Miracle-dz/UniFAR-FTUs huggingface dataset:
As the Population is Aging the Surgical Care Model Needs Refreshment: Implementation of a Vasculogeriatric Service for Vascular Surgical Patients in Australia

Introduction: Vascular surgical patients are elderly with multiple, significant co-morbidities requiring multispecialist management. Optimal perioperative inpatient medical management may have a positive impact on timely discharge. This study aims to investigate whether an integrated geriatric service in a vascular surgical department reduces length of stay and cost. Methods: A geriatric service was created as an integral part of a Vascular Surgical Unit in a tertiary state referral center. In a retrospective cohort study design patient data was investigated six months prior to installment of the service and the six months after. Results: Total number of patients admitted was 669 (300 before, and 369 after institution of the vasculogeiatric service. Patients with multiple admissions were excluded resulting in a patient volume of 497. A significant reduction of length of stay was found after the introduction of the vascular geriatric service (p= 0.003). This difference remains true when sub analysed for gender (p= 0.014), admission type (p< 0.001) and age (p= 0.012). Conclusion: In this pilot study the institution of a vasculogeriatric service lead to a significant reduction in LOS. The authors highly recommend this model of care, however specific benefits inpatient care and impact on mortality needs evaluation in further studies.
Improved Model Based Predictive Torque Control Strategy with Fast Dynamic Response for Flux-Switching Permanent Magnet Machines

The flux-switching permanent magnet machine (FSPMM) has got great attention by academic researchers during the past couple of years for its merits of strong mechanical robustness, high efficiency, strong thermal dissipation ability, etc. However, for its inherited double salient structure in both stator and rotor, the FSPMM suffers from severe torque and flux ripples at different rotor positions for its variable magnetic resistance, which cannot be solved completely only by electromagnetic optimal design. In order to increase the drive performance of FSPMM, such as dynamic response and stable torque smoothness, an improved model based predictive torque control (MPTC) algorithm is proposed. By using the cost function modulation strategy, the torque and flux ripples of FSPMM are reduced evidently, accompanying with the minimized converter switching frequency and power loss. Comprehensive simulation investigations are finally carried out to validate relevant theoretical analysis.
Patterns of Convergence and Divergence Between Bipolar Disorder Type I and Type II: Evidence From Integrative Genomic Analyses

Aim: Genome-wide association studies (GWAS) analyses have revealed genetic evidence of bipolar disorder (BD), but little is known about the genetic structure of BD subtypes. We aimed to investigate the genetic overlap and distinction of bipolar type I (BD I) & type II (BD II) by conducting integrative post-GWAS analyses. Methods: We utilized single nucleotide polymorphism (SNP)–level approaches to uncover correlated and distinct genetic loci. Transcriptome-wide association analyses (TWAS) were then approached to pinpoint functional genes expressed in specific brain tissues and blood. Next, we performed cross-phenotype analysis, including exploring the potential causal associations between two BD subtypes and lithium responses and comparing the difference in genetic structures among four different psychiatric traits. Results: SNP-level evidence revealed three genomic loci, SLC25A17, ZNF184, and RPL10AP3, shared by BD I and II, and one locus (MAD1L1) and significant gene sets involved in calcium channel activity, neural and synapsed signals that distinguished two subtypes. TWAS data implicated different genes affecting BD I and II through expression in specific brain regions (nucleus accumbens for BD I). Cross-phenotype analyses indicated that BD I and II share continuous genetic structures with schizophrenia and major depressive disorder, which help fill the gaps left by the dichotomy of mental disorders. Conclusion: These combined evidences illustrate genetic convergence and divergence between BD I and II and provide an underlying biological and trans-diagnostic insight into major psychiatric disorders.
ASSESSMENT OF GENETIC DIVERSITY AMONG SOYBEAN (Glycine max (L.) Merr.) GENOTYPES MAKING USE OF AGROMORPHOLOGICAL BASED ON NUTRITIONAL QUALITY TRAITS

Present study aims to assess the genetic diversity of 30 soybean genotypes based on agronomic and quality parameters using near infrared spectroscopy. Oribi, Crawford, BD 1601, Egret, B 66 S 41, B 66 S 387, Dundee, B 66 S 37 and Santa Rosa flowered earlier than B 66 S 256, Solar 12 and B 66 S 8. Yeluanda, 15/06/2012, B 66 S 31 had the highest fibre contents and height. DB 1601 had the highest number of seeds per pod. Oribi had the highest number of branches with PR 165-52 and B 66 S 24 having the highest yields. Dundee and N69-2774 were associated with high oil content while Ex RHOD, R 5-4-2 M, N69-2774 and DB 1601 were associated with both ash and stearic acid. Crawford, Egret and B 66-387 accessions were associated with palmitic acid. B 66 S 8 had the highest oleic acid content. PR 165-52 and B 66 S 8 contained the highest amounts of oil. Multivariate analysis helped us understand important agronomic and genetic traits when selecting soybean genotypes.
Monitoring and control of polymer production line based on machine learning

The work is devoted to the development of an application for monitoring and controlling the state of equipment (extruder) for the petrochemical industry based on sensor readings using a machine learning model. The statistical relationships of the technological process parameters are analyzed, the most significant parameters influencing the occurrence of failures are determined using SHAP values. The hypotheses regarding the effectiveness of various machine learning algorithms in relation to the real problem of predicting the technical state of the extruder are tested. A gradient boosting model has been developed to predict the probability of extruder shutdown due to the formation of polypropylene agglomerates. The developed application allows interpreting the results of the model, which makes it possible to select the most important process parameters that have the greatest impact on the probability of extruder failure, and also proposing a prototype of an extruder monitoring system based on sensor readings using a machine learning model.
CUSTOMER-FOCUSED PRODUCT DEVELOPMENT AN OUTDOOR INDUSTRY PERSPECTIVE

Purpose– The purpose of this paper is to introduce a methodology that can support the process of understanding and designing for the satisfaction of high-level needs in practice. The satisfaction of high-level needs has seldom been in focus when it comes to customer satisfaction surveys or the process of new product or service development. However, needs do occur on various levels, and the satisfaction of high-level needs actually appears to have the greatest potential for the creation of loyalty among customers and customer satisfaction. The satisfaction of high-level needs has furthermore been pointed out as a strategy for the creation of attractive quality.Design/methodology/approach– The paper is based on literature studies and the application of the Ideation Need Mapping (INM) methodology in a specific case.Findings– The paper presents the INM methodology that could be used for guiding product and service innovation in practice. More specifically, the methodology supports the process of understanding and designing for the satisfaction of high-level needs.Originality/value– This paper aims to contribute to envisioning and demonstrating how the understanding of, and design for, satisfaction of high-level needs can be done in practice.
Theoretical Aspects of the Roots Air Motor

The following analyses constitute a preliminary attempt to provide a sound theoretical basis for future work to be carried out on Roots motors and blowers, the former only being considered in the present paper.An ideal motor is defined, thereby facilitating the analyses and setting a standard of performance for the conventional motor operating on the non-expansive cycle. The low absolute thermodynamic efficiency of the ideal being a direct consequence of non-expansive working, a standard of performance for expansive working is set by the definition of a perfect motor.It is shown that ideal motor torque is reduced as the number of rotor lobes increases, and that severe alternating torques, which are largely tied up with the motor mean torque, occur in the rotors themselves.In the real motor, deviations from the ideal are due to a number of separate causes. A quasi-analytical solution has been obtained for the adverse torque due to subsonic discharge, whilst a pair of simultaneous differential equations per.
Potential Mechanisms of Triptolide against Diabetic Cardiomyopathy Based on Network Pharmacology Analysis and Molecular Docking

The incidence of heart failure was significantly increased in patients with diabetic cardiomyopathy (DCM). The therapeutic effect of triptolide on DCM has been reported, but the underlying mechanisms remain to be elucidated. This study is aimed at investigating the potential targets of triptolide as a therapeutic strategy for DCM using a network pharmacology approach. Triptolide and its targets were identified by the Traditional Chinese Medicine Systems Pharmacology database. DCM-associated protein targets were identified using the comparative toxicogenomics database and the GeneCards database. The networks of triptolide-target genes and DCM-associated target genes were created by Cytoscape. The common targets and enriched pathways were identified by the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. The gene-gene interaction network was analyzed by the GeneMANIA database. The drug-target-pathway network was constructed by Cytoscape. Six candidate protein targets were identified in both triptolide target network and DCM-associated network: STAT3, VEGFA, FOS, TNF, TP53, and TGFB1. The gene-gene interaction based on the targets of triptolide in DCM revealed the interaction of these targets. Additionally, five key targets that were linked to more than three genes were determined as crucial genes. The GO analysis identified 10 biological processes, 2 cellular components, and 10 molecular functions. The KEGG analysis identified 10 signaling pathways. The docking analysis showed that triptolide fits in the binding pockets of all six candidate targets. In conclusion, the present study explored the potential targets and signaling pathways of triptolide as a treatment for DCM. These results illustrate the mechanism of action of triptolide as an anti-DCM agent and contribute to a better understanding of triptolide as a transcriptional regulator of cytokine mRNA expression.
Design and Performance Evaluation of Energy Efficient 8-Bit ALU At Ultra Low Supply Voltages Using FinFET With 20 nm Technology

Since last few years, the tiny size of MOSFET, that is less than tens of nanometers, created some operational problems such as increased gate-oxide leakage, amplified junction leakage, high sub-threshold conduction, and reduced output resistance. To overcome the above challenges, FinFET has the advantages of an increase in the operating speed, reduced power consumption, decreased static leakage current is used to realize the majority of the applications by replacing MOSFET. By considering the attractive features of the FinFET, an ALU is designed as an application. In the digital processor, the arithmetic and logical operations are executed using the Arithmetic logic unit (ALU). In this paper, power efficient 8-bit ALU is designed with Full adder (FA) and multiplexers composed of Gate diffusion input (GDI) which gained designer's choice for digital combinational circuit realization at minimum power consumption. The design is simulated using Cadence virtuoso with 20nm technology. Comparative performance analysis is carried out in contrast to the other standard circuits by taking the critical performance metrics such as delay, power, and power delay product (PDP), energy-delay product (EDP) metrics into consideration. Keywords—8-bit ALU, EDP, FinFET, Full Adder, GDI, PDP, Static leakage current.
Therapeutic effect of mesenchymal stem cell in spinal cord injury

Objective: To explore the effect of bone marrow mesenchymal stem cells (BMSCs) transplantation in the treatment of spinal cord injury (SCI). Method: 68 SCI patients admitted to our hospital were randomly divided into group A and group B, with 34 cases in each group. Group A received autologous BMSCs transplantation therapy and group B received standard occupational therapy. The American Spinal Injury Association (ASIA) score was used to assess the recovery of neurological function before and after treatment. The Self-rating Anxiety Scale (SAS) and the Self-rating Depression Scale (SDS) were used to evaluate the psychological state of the two groups before and after treatment. The Health Status Survey (SF-36) was used to evaluate the life quality of the two groups after 6 months of discharge. The clinical efficacy and adverse reactions of the two groups were observed. Result: The effective rate of group A was significantly higher than that of group B (P<0.05). The motor, pain and tactile scores of the ASIA scores in group A were significantly higher than those in group B (P<0.05). The SAS and SDS scores of group A were significantly lower than those of group B (P<0.05). The scores of overall health, physiological function, social function, physical pain, physiological function, vitality, mental health and emotional function in SF-36 of group A were significantly higher than those of group B (P<0.001). After BMSCs transplantation, 1 patient developed low back pain, 2 patients developed fever, and 1 patient developed headache but did not receive special treatment. Conclusion: BMSCs transplantation has a good therapeutic effect on SCI compared with the standard occupational therapy. It can promote the recovery of neurological function in patients, and alleviate the patient’s bad mood and improve the quality of life, with fewer adverse reactions, which may be an ideal treatment for SCI.
International Journal of Geography and Geography Education (IGGE)

Land cover changes due to many factors ranging initially from the numerical size and density of the population to socio-economic characteristics and technology, from land policies to natural factors. Most of the land cover change is human-induced and involves many economic and ecological problems. Using the land by protecting it is of great importance to solve these problems before they worsen and to take precautions. Temporal monitoring of land cover changes and improving Geographical Information Systems and Remote Sensing technologies provide decision makers with many opportunities for the future of this change. The speed, direction and type of land cover change are identified with the developed forecast models, thus forming a basis for planning for sustainable land use. Although many probability methods are used to make land cover estimations, one common practice is Markov chain model. In Markov chains, a special class of random processes, two or more results that emerge based on repeated observations can be determined by means of probability laws. The main objective of this study is to estimate the 2018 land cover of Ankara province with Markov chains technique. In this context, CORINE 1990, 2000, 2006 and 2012 data sets were evaluated according to first level land cover classes. In the model for 2012 land cover estimation, 92% accuracy was achieved based on the first three data sets. With the addition of 2012 CORINE data to the model, evaluation for 2018 was made and the accuracy of the model was found to be 90% for 2018 based on the added data set. According to these latest estimation data, the change of land cover in the province will continue to develop in favor of artificial areas and against agricultural areas.
Automatically hemodynamic analysis of AAA from CT images based on deep learning and CFD approaches

Abdominal aortic aneurysm is a serious disease which course is accompanied by the development of health complications and often leads to patient death due to aortic rupture. One of the powerful methods to estimate the risk of rupture is three-dimensional patient-specific hemodynamic analysis. In this study, we develop a software tool based on deep learning and CFD methods to perform automated computational hemodynamics with patient-specific geometry reconstructed from computed tomography images.
Asymptotics of the Hausdorff dimensions of the Julia sets of McMullen maps with error bounds

For McMullen maps f λ (z) = z p + λ/z p , where λ∈C\{0} , it is known that if p ⩾ 3 and λ is small enough, then the Julia set J(f λ ) of f λ is a Cantor set of circles. In this paper we show that the Hausdorff dimension of J(f λ ) has the following asymptotic behavior dimHJ(fλ)=1+log2logp+O(|λ|2−4/p),asλ→0. An explicit error estimation of the remainder is also obtained. We also observe a ‘dimension paradox’ for the Julia set of Cantor set of circles.
Converged Charging Method for Transportation Satellite SMS Service

This article introduces a billing method of pre-paid and post-paid transportation satellite short message service. It aims to solve the problem that the handheld transportation satellite equipment in China cannot use the SMS service due to the two staged dial prepaid system can’t charge and bill. The billing platform is connected with the SMS billing of the post-paid business support system, and uses the time series prediction algorithm Prophet to capture and early warning of abnormal SMS entries.
Análisis de un esquema de diferencias finitas para la solución numérica de una ecuación de convección difusión fraccionaria

The nonlinear time fractional convection diffusion equation (TFCDE) is obtained from a standard nonlinear convection diffusion equation by replacing the first-order time derivative with a fractional derivative (in Caputo sense) of order α ∈ (0, 1). Developing numerical methods for solving fractional partial differential equations is of increasing interest in many areas of Science and Engineering. In this thesis an explicit conservative finite difference scheme for TFCDE is introduced. We find its CFL condition and prove encouraging results regarding stability, namely, monotonicity, the TVD property and several bounds. Illustrative numerical examples are included in order to evaluate potential uses of the new method. Finally, we develop a graphical user interface (GUI) based in tool GUIDE of MATLAB for numerical solution TFCDE.
AINTUITIONISTIC FUZZY C-MEANS CLUSTERING BASED FEATURE SELECTION AND ENSEMBLE SUPPORT VECTOR MACHINE FRAMEWORK FOR BIG DATA CYBER-SECURITY

In enterprises and industries, rapid progress in attained by information technology in recent days and which makes the term big data, a very popular one. Various sources like business record, digital videos, pictures in digital format, social media are producing data and which makes the expansion in data growth is a rapid one. Big data corresponds to management of this huge data and it is a challenging task. In apps and operating systems, there is a malicious software insertion, which makes big data hacking as a serious threat. So, enhanced security techniques are required for securing big data from cyber threats. Malicious software and unknown patterns are classified by proposing Machine Learning (ML) algorithms. For big data cyber security, an Ensemble Support Vector Machine (ESVM) framework is designed in previous system. Improved K-means clustering is used initially for selecting features. A bi-objective optimization problem is processed in this work by formulating ESVM configuration, where complexity of model and accuracy are considered as two conflicting objectives. Cuckoo Search (CS) optimization algorithm is used for performing bi-objective optimization. Due to local optimum results and slow convergence rate, CS is not used very often. So, it requires a better algorithm for selecting features and for enhancing accuracy. For avoiding this, for big data cyber security, a new framework is introduced in this work. In which, detection of malicious activity is done using an Ensemble Support Vector Machine (ESVM) framework. Intuitionistic fuzzy C-means clustering is used for selecting features initially in this work. ESVM technique is for performing malware and intrusion detection based on selected features. For solving multi objective function and optimizing parameters, a hyper metaheuristics algorithm framework is introduced in this work. Improved Artificial Bee Colony (IABC) optimization algorithm is used for parameter optimization.
An efficient Decision Tree Algorithm for analyzing the Twitter Sentiment Analysis

: Opinion mining and sentiment analysis are valuable to extract the useful subjective information out of text documents. The huge amount of information from this medium has become an attractive resource for organizations to monitor the opinions of users, and therefore, it is receiving a lot of attention in the field of sentiment analysis. However, performing sentiment analysis is a challenging task for the researchers in order to find the users sentiments from the large datasets, because of its unstructured nature, slangs, misspells and abbreviations. To address this problem, a new proposed system is developed in this research study. Here, the proposed system comprises of four major phases; data collection, pre-processing, key word extraction, and classification. Initially, the input data were collected from the twitter dataset. After collecting the data, pre-processing was carried-out for enhancing the quality of collected data. The pre-processing phase comprises of two systems; lemmatization, and removal of stop-words and URLs. Then, an effective topic modelling approach Latent Dirichlet Allocation (LDA) was applied to extract the keywords and also helps in identifying the concerned topics. The extracted key-words were classified into three forms (positive, negative and neutral) by applying an effective machine learning classifier: Decision Tree (DT). The experimental outcome showed that the proposed system enhanced the accuracy in sentiment analysis up to 6-20% related to the existing systems.
AGE RELATED CHANGES IN LOWER EXTREMITY COORDINATION PATTERNS IN FEMALE RUNNERS

The aim of this study was to compare the coordination patterns of the lower extremity joints in older and younger females during running. The results of this study show that the older runners have maintained similar movement and coordination patterns to younger runners as a result of years of running experience. The results in this study could provide researchers and clinicians the information necessary to develop shoe wear and orthotic devices to reduce the risk of injury in specific populations.
Corrosion sensors for concrete bridges

Corrosion of rebar in concrete bridges is a problem for the infrastructure and is difficult to detect. Engineers and scientists at the Johns Hopkins University Applied Physics Laboratory (JHU/APL) have developed a device that is designed to be buried in the concrete when the bridge deck is poured. The device, known as a wireless embeddable sensor platform/smart aggregate (WESP/SA), can provide data about conditions within the bridge deck. This data will assist highway maintenance engineers in determining corrosive actions.