indicate that the MISSA can find a better optimal solution. Aiming at the problem of high energy consumption caused by the nonlinear, strong coupling, and large hysteresis characteristics of central air conditioning (CAC) system, an energy-saving optimization method based on WTD–CNN–LSTM and multi-strategy improved sparrow search algorithm (MISSA) is proposed, which works to minimize the total energy consumption during the operation of the CAC system. Firstly, minimize the sum of energy consumption of chillers, freezing pumps, cooling pumps, and cooling towers as the objective function, and use the range of operating parameters of each equipment as the basic constraint conditions to establish an energy-saving optimization model for the CAC system. Then, WTD–CNN–-LSTM is used to predict the future cooling load, and the predicted results are used as key constraint condition to achieve on-demand cooling. Finally, MISSA is proposed to improve the initialization, update and convergence stages of the CAC system operation parameter optimization process, accurately obtaining the optimal operating parameters. Compared with manual experience, MISSA reduces energy consumption by 15.32 % and improves energy efficiency ratio by 20.76 %. Meanwhile, compared with other optimization algorithms, MISSA reduces energy consumption by 4.85–13.26 % and improves energy efficiency ratio by 6.53–16.33 % after optimizing the CAC system. The experiment verifies that MISSA is more energy efficient when applied to the CAC system and has the advantages of accuracy, fast convergence, strong global search capability, and the ability to jump out of local optimization. [ABSTRACT FROM AUTHOR]

Ensuring the safe operation of trains hinges on precise bearing condition monitoring, given the pivotal role bearings play in railway wagons. The status and maintenance of wagon bearings are of paramount concern, necessitating a shift from traditional maintenance approaches reliant on schedules and experience, which often lack real-time precision and efficiency. To address this challenge, our research focuses on enhancing the sparrow search algorithm by incorporating logistic chaos mapping and the levy flight strategy. This enhanced algorithm optimizes variational mode decomposition parameters, utilizing intrinsic mode components' average dispersion entropy as the fitness function. This optimization is integrated with a multi-level convolutional neural network for bearing fault diagnosis. Our findings demonstrate the improved algorithm's enhanced spatial search capabilities and reduced modal aliasing in the frequency components. Experimental validation on public datasets and the group's experimental platform for railway wagons shows that multi-level convolutional neural networks have higher diagnostic accuracy and faster convergence speeds than traditional models such as LeNet-5, AlexNet, and convolutional neural network. Our research introduces a highly accurate and widely applicable methodology for mechanical equipment fault diagnosis, aligning with the requirements of the "smart" era. [ABSTRACT FROM AUTHOR]

Sparrow Search Algorithm (SSA) has recently received more attention in the intelligent optimization group. However, the high randomness and local optimal problems of the algorithm limit the application of the algorithm. In this paper, a novel sparrow search algorithm (NSSA) combined with spawning technology is proposed, which effectively improves the problem of the algorithm falling into local optimization. First of all, it is proposed to replace the traditional stochastic method with the good point set theory to find the initial individual, so that the initial population is more evenly distributed in the search space and improve the quality of the initial solution. Secondly, the spawning strategy of the cuckoo algorithm is integrated into the discoverer stage, which improves the search method of the discoverer and enhances the global search ability, which makes the algorithm avoids the sudden decline of population diversity and premature convergence. Finally, Levy flight and Brownian motion are used to disturb the position of the sparrow dimension by dimension to improve the ability of the algorithm to jump out of the local optimal value in subsequent iterations. By comparing and analyzing NSSA and SSA, DE, ALO, GOA, DOA, HSSA and XSSA algorithms in 12 common evaluation functions and CEC-2017 test functions, and apply NSSA to the TSP problem. The simulation results show the effectiveness and superiority of the proposed scheme. [ABSTRACT FROM AUTHOR]

Noise from oil and gas development is pervasive across many landscapes and creates a novel soundscape that wildlife must adapt to or avoid. In response to anthropogenic noise, many wildlife species alter their vocalizations. Some adjusted vocalizations may promote effective communication in the presence of noise by improving detection and preserving information about the sender's status. However, if adjusted vocalizations fail to improve communication in noise, both missed detections and misinterpretations of vocalizations could impact the fitness of individuals and ultimately contribute to population declines. Baird's sparrow is a species at risk in Canada that adjusts its songs in response to oil well drilling noise by altering whole-song elements such as decreasing the peak frequency of songs (Curry et al., 2017, Bioacoustics, 27 (2), 105–130). We examined the efficacy of these adjusted songs in the mixed-grass prairies of southern Alberta, Canada during the Baird's sparrow breeding season (May–July 2018 and 2019) using a repeated measures study design (N = 69 dyads) in which we simulated territorial intrusions by broadcasting adjusted songs and unadjusted songs in the presence and absence of oil well drilling noise recordings. We found that focal male behaviour was mainly mediated by noise treatment when compared to song treatment. In noisy trials, males sang less, called more and performed more flybys, regardless of song treatment type. However, in noisy trials, males displayed longer song latency in response to unadjusted songs compared to adjusted songs. The results of our novel study suggest that the presence of oil well drilling noise elicits more aggressive territorial defence behaviour in Baird's sparrows or hinders the ability of individuals to locate or assess rivals. Additionally, our results suggest that adjusted songs only partially restore effective communication in noise. • Focal male behaviour was mainly influenced by the presence or absence of noise. • Noise may have caused increased aggression or impaired detection in focal males. • Focal males did not generally discriminate between adjusted and unadjusted songs. • Adjusted songs may partially improve acoustic detection in noise. • Unadjusted songs may experience partial acoustic masking in noise. [ABSTRACT FROM AUTHOR]

Precise and timely prediction of crop yields is crucial for food security and the development of agricultural policies. However, crop yield is influenced by multiple factors within complex growth environments. Previous research has paid relatively little attention to the interference of environmental factors and drought on the growth of winter wheat. Therefore, there is an urgent need for more effective methods to explore the inherent relationship between these factors and crop yield, making precise yield prediction increasingly important. This study was based on four type of indicators including meteorological, crop growth status, environmental, and drought index, from October 2003 to June 2019 in Henan Province as the basic data for predicting winter wheat yield. Using the sparrow search algorithm combined with random forest (SSA-RF) under different input indicators, accuracy of winter wheat yield estimation was calculated. The estimation accuracy of SSA-RF was compared with partial least squares regression (PLSR), extreme gradient boosting (XG-Boost), and random forest (RF) models. Finally, the determined optimal yield estimation method was used to predict winter wheat yield in three typical years. Following are the findings: 1) the SSA-RF demonstrates superior performance in estimating winter wheat yield compared to other algorithms. The best yield estimation method is achieved by four types indicators' composition with SSA-RF) (R2 = 0.805, RRMSE = 9.9%. 2) Crops growth status and environmental indicators play significant roles in wheat yield estimation, accounting for 46% and 22% of the yield importance among all indicators, respectively. 3) Selecting indicators from October to April of the following year yielded the highest accuracy in winter wheat yield estimation, with an R2 of 0.826 and an RAISE of 9.0%. Yield estimates can be completed two months before the winter wheat harvest in June. 4) The predicted performance will be slightly affected by severe drought. Compared with severe drought year (2011) (R2 = 0.680) and normal year (2017) (R2 = 0.790), the SSA-RF model has higher prediction accuracy for wet year (2018) (R2 = 0.820). This study could provide an innovative approach for remote sensing estimation of winter wheat yield, yield. [ABSTRACT FROM AUTHOR]