Miracle-dz/UniFAR-FTUs huggingface dataset:
Using Simulation Exercises to Deal With the Death of a Child as Part of Healthcare Studies

Background: Almost all healthcare professionals find themselves in a situation where they witness death in their work. Meeting a family in crisis is challenging for healthcare professionals and students. Simulation is an effective tool to practice complex and emotionally challenging situations in healthcare education. Methods: The aims of the study were to find out what challenges healthcare students experience when facing a family in crisis and to assess the usability of simulation in teaching healthcare students how to manage this situation. Voluntary simulations for paramedic students and medical students (a total of 29 students) were held in the autumn of 2021. Before and after the simulations, the students evaluated their skills to meet a family in crisis (the loss of a child) with the help of a questionnaire that contained mostly open-ended questions. The study was completed using a qualitative method. Results: The challenges raised by our students were divided into three categories: child-, family-, and self/student-related. Child-related challenges included the developmental stage which impacted communication. Family-related challenges included family members’ different reactions to a devastating situation. Student/self-related challenges were few previous child contacts, communication with the family, and a situation that requires paying attention to multiple things at the same time. Conclusion: According to our study, the students find simulation as a useful method for preparing to communicate with a family in crisis. The students see that with the help of simulation, they can practice different modes of operation and communication when facing a family in crisis. They also have the possibility to think about the processing of their own emotions during a crisis.
Extraction Optimization and Qualitative/Quantitative Determination of Bioactive Abietane-Type Diterpenes from Three Salvia Species (Common Sage, Greek Sage and Rosemary) by 1H-qNMR

The objective of this study was the optimization of the extraction process and the qualitative and quantitative determination of the bioactive metabolites: 12-O-methylcarnosic acid (12MCA), carnosic acid (CA), carnosol (CS), 7-O-methyl-epi-rosmanol (7MER) and rosmanol (RO) in infusions, decoctions, turbulent flow extracts, tinctures and oleolites from three Salvia species: Salvia officinalis L. (common sage, SO), Salvia fruticosa Mill. (Greek sage, SF) and Salvia rosmarinus Spenn (syn Rosmarinus officinalis L.) (rosemary, SR), using Quantitative Proton Nuclear Magnetic Resonance Spectroscopy (1H-qNMR). Regarding the aqueous extracts, decoctions appeared to be richer sources of the studied metabolites than infusions among the three plants. For SR, the turbulent flow extraction under heating was the most efficient one. The optimum time for the preparation of decoctions was found to be 5 min for SF and SO and 15 min for SR. It is noteworthy that SR tinctures were not stable in time due to decomposition of the abietane-type diterpenes CA and CS because of the polar solvent used for their preparation. Contrary to this finding, the oleolites of SR appeared to be very stable. Olive oil as a solvent for extraction was very protective for the contained abietane-type diterpenes. A preliminary stability study on the effect of the storage time of the SF on the abietane-type diterpenes content showed that the total quantity of abietanes decreased by 16.51% and 40.79% after 12 and 36 months, respectively. The results of this investigation also demonstrated that 1H-qNMR is very useful for the analysis of sensitive metabolites, like abietane-type diterpenes, that can be influenced by solvents used in chromatographic analysis.
Research on the Algorithm of Position Correction for High-Speed Moving Express Packages Based on Traditional Vision and AI Vision

The rapid development of the logistics industry poses significant challenges to the sorting work within this sector. The fast and precise identification of moving express parcels holds immense significance for the performance of logistics sorting systems. This paper proposes a motion express parcel positioning algorithm that combines traditional vision and AI-based vision. In the traditional vision aspect, we employ a brightness-based traditional visual parcel detection algorithm. In the AI vision aspect, we introduce a Convolutional Block Attention Module (CBAM) and Focal-EIoU to enhance YOLOv5, improving the model’s recall rate and robustness. Additionally, we adopt an Optimal Transport Assignment (OTA) label assignment strategy to provide a training dataset based on global optimality for the model training phase. Our experimental results demonstrate that our modified AI model surpasses traditional algorithms in both parcel recognition accuracy and inference speed. The combined approach of traditional vision and AI vision in the motion express parcel positioning algorithm proves applicable for practical logistics sorting systems.
Multi-decadal Changes of the Impact of El Niño Events on Tibetan Plateau Summer Precipitation

Precipitation on the Tibetan Plateau (TP) has an important effect on the water supply and demand of the downstream population. Involving recent climate change, the multi-decadal variations of the impact of El Niño-Southern Oscillation (ENSO) events on regional climate were observed. In this work, the authors investigated the changes in summer precipitation over TP during 1950–2019. At the multi-decadal scale, the authors found that the inhabiting impact of El Niño events on the TP summer precipitation has strengthened since the late 1970s. The main factor contributing to this phenomenon is the significant amplification in the decadal amplitude of El Niño during 1978–2019 accompanied by a discernible escalation in the frequency of El Niño events. This phenomenon induces anomalous perturbations in sea surface temperatures (SST) within the tropical Indo-Pacific region, consequently weakening the atmospheric vapor transport from the western Pacific to the TP. Additionally, conspicuous anomalies in subsidence motion are observed longitudinally and latitudinally across the TP which significantly contributes to a curtailed supply of atmospheric moisture. These results bear profound implications for the multi-decadal prediction of the TP climate.
Spatio-temporal changes in heat waves and cold spells: an analysis of 55 U.S. cities

Abstract With observed increases in global temperatures indicating changes to anomalous temperature events (ATE), few studies have considered the changes associated with both heat and cold together. This study evaluates the changes in heat waves and cold spells for 55 U.S. metropolitan areas (1948–2012). Using surface observations, thresholds of mean apparent temperature were used to define heat, extreme heat, cold, and extreme cold events. Days that exceeded the 95th temperature percentile were considered heat days. Similar values were used to define extreme heat (97.5th), cold (5th), and extreme cold (2.5th). Thresholds were calculated independently for each of the locations, incorporating spatial variability into the ATE definition. Changes in duration, seasonal timing, and frequency, all of which have been shown to be important characteristics in regard to heat and cold events, were evaluated. Significant changes in some characteristics were found. Across many locations, heat events have become more frequent, longer lasting, and earlier occurring, while cold spells have experienced an opposite trend. Since heat and cold events impact a range of ecological and bioclimatological processes, understanding the variability and changes associated with ATE remains an important aspect to consider as society prepares for future events.
Sparsity and Endogeneity in High Dimensional Models: A Difference in Difference Approach

Technological development have contributed to several sciences and disciplines. Econometrics has bene ted from it and has developed a series of new methodologies to obtain the most of this wide supply of Information. This achievements have not been in all areas, especially in impact evaluation. Is because of this that adapting current methodologies and developing new ones in this area is essential.
Vision-Based Self-Motion Estimation in a Fixed-Wing Aerial Vehicle

This paper describes a complete algorithm to estimate the motion of a fixed-wing aircraft given a series of digitized flight images. The algorithm was designed for fixedwing aircraft because carefully procured flight images and corresponding navigation data were available to us for testing. After image pre-processing, optic flow data is determined by automatically finding and tracking good features between pairs of images. The image coordinates of matched features are then processed by a rigid-object linear optic flow-motion estimation algorithm. Input factors are weighed to provide good testing techniques. Error analysis is performed with simulation data keeping these factors in mind to determine the effectiveness of the optic flow algorithm. The output of this program is an estimate of rotation and translation of the imaged environment in relation to the camera, and thereby the airplane. Real flight images from NASA test flights are used to confirm the accuracy of the algorithm. Where possible, the estimated motion parameters are compared with recorded flight instrument data to confirm the correctness of the algorithm. Results show that the algorithm is accurate to within a degree provided that enough optic flow feature points are tracked.
Heart Reaction to Nandrolone Decanoate plus Two Different Intensities of Endurance Exercise: Electrocardiography and Stereological Approach

Background Regarding the negative effects of androgenic anabolic steroids (AASs) abuse, the long-term effect of nandrolone decanoate with/without two intensities of endurance exercise training was investigated on heart tissue and electrocardiogram (ECG) in rats. Methods The experiment was conducted on 63 ýýmale Wistar rats, which were ý4 months old. The rats were divide into groups of control (CTL), arachis oilasnandrolone solvent (Ar), nandrolone (Nan) (received a dose of 5 mg/kg twice/week for 8 weeks), mild swimming exercise training (mEx), severe exercise (sEx), sEx + Nan, mEx + Nan, mEx + Ar, and sEx + Ar. During the 8 weeks of swimming exercise, the animals carried dumbbells equivalent to 2% of their body weight, which was gradually increased and reached 5% and 8% in the 6th week for mild and severe exercises, respectively. Finally, ECGs recording and samplings were done. Findings Both types of exercise, without nandrolone, significantly reduced the heart rate and increased the RR interval of ECG. Nandrolone alone and with mild (P < 0.050) and intense exercise (P < 0.010 vs. CTL) increased the left ventricular hypertrophy (LVH) index. Left ventricular volume was significantly higher in the Nan group (P < 0.050) compared to the CTL group and all exercise groups (P < 0.010) compared to the Nan, CTL, and Ar groups. Myocytes volume increased in the presence of both of mild and high-intensity exercise plus nandrolone (P < 0.050 vs. CTL and Ar groups). Hydroxyproline value of the heart was significantly higher in the nandrolone group compared to all other groups (P < 0.001). Exercise prevented the effect of nandrolone on hydroxyproline. Conclusion Both levels of swimming exercise prevent the effect of nandrolone on the production of hydroxyproline and fibrotic cardiac remodeling.
Leak with Digital Subtraction Myelography Localization of a Rapid Cerebrospinal Fluid

SUMMARY: A 53-year-old woman with superficial siderosis underwent spinal MR imaging, which demonstrated a large cervicothoracic epidural fluid collection compatible with a CSF leak. Conventional and dynamic CT myelography failed to localize the dural tear because of rapid equilibration of myelographic contrast between the thecal sac and the extradural collection. The superior temporal resolution of digital subtraction myelography precisely localized the CSF leak preoperatively and led to the successful surgical correction of the dural tear.
Real-Time Ultraviolet Flame Detection System Based on 4H-SiC Phototransistor

High-sensitivity ultraviolet (UV) photoelectric detection technology is significant in UV communication, missile tail flame radiation detection, and fire early warning. This article utilized a 4H-SiC phototransistor detector to respond to weak UV light. A corresponding detection circuit system was developed to convert the photogenerated analog current signal to a digital voltage signal and transmit the digital signal to the host computer. A trans-impedance amplifier amplifies and transfers the weak photocurrent into a large voltage signal, which possesses a stronger antijamming ability. The analog-to-digital converter converts the analog voltage signal into a digital signal for convenient transmission in the following circuits. A universal serial bus interface module transmits the digital signal to the host computer using a serial transmission method with a field-programmable gate array controlling the timing. A graphical software interface based on LabVIEW was developed to display the collected digital data that stand for real-time UV intensity. Monitoring and recording the weak optical power of the UV light emitted by candlelight can be realized via the system. The UV power density range was between 50 nW/cm2 and $3~\mu \text{W}$ /cm2, and the corresponding output voltages ranged from 0.095 to 3.45 V. Compared with traditional high-voltage biased p-i-n avalanche diode detection system, the flame detection system demonstrated in this article was based on a 4H-SiC phototransistor, which has the advantages of low voltage and high stability.
The Effect of Healthcare Expenditure on Maternal Mortality in Uganda

The maternal mortality rate in Uganda of 189 per 100,000 live births remains above the global target of 70 per 100,000. Evidence shows that the mode of healthcare expenditure impacts the provision of maternal healthcare services and reduction in maternal mortality. Understanding how healthcare expenditure impacts maternal health outcomes in Uganda is important for policy. Data from World Development Indicators for the period 1985-2019 were used to explore the effect of healthcare expenditure on maternal mortality using an auto-regressive distributed lag model. The findings showed that domestic government expenditure targeted on maternal health conditions significantly reduces maternal mortality. An increase in nurses was also associated with significant reductions in maternal mortality; hence, the government should increase investment in training and posting adequate nurses at public health facilities, where the majority of Ugandans seek care. Similarly, out-of-pocket health expenditure and total health expenditure per capita were both associated with a reduction in maternal deaths in the short-run. However, while out-of-pocket health expenditure is associated with a reduction in maternal mortality, it is inequitable. The Uganda National Health Insurance should ensure that financial risk protection for the poor is implemented to move towards universal health coverage.
Human Betacoronavirus OC43 Interferes with the Integrated Stress Response Pathway in Infected Cells

Viruses evolve many strategies to ensure the efficient synthesis of their proteins. One such strategy is the inhibition of the integrated stress response—the mechanism through which infected cells arrest translation through the phosphorylation of the alpha subunit of the eukaryotic translation initiation factor 2 (eIF2α). We have recently shown that the human common cold betacoronavirus OC43 actively inhibits eIF2α phosphorylation in response to sodium arsenite, a potent inducer of oxidative stress. In this work, we examined the modulation of integrated stress responses by OC43 and demonstrated that the negative feedback regulator of eIF2α phosphorylation GADD34 is strongly induced in infected cells. However, the upregulation of GADD34 expression induced by OC43 was independent from the activation of the integrated stress response and was not required for the inhibition of eIF2α phosphorylation in virus-infected cells. Our work reveals a complex interplay between the common cold coronavirus and the integrated stress response, in which efficient viral protein synthesis is ensured by the inhibition of eIF2α phosphorylation but the GADD34 negative feedback loop is disrupted.
ElastographyPressure Hydrocephalus by MR Prediction of Surgical Outcomes in Normal

BACKGROUND AND PURPOSE: Normal pressure hydrocephalus is a treatable cause of dementia associated with distinct mechanical property signatures in the brain as measured by MR elastography. In this study, we tested the hypothesis that speci ﬁ c anatomic features of normal pressure hydrocephalus are associated with unique mechanical property alterations. Then, we tested the hypothesis that summary measures of these mechanical signatures can be used to predict clinical outcomes. MATERIALS AND METHODS: MR elastography and structural imaging were performed in 128 patients with suspected normal pressure hydrocephalus and 44 control participants. Patients were categorized into 4 subgroups based on their anatomic features. Surgery outcome was acquired for 68 patients. Voxelwise modeling was performed to detect regions with signi ﬁ cantly different mechanical properties between each group. Mechanical signatures were summarized using pattern analysis and were used as features to train classi ﬁ cation models and predict shunt outcomes for 2 sets of feature spaces: a limited 2D feature space that included the most common features found in normal pressure hydrocephalus and an expanded 20-dimensional (20D) feature space that included features from all 4 morphologic subgroups. RESULTS: Both the 2D and 20D classi ﬁ ers performed signi ﬁ cantly better than chance for predicting clinical outcomes with estimated areas under the receiver operating characteristic curve of 0.66 and 0.77, respectively ( P , .05, permutation test). The 20D classi ﬁ er signi ﬁ cantly improved the diagnostic OR and positive predictive value compared with the 2D classi ﬁ er ( P , .05, permutation test). CONCLUSIONS: MR elastography provides further insight into mechanical alterations in the normal pressure hydrocephalus brain and is a promising, noninvasive
Randomized clinical trial evaluating the efficacy of synbiotic supplementation on serum endotoxin and trimethylamine N-oxide levels in patients with dyslipidaemia

Introduction Elevated serum endotoxin and trimethylamine N-oxide (TMAO) are associated with metabolic disorders including dyslipidaemia and insulin resistance. This study aimed to evaluate the impact of a 12-week treatment with a synbiotic supplement on serum endotoxin and TMAO levels in patients diagnosed with dyslipidaemia. Material and methods A total of 56 patients who met the study inclusion criteria were recruited in this randomized, double-blind clinical trial. Participants were randomly assigned into intervention and control groups and received either synbiotic or placebo sachets twice a day for 12 weeks. The sociodemographic data, food intake, physical activity, and anthropometric indices of participants were assessed before and after intervention. Serum endotoxin, TMAO, and fasting blood glucose (FBG) levels were measured at the baseline and end of the study. Results No significant difference in the baseline characteristics of participants in the 2 groups was observed. After the 12 weeks of intervention, the mean of serum endotoxin (p < 0.0001), TMAO (p < 0.0001), and FBG (p < 0.0001) was decreased in patients who received synbiotic supplements while no significant change was observed in the control group. Moreover, a significant positive correlation between changes in endotoxin (r = 0.41, p = 0.041) and TMAO (r = 0.40, p = 0.047) with FBG changes was observed. Conclusions A significant reduction in serum endotoxin and TMAO levels, as well as improvements in FBG, following 12 weeks of supplementation with synbiotics, may offer a potential approach for improving metabolic status in patients with dyslipidaemia.
Potential antibacterial mechanism of silver nanoparticles and the optimization of orthopedic implants by advanced modification technologies

: Infection, as a common postoperative complication of orthopedic surgery, is the main reason leading to implant failure. Silver nanoparticles (AgNPs) are considered as a promising antibacterial agent and always used to modify orthopedic implants to prevent infection. To optimize the implants in a reasonable manner, it is critical for us to know the specific antibacterial mechanism, which is still unclear. In this review, we analyzed the potential antibacterial mechanisms of AgNPs, and the influences of AgNPs on osteogenic-related cells, including cellular adhesion, proliferation, and differentiation, were also discussed. In addition, methods to enhance biocompatibility of AgNPs as well as advanced implants modifications technologies were also summarized.
Synthesis of Silver nanoparticles using leaves extract: Characterization Ficus palmata and evaluation for its Antimicrobial and Antioxidant activities

Objective: Metallic nanoparticles are usually synthesized by chemical methods which require toxic chemicals. The objective of present study was to prepare silver nanoparticles of leaves extract and evaluated for Ficus palmata antimicrobial and antioxidant activities. In this current research article we report the eco-Materials and methods: friendly green synthesis of silver nanoparticles from leaf extract of without using any external reducing Ficus palmata or capping agent. The synthesized nanoparticles were characterized by UV-Vis Spectroscopy, X-ray diﬀraction (XRD), Scanning Electron Microscopy and Transmission Electron Microscopy (TEM) techniques. UV-Vis Results: Spectroscopy studies showed the absorbance peak at 452 nm. The XRD patterns indicated the formation of crystalline silver nanoparticles having face-centered cubic geometry. The spherical nature and size of synthesized nanoparticles was further conﬁrmed by TEM. The synthesized nanoparticles showed considerable antibacterial and Conclusion: antioxidant activities.
Comparison of machine learning algorithms in statistically imputed water potability dataset

Lack of safe drinking water is a growing concern in the present day and age. Since missing data is commonly found among most of the available datasets, the main purpose of this study is to find the best algorithm that works in the dataset that is statistically imputed and find the algorithm that gives the best prediction on whether water is potable or not. Water potability is predicted using its datasets with the help of the four algorithms evaluating nine features. Some values of the three features, specifically pH, chloramine, and trihalomethane, are found to be missing in the dataset. Missing values are filled in by the median of that particular feature. The performance of machine learning algorithms called LR, K-NN, RF, and ANN is compared in these given conditions. As per our research, RF, with 700 decision trees at a maximum depth of 30, is found to be the best-performing algorithm for the statically imputed water potability dataset. The study most certainly answers the question concerning the best algorithm, but still, further study is needed to optimize the algorithm in order to provide the best prediction.