Miracle-dz/UniFAR-FTUs huggingface dataset:
New Application of Molybdenite in the Study on Ore Deposits:Rare Earth Elements Geochemistry

As the common sulfide mineral in metallic mineral deposits,the molybdenite has played an important role on the isotopic dating of the deposit.Besides the widely application of Re-Os method of isotopic dating,the new application of this mineral on the REE(rare earth elements) geochemical study is reported in this paper.Based on the studies of 14 Mesozoic W,Mo and poly-metallic ore deposits from Hunan,Jiangxi and Fujian Province,it's a fact that the REE contents and normalized pattern of molybdenite samples from different types of deposits are different from each other.Although these differences are resulted from multi-causes,such general features of REE content,ratios and chondrite-normalized patterns can be outlined as followings:1)different types of ore deposits have different REE geochemical features.For example,porphyry Cu-Mo deposits,hydrothermal Mo deposits hosted within volcanic rocks and hydrothermal W deposits originated from granite can be distinguished by REE features of the molybdenite;2)different deposits belonging to the same type and with the same mineral resources have similar REE features,no matter what geological settings they are located in.The tungsten deposits in Southern Hunan have similar REE features,recorded in the molybdenite,as those in South Jiangxi;3)the molybdenite formed at different stages have different REE geochemical features,but those formed at the same stage and in the same geological environment might have similar features.For instance,all of the studied middle-later Jurassic molybdenites construct a trend of gradually increasing of ∑Ce/∑Y ratio with ages;4)because the molybdenite formed at different settings can have similar feature,while those formed at the same metallogenic belt might have different features,the conclusion is that the ore-forming tectonic settings cannot be discriminated only by REE geochemistry of molybdenite;5)the comparison of the molybdenite from different mines can provide valuable information for mineral exploration.Although the tracing of REE in the molybdenite is preliminary and is to be further studied,it provides a new method for studying the genesis of the ore deposit.
Numerical Simulation of Salt and Water Distribution from Surface Drip Irrigation of Cotton Seedling

A model simulating water and solute distribution under from drip irrigation is established,which is numerically solved using Hydrus 2D software.The simulation result of water and salt distribution is verified using data obtained from experiment that is conducted in the greenhouse.The result indicts that good agreement is obtained between simulated and measured water and solute distribution,and simulating precision of Hydrus 2D is good enough for selecting the design and running parameters of drip irrigation system.When the total irrigation water amount is equal,low irrigation frequency has tendency to reduce salt accumulation in upper soil lay.
Selective Probing of Longitudinal and Transverse Plasmon Modes with Electron Phase-Matching

The optical properties of metallic nanoparticles are dominated by localized surface plasmons (LSPs). Their properties only depend on the constituting material, the size and shape of the nano-object as well as its surrounding medium. In anisotropic structures, such as metallic nanorods, two families of modes generally exist, transverse and longitudinal. Their spectral and spatial overlaps usually impede their separate measurements in electron energy loss spectroscopy (EELS). In this work, we propose three different strategies enabling to overcome this difficulty and selectively probe longitudinal and transverse modes. The first strategy is numeric and relies on morphing of nano-structures, rooted in the geometrical nature of LSPs. The two other strategies exploit the relativistic and wave nature of the electrons in an EELS experiment. The first one is the phase-matching between the electron and the plasmon excitation to enhance their coupling by either tilting the sample and modifying the electron kinetic energy. The second one - polarized EELS (pEELS) - exploits the wave nature of electrons to mimic selection rules analogous to the one existing in light spectroscopies. The above-mentioned strategies are exemplified - either experimentally or numerically - on a canonical plasmonic toy model: the nano-rod. The goal of the paper is to bring together the state-of-the-art concepts of EELS for plasmonics to tackle a pedestrian problem in this field.
Non-Contact Estimation of Cardiac Inter-Beat Interval and Heart Rate Variability Using Time-Frequency Domain Analysis for CW Radar

Vital signs are objective indices of health status. Heart rate variability (HRV) is the physiological phenomenon of variation in interval between consecutive heartbeats, which carries more physiological information than respiration rate (RR) or heart rate (HR). Medical radar isa potential sensor for non-contact vital sign monitoring. However, this sensor requires a more complicated process for HRV extraction. In this study, the HRV was obtained as follows: The heartbeat component was extracted from the radar signal using a locally projected noise reduction (LPNR)-based nonlinear adaptive filter and a convolution-based smoothing filter. The heartbeat component still includes undesired peaks; therefore, a derivative function was used to detect the sharpest slope peak, which is the desired peak, followed by R-peak detection to obtain HRV. To evaluate the performance of the proposed method, we tested the system on 18 healthy subjects and compared the HRV determined by this sensor with that measured by contact-type electrocardiography (ECG). The results show a correlation of 97.43% between HRV by radar and HRV by ECG; the 95% confidence of inter-beat interval (IBI) is 59.5 ms. In addition, the proposed method was applied to monitor the change in HRV of an inpatient from Yokohama Hospital, Japan. The clinical data processing results provided consent for the nurse's daily check.
Prescribed fire applications in Forest and Woodlands: Integration of models and field studies to guide fire use

Globally prescribed burning is widely used for agro-forestry, restoration, and conservation to modify species composition and stand structure. Commonly stated goals of prescribed burns include to reduce hazardous fuels, improve species’ habitat, reduce the potential for severe fires in the wildland urban interface or protect municipal watersheds. Treatments may focus primarily on modifying conditions at the stand level or to change the mix of mosaic patches at the landscape level. Given the wide range of vegetation/fuel types, management objectives, and human resources it is not possible to conduct field studies to acquire the empirical data to guide fire use in the full range of applications. The integration of empirically-based models with biophysical process models holds promise for being able to extend our knowledge, develop interim guidelines for prescribed fire applications in novel situations, identify knowledge gaps, and set future research priorities. Based on their experience in forests from North America and Eurasia, the authors have begun to synthesize the fire effects literature and identify common data sets for comparative analysis. This paper reports preliminary results of this effort.
PENGGUNAAN E-WALLET DALAM TRANSAKSI E-COMMERCE: ANALISIS UNIFIED THEORY OF ACCEPTANCE AND USE OF TECHNOLOGY (UTAUT)

E-wallet is an electronic service used as a payment tool in electronic form where the amount of money will be stored in a certain electronic medium. However, there are still problems related to privacy and data security also network access while using e-wallet. The use of e-wallet is expected to increase efficiency and effectiveness in conducting transactions, helping the smoothness of small but frequent mass transactions. The Unified Theory of Acceptance and Use of Technology (UTAUT) analysis is used to predict a person's attitudes and acceptance of the technology used and provide information on what factors can encourage the user's attitudes. The purpose of this study is to analyze the use of e-wallets in transactions on e-commerce using the UTAUT framework and to determine the impact of using E-wallets as a non-cash payment tool for e-commerce using the Unified Theory of Acceptance and Use of Technology (UTAUT) framework on 109 student respondents of Faculty Economics and Business Satya Wacana Christian University using explanatory research through a quantitative approach. The results of this study prove that there are two accepted hypotheses. Only the social influence and facilitating conditions variables have a positive and significant effect on the user's interest in using the ShopeePay service with an interest in transactions on e-commerce.
Comparison between percutaneous transhepatic gallbladder drainage and upfront laparoscopic cholecystectomy in patients with moderate-to-severe acute cholecystitis: a propensity score-matched analysis

Purpose In the Tokyo Guidelines 2018 (TG18), emergency laparoscopic cholecystectomy is recognized as a crucial early treatment option for acute cholecystitis. However, early laparoscopic intervention in patients with moderate-to-severe acute cholecystitis or those with severe comorbidities may increase the risk of complications. Therefore, in the present study, we investigated the association between early laparoscopic cholecystectomy and percutaneous transhepatic gallbladder drainage (PTGBD) in moderate-to-severe acute cholecystitis patients. Methods We retrospectively analyzed 835 TG18 grade II or III acute cholecystitis patients who underwent laparoscopic cholecystectomy at 4 tertiary medical centers in the Republic of Korea. Patients were classified into 2 groups according to whether PTGBD was performed before surgery, and their short-term postoperative outcomes were analyzed retrospectively. Results The patients were divided into 2 groups, and 1:1 propensity score matching was conducted to establish the PTGBD group (n = 201) and the early laparoscopic cholecystectomy group (n = 201). The PTGBD group experienced significantly higher rates of preoperative systemic inflammatory response syndrome (24.9% vs. 6.5%, P < 0.001), pneumonia (7.5% vs. 3.0%, P = 0.045), and cardiac disease (67.2% vs. 57.7%, P = 0.041) than the early operation group. However, there was no difference in biliary complication (hazard ratio, 1.103; 95% confidence interval, 0.519–2.343; P = 0.799) between the PTGBD group and early laparoscopic cholecystectomy group. Conclusion In most cases of moderate-to-severe cholecystitis, early laparoscopic cholecystectomy was relatively feasible. However, PTGBD should be considered if patients have the risk factor of underlying disease when experiencing general anesthesia.
Econometric Modeling and Forecasting of Interest Rates in Montenegro

In contemporary conditions, the econometric modeling and forecast have a growing importance in both the development of several theoretic models and approaches that may be used and in the necessity of forecasting to make proper decisions by authorities for their making. The paper tests the possibility of applying the Box-Jenkins approach and vector autoregressive models for modeling and forecasting interest rates in Montenegro. Box-Jenkins approach and vector autoregressive models are one of multiple, yet the most used approaches and models used for forecasting time series values. Thee comparison of forecasting models determines the more superior model. The time series of the interest rate to be modeled and forecasted is a monthly weighted average lending interest rate of banks on new loans in the period from December 2011 to January 2018. An example of the interest rate, which is extremely important in quite a bank-centric system in Montenegro, proved that the Box-Jenkins approach and VAR models may be used successfully for modeling and forecast. Moreover, the paper recommends the use of the Box-Jenkins approach and the assessed AR model for forecasting interest rate since it has better forecasting performances than the VAR model. Despite numerous limitations, primarily the inadequate statistical base, the AR model may find its application and help the decision-makers in the process of making economic decisions. Keywords: forecasting, autoregressive models, interest rate forecasting, Box-Jenkins, VAR, AR.
A Novel Energy Proficient Computing Framework for Green Computing Using Sustainable Energy Sources

Numerous green computing applications employ sustainable energy sources to abate redundant energy consumption. Renewable energy sources are vital to improving energy efficiency and should be used optimally. This paper introduces the Energy Proficient Computing Framework (EPCF) in the resource-centric cloud environment. The main objective of the EPCF is to improve the shared efficiency of energy distribution in the computing systems. Renewable energy is distributed among computers according to their running status and the number of calculations available. Traditional k-means clustering separates the states and computations when making this determination. This mapping procedure is repeated throughout the computation until the energy is dispersed without waste. Energy is conserved for the later use if the sources of the leak can be located in advance. As a result, we can conserve and use energy more effectively. In addition, it speeds up calculations and decreases service allocation waiting times. The proposed framework achieves 14.69% less energy cost for the different service al-location rates, 6.34% less energy drain, and 14.4% high efficiency.
Hospital Discharge Planners Need More Information When Referring Patients to Home Health Care: Insights From the Coronavirus Disease 2019 Pandemic

Hospital discharge planners play an important role in helping patients choose appropriate home health care. However, during the COVID-19 pandemic, they may not have had enough information to make the best decisions for their patients. A study of 58 discharge planners from Michigan hospitals found that 90% of them wanted information about the quality of home health agencies and whether they were prepared for COVID-19. However, only about 20% had this information readily available. The study also found that discharge planners varied in how they incorporated quality information. Some did not incorporate any quality information at all, while others provided it to patients without explaining its significance. Only about 25% of discharge planners helped patients interpret different sources of information. These findings suggest that hospital discharge planners had an unmet need for quality information, and they also provided limited assistance to patients. This may have led to some patients receiving suboptimal care. Thus, we proposed that hospital discharge planners need more information about the quality of home health agencies. Discharge planners should be more transparent about the quality of information they have, and they should help patients interpret it.
Improved U-Net Model to Estimate Cardiac Output Based on Photoplethysmography and Arterial Pressure Waveform

We aimed to estimate cardiac output (CO) from photoplethysmography (PPG) and the arterial pressure waveform (ART) using a deep learning approach, which is minimally invasive, does not require patient demographic information, and is operator-independent, eliminating the need to artificially extract a feature of the waveform by implementing a traditional formula. We aimed to present an alternative to measuring cardiac output with greater accuracy for a wider range of patients. Using a publicly available dataset, we selected 543 eligible patients and divided them into test and training sets after preprocessing. The data consisted of PPG and ART waveforms containing 2048 points with the corresponding CO. We achieved an improvement based on the U-Net modeling framework and built a two-channel deep learning model to automatically extract the waveform features to estimate the CO in the dataset as the reference, acquired using the EV1000, a commercially available instrument. The model demonstrated strong consistency with the reference values on the test dataset. The mean CO was 5.01 ± 1.60 L/min and 4.98 ± 1.59 L/min for the reference value and the predicted value, respectively. The average bias was −0.04 L/min with a −1.025 and 0.944 L/min 95% limit of agreement (LOA). The bias was 0.79% with a 95% LOA between −20.4% and 18.8% when calculating the percentage of the difference from the reference. The normalized root-mean-squared error (RMSNE) was 10.0%. The Pearson correlation coefficient (r) was 0.951. The percentage error (PE) was 19.5%, being below 30%. These results surpassed the performance of traditional formula-based calculation methods, meeting clinical acceptability standards. We propose a dual-channel, improved U-Net deep learning model for estimating cardiac output, demonstrating excellent and consistent results. This method offers a superior reference method for assessing cardiac output in cases where it is unnecessary to employ specialized cardiac output measurement devices or when patients are not suitable for pulmonary-artery-catheter-based measurements, providing a viable alternative solution.
Carbonate facies patterns in surface sediments of upwelling and non‐upwelling shelf environments (Panama, East Pacific)

In this study two carbonate environments are compared and contrasted; the Gulf of Panama and the Gulf of Chiriquí on the Pacific side of Panama. These two embayments are in close geographic proximity at latitudes between 7° N and 9° N. The Gulf of Panama and the Gulf of Chiriquí are characterized by contrasting oceanographic conditions with year‐round stable non‐upwelling conditions in the Gulf of Chiriquí and strong seasonal upwelling in the dry season (December to April) in the Gulf of Panama. The upwelling variations only have a limited influence on the amount of carbonate produced; however, they do have a major impact on the occurrence of specific carbonate producing biota. In addition, carbonate production and distribution is influenced in both gulfs by the occurrence of islands and by terrigenous input. Terrigenous material is found mainly in the smaller grain sizes (<63 to 250 μm) that can be transported easily by currents and waves. Carbonate dominant sediments (carbonate sands and mixed carbonate–siliciclastic sands) mainly occur around the islands and are dominated by larger grain‐sizes (>500 μm). The Gulf of Panama and the Gulf of Chiriquí both show warm and temperate carbonate‐producing biota, with carbonate producers from tropical (corals) to mixed tropical to cool‐water (coralline red algae) and cold‐water (balanids) environments. The Gulf of Chiriquí is characterized by oligotrophic to mesotrophic conditions resulting in a photozoan (coral) and/or rhodolith‐facies in shallow‐water areas surrounding the islands and a mollusc‐dominated facies in deeper waters towards the shelf edge. Seasonal upwelling causes temporary eutrophic conditions in the Gulf of Panama, which results in a heterozoan facies around the islands dominated by balanids, echinoderms and molluscs. Thus a ‘cool‐water’ carbonate fauna and eutrophic conditions can exist in the tropics within an area prone to seasonal upwelling. The distinct facies differences found on the Pacific shelf of Panama stress the importance of variations in oceanographic conditions, upwelling versus non‐upwelling, in determining carbonate production and associated facies patterns in the tropics.
An Approach for Behavioral Fingerprinting of P4 Programmable Switches

Behavioral Fingerprinting is a technique used to understand the behavior of devices, enabling a better understanding of their functionality and improved anomaly detection. This paper proposes a methodology for generating the behavioral fingerprint of programmable switches. The methodology outlines the process of selecting metrics for analysis, extracting data from them, and organizing the information to construct a behavioral fingerprint for a programmable device within a network.
Viral Subversion of the Chromosome Region Maintenance 1 Export Pathway and Its Consequences for the Cell Host

In eukaryotic cells, the spatial distribution between cytoplasm and nucleus is essential for cell homeostasis. This dynamic distribution is selectively regulated by the nuclear pore complex (NPC), which allows the passive or energy-dependent transport of proteins between these two compartments. Viruses possess many strategies to hijack nucleocytoplasmic shuttling for the benefit of their viral replication. Here, we review how viruses interfere with the karyopherin CRM1 that controls the nuclear export of protein cargoes. We analyze the fact that the viral hijacking of CRM1 provokes are-localization of numerous cellular factors in a suitable place for specific steps of viral replication. While CRM1 emerges as a critical partner for viruses, it also takes part in antiviral and inflammatory response regulation. This review also addresses how CRM1 hijacking affects it and the benefits of CRM1 inhibitors as antiviral treatments.
Identification and validation of ubiquitination-related signature and subgroups in immune microenvironment of tuberculosis

Background: Mycobacterium tuberculosis (Mtb) is the bacterial pathogen responsible for causing tuberculosis (TB), a severe public health concern that results in numerous deaths worldwide. Ubiquitination (Ub) is an essential physiological process that aids in maintaining homeostasis and contributes to the development of TB. Therefore, the main objective of our study was to investigate the potential role of Ub-related genes in TB. Methods: Our research entailed utilizing single sample gene set enrichment analysis (ssGSEA) in combination with several machine learning techniques to discern the Ub-related signature of TB and identify potential diagnostic markers that distinguish TB from healthy controls (HC). Results: In summary, we used the ssGSEA algorithm to determine the score of Ub families (E1, E2, E3, DUB, UBD, and ULD). Notably, the score of E1, E3, and UBD were lower in TB patients than in HC individuals, and we identified 96 Ub-related differentially expressed genes (UbDEGs). Employing machine learning algorithms, we identified 11 Ub-related hub genes and defined two distinct Ub-related subclusters. Notably, through GSVA and functional analysis, it was determined that these subclusters were implicated in numerous immune-related processes. We further investigated these Ub-related hub genes in four TB-related diseases and found that TRIM68 exhibited higher correlations with various immune cells in different conditions, indicating that it may play a crucial role in the immune process of these diseases. Conclusion: The observed enrichment of Ub-related gene expression in TB patients emphasizes the potential involvement of ubiquitination in the progression of TB. These significant findings establish a basis for future investigations to elucidate the molecular mechanisms associated with TB, select suitable diagnostic biomarkers, and design innovative therapeutic interventions for combating this fatal infectious disease.