Miracle-dz/UniFAR-FTUs huggingface dataset:
Synergistic Retrieval of Temperature and Humidity Profiles from Space-Based and Ground-Based Infrared Sounders Using an Optimal Estimation Method

The atmospheric temperature and humidity profiles of the troposphere are generally measured by radiosondes and satellites, which are essential for analyzing and predicting weather. Nevertheless, the insufficient observation frequencies and low detection accuracy of the boundary layer restricts the description of atmospheric state changes by the temperature and humidity profiles. Therefore, this work focus on retrieving the temperature and humidity profiles using observations of the FengYun-4 (FY-4) Geostationary Interferometric Infrared Sounder (GIIRS) combined with ground-based infrared spectral observations from the Atmospheric Emitted Radiance Interferometer (AERI), which are more accurate than space-based individual retrieval results and have a wider effective retrieval range than ground-based individual retrieval results. Based on the synergistic observations, which are made by matching the space-based and ground-based data with those of different spatial and temporal resolutions, a synergistic retrieval process is proposed to obtain the temperature and humidity profiles at a high frequency under clear-sky conditions based on the optimal estimation method. In this research, using the line-by-line radiative transfer model (LBLRTM) as the forward model for observing simulations, a retrieval experiment was carried out in Qingdao, China, where an AERI is situated. Taking radiosonde data as a reference for comparing the retrieval results of the temperature and humidity profiles of the troposphere, the root-mean-square error (RMSE) of the synergistic retrieval algorithm below 400 hPa is within 2 K for temperature and within 12% for relative humidity. Compared with the GIIRS individual retrieval, the RMSE of temperature and relative humidity for the synergistic method is reduced by 0.13–1.5 K and 2.7–4.4% at 500 hPa, and 0.13–2.1 K and 2.5–7.2% at 900 hPa. Moreover, the forecast index (FI) calculated from the retrieval results shows reasonable consistency with the FIs calculated from the ERA5 reanalysis and from radiosonde data. The synergistic retrieval results have higher temporal resolution than space-based retrieval results and can reflect the changes in the atmospheric state more accurately. Overall, the results demonstrated the promising potential of the synergistic retrieval of temperature and humidity profiles at high accuracy and high temporal resolution under clear-sky conditions from FY-4/GIIRS and AERI.
Bioconversão de coprodutos agroindustriais utilizando fungos Pleurotus ostreatus e Pleurotus sajor-caju para alimentação de ruminantes

O uso de coprodutos para a alimentação de ruminantes, aliado a biotecnologias, apresenta-se como solução paraentraves ambientais, sociais e econômicos. Objetivou-se com este trabalho avaliar o enriquecimento nutri-cional de coprodutos agroindustriais (torta de nabo forrageiro, TN; farelo de soja, FS; e bagaço-de-cana-de açúcar, BC), bioconvertidos por fungos Pleurotus ostreatus e Pleurotus sajor-caju, na alimentação de ruminantes. Foram realizadas análises de matéria seca, proteína bruta, extrato etéreo, cinzas e digestibilidade in vitro da matéria seca (DIVMS) antes e após a bioconversão dos alimentos pelos fungos P. ostreatus e P. sajor-caju. Foram utilizados os três substratos nas seguintes proporções: 100% e nas associações TN+FS (50:50), TN+BC (70:30), TN+FS (70:30) e TN+FS+BC (40:40:20). A bioconversãosão alterou os teores de proteína bruta, cinzas e DIVMS dos coprodutos utilizados, propor-cionando um enriquecimento nutricional desses substratos para serem utilizados na alimentação de ruminantes. Os teores de celulose e hemicelulose não foram reduzidos na maioria dos substratos avaliados, no entanto houve diminuição dos níveis de extrato etéreo e lignina. Os resultados obtidos nesse estudo demons-tram que tratamentos biotecnológicos de coprodutos agroindustriais por fungos se tornam uma solução ambientalmente correta devido ao reaproveitamento, transformando-os em produtos com maior valor agregado para a alimentação de ruminantes.
Metal Resistant Enterobacter cloacae ZA14 Enhanced Seedling Vigor and Metal Tolerance through Improved Growth, Physiology and Antioxidants in Tomato (Solanum lycopersicum) Irrigated with Textile Effluents

The presence of toxic heavy metals and dyes in textile wastewater is a serious problem contaminating vegetables by irrigation. This contaminated food upon consumption undermines human health and is lethal for human life. The endophytic bacteria have the ability to degrade textile dyes and remediate heavy metals. The purpose of the present investigation was to evaluate useful concentration levels of textile wastewater (TWW) for irrigation in combination with the endophytic bacterium Enterobacter cloacae ZA14 to remediate heavy metals for improving growth of the tomato (Solanum lycopersicum) plant. The tomato seedlings showed inhibited germination (52%); suppressed root length (55%) and shoot length (53%); declined RWC (47%); lowest CSI (34%); reduced MSI (32%); increased accumulation of heavy metals Cr, Pb, and Cd in roots and shoots; with decreased metal tolerance index; and rise in production of total thiols (57%) at use of 100% TWW without bacterial application. On the contrary, the supplementation of endophytic bacterium ZA14 showed improved germination (100%), a decline of 3 and 5% in root and shoot length respectively, increased CSI (13%), decrease in MSI (6%), reduced bioaccumulation of Cr (root 30 and shoot 56%), Pb (root 58 and shoot 65%), and Cd (root 21 and shoot 58%), total thiols (76%), when irrigated with 25% TWW. Hence, it is concluded that the irrigation with 25% TWW, along with the application of Enterobacter cloacae ZA14, may improve the growth of tomato by mitigating the phytotoxicity of dyes and heavy metals from textile wastewater.
THE PHOTOMETRIC AND SPECTRAL EVOLUTION OF THE 2008 LUMINOUS OPTICAL TRANSIENT IN NGC 300

The 2008 optical transient in NGC 300 is one of a growing class of intermediate-luminosity transients that brighten several orders of magnitude from a previously optically obscured state. The origin of their eruptions is not understood. Our multi-wavelength photometry and spectroscopy from maximum light to more than a year later provide a record of its post-eruption behavior. We describe its changing spectral energy distribution, the evolution of its absorption- and emission-line spectrum, the development of a bipolar outflow, and the rapid transition from a dense wind to an optically thin ionized wind. In addition to strong, narrow hydrogen lines, the F-type absorption-line spectrum of the transient is characterized by strong Ca ii and [Ca ii] emission. The very broad wings of the Ca ii triplet and the asymmetric [Ca ii] emission lines are due to strong Thomson scattering in the expanding ejecta. Post-maximum, the hydrogen and Ca ii lines developed double-peaked emission profiles that we attribute to a bipolar outflow. Between approximately 60 and 100 days after maximum, the F-type absorption spectrum, formed in its dense wind, weakened and the wind became transparent to ionizing radiation. We discuss the probable evolutionary state of the transient and similar objects such as SN 2008S and conclude that they were most likely post-red supergiants or post-asymptotic giant branch stars on a blue loop to warmer temperatures when the eruption occurred. These objects are not luminous blue variables.
Event-triggered-based online integral reinforcement learning for optimal control of unknown constrained nonlinear systems

For unknown nonlinear systems with actuator saturation, an online policy iteration-based algorithm is employed to solve the optimal event-triggered control problem. To learn the system dynamics, a novel identifier is proposed to make the estimation error converge quickly and the experience replay technique is employed to release the persistence of the excitation condition. To approximate the cost function and the event-triggered control law, we present event-triggered-based critic and actor networks, whose weights are updated only at triggered instants. During the policy iteration process, an event-triggered-based integral reinforcement learning method is proposed to solve the Hamilton–Jacobi–Bellman equation. By utilising the integral reinforcement learning, the network resource is saved and learning efficiency is improved. Based on the Lyapunov method, stability for the closed-loop system and estimation errors for the three networks are analysed. At last, simulation results of two numerical examples are used to show the effectiveness of the proposed method.
Polymer–fullerene bulk heterojunction solar cells

Organic solar cells have the potential to be low-cost and efficient solar energy converters, with a promising energy balance. They are made of carbon-based semiconductors, which exhibit favourable light absorption and charge generation properties, and can be manufactured by low temperature processes such as printing from solvent-based inks, which are compatible with flexible plastic substrates or even paper. In this review, we will present an overview of the physical function of organic solar cells, their state-of-the-art performance and limitations, as well as novel concepts to achieve a better material stability and higher power conversion efficiencies. We will also briefly review processing and cost in view of the market potential.
Mode-Locking Dynamics in an All-PM Figure-Nine Tm-Doped Fiber Laser

We report a study on pulse dynamics in figure-nine Tm-doped all-polarization maintaining fiber laser. We analyzed laser operation from self-starting with multi-pulse dynamic to single-pulse operation by decreasing the pump power from the mode-locking threshold. By choosing a reliable setting of waveplates, our laser was generating pulses at the central wavelength of 1985 nm with a half-width of the spectrum, pulse duration, and pulse energy equal 6.4 nm, 650 fs, 177 pJ for the output port and 19.2 nm, 1279 fs, 57 pJ for the reject port in the single-pulse state. In the multi-pulse state, we recorded optical spectra, temporal waveforms, and average power at both exit ports. By analyzing temporal traces and output to reject port ratio power, we can distinguish between eight states of operation which follow an exact pattern. In the case of the single-pulse regime, we performed a further laser characterization, including relative intensity noise.
A Chain of Dark Clouds in Projection Against the Galactic Center

In a J , H ,a ndKS band survey of the Galactic center region over an area of 2 i � 5 i , we have found many dark clouds, among which a distinguished chain of dark clouds can be identified with a quiescent CO cloud. The distances of the clouds are estimated to be 3.2-4.2 kpc, corresponding to the Norma arm, by our new method for determining the distances to dark clouds using the cumulative number of stars against the JKS color. By adopting these estimated distances, the size and total mass of the cloud are estimated to be � 70 pc in length and 6 � 10 4 Mˇ, respectively. Three compact H II regions exist in the cloud, indicating that star-forming activities are going on at the cores of the quiescent CO cloud on the spiral arm.
Expanding the Mind–Body–Environment Connection to Enhance the Development of Cultural Humility

The unique health care needs of diverse individuals and communities are complex. To meet these needs, healthcare professionals are being called upon to alter traditional ways of thinking, perceiving, and acting in order to create more inclusive environments. Research shows that using mindsight, a process that increases both insight and empathy, can enhance an individual’s mind–body-environment connection, increase self-awareness, and promote the development of cultural humility. This paper will discuss the current perspectives on the mind/body/environment connection from a Western lens that may impact the enactment of cultural humility for healthcare providers. Two evidence-based approaches, yoga and forest therapy, are recommended as effective intervention tools in fostering mindsight and cultural humility. Blending traditional cognitive learning with techniques anchored in the physical body may hold promise in supporting the development of mindsight and cultural humility in healthcare education and practice.
Heat Transport during Colloidal Mixture of Water with Al2O3-SiO2 Nanoparticles within Porous Medium: Semi-Analytical Solutions

In recent years, energy consumption has become an essential aspect in the manufacturing industry, and low heat transfer is one of the obstacles that affect the quality of the final product. This situation can be managed by suspending nanoparticles into ordinary heat transferring fluid (the base fluid). This newly prepared colloidal suspension has better heat transport capabilities. Keeping such usage of nanofluids in mind, this research was performed to better understand the heat transport characteristics during flow analysis saturated in porous media subject to Al2O3-SiO2/water hybrid nanofluids. This flow problem was generated by a stretching/shrinking surface. The surface of the sheet was under the influence of mass suction and second-order partial slip. The boundary layer flow was formulated in a system of partial differential equations by utilizing basic conservation laws in conjunction with the Tiwari and Das nanofluid model. Then, the appropriate form of the similarity transformation was adapted to transform the model into a system of ordinary differential equations. The built-in function, i.e., the bvp4c function in the MATLAB software, solved the reduced form of the boundary layer model. The novelty of this study lay in the predicting of two different exact and numerical solutions for both the flow and temperature fields. The computed results showed that the medium porosity as well as the nanoparticle volume fraction widened the existence range of the dual solutions. In addition, the investigational output exposed the fact that the temperature fields were significantly enhanced by the higher nanoparticle volume fraction. Moreover, the outcomes of this study showed a superb correlation with existing works. The present results can be utilized in various branches of science and engineering such as the polymer industry and in the treatment of different diseases.
Effects of thermal expansion on moderately intense turbulence in premixed flames

The study aims at analytically and numerically exploring the influence of combustion-induced thermal expansion on turbulence in premixed flames. In the theoretical part, contributions of solenoidal and potential velocity fluctuations to the unclosed component of the advection term in the Reynolds-averaged Navier-Stokes equations are compared and a new criterion for assessing the importance of the thermal expansion effects is introduced. The criterion highlights a ratio of the dilatation in the laminar flame to the large-scale gradient of root-mean-square (rms) velocity in the turbulent flame brush. To support the theoretical study, direct numerical simulation (DNS) data obtained earlier from two complex-chemistry, lean H2-air flames are analyzed. In line with the new criterion, even at sufficiently high Karlovitz numbers, results show significant influence of combustion-induced potential velocity fluctuations on the second moments of the turbulent velocity upstream of and within the flame brush. In particular, the DNS data demonstrate that (i) potential and solenoidal rms velocities are comparable in the unburnt gas close to the leading edge of the flame brush and (ii) potential and solenoidal rms velocities conditioned to unburnt gas are comparable within the entire flame brush. Moreover, combustion-induced thermal expansion affects not only the potential velocity, but even the solenoidal one. The latter effects manifest themselves in a negative correlation between solenoidal velocity fluctuations and dilatation or in the counter-gradient behavior of the solenoidal scalar flux. Finally, a turbulence-in-premixed-flame diagram is sketched to discuss the influence of combustion-induced thermal expansion on various ranges of turbulence spectrum.
Higher‐latitude spring‐flowering herbs advance their phenology more than trees with warming temperatures

The phenologies of co‐occurring trees and spring‐blooming understory herbs in northeastern North American hardwood forests appear to be regulated by different environmental drivers – air temperature and soil temperature/snowpack, respectively. Accordingly, it has been hypothesized that climate change–driven asymmetry in the advancement of canopy leaf‐out relative to the timing of understory growth could reduce photosynthetic rates and reproductive success of understory herbs through greater early‐season shading. To determine whether trees and spring‐flowering forest herbs are advancing their phenologies at different rates with respect to increasing global temperatures, we examined the phenological responses to warming of 10 species of trees and 11 species of spring‐flowering forest herbs (8045 observations from 965 sites) in northeastern North America using 13 years of data collected by citizen scientists under the auspices of the USA‐National Phenology Network. Contrary to expectation, the degree of advancement of leaf‐out as a function of temperature was greater in spring‐flowering forest herbs than in trees, with a mean response rate of −4.9 days/°C (95% BCI [−5.2, −4.6]) for spring‐flowering forest herbs vs. −3.3 days/°C (95% BCI [−3.5, −3.1]) for trees. However, the response to temperature was not consistent across the latitudinal range, with spring‐flowering forest herbs responding more strongly to warming than trees at middle (40–44°N) and higher (45–48°N) latitudes but not at lower latitudes (35–39°N). Synthesis. In contrast to previous suggestions, our study shows spring‐flowering forest herbs advancing their phenology at a higher rate than trees with respect to warming through most of the latitudinal range investigated, which could translate into a longer growing season and increased carbon uptake for spring‐flowering forest herbs as spring temperatures rise.
Finger Vein Verification on Different Datasets Based on Deep Learning with Triplet Loss

In this study, deep learning and triplet loss function methods are used for finger vein verification research, and the model is trained and validated between different kinds of datasets including FV-USM, HKPU, and SDUMLA-HMT datasets. This work gives the accuracy and other evaluation indexes of finger vein verification calculated for different training-validation set combinations and gives the corresponding ROC curves and AUC values. The accuracy of the best result has reached 98%, and all the ROC AUC values are above 0.98, indicating that the obtained model can identify the finger veins well. Since the experiments are cross-validated between different kinds of datasets, the model has good adaptability and applicability. From the experimental results, it is also found that the model trained on the dataset that is more difficult to be distinguished will be a better and more robust model.
The Role of Novel Cardiac Imaging for Contemporary Management of Heart Failure

Heart failure is becoming the central problem in cardiology. Its recognition, differential diagnosis, and the monitoring of therapy are intimately coupled with cardiac imaging. Cardiac imaging has witnessed an explosive growth and differentiation, with echocardiography continuing as the first diagnostic step; the echocardiographic exam itself has become considerably more complex than in the last century, with the assessment of diastolic left ventricular function and strain imaging contributing important information, especially in heart failure. Very often, however, echocardiography can only describe the fact of functional impairment and morphologic remodeling, whereas further clarification of the underlying disease, such as cardiomyopathy, myocarditis, storage diseases, sarcoidosis, and others, remains elusive. Here, cardiovascular magnetic resonance and perfusion imaging should be used judiciously to arrive as often as possible at a clear diagnosis which ideally enables specific therapy.
Applying Artificial Intelligence to Wearable Sensor Data to Diagnose and Predict Cardiovascular Disease: A Review

Cardiovascular disease (CVD) is the world’s leading cause of mortality. There is significant interest in using Artificial Intelligence (AI) to analyse data from novel sensors such as wearables to provide an earlier and more accurate prediction and diagnosis of heart disease. Digital health technologies that fuse AI and sensing devices may help disease prevention and reduce the substantial morbidity and mortality caused by CVD worldwide. In this review, we identify and describe recent developments in the application of digital health for CVD, focusing on AI approaches for CVD detection, diagnosis, and prediction through AI models driven by data collected from wearables. We summarise the literature on the use of wearables and AI in cardiovascular disease diagnosis, followed by a detailed description of the dominant AI approaches applied for modelling and prediction using data acquired from sensors such as wearables. We discuss the AI algorithms and models and clinical applications and find that AI and machine-learning-based approaches are superior to traditional or conventional statistical methods for predicting cardiovascular events. However, further studies evaluating the applicability of such algorithms in the real world are needed. In addition, improvements in wearable device data accuracy and better management of their application are required. Lastly, we discuss the challenges that the introduction of such technologies into routine healthcare may face.
Academic Motivation of Indonesian University Students: Relationship with Self-Compassion and Resilience

Academic motivation is an important construct for university students, associated with student wellbeing and academic performance. Students who are motivated tend to feel and perform well. Self-compassion, that is kindness and understanding towards oneself in difficult times, and resilience, an ability to bounce back from difficulties, are also associated with student wellbeing and academic achievement. However, how these variables are related to each other has not been evaluated in Indonesian university students. Indonesian higher education has rapidly developed, focusing on student achievement while their wellbeing suffers. Understanding how academic motivation is linked with self-compassion and resilience can inform an effective way to augment their motivation. Accordingly, this cross-sectional study evaluated the relationship among these three variables. An opportunity sample of 156 students in Indonesia completed measures about those three constructs. Correlation, regression and moderation analyses were used. Intrinsic motivation was positively associated with extrinsic motivation and resilience. Amotivation was negatively associated with self-compassion and resilience. Self-compassion was positively associated with resilience. Both self-compassion and resilience predicted all three types of motivation apart from self-compassion for intrinsic motivation. Lastly, self-compassion nor resilience moderated the pathway from extrinsic motivation to intrinsic motivation. Resilience interventions are recommended for Indonesian students to maintain intrinsic motivation. Our findings will help educators and wellbeing staff to identify helpful ways to support healthful motivation in this student population that is undergoing drastic changes.