Adaptive Function Research Articles

A Multiple Environment Available Path Planning Based on an Improved A* Algorithm

The objective of the path planning for a mobile robot is to generate a collision-free path from a starting position to a target position, aiming to realize a higher quality of path planning, an improved A* algorithm and a hybrid approach incorporating the dynamic window algorithm have been proposed for robot path planning in various environments in this paper. In global path planning, first, a bidirectional search strategy was introduced into to improve the searching efficiency, and an adaptive heuristic function was designed to reduce redundant search nodes. In the meantime, a filtering function for key path nodes and an enhanced jump point optimization method help to remove redundant nodes in the path, reduce turning angles, greatly shorten the path length, and smooth the path using cubic B-spline curves. Furthermore, in local path planning, the combination of key path nodes and the dynamic window approach (DWA) algorithm is utilized to achieve obstacle avoidance in dynamic environments and adjust the heading angle of the section enables seamless locomotion of the robot. Finally, the simulation experiments and physical experiments on the robot were conducted to validate that the proposed improved algorithm significantly improves the speed of path planning while also reducing the length of the planned path and improve the reliability of the algorithm when compared with other algorithms.

Exploring the Clinical Characteristics and Comorbid Disorders of Borderline Intellectual Functioning.

Borderline intellectual functioning (BIF) is characterized by cognitive impairment and deficits in adaptive functioning. Despite affecting a significant proportion of the population, BIF still remains underdiagnosed and poorly understood. In addition to cognitive impairments across a range of domains, individuals with BIF face a greater risk of academic failure and often require special educational support. They suffer from emotional problems, such as difficulties with emotional awareness, anxiety, depressed mood, and unhappiness. Individuals with BIF are more likely to have an impairment of social and adaptive functioning. Furthermore, individuals with BIF are at higher risk of physical and mental health problems, often receive inadequate treatment, and have a poorer prognosis. This review aims to enhance the understanding of clinicians, educators, and policymakers by providing an overview of the characteristics of BIF and its associated challenges, ultimately contributing to the improvement of support systems for individuals with BIF.

Epidemiology and Diagnosis of Slow Learners (Borderline Intellectual Functioning).

The definitions of "slow learners" and "borderline intellectual functioning (BIF)" have not reached a consensus and have continually evolved in terminology. The criteria for diagnosing BIF include the Full-Scale Intelligence Quotient, adaptive functioning, and onset of symptoms from the developmental period; however, specific standards have not been provided. Until the Diagnostic and Statistical Manual of Mental Disorders-IV, a range for the Full-Scale Intelligence Quotient was provided, but due to its limitations in reflecting the actual functioning of individuals with BIF, this criterion was removed from the Diagnostic and Statistical Manual of Mental Disorders-5. The absence of specific diagnostic criteria complicates the identification of individuals with BIF, highlighting the need for a more precise classification and definition.

The role of male quality in sequential mate choice: pregnancy replacement in small mammals?

Females mainly increase their reproductive success by improving the quality of their mates and need to be discriminative in their mate choices. Here, we investigate whether female mammals can trade up sire quality in sequential mate choice during already progressed pregnancies. A male-induced pregnancy termination (functional 'Bruce effect') could thus have an adaptive function in mate choice as a functional part of a pregnancy replacement. We used bank voles (Myodes glareolus) as a model system and exchanged the breeding male in the early second trimester of a potential pregnancy. Male quality was determined using urine marking values. Females were offered a sequence of either high- then low-quality male (HL) or a low- then high-quality male (LH). The majority of females bred with high-quality males independent of their position in the sequence, which may indicate a pregnancy replacement in LH but not in HL. The body size of the second male, which could have been related to the coercion of females by males into remating, did not explain late pregnancies. Thus, pregnancy replacement, often discussed as a counterstrategy to infanticide, may constitute adaptive mate choice in female mammals, and female choice may induce pregnancy replacement in mammals.

Narrative coherence and mentalizing complexity are associated in fictive storytelling and autobiographical memories in typically developing children and adolescents

Different lines of evidence indicate that the ability to communicate narratives coherently is related to children's social-emotional development. However, it is unknown whether narrative coherence is genre-specific or generalizes across autobiographical memories and fictive stories, and if autobiographical and fictive narratives show different or similar associations with mentalizing language, cognitive functions, social and daily functioning. Addressing these questions may provide important clues about the development of narrative communication skills in children and adolescents. We assessed 86 typically developing children and adolescents aged 7–14, examining narrative coherence and mentalizing complexity in six autobiographical memories and five fictional stories, alongside intellectual functioning and parent and teacher reports on social and adaptive functioning. Results showed that the measures on narrative coherence and mentalizing complexity, respectively, were associated across autobiographical memories and fictional stories. Moreover, narrative coherence and mentalizing complexity was related to each other on both fictive and autobiographical stories. Higher narrative coherence and mentalizing complexity on autobiographical memories were specifically related to better social-emotional reciprocity reported by teachers, who likely have more opportunities than parents to observe the child’s daily interaction with peers. Our findings suggest that narrative coherence and mentalizing language in school-aged children generalize across genres. Being able to communicate personal narratives coherently with use of mentalizing language appears to be important for the social-emotional interplay of children and adolescents.

WITHDRAWN: CSCAD: An adaptive LightGBM algorithm to detect cache side-channel attacks

Cache side-channel attacks have become more sophisticated and more destructive to the security of computer architectures than ever before, which can obtain the plaintext of a victim without key. To address the challenges introduced by cache side-channel attacks, anomaly detection and feature detection were proposed. However, these methods have drawbacks in terms of computational performance and detection effectiveness. In this paper, we proposed a Cache Side-Channel Attack Detector (CSCAD), a novel tool for detecting cache side-channel attacks against memory events. Specifically, we design a collector using Hardware Performance Counters and use improved Maximum Information Coefficient to generate feature vectors. Meanwhile, an adaptive genetic algorithm with crossover and mutation probability is proposed to optimize the hyperparameters of LightGBM. Additionally, an adaptive loss function weight model with low overhead is introduced to enhance the efficiency of attack detection. It is encouraging to see that CSCAD achieved a recall of 98.14%. In detecting 1000 samples, it boosted the detection speed by approximately 75% compared to conventional machine learning methods. CSCAD has outperformed the state-of-the-art methods by simultaneously achieving excellent detection speed and effectiveness.

Enhancing ECG Heartbeat classification with feature fusion neural networks and dynamic minority-biased batch weighting loss function

Objective. This study aims to address the challenges of imbalanced heartbeat classification using electrocardiogram (ECG). In this proposed novel deep-learning method, the focus is on accurately identifying minority classes in conditions characterized by significant imbalances in ECG data. Approach. We propose a feature fusion neural network enhanced by a dynamic minority-biased batch weighting loss function. This network comprises three specialized branches: the complete ECG data branch for a comprehensive view of ECG signals, the local QRS wave branch for detailed features of the QRS complex, and the R wave information branch to analyze R wave characteristics. This structure is designed to extract diverse aspects of ECG data. The dynamic loss function prioritizes minority classes while maintaining the recognition of majority classes, adjusting the network’s learning focus without altering the original data distribution. Together, this fusion structure and adaptive loss function significantly improve the network’s ability to distinguish between various heartbeat classes, enhancing the accuracy of minority class identification. Main results. The proposed method demonstrated balanced performance within the MIT-BIH dataset, especially for minority classes. Under the intra-patient paradigm, the accuracy, sensitivity, specificity, and positive predictive value for Supraventricular ectopic beat were 99.63 % , 93.62 % , 99.81 % , and 92.98 % , respectively, and for Fusion beat were 99.76 % , 85.56 % , 99.87 % , and 84.16 % , respectively. Under the inter-patient paradigm, these metrics were 96.56 % , 89.16 % , 96.84 % , and 51.99 % for Supraventricular ectopic beat, and 96.10 % , 77.06 % , 96.25 % , and 13.92 % for Fusion beat, respectively. Significance. This method effectively addresses the class imbalance in ECG datasets. By leveraging diverse ECG signal information and a novel loss function, this approach offers a promising tool for aiding in the diagnosis and treatment of cardiac conditions.

Research on fixed-time time-varying formation of heterogeneous multi-agent systems based on tracking error observer under DoS attacks

In this paper, the fixed-time time-varying formation of heterogeneous multi-agent systems (MASs) based on tracking error observer under denial-of-service (DoS) attacks is investigated. Firstly, the dynamic pinning strategy is used to reconstruct the communication channel for the system that suffers from DoS attacks to prevent the discontinuous transmission information of the communication network from affecting MASs formation. Then, considering that the leader state is not available to each follower under DoS attacks, a fixed-time distributed observer without velocity information is constructed to estimate the tracking error between followers and the leader. Finally, adaptive radial basis function neural network (RBFNN) is used to approximate the unknown ensemble disturbances in the system, and the fixed-time time-varying formation scheme is designed with the constructed observer. The effectiveness of the proposed control algorithm is demonstrated by the numerical simulation.

Genome-wide comparative analyses highlight selection signatures underlying saline adaptation in Chilika buffalo.

Chilika, a native buffalo breed of the Eastern coast of India, is mainly distributed around the Chilika brackish water lake connected with the Bay of Bengal Sea. This breed possesses a unique ability to delve deep into the salty water of the lake and stay there to feed on local vegetation of saline nature. Adaptation to salinity is a genetic phenomenon; however, the genetic basis underlying salinity tolerance is still limited in animals, specifically in livestock. The present study explores the genetic evolution that unveils the Chilika buffalo's adaptation to the harsh saline habitat, including both water and food systems. For this study, whole genome resequencing data on 18 Chilika buffalo and for comparison 10 Murrah buffalo of normal habitat were generated. For identification of selection sweeps, intrapopulation and interpopulation statistics were used. A total of 709, 309, 468, and 354 genes were detected to possess selection sweeps in Chilika buffalo using the nucleotide diversity (θπ), Tajima's D, nucleotide diversity ratio (θπ-ratio), and FST methods, respectively. Further analysis revealed a total of 23 genes including EXOC6B, VPS8, LYPD1, VPS35, CAMKMT, NCKAP5, COMMD1, myosin light chain kinase 3 (MYLK3), and B3GNT2 were found to be common by all the methods. Furthermore, functional annotation study of identified genes provided pathways such as MAPK signaling, renin secretion, endocytosis, oxytocin signaling pathway, etc. Gene network analysis enlists that hub genes provide insights into their interactions with each other. In conclusion, this study has highlighted the genetic basis underlying the local adaptive function of Chilika buffalo under saline environment.NEW & NOTEWORTHY Indian Chilika buffaloes are being maintained on extensive grazing system and have a unique ability to convert local salty vegetation into valuable human food. However, adaptability to saline habitat of Chilika buffalo has not been explored to date. Here, we identified genes and biological pathways involved, such as MAPK signaling, renin secretion, endocytosis, and oxytocin signaling pathway, underlying adaptability of Chilika buffalo to saline environment. This investigation shed light on the mechanisms underlying the buffalo's resilience in its native surroundings.

Mid-childhood autism sibling recurrence in infants with a family history of autism.

Autism sibling recurrence in prospective infant family history studies is ~20% at 3 years but systematic follow-up to mid-childhood is rare. In population and clinical cohorts autism is not recognized in some children until school-age or later. One hundred and fifty-nine infants with an older sibling with autism underwent research diagnostic assessments at 3 years and mid-childhood (6 to 12 years (mean 9)). We report the autism sibling recurrence rate in mid-childhood and compare developmental and behavioral profiles at mid-childhood and 3 years in those with earlier versus later recognized autism, and those who had, or had not, received a community autism diagnosis. The autism recurrence rate in this sample in mid-childhood was 37.1%, 95% CI [29.9%, 44.9%] and higher in boys than girls. Around half of those diagnosed with autism in mid-childhood had not received a diagnosis at 3 years. Later, diagnosis was more common in girls than boys. While some had sub-threshold symptoms at 3, in others late diagnosis followed a largely typical early presentation. Sibling recurrence based on community clinical diagnosis was 24.5%, 95% CI [18.4%, 31.9%]. Those who also had a community diagnosis tended to be older, have lower adaptive function and higher autism and inattention symptoms. Notwithstanding limitations of a single site study, modest sample size and limits to generalisability, autism sibling recurrence in family history infants may be higher in mid-childhood than in studies reporting diagnostic outcome at 3 years. Findings have implications for families and clinical services, and for prospective family history studies.

Research on Unmanned Vehicle Path Planning Based on the Fusion of an Improved Rapidly Exploring Random Tree Algorithm and an Improved Dynamic Window Approach Algorithm

Aiming at the problem that the traditional rapidly exploring random tree (RRT) algorithm only considers the global path of unmanned vehicles in a static environment, which has the limitation of not being able to avoid unknown dynamic obstacles in real time, and that the traditional dynamic window approach (DWA) algorithm is prone to fall into a local optimum during local path planning, this paper proposes a path planning method for unmanned vehicles that integrates improved RRT and DWA algorithms. The RRT algorithm is improved by introducing strategies such as target-biased random sampling, adaptive step size, and adaptive radius node screening, which enhance the efficiency and safety of path planning. The global path key points generated by the improved RRT algorithm are used as the subtarget points of the DWA algorithm, and the DWA algorithm is optimized through the design of an adaptive evaluation function weighting method based on real-time obstacle distances to achieve more reasonable local path planning. Through simulation experiments, the fusion algorithm shows promising results in a variety of typical static and dynamic mixed driving scenarios, can effectively plan a path that meets the driving requirements of an unmanned vehicle, avoids unknown dynamic obstacles, and shows higher path optimization efficiency and driving stability in complex environments, which provides strong support for an unmanned vehicle’s path planning in complex environments.

Boosting semi‐supervised learning under imbalanced regression via pseudo‐labeling

SummaryImbalanced samples are widespread, which impairs the generalization and fairness of models. Semi‐supervised learning can overcome the deficiency of rare labeled samples, but it is challenging to select high‐quality pseudo‐label data. Unlike discrete labels that can be matched one‐to‐one with points on a numerical axis, labels in regression tasks are consecutive and cannot be directly chosen. Besides, the distribution of unlabeled data is imbalanced, which easily leads to an imbalanced distribution of pseudo‐label data, exacerbating the imbalance in the semi‐supervised dataset. To solve this problem, this article proposes a semi‐supervised imbalanced regression network (SIRN), which consists of two components: A, designed to learn the relationship between features and labels (targets), and B, dedicated to learning the relationship between features and target deviations. To measure target deviations under imbalanced distribution, the target deviation function is introduced. To select continuous pseudo‐labels, the deviation matching strategy is designed. Furthermore, an adaptive selection function is developed to mitigate the risk of skewed distributions due to imbalanced pseudo‐label data. Finally, the effectiveness of the proposed method is validated through evaluations of two regression tasks. The results show a great reduction in predicted value error, particularly in few‐shot regions. This empirical evidence confirms the efficacy of our method in addressing the issue of imbalanced samples in regression tasks.

Adaptive Functioning and Sleep Quality: Associations in Young Children with Congenital Heart Disease

Adaptive Functioning and Sleep Quality: Associations in Young Children with Congenital Heart Disease

Development path of digital cultural industry by integrating proportional integral and differential algorithm from the perspective of cultural innovation

The digital culture business benefits from quick technical updates, digital manufacturing, communication networks, and customized consumption, which promotes new supply and consumption. The innovation and development of the digital cultural industry cannot be separated from the physical industry. The efficient production of the physical industry can ensure the innovative development of digital cultural industry. Therefore, to improve the production efficiency of physical industry, this research proposes a temperature control model combining whale optimization algorithm and fuzzy proportional integral differential algorithm based on the traditional temperature control in ceramic firing and wine making process. Firstly, fuzzy control theory is used to improve the proportional integral differential algorithm, and a fuzzy proportional integral differential model with adaptive adjustment function is established. On this basis, to quickly confirm the optimal parameters of the proportional integral differential model, the whale optimization algorithm is introduced to further optimize its weight factors. It was verified that the temperature control adjustment model combining whale optimization algorithm and fuzzy proportional integral differential model was 2.77 s, which was shortened by 1.11 s, 2.37 s and 3.13 s, respectively than the other three models. Therefore, the constructed temperature control model can effectively realize high-precision intelligent temperature control in industrial production, laying the cornerstone for promoting the innovative development of digital culture industry. The proposed model can improve the production efficiency of physical industries, promote digital transformation, and optimize the innovative development of digital cultural industries.

3D Printing of Customizable Transient Bioelectronics and Sensors

AbstractTransient electronics have emerged as a new category of devices that can degrade after their functional lifetime, offering tremendous potential as disposable sensors, actuators, wearables, and implants. Additive manufacturing methods represent a promising approach for patterning transient materials, yet examples of fully printed bioelectronic devices are scarce. This study introduces a fully digital 3D printing approach enabling the prototyping and customization of soft bioelectronics made of transient materials. The direct ink writing of poly(octamethylene maleate (anhydride) citrate) (POMaC) as an elastomeric matrix and of a shellac‐carbon ink as a conductor is investigated. Precise and repeatable deposition of both structural and conductive features is achieved by optimizing printing parameters, i.e., the dispense gap, printing speed, and inlet pressure. Multi‐material 3D printing enables the fabrication of functional transient devices. Notably, pressure and strain sensors are shown to operate in ranges relevant to implanted biomechanical monitoring. 3D‐printed transient electrodes are demonstrated to be comparable to state‐of‐the‐art devices in terms of impedance behavior. Finally, physical degradation of the materials is confirmed at physiological conditions. These fully digital additive manufacturing processes enable the monolithic fabrication of customizable transient bioelectronics with adaptable functions and geometries.

Mechanical stretching triggered smart Janus fabrics for dynamic personal moisture/heat management

Janus fabrics, exhibiting unidirectional liquid transportation capability to foster a comfortable microclimate for our human body, have become a new research hotspot. However, integrating intelligent, adaptable functionalities into Janus fabrics to suit fluctuating environmental conditions and personal thermal perceptions remains a challenge. Herein, we proposed a smart pore-actuated Janus fabric with tunable functions based on mechanical stretching. Specifically, when the human feels hot and sweaty, they can manually stretch the spandex fabric to “open” its pores (from ∼ 41 μm to ∼ 138 μm) according to the actual situation. Meanwhile, the stretching can significantly decrease the positive breakthrough pressure of the fabric from 9.5 to 0.8 cm H2O, which can facilitate one-way sweat transportation and rapid heat dissipation (0.3 ℃ lower than that of the original fabric). Moreover, timely stopping stretching can “close” the fabric’s pores, avoiding excessive evaporation-induced chill. For comparison, in cold or rainy weather, the pores of the unstretched fabric are set to be “closed” (diameter: ∼41 μm) to prevent excessive heat loss (2.4 ℃ higher than that covered by the spandex fabric) and safeguard against external rainwater intrusion. This innovative design can maximize human comfort in diverse environments, and the underlying concept holds promise for the development of new-generation smart fabrics.

Unknown input observer based neuro-adaptive fault-tolerant control for vehicle platoons with sensor fault and output quantization

Sensor fault and output quantization are common issues acting on vehicle platoon, and they may lead to performance deterioration, instability and even insecurity of the platoon. Therefore, this paper investigates the fault-tolerant control (FTC) problem of vehicle platoons with regard to the above two issues. Considering the probabilistic sensor fault and quantized measurement signals, an unknown input observer (UIO) based fault detection algorithm with adaptive threshold is developed for sensor health status monitoring. Then, an augmented vehicle platoon model is constructed by introducing a low-pass output filter, and a robust UIO is established for state reconstruction. Based on the above results, a fault-tolerant control scheme is exploited by employing the back-stepping control method and adaptive radial basis function neural network (RBF NN) approximation technique, which is proved to be capable of achieving the time-domain string stability (TSS) of vehicle platoons in the presence of sensor fault and output quantization. Simulation results demonstrate the effectiveness of the proposed algorithms.

Charting the evolutionary path of the SUMO modification system in plants reveals molecular hardwiring of development to stress adaptation.

SUMO modification is part of the spectrum of Ubiquitin-like (UBL) systems that give rise to proteoform complexity through post-translational modifications (PTMs). Proteoforms are essential modifiers of cell signaling for plant adaptation to changing environments. Exploration of the evolutionary emergence of Ubiquitin-like (UBL) systems unveils their origin from prokaryotes, where it is linked to the mechanisms that enable sulfur uptake into biomolecules. We explore the emergence of the SUMO machinery across the plant lineage from single-cell to land plants. We reveal the evolutionary point at which plants acquired the ability to form SUMO chains through the emergence of SUMO E4 ligases, hinting at its role in facilitating multicellularity. Additionally, we explore the possible mechanism for the neofunctionalization of SUMO proteases through the fusion of conserved catalytic domains with divergent sequences. We highlight the pivotal role of SUMO proteases in plant development and adaptation, offering new insights into target specificity mechanisms of SUMO modification during plant evolution. Correlating the emergence of adaptive traits in the plant lineage with established experimental evidence for SUMO in developmental processes, we propose that SUMO modification has evolved to link developmental processes to adaptive functions in land plants.

Characteristics of Psychrotolerant Bacteria Isolated from Clay Organogenic Deposits of Mramorny Cave (Primorsky Territory)

A cultivated community of bacteria of the genus Pseudomonas was researched in clayey organogenic deposits of the Mramorny Cave (Primorsky Territory). The bacterial strains studied in this work are eurythermal and psychrotolerant. Their phylogenetic affiliation was found by high throughput sequencing of 16S rRNA gene fragments. It is known that bacteria of the genus Pseudomonas represent all the Earth ecological niches and, accordingly, have a wide range of adaptive functions. Using microscopy methods, a change in the nature of mobility and the cell size stability with changes in the temperatures of cultivating bacteria were established. The studied strains are of scientific and practical interest due to the enzymatic activity detection to several substrates simultaneously at different temperatures (25 and 4℃), as well as the ability to secrete cold active pectinase, protease and lipase. However, phosphate-solubilizing activity both at 4 and at 25℃ became preferable for the strains. The Mramorny Cave is karst and is characterized by carbonate karst, which explains the preference for the studied strains in calcium phosphate. An analysis of the obtained data shows that the collection of cultivated bacteria obtained by us includes both typical psychrotolerant ones, which exhibit enzymatic activity under conditions of optimal growth temperature, and unique ones, capable of synthesizing a wide range of enzymes under conditions not characteristic of its optimum growth.

A Multi-Task Joint Learning Model Based on Transformer and Customized Gate Control for Predicting Remaining Useful Life and Health Status of Tools.

Previous studies have primarily focused on predicting the remaining useful life (RUL) of tools as an independent process. However, the RUL of a tool is closely related to its wear stage. In light of this, a multi-task joint learning model based on a transformer encoder and customized gate control (TECGC) is proposed for simultaneous prediction of tool RUL and tool wear stages. Specifically, the transformer encoder is employed as the backbone of the TECGC model for extracting shared features from the original data. The customized gate control (CGC) is utilized to extract task-specific features relevant to tool RUL prediction and tool wear stage and shared features. Finally, by integrating these components, the tool RUL and the tool wear stage can be predicted simultaneously by the TECGC model. In addition, a dynamic adaptive multi-task learning loss function is proposed for the model's training to enhance its calculation efficiency. This approach avoids unsatisfactory prediction performance of the model caused by unreasonable selection of trade-off parameters of the loss function. The effectiveness of the TECGC model is evaluated using the PHM2010 dataset. The results demonstrate its capability to accurately predict tool RUL and tool wear stages.