Selection Mechanism Research Articles

Challenges in using data on fathers/partners to study prenatal exposures and offspring health.

Paternal exposures (and other non-maternal factors) around pregnancy could have important effects on offspring health. One challenge is that data on partners are usually from a subgroup of mothers with data, potentially introducing selection bias, limiting generalisability of findings. We aimed to investigate the potential for selection bias in studies using partner data.We characterise availability of data on father/partner and mother health behaviours (smoking, alcohol, caffeine and physical activity) around pregnancy from three UK cohort studies: the Avon Longitudinal Study of Parents and Children (ALSPAC), Born in Bradford and the Millennium Cohort Study. We assess the extent of sample selection by comparing characteristics of families where fathers/partners do and do not participate. Using the association of parental smoking during pregnancy and child birthweight as an example, we perform simulations to investigate the extent to which missing father/partner data may induce bias in analyses conducted only in families with participating fathers/partners.In all cohorts, father/partner data were less detailed and collected at fewer timepoints than mothers. Partners with a lower socio-economic position were less likely to participate. In simulations based on ALSPAC data, there was little evidence of selection bias in associations of maternal smoking with birthweight, and bias for father/partner smoking was relatively small. Missing partner data can induce selection bias. In our example analyses of the effect of parental smoking on offspring birthweight, the bias had a relatively small impact. In practice, the impact of selection bias will depend on both the analysis model and the selection mechanism.

Adaptive time step selection for spectral deferred correction

AbstractSpectral Deferred Correction (SDC) is an iterative method for the numerical solution of ordinary differential equations. It works by refining the numerical solution for an initial value problem by approximately solving differential equations for the error, and can be interpreted as a preconditioned fixed-point iteration for solving the fully implicit collocation problem. We adopt techniques from embedded Runge-Kutta Methods (RKM) to SDC in order to provide a mechanism for adaptive time step size selection and thus increase computational efficiency of SDC. We propose two SDC-specific estimates of the local error that are generic and do not rely on problem specific quantities. We demonstrate a gain in efficiency over standard SDC with fixed step size and compare efficiency favorably against state-of-the-art adaptive RKM.

Attrition in a large-scale habituation task administered at home.

Infant research often struggles with selective samples, especially when focusing on behavioural measures, such as those drawn from habituation tasks. However, selectivity may threaten the generalizability and interpretation of results, which is why the current study investigates attrition in a habituation task administered in a household setting in 7-month-old infants. We used a large-scale German dataset, focusing on the children's socioeconomic background, and investigated two aspects of attrition, namely, participation and task completion. The findings suggest significant effects of the children's socioeconomic background on attrition: Maternal education, parental occupation, household income and household language (German vs. other) were positively related to participation and task completion. The analyses indicate that multiple barriers may prevent parents from lower socioeconomic backgrounds from letting their children participate. The study concludes with a critical discussion of possible mechanisms of selectivity in behavioural measures as well as the household setting, in which the data were collected.

Fault diagnosis of driving gear in battery swapping system based on auditory bionics

Rack and pinion drives (RPD) are widely used in battery swapping system (BSS) for electric heavy trucks (EHT), and due to the continuous heavy-load and high-intensity operation, along with the electric erosion, the gears in the RPD are always damaged, which causes unexpected consequences such as downtime or safety incidents. The working conditions of the RPD in BSS include uncertain noises, fluctuant and low speed, which pose steep challenges to accurate fault diagnosis. Considering the auditory resistance of interference, the low-frequency sensitivity of auditory perception, and the auditory saliency mechanism, to leverage the advantages of auditory perceptual mechanism in addressing the above challenges, as the contribution in artificial intelligence, we propose an entire vibration signal processing scheme based on auditory bionics, including some mathematical models for auditory mechanisms. For the application in engineering, the proposed scheme is employed for fault diagnosis of RPD in BSS in unique working conditions. First, adaptive resampling is used to smooth the speed fluctuation, then, Gammatone filters are employed to transform vibration signals to cochleograms, after that, based on auditory stream segregation and selective attention mechanisms, effective frequency channels and salient features are extracted from the cochleograms, besides, to improve the diagnosis accuracy, binaural features are also extracted, finally, based on (sectional) sparse representation and fusion, fault diagnosis is achieved. The effectiveness of the fault diagnosis scheme is demonstrated using a BSS prototype system.

The effect of grandparental involvement on grandchildren's school grades: Heterogeneity by the extended family characteristics

As the early years are crucial for individuals' lifelong socioeconomic success, extensive research has examined the impact of non-maternal childcare on children's development. This study aims to enhance the understanding of the relationship between grandparental involvement (defined as grandparent childcare, frequency of contact, and financial support) and grandchildren's school grades, exploring a mechanism of positive selection: children from extended families with specific socioeconomic characteristics are more likely to spend time with grandparents and benefit the most from this involvement.We utilize data from the German Pairfam survey, which uniquely provides rich information on three family generations. By conducting a heterogeneous treatment effect analysis, we account for confounding factors associated with grandparental involvement and school performance that could bias our findings. For example, children from advantaged families could be more likely to spend time with grandparents and achieve better school grades. Additionally, this approach examines whether the effect of grandparental involvement systematically varies across children based on the extended family's characteristics. For example, children from advantaged families may benefit the most from spending time with grandparents who possess social, cultural, and cognitive resources conducive to their development.After accounting for confounding factors and heterogeneity, our analyses do not reveal a statistically significant effect of grandparental investment on children's school grades. The study concludes by discussing possible reasons for this result and highlighting the implications for the intergenerational transmission of inequality.

Cognitive Radar Waveform Selection for Low-Altitude Maneuvering-Target Tracking: A Robust Information-Aided Fusion Method

In this paper, we introduce an innovative interacting multiple-criterion selection (IMCS) idea to design the optimal radar waveform, aimingto reduce tracking error and enhance tracking performance. This method integrates the multiple-hypothesis tracking (MHT) and Rao–Blackwellized particle filter (RBPF) algorithms to tackle maneuvering First-Person-View (FPV) drones in a three-dimensional low-altitude cluttered environment. A complex hybrid model, combining linear and nonlinear states, is constructed to describe the high maneuverability of the target. Based on the interacting multiple model (IMM) framework, our proposed IMCS method employs several waveform selection criteria as models and determines the optimal criterion with the highest probability to select waveform parameters. The simulation results indicate that the MHT–RBPF algorithm, using the IMCS method for adaptive parameter selection, exhibits high accuracy and robustness in tracking a low-altitude maneuvering target, resulting in lower root mean square error (RMSE) compared with fixed- or single-waveform selection mechanisms.

Advanced Object Tracking through Conditional Online Updates and Noise Suppression

Object tracking under complex environmental conditions, such as background clutter, occlusion, and rapid motion, presents significant challenges. This paper addresses these issues by proposing a tracking algorithm integrating background suppression, target region enhancement, and an adaptive online template update mechanism to improve tracking accuracy. The proposed method uses the initial bounding box of the target object as a reference template and selectively updates specific regions online to suppress noise and retain critical features. We evaluated the proposed method using the OTB dataset to validate it. The baseline model without the proposed method showed a success rate of 0.417 and a precision of 0.586, while the algorithm with the proposed method achieved improved values of 0.524 and 0.728, respectively. Qualitative evaluations further confirmed the robustness of the proposed method, demonstrating high performance in scenarios with occlusion and complex backgrounds. Rather than updating all regions indiscriminately, the proposed method selectively updates the template using representative values from the target object's information. This selective update mechanism ensures the incorporation of the most relevant and accurate features, enabling the algorithm to adapt to changes in the target's appearance while minimizing noise integration. Emphasizing the feature regions and suppressing noise are also critical for maintaining a clear and precise representation of the target object, reducing the likelihood of confusion by irrelevant background information. Future research will focus on developing balanced update strategies that integrate new information while maintaining stable and reliable target characteristics.

Maintenance of inflorescence size variability within common buckwheat (Fagopyrum esculentum Moench) cultivars of various origin: the phenomenon and its possible causes

Background. Intrapopulation polymorphism usually can be interpreted as a result of balancing selection, but the basic mechanisms of such selection are not always clear. Earlier we documented a wide variation in the inflorescence size within one of the most genetically uniform common buckwheat (Fagopyrum esculentum Moench) cultivar ‘Skorospelaya 86’, with larger sizes controlled by recessive alleles of multiple genes. The objective of the present work was to analyze the variation within a set of buckwheat cultivars representing different morphological types of the crop, and try to make out possible mechanisms that underlie it. Material and methods. A set of buckwheat cultivars of various origin and belonging to different morphological types were analyzed. The size of inflorescences was assessed as a number of their iterative subunits, i.e., partial floret clusters (PFCs). The number of mature seeds in the entire inflorescence and various PFCs was also counted. Results and conclusions. All the cultivars were variable, with the range at the species level from 6 to 24 PFCs. Accumulation of the genes determining a larger inflorescence seems to be simply explained: larger inflorescences tended to produce more seeds than smaller ones. However, it is not so easy to explain the maintenance of the inflorescence size variability, i.e., why the alleles determining small inflorescences were not lost during the artificial selection for higher seed productivity. Probably, such polymorphism may be useful for maintaining heterosis at the population level.

Mucormycosis: Current Perspectives on Treatment, Diagnosis, and Advancements

Objective: This review study examines mucormycosis treatment challenges, gaps in medicines, and COVID-19-related effects. This paper examines diagnostic and drug development advances while addressing safety and specificity. Methods: This review study searches PubMed, Web Science, and Scopus for relevant material. Keywords associated with mucormycosis, therapy, diagnosis, medication advancement, and COVID- 19 are used to identify pertinent articles. Data extraction summarizes therapeutic obstacles, diagnostic advances, and innovative drug options. Results: This review article covers mucormycosis therapy, diagnostics, and drug development. It reveals limitations in present medicines, such as selectivity, safety, and resistance mechanisms. Diagnostic advances and mucormycosis in COVID-19 have also been explored. Conclusion: This review emphasizes the need for more secure, specific mucormycosis therapies. It discusses therapy obstacles, diagnostic advances, and new drug techniques. Overall, this research emphasizes improving mucormycosis therapy to enhance patient outcomes.

Cryo-EM structures of ryanodine receptors and diamide insecticides reveal the mechanisms of selectivity and resistance

The resistance of pests to common insecticides is a global issue that threatens food production worldwide. Diamide insecticides target insect ryanodine receptors (RyRs), causing uncontrolled calcium release from the sarcoplasmic and endoplasmic reticulum. Despite their high potency and species selectivity, several resistance mutations have emerged. Using a chimeric RyR (chiRyR) approach and cryo-electron microscopy (cryo-EM), we investigate how insect RyRs engage two different diamide insecticides from separate families: flubendiamide, a phthalic acid derivative, and tetraniliprole, an anthranilic compound. Both compounds target the same site in the transmembrane region of the RyR, albeit with different poses, and promote channel opening through coupling with the pore-forming domain. To explore the resistance mechanisms, we also solve two cryo-EM structures of chiRyR carrying the two most common resistance mutations, I4790M and G4946E, both alone and in complex with the diamide insecticide chlorantraniliprole. The resistance mutations perturb the local structure, directly reducing the binding affinity and altering the binding pose. Our findings elucidate the mode of action of different diamide insecticides, reveal the molecular mechanism of resistance mutations, and provide important clues for the development of novel pesticides that can bypass the resistance mutations.

Hyperspectral Image Classification Algorithm for Forest Analysis Based on a Group-Sensitive Selective Perceptual Transformer

Substantial advancements have been achieved in hyperspectral image (HSI) classification through contemporary deep learning techniques. Nevertheless, the incorporation of an excessive number of irrelevant tokens in large-scale remote sensing data results in inefficient long-range modeling. To overcome this hurdle, this study introduces the Group-Sensitive Selective Perception Transformer (GSAT) framework, which builds upon the Vision Transformer (ViT) to enhance HSI classification outcomes. The innovation of the GSAT architecture is primarily evident in several key aspects. Firstly, the GSAT incorporates a Group-Sensitive Pixel Group Mapping (PGM) module, which organizes pixels into distinct groups. This allows the global self-attention mechanism to function within these groupings, effectively capturing local interdependencies within spectral channels. This grouping tactic not only boosts the model’s spatial awareness but also lessens computational complexity, enhancing overall efficiency. Secondly, the GSAT addresses the detrimental effects of superfluous tokens on model efficacy by introducing the Sensitivity Selection Framework (SSF) module. This module selectively identifies the most pertinent tokens for classification purposes, thereby minimizing distractions from extraneous information and bolstering the model’s representational strength. Furthermore, the SSF refines local representation through multi-scale feature selection, enabling the model to more effectively encapsulate feature data across various scales. Additionally, the GSAT architecture adeptly represents both global and local features of HSI data by merging global self-attention with local feature extraction. This integration strategy not only elevates classification precision but also enhances the model’s versatility in navigating complex scenes, particularly in urban mapping scenarios where it significantly outclasses previous deep learning methods. The advent of the GSAT architecture not only rectifies the inefficiencies of traditional deep learning approaches in processing extensive remote sensing imagery but also markededly enhances the performance of HSI classification tasks through the deployment of group-sensitive and selective perception mechanisms. It presents a novel viewpoint within the domain of hyperspectral image classification and is poised to propel further advancements in the field. Empirical testing on six standard HSI datasets confirms the superior performance of the proposed GSAT method in HSI classification, especially within urban mapping contexts, where it exceeds the capabilities of prior deep learning techniques. In essence, the GSAT architecture markedly refines HSI classification by pioneering group-sensitive pixel group mapping and selective perception mechanisms, heralding a significant breakthrough in hyperspectral image processing.

TsRNA-00764 Regulates Estrogen and Progesterone Synthesis and Lipid Deposition by Targeting PPAR-γ in Duck Granulosa Cells.

Transfer RNA-derived small RNAs (tsRNAs) are novel regulatory small non-coding RNAs that have been found to modulate many life activities in recent years. However, the exact functions of tsRNAs in follicle development remain unclear. Follicle development is a remarkably complex process that follows a strict hierarchy and is strongly associated with reproductive performance in ducks. The process of converting small yellow follicles into hierarchal follicles is known as follicle selection, which directly determines the number of mature follicles. We performed small RNA sequencing during follicle selection in ducks and identified tsRNA-00764 as the target of interest based on tsRNA expression profiles in this study. Bioinformatics analyses and luciferase reporter assays further revealed that peroxisome proliferator-activated receptor-γ (PPAR-γ) was the target gene of tsRNA-00764. Moreover, tsRNA-00764 knockdown promoted estrogen and progesterone synthesis and lipid deposition in duck granulosa cells, while a PPAR-γ inhibitor reversed the above phenomenon. Taken together, these results demonstrate that tsRNA-00764, differentially expressed in pre-hierarchal and hierarchy follicles, modulates estrogen and progesterone synthesis and lipid deposition by targeting PPAR-γ in duck granulosa cells, serving as a potential novel mechanism of follicle selection. Overall, our findings provide a theoretical foundation for further exploration of the molecular mechanisms underlying follicle development and production performance in ducks.

ASK-HAR: Attention-Based Multi-Core Selective Kernel Convolution Network for human activity recognition

Human Activity Recognition (HAR) is an increasingly popular field of study aimed at automatically identifying and categorizing human movements and activities using various tracking devices and measurements, such as sensors and cameras. Smartphones have emerged as a prevalent sensor modality for HAR, offering abundant data on an individual’s movements through GPS, accelerometers, and gyroscopes. In conventional convolutional neural networks (CNNs), the artificial neurons within each feature layer typically possess identical receptive fields (RFs). This paper introduces a novel model, ASK-HAR (Attention-based Multi-Core Selective Kernel Convolution Network for HAR), which enhances HAR by performing kernel selection between multiple branches with different RFs using attention mechanisms. The selective kernel mechanism is leveraged to optimize HAR performance. Additionally, the CBAM attention module is employed for time series feature extraction and activity recognition within the overall framework. Extensive experiments conducted on benchmark HAR datasets, including UCI-HAR, USC-HAD, WISDM, PAMAP2, and DSADS, demonstrate that the ASK-HAR model consistently achieves high accuracy across all datasets.

Enhancing Machine Learning-Based Autoscaling for Cloud Resource Orchestration

Performance and cost-effectiveness are sustained by efficient management of resources in cloud computing. Current autoscaling approaches, when trying to balance between the consumption of resources and QoS requirements, usually fall short and end up being inefficient and leading to service disruptions. The existing literature has primarily focuses on static metrics and/or proactive scaling approaches which do not align with dynamically changing tasks, jobs or service calls. The key concept of our approach is the use of statistical analysis to select the most relevant metrics for the specific application being scaled. We demonstrated that different applications require different metrics to accurately estimate the necessary resources, highlighting that what is critical for an application may not be for the other. The proper metrics selection for control mechanism which regulates the requried recources of application are described in this study. Introduced selection mechanism enables us to improve previously designed autoscaler by allowing them to react more quickly to sudden load changes, use fewer resources, and maintain more stable service QoS due to the more accurate machine learning models. We compared our method with previous approaches through a carefully designed series of experiments, and the results showed that this approach brings significant improvements, such as reducing QoS violations by up to 80% and reducing VM usage by 3% to 50%. Testing and measurements were conducted on the Hungarian Research Network (HUN-REN) Cloud, which supports the operation of over 300 scientific projects.

An environmentally friendly depressant for magnesite and calcite flotation separation: Selective depression and adsorption mechanism

The similar crystal structures and chemical properties of magnesite and calcite make their effective separation through flotation challenging. This study explores the use of Glutamic acid diacetic acid tetrasodium salt (GLDA), a novel environmentally friendly chelating reagent, to enhance the flotation separation of magnesite and calcite. Flotation test results indicate that in a sodium oleate (NaOL) system, GLDA selectively inhibits the flotation of calcite. Under optimal conditions (slurry pH=9.20, GLDA 15 mg/L, and NaOL 140 mg/L), a flotation concentrate with an MgO grade of 46.21 %, CaO grade of 1.39 %, and an MgO recovery rate of 73.17 % was achieved. GLDA minimally affects the hydrophobicity of magnesite but significantly reduces the hydrophobicity of calcite. The selective inhibition mechanism of GLDA was investigated using zeta potential measurements, FTIR, and XPS analyses. Results show that GLDA selectively reacts with Ca sites on the calcite surface, adsorbing and covering it, thereby preventing NaOL from adsorbing on the calcite. Conversely, GLDA has a minor impact on the Mg sites on the magnesite surface, resulting in a negligible effect on NaOL adsorption by magnesite. Consequently, GLDA enhances the flotation separation efficiency of magnesite and calcite. The research results have potential for application in the purification of low-grade magnesite ore.

Enhancing gait recognition in lower limb exoskeletons: adaptive feature selection and random forest with Bayesian optimization

Abstract To improve human-machine cooperation and enhance the accuracy of gait recognition in wearable lower limb exoskeletons, an enhancement method of gait recognition based on adaptive feature selection and an optimized machine learning algorithm was proposed. In this study, surface electromyography (sEMG) signals of rectus femoris, medialis femoris, lateralis femoris, semitendinosus and biceps femoris were recorded during level-ground walking. Then, time domain, frequency domain, time-frequency domain and nonlinear features were extracted. The integrated value of electromyography, variance, root mean square and wavelength were selected as the time domain features, and the mean power frequency and median frequency were selected as the frequency domain features. Wavelet packet energy was selected as the time-frequency domain feature. Nonlinear features including approximate entropy, sample entropy and fuzzy entropy of sEMG were extracted. To reduce feature dimension and improve the calculation efficiency, adaptive feature selection was performed by particle swarm optimization combined with sigmoid function. Then, the feature matrix was determined as the input for a random forest classifier to recognize different gait phases. An adaptive optimization mechanism based on Bayesian optimization was applied to random forest. Compared with random forest, the overall performance of the optimized model was improved. Its accuracy was increased by 3.57%. The feature selection and adaptive optimization mechanisms in gait recognition not only enhance the accuracy of random forest algorithms applied to sEMG for gait prediction, but also facilitate the flexibility and consistency required for lower limb exoskeleton gait control.

A Platform of Federated Learning Management for Enhanced Mobile Collaboration

Federated learning (FL) has emerged as a crucial technology in today’s data-centric environment, enabling decentralized machine learning while safeguarding user privacy. This study introduces “Federated Learning ML Operations (FedOps) Mobile”, a novel FL framework optimized for the dynamic and heterogeneous ecosystem of mobile devices. FedOps Mobile addresses the inherent challenges of FL—such as system scalability, device heterogeneity, and operational efficiency—through advanced on-device training using TensorFlow Lite and CoreML. The framework’s innovative approach includes sophisticated client selection mechanisms that assess device readiness and capabilities, ensuring equitable and efficient participation across the network. Additionally, FedOps Mobile leverages remote device control for seamless task management and continuous learning, all without compromising the user experience. The main contribution of this study is the demonstration that federated learning across heterogeneous devices, especially those using different operating systems, can be both practical and efficient using the FedOps Mobile framework. This was validated through experiments that evaluated three key areas: operational efficiency, model personalization, and resource optimization in multi-device settings. The results showed that the proposed method excels in client selection, energy consumption, and model optimization, establishing a new benchmark for federated learning in diverse and complex environments.

Adaptive feature fusion and disturbance correction for accurate remaining useful life prediction of rolling bearings

As a key component in the transmission system of high-speed trains, bearings need to withstand certain loads while rotating at high speeds. Once a failure occurs, it can directly affect the safety of train operations. Therefore, it is of great significance to establish a reliable remaining useful life model to ensure train operation safety. Addressing the problems of redundant features in the existing multi-feature fusion process, which affects diagnostic performance, and the spurious fluctuations in the fusion features, which cause inaccurate determination of the start fault time and lead to low prediction accuracy of remaining useful life, we propose a method based on adaptive feature fusion and the autoregressive integrated moving average model for rolling bearing prediction. Firstly, features from the time domain, frequency domain, and entropy are extracted. A feature selection mechanism with minimum redundancy is constructed to screen the optimal sensitive feature set. Secondly, based on adaptive feature fusion, the optimal sensitive feature set is dynamically fused, and the spurious fluctuations of the health index are corrected using linear regression and the 3σ principle. Next, a bottom-up time series segmentation method is employed to divide the health status of the Improved Health Indicators. Finally, a remaining useful life prediction model based on the autoregressive integrated moving average model is established. This study demonstrates that the proposed method effectively identifies features that are most sensitive to degradation trends, accurately determines the initial fault moment of bearings, and achieves effective prediction of the remaining useful life of bearings.

Exploration of Cryptic Pockets Using Enhanced Sampling Along Normal Modes: A Case Study of KRAS G12D.

Identification of cryptic pockets has the potential to open new therapeutic opportunities by discovering ligand binding sites that remain hidden in static apo structures of a target protein. Moreover, allosteric cryptic pockets can become valuable for designing target-selective ligands when the natural ligand binding sites are conserved in variants of a protein. For example, before an allosteric cryptic pocket was discovered, KRAS was considered undruggable due to its smooth surface and conservation of the GDP/GTP binding pocket across the wild type and oncogenic isoforms. Recent identification of the Switch-II cryptic pocket in the KRASG12C mutant and FDA approval of anticancer drugs targeting this site underscores the importance of cryptic pockets in solving pharmaceutical challenges. Here, we present a newly developed approach for the exploration of cryptic pockets using weighted ensemble molecular dynamics simulations with inherent normal modes as progress coordinates applied to the wild type KRAS and the G12D mutant. We performed extensive all-atomic simulations (>400 μs) with and without several cosolvents (xenon, ethanol, benzene), and analyzed trajectories using three distinct methods to search for potential binding pockets. These methods have been applied as a proof-of-concept to KRAS and have shown they can predict known cryptic binding sites. Furthermore, we performed ligand-binding simulations of a known inhibitor (MRTX1133) to shed light on the nature of cryptic pockets in KRASG12D and the role of conformational selection vs induced-fit mechanism in the formation of these cryptic pockets.

Establishment of characteristic fingerprint of volatile components in Batocera horsfieldi (Coleoptera: Cerambycidae) host plants and correlation analysis with climatic factors.

Batocera horsfieldi is the primary stemboring pest of timber forests and economic forests in China, belonging to the Coleoptera Cerambycidae. In this study, gas chromatography-mass spectrometry was used to analyze the volatile components in the supplementary feeding hosts and oviposition hosts of B. horsfieldi, and characteristic fingerprints were constructed. A total of 168 compounds were detected, primarily consisting of terpenes, hydrocarbons, and aldehydes, with 75, 23, and 14 compounds, respectively. Hierarchical cluster analysis and principal component analysis yielded consistent results. The similarity evaluation results showed that the similarity range between healthy poplar tree bark and healthy poplar leaves was the highest, reaching 0.953-0.98, and 10 common peaks were identified. The analysis of the correlation with climatic factors shows that most compounds are positively correlated with mean annual humidity, mean annual temperature, mean annual minimum temperature, and mean annual maximum temperature. Only caryophyllene and alpha-guaiene are negatively correlated with temperature-related climatic factors. Pathway analysis also reveals differences in the direct effects of different compounds. These analytical results provide a theoretical basis for further studying the selection mechanism of B. horsfieldi on hosts and offer theoretical guidance for identifying plant-derived attractants with biological activity.