Search Results (801)

Tropospheric ozone (O₃), a secondary pollutant of mounting global concern, emerges from complex, nonlinear photochemical reactions involving nitrogen oxides (NO_x) and volatile organic compounds (VOCs) under dynamically evolving meteorological conditions. Accurately characterizing and effectively regulating O₃ formation necessitates not only precise and multi-dimensional precursor observations but also modeling frameworks that are structurally coherent, chemically interpretable, and sensitive to regime variability. Despite significant technological progress, current research remains markedly fragmented: observational platforms often operate in isolation with limited vertical and spatial interoperability, while modeling paradigms—ranging from mechanistic chemical transport models (CTMs) to data-driven machine learning approaches—frequently trade interpretability for predictive performance and struggle to capture regime transitions across heterogeneous environments. This review provides a dual-perspective synthesis of recent advances and enduring challenges in the VOC–O₃ research landscape. We first establish a typology of ground-based, airborne, and satellite-based VOC monitoring systems, evaluating their capabilities, limitations, and roles within a vertically structured sensing architecture. We then examine the evolution of O₃ modeling strategies, from empirical and semi-mechanistic models to hybrid frameworks that integrate physical knowledge with algorithmic flexibility. By diagnosing the structural decoupling between observation and inference, we identify key methodological bottlenecks and advocate for a system-level redesign of the VOC–O₃ research paradigm. Finally, we propose a forward-looking framework for next-generation atmospheric governance—one that fuses cross-platform sensing, regime-aware modeling, and policy-relevant diagnostics into an integrated, adaptive, and chemically robust decision-support system. Full article

In this work, a novel polarization-maintaining hollow-core fiber structure featuring a semi-circular nested dual-ring geometry is proposed. To simultaneously optimize two inherently conflicting performance metrics, namely, birefringence and confinement loss, a multi objective genetic algorithm is employed for geometric parameter tuning, resulting in a set of Pareto-optimal solutions. At the target wavelength of 1550 nm, the first optimal design achieves birefringence exceeding $1\times {10}^{-4}$ over a 1275 nm bandwidth while maintaining confinement loss around ${10}^0$ dB/m; the second design maintains birefringence above $1\times {10}^{-4}$ across a 1000 nm spectral range, with confinement loss on the order of ${10}^{-1}$ dB/m. These optimized designs offer a promising approach for improving the performance of polarization-sensitive applications such as interferometric sensing and high coherence laser systems. The results confirm the suitability of multi-objective genetic algorithms for integrated multi-objective fiber optimization and provide a new strategy for designing low-loss and high-birefringence fiber devices. Full article

From the moment we are born, and even before, in the womb, and until our last breath, our bodies move all the time. Adaptive behaviors necessarily depend not only on the successful integration of multisensory bodily signals but also on how we move our bodies in the world. This paper considers the notion of embodied selfhood through the perspective of dynamic and rhymical coupling between bodily movements and bodily actions. We propose a new theoretical framework suggesting that the dynamic coupling between bodily movements and bodily actions in the world are fundamental in constructing and maintaining a coherent sense of self. To support this idea, we use the Predictive Processing (PP) and Active Inference frameworks as our background theoretical canvas. Specifically, we will focus on the phenomenon of somatosensory attenuation in relation to dynamic selfhood and argue that rhythmic bodily signals such as heartbeats, breathing, and walking patterns are predictable and, thus, can be smoothly attenuated, i.e., processed in the background. We illustrate this hypothesis by discussing the case of Depersonalization Disorder as a failure to self-attenuate self-related information processing, leading to feelings of unreality and self ‘loss’. We conclude with potential implications of our hypothesis for therapy. Full article

For applications like smart cities and autonomous driving, high-precision direction-of-arrival (DOA) estimation for 5G broadband signals is essential. A primary obstacle for existing methods is the spatial incoherence caused by multi-frequency propagation. We present a sparse Bayesian learning (SBL) algorithm specifically designed to resolve this issue while also minimizing computational load. The algorithm synergistically combines three key components: first, a multiple-signal classification (MUSIC)-like focusing technique ensures a coherent sparse model; second, a real-valued transformation significantly cuts down on computational complexity; and third, an optimized variational Bayesian inference accelerates convergence via root-finding. Validation against MUSIC and rootSBL confirms our method’s marked superiority in low-SNR, limited-snapshot, and multipath conditions delivering both higher accuracy and faster convergence. This work, thus, contributes an effective and practical solution for real-time 5G DOA sensing. Full article

Recently, more and more attention has been paid to human health with the rapid development of society. A designed virtual polarizer (VP) can realize multiscale and multi-biomass sensing, including temperature, cancerous cells, and COVID-19. Based on coherent perfect polarization conversion, a certain polarization conversion can be fulfilled within 1.72~2.14 THz. Then, through observing the displacement of a perfect matching point (PMP), variations in temperature can be accurately determined, covering from 299 K to 315 K, with a sensitivity (S) of 0.0198 THz/K. Moreover, a sharp coherent perfect absorption (CPA) peak generated from the VP can be employed for the detection of cancerous cells and COVID-19. The refractive index (RI) detection range of cancerous cells is from 1.36 RIU to 1.41 RIU with the sensitivity being −4.45881 THz/RIU. The average quality factor (Q), figure of merit (FOM), and detection limit (DL) are 825.36, 241.11 RIU⁻¹, and −36.83 dB. For the COVID-19 solution concentration (SC) from 0 mM to 525 mM, by mapping SC to RI, the RI sensing range is 1.344 RIU–1.355 RIU with the S being −5.03467 THz/RIU. The relevant Q, FOM, and DL are 760.85, 244.94 RIU⁻¹, and −36.89 dB. Based on two strategies of PMP and CPA, the proposed VP is capable of multiscale and multi-biomass sensing with excellent detection performance, providing a new detection method for biosensing. Full article

Mangroves play a crucial part in the worldwide blue carbon cycle because they store a lot of carbon in their biomass and soil. Accurate estimation of aboveground biomass (AGB) is essential for quantifying carbon stocks and understanding ecological responses to climate and human disturbances. However, regional-scale AGB mapping remains difficult due to fragmented mangrove distributions, limited field data, and cross-site heterogeneity. To address these challenges, we propose a Spatial Attention Coupled Bayesian Aggregator (SAC-BA), which integrates field measurements with multi-source remote sensing (Landsat 8, Sentinel-1), terrain data, and climate variables using advanced ensemble learning. Four machine learning models (Random Forest (RF), Cubist, Support Vector Regression (SVR), and Extreme Gradient Boosting (XGBoost)) were first trained, and their outputs were fused using Bayesian model averaging with spatial attention weights and constraints based on Local Indicators of Spatial Association (LISAs), which identify spatial clusters (e.g., high–high, low–low) to improve accuracy and spatial coherence. SAC-BA achieved the highest performance (coefficient of determination (${\mathrm{R}}^2$) = 0.82, root mean square error = 29.90 Mg/ha), outperforming all individual models and traditional BMA. The resulting 30-m AGB map of Indonesian mangroves in 2017 estimated a total of 217.17 × ${\mathrm{10}}^6$ Mg, with a mean of 103.20 Mg/ha. The predicted AGB map effectively captured spatial variability, reduced noise at ecological boundaries, and maintained high confidence predictions in core mangrove zones. These results highlight the advantages of incorporating spatial structure and uncertainty into ensemble modeling. SAC-BA provides a reliable and transferable framework for regional AGB estimation, supporting improved carbon assessment and mangrove conservation efforts. Full article

Background: Nursing students often face high academic and emotional demands, which can negatively affect both their mental health and academic performance. From a salutogenic perspective, the sense of coherence (SOC) is considered a key protective factor in managing stress and fostering resilience. Objectives: This study aimed to explore the SOC levels among nursing students and examine their associations with perceived academic stress and sociodemographic variables. Methods: A multicenter, cross-sectional, exploratory study was conducted in a sample of 1301 undergraduate nursing students from four universities in Spain and Chile. Participants completed the Orientation to Life Questionnaire, a validated instrument assessing SOC and its three dimensions: comprehensibility, manageability, and meaningfulness. Sociodemographic data and students’ perceived stress in relation to key academic activities were also collected. Descriptive and inferential statistical analyses were performed, including t-tests and ANOVA. Results: The mean SOC score was 62.65 (SD = 12.36), with no significant differences between universities. Significant associations (p < 0.05) were found between SOC scores and age, marital status, academic year, work status, and university entry path, but not with gender or caregiving responsibilities. Students aged ≥29 years and those who were married or working had higher SOC scores. Higher levels of perceived stress in lectures, seminars, clinical practice, group work, and written assignments were significantly associated with lower SOC scores. Conclusions: This study’s findings suggest that a stronger SOC is associated with lower perceived academic stress and certain sociodemographic characteristics. Integrating salutogenic approaches into nursing curricula could strengthen students’ SOC, promoting their mental well-being and academic resilience. Full article

The paradigm-shift idea of murburn concept is no hypothesis but developed directly from fundamental facts of cellular/ecological existence. Murburn involves spontaneous and stochastic interactions (mediated by murzymes) amongst the molecules and unbound ions of cells. It leads to effective charge separation (ECS) and formation/recruitment of diffusible reactive species (DRS, like radicals whose reactions enable ATP-synthesis and thermogenesis) and emission of radiations (UV/Vis to ELF). These processes also lead to a chemo-electromagnetic matrix (CEM), ascertaining that living cell/organism react/function as a coherent unit. Murburn concept propounds the true utility of oxygen: generating DRS (with catalytic and electrical properties) on the way to becoming water, the life solvent, and ultimately also leading to phase-based macroscopic homeostatic outcomes. Such a layout enables cells to become simple chemical engines (SCEs) with powering, coherence, homeostasis, electro-mechanical and sensing–response (PCHEMS; life’s short-term “intelligence”) abilities. In the current review, we discuss the coacervate nature of cells and dwell upon the ways and contexts in which various radiations (either incident or endogenously generated) could interact in the new scheme of cellular function. Presenting comparative evidence/arguments and listing of systems with murburn models, we argue that the new perceptions explain life processes better and urge the community to urgently adopt murburn bioenergetics and adapt to its views. Further, we touch upon some distinct scientific and sociological contexts with respect to the outreach of murburn concept. It is envisaged that greater awareness of murburn could enhance the longevity and quality of life and afford better approaches to therapies. Full article

Interferometric fiber optic hydrophones (IFOHs) are highly sensitive for underwater acoustic detection but face challenges owing to the trade-off between laser monochromaticity and coherence length. In this study, we propose a pseudo-random binary sequence (PRBS) phase modulation method for laser coherence length control, establishing the first theoretical model that quantitatively links PRBS parameter to coherence length, elucidating the mechanism underlying its suppression of parasitic interference noise. Furthermore, our research findings demonstrate that while reducing the laser coherence length effectively mitigates parasitic interference noise in IFOHs, this reduction also leads to elevated background noise caused by diminished interference visibility. Consequently, the modulation of coherence length requires a balanced optimization approach that not only suppresses parasitic noise but also minimizes visibility-introduced background noise, thereby determining the system-specific optimal coherence length. Through theoretical modeling and experimental validation, we determined that for IFOH systems with a 500 ns delay, the optimal coherence lengths for link fibers of 3.3 km and 10 km are 0.93 m and 0.78 m, respectively. At the optimal coherence length, the background noise level in the 3.3 km system reaches −84.5 dB (re: rad/√Hz @1 kHz), representing an additional noise suppression of 4.5 dB beyond the original suppression. This study provides a comprehensive theoretical and experimental solution to the long-standing contradiction between high laser monochromaticity, stability and appropriate coherence length, establishing a coherence modulation noise suppression framework for hydrophones, gyroscopes, distributed acoustic sensing (DAS), and other fields. Full article

Background: Intermediate care units face growing complexity due to aging populations and chronic illnesses. Non-technical skills such as empathy and communication are crucial for quality care. We aimed to examine the relationship between communication skills, self-efficacy, and sense of coherence among intermediate care nurses. Methods: We conducted an observational, cross-sectional study with 60 intermediate care nurses from three units in a Catalan hospital, Spain. Participants engaged in high-fidelity simulation using geriatric end-of-life scenarios with an ultra-realistic manikin representing a geriatric patient at the end of life. NTSs were measured using validated tools: the Health Professionals Communication Skills Scale (HP-CSS), the General Self-Efficacy Scale, and the Sense of Coherence Questionnaire (OLQ-13). Sessions followed INACSL standards, including prebriefing, simulation, and debriefing phases. Results: Post-simulation outcomes revealed significant gains in interpersonal competencies, with men reporting higher assertiveness (p = 0.015) and greater satisfaction with both the simulation experience (p = 0.003) and the instructor (p = 0.008), underscoring gender-related perceptions in immersive training. Conclusions: Ultra-realistic clinical simulation is effective in enhancing NTS among intermediate care nurses, contributing to improved care quality and clearer professional profiles in geriatric nursing. Full article

Spiritual intelligence (SI) is defined as a unique form of hermeneutic–relational intelligence that enables individuals to integrate cognitive, emotional, and symbolic dimensions to guide their thoughts and actions with reflection, aiming for existential coherence rooted in a transcendent system of meaning. It functions as a metacognitive framework that unites affective, cognitive, and symbolic levels in dialog with a sense of meaning that is considered sacred or transcendent, where “sacred,” in this context, refers inclusively to any symbolic reference or value that a person or culture perceives as inviolable, fundamental, or orienting. It can derive from religious traditions but also from ethical, philosophical, or civil visions. It functions as a horizon of meaning from which to draw coherence and guidance and which orients the understanding of oneself, the world, and action. SI appears as the ability to interpret one’s experiences through the lens of values and principles, maintaining a sense of continuity in meaning even during times of ambiguity, conflict, or discontinuity. It therefore functions as a metacognitive ability that brings together various mental functions into a cohesive view of reality, rooted in a dynamic dialog between the self and a value system seen as sacred. Full article

Providing long-term care for a person with Alzheimer’s disease is associated with chronic stress and emotional overload. One of the key predictors of emotional burden is the amount of time devoted to caregiving, which intensifies the experienced stress. Additional risk factors include the stage of the illness, difficulties in the care recipient’s activities of daily living, the caregiver’s neglect of their own needs, and challenging behaviours exhibited by the person receiving care. Therefore, it is essential to identify the psychological protective resources of caregivers that can buffer the impact of stress. Background/Objectives: The objective of the study was to explore the psychological mechanisms underlying the involvement of caregivers supporting individuals with Alzheimer’s disease. A moderated mediation model was employed, in which stress indirectly affects caregiver involvement through a sense of coherence, and the strength of this relationship is moderated by the amount of time devoted to caregiving. Methods: The bootstrapping method was applied using 5000 resamples within a 95% bias-corrected confidence interval. The analysis accounted for variables such as stress levels, sense of coherence, involvement in caregiving, duration of care, education, gender, age, and stage of the illness. Results: The sense of coherence mediated the relationship between stress and involvement in caring (B = 0.0063, SE = 0.0031, 95% CI [0.0012, 0.0135]), and this indirect effect was contingent upon the amount of time devoted to helping. The relationship between sense of coherence and involvement in caring was significant at the mean level (B = 0.005, SE = 0.002, 95% CI [0.0004, 0.0101]) and became stronger at high levels of time devoted to caring (+1 SD; B = 0.009, SE = 0.003, 95% CI [0.0030, 0.0148]). These results indicate that the positive association between sense of coherence and caregiver involvement increases with the amount of time spent caring. Conclusions: The results highlight the importance of strengthening caregivers’ resilience resources—such as a sense of coherence—in preventing overload. The model may serve as a foundation for developing interventions aimed at supporting caregivers’ mental health. Full article

Accurate feature matching between optical and synthetic aperture radar (SAR) images remains a significant challenge in remote sensing due to substantial modality discrepancies in texture, intensity, and geometric structure. In this study, we proposed an attention-context-aware deep learning framework (ACAMatch) for robust and efficient optical–SAR image registration. The proposed method integrates a structure-enhanced feature extractor, RS2FNet, which combines dual-stage Res2Net modules with a bi-level routing attention mechanism to capture multi-scale local textures and global structural semantics. A context-aware matching module refines correspondences through self- and cross-attention, coupled with a confidence-driven early-exit pruning strategy to reduce computational cost while maintaining accuracy. Additionally, a match-aware multi-task loss function jointly enforces spatial consistency, affine invariance, and structural coherence for end-to-end optimization. Experiments on public datasets (SEN1-2 and WHU-OPT-SAR) and a self-collected Gaofen (GF) dataset demonstrated that ACAMatch significantly outperformed existing state-of-the-art methods in terms of the number of correct matches, matching accuracy, and inference speed, especially under challenging conditions such as resolution differences and severe structural distortions. These results indicate the effectiveness and generalizability of the proposed approach for multimodal image registration, making ACAMatch a promising solution for remote sensing applications such as change detection and multi-sensor data fusion. Full article

In this work, we present a new partially coherent adjustable anomalous vortex laser beam (PCAAVLB) and introduce it into turbulent biological tissue. The equation of such PCAAVLB in turbulent biological tissue is obtained. By numerical analysis, the evolution of the intensity of such PCAAVLB in turbulent biological tissue is analyzed. It is found that the PCAAVLB in biological tissue can lose its ring shape and become a Gaussian beam, and a PCAAVLB with smaller topological charge $M$ or coherence length $\sigma$ will evolve into a Gaussian profile faster. The PCAAVLB in turbulent biological tissue with a smaller small-length-scale factor $l_0$ or larger fractal dimension $D$ will evolve into a Gaussian profile faster and have a larger intensity as z increases. The results may have potential applications in sensing under biological tissue environments and laser imaging in biology. Full article

Accurate alignment of 3D point clouds, achieved by ubiquitous sensors such as LiDAR and depth cameras, is critical for enhancing perception capabilities in robotics, autonomous navigation, and environmental reconstruction. However, low-overlap scenarios—common due to limited sensor field-of-view or occlusions—severely degrade registration robustness and sensing reliability. To address this challenge, this paper proposes a novel geometric consistency optimization and rectification deep learning network named GeoCORNet. By synergistically designing a geometric consistency enhancement module, a bidirectional cross-attention mechanism, a predictive displacement rectification strategy, and joint optimization of overlap loss with displacement loss, GeoCORNet significantly improves registration accuracy and robustness in complex scenarios. The Attentive Cross-Consistency module of GeoCORNet integrates distance and angular consistency constraints with bidirectional cross-attention to significantly suppress noise from non-overlapping regions while reinforcing geometric coherence in overlapping areas. The predictive displacement rectification strategy dynamically rectifies erroneous correspondences through predicted 3D displacements instead of discarding them, maximizing the utility of sparse sensor data. Furthermore, a novel displacement loss function was developed to effectively constrain the geometric distribution of corrected point-pairs. Experimental results demonstrate that our method outperformed existing approaches in the aspects of registration recall, rotation error, and algorithm robustness under low-overlap conditions. These advances establish a new paradigm for robust 3D sensing in real-world applications where partial sensor data is prevalent. Full article