Miracle-dz/UniFAR-FTUs huggingface dataset:
NÍVEL DE INTEGRAÇÃO DOS SISTEMAS DE CONTABILIDADE FINANCEIRA E GERENCIAL COM O PROCESSO DE CONVERGÊNCIA CONTÁBIL E A EFETIVIDADE DA CONTROLADORIA

This study aims to verify the integration level between the financial and management accounting systems as a result of the convergence process with the international accounting standards and of the effectiveness of controllership in Brazilian companies. A descriptive research was undertaken, based on the application of the questionnaire by Angelkort and Weisenberger (2011) to the 500 Best and Biggest of Revista Exame, issue 2011, using a sample of 32 companies that answered the research. The correlations between the integration level of the financial and management accounting systems and the variables “consistency of financial language”, “quality of services provided” and “degree of influence in decision making”, during the convergence period with the international accounting standards, were positive and moderate. It was also observed that the period before the accounting convergence (2004 till 2007) showed a better integration level of the financial and management accounting systems than the accounting convergence period (2008 till 2011). In conclusion, the accounting convergence process increased the integration level of the financial and management accounting systems in the investigated companies, as well as the effectiveness of controllership, particularly in the consistency of the financial language, in the quality of the services provided and in the influence of the controllers’ services on these companies’ decisions.
Nonlinear dynamics and acoustic radiation of coherent structures consisting of multiple ring–helical modes in the near-nozzle region of a subsonic turbulent circular jet

Abstract This paper investigates the nonlinear evolution and acoustic radiation of coherent structures (CS) in the near-nozzle region of a subsonic turbulent circular jet. A CS is taken to be a wavepacket consisting of multiple ring/helical modes, which are considered to be inviscid instability waves supported by the mean-flow profile. As the three-dimensionality of helical modes is weak in the near-nozzle region, the ring and helical modes with the same frequency have nearly the same growth rates and critical levels. They coexist and interact with each other in their common critical layer at high Reynolds numbers. The self and mutual quadratic interactions generate a mean-flow distortion and streaks, which act back on the fundamental components through the cubic interaction. The amplitude of the CS is governed by an integro-partial-differential equation, a significant feature of which is that differentiations with respect to the azimuthal coordinate appear in the history-dependent nonlinear terms. The non-parallelism of the mean flow as well as the impact of fine-scale turbulence on CS are taken into account and found to affect the nonlinear terms. By solving the amplitude equation, the development of the constituting modes, streamwise vortices and streaks are described. For CS consisting of frequency sideband, low-frequency components are excited nonlinearly and amplify to reach a considerable level. By analysing the large-distance asymptote of the perturbation, the low-frequency acoustic waves are found to be emitted by the temporally–spatially varying mean-flow distortion and streaks generated by the nonlinear interactions of the CS, and are thereby determined on the basis of first principles. Interestingly, the energetic part of the streaky structure that contributes to the nonlinear dynamics does not radiate directly, and instead the Reynolds stresses driving the subdominant radiating components represent the true physical sources.
A DOA estimation method for array signals based on deep learning

The traditional Direction of Arrival (DOA) estimation algorithms are based on model parameters, which depends on the accuracy of the array model. When the array model has errors, the matching between the model and the data will fail, which affects the estimation performance to some extent. Therefore, this paper constructs the nonlinear relationship between the received signal and its spatial spectrum through the neural network framework, and uses the data-driven of deep learning to realize the DOA estimation. The neural network consists of an autoencoder network and multiple parallel 1-D VGG networks to achieve spatial spectrum estimation of the angle region. The simulation results show that the DOA estimation method proposed in this paper has good generalization ability, and also show good robustness under array error conditions.
A visual window convolutional attention mechanism based building extraction method for remote sensing images

To solve the problem of low accuracy and slow speed of building extraction due to complex semantic information of remote sensing image, Visual Window Convolutional Attention-Net(VWCA-Net) is constructed in this paper. VWCA-Net is based on an encoding-decoding framework, with the ResNet-50 used as the encoding backbone network in the encoding phase; the Visual Window Convolutional Attention Fusion (VWCAF) module is used to fuse shallow and deep features in the decoding phase. The VWCAF module first filters the feature maps containing buildings and generates Mask by windowing the sliced deep feature maps and fusing visual features, and then fuses the weights of CBAM to remove the features that do not contain the building feature maps, thus making the building boundary segmentation more accurate and faster. The experiment show that the Recall and mIoU of VWCA-Net on WHU dataset are better than SegNeXt network by 0.87% and 0.89%, respectively, but the computational effort is reduced by 17.38 FLOPs/G, which is a more effective and practical application method for building extraction.
Classification of X-ray images of COVID-19 based on CNN and improved swin transformer model

Faced with the rapid spread of COVID-19, nucleic acid testing methods can detect positive cases relatively quickly. Still, the time-consuming detection and frequent false-negative issues have led to a sharp increase in the demand for alternative diagnostic tools for COVID-19. In this paper, using medical imaging technology and deep learning technology, a model combining a convolutional neural network and an improved Swin Transformer network is designed to detect chest X-ray images of COVID-19. The image is input into the convolutional layer to extract the local details of the image. Then, to solve the problem that some heads do not play a role in calculating multi-head self-attention due to too small a weight, a learnable bias parameter is added to each individual computing head to enhance the specific weight of each head. Experiments show that this method has a recognition rate of 98.25% for chest X-ray images of COVID-19. Indicators such as recall rate and F1 score have been improved compared with some current methods.
Deep learning based secreted protein prediction

Proteins secreted by various cells and tissues into different body fluids can signal several physiological disorders. However, the degree of complexity in different body fluids and the presence of a large number of proteins in fluids can make studying them with existing proteomics techniques complex and result in large discrepancies between experimental studies. To address this, we developed a deep learning framework called SecBert that identifies secreted proteins in two human body fluids. SecBert uses a sequence-based approach with end-to-end automatic feature extraction for protein classification. Our results show that SecBert performs well, achieving an average area under the ROC curve of 0.94-0.95 on each fluid test dataset.
A semantic SLAM system for low texture and dynamic environments

Visual Simultaneous Localization and Mapping(vSLAM) is considered the foundation technology for augmented reality and mobile robotics, and a number of impressive systems have been developed over the decades. However, some issues are still not well addressed, such as poor positioning accuracy and even tracking loss in low texture and dynamic environments. We propose a robust semantic visual SLAM for indoor low-texture and dynamic environments. It uses deep learning image feature point extraction method to replace the traditional feature point extraction method and uses case segmentation network to provide image semantic information combined with motion consistency check to eliminate the influence of dynamic objects. Finally, we verify the accuracy and robustness of the proposed system on the TUM RGB-D dataset. The results show that the proposed system has higher accuracy than ORB-SLAM2 and other semantic SLAM.
A hybrid significant wave height prediction model based on ensemble empirical mode decomposition and generative adversarial networks

Short-term prediction of ocean significant wave height is crucial for human maritime activities. However, historical wave height data possess characteristics such as high complexity and strong randomness. To address these issues, this paper proposes a hybrid model that combines Ensemble Empirical Mode Decomposition (EEMD) and Generative Adversarial Networks (GANs). The model first decomposes the raw wave height data using EEMD to select effective sub-series for reconstruction and implements denoising of the original data. Then, the reconstructed wave height sequence and relevant features are fed into a GAN-based prediction model. The generator in the model comprises of Long Short-Term Memory Network (LSTM) networks, while the discriminator consists of Convolutional Neural Networks (CNNs). Finally, the generator and discriminator are trained adversarially using North Pacific oceanographic data to achieve short-term predictions of ocean wave height. This proposed model is then compared with three baseline models, and the results show that our model performs the best in all three evaluation metrics, making it a valuable tool for ocean engineering.
Classification of skin cancer based on composite scaling and attention

A suitable neural network can greatly improve the diagnostic performance of skin diseases. Based on the Compound Scaling Method, EfficientNet [8] combined with attention mechanism can achieve this goal. This article investigates a deep learning model that is more suitable for classifying skin cancer datasets, as well as the effectiveness of attention mechanisms in deep neural architecture. EfficientNet [8] based on the Compound Scaling Method is a more suitable model for skin cancer dataset classification, and the attention mechanism can enhance the attention to important features on the basic classification model. We compared the classification performance of Alex, VGG, ResNet, and EfficientNet [8] models on the skin cancer dataset HAM10000[1]. Among the above four models, EfficientNet has better classification performance, achieving the highest accuracy of 91.65% on the HAM10000 dataset [1]. When EfficientNet [8] is combined with attention mechanism, performance is improved to a certain extent, achieving a precision of 92.98% on the HAM10000 dataset [1].
Fault identification method for substation power equipment based on infrared, visible light images, and YOLOv5 algorithm

For the issues of cable local heating, isolator ceramic disc breakage, lightning arrester and transformer local breakage among substation power equipment fault diagnosis, this study presented a YOLOv5 target detection approach based on deep convolutional neural network to identify and locate power equipment such as insulators, disconnect switch contacts, bushings, wire clips, lightning arresters and their abnormal heating areas. In the testing stage of the YOLOv5 process, the network divides the image in the database into 416*416 pixels. By using the Yolomark tool to annotate the image, the labels are fed into the deep learning convolutional neural network together with the sample set for training, and the ultimate module is generated after several iterations. Finally, the infrared and visible spectral materials of electronic facilities gathered by the operation and monitoring units with infrared thermographers are used for effect testing. Then the fault diagnosis and grading of various power equipment in substation are carried out. The detective findings are evaluated with the quicker regional convolutional neural network algorithm, YOLOv3 algorithm and SSD algorithm for comparison. Through the experimental analysis, the average accuracy of the model is 96.7%, which can achieve the fault diagnosis target of substation power equipment， and thus enhance the daily running, maintenance and management efficient of the power grid.
Food adulteration identification framework via unsupervised Anomaly Detection algorithm: applied to camel milk (FIAD）

Food adulteration driven by economic interests is an important cause of food safety. Camel milk is widely sought for its high nutritional and medicinal value; some businesses adulterate it for profiteering due to its low yield and high price. Traditional adulteration detection methods rely on supervised learning, which is limited by the data of unknown categories in practical application scenarios, and it is difficult to solve the adulteration problem of category imbalance. For the above scenario, this paper proposes a camel milk adulteration detection framework FIAD based on an unsupervised anomaly detection algorithm, which starts from the perspective of anomaly detection and automatically captures and isolates anomalous features in the data through a tree algorithm without manual labeling of data, directly targeting the adulteration identification problem of category imbalance. We tested the discrimination performance of FIAD in a batch of category-imbalanced camel milk adulteration datasets. At 10% and 20% category imbalance, FIAD achieved AUCs of 0.943 and 0.959 and Recall of 0.915 and 0.949, while occupying less memory, better than eight baseline models. The results show that FIAD has excellent comprehensive identification performance and provides a low-cost and high-efficiency identification method for camel milk adulteration identification.
An effective and efficient real-time facial expression recognition method in the wild

In human communication, besides direct verbal speech, facial expressions are also the main method to convey inner thoughts and emotions. By analyzing facial expressions, it is possible to obtain information about human inner emotions. Applying expression analysis algorithms in social robots can help robots accurately understand users' emotions and intentions, which in turn leads to better human-computer interaction.Therefore, in this paper, an effective & efficient expression recognition method is designed. The network uses the Ghost Module as the core module, and the lightweight attention module is used to increase the accuracy of expression recognition. In addition, the network is trained with a distributed label loss in order to solve the problems of insufficient data and long-tail of the facial expression dataset. Experiments prove that the network is superior, performs well on the RAF-DB dataset, with 86.8% accuracy without pretraining and has a fast processing speed. We also designed a real-time expression analysis system with this network as the backbone to simulate the actual working scenario of the robot, and found superior results to satisfy the functions of real-time human-computer interaction and efficient expression recognition.
Modified DeepLabV3+ algorithm for casting defect detection

The occurrence of internal defects is inevitable in the casting process. It affects the reliability and durability of products and even gives rise to potential economic losses and security issues. Therefore, it appears essential to identify and eliminate defects in time. Deep learning algorithms can acquire more effective feature representation in complex scenes. Hence, a lightweight modified DeepLabV3+ algorithm is proposed with the following improvements. First, the ImageNet pre-trained MobileNetV2 is adopted as the backbone network to enhance the model feature extraction and improve the detection speed. Second, the atrous spatial pyramid pooling (ASPP) module is constructed for multi-scale feature fusion using atrous separable convolutions with different dilation rates. Third, the decoder is modified to fuse feature maps of different levels. Moreover, efficient channel attention modules are introduced to obtain more effective features and thus improve the defect edge segmentation effect. Last, a hybrid loss function of Focal Loss and Lovaszsoftmax Loss is used to reduce the influence of sample imbalance on model performance. The experimental results show that compared with the classical algorithms, the modified algorithm has higher accuracy and efficiency in the X-ray casting defect segmentation dataset CDSXray with less computational complexity.
Model and theoretical seismicity

abstract A laboratory and a numerical model have been constructed to explore the role of friction along a fault as a factor in the earthquake mechanism. The laboratory model demonstrates that small shocks are necessary to the loading of potential energy into the focal structure; a large part, but not all, of the stored potential energy is later released in a major shock, at the end of a period of loading energy into the system. By the introduction of viscosity into the numerical model, aftershocks take place following a major shock. Both models have features which describe the statistics of shocks in the main sequence, the statistics of aftershocks and the energy-magnitude scale, among others.
A review of intelligent vehicle motion planning algorithms

With vehicles becoming a necessity for people's life and work, the vigorous development of intelligent vehicles is of great significance in today's society in order to reduce traffic accidents, lower operating costs and achieve environmentally friendly travel. Motion planning, as the functional core of intelligent driving systems, is crucial to the driving safety of intelligent vehicles. This paper introduces the development status of intelligent vehicles at home and abroad, studies the current research status of mainstream motion planning techniques, analyses the technical challenges encountered in motion planning for intelligent vehicles in urban roads, and illustrates the development goal of establishing efficient and reliable motion planning algorithms in complex scenarios.
New approach to testing treatments for osteoarthritis: FastOA

Animal models of post traumatic osteoarthritis have shown many promising treatments for disease, but human trials have mostly failed to identify effective treatments. This viewpoint suggests that the frequent failure of drug and treatment development in osteoarthritis is due, in part, to the advanced stage of disease of patients in trials and suggests that mirroring the animal model approach might be more successful. It suggests a path forward by enriching trial enrollees with those likely to develop post traumatic OA quickly.
Association between self-reported alcohol consumption and diastolic dysfunction: a cross-sectional study

Background and objectives While alcohol consumption is associated with common risk factors for diastolic dysfunction the independent impact of low levels of alcohol consumption on this condition in a community setting is still unclear. Thus, the aim of this study was to explore this association in a representative population sample employing optimal echocardiographic techniques. Design Cross-sectional observational study in community-based population. Settings, participants and methods Participants between 30 and 75 years of age were consecutively invited to a physical examination, interview, conventional echocardiography, including Tissue Velocity Imaging. Diastolic dysfunction was defined according to the European Society of Cardiology criteria, excluding subjects with ejection fraction <45%, self-reported history of heart failure or atrial fibrillation on ECG. Self-reported alcohol intake using a validated questionnaire was categorised as no intake, low and medium-high intake. Results In total, 500 men and 538 women (mean age 55.4±13) were successfully examined. Diastolic dysfunction was identified in 16% (79/500) of the men and 13% (58/538) of the women. The multivariable adjusted model revealed a strong and independent association between alcohol intake and diastolic dysfunction. In fact, using no alcohol intake as reference, diastolic dysfunction was independently associated with alcohol consumption in a dose-dependent fashion; low consumption, OR 2.3 (95% CI 1.3 to 4.0) and medium-high consumption OR 3.1 (95% CI 1.6 to 6.2), respectively. Conclusion There was a significant association between alcohol consumption and diastolic dysfunction starting already at low levels that was supported by a dose-dependent pattern. These results need confirmatory studies and are important in public health policies.
Performance of predictive algorithms in estimating the risk of being a zero-dose child in India, Mali and Nigeria

Introduction Many children in low-income and middle-income countries fail to receive any routine vaccinations. There is little evidence on how to effectively and efficiently identify and target such ‘zero-dose’ (ZD) children. Methods We examined how well predictive algorithms can characterise a child’s risk of being ZD based on predictor variables that are available in routine administrative data. We applied supervised learning algorithms with three increasingly rich sets of predictors and multiple years of data from India, Mali and Nigeria. We assessed performance based on specificity, sensitivity and the F1 Score and investigated feature importance. We also examined how performance decays when the model is trained on older data. For data from India in 2015, we further compared the inclusion and exclusion errors of the algorithmic approach with a simple geographical targeting approach based on district full-immunisation coverage. Results Cost-sensitive Ridge classification correctly classifies most ZD children as being at high risk in most country-years (high specificity). Performance did not meaningfully increase when predictors were added beyond an initial sparse set of seven variables. Region and measures of contact with the health system (antenatal care and birth in a facility) had the highest feature importance. Model performance decreased in the time between the data on which the model was trained and the data to which it was applied (test data). The exclusion error of the algorithmic approach was about 9.1% lower than the exclusion error of the geographical approach. Furthermore, the algorithmic approach was able to detect ZD children across 176 more areas as compared with the geographical rule, for the same number of children targeted. Interpretation Predictive algorithms applied to existing data can effectively identify ZD children and could be deployed at low cost to target interventions to reduce ZD prevalence and inequities in vaccination coverage.