Adaptive Component Research Articles

Rational Polynomial Coefficient Estimation via Adaptive Sparse PCA-Based Method

The Rational Function Model (RFM) is composed of numerous highly correlated Rational Polynomial Coefficients (RPCs), establishing a mathematical relationship between two-dimensional images and three-dimensional spatial coordinates. Due to the existence of ill-posedness and overparameterization, the estimated RPCs are sensitive to any slight perturbations in the observation data, particularly when handling a limited number of Ground Control Points (GCPs). Recently, Principal Component Analysis (PCA) has demonstrated significant performance improvements in the RFM optimization problem. In the PCA-based RFM, each Principal Component (PC) is a linear combination of all variables in the design matrix. However, some original variables are noise related and have very small or almost zero contributions to the construction of PCs, which leads to the overparameterization problem and makes the RPC estimation process ill posed. To address this problem, in this paper, we propose an Adaptive Sparse Principal Component Analysis-based RFM method (ASPCA-RFM) for RPC estimation. In this method, the Elastic Net sparsity constraint is introduced to ensure that each PC contains only a small number of original variables, which automatically eliminates unnecessary variables during PC computation. Since the optimal regularization parameters of the Elastic Net vary significantly in different scenarios, an adaptive regularization parameter approach is proposed to dynamically adjust the regularization parameters according to the explained variance of PCs and degrees of freedom. By adopting the proposed method, the noise and error in the design matrix can be reduced, and the ill-posedness and overparameterization of the RPC estimation can be significantly mitigated. Additionally, we conduct extensive experiments to validate the effectiveness of our method. Compared to existing state-of-the-art methods, the proposed method yields markedly improved or competitive performance.

Heatwave vulnerability of large metropolitans in Bangladesh: An evaluation

Heatwaves pose a significant risk to human society, and assessing vulnerability at the local scale is challenging due to the multifaceted nature of contributing factors. This study focuses on evaluating the heatwave vulnerability of five major cities in Bangladesh: Chittagong, Dhaka, Khulna, Rajshahi, and Sylhet. Heatwave vulnerability index (HVI) was estimated, integrating exposure, sensitivity, and adaptive capacity components. Time-series data from the Moderate Resolution Imaging Spectroradiometer (MODIS) for land surface temperature (LST) spanning 2000–2019 served as the primary dataset. The HVI calculation employed a principal component analysis method. The spatial distribution of the resulting HVI across five metropolitan areas were evaluated by estimating spatial autocorrelation and identifying clusters and hot spots in vulnerable areas. The findings revealed that urban centers characterized by extensive built-up areas exhibited higher exposure to urban heat compared to their surroundings. Dhaka, the capital, recorded the highest mean annual LST, while Sylhet registered the lowest. Rajshahi and Sylhet stood out for having the largest percentage of heat-exposed and sensitive areas, respectively. Sylhet also had the highest percentage of heat-vulnerable areas, reaching 63 %, whereas Dhaka had the highest number of vulnerable individuals, totaling 12.5 million. The findings also suggested that augmenting urban green and blue infrastructures holds potential for mitigating the adverse impacts of heatwaves. The methodology and outcomes of this study provide a valuable foundation for devising heatwave adaptation strategies not only in Bangladesh but also in other regions facing similar challenges.

UDANet: An unsupervised domain adaptive vehicle density estimation network based on joint adversarial learning

Unsupervised domain adaptive vehicle density estimation aims to transfer the knowledge learned from the labeled source domain to the unlabeled target domain, which has received extensive attention due to its practicality and effectiveness. However, the existing methods often rely on a large amount of expensive labeled data, and ignore the large differences between different data domains, making the network trained in one scene difficult to apply to the target scene. To solve these problems, this paper proposes an unsupervised domain adaptive vehicle density estimation network based on joint adversarial learning (UDANet), which effectively extends the model to unlabeled target domain data by adding two domain adaptive components to the pre-trained backbone. Specifically, a multi-level feature discriminator is designed to align vehicle features from the image level and refine features from the pixel level by joint adversarial learning, thus achieving cross-domain transfer of features from the source domain to the target domain. In addition, to address the shared convolutional structure that incorrectly highlights irrelevant information, a feature correction module is designed to improve the adaptability of the network to target domain data by re-modeling the source and target data according to channel independence. Several well-known backbones are used to prove the superiority of the backbone and the mobility of the two domain adaptive components. The domain adaptation experimental results on four vehicle datasets show that UDANet achieves high estimation accuracy compared to current state-of-the-art unsupervised methods, and is suitable for the task of vehicle density estimation in complex scenes.

Real-Time Motor Unit Tracking from sEMG Signals with Adaptive ICA on a Parallel Ultra-Low Power Processor.

Spike extraction by blind source separation (BSS) algorithms can successfully extract physiologically meaningful information from the sEMG signal, as they are able to identify motor unit (MU) discharges involved in muscle contractions. However, BSS approaches are currently restricted to isometric contractions, limiting their applicability in real-world scenarios. We present a strategy to track MUs across different dynamic hand gestures using adaptive independent component analysis (ICA): first, a pool of MUs is identified during isometric contractions, and the decomposition parameters are stored; during dynamic gestures, the decomposition parameters are updated online in an unsupervised fashion, yielding the refined MUs; then, a Pan-Tompkins-inspired algorithm detects the spikes in each MUs; finally, the identified spikes are fed to a classifier to recognize the gesture. We validate our approach on a 4-subject, 7-gesture + rest dataset collected with our custom 16-channel dry sEMG armband, achieving an average balanced accuracy of 85.58±14.91% and macro-F1 score of 85.86±14.48%. We deploy our solution onto GAP9, a parallel ultra-low-power microcontroller specialized for computation-intensive linear algebra applications at the edge, obtaining an energy consumption of 4.72 mJ @ 240 MHz and a latency of 121.3 ms for each 200 ms-long window of sEMG signal.

Adaptive grey wolf optimizer based on transfer function inertia weight of second-order high-pass filter

This paper proposes an Adaptive Grey Wolf Optimizer based on Transfer function Inertia Weight of Second-order High-pass Filter (SAGWO), aiming to overcome the limitations of Grey Wolf Optimizer (GWO) in terms of convergence speed and solution accuracy. SAGWO enhances the algorithm's dynamic adaptability and greatly speeds up convergence by incorporating the transfer function of the second-order high-pass filter into the modifications of inertia and leader wolves' weight. Furthermore, SAGWO incorporates an adaptive random search component, which significantly enhances the search range and the ability to explore globally. The performance of SAGWO is assessed and compared against three prominent Swarm Intelligence Algorithms (ALO, WOA, and EHO), along with GWO and GWO optimized by PSO. This evaluation is conducted through trials on the CEC2017 standard test set. The experimental results indicate that SAGWO outperforms in terms of both convergence speed and solution correctness. Moreover, SAGWO is utilized to address practical engineering problems such as pressure vessel design and robot gripper design, showcasing its remarkable effectiveness once more. This research enhances the theoretical development of Swarm Intelligence Algorithms and demonstrates the significant utility of SAGWO in practical applications, establishing it as a powerful instrument for scientific investigation and engineering optimization.

Adaptive importance sampling approach for structural time-variant reliability analysis

A novel sampling approach, called adaptive importance sampling (AIS), is proposed to efficiently perform time-variant reliability analysis. In practice, structures are generally subject to time-variant deterioration processes and external loads, and the Time-variant Failure Probability Function (TFPF), which is the failure probability as a function of time, is a critical quantity of interest in engineering applications. The proposed approach leverages an adaptive strategy and an optimal combination algorithm to further improve the accuracy and efficiency of TFPF estimation using the importance sampling approach. The adaptive strategy is to seek for the best setting of importance sampling components to iteratively obtain estimator components of the TFPF. The optimal combination algorithm is to collect all these adaptive estimator components to form an overall estimator by its coefficient of variation (C.o.V.). The proposed approach outperforms traditional importance sampling methods in the sense that it ensures the convergence with minimal computational cost, specifically the C.o.V. of the TFPF estimator is below a predetermined threshold over the entire time domain. Therefore, the proposed approach offers an extension to traditional importance sampling methods for time-variant reliability assessment. Numerical examples are provided to demonstrate the effectiveness of the proposed approach in accurately estimating the TFPF of structures subjected to time-variant loads and deterioration processes.

Mastering complexity in Life Cycle Assessment for product development: Evaluating the impacts of adaptive façades

Adaptive façades, which can manipulate the physical properties of buildings, represent a promising approach to reducing the environmental impacts of buildings, which are usually assessed using the Life Cycle Assessment (LCA) method. However, the inclusion of additional fields of engineering with their corresponding simulation tools and inclusion of additional adaptive components in building designs leads to an increased number of possible design configurations – in the order of several millions – as well as stronger dependencies between the design and the use phase. This paper proposes a workflow to automatically generating, analysing and evaluating LCA results of all potential configurations within a defined parameter space. It combines and processes data from multiple sources, including process-based LCA models, generic LCA databases, and simulation tools. Various methods for combining and analysing the data are provided in the form of parameter sensitivity and statistical evaluations as well as visualisations. Depending on the area of interest, dynamic benchmarks can be derived and visualised. The results can be structured according to the life cycle phases and exported in common spreadsheet formats. This enables comparison to other LCA results and ensures seamless integration in existing reporting structures. The primary beneficiaries are LCA experts who require ex-ante evaluations for (building) products with a large number of potential configurations and complex interactions between the design and use phases. Identifying hotspots and assessing the sensitivity of parameters at an early stage of product development enables product designers to consider environmental aspects and optimise the product in this respect.

Differential Aging Effects on Implicit and Explicit Sensorimotor Learning.

Deterioration in motor control is a hallmark of aging, significantly contributing to a decline in quality of life. More controversial is the question of whether and how aging impacts sensorimotor learning. We hypothesized that the inconsistent picture observed in the current literature can be attributed to at least two factors. First, aging studies tend to be underpowered. Second, the learning assays used in these experiments tend to reflect, to varying degrees, the operation of multiple learning processes, making it difficult to make inferences across studies. We took a two-pronged approach to address these issues. We first performed a meta-analysis of the sensorimotor adaptation literature focusing on outcome measures that provide estimates of explicit and implicit components of adaptation. We then conducted two well-powered experiments to re-examine the effect of aging on sensorimotor adaptation, using behavioral tasks designed to isolate explicit and implicit processes. Convergently, both approaches revealed a striking dissociation: Older individuals exhibited a marked impairment in their ability to discover an explicit strategy to counteract a visuomotor perturbation. However, they exhibited enhanced implicit recalibration. We hypothesize that the effect of aging on explicit learning reflects an age-related decline in reasoning and problem solving, and the effect of aging on implicit learning reflects age-related changes in multisensory integration. Taken together, these findings deepen our understanding of the impact of aging on sensorimotor learning.

Motor adaptation is reduced by symbolic compared to sensory feedback.

Motor adaptation - the process of reducing motor errors through feedback and practice - is an essential feature of human competence, allowing us to move accurately in dynamic and novel environments. Adaptation typically results from sensory feedback, with most learning driven by visual and proprioceptive feedback that arises with the movement. In humans, motor adaptation can also be driven by symbolic feedback. In the present study, we examine how implicit and explicit components of motor adaptation are modulated by symbolic feedback. We conducted three reaching experiments involving over 400 human participants to compare sensory and symbolic feedback using a task in which both types of learning processes could be operative (Experiment 1) or tasks in which learning was expected to be limited to only an explicit process (Experiments 2 and 3). Adaptation with symbolic feedback was dominated by explicit strategy use, with minimal evidence of implicit recalibration. Even when matched in terms of information content, adaptation to rotational and mirror reversal perturbations was slower in response to symbolic feedback compared to sensory feedback. Our results suggest that the abstract and indirect nature of symbolic feedback disrupts strategic reasoning and/or refinement, deepening our understanding of how feedback type influences the mechanisms of sensorimotor learning.

Research on load-resistant enhancement control optimization of composite energy power supply system based on deep reinforcement learning

Abstract To solve the problem of insufficient inertia in the power electronics components under the development trend of power electronics in the transmission system of heavy-duty electric vehicles, combined with the composite energy power supply mode and control method, voltage-based feedback channels and virtual capacitor branches are designed for both engine-generator sets and DCDC control loops. These innovations compensate for system inertia and introduce a TD3 deep reinforcement learning-based adaptive regulation component to manage electrical systems in response to DC voltage fluctuations. This approach enhances the stability of the entire system in the face of load disturbances. Strategy validations demonstrate that this method ensures stable system operation, significantly mitigates DC voltage fluctuations during sudden load changes and is highly adaptable without relying on system models.

Applying sequential adaptive strategies for sampling animal populations: An empirical study

AbstractTraditional sampling methods may prove inadequate when dealing with spatially clustered populations or when studying rare events or traits that are not easily detectable across the target population. When both scenarios occur simultaneously, adaptive sampling strategies can represent a viable option to enhance the detectability of cases of interest. This paper delves into the application of a novel class of sequential adaptive sampling strategies to animal surveys. These strategies, originally proposed for human population tuberculosis prevalence surveys, allow oversampling of the rare interest variables while managing on‐field constraints. This ensures that the unfixed sample size, typical of adaptive sampling, does not compromise overall cost‐effectiveness. We explore a strategy within this class that integrates an adaptive component into a Poisson sequential selection. The aim is twofold: to intensify the detection of cases by exploiting the spatial clustering and to provide a flexible framework for managing logistics and budget constraints. To illustrate the strengths and weaknesses of this Poisson‐based sequential adaptive sampling strategy compared to traditional sampling methods, a simulation study was conducted on a blue‐winged teal population in Florida, USA. The results showcase the benefits of the proposed strategy and open avenues for future methodological and practical improvements.

Exploring Sensitivity Factors Influencing Technology Adoption for Climate Change Adaptation among Agricultural Communities of Kedah and Kelantan, Malaysia

The agricultural sector is exceptionally susceptible to fluctuations in climatic conditions, and such changes significantly impact agricultural productivity as well as the welfare of agricultural communities. Therefore, it is crucial to comprehend the vulnerability and risk of the agricultural communities to these effects. The agriculture ability of the sector to successfully adapt over the long term will depend heavily on the adoption of new technologies. Therefore, the adoption of emerging technologies into agriculture is a key component of climate change adaptation. Nguyen et al. (2021) suggest that it would be great for future study to conceptualize the integration of economic, social, and political issues in improving adaptation among agricultural communities. Therefore, current study attempts to determine social vulnerability of the agricultural communities particularly in terms of sensitivity which consist of economic, political, cultural and institutional factors that influence technology adoption for climate change adaptation. The study will be using questionnaire as the main research instrument. A descriptive, correlation and regression analysis will be employed in order to achieve the objectives of the study. A non-probability sampling known as convenient sampling technique will be employed as the sampling technique. The study will be conducted in Kedah and Kelantan as both states are predominantly characterized by an agriculture-based economy which currently have been reported to be affected by climate change. Results of the study will contribute to the making of policy related to agriculture and climate change adaptation as well as for future study related to the field.

Evidence for immune priming specificity and cross-protection against sympatric and allopatric Vibrio splendidus strains in the oyster Magalana (Crassostrea) gigas

Infections with pathogenic Vibrio strains are associated with high summer mortalities of Pacific oysters Magalana (Crassostrea) gigas, affecting production worldwide. This raises the question of how M. gigas cultures can be protected against deadly Vibro infection. There is increasing experimental evidence of immune priming in invertebrates, where previous exposure to a low pathogen load boosts the immune response upon secondary exposure. Priming responses, however, appear to vary in their specificity across host and parasite taxa. To test priming specificity in the Vibrio – M. gigas system, we used two closely related Vibrio splendidus strains with differing degrees of virulence towards M. gigas. These V. splendidus strains were either isolated in the same location as the oysters (sympatric, opening up the potential for co-evolution) or in a different location (allopatric). We extracted cell-free haemolymph plasma from infected and control oysters to test the influence of humoral immune effectors on bacterial growth in vitro. While addition of haemolypmph plasma in general promoted growth of both strains, priming by an exposure to a sublethal dose of bacterial cells lead to inhibitory effects against a subsequent challenge with a potentially lethal dose in vitro. Inhibitory effects and immune priming was strongest when oysters had been primed with the sympatric Vibrio strain, but inhibitory effects were seen both when challenged with the sympatric as well as against allopatric V. splendidus, suggesting some degree of cross protection. The stronger immune priming against the sympatric strain suggests that priming could be more efficient against matching local strains potentially adding a component of local adaptation or co-evolution to immune priming in oysters. These in vitro results, however, were not reflected in the in vivo infection data, where we saw increased bacterial loads following an initial challenge. This discrepancy might suggests that that it is the humoral part of the oyster immune system that produces the priming effects seen in our in vitro experiments.

Структура и факторы академической адаптации студентов-иностранцев в российских университетах

Introduction. Academic adaptation of foreign students in terms of academic mobility is the basis of academic achievements and is associated with psychological well-being, which serves as a condition for a positive attitude towards the host country. Therefore, the study of the peculiarities of academic adaptation of foreign students will allow us to determine the conditions for their secondary socialization and psychological acceptance of the host country. Theoretical analysis. Academic adaptation of foreign students is associated with problems of cultural stress, awareness, socio-psychological relations of the host party, knowledge of the language for establishing contacts and interaction with local students and everyday problems. Academic problems themselves are considered to a lesser extent. Empirical analysis. The purpose of the study is to analyze the structure and factors of academic adaptation of foreign students. It is assumed that the social characteristics of the academic adaptation of foreigners (communicative, personal) are more pronounced than the psychophysiological ones, reflecting the physical well-being of students, the subjective assessment of their exhaustion and fatigue. Conclusion. As a result of the research, the indicators of personal, emotional and motivational and lower psychophysiological components of academic adaptation were found to be more pronounced than those of Russians. The adaptation of foreign students is conditioned by satisfaction of the basic need for connectedness with others, characterizing the possibilities of their support from relatives, connectedness with them, adaptation of Russian students - with satisfaction of the need for connectedness with others, competence, happiness and life satisfaction. Involvement in the educational activity of students, regardless of their residence status, is the most important factor in academic adaptation.

Comparative analysis of components of university adjustment of medical students studying on different financial basis

The purpose of the study is to conduct empirical and statistical evaluation, as well as subsequent comparative analysis of the expression of university adaptation components (social, didactic, and professional) in 1st year medical students studying on different financial basis (commercial, budgetary, and target contract). The scientific novelty of the study lies in the research of medical students’ adaptation processes in the context of their financial basis of education. The online testing method with the subsequent statistical analysis of the obtained data has been applied. To identify the level of expression of the university adaptation components, the method “Students’ adaptation to university” by M.S. Yurkina was used as a diagnostic tool. According to the obtained data, there are statistically significant differences in the levels of expression of the university adaptation components in 1st year medical students studying on a different financial basis. The least psychologically stable from the point of view of social infusion into the university environment are the students studying under the target contract. Didactic and professional adaptation components in higher education are least expressed in students studying on a commercial basis. The data obtained as a result of the study can be used in university educational activity to improve freshmen’s adaptation processes management considering the financial basis of their education, which will optimize the issue of preserving the contingent of students.

Comparative genomics of dusky kob (Argyrosomus japonicus, Sciaenidae) conspecifics: Evidence for speciation and the genetic mechanisms underlying traits.

Dusky kob (Argyrosomus japonicus) is a commercially important finfish, indigenous to South Africa, Australia, and China. Previous studies highlighted differences in genetic composition, life history, and morphology of the species across geographic regions. A draft genome sequence of 0.742 Gb (N50 = 5.49 Mb; BUSCO completeness = 97.8%) and 22,438 predicted protein-coding genes was generated for the South African (SA) conspecific. A comparison with the Chinese (CN) conspecific revealed a core set of 32,068 orthologous protein clusters across both genomes. The SA genome exhibited 440 unique clusters compared to 1928 unique clusters in the CN genome. Transportation and immune response processes were overrepresented among the SA accessory genome, whereas the CN accessory genome was enriched for immune response, DNA transposition, and sensory detection (FDR-adjusted p < 0.01). These unique clusters may represent an adaptive component of the species' pangenome that could explain population divergence due to differential environmental specialisation. Furthermore, 700 single-copy orthologues (SCOs) displayed evidence of positive selection between the SA and CN genomes, and globally these genomes shared only 92% similarity, suggesting they might be distinct species. These genes primarily play roles in metabolism and digestion, illustrating the evolutionary pathways that differentiate the species. Understanding these genomic mechanisms underlying adaptation and evolution within and between species provides valuable insights into growth and maturation of kob, traits that are particularly relevant to commercial aquaculture.

Receptors Implicated in Microgravity-Induced Bone Loss

For humans to explore and colonize the universe, both engineering and physiological obstacles must be successfully addressed. A major physiological problem is that humans lose bone rapidly in microgravity. Understanding the underlying mechanisms for this bone loss is crucial for designing strategies to ameliorate these effects. Because bone physiology is entangled with other organ systems, and bone loss is a component of human adaptation to microgravity, strategies to reduce bone loss must also account for potential effects on other systems. Here, we consider the receptors involved in normal bone remodeling and how this regulation is altered in low-gravity environments. We examine how single cells, tissues and organs, and humans as a whole are affected by low gravity, and the role of receptors that have been implicated in responses leading to bone loss. These include receptors linking cells to the extracellular matrix and to each other, alterations in the extracellular matrix associated with changes in gravity, and changes in fluid distribution and fluid behavior due to lack of gravity that may have effects on receptor-based signaling shared by bone and other regulatory systems. Inflammatory responses associated with the environment in space, which include microgravity and radiation, can also potentially trigger bone loss.

Assessment of the Vulnerability of Households Led by Men and Women to the Impacts of Climate-Related Natural Disasters in the Coastal Areas of Myanmar and Vietnam

Farm households along the coastlines of Myanmar and Vietnam are becoming increasingly vulnerable to flooding, saltwater intrusion, and rising sea levels. There is little information available on the relative vulnerability of men- and women-headed households, and the governments of Myanmar and Vietnam have not identified or implemented any adaptive measures aimed specifically at vulnerable peoples. This study aims to fill these gaps and assess the relative climate change vulnerability of men- and women-headed farm households. This study considers 599 farm households from two regions of Myanmar and 300 households from Thua Thien Hue province of Vietnam for the period 2021–2022. We offer a livelihood vulnerability index (LVI) analysis of men- and women-headed farm households using 46 indicators arranged into seven major components. The aggregate LVI scores indicate that farm households in Myanmar are more vulnerable (scores of 0.459 for men and 0.476 for women) to climate-related natural disasters than farm households in Vietnam (scores of 0.288 for men and 0.292 for women), regardless of the gender of the head of household. Total vulnerability indexing scores indicate that women-headed households are more vulnerable than men-headed households in both countries. Poor adaptive capacity and highly sensitive LVI dimensional scores explain the greater vulnerability of women-headed farm households. The findings also highlight the importance of the adaptive capacity components reflected in the LVI analysis in reducing farm households’ vulnerability.

Adaptive Learning Components for Pre-University Students

Abstract The majority of those teaching students in Romanian pre-university education use digital educational resources, educational platforms, and applications that employ artificial intelligence both in the teaching process and in evaluation to support student motivation and improve school results through learning adapted to the needs of the students. During the COVID-19 pandemic restrictions, teachers sought new methods and tools to use in online teaching and assessment. Many of them created digital educational resources that they use both for teaching and for evaluating students. In this study, I present the results of research conducted by applying an online questionnaire created with Google Forms to a number of 107 teachers who teach students in Romanian pre-university education. The questionnaire was sent individually through the WhatsApp application to identify the factors that lead to the motivation and improvement of the students' school results. The questionnaire responses were provided during February 20-23, 2024. Digital educational resources, educational platforms, and applications that use artificial intelligence were identified, which are used by teachers to motivate students to learn and to enhance their learning outcomes. The study underscores the significance of tailoring teaching methods to the specific needs of students and highlights the pivotal role of technology in enabling this adaptation process.

Urban growth and vulnerability to climate change of Calderón in the Metropolitan district of Quito, Ecuador

The parish of Calderón, located in the Metropolitan District of Quito, has a spatial dynamic characteristic of the expansion processes, with notable population growth, loss of agricultural land, and settlements in vulnerable areas. This research aimed to identify the vulnerability to climate change by analyzing urban growth in this parish to define measures for its climate change adaptation. A three-stage mixed approach methodology was used: the historical and spatial analysis of urban growth, the relationship between urban growth and vulnerability to climate change, and climate change adaptation measures in the parish of Calderón. The thirteen indicators analyzed show a medium-low risk level in their exposure and sensitivity components. However, the opposite is true for the adaptive capacity component, where vulnerability is high. The relationship between the parish’s urban growth and vulnerability to climate change is not direct. On the one hand, the expansion processes affect the territory and the population, causing environmental and social conflicts. On the other hand, factors immersed in urban expansion's spatial dynamics are revealed.