Miracle-dz/UniFAR-FTUs huggingface dataset:
Feasibility and Safety of Physical Exercise to Preserve Bone Health in Men With Prostate Cancer Receiving Androgen Deprivation Therapy: A Systematic Review

Abstract Objective Men with prostate cancer (PCa) receiving androgen deprivation therapy (ADT) experience the loss of bone mineral density (BMD) and lean body mass, which can increase their risk of falls and fractures. Physical exercise programs with appropriate components and dosage are suggested to preserve BMD and muscle strength, thereby potentially reducing accidental falls and fractures and associated morbidity and mortality. These benefits can be obtained if exercise programs are feasible and safe and if patient adherence is adequate. This systematic review investigates the feasibility and safety of exercise programs aimed at preventing the risk of accidental falls and fractures and BMD loss in men with PCa undergoing ADT. Methods MEDLINE, Embase, CINAHL, and the Cochrane Library were searched from database inception to June 7, 2021. Randomized controlled trials were included when they analyzed the feasibility and safety of experimental exercise programs targeting bone health in men with PCa receiving ADT. Two reviewers independently selected the studies, assessed their methodological quality, and extracted the data. Exercise feasibility was measured through recruitment, retention, and adherence rates. Exercise safety was measured through the number, type, and severity of adverse events. Furthermore, the components, setting, intensity, frequency, and duration of exercise programs were extracted. Results Ten studies were included, with a total of 633 participants. Exercise consisted of a combination of aerobic, resistance, and impact-loading exercise or football training. Exercise is feasible in men with PCa undergoing ADT, although football training should be prescribed with caution for safety reasons. Conclusion Multicomponent exercise programs targeting bone health seem feasible and safe in this population; however, adverse events should be systematically documented according to current guidelines. Impact The study shows that men with PCa receiving ADT can safely perform exercise programs to preserve bone health and supports that those programs should become part of lifestyle habits. Lay Summary Men with PCa who are receiving ADT can safely perform exercise programs to preserve bone health and should make exercise an important part of their lifestyle.
Biogas and calcite handling in calcium fluoride precipitation from aqueous solution guided by thermodynamic simulation with PHREEQC

Abstract In this innovative study, biogas has been associated with calcium carbonate [CaCO3] to promote the precipitation of fluorite, aiming at the treatment of wastewater with high content of fluoride. The work associates distinct sources of calcium and CO2 for the precipitation of fluorite according to previous simulation with the free software PHREEQC. Considering the reaction at equilibrium, the minimal predicted Ca dosage was 215 mg/L, lower than the 430 mg/L that was experimentally determined, independent of Ca source. The simultaneous use of CaCO3 and CO2 from distinct gas sources (pure CO2, 1:1 CO2:N2, and biogas) exhibited high performance permitting the reduction of fluoride content from 134 to 10 mg/L, with low gas consumption. The biogas consumption of 66.0 mmol/L, equivalent to 33.4 mmol/L of CO2 (1.47 kgCO2/m3 treated wastewater) was predicted, indicating that the biogas storage bag of 700 L would be able to treat 469 L of wastewater. Furthermore, the inert fraction of biogas (CH4) did not impact the reaction and it may be used after the reaction as an alternative source of power, equivalent to 8.25 kWh/m3 treated wastewater. Final solids were composed by fluorite and non-dissolved calcite, confirming the predictions obtained by PHREEQC.
A Comparative Study on Language Models for the Kannada Language

We train word embeddings for Kannada, a Dra-vidian language spoken by the people of Kar-nataka, a southern state in India. The word embeddings are trained using the latest deep learning language models, to successfully encode semantic properties of words. We release our best models on HuggingFace, a popular open source repository of language models to be used for further Indic NLP research. We evaluate our models on the downstream task of text classiﬁcation and small custom analogy and similarity tasks. Our best model attains accuracy on par with the current State of the Art while being only a fraction of its size. We hope that by publicly releasing our trained models, we will help in accelerating research and easing the effort involved in training embeddings for downstream tasks.
Compressive Performers in Language Modelling

This work introduces the Compressive Performer, a hybrid Transformer variant based on two existing model architectures: the Performer, which reduces the memory requirement and processing time of the Transformer to linear complexity, and the Compressive Transformer, which re-tains contextual dependencies over a long range by compressing old activations instead of discarding them. Experiments in language modelling at the character level, the word level, and the sub-word level demonstrate that the Compressive Per-former shows improved perplexity scores on the enwik-8 dataset, compared to its base models. This work also compares convolutional compression with autoen-coder compression, determining that both show similar perplexity scores.
Formulating Automated Responses to Cognitive Distortions for CBT Interactions

One of the key ideas of Cognitive Behavioural Therapy (CBT) is the ability to convert negative or distorted thoughts into more realistic alternatives. Although modern machine learning techniques can be successfully applied to a variety of Natural Language Processing tasks, including Cognitive Behavioural Therapy, the lack of a publicly available dataset makes supervised training difﬁcult for tasks such as reforming distorted thoughts. In this research, we constructed a small CBT dataset via crowd-sourcing, and leveraged state of the art pre-trained architectures to transform cognitive distortions, producing text that is relevant and more positive than the original negative thoughts. In particular, the T5 transformer approach to multitask pre-training on a sequence-to-sequence framework, allows for higher ﬂexibility when ﬁne-tuning on the CBT dataset. Human evaluation of the automatically generated responses showcases results that are not far behind from the overall quality of the ground truth scores.
Automated Recognition of Hindi Word Audio Clips for Indian Children using Clustering-based Filters and Binary Classifier

Speech recognition systems have made remarkable progress in the last few decades but most of the work has been done for adult speech. The rise of online learning during Covid-19 pandemic highlights the need for voice-enabled assistants for children so that they can navigate the menus and interfaces seamlessly. Speech recognition for children will also be very useful to develop automated reading assessment tools. However, such technology for children is challenging for a country like India where differences in accents, diction and enunciation is significant but available children speech data is limited. Through this paper, I tried various approaches to recognize hindi word audios. Commercially available Google Speech-to-Text performs poorly with only 49.7% accuracy at recall of 0.24 while recognising audio samples containing hindi words spoken by children. Using the same dataset, I experimented with clustering algorithm and logistic regression and found that the accuracy improves upto 81% with logistic regression. The paper also highlights the importance of data preprocessing by performing noise reduction using Butterworth low pass filters. © ICNLSP 2021. All Rights Reserved.
Predictors of nurse's happiness: a systematic review

Abstract Objective An acute shortage of nurses exists all over the world. Part of this shortage appears to be due to nurses’ low job satisfaction, low retention, and high turnover. Happiness at work is identified as a contributing factor that determines the shortage of nurses across the world. This paper critically appraised the existing scientific articles that assessed the individual and organizational predictors of happiness among nurses. Methods The systematic review was conducted from August 2010 to August 2020 using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Scientific articles were searched in the electronic databases (Scopus, Medline, CINAHL, PsychINFO, and Pubmed Central) on the predictors of happiness among nurses. Results A total of 13 articles were selected following the literature search and a thematic analysis was done. This review provides updated evidence on the predictors of nurses’ happiness. Job-related predictors were identified as organizational predictors; and personal, psychological, family, social, and spiritual predictors were reported to be the individual predictors. Conclusions As both individual and organizational predictors determine the happiness of nurses, the authors recommend the need for future interventional studies to increase the nurses’ happiness, their mental health, and health-related quality of life.
Cavitation inception tests on a systematic series of two- bladed propellers

The paper aims at propeller design techniques which suppress inception of tip vortex cavitation. Model tests provide insight in the phenomena associated with tip vortex cavitation inception on propeller blades and the important parameters are systematically varied and investigated. The application of this work is primarily directed to improvements on naval propellers. However, lessons learned are also useful in the design of propellers for merchant ships, where there is a risk of cavitation hindrance due to vibrations or noise. A systematic series of propeller blade designs has been made, based on an existing state of the art controllable pitch Naval propeller design. Geometric parameters were varied in the tip region including thickness, planform, skew, chord distribution and rake. All these parameters were varied while maintaining a constant radial loading distribution. The results of the cavitation inception tests are presented in the form of inception diagrams for the distinct types of tip vortex cavitation that can be distinguished (trailing, local tip and leading edge vortex cavitation). Cavitation inception performance is quantified by the width of the non cavitating range of operating conditions at constant σ, referred to as the cavitation bucket. The systematic tests indicate that the blade contour is an important parameter in tip vortex inception. Using the results of the systematic tests a series of propeller models were designed in an effort to improve the results further by combinations of the parameters in the systematic tests. As yet the results have shown no significant further improvements, but indicated the pitfalls and problems which have to be avoided. A low skew planform was finally designed aiming at the delay of cavitation inception. The model test results confirmed the design guidance derived from the systematic tests. Recent full scale observations confirmed the model test results and provide an illustration of the potential of the new design technique. Panel Method analysis was used to assist in the design of the various tip variants. The possibilities and shortcomings in application of inviscid computations for the analysis of tip flow are shown.
Predefined-Time Tracking Control for Second-Order Leader-Following Multi-Agent Systems via Prescribed-Time Observers and Sliding Mode Control Strategy

Predefined-time tracking problem for second-order nonlinear Multi-Agent Systems (MASs) under undirected graph is investigated in this paper. Firstly, by designing a group of observers, each follower can obtain the position and velocity tracking errors between it and the leader within any specified time. Secondly, an observer-based sliding surface is designed, so that the system trajectories will reach the sliding manifold within another specified time. Finally, by combining the proposed observer and sliding surface, we propose a distributed controller which can guarantee the predefined-time leader-following consensus. In order to verify the proposed solution, a simulation is executed and followed by some analysis of its results.
An analysis of Twitter Users' Political Tendency Based on Integrating of Topics and Multi-relation with Graph Embedding

The analysis of user's political tendency is a research hotspot in the field of social science. At present, most related researches only detect users' political inclination from the content or behavior relationship, ignoring that the fusion of two heterogeneous data can bring more comprehensive information. Moreover, the political tendency of users is simply divided into two categories, ignoring the research on neutral users. Aiming at the problem of analyzing users' political tendency from content and behavior, this paper puts forward a graph embedding method which integrates topics and five social relationships of users, such as Follow, Like and so on. This method can efficiently integrate various types of data in real heterogeneous networks. Experiments show that the proposed method is superior to baseline methods in classifying Twitter users' political tendency. The analysis result of multi-type data fusion is better than that of single type's result. In addition, the case of predicting the general election proves the effectiveness of the proposed method.
Industrial Parts Detection Based on Deep Learning

Object detection is the core technique of industrial sorting based on machine vision. However, traditional object detection algorithm is difficult to solve sorting tasks in complex industrial scenarios. To this end, this paper proposes an industrial parts detection network based on deep learning. This method is to use the object detection network to detect the parts on the conveyor belts and obtain the category and location information of the parts. In order to improve the network's detection accuracy of multi-scale parts, this paper use the K-Means algorithm to redesign the size of the anchor. In addition, we construct a private dataset for model training and use dataset augmentation to expand our dataset, then using the pre-trained weights based on the VOC dataset for transfer learning. Experiments based on self-constructed dataset show that the mAP of our method achieves 97.03%, which can satisfy the requirements of practical applications.
Integrated Navigation in Indoor NLOS Environment Based on Kalman Filter

In view of the fact that the global satellite navigation system can not be applied to the indoor environment, it is of great practical significance to study the navigation problems in the indoor environment. This paper first introduces the basic principles of inertial navigation and ultra-wideband navigation, then analyzes the practical problems of TOA estimation of UWB signals in indoor NLOS environment, and considers the use of Kalman filter to optimize the integrated navigation error in NLOS environment. The simulation experiment is used to assist the analysis and the optimization results are given. Through the analysis of the results, the NLOS error is reduced by 27.7%.
Increment-aware Dynamic Propagation Embedding for Rumor Detection

Automatically detecting rumors on social media has become an urgent task in recent years. Most of the existing methods either focus on learning sequential diffusion features along linear propagation paths or model the non-linear structure relations among posts based on static propagation networks. However, these methods largely ignored the dynamic changes of propagation networks over time, which could provide informative clues to distinguish rumors and non-rumors. To fill this gap, we propose a novel model named Increment-aware Dynamic Propagation Embedding (IDPE) for rumor detection, which can capture the temporal evolution of propagation networks without compromising the efficiency of rumor detection. At first, a propagation network is represented as a snapshot sequence. Then, a Local Graph Attention Network (LGAT) is built to embed each snapshot in the sequence incrementally. Further, a propagation embedding is employed to encode the temporal variations of the snapshot sequence to generate the propagation representation for identifying rumors. The experiments conducted on real datasets demonstrate the effectiveness of IDPE in rumor detection.
Ship Classification for Space-based AIS Data Using 1D-CNN

The wild application of AIS (Automatic Identification System) provides large quantities of data for maritime traffic research. There are many ships with types unknown in AIS data. The data-driven methods to identify the ship's class is one of the research hotspots in AIS data mining. In this paper, a 1D-CNN (one-dimensional convolutional neural network) method is applied to classify ships from the distribution of ships motion features, which achieves an accuracy of 78.83%. Moreover, the importance of features is discussed, which explains how the 1D-CNN works in this task and the reason why some classes of ships are mislabeled. The method in this paper is proved to be effective in ship classification using dynamic data in AIS.
Feature Fusion based Re-voting for 3D Object Detection

3D object detection based on point cloud is a challenging visual task, which is helpful to the realization of various 3D visual applications. A few recent works based votenet recognize objects by using hough voting. However, the voting strategy in votenet can only obtain some sampling points from incomplete surfaces and chaotic backgrounds, without considering the features and position relation of the original cloud points. In this work, we introduce a new 3D object detection method called feature fusion based revoting network (FFRNet). Our method adds a supervision mechanism to the simple voting mechanism and fuses the feature of seed points and voting points to increase the richness of information in the re-voting module. The feature fusion operation enhances the acquisition of effective information of the original surface points, So as to achieve more reliable and flexible object positioning and category prediction results. We validate our model on the challenging ScanNet V2 dataset, advancing votenet results by 3.6 mAP@0.25.
Using the Loess Method to Extract Tree-ring Information from the Profiles of a Drill Resistant Device

As an alternative to the traditional methods, micro-drilling devices can provide a faster and more-efficient way of collecting tree-ring data nondestructively. Data generated by such devices need to be further processed to yield useful tree-ring information. In this paper; we used a nonparametric regression method (loess) to extract tree-ring numbers and widths from the drilling profiles generated by a RESISTOGRAPH(superscript ®). The extracted information was then compared with measurements from scanned cross-sectional images of the disc. The results suggested that the loess method indeed provided accurate estimates of ring numbers and widths, except near the pith area. Overall, the actual drilling paths controlled the accuracy of the extracted tree-ring information. Some issues which one needs to be aware of to ensure successful drilling were also discussed.
Progressive Entity Matching via Cost Benefit Analysis

Entity matching (EM) is a fundamental problem in data preprocessing, and is a long running topic in big data analytics and mining communities. In big data era, (nearly) real-time data applications become popular, and call for progressive EM, which produces as many match pairs as possible in very limited time. Previous progressive EM focus on memory based solutions, but disk based solutions are necessary when dirty datasets cannot be fully loaded into memory. To this end, we propose a cost benefit analysis based progressive EM approach, which partitions data according to coarse clustering results and then iteratively schedules data partitions in a greedy way for high progressive resolution. At first, based on estimated record pair similarities, records are fast coarsely clustered; then, record clusters with near average similarities are greedily distributed to the same partitions, and data partitions are cached. After that, cost model is defined with time and space constrains, and benefit model is defined with expected resolution results. On the basis of the cost benefit model, a greedy approximate method is proposed to effectively schedule data for high progressiveness of EM. Finally, we implement extensive experiments over several datasets to evaluate our approach, and show its advantages over existing works.
Usabilidade da Biblioteca Virtual em Saúde: avaliando a eficácia, eficiência e satisfação 1 Usability of Virtual Health Library: evaluating eff ectiveness, efficiency and satisfaction

Aborda aspectos relativos a avaliacao de bibliotecas digitais, consideradas como dispositivos informacionais que podem auxiliar na democratizacao da informacao mediada pelas tecnologias digitais. Tais bibliotecas podem ser compreendidas como um espaco de organizacao, armazenamento, disseminacao e acesso a informacao por meio de uma rede de comunicacao. Discute a importância da avaliacao dessas bibliotecas, observando a ausencia de normas internacionais destinadas a mensuracao dessas. Objetiva avaliar a usabilidade da Biblioteca Virtual em Saude (BVS). Metodologicamente caracteriza-se como um teste formal de usabilidade, com a finalidade de medir a eficiencia, a eficacia e a satisfacao de usuarios de bibliotecas digitais. O teste foi composto por tres instrumentos de coleta de dados, um questionario de perfil/experiencia; uma lista de dez tarefas a serem realizadas, utilizando o site da BVS; e um questionario com oito perguntas abertas que extraia percepcoes sobre o uso da biblioteca e seus recursos. O modelo metodologico foi aplicado entre os dias 05 e 21 de dezembro de 2011 no laboratorio de informatica do Centro de Ciencias da Saude da Universidade Federal da Paraiba. Os resultados do teste de usabilidade possibilitam inferir que a BVS apresenta um bom nivel de eficacia e boa eficiencia, tendo o quesito satisfacao atingido o nivel satisfatorio, conforme as respostas apresentadas nas questoes abertas pelos participantes da pesquisa. Foram detectados alguns problemas de usabilidade e apresentadas sugestoes para melhorar a interface e, consequentemente, a interacao usuario/biblioteca digital.