Miracle-dz/UniFAR-FTUs huggingface dataset:
HIGH-RESOLUTION STABLE ISOTOPE PROFILES OF A DIMITOBELID BELEMNITE: IMPLICATIONS FOR PALEODEPTH HABITAT AND LATE MAASTRICHTIAN CLIMATE SEASONALITY

Abstract Oxygen and carbon isotope ratios were measured on belemnites, planktonic foraminifera, and benthic foraminifera collected from Late Cretaceous sediments on Seymour Island, Antarctic Peninsula, to compare the relative depth habitats of these organisms and to provide insight on temperature seasonality at high southern latitudes near the end of the Cretaceous. Average δ18O and δ13C values of the belemnite Dimitobelus seymouriensis overlap with those of benthic foraminifera; these values are significantly different (p < 0.01) from isotopic compositions recorded by planktonic foraminifera. This comparison shows that D. seymouriensis likely inhabited waters below the surface summer mixed layer of the outer shelf. These results suggest that average δ18O of D. seymouriensis is a good indicator of intermediate-to-deep-shelf water conditions, which in this locality is a good approximation for pelagic water temperatures in the Southern Ocean. Measurements of δ18O on high-resolution samples across belemnite growth bands indicate that annual temperature variability of sub-mixed-layer neritic water along the Antarctic coastline was 5°C on average with a mean annual temperature of 6°C.
Academy of Breastfeeding Medicine Clinical Protocol #21: Breastfeeding in the Setting of Substance Use and Substance Use Disorder (Revised 2023).

Background: The Academy of Breastfeeding Medicine (ABM) revised the 2015 version of the substance use disorder (SUD) clinical protocol to review the evidence and provide updated literature-based recommendations related to breastfeeding in the setting of substance use and SUD treatments. Key Information: Decisions around breastfeeding are an important aspect of care during the peripartum period, and there are specific benefits and risks for substance-exposed mother-infant dyads. Recommendations: This protocol provides breastfeeding recommendations in the setting of nonprescribed opioid, stimulant, sedative-hypnotic, alcohol, nicotine, and cannabis use, and SUD treatments. Additionally, we offer guidance on the utility of toxicology testing in breastfeeding recommendations. Individual programs and institutions should establish consistent breastfeeding approaches that mitigate bias, facilitate consistency, and empower mothers with SUD. For specific breastfeeding recommendations, given the complexity of breastfeeding in mothers with SUD, individualized care plans should be created in partnership with the patient and multidisciplinary team with appropriate clinical support and follow-up. In general, breastfeeding is recommended among mothers who stop nonprescribed substance use by the time of delivery, and they should continue to receive ongoing postpartum care, such as lactation support and SUD treatment. Overall, enhancing breastfeeding education regarding substance use in pregnancy and lactation is essential to allow for patient-centered guidance.
Clinical trials aimed at HIV cure or remission: new pathways and lessons learned

ABSTRACT Introduction The main barrier to finding a cure against HIV is the latent HIV reservoir, which persists in people living with HIV (PLWH) despite antiretroviral treatment (ART). Here, we discuss recent findings from interventional studies using mono- and combination therapies aimed at enhancing immune-mediated killing of the virus with or without activating HIV from latency. Areas covered We discuss latency reversal agents (LRAs), broadly neutralizing antibodies, immunomodulatory therapies, and studies aimed at inducing apoptosis. Expert opinion The landscape of clinical trials for HIV cure and remission has evolved considerably over the past 10 years. Several novel interventions such as immune checkpoint inhibitors, therapeutic vaccines, and broadly neutralizing antibodies have been tested either alone or in combination with LRAs but studies have so far not shown a meaningful impact on the frequency of latently infected cells. Immunomodulatory therapies could work differently in the setting of antigen expression, that is, during active viremia, and timing of interventions could therefore, be key to future therapeutic success. Lessons learned from clinical trials aimed at HIV cure indicate that while we are still far from reaching a complete eradication cure of HIV, clinical interventions capable of inducing enhanced control of HIV replication in the absence of ART might be a more feasible goal.
Statistical Downscaling Prediction of Sea Surface Winds over the Global Ocean

AbstractThe statistical prediction of local sea surface winds from large-scale, free-tropospheric fields is investigated at a number of locations over the global ocean using a statistical downscaling model based on multiple linear regression. The predictands (the mean and standard deviation of both vector wind components and wind speed) calculated from ocean buoy observations on daily, weekly, and monthly scales are regressed on upper-level predictor fields from reanalysis products. It is found that in general the mean vector wind components are more predictable than mean wind speed in the North Pacific and Atlantic, while in the tropical Pacific and Atlantic the difference in predictive skill between mean vector wind components and wind speed is not substantial. The predictability of wind speed relative to vector wind components is interpreted by an idealized model of the wind speed probability density function, which indicates that in the midlatitudes the mean wind speed is more sensitive to the vector .
Myth or truth: investigation of the jumping ability of Tunga penetrans (Siphonaptera: Tungidae)

Abstract Female sand fleas (Tunga penetrans Linnaeus, 1758, Siphonaptera: Tungidae) cause a severe parasitic skin disease known as tungiasis. T. penetrans is a small flea, measuring less than 1 mm in length. The females of this species burrow into the skin of human and animal hosts and mostly affect the feet. This has led to the anecdotal assumption that T. penetrans, unlike its relatives in the Siphonaptera family, would have a limited jumping ability potentially not reaching higher body parts. However, there is no data supporting this. This study evaluated the jumping capabilities of T. penetrans for height and distance using sticky tapes. The vertical jump of the female T. penetrans ranged from 4.5 to 100 mm with a mean of 40 mm whereas the vertical jump of the male T. penetrans ranged from 1.2 to 138 mm with a mean of 46 mm. The horizontal jump of the female T. penetrans ranged from 18 to 138 mm with a mean of 64 mm and that of the male ranged from 9 to 251 mm with a mean of 80 mm. Based on the literature, fleas of various species have been described as jumping vertically 50–100 times their size and horizontally 5–100 times their size. In this respect, sand fleas appear to have equal expert jumping abilities to their relatives. Their aggregation on people’s feet is not likely a result of their poor jumping ability but might be an adaptation to the host’s behavior which would require further investigations.
The increasing threat of Rift Valley fever virus globalization: strategic guidance for protection and preparation

Rift Valley fever virus (RVFV) (Bunyavirales: Phlebovirus) is a prominent vector-borne zoonotic disease threat to global agriculture and public health. Risks of introduction into nonendemic regions are tied to changing climate regimes and other dynamic environmental factors that are becoming more prevalent, as well as virus evolutionary factors and human/animal movement. Endemic to the African continent, RVFV has caused large epizootics at the decadal scale since the early 20th century but has spread to the Arabian Peninsula and shows increasing patterns of interepizootic transmission on the annual scale. This virus can be transmitted by mosquitoes as well as through direct contact with infected tissues and can cause sporadic to widespread morbidity and mortality in domestic ungulate livestock as well as humans. High viremias in infected livestock moved for legal and illegal trade as well as in infected mosquitoes or human travelers can spread this virus worldwide. With increasing global commerce, it is likely RVFV will be introduced to new areas with suitable hosts, mosquito vector species, and environments. However, the strong mosquito component of RVFV epidemiology combined with advancements in vaccines, diagnostics, and virus evolutionary factors create opportunities for strategies to leverage models of connectivity among potential source and emerging regions to target surveillance and mitigation activities to reduce the risk of RVFV introduction, or contain the virus should it be introduced, into new regions.
Molecular analysis of secondary organic aerosol and brown carbon from the oxidation of indole

Abstract. Indole (ind) is a nitrogen-containing heterocyclic volatile organic compound commonly emitted from animal husbandry and from different plants like maize with global emissions of 0.1 Tg yr−1. The chemical composition and optical properties of indole secondary organic aerosol (SOA) and brown carbon (BrC) are still not well understood. To address this, environmental chamber experiments were conducted to investigate the oxidation of indole at atmospherically relevant concentrations of selected oxidants (OH radicals and O3) with or without NO2. In the presence of NO2, the SOA yields decreased by more than a factor of 2, but the mass absorption coefficient at 365 nm (MAC365) of ind-SOA was 4.3 ± 0.4 m2 g−1, which was 5 times higher than that in experiments without NO2. In the presence of NO2, C8H6N2O2 (identified as 3-nitroindole) contributed 76 % to all organic compounds detected by a chemical ionization mass spectrometer, contributing ∼ 50 % of the light absorption at 365 nm (Abs365). In the absence of NO2, the dominating chromophore was C8H7O3N, contributing to 20 %–30 % of Abs365. Indole contributes substantially to the formation of secondary BrC and its potential impact on the atmospheric radiative transfer is further enhanced in the presence of NO2, as it significantly increases the specific light absorption of ind-SOA by facilitating the formation of 3-nitroindole. This work provides new insights into an important process of brown carbon formation by interaction of two pollutants, NO2 and indole, mainly emitted by anthropogenic activities.
Vergleich zweier Bandagetechniken bei sekundärem Armlymphödem nach Brustkrebs – Wirkung des Zweilagen-Kompressionssystems „3M Coban 2 Lite“ auf das Ödemvolumen und die Lebensqualität bei brustkrebsbedingtem Armlymphödem im Vergleich zur Dreischichten-Kompressionsbandage: eine Kohortenstudie

Zusammenfassung Hintergrund Das Armlymphödem ist eine mögliche Folge bei axillärer Lymphknotenentfernung aufgrund von Brustkrebs und führt zu Einschränkungen im Alltag sowie der Lebensqualität. Aktuell ist unklar, durch welches Bandagematerial während der komplexen physikalischen Entstauungstherapie die größere Volumenreduktion bei bestmöglicher Lebensqualität erfolgen kann. Ziel Beantwortung der Frage, ob das Zweilagen-Kompressionssystem „3M™ Coban™ 2 Lite“ bei Patientinnen mit sekundärem Armlymphödem innerhalb einer einwöchigen Intensivphase eine größere prozentuale Ödemvolumenreduktion bewirken kann als die Therapie mit klassischen Kurzzugbinden. Zudem wurde evaluiert, welches der beiden Materialien mit einem besseren Therapieempfinden und einer höheren Lebensqualität verknüpft ist. Methode Von März 2021 bis November 2021 wurden im Kantonsspital Winterthur Intensivphasen bei Frauen mit sekundärem Lymphödem durchgeführt. Im Rahmen der Standardtherapie erhielten sie während einer Woche entweder 3-mal pro Woche eine Coban-Lite-Bandage oder täglich eine klassische Kurzzugbandage. Ausgewertet wurde die prozentuale Ödemvolumenreduktion, die Lebensqualität und das Therapieempfinden nach einer Woche Therapie. Ergebnisse Daten von 34 Patientinnen, jeweils 17 pro Gruppe, wurden ausgewertet. Die prozentuale Ödemvolumenreduktion von 55,1 % in der Coban-Lite-Gruppe und 35,0 % in der Klassische-Bandage-Gruppe (p = 0,067; 95 % KI [−0,646; 28,809]) sowie die Lebensqualität (p = 0,202; 95 % KI [−1,11; 0,22]) unterschieden sich in beiden Gruppen nicht signifikant voneinander. Das Therapieempfinden war jedoch in der Coban-Lite-Gruppe signifikant besser als in der Klassische-Bandage-Gruppe (p = 0,004; 95 % KI [−117; −0,17]) und wies eine mittlere Effektstärke (r = 0,488) auf. Schlussfolgerung Die Anwendung der beiden Bandagetechniken zeigten keinen statistisch signifikanten Gruppenunterschied hinsichtlich der Ödemvolumenreduktion nach einer Woche. Während dieser Zeit wurden mit der Coban-Lite-Bandage weniger Therapiesitzungen durchgeführt als mit der klassischen Bandage. Zudem erzielte die Coban-Lite-Bandage beim Therapieempfinden ein signifikant besseres Ergebnis.
Research on XSS adversarial attack model based on reinforcement learning

Cross-site scripting (XSS) is an important issue in the field of network security, and there have been many studies on XSS detection models. However, the emergence of XSS adversarial attack samples has affected the detection accuracy of these models. Therefore, this paper proposes a reinforcement learning-based XSS adversarial attack model, which aims to generate XSS adversarial attack samples and consists of a detection module and an adversarial attack module. In the detection stage, the detection module cleans the original XSS script data and vectorizes it using Word2Vec. Then, the processed data is input into the XSS detection model of the detection module. The detection module uses an ensemble learning method to construct an XSS detection model by combining LSTM, MLP, and SVM to form a higher accuracy model. Finally, the detection module obtains the classification result of the original XSS data as a preparation for the next escape stage. In the escape stage, the adversarial attack module is designed using the reinforcement learning algorithm TD3, and uses an adversarial generation module to generate legitimate adversarial samples that can bypass the XSS detection model. Experimental results show that the XSS adversarial attack samples generated based on TD3 have an evasion rate nearly 6% higher than those based on Soft-Q learning. This model provides a new idea and method for improving the accuracy of XSS detection models and providing more valuable XSS attack data samples.
Fault detection method of DC distribution network based on GRNN

DC distribution network has a good consumption effect on new energy, and DC fault detection is one of the key technical problems for the large-scale application of DC distribution network. Aiming at the problems of difficult threshold setting and long detection time in existing fault detection schemes, a fault detection scheme for DC distribution network based on generalized regression neural network is proposed. Firstly, the transient voltage change rate of positive and negative lines is used as the fault start criterion. Then the disturbance of DC distribution network is eliminated by low voltage protection. Secondly, the Spearman correlation coefficient is used to analyze the correlation of the transient voltage of the positive and negative pole lines to distinguish symmetric faults and asymmetric faults. For symmetric faults combined with the transient voltage change rate of positive and negative pole lines, bipolar short circuit faults and AC faults can be distinguished. For asymmetric faults, GRNN is trained offline with simulation data, and the fault poles are distinguished according to the online output results of GRNN. The fault detection scheme is verified on the MATLAB / simulink platform to build a simulation model of the DC distribution network at both ends, which can quickly and accurately identify the fault.
DDoS attach detection technology based on CNN and transformer

In view of the problems of high data dimension, redundant features and low detection accuracy in current DDoS attack detection, This paper proposes a DDoS attack detection model CNN-Trans based on the combination of Convolutional Neural Network (CNN) and Transformer. The detection model firstly uses CNN integrated with CBAM attention for feature extraction. The model can better learn the depth characteristics of the input data, and then use Transformer to train the processed data to get classification results. Experimental results show that the accuracy and other aspects of the proposed model CNN-Trans have been improved
A blind source separation algorithm based on the dynamic similarity of signals

Blind source separation (BSS) refers to obtain source signals by means of their linear commixture with unknown mixing channel. Existing BSS methods mainly rely on the basic hypothesis that the source signal is non-Gaussian, which leads to its inability to separate the mixed Gaussian signals, and severely limits the application of BSS. In order to solve the above problem and the shortcomings that cosine similarity will encounter in the measurement of dynamic similarity of signals, this paper utilizes the generalized Jaccard index to quantify the dynamic similarity of signals, and then proposes the BSS method based on dynamic similarity of signals to realize the separation of nonlinear chaotic Gaussian signals. This method introduces the exponential function consisting of dynamic stationarity factor and independence factor of signal as the cost function of blind source separation, and then employs the imperialist competition algorithm with fast convergence speed to solve cost function to achieve the separation of signal. The effectiveness of proposed method is verified by conducting simulation experiments on the synthesized nonlinear chaotic Gaussian signals and ECG signals. Simultaneously, in comparison with the BSS method based on cosine index, the experimental results show that the proposed method has smaller cross-talking error and better separation effect.
Short-term power prediction of distributed photovoltaic based on CNN-LSTM and meteorological interpolation

The photovoltaic power generation has strong randomness and fluctuation. Considering the traditional photovoltaic power prediction methods mainly focus on centralized photovoltaic and rely heavily on meteorological data, while distributed photovoltaic usually does not have perfect meteorological data, so the existing photovoltaic power prediction methods are difficult to apply to distributed photovoltaic. Based on the known wide-area meteorological resource data, the model using the Kriging method and the inverse distance weighting (IDW) method to interpolate the meteorological data of the of the distributed photovoltaic power stations by its longitude and latitude positions, using the CNN network to extract the hidden feature information, and constructs a prediction model based on LSTM. The model verifies the effectiveness of the method through actual data.
Optimized carrier tracking loop design for high dynamic and low CNR conditions

The carrier tracking loop face extreme difficulty in entering a locked-in state for high dynamic and low carrier-to-noise ratio (CNR) environment. To address this issue, this paper presents an improved Frequency-Locked Loop (FLL) serially assisted Phase-Locked Loop (PLL). The improved carrier tracking loop design consists of a high-dynamic frequency discriminator and a high-dynamic loop filter based on second-order FLL serially assisted third-order PLL. In particular, this proposed frequency discriminator executes an adaptive switching mechanism between the maximum likelihood estimation (MLE) discriminator and the conventional frequency discriminator. Once the Doppler frequency estimation error begins to accumulate, the MLE discriminator will be selected which can pull the Doppler frequency into the working range of the conventional frequency discriminator. Thus, a wider pull-in range can be obtained to provide good frequency discrimination capabilities under high dynamic conditions. The experimental results and comparative analysis demonstrate that the improved FLL serially assisted PLL can achieve an improved tracking performance under high dynamic and low CNR conditions.
A world heritage industry?: tourism at Hungarian World Heritage sites = Une industrie patrimoniale mondiale? le tourisme dans les sites du patrimoine mondial en Hongrie

It is the task of each individual country to nominate heritage sites within its borders for inclusion on the World Heritage List. This paper analyses the tangible and intangible heritage requirements that go into nominating World Heritage sites in Hungary. The authors also investigate and analyze tourism development at the Hungarian World Heritage Sites, with special emphasis on the village of Holloko. Tourism can have both beneficial and damaging effects on natural and cultural heritage. The increase in a site's popularity, once it is included on the WHL, often leads to the commodification of its patrimony. In order to protect the authenticity and the unique characteristics of the World Heritage Sites while still providing a high quality experience for visitors, an appropriate management system must be developed for each site. In the paper, the current Hungarian management and planning practices are discussed, and recommendations are made for their improvement and development.
Simulation-based machine learning training data generation system

In recent years, machine learning has made considerable progress in various fields. The training of deep learning algorithm model requires a large amount of data, and a large amount of data annotation is very costly. In this case, the use of simulation data can provide great convenience. Using the three-dimensional reconstruction algorithm to construct a three-dimensional model of the real scene can speed up the construction of simulation data. The model has problems such as model damage and model fragmentation. The repair model takes a long time and the repair effect is poor. To solve these problems, we designed and developed a simulation training data generation system. The system uses Blender as a development platform, introduces a three-dimensional reconstruction algorithm to initialize the model, transforms the model into a point cloud for repair operation, and proposes a point cloud region adjustment method and a point cloud fast repair method. Finally, the refined model is used to generate a data set with true values. The experimental results show that the system we designed can improve the efficiency and accuracy of data generation and efficiently generate simulation training data for machine learning.
Agent model for calculating gas radiation properties based on gradient boosting decision tree algorithm

The calculation of gas radiation properties has important applications in many fields such as infrared target detection, remote sensing atmospheric transmittance calculation and so on. Traditionally, the line by line calculation method is used for the calculation of gas radiation properties. However, this method is inefficient for the calculation of large scale flow field radiation data, so this paper proposes a method using a pre-training agent model based on gradient boosting decision tree algorithm instead of the line by line calculation method. After experimental comparison, it is found that the new model based on gradient boosting decision tree algorithm greatly improves the computational efficiency compared with the traditional line by line calculation method on the basis of guaranteed computational accuracy, and greatly reduces the storage space occupation compared with the pre-tabulation method. It can replace the line by line model to a certain extent when dealing with the needs of extensive and complex flow field calculations and it can greatly reduce the consumption of computing time.