Search Results (1,733)

In this article, we present a method for extracting the complex permittivity of high-conductivity solutions up to 9 GHz. Microwave measurements were performed using a low-cost SMA connector, employed as an open-circuit coaxial probe, which was subsequently brought into contact with the liquids under characterization. Compared to state-of-the-art techniques, this method offers the advantage of good accuracy while remaining simple to implement with a low-cost sensor. The affordability of the sensor is crucial because the sensor must operate in a corrosive environment. The use of existing but expensive commercial solutions is prohibitive. Therefore, sensor replacement must be straightforward and inexpensive in case of damage. Two permittivity extraction methods were studied, both relying on a straightforward experimental approach and knowledge of the complex permittivity of reference liquids (deionized water, ethanol, methanol). The technique was initially validated on saline solutions (NaCl) known from the literature before being applied to aqueous lithium bromide (LiBr water) solutions. Eight LiBr water solutions, known to be highly corrosive, were measured for LiBr mass concentrations ranging from 1% to 54% and for conductivities up to 14 S/m. The high conductivity of these solutions brings challenges to extract the real part of the permittivity, which is underestimated by both methods. In contrast, the imaginary part exhibits consistent results with variations strongly correlated to the concentration. Notably, an inversion of the direction of variation was observed for mass concentration in LiBr exceeding 35% aligning with the conductivity curve. Full article

Net ecosystem productivity (NEP) is an extremely important flux for terrestrial ecosystems, indicating the value of net ecosystem exchange (NEE) between terrestrial ecosystems and the atmosphere, excluding carbon fluxes from disturbances. Leveraging flux network NEE annual measurements, this study focuses on upscaling the tower-based NEP to a global 250 m resolution dataset with flux site distribution considerations. Firstly, the data augmentation method was presented to address issues related to the uneven spatial distribution of flux sites. Secondly, a random forest model was developed for NEP estimation using the optimized tower-based NEP and remotely sensed and meteorological gridded sample sets, giving an R² value of 0.73 and an RMSE value of 149.83 gC m⁻² yr⁻¹. Finally, a global NEP product at a 250 m resolution was generated (2001–2022, average 13.79 PgC yr⁻¹) and evaluated. In summary, we present a solution to the overestimation of global NEP by data-driven methods, producing a long-time-series, high-resolution NEP dataset that is more comparable to atmospheric inversion results. This dataset enhances comparability with atmospheric inversion results, thereby boosting our confidence in conducting a consistency analysis of terrestrial carbon sinks across different methods within the framework. Full article

Background/Objectives: In recent years, there has been an increased interest in reducing suicide rates through dietary modification; however, the relationship between riboflavin intake and suicide risk remains unclear. This study aims to examine the association between dietary riboflavin and suicidal ideation. Methods: A total of 17,320 participants from the Korean National Health and Nutrition Examination Survey (KNHANES) 2014–2020 were included. Suicidal ideation was assessed using the ninth item of the Patient Health Questionnaire-9 (PHQ-9). Riboflavin intake was evaluated through dietary assessments. Multivariate logistic regression, restricted cubic spline (RCS) regression analysis, subgroup analysis, and interaction tests were conducted to explore the relationship between riboflavin intake and suicidal ideation. Results: There was a statistically significant association between riboflavin intake and suicidal ideation [OR (95%CI): 0.83 (0.77, 0.91), p < 0.001], after full adjustment for covariates. The linear trend test, using Q1 as the reference, showed ORs (95% CI) for Q2 and Q3 of 0.96 (0.81, 1.15) and 1.06 (0.80, 1.42), respectively. The RCS analysis revealed a non-linear pattern in the relationship between riboflavin intake and suicidal thoughts. This association was particularly significant among women and individuals younger than 60 years. Subgroup analyses and interaction tests indicated that the associations remained consistent across subgroups and were not influenced by factors other than anaerobic exercise. Conclusions: Our findings suggest a non-linear inverse relationship between riboflavin intake and suicidal ideation, with notable variations by sex and age. Modifying dietary riboflavin intake may be a crucial strategy for reducing suicide risk. Full article

The search for mineral resources presents an enduring challenge as these demands consistently surge, and the utilization of geophysics is undeniably intertwined with the pursuit of novel prospects. Technological advancements over recent decades have facilitated access to 2D and 3D visualization software, enabling robust data integrations. Consequently, interpreters possess the latitude to harness their ingenuity and technical acumen in conducting multifarious analyses. Mineral exploration in greenfield areas, a particularly challenging endeavor, often commences with regional surveys and circumscribed information about the terrain. Notwithstanding limited preliminary data, the judicious deployment of filtering, modeling, and inversion techniques with geophysical data holds sway in catalyzing discoveries. This study, with its comprehensive amalgamation of diverse copper occurrence indicators and the novel procedural framework it establishes for processing and integrating airborne gamma-ray spectrometry and magnetometry geophysical and geological data, exemplifies the complexity and depth of our field. Elaborate litho-geophysical profiles, linked with data concerning mineral occurrences and geochemistry, pinpoint potential copper deposits in the area. This multidisciplinary approach and inversion mode provide detailed insights into probable mineralized body continuity and regional structural frameworks, offering valuable guidance for future regional mineral exploration efforts. Full article

Leaf area index (LAI) serves as a crucial indicator for characterizing the growth and development process of maize. However, the LAI inversion of maize based on unmanned aerial vehicles (UAVs) is highly susceptible to various factors such as weather conditions, light intensity, and sensor performance. In contrast to satellites, the spectral stability of UAV-based data is relatively inferior, and the phenomenon of “spectral fragmentation” is prone to occur during large-scale monitoring. This study was designed to solve the problem that maize LAI inversion based on UAVs is difficult to achieve both high spatial resolution and spectral consistency. A two-stage remote sensing data fusion method integrating coarse and fine fusion was proposed. The SHapley Additive exPlanations (SHAP) model was introduced to investigate the contributions of 20 features in 7 categories to LAI inversion of maize, and canopy temperature extracted from thermal infrared images was one of them. Additionally, the most suitable feature sampling window was determined through multi-scale sampling experiments. The grid search method was used to optimize the hyperparameters of models such as Gradient Boosting, XGBoost, and Random Forest, and their accuracy was compared. The results showed that, by utilizing a 3 × 3 feature sampling window and 9 features with the highest contributions, the LAI inversion accuracy of the whole growth stage based on Random Forest could reach R² = 0.90 and RMSE = 0.38 m²/m². Compared with the single UAV data source mode, the inversion accuracy was enhanced by nearly 25%. The R² in the jointing, tasseling, and filling stages were 0.87, 0.86, and 0.62, respectively. Moreover, this study verified the significant role of thermal infrared data in LAI inversion, providing a new method for fine LAI inversion of maize. Full article

Macroseismic intensity data are the only source of information for historical earthquakes; it is therefore necessary to devise methods that allow us to retrieve as many source parameters as possible on the basis of these data. We present the inversion of macroseismic data as a first validation of an improved version of the kinematic function, KF. Following the previous results of some earthquakes on Italian territory and several validations by Californian events provided with instrumental solutions, we have now simplified the KF by reducing some degrees of freedom of the parameters and rearranging the code for parallel calculation. This approach will allow for a more extensive application of the KF technique. We present the inversion of the macroseismic intensity pattern of the M_wp6 earthquake of 27 March 1928 (8:32 GMT), which occurred in Northeastern Italy (Carnia), and we retrieved source parameters that are compatible with the solutions of other authors who independently treat instrumental data. The 1928 event is located a few tens of kilometers west of the more destructive M_w6.5 of 6 May 1976 and northeast of the subsequent earthquake M_wp6.1 of 18 October 1936. The inversion was performed as a blind test, without prior knowledge for fault plane solutions and tectonic information; it resulted in a minimum variance model with a strike of 62°, a dip of 10°, and a rake of 101°. This solution is not consistent with the entire tectonic framework of the eastern Southalpine chain, but it is in agreement with the But-Chiarsò line. This result encourages us to test further improvements to the KF method and to treat other cases from the Italian macroseismic catalog. Full article

F8 gene inversion variants originate in male germ cells during spermatogenesis. Our recent study revealed that de novo variants (DNVs) caused F8 noninversion variants (NIVs) in sporadic hemophilia A (HA). Here, we conducted a direct clinical determination of sex differences in the origin of sporadic HA-NIV according to the data of two new HA-NIV families, as well as of the families demonstrated in the previous study. Of the 126 registered families with HA, 23 were eligible for inclusion. We conducted a linkage analysis with F8 gene markers and an amplification refractory mutation system–quantitative polymerase chain reaction to confirm the origin of the sporadic NIVs (~0% mutant cells) or the presence of a mosaic variant, requiring further confirmation of the origin in the parent. Two sporadic DNV events were determined. One event occurred in grandparents, consisting of five maternal grandmothers and seven maternal grandfathers, who were the origins; their respective daughters became carrier mothers who gave birth to probands. The other event included 11 mothers, who were the origins exclusively; their respective sons became probands. In this study, we found that sporadic HA-NIVs originate mostly from females (16 out of 23). Our study contributes to a better understanding of the genetic pathogenesis of HA. Full article

Meteor radar is a widely used technique for measuring wind in the mesosphere and lower thermosphere, with the key advantage of being unaffected by terrestrial weather conditions, thus enabling continuous operation. In all-sky interferometric meteor radar systems, amplitude and phase consistencies between multiple channels exhibit dynamic variations over time, which can significantly degrade the accuracy of wind measurements. Despite the inherently dynamic nature of these inconsistencies, the majority of existing research predominantly employs static calibration methods to address these issues. In this study, we propose a dynamic adaptive calibration method that combines normalized least mean square and correlation algorithms, integrated with hardware design. We further assess the effectiveness of this method through numerical simulations and practical implementation on an independently developed meteor radar system with a five-channel receiver. The receiver facilitates the practical application of the proposed method by incorporating variable gain control circuits and high-precision synchronization analog-to-digital acquisition units, ensuring initial amplitude and phase consistency accuracy. In our dynamic calibration, initial coefficients are determined using a sliding correlation algorithm to assign preliminary weights, which are then refined through the proposed method. This method maximizes cross-channel consistencies, resulting in amplitude inconsistency of <0.0173 dB and phase inconsistency of <0.2064°. Repeated calibration experiments and their comparison with conventional static calibration methods demonstrate significant improvements in amplitude and phase consistency. These results validate the potential of the proposed method to enhance both the detection accuracy and wind inversion precision of meteor radar systems. Full article

A comprehensive flight control law design method based on adaptive control is presented in this paper. The proposed method consists of three basic modules—model decoupling, online system identification and adaptive pole placement. The model decoupling module decouples the helicopter flight dynamics model based on dynamic inversion technique. This procedure helps to reduce the difficulties in online system identification and adaptive controller design. In online system identification module, a recursive extended least squares algorithm is established to identify the augmented linear flight dynamics model which is composed of helicopter model and unideal noise model. The helicopter model parameters and the noise parameters are identified simultaneously which improves the identification accuracy as well as robustness. Pole placement is implemented in the last module, and an optimization method is developed to help selecting ideal poles. The adaptive rule in this step is designed based on eigenvalue analysis of the model to remove all unnecessary oscillations of the control parameters. An adaptive controller is designed according to the developed method for the UH-60A helicopter based on a nonlinear simulation program. Typical response types are also implemented. The simulation results show that the designed adaptive controller has high performance as well as robustness in both hover and forward flight. Full article

The human nuclear receptor (NR) superfamily consists of 48 genes that are ligand-activated transcription factors that play a key role in maintaining cellular homeostasis and in pathophysiology. NRs are important drug targets for both cancer and non-cancer endpoints as ligands for these receptors can act as agonists, antagonists or inverse agonists to modulate gene expression. With two exceptions, the classical mechanism of action of NRs involves their interactions as monomers, dimers or heterodimers with their cognate response elements (cis-elements) in target gene promoters. Several studies showed that a number of NR-regulated genes did not directly bind their corresponding cis-elements and promoter analysis identified that NR-responsive gene promoters contained GC-rich sequences that bind specificity protein 1 (Sp1), Sp3 and Sp4 transcription factors (TFs). This review is focused on identifying an important sub-set of Sp-regulated genes that are indirectly coregulated through interactions with NRs. Subsequent studies showed that many NRs directly bind Sp1 (or Sp3 and Sp4), the NR/Sp complexes bind GC-rich sites to regulate gene expression and the NR acts as a ligand-modulated nuclear cofactor. In addition, several reports show that NR-responsive genes contain cis-elements that bind both Sp TFs and NRs, and mutation of either cis-element results in loss of NR-responsive (inducible and/or basal). Regulation of these genes involves interactions between DNA-bound Sp TFs with proximal or distal DNA-bound NRs, and, in some cases, other nuclear cofactors are required for gene expression. Thus, many NR-responsive genes are regulated by NR/Sp complexes, and these genes can be targeted by ligands that target NRs and also by drugs that induce degradation of Sp1, Sp3 and Sp4. Full article

Infrared target detection has a wide range of application value, but due to the characteristics of infrared images, infrared targets are easily submerged in the complex background. Therefore, in complex scenes, it is difficult to effectively and accurately detect infrared dim small targets. For this reason, we design an infrared dim small target (IDST) detection algorithm containing Large-size Receptive Fields (LRFNet). It uses the Residual network with an Inverted Pyramid Structure (RIPS), which consists of convolutional layers that become progressively smaller, so it can have a larger effective receptive field and can improve the robustness of the model. In addition, through the Attention Mechanisms with Large Receptive Fields and Inverse Bottlenecks (LRIB), it can make the network better localize the region where the target is located and improve the detection effect of the model. The experimental results show that our proposed algorithm outperforms other state-of-the-art algorithms in all evaluation metrics. Full article

This paper presents an algorithm for solving the inverse fractional Stefan problem. The considered inverse problem consists of determining the heat transfer coefficient at one of the boundaries of the considered region. The additional information necessary for solving the inverse problem is the set of temperature values in selected points of the region. The fractional derivative with respect to time used in the considered Stefan problem is of the Caputo type. The direct problem was solved by using the alternating phase truncation method adapted to the model with the fractional derivative. To solve the inverse problem, the ant colony algorithm was used. This paper contains an example illustrating the accuracy and stability of the presented algorithm. Full article

The phononic crystals composed of soft materials have received extensive attention owing to the extraordinary behavior when undergoing large deformations, making it possible to provide tunable band gaps actively. However, the inverse designs of them mainly rely on the gradient-driven or gradient-free optimization schemes, which require sensitivity analysis or cause time-consuming, lacking intelligence and flexibility. To this end, a deep learning-based framework composed of a conditional variational autoencoder and multilayer perceptron is proposed to discover the mapping relation from the band gaps to the topology layout applied with prestress. The nonlinear superelastic neo-Hookean model is employed to describe the constitutive characteristics, based on which the band structures are obtained via the transfer matrix method accompanied with Bloch theory. The results show that the proposed data-driven approach can efficiently and rapidly generate multiple candidates applied with predicted prestress. The band gaps are in accord with each other and also consistent with the prescribed targets, verifying the accuracy and flexibility simultaneously. Furthermore, based on the generalization performance, the design space is deeply exploited to obtain desired soft structures whose stop bands are characterized by wider bandwidth, lower location, and enhanced wave attenuation performance. Full article

Background: Extracorporeal cardiopulmonary resuscitation (ECPR) has the potential to improve neurological outcomes in patients with refractory out-of-hospital cardiac arrest (OHCA), offering an alternative to conventional cardiopulmonary resuscitation (CCPR). However, its effectiveness in OHCA remains controversial despite advancements in resuscitation techniques. Methods: This retrospective single-center study compared neurological outcomes and 30-day survival between ECPR and CCPR patients from January 2014 to January 2022. Patients aged 18–75 with witnessed OHCA, minimal no flow and low flow times, and cardiac arrests occurring at home or in public places were included. All patients were transported directly to our institution, a tertiary medical center serving the southeastern region of Seoul, where extracorporeal membrane oxygenation implantation was consistently performed in the emergency department. Neurological outcomes were assessed using Cerebral Performance Category scores, with good outcomes defined as scores of 1–2. Statistical analyses included logistic regression models and Kaplan–Meier survival curves, adjusted for confounders using inverse probability of treatment weighting. Results: ECPR was associated with significantly better neurological outcomes than CCPR (p < 0.001). Factors predicting poor outcomes included older age and longer low flow times, while male sex and shockable rhythms were protective. No significant difference was found in 30-day survival between the ECPR and CCPR groups, although a trend toward better survival was noted with ECPR. Conclusions: ECPR may improve neurological outcomes in patients with refractory OHCA compared to CCPR, although it does not significantly affect 30-day survival. Further studies are necessary to validate these findings and explore the long-term impacts of ECPR. Full article

In the context of data security, this work aims to present a novel solution that, rather than addressing the topic of endpoint security—which has already garnered significant attention within the international scientific community—offers a different perspective on the subject. In other words, the focus is not on device security but rather on the protection and security of the information contained within those devices. As we will see, the result is a next-generation decentralized infrastructure that simultaneously integrates two cognitive areas: data storage and its protection and security. In this context, an innovative Multiscale Relativistic Quantum (MuReQua) chain is considered to realize a novel decentralized and security solution for storing data. This engine is based on the principles of Quantum Mechanics, stochastic processes, and a new approach of decentralization for data storage focused on information security. The solution is broken down into four main components, considered four levels of security against attackers: (i) defocusing, (ii) fogging, (iii) puzzling, and (iv) crypto agility. The defocusing is realized thanks to a fragmentation of the contents and their distributions on different allocations, while the fogging is a component consisting of a solution of hybrid cyphering. Then, the puzzling is a unit of Information Fusion and Inverse Information Fusion, while the crypto agility component is a frontier component based on Quantum Computing, which gives a stochastic dynamic to the information and, in particular, to its data fragments. The data analytics show a very effective and robust solution, with executions time comparable with cloud technologies, but with a level of security that is a post quantum one. In the end, thanks to a specific application example, going beyond purely technical and technological aspects, this work introduces a new cognitive perspective regarding (i) the distinction between data and information, and (ii) the differentiation between the owner and the custodian of data. Full article