Search Results (233)

Background: The use of foot orthoses to treat different pathologies in pronated feet in adults is widespread among podiatric professionals, although it has not been conclusively demonstrated to modify foot posture in the short or medium term. Objective: The aim of this study was to evaluate whether prefabricated foot supports reduce pronated foot posture in adults, as measured by the foot posture index (FPI). Methods: A randomised controlled clinical trial was conducted in 109 subjects with pronated feet. The participants were randomly placed into a control group that did not receive any intervention and an experimental group that used prefabricated orthoses for 6 months. The changes in the FPI were evaluated in both groups at 6 months. Results: Over the six-month follow-up period, the delta FPI variable was changed by −1.1 ± 2.2 points in the experimental group, whereas the same variable was reduced by 1.2 ± 2.1 points in the control group (p = 0.001). The participants in the experimental group neutralised their FPIs significantly more than those in the control group did (39.3% vs. 8.5%; p = 0.041). Moreover, individuals in the experimental group were more likely to migrate from highly pronated feet to pronated feet than those in the control group were (45.8% vs. 20%; p < 0.001). Finally, multivariate analysis indicated that prefabricated foot orthoses were associated with an improved FPI (OR: 6.23, CI%95: 2.72–17.09; p < 0.001). However, the corrective effect provided by the prefabricated foot orthoses, which neutralised the pronated posture, was nullified by the presence of index minus metatarsal formula. Conclusions: The use of prefabricated orthoses resulted in a decreased FPI in adults, especially in those with highly pronated feet. However, the index minus presence nullified the effect of prefabricated orthoses on foot posture neutralisation. Full article

The goal of this study is to look into the dynamic relationship between stock prices, foreign portfolio investment, and financial development in the South African economy. Federal Reserve Economic Data (FRED) provided quarterly time series data from 1960 (Q1) to 2024 (Q2). This study uses a structural VAR estimation approach and dynamic conditional correlation (DCC GARCH model). The DCC GARCH approach displays time-varying correlations between stock prices, credit given to the private sector as a measure of financial growth, and foreign portfolio investments. The dynamic links between stock prices, financial development, and foreign private investment (FPI) are examined using the SVAR technique. Our findings show that a financial development shock encourages and provokes a substantial influx of foreign portfolio investment into the South African economy. This suggests that overseas portfolio investments react favorably and notably well to favorable shocks in the financial development process. We suggest that a stable financial system framework and lower credit costs would strengthen the impact of higher stock prices on private sector credit and guarantee that higher stock prices have a beneficial impact on other financial development metrics. Better financial development metrics, such as credit to the private sector, will therefore increase foreign portfolio investment. Full article

The focus of this investigation was to examine the effects of energy consumption, agricultural commerce, and productivity on CO₂ emissions in Nigeria using quantile regression. Time series data from 1960 to 2021 were used. The findings revealed that the impact of agricultural raw materials imports (AGRIMs) and exports on carbon footprints is positive. There is a prevalence of a set of notable percentile differences in the conditional distribution of the variables on CO₂ emissions. Initially, the coefficient of energy consumption (EnCons) was high, but constantly nosedived from the 25th quantile until it reached the 90th quantile when it picked up again, and the same was true in the case of AGRIM. Thus, a 1% increase in agricultural imports will bring about 0.0047—a significant unit increase in CO₂ emissions in Nigeria from the 0.382946 coefficient in the 10th quantile to the 0.264392 coefficient in the 50th quantile, and thereafter, the effects become insignificant. Profound significant variance across disparate percentiles in the conditional spread of AGRIM, food production index (FPI), CPI, and FDI was found. It further showed that the effects of the regressors on carbon emissions differ over the quantiles. Overall, AGRIM and EnCons have positive and significant effects on carbon emission. However, the agricultural raw material export has significant negative effects on CO₂ emissions as the movement (transportation) of goods within a country prior to export involves a huge level of carbon release. This study provides recommendations and policy implications. Full article

Floods are a significant and pervasive threat globally, exacerbated by climate change and increasing extreme weather events. The Gravity Recovery and Climate Experiment (GRACE) and its follow-on mission (GRACE-FO) provide crucial insights into terrestrial water storage anomalies (TWSA), which are vital for understanding flood dynamics. However, the observational gap between these missions presents challenges for flood monitoring, affecting the estimation of long-term trends and limiting the analysis of interannual variability, thereby impacting overall analysis accuracy. Reconstructing the missing data between GRACE and GRACE-FO is essential for systematically understanding the spatiotemporal distribution characteristics and driving mechanisms of interannual changes in regional water reserves. In this study, the Generative Adversarial Imputation Network (GAIN) is applied to improve the monitoring capability for flood events in the Pearl River Basin (PRB). First, the GRACE/GRACE-FO TWSA data gap is imputed with GAIN and compared with long short-term memory (LSTM) and k-Nearest Neighbors (KNN) methods. Using the reconstructed data, we develop the Flood Potential Index (FPI) by integrating GRACE-based TWSA with precipitation data and analyze key characteristics of FPI variability against actual flood events. The results indicate that GAIN effectively predicts the GRACE/GRACE-FO TWSA gap, with an average improvement of approximately 50.94% over LSTM and 68.27% over KNN. The reconstructed FPI proves effective in monitoring flood events in the PRB, validating the reliability of the reconstructed TWSA. Additionally, the FPI achieves a predictive accuracy of 79.7% for real flood events, indicating that short-term flood characteristics are better captured using TWSA. This study demonstrates the effectiveness of GAIN in enhancing data continuity, providing a reliable framework for large-scale flood risk assessment and offering valuable insights for flood management in vulnerable regions. Full article

Minimizing optical losses in resonant cavities is crucial for improving photonic device performance. This study focuses on the development of a simulation tool to analyze scattering losses in Fabry–Pérot interferometers (FPIs), offering precise modeling of waveguide dynamics and contributing to accurate loss predictions across various platforms. Optical cavities often suffer from scattering losses due to surface roughness and material defects. Our approach integrates theoretical models and simulations to quantify these losses, utilizing the FPI as a model system. We identified upper and lower reflectivity thresholds, beyond which accurate measurement of losses becomes unreliable. For reflectivity below a certain threshold, measurement errors arise, while excessively high reflectivity can reduce fringe visibility and introduce detector sensitivity issues. Simulations were used to validate the model’s ability to predict reflectivity and attenuation in waveguides with varying loss levels. The software’s flexibility to adjust transmission parameters for different cavity configurations enhances its utility for a broad range of photonic systems. Our study offers a novel methodology for optical loss analysis, with practical applications in optimizing photonic devices. By providing a reliable tool for precise loss measurement, this work supports advancements in optical technologies, enabling the design of more efficient, high-performance devices across various applications. Full article

The protein transition motivates the use of plant proteins, but their application in food emulsions is challenging, especially when high concentrations of oil and salt are needed for formulation and sensory properties. In the present work, we connect the iso-electric point of two potato protein isolates (patatin-rich, POPI-200; protease inhibitor-rich, POPI-300) and a faba protein isolate (FPI) to the behavior in the bulk phase and at the interface, and relate this to the physical stability of 45 wt% oil-in-water (O/W) emulsions in the presence of NaCl at pH 4.0–7.0. In the absence of NaCl, a higher bulk viscosity was found at the iso-electric point (IEP), especially for the FPI. In the presence of NaCl, the viscosity of the POPI-200 solutions was highest, followed by POPI-300, and that of the FPI was lowest, irrespective of the pH. Both POPIs showed faster initial adsorption at the O/W interface in the absence of NaCl, and formed a more elastic layer compared to the FPI. For all proteins, salt addition leads to less elastic films. Interestingly, the interfaces were more elastic at a pH close to the IEP of the protein in the presence of NaCl. Both POPI-stabilized emulsions showed higher stability (smaller size and less oiling off) than the FPI-stabilized emulsions, which makes potato proteins relevant for food emulsion product formulation, even under high salt conditions. Full article

In this study, a low-temperature co-fired ceramic (LTCC)-based circular diaphragm design was considered for Fabry–Pérot Interferometer (FPI) pressure sensor applications. The characteristics of the LTCC-based circular diaphragm were analyzed using FEM analysis. The selected thicknesses of the LTCC diaphragms were 50 μm, 75 μm and 100 μm, with diameters of 3 mm, 4 mm and 5 mm, respectively. Our results showed that the sensitivity and frequency response of this structure can be designed flexibly by adjusting the parameters of the ceramic diaphragm size, including the radius and thickness. The key contribution of this work is that it demonstrates the performance of LTCC diaphragms with different sizes, which could be useful for future works. Full article

Introduction: The SARS-CoV-2 pandemic presented unique challenges to the health-care system and prehospital emergency medical services. An increasing prevalence of burnout has been described, which in turn is associated with mental illness. The aim of this paper was to evaluate burnout through a sex comparison and to analyze associations of burnout with personality traits during the SARS-CoV-2 pandemic. Methods: Eighty-eight emergency physicians and field shearers of Kharkiv City (Ukraine) emergency medical services (52% women) participated in the quantitative cross-sectional study. In addition to sociodemographic and occupational data, the Maslach Burnout Inventory (MBI) and the Freiburg Personality Inventory (FPI) were applied and analyzed in the sex comparison. Correlation analyses were performed to describe the relationships between the MBI dimensions and FPI traits. Results: The average age of the respondents was 35.1 ± 13.5 years. The prevalence of burnout during the pandemic was 6.5% in women and 2.4% in men. Only the scores on the MBI dimension cynicism were significantly (p = 0.027) higher in women than in men. Two personality traits differed between sexes: inhibition and male/female self-reports. Predominantly moderate correlations were found between the FPI traits and the MBI dimensions. Conclusions: Although the prevalence of burnout in this occupational group during the pandemic was similar to prepandemic figures reported in the literature, more than half of the male and female paramedics showed average-to-high scores on the three MBI dimensions. Because burnout is associated with other mental illnesses and prolonged incapacity, workplace-based interventions should be implemented. Full article

An all-fiber vibration sensor based on the Fabry-Perot interferometer (FPI) is proposed and experimentally evaluated in this study. The sensor is fabricated by introducing a Fabry-Perot cavity to the single-mode fiber using femtosecond laser ablation. The cavity and the tail act together as a cantilever beam, which can be used as a vibration receiver. When mechanical vibrations are applied, the cavity length of the Fabry-Perot interferometer changes accordingly, altering the interference fringes. Due to the low moment of inertia of the fiber optic cantilever beam, the sensor can achieve broadband frequency responses and high vibration sensitivity without an external vibration receiver structure. The frequency range of sensor detection is 70 Hz–110 kHz, and the sensitivity of the sensor is 60 mV/V. The sensor’s signal-to-noise ratio (SNR) can reach 56 dB. The influence of the sensor parameters (cavity depth and fiber tail length) on the sensing performance are also investigated in this study. The sensor has the advantages of compact structure, high sensitivity, and wideband frequency response, which could be a promising candidate for vibration sensing. Full article

With the introduction of advanced Fiber Bragg Grating (FBG) technology, Fabry–Pérot (FP) interferometers have become widely used in fiber-optic ultrasound detection. In these applications, the slope of the reflectance is a critical factor influencing detection results. Due to the intensity limitations of the laser source in fiber-optic ultrasound detection, the reflectance of the FBG is generally increased to enhance the signal-to-noise ratio (SNR). However, increasing reflectance can cause the reflectance curve to deviate from a sinusoidal shape, which in turn affects the slope of the reflectance and introduces greater errors. This paper first investigates the relationship between the transmission curve of the FP interferometer and reflectance, with a focus on the errors introduced by simplified assumptions. Further research shows that in sensors with asymmetric reflectance slopes, their transmittance curves deviate significantly from sinusoidal signals. This discrepancy highlights the importance of achieving symmetrical slopes to ensure consistent and accurate detection. To address this issue, this paper proposes an innovative method to adjust the rear-end reflectance of the FP interferometer by combining stress modulation, UV adhesive, and a high-reflectivity metal disk. Additionally, by adjusting the rear-end reflectance to ensure that the transmittance curve approximates a sinusoidal signal, the symmetry of the slope is maintained. Finally, through practical ultrasound testing, by adjusting the incident wavelength to the positions of slope extrema (or zero) at equal intervals, the expected ultrasound signals at extrema (or zero) can be detected. This method converts the problem of approximating a sinusoidal signal into a problem of the slope adjustment of the transmittance curve, making it easier and more direct to determine its impact on detection results. The proposed method not only improves the performance of fiber-optic ultrasound sensors but also reduces costs, paving the way for broader applications in medical diagnostics and structural health monitoring. Full article

Vector bending sensors can be utilized to detect the bending curvature and direction, which is essential for various applications such as structural health monitoring, mechanical deformation measurement, and shape sensing. In this work, we demonstrate a temperature-insensitive vector bending sensor via parallel Farby–Perot interferometers (FPIs) fabricated by etching and splicing a multicore fiber (MCF). The parallel FPIs made in this simple and effective way exhibit significant interferometric visibility with a fringe contrast over 20 dB in the reflection spectra, which is 6 dB larger than the previous MCF-based FPIs. And such a device exhibits a curvature sensitivity of 0.207 nm/m⁻¹ with strong bending-direction discrimination. The curvature magnitude and orientation angle can be reconstructed through the dip wavelength shifts in two off-diagonal outer-core FPIs. The reconstruction results of nine randomly selected pairs of bending magnitudes and directions show that the average relative error of magnitude is ~4.5%, and the average absolute error of orientation angle is less than 2.0°. Furthermore, the proposed bending sensor is temperature-insensitive, with temperature at a lower sensitivity than 10 pm/°C. The fabrication simplicity, high interferometric visibility, compactness, and temperature insensitivity of the device may accelerate MCF-based FPI applications. Full article

Background: In the United States, traumatic brain injury (TBI) contributes significantly to mortality and morbidity. Elovanoids (ELVs), a novel class of homeostatic lipid mediators we recently discovered and characterized, have demonstrated neuroprotection in experimental stroke models but have never been tested after TBI. Methods: A moderate fluid-percussion injury (FPI) model was used on male rats that were treated with ELVs by intravenous (IV) or intranasal (IN) delivery. In addition, using liquid chromatography-mass spectrometry (LC-MS/MS), we examined whether ELVs could be detected in brain tissue after IN delivery. Results: ELVs administered intravenously 1 h after FPI improved behavior on days 2, 3, 7, and 14 by 20, 23, 31, and 34%, respectively, and preserved hippocampal CA3 and dentate gyrus (DG) volume loss compared to the vehicle. Whole-brain tractography revealed that ELV-IV treatment increased corpus callosum white matter fibers at the injury site. In comparison to treatment with saline on days 2, 3, 7, and 14, ELVs administered intranasally at 1 h and 24 h after FPI showed improved neurological scores by 37, 45, 41, and 41%. T2-weighted imaging (T2WI) abnormalities, such as enlarged ventricles and cortical thinning, were reduced in rats treated by ELV-IN delivery compared to the vehicle. On day 3, ELVs were detected in the striatum and ipsilateral cortex of ELV-IN-treated rats. Conclusion: We have demonstrated that both ELV-IN and ELV-IV administration offer high-grade neuroprotection that can be selectively supplied to the brain. This discovery may lead to innovative therapeutic targets for secondary injury cascade prevention following TBI. Full article

In the field of fiber-optic sensing, effectively reducing the noise of sensing spectra and achieving a high signal-to-noise ratio (SNR) has consistently been a focal point of research. This study proposes a deep-learning-based denoising method for fiber-optic sensors, which involves pre-processing the sensor spectrum into a 2D image and training with a cycle-consistent generative adversarial network (Cycle-GAN) model. The pre-trained algorithm demonstrates the ability to effectively denoise various spectrum types and noise profiles. This study evaluates the denoising performance of simulated spectra obtained from four different types of fiber-optic sensors: fiber Fabry–Perot interferometer (FPI), regular fiber Bragg grating (FBG), chirped FBG, and FBG pair. Compared to traditional denoising algorithms such as wavelet transform (WT) and empirical mode decomposition (EMD), the proposed method achieves an SNR improvement of up to $13.71 \mathrm{dB}$, an RMSE that is up to three times smaller, and a minimum correlation coefficient ($R^2$) of no less than 99.70% with the original high-SNR signals. Additionally, the proposed algorithm was tested for multimode noise reduction, demonstrating an excellent linearity in temperature response with a $R^2$ of 99.95% for its linear fitting and $99.74\%$ for the temperature response obtained from single-mode fiber sensors. The proposed denoising approach effectively reduces the impact of various noises from the sensing system, enhancing the practicality of fiber-optic sensing, especially for specialized fiber applications in research and industrial domains. Full article

The stable flight of drones relies on Global Navigation Satellite Systems (GNSS). However, in complex environments, GNSS signals are prone to interference, leading to flight instability. Inspired by cross-view machine learning, this paper introduces the VDUAV dataset and designs the VRLM network architecture, opening new avenues for cross-view geolocation. First, to address the limitations of traditional datasets with limited scenarios, we propose the VDUAV dataset. By leveraging the virtual–real mapping of latitude and longitude coordinates, we establish a digital twin platform that incorporates 3D models of real-world environments. This platform facilitates the creation of the VDUAV dataset for cross-view drone localization, significantly reducing the cost of dataset production. Second, we introduce a new baseline model for cross-view matching, the Virtual Reality Localization Method (VRLM). The model uses FocalNet as its backbone and extracts multi-scale features from both drone and satellite images through two separate branches. These features are then fused using a Similarity Computation and Feature Fusion (SCFF) module. By applying a weighted fusion of multi-scale features, the model preserves critical distinguishing features in the images, leading to substantial improvements in both processing speed and localization accuracy. Experimental results demonstrate that the VRLM model outperforms FPI on the VDUAV dataset, achieving an accuracy increase to 83.35% on the MA@20 metric and a precision of 74.13% on the RDS metric. Full article

Objective: To evaluate the morphofunctional alterations in the foot and their association with functionality, considering aspects such as disability, pain, and limitations in daily activities in patients with fibromyalgia syndrome (FMS). Methods: A case–control study was conducted in patients with FMS (case group) and without FMS (control group), matched by age and sex. Foot posture was assessed using the foot posture index (FPI), along with the presence of hallux valgus (HV), trigger points, hyperkeratosis, and dorsiflexion of the first metatarsophalangeal joint and ankle. Foot functionality was evaluated using the foot function index (FFI) questionnaire. Results: A total of 100 women with FMS and 100 women without FMS, with a mean age of 61.97 ± 9.26 years, were recruited. HV (p < 0.001), hyperkeratosis (p < 0.001), pronated and supinated foot (p < 0.001), as well as limitations in dorsiflexion of the first metatarsophalangeal joint (p < 0.001) and the ankle with the knee flexed (p < 0.001) and extended (p < 0.001), along with the activity of the flexor hallucis brevis (p = 0.006), adductor hallucis (p = 0.006), and dorsal interosseous (p = 0.002) muscles, were significantly associated with the FFI, being higher in individuals with FMS, indicating greater impairment of foot functionality in these patients. Multivariate analysis revealed a statistical association between FMS and low educational level (OR = 2.57, 95% CI 1.05–5.72), the presence of another rheumatic disease (OR = 5.07, 95% CI 2.34–11), and the presence of any active trigger point (OR = 11.15, 95% CI 3.97–31.31). Conclusions: The study highlights the relationship between morphofunctional foot alterations, specifically the presence of active myofascial trigger points, and functionality in patients with FMS. Full article