Miracle-dz/UniFAR-FTUs huggingface dataset:
Short-Term and Long-Term Renal Outcomes in Patients With Obesity After Minimally Invasive Versus Open Partial Nephrectomy for the Treatment of Renal Cancer: Retrospective Study

Background Obesity is significantly associated with renal cell carcinoma. Surgery is the preferred treatment for demarcated lesions of renal cell carcinoma; however, obesity increases the complexity of surgical outcomes. Minimally invasive surgical techniques are preferred over open partial nephrectomy (OPN), but controversy remains regarding the most efficacious technique in patients with obesity. Objective This study aims to determine whether minimally invasive partial nephrectomy (MIPN) or OPN better preserves renal function and investigate short- and long-term renal outcomes in patients with obesity undergoing a partial nephrectomy. Methods We conducted a retrospective chart review of 242 adult patients aged ≥18 years who underwent MIPN or OPN between January 1, 2005, and December 31, 2016, at the University of Arkansas for Medical Sciences. Using creatinine as a measure of kidney function, patients’ preoperative levels were compared with their postoperative levels in 2-time frames: short (3-6 months postsurgery) or long (>6 months). The primary outcome was the change in creatinine values from preoperative to >6 months postoperatively in patients with obesity. Secondary outcomes included the change in creatinine values from preoperative to 3 to 6 months postoperatively in patients with obesity who underwent MIPN versus OPN. We also analyzed the creatinine values of nonobese patients (BMI <30) who underwent partial nephrectomy using the same time frames. Unconditional logistic regression was used to estimate crude and multivariable-adjusted odds ratios (ORs) and 95% CI to observe associations between surgery type and changes in creatinine values from while stratifying for obesity. Results A total of 140 patients were included in the study, of whom 75 were obese and 65 were nonobese. At >6 months after MIPN (n=20), the odds of patients with obesity having a decrease or no change in creatinine values was 1.24 times higher than those who had OPN (n=13; OR 1.24, 95% CI 0.299-6.729; P=.80). At 3 to 6 months after MIPN (n=27), the odds were 0.62 times lower than those after OPN (n=17; OR 0.62, 95% CI 0.140-2.753; P=.56). In the nonobese group, at 3 to 6 months after undergoing minimally invasive surgery (n=18), the odds of having a decrease or no change in creatinine values was 4.86 times higher than those who had open surgery (n=21; OR 4.86, 95% CI 1.085-21.809; P=.04). At more than 6 months after MIPN (n=14), the odds were 4.13 times higher than those after OPN (n=11; OR 4.13, 95% CI 0.579-29.485; P=.16). Conclusions We observed a nonstatistically significant preservation of renal function in patients with obesity who underwent OPN at 3 to 6 months postoperatively. Conversely, after 6 months, the same was true for MIPN, indicating the long-term benefit of MIPN. In the nonobese group, MIPN was favored over OPN.
A Reversible Data Hiding Algorithm Based on Secret Sharing

: In traditional secret sharing schemes, all shared images containing secret segments are needed to recover secret information. In this paper, a reversible data hiding scheme based on Shamir secret sharing is used. Secret information can be recovered even if only part of the encrypted sharing is owned. This method can reduce the vulnerability of traditional encryption sharing schemes to attack. Before uploading the secret information to the cloud server, embed the encrypted n segments of secret information into n different pictures. The receiver downloads t images from the cloud server (t < n), extracts the encrypted information using the watermark extraction algorithm, and obtains the original secret information after decryption through Shamir secret sharing.
A Redesign Decision Model for Large-Scale Complex Sustainment-Dominated Systems

This article presents a fuzzy, multiattribute decision-making method to assist managers in making more informed technology refresh planning decisions; especially when evaluating large-scale complex sustainment-dominated systems. These types of systems have inherently long operational lives, thereby incurring multiple technology design refreshes throughout their extensive lifecycles. Strategically planning for these technology refreshes presents a significant engineering, business, and logistical challenge. Existing refresh planning models are limited as they do not consider a systems’ view when forming recommendations, which can lead to suboptimal decisions. The mathematical models developed in this article are the foundation of the fuzzy, multiattribute decision-making method that allows engineering managers to use a systems’ thinking approach to evaluate competing technology refresh options. When applied to multidisciplinary projects, the fuzzy, multiattribute decision-making method evaluates several competing and complementary redesign drivers, leading to more informed systems refresh recommendations.
Predictors of Student Teachers’ ESD Implementation Intention and Their Implications for Improving Teacher Education

Recently, the German state of Baden-Württemberg included ESD as a guiding perspective for all school curricula. Consequently, teacher education needs to empower and motivate student teachers to implement ESD in schools. In previous ESD research, however, the motivational dimension rarely has been investigated. Psychological theories stress the importance of developing an intention for the actual realization of a behavior. Therefore, we investigated, in an online survey (N = 366), which factors influence student teachers’ ESD implementation intention. Furthermore, we investigated how student teachers understand the guiding perspectives and what influences the actual ESD implementation from the respondents’ point of view. Via structural equation modelling, we found direct effects of subjective task value, expectation of success and ESD knowledge, as well as indirect effects of SD attitudes and ESD implementation beliefs on ESD implementation intention, but no effects for subjective norm and perceived costs. Analyses of open answers revealed complex understandings of ESD, but also misinterpretations mainly in terms of ESD as equivalent to environmental education. Furthermore, student teachers expected barriers (e.g., lack of time) and formulated support needs (e.g., teaching material) for implementing ESD in schools. Our findings stress the importance of advancing ESD implementation in teacher education.
Detection and Mitigation of MAC Spoofing

Wireless Local Area Network (WLAN) are generally utilized and getting more in number step by step because of the simplicity of spread signs, quality, and quality. WLAN is additionally simple to actualize in any association. Without any difficulty of utilization, the remote system is likewise simple to produce or bargain because of some shortcoming. Macintosh satirizing is one of the provoking system to be maintained a strategic distance from Macintosh satirizing because conveyable due to devices used to produce the MAC address of system card on the product level. Approval with MAC address got dubious. To beat this issue, we proposed a structure in which client approval process completed by getting three one of kind boundaries of the machine, get hashed and contrasted and the database.
The functional state of girls with high motor fitness in the conditions of digitalization

The article aims at revealing the functional state of girls aged between six-seven and 10-11 years with a generally high level of key motor skills due to the intensive use of digital technologies. Experimental studies comprised practically healthy girls aged between six-seven (n>1,200 and n=60) and 10-11 years (n>1,000 and n=74). To assess the information load of children, the authors of the article determined the total screen time and the digitalization of their living conditions. The level of digitalization was studied based on a modified Informatization Index (Imod). The heart rate and blood pressure were recorded. Based on these measurements, the authors calculated average pressure, the vegetative index of Kerdo, double product, the Myznikov index and changes in the functional index. They measured chest circumference, the length and weight of the body to calculate the body mass index. The battery of motor fitness tests included a 30-meter sprint, front bend, three sets of a 10-meter shuttle run, sit-ups, a long jump, a 6-minute run, PWC170. Physical activity was studied using the adapted questionnaire “Global Physical Activity Questionnaire (GPAQ)” and the timing of motor actions. During the intensive use of digital technologies, girls aged between six-seven and 10-11 years with a high level of motor abilities could be characterized by the most favorable functional state of their body in comparison with the same children with insufficient motor fitness. They had the hypertonicity of the parasympathetic division of the vegetative nervous system, the predominance of the autonomous regulation of the heart rate over central regulation, the expanded adaptive capabilities of the organism, harmonious physical development and high physical activity. The study results give reason to believe that an increase in the level of motor fitness of preschoolers and primary school children can have a significant effect on improving the functional state of their bodies due to the intensive use of digital technologies.
Analyzing Gaussian distribution of semantic shifts in Lexical Semantic Change Models

In recent years, there has been a significant increase in interest in lexical semantic change detection. Many are the existing approaches, data used, and evaluation strategies to detect semantic shifts. The classification of change words against stable words requires thresholds to label the degree of semantic change. In this work, we compare state-of-the-art computational historical linguistics approaches to evaluate the efficacy of thresholds based on the Gaussian Distribution of semantic shifts. We present the results of an in-depth analysis conducted on both SemEval-2020 Task 1 Subtask 1 and DIACR-Ita tasks. Specifically, we compare Temporal Random Indexing, Temporal Referencing, Orthogonal Procrustes Alignment, Dynamic Word Embeddings and Temporal Word Embedding with a Compass. While results obtained with Gaussian thresholds achieve state-of-the-art performance in English, German, Swedish and Italian, they remain far from results obtained using the optimal threshold.
Parental Attachment and Psychological Wellbeing in Adolescents: Mediating Role of Self-esteem

Introduction: Adolescence is amongst the most rapid stage of human development. It is the period to create knowledge and skills, learn to mange emotions, obtain attributes and capacities and relationships with parents and peers. All these aspects are important for enjoying these years and assuming the roles of adults. The purpose of the study was to examine the relationship between parental attachment, self-esteem and psychological wellbeing of adolescents. Methods: The participants were 292 adolescents with age ranging from 13 to 18 years. Inventory for parent and peer attachment (IPPA) by Armsden and Greenberg self-esteem scale by Rosenberg and psychological wellbeing scale by Ryff were used to measure the parental attachment, self-esteem, and psychological wellbeing in adolescents. Result: Results indicated that the the correlation value of parental attachment and psychological wellbeing was found be 0.306. The value of correlation value for self-esteem and psychological wellbeing was found to be 0.342. Conclusion: Significant positive relationship was found between parental attachment, self-esteem, and psychological wellbeing. The mediation analysis has shown that self-esteem partially mediates the relationship between parental attachment and psychological wellbeing among adolescents.
Signal Intensity Changes in the Dentate Nucleus and Globus Pallidus of Multiple Sclerosis Patients Undergoing Multiple Brain Magnetic Resonance Imaging with Gadolinium-Based Contrast Agents

Objective: To investigate the correlation between cumulative doses of gadolinium-based contrast agents and increased signal intensity (SI) in the dentate nucleus (DN) and globus pallidus (GP). Methods: Our retrospective trial involved 21 multiple sclerosis patients (11 women, 10 men; mean age: 39.21±10.24, range: 20 to 57 years) who underwent a serial number of multiple cranial magnetic resonance imaging (MRI) examinations in the radiology department of our tertiary care center. Average signal intensities on the DN, pons, GP and thalamus on unenhanced T1-weighted magnetic resonance images were taken into account. The signal intensity in the DN was proportioned to the signal intensity in the pons, whereas the signal intensity in GP to the signal intensity in the thalamus. Any relationship between the DN-to-pons or GP-to-thalamus signal intensity ratios and previous gadolinium-based contrast administrations was sought by means of repeated measures ANOVA. Results: Increases in both DN-to-pons and GP-to-thalamus signal intensity ratios displayed a significant correlation with previous administrations of gadolinium-based contrast agents (p<0.001 for both). A remarkable increase was detected in DN-to-pons and GP-to-thalamus signal intensity ratios between the first and last MRI examinations (p<0.001 for both). Conclusion: Our results support the association between increase in the SI of the DN and GP to the number of gadolinium-enhanced MRI scans in MS patients. The increase in T1 SI seems to be linked with the number of enhanced MRI scans.
Machine Learning-based Detection and Classification of Walnut Fungi Diseases

Fungi disease affects walnut trees worldwide because it damages the canopies of the trees and can easily spread to neighboring trees, resulting in low quality and less yield. The fungal disease can be treated relatively easily, and the main goal is preventing its spread by automatic early-detection systems. Recently, machine learning techniques have achieved promising results in many applications in the agricultural field, including plant disease detection. In this paper, an automatic machine learning-based detection method for identifying walnut diseases is proposed. The proposed method first resizes a leaf’s input image and pre-processes it using intensity adjustment and histogram equalization. After that, the detected infected area of the leaf is segmented using the Otsu thresholding algorithm. The proposed method extracts color and shape features from the leaf’s segmented area using the gray level co-occurrence matrix (GLCM) and color moments. Finally, the extracted features are provided to the back-propagation neural network (BPNN) classifier to detect and classify walnut leaf diseases. Experimental results demonstrate that the proposed method’s detection accuracy is 95.3%, which is significantly higher than those of the state-of-the-art techniques. The proposed method assists farmers in detecting diseases affecting walnut trees and thus enables them to generate more revenue by improving the productivity and quality of their walnuts.
Sentiment Analytics: Extraction of Challenging Influencing Factors from COVID-19 Pandemics

The advancement in electronic devices and communication technologies in social media have introduced major changes in today’s communication and people have accepted such communicational habits at a rapid pace. The changes involve the way people started interacting with each other, and modern mean of discovering new groups of people, and individuals with similar mindsets, mutual interests, and ideas to share with. As far as the communities are concerned, there are so many social drives (such as “Say No to Plastic”) that need to be dis-cussed on a certain platform for their promotion. Although, it’s quit is challen-ging, but with the advancement of communication technologies propagations and the increasing engagement of the youth on social media towards social drives made this achievable. There exists a need to examine the influencing factors that peoples express when such drives are promoted on any social media forum in order to evaluate their sentiments. The objective of this research is to explore the influencing factors of pandemic Corona Virus Disease 2019 (COVID-19) when it has been promoted as a social drive on social media forums. In this research effort, sentiment analysis along with novel similarity measures such as THINKMAP, VADER++ and lexicons have been utilized to analyze the influencing factors of those peoples who are affected by COVID-19 when social media forums promote it as a common social drive. The significance of this research is that it would help to demonstrate that participation of youth of different countries could boost up the promotion of social drives and fasten the process which leads towards the positive awareness about any common social drive. Moreover, it would cover the gap of common issues on social media that are not in practice in past studies. It also strengthens the awareness phenomena in youth beyond boundaries. From the experimental results and discussion, it has been observed that the proposed research performed well in term of accuracy. © 2021, Tech Science Press. All rights reserved.
Movement-based mind-body interventions for cardiac rehabilitation: An updated systematic review of randomized controlled trials

This study aimed to assess evidence for the effect of movement-based mind-body interventions (MMBIs) for cardiac rehabilitation (CR). Six databases were searched from January 1995 to September 2020. All randomized controlled trials (RCTs) evaluated the effect of MMBIs on heart disease (HD) patients' physical and psychological outcomes. Two reviewers independently assessed the quality of all the included studies using the revised Cochrane risk-of-bias tool for RCTs. Sixteen RCTs (5160 participants) published between 1996 and 2020 met all inclusion criteria. In total, these studies investigated the effect of MMBIs for CR. Outcome measures that emerged in these studies included physical and psychological, and/or biochemical parameters to comprehensively evaluate the effects of MMBIs on HD patients. Overall, these studies suggest that MMBIs seem to be an alternative with the optimal CR option.
Transformer Neural Networks for Automated Rhythm Generation

We propose a novel approach to automated rhythm generation in which a TransformerXL model is employed to model and generate rhythm from the Magenta Groove MIDI Dataset. Recent applications of this high-dimensional language framework in the field of music have demonstrated it’s ability to effectively capture and emulate long-term dependency in musical sequences dependencies characteristic of human notions of musicality and creative merit making it an ideal candidate to experiment with for the task of rhythm-specific generation. We evaluate hundreds of generations from our optimum model using a variety of methods; probabilistic, musicological and in blind listening tests to determine the extent to which our framework has learnt and reproduced the aspects of rhythm we understand to be valuable. Our model is able to achieve a standard of rhythmic production comparable to human playing across arbitrarily long time periods and multiple playing styles.
Mother’s Lifestyle Feature Relevance for NICU and Preterm Birth Prediction

Maternal health plays an important role in defining the health of mother, child and childbirth experience. With the change in lifestyle over the decades, there have been many health challenges faced by woman, which makes it important for women to understand the impact of their lifestyle and physical health features on their wellbeing. In this study, we have realised the importance of mother’s features with respect to preterm childbirth prediction and prediction for neonatal intensive care unit(NICU) facility requirement for newborn. Experiments are performed on MSF dataset which consists of records of 1000 women, 21 physical features and 78 lifestyle features are taken into consideration. Random forest based hybrid model using F-score and Mutual information is used to evaluate each features for their capability of True positive(TP) and False Negative(FN) predictions. For preterm birth prediction, out of all the features hypertension, diabetes, PCOS and consumption of outside food during teenage are found to be the most relevant features. While for NICU prediction diabetes, low amniotic fluid during pregnancy, exposure to air and noise pollution during teenage and consumption of alcohol after marriage are found to be relevant.
A posteriori error estimates of spectral method for the fractional optimal control problems with non-homogeneous initial conditions

In this paper we consider an optimal control problem governed by a space-time fractional diffusion equation with non-homogeneous initial conditions. A spectral method is proposed to discretize the problem in both time and space directions. The contribution of the paper is threefold: (1) A discussion and better understanding of the initial conditions for fractional differential equations with Riemann-Liouville and Caputo derivatives are presented. (2) A posteriori error estimates are obtained for both the state and the control approximations. (3) Numerical experiments are performed to verify that the obtained a posteriori error estimates are reliable.
Positioning Unit Cell Model Duplication With Residual Concatenation Neural Network (RCNN) and Transfer Learning for Visible Light Positioning (VLP)

Machine-learning (ML) can be employed to enhance the positioning accuracy of visible-light-positioning (VLP) system. To diminish the training time and complexity, the whole area is usually divided into several positioning unit cells. Most literatures only focus on the positioning performance within an unit cell, and assume the unit cell can be repeatedly duplicated to cover the whole area. In this work, we propose and demonstrate a positioning unit cell model duplication scheme, named as spatial sequence adaptation (SSA) process. We also propose and demonstrate a residual concatenation neural network (RCNN) and transfer learning (TL) to refine the model of the target positioning unit cell. A practical test-bed with vertical distance of 2.8 m consisting of two unit cells with dimensions of about 1.55 m × 2 m per cell is constructed. The client side is an autonomous mobile robot (AMR) for acquiring continuous training and testing data. Our experimental results reveal that high precision positioning in the duplicated unit cell duplication can be achieved.