Within the broad landscape of dynamic covalent chemistry (DCC), the coordination chemistry of primary-amine-based aminals has received comparatively little systematic investigation. Here we demonstrate that a primary-amine-based dynamic covalent aminal (LA2) can serve as an adaptive scaffold for coordination chemistry diversity, where solvent and metal ion choice govern distinct outcomes. Coordination stabilizes otherwise transient ligand forms and directs divergent pathways: Co(III) stabilizes hemiaminalates in methanol, with [Co(LO2)2](ClO4) exhibiting fluxional behavior, and templates macrocyclization in acetonitrile to yield [Co(LM)](ClO4)3; Cu(II) affords hemiaminal and hemiaminal ether complexes, and Fe(III) promotes pyridinium dimerization to give cis-[(LI2)2](ClO4). These findings establish that dynamic ligands can actively generate structural diversity in metal complexes, opening new directions for adaptive ligand design and functional coordination architectures.

Adaptive parallel design criterion for failure probability estimation with Student-t likelihood

An adaptive Kriging-assisted reliability-based design optimization framework with reliability assurance and high efficiency

Adaptive hear-through filter design and implementation for earphones

Adaptive Experiment Design for Nonlinear System Identification With Operational Constraints

An improved target clustering algorithm for adaptive design of repeat ground track constellations

Background/Objectives: This systematic review examines neuroplasticity-informed approaches to learning under cognitive load, synthesizing evidence from functional imaging, brain stimulation, and educational technology research. As digital learning environments increasingly challenge learners with complex cognitive demands, understanding how neuroplasticity principles can inform adaptive educational design becomes critical. This review examines how neural mechanisms underlying learning under cognitive load can inform the development of evidence-based educational technologies that optimize neuroplastic potential while mitigating cognitive overload. Methods: Following PRISMA guidelines, we synthesized 94 empirical studies published between 2005 and 2025 across PubMed, Scopus, Web of Science, and PsycINFO. Studies were selected based on rigorous inclusion criteria that emphasized functional neuroimaging (fMRI, EEG), non-invasive brain stimulation (tDCS, TMS), and educational technology applications, which examined learning outcomes under varying cognitive load conditions. Priority was given to research with translational implications for adaptive learning systems and personalized educational interventions. Results: Functional imaging studies reveal an inverted-U relationship between cognitive load and neuroplasticity, with a moderate challenge in optimizing prefrontal-parietal network activation and learning-related neural adaptations. Brain stimulation research demonstrates that tDCS and TMS can enhance neuroplastic responses under cognitive load, particularly benefiting learners with lower baseline abilities. Educational technology applications demonstrate that neuroplasticity-informed adaptive systems, which incorporate real-time cognitive load monitoring and dynamic difficulty adjustment, significantly enhance learning outcomes compared to traditional approaches. Individual differences in cognitive capacity, neurodiversity, and baseline brain states substantially moderate these effects, necessitating the development of personalized intervention strategies. Conclusions: Neuroplasticity-informed learning approaches offer a robust framework for educational technology design that respects cognitive load limitations while maximizing adaptive neural changes. Integration of functional imaging insights, brain stimulation protocols, and adaptive algorithms enables the development of inclusive educational technologies that support diverse learners under cognitive stress. Future research should focus on scalable implementations of real-time neuroplasticity monitoring in authentic educational settings, as well as on developing ethical frameworks for deploying neurotechnology-enhanced learning systems across diverse populations.

In the context of deep media society, digital intimacy has become one of the core forms of social media user interaction. As a group of content creators relying on emotional needs, the emotional connection and trust relationship built on youtube and tiktok platforms has typical research value. Supported by digital intimacy theory, use and satisfaction theory and post subculture theory, this paper compares and analyzes the content production logic, interaction mechanism and trust building path of the two platforms. It is found that through the transformation of "local knowledge", algorithmic adaptive content design, ritual interaction and personalized modeling, fortune telling influencers build differentiated digital intimacy in two scenarios of fragmentation and deep immersion; Trust building has experienced a progressive process from emotional resonance, value recognition to decision dependence, accompanied by the potential risk of algorithmic alienation. The conclusions provide an empirical reference for understanding the new form of emotional labor in the social media era and the construction logic of digital intimacy.

The comprehensive research of modern energy-efficient and adaptive designs of bulldozer blades used in transport construction, mining and special engineering works, are carried out in the paper. The geometric parameters of the blades, the regularities of the drag prism formation, the force interaction of the blade with the soil, as well as the influence of various profiles (S-blade, SU-blade, U-blade, Σ-blade, VPAT and DSAB) on cutting resistance, performance and specific energy consumption are considered. The research is based on the principles of soil mechanics, modeling of cutting forces, estimating the soil drag prism volume, determining the resistance to movement and calculating the technical performance of bulldozers with different types of blades. Particular attention is paid to innovative designs of Σ-blade and dual-stabilized angle blade, which provide a reduction in soil resistance by up to 15–28% and an increase in performance by up to 45% compared to blade traditional solutions. The results obtained confirm the effectiveness of profiled and adaptive blades by optimizing the geometry of the working surface and adjusting the cutting angle. Engineering recommendations are proposed for the implementation of adaptive systems in modern bulldozers in order to reduce fuel consumption, reduce the load on the transmission and increase the energy efficiency of the machines.

Abstract: Capital market or financial market volatility is pervasive and complex. It is the pulse of the market that reflects balance between opportunity and uncertainty. In the fast-growing Indian stock market, accurately modelling and predicting this volatility is vital for achieving superior performance. Traditional econometric models like ARCH and GARCH have offered fundamental insights but fail to explain the nonlinear and regime shifting patterns of volatility. This paper introduces AI Volatility Adaption Cycle (AIVAC) i.e., a conceptual framework that integrates volatility theory, complexity economics and artificial intelligence to build adaptive trading strategies. The AIVAC blends econometric reasoning with neural and reinforcement learning to form a self-evolving intelligent framework. The proposed framework has five layers namely, data fusion and conceptualization, feature transformation, predictive intelligence and regime detection, adaptive strategy engine and feedback driven evolutionary learning. This will allow the users to automatically adjust their settings like risk levels and trading methods on real time basis. This paper contributes to framing a practically applicable adaptive intelligent framework fit for Indian market. The paper also discusses the implications for the stakeholders while emphasizing transparency, interpretability and ethical AI adoption. Keywords: Volatility, Deep Learning, Adaptive Trading, Artificial Intelligence, Indian Stock Market

This study aims to comprehensively evaluate the enabling and constraining factors that shape the implementation of halal certification within the tourism-based economy of Bali Province. Although halal tourism has emerged as a global trend and aligns with Indonesia’s national regulatory agenda, the adoption rate of halal certification in Bali remains remarkably low—covering less than 2% of its industrial potential—despite the dominant proportion of Muslim domestic tourists. Employing a socio-legal qualitative approach, the findings reveal that the primary enablers of halal certification include the substantial market of Muslim tourists, the advanced readiness of Bali’s tourism infrastructure, and strong socio-cultural support rooted in long-standing traditions of tolerance and cultural innovation, as reflected in the emergence of the Halal–Balinese Fusion concept. Conversely, the implementation of halal certification is hindered by multiple multidimensional barriers, encompassing: (1) technical and bureaucratic obstacles, including lengthy certification procedures (45–90 days) and relatively high associated costs; (2) human resource limitations, evidenced by the presence of only 12 certified halal auditors across Bali; (3) cultural and perceptual resistance stemming from concerns that halal initiatives may disrupt the philosophical foundation of Tri Hita Karana; and (4) critical infrastructural constraints, particularly the scarcity of halal-certified slaughterhouses that contributes to increased logistical costs, coupled with limited availability of permanent prayer facilities in hotels and tourist destinations. The study concludes that accelerating the implementation of halal certification in Bali requires a more adaptive policy design through procedural simplification, provision of financial incentives to industry actors, and the deployment of culturally sensitive communication strategies. These measures are necessary to ensure the creation of synergy between the needs of Muslim tourists and the preservation of Bali’s cultural heritage.

As adaptive learning systems are increasingly implemented to support personalized education, concerns have emerged regarding their tendency to rely on narrow and reductionist intelligence models. This study aims to analyze the influence of intelligence variations in adaptive learning design through a systematic literature review. Using the PRISMA 2020 protocol, fifteen empirical articles from the period 2010–2025 were critically reviewed. The results show the dominance of rule-based approaches with static profiles that rely on the linguistic-logical-mathematical dimension as the default parameter, ignoring the potential of other dimensions such as naturalist and existential. Artificial intelligence integration offers dynamic personalization potential but poses pedagogical and ethical dilemmas. Empirical evidence in Indonesia confirms the effectiveness of multiple intelligences-based adaptive learning on reading literacy and science creativity, despite constraints related to infrastructure and teacher capacity. The findings lead to three design principles: multimodal flexibility, cultural calibration of measurement instruments, and technology–pedagogical balance. This study recommends the development of hybrid prototypes and ethical standards for the use of cognitive data to realize an inclusive learning ecosystem. The implications of this review indicate that adaptive learning design should move beyond single-intelligence models by adopting culturally calibrated, multimodal, and pedagogically guided adaptation strategies. These implications provide a concrete framework for educators, designers, and developers to design more inclusive and context-responsive adaptive learning systems.

Inclusive mathematics education aims to ensure that students with special educational needs (SEN) have equal learning opportunities alongside their peers. However, its implementation still faces multidimensional barriers, including limited teacher competence, rigid curriculum, low student motivation and self-concept, restricted use of assistive technology, communication challenges, classroom management difficulties, and the absence of shadow teachers. This study seeks to identify these barriers while offering a conceptual framework mapping the relationships between barriers and potential solutions. The methodology employed is a PRISMA-guided systematic literature review (SLR), analyzing 12 selected articles from an initial pool of 147 obtained from Scopus, Google Scholar, and DOAJ (2021-2025). Findings reveal that these barriers are interrelated, reinforcing one another and exacerbating learning difficulties. The novelty of this study lies in the development of a barrier-solution conceptual framework and the application of a more transparent review method with critical appraisal. Policy and practice implications highlight the urgency of continuous inclusive teacher training, adaptive curriculum design based on Universal Design for Learning, integration of assistive technology, provision of shadow teachers, and affective strategies to strengthen SEN students’ confidence. These findings provide a foundation for future interventions and inclusive education policies in Indonesia and beyond.

ABSTRACT Clinical trial design efficiency is often defined as a design using the fewest subjects to achieve the trial goal with high probability. Multi-arm trials are more efficient than two-arm trials as they compare multiple therapies in a single trial. There has been an increase in the use of adaptive designs, particularly in response adaptive randomization (RAR) and early stopping, to further improve efficiencies in trial design. Designs with a higher probability of participants receiving better treatment and/or the ability to end early can be more efficient and attractive to investigators, participants, and stakeholders, but come with their criticisms. We set out to find the most efficient design for a future study. The purpose of this motivating study is to identify the application of docosahexaenoic acid (DHA) (control, chew, or patient choice of capsule or chew) that results in the highest adherence among pregnant women. The primary goal of this research was to find the most efficient trial design by comparing the performance of fixed and Bayesian adaptive designs (BAD) through operating characteristics using simulations. To evaluate the performance of competing designs, ‘efficiency’ was defined through a novel optimality assessment using a data envelopment analysis (DEA). Several fixed and adaptive trial designs with response adaptive randomization (RAR) and early stopping criteria across different accrual rates were considered. The DEA incorporated a balance in power, total sample size, and expected response outcome. The BAD utilizing RAR and early stopping for success and/or futility was determined to be the most efficient study design.

The article explores bionics as a scientific and methodological foundation for creating sustainable architecture capable of energy-efficient functioning and achieving synergy with the natural environment. It examines the evolution of bionics from the formal imitation of organic forms to the comprehensive application of biomimetic principles. Through specific examples – such as the passive ventilation system of the Eastgate Centre in Zimbabwe, inspired by termite mound architecture, and façade concepts that mimic photosynthesis – the article reveals mechanisms for implementing bionic design solutions. Particular attention is given to analyzing the energy efficiency, adaptability, and resource-saving characteristics of bio-inspired architectural objects. The study highlights the contemporary understanding of bionics, which focuses on the principles of cyclicality, adaptability, and zero-waste design derived from natural ecosystems. It provides a detailed analysis of examples ranging from passive ventilation and thermal regulation systems modeled after termite mounds to adaptive façade systems that imitate photosynthesis and plant regulatory mechanisms. Special attention is paid to environmental synergy achieved through efficient resource management – for instance, mimicking water-harvesting strategies of desert insects or adopting lightweight yet durable structural analogues inspired by biological prototypes such as bone or spider silk to minimize material consumption. The discussion systematizes the advantages of the bionic approach – including enhanced energy efficiency, reduced operational costs, and improved comfort – while also addressing the challenges of its implementation, such as high research costs and the need for interdisciplinary collaboration. The article substantiates the idea that bionics serves not only as a tool for solving engineering problems but also as a catalyst for shaping a new architectural philosophy aimed at fostering harmony between the built and natural environments. The practical significance of the study lies in its potential use by researchers, educators, graduate students, and practitioners engaged in related scientific and design inquiries.

This study develops a contextualized model for implementing the Individualized Learning Program (ILP) in inclusive early childhood education. The study was conducted in an inclusive early childhood education institution in Malang, East Java, Indonesia. Using a qualitative descriptive case-study design with embedded descriptive quantification, data were collected through semi-structured interviews, classroom observations, and document analysis. Participants included 12 classroom teachers, 1 special-education companion teacher (GPK), the school principal, and 4 parents of children with special educational needs (SEN). Data collection was conducted in March 2025 and was corroborated by institutional ILP documents and communication logs from the same program cycle. Thematic analysis generated a three-phase ILP implementation cycle—planning, implementation, and evaluation—supported by collaborative practices among teachers, parents, and administrators. To resolve the design–results consistency, quantitative information reported in the Results (percentages/means) is treated as descriptive summaries extracted from school records, checklists, and logs to complement qualitative themes rather than as inferential outcomes. The findings indicate that effective ILP implementation depends on three interrelated dimensions: (1) Institutional Support, (2) Adaptive Learning Design, and (3) Parental Partnership, while key constraints include limited trained staff and facility readiness. The resulting ILP Implementation Model offers an operationalizable, evidence-based framework for strengthening inclusive practices and aligning early childhood services with equitable, quality education goals.

Purpose of the Article. This study aims to substantiate the principles of interface design based on cognitive load theory, with a focus on balancing the reduction of excessive perceptual complexity and providing users with control capabilities in situations of uncertainty or conflicting outcomes. Research Methods. The methodological foundation is cognitive load theory, which distinguishes between intrinsic, extraneous, and germane load. The study employs comparative analysis of scientific publications, international accessibility standards (particularly WCAG 2.2), and modern software solutions oriented toward inclusive and adaptive design. Scientific Novelty. For the first time, practices for reducing cognitive load in web application interfaces are systematized from the perspective of combining extraneous load minimization with the integration of user control tools. Recommendations are proposed for designers and developers regarding the implementation of inclusive interface adaptation mechanisms, including tools based on artificial intelligence. Conclusions. Influence on intrinsic cognitive load is primarily achievable through the optimization of system messages, button texts, and hints, whereas user-generated content remains beyond the designer’s control. Excessive cognitive load can be reduced by adhering to standards for font size, line spacing, contrast, and clear visual structure. At the same time, parameters such as font choice or color scheme yield contradictory results and largely depend on users’ individual cognitive characteristics and prior experience. The optimal approach involves implementing personalization and adaptability mechanisms, allowing each user to set a comfortable level of complexity and find a balance between simplicity and functionality.

The purpose of the study is to identify, systematise and critically analyse the psycho-visual mechanisms used in contemporary graphic design to manage perception in conditions of information overload. Research methodology. An interdisciplinary approach was used, combining the principles of cognitive psychology, neuroaesthetics, Gestalt theory and design analytics. The primary research methods include: critical visual analysis, cognitive-perceptual interpretation, philosophical conceptualisation of visual experience, and content analysis of examples of visual communication in conditions of information saturation. Scientific novelty. The article offers an interdisciplinary analysis of psycho-visual mechanisms for managing perception through graphic design in conditions of information overload, examining the intersection of cognitive aesthetics and visual semiotics. The application of psycho-visual consistency as a factor in effective communication in overloaded conditions is clarified. A typology of cognitive functions of forms is proposed, which allows visual elements to be considered as carriers of perceptual influence. Psycho-visual mechanisms of perception control, facilitated by graphic design, are generalised in conditions of information overload. The results are of practical importance for the design of visual communications, can be used as analytical tools in the creation of visual strategies, and can be incorporated into the training of future graphic designers. Conclusions. Research into the psycho-visual mechanisms of perception control through graphic design confirms their leading role in structuring visual information in conditions of information overload. It is essential to focus not on visual complexity, but on the informativeness and controllability of attention, which provide grounds for creating adaptive and intuitive design solutions. The graphic designer acts as an intermediary between complex information flows and the user’s cognitive abilities, while graphic design performs regulatory and meaning-making functions. The effectiveness of graphic design depends on the graphic designer’s ability to apply Gestalt principles, visual accents, rhythm, and compositional balance, which ensure cognitive economy and facilitate perception.

ABSTRACT We extend the recent work of Selivanov and Fridman on control design for partial differential equations (PDEs) of Euler–Bernoulli type with viscous and Kelvin–Voigt damping. The extension is focused on adaptive observer design in the presence of sensor delay and parameter uncertainties. The considered formulation of the uncertainties makes it possible to account for the effect of structured disturbances entering the sensor and the state equations. The observer design approach combines the modal decomposition method and the decoupling transformation technique. Making use of these techniques, we design an adaptive observer that includes a (finite‐dimensional) state estimator, a parameter estimator of least‐squares type, and an auxiliary filter generating the regressor of the parameter estimator. The resulting estimation error system turns out to be an interconnected system driven by a signal depending on the neglected PDE modes. Exponential stability of the whole estimation error system is analyzed using suitable Lyapunov and Lyapunov–Krasovskii functionals. We show that exponential stability is guaranteed for not too large delay values, under a well‐defined persistent excitation (PE) condition.

Accurate estimation of rare events in clustered populations is paramount for effective public health planning, yet traditional estimators often assume population homogeneity, leading to inefficiencies and potential bias. This study employs a case study approach using a real-world public health dataset to compare two estimators for the proportion of a rare event (incomplete Hepatitis B vaccination) within an Adaptive Cluster Sampling (ACS) frame-work: a classical ACS estimator and a heterogeneous estimator derived from a Bayesian hierarchical model. The hierarchical structure accounts for clustering at the College-Department level (networks, representing 279 distinct net-work units identified through the adaptive sampling process, including both expanded clusters and singleton networks) and the College-Year level (sub-networks, representing 45 nested groupings within the 380-student population). By treating a complete dataset as a known (population), we rigorously assess estimator performance in terms of bias, variance, and Mean Squared Error (MSE). Our findings demonstrate that the Bayesian hierarchical estimator (estimate: 0.182, variance: 0.00038, MSE: 0.00038) consistently yields estimates with negligible bias, lower variance, and a substantially lower MSE compared to the classical ACS estimator (estimate: 0.172, variance: 0.00050, MSE: 0.00059). This represents a 1.31-fold reduction in variance and a 1.55-fold reduction in MSE for the Bayesian approach. Posterior predictive checks further confirm the good fit of the Bayesian model to the observed data. This underscores the critical importance of explicitly accounting for population heterogeneity and employing robust model-based inference in adaptive sampling designs to generate reliable estimates for informed public health policy.