Miracle-dz/UniFAR-FTUs huggingface dataset:
Asymptotic Analysis of Equilibrium in Radiation Fog

Abstract A vertical distribution formulation of liquid water content (LWC) for steady radiation fog was obtained and examined through the singular perturbation method. The asymptotic LWC distribution is a consequential balance among cooling, droplet gravitational settling, and turbulence in the liquid water budget of radiation fog. The cooling produces liquid water, which is depleted by turbulence near the surface. The influence of turbulence on the liquid water budget decreases with height and is more significant for shallow fogs than for deep fogs. The depth of the region of surface-induced turbulence can be characterized with a fog boundary layer (FBL). The behavior of the FBL bears some resemblance to the surface mixing layer in radiation fog. The characteristic depth of the FBL is thinner for weaker turbulence and stronger cooling, whereas if turbulence intensity increases or cooling rate decreases then the FBL will develop from the ground. The asymptotic formulation also reveals a critical turbulent.
High Accuracy and Cost-Effective Fiber Optic Liquid Level Sensing System Based on Deep Neural Network

In this paper, a novel liquid level sensing system is proposed to enhance the capacity of the sensing system, as well as reduce the cost and increase the sensing accuracy. The proposed sensing system can monitor the liquid level of several points at the same time in the sensing unit. Additionally, for cost efficiency, the proposed system employs only one sensor at each spot and all the sensors are multiplexed. In multiplexed systems, when changing the liquid level inside the container, the float position is changed and leads to an overlap or cross-talk between two sensors. To solve this overlap problem and to accurately predict the liquid level of each container, we proposed a deep neural network (DNN) approach to properly identify the water level. The performance of the proposed DNN model is evaluated via two different scenarios and the result proves that the proposed DNN model can accurately predict the liquid level of each point. Furthermore, when comparing the DNN model with the conventional machine learning schemes, including random forest (RF) and support vector machines (SVM), the DNN model exhibits the best performance.
Value-Driven System Design of Utility-Scale Airborne Wind Energy

In the current auction-based electricity market, the design of utility-scale renewable energy systems has traditionally been driven by the levelised cost of energy (LCoE). However, the market is gradually moving towards a subsidy-free era, which will expose the power plant owners to the fluctuating prices of electricity. This paper presents a computational approach to account for the influence of time-varying electricity prices on the design of airborne wind energy (AWE) systems. The framework combines an analytical performance model, providing the power curve of the system, with a wind resource characterisation based on ERA5 reanalysis data. The resulting annual energy production (AEP) model is coupled with a parametric cost model based on reference prototype data from Ampyx Power B.V. extended by scaling laws. Ultimately, an energy price model using real-life data from the ENTSO-E platform maintained by the association of EU transmission system operators was used to estimate the revenue profile. This framework was then used to compare the performance of systems based on multiple economic metrics within a chosen design space. The simulation results confirmed the expected behaviour that the electricity produced at lower wind speeds has a higher value than that produced at higher wind speeds. To account for this electricity price dependency on wind speeds in the design process, we propose an economic metric defined as the levelised profit of energy (LPoE). This approach determines the trade-offs between designing a system that minimises cost and designing a system that maximises value.
Eagle Syndrome: Pathophysiology, Differential Diagnosis and Treatment Options

The present investigation summarizes relevant symptoms, differential diagnosis, imaging, and treatment options of Eagle Syndrome. A comprehensive literature review of peer-reviewed literature was employed utilizing most relevant databases. The diagnoses of Eagle Syndrome have recently increased because of increased awareness of physicians of Eagle Syndrome and the earlier identification of the cardinal symptoms of the disease. The most important symptoms are dysphagia in the setting of cervical neck pain, but there is a wide variety of symptomatology that make Eagle Syndrome a challenge to recognize and diagnose clinically. CT scan continues to be the standard of care for diagnosing Eagle Syndrome and CT Angiography has an important role in aiding diagnosis as well. Medical treatment options include steroids, antidepressants, and anticonvulsants however not all cases of Eagle Syndrome can be managed medically. Surgical approaches are varied but typically are either extraoral or transoral. This report aims to update providers on the important diagnostic criteria of Eagle Syndrome and how physicians can develop a treatment plan that addresses all the symptoms of patients with Eagle Syndrome because it can be treated safely and appropriately.
GOLD Synoptic Observations of Thermospheric Annual and Semiannual Variations in Composition During Solar Minimum Years

This study investigates thermospheric seasonal variations composed of annual and semiannual oscillations (AO and SAO) in the column density ratio of atomic oxygen to molecular nitrogen observed by Global‐scale Observations of Limb and Disk. On the basis of multiple analysis approaches, it is revealed that the AO is the predominant component at middle latitudes, while the SAO tends to be increasingly important toward the lower latitudes. The SAO is also evident in the southern mid‐latitudes. Quantitative analyses demonstrate that AO (SAO) peaks around winter solstices (equinoxes). Additionally, the seasonal variation at low‐latitudes exhibits greater amplitudes in the early morning and around noon, whereas it gradually weakens from early morning toward late afternoon at mid‐latitudes. A hypothesis is that seasonal variations are likely modulated by atmospheric tides, including those generated in the thermosphere and those propagating upward from the lower atmosphere.
Improvement of residual bilinearization by particle swarm optimization for achieving the second‐order advantage with unfolded partial least‐squares

The combination of unfolded partial least‐squares (U‐PLS) with residual bilinearization (RBL) provides a second‐order multivariate calibration method capable of achieving the second‐order advantage. RBL is performed by varying the test sample scores in order to minimize the residues of a combined U‐PLS model for the calibrated components and a principal component model for the potential interferents. The sample scores are then employed to predict the analyte concentration, with regression coefficients taken from the calibration step. When the contribution of multiple potential interferents is severe, particle swarm optimization (PSO) helps in preventing RBL to be trapped by false minima, restoring its predictive ability and making it comparable to the standard parallel factor (PARAFAC) analysis. Both simulated and experimental systems are analyzed in order to show the potentiality of the new technique. Copyright © 2007 John Wiley & Sons, Ltd.
Design the T-S fuzzy controller for a class of T-S fuzzy models via genetic algorithm

A simple Takagi-Sugeno (TS) fuzzy controller design method is proposed, which is based on the genetic algorithm (GA) and generalized inverse theory. The eigenvalues problems of LMI could be implemented by this approach. The proposed method can automatically seek the feedback gains without complex mathematical derivations. In this paper, we implement the TS fuzzy controller design problems in an alternative way.
When Artificial Intelligence Voices Human Concerns: The Paradoxical Effects of AI Voice on Climate Risk Perception and Pro-Environmental Behavioral Intention

Artificial intelligence (AI)-enabled text-to-speech transformation has been widely employed to deliver online information in various fields. However, few studies have investigated the effect of the AI voice in environmental risk communication, especially in the field of climate change, an issue that poses a severe threat to global public health. To address this gap, the current study examines how the AI voice impacts the persuasive outcome of climate-related information and the potential mechanism that underlies this process. Based on the social and affect heuristics of voice, we propose a serial mediation model to test the effect of climate-related information delivered by different voice types (AI voice vs. human voice) in eliciting risk perception and motivating pro-environmental behavioral intention. Through an online auditory experiment (N = 397), we found the following. First, the AI voice was as effective as the human voice in eliciting risk perception and motivating pro-environmental behavioral intention. Second, compared with human voice, the AI voice yielded a listener’s lower level of perceived identity oneness with the speaker, which decreased risk perception and subsequently inhibited pro-environmental behavioral intention. Third, compared with human voice, the AI voice produced a higher level of auditory fear, which increased risk perception and thereby led to stronger pro-environmental behavioral intention. The paradoxical role of the AI voice and its wise use in environmental risk communication for promoting global public health are discussed.
Analysis of the Difference between Climate Aridity Index and Meteorological Drought Index in the Summer Monsoon Transition Zone

The summer monsoon transition zone (SMTZ) in China represents an unusual land type with an agro-pasture ecotone, and it is a climate-sensitive region. Changes in climate aridity and changes in meteorological drought are mutually related yet fundamentally different. In this study, potential evapotranspiration (ETO) is calculated using Penman–Monteith, based on China’s national meteorological stations data from 1961 to 2013. An ETO-based climate aridity index (IAI) and ETO-based standardized precipitation evapotranspiration index (SPEI) are used as the metrics for climate aridity and meteorological drought, respectively. The result shows a significant difference between climate aridity and meteorological drought in the SMTZ, compared with the monsoon and non-monsoon zone. This difference varies on different time scales (1–48 months), and the greatest differences between IAI and SPEI are on seasonal and monthly scales (1–12 months), but lower at longer time scales (>12 months). The first reason for the difference is the desynchronicity of meteorological drought and the background climate. After the background climate becomes a relatively arid state (such as 0.96 < IAI < 1) from a semi-arid state (0.50 < IAI < 0.80), the continued arid state with weak IAI fluctuations eventually results in increasingly severe meteorological droughts, or the recurrence of equally severe droughts with drastic reduction. Consequently, the onset of the most severe climate aridity is two to seven months (mostly three to four months) ahead of the onset of the most severe drought events, until the climate returns to a semi-arid state. Second, climate aridity represents the average state of the background climate over a long time period and changes gently, while meteorological droughts are stochastic climate events and change drastically. These findings indicate that IAI can serve as a predictor of the onset of meteorological drought events, especially in the SMTZ, but it fails to characterize the progression of meteorological drought events well. Therefore, this result is of great significance for drought prediction and early warning.
Apelin-13 alleviates contrast-induced acute kidney injury by inhibiting endoplasmic reticulum stress

Abstract Contrast-induced acute kidney injury (CI-AKI) is a severe complication associated with significant morbidity and mortality, and effective therapeutic strategies are still lacking. Apelin is an endogenous physiological regulator with antioxidative, anti-inflammatory and antiapoptotic properties. However, the role of apelin-13 in CI-AKI remains unclear. In our study, we found that the protein expression levels of apelin were significantly downregulated in rat kidney tissues and HK-2 cells during contrast media treatment. Moreover, we explored the protective effect of apelin-13 on renal tubule damage using in vitro and in vivo models of CI-AKI. Exogenous apelin-13 ameliorated endoplasmic reticulum stress, reactive oxygen species and apoptosis protein expression in contrast media-treated cells and rat kidney tissues. Mechanistically, the downregulation of endoplasmic reticulum stress contributed critically to the antiapoptotic effect of apelin-13. Collectively, our findings reveal the inherent mechanisms by which apelin-13 regulates CI-AKI and provide a prospective target for the prevention of CI-AKI. Graphical Abstract
Prognostic value of the immunohistochemical score based on four markers in head and neck squamous cell carcinoma

Purpose Head and neck squamous cell carcinoma (HNSCC) ranks sixth among all cancers globally regarding morbidity, and it has a poor prognosis, high mortality, and highly aggressive properties. In this study, we established a model for predicting prognosis based on immunohistochemical (IHC) scores. Methods Data on 402 HNSCC cases were collected, the glmnet Cox proportional hazards model was used, risk factors were analyzed for predicting the prognosis of survival, and the IHC score was established. We used the IHC score to predict disease-free survival (DFS) using training and independent validation cohorts, including 264 cases in total. Additionally, the accuracy of the IHC score and the TNM system (8th edition) was compared. A DFS prediction nomogram was established by combining the prognostic factors. Results The IHC scores included CK, Ki-67, p16, and p40 staining intensity. The concordance index and the Kaplan-Meier survival analysis showed that the IHC scores had high predictive power for HNSCC. Our results showed that the IHC score is an independent factor that can predict prognosis in a multivariate Cox regression analysis. When predicting DFS, the IHC score had a significantly higher value for the area under the ROC curve (AUC) than that of the TNM system. A nomogram was established and included the IHC score, age, tumor location, and the TNM stage. The calibration curves exhibited high consistency between the prognosis predicted by our nomogram and the actual prognosis. Conclusions The IHC score was more accurate than the eighth edition of the TNM system in predicting HNSCC prognosis. Therefore, combining the two methods can facilitate individualized patient consultation and care.
The Impact of Dichotomization on the Efficiency of Testing for an Interaction Effect in Exponential Family Models

In a number of special cases, it has been shown that categorization of a continuous explanatory variable for use in a regression model can lead to efficiency losses. Nevertheless, categorization remains a popular means of dealing with continuous variables, particularly in medical statistics. Two topics of current interest in medical statistics are subgroup effects in clinical trials and gene–environment interactions in genetic studies. In a regression model setting, these topics involve the examination of interaction effects, often between a binary explanatory variable and a continuous explanatory variable. In this article the efficiency losses associated with dichotomization of a continuous explanatory variable for the testing of such interaction effects are calculated and compared with those associated with the testing of main effects. It is shown that considerable additional efficiency loss can arise because of dichotomization in both a main effect and an interaction. The theoretical development is done in the context of exponential family models, thus also generalizing earlier results on efficiency loss for main effects. Some indication is also given of the losses in efficiency associated with more detailed categorization. The further impact of censoring in time-to-event models is investigated and shown to not alter the qualitative conclusions. The practical importance of the findings is illustrated through the analysis of data from a clinical trial in patients with prostate cancer.
Synchronization of coupled augmented Lorenz oscillators with parameter mismatch

We have recently developed a chaotic gas turbine, the rotational motion of which might simulate the random reversals of large-scale circulation in turbulent Rayleigh-Bénard convection. The nondimensionalized equations of motion of the turbine are represented as a star network of many Lorenz subsystems sharing dimensionless angular velocity as the central node, referred to as augmented Lorenz equations. In this study, we investigate the dynamical nature of the augmented Lorenz model, focusing on the chaotic synchronizability of coupled augmented Lorenz oscillators with parameter mismatch.
Connection-graph-based event-triggered output consensus in multi-agent systems with time-varying couplings

Event-triggered strategies are effective to update local controllers in multi-agent systems (MASs) at necessary discrete times. Based on graph theoretical reasoning, the authors study the distributed event-triggered output consensus in MASs with time-varying couplings. Graph theoretical reasoning provides us a Lyapunov function that fully utilises the properties of a graph such as the path between two arbitrary agents. With this function, the updating of local output-feedback controllers is determined by connection-graph-based state-dependent or time-dependent event functions. Interesting enough, the proposed event-triggered schemes can also be effectively extended to non-linear MASs with time-varying couplings. Theoretical analysis and numerical simulations verify the main results.
Fabrication of a Lactate-Specific Molecularly Imprinted Polymer toward Disease Detection

The development of sensitive and selective robust sensor materials for targeted biomarker detection aims to contribute to self-health monitoring and management. Molecularly imprinted polymeric (MIP) materials can perform as biomimetic recognition elements via tailored routes of synthesis for specific target analyte extraction and/or detection. In this work, a sensitive- and selective-lactate MIP has been developed utilizing methacrylic acid and ethylene glycol dimethacrylate as the functional monomer and cross-linker, respectively. The sensitivity of the as-synthesized imprinted species was evaluated by determining the target analyte retention, imprinting factor, and selectivity adsorption of up to 63.5%, 6.86, and 0.82, respectively. MIP selectivity elucidated the imprinting mechanism between the functional monomers and target analyte lactate, further experimentally evidenced by using structurally competitive analytes malic acid and sodium 2-hydroxybutyrate, where retentions of 22.6 and 25.2%, respectively, were observed. Understanding the specific intermolecular mechanisms of both the template analyte and structural interferents with the MIP enables experimentalists to make informed decisions regarding monomer-target and porogen selections and possible sites of interaction for improved molecular imprinting. This imprinting system highlights the potential to be further developed into artificial receptor sensor materials for the detection of disease.
When We Practice to Deceive: Service Worker Manipulation and (Mis)Behavior on Cruise Ships

Studies of service delivery often assume that any deviance from service protocols when interacting with customers is largely due to service worker ineptitude or incompetence; this is particularly the case with customer deception or manipulation. This contrasts with studies which report that service workers frequently act in deviant ways. In tourist settings, service quality is paramount, and any attempts to deceive or manipulate customers is tantamount to service failure. This study explores service worker deception and manipulation on cruise ships through a series of in-depth interviews (n = 50) via the lens of “Deviant Leisure. ” This study finds that manipulation is a quotidian practice on cruise ships, including the manipulation by cruise ship frontline employees (including the manipulation of customers, co-workers, subordinates, and superordinates), and the manipulation of cruise ship frontline employees (including manipulation by customers, co-workers, and managers). The paper concludes with a discussion of contributions to theory and practice.
Neutrino--proton elastic scattering: Implications for weak-interaction models

We test the predictions of various gauge models for weak-neutral-current interactions by comparing them with experimental results on inclusive and elastic neutrino interactions. The Weinberg--Salam model is in fair agreement with the data; however, vectorlike theories can be ruled out. We also consider certain gauge models with five or six quarks and one or two right-handed doublets, and find that they fit the data rather well. (AIP)
Event entity alignment for multi-source encyclopedia knowledge bases with the similarity of event element sets

The key to constructing an event knowledge graph is to acquire event knowledge. At present, the method of event extraction from text is not accurate enough, while the event information obtained through encyclopedia knowledge bases has the advantages of high accuracy, good structure and rich multimedia resources. However, acquiring event entities from a single encyclopedia knowledge base has the problem of missing information, so the fusion technology for multisource encyclopedia knowledge bases is needed urgently, in which entity alignment is the core technology. Aiming at the shortcomings of current alignment methods focusing on static entity in event entity alignment, we propose an event entity alignment method based on event elements, which calculates entity similarity according to multiple event elements. In contrast to the algorithm based on latent Dirichlet allocation (LDA) model and the method based on representation learning using BERT, the proposed method provides a significant performance improvement in event entity alignment. Especially, the method optimizes the threshold setting so that it enhances the ability to identify the presence of aligned entities.
A new method for computing delay margins for stability of linear delay systems

This note is concerned with stability properties of linear time-invariant delay systems. We consider retarded delay systems modeled both as a high order scalar differential difference equation and as a set of first order differential-difference equations expressed in state space form. We provide a computational method that can be used to compute a delay interval such that the delay system under consideration is stable for all delay values that lie in the computed interval. This method requires computing only the eigenvalues and generalized eigenvalues of certain constant matrices and it can be implemented efficiently. Based on this method, we further state a simple necessary and sufficient condition concerning stability independent of delay for each of the two types of the models. <<ETX>>