Search Results (2,475)

Defect modes in phononic crystals (PnCs) provide strongly localized resonances that are essential for frequency-dependent wave filtering and highly sensitive sensing. Their functionality increases greatly when their spectral characteristics can be externally tuned without altering the structural configuration. However, existing feedback control strategies rely on laminated piezoelectric defects, which have uniform electromechanical loading that causes voltage cancellation for even-symmetric defect modes. Consequently, only odd-symmetric defect bands can be manipulated effectively, which limits multi-band tunability. To overcome this constraint, we propose a segmented piezoelectric sensor–actuator design that enables symmetry-dependent feedback at the defect site. We develop a transfer-matrix analytical framework to incorporate complex-valued feedback gains directly into dispersion and transmission calculations. Analytical predictions demonstrate that real-valued feedback yields opposite stiffness modifications for odd- and even-symmetric modes. This enables the simultaneous tuning of both defect bands and induces an exceptional-point-like coalescence. In contrast, imaginary feedback preserves stiffness but modulates effective damping, generating a parity-dependent amplification-suppression response. The analytical results closely match those of fully coupled finite-element simulations, reducing computation time by more than two orders of magnitude. These findings demonstrate that segmentation-enabled feedback provides an efficient and scalable approach to tunable, multi-band, non-Hermitian wave control in piezoelectric PnCs. Full article

Workers with upper-limb disabilities face difficulties in performing manufacturing tasks requiring fine manipulation, stable handling, and multistep procedural understanding. To address these limitations, this paper presents an integrated collaborative workcell designed to support disability-inclusive manufacturing. The system comprises four core modules: a JSON-based collaboration database that structures manufacturing processes into robot–human cooperative units; a projection-based augmented reality (AR) interface that provides spatially aligned task guidance and virtual interaction elements; a multimodal interaction channel combining gesture tracking with speech and language-based communication; and a personalization mechanism that enables users to adjust robot behaviors—such as delivery poses and user-driven task role switching—which are then stored for future operations. The system is implemented using ROS-style modular nodes with an external WPF-based projection module and evaluated through scenario-based experiments involving workers with upper-limb impairments. The experimental scenarios illustrate that the proposed workcell is capable of supporting step transitions, part handover, contextual feedback, and user-preference adaptation within a unified system framework, suggesting its feasibility as an integrated foundation for disability-inclusive human–robot collaboration in manufacturing environments. Full article

Integrating people with hearing disabilities into schools is one of the biggest problems that Latin American societies face. Mexican Sign Language (MSL) is the main language and culture of the deaf community in Mexico. However, its use in formal education is still limited by structural inequalities, a lack of qualified interpreters, and a lack of technology that can support personalized instruction. This study outlines the conceptualization and development of a mobile application designed as an adaptive assistive technology for learning MSL, utilizing a combination of computer vision techniques, deep learning algorithms, and personalized pedagogical interaction. The suggested system uses convolutional neural networks (CNNs) and pose-estimation models to recognize hand gestures in real time with 95.7% accuracy. It then gives the learner instant feedback by changing the difficulty level. A dynamic learning engine automatically changes the level of difficulty based on how well the learner is doing, which helps them learn signs and phrases over time. The Scrum agile methodology was used during the development process. This meant that educators, linguists, and members of the deaf community all worked together to design the product. Early tests show that sign recognition accuracy and indicators of user engagement and motivation show favorable performance and are at appropriate levels. This proposal aims to enhance inclusive digital ecosystems and foster linguistic equity in Mexican education through scalable, mobile, and culturally relevant technologies, in addition to its technical contributions. Full article

The integration of complex system concepts and sustainability in chemical engineering education is often limited to elective or separate courses rather than their integration into the core curriculum. This pedagogical gap can lead to graduates who lack a holistic understanding of the intricate interplay between industrial processes and the Earth’s ecological limits, and the feedback loops required to address complex global challenges. This paper presents a transformative approach to close this gap by embedding the Planetary Boundaries framework and system thinking across core chemical engineering courses, such as Material and Energy Balances, Reaction Engineering, and Process Design, and extending this integration to capstone projects. The framework treats the curriculum itself as an interconnected learning system in which key systems concepts are revisited and deepened through contextualized examples and digital modeling tools, including process simulators and life-cycle assessment. We map each boundary to illustrative process examples and learning activities and discuss practical implementation issues such as curriculum crowding, educator readiness, and data availability. This approach aligns with outcome-based education goals by making system thinking and absolute sustainability explicit learning outcomes, preparing future chemical engineers to design processes that respect planetary limits while balancing technical performance, economic feasibility, and societal needs. Full article

This study presents a standardized method for multimodal monitoring of exercise execution in isolated, confined, and extreme (ICE) environments, addressing the need for reproducible assessment of neuromuscular and cardiovascular responses under space- and equipment-limited conditions. The method integrates wearable surface electromyography (sEMG), inertial measurement units (IMU), and electrocardiography (ECG) to capture muscle activation, movement, and cardiac dynamics during space-efficient exercise. Ten exercises suitable for confined habitats were implemented during analog missions conducted in the DeepLabH03 facility, with feasibility evaluated in a seven-day campaign involving three adult participants. Signals were synchronized using video-verified repetition boundaries, sEMG was normalized to maximum voluntary contraction, and sEMG amplitude- and frequency-domain features were extracted alongside heart rate variability indices. The protocol enabled stable real-time data acquisition, reliable repetition-level segmentation, and consistent detection of muscle-specific activation patterns across exercises. While amplitude-based sEMG indices showed no uniform main effect of exercise, robust exercise-by-muscle interactions were observed, and sEMG mean frequency demonstrated sensitivity to differences in movement strategy. Cardiac measures showed limited condition-specific modulation, consistent with short exercise bouts and small sample size. As a proof-of-concept feasibility study, the proposed protocol provides a practical and reproducible framework for multimodal physiological monitoring of exercise in ICE analogs and other constrained environments, supporting future studies on exercise quality, training load, and adaptive feedback systems. The protocol is designed to support near-real-time monitoring and forms a technical basis for future exercise-quality feedback in confined habitats. Full article

This study investigates the relationship between stock returns and income inequality in South Africa, a country marked by persistently high levels of income disparities and a sophisticated and structurally unique financial market. Despite the Johannesburg Stock Exchange (JSE) being one of the most developed and liquid markets in Africa, stock ownership remains limited to a small segment of the population, often reinforcing pre-existing income inequalities. This study determines the relationship between stock returns and income distribution using the ARDL bound test methodology. Using time series data from 1975 to 2024, the study examines the extent to which stock market returns influence income distribution. The findings of the study suggest a positive relationship between stock returns and income distribution. This relationship suggests that higher stock market development disproportionately benefits capital holders. The long-term relationship seems to have limited feedback from inequality to stock returns. The findings aim to inform policies on inclusive financial participation and broad-based wealth generation to address South Africa’s structural inequalities. Full article

This paper presents the design, modeling and control of a flywheel actuator for mobile platform stabilization. A Lagrangian-based model couples platform mechanics with DC-motor electromechanics. Analytical calculations estimate natural frequencies, damping and actuator limits. Numerical simulations in Python 3.12 evaluate cascade and state-feedback controllers for suppressing free oscillations and rejecting external disturbances. Additional studies examine filtering to improve measurement quality and unloading strategies to avoid actuator saturation. The results validate the proposed control architecture and demonstrate its applicability to robotic and energy systems operating under dynamic loads. Full article

Family-implemented interventions are evidence-based practices used to support a range of developmental outcomes, including social communication. Social communication is a broad construct that encompasses a variety of skills, from foundational abilities such as joint attention (i.e., two people attending to the same object or event) to more advanced behaviors like triadic interactions (i.e., responding to or initiating conversation that involves reciprocal interactions). In a previous study, we examined the effects of a brief, parent-implemented Naturalistic Developmental Behavioral Intervention (NDBI), delivered over telepractice with video feedback coaching. The intervention resulted in increased strategy use by all mothers and the frequency of communication for three young children. In the current study, we conducted a secondary analysis of those data to explore whether the communication-focused intervention produced a collateral effect on activity-engaged triadic interactions (i.e., mother–child–mother or child–mother–child exchanges while simultaneously engaging in a joint activity). Although a functional relation was not established, critical theoretical implications are posed. These findings highlight the need for future research to break apart complex skills into subskills to detect any subtle changes in child outcomes. Limitations and directions for future research are discussed. Full article

Lymph node evaluation is a central determinant of oncologic quality in the surgical management of non-small-cell lung cancer (NSCLC). Accurate assessment of hilar and mediastinal lymph nodes underpins pathologic staging, informs postoperative treatment decisions, and remains essential for prognostic stratification and assessment of resection completeness. Although international guidelines provide clear recommendations, real-world data consistently demonstrate substantial variability in lymph node staging practices, with inadequate evaluation frequently observed across institutions and surgical settings. Insufficient nodal assessment, manifested as the omission of mediastinal staging, limited station sampling, or low lymph node yield, is associated with reduced nodal upstaging, inappropriate omission of adjuvant therapy, higher recurrence rates, and inferior long-term survival. Contemporary guidance from major societies, including the National Comprehensive Cancer Network, European Society of Thoracic Surgeons, International Association for the Study of Lung Cancer, and the Commission on Cancer, has increasingly converged on a station-based definition of adequacy, emphasizing systematic evaluation of both N1 and N2 nodal stations rather than reliance on absolute node counts alone. In parallel, preoperative mediastinal staging algorithms have evolved toward routine use of endobronchial and esophageal ultrasound as first-line invasive modalities, reserving surgical mediastinoscopy for selected high-risk or inconclusive cases. Evidence from randomized trials, population-level databases, and meta-analyses indicates that thorough nodal assessment improves staging accuracy and survival, while recent data support the selective use of lobe-specific or tailored lymphadenectomy in carefully staged, low-risk early disease. Finally, emerging quality improvement interventions, including standardized specimen handling, operative checklists, and multidisciplinary feedback mechanisms, have demonstrated measurable improvements in guideline adherence and patient outcomes. This narrative review integrates contemporary evidence and guideline recommendations to outline a practical framework for implementing reliable, high-quality lymph node staging in modern lung cancer surgery. Full article

Background/Objectives: Given the critical role of vaccinations and venipunctures in disease prevention and health monitoring, it is concerning that over half of children ages 4 to 8 experience some level of needle fear. Higher levels of fear result in longer procedure times, ineffective pain management, distressing memories of needles, and ultimately, healthcare avoidance. Exposure-based therapy with a therapist is recommended for high levels of fear. However, access is limited due to cost, wait times, clinician shortages, system barriers, and social stigma. Thus, there is a need for an evidence-informed, caregiver-directed educational resource for management of moderate to high needle fear in young children. Methods: To address this gap, such a resource was drafted which included a caregiver guide and an illustrated children’s book. The current objective was to gather key user feedback on this initial version of the resource. Participants reported their perceptions of the content, coping strategies, design, organization, and accessibility of the resource through semi-structured interviews and limited quantitative ratings. Participants were children with moderate to high levels of needle fear (N = 6), their caregivers (N = 6), and healthcare professionals (N = 6; including needle providers, child life specialists, and mental health clinicians). Interviews were coded with inductive content analysis; descriptive statistics were calculated for quantitative ratings. Results: Participants reported satisfaction with the e-resource and highlighted strengths (e.g., CARD^TM system, children’s book) and improvement areas (e.g., length, language). Conclusion: Feedback informed revisions to the e-resource in preparation for further evaluation in a follow-up study. Full article

Tax evasion is a dynamic process reflecting continuous interaction between taxpayers and regulatory institutions rather than a static deviation from fiscal equilibrium. This study introduces a predator-prey model of tax evasion that translates the Lotka-Volterra framework from biology into budgetary dynamics. The model captures the feedback between the volume of tax evasion and the intensity of regulation, incorporating nonlinearity, implicit reactive lag, and adaptive response. Theoretical derivation and numerical simulation identify three dynamic regimes—stable equilibrium, limit-cycle oscillation, and instability—that arise through a Hopf bifurcation. Bifurcation maps in the (r, a), (r, b), and (r, c) parameter spaces reveal how control efficiency, institutional inertia, and behavioral feedback jointly determine fiscal stability. Results show that excessive enforcement may destabilize the system by inducing regulatory fatigue, while weak control enables exponential growth in evasion. The model provides a dynamic analytical tool for evaluating fiscal policy efficiency and identifying stability thresholds. Its findings suggest that adaptive, feedback-based regulation is essential for maintaining long-term tax discipline. The study contributes to closing the research gap by providing a unified dynamic framework linking micro-behavioral decision-making with macro-fiscal stability, offering a foundation for future empirical calibration and behavioral extensions of fiscal systems. Full article

This article describes a global structure optimization methodology for microelectromechanical system devices based on a multi-objective elitist genetic algorithm. By integrating a parameterized model with a multi-objective evolutionary framework, the approach can efficiently explore design space and concurrently optimize multiple metrics. A differential capacitive MEMS accelerometer is presented to demonstrate the method. Four key objectives, including resonant frequency, static capacitance, dynamic capacitance, and feedback force, are simultaneously optimized to enhance sensitivity, bandwidth, and closed-loop driving capability. After 25 generations, the algorithm converged to a uniformly distributed Pareto front. The experimental results indicate that, compared with the initial design, the sensitivity-oriented design achieves a 56.1% reduction in static capacitance and an 85.5% improvement in sensitivity. The global multi-objective optimization achieves a normalized hypervolume of 35.8%, notably higher than the local structure optimization, demonstrating its superior design space coverage and trade-off capability. Compared to single-objective optimization, the multi-objective approach offers a superior strategy by avoiding the limitation of overemphasizing resonant frequency at the expense of other metrics, thereby enabling a comprehensive exploration of the design space. Full article

To address the limitations of fixed-order update mechanisms in convolutional neural network parameter training, an adaptive parameter training method based on the Hausdorff difference is proposed in this paper. By deriving a Hausdorff difference formula that is suitable for discrete training processes and embedding it into the adaptive moment estimation framework, a generalized Hausdorff difference-based Adam algorithm (HAdam) is constructed. This algorithm introduces an order parameter to achieve joint dynamic control of the momentum intensity and the effective learning rate. Through theoretical analysis and numerical simulations, the influence of the order parameter and its value range on algorithm stability, parameter evolution trajectories, and convergence speed is investigated, and two adaptive order adjustment strategies based on iteration cycles and gradient feedback are designed. The experimental results on the Fashion-MNIST and CIFAR-10 benchmark datasets show that, compared with the standard Adam algorithm, the HAdam algorithm exhibits clear advantages in both convergence efficiency and recognition accuracy. Full article

Background/Objectives: This study explores the diagnostic and management pathways for dyspareunia in women seeking specialist care, focusing on gynecologists’ feedback and women’s perceptions of their experience. Methods: An online survey was conducted among 225 sexually active women to explore their perceptions of dyspareunia, its impact on relationships, and experiences with healthcare feedback, diagnosis, and treatment. The Numeric Rating Scale (NRS) for pain assessment and the Female Sexual Function Index (FSFI) were used. Gynecologists’ feedback was classified as positive, neutral, or negative based on its influence on the therapeutic pathway. Results: Of 78 women reporting dyspareunia, 12 with pain level ≥5 on NRS were selected for in-depth analysis. The mean pain score was 7.0 ± 1.53, with symptoms lasting from several months to over two years and occurring during most sexual encounters. The mean FSFI score was 24.86 ± 4.54, with half of the participants scoring within the sexual dysfunction range. Qualitative findings revealed frequent dismissive responses from healthcare professionals and limited access to appropriate management. Common self-management strategies included changing sexual positions and using lubricants, while half of the participants had not undergone a formal diagnostic process. Most frequent diagnoses were hormonal disorders and recurrent genital tract infections, and women were advised to undergo pharmacological treatment. Half of the participants were unaware of the possibility of physiotherapeutic management. Conclusions: Women with dyspareunia often face an inadequate diagnostic and therapeutic process. The care received is often insufficient and not aligned with a biopsychosocial model. Full article

Satellite communication systems experience significant signal degradation during rain events, a phenomenon that can be leveraged for meteorological applications. This study introduces a novel hybrid machine learning framework combining unsupervised clustering with cluster-specific supervised deep learning models to transform satellite signal attenuation into a predictive tool for rainfall prediction. Unlike conventional single-model approaches treating all atmospheric conditions uniformly, our methodology employs K-Means Clustering with the Elbow Method to identify four distinct atmospheric regimes based on Signal-to-Noise Ratio (SNR) patterns from a 12-m Ku-band satellite ground station at King Mongkut’s Institute of Technology Ladkrabang (KMITL), Bangkok, Thailand, combined with absolute pressure and hourly rainfall measurements. The dataset comprises 98,483 observations collected with 30-s temporal resolutions, providing comprehensive coverage of diverse tropical atmospheric conditions. The experimental platform integrates three subsystems: a receiver chain featuring a Low-Noise Block (LNB) converter and Software-Defined Radio (SDR) platform for real-time data acquisition; a control system with two-axis motorized pointing incorporating dual-encoder feedback; and a preprocessing workflow implementing data cleaning, K-Means Clustering (k = 4), Synthetic Minority Over-Sampling Technique (SMOTE) for balanced representation, and standardization. Specialized Long Short-Term Memory (LSTM) networks trained for each identified cluster enable capture of regime-specific temporal dynamics. Experimental validation demonstrates substantial performance improvements, with cluster-specific LSTM models achieving R² values exceeding 0.92 across all atmospheric regimes. Comparative analysis confirms LSTM superiority over RNN and GRU. Classification performance evaluation reveals exceptional detection capabilities with Probability of Detection ranging from 0.75 to 0.99 and False Alarm Ratios below 0.23. This work presents a scalable approach to weather radar systems for tropical regions with limited ground-based infrastructure, particularly during rapid meteorological transitions characteristic of tropical climates. Full article