Miracle-dz/UniFAR-FTUs huggingface dataset:
Incretin Response to Mixed Meal Challenge in Active Cushing’s Disease and after Pasireotide Therapy

Cushing’s disease (CD) causes diabetes mellitus (DM) through different mechanisms in a significant proportion of patients. Glucose metabolism has rarely been assessed with appropriate testing in CD; we aimed to evaluate hormonal response to a mixed meal tolerance test (MMTT) in CD patients and analyzed the effect of pasireotide (PAS) on glucose homeostasis. To assess gastro-entero-pancreatic hormones response in diabetic (DM+) and non-diabetic (DM–) patients, 26 patients with CD underwent an MMTT. Ten patients were submitted to a second MMTT after two months of PAS 600 µg twice daily. The DM+ group had significantly higher BMI, waist circumference, glycemia, HbA1c, ACTH levels and insulin resistance indexes than DM− (p < 0.05). Moreover, DM+ patients exhibited increased C-peptide (p = 0.004) and glucose area under the curve (AUC) (p = 0.021) during MMTT, with a blunted insulinotropic peptide (GIP) response (p = 0.035). Glucagon levels were similar in both groups, showing a quick rise after meals. No difference in estimated insulin secretion and insulin:glucagon ratio was found. After two months, PAS induced an increase in both fasting glycemia and HbA1c compared to baseline (p < 0.05). However, this glucose trend after meal did not worsen despite the blunted insulin and C-peptide response to MMTT. After PAS treatment, patients exhibited reduced insulin secretion (p = 0.005) and resistance (p = 0.007) indexes. Conversely, glucagon did not change with a consequent impairment of insulin:glucagon ratio (p = 0.009). No significant differences were observed in incretins basal and meal-induced levels. Insulin resistance confirmed its pivotal role in glucocorticoid-induced DM. A blunted GIP response to MMTT in the DM+ group might suggest a potential inhibitory role of hypercortisolism on enteropancreatic axis. As expected, PAS reduced insulin secretion but also induced an improvement in insulin sensitivity as a result of cortisol reduction. No differences in incretin response to MMTT were recorded during PAS therapy. The discrepancy between insulin and glucagon trends while on PAS may be an important pathophysiological mechanism in this iatrogenic DM; hence restoring insulin:glucagon ratio by either enhancing insulin secretion or reducing glucagon tone can be a potential therapeutic target.
Shannon information entropy of eddy current density distribution

Abstract This paper presents a new method to evaluate the distribution of eddy current density excited by four different planar eddy current probe inducers. The proposed method uses the information entropy to analyse eddy current distribution in different directions in the whole, or in part, of surface. Four finite-element models show the eddy current distribution and allow the entropy calculation. Finally, the paper highlights strengths and weaknesses of the studied current probe inducers.
The Hitchhiker's guide to intellectual capital

Introduction What do numbers mean, especially in accounting for non-financial aspects of businesses, such as intellectual capital (IC)? This was a question pose d by some of my students while teaching in Japan in 2012. To me this is an important question because it delves into the heart of how we teach accounting and accounting related subjects to our students and hence how accounting is applied in practice. Since beginning my research in to IC I have been critical of traditional accounting concepts such as the balance sheet and t raditional forms of reporting that have dominated thinking and thus IC practice, education and research, resulting in what is termed an “accountingisation” of IC (Dumay, 2009; Habersam et al., forthcoming). Thus, in order to help break free from the domination of accountingisation his paper offers a personal and reflective narrative about numbers and IC.
The Relevance of Calibration in Machine Learning-Based Hypertension Risk Assessment Combining Photoplethysmography and Electrocardiography

The detection of hypertension (HT) is of great importance for the early diagnosis of cardiovascular diseases (CVDs), as subjects with high blood pressure (BP) are asymptomatic until advanced stages of the disease. The present study proposes a classification model to discriminate between normotensive (NTS) and hypertensive (HTS) subjects employing electrocardiographic (ECG) and photoplethysmographic (PPG) recordings as an alternative to traditional cuff-based methods. A total of 913 ECG, PPG and BP recordings from 69 subjects were analyzed. Then, signal preprocessing, fiducial points extraction and feature selection were performed, providing 17 discriminatory features, such as pulse arrival and transit times, that fed machine-learning-based classifiers. The main innovation proposed in this research uncovers the relevance of previous calibration to obtain accurate HT risk assessment. This aspect has been assessed using both close and distant time test measurements with respect to calibration. The k-nearest neighbors-classifier provided the best outcomes with an accuracy for new subjects before calibration of 51.48%. The inclusion of just one calibration measurement into the model improved classification accuracy by 30%, reaching gradually more than 96% with more than six calibration measurements. Accuracy decreased with distance to calibration, but remained outstanding even days after calibration. Thus, the use of PPG and ECG recordings combined with previous subject calibration can significantly improve discrimination between NTS and HTS individuals. This strategy could be implemented in wearable devices for HT risk assessment as well as to prevent CVDs.
High hydrogen production rate from potassium borohydride hydrolysis with an efficient catalyst: CNT@Ru(0)

We describe the production and catalytic activity of a carbon nanotube supported-Ru(0) (CNT@ Ru(0)) catalyst. For the first time, the produced CNT@Ru(0) catalyst is used to achieve the greatest hydrogen production rate from potassium borohydride (KBH 4 ) hydrolysis. The produced CNT@ Ru(0) catalyst shows promise in the creation of hydrogen from the degradation of KBh 4 . The hydrogen generation rate of CNT@Ru(0) is determined to be 86,264.85 mL min –1 can with a low activa- tion energy of 30.18 kJ mol −1 . CNTs are potential support for distributing metal catalysts, according to the current work. Furthermore, structural, morphological, and elemental characteristics of the produced CNT@Ru(0) catalyst are investigated.
Abordagem cirúrgica de megaceco secundário a tumor de cólon descendente

Obstrução intestinal é a manifestação clínica mais frequente de tumores de cólon sendo esses, em sua maioria, localizados no cólon descendente e reto-sigmoide. A cirurgia de emergência para obstrução intestinal é associada a altos riscos de mortalidade e de morbidade e a abordagem ideal permanece controversa. Procedimentos em vários estágios e o uso de stents como ponte para cirurgia são opções promissoras. É apresentado um caso de paciente de 61 anos, com abdome agudo obstrutivo secundário à neoplasia colorretal, com ênfase em seu diagnóstico e tratamento.
Residues from Cajanus cajan Plant Provide Natural Cellulose Fibers Similar to Flax

ABSTRACT With an annual production of about 5 million tons, Cajanus cajan (pigeon pea) generates at least 20 to 25 million tons of stems as residues. These residues are generally disposed of as waste leading to environmental pollution and loss of a valuable raw material. In this report, we demonstrate that the stems of pigeon pea could be used as a source for developing natural cellulose fibers. Fibers were extracted from the stems using only water and also with water followed by alkali treatment. Fibers with high strength (984 MPa) and elongation (3.4%) similar to the properties of flax fibers were obtained. The physical and morphological properties of the fibers were similar to the common fibers. The fibers had good thermal stability and showed potential to be useful for textile, composite, and other applications. Such applications will provide a considerably high value addition to the stems of C. cajan and also be a renewable and sustainable source for fibers.
EMG FEATURE EXTRACTION FOR TOLERANCE OF 50 HZ INTERFERENCE

Hz interference is a major problem in analysis of surface electromyography signals. Therefore, noise reduction is an important step before performing feature extraction, which is used in EMG-based gestures classification. However, the solutions to remove noise are limited. This research is aimed at selecting a feature that tolerates 50 Hz interference. As a result, noise removal algorithms are not needed. Eight features in time and frequency domain are tested with additive 50 Hz interference at various signal-to-noise ratios. Results show that Willison amplitude is the best feature compared to others. Subsequently, evaluations of threshold of Willison amplitude are tested. Willison amplitude with 5 mV threshold shows better than others.
Effects of a Formula with scGOS/lcFOS (9:1) and Glycomacropeptide (GMP) Supplementation on the Gut Microbiota of Very Preterm Infants

Microbial colonization of very preterm (VPT) infants is detrimentally affected by the complex interplay of physiological, dietary, medical, and environmental factors. The aim of this study was to evaluate the effects of an infant formula containing the specific prebiotic mixture of scGOS/lcFOS (9:1) and glycomacropeptide (GMP) on the composition and function of VPT infants’ gut microbiota. Metagenomic analysis was performed on the gut microbiota of VPT infants sampled at four time points: 24 h before the trial and 7, 14, and 28 days after the trial. Functional profiling was aggregated into gut and brain modules (GBMs) and gut metabolic modules (GMMs) based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Enterococcus faecium, Escherichia coli, Klebsiella aerogenes, and Klebsiella pneumoniae were dominant species in both the test group and the control group. After the 4-week intervention, the abundance of Bifidobacterium in the test group was significantly increased. We found two GBMs (quinolinic acid synthesis and kynurenine degradation) and four GMMs (glutamine degradation, glyoxylate bypass, dissimilatory nitrate reduction, and preparatory phase of glycolysis) were significantly enriched in the test group, respectively. The results of this study suggested that formula enriched with scGOS/lcFOS (9:1) and GPM is beneficial to the intestinal microecology of VPT infants.
Empirical Analysis of Forest Penalizing Attribute and Its Enhanced Variations for Android Malware Detection

As a result of the rapid advancement of mobile and internet technology, a plethora of new mobile security risks has recently emerged. Many techniques have been developed to address the risks associated with Android malware. The most extensively used method for identifying Android malware is signature-based detection. The drawback of this method, however, is that it is unable to detect unknown malware. As a consequence of this problem, machine learning (ML) methods for detecting and classifying malware applications were developed. The goal of conventional ML approaches is to improve classification accuracy. However, owing to imbalanced real-world datasets, the traditional classification algorithms perform poorly in detecting malicious apps. As a result, in this study, we developed a meta-learning approach based on the forest penalizing attribute (FPA) classification algorithm for detecting malware applications. In other words, with this research, we investigated how to improve Android malware detection by applying empirical analysis of FPA and its enhanced variants (Cas_FPA and RoF_FPA). The proposed FPA and its enhanced variants were tested using the Malgenome and Drebin Android malware datasets, which contain features gathered from both static and dynamic Android malware analysis. Furthermore, the findings obtained using the proposed technique were compared with baseline classifiers and existing malware detection methods to validate their effectiveness in detecting malware application families. Based on the findings, FPA outperforms the baseline classifiers and existing ML-based Android malware detection models in dealing with the unbalanced family categorization of Android malware apps, with an accuracy of 98.94% and an area under curve (AUC) value of 0.999. Hence, further development and deployment of FPA-based meta-learners for Android malware detection and other cybersecurity threats is recommended.
Comparison of Improved Oil Recovery Processes on the Norne Field, C-Segment

According to current prognosis, oil production in matured reservoirs is expected to decline and this could create gap between supply and demand of hydrocarbons in various parts of the world. Inspite of the huge volumes in the unconventional hydrocarbons reservoirs, they require intensive high energy to recover oil while developing new fields is becoming complicated and expensive. There is, therefore a growing interest in development of efficient and effective improved oil recovery methods so as to increase oil recovery from ageing resources. This study compares application of three IOR methods namely polymer flooding, well location optimization and low salinity water flooding to the Norne field C segment. The main objective was to carry out comparative simulation study for the three IOR methods and then comparing their performance in terms of net present value. Sensitivity analysis for polymer flooding and low saline water flooding was carried out to analyse suitable well for injection, appropriate composition of polymer and salt as well as right time for injections. Economic evaluation was done to identify which method is most profitable in terms of net present value. Well C-3H found to be the best injector to use with polymer flooding in Norne C-segment. Low polymer concentration of 0.2 kg/m gave high oil production with less polymer consumption when injected at 3 months cycle interval in early timing. Low saline water injection found also to improve oil production of this field and significant improvements were observed by continuous early injection of low saline water. Furthermore, sidetracking the wells horizontally in the regions of high oil saturation gave high oil production than sidetracking vertically. Economic evaluation showed that polymer flooding gives the lowest incremental NPV while combination of polymer flooding and well location optimization had the highest incremental NPV at all oil prices tested. The study recommends further studies to be carried for low saline water injection with the use of real field data for saturation and relative permeability end points.
How Orthogonal is LoRa Modulation?

In this article, we provide, for the first time, a comprehensive understanding of long-range (LoRa) waveform theory in order to quantify its orthogonality. We present LoRa waveform expressions in continuous- and discrete-time domains, and analyze measures of orthogonality between different LoRa spreading factors (SFs) through cross-correlation functions. The cross-correlation functions are analytically expressed in a general form and they account for diverse configuration parameters (bandwidth, SF, etc.) and different cases of signal displacements (time delay shift, frequency shift, etc.). We quantify their mean and maximum in all time domains. We highlight the impact of the temporal displacement and different bandwidths. The general result is that LoRa modulation is nonorthogonal. First, we observe that for same bandwidths, the largest maximum cross-correlation happens for same SF and is equal to 100% due to same symbols; whereas for different bandwidths, the largest maximum cross-correlation is no longer observed at the same SF. Second, the maximum cross-correlation is less than 26% between different SFs, is higher for closer SFs, and decreases as the difference between SFs increases. After downchirping, the maximum cross-correlation increases and the mean decreases compared to those before downchirping. Moreover, the maximum cross-correlation is insignificantly impacted by the temporal delay, which makes it valid to adopt for the performance analysis of both synchronous and asynchronous systems. Finally, we analyze by simulating the bit error probability statistics for different bandwidth ratios and highlighting their correlated behavior with the insights obtained from the maximum cross-correlation expressions.
An Empirical Model of cooperative communication Model in Mobile Adhoc Networks

Cooperative communication is the one of the current interesting research issue in the field of wireless sensors, various topology architectures defined for data transmission and cooperative communication in MANET, In this paper we are introducing an empirical model for cooperative communication with one of the evolutionary algorithm(i.e genetic algorithm),In this approach we consider the factors of signal strength and channel capacity for calculating the communication cost, then we generates the chromosomes for data transmission between source and destination through relay nodes.
To assess the effectiveness of shorter MDR Regimen in tertiary care centre in Western UP

Background: In an attempt to reduce the length of conventional MDR TB regimen for 18months or more WHO updated its treatment guidelines in May 2016and included a recommendation on the use of Standardised Shorter MDR TB regimen with seven drugs and a treatment duration of 9to 12 months. The study explored the experience of the programmatic management of tuberculosis on a shorter injectable containing MDRTB regimen in a tertiary care centre in western Uttar Pradesh, India. Materials and Methods: Retrospective analysis of the data in a tertiary care centre in western up was carried out. The following attributes were included in outcome analysis: 1) Cured, 2) Treatment completed, 3) Treatment failure, Treatment regimen changed, 4) Died, 5) Lost to follow up. Results: Out of 87cases 43 are Male and 44 are female. Out Of the 87cases, 15 Cases were Cured ,57 Cases Treatment Completed, in 7 Patient treatment is changed or treatment is failed, 7 patients died in which 3 patients are female and 4 patients are male and 1 lost to follow up.
Classroom Pivotal Response Teaching

The Individuals with Disabilities Education Act mandates the use of evidencebased practices (EBPs) for students with disabilities, making it essential for teachers to identify, select, and use EBPs in their classrooms. Many EBPs for autistic learners have a foundation in applied behavior analysis (ABA; see Steinbrenner et al., 2020). Unfortunately, some interventions based on ABA can feel out of place in the school environment, and teachers might find it difficult to determine how they fit within their current classroom routines. This presents a challenge for teachers and school administrators in identifying practices that strike a balance between research support and feasibility in classrooms.
Integration of Convolutional Neural Networks and Recurrent Neural Networks for Foliar Disease Classification in Apple Trees

—Automated methods intended for image classification have become increasingly popular in recent years, with applications in the agriculture field including weed identification, fruit classification, and disease detection in plants and trees. In image classification, convolutional neural networks (CNN) have already shown exceptional results but the problem with these models is that these models cannot extract some relevant image features of the input image. On the other hand, the recurrent neural network (RNN) can fully exploit the relationship among image features. In this paper, the performance of combined CNN and RNN models is evaluated by extracting relevant image features on images of diseased apple leaves. This article suggested a combination of pre-trained CNN network and LSTM, a particular type of RNN. With the use of transfer learning, the deep features were extracted from several fully connected layers of pre-trained deep models i.e. Xception, VGG16, and InceptionV3. The extracted deep features from the CNN layer and RNN layer were concatenated and fed into the fully connected layer to allow the proposed model to be more focused on finding relevant information in the input data. Finally, the class labels of apple foliar disease images are determined by the integrated model for apple foliar disease classification, experimental findings demonstrate that the proposed approach outperforms individual pre-trained models.
Robust score test for treatment effects for paired continuous and ordinal data

By assuming independence between the paired continuous and ordinal responses, we develop a score statistic for testing the difference between two treatment effects for the paired mixed data. The test is easy to implement and is legitimate for general marginal distributions and in the presence of within-pair correlation. Simulations show that our new method outperforms the generalized estimating equations (GEE) and the generalized linear mixed model (GLMM) in terms of performance measures most concerned.
Utilization from Okara to Replace Wheat Flour in Ice-Cream Cone

Research objectives were to study the formula, process and the ratio of Okara (Dry basis) to reduce the amount of wheat flour to produce the ice-cream cone. Gluten is a protein composite found in wheat (Triticum spp.) and related grains, including barley and rye which causes health problems in sufferers of celiac disease and some cases of wheat allergy. The Okara was prepared by dried at 70 degree Celsius, 10 hours after that grind and screen size unless 60 mesh. The experimental design was CRD at 3 levels (Okara:wheat flour) such as 30:70, 35:65 and 40:60. Use 9 Points Hedonic Scale test was performed for each treatment. Thirty panelists were most acceptable at 35:65 the result found the characteristic for odor was 6.80+ 1.03, taste was 7.23+ 0.97, texture was 7.23+ 0.72, and overall acceptance was 7.30+ 0.70 there were significant differences (p<0.05) which was well accepted with score 6-7 (like slightly-like moderately) which there was significant differences (p<0.05) with 30:70 and 40:60. The finished product had Aw 0.31±0.05, 16.12±0.58 %hardness, and could be stored at room temperature for 23 days and not found Staphylococcus aureus, yeast and mold. Nutrients content 1.55±0.05%fiber, 17.22±0.05%fat, 13.56±0.04%protein, 63.61±0.24%carbohydrate and 1.14±0.02%ash.
A Recommendation System Framework for Educational Content Reinforcement in Virtual Learning Environments

: In Virtual Learning Environments, tutors play a vital role by supporting students and improving their learning through the courses. One important task is to identify content with which the students struggle and give them suggestions for educational resources to reinforce their learning and overcome difﬁculties. However, providing individualized suggestions for each student may be infeasible, especially for courses with many enrolled students. In this work, we propose and validate a framework for building recommendation systems of educational content for Virtual Learning Environments. Our proposed system identiﬁes the content that a student needs to reinforce based on the results of his assessments and recommends resources that best relate to the questions that he answered incorrectly, using Information Retrieval, Machine Learning, and Natural Language Processing techniques. We validate our proposed solution by taking as a case study data collected from DAL - Dell Accessible Learning, an distance learning platform. We built a dataset with content from 8 courses to compare the performance of different methods and text representations in our framework. Our best result achieved an accuracy of 0.89 using a Nearest Neighbor method with TF-IDF representation.