Miracle-dz/UniFAR-FTUs huggingface dataset:
The Impact of Lymph Node Dissection on Survival in Intermediate- and High-Risk Prostate Cancer: A Population-Based Study

Zhen Zhou Shanghai Tenth People's Hospital Shi-Yong Xin Henan University of Science and Technology Ting-Shuai Zhai Shanghai Tenth People's Hospital Liang Jin Shanghai Tenth People's Hospital Xiang Liu Shanghai Tenth People's Hospital Jia-Xin Zhang Shanghai Tenth People's Hospital Xian-Chao Sun Shanghai Tenth People's Hospital Wang-Li Mei Shanghai Tenth People's Hospital Bi-Hui Zhang Shanghai Tenth People's Hospital Xu-Dong Yao Shanghai Tenth People's Hospital Lin Ye (  ericyelin@163.com ) Shanghai Tenth People's Hospital
A Review on Cancer Dataset Classification using Data Mining Methods

Data analytics is observed as routine biomedical research domain focused on relevant clinical condition of cancer. Bioinformatics is characterized with consideration of several disease aspects. At present, several research focused on data processing of medical data. The medical data incorporates vast range of data such as miRNA, DNA with consideration of instances. The analysis of medical data expressed the micro array expression of gene with consideration of data attributes for minimal sample data count. For clinical and research process by using available data, medical data analysis extract equivalent data about genes. Data mining concentrated on analysis of cancer data with complexity analysis of underlying molecular diseases. The data mining of medical data is provided for processing reliable information and mechanism. However, construction of building model for medical data is complex for achieving reliable data. In this review, presented about contribution of data analysis for cancer data. The research concentrated on gene selection and data integration. Further, the analysis is based on the examination of data mining method analysis with consideration of gene data.
Automated extraction of structural beam lines and connections from point clouds of steel buildings

This paper presents an alternative to segmentation of point clouds tailored specifically to large‐scale steel buildings. Typical segmentation approaches process the 3D point data directly and focus on large blocky structures such as concrete; these are not generalizable to smaller, more complex geometries found in steel elements. The method takes advantage of image processing techniques by utilizing 2D “slices” of the point cloud, rather than the original 3D point cloud. Centroids of targeted structural cross sections are extracted from these slices using 2D convolution as a template‐matching operation, and then projected back to 3D. From this, member centroidal axes are extracted using a custom linear region growing algorithm to create a 1D beam line model, including connections. Experimental results from scans of four prefabricated steel buildings indicate that the method is robust to variations in framing systems, clutter (e.g., obstructions, nonstructural elements), and point cloud sparsity.
On–off–on fluorescence sensing of glutathione in food samples based on a graphitic carbon nitride (g-C3N4)–Cu2+ strategy

A novel fluorescence sensor based on a g-C3N4 nanosheet–Cu2+ system has been developed for rapid, sensitive and selective sensing of glutathione (GSH) in food samples. The g-C3N4 nanosheets were used here as a new type of carbon-based nanomaterial with high fluorescence quantum yield and specific surface area. The fluorescence of the g-C3N4 nanosheets was quenched by Cu2+via a photoinduced electron transfer (PET) process. In the presence of GSH, the fluorescence sensor was switched on, which was attributed to coordination of Cu2+ to GSH and the functional groups on the surface of the g-C3N4 nanosheets. Under optimal conditions, a limit of detection (LOD) of 20 nM for GSH was reached over a wide GSH concentration range of 0.05 to 900 μM. Moreover, the proposed sensor shows excellent performance, and has advantages such as low cost, easy preparation, rapid detection and switch-on fluorescence response. The fluorescence sensor was successfully applied for GSH detection in different food samples.
The video game as agencement and the image of new gaming experiences: the work of indie video game developers

ABSTRACT The growth of the global video game industry has resulted in an inflow of new entrants who aspire to create novel video games and preferably new video game genres. From an empiricist perspective presented by Gilles Deleuze, a video game is an agencement that materialize on the basis of the relations between the elements included in the game (e.g. computer code, game design ideas, the narrative structure of the game, interface design, etc.). A study of indie video game developers examines how the video game as agencement is composed of technical and narrative elements, and how the subject-formation process of the developer is bound up with the creation of video games. That is, indie video game developers are part of the creative work to develop new digital artefacts that per se are relational and composite in nature, as premised by the concept of agencement.
Impact on Financial Decisions through Resources and Education

In high stakes financial decisions, people leave a substantial amount of money on the table, even when financial education is available. The ubiquity of financial choices makes understanding the effects of incentives and education on mistakes crucial. This research experimentally examines the impact of changes in incentives and educational availability on incentivized but hypothetical healthcare choices using Amazon Mechanical Turk. We find that increasing incentives are ineffective in increasing decision-making effort, even when these changes are made clear and salient to the subjects. Yet, surprisingly, despite this lack of effort response, subjects’ choices improve when incentives are high. This result highlights an under-appreciated channel of incentives: when stakes become larger, often, the problems become simpler too. We next investigate the effect of available education. Overall, education leads to an increase in decision-making effort and an improvement in choice quality. However, this average effect masks significant heterogeneity across incentive treatments. Subjects are willing to put in the educational effort when either the problems are hard or mistakes are highly costly, but the return of the educational effort is zero for hard problems and positive for easy ones. Thus, only when stakes are high and the problem is easy, does education have an effect. These findings suggest that people can be encouraged to get education for high-stakes decisions, and policy-makers have a role in simplifying problems to translate the extra effort into better choices.
Prevalence and Covariates of Metabolic Syndrome among Elderly People Living in Nursing Homes, Semnan, Iran

Backgrounds: The study aimed to determine the prevalence and covariates of metabolic syndrome among the elderly residents in nursing homes in Semnan Province, Iran. Methods: This cross-sectional study was conducted among aged participants institutionalized in nursing home in 2018. The study participants’ characteristics were assessed using a demographic assessment form. Moreover, the Mini Nutritional Assessment (MNA) questionnaire, Mentally Assessing Nutritional Assessment (MANA), Katz's Index of Independence in Activity of Daily Living, Mini-Mental State Examination (MMSE) and well-being were applied to assess the participants’ physical and mental status. In addition, Para-clinical and biochemical tests for diagnosis of metabolic syndrome used based on the Adult Treatment Panel (ATPIII). Results: In this study, of 129 eligible participants, 27.7% were suffering from metabolic syndrome. The prevalence of MS in male and female subjects were 26.6% and 28%, respectively. The results of the study indicated a significant relationship between metabolic syndrome and nutritional statues (Pvalue=0.004), mental status (Pvalue=0.001) and ADL (Pvalue=0.003). Moreover, the results showed no significant relationship between well-being and metabolic syndrome. Of all variables of the study, significant relationship between smoking habit, lack of daily activity, abdominal obesity, high systolic and blood pressure, high density lipoprotein cholesterol (HDL-C) HDL-cholesterol, Waist Circumference (WC), systole blood pressure (SBP) were found to be statistically significant. Conclusions: The findings of the study indicated the prevalence of metabolic syndrome among institutionalized elderlies is high. It is recommended to implement preventive and therapeutic measures in this vulnerable group of elderly people. Key words: Metabolic Syndrome, Elderly People, Nursing Homes, Prevalence
On estimation of the PDF and the CDF of the one-parameter polynomial exponential family of distributions

Abstract In this article, we have considered the estimation of the probability density function and cumulative distribution function of the one-parameter polynomial exponential family of distributions. A number of probability distributions like the exponential, Lindley, length-biased Lindley and Sujatha are particular cases. Two estimators—maximum likelihood and uniformly minimum variance unbiased estimators of the probability density function and cumulative distribution function of the family have been discussed. The estimation issues of the length-biased Lindley and Sujatha distribution have been considered in detail. The estimators have been compared in mean squared error sense. Monte Carlo simulations and real data analysis are performed to compare the performances of the proposed estimators.
AnimA-K: recognizing student’s mood during the learning process

: This article discusses the introduction of affectivity studies in virtual learning environments (VLE). The idea is to identify the mood of the student in order to encourage more appropriate teaching strategies. A historical and theoretical review on the importance of the affective dimension in human subjectivity is shown. Also, the challenges of using computational models for recognition and representation of mood into ROODA VLE are discussed. The prototype AnimA-K is presented to check this possibility
Introducing The Index of Balanced Affect Change (TIBACh): a study among Church of England clergy and laity

ABSTRACT Drawing on the classic model of balanced affect as developed and operationalised in the Francis Burnout Inventory, the present study describes the development of, and tests the construct validity of The Index of Balanced Affect Change (TIBACh) among a sample of 4,449 Church of England clergy (29%) and laity (71%). The two five-item measures showed acceptable internal consistency reliability, with Cronbach’s alphas of . 70 for positive affect and . 83 for negative affect. Construct validity was tested against an independent measure of coping during lockdown. Coping was positively correlated with positive affect and negatively correlated with negative affect. Crucially, for the balanced affect model, there was a significant interaction effect of positive and negative affect on coping, showing that the ameliorating effect of positive affect on coping increased with increasing levels of negative affect.
Online Learning Communities Amid the COVID-19 Pandemic: An Agent-Based Model

This study has adopted an agent-based model to examine the factors that influenced the learning experiences in online learning communities. A community of inquiry (CoI) is an online learning framework which posits that positive learning experiences are created through cognitive, social, and teaching dimensions. It is based on the distance learning system during the COVID-19 pandemic. During this challenging time, schools worldwide have shifted to the distance learning system. The factors of this model are carefully translated to correspond with the parameters from Netlogo's HIV model through a coherent approach. These are the number of contacts, the length of time for online unreadiness, and the absence of learner control measures. This paper used a three-factorial research design where a series of simulations are carried out in the Netlogo software. The generated data reveal how these factors significantly contribute to the students' meaningful learning experiences. It has shown that CoI supports deep learning and how it provides meaningful learning experiences. Hence, this paper calls for further research in online learning to sustain a quality online learning environment based on the CoI framework, equipping tutors to varying teaching methods in online learning and contextualizing CoI from perspectives across disciplines.
Hybrid of K-means clustering and naive Bayes classifier for predicting performance of an employee

Predicting the performance of an employee in the future is a requirement for companies to succeed. The employee is the organization's main component, the failure or organization’s success based on the performance of an employee, this has become an important interest in almost all types of companies for decision-makers and managers in the implementation of plans to find highly skilled employees correctly. Management thus becomes involved in the success of these employees. Particularly to guarantee that the right employee at the right time is assigned to the convenient job. The forecasting of analytics is a modern human resource trend. In the field of predictive analytics, data mining plays a useful role. To obtain a highly precise model, the proposed framework incorporates the K-Means clustering approach and the Naive Bayes (NB) classification for better results in processing performance data of employees, implemented in WEKA, which enables personnel professionals and decision-makers to predict and optimize their employees' performance. The data were taken from the previous works, this was used as a test case to illustrate how the incorporates of K-Media and Naive Bayes algorithms increases the exactness of employee performance predicting, compared with the K-Means and Naive Bayes methods, the proposed framework increases the accuracy of predicting the performance of an employee.
Analysis of Geomagnetic Ap Index on Worldwide Earthquake Occurrence using the Principal Component Analysis and Hierarchical Cluster Analysis

Geoeffective solar events, especially the coronal mass ejection (CME) and the high-speed solar wind (HSSW) will induce geomagnetic storm upon its arrival to Earth. The solar events could trigger an earthquake occurred during the arrival. In this study, the focus is on the proxy of the geoeffective solar events, which is the geomagnetic Ap index and the data of shallow worldwide earthquakes. The main objective was to investigate the impact of geomagnetic storms on the occurrences of earthquakes from 1994 to 2017 from a statistical perspective. The geomagnetic Ap index data was obtained from the Helmholtz-Centre Postdam GFZ German Research Centre for Geosciences and the shallow worldwide earthquake data were from the United States Geological Survey (USGS) earthquake catalogue. The Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) were used to analyse the data. Two groups were obtained from the PCA biplot: Group 1 before the event (Day-4 to Day-1) and Group 2 after the event group (Day 0 to Day+4). A two-cluster solution was obtained from the HCA, which shows that days before and after geostorm are divided into two main clusters. The statistical results show that earthquakes activity might have different behaviour before and after the geostorm occurred. In conclusion, the results emphasize that there are differences between days before and after the geostorm occurrence, hence, the solar influence upon earthquake occurrences cannot be neglected entirely.
Ppe.XapF: High throughput KASP assays to identify fruit response to Xanthomonas arboricola pv. pruni (Xap) in peach

Bacterial spot, caused by Xanthomonas arboricola pv. pruni (Xap), is a serious peach disease with symptoms that traverse severe defoliation and black surface pitting, cracking or blemishes on peach fruit with global economic impacts. A management option for control and meeting consumer demand for chemical-free, environmentally friendly fruit production is the development of resistant or tolerant cultivars. We developed simple, accurate, and efficient DNA assays (Ppe.XapF) based on SNP genotyping with KASP technology to quickly test for bacterial spot resistance alleles in peach fruit that allows breeders to cull seedlings at the greenhouse stage. The objective of this research was to validate newly developed DNA tests that target the two major QTLs for fruit resistance in peach with diagnostic utility in predicting fruit response to bacterial spot infection. Our study confirms that with only two Ppe.XapF DNA tests, Ppe.XapF1-1 and Ppe.XapF6-2, individuals carrying susceptible alleles can be identified. Use of these efficient and accurate Ppe.XapF KASP tests resulted in 44% reduction in seedling planting rate in the Clemson University peach breeding program.
Integrated Capacitive and Resistive Humidity Transduction via Surface Type Nickel Phthalocyanine Based Sensor

1 Center for Advanced Materials (CAM), Qatar University, P. O. Box 2713, Doha, Qatar. 2 Department of Electrical Engineering, College of Engineering, Qatar University, P. O. Box 2713, Doha, Qatar. 3 Low Dimensional Materials Research Centre, Department of Physics, University of Malaya, Kuala Lumpur 50603, Malaysia 4 Institute of Physics and Electronics, University of Peshawar, 25120 Peshawar, Khyber Pakhtunkhwa, Pakistan * E-mail: zubairtarar@qu.edu. qa
Milk Production Performance of Red Sokoto Goats supplemented with Crop Residues in Potiskum Area of Yobe State, Nigeria

The feeding value of groundnut seed cake and millet bran as protein and energy supplements in the diets of Red Sokoto does was investigated in an experiment at different quantities of 200g mixture of ground nut seed cake/millet bran (50:50), 200g groundnut seed cake alone and 200g millet bran alone using groundnut haulms as basal diet for the control. The experiment lasted for twelve weeks during which feed intake and milk production performance were recorded in twenty Red Sokoto goats with five does per treatment using the completely randomized design. The daily supplement intake, total feed intake and milk yield were significant (P<0.01) but differed according to the diet supplement. The milk yield values ranged between 0.2 and 0.6kg. The daily basal feed intake was also significant (P<0.05) but higher in the control. The lactation length was significant (P<0.001) and last longer in the group fed with mixed supplements. The values of lactation length ranged between 46 and 84 days. The daily weight gain and weaning weights were not significant (P>0.05) but the highest weights were recorded for goats in supplements B fed groundnut seed cake/millet bran mixture. The regression coefficient of daily supplement intake, daily weight gain and feed conversion ratios were significant (P<0.01). Findings from the study revealed that the mixture of groundnut seed cake and millet bran supplement diets can increase milk yield and lactation length of lactating goats significantly during dry season.
Effect of the COVID-19 pandemic on the status of overweight and obesity among children and adolescents: a retrospective study in Chengdu, China

Background: The prevalence of overweight and obesity among children and adolescents is steadily increasing and has become a public health concern. Lifestyle changes due to the COVID-19 pandemic may have an impact on the status of overweight and obesity among children and adolescents. This study aimed to analyze the effect of the COVID-19 pandemic on the status of overweight and obesity among children and adolescents. Methods: We retrospectively analyzed the children and adolescents who visited the West China Second University Hospital, Sichuan University from January 1st, 2018 to June 30st, 2020. We included obese children who met the criteria and divided them into 5 groups with 6 months as the unit according to the time of their visit. The national lockdown time was used as a segmentation point to study the changes of obesity status in the same children before and after lockdown. Results: A total of 140,526 children and adolescents visited the outpatient department from January 1st, 2018 to June 30st, 2020, and 1,740 of them were diagnosed as overweight or obese at the time of their first visit. The study found that there was a significant difference in the obesity rate among the groups (P < 0.01). However, there was no difference between January to June, 2020 and the previous period. Except for the increased incidence of VD deficiency (P < 0.01), the severity of obesity, insulin resistance and dyslipidemia of obese children did not change before and after COVID-19 (P=0.303, 0.663, 0.106, respectively). A total of 65 obese children were followed up in the outpatient department before and after COVID-19 lockdown. There were no significant differences in BMI-SDS, HOMA-IR and 25(OH)VD among obese children before and after lockdown (p = 0.626, 0.386, 0.251, respectively). Conclusions: The available evidence cannot prove that the COVID-19 pandemic affects the status of overweight and obesity among children and adolescents who visited hospitals. It may be related to the multiple effects of the COVID-19 pandemic on children.
Classification of Cyber-Attack using Adaboost Regression Classifier and Securing the Network

In recent years, with adverse development of technology leads to several security breaches. To withstand those security threats and breaches especially for cyber attacks resources are optimized with improved network lifetime. Those security challenges are lead to confidentiality, privacy, integrity and availability. To prevent cyberattacks artificial intelligence-based technology is evolved. To adopt appropriate cybersecurity wireless communication systems are intended to withstand threats and challenges. This paper, presented a deep learning-based classification technique for cyber attack detection. Deep learning structure involved in attack detection with proposed AdaBoost Regression Classifier (ABRC). The proposed ABRC with deep learning involved in estimation of attacks in the network security with deep learning structure. The proposed classifier model is involved in estimation of threats. The developed algorithm integrates AdaBoost and Regression classifier for threat detection and classification. The performance analysis expressed that proposed ABRC exhibits significant performance for cyber-attack detection than the existing deep learning technique.
Fungi Classification using Convolution Neural Network

This paper presents a model based on Convolution Neural Network (CNN) to identify and classify the fungi those causes disease to apple plant leaf. In this paper, apple scab, rust, black rot, and healthy leaf are studied and classified. The plant pathology dataset (publically available) consists of 9164 images are used for experimentation. The proposed CNN model identifies and classifies the apple leaves into these four categories. This model can successfully detect and classify diseases with an accuracy of 88.9%.