Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic Identification of Failure in Hip Replacement: An Artificial Intelligence Approach

Background: Total hip arthroplasty (THA) follow-up is conventionally conducted with serial X-ray imaging in order to ensure the early identification of implant failure. The purpose of this study is to develop an automated radiographic failure detection system. Methods: 630 patients with THA were included in the study, two thirds of which needed total or partial revision for prosthetic loosening. The analysis is based on one antero-posterior and one lateral radiographic view obtained from each patient during routine post-surgery follow-up. After pre-processing for proper standardization, images were analyzed through a convolutional neural network (the DenseNet169 network), aiming to predict prosthesis failure. The entire dataset was divided in three subsets: training, validation, and test. These contained transfer learning and fine-tuning algorithms, based on the training dataset, and were implemented to adapt the DenseNet169 network to the specific data and clinical problem. Results: After the training procedures, in the test set, the classification accuracy was 0.97, the sensitivity 0.97, the specificity 0.97, and the ROC AUC was 0.99. Only five images were incorrectly classified. Seventy-four images were classified as failed, and eighty as non-failed with a probability >0.999. Conclusion: The proposed deep learning procedure can detect the loosening of the hip prosthesis with a very high degree of precision.
Fowl Aviadenovirus E associated with hepatitis-hydropericardium syndrome in broiler breeders

ABSTRACT: Fowl aviadenovirus (FAdV) is an important pathogen in the global poultry industry and the etiology of inclusion body hepatitis-hydropericardium syndrome (HHS) in chickens. Since the 1990s, several outbreaks of HHS have occurred in poultry producing areas, including South America. The coinfection of FAdV and chicken anemia virus (CAV) may markedly impact the incidence of HHS. This study describes an outbreak of HHS in coinfection with CAV in industrial broiler breeders and characterizes the FAdV isolate. The three-week-old male broiler breeders had pale bone marrow, enlarged and yellowish liver, splenomegaly, and atrophied thymus; one chicken was also found with hydropericardium. Virus isolation was performed in SPF chicken embryos of liver and thymus. Tissues of the naturally infected chickens and the inoculated embryos were evaluated by PCR and histopathology. All affected chickens and inoculated embryos were positive for FAdV and CAV. The inoculated embryos had enlarged, greenish and hemorrhagic livers, and 30% died within 7 days of inoculation. Phylogenetic analysis of the FAdV isolate hexon gene partial sequence enabled grouping with E species. The E species has recently become a relevant species in several countries. The association of FAdV with CAV in breeders is of further concern due to both being capable of vertical transmission. Within the last decade, a worldwide upsurge of HHS in broiler breeders owing to failed biosecurity has occurred. In this episode, the failure on biosecurity may have enabled challenge with both FAdV and CAV, with pathological synergism. The CAV-impaired adaptive immunity may have benefited the FAdV infection.
Methods for segmentation of spinal cord and esophagus in radiotherapy planning computed tomography

Organs at Risk (OARs) are healthy tissues around cancer that must be preserved in radiotherapy (RT). The spinal cord and esophagus are crucial OARs. In this work, we proposed methods for the segmentation of these OARs from the CT using image processing techniques and deep convolutional neural network (CNN). For spinal cord segmentation, two methods are proposed, the first using techniques such as template matching, superpixel, and CNN. The second method, use adaptive template matching and CNN. In the esophagus segmentation, we proposed a method composed of registration techniques, atlas, pre-processing, U-Net, and post-processing. The methods were applied to 36 planning CT images provided by The Cancer Imaging Archive. The first method for spinal cord segmentation obtained 78.20% Dice. The second method for spinal cord segmentation obtained 81.69% Dice. The esophagus segmentation method obtained an accuracy of 82.15% Dice.
Impacts of Perceived Justice and Emotional Intelligence on Organizational Commitment and Organizational Citizenship Behavior

How to motivate employees to commit to the organization and to have more voluntary, beneficial behaviors for the organization is an interesting topic for researchers and managers. Based on the theory of equity, authors analyze the impacts of perceived justice and emotional intelligence on employees’ organizational commitment and organizational citizenship behaviors. The mixed research method is used to build a research model and to verify hypotheses with the data of 171 respondents working in different industries which deal with customers in HCM city. The partial least squares structural equation modeling (PLS-SEM) is used in order to explore the impacts of the two high order construct: Emotion intelligence and perceived justice on the organizational commitment, then onto organizational citizen behavior (OCB). The research results pointed out that all hypotheses are accepted. Perceived justice has greater impacts on employees’ organizational commitment than emotional intelligence. In contrast, emotional intelligence has greater influences on employees’ OCB than perceived justice. Managers should establish clear working process, offer opportunities for employees to express their feelings and recognize exactly employees’ contributions in order to improve the employees’ perception of fairness. Selecting carefully candidates and providing training programs are best ways to increase employees’ emotional intelligence.
Can machine understand public administration literature? Applying text mining for systematic review

Systematic reviews summarize the progress of studies and pave roads for future research in an academic field. However, conducting a systematic literature review can be burdensome and time-consuming. Computer-assisted methods such as text mining techniques have been increasingly applied to improve systematic reviews in public administration. To test the reliability of using text mining for systematic literature reviews, this study uses clustering, topic modeling, automatic multi-term extraction, and text network to systematically review articles published in Chinese Public Administration Review from 2002 to 2019. By comparing machine-produced topics with existing human-coded themes, findings show that applying text mining methods for systematic reviews can be reliable and effective with cautions. The study also offers practical suggestions for researchers to apply text mining methods for systematic literature reviews.
A review on threat assessment, path planning and path tracking strategies for collision avoidance systems of autonomous vehicles

The growth of automation sector has brought numerous autonomous technology developments in many sectors, especially automotive. The autonomous features are proven to be helpful in reducing road fatalities globally. Advanced Driver Assistance Systems (ADAS), a system which helps the driving process automation, has growing roles in recent road vehicle features. It is the base for the development of a fully autonomous vehicle. One of the main features of ADAS is Collision Avoidance (CA) system. A sufficient CA architecture usually encompasses threat assessment, path planning and path tracking strategies. There are many ways of developing precise CA architecture using the combination of these strategies. This paper aims to review current available methods for CA as an introductory idea for researchers who are new to this field. Each of the methods in each strategy is categorised into several groups. Their advantages and drawbacks are discussed. In addition to that, several improvement suggestions for a comprehensive CA system were highlighted.
Definition of Damage Parameter in Low-Cycle Fatigue of Gray Cast Iron

Gray cast iron shows large asymmetrical features by the graphite flake when tensile and compressive stresses are applied. The plastic strain rage which is used in low-cycle fatigue life prediction by many researchers is hardly defined and gives very different values by the Standards in this case. From the results of this study, it is not reliable to use the plastic strain range as a low-cycle fatigue damage parameter. Therefore, the plastic strain energy density which is uniquely defined was suggested as a damage parameter and it showed good correlation in low-cycle fatigue in gray cast iron.
Methionine Promotes the Growth and Yield of Wheat under Water Deficit Conditions by Regulating the Antioxidant Enzymes, Reactive Oxygen Species, and Ions

The individual application of pure and active compounds such as methionine may help to address water scarcity issues without compromising the yield of wheat. As organic plant growth stimulants, amino acids are popularly used to promote the productivity of crops. However, the influence of the exogenous application of methionine in wheat remains elusive. The present investigation was planned in order to understand the impact of methionine in wheat under drought stress. Two wheat genotypes were allowed to grow with 100% field capacity (FC) up to the three-leaf stage. Twenty-five-day-old seedlings of two wheat genotypes, Galaxy-13 and Johar-16, were subjected to 40% FC, denoted as water deficit-stress (D), along with 100% FC, called control (C), with and without L-methionine (Met; 4 mM) foliar treatment. Water deficit significantly reduced shoot length, shoot fresh and dry weights, seed yield, photosynthetic, gas exchange attributes except for transpiration rate (E), and shoot mineral ions (potassium, calcium, and phosphorus) in both genotypes. A significant increase was recorded in superoxide dismutase (SOD), catalase (CAT), hydrogen peroxide (H2O2), malondialdehyde (MDA), and sodium ions (Na+) due to water deficiency. However, foliar application of Met substantially improved the studied growth, photosynthetic, and gas exchange attributes with water deficit conditions in both genotypes. The activities of SOD, POD, and CAT were further enhanced under stress with Met application. Met improved potassium (K), calcium (Ca2+), and phosphorus (P) content. In a nutshell, the foliar application of Met effectively amended water deficit stress tolerance by reducing MDA and H2O2 content under water deficit conditions in wheat plants. Thus, we are able to deduce a positive association between Met-induced improved growth attributes and drought tolerance.
HE-DFNETS: A Novel Hybrid Deep Learning Architecture for the Prediction of Potential Fishing Zone Areas in Indian Ocean Using Remote Sensing Images

The Indian subcontinent is known for its larger coastline spanning, over 8100 km and is considered the habitat for many millions of people. The livelihood of their habitat is purely dependent upon the fishing activities. Often, the search for fish requires more time for catching and more resources, thus increasing the operational cost leading to low profitability. With the advent of artificial intelligence algorithms, designing intelligent algorithms for an effective prediction of fishing areas has reached new heights in terms of high accuracy (Acy) and less time. But still, predicting the location of potential fishing zones (PFZs) is always a daunting task. To reduce these issues, this work presented the novel hybrid prediction architecture of PFZs using remote sensing images. The proposed architecture integrates the deep convolutional layers and flitter bat optimized long short-term memory (FB-LSTM)-based recurrent neural networks (RNN). These convolutional layers are utilized to remove the various color features such as chlorophyll, sea surface temperature (SST), and GPS location from the satellite images, and FB-LTSM is utilized to predict the potential locations for fishing. The extensive experimentations are carried out utilizing the satellite data from Indian National Centre for Ocean Information Services (INCOIS) and implemented using TensorFlow 1.18 with Keras API. The performance metrics such as prediction Acy, precision (Pscn), recall (Rcl) or sensitivity (Sty), specificity (Sfy), and F1-score and compared with other existing intelligent learning models. From our observations, the proposed architecture (99% prediction Acy) has outperformed the other existing algorithms and finds its best place in designing an intelligent system for better predicting of PFZs.
Behavior Pattern Mining from Traffic and Its Application to Network Anomaly Detection

Accurately detecting and identifying abnormal behaviors on the Internet are a challenging task. In this work, an anomaly detection scheme is proposed that employs the behavior attribute matrix and adjacency matrix to characterize user behavior patterns. Then, anomaly detection is conducted by analyzing the residual matrix. By analyzing network traffic and anomaly characteristics, we construct the behavior attribute matrix, which incorporates seven features that characterize user behavior patterns. To include the effects of network environment, we employ the similarity between IP addresses to form the adjacency matrix. Further, we employ CUR matrix decomposition to mine the changing trends of the matrices and obtain the residual pattern characteristics that are used to detect anomalies. To validate the effectiveness and accuracy of the proposed scheme, two datasets are used: (1) the public MAWI dataset, collected from the WIDE backbone network, which is used to validate accuracy; (2) the campus network dataset, collected from the northwest center of Chinese Education and Research Network (CERNET), which is used to verify practicability. The experimental results demonstrate that the proposed scheme can not only accurately detect and identify abnormal behaviors but also trace the source of anomalies.
Specifying, Analyzing, Integrating Mobile Apps and Location Sensors as part of Cyber-Physical Systems in the Classroom Environment

Cyber-Physical Systems (CPS) are characterized as complex systems usually networked, composed of several heterogeneous components that make the connection between events in the physical environment with computation. We can observe that this kind of systems is increasingly used in different areas such as automotive facilities, construction (civil engineering), health care and energy industry, providing a service or activity which depends on the interaction with users and the physical environment in which they are installed. Nowadays, in the educational context, the process of control and monitor of evaluation activities is conducted in a non-automated way by lecturers. This control is performed before, during and after the beginning of the evaluation activity, and include logistical processes such as classroom reservation, distribution of students per classroom, attendance record or fraud control. However, in an environment involving a large number of students, the execution of these tasks becomes difficult to perform efficiently and safely, requiring innovative techniques or assistance tools. In this work, the creation/design of a cyber-physical system through a modeling approach is proposed, aiming to help teachers to control and monitor evaluation activities. Based on a systematic literature study, we claim that there are no studies presenting the modeling of cyber-physical systems in an educational context, enhancing the interest of the proposed case study. In this document, we show how we used a framework named ModelicaML to model this system during the design phase. Also, this framework will offer a simulation component to simulate the behavior of the prescribed system. On the side of the hardware architecture, for the purpose of identifying the valid seats for the specific students inclass during the examination period, an indoor location system will be used, allowing to blueprint the physical layout of the room and globally manage the activity workflow. We finish this work by showing with empirical studies the gains of our solution when compared to the traditional method.
Carga de trabalho de enfermagem em uma unidade de internação

O objetivo deste estudo foi avaliar a carga de trabalho de enfermagem em uma unidade de internacao de um hospital de ensino. Trata-se de estudo descritivo, com a aplicacao do Nursing Activities Score (NAS), por 30 dias consecutivos. Foram 1.080 observacoes obtidas do registro de 156 pacientes, e a media do NAS foi de 47,31%. Considerando que cada ponto do NAS corresponde a 0,24h, foram requeridas, em media, 11,35 horas de enfermagem na assistencia em 24 horas, o que corresponde a um perfil de pacientes que requer cuidados semi-intensivos e intensivos, sugerindo que o NAS pode ser utilizado para avaliar a carga de trabalho de enfermagem nessa unidade.
Finite element characterisation of plasmonic waveguides in terahertz and optical frequencies

In recent years plasmonic devices have become an interesting area of research due to the sub-wavelength confinement and propagation of radiation, allowing the design of very compact structures. Compact structures are necessary to make smaller integrated optical circuits. Due to the use of metals, plasmonic guides usually show more losses compared to the conventional dielectric guides. Therefore, plasmonic waveguides are not normally used for long distance transmission. However, they are promising for inter-chip or intra-chip communication and also have seen a lot of sensor applications. There has been considerable interest in exploiting the frequency bands in the terahertz regime to open up new frontiers of research across a diverse range of applications. An array of opportunities for creating novel technologies using this frequency band had remained largely unexplored and undeveloped for a considerable period of time due to the lack of suitable sources, as well as lack of guiding and detecting devices. This thesis describes the design, analysis and optimisation of plasmonic devices in optical and terahertz frequencies. A fully vectorial H-field based finite element method has been used in the research reported in this thesis to reveal the modal characteristics of different plasmonic structures. A six layer planar contra-directional nano-coupler has been analysed at optical frequency. Three different modes of propagation were considered to study the characteristics of different properties of the structure, including the coupling length. A design approach has been proposed to make the coupler low loss as well as smaller in length. For the terahertz plasmonics, a rectangular metallic hollow core guide was considered at terahertz frequency. Several modes were considered for the modal analysis of the structure. Modal analysis was performed by changing metal, introducing different dielectric coating in the hollow core, changing the thickness of the metal and dielectric layers and changing the dimensions of the guide. A dispersion analysis was also performed. The criteria for designing very low loss, compact and low dispersion guide have been presented for the structure at the end of the study.
Model Recurent Neural Network untuk Peramalan Produksi Tebu Nasional

Abstrak: Produksi tebu di Indonesia tersebar di beberapa wilayah yang mengakibatkan variabilitas yang tinggi dari variabel-variabel yang mempengaruhi produksi tebu nasional. Di samping itu, tidak mudah untuk mendapatkan data-data tersebut dalam waktu yang cukup panjang. Oleh karena itu peramalan produksi tebu nasional berdasarkan variabel-variabel tersebut sangat sulit dilakukan. Sebagai solusi dari masalah tersebut, maka peramalan produksi tebu nasional dilakukan berdasarkan data historisnya. Penelitian ini bertujuan untuk mengembangkan model recurrent neural networks (RNN) untuk peramalan produksi tebu nasional berdasarkan data historisnya. Data yang digunakan adalah data produksi tebu nasional dari tahun 1967 sampai dengan tahun 2019 dalam satuan ton. Sebagai data latih digunakan data tahun 1967 sampai dengan tahun 2006 dan sisanya dipakai sebagai data uji. Pada penelitian ini dilakukan percobaan untuk mengetahui pengaruh panjang deret waktu dan ukuran batch terhadap kinerja model RNN dengan tiga ulangan. Hasil penelitian menunjukkan bahwa model RNN dengan panjang deret waktu 4 dan ukuran batch 16 menghasilkan nilai mean absolut percentage error (MAPE) sebesar 9.0% dengan nilai korelasi 0.77. Secara umum, model RNN yang dibangun mampu menangkap pola produksi tebu nasional dengan tingkat kesalahan yang masih dapat ditoleransi. Kata kunci: deret waktu, peramalan, produksi tebu, recurrent neural networks Abstract: Sugarcane production in Indonesia is spread over several regions. This condition results in high variability of the variables that affect national sugarcane production. In addition, it is not easy to obtain these data over a long period. As a result, it is very difficult to forecast the production of national sugarcane based on the influencing variables. Therefore, the forecasting was based on historical data of the national sugarcane production. This study aims to develop a recurrent neural networks (RNN) model for forecasting national sugarcane production based on historical data. The data used is national sugarcane production data from 1967 to 2019 in tons. As training data, data from 1967 to 2006 were used and the rest was used as test data. In this study, an experiment was conducted to determine the effect of time series length and batch size on the performance of the RNN model with three replications. The results showed that the RNN model with a time series length of 4 and a batch size of 16 produced a mean absolute percentage error (MAPE) of 9.0% with a correlation value of 0.77. In general, the RNN model is able to capture the national sugarcane production pattern with a tolerable error rate. Keywords: forecasting, recurrent neural networks, sugarcane production, time series
A Voxel-Based Method for Automated Detection and Mapping of Light Poles on Rural Highways using LiDAR Data

The number of light poles and their position (in terms of density and offset off the roadside) have significant impacts on the safe operation of highways. In current practice, inventory of such information is performed in periodic site visits, which are tedious and time consuming. This makes inventory and health monitoring of poles at a network level extremely challenging. To relieve the burden associated with manual inventory of poles, this paper proposes a novel algorithm which can automatically obtain such information from remotely sensing data. The proposed algorithm works by first tiling point cloud data collected using light detection and ranging (LiDAR) technology into manageable data tiles of fixed dimensions. The data are voxelized and attributes for each data voxel are calculated to classify them into ground and nonground points. Connected components labeling is then used to perform 3D clustering of the data voxels. Further clustering is performed using a density-based clustering to combine connected components of the same object. The final step involves classifying different objects into poles and non-poles based on a set of decision rules related to the geometric properties of the clusters. The proposed algorithm was tested on a 4 km rural highway segment in Alberta, Canada, which had substantial variation in its vertical alignment. The algorithm was accurate in detecting nonground objects, including poles. Moreover, the results also highlight the importance of considering the length of the highway and its terrain when detecting nonground objects from LiDAR.