Miracle-dz/UniFAR-FTUs huggingface dataset:
Gamma Ray Bursts

Gamma Ray Bursts (GRBs) are short bursts of very high energy photons which were discovered in the late 1960s. Ever since their discovery, scientists have wondered about their origin. Nowadays it is known that they originate outside the Milky Way because of their high red shift first measured in the afterglows thanks to the Beppo-SAX satellite and ground-based observations. However, theoreticians still do not agree about the mechanism that generates the bursts, and different competing models are animatedly debated. Current GRB experiments include the Swift satellite and the Pierre Auger Observatory that could detect GRBs with an increase of the background. A forthcoming dedicated experiment is GLAST, a satellite observatory for detecting gamma rays with energies up to 300 GeV, whose launch is scheduled for early 2008. 1 Observational progress Gamma Ray Bursts (GRBs) were discovered by the US military satellite Vela in 1967 while monitoring for clandestine tests of nuclear weapons. This information was not released to the scientific community until the following decade. GRBs are among the most energetic known objects ever measured and their origin and mechanism became a puzzle to the community [1, 2]. Fig. 1: GRBs measured by BATSE in Galactic coordinates [3] In the early 1990s the Compton Gamma Ray Observatory (CGRO) was launched and started to unravel these questions. It included four different detectors to measure gamma rays, one of which was BATSE with a 4π sr field of view, 0.3–1.2 MeV energy range, and a precision of 2 degrees. At that moment, not only was the origin of GRBs unknown, but also their location in space, and many scientists speculated that they were explosions occurring within our Galaxy. When the map showing an isotropical distribution of the GRBs as measured by BATSE was published (Fig. 1), it was clear *Work performed as a student project under the supervision of E. Nardi.
“To Be or Not to Be”—Cardiopulmonary Resuscitation for Hospitalized People Who Have a Low Probability of Benefit: Qualitative Analysis of Semi-structured Interviews

Purpose Our aim was to understand the decision making of patients in hospital who wanted cardiopulmonary resuscitation despite low probability of benefit. Methods We included patients admitted to general medical wards who had a low chance of surviving in-hospital cardiopulmonary resuscitation (CPR) and had an order in the chart to administer CPR. We developed an interview guide to explore participants’ decision-making process, sources of information, and emotions associated with this decision. Results We developed 3 themes from the data. 1) “Life is worth living . for now”: Participants describe their enjoyment of life and desire to carry on in their current state. 2) “Making sense of CPR outcomes”: Participants saw CPR outcomes as binary, either they live, or they die; deciding not to receive CPR means choosing death. Participants were optimistic they would survive CPR and cited personal experience and TV as information sources. 3) “Decision process”: Participants did not engage in shared decision making. Instead, they were asked a binary yes/no question with no reflection on their values or discussion about harms or benefits. Limitations The probability of successful CPR in our sample is unknown. Findings may be different in a population who is imminently dying but still requesting CPR. Conclusions Participants chose CPR because they perceived life as worth living and CPR as a chance worth taking. Participants did not want to be left in a severely debilitated state but did not have accurate information about this risk. Implications Decision making about CPR in-hospital can be improved if it is grounded in accurate risk understanding and the patient’s values and wishes.
The Influence of Social Support on Higher Vocational Students’ Learning Motivation: The Mediating Role of Belief in a Just World and the Moderating Role of Gender

Purpose Social support is a valid predictor of individuals’ learning motivation, but the specific mechanism of the two is still unclear. In order to explore the specific mechanism between them, we examined the mediating role of belief in a just world (BJW) and the moderating role of gender in the relationship between social support and learning motivation. Methods A total of 1320 students enrolled in three higher vocational colleges in eastern China were surveyed using the adolescent Social Support Scale, the college students’ Motivation to Learn questionnaire, and the College Students’ Belief in a Just World Scale. Descriptive statistics and correlation analyses were performed for all study variables, and finally, mediating and moderating effects were tested using the process developed by Hayes. Results In China, the relationship between social support, BJW, and learning motivation of higher vocational college students shows a two-by-two positive correlation. Social support can directly influence learning motivation and function through the mediation of BJW. Gender moderates the first half of the pathway of the mediating effect of social support – BJW – learning motivation and the direct pathway of social support – learning motivation, ie, the positive effect of social support received on BJW and is more remarkable for boys when compared to girls. In addition, among the mediating effects played by BJW, the intrinsic justice dimension contributed the most, the ultimate justice dimension the second, and the intrinsic injustice dimension the least. Conclusion This study adds to and extends the relevant research on the influence of social support on individuals. It confirms the moderating effect of gender and provides a new idea for enhancing the learning motivation of disadvantaged student groups. The results of the study can provide a reference for researchers and educators to further explore how to enhance the learning motivation of higher education students.
Stable Communications in Green Unmanned Aerial Relaying Systems

Green unmanned aerial relaying (UAR) systems, featuring solar-powered unmanned aerial vehicles (UAVs) to provide agile and flexible communication services, have recently gained significant attention for their wide applications. Nonetheless, due to the essential dynamics of wireless channel conditions and solar energy supply, maintaining system stability is a critical concern in practical green UAR systems, as fluctuation in these two factors can significantly affect communication performance. To address this issue, this article proposes a scheme design for stable communications in a UAR system with dynamic solar energy supply and wireless channel conditions. Specifically, we first formulate an optimization problem, called OFL, to control the power allocation and flow data rate assignment to minimize long-term time-averaged energy consumption while ensuring the demanded data rate and system stability. Considering that solving OFL needs complete prior information about the solar power supply and wireless channel conditions, which is hardly acquired, we then explore the Lyapunov theory to transform OFL as an online optimization problem, called ONL. To find the solution to ONL, we subsequently design a distributed algorithm that enables each UAV to make its own decision locally, thus, reducing the computational overhead of each UAV. Particularly, we prove that stability can be guaranteed by employing the designed algorithm. Extensive simulations are conducted to show the performance achieved by the proposed scheme.
An adaptive method for weighted median priors in transmission tomography reconstruction

We present an adaptive method of selecting the center weight in the weighted-median prior for penalized-likelihood (PL) transmission tomography reconstruction. While the well-known median filter, which is closely related to the median prior, preserves edges, it is known to have an unfortunate effect of removing fine details because it tends to eliminate any structure that occupies less than half of the window elements. On the other hand, center-weighted median filters can preserve fine details by using relatively large center weights. But the large center weights can degrade monotonic regions due to insufficient noise suppression. In this work, to adaptively select the center weight, we first calculate pixelwise standard deviation over 3×3 neighbors of each pixel at every PL iteration and measure its cumulative histogram, which is a monotonically non-decreasing 1-D function. We then normalize the resulting function to maintain its range over [1,9]. In this case the domain of the normalized function represents the standard deviation at each pixel, and the range can be used for the center weight of a 3×3 median window. We implemented the median prior within the PL framework and used an alternating joint minimization algorithm based on a separable paraboloidal surrogates algorithm. The experimental results demonstrate that our proposed method not only compromises the two extreme cases (the largest and smallest center weights) yielding a good reconstruction over the entire image in terms of the percentage error, but also outperforms the standard method in terms of the contrast recovery coefficient measured in several regions of interest.
Preliminary Study of Iron Concentration in the Human Placenta in Twin Pregnancies

Background: Pregnancy significantly increases the demand for iron (Fe) in the female body to facilitate maternal blood volume expansion, placental development, and fetal growth. As Fe flux in pregnancy is significantly influenced by the placenta, the aim of this study was to determine the dependencies between the Fe concentration in the placenta, the infant’s morphometric parameters and the woman’s morphological blood parameters in the last trimester. Methods: The study was conducted on 33 women with multiple (dichorionic–diamniotic) pregnancies from whom the placentas were drawn, and their 66 infants, including pairs of monozygotic (n = 23) and mixed-sex twins (n = 10). Fe concentrations were determined based on inductively coupled plasma atomic emission spectroscopy (ICP-OES) using ICAP 7400 Duo, Thermo Scientific. Results: The results of the analysis showed that lower placental Fe concentrations were associated with deteriorated morphometric parameters of infants, including weight and head circumference. Although we found no statistically significant dependencies between Fe concentration in the placenta and the women’s morphological blood parameters, higher Fe concentration in the placenta of mothers supplemented with Fe correlated with better morphometric parameters in infants compared to those whose mothers received no Fe supplementation. Conclusions: The research adds additional knowledge for placental iron-related processes during multiple pregnancies. However, many limitations of the study do not allow detailed conclusions to be assessed, and statistical data should be assessed conservatively.
Estimation of Single-Generation Sibling Relationships Based on DNA Markers

An algorithm for infering joint sibling relationships for a single generation sample of organisms based on DNA marker data is presented. The technique is based primarily on the exclusion principle, i.e., the process of systematically eliminating hypothetical kinship types between individuals in the basis of DNA incompatibility. The result is an enumeration of all genetically compatible sibling groupings, which is maximal in the sense that no other individual can be added to the sibling group. The technique is computationally feasible for large samples given enough variablility in the DNA markers used. From this, we construct a partition of the individuals into sibling groups based on a score. In addition, we derive some statistical properties of sibling compatibility events as well as various types of error probabilities associated with sibling identification. The techniques are illustrated using fisheries data.
Interferential therapy for chronic constipation in adults: The CON‐COUR randomizedcontrolled trial

Abstract Background Chronic constipation (CC) is a frequent condition, and the first‐line treatment includes dietary rules, the use of laxatives, and biofeedback for evacuation disorders. However, almost half of the patients are dissatisfied with their current treatment. We report the first double‐blind randomized multicenter trial assessing the effectiveness of transabdominal electrical stimulation by interferential therapy (IFT) in the treatment of CC in adults. Methods A prospective, multicenter, randomized, double‐blind, sham‐controlled, parallel‐group, phase 3 trial was conducted at 7 centers in France. The primary endpoint was 8‐week efficacy as defined by the number of complete spontaneous bowel movements during the last 4 weeks of the 8‐week stimulation period. Secondary endpoints included the evaluation of the effects of IFT on symptoms (Patient Assessment of Constipation Symptoms questionnair (PAC‐SYM)), quality of life (QOL) (Patient Assessment of Constipation Quality of Life), Colonic transit time (CTT), anorectal manometry, and patient satisfaction. Results The proportion of 8‐week responders was not significantly different between the two groups (73.2% in the IFT group vs. 67.1% in the sham group). After 8 weeks of stimulation, the mean overall PAC‐SYM score and the mean reduction in the overall PAC‐SYM score were significantly greater in the IFT group than in the sham group. No differences were observed concerning CTT, anorectal manometry, or patient satisfaction. Conclusions Although the primary endpoint was not reached, IFT can significantly alleviate the symptoms and improve the QOL of CC patients. It can be assumed that new treatments require different modes of evaluation and that the assessment of patient‐reported outcomes may become a priority among therapeutic targets of CC. Trial registration number NCT02381665.
The Infinite Apparatus in the Quantum Theory of Measurement

It has been suggested that we ought to idealize the apparatus used to measure quantum systems as consisting of an infinite number of particles. Various authors have claimed that if we do so we do not need to take seriously the limitations on measurement incorporated into the Wigner-Araki-Yanase quantum theory of measurement. Bub (1988) and (1989) claims we can solve the measurement problem if we make this assumption. I argue against both claims on the basis of differences between the role of such idealizations in classical and quantum mechanics.
Tobacco use in first-episode psychosis, a multinational EU-GEI study

Abstract Background Tobacco is a highly prevalent substance of abuse in patients with psychosis. Previous studies have reported an association between tobacco use and schizophrenia. The aim of this study was to analyze the relationship between tobacco use and first-episode psychosis (FEP), age at onset of psychosis, and specific diagnosis of psychosis. Methods The sample consisted of 1105 FEP patients and 1355 controls from the European Network of National Schizophrenia Networks Studying Gene–Environment Interactions (EU-GEI) study. We assessed substance use with the Tobacco and Alcohol Questionnaire and performed a series of regression analyses using case-control status, age of onset of psychosis, and diagnosis as outcomes and tobacco use and frequency of tobacco use as predictors. Analyses were adjusted for sociodemographic characteristics, alcohol, and cannabis use. Results After controlling for cannabis use, FEP patients were 2.6 times more likely to use tobacco [p ⩽ 0.001; adjusted odds ratio (AOR) 2.6; 95% confidence interval (CI) [2.1–3.2]] and 1.7 times more likely to smoke 20 or more cigarettes a day (p = 0.003; AOR 1.7; 95% CI [1.2–2.4]) than controls. Tobacco use was associated with an earlier age at psychosis onset (β = −2.3; p ⩽ 0.001; 95% CI [−3.7 to −0.9]) and was 1.3 times more frequent in FEP patients with a diagnosis of schizophrenia than in other diagnoses of psychosis (AOR 1.3; 95% CI [1.0–1.8]); however, these results were no longer significant after controlling for cannabis use. Conclusions Tobacco and heavy-tobacco use are associated with increased odds of FEP. These findings further support the relevance of tobacco prevention in young populations.
Identification of a Continuous Stirring Tank Reactor Using Two-layer Perceptron Neural Network Model

This paper investigates the identification of a continuous stirring tank reactor using a two-layer multi-layer perceptron neural network. It is shown that the neural network based state-space innovations form model clearly outperforms the equivalent linear model in simulation performance and correlation functions of prediction errors and thus, it is possible to obtain hopeful results for this system with neural network based state-space innovations form model. It is by no means claimed that the “optimal” solution is found, however, it is shown that the proposed neural network based model has not only significantly performed better in simulation but also in correlation characteristics than the equivalent linear model in the sense that the correlation functions tend to lie more closely within the confidence regions. This also means that most of the information has been extracted from the training set and that the model approximates the given system well.
Recent Advances in Brillouin Optical Time Domain Reflectometry

In the past two decades Brillouin-based sensors have emerged as a newly-developed optical fiber sensing technology for distributed temperature and strain measurements. Among these, the Brillouin optical time domain reflectometer (BOTDR) has attracted more and more research attention, because of its exclusive advantages, including single-end access, simple system architecture, easy implementation and widespread field applications. It is realized mainly by injecting optical pulses into the fiber and detecting the Brillouin frequency shift (BFS), which is linearly related to the change of ambient temperature and axial strain of the sensing fiber. In this paper, the authors provide a review of new progress on performance improvement and applications of BOTDR in the last decade. Firstly, the recent advances in improving the performance of BOTDRs are summarized, such as spatial resolution, signal-to-noise ratio and measurement accuracy, measurement speed, cross sensitivity and other properties. Moreover, novel-type optical fibers bring new characteristics to optic fiber sensors, hence we introduce the different Brillouin sensing features of special fibers, mainly covering the plastic optical fiber, photonic crystal fiber, few-mode fiber and other special fibers. Additionally, we present a brief overview of BOTDR application scenarios in many industrial fields and intelligent perception, including structural health monitoring of large-range infrastructure, geological disaster prewarning and other applications. To conclude, we discuss several challenges and prospects in the future development of BOTDRs.
Remote sensing image analysis and prediction based on improved Pix2Pix model for water environment protection of smart cities

Background As an important part of smart cities, smart water environmental protection has become an important way to solve water environmental pollution problems. It is proposed in this article to develop a water quality remote sensing image analysis and prediction method based on the improved Pix2Pix (3D-GAN) model to overcome the problems associated with water environment prediction of smart cities based on remote sensing image data having low accuracy in predicting image information, as well as being difficult to train. Methods Firstly, due to inversion differences and weather conditions, water quality remote sensing images are not perfect, which leads to the creation of time series data that cannot be used directly in prediction modeling. Therefore, a method for preprocessing time series of remote sensing images has been proposed in this article. The original remote sensing image was unified by pixel substitution, the image was repaired by spatial weight matrix, and the time series data was supplemented by linear interpolation. Secondly, in order to enhance the ability of the prediction model to process spatial-temporal data and improve the prediction accuracy of remote sensing images, the convolutional gated recurrent unit network is concatenated with the U-net network as the generator of the improved Pix2Pix model. At the same time, the channel attention mechanism is introduced into the convolutional gated recurrent unit network to enhance the ability of extracting image time series information, and the residual structure is introduced into the downsampling of the U-net network to avoid gradient explosion or disappearance. After that, the remote sensing images of historical moments are superimposed on the channels as labels and sent to the discriminator for adversarial training. The improved Pix2Pix model no longer translates images, but can predict two dimensions of space and one dimension of time, so it is actually a 3D-GAN model. Third, remote sensing image inversion data of chlorophyll-a concentrations in the Taihu Lake basin are used to verify and predict the water environment at future moments. Results The results show that the mean value of structural similarity, peak signal-to-noise ratio, cosine similarity, and mutual information between the predicted value of the proposed method and the real remote sensing image is higher than that of existing methods, which indicates that the proposed method is effective in predicting water environment of smart cities.
The Benefits of Olive Oil for Skin Health: Study on the Effect of Hydroxytyrosol, Tyrosol, and Oleocanthal on Human Fibroblasts

Fibroblasts contribute to maintaining tissue integrity and homeostasis and are a key cell population in wound healing. This cell population can be stimulated by some bioactive compounds such as extra virgin olive oil (EVOO) polyphenols. The aim of this study was to determine the effects of hydroxytyrosol (htyr), tyrosol (tyr), and oleocanthal (ole) phenolic compounds present in EVOO on the proliferation, migration, cell cycle, and antigenic profile of cultured human fibroblasts. CCD-1064Sk human fibroblast cells were treated for 24 h with each polyphenol at doses ranging 10−5 to 10−9 M. Cell proliferation was evaluated using the MTT spectrophotometric technique, migration capacity by culture insert assay, and cell cycle and antigenic profile with flow cytometry. Cell proliferation was significantly increased by treatment with all compounds. The highest increases followed treatments with htyr or tyr at doses of 10−5 or 10−6 M and with ole at 10−6 and 10−7 M, and these compounds and doses were used for assays of antigenic profile, cell cycle, and migration. During the first few hours after treatment, increased fibronectin and α-actin expressions and greater cell migration were observed, with no cell cycle changes. In conclusion, these in vitro results suggest that phenolic compounds in EVOO might contribute to wound healing through action on fibroblasts related to tissue regeneration.
Malicious code detection based on many‐objective transfer model

With the rapid growth of malicious codes, personal privacy, and Internet security are seriously threatened. Existing transfer learning‐based malicious code detection improves detection accuracy by transferring pre‐trained neural networks. However, it cannot efficiently tune the structure and parameters of the neural networks. Here, we first propose a novel many‐objective transfer model. It mainly focuses on the detection accuracy and the total number of parameters of the neural network model. The optimal structure and parameters are captured from the pre‐trained neural network by many‐objective optimization algorithm. Second, the partitioned crossover‐mutation vector angle‐based evolutionary algorithm for unconstrained many‐objective optimization is proposed to solve the model. The algorithm performs crossover mutation operations in different ways on different regions of the candidate solution to improve population diversity. The simulation results show that the model can reduce the pre‐trained neural network structure by 49% while maintaining the accuracy in malicious code detection.