Functional Components Research Articles

Improving long‐tail classification via decoupling and regularisation

AbstractReal‐world data always exhibit an imbalanced and long‐tailed distribution, which leads to poor performance for neural network‐based classification. Existing methods mainly tackle this problem by reweighting the loss function or rebalancing the classifier. However, one crucial aspect overlooked by previous research studies is the imbalanced feature space problem caused by the imbalanced angle distribution. In this paper, the authors shed light on the significance of the angle distribution in achieving a balanced feature space, which is essential for improving model performance under long‐tailed distributions. Nevertheless, it is challenging to effectively balance both the classifier norms and angle distribution due to problems such as the low feature norm. To tackle these challenges, the authors first thoroughly analyse the classifier and feature space by decoupling the classification logits into three key components: classifier norm (i.e. the magnitude of the classifier vector), feature norm (i.e. the magnitude of the feature vector), and cosine similarity between the classifier vector and feature vector. In this way, the authors analyse the change of each component in the training process and reveal three critical problems that should be solved, that is, the imbalanced angle distribution, the lack of feature discrimination, and the low feature norm. Drawing from this analysis, the authors propose a novel loss function that incorporates hyperspherical uniformity, additive angular margin, and feature norm regularisation. Each component of the loss function addresses a specific problem and synergistically contributes to achieving a balanced classifier and feature space. The authors conduct extensive experiments on three popular benchmark datasets including CIFAR‐10/100‐LT, ImageNet‐LT, and iNaturalist 2018. The experimental results demonstrate that the authors’ loss function outperforms several previous state‐of‐the‐art methods in addressing the challenges posed by imbalanced and long‐tailed datasets, that is, by improving upon the best‐performing baselines on CIFAR‐100‐LT by 1.34, 1.41, 1.41 and 1.33, respectively.

Advanced X/γ-Ray-Shielding Materials Enabled by the In Situ Generation of Cerium-Tungsten Nanoparticles Within Regenerated Collagen Fibers.

Advanced X- and γ-ray-shielding materials are conventionally designed and fabricated via the uniform dispersion of high-Z elements into the substrate. These soluble high-Z elements considerably reduced the particle size of functional components; however, the as-obtained composites exhibited weak absorption region at 40-80keV and poor water resistance. To address these issues, such materials are fabricated by introducing cerium and tungsten into regenerated collagen fibers (RCFs) using a "dual impregnation-desolvation" strategy. The uniform dispersion of functional components is achieved via the in situ generation of cerium-tungsten nanoparticles (CeW NPs) under multiple impregnating and desolvating cycles; as a result, the CeW NPs achieved an ultrasmall particle size of 17.15nm. Benefiting from the ultrasmall particle size and uniform dispersion of CeW NPs, the fabricated CeW-RCF composites exhibit satisfactory X- and γ-ray-shielding capabilities with an ultrahigh mass attenuation coefficient (MAC) of 5.9cm2g-1 at 83keV, higher than that of lead plates. The CeW-RCF composites also exhibit outstanding mechanical strength, low density, and high air permeability, demonstrating their superior wearability. This work provides novel insights into the design and fabrication of advanced X- and γ-ray-shielding materials with high radiation-shielding performance and enhanced wearability.

Functional quantile principal component analysis.

This paper introduces functional quantile principal component analysis (FQPCA), a dimensionality reduction technique that extends the concept of functional principal components analysis (FPCA) to the examination of participant-specific quantiles curves. Our approach borrows strength across participants to estimate patterns in quantiles, and uses participant-level data to estimate loadings on those patterns. As a result, FQPCA is able to capture shifts in the scale and distribution of data that affect participant-level quantile curves, and is also a robust methodology suitable for dealing with outliers, heteroscedastic data or skewed data. The need for such methodology is exemplified by physical activity data collected using wearable devices. Participants often differ in the timing and intensity of physical activity behaviors, and capturing information beyond the participant-level expected value curves produced by FPCA is necessary for a robust quantification of diurnal patterns of activity. We illustrate our methods using accelerometer data from the National Health and Nutrition Examination Survey, and produce participant-level 10%, 50%, and 90% quantile curves over 24 h of activity. The proposed methodology is supported by simulation results, and is available as an R package.

Functional Principal Component Analysis for Continuous Non-Gaussian, Truncated, and Discrete Functional Data.

Mobile health studies often collect multiple within-day self-reported assessments of participants' behavior and well-being on different scales such as physical activity (continuous scale), pain levels (truncated scale), mood states (ordinal scale), and the occurrence of daily life events (binary scale). These assessments, when indexed by time of day, can be treated and analyzed as functional data corresponding to their respective types: continuous, truncated, ordinal, and binary. Motivated by these examples, we develop a functional principal component analysis that deals with all four types of functional data in a unified manner. It employs a semiparametric Gaussian copula model, assuming a generalized latent non-paranormal process as the underlying generating mechanism for these four types of functional data. We specify latent temporal dependence using a covariance estimated through Kendall's bridging method, incorporating smoothness in the bridging process. The approach is then extended with methods for handling both dense and sparse sampling designs, calculating subject-specific latent representations of observed data, latent principal components and principal component scores. Simulation studies demonstrate the method's competitive performance under both dense and sparse sampling designs. The method is applied to data from 497 participants in the National Institute of Mental Health Family Study of Mood Spectrum Disorders to characterize differences in within-day temporal patterns of mood in individuals with the major mood disorder subtypes, including Major Depressive Disorder and Type 1 and 2 Bipolar Disorder. Software implementation of the proposed method is provided in an R-package.

Adaptive Feature Extraction Using Sparrow Search Algorithm-Variational Mode Decomposition for Low-Speed Bearing Fault Diagnosis.

To address the challenge of extracting effective fault features at low speeds, where fault information is weak and heavily influenced by environmental noise, a parameter-adaptive variational mode decomposition (VMD) method is proposed. This method aims to overcome the limitations of traditional VMD, which relies on manually set parameters. The sparrow search algorithm is used to calculate the fitness function based on mean envelope entropy, enabling the adaptive determination of the number of mode decompositions and the penalty factor in VMD. Afterward, the optimised parameters are used to enhance traditional VMD, enabling the decomposition of the raw signal to obtain intrinsic mode function components. The kurtosis criterion is then used to select relevant intrinsic mode functions for signal reconstruction. Finally, envelope analysis is applied to the reconstructed signal, and the results reveal the relationship between fault characteristic frequencies and their harmonics. The experimental results demonstrate that compared with other advanced methods, the proposed approach effectively reduces noise interference and extracts fault features for diagnosing low-speed bearing faults.

Assessing clinical progression measures in Alzheimer's disease trials: A systematic review and meta-analysis.

Assessing treatments for Alzheimer's disease (AD) relies on reliable tools for measuring AD progression. In this analysis, we evaluate the sensitivity of clinical progression measures in AD within randomized controlled trials (RCTs) with confirmed positive amyloid (Aβ+) status prior to trial enrollment. Excluding trials targeting non-cognitive symptoms, we conducted meta-analyses on progression measures from 25 selected RCTs using R version 4.2.0, along with the metafor and emmeans libraries. The Functional Activities Questionnaire (FAQ) demonstrated the greatest sensitivity over 12weeks. Other cognitive measures demonstrated lower sensitivity. The integrated Alzheimer's Disease Rating Scale (iADRS) and Clinical Dementia Rating-Sum of Boxes (CDR-SB) seemed more effective than their individual cognitive components. Neuropsychiatric measures were the least sensitive in measuring progression. Functional measures generally outperformed other measure categories. Purely cognitive domain-based measures were suboptimal for tracking early AD progression. Ideally, future measures should incorporate both cognitive and functional components to enhance sensitivity. Concerns remain regarding the limitations of current outcome measures used in AD clinical trials, particularly their sensitivity in the early and preclinical stages of the disease, which hampers their reliability as indicators of AD progression. The Functional Activities Questionnaire (FAQ) demonstrated the most substantial weighted mean change over 12weeks, followed by the Mini-Mental State Examination (MMSE). Functional measures outperformed other measure categories. Composite scores of integrated Alzheimer's Disease Rating Scale and Clinical Dementia Rating-Sum of Boxes are more sensitive to change than their individual cognitive components, possibly driven by the functional components of the score. Neuropsychiatric measures analyzed in this study appeared to be the least sensitive in measuring progression.

New insights into the roles of lactylation in cancer.

Lactylation, a novel discovered posttranslational modification, is a vital component of lactate function and is prevalent in a wide range of cells, interacting with both histone and non-histone proteins. Recent studies have confirmed that lactylation as a new contributor to epigenetic landscape is involved in multiple pathological processes. Accumulating evidence reveals that lactylation exists in different pathophysiological states and leads to inflammation and cancer; however, few mechanisms of lactylation have been elaborated. This review summarizes the biological processes and pathophysiological roles of lactylation in cancer, as well as discusses the relevant mechanisms and potential therapeutic targets, aiming to provide new insights for targeted cancer therapy.

Rola mikrobioty jelit i jej metabolitów w przebiegu otyłości i cukrzycy

The gut microbiota is an indispensable component of the proper functioning of the human body, as gut microorganisms and their metabolites strongly influence the host's metabolism and immune functions. They also contribute to the biosynthesis of vitamins, production of gut hormones, maintenance of intestinal barrier integrity, protection against pathogens, as well as digestion and absorption of nutrients. Increasingly, the relationship between disturbances in the composition of gut microbiota and the onset of metabolic diseases such as obesity and type 2 diabetes is being emphasized. Understanding the significance of the microbiota in the course of these diseases, its composition, and activity may offer new approaches to their treatment. Increasing attention is being paid to individual species of gut bacteria, among which Akkermansia muciniphila holds a special position, as a decrease in the abundance of this commensal bacterium in the gut is associated with many diseases, including obesity and diabetes.

Is transcranial alternating current stimulation effective for improving working memory? A three-level meta-analysis.

Working memory, an essential component of cognitive function, can be improved through specific methods. This meta-analysis evaluates the effectiveness of transcranial alternating current stimulation (tACS), an emerging technique for enhancing working memory, and explores its efficacy, influencing factors, and underlying mechanisms. A PRISMA systematic search was conducted. Hedges's g was used to quantify effect sizes. We constructed a three-level meta-analytic model to account for all effect sizes and performed subgroup analyses to assess moderating factors. Recognizing the distinct neural underpinnings of various working memory processes, we separately assessed the effects on n-back tasks and traditional working memory tasks. A total of 39 studies with 405 effect sizes were included (170 from n-back tasks and 235 from other tasks). The overall analysis indicated a net benefit of g = 0.060 of tACS on working memory. Separate analyses showed that tACS had a small positive effect on n-back tasks (g = 0.102), but almost no effect on traditional working memory tasks (g = 0.045). Further analyses revealed mainly: A moderately positive effect of theta tACS (without anti-phase stimulation) on n-back tasks (g = 0.207); and a small effect of offline stimulation on working memory maintenance (g = 0.127). Overall, tACS has minimal impact on working memory improvement, but it shows potential under certain conditions. Specifically, both online and offline theta tACS can improve n-back task performance, while only offline stimulation enhances working memory maintenance. More research is needed to understand the mechanisms behind these effects to make tACS an effective method.

Social Relationships and Tooth Loss in Adults Aged 60 Years and Older: A Systematic Review and Meta-Analysis.

To systematically assess current evidence on the extent to which social relationships are associated with tooth loss in adults aged 60 years and older. A systematic literature search was conducted on PubMed, Embase, Web of Science, CINAHL and The Cochrane Library databases to identify relevant studies published from 1966 up to March 2024. Cross-sectional or cohort studies investigating the association between structural, functional and/or combined (structural and functional) components of social relationships and the number of remaining teeth or edentulism among community-dwelling or institutionalised older adults were included. Data were extracted on participants' and study characteristics, including study design, the type of measures used to assess social relationships (structural, functional, and combined), outcome measures and association estimates. The quality of the studies was assessed using the Newcastle-Ottawa Scale (NOS) for cohort studies and the adapted NOS for cross-sectional studies. The reported association between social relationships and the number of remaining teeth or edentulism was summarised using meta-analysis with robust variance estimation. Twenty studies were included in the review and 12 studies (125 553 participants) in the meta-analysis. Across the 12 studies, the average odds ratio (95% confidence interval) was 1.15 (1.01-1.32), indicating a 15% higher likelihood of having a lower number of teeth or edentulism for those with weaker social relationships. The GRADE certainty of the body of evidence was low. Weak social relationships were associated with a lower number of teeth or edentulism in older adults. Our findings may inform potential public health approaches that target and modify social relationships to prevent and address older adults' oral diseases. Still, the directionality and the underlying mechanisms connecting social relationships and tooth loss need to be further explored by longitudinal studies with follow-up long enough for oral health outcomes or changes in social relationships to occur. Protocol Registration: PROSPERO (CRD42023417845).

Use of the international classification of functioning, disability and health in a group of older persons with osteoarticular disorders

Objective: to describe the main structural and functional impairments, activity limitations, and social participation restrictions in elderly individuals with myoarticular dysfunctions using the International Classification of Functioning, Disability, and Health (ICF). Methods: a descriptive, cross-sectional, and retrospective study based on the analysis of clinical and functional data of elderly individuals with osteomyoarticular dysfunctions. A questionnaire including components of Functionality and Disability (ICF, 2004), mobility and stability (Timed Up and Go), lower limb muscle strength (Sit-To-Stand Test), and pain (visual analog scale) was used. Results: a total of 11 elderly individuals (70±1.4 years) with at least two comorbidities (100%) were included, with the majority reporting episodes of falls (54.5%). Pain was the primary complaint in "Body functions," reported by all participants. In "Body structures," the lower limbs (45.4%) and upper limbs (18.1%) had the highest prevalence of complaints. In "Activities and Participation," the most frequently mentioned items were performing housework tasks (31.8%), moving around (31.8%), and recreation and leisure (27.3%). Conclusion: among the ICF components, pain sensation was the most common finding in elderly individuals, especially affecting the lower limbs. Regarding activity limitations and social participation restrictions, performing housework tasks, moving around, and recreation/leisure were prevalent.

Effects of Different Fertilization Measures on Bacterial Community Structure in Seed Production Corn Fields

Different fertilization measures affect the soil’s physical and chemical properties and bacterial community structure, which in turn affects the growth environment and yield of maize seed production. Therefore, rational fertilization measures are important in maintaining and improving soil fertility and promoting maize crop growth. Research on fertilization practices in maize fields for seed production can help to increase agricultural productivity while protecting and enhancing soil health and achieving sustainable agricultural development. To clarify the effects of different fertilization measures on soil bacterial communities in seed corn fields, 16S rRNA high-throughput sequencing technology and PICRUSt method were used to explore the soil under different fertilization measures (CK as control, effects of single application of liquid organic fertilizer (M), single application of bacterial agents (BF), and combined application of liquid organic fertilizer and bacterial agents (M + BF)) on soil bacterial community structure characteristics and ecological functions. Proteobacteria (20.14–25.30%), Actinobacteriota (18.21–20.47%), Actinobacteriota (13.55–22.00%), and Chloroflexi (14.24–17.59%) were the dominant phyla. Bacillus, RB41, Arthrobacter, and Sphingomonas were the dominant genera. M + BF treatment significantly increased the relative abundance of Planctomycetota. The relative abundance of Bacillus and PaeniBacillus in M treatment decreased considerably, while the relative abundance of Turicibacter increased significantly. The relative abundance of Sphingomonas was reduced considerably in M and M + BF treatments. The relative abundance of Subgroup 10 decreased significantly after BF and M + BF treatments. BF treatment significantly increased the relative abundance of Skermanella. Redundancy analysis showed that alkali-hydrolyzed nitrogen (p = 0.044) was the main environmental factor affecting soil bacterial communities under different fertilizer treatments. PICRUSt function prediction results showed that metabolism was the main functional component of bacteria, accounting for 78.45–78.94%. The abundance of functional genes for terpenoid and polyketone metabolism, the endocrine system, the excretory system, and the immune system of the soil bacterial community was significantly increased under M treatment, while the abundance of functional genes for the digestive system was decreased considerably. Different fertilizer cultivation measures changed soil bacterial community composition and ecological function in maize fields. These results can provide a theoretical reference for the study of bacterial community succession characteristics in maize fields and the determination of appropriate fertilizer cultivation measures.

An Introduction to Semantic Communication and Semantic-Aware Networking Standardization for 6G

Recent developments of networking technologies have witnessed a growing interest on human-oriented services such as Tactile Internet and VR/AR/XR, most of which are data-hungry and computationally expensive. Semantic communication and semantic-ware networking (SAN) are promising solutions to meet the above need and support emerging smart applications and services based on the network and user generated data. In this paper, we provide an overview of the ongoing standardization activity of semantic communication and semantic-aware networking at IMT-2030 (6G) standard promotion group, including the overview of the concepts, key functional components, reference architectures, and use cases.

Structural characteristics and antioxidant activity of a low-molecular-weight jujube polysaccharide by ultrasound assisted metal-free Fenton reaction

This study used an ultrasonically accelerated metal-free Fenton (H2O2-Vc system) reaction to promote water-extracted degrading polysaccharides from Ziziphus Jujuba cv. Muzao (DZMP). A novel jujube polysaccharide (DPZMP3) was obtained by degradation using DEAE-Sepharose Fast Flow and Sephacryl S-100 column chromatography. Methylation analysis, HPGPC, ion chromatography, FT-IR, and NMR spectroscopies were used to clarify the chemical structures of DPZMP3. Monosaccharide compositional analysis of DPZMP3 revealed the presence of Rha, Ara, Gal, and GalA at a molar ratio of 1.00:1.49:1.60:7.68, and the HPGPC data demonstrated the average Mw of 34.3 kDa. Based on the structural and linkage research using NMR spectroscopy and GC–MS, it was determined that DPZMP3 was a homogalacturonan pectic polysaccharide with a (1 → 4)-Galp branch at C-6 and a small amount of Araf and Rhap residues. The ultrasonic-aided Fenton treatment did not significantly alter the structure of DPZMP3. It may also be useful for DZMP and enhancing their antioxidant activity in vitro. The current study's findings could pave the way for the food sector to use jujube polysaccharides obtained by degradation as a functional food component.

Research Progress on Genomic Regions and Candidate Genes Related to Milk Composition Traits of Dairy Goats Based on Functional Genomics: A Narrative Review.

Goat milk has gained global attention for its unique nutritional properties and potential health benefits. Advancements in functional genomic technologies have significantly progressed genetic research on milk composition traits in dairy goats. This review summarizes various research methodologies applied in this field. Genome-wide association studies (GWAS) have identified genomic regions associated with major milk components, with the diacylglycerol acyltransferase 1 (DGAT1) gene and casein gene cluster consistently linked to milk composition traits. Transcriptomics has revealed gene expression patterns in mammary tissue across lactation stages, while the role of non-coding RNAs (such as miRNAs and circRNAs) in regulating milk composition has been confirmed. Proteomic and metabolomic studies have not only helped us gain a more comprehensive understanding of goat milk composition characteristics but have also provided crucial support for the functional validation of genes related to milk components. The integration of multi-omics data has emerged as an effective strategy for elucidating complex regulatory networks from a systems biology perspective. Despite progress, challenges remain, including refining reference genomes, collecting large-scale phenotypic data, and conducting functional validations. Future research should focus on improving reference genomes, expanding study populations, investigating functional milk components, exploring epigenetic regulation and non-coding RNAs, and studying microbiome-host genome interactions. These efforts will inform more precise genomic and marker-assisted selection strategies, advancing genetic improvements in milk composition traits in dairy goats.

Enhancing combat readiness via evidence-based pedagogical innovations in National Guard of Ukraine officer training

This study examines the effectiveness of innovative pedagogical conditions in enhancing combat readiness among future officers of the National Guard of Ukraine (NGU). Utilising a quasi-experimental design, we implemented and evaluated a set of pedagogical interventions integrating recent combat experience into officer training at the Kyiv Institute of the National Guard of Ukraine. The study involved 74 trainee officers divided into experimental (n=35) and control (n=39) groups. Results demonstrated significant improvements in the experimental group across functional, psychological, and reflective components of combat readiness, with increases of 43%, 25%, and 32%, respectively, in high-level readiness. These findings underscore the importance of rapidly integrating field experiences into military education, offering valuable insights for enhancing officer training in dynamic conflict environments. The study contributes to the broader understanding of adaptive military education strategies and provides a framework for implementing evidence-based training reforms in military institutions globally.

The productivity of selected soybean cultivars grown using various cultivation methods

Soybean is an important legume crop globally due to its rich protein, oil content, and functional components. The purpose of this study was to evaluate the yield of selected soybean cultivars depending on cultivation methods. The three-year field experiment, conducted from 2018 to 2020 at the Agricultural Experimental Station in Kępa-Puławy, Osiny farm (Institute of Soil Science and Plant Cultivation – State Research Institute in Puławy), investigated these variations. The first experimental factor was the soil cultivation method: A – conventional tillage, B – reduced tillage, and C – strip tillage. The second variable was soybean cultivar: ‘Aldana’ and ‘Merlin’. The soybean cultivars were selected for their differing maturity rates: ‘Aldana’ (000) is an early cultivar, while ‘Merlin’ (000++) semi-late cultivar. The field experiment utilised a split-plot design on Luvisol soil with sandy loam texture, belonging to a good rye complex, class IIIb–IVa, and was replicated four times. The study showed that the productivity (seed and protein yield) of the ‘Merlin’ cultivar grown in the central-eastern part of Poland was approximately 8% higher than that of the ‘Aldana’ cultivar. The cultivation method had a relatively minor influence on soybean yield, the content of selected nutrients, morphological features, and elements of the yield structure. The soil in strip-tillage method was more compact than the soil cultivated with a plough. After harvesting soybeans at a depth of 30, and 40 cm, the compactness of soil in strip-tillage or with reduced tillage was much lower than in spring, highlighting a positive effect of soybean cultivation on loosening the arable layer.

Exploring Working Memory in Context Sensitivity

ABSTRACT Context sensitivity refers to the ability to identify cues regarding the nature of stressor situations. This skill is a necessary precursor to successful emotion regulation and may involve detecting the presence or absence of stressor cues. Previous research has suggested that context sensitivity relies in part on working memory (WM), one component of cognitive control or executive functioning. We explored this potential relationship in an empirical study (N = 112) that assessed WM via WAIS-IV Digit Span as well as the ability to detect the presence or absence of stressor cues via the Context Sensitivity Index. Results demonstrated that detection of both cue presence and absence were significantly associated with the ability to apply semantic knowledge to information held in WM. Our findings expand upon a potential cognitive mechanism for context sensitivity.

Investigation of electronic, optical and thermoelectric properties in orthorhombic perovskite SmFeO3

In this work, we have investigated the electronic, optical, and thermoelectric properties of orthorhombic perovskite SmFeO3 using density functional and Boltzmann transport theories, for renewable energy applications. The modeling of the exact electron exchange-correlation has been carried out using both Generalized Gradient Approximation (GGA) and a functional modified with Hubbard interactions. The spin-polarized calculations show that SmFeO3 adopts a G-type antiferromagnetic configuration. The studied compound's electronic band structure and density of states analysis reveal its p-type semiconductor behavior. Furthermore, the optical properties such as real ε1(ω) and imaginary ε2(ω) components of the dielectric function as well as other conventional optical parameters, were investigated. The results show that SmFeO3 is a promising material for optoelectronic applications. The calculated transport properties including the Seebeck coefficient (S), the electrical conductivity (σ/τ), the electronic thermal conductivity (k0/τ) and the electronic figure of merit (ZTe) show significant results. The studied perovskite is also a potential candidate for thermoelectric applications, due to its high Seebeck coefficient, reaching 2388 μV/K, and the large electronic figure of merit which is close to 1 at room temperature. To improve the thermoelectric performance of SmFeO3 material, we have studied the effect of both p-type and n-type doping on its thermoelectric behavior at different temperature values.

Assessment of the Adaptive Behavior of Young Children with Visual Impairments in an Early Intervention Service: A Pilot Study.

Adaptive behavior, defined as a critical set of skills learned and performed throughout daily life to cope with society's age-appropriate expectations, is a central concept for people with disabilities in both clinical and research contexts. As AB is an essential component of daily functioning, assessment is necessary both for the diagnostic process and for intervention, as it enables scores to be compared with the developmental norm, identifies strengths and weaknesses of the persons and monitors the progress of interventions. AB assessment is common in children with developmental delays but less common in children with visual impairment (VI). The aim of this study was to evaluate the AB of young children with VI through a pilot study and descriptive data. The participants were recruited through an early childhood special needs education service specialized in VI in the French-speaking part of Switzerland. Overall, 10 families gave their consent for their child to be assessed using the Vineland Adaptative Behavior Scale-II (VABS-II) completed by their early childhood educator. Globally, the results showed that participants were rated between the Adapted to Moderately High levels. The highest domains were in the areas of communication, daily living skills and socialization. The results showed a trend that is superior to previous studies assessing the AB of a similar population. As a result, one obvious perspective would be to adapt the scale to ensure that the items assessed are more consistent with the specificities of their development and the intervention priorities, enabling them to achieve adaptive behavior and independence in carrying out activities of daily living.