Miracle-dz/UniFAR-FTUs huggingface dataset:
Operational predictability of monthly average maximum temperature over the Iberian Peninsula using DEMETER simulations and downscaling

The multi-model ensemble for seasonal to interannual prediction developed in the European Union project DEMETER has been used to quantify the predictability of monthly average maximum temperature that could be achieved operationally over the Iberian Peninsula. Statistical downscaling based on canonical correlation analysis is applied to increase the spatial resolution available from the global models. The downscaling is based on empirical connections between the North Atlantic sea level pressure and monthly average maximum temperature over the Iberian Peninsula. The maximum temperature estimated from the multi-model ensemble and the single models is compared to the observations. The statistical downscaling model skill is characterized by means of the correlation, variance fraction and the Brier skill score. The results suggest the following: the downscaling model works properly when driven by observed large-scale fields in terms of the correlation and the variance fraction scores, despite some problems owing to sample degeneracy; the predictability is almost limited to February, which is one of the initialization months of the DEMETER ensemble, and it is lost when this month is not considered as starting month. This result is supported by the fact that the areally averaged reproducibility is lower during non-initialization months. In any case, the analysis of the variance test performed reveals that the monthly average maximum temperature is scarcely predictable. Finally, the results also support the advantage of using a multi-model ensemble approach instead of single models participating in DEMETER.
Novel Hybrid Composites Based on Polymers of Diphenyl-Amine-2-Carboxylic Acid and Highly Porous Activated IR-Pyrolyzed Polyacrylonitrile

Hybrid composites based on electroactive polymers of diphenylamine-2-carboxylic acid (PDPAC) and highly porous carbon with a hierarchical pore structure were prepared for the first time. Activated IR-pyrolyzed polyacrylonitrile (IR-PAN-a), characterized by a highly developed surface, was chosen as a highly porous N-doped carbon component of the hybrid materials. IR-PAN-a was prepared using pyrolysis of polyacrylonitrile (PAN) in the presence of potassium hydroxide under IR radiation. Composite materials were obtained using oxidative polymerization of diphenylamine-2-carboxylic acid (DPAC) in the presence of IR-PAN-a both in an acidic and an alkaline medium. The composite materials were IR-heated to reduce the oxygen content and enhance their physical and chemical properties. The chemical structure, morphology, and electrical and thermal properties of the developed IR-PAN-a/PDPAC composites were investigated. The IR-PAN-a/PDPAC composites are thermally stable and electrically conductive. During the synthesis of the composites in an acidic medium, doping of the polymer component occurs, which makes the main contribution to the composite conductivity (1.3 × 10–5 S/cm). A sharp drop in the electrical conductivity of the IR-PAN-a/PDPACac-IR composites to 3.4 × 10–10 S/cm is associated with the removal of the dopant during IR heating. The IR-PAN-a/PDPACalk composites prepared before and after IR heating show a gradual increase in electrical conductivity by five orders of magnitude to 1.6 × 10–5 S/cm at 25–106 Hz. IR heating of the obtained materials leads to a significant increase in their thermal properties. The IR-heated composites lose half of their initial weight in an inert atmosphere at temperatures above 1000 °C, whereas for IR-PAN-a/PDPAC, the temperature range is 840–849 °C.
Femtosecond time-resolved observation of above-threshold ionization in Na 2

Femtosecond pump-probe techniques are applied to the measurement of above-threshold ionization (ATI) electrons to observe the influence of vibrational wave-packet dynamics on molecular above threshold ionization in real time. When ionized by a single laser pulse structural changes in the ATI electron spectra map the changes of the vibrational wave-packet evolution in the resonant intermediate electronic states as a function of laser intensity. Molecular vibrational motion in the coherently coupled electronic states dominates the electron energy distribution rather than the energy splitting induced by Rabi oscillations.
Estimates of coda-Q using a non-linear regression

A non-linear regression technique (Gauss-Newton method) is used to fit the single-scattering model of coda wave generation to experimental data. Comparisons between these results and those obtained applying the technique used in seismological practice (logarithmic linearization of the model equation and least-squares fit) are made. Data used are: a) a synthetic coda generated by white Gaussian noise modulated with an attenuation factor, exp(-kt), and a geometrical spreading factor; b) seismograms provided by two seismic series that occurred in the Abruzzo (Italy) and Antequera (Southern Spain) regions.The study from synthetic data shows that when the signal-to-noise ratio of the final part of the coda is greater or equal to 5, there are no significant differences in the values of Qc inferred by both techniques. When the signal-to-noise ratio is less than 5, we observed that the log-log technique produces a systematic overestimation of the values of Qc. Results obtained with real seismograms show that the log-log technique overestimates the Qc values when compared with the Gauss-Newton method, when the whole coda is used. When a long coda is used, the Qc dependence on frequency estimated using log-log is different in some cases from the one obtained with the Gauss-Newton technique. The Qc dependence on the lapse-time is less pronounced when the Gauss-Newton technique is used. This effect is more evident when different coda durations are obtained fixing the start-time of coda analysis, and increasing the end-time of the same coda. This pattern is also observed for the Abruzzo (Italy) and Antequera (Spain) series; when the log-log technique is used, a lapse-time dependence is observed, but, when the Gauss-Newton technique is used, this dependence is less significant.
Pathogenicity of phospholipase B1 of Trichosporon asahii in immunosuppressed mice

Trichosporon asahii is an opportunistic pathogenic yeast‐like fungus. Phospholipase B1 (PLB1) is an important virulence factor of pathogenic fungi such as Candida albicans and Cryptococcus neoformans, and there are few studies on the role of PLB1 in the pathogenicity of T. asahii.
A Critical Examination of the Observed First Aerosol Indirect Effect

Abstract The relative change in cloud droplet number concentration with respect to the relative change in aerosol number concentration, α, is an indicator of the strength of the aerosol indirect effect and is commonly used in models to parameterize this effect. Based on Twomey’s analytical expression, the values of α derived from measurements of an individual cloud (i.e., αT) can be as large as 0.60–0.90. In contrast, the values of α derived from direct measurements of polluted and clean clouds (i.e., αΔ) typically range from 0.25 to 0.85, corresponding to a weaker but more uncertain cooling effect. Clearly, reconciling αΔ with αT is necessary to properly calculate the indirect aerosol forcing. In this study, the terms that are involved in determining αT and αΔ are first analytically examined. Then, by analyzing satellite data over subtropical oceans, the satellite-observed αΔ can be successfully related to Twomey’s analytical solution. It is found that except for the dust-influenced region of the northea.
Throughput Maximization Using Deep Complex Networks for Industrial Internet of Things

The high-density Industrial Internet of Things needs to meet the requirements of high-density device access and massive data transmission, which requires the support of multiple-input multiple-output (MIMO) antenna cognitive systems to keep high throughput. In such a system, spectral efficiency (SE) optimization based on dynamic power allocation is an effective way to enhance the network throughput as the channel quality variations significantly affect the spectral efficiency performance. Deep learning methods have illustrated the ability to efficiently solve the non-convexity of resource allocation problems induced by the channel multi-path and inter-user interference effects. However, current real-valued deep-learning-based power allocation methods have failed to utilize the representational capacity of complex-valued data as they regard the complex-valued channel data as two parts: real and imaginary data. In this paper, we propose a complex-valued power allocation network (AttCVNN) with cross-channel and in-channel attention mechanisms to improve the model performance where the former considers the relationship between cognitive users and the primary user, i.e., inter-network users, while the latter focuses on the relationship among cognitive users, i.e., intra-network users. Comparison experiments indicate that the proposed AttCVNN notably outperforms both the equal power allocation method (EPM) and the real-valued and the complex-valued fully connected network (FNN, CVFNN) and shows a better convergence rate in the training phase than the real-valued convolutional neural network (AttCNN).
The role of p63 in embryonic external genitalia outgrowth in mice

Embryonic external genitalia (genital tubercle [GT]) protrude from the cloaca and outgrow as cloacal development progresses. Individual gene functions and knockout phenotypes in GT development have been extensively analyzed; however, the interactions between these genes are not fully understood. In this study, we investigated the role of p63, focusing on its interaction with the Shh–Wnt/Ctnnb1–Fgf8 pathway, a signaling network that is known to play a role in GT outgrowth. p63 was expressed in the epithelial tissues of the GT at E11.5, and the distal tip of the GT predominantly expressed the ΔNp63α isoform. The GTs in p63 knockout embryos had normal Shh expression, but CTNNB1 protein and Fgf8 gene expression in the distal urethral epithelium was decreased or lost. Constitutive expression of CTNNB1 in p63‐null embryos restored Fgf8 expression, accompanied by small bud structure development; however, such bud structures could not be maintained by E13.5, at which point mutant GTs exhibited severe abnormalities showing a split shape with a hemorrhagic cloaca. Therefore, p63 is a key component of the signaling pathway that triggers Fgf8 expression in the distal urethral epithelium and contributes to GT outgrowth by ensuring the structural integrity of the cloacal epithelia. Altogether, we propose that p63 plays an essential role in the signaling network for the development of external genitalia.
Impact of Long Working Hours on Mental Health: Evidence from China

Although previous studies have examined the impact of long working hours on mental health in China, they have not addressed the initial value and reverse causality issues. To bridge this gap in the literature, I conducted a dynamic longitudinal analysis to investigate the association between long working hours and the risk of mental illness nationwide. Using three-wave longitudinal data from the China Family Panel Studies conducted in 2014, 2016, and 2018, I adopted dynamic regression models with lagged long working hours variables to examine their association with the risk of mental illness. The results indicate that long working hours have positive and significant (p < 0.01 or p < 0.05) associations with the risk of mental illness (OR: 1.12~1.22). The effect is more significant for women, white-collar workers, and employees in micro-firms, compared with their counterparts (i.e., men, pink- and blue-collar workers, employees of large firms, and self-employed individuals). The results provide empirical evidence of the effects of long working hours on mental health in China, confirming the need to enforce the regulations regarding standard working hours and monitor regulatory compliance by companies, as these factors are expected to improve mental health.
A Novel Point Cloud Registration Method Based on ROPNet

Point cloud registration is a crucial preprocessing step for point cloud data analysis and applications. Nowadays, many deep-learning-based methods have been proposed to improve the registration quality. These methods always use the sum of two cross-entropy as a loss function to train the model, which may lead to mismatching in overlapping regions. In this paper, we designed a new loss function based on the cross-entropy and applied it to the ROPNet point cloud registration model. Meanwhile, we improved the ROPNet by adding the channel attention mechanism to make the network focus on both global and local important information, thus improving the registration performance and reducing the point cloud registration error. We tested our method on ModelNet40 dataset, and the experimental results demonstrate the effectiveness of our proposed method.
ATMOSPHERIC EXTINCTION IN THE NEAR INFRARED.

A terrestrial model atmosphere has been used to compute theoretical atmospheric transmissions at very high resolution for various column densities of H2O, CO2, and other molecules. These calculations were used to study the atmospheric extinction for various narrow- and broad-band infrared colors. These computations show that conventional corrections for this extinction by linear extrapolation of stellar observations to zero air mass will yield stellar magnitudes which are too faint, by as much as 0.24 magnitude. This extinction error depends slightly on stellar color. Quantitative results are presented specifically for Kitt Peak and Mauna Kea but should be applicable for the reduction of data obtained at different sites. The extinction problems are found to be especially severe for bandpasses designed to measure stellar water band strengths.
Cross-Cultural Adaption and Validation of SERVPERF Tool for Measuring Healthcare Quality in an Oncology Public Hospital, Vietnam

SERVPERF (Service Performance) tool needs to be adapted to the context and the usage purpose. Our study aimed to validate the context adapted SERVPERF tool in an Oncology public hospital in Vietnam. A study was conducted in 2020 with 227 in-patients as respondents the modified SERVPERF tool. Data collected were analyzed for tool assessment (reliability and validity). The new order in 5 factors in the modified tool were: (1) Responsiveness; (2) Empathy; (3) Reliability; (4) Tangible and (5) Assurance. The modified tool has a high Cronbach’s alpha of . 94. The tool validity was confirmed by Confirmatory Factor Analysis (CFA) where the healthcare service quality item corresponds as χ2/df = 3.79, Comparative Fit Index (CFI) = 0.85, Tucker Lewis Index (TLI) = 0.83, and Root Mean Squared Error of Approximation (RMSEA) = 0.08 are good fit indices. The modified tool SERVPERF with high reliability and validity could be applied for measuring the clients’ perceptions about healthcare service quality in other Oncology public hospitals in Vietnam.
Histopathological Changes in the Myocardium Caused by Energy Drinks and Alcohol in the Mid-term and Their Effects on Skeletal Muscle Following Ischemia-reperfusion in a Rat Model

Background: Although energy drinks have been consumed for many years, their effects on the cardiovascular system continue to be investigated. Today, the most frequently used area of energy drinks is the entertainment sector, and this study investigates the effects of energy drinks and alcohol consumption on rats’ limb and myocardium tissue. Methods: Forty Wistar Albino rats were used and divided into 4 groups. Energy drinks were given to the first group (the energy drink group), alcohol was given to the second group, and energy drinks and alcohol were given to the third group Redbull-Alcohol (RA). Blood samples, leg muscles, and heart tissues were studied after the ischemia–reperfusion model was created at the infrarenal level. Results: In the histopathological examination of heart muscles, the damage was significantly more severe in the RA group than in the control group (P < .05). There was no significant change in the RA group in the limb muscle; however, muscle fiber abnormality was higher. The energy drink group was more prone to carbon dioxide retention and hypoxia, resulting in respiratory acidosis. (P = .05). Lactate was significantly higher in the energy drink group (P = .002). Glucose concentrations of energy drink and RA groups were higher (P = .02). Conclusion: The high lactate values of the energy drink group and more damaged fibers in the striated muscles in the RA group showed that they are more susceptible to ischemia. Long-term energy drinks and alcohol use may cause damage to the heart muscle and endothelium. Also, the effects of long-term alcohol and energy drink use on the respiratory system should be investigated with more specific studies.
Herbal Products and Their Active Constituents for Diabetic Wound Healing—Preclinical and Clinical Studies: A Systematic Review

The purpose of this review is to provide verified data on the current knowledge acquired in preclinical and clinical studies regarding topically used herbal products and their active constituents (formulations and dressings) with diabetic wound healing activity. Moreover, herbal products and their active constituents used for diabetic wound infections, and various cellular and molecular mechanisms of their actions will also be described. The electronic databases were searched for articles published from 2012 to 2022. Publications with oral or systemic administration of herbal products in diabetic wound healing, published before 2012, available only as an abstract, or in languages other than English were excluded from the study. The 59 articles comparing topically used herbal products in diabetic wound healing treatment versus control treatments (placebo or active therapy) were selected. Herbal products through different mechanisms of action, including antimicrobial, anti-inflammatory, antioxidant activity, stimulation of angiogenesis, production of cytokines and growth factors, keratinocytes, and fibroblast migration and proliferation may be considered as an important support during conventional therapy or even as a substitute for synthetic drugs used for diabetic wound treatment.
Using Deep Learning to Recognize Therapeutic Effects of Music Based on Emotions

Music is important in everyday life, and music therapy can help treat a variety of health issues. Music listening is a technique used by music therapists in various clinical treatments. As a result, music therapists must have an intelligent system at their disposal to assist and support them in selecting the most appropriate music for each patient. Previous research has not thoroughly addressed the relationship between music features and their effects on patients. The current paper focuses on identifying and predicting whether music has therapeutic benefits. A machine learning model is developed, using a multi-class neural network to classify emotions into four categories and then predict the output. The neural network developed has three layers: (i) an input layer with multiple features; (ii) a deep connected hidden layer; (iii) an output layer. K-Fold Cross Validation was used to assess the estimator. The experiment aims to create a machine-learning model that can predict whether a specific song has therapeutic effects on a specific person. The model considers a person’s musical and emotional characteristics but is also trained to consider solfeggio frequencies. During the training phase, a subset of the Million Dataset is used. The user selects their favorite type of music and their current mood to allow the model to make a prediction. If the selected song is inappropriate, the application, using Machine Learning, recommends another type of music that may be useful for that specific user. An ongoing study is underway to validate the Machine Learning model. The developed system has been tested on many individuals. Because it achieved very good performance indicators, the proposed solution can be used by music therapists or even patients to select the appropriate song for their treatment.
Randomized Clinical Trial Comparing Insertion Torque and Implant Stability of Two Different Implant Macrogeometries in the Initial Periods of Osseointegration

Objectives: The present study compared two implants with different macrogeometries placed in healed alveolar sites, evaluating the insertion torque (ITV) and implant stability quotient (ISQ) values at three different periods. Methods: Seventy patients with a total of 100 dental implants were allocated into two groups (n = 50 per group): DuoCone implants (DC group) that included 28 implants in the maxilla and 22 in the mandible, and Maestro implants (MAE group) that included 26 in the maxilla and 24 in the mandible. The ITV was measured during the implant placement, and the ISQ values were measured immediately at implant placement (baseline) and after 30 and 45 days. Results: The mean and standard deviations of the ITV were statistically significant (p < 0.0001), 56.4 ± 6.41 Ncm for the DC group and 29.3 ± 9.65 Ncm for the MAE group. In the DC group, the ISQs ranged between 61.1 ± 3.78 and 69.8 ± 3.86, while the MAE group presented similar values compared with the other group, ranging between 61.9 ± 3.92 and 72.1 ± 2.37. Conclusions: The value of implant insertion torque did not influence the ISQ values measured immediately after implant placement. However, the ITV influenced the ISQ values measured in the two initial periods of osseointegration, with implants installed with lower torques presenting higher ISQ values.
COSIBAS Platform—Cognitive Services for IoT-Based Scenarios: Application in P2P Networks for Energy Exchange

The revolution generated by the Internet of Things (IoT) has radically changed the world; countless objects with remote sensing, actuation, analysis and sharing capabilities are interconnected over heterogeneous communication networks. Consequently, all of today’s devices can connect to the internet and can provide valuable information for decision making. However, the data collected by different devices are in different formats, which makes it necessary to develop a solution that integrates comprehensive semantic tools to represent, integrate and acquire knowledge, which is a major challenge for IoT environments. The proposed solution addresses this challenge by using IoT semantic data to reason about actionable knowledge, combining next-generation semantic technologies and artificial intelligence through a set of cognitive components that enables easy interoperability and integration for both legacy systems and emerging technologies, such as IoT, to generate business value in terms of faster analytics and improved decision making. Thus, combining IoT environments with cognitive artificial intelligence services, COSIBAS builds an abstraction layer between existing platforms for IoT and AI technologies to enable cognitive solutions and increase interoperability across multiple domains. The resulting low-cost cross platform supports scalability and the evolution of large-scale heterogeneous systems and allows the modernization of legacy infrastructures with cognitive tools and communication mechanisms while reusing assets.