Miracle-dz/UniFAR-FTUs huggingface dataset:
Transaction Volume Estimation in Financial Markets with LSTM

In this study, it was aimed to determine the transaction volume that will be encountered in the future (hourly) in the factoring sector, and then to take financial and operational action early. For the study, the LSTM model, which is a kind of recurrent neural network (RNN) that can capture long and short- term dependencies, was applied by using data-driven approaches to estimate the check amounts of hourly transactions. As a result of the results, it was aimed to increase the operational efficiency in a broad scope by allowing the factoring company to determine the loan amounts to be obtained from banks in the most optimal way, and then to take early action within the scope of both the workforce and business management of the financial resource allocation management process and operational activities. MAPE score was used as a measure of error in the time series analysis model. MAPE scores were found as %5.05 for 30 days, %4.18 for 10 days, %3.47 for 5 days, %3.09 for 3 days and %1.83 for 1 day. According to the MAPE scores calculated for different days, the enterprise will be able to decide on the loan to be drawn from banks both in terms of time and amount, and the necessary action will be taken.
Dynamic Perturbation Spreading in Networks

Understanding how local perturbations induce the transient dynamics of a network of coupled units is essential to control and operate such systems. Often a perturbation initiated in one unit spreads to other units whose dynamical state they transiently alter. The maximum state changes at those units and the timings of these changes constitute key characteristics of such transient response dynamics. However, even for linear dynamical systems it is not possible to analytically determine time and amplitude of the maximal response of a unit to a perturbation. Here, we propose to extract approximate peak times and amplitudes from effective expectation values used to characterize the typical time and magnitude of the response of a unit by interpreting the system's response as a probability distribution over time. We derive analytic estimators for the peak response based on these expectation value measures in linearized systems operating close to a stable fixed point. These estimators can be expressed in terms of the inverse of the system's Jacobian. We obtain identical results with different approximations for the response dynamics, indicating that these estimators become exact in the limit of weak coupling. Furthermore, the results suggest that perturbations spread ballistically in networks with diffusive coupling.
Association of Catechol-O-Methyl-Transferase and Estrogen Receptors polymorphism with Severity of Temporomandibular Disorder in Iranian Patients

Background: There are many studies which strongly suggest that the pathophysiology of Temporomandibular joint Disorder (TMD) may also be influenced by genetic conditions. The current study was aimed to evaluate the hypothesis that the polymorphism of estrogen receptor genes, estrogen receptor 1 and 2 (ESR1 and ESR2), and the gene Catechol-O-Methyl-Transferase (COMT) could be Predisposing factor for TMD. Methods: In this case-control study, blood sample were taken from 100 TMD diagnosed patients based on Research Diagnostic Criteria for Temporomandibular Disorders (RDC/TMD) and 103 healthy individuals as the control group. Tetra ARMS-PCR method was used to amplify and identify COMT rs4680, ESR1 rs1643821, and ESR2 rs1676303 gene polymorphism. Results: ESR1 genotype AA and GA showed significantly increase probability (OR= 4.80, OR=2.98, respectively) of TMD. ESR2 T/T homozygosity was associated with decreased risk for TMD (OR=0.41). The relationship between COMT and TMD was not statistically significant (p>00.05). The relationship between the severity of TMD and ESR1 was significant (p=0.003). According to the inheritance pattern the COMT and ESR1 gene, in the dominant pattern can be susceptible to TMD and in ESR2 gene, in the recessive pattern can be protective to TMD. Conclusion: It seems that SNPs of ESR1 rs1643821 has a susceptible role and ESR2 rs1676303 has a protective role against TMD. Also, we add evidences that various genotype of COMT rs4680 were not statistically different between case and control, but allele A in the dominant inherence pattern can be susceptible to TMD.
Change Alignment-Based Graph Structure Learning for Unsupervised Heterogeneous Change Detection

Heterogeneous change detection (HCD) in remote sensing has gained significant attention. Heterogeneous images come from different sensors, which cannot be compared directly to detect changes. This letter proposes a change alignment-based graph structure learning (CAGSL) method for unsupervised HCD, which detects changes by calculating forward and backward structure differences. To achieve this objective, CAGSL incorporates two pivotal improvements. First, CAGSL utilizes a graph autoencoder (GAE) to optimize the graph structure, enabling a more accurate representation of the topological relationships between the real land covers. Second, CAGSL introduces a change alignment constraint based on the HCD task property that the forward and backward structural differences represent the same change event to enhance the optimization of the graph structure. Subsequently, the optimized graph structure is used to compute the structure difference images through graph mapping. Finally, the change map (CM) is obtained through Otsu segmentation. Experimental results demonstrate the effectiveness of the proposed CAGSL when compared to some state-of-the-art (SOTA) methods.
Multi-Request Data Cache Optimization Strategy in Edge Computing

With the rapid growth in the number of mobile devices, more and more data is created and requested by users. By caching the data to the edge server, users can obtain the content they request in a closer place, which reduces the network load and improves the user experience. However, existing articles only focus on a single request of users, while users often request multiple content at one time, which leads to more complex caching scenarios. In this paper, we will study an edge data cache scenario with multiple requests, and design a cost function to optimize the cache results. In this paper, we formulate the data cache problem as a mixed integer programming problem and minimize our cost function. Extensive experiments are conducted on a real-world dataset that contains the locations of edge servers and mobile users, and the results reveal that our approach significantly outperform the baseline approaches.
Deforestation and Secondary Growth in Rondonia, Brazil from SIR-C SAR and Landsat.SPOT data

Covers problems with existing data collected with high-resolution optical sensors. They say active microwave sensors could complement other sensors in getting through things like cloud cover. They analyzed SIR-C data in combination with Landsat TM data, a 9-year time series of SPOT XS data, and a preliminary field survey. They report findings and draw conclusions, including that SARs operating at long radar wavelengths, with both like and cross-polarizations, are needed for tropical deforestation studies.
Total and Extracellular Vesicle cAMP Contents in Urine Are Associated with Autosomal Dominant Polycystic Kidney Disease (ADPKD) Progression

ADPKD is the most common genetic renal disease, characterized by the presence of multiple cysts which, through slow and gradual growth, lead to glomerular filtration rate (GFR) decline and end-stage renal disease. Cystic growth is associated with increased intracellular levels of 3′,5′-cyclic adenosine monophosphate (cAMP). Extracellular vesicles (EVs) are proposed to participate in “remote sensing” by transporting different cargoes, but their relevance to ADPKD progression is poorly understood. This study aimed to determine whether cAMP is contained in urinary EVs and, if so, how total and/or EV cAMP contents participate in disease progression. Fourteen ADPKD patients, naïve for V2 receptor antagonism treatment, and seven controls were studied. Progression was evaluated by estimating GFR (eGFR) and height-adjusted total kidney volume (htTKV). Fresh morning urine was collected to determine cAMP by the competitive radioligand assay. Urine EVs were isolated using an adapted centrifugation method and characterized by electron microscopy, dynamic light scanning, flow cytometry with FITC CD63 labeling, protein and RNA content, and AQP2 and GAPDH mRNA detection. Total and EV cAMP was measurable in both control and patient urine samples. Total cAMP was significantly correlated with eGFR and its annual change but inversely correlated with htTKV. The cAMP-EVs showed a bimodal pattern with htTKV, increasing to ~1 L/m and falling at larger sizes. Our results demonstrate that urine cAMP correlates with ADPKD progression markers, and that its extracellular delivery by EVs could reflect the architectural disturbances of the organ.
Metabolomics in Pulmonary Hypertension—A Useful Tool to Provide Insights into the Dark Side of a Tricky Pathology

Pulmonary hypertension (PH) is a multifaceted illness causing clinical manifestations like dyspnea, fatigue, and cyanosis. If left untreated, it often evolves into irreversible pulmonary arterial hypertension (PAH), leading to death. Metabolomics is a laboratory technique capable of providing insights into the metabolic pathways that are responsible for a number of physiologic or pathologic events through the analysis of a biological fluid (such as blood, urine, and sputum) using proton nuclear magnetic resonance spectroscopy or mass spectrometry. A systematic review was finalized according to the PRISMA scheme, with the goal of providing an overview of the research papers released up to now on the application of metabolomics to PH/PAH. So, eighty-five papers were identified, of which twenty-four concerning PH, and sixty-one regarding PAH. We found that, from a metabolic standpoint, the hallmarks of the disease onset and progression are an increase in glycolysis and impaired mitochondrial respiration. Oxidation is exacerbated as well. Specific metabolic fingerprints allow the characterization of some of the specific PH and PAH subtypes. Overall, metabolomics provides insights into the biological processes happening in the body of a subject suffering from PH/PAH. The disarranged metabolic pathways underpinning the disease may be the target of new therapeutic agents. Metabolomics will allow investigators to make a step forward towards personalized medicine.
RDFM: Robust Deep Feature Matching for Multimodal Remote-Sensing Images

Robust feature matching for multimodal remote-sensing images remains challenging due to the significant nonlinear radiation difference (NRD) caused by modality variations. In this letter, we present a novel feature-matching method for multimodal remote-sensing images, called robust deep feature matching (RDFM), which exploits only deep features extracted by a pretrained Visual Geometry Group (VGG) network to achieve competitive performance. It is shown that template matching of these pretrained features is robust to NRD for various multimodal remote-sensing images, and no additional training is required to improve the matching performance. To extract as many correspondences as possible, we use dense template matching to obtain point correspondences and introduce a 4-D convolution-based implementation of dense template matching for the sake of computational efficiency. RDFM consists of two main steps. First, enormous coarse correspondences are extracted by applying dense template matching at the deep layer of the pretrained network, and then a coarse-to-fine hierarchical refinement is performed to obtain high-quality correspondences. To verify the effectiveness of RDFM, six different types of multimodal image datasets are used in our experiments, including day–night, depth–optical, infrared–optical, map–optical, optical–optical, and SAR–optical datasets. The comprehensive experimental results show that RDFM can overcome the problem caused by NRD and achieves a better performance than the state-of-the-art methods for multimodal remote-sensing image matching. The code of RDFM is publicly available at https://github.com/Fans2017/RDFM.
Class-wise Graph Embedding-Based Active Learning for Hyperspectral Image Classification

Deep learning (DL) techniques have shown remarkable progress in remotely sensed hyperspectral image (HSI) classification tasks. The performance of DL-based models highly relies on the quality and quantity of labeled data. However, manual labeling is a laborious and expensive process that requires substantial efforts from human experts. Active learning (AL) techniques have been developed to alleviate the burden of manual annotation by selecting the most informative and uncertain samples for labeling. In this article, we propose a new class-wise graph-embedding-based AL (CGE-AL) framework implemented by a class-wise graph convolutional network (CGCN). First, we train a classifier with labeled data and infer latent features from labeled and unlabeled samples with the trained parameter. Then, we group the labeled data into multiple one-label sets by category. In a class-wise manner, we initialize the nodes of the graph with one-label and unlabeled features, which are then fed into CGCN. By updating the graph parameters with binary loss, CGCNs measure the uncertainty between labeled nodes and unlabeled nodes. To select the most valuable sample for labeling, we adopt the class minimum uncertainty to query the unlabeled nodes with higher overall uncertainty. We repeat this process with the updated labeled set to retrain our classification model and CGCNs. Extensive experiments demonstrate the outstanding performance of our method compared with other state-of-the-art AL-based approaches.
Non-Invasive Risk Assessment and Prediction of Mortality in Patients Undergoing Coronary Artery Bypass Graft Surgery

Abstract Objectives: Heart rate turbulence (HRT) and T-wave alternans (TWA), non-invasive markers of cardiac autonomic dysfunction, and ventricular repolarization abnormality, reportedly, predict the risk of cardiovascular death after myocardial infarction. We investigated whether pre-operative assessment of HRT and/or TWA could predict long-term mortality following coronary artery bypass graft (CABG) surgery. Methods: From May 2010 to December 2017, patients undergoing elective CABG and receiving 24 h ambulatory electrocardiogram monitoring 1 to 5 days prior to CABG surgery were prospectively enrolled. Pre-operative HRT and TWA were measured using a 24 h ambulatory electrocardiogram. The relative risk of cardiac or overall death was assessed according to abnormalities of HRT, TWA, or left ventricular ejection fraction (LV EF). Results: During the mean follow-up period of 4.6 ± 3.9 years, 40 adjudicated overall (5.9%/yr) and 5 cardiac deaths (0.9%/yr) occurred in 146 enrolled patients (64.9 ± 9.3 years; 108 males). Patients with abnormal HRT exhibited significantly higher relative risks of cardiac death (adjusted hazard ratio [HR] 24.9, 95% confidence interval [CI] 1.46–427) and all-cause death (adjusted HR 5.77, 95% CI 2.34–14.2) compared to those with normal HRT. Moreover, abnormal HRT plus abnormal TWA and LV EF < 50% was associated with a greater elevation in cardiac and overall mortality risk. The predictive role of abnormal HRT with/without abnormal TWA for all-cause death was likely more prominent in patients with mildly reduced (35 to 50%) or preserved (≥50%) LV EF. Abnormal HRT plus abnormal TWA and LV EF < 50% showed high negative predictive value in cardiac and overall mortality risk. Conclusions: Assessment of pre-operative HRT and/or TWA predicted mortality risk in patients undergoing elective CABG. Combined analysis of HRT, TWA, and LVEF enhanced the prognostic power. In particular, the predictive value of HRT was enhanced in patients with preserved or mid-range LV EF.
Two new species of soldier beetles (Coleoptera: Cantharidae) from Eocene Baltic amber, including one with a rare type of antennae

Two new species of the family Cantharidae from Baltic amber are described and illustrated: Sucinorhagonycha fabrizioi sp. nov. (Cantharinae, Cacomorphocerini) and Cantharis (Cantharis) samsocki sp. nov. (Cantharinae, Cantharini). The former is particularly notable because it has 12 antennomeres, a characteristic that is relatively rare among the world’s extant beetles, particularly in soldier beetles. This fascinating characteristic and why it may have evolved are explored here.
Towards Infinite Tilings with Symmetric Boundaries

Large-time coarsening and the associated scaling and statistically self-similar properties are used to construct infinite tilings. This is realized using a Cahn–Hilliard equation and special boundaries on each tile. Within a compromise between computational effort and the goal to reduce recurrences, an infinite tiling has been created and software which zooms in and out evolve forward and backward in time as well as traverse the infinite tiling horizontally and vertically. We also analyze the scaling behavior and the statistically self-similar properties and describe the numerical approach, which is based on finite elements and an energy-stable time discretization.
Genomic Colinearity and Transcriptional Regulatory Networks of BES1 Gene Family in Horticultural Plants Particularly Kiwifruit and Peach

The BES1 transcription factor family was unique and critical in plants. The BES1s played roles in the Brassinosteroid (BR) signaling pathway and participated in the plant’s development, maturation, and stress response process. This study investigated the function of the BES1 gene family of 48 horticultural crops by phylogenetic and genomic colinearity network analysis. In addition, the transcriptional regulatory networks had analyzed the process during biotic stress, abiotic stress, fruit development, and postharvest of kiwifruit and peach. The study illustrated a comprehensive understanding of the phylogenetic relationships of the BES1 family in plant genomes and the prediction of growth and development of kiwifruit and peach fruits and maturation-related BES1 members, laying the foundation for further functional studies of BES1 genes in the future.
Attenuation of Cardiovascular Responses to Laryngoscopy and Intubation-A Comparative Study between IV Esmolol hydrochloride and Lignocaine Hydrochloride

Background: Laryngoscopy and tracheal intubation is invariably associated with a reflex Sympathetic pressor response resulting in elevated heart rate and blood pressures. This may prove detrimental in high-risk patients. Objective of this study is to compare the effects of lignocaine and esmolol in attenuation of this response. Methods: 150 ASA I II status normotensive patients scheduled for elective surgical procedures were selected randomly and divided into three groups of 50 each. All patients received premedication with pentazocine 0.5mg/kg i.m and midazolam 0.05 mg/kg i.m. Induction of anesthesia was standardized for all patients who received, thiopentone 5-mg/kg i.v. and Glycopyrrolate 0.01. mg/kg and were relaxed with succinylcholine 2mg/kg i.v. First group did not receive any attenuation. The second group received 2mg/kg lignocaine i.v. bolus and the third group received 1mg/kg Esmolol iv. bolus, 3 minutes before laryngoscopy and intubation. HR, systolic, diastolic blood pressure was recorded noninvasively before induction, post induction-1,3,5, 7 and 10 minutes from the onset of laryngoscopy. ‘z’ test was used for statistical analysis. Results: After intubation incidence of tachycardia (HR>100/min) was significantly greater in control and lignocaine group than in esmolol group (z>1.96, p<0.050.001). Rise in SBP and DBP were also statistically significant in control and lignocaine group than in esmolol group (z>1.96, p<0.05). Conclusion: Attenuation of pressor response is seen both with lignocaine and with esmolol. Of the two drugs Esmolol 1mg/kg i.v. bolus provides a consistent, reliable and effective attenuation as compared to lignocaine 2mg/kg iv. bolus.
Incorporating electrical impedance tomography to transpulmonary pressure-guided PEEP in severe ARDS with pneumothorax and multiple cavitations: a case report

Pneumothorax is a potentially fatal complication in patients with acute respiratory distress syndrome (ARDS), presenting challenges in determining the optimal positive end-expiratory pressure (PEEP) level to prevent atelectasis without exacerbating the pneumothorax. This case report describes the successful application of transpulmonary pressure and electrical impedance tomography (EIT) at the bedside to guide PEEP selection in a patient with ARDS complicated by pneumothorax due to methicillin-resistant Staphylococcus aureus infection. By using minimal PEEP to maintain positive end-expiratory transpulmonary pressure and visualizing lung reopening with EIT, the optimal PEEP level was reaffirmed, even if traditionally considered high. The patient’s condition improved, and successful weaning from the ventilator was achieved, leading to a transfer out of the intensive care unit. Clinical trial registration: https://clinicaltrials.gov/show/NCT04081142, identifier NCT04081142.
Attention and Concentration for Software Developers

Attention and concentration has been claimed as reasons for higher quality code and more productive development, also with some empirical evidence. In this paper we investigate the matter further. We present a systematic literature review focused on investigating the existing methods for measuring attention in the software development processes especially team meetings. Based on the research gap revealed by literature review, we conducted experiments measuring attention levels using portable electroencephalography (EEG) device. Attention was measured while the software developers took part in stand-up and sit-down meetings. The experimental results revealed that stand-up meetings foster higher level of attention compared to sit-down meetings.
Hierarchical Classic Controllers (HCC) with an Enhanced SVM Method for DDoS Attack Detection in SDN

A centralized Software-defined Network (SDN) controller, due to its nature, faces many issues such as a single point of failure, computational complexity growth, different types of attacks, reliability challenges and scalability concerns. One of the most common fifth generation cyber-attacks is the Distributed Denial of Service (DDoS) attack. Having a single SDN controller can lead to a plethora of issues with respect to latency, computational complexity in the control plane, reachability, and scalability as the network scale increases. To address these issues, state-of-the-art approaches have investigated multiple SDN controllers in the network. The placement of these multiple controllers has drawn more attention in recent studies. In our previous work, we evaluated an Entropy-based technique and a machine learning-based Support Vector Machine (SVM) to detect DDoS using a single SDN controller. In this paper, we extend our previous work to further decrease the impact of the DDoS attacks on the SDN controller. Our new technique called Hierarchical Classic Controllers (HCC) uses SVM and Entropy methods to detect abnormal traffic which can lead to network failures caused by overwhelming a single controller. Determining the number of controllers and their best placement are major contributions in our new method. Our results show that the combination of the above three methods (HCC with SVM and Entropy), in the case of a network with 3 controllers provides greater accuracy and improves the DDoS attack detection rate to 86.12% compared to 79.03% and 81.33% using Entropy-based HCC and SVM-based HCC, respectively.