Adaptive Function Research Articles

Event-Trigger-Based Adaptive Barrier Function Higher-Order Global Sliding Mode Control Technique for Quadrotor UAVs

Event-Trigger-Based Adaptive Barrier Function Higher-Order Global Sliding Mode Control Technique for Quadrotor UAVs

Jealousy as Predicted by Allocation and Reception of Resources in an Economic Game.

Evidence is abundant that evolution by selection has produced sex differences in the design of adaptations to solve the problems surrounding reproduction. A prime example is the design of human jealousy, which research suggests is triggered by distinct evoking acts that are specific challenges for women and men in their exclusive reproductive bond. It follows that jealousy would be directed toward driving away interlopers who could potentially threaten the bond with the romantic partner or increase mate retention efforts in response to sex-specific threats. To explore this possibility, we use as a methodological innovation an economic game for the evocation of jealousy. With a modified dictator game, we showed men and women in a committed relationship, conditions in which the partner or an intrasexual rival allocates money to (investing condition), or obtains money from (receiving condition), the partner or an opposite sex third party that they recently met. A sample of 56 heterosexual couples (n = 112) participated in a laboratory setting. Our results show the different scenarios of this dictator game exerted the expected evocation of jealousy (controlling individual differences), with women being more jealous by the partner's allocation of resources to a rival, and men reporting slightly more jealousy by their partner receiving money from a rival. We discuss the implications of this method to advance the comprehension of the adaptive function of sex differences in jealousy, the use of economic games, and possible modifications to improve the similarity of the game to a real assessment of actual male jealousy.

Attention control mediates the relationship between mental imagery vividness and emotion regulation

Contradictory findings suggest mental imagery may both exacerbate and protect against negative affect. We aimed to reconcile these contradictory findings by considering individual differences (N=989) in imagery vividness, attention control, resilience, emotion regulation strategy, and negative affect (depressive, anxious, and posttraumatic stress symptomology). We hypothesized that attention control would mediate relationships between imagery vividness and emotion regulation strategy use, and psychopathology symptomology. Results revealed that imagery vividness, as mediated by attention control, predicted greater levels of healthy reappraisal and deleterious rumination. Attention control also mediated negative relationships between imagery vividness and catastrophizing, self-blame, and psychopathology symptomology. An exploratory latent structural equation model revealed that imagery vividness and attention control aggregated positively with reappraisal and resilience scores. The present investigation suggests an adaptive function of imagery vividness via the indirect effects of attention control, facilitating adaptive emotion regulation and limiting maladaptive strategy use, thereby protecting against negative affect.

A 3D approach to understanding heterogeneity in early developing autisms.

Phenotypic heterogeneity in early language, intellectual, motor, and adaptive functioning (LIMA) features are amongst the most striking features that distinguish different types of autistic individuals. Yet the current diagnostic criteria uses a single label of autism and implicitly emphasizes what individuals have in common as core social-communicative and restricted repetitive behavior difficulties. Subtype labels based on the non-core LIMA features may help to more meaningfully distinguish types of autisms with differing developmental paths and differential underlying biology. Unsupervised data-driven subtypes were identified using stability-based relative clustering validation on publicly available Mullen Scales of Early Learning (MSEL) and Vineland Adaptive Behavior Scales (VABS) data (n = 615; age = 24-68months) from the National Institute of Mental Health Data Archive (NDA). Differential developmental trajectories between subtypes were tested on longitudinal data from NDA and from an independent in-house dataset from UCSD. A subset of the UCSD dataset was also tested for subtype differences in functional and structural neuroimaging phenotypes and relationships with blood gene expression. The current subtyping model was also compared to early language outcome subtypes derived from past work. Two autism subtypes can be identified based on early phenotypic LIMA features. These data-driven subtypes are robust in the population and can be identified in independent data with 98% accuracy. The subtypes can be described as Type I versus Type II autisms differentiated by relatively high versus low scores on LIMA features. These two types of autisms are also distinguished by different developmental trajectories over the first decade of life. Finally, these two types of autisms reveal striking differences in functional and structural neuroimaging phenotypes and their relationships with gene expression and may highlight unique biological mechanisms. Sample sizes for the neuroimaging and gene expression dataset are relatively small and require further independent replication. The current work is also limited to subtyping based on MSEL and VABS phenotypic measures. This work emphasizes the potential importance of stratifying autism by a Type I versus Type II distinction focused on LIMA features and which may be of high prognostic and biological significance.

Evolutionary Role of the Female Orgasm: Insights into Mate Choice and Beyond.

The female orgasm remains a subject of extensive debate within evolutionary sciences, primarily due to the absence of a consensus regarding its adaptive function. Some hypotheses propose that it could function as a mechanism for mate selection, possibly linked to reproductive strategies. Studies have observed that lesbian women tend to experience orgasms more frequently than straight women, suggesting a potential link to advantageous partner characteristics. We explored the connection between the female orgasm and mate selection, aiming to ascertain its role as an adaptive tool for mate choice. A total of 939 sexually active Brazilian women (Mage = 28.3years) engaged in committed relationships participated by completing the Marital Empathy Questionnaire, the Female Sexual Function Index, and evaluations of their partners' attributes. Analysis using structural equation models revealed that higher orgasm scores were positively associated with perceptions of partner kindness, intelligence, good health, physical attractiveness, and empathy. However, the small effect sizes of these associations warrant caution in their interpretation, and no significant association was found for relationship duration, financial prospects, dominance, and masculinity/femininity. Additionally, no differences were found between women in same-sex and other-sex relationships regarding orgasm frequency and its association with partner traits. The findings partially support the mate-choice theory, encompassing both sire-choice and pair-bond hypotheses, suggesting that female orgasm is positively associated with some partner attributes related to genetic quality and the capability for emotional connection and resource investment. Therefore, female orgasm may offer information about the partner that might not be primarily accessed and contribute to relationship maintenance.

Pan-genome analysis of GT64 gene family and expression response to Verticillium wilt in cotton

BackgroundThe GT64 subfamily, belonging to the glycosyltransferase family, plays a critical function in plant adaptation to stress conditions and the modulation of plant growth, development, and organogenesis processes. However, a comprehensive identification and systematic analysis of GT64 in cotton are still lacking.ResultsThis study used bioinformatics techniques to conduct a detailed investigation on the GT64 gene family members of eight cotton species for the first time. A total of 39 GT64 genes were detected, which could be classified into five subfamilies according to the phylogenetic tree. Among them, six genes were found in upland cotton. Furthermore, investigated the precise chromosomal positions of these genes and visually represented their gene structure details. Moreover, forecasted cis-regulatory elements in GhGT64s and ascertained the duplication type of the GT64 in the eight cotton species. Evaluation of the Ka/Ks ratio for similar gene pairs among the eight cotton species provided insights into the selective pressures acting on these homologous genes. Additionally, analyzed the expression profiles of the GT64 gene family. Overexpressing GhGT64_4 in tobacco improved its disease resistance. Subsequently, VIGS experiments conducted in cotton demonstrated reduced disease resistance upon silencing of the GhGT64_4, may indicate its involvement in affecting lignin and jasmonic acid biosynthesis pathways, thus impacting cotton resistance. Weighted Gene Co-expression Network Analysis (WGCNA) revealed an early immune response against Verticillium dahliae in G. barbadense compared to G. hirsutum. Quantitative Reverse Transcription Polymerase Chain Reaction (qRT-PCR) analysis indicated that some GT64 genes might play a role under various biotic and abiotic stress conditions.ConclusionsThese discoveries enhance our knowledge of GT64 family members and lay the groundwork for future investigations into the disease resistance mechanisms of this gene in cotton.

Layout Congestion Prediction Based on Regression-ViT

To accelerate the back-end design flow of integrated circuit (IC), numerous studies have made exploratory advancements in machine learning (ML) for electronic design automation (EDA). However, most research works are limited to deep learning (DL) models predominantly based on convolutional neural networks, and the models often suffer from poor generalization due to the scarcity of data. In this study, we propose the Double generative adversarial networks (D-GAN) model to enrich the dataset and propose the Regression Vision Transformer (R-ViT) model to predict layout congestion information. Compared to the baseline model, experimental results show improvements of 3.03% and 2.64% in Receiver Operating Characteristic-Area under Curve (ROC-AUC) and Precision-Recall Curve-Area under Curve (PRC-AUC) respectively. To further enhance the prediction accuracy of the model, an adaptive Huber loss function is designed to optimize the training process, resulting in an improvement of up to 11.03% in ROC-AUC compared with the baseline model. Lastly, extended experiments are conducted to study the effects of parameters and convolutional kernel size on performance, which find a better configuration.

Perspectives on Intelligence in Soft Robotics

Engineers frequently aim to streamline environmental factors to facilitate the effective operation of robots. However, in nature, environmental considerations play a crucial role in shaping the embodiment of organisms. To comply robots with the complexity of real‐world environments, embedding similar intelligence is key. In the field of soft robotics, various approaches offer insight into how intelligence can be integrated into artificial agents. A discussed topic is the intricate relationship between the brain and the body at the core of intelligence in robots. The goal of this article is, therefore, to unravel the strategies to implement different types of intelligence currently adopted in soft robots. A classification is made by making a distinction between agents that adapt to their environment by 1) their adaptive shape, 2) their adaptive functionality, and 3) their adaptive mechanics. Additionally, the perspectives on intelligence based on their computational approach are distinguished: centralized computation, decentralized computation, or embedded computation. It is concluded that a tailored robotic design approach attuned to specific environmental demands is needed. To unlock the full potential of soft robots, a fresh perspective on embodied intelligence is described, so‐called mechanical intelligence, emphasizing the robot's responsiveness to changing external conditions of a real‐world environment.

Emotional state dynamics impacts temporal memory

ABSTRACT Emotional fluctuations are ubiquitous in everyday life, but precisely how they sculpt the temporal organisation of memories remains unclear. Here, we designed a novel task – the Emotion Boundary Task – wherein participants viewed sequences of negative and neutral images surrounded by a colour border. We manipulated perceptual context (border colour), emotional-picture valence, as well as the direction of emotional-valence shifts (i.e., shifts from neutral-to-negative and negative-to-neutral events) to create events with a shared perceptual and/or emotional context. We measured memory for temporal order and temporal distances for images processed within and across events. Negative images processed within events were remembered as closer in time compared to neutral ones. In contrast, temporal distances were remembered as longer for images spanning neutral-to-negative shifts – suggesting temporal dilation in memory with the onset of a negative event following a previously-neutral state. The extent of negative-picture induced temporal dilation in memory correlated with dispositional negativity across individuals. Lastly, temporal order memory was enhanced for recently-presented negative (versus neutral) images. These findings suggest that emotional-state dynamics matters when considering emotion-temporal memory interactions: While persistent negative events may compress subjectively remembered time, dynamic shifts from neutral-to-negative events produce temporal dilation in memory, with implications for adaptive emotional functioning.

Stress-induced overeating behaviors explained from a (transitory) relief-learning perspective

People use various behaviors to cope with stressful events. These behaviors are mostly adaptive, as they allow a successful release of stress without impacting other aspects of life: How nice is it to have a break with a few spoons of that favorite ice cream during a hectic working day? However, when excessive consumption of high-sugar/salt ultra-processed food becomes the gateway to find relief from stress, eating loses this adaptive function and may escalate to binge eating, lead to obesity, and other medical conditions linked to overweight.Several etiological models attempt to explain stress-induced eating and excessive overeating behaviors characterizing these clinical conditions. The popular Emotional Eating Theory proposes that stress-related (over-)eating, a major predictor of obesity and diagnosed binge eating disorders, develops based on negative reinforcement learning since food consumption regulates the negative affective state associated with stressful circumstances. Differently, the prominent Incentive Sensitization Theory explains overeating, binge eating disorders (including bulimia), and obesity in terms of excessive amplification of reward ‘wanting’, which is thought to emerge from overexposure to obesogenic (food)cues. The several studies oriented by these theories have paved the way to better understand stress-related (over-)eating and its clinical excesses. However, a deep mechanistic understanding of how and why stress-induced (over-)eating can escalate till clinical forms of overeating remain elusive. A well-funded connection of the mechanisms proposed by the Emotional Eating Theory and the Incentive Sensitization Theory might address this etiological open question. To avoid erroneous arguments, it is however essential to first address the internal theoretical and methodological shortcomings of each theory and connected studies. These shortcomings stem from conceptual fallacies and poorly implemented designs, which might partially explain the ‘high variability and low replicability’ problem of empirical findings. Next, the formulation of a new integrative model could provide fresh insight into the deep learning and biological mechanisms of this escalation. A successful formalization of this model could then create the much-needed impact in clinical and preventive research since excessive overeating is a behavior hard to change once established.In this opinion paper, I propose to apply recent insights we gathered on the role of relief from the field of safety learning to stress (over-)eating. I will present a new relief-based model that, as a starting point, has the potential to connect the Emotional Eating Theory with the Incentive Sensitization Theory, setting the base for more integrative science.

Evaluating the effectiveness of the CAIDS-Q for screening for intellectual disability in mainstream schools

Objectives The identification of intellectual disability in children can help them access targeted support and interventions. Research suggests that current methods of identifying intellectual disability in schools in the United Kingdom need to be improved. Screening tools can play a role in this. The study examined the performance of the Child and Adolescent Intellectual Disability Screening Questionnaire (CAIDS-Q) in mainstream schools as a means of facilitating identification of intellectual disability. Methods Parents and/or teachers of 70 children completed the CAIDS-Q and an assessment of adaptive functioning about them. Children who were indicated as having difficulties completed an intellectual assessment. The data were used to explore some psychometric properties of the CAIDS-Q. Results The CAIDS-Q had sensitivity and specificity values of 100% and 79% respectively when completed by teachers and 92% and 70% when completed by parents. CAIDS-Q scores correlated positively with intellectual and adaptive functioning scores, with large and medium effect sizes respectively. There was moderate agreement between parents and teachers on the outcome of the CAIDS-Q. Conclusions The CAIDS-Q, when used in mainstream schools, had psychometric properties consistent with those found in health service settings. It may offer a way of improving identification of intellectual disability in educational settings.

Optimizing electric load forecasting with support vector regression/LSTM optimized by flexible Gorilla troops algorithm and neural networks a case study

This research work focuses on addressing the challenges of electric load forecasting through the combination of Support Vector Regression and Long Short-Term Memory (SVR/LSTM) methodology. The model has been modified by a flexible version of the Gorilla Troops optimization algorithm. The objective of this study is to enhance the precision and effectiveness of load forecasting models by integrating the adaptive functionalities of the Gorilla Troops algorithm within the SVR/LSTM framework. To assess the efficacy of the proposed methodology, a comprehensive series of experiments and evaluations have been undertaken, utilizing authentic data obtained from 200 residential properties located in Texas, United States of America. The dataset comprises historical records of electricity consumption, meteorological data, and other pertinent variables that exert an impact on energy demand. The presence of this general dataset enhances the dependability and inclusiveness of the empirical findings. The proposed methodology was evaluated against various contemporary load forecasting techniques that are widely employed in the industry. The results of a comprehensive evaluation and performance analysis indicate that the modified SVR/LSTM model exhibits superior performance compared to the existing methods in terms of accuracy and robustness. The comparison results unequivocally demonstrate the superiority of the proposed method in accurately forecasting electric load demand.

An adaptive trajectory segmentation and simplification algorithm based on vessel behavioral features

Existing algorithms for trajectory simplification in AIS data processing have issues such as loss of key feature points, high subjectivity in threshold selection, and incomplete consideration of factors. In this paper, an adaptive trajectory segmentation and simplification algorithm based on vessel behavioral features is proposed. The algorithm identifies behavioral feature intervals by extracting vessel behavioral features and constructs an adaptive segmentation threshold function to achieve adaptive segmentation of trajectories and extraction of key feature points. Then, considering the quality, similarity, and spatial variability of trajectory simplification, an adaptive simplification threshold function is constructed to automatically select the best threshold for each sub-trajectory to generate the simplified trajectory. Our experiments demonstrate that the algorithm maintains a high simplification rate while enabling adaptive simplification of the trajectory, and also retains the key feature points and trajectory shapes to the maximum extent. The effectiveness and robustness of the algorithm are verified through comparison with other algorithms, effectively avoiding the distortion of navigational behavior that may be caused by traditional methods. This provides reliable data support for maritime traffic management and decision-making.

Remaining useful life prognostics of bearings based on convolution attention networks and enhanced transformer

Rolling bearings are critical components of industrial equipment, and predicting their remaining useful life (RUL) is a challenging task. This article proposes a new end-to-end model (MDSCT) to achieve efficient and accurate prediction of bearing RUL. MDSCT uses the raw vibration signals collected by sensors for prediction, without relying on a large amount of prior knowledge. The feature extraction backbone of this model is composed of an MDSC attention module that integrates multiple parallel depth-wise separable convolutions and an efficient attention mechanism, combined with a tansformer encoder (PPSformer) optimized using patch embedding and probesparse self attention techniques. They are respectively used to capture local subtle features and global dependent features in degraded signals. This article also proposes an improved adaptive activation function AdaptH_Swish to enhance the model's ability to model nonlinear relationships. To comprehensively verify the comprehensive performance of the model, this paper conducted detailed ablation and comparative experiments using two standard datasets, PHM2012 and XJTU-SY. The experimental results not only confirm the rationality and efficiency of the model structure design, but also demonstrate significant advantages in three evaluation indicators compared to various existing methods, fully demonstrating the high generalization and strong robustness of the MDSCT model in bearing RUL prediction tasks.

Effects of a combined neuropsychological and cognitive behavioral group therapy on young adults with Fragile X Syndrome: An explorative study

BackgroundFragile X Syndrome (FXS) is an X-linked neurodevelopmental disorder that leads to intellectual disability (ID) along with cognitive-behavioral difficulties. Research on psychosocial treatments in individuals FXS and ID is still lacking. This study aimed to investigate the effectiveness of a combined neuropsychological and cognitive behavioral group therapy (nCBT) among young adults with FXS. MethodTen young adults diagnosed with FXS took part in the second stage intervention of "Corp-osa-Mente" (CoM II), a group nCBT program previously outlined by Montanaro and colleagues in an earlier study, with the participants being the same as in the previous research. This report details the outcomes of an additional twelve-month group sections aimed at enhancing the ability to manage emotions and the socio-communicative skills of these young adults. Caregivers completed measures of adaptive functioning, emotional and behavior problems, executive function, communication skills and family quality of life at pre-treatment (T0) and post-treatment (T1). ResultsCoM II showed a decrease in depressive and anxiety symptoms from T0 to T1, along with increased socio-pragmatic and communication skills from pre-test to post-test intervention. Additionally, our analysis revealed improvements in the adapative behavior of participants and in the family quality of life. ConclusionsThese preliminary findings suggest that young adults with FXS and ID experienced positive outcomes through participation in CoM II, a group nCBT. However, it is recommended to undertake additional methodologically rigorous studies, such as randomized controlled trials (RCTs), to substantiate these initially promising findings.

Emotional regulation, self-compassion and adaptive functioning in parents: a comparative study in families of neurodevelopmental disorder and typically developing children

PurposeThis study aims to investigate the associations between emotional regulation strategies, self-compassion and adaptive functioning in families of children with neurodevelopmental disorders (NDD) in comparison to families with typically developing (TD) children.Design/methodology/approachThis cross-sectional study used a comprehensive set of validated measures to assess emotional regulation strategies, self-compassion and adaptive functioning. The study cohort consisted of 116 parents; 58 parents of children with NDD and 58 parents of TD children. The mean age of the children was 6.40 years (SD = 1.82).FindingsThe findings indicate that higher self-compassion is associated with improved adaptive functioning, evidenced by reduced levels of internalizing (depression, anxiety, withdrawal, somatic complaints) and externalizing (aggressive behaviors) problems. Parents of children with NDD reported greater levels of depression, anxiety and withdrawal compared to parents of TD children. Parents of TD children used cognitive reappraisal more frequently and exhibited higher levels of self-compassion, whereas the use of expressive suppression was more prevalent among parents of children with NDD.Practical implicationsThese findings highlight the need for family-centered and tailored interventions that promote self-compassion and adaptive emotional regulation strategies to reduce internalizing and externalizing problems among parents of children with NDD. By providing comprehensive psychological support and accessible community resources, parental ability to cope with stress may be improved, fostering adaptive functioning that benefits both parents and children with NDD.Originality/valueThis study introduces a novel perspective on the importance of self-compassion and emotional regulation strategies in protecting and improving the mental well-being of parents with neurodevelopmental disorder children. It provides valuable insights for targeted interventions in NDD families, focusing on reducing the risk of mental health deterioration while promoting emotional resilience.

Mechanically activated and deactivated ion transport across nanopores with heterogeneous surface charge distributions

To mimic the intricate and adaptive functionalities of biological ion channels, electrohydrodynamic ion transport has been studied extensively, albeit mostly, across uniformly charged nanochannels. Here, we analyze the ion transport under coupled electric field and pressure across heterogeneously charged nanopores with oppositely charged sections on their lateral surface. We only consider such pores with symmetric hourglass-like and cylindrical shapes to focus on the effects of the non-uniform surface charge distribution. Finite-element simulations of a continuum model demonstrate that a pressure applied in either direction of the pore-axis equally suppresses or amplifies the ionic conductance, depending on the electric field polarity, by distorting the quasi-static distribution of ions in the pore. The resulting anomalous mechanical deactivation and activation of ionic current under opposite voltage biases exhibit the functional modularity of our setup, while their intensities are highly tunable, substantially greater than those of analogous behaviors in other nanochannels, and fundamentally correlated to ionic current rectification (ICR) in our pores. A detailed study of ICR subsequently reveals its counterintuitive non-monotonous variations, in the pores, with the magnitude of applied voltage and the pore length, that can help optimize their diode-like behavior. We further illustrate that while the hourglass-shaped nanopores yield the more efficient mechanical suppressors of ion transport, their cylindrical analogs are the superior rectifiers and mechanical amplifiers of ion conduction. Therefore, this article provides a blueprint for the strategic design of nanofluidic circuits to attain a robust, modular, and tunable control of ion transport under external electrical and mechanical stimuli.

Parent Perceptions of Occurrence, Predictors, and Treatment of Mental Health Concerns in Youth with Intellectual Disability with or without Autism Spectrum Disorder

ABSTRACT Introduction Mental health concerns have been noted to be highly prevalent for youth with intellectual disability (ID), with or without autism spectrum disorder (ASD). The purpose of the current study was to examine a Canadian sample of youth with ID, with or without ASD, to explore caregiver-reported percentages, predictors, and treatment methods for mental health concerns. Method The sample included 358 caregivers who completed the GO4KIDDS survey on behalf of their child between the ages of 4 and 20 years (M = 11.36; SD = 3.82). Results In total, 56% of youth with ID (with or without ASD) were reported to experience mental health concerns. Adaptive functioning and diagnosis were significant predictors of mental health concerns. Of the youth who were reported to experience mental health concerns, 80% received some type of treatment, most commonly informal therapies (52%). Conclusion More than half of the samples were reported to experience mental health concerns and while the majority received some type of treatment, many of the treatments utilized are not evidence-based.

Coping with climate anxiety: impacts on functioning in Australian adolescents

ABSTRACT Objective Being aware of climatic threats can evoke climate anxiety in youth, wherein young people may draw on coping strategies to manage this experience. To further understand the helpfulness of climate anxiety and coping, this study explored whether these experiences in adolescence are related to adaptive functioning or impairment. Method Australian adolescents aged 12–20 years (n = 261), recruited through social media or school-based participation, completed an online survey. Various cognitive styles of coping and behavioural coping strategies were assessed. Results Several styles of coping were found to be differentially related to functioning. Positive reappraisal of climate change was associated with reduced personal and occupational functional impairment. Meaning-focused reappraisal and pro-environmental coping were positively associated with adaptive functioning (e.g. higher motivation, greater connection to others). In contrast, catastrophising was inversely associated with adaptive functioning. De-emphasising the threat of climate change was related to social functional impairment, but also adaptive functioning. Finally, the more a young person experienced physiological symptoms of climate anxiety, they more they reported personal, occupational and social functional impairment. Conclusions Supporting adolescents as they learn to navigate and cope with climate anxiety, as well as climate change more broadly, may help protect youth wellbeing and functioning long-term.

Sparse large-scale high-order fuzzy cognitive maps guided by spearman correlation coefficient

Time series prediction is one of the most important applications of Fuzzy Cognitive Maps (FCMs). In general, the state of FCMs in forecasting depends only on the state of the previous moment, but in fact it is also affected by the past state. Hence Higher-Order Fuzzy Cognitive Maps (HFCMs) are proposed based on FCMs considering historical information and have been widely used for time series forecasting. However, using HFCMs to deal with sparse and large-scale multivariate time series are still a challenge, while large-scale data makes it difficult to determine the causal relationship between nodes because of the increased number of nodes, so it is necessary to explore the relationship between nodes to guide large-scale HFCMs learning. Therefore, a sparse large-scale HFCMs learning algorithm guided by Spearman correlation coefficient, called SG-HFCM, is proposed in the paper. The SG-HFCM model is specified as follows: First, the solving of HFCMs model is transform into a regression model and an adaptive loss function is utilized to enhance the robustness of the model. Second, l1-norm is used to improve the sparsity of the weight matrix. Third, in order to more accurately characterize the correlation relationship between variables, the Spearman correlation coefficients is added as a regular term to guide the learning of weight matrices. When calculating the Spearman correlation coefficient, through splitting domain interval method, we can better understand the characteristics of the data, and get better correlation in different small intervals, and more accurately characterize the relationship between the variables in order to guide the weight matrix. In addition, the Alternating Direction Multiplication Method and quadratic programming method are used to solve the algorithms to get better solutions for the SG-HFCM, where the quadratic programming can well ensure that the range of the weights and obtaining the optimal solution. Finally, by comparing with five algorithms, the SG-HFCM model showed an average improvement of 11.93% in prediction accuracy for GRNs, indicating that our proposed model has good predictive performance.