.
The accelerated integration of intelligent agents in user-centered digital environments has intensified research in the field of Human-Robot Interaction, especially regarding mechanisms for adaptive, intuitive, and cognitively aligned communication. The present study develops and empirically examines a structural model of BCI-inspired adaptive agents designed to support coordinated interaction in HRI contexts. The study analyzes users' perceptions of standardized hypothetical interaction scenarios involving BCI-inspired adaptive digital agents, where BCI inspiration is conceptual and refers to adaptive architectures interpreting behavioral cues rather than direct neural signal acquisition. The proposed model integrates four main constructs-perceived technological innovation, user involvement, agent adaptivity, and digital synergy-and examines their associations with user satisfaction in digital collaborative environments. Data were collected through an anonymous questionnaire (N = 268) and analyzed using structural equation modeling with the PLS-SEM method. The structural model demonstrates substantial explanatory power, accounting for 66.8% of the variance in user satisfaction (R2 = 0.668). The study contributes by empirically supporting a scenario-based structural evaluation framework suitable for early-stage adaptive HRI system design. The results highlight the role of digital synergy in aligning innovation, engagement, and adaptive behavior in BCI-inspired adaptive HRI systems, providing directions for the design of adaptive robotic agents oriented toward coordinated interaction, user-centered integration, and responsible use in collaborative digital ecosystems.
The present study investigates the relationship between selective attention and learning capacity in young adults, grounded in contemporary cognitive and neuropsychological models of information processing. Selective attention is conceptualised as a central executive mechanism responsible for filtering relevant stimuli and inhibiting interference (Broadbent, 1958; Desimone and Duncan, 1995; Petersen and Posner, 2012). Learning efficiency is conceptualised as dependent on attentional gating processes that regulate encoding and consolidation in working and long-term memory systems (Atkinson and Shiffrin, 1968; Baddeley, 2012; Kandel et al., 2014). Results indicate an exceptionally strong positive association between selective attention and verbal learning performance (r = .97, p < .001). Regression analyses suggest that selective attention accounts for a substantial proportion of variance in learning performance within the present sample. Differential analyses further indicate significant gender and residential environmental differences. These findings provide empirical support for theoretical assumptions regarding the central role of executive attention in facilitating encoding and consolidation processes (Engle, 2002; Miller and Cohen, 2001). Implications are discussed in relation to education, cognitive neuroscience, and cognitive performance optimisation.
We propose a strategy for managing the issue of multiplicity in clinical trials with adaptive selection followed by group-sequential testing. The approach employs a two-stage design and addresses trials with multiple hypotheses. The first stage adaptively selects a subset of hypotheses for further testing, while the second stage monitors the remaining hypotheses based on group-sequential procedures. We provide a rigorous framework for controlling the overall Type-1 error rate across both stages, utilizing group-sequential p-values and the closed testing principle to ensure statistical validity in the adaptive setting. Through a simulation study based on an oncology trial example, we demonstrate the effectiveness of the proposed method in controlling Type-1 error rate while maintaining sufficient power.
This study examines the relationship between early maladaptive schemas and attachment styles in emerging adulthood, within the theoretical frameworks of schema therapy (Young, 1990; Young et al., 2003) and attachment theory (Bowlby, 1969). Using a non-probabilistic sample of 170 participants (equally distributed by gender), Pearson correlation analyses revealed significant associations between schemas and attachment styles. Anxious attachment was positively correlated primarily with vulnerability, dependence, and subjugation, highlighting the role of threat anticipation and increased need for reassurance (Rad et al. (2025). Secure attachment was negatively correlated with strong negative correlations with defectiveness/shame, social isolation, failure, and vulnerability, confirming its protective function. Disorganised attachment was the most sensitive to schema activation, showing positive correlations with a broad spectrum of maladaptive patterns. Avoidant attachment was weakly associated with classical schemas and was modestly associated with unrelenting standards. Gender differences were observed primarily in attachment styles and vulnerability.
Recently, Wireless Sensor Networks (WSNs) have proven their pivotal role across several domains, such as battlefield surveillance, patient monitoring, and climate data collection. However, implementing security solutions in WSNs is very challenging due to the inherent constraints of computing power, memory, and energy in sensor nodes. To develop a novel epidemic-inspired compartmental model, five unique states: susceptible (S), exposed (E), two infectious classes (I₁ and I₂), and recovered (R) (SEI1I2R), serve as the basis to capture the malware propagation dynamics in WSNs. In contrast to classical approaches, our approach introduces a dual infection model, where exposed nodes transition into either I₁ or I₂ with distinct probabilities, enabling the realisation of real-world-like conditions of two distinct malware behaviours. Furthermore, the effects of node density (ρ) and the communication radius (r) are also probed for malware transmission by integrating them with the infection rate explicitly. A key contribution of this research is the derivation of the basic reproduction number ( R_0^th ). It is considered a key threshold parameter that delineates the malware propagation in WSNs, apprehending the local dynamics of malware transmission. Furthermore, the expressions for threshold node density and communication radius are derived and validated through simulation results. Rigorous mathematical analysis establishes that the malware-free equilibrium is locally and globally stable whenever the basic reproduction number R_0^th is less than unity, whereas the endemic equilibrium emerges and remains stable when R_0^th exceeds one, for which local stability conditions are explicitly derived. These observations reveal the relationship between network topology, infection heterogeneity, and recovery strategies. The proposed framework, in addition to the advancement of theoretical understanding of malware propagation, also delineates a foundation for designing effective defence mechanisms to safeguard WSN infrastructure.
