Search Results (1,002)

The airborne Synthetic Aperture Radar (SAR) has the advantages of high-precision real-time observation of wave height variations and portability in the high frequency band, such as the Ku band. In view of the Four Fast Fourier Transform (4-FFT) algorithm, combined with a Gaussian operator, a Laplacian of Gaussian (LoG) Phase Unwrapping (PU) expression was derived. Then, an Adaptive LoG (ALoG) algorithm was proposed based on adaptive variance, further optimizing the algorithm through iteration. Building the models of Kelvin wake on the sea surface and height to phase, the interferometric phase of wave height can be simulated. These PU algorithms were qualitatively and quantitatively evaluated. The Principal Component Analysis (PCA) scores of the ALoG iteration (ALoGI) algorithm are the best under the tested noise levels of the simulation. Through a simulation experiment, it has been proven that the superiority of the ALoGI algorithm in high spatial resolution inversion for the sea-ship surface height of the Kelvin wake, with good stability and noise resistance. Full article

Background: The brain is never truly at rest. Even in the absence of external tasks, it remains active, continuously organizing itself into large-scale resting-state networks involved in shaping our internal thoughts and experiences. Understanding the networks’ structure and dynamics is key to uncovering how the brain functions as a whole. While previous studies have mapped resting-state networks and explored the roles of individual brain regions (network vertices), the relevance of the time-dependent functional interactions (network edges) between them remains largely unexplored. Methods: Here, we assess this relevance by elucidating the time-evolving importance of both brain regions and their interactions, associated with the networks’ constituents, using the fundamental concept of centrality. We investigate long-term electrophysiological recordings of brain dynamics from more than 100 participants and reveal new insights into how resting-state networks are organized over longer times. Results: Our findings reveal that the functional architecture of brain networks in a resting state is critically shaped by the dynamic interplay between brain regions. We identified functional importance backbones–core sets of dynamically central vertices and edges–whose configuration varies significantly between subgroups and further varies with different brain states, including wakefulness and sleep. Notably, regions associated with the default mode network exhibited adaptable patterns of centrality, challenging the notion of static network cores. Conclusions: By considering the temporal evolution of both vertices and edges, we provide a more comprehensive understanding of intrinsic brain activity and its functional relevance. This dynamic perspective reveals how the brain’s intrinsic activity is coordinated across space and time, highlighting the existence of functional importance backbones that adapt to different brain states. Full article

This paper examines a previously unknown anonymous Hebrew letter inserted into a postwar edition of Shem HaGedolim, found in the library of the Jewish University in Budapest. The letter, composed in Győr in 1947, consists almost entirely of passages copied from Tiferet Chayim, a hagiographic genealogy of the Sanz Hasidic dynasty. Although derivative in content, the letter’s form and placement suggest it was not meant for transmission but instead served as a private act of mourning and historiographical preservation. By situating the letter within the broader context of post-Holocaust Jewish and Hasidic memory practices, including yizkor books, rabbinic memoirs, and grassroots commemorative writing, this study proposes that the document constitutes a “micro-yizkor”: a bibliographic ritual that aimed to re-inscribe lost tzaddikim into sacred memory. Drawing on theories of trauma, religious coping, and bereavement psychology, particularly the Two-Track Model of Bereavement, the paper examines the letter as both a therapeutic and historiographical gesture. The author’s meticulous copying, selective omissions, and personalized touches (such as modified honorifics and emotive phrases) reflect an attempt to maintain spiritual continuity in the wake of communal devastation. Engaging scholarship by Michal Shaul, Lior Becker, Gershon Greenberg, and others, the analysis demonstrates how citation, far from being a passive act, functions here as an instrument of resistance, memory, and redemptive reconstruction. The existence of such a document can also be examined through the lens of Maurice Rickards’ insights, particularly his characterization of the “compulsive note” as a salient form of ephemera, materials often inserted between the pages of books, which pose unique challenges for interpreting the time capsule their authors sought to construct. Ultimately, the paper argues that this modest and anonymous document offers a rare window into postwar Ultra-orthodox religious subjectivity. It challenges prevailing assumptions about Hasidic silence after the Holocaust and demonstarates how even derivative texts can serve as potent sites of historical testimony, spiritual resilience, and bibliographic mourning. The letter thus sheds light on a neglected form of Hasidic historiography, one authored not by professional historians, but by the broken-hearted, writing in the margins of sacred books. Full article

Background/Objectives: The glymphatic system plays a crucial role in clearing brain metabolic waste, and its dysfunction has been correlated to various neurological disorders. The Diffusion Tensor Imaging Analysis Along the Perivascular Space (DTI-ALPS) index has been proposed as a non-invasive marker of glymphatic function by measuring diffusivity along perivascular spaces; however, its sensitivity to sleep-related changes in glymphatic activity has not yet been validated. This study aimed to evaluate the feasibility of using the DTI-ALPS index as a quantitative marker of dynamic glymphatic activity during sleep. Methods: Diffusion tensor imaging (DTI) data were obtained from 12 healthy male participants (age = 24.44 ± 2.5 years; Pittsburgh Sleep Quality Index (PSQI) < 5), once while awake and 16 times during sleep, following 24 h sleep deprivation and administration of 10 mg zolpidem. Simultaneous MR-compatible electroencephalography was used to determine whether the subject was asleep or awake. DTI preprocessing included eddy current correction and tensor fitting. The DTI-ALPS index was calculated from nine regions of interest in projection and association areas aligned to standard space. The final analysis included nine participants (age = 24.56 ± 2.74 years; PSQI < 5) who maintained a continuous sleep state for 1 h without awakening. Results: Among nine ROI pairs, three showed significant increases in the DTI-ALPS index during sleep compared to wakefulness (Friedman test; p = 0.027, 0.029, 0.034). These ROIs showed changes at 14, 19, and 25 min after sleep induction, with FDR-corrected p-values of 0.024, 0.018, and 0.018, respectively. Conclusions: This study demonstrated a statistically significant increase in the DTI-ALPS index within 30 min after sleep induction through time-series DTI analysis during wakefulness and sleep, supporting its potential as a biomarker reflecting glymphatic activity. Full article

Maximizing the power output of wind farms is critical for improving the economic viability and grid integration of renewable energy. Active wake control (AWC) strategies, such as yaw-based wake steering, offer significant potential for power generation increase but require predictive models that are both accurate and computationally efficient for real-time implementation. This paper proposes a data-driven observer to rapidly estimate the potential power gain achievable through AWC as a function of the ambient wind direction. The approach is rooted in Koopman operator theory, which allows a linear representation of nonlinear dynamics. Specifically, a model is developed using an Input–Output Extended Dynamic Mode Decomposition framework combined with Sparse Identification (IOEDMDSINDy). This method lifts the low-dimensional wind direction input into a high-dimensional space of observable functions and then employs iterative sparse regression to identify a minimal, interpretable linear model in this lifted space. By training on offline simulation data, the resulting observer serves as an ultra-fast surrogate model, capable of providing instantaneous predictions to inform online control decisions. The methodology is demonstrated and its performance is validated using two case studies: a 9-turbine and a 20-turbine wind farm. The results show that the observer accurately captures the complex, nonlinear relationship between wind direction and power gain, significantly outperforming simpler models. This work provides a key enabling technology for advanced, real-time wind farm control systems. Full article

Biomimetic design for engineering applications may suggest the optimal performance of engineering devices. In this work the passive/pure pitching characteristics of a hydrofoil are investigated experimentally with and without a pair of biomimetic fin strips placed symmetrically on the two sides of the foil leading edge. The work is performed in a recirculating water channel at low Reynolds numbers (Re) with a range of 1300 ≤ Re ≤ 3200. Using high-speed videography and Particle Image Velocimetry (PIV), the pitching characteristics and wakes are visualized. Passive pitching characteristics, i.e., the pitching amplitude and pitching frequency of the hydrofoils, are investigated based on their trailing edge movement. Significant improvement in both pitching frequency and amplitudes are observed for the foil with fin strips compared to the baseline simple foil. Comparing the pitching characteristics of the two foils, it is observed that the hydrofoil with biomimetic fin strips exhibits 25% and 21% higher pitching amplitude and pitching frequency, respectively, compared to that of the baseline at comparable Reynolds numbers. The initiation of pitching for the finned foil is also observed at comparatively low Reynolds numbers. The wake is also studied using time mean and fluctuating velocity profiles obtained using PIV. Full article

Morningness-eveningness is usually assessed as either a trait or a state using either a morning–evening preference scale or sleep timing reported for free days, respectively. These assessments were implemented in numerous studies exploring the associations between morningness-eveningness and health, mood, and sleep problems. Evening types almost always had more problems than morning types. We examined these associations in university students with conflicting results of trait and state assessments of morningness-eveningness and tried to confirm their chronotype using a multidimensional chronotyping approach that recognizes four types other than morning and evening (lethargic, vigilant, napping, and afternoon). The conflicting trait and state assessments of morningness-eveningness were found in 141 of 1582 students. Multidimensional chronotyping supported morningness of morning types with late weekend sleep timing, and the associations with health, mood, and sleep problems resembled the associations of other morning types (i.e., these associations persisted despite late sleep timing). In contrast, evening types with early weekend sleep timing were more likely classified as lethargic or napping types rather than evening types. They did not resemble evening types in their associations with health, mood, and sleep problems (i.e., early sleep timing did not change these associations). Model-based simulations of the sleep–wake cycles of students with conflicting trait and state assessments suggested that their bedtimes cannot be solely determined by their biological clocks. On weekdays or weekends, mind-bedtime procrastination can lead to missing the bedtime signal from their biological clocks (i.e., self-deprivation of sleep or, in other words, voluntary prolongation of the wake phase of the sleep–wake cycle). Full article

In recent times, blades that have the ability to change shape passively or actively have garnered interest due to their ability to optimize blade performance for varying flow conditions. Various versions of morphing exist, from simple chord length changes to full blade morphing with multiple degrees of freedom. These blades can incorporate smart materials or mechanical actuators to modify the blade shape to suit the wind conditions. Morphing blades have shown an ability to improve performance in simulations. These simulations show increased performance in Region 2 (partial load) operating conditions. This study focuses on the effects of the wake for a flexible wind turbine with actively variable twist angle distribution (TAD) to improve the energy production capabilities of morphing structures. These wake effects influence wind farm performance for locally clustered turbines by extracting energy from the free stream. Hence, the development of better wake models is critical for better turbine design and controls. This paper provides an outline of some approaches available for wake modeling. FLORIS (FLow Redirection and Induction Steady-State) is a program used to predict steady-state wake characteristics. Alongside that, the Materials and Methods section shows different modeling environments and their possible integration into FLORIS. The Results and Discussion section analyzes the 20 kW wind turbine with previously acquired data from the National Renewable Energy Laboratory’s (NREL) AeroDyn v13 software. The study employs FLORIS to simulate steady-state non-linear wake interactions for the nine TAD shapes. These TAD shapes are evaluated across Region 2 operating conditions. The previous study used a genetic algorithm to obtain nine TAD shapes that maximized aerodynamic efficiency in Region 2. The Results and Discussion section compares these TAD shapes to the original blade design regarding the wake characteristics. The project aims to enhance the understanding of FLORIS for studying wake characteristics for morphing blades. Full article

Dust emissions from unpaved haul roads in open-pit coal mining pose a significant risk to air quality, health, and operational efficiency of mining operations. This study integrated real-time field monitoring with numerical simulations using ANSYS Fluent 2023 R1 to investigate the generation, dispersion, and migration of particulate matter (PM) at the Ha’erwusu open-pit coal mine under varying meteorological conditions. Real-time measurements of PM2.5, PM10, and TSP, along with meteorological variables (wind speed, wind direction, humidity, temperature, and air pressure), were collected and analyzed using Pearson’s correlation and multivariate linear regression analyses. Wind speed and air pressure emerged as dominant factors in winter, whereas wind and temperature were more influential in summer (R² = 0.391 for temperature vs. PM2.5). External airflow simulations revealed that truck-induced turbulence and high wind speeds generated wake vortices with turbulent kinetic energy (TKE) peaking at 5.02 m²/s², thereby accelerating particle dispersion. The dust migration rates reached 3.33 m/s within 6 s after emission and gradually decreased with distance. The particle settling velocities ranged from 0.218 m/s for coarse dust to 0.035 m/s for PM2.5, with dispersion extending up to 37 m downwind. The highest simulated dust concentration reached 4.34 × 10⁻² g/m³ near a single truck and increased to 2.51 × 10⁻¹ g/m³ under multiple-truck operations. Based on spatial attenuation trends, a minimum safety buffer of 55 m downwind and 45 m crosswind is recommended to minimize occupational exposure. These findings contribute to data-driven, weather-responsive dust suppression planning in open-pit mining operations and establish a validated modeling framework for future mitigation strategies in this field. Full article

The convergence of next-generation wireless communication technologies and modern energy infrastructure presents a promising path toward sustainable and intelligent systems. This survey explores how beyond-5G and 6G communication technologies can support the greening of Industrial Internet of Things (IIoT) systems and smart grids. It highlights the critical challenges in achieving energy efficiency, interoperability, and real-time responsiveness across different domains. The paper reviews key enablers such as LPWAN, wake-up radios, mobile edge computing, and energy harvesting techniques for green IoT, as well as optimization strategies for 5G/6G networks and data center operations. Furthermore, it examines the role of 5G in enabling reliable, ultra-low-latency data communication for advanced smart grid applications, such as distributed generation, precise load control, and intelligent feeder automation. Through a structured analysis of recent advances and open research problems, the paper aims to identify essential directions for future research and development in building energy-efficient, resilient, and scalable smart infrastructures powered by intelligent wireless networks. Full article

Background/Objectives: The COVID-19 pandemic intensified stress among students, though its impact on burnout symptoms remains mixed. Previous research emphasized examining both study-related behavior such as academic engagement and burnout for a fuller understanding of students’ well-being in the wake of the COVID-19 pandemic. Methods: In this cross-sectional study we examined stress, burnout, study-related behavior, and typical coping patterns among three cohorts of university students before (2016), at the start of (2020), and after (2024) the pandemic, with 1016 students participating. Results: Perceived stress was significantly higher during the pandemic but returned to pre-COVID-19 levels afterward. Depression scores remained stable across cohorts. Burnout symptoms, particularly cynicism and academic efficacy, were significantly lower in the COVID-19 cohort. Study commitment, including subjective importance of studying, academic goals/ambition, willingness to exert oneself, and striving for perfection were lower during and after the pandemic than before. Emotional distancing peaked in 2020, suggesting disengagement as a coping strategy. Pre-COVID-19 students exhibited higher active coping scores than the COVID-19 and post-COVID-19 cohorts, while satisfaction with studies was highest post-pandemic, likely due to the return of in-person academic and social experiences. Conclusions: These findings reveal fluctuations in students’ stress, burnout, and study-related behavior over time. While stress-levels have normalized, study commitment and typical coping patterns such as active coping remain altered, indicating the pandemic’s lasting impact on students’ academic behavior and mental health. Full article

The COVID-19 pandemic sparked changes globally, as leaders scrambled to protect wellbeing and safety. With many U.S. students sent away from their campuses, undergraduate students still grappled with the time-old question: “what will I do after college,” except during an unprecedented time in history rife with heightened career uncertainty. This qualitative study presents the results of a survey administered as part of a mind–body health project conducted in the wake of the pandemic, speaking directly to undergraduate college students’ health-related career aspirations. Two open-ended survey questions—(1) what is your intended career, and (2) how (if at all) has the COVID-19 pandemic changed your perspective about your future career—were administered with thematic analysis conducted. Qualitative analysis using hybrid data and theory-driven approaches revealed several themes around an increased desire to work within health-related fields, decreased desire to work in healthcare, non-medical to medical career shift, and additional undecidedness. Connecting theory to practice, Super’s Life-Space, Life-Span Career Theory and Krumboltz’s Social Learning Theory of Career Decision-Making guide practical implications and grander discussion around career development during periods of crisis. Full article

Wake steering has emerged as a promising strategy to mitigate turbine wake losses, with existing research largely focusing on the aerodynamic optimization of yaw angles. However, many prior approaches rely on static look-up tables (LUTs), offering limited adaptability to real-world wind variability and leading to non-optimal results. More importantly, these energy-focused strategies overlook the mechanical implications of frequent yaw activities in pursuit of the maximum power output, which may lead to premature exhaustion of the yaw system’s design life, thereby accelerating structural degradation. This study proposes a supervisory control framework that balances energy capture with structural reliability through three key innovations: (1) upstream-based inflow sensing for real-time capture of free-stream wind, (2) fatigue-responsive optimization constrained by a dynamic actuation quota system with adaptive yaw activation, and (3) a bidirectional threshold adjustment mechanism that redistributes unused actuation allowances and compensates for transient quota overruns. A case study at an offshore wind farm shows that the framework improves energy yield by 3.94%, which is only 0.29% below conventional optimization, while reducing yaw duration and activation frequency by 48.5% and 74.6%, respectively. These findings demonstrate the framework’s potential as a fatigue-aware control paradigm that balances energy efficiency with system longevity. Full article

Background/Objectives Lettuce is known to contain compounds that promote sleep. This study aims to evaluate the effects of lettuce extract on Korean adults experiencing poor sleep quality. Methods In this randomized, double-blind, placebo-controlled trial, participants aged 30–65 with poor sleep quality (Pittsburgh Sleep Quality Index (PSQI) > 5) were recruited. Over 4 weeks, participants took two capsules daily of either the test extract or placebo. Sleep quality and quantity were assessed using the PSQI, actigraphy and polysomnography, and analyzed using ANCOVA adjusting for baseline, age, and sex. Results The adjusted final PSQI scores showed greater improvement in the test group than in the placebo group for both the global scores (6.48 ± 0.63 vs. 7.41 ± 0.57, p = 0.0462). Regarding actigraphy measurements, the adjusted final means showed significant improvements in the test group compared to the placebo group for total sleep time (TST) (421.68 ± 13.29 vs. 386.57 ± 12.27 min, p = 0.0023) and sleep efficiency (SE) (83.90 ± 1.6 vs. 81.01 ± 1.50%, p = 0.0342). Polysomnography results also favored the test group, with higher adjusted final means TST (358.90 ± 19.75 vs. 322.11 ± 17.66 min, p = 0.0457) and SE (86.86 ± 3.31 vs. 79.60 ± 2.99%, p = 0.0182), and lower wake after sleep onset (39.26 ± 10.57 vs. 68.15 ± 9.60 min, p = 0.0042). Conclusions Heukharang extract may enhance sleep quality and quantity and is deemed safe, suggesting its potential as a functional food for improving sleep. Full article

In this paper, the aerodynamic performance of an improved hybrid vertical-axis wind turbine is investigated, and the performance of the hybrid turbine at high tip–speed ratios is significantly enhanced by adding a spoiler at the end of the inner rotor. The improved design increases the average torque coefficient by 7.4% and the peak power coefficient by 32.4%, which effectively solves the problem of power loss due to the negative torque of the inner rotor in the conventional hybrid turbine at high TSR; the spoiler improves the performance of the outer rotor in the wake region by optimizing the airflow distribution, reducing the counter-pressure differential, lowering the inner rotor drag and at the same time attenuating the wake turbulence intensity. The study verifies the validity of the design through 2D CFD simulation, and provides a new idea for the optimization of hybrid wind turbines, which is especially suitable for low wind speed and complex terrain environments, and is of great significance for the promotion of renewable energy technology development. Full article