Miracle-dz/UniFAR-FTUs huggingface dataset:
A solvable singular control problem driven by a jump diffusion process with applications

ABSTRACT We consider a class of singular control problems driven by a double exponential jump diffusion process, which come from the reversible investment problem. In some interesting cases (e.g., the running cost function is given by the so-called Cobb-Douglas production function), we give the explicit solutions to the singular control problem by using the connection between singular control and optimal switching. We solve a collection of consistent optimal switching problems and yield the explicit solution for the singular control problem. We then give an application to a particular inventory control problem in a single random period.
Triple Hydrophilic Statistical Terpolymers via RAFT Polymerization: Synthesis and Properties in Aqueous Solutions

In this work, we report the synthesis of novel triple hydrophilic statistical terpolymers consisting of three different methacrylate monomers with varying degrees of responsivity to solution conditions. Terpolymers of the type poly(di(ethylene glycol) methyl ether methacrylate-co-2-(dimethylamino)ethylmethacrylate-co-oligoethylene glycol methyl ether methacrylate), P(DEGMA-co-DMAEMA-co-OEGMA), and of different compositions, were prepared by using the RAFT methodology. Their molecular characterization was carried out using size exclusion chromatography (SEC) and spectroscopic techniques, including 1H-NMR and ATR-FTIR. Studies in dilute aqueous media by dynamic and electrophoretic light scattering (DLS and ELS) show their potential responsiveness regarding changes in temperature, pH, and kosmotropic salt concentration. Finally, the change in hydrophilic/hydrophobic balance of the formed terpolymer nanoparticles during heating and cooling was studied using fluorescence spectroscopy (FS) in conjunction with pyrene giving additional information on the responsiveness and internal structure of the self-assembled nanoaggregates.
A CBCT Study of Labial Alveolar Bone Thickness in the Maxillary Anterior Region in a Teaching Hospital Population in the Eastern Province of Saudi Arabia

Background and Objectives: Labial alveolar bone thickness in the maxillary anterior region is the key factor in the placement of implants. Differences in the thickness of the bone are reported among different ethnic groups. Thus, the present study was aimed at assessing labial alveolar bone thickness in the maxillary anterior region in the population of the eastern province of Saudi Arabia. Materials and Methods: The six anterior teeth in each of the 186 CBCT sagittal images were analyzed at three points: Point A from the facial plate at the level of the bone crest to the coronal root third, Point B to the mid-root surface, and Point C to the apical third. Crest height (Point D) was measured as the distance from the CEJ to the alveolar bone crest. The analysis was done using SPSS version 20. A p-value of <0.05 was considered statistically significant. Results: The bone thickness at any given point (Point A, Point B, or Point C) was less than the preferred bone thickness of 2 mm in all six teeth. The thickness was minimum at Point B, maximum at Point C, and intermediate at Point A. This difference was found to be statistically significant (p-value < 0.05). The crestal height (Point D) was less than 3 mm, and it was not statistically significant. Comparison of bone thickness on the right and left sides for any given point was not statistically significant except at Point A in the central incisor, where it was statistically significant (p-value = 0.035). Gender comparison of bone thickness showed no difference at Point A; however, at Points B and C, it showed statistical significance (p-value < 0.05). Conclusions: The alveolar bone thickness being <2 mm in the labial anterior region warns of the importance of the proper assessment of bone during implant placement to have a predictable outcome.
Demographic features, laboratory findings and outcomes of ICU patients with COVID-19 in Ahvaz, Iran: A single-centered prospective study

Background: COVID-19 can lead to severe acute respiratory syndrome so that some patients need to be admitted to the Intensive Care Unit (ICU). The aim of the current study is to investigate the frequency of demographic, laboratory and imaging findings and type of treatment and their relationship with disease outcomes in patients with COVID-19. Material and Methods: This prospective cross-sectional study was conducted on all patients with COVID-19 who were admitted in the ICU of Razi Hospital in Ahvaz, Iran from January 20 to February 20, 2021. Patient information including demographic features, laboratory and imaging findings and clinical outcomes was recorded. Results: One hundred and thirty-three patients were recruited in the present study, out of which 74 patients (55.6%) were males and 59 patients (44.4%) were females. The overall mortality rate of patients was 35.3% (47 patients) and was higher in patients over 65 years of age. There was a significant difference in terms of thrombocytopenia (P value: 0.001), lymphopenia (P value: 0.004), progression of lung involvement in imaging, shock, disseminated intravascular coagulation (DIC), sepsis and receiving invasive respiratory support in living and deceased patients (P value < 0.001). Furthermore, the difference in life status and the length of in-ICU stay in patients with hyperkalemia and renal failure was statistically significant (P value = 0.033, P value < 0.001 respectively). Conclusion: Mortality rate of patients with COVID- 19 admitted to ICU is generally high. According to the findings of this study, thrombocytopenia, lymphopenia, hyperkalemia and AKI are laboratory disorders associated with increased mortality. Moreover, the progression of pulmonary involvement in imaging, shock, DIC, sepsis, and need to invasive respiratory support is associated with low survival of patients.
Monitoring Method of Non-Gaussian Process Based on Fractal Analysis With Kernel Independent Component Regression

Process monitoring is very important for the safety of industrial production processes. The traditional monitoring method based on independent component analysis (ICA) has the disadvantages as follows: 1) the importance ordering problem of independent components has not been solved and 2) the dynamic problem is not considered. To address these issues, a fractal dimension-based dynamic kernel independent component regression (FD-KICR) method is proposed. The contributions of the proposed method are given as follows: 1) the intrinsic dimension of the data is calculated through the improved fractal dimension (FDim), and then, the number of selected nonlinear independent components (nICs) is determined; 2) the time lags are computed with FDim to effectively describe the dynamic structure of data; and 3) by correlating temperature with independent components selection and indirectly monitoring temperature changes through bands of independent components, this method can effectively monitor the safety of the production process. This proposed method is applied to the electrical fused magnesia furnace (EFMF). The experience results show the effectiveness of this method.
Surgery of Colorectal Liver Metastases Involving the Inferior Vena Cava: A Systematic Review

Simple Summary Most of the existing data on the resection of liver metastases with inferior vena cava infiltration come from case reports or small case series. This systematic review on the subject found postoperative morbidity and mortality rates and overall survival to be acceptable, offering a surgical alternative to these patients previously considered unresectable. Abstract Combined hepatic and inferior vena cava (IVC) resection is the only potentially curative treatment for patients with colorectal liver metastases (CRLM) involving the IVC. Most of the existing data come from case reports or small case series. In this paper, a systematic review based on the PICO strategy was performed in accordance with the PRISMA statement. Papers from January 1980 to December 2022 were searched in Embase, PubMed, and the Cochrane Library databases. Articles considered for inclusion had to present data on simultaneous liver and IVC resection for CRLM and report surgical and/or oncological outcomes. From a total of 1175 articles retrieved, 29, including a total of 188 patients, met the inclusion criteria. The mean age was 58.3 ± 10.8 years. The most frequent techniques used were right hepatectomy ± caudate lobe for hepatic resections (37.8%), lateral clamping (44.8%) for vascular control, and primary closure (56.8%) for IVC repair. The thirty-day mortality reached 4.6%. Tumour relapse was reported in 65.8% of the cases. The median overall survival (OS) was 34 months (with a confidence interval of 30–40 months), and the 1-year, 3-year, and 5-year OS were 71.4%, 19.8%, and 7.1%, respectively. In the absence of prospective randomized studies, which are difficult to perform, IVC resection seems to be safe and feasible.
The Effect of Product Quality on Purchase Decision Mediated by Brand Image in Zara Women’s Bag Consumers

This study examines the effect of product quality on purchasing decisions with the brand image as a mediating variable. The research was conducted in Tunjungan Plaza, Galaxy Mall, and Pakuwon Mall with a total sample of 150 respondents of Zara Women’s consumers. The data collection technique in this study used an online questionnaire. The data analysis technique in this study uses partial least squares using Smart PLS software. The results of the partial least squares (PLS) analysis show that product quality has a positive and significant effect on purchasing decisions, product quality has a positive and significant effect on brand image, brand image has a positive and significant effect on purchasing decisions, and product quality has a positive and significant effect on purchasing decisions.
SentiTrust: A New Trust Model for Decentralized Online Social Media

Online Social Media (OSM) are dominating the wide range of Internet services. Due to their vast audience, it is crucial to evaluate the interpersonal trust among OSM users that can identify reliable sources of information, the meaningfulness of a relationship, or the trustworthiness of other users. SentiTrust is an innovative trust model for Decentralized Online Social Networks that is based on AI-powered Sentiment Analysis. It enriches the trust definition by exploiting important features that are enabled because of the adoption of Social Media through mobile devices. The model can be easily extended and customized according to the scenario of interest. The sentiment analysis component has been tested by involving 30 participants who completed several guided tasks using a social media application while their electrodermal activity and rate responses were measured. The results suggest that low arousal states are related to receiving happy faces and to sending more messages per minute. Furthermore, positive interactions result in shorter interactions and multimedia exchanges.
Modeling of Diamond Based Devices for Beam Diagnostics

Beamlines at new light sources, such as the National Synchrotron Light Source II will operate at flux levels beyond the saturation level of existing diagnostics, necessitating the development of new devices. Currently, there is no detector which can span the entire flux range that is possible even in a second generation light source and will become crucial for next generation light sources. One new approach [1] is a diamond-based detector that will be able to monitor beam position, flux and timing to much better resolution. Furthermore, this detector also has linear response to flux over 11 orders of magnitude. However, the successful development of the detector requires thorough understanding and optimization of the physical processes involved. We will discuss the new modeling capabilities we have been implementing in the VORPAL 3D code to investigate the effects of charge generation due to absorption of x-ray photons, transport, and charge trapping. We will report results from VORPAL simulations on charge collection and how it depends on applied field, charge trapping, and the energy of absorbed photons.
The relationship between demographic variables and secondary school teachers’ knowledge about adolescent growth and development in the Ramadi Education Directorate in Iraq

Background: Teachers are influencing not only the educational levels of students but also their normal growth and development, so their knowledge regarding this important aspect is crucial. Aims of the study: To clarify the relationship between demographic data and secondary school teachers' knowledge about adolescent growth and development in the Directorate of Education in Ramadi. Methodology: A descriptive study was conducted to clarify the relationship between demographic data and secondary school teachers' knowledge about adolescent growth and development in the Directorate of Education in Ramadi from 1 / October / 2020 to 31 / May / 2021 in secondary schools in Ramadi, Iraq. A sample of (60) teachers from the above mentioned schools was selected using the non-probability sampling method to collect an appropriate sample. The sample consisted of (30) teachers of the control group, and (30) teachers of the case group, and a self-report questionnaire was used for collecting the required data. Results: Indicate that there is no relationship between demographic data and the knowledge of secondary school teachers. Conclusion: The study concluded that there were no statistically significant differences between teachers' demographic data and their knowledge of adolescent growth and development. Recommendations: The study also recommended the necessity of intensifying educational programs in this field for the benefit of secondary schools, faculty members and students
Enhancing predictive accuracy of asset returns by experimenting with ML techniques

The unparalleled success of machine learning is indisputable. It has transformed the world with unimaginable solutions to insistent problems. The remarkable accuracy that machine learning manifests for making estimations is an object of fascination for plenty of researchers all over the world. The financial industry has also benefited from the growth of this electrifying field to predict asset returns, creditworthiness of a customer, and portfolio management, among others. In this research, we spotlight how this accuracy is contingent upon the analysis of various aspects of the data. We also experiment with simple techniques to make predictions and our findings suggest how these methods overshadow neural nets. The results indicate that the penalized linear models deliver the best performance. Random forest models had not been effective though. Machine learning models fitted with respect to median quantile loss were similarly observed to typically offer improvements across all machine learning models across all loss metrics. While little is known about the future of asset return that involves various risk and uncertainty, the recent enhancements in a machine learning field can contribute to deep domain training. Machine learning is increasingly gaining popularity nowadays in sectors including engineering, charity work, etc. Recently, even behavioral economics has started to leverage machine-learning expertise.
The Advection of High-Resolution Tracers by Low-Resolution Winds

Abstract The usefulness of any simulation of atmospheric tracers using low-resolution winds relies on both the dominance of large spatial scales in the strain and time dependence that results in a cascade in tracer scales. Here, a quantitative study on the accuracy of such tracer studies is made using the contour advection technique. It is shown that, although contour stretching rates are very insensitive to the spatial truncation of the wind field, the displacement errors in filament position are sensitive. A knowledge of displacement characteristics is essential if Lagrangian simulations are to be used for the inference of airmass origin. A quantitative lower estimate is obtained for the tracer scale factor (TSF): the ratio of the smallest resolved scale in the advecting wind field to the smallest “trustworthy” scale in the tracer field. For a baroclinic wave life cycle the TSF = 6.1 ± 0.3 while for the Northern Hemisphere wintertime lower stratosphere the TSF = 5.5 ± 0.5, when using the most stringent .
SUSTAINABLE FORAGE PRODUCTION IN MELIA AZEDARACH BASED SILVOPASTORAL SYSTEM

Silvopasture experiment was conducted to find out the performance of Melia azedarach (Bakain) with fodder crops in silvopastoral model of agroforestry under rainfed conditions of Ranchi, Jharkhand. Four kinds of fodder grasses viz., Stylosanthes hamata (Stylo), Arachis glabrata (Charabadam), Brachiaria mutica (Para grass) and Pennisetum purpureum x Pennisetum glaucum (NB Hybrid). Maximum growth was seen in Bakain in terms of tree height (185 cm) and collar diameter (24.97 mm) when intercropped with Stylo. Maximum green & dry fodder yield was observed in sole NB Hybrid grass, whereas Charabadam recorded maximum crude protein content in silvopastoral condition. The soil nutrient status of silvopasture was also raised as compared to sole tree/crops.
A Review of Recent Pharmacological Advances in the Management of Diabetes-Associated Peripheral Neuropathy

Diabetic peripheral neuropathy is a common complication of longstanding diabetes mellitus. These neuropathies can present in various forms, and with the increasing prevalence of diabetes mellitus, a subsequent increase in peripheral neuropathy cases has been noted. Peripheral neuropathy has a significant societal and economic burden, with patients requiring concomitant medication and often experiencing a decline in their quality of life. There is currently a wide variety of pharmacological interventions, including serotonin norepinephrine reuptake inhibitors, gapentanoids, sodium channel blockers, and tricyclic antidepressants. These medications will be discussed, as well as their respective efficacies. Recent advances in the treatment of diabetes mellitus with incretin system-modulating drugs, specifically glucagon-like peptide-1 agonists, have been promising, and their potential implication in the treatment of peripheral diabetic neuropathy is discussed in this review.
Co-Attention-Based Cross-Stitch Network for Parameter Prediction of Two-Phase Flow

Gas–liquid two-phase flow is pervasive in industrial processes, and accurate measurement of its parameters can effectively improve industrial efficiency. Void fraction, a key parameter in gas–liquid two-phase flow, plays a crucial role in mixture density determination and flow structure analysis. In this article, a novel multitask learning (MTL) method is proposed to predict the gas void fraction. First, we conduct vertical upward gas–liquid two-phase flow experiments to obtain fluid signals relying on the four-sector distributed conductance sensor. Then, we pair the measurement of gas void fraction and its classification and design a co-attention-based cross-stitch network (CACSNet) to process these two closely related tasks simultaneously. In CACSNet, two parallel embedding branches are adopted to extract task-specific features. There are three feature extractors in each branch to capture in-depth representations of temporal and spatial. To learn an optimal combination of these task-specific features and generate shared representations, we propose cross-stitch embedding with attention modules (CEAMs), which is incorporated the channel co-attention module (CCAM) and the temporal co-attention module (TCAM). Finally, we compare our CACSNet with single-task baseline and other competitive methods, which proves that our framework achieves superior performance in gas void fraction prediction. We also conduct detailed model ablation and parameter analysis to illustrate the efficiency of our proposed structure.
Abundance analysis of three metal poor stars: CS 22166-0030, CS 22186-0005, and CS 30344-0033

Abstract We present the abundance analysis of three very metal poor stars, CS 22166-0030 ([Fe/H]=−2.96), CS 22186-0005 ([Fe/H]=−2.70), and CS 30344-0033 ([Fe/H]=−2.90). Our study is based on high resolution spectra which were obtained from SARG (on TNG), HARPS (on 3.6m), and UVES (on VLT) spectrographs and one-dimensional ATLAS9 model atmospheres. We derived the abundances for 2, 9, and 16 atomic species in the spectrum of CS 22166-0030, CS 22186-0005, and CS 30344-0033, respectively. The Na and Mg abundances of CS 22166-0030 are highly under-abundant with respect to the solar values. The abundance patterns of CS 22186-0005 and CS 30344-0033 are consistent with the other halo stars within abundance uncertainties.
The Effect of Store Atmosphere on Repurchase Intention with Customer Satisfaction as Mediation Variable

This study aims to analyze the effect of the Store Atmosphere variable on the Repurchase Intention variable through Satisfaction as a mediating variable. A non-probability sampling of purposive sampling was used for the sampling technique, with the answer from 190 customers of Sinar Anugrah Store as the sample for this research. Furthermore, this research uses structural Equation Modeling – Partial Least Square (SEM-PLS) with the SmartPLS 3 program as a data analysis tool. The results show that the store atmosphere has a significant effect on repurchase intention, and the store atmosphere has a significant effect on customer satisfaction. Customer Satisfaction has no significant effect on Repurchase Intention. In addition, the results showed that customer satisfaction was not able to significantly mediate the store atmosphere on repurchase intentions.
Pancreatic Cancer Biopsy Modalities: Comparing Insurance Status, Length of Stay, and Hospital Complications Based on Percutaneous, Endoscopic, and Surgical Biopsy Methods

Background: Pancreatic cancer is diagnosed histologically through percutaneous biopsy (PB), endoscopic biopsy (EB), or surgical biopsy (SB). Factors and outcomes associated with method type are not clearly understood. We aimed to evaluate the relationship between insurance status, length of hospital stay (LOS), complications, and different pancreatic biopsy modalities. Study: The 2001-2013 database from the National (Nationwide) Inpatient Sample (NIS) was queried for those with pancreatic cancer who underwent biopsies using International Classification of Diseases, Ninth Revision (ICD-9) codes. Data regarding insurance status, hospital stay, demographics, and complications were analyzed using chi-square and multivariate analysis with α < 0.001. Results: A total of 824,162 patients with pancreatic cancer were identified. Uninsured and Medicaid patients were more likely to get PB compared to SB. Patients were more likely to have acute renal failure (ARF) with an EB compared to SB. Patients were more likely to have a urinary tract infection (UTI) with EB or PB compared to SB. All biopsy types were less likely to have pneumonia; pancreatitis was more prevalent in EB compared to PB and SB. Conclusions: Uninsured and Medicaid patients were most likely to have a PB compared to EB despite unclear indications which may represent an underlying discrepancy in healthcare utilization. EB patients had the shortest LOS while SB patients stayed three more days; those who underwent a combination of biopsies had the greatest LOS. Patients with EB were more likely to develop ARF, UTI, and pancreatitis than SB, possibly attributed to the advanced nature of endoscopic ultrasound. It is important to establish appropriate algorithm contributors to guide decision-making.