Miracle-dz/UniFAR-FTUs huggingface dataset:
Self-Rated Anxiety and Attitude Responses of Pediatric Nurses Surveyed about Providing End of Life Care

Background: End of Life care is a critical topic for pediatric patients with terminal conditions, aiming at improving their life in spite of prognosis. It is the most traumatic aspect of pediatric nursing due to unpleasant sensations and experiences that obstruct nurses’ abilities to apply satisfactory care regarding End of Life. Aim: to identify the association between self-rated anxiety and attitude responses of pediatric nurses surveyed about providing End of Life care. Method: A descriptive correlational design was utilized. The sample composed of 48 nurses in neonatal and pediatric intensive care units in general hospitals in Port Said Governorate. Tools: The 20-item state anxiety questionnaire and the nurses’ attitudes toward pediatric end of life care were used to collect data. Results: 46.8% of the studied nurses had demonstrated generally positive attitude toward End of Life care. 33.3% of them had tendered to reported sometimes to have anxiety symptoms, there is a statistical significant relation between level of anxiety and the attitude toward areas with comfort in discussing the pediatric End of Life care, and physician involvement. Conclusion & Recommendation: positive attitude of pediatric nurses concerning End of Life care. Moreover, negative nurses’ attitude toward the benefits of pediatric nursing care relating End of Life, and there was a statistical significant relation between level of anxiety and the attitude toward comfort in discussing the pediatric nursing care regarding End of Life, and physician involvement. Therefore, The study findings emphasize the need for acquainting pediatric nurses with effective coping strategies to overcome anxiety to improve their attitude toward End of Life care.
Enterprise Value Assessment Based on Real Option Approach: The Case of SEMCORP

As the core power source of new energy vehicles, the lithium battery industry has ushered in a high-speed development, and has become the most widely used battery technology system. Due to the industry's high technical barriers, imperfect system, high volatility risk and other characteristics of the lithium battery enterprise value the overall assessment of the difficulty of ordinary investors to make a more accurate judgment. As an important representative of the lithium new energy materials industry, SEMCORP is in a golden development period. Based on the research results of previous researchers on enterprise value assessment, this paper examines the value assessment of n SEMCORP. Specifically, this paper applies a two-stage growth model to calculate the present value and evaluates the potential value separately with the help of an option pricing model to obtain its overall value. After analyzing, conclude that SEMCORP 's shares are overvalued. This paper can provide decision-making references for the investment and financing of Chinese lithium new energy materials enterprises, as well as further enriching the application of enterprise value assessment methods.
The coastal streamflow flux in the Regional Arctic System Model

The coastal streamflow flux from the Arctic drainage basin is an important driver of dynamics in the coupled ice-ocean system. Comprising more than one-third of the total freshwater flux into the Arctic Ocean, streamflow is a key component of the regional and global freshwater cycle. To better represent the coupling of the streamflow flux to the ocean, we have developed and applied the RVIC streamflow routing model within the Regional Arctic System Model (RASM). The RASM is a high-resolution regional Earth System Model whose domain includes all of the Arctic drainage basin. In this paper, we introduce the RVIC streamflow routing model, detailing its application within RASM and its advancements in terms of representing high-resolution streamflow processes. We evaluate model simulated streamflow relative to in-situ observations and demonstrate a method for improving model performance using a simple optimization procedure. We also present a new, spatially and temporally consistent, high-resolution dataset of coastal freshwater fluxes for the Arctic drainage basin and surrounding areas that is based on a fully-coupled RASM simulation and intended for use in Arctic Ocean modeling applications. This dataset is evaluated relative to other coastal streamflow datasets commonly used by the ocean modeling community. We demonstrate that the RASM-simulated streamflow flux better represents the annual cycle than existing datasets, especially in ungauged areas. Finally, we assess the impact that streamflow has on the coupled ice-ocean system, finding that the presence of streamflow leads to reduced sea surface salinity, increased sea surface temperatures, and decreased sea ice thickness. This article is protected by copyright. All rights reserved.
The growth and phase distribution of ultrathin SnTe on graphene

Recently, a monolayer of SnTe was discovered to be a two-dimensional ferroelectric with an in-plane polarization, and, most dramatically, it exhibits a significant enhancement of the ferroelectric phase transition temperature compared to its bulk counterpart. This phenomenon is due to a structural phase transition from bulk-like α/β-SnTe, a topological crystalline insulator, to layered γ-SnTe as the thickness is decreased to a few atomic layers. A detailed understanding of the growth mechanism and phase distribution of ultrathin SnTe films are of great interest for potential applications. Here, we report detailed studies of the molecular beam epitaxial growth and in situ scanning tunneling microscopy characterization of ultrathin SnTe films on graphene substrates. By varying the growth conditions, SnTe can be prepared as either a continuous film or in the form of large rectangular plates. The rate of nucleation of SnTe was found to be highly sensitive to the substrate temperature. The coexistence and competition between the β and γ phases formed at room temperature was studied, and the phase diagram with respect to the average thickness of SnTe and the substrate temperature during growth is drawn.
Improving Materials Accountancy for Reprocessing using hiRX.

The High Resolution X-Ray (HiRX) technology has the potential to replace K-Edge and Hybrid K-Edge Densitometry (HKED) for routine accountability measurements in reprocessing. This technology may significantly reduce plutonium measurement uncertainty in a simpler and less costly instrument. X-ray optics are used to generate monochromatic excitation of a sample and selectively collect emitted x-rays of the target elements. The result is a spectrum with a peak specific to one element with negligible background. Modeling was used to examine how safeguards could be improved through the use of HiRX at existing aqueous reprocessing plants. This work utilized the Separation and Safeguards Performance Model (SSPM), developed at Sandia National Laboratories, to examine how reduced measurement uncertainty decreases the overall measurement error. Material loss scenarios were also modeled to determine the effect on detection probability for protracted diversion of nuclear material. Current testing of HiRX is being used to inform the modeling effort, but a 0.1% measurement uncertainty for uranium and plutonium concentration is an optimistic goal based on laboratory results. Modeling results showed that a three-fold improvement in the overall safeguards performance of a plant if the 0.1% uncertainty goal can be achieved. The modeling results will be presented along with a discussion of the current experimental campaign results. In addition, a qualitative cost analysis will be presented to compare the use of HiRX with HKED.
Effect of citric acid pretreatment on drying kinetics of tamarillo in a greenhouse solar dryer

The production and consumption of tamarillo (Solanum betaceum) is gaining momentum due to its immense nutritional, health, and commercial potential. However, like many other horticultural products, high postharvest losses are incurred in tamarillo production. In particular, decay is a major problem. Drying is a viable option that can address this problem and significantly increase the shelf life of tamarillo. Consequently, the objective of this study was to evaluate the effect of citric acid pretreatment on the drying kinetics and quality of tamarillo slices. The tamarillos were dried in a passively operated even-span greenhouse solar dryer (8 m long, 4 m wide, and 3.6 m high to the ridge) covered with a 200 μm thick ultraviolet-stabilised polyethylene film. Solar radiation, temperature, relative humidity, moisture content, colour, firmness, and vitamin C content were periodically measured, and the data were analysed statistically. The citric acid-pretreated and control (untreated) tamarillo slices were dried from an initial moisture content of 655% (db) to a final moisture content of 16.25% and 22.50% (db), respectively, in 10 hours. In addition, the pretreated slices attained a higher average drying rate of 42.01±0.23 g/g/hr compared to 40.23±0.12 g/g/hr attained by the control slices. Further, the Page model best described the drying kinetics of both tamarillo samples with R², χ2, and RMSE values of 0.9975, 0.0004, and 0.0005, respectively. The pre-treated samples had better colour retention with a total colour change of 15.07±1.12 compared to 35.99±0.98 attained in the control samples. Moreover, the pretreated tamarillo slices attained a lower percentage firmness increment of 281.82±3.21% compared to 337.5%±1.54 attained in the untreated samples. Further, the pretreated tamarillo slices had 42.94±0.41% vitamin C retention compared to 40.17±0.67% retention in the control samples. Generally, pre-treating tamarillo slices with citric acid improves their drying kinetics and both physical and nutritional qualities.
Nine years of warming and nitrogen addition in the Tibetan grassland promoted loss of soil organic carbon but did not alter the bulk change in chemical structure

Abstract. Nitrogen (N) and warming effects on ecosystem carbon (C) budgets and stabilization are critical to understand as C sequestration is considered as a mechanism to offset anthropogenic CO2 emissions, which is important for accurately predicting ecosystem C sequestration and/or potential C loss, remaining controversial though. However, the relevant information, especially for the intervention of environmental controls on grassland soil, is limited in Tibetan Plateau (TP) regions. Here we used a 9-year two-way factorial experiment involving warming with open top chambers (+1.80 ∘C in the daytime and +0.77 ∘C in the nighttime at the soil surface) and multilevel nitrogen (N) enrichment treatments (0, 5, 10, and 15 gm-2yr-1) in the TP to investigate the changes in soil organic carbon (SOC) pool size and chemical structure. Nine-year warming treatment significantly decreased SOC stock in the Tibetan grassland. We observed decreasing SOC concentrations which may be related to changes in the C-degrading enzymes. Surprisingly, the SOC molecular structure remained unchanged in all N-enrichment and warmed plots, suggesting that both treatments had affected all forms of SOC, from simple and complex polymeric in a similar way. Our results suggest that long-term warming stimulates soil C loss but no preference in SOC loss with different chemical structures.
The Relationship between SOFR and Market Yield of U.S. Treasury Securities

This paper introduces the characteristic of the new benchmark rate Secured Overnight Financing Rate (SOFR), explain its connection to the futures price, and show its potential to provide new insight into the argument on interest rate term structure. Based on the future price discovery theory, this study set up a hypothesis that short-term interest rates should contribute to forecasting long-term ones. The causality between interest rates in different terms of, which are separately represented by bilateral repurchase rate and the market yield of long-term U.S. government bonds, is explored in this research using the VAR model, as well as the Granger causality test. The conclusion of this research proves the hypothesis and indicates a single-direction causality between interest rate of different terms. However, the research also has drawbacks such as ignoring non-linear relations and evading the change of government policy. These problems need to be solved in the following study.
First Old WomanMan and the Mesoamerican Diphrastic Kenning of Engendering

The royalty of the Classic Maya of Mesoamerica, and later sages of the Maya, used a powerful diphrastic kenning chab akab’, glossed as “generation-darkness” to convey a range of objectives, conjuring foremost among them. Known principally from hieroglyphic written expressions, but also depicted in the form of sacrificial instruments and offerings, Eleanor Harrison-Buck, following Timothy Knowlton, proposed that the kenning references sexual intercourse. This essay proposes that a black steatite carved figure stylistically dating to the Middle Preclassic period (900–350 CE) depicts this incantation as an old woman giving birth to her maleness in the form of a circumcised penis. A second Middle Preclassic figure of a dancing child, found as an heirloom in a Classic tomb, is compared to show the link between Preclassic and Classic meaning.
Developing mathematical models and intelligent sustainable supply chains by uncertain parameters and algorithms

In the modern era, uncertainty is a common feature of modeling techniques for designing sustainable supply chains. The increasing severity of environmental issues necessitates the integration of sustainable production in supply chain management. The present study aims to develop mathematical models and intelligent sustainable supply chains with uncertain parameters and algorithms. The goal is to design a sustainable and eco-friendly model that minimizes environmental contaminants and system costs. This descriptive-analytical study employs a novel hybrid technique to manage the uncertainty associated with the model parameters, research problems, and problem complexity, and tackle large-scale problems. The automotive industry was selected to implement the mathematical model. These combined techniques consider the disruption-induced capacity reduction and the uncertainties surrounding shipping costs and demand. Results suggest that hybrid models and techniques are efficient in solving large-scale problems and delivering high-quality processing. Further, the findings show that heuristic solutions can significantly reduce computation time for larger problems.
Study on Voids and Seepage Characteristics within Rock Fracture after Shear Dislocation Viewing from CT Test and Numerical Modeling

In rock mass engineering, stress balance changes often cause the relative slip of fractures along a wall surface, impacting the seepage behavior of fluid in the fractures. Using computer tomography (CT) scanning, spatial models of fractures with dislocations ranging from 0 to 10 mm were created to explore the relationship between changes in fracture dislocation and changes in fluid flow behavior, respectively. The spatial fractal dimension of cavity distribution within the fractures was calculated using a thin-plate filling approach to characterize the complexity of the fracture cavity distribution. The fluid flow within the dislocation fractures was then simulated using COMSOL, and the effect of cavity alterations in the form of dislocation on the fluid seepage behavior was analyzed using the spatial fractal. The results show that the values of mechanical aperture after dislocation of the fracture obtained by a CT test are normally distributed, the distribution range of mechanical aperture gradually widens with an increase in the dislocation distance, and the average mechanical aperture increases on a logarithmic curve. The relative spatial fractal dimension decreases gradually with an increase in dislocation distance, and the interconnected pathways within the fracture decrease; in addition, it is observed that the change in the relative spatial fractal dimension is closely correlated with the change in the mean mechanical aperture. Numerical simulations of dislocation fracture seepage found that the permeability increases nonlinearly with increasing dislocation distance. When the dislocation distance reaches 5 mm, nonlinear behaviors such as eddy currents occur, and the influence range of eddy currents gradually expands with the increase in dislocation distance under the influence of the boundary. Moreover, the inertia coefficient B in the Forchheimer equation and the critical hydraulic gradient Jc, which can describe the nonlinear seepage characteristics, show a power function decreasing trend with increasing dislocation distance, and the fluid in the fracture is more likely to produce nonlinear flow.
A Study of Entity Relationship Extraction Algorithms Based on Symmetric Interaction between Data, Models, and Inference Algorithms

The purpose of this paper is to address the extraction of entities and relationships from unstructured Chinese text, with a particular emphasis on the challenges of Named Entity Recognition (NER) and Relation Extraction (RE). This will be achieved by integrating external lexical information and utilizing the abundant semantic information available in Chinese. We utilize a pipeline model that is applied separately to NER and RE by introducing an innovative NER model that integrates Chinese pinyin, characters, and words to enhance recognition capabilities. Simultaneously, we incorporate information such as entity distance, sentence length, and part-of-speech to improve the performance of relation extraction. We also delve into the interactions among data, models, and inference algorithms to improve learning efficiency in addressing this challenge. In comparison to existing methods, our model has achieved significant results.
A Comprehensive Investigation of Active Learning Strategies for Conducting Anti-Cancer Drug Screening

Simple Summary Preclinical drug screening experiments for anti-cancer drug discovery typically involve testing candidate drugs against cancer cell lines. This process can be expensive and time consuming since the possible experimental space can be quite huge, involving all of the combinations of candidate cell lines and drugs. Guiding drug screening experiments with active learning strategies could potentially identify promising candidates for successful experimentation. This study investigates various active learning strategies for selecting experiments to generate response data for identifying effective treatments and improving the performance of drug response prediction models. We have demonstrated that most active learning strategies are more efficient than random selection for identifying effective treatments. Abstract It is well-known that cancers of the same histology type can respond differently to a treatment. Thus, computational drug response prediction is of paramount importance for both preclinical drug screening studies and clinical treatment design. To build drug response prediction models, treatment response data need to be generated through screening experiments and used as input to train the prediction models. In this study, we investigate various active learning strategies of selecting experiments to generate response data for the purposes of (1) improving the performance of drug response prediction models built on the data and (2) identifying effective treatments. Here, we focus on constructing drug-specific response prediction models for cancer cell lines. Various approaches have been designed and applied to select cell lines for screening, including a random, greedy, uncertainty, diversity, combination of greedy and uncertainty, sampling-based hybrid, and iteration-based hybrid approach. All of these approaches are evaluated and compared using two criteria: (1) the number of identified hits that are selected experiments validated to be responsive, and (2) the performance of the response prediction model trained on the data of selected experiments. The analysis was conducted for 57 drugs and the results show a significant improvement on identifying hits using active learning approaches compared with the random and greedy sampling method. Active learning approaches also show an improvement on response prediction performance for some of the drugs and analysis runs compared with the greedy sampling method.
HOPE: Holistic STT-RAM Architecture Exploration Framework for Future Cross-Platform Analysis

Spin Transfer Torque Random Access Memory (STT-RAM) is an emerging Non-Volatile Memory (NVM) technology that has garnered attention to overcome the drawbacks of conventional CMOS-based technologies. However, such technologies must be evaluated before deployment under real workloads and architecture. But there is a lack of available open-source STT-RAM-based system evaluation framework, which hampers research and experimentation and impacts the adoption of STT-RAM in a system. This paper proposes a novel, extendable STT-RAM memory controller design integrated inside the gem5 simulator. Our framework enables understanding various aspects of STT-RAM, i.e., power, delay, clock cycles, energy, and system throughput. We will open-source our HOPE framework, which will fuel research and aid in accelerating the development of future system architectures based on STT-RAM. It will also facilitate the user for further tool enhancement.
A review on machine learning based intrusion detection system for internet of things enabled environment

Within an internet of things (IoT) environment, the fundamental purpose of various devices is to gather the abundant amount of data that is being generated and then transmit this data to the predetermined server over the internet. IoT connects billions of objects and the internet to communicate without human intervention. But network security and privacy issues are increasing very fast, in today's world. Because of the prevalence of technological advancement in regular activities, internet security has evolved into a necessary requirement. Because technology is integrated into every aspect of contemporary life, cyberattacks on the internet of things represent a bigger danger than attacks against traditional networks. Researchers have found that combining machine learning techniques into an intrusion detection system (IDS) is an efficient way to get beyond the limitations of conventional IDSs in an IoT context. This research presents a comprehensive literature assessment and develops an intrusion detection system that makes use of machine learning techniques to address security problems in an IoT environment. Along with a comprehensive look at the state of the art in terms of intrusion detection systems for IoT-enabled environments, this study also examines the attributes of approaches, common datasets, and existing methods utilized to construct such systems.
SFR and Abundances of Nearby Galaxies

Abstract We present star formation rates and nebular abundances of 59 different star-forming regions in 16 nearby galaxies. The star-forming regions were selected to be bright in Hα and were observed with the SNIFS integral field spectrograph on the UH 2.2m telescope. The spectra span the wavelength range between 3200Å and 1μm. We find that the local star formation rates depend on the local abundances in that low SFRs show a dependence but high SFR appear insensitive to it.