Search Results (570)

This study examines how the sense of home for people with dementia is shaped not only by physical settings but by dynamic atmospheric compositions emerging through memory, sensation, and everyday practices. Building on a preliminary literature mapping that identified three dimensions of home in later-life care environments—safe space, small world, and connection—we developed a multisensory co-design toolkit combining key-element cards and curated olfactory prompts. The study was conducted in a dementia-friendly residential care facility in Italy. Nine residents with mild–moderate dementia (aged 75–84) participated in two group sessions and six individual sessions, facilitated by two design researchers with care staff present. Data consist of audio-recorded and transcribed interviews, guided olfactory sessions, and researcher fieldnotes. Across sessions, participants articulated “small worlds” as micro-environments composed of meaningful objects, bodily comfort, routines, and sensory cues that supported emotional regulation and identity continuity. Olfactory prompts, administered through a low-intensity and participant-controlled protocol, supported scene-based autobiographical recall for some participants, often eliciting memories of domestic rituals, places, and relationships. Rather than treating home-like design as a fixed architectural style, we interpret home as continuously re-made through situated sensory–temporal patterns and relational practices. We translate these findings into atmospheric design directions for dementia care: designing places of self and refuge, staging accessible material memory devices, embedding gentle olfactory micro-worlds within daily routines, and approaching atmosphere as an ongoing process of co-attunement among residents, staff, and environmental conditions. The study contributes a methodological and conceptual framework for multisensory, narrative-driven approaches to designing home-like environments in long-term care. Full article

As artificial intelligence (AI) is increasingly used in classrooms, tutoring systems, and learning platforms, it is essential that these tools are not only powerful, but also easy to understand, fair, and supportive of real learning. Many current AI systems can generate fluent responses or accurate predictions, yet they often fail to clearly explain their decisions, reflect students’ cultural contexts, or give learners and educators meaningful control. This gap can reduce trust and limit the educational value of AI-supported learning. This paper introduces the PEARL framework, a human-centered approach for designing and evaluating explainable AI in education. PEARL is built around five core principles: Pedagogical Personalization (adapting support to learners’ levels and curriculum goals), Explainability and Engagement (providing clear, motivating explanations in everyday language), Attribution and Accountability (making AI decisions traceable and justifiable), Representation and Reflection (supporting fairness, diversity, and learner self-reflection), and Localized Learner Agency (giving learners control over how AI explains and supports them). Unlike many existing explainability approaches that focus mainly on technical performance, PEARL emphasizes how students, teachers, and administrators experience and make sense of AI decisions. The framework is demonstrated through simulated examples using an AI-based tutoring system, showing how PEARL can improve feedback clarity, support different stakeholder needs, reduce bias, and promote culturally relevant learning. The paper also introduces the PEARL Composite Score, a practical evaluation tool that helps assess how well educational AI systems align with ethical, pedagogical, and human-centered principles. This study includes a small exploratory mixed-methods user study (N = 17) evaluating example AI tutor interactions; no live classroom deployment was conducted. Together, these contributions offer a practical roadmap for building educational AI systems that are not only effective, but also trustworthy, inclusive, and genuinely supportive of human learning. Full article

Chatter is a self-excited vibration that limits productivity, accelerates roll wear and compromises strip surface quality in high-speed tandem cold-rolling. This work presents a predictive hybrid model that couples the strip-deformation physics to the structural dynamics of a five-stand, 4-high mill, providing a fast decision tool for process optimization and real-time control. The model represents each stand as a four-degree-of-freedom mass–spring–damper system whose parameters are extracted from manufacturing automation datasheets and roll-gap sensing. Linearization about the nominal point yields analytical sensitivity matrices that close the electromechanical loop; the delay between stands is also included in the model. Implemented in MATLAB/Simulink, the computational model, based on data provided by Danieli & C. Officine Meccaniche S.p.A., reproduces the onset of chatter for two types of steel. The framework therefore supports automation-ready scheduling, active vibration mitigation and design-space exploration for next-generation mechatronic cold-rolling systems. Full article

This paper addresses the problem of cooperative coverage control for heterogeneous Autonomous Underwater Vehicle (AUV) swarms operating in complex underwater environments. The objective is to achieve optimal coverage of a target region while simultaneously ensuring collision avoidance—both among AUVs and with static obstacles—and satisfying the inherent dynamic constraints of the AUVs. To this end, we propose a hierarchical control framework that fuses Control Barrier Functions (CBFs) with consensus theory. First, addressing the heterogeneity and limited sensing ranges of the AUVs, a cooperative coverage model based on a modified Voronoi partition is constructed. A nominal controller based on consensus theory is designed to balance the ratio of task workload to individual capability for each AUV. By minimizing a Lyapunov-like function via gradient descent, the swarm achieves self-organized optimal coverage. Second, to guarantee system safety, multiple safety constraints are designed for the AUV double-integrator dynamics, utilizing Zeroing Control Barrier Functions (ZCBFs) and High-Order Control Barrier Functions (HOCBFs). This approach unifies the handling of collision avoidance and velocity limitations. Finally, the nominal coverage controller and safety constraints are integrated into a Quadratic Programming (QP) formulation. This constitutes a safety-critical layer that modifies the control commands in a minimally invasive manner. Theoretical analysis demonstrates the stability of the framework, the forward invariance of the safe set, and the convergence of the coverage task. Simulation experiments verify the effectiveness and robustness of the proposed method in navigating obstacles and efficiently completing heterogeneous cooperative coverage tasks in complex environments. Full article

Background/Objectives: Urine drug testing (UDT) is a core component of nursing interventions within the treatment of substance use disorder (SUD). Beyond the detection of psychoactive substance use and medication adherence, UDT also provides opportunities for therapeutic dialogue, patient support, and recovery monitoring. Despite its routine use, little is known about how patients experience UDT and its potential as a therapeutic nursing tool within recovery-oriented care. This study aimed to explore patients’ lived experiences with UDT to understand its role in recovery-oriented addiction treatment. Methods: A phenomenological study with in-depth, semi-structured interviews was conducted among 12 residents of a supervised living facility at Addiction Care North Netherlands. Data were analyzed using Colaizzi’s seven-step method. Results: Four main themes were constructed in relation to trust within the therapeutic relationship—empowerment, accountability, and autonomy. Patients stated that their perception of UDTs as either supportive or punitive depended strongly on the level of trust within the therapeutic relationship. When trust was present, UDTs were experienced as supportive nursing tools that fostered empowerment and positive self-image, reinforced accountability for recovery goals, and upheld autonomy in decision-making. Conversely, in the absence of trust, UDTs were often perceived as punitive, coercive measures that undermined self-confidence and diminished accountability, ultimately hindering recovery progress. Nursing practices that emphasized nonjudgmental interpretation of results, collaborative decision-making, and patient-centered support contributed to positive experiences. Conclusions: Patients’ experiences indicate that the therapeutic value of UDT is highly dependent on the quality of the patient–nurse relationship. Nurses play a key role in ensuring that UDT is used as a supportive intervention rather than merely a control measure. Integrating UDT into holistic, recovery-oriented care can foster engagement, empowerment, and a sense of accountability. Future research should investigate nursing-led strategies to optimize UDT implementation tailored to treatment phase and patient needs. Full article

The pharmaceutical industry faces a broken drug development pipeline, characterized by high costs, slow timelines and is prone to high failure rates. The convergence of Artificial Intelligence (AI) and quantum technologies is poised to fundamentally transform this landscape. AI excels in interpreting complex data, optimizing processes and designing drug candidates, while quantum systems enable unprecedented molecular simulation, ultra-sensitive sensing and precise physical control. This convergence establishes an integrated, self-learning ecosystem for the discovery, development, and delivery of therapeutics. This framework co-designs strategies from molecular targeting to formulation stability, compressing timelines and enhancing precision, which may enable safer, faster, and more adaptive medicines. Full article

The integration of flexible electronics and machine learning (ML) algorithms has become a revolutionary force driving the field of intelligent sensing, giving rise to a new generation of intelligent devices and systems. This article provides a systematic review of core technologies and practical applications of ML in flexible electronics. It focuses on analyzing the theoretical frameworks of algorithms such as the Long Short-Term Memory Network (LSTM), Convolutional Neural Network (CNN), and Reinforcement Learning (RL) in the intelligent processing of sensor signals (IPSS), multimodal feature extraction (MFE), process defect and anomaly detection (PDAD), and data compression and edge computing (DCEC). This study explores the performance advantages of these technologies in optimizing signal analysis accuracy, compensating for interference in high-noise environments, optimizing manufacturing process parameters, etc., and empirically analyzes their potential applications in wearable health monitoring systems, intelligent control of soft robots, performance optimization of self-powered devices, and intelligent perception of epidermal electronic systems. Full article

Training adaptation involves muscular–metabolic remodeling and personality-linked traits such as motivation, self-regulation, and resilience. This narrative review examines how training load oscillation (TLO)—the deliberate variation in exercise intensity, volume, and substrate availability—may function as a systemic epigenetic stimulus capable of shaping both physiological and psychological adaptation. Fluctuating energetic states reconfigure key energy-sensing pathways (AMPK, mTOR, CaMKII, and SIRT1), thereby potentially influencing DNA methylation, histone acetylation, and microRNA programs linked to PGC-1α and BDNF. This review synthesizes converging evidence suggesting links between these molecular responses and behavioral consistency, cognitive control, and stress tolerance. Building on this literature, a systems model of molecular–behavioral coupling is proposed, in which TLO is hypothesized to entrain phase-shifted AMPK/SIRT1 and mTOR windows, alongside CaMKII intensity pulses and a delayed BDNF crest. The model generates testable predictions—such as amplitude-dependent PGC-1α demethylation, BDNF promoter acetylation, and NR3C1 recalibration under recovery-weighted cycles—and highlights practical implications for timing nutritional, cognitive, and recovery inputs to molecular windows. Understanding TLO as an entrainment signal may help integrate physiology and psychology within a coherent, durable performance strategy. This framework is conceptual in scope and intended to generate testable hypotheses rather than assert definitive mechanisms, providing a structured basis for future empirical investigations integrating molecular, physiological, and behavioral outcomes. Full article

Various recurring medical events encourage innovative patient well-being through connected health strategies based on an elegant digital environment that prioritizes safety, comfort, and beneficial outcomes for both patients and medical staff. This narrative review article aims to investigate and highlight the potential of advanced, reliable, high-precision, and secure medical observation and intervention missions. These involve a smart digital environment integrating smart materials combined with smart digital monitoring. These medical implications concern robotic surgery and drug delivery through image-assisted implantation, as well as wearable observation and assistive tools. The former requires high-precision motion and positioning strategies, while the latter enables sensing, diagnosis, monitoring, and central task assistance. Both advocate minimally invasive or noninvasive procedures and precise supervision through autonomously controlled processes with staff participation. The article analyzes the requirements and evolution of medical interventions, robotic actuation technologies for positioning actuated and self-moving instances, monitoring of image-assisted robotic procedures using digital twins and augmented digital tools, and wearable medical detection and assistance devices. A discussion including future research perspectives and conclusions complete the article. The different themes addressed in the proposed paper, although self-sufficient, are supported by examples of the literature, allowing a deeper understanding. Full article

Preschool teaching is a highly demanding profession that requires constant socio-emotional attunement and the ability to engage in reflective reasoning. Despite the central role of these skills in effective early childhood education, little is known about whether preschool teachers’ socio-affective and cognitive capacities vary as a function of accumulated professional experience. To address this knowledge gap, we compared the performance of 30 professional preschool teachers with a matched control group of 30 non-teachers on tests measuring emotion recognition, active-empathic listening, interpersonal reactivity, and abstract reasoning. We found that preschool teachers were significantly better on all dimensions of active-empathic listening (sensing, processing, and responding) and better in emotional self-regulation than controls. Moreover, years of preschool teaching experience were positively correlated with emotion recognition, improved listening skills, and more deliberate abstract reasoning strategies. Notably, socio-affective competencies were correlated with abstract reasoning performance within the preschool teacher group. According to these results, long-term professional involvement in preschool teaching enhances socio-affective skills and integrates them with higher-order cognitive processes, both of which are essential for responsive teaching, efficient classroom management, and the development of children’s social and cognitive abilities. Full article

Background/Objectives: Mothers expecting a second child experience the parenting of multiple children for the first time, differing from first-time motherhood. This highlights the need for childbirth preparation education tailored to families expecting a second child. Parental self-efficacy influences maternal mental health, child development, and parent–child interactions. This non-randomized pilot exploratory study aimed to examine the association between childbirth preparation education for families expecting a second child and maternal parental self-efficacy at 1-month postpartum, focusing on a family-based, single-session program actively involving firstborn children. Methods: The intervention group (n = 18) received childbirth preparation education during pregnancy and completed questionnaires and semi-structured interviews at 1-month postpartum. The control group (n = 34) completed questionnaires only at 1-month postpartum. Questionnaires included the Parenting Sense of Competence Scale, Rosenberg Self-Esteem Scale, Maternal Attachment Inventory, Edinburgh Postnatal Depression Scale, and demographic information. Semi-structured interviews explored participants’ experiences and feelings after attending the childbirth preparation class. Results: Compared to the control group, the intervention group had higher Parenting Sense of Competence Scale scores; mothers in the intervention group reported smoother family-wide adaptation to life with a second child, greater confidence in child-rearing, recognition of the firstborn’s growth into an older sibling, and effective use of hands-on experiences from the class. Conclusions: Childbirth preparation education for families expecting a second child may be associated with higher maternal parental self-efficacy at 1-month postpartum. This association may reflect collective family preparation and adjustment supporting adaptation to life with a second child. Full article

The growing demand for miniature mechanical components has increased the importance of ultra-precision micro machine tools and real-time monitoring. This study examines acoustic emission (AE) sensing for the intelligent control of an ultra-precision micro lathe. AE signals were measured while brass and aluminum alloys were turned with cermet and diamond tools at different spindle speeds and cutting depths. Finite element simulations were performed to clarify the AE generation mechanisms. The AE waveform amplitude changed stepwise corresponding to tool–workpiece contact, elastoplastic deformation, and chip formation, enabling precise contact detection at the 0.1 μm level. The AE amplitude increased with increasing spindle speed and increasing depth of cut except during abnormal conditions (e.g., workpiece adhesion). Frequency analysis revealed a dominant peak near 0.2 MHz during normal cutting, as well as high-frequency (>1 MHz) components linked to built-up edge formation. Simulations confirmed that these AE features reflect variations in the strain rate in the shear zone and on the rake face. They also confirmed that cutting force spectra under high friction reproduce the experimentally observed high-frequency peaks. These findings demonstrate the feasibility of using AE sensing to identify the cutting state and support the development of self-optimizing micro machine tools. Full article

Immersive methods and biometric tools provide a rigorous, context-rich way to study how people perceive and choose food. Immersive methods use extended reality, including virtual, augmented, mixed, and augmented virtual environments, to recreate settings such as homes, shops, and restaurants. They increase participants’ sense of presence and the ecological validity (realism of conditions) of experiments, while still tightly controlling sensory and social cues like lighting, sound, and surroundings. Biometric tools record objective signals linked to attention, emotion, and cognitive load via sensors such as eye-tracking, galvanic skin response (GSR), heart rate (and variability), facial electromyography, electroencephalography, and functional near-infrared spectroscopy. Researchers align stimuli presentation, gaze, and physiology on a common temporal reference and link these data to outcomes like liking, choice, or willingness-to-buy. This approach reveals implicit responses that self-reports may miss, clarifies how changes in context shift perception, and improves predictive power. It enables faster, lower-risk product and packaging development, better-informed labeling and retail design, and more targeted nutrition and health communication. Good practices emphasize careful system calibration, adequate statistical power, participant comfort and safety, robust data protection, and transparent analysis. In food science and consumer behavior, combining immersive environments with biometrics yields valid, reproducible evidence about what captures attention, creates value, and drives food choice. Full article

The operational efficiency and precision of boom sprayers, as critical equipment for protecting field crops, are vital to global food security and agricultural sustainability. In precision agriculture systems, achieving uniform pesticide application fundamentally depends on maintaining stable boom posture during operation. However, severe boom vibration not only directly causes issues like missed spraying, double spraying, and pesticide drift but also represents a critical bottleneck constraining its functional realization in cutting-edge applications. Despite its importance, achieving absolute boom stability is a complex task. Its suspension system design faces a fundamental technical contradiction: effectively isolating high-frequency vehicle vibrations caused by ground surfaces while precisely following large-scale, low-frequency slope variations in the field. This paper systematically traces the evolutionary path of self-balancing boom technology in addressing this core contradiction. First, the paper conducts a dynamic analysis of the root causes of boom instability and the mechanism of its detrimental physical effects on spray quality. This serves as a foundation for the subsequent discussion on technical approaches for boom support and balancing systems. The paper also delves into the evolution of sensing technology, from “single-point height measurement” to “point cloud morphology perception,” and provides a detailed analysis of control strategies from classical PID to modern robust control and artificial intelligence methods. Furthermore, this paper explores the deep integration of this technology with precision agriculture applications, such as variable rate application and autonomous navigation. In conclusion, the paper summarizes the main challenges facing current technology and outlines future development trends, aiming to provide a comprehensive reference for research and development in this field. Full article

Despite the large body of research showing that students in STEM classes at all levels learn better via active learning than they do via lecture, post-secondary physics and astronomy (P&A) faculty members continue to primarily use teacher-focused, lecture pedagogy in their classes. Methods include answers from eight faculty members, and interviews with five faculty members who self-identified as primarily using lecture were conducted to determine their perceptions of why they use lecture. During analysis coding, results show that an unanticipated theme not sufficiently represented in the pre-existing literature rose to the forefront: that many of these faculty members feel the decision of pedagogy is out of their control. In conclusion, a grounded theory was developed and is proposed herein that these faculty feel a sense of powerlessness. Reasons offered include administrators often make decisions based on the financial needs of the school, which then force the faculty into using lecture as their primary pedagogy. Implications include that providing professional development in active pedagogies may not be sufficient to help faculty members change pedagogy, as they may need to be convinced that they have the power to make change and use student-centered, active learning pedagogies within their own individual constraints and settings. Understanding that some instructors may feel powerless in choosing how to teach is an important step for professional development providers toward ensuring that faculty have a voice and can choose the best teaching methods for their classrooms. Full article