Search Results (19)

Holometabolous insects display drastically different morphologies across life stages (i.e., larvae vs. adults). Morphological differences across life stages, such as different sizes and coloration, likely result in differential survival, as predators may find individuals of one life stage more conspicuous and/or more energetically profitable than another. Furthermore, prey conspicuousness may vary temporally because both the sensory environment and predator sensory abilities differ between day and night. Here, we investigated how the interaction between life stage (caterpillar vs. moth) and time of day (day vs. night) influences predation of the white-lined sphinx (Lepidoptera: Hyles lineata). We predicted that caterpillars would be less susceptible to predation than adult moths, as adults are larger and have a more conspicuous shape. After quantifying predation for 72 h during dawn and dusk using 199 plasticine replicas each of adults and caterpillars, predation on adult replicas was twice that of predation on caterpillar replicas. Furthermore, replicas were six times more likely to be predated on during the day than during the night. Lastly, attacks were made mainly by birds, which carried out 86% of the attacks on adult models and 85% of those on caterpillar models. These data support the hypothesis that predation rates differ across life stages in holometabolous insects. This research lays a foundation for further investigation into how specific differences in morphology across life stages affect predation and survival in holometabolous insects. Full article

Traditional sweet potato disease and pest detection methods have the limitations of low efficiency, poor accuracy and manual dependence, while deep learning-based target detection can achieve an efficient and accurate detection. This paper proposed an efficient sweet potato leaf disease and pest detection method SPLDPvB, as well as a low-complexity version SPLDPvT, to achieve accurate identification of sweet potato leaf spots and pests, such as hawk moth and wheat moth. First, a residual module containing three depthwise separable convolutional layers and a skip connection was proposed to effectively retain key feature information. Then, an efficient feature extraction module integrating the residual module and the attention mechanism was designed to significantly improve the feature extraction capability. Finally, in the model architecture, only the structure of the backbone network and the decoupling head combination was retained, and the traditional backbone network was replaced by an efficient feature extraction module, which greatly reduced the model complexity. The experimental results showed that the mAP0.5 and mAP0.5:0.95 of the proposed SPLDPvB model were 88.7% and 74.6%, respectively, and the number of parameters and the amount of calculation were 1.1 M and 7.7 G, respectively. Compared with YOLOv11S, mAP0.5 and mAP0.5:0.95 increased by 2.3% and 2.8%, respectively, and the number of parameters and the amount of calculation were reduced by 88.2% and 63.8%, respectively. The proposed model achieves higher detection accuracy with significantly reduced complexity, demonstrating excellent performance in detecting sweet potato leaf pests and diseases. This method realizes the automatic detection of sweet potato leaf pests and diseases and provides technical guidance for the accurate identification and spraying of pests and diseases. Full article

Orchids have evolved flowers with unique morphologies through coevolution with pollinators, such as insects. Among the floral organs, the lip (labellum), one of the three petals, exhibits a distinctive shape and plays a crucial role in attracting pollinators and facilitating pollination in many orchids. The lip of the terrestrial orchid Habenaria radiata is shaped like a flying white bird and is believed to attract and provide a platform for nectar-feeding pollinators, such as hawk moths. To elucidate the mechanism of lip morphogenesis, we conducted time-lapse imaging of blooming flowers to observe the extension process of the lip and analyzed the cellular morphology during the generation of serrations. We found that the wing part of the lip folds inward in the bud and fully expands in two hours after blooming. The serrations of the lip were initially formed through cell division and later deepened through polar cell elongation. Transcriptome analysis of floral buds revealed the expression of genes involved in floral organ development, cell division, and meiosis. Additionally, genes involved in serration formation are also expressed in floral buds. This study provides insights into the mechanism underlying the formation of the unique lip morphology in Habenaria radiata. Full article

The primary goal of enhancing automatic generation control (AGC) in interconnected multi-area power systems is to ensure high-quality power generation and reliable distribution during emergencies. These systems still struggle with consistent stability and effective response under dynamic load conditions despite technological advancements. This research introduces a secondary controller designed for load frequency control (LFC) to maintain stability during unexpected load changes by optimally tuning the parameters of a Proportional–Integral–Derivative (PID) controller using pelican optimization algorithm (POA). An interconnected power system for ith multi-area is modeled in this study; meanwhile, for determining the optimal PID gain settings, a four-area interconnected power system is developed consisting of thermal, reheat thermal, hydroelectric, and gas turbine units based on the ith area model. A sensitivity analysis was conducted to validate the proposed controller’s robustness under different load conditions (1%, 2%, and 10% step load perturbation) and adjusting nominal parameters (R, T_p, and T_ij) within a range of ±25% and ±50%. The performance response indicates that the POA-optimized PID controller achieves superior performance in frequency stabilization and oscillation reduction, with the lowest integral time absolute error (ITAE) value showing improvements of 7.01%, 7.31%, 45.97%, and 50.57% over gray wolf optimization (GWO), Moth Flame Optimization Algorithm (MFOA), Particle Swarm Optimization (PSO), and Harris Hawks Optimization (HHO), respectively. Full article

Integration testing is an essential activity in software testing, especially in object-oriented software development. Determining the sequence of classes to be integrated, i.e., the class integration test order (CITO) problem, is of great importance but computationally challenging. Previous research has shown that meta heuristic algorithms can devise class integration test orders with lower test stubbing complexity, resulting in software testing cost reduction. This study focuses on the comparable performance evaluation of ten commonly used meta heuristic algorithms: genetic algorithm (GA), particle swarm optimization (PSO), cuckoo search algorithm (CS), firefly algorithm (FA), bat algorithm (BA), grey wolf algorithm (GWO), moth flame optimization (MFO), sine cosine algorithm (SCA), salp swarm algorithm (SSA) and Harris hawk optimization (HHO). The objective of this study is to identify the most suited algorithms, narrowing down potential avenues for future researches in the field of search-based class integration test order generation. The standard implementations of these algorithms are employed to generate integration test orders. Additionally, these test orders are evaluated and compared in terms of stubbing complexity, convergence speed, average runtime, and memory consumption. The experimental results suggest that MFO, SSA, GWO and CS are the most suited algorithms. MFO, SSA and GWO exhibit excellent optimization performance in systems where fitness values are heavily impacted by attribute coupling. Meanwhile, MFO, GWO and CS are recommended for systems where the fitness values are strongly influenced by method coupling. BA and FA emerge as the slowest algorithms, while the remaining algorithms exhibit intermediate performance. The performance analysis may be used to select and improve appropriate algorithms for the CITO problem, providing a cornerstone for future scientific research and practical applications. Full article

A novel hybrid Harris Hawk-Arithmetic Optimization Algorithm (HHAOA) for optimizing the Industrial Wireless Mesh Networks (WMNs) and real-time pressure process control was proposed in this research article. The proposed algorithm uses inspiration from Harris Hawk Optimization and the Arithmetic Optimization Algorithm to improve position relocation problems, premature convergence, and the poor accuracy the existing techniques face. The HHAOA algorithm was evaluated on various benchmark functions and compared with other optimization algorithms, namely Arithmetic Optimization Algorithm, Moth Flame Optimization, Sine Cosine Algorithm, Grey Wolf Optimization, and Harris Hawk Optimization. The proposed algorithm was also applied to a real-world industrial wireless mesh network simulation and experimentation on the real-time pressure process control system. All the results demonstrate that the HHAOA algorithm outperforms different algorithms regarding mean, standard deviation, convergence speed, accuracy, and robustness and improves client router connectivity and network congestion with a 31.7% reduction in Wireless Mesh Network routers. In the real-time pressure process, the HHAOA optimized Fractional-order Predictive PI (FOPPI) Controller produced a robust and smoother control signal leading to minimal peak overshoot and an average of a 53.244% faster settling. Based on the results, the algorithm enhanced the efficiency and reliability of industrial wireless networks and real-time pressure process control systems, which are critical for industrial automation and control applications. Full article

Malicious apps specifically aimed at the Android platform have increased in tandem with the proliferation of mobile devices. Malware is now so carefully written that it is difficult to detect. Due to the exponential growth in malware, manual methods of malware are increasingly ineffective. Although prior writers have proposed numerous high-quality approaches, static and dynamic assessments inherently necessitate intricate procedures. The obfuscation methods used by modern malware are incredibly complex and clever. As a result, it cannot be detected using only static malware analysis. As a result, this work presents a hybrid analysis approach, partially tailored for multiple-feature data, for identifying Android malware and classifying malware families to improve Android malware detection and classification. This paper offers a hybrid method that combines static and dynamic malware analysis to give a full view of the threat. Three distinct phases make up the framework proposed in this research. Normalization and feature extraction procedures are used in the first phase of pre-processing. Both static and dynamic features undergo feature selection in the second phase. Two feature selection strategies are proposed to choose the best subset of features to use for both static and dynamic features. The third phase involves applying a newly proposed detection model to classify android apps; this model uses a neural network optimized with an improved version of HHO. Application of binary and multi-class classification is used, with binary classification for benign and malware apps and multi-class classification for detecting malware categories and families. By utilizing the features gleaned from static and dynamic malware analysis, several machine-learning methods are used for malware classification. According to the results of the experiments, the hybrid approach improves the accuracy of detection and classification of Android malware compared to the scenario when considering static and dynamic information separately. Full article

Brazil is one of the centers of diversity of Cactaceae, yet studies addressing both pollination biology and the breeding system in Brazilian cacti are scarce. We herein present a detailed analysis of two native species with economic relevance: Cereus hildmannianus and Pereskia aculeata. The first species produce edible, sweet, spineless fruits and the second species produces leaves with high protein content. Pollination studies were undertaken through fieldwork observations in three localities of Rio Grande do Sul, Brazil, over two flowering seasons, totaling over 130 observation hours. Breeding systems were elucidated utilizing controlled pollinations. Cereus hildmannianus is solely pollinated by nectar-gathering species of Sphingidae hawk moths. In contrast, the flowers of P. aculeata are pollinated by predominantly native Hymenoptera but also by Coleoptera and Diptera, which gather pollen and/or nectar. Both cacti species are pollinator-dependent; neither intact nor emasculated flowers turn into fruit, yet whereas C. hildmannianus is self-incompatible, P. aculeata is fully self-compatible. In sum, C. hildmannianus is more restrictive and specialized regarding its pollination and breeding system, whereas P. aculeata is more generalist. Understanding the pollination needs of these species is a necessary starting point towards their conservation but also for their proper management and eventual domestication. Full article

The environmental and technical benefits of renewable energy sources make expanding their use essential in our lives. The main source of renewable energy used in this work is photovoltaic energy. Photovoltaic cells are a clean energy source dependent on solar irradiance to generate electricity from sunlight. The identification of solar cell variables is one of the main items in the simulation and modeling of photovoltaic models. The models used in this work are triple-diode, double-diode, and single-diode solar cells. A novel optimization method called weighted mean of vectors (INFO) is applied for estimating the solar cell variables in the three models. The fitness function of identification is to minimize the root-mean-square error (RMSE) between the measured data of current and the data of simulated current based on the parameters identified from the algorithms. The INFO technique is compared with another seven methods: Harris hawk optimization (HHO), tunicate swarm algorithm (TSA), sine—cosine algorithm (SCA), moth–flame optimizer (MFO), grey wolf optimization (GWO), chimp optimization algorithm (ChOA), and Runge–Kutta optimization (RUN). Full article

Internet technology has provided an indescribable new way for businesses to attract new customers, track their behaviour, customise services, products, and advertising. Internet technology and the new trend of online shopping have resulted in the establishment of numerous websites to sell products on a daily basis. Products compete to be displayed on the limited pages of a website in online shopping because it has a significant impact on sales. Website designers carefully select which products to display on a page in order to influence the customers’ purchasing decisions. However, concerns regarding appropriate decision making have not been fully addressed. As a result, this study conducts a comprehensive comparative analysis of the performance of ten different metaheuristics. The ant lion optimiser (ALO), Dragonfly algorithm (DA), Grasshopper optimisation algorithm (GOA), Harris hawks optimisation (HHO), Moth-flame optimisation algorithm (MFO), Multi-verse optimiser (MVO), sine cosine algorithm (SCA), Salp Swarm Algorithm (SSA), The whale optimisation algorithm (WOA), and Grey wolf optimiser (GWO) are some of the recent algorithms that were chosen for this study. The results show that the MFO outperforms the other methods in all sizes. MFO has an average normalised objective function of 81%, while ALO has a normalised objective function of 77%. In contrast, HHO has the worst performance of 16%. The study’s findings add new theoretical and practical insights to the growing body of knowledge about e-commerce environments and have implications for planners, policymakers, and managers, particularly in companies where an unplanned advertisement wastes the budget. Full article

Successful implementation of construction projects worldwide calls for a set of effective risk management plans in which uncertainties associated with risks and effective response strategies are addressed meticulously. Thus, this study aims to provide an optimization approach with which risk response strategies that maximize the utility function are selected. This selection is by opting for the most appropriate strategies with the highest impact on the project regarding the weight of each risk and budget constraints. Moreover, the risk assessment and response strategy of a construction project in Iran as a case study, based on the global standard of the project management body of knowledge (PMBOK) and related literature, is evaluated. To handle the complexity of the proposed model, different state of the art metaheuristic algorithms including the ant lion optimizer (ALO), dragonfly algorithm (DA), grasshopper optimization algorithm (GOA), Harris hawks optimization (HHO), moth-flame optimization algorithm (MFO), multi-verse optimizer (MVO), sine cosine algorithm (SCA), salp swarm algorithm (SSA), whale optimization algorithm (WOA), and grey wolf optimizer (GWO). These algorithms are validated by the exact solver from CPLEX software and compare with each other. One finding from this comparison is the high performance of MFO and HHO algorithms. Based on some sensitivity analyses, an extensive discussion is provided to suggest managerial insights for real-world construction projects. Full article

The Filippi’s glands (FGs), formerly “Lyonet’s glands”, are paired accessory organs associated with the silk glands. They are unique to Lepidoptera caterpillars and their exact role is not clear. The FGs are thought to be involved in the construction of a silk cocoon in bombycoid moths. FGs can differ in size and shape, therefore, in this study we attempt to find a correlation between FG morphology and phylogenetic position within the Bombycoidea. We use light and electron microscopy to examine the presence and morphology of FGs in a range of wild (giant) silk moths and several related species. Our results confirm that the majority of studied silk moth species have complex type of FGs that continuously increase in size during larval development. We identified several species of giant silk moths and two hawk moth species that completely lack FGs throughout their larval development. Finally, in several hawk moth species in which FGs are well developed during the first larval stage, these glands do not grow and remain small during later larval growth. Our results suggest that FGs are not critical for spinning and that loss of FGs occurred several times during the evolution of saturniids and sphingids. Comparison of FGs in different moths is an important first step in the elucidation of their physiological significance. Full article

The spurge hawkmoth Hyles euphorbiae L. (Sphingidae) comprises a remarkable species complex with still not fully resolved taxonomy. Its extensive natural distribution range covers diverse climatic zones. This predestinates particular populations to cope with different local seasonally unfavorable environmental conditions. The ability of the pupae to overcome outer frosty conditions is well known. However, the differences between two main ecotypes (‘euphorbiae’ and ‘tithymali’) in terms of the inherent degree of frost tolerance, its corresponding survival strategy, and underlying mechanism have not been studied in detail so far. The main aim of our study was to test the phenotypic exhibition of pupae (as the relevant life cycle stadia to outlast unfavorable conditions) in response to combined effects of exogenous stimuli, such as daylight length and cooling regime. Namely, we tested the turnout of subitan (with fast development, unadapted to unfavorable conditions) or diapause (paused development, adapted to unfavorable external influences and increased resistance) pupae under different conditions, as well as their mortality, and we measured the super cooling point (SCP) of whole pupae (in vivo) and pupal hemolymph (in vitro) as phenotypic indicators of cold acclimation. Our results show higher cold sensitivity in ‘tithymali’ populations, exhibiting rather opportunistic and short-termed cold hardiness, while ‘euphorbiae’ produces a phenotype of seasonal cold-hardy diapause pupae under a combined effect of short daylight length and continuous cold treatment. Further differences include the variability in duration and mortality of diapause pupae. This suggests different pre-adaptations to seasonal environmental conditions in each ecotype and may indicate a state of incipient speciation within the H. euphorbiae complex. Full article

There is an increasing concentration in the influences of nonconventional power sources on power system process and management, as the application of these sources upsurges worldwide. Renewable energy technologies are one of the best technologies for generating electrical power with zero fuel cost, a clean environment, and are available almost throughout the year. Some of the widespread renewable energy sources are tidal energy, geothermal energy, wind energy, and solar energy. Among many renewable energy sources, wind and solar energy sources are more popular because they are easy to install and operate. Due to their high flexibility, wind and solar power generation units are easily integrated with conventional power generation systems. Traditional generating units primarily use synchronous generators that enable them to ensure the process during significant transient errors. If massive wind generation is faltered due to error, it may harm the power system’s operation and lead to the load frequency control issue. This work proposes binary moth flame optimizer (MFO) variants to mitigate the frequency constraint issue. Two different binary variants are implemented for improving the performance of MFO for discrete optimization problems. The proposed model was evaluated and compared with existing algorithms in terms of standard testing benchmarks and showed improved results in terms of average and standard deviation. Full article

The electric sector is majorly concerned about the greenhouse and non-greenhouse gas emissions generated from both conventional and renewable energy sources, as this is becoming a major issue globally. Thus, the utilities must adhere to certain environmental guidelines for sustainable power generation. Therefore, this paper presents a novel nature-inspired and population-based Harris Hawks Optimization (HHO) methodology for controlling the emissions from thermal generating sources by solving single and multi-objective Optimal Power Flow (OPF) problems. The OPF is a non-linear, non-convex, constrained optimization problem that primarily aims to minimize the fitness function by satisfying the equality and inequality constraints of the system. The cooperative behavior and dynamic chasing patterns of hawks to pounce on escaping prey is modeled mathematically to minimize the objective function. In this paper, fuel cost, real power loss and environment emissions are regarded as single and multi-objective functions for optimal adjustments of power system control variables. The different conflicting framed multi-objective functions have been solved using weighted sums using a no-preference method. The presented method is coded using MATLAB software and an IEEE (Institute of Electrical and Electronics Engineers) 30-bus. The system was used to demonstrate the effectiveness of selective objectives. The obtained results are compared with the other Artificial Intelligence (AI) techniques such as the Whale Optimization Algorithm (WOA), the Salp Swarm Algorithm (SSA), Moth Flame (MF) and Glow Warm Optimization (GWO). Additionally, the study on placement of Distributed Generation (DG) reveals that the system losses and emissions are reduced by an amount of 9.8355% and 26.2%, respectively. Full article