Miracle-dz/UniFAR-FTUs huggingface dataset:
Progesterone Receptor Status Analysis in Breast Cancer Patients using DCE- MR Images and Gabor Derived Anisotropy Index

Hormone receptors play a key role in female breast cancers as predictive biomarkers. Breast cancer subtype with Progesterone receptor (PgR) expression is one of the important hormone receptors in predicting prognosis and evaluating the Neoadjuvant Chemotherapy (NAC) treatment response. PgR (-) breast cancers are associated with a higher response to NAC compared to PgR (+) breast cancer patients. Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) is the widely used imaging modality in assessing the NAC response in patients. However, evaluating the treatment response of PgR breast cancers is complicated and challenging since breast cancer with positive receptor statuses will respond differently to NAC. Therefore, in this work, an attempt has been made to differentiate the PgR (+) and PgR (-) breast cancer patients due to NAC using Gabor derived Anisotropy Index (AI). A total of 50 PgR (+) and 63 PgR (-) DCE-MR images at 4 time points of NAC treatment are considered from the openly available I-SPY1 of the TCIA database. AI is calculated within the PgR status groups from Gabor energies that are acquired after designing the Gabor filter bank with 5 scales and 7 orientations. Results demonstrate that the AI values can significantly differentiate PgR (+) and PgR (-) breast cancer patients $(\mathrm{p}\leq 0.05)$ due to NAC. The mean AI values are observed to be high in PgR (+) patients $(4.14\mathrm{E}+10\pm$ 1.17E+ 11) than PgR (-) patients $(1.95\mathrm{E}+10\pm 8.06\mathrm{E}+10)$. AI could statistically differentiate visit 1 & visit 4 of NAC treatment in both PgR status patients with a p-value of 0.0246 and 0.0387 respectively. Further, the percentage difference in the mean value of AI is observed to be high in PgR (-) between visit 1 V s 4, visit 2 V s 4, visit 1 V s 3, and visit 2 Vs 3 compared to PgR (+) subjects. Hence, AI could be used as a single index value in assessing the treatment response in both PgR (+) and PgR (-) subjects.
Epileptic Seizure Detection Using Convolution Neural Networks

Epilepsy is a central nervous system disorder that affects a substantial number of world's population and disrupts the quality of life of the sufferers. A number of diagnostic techniques evolved over the years for the detection of epileptic seizures using encephalograms. The subject paper presents design and implementation of a classification model based on convolution neural networks that is capable of detecting epileptic seizures using computational methods utilizing encephalogram data. The study used convolution neural networks that have unique characteristics for recognizing patterns and images and in classifying their features. The neural network architecture proposed herein comprises of layers for input and output with several hidden convolution layers. The electroencephalogram database that was used in this work is the freely accessible CHB-MIT scalp encephalogram database. The developed approach was implemented using the 22 subject database and testing was carried out on patients a few days after the withdrawal of the anti-seizure medications. The test subjects were composed of 5 males and 17 females from various age groups. It was observed that the suggested algorithm could detect about 94.6 percent of the 198 tested seizure records, indicating a good performance of the proposed seizure detection algorithm.
Application of Low-Altitude UAV Remote Sensing Image Object Detection Based on Improved YOLOv5

With the development of science and technology, the traditional industrial structures are constantly being upgraded. As far as drones are concerned, an increasing number of researchers are using reinforcement learning or deep learning to make drones more intelligent. At present, there are many algorithms for object detection. Although many models have a high accuracy of detection, these models have many parameters and high complexity, making them unable to perform real-time detection. Therefore, it is particularly important to design a lightweight object detection algorithm that is able to meet the needs of real-time detection using UAVs. In response to the above problems, this paper establishes a dataset of six animals in grassland from different angles and during different time periods on the basis of the remote sensing images of drones. In addition, on the basis of the Yolov5s network model, a lightweight object detector is designed. First, Squeeze-and-Excitation Networks are introduced to improve the expressiveness of the network model. Secondly, the convolutional layer of branch 2 in the BottleNeckCSP structure is deleted, and 3/4 of its input channels are directly merged with the results of branch 1 processing, which reduces the number of model parameters. Next, in the SPP module of the network model, a 3 × 3 maximum pooling layer is added to improve the receptive field of the model. Finally, the trained model is applied to NVIDIA-TX2 processor for real-time object detection. After testing, the optimized YOLOv5 grassland animal detection model was able to effectively identify six different forms of grassland animal. Compared with the YOLOv3, EfficientDet-D0, YOLOv4 and YOLOv5s network models, the mAP_0. 5 value was improved by 0.186, 0.03, 0.007 and 0.011, respectively, and the mAP_0. 5:0.95 value was improved by 0.216, 0.066, 0.034 and 0.051, respectively, with an average detection speed of 26 fps. The experimental results show that the grassland animal detection model based on the YOLOv5 network has high detection accuracy, good robustness, and faster calculation speed in different time periods and at different viewing angles.
A Conceptual Framework For The Factors Influencing The Accounting Students Career Choice As Public Accountants

Purpose: The aim of this research is to provide a critical review of literature on the factors influencing accounting students’ career choice to be public accountants and develop a conceptual framework. Methodology: The study follows a qualitative mythological approach by reviewing the relevant literature pertaining to factors such as financial rewards, prestige in the society, work environment, continuous education opportunities, professional independence opportunities, family and closest friends, GPA or grade of the students, labour market considerations, students’ future educational plan, self-motivation, cultural, and social media along with the relevant applicable theories. Findings: These factors indicate the trending pattern which influences the decision choices of accounting students to become public accountants. The analysis indicates that in previous studies, several factors showed a positive and significant influence on accounting students’ career choices to be public accountants. The analysis also provides implications of these studies’ findings and directions for future research by incorporating new variables in future studies. Originality/Value: This literature review is meant to raise more awareness on the critical factors influencing the accounting students’ career choice to be public accountants. The suggested conceptual framework will help the researchers to provide new evidence on the critical factors. College and university Professors should be able to execute activities that back and help the students to have a positive attitude about public accountancy courses. Universities can produce policies and procedures focusing on students’ extrinsic motivation, career exposure, social values, etc. which can help in motivating them to choose a public accountancy career. Building a strong network with professional bodies such as ACCA, CIMA, and Institute of Chartered Accountancy by developing a synergistic relationship with the real business world.
Analysis of Factors Associated with Subjective Mild Cognitive Impairment (MCI) among Older Adults Resident in the Community

This study explored the associated factors of mild cognitive impairment (MCI) in older adults, focusing on personal characteristics and health promotion activities. The research design of this study was a non-experimental, cross-sectional design. This study used secondary data from the 2019 community health survey conducted by the Korea Disease Control and Prevention Agency (KDCA). In this study, 20,041 older adults with subjective MCI and 52,587 healthy older adults—a total of 72,628 subjects—were analyzed as the final subjects in this study. The data were analyzed by using descriptive statistics, a chi-square test, an independent t-test, and logistic regression. The results indicate that the probability of experiencing subjective MCI significantly decreased with an increase in monthly income (odds ratio (OR) = 1.71, 95% confidence interval (CI) = 1.19–2.46); increased with an increase in depressive symptoms (odds ratio (OR) = 1.09, 95% confidence interval (CI) = 1.04–1.15); and decreased with an increase in the quality of life level (OR = 0.90, 95% CI = 0.82–0.99). Through the results of this study, several factors related to MCI in older adults were identified. If these related factors are properly managed, the possibility of MCI in older adults may be reduced. Therefore, MCI in older adults should be addressed as a preventable disease rather than a natural aging process.
On a family of Ramanujan's formulas for sums of fourth powers

Review: In his third notebook, Ramanujan stated four formulas for fourth power sums. The simplest of these is the assertion a4+b4+c4=2(ab+bc+ca)2 provided a+b+c=0. In addition, after the four formulas Ramanujan concludes with "and so on''. The author provides a unified general result which contains Ramanujan's four results and is plausibly the full idea Ramanujan had in mind.
Study on Interaction of Dopamine Reward Prediction Error Pathway and VTA-CA1 Novelty Pathway in VTA Region

: The function of dopamine neurons (DA) and the dopaminergic pathways in the brain have been studied for scientists for years. However, people still have questions about how these neural pathways interact with each other, building “bridges” between different regions of the brain. This paper investigates the mechanism of dopamine reward prediction error pathway and VTA-hippocampus novelty pathway in VTA Region, and hypothesized that there may exist some interactions between the RPE pathway and VTA-hippocampus novelty pathway in the VTA. Behavioral experiments are designed to further investigate the dopamine activity on VTA and hippocampus when the two pathways work together. The experiments will compare the DA firing in transgenic mice’s VTA and CA1 regions in the hippocampus when prediction error happens in novel environments and familiar environments. The results are predicted to show that novelty loop and RPE loop might interact with each other.
Quality Analysis and Key Factor Research in Japan's Economic Growth Based on Factor Analysis

Factor analysis is suitable for the conditions of complex correlation, large sample size, many influencing factors, and many selected indicators; able to conduct research on a variety of subjects; and can better apply multivariate statistical methods to the evaluation of financial performance. Among other common methods of evaluating financial performance, factor analysis methods can reduce dimensionality and simplify data basically design weights. Factor analysis can reduce the dimensionality of data even with large sample sizes. It avoids a lot of complicated calculations, reduces the difficulty of calculation, and solves the problem of overlapping factor information, reflecting the advantages of multivariate statistics. Based on the factor analysis method, this study analyzes the quality analysis and key factors in Japan’s economic growth: (1) integrity principle residents’ financial risk is composed of multiple factors, not only unilateral factors but also the following factors: determine the influencing factors, select indicators, and identify risks and potential hazards. Financial risk itself is comprehensive, and the assessment of risk arising from a single factor is less important. (2) The requirements for the selected financial indicators are that the unit and order of magnitude should not only be accurate but also consistent, the financial information reflected by the selected financial indicators should be direct and clear, and financial data should be obtained in multiple ways, through annual reports and the Internet, to collect financial data. The analysis of financial performance based on scientific principles can show the current operating conditions, so the selection of financial indicators must be true, effective, and objective, so as to correctly reflect the real situation, and it is beneficial to combine the analysis results and carry out the following steps. (3) Financial risks may arise in the process of financing or in the process of investment. Financial risk in a broad sense is more in line with the characteristics of risk, and it arises in every link related to financial activities. The analysis is carried out in terms of financial risk in a broad sense. It can be seen that the economic quality scores were higher in 2011 and 2017. (4) Use several representative factors to replace the original variables for analysis, and explain the problems existing in the Japanese economy according to the results. The results show that the key factors of economic growth include gross national product, inflation, employment rate, and the balance of payments.
Establishing the EMC Model of a Three-Terminal Voltage Regulator Based on Electrical Characteristic

The electromagnetic compatibility (EMC) will affect the normal operation of electromechanical systems and is an important indicator of the security and the stability. Three-terminal voltage regulators are widely used in various electromechanical systems, and modeling of three-terminal voltage regulators is of significance for EMC analysis of systems. However, there are some challenges in the modeling of three-terminal voltage regulators. In this paper, a modeling method of three-terminal voltage regulators based on electrical characteristic data, which can be used for establishing EMC model of a real physic object of voltage regulator conveniently, is proposed. Firstly, the electrical characteristic parameters of three-terminal voltage regulators are analyzed, and the experimental test methods of some parameters are given, which is useful for getting the needed data of modeling. Then, based on the data obtained through experimental tests and datasheets, a preliminary model is established using PSpice Model Editor. Finally, the final model is formed by adding external circuits to modify the preliminary model. The comparison of the simulated and experimental results shows that the model has satisfactory accuracy and the method is feasible. This method is very useful for future components modeling when doing EMC analysis and other circuit analysis.
Developing a Reliable Holographic Flow Cyto-Tomography Apparatus by Optimizing the Experimental Layout and Computational Processing

Digital Holographic Tomography (DHT) has recently been established as a means of retrieving the 3D refractive index mapping of single cells. To make DHT a viable system, it is necessary to develop a reliable and robust holographic apparatus in order that such technology can be utilized outside of specialized optics laboratories and operated in the in-flow modality. In this paper, we propose a quasi-common-path lateral-shearing holographic optical set-up to be used, for the first time, for DHT in a flow-cytometer modality. The proposed solution is able to withstand environmental vibrations that can severely affect the interference process. Furthermore, we have scaled down the system while ensuring that a full 360° rotation of the cells occurs in the field-of-view, in order to retrieve 3D phase-contrast tomograms of single cells flowing along a microfluidic channel. This was achieved by setting the camera sensor at 45° with respect to the microfluidic direction. Additional optimizations were made to the computational elements to ensure the reliable retrieval of 3D refractive index distributions by demonstrating an effective method of tomographic reconstruction, based on high-order total variation. The results were first demonstrated using realistic 3D numerical phantom cells to assess the performance of the proposed high-order total variation method in comparison with the gold-standard algorithm for tomographic reconstructions: namely, filtered back projection. Then, the proposed DHT system and the processing pipeline were experimentally validated for monocytes and mouse embryonic fibroblast NIH-3T3 cells lines. Moreover, the repeatability of these tomographic measurements was also investigated by recording the same cell multiple times and quantifying the ability to provide reliable and comparable tomographic reconstructions, as confirmed by a correlation coefficient greater than 95%. The reported results represent various steps forward in several key aspects of in-flow DHT, thus paving the way for its use in real-world applications.
Overnight GARCH-Itô Volatility Models

Abstract Various parametric volatility models for financial data have been developed to incorporate high-frequency realized volatilities and better capture market dynamics. However, because high-frequency trading data are not available during the close-to-open period, the volatility models often ignore volatility information over the close-to-open period and thus may suffer from loss of important information relevant to market dynamics. In this article, to account for whole-day market dynamics, we propose an overnight volatility model based on Itô diffusions to accommodate two different instantaneous volatility processes for the open-to-close and close-to-open periods. We develop a weighted least squares method to estimate model parameters for two different periods and investigate its asymptotic properties.
Awake videolaryngoscopic orotracheal intubation in patients with laryngeal tumour using the C-MAC D-Blade

Background and Aims: This study assessed the applicability of C-MAC videolaryngoscope (VL) D-blade for awake intubation in patients with laryngeal tumour. The primary study objective was to determine the rate of successful intubation in the first attempt. The other parameters recorded were number of attempts required for intubation, duration of different stages of intubation, haemodynamics, ease of intubation and patient comfort on visual analogue scale (VAS) postoperatively. Methods: Thirty patients were studied. Patients were sedated with dexmedetomidine and fentanyl as a slow bolus (over 20 min) and Ramsay sedation score was assessed. Topicalisation of the oropharynx, tonsillar pillars and base of the tongue was done with lignocaine 10% spray. Four ml of 4% lignocaine using MADgic atomiser was used for anaesthetising the glottis and the tracheal lumen. Results: Successful intubation was achieved in 86.6% patients in first attempt and 13.3% in two attempts. Total time for all intubations was less than 30 seconds. Fremantle score was F-1-C-MAC D-blade (easy intubation with full view) in 60% patients, while 23.3% had F-2-C-MAC D-blade (full view and either required more than one attempt or a modified technique), 13.3% had P-1-C-MAC D-blade (partial view with easy intubation) and 3.3% had P-2-C-MAC D-blade (partial view and required more than one attempt or a modified technique). The VAS score for anaesthesiologist's ease and for patient's experience was 85.83 ± 7.20 and 86.66 ± 14.46, respectively. Conclusion: C-MAC VL D-blade-assisted awake intubation is an effective and safe method to manage the airway of patients with laryngeal tumour once adequate topicalisation is ensured before the procedure.
rhEGF-Loaded Hydrogel in the Treatment of Chronic Wounds in Patients with Diabetes: Clinical Cases

The aim of the study was to evaluate the healing process of chronic wounds treated with carboxymethylcellulose loaded with recombinant human epidermal growth factor in patients with diabetes. The case series consisted of 10 patients treated at the university hospital for 12 weeks. Data were analyzed using SPSS version 22.0. according to the intention to treat the principle, without the loss or exclusion of the participants. The sample consisted of 70% (7/10) males with a mean age of 61.9 years (±9.4); all (100%) had diabetes mellitus and 70% (7/10) had systolic hypertension associated with diabetes mellitus. Sixty percent (6/10) presented lesions of diabetic etiology and 40% (4/10) presented lesions of venous etiology; 70% (7/10) had had lesions for less than 5 years. The mean glycated hemoglobin was 7.8% (±2.7%), while the mean ankle-arm index (AAI) was 0.94 (±0.21). The mean initial area of all wounds was 13.4 cm², and the mean final area was 7.8 cm2, with a reduction rate of 28.9% over the 12 weeks of treatment. The reduction rate of diabetic ulcers was higher (33.4%) than that of venous ulcers (22.1%). Regarding the type of tissue, there was an increase in granulation and epithelialization, and a decrease in slough and the amount of exudate that were statistically significant (p = 0.021). No participant had severe or local adverse events during the study period. Epidermal growth factor was effective in the treatment of chronic wounds, especially diabetic ulcers, resulting in the reduction of the wound area and the improvement of tissue and exudate quality.
L-Ergothioneine protects hepatocytes against azathioprine-Induced toxicity via NIK/NF-ΚB signaling axis

This research study has major implications for the development of ESH as a safe and holistic hepatoprotectant and could be used as an adjuvant in the treatment of IBD to minimize DILI. We demonstrate that ESH regulates oxidative stress, NIK/NF-κB-mediated inflammatory axis, and improves the hepatocellular integrity against AZA-provoked deleterious events in the hepatocytes (Figure 5). However, further research is warranted to elucidate the efficacy, specificity, and safety of ESH in AZA-treated patients with normal genetic makeup and gene polymorphisms.
A Novel Approach for Send Time Prediction on Email Marketing

In the digital world, the demand for better interactions between subscribers and companies is growing, creating the need for personalized and individualized experiences. With the exponential growth of email usage over the years, broad flows of campaigns are sent and received by subscribers, which reveals itself to be a problem for both companies and subscribers. In this work, subscribers are segmented by their behaviors and profiles, such as (i) open rates, (ii) click-through rates, (iii) frequency, and (iv) period of interactions with the companies. Different regressions are used: (i) Random Forest Regressor, (ii) Multiple Linear Regression, (iii) K-Neighbors Regressor, and (iv) Support Vector Regressor. All these regressions’ results were aggregated into a final prediction achieved by an ensemble approach, which uses averaging and stacking methods. The use of Long Short-Term Memory is also considered in the presented case. The stacking model obtained the best performance, with an R2 score of 0.91 and a Mean Absolute Error of 0.204. This allows us to estimate the week’s days with a half-day error difference. This work presents promising results for subscriber segmentation based on profile information for predicting the best period for email marketing. In the future, subscribers can be segmented using the Recency, Frequency and Monetary value, the Lifetime Value, or Stream Clustering approaches that allow more personalized and tailored experiences for subscribers. The latter tracks segments over time without costly recalculations and handles continuous streams of new observations without the necessity to recompile the entire model.