Miracle-dz/UniFAR-FTUs huggingface dataset:
ArticleWearable Bluetooth Triage Healthcare Monitoring System Caitlin Polley 1*

Triage is the first interaction between a patient and a nurse/paramedic. This assessment, usually performed at Emergency departments, is a highly dynamic process and there are international grading systems that according to the patient condition initiate the patient journey. Triage requires an initial rapid assessment followed by routine checks of the patients’ vitals, including respiratory rate, temperature, and pulse rate. Ideally, these checks should be performed continuously and remotely to reduce the workload on triage nurses; optimizing tools and monitoring systems can be introduced and include a wearable patient monitoring system that is not at the expense of the patient’s comfort and can be remotely monitored through wireless connectivity. In this study, we assessed the suitability of a small ceramic piezoelectric disk submerged in a skin-safe silicone dome that enhances contact with skin, to detect wirelessly both respiration and cardiac events at several positions on the human body. For the purposes of this evaluation, we fitted the sensor with a respiratory belt as well as a single lead ECG, all acquired simultaneously. To complete Triage parameter collection, we also included a medical-grade contact thermometer. Performances of cardiac and respiratory events detection were assessed. The instantaneous heart and respiratory rates provided by the proposed sensor, the ECG and the respiratory belt were compared via statistical analyses. In all considered sensor positions, very high performances were achieved for the detection of both cardiac and respiratory events, except for the wrist, which provided lower performances for respiratory rates. These promising yet preliminary results suggest the proposed wireless sensor could be used as a wearable, hands-free monitoring device for triage assessment within emergency departments. Further tests are foreseen to assess sensor performances in real operating environ-
Welfare problems in stall-housing of gestating sows

be inadequate. Nevertheless, most of the commercially available stalls provide only the minimum space required for average sized sows and have similar designs and measurements. Many of the studies dealing with the welfare implications of the stall system have made direct and indirect comparisons of confined housing with non-confined systems, and the differences are obvious. However, what is equally important is to know how the welfare status of the sow in a stall changes due to changes in body size, weight, and other physiological changes associated with
Assessment of Major Reproductive Health Problems of Dairy Cows in Dairy Farms of Wolaita Sodo District, Southern Ethiopia

A cross-sectional questionnaire-based study was conducted in 103 dairy farms to identify the major reproductive health problems of dairy cattle in Wolaita Sodo district. The dairy farm owners were interviewed with their own languages and data on dairy reproductive performances, particularly the history of reproductive health problems, were collected. The current finding revealed that the most frequently recorded reproductive health problems were repeat breeding (85.55%), retained fetal membrane (23.3%), anestrous (17.47%), dystocia (12.6%), abortion (10.67%), uterine (6.79%) and vaginal prolapse (3.88%), and pyometra (1.94%). Overall, 55% (57/103) of the farm owners responded that their dairy cows were affected by either one or more of the above reproductive health disorders. In the present study, there was a significant association (p< 0.05) between the educational levels of dairy farm owners and the feeding frequency as well as the health management system of dairy farms. Whereas there was no significant association (p> 0.05) between work experience and farm management practice as well as health management. Thus, repeat breeding was the most common reproductive health problem recorded in the study area that has an impact on the reproductive health of dairy cows. Further studies should be conducted at different blood levels, and awareness is given to farm owners and attendants to improve dairy management with increased parity.
Algorithm for Estimating Natural Frequencies and Mode Shapes of Cantilever MEMS Microbeams

The use in the past of mathematical techniques and methodologies to design macro-world structures in the past are now considered for designing micro-world structures. In this work, a computational algorithm for estimating natural frequencies and mode shapes of cantilever microbeams is presented. The methodology is based on the Rayleigh-Ritz method, which considers the potential and kinetic energies of the mechanical system. A proposed polynomial equation is first considered to establish the behavior of the microstructure. Together with the energy equations, an eigenvalue problem is constructed. The corresponding eigenvalues give an approximate estimation of the frequency values; the corresponding eigenvectors once substituted in the proposed polynomial describe the mode shapes of the microbeam for each frequency value. The results obtained with this methodology were compared with the finite element method. An interesting difference between the methodologies and the algorithm was found and the corresponding results are shown.
Eco-Certification as a Tool of Sustainable Tourism: A Preliminary Approach

As tourism is the fastest growing sectors of the global economy, environmental quality acquires a decisive role in sustainable tourism development. Tourism, like any activity, has environmental impacts that directly or indirectly affect and alter the resources that make up the tourism product. Adoption of the principles of sustainable development is essential in tourism since the protection and safeguarding of environmental quality is a prerequisite for the development and maintenance of tourism activity. Environmental management systems and ecolabel scan help business to minimize their environmental impact and furthermore to set a communication channel between actors. Also, may inform customers of new green features of tourism services, on in a visual way. The paper highlights the role of environmental certification in protecting and upgrading tourism product.
Simulation modelling of material handling using AGV

Modern production systems are mainly characterized by, among others, the use of automated transport systems. One of the components of such transport systems are automated guided vehicles (AGV). These vehicles are primarily used in the material handling due to their greater efficiency, flexibility and lower operating costs. The main aim of the article is to present the possibility of using simulation modelling in the analysis of internal transport operations using automated guided vehicles.
Automated Classification of High-resolution Rock Image Based on Residual Neural Network

The identification and classification of high-resolution rock images are significant for oil and gas exploration. In recent years, deep learning has been applied in various fields and achieved satisfactory results. This paper presents a rock classification method based on deep learning. Firstly, the high-resolution rock images are randomly divided into several small images as a training set. According to the characteristics of the datasets, the ResNet (Residual Neural Network) is optimized and trained. The local images obtained by random segmentation are predicted by using the model obtained by training. Finally, all probability values corresponding to each category of the local image are combined for statistics and voting. The maximum probability value and the corresponding category are taken as the final classification result of the classified image. Experimental results show that the classification accuracy of this method is 99.6%, which proves the algorithm’s effectiveness in high-resolution rock images classification.
Elastic modulus of defected graphene sheets

In this paper, the elastic modulus of single-layered graphene sheets (SLGSs) with and without defects is investigated using the finite element method. The SLGSs with two chiralities (armchair and zigzag) are modeled by beam elements. At first, the SLGSs without defects are investigated then the carbon atoms and corresponding beam elements are removed and the elastic modulus of SLGSs is determined. The increasing number of defects apparently decreased the elastic modulus of graphene sheets.
Practical Time Optimization of DSP Program for High-frequency Applications

The rapid development of the high frequency power conversion techniques makes great demands on the methods that can reduce the execution time of the program effectively. This paper is aiming at reducing the execution time of the program in several aspects such as sampling, complex expressions, and so on. As one of the most widely applied methods, reducing the execution time of the program at the cost of the memory space is adopted in this paper. Furthermore, in order to confirm the feasibility and superiority of programs that are proposed in this paper, they are compared with other programs that can realize the same function in terms of the execution time.
Optimization of the Layout and Control Strategy for Parallel Through-the-Road Hybrid Electric Vehicles

This paper describes the optimization of the layout and of the control strategy of through-the-road (TTR) parallel hybrid electric vehicles equipped with two compression-ignition engines that feature different values of maximum output power. First, a tool has been developed to define the optimal layout of each TTR vehicle. This is based on the minimization of the powertrain and fuel cost over a 10-year time span, taking into account the fuel consumption. Several performance requirements are guaranteed during the optimization, namely maximum vehicle velocity, 0-100 km/h acceleration time, gradeability and the all-electric range. A benchmark optimizer that is based on the dynamic programming theory has been developed to identify the optimal working mode and the gear number, which are the control variables of the problem. A mathematical technique, based on the pre-processing of a configuration matrix, has been developed in order to speed up the calculation time. After the layout optimization, the potential of the two identified hybrid vehicles in improving the fuel economy, compared with the conventional vehicle, has been analyzed and discussed over several driving missions, i.e., the New European Driving Cycle, the Artemis Urban Driving Cycle, the Artemis Rural Driving Cycle, the Artemis Motorway Driving Cycle and the Federal Test Procedure. The contributions related to the vehicle electrification and to the control strategy were identified separately. Finally, a real-time optimizer has also been developed, which is based on the instantaneous maximization of an equivalent powertrain efficiency.
Multi-input Convolutional Neural Network Fault Diagnosis Algorithm Based on the Hydraulic Pump

Convolutional neural network used in fault diagnosis can effectively extract fault features in vibration signals. However, in the feature extraction of mechanical fault diagnosis, usually more than two feature signals including at least axial and radial vibration signals can be extracted. This paper proposes two multi-input convolutional neural network models based on the fault data of the aircraft hydraulic pump including axial and radial vibration. The first is the Independent Input Multi-input Convolutional Neural Network model. The two inputs are respectively used for convolution pooling operation with CNN, and are combined through the concatenate function before the fully connected layer, and then all frames are integrated and flattened by the flatten function. A one-dimensional array, finally enters the fully connected layer and outputs the result through the softmax function. The second is the Combined Input Multiinput Convolutional Neural Network, that is, combine two one-dimensional signals into a twodimensional signal in the input layer of the convolutional neural network and then perform convolution pooling, and finally output the result through the softmax function. The results show that the two models have good accuracy and stability, and the second one has a higher convergence and fitting efficiency than the first one.
A Deep Learning Based Automatic Interference Avoidance Resource Allocation Scheme for SCMA Systems

Sparse code multiple access (SCMA) is able to provide high spectral efficiency and massive connectivity, hence it is considered as a promising scheme for the fifth generation (5G) systems. This paper proposed a radio resource allocation scheme based on deep learning for SCMA systems, with the aim to automatically avoid the inter-cell interference. A long short term memory (LSTM) network is adopted to learn the past interference characteristics and predict the interference power in the current subframe. Radio resource blocks with less predicted interference power are then selected for users to transmit signals. Simulation results show that the proposed scheme outperforms the moving average prediction method and has significant gains over the random radio resource block allocation in terms of achievable bit error rate in SCMA systems.
Knowledge Graph Embeddings or Bias Graph Embeddings? A Study of Bias in Link Prediction Models

Link Prediction aims at tackling Knowledge Graph incompleteness by inferring new facts based on the existing, already known ones. Nowadays most Link Prediction systems rely on Machine Learning and Deep Learning approaches; this results in inherent opaque models in which assessing the robustness to data biases is not trivial. We define 3 specific types of Sample Selection Bias and estimate their presence in the 5 best-established Link Prediction datasets. We then verify how these biases affect the behaviour of 9 systems representative for every major family of Link Prediction models. We find that these models do indeed learn and incorporate each of the presented biases, with a heavily negative effect on their behaviour. We thus advocate for the creation of novel more robust datasets and of more effective evaluation practices.
A conceptualisation of process mining impact

As the process mining domain continues to see growing interest among various industry sectors globally, it is expedient to study its impact in organisations. Currently, no models or frameworks exist specifically to assess the impact of process mining. Although generic IT/ IS impact models or frameworks could be adopted, they are unlikely to cater to the contextual nuances particular to the process mining environment. This study aims to provide a clear conceptualised understanding of process mining impact and its success factors and contextual elements. The extent to which these contextual factors contribute to Process Mining impact will also be ascertained. Applying a multi-phased qualitative analysis approach, secondary data from published case studies and empirical data from real-life case studies will be analysed to derive an empirically validated process mining impact model. Current research progress includes a structured literature review on Success literature and building an a priori model from the analysis of secondary case studies.
A smart computer mouse with biometric sensors for unobtrusive office work-related stress monitoring

Health disorders related to the prolonged exposure to stress are very common among office workers. The need for an automated and unobtrusive method of detecting and monitoring occupational stress is imperative and intensifies in the current conditions, where the pandemic COVID-19 causes changes in the working norms globally. In this study, we present a smart computer mouse with biometric sensors integrated in such a way that its structure and functionality remain unaffected. Photoplethysmography (PPG) signal is collected from user’s thumb by a PPG sensor placed on the side wall of the mouse, while galvanic skin response (GSR) is measured from the palm through two electrodes placed on the top surface of the mouse. Biosignals are processed by a microcontroller and can be transferred wirelessly over Wi-Fi connection. Both the sensors and the microcontroller have been placed inside the mouse, enabling its plug and play use, without any additional equipment. The proposed module has been developed as part of a system that infers about the stress levels of office workers, based on their interactions with the computer and its peripheral devices.
1 Freshwater in the Arctic Ocean 2010-2019

The Arctic climate system is rapidly transitioning into a new regime with a reduction in the extent of sea ice, enhanced mixing in the ocean and atmosphere, and thus enhanced coupling within the ocean-ice-atmosphere system; these physical changes are leading to ecosystem changes in the Arctic Ocean. In this review paper, we assess one of the critically important aspects of this new regime, the variability of Arctic freshwater, which plays a fundamental role in the Arctic climate 20 system by impacting ocean stratification and sea ice formation. Liquid and solid freshwater exports also affect the global climate system, notably by impacting the global ocean overturning circulation. We assess how this budget has changed relative to the 2000-2010 period. We include discussions of processes not included in all previous assessments, such as runoff from the Greenland Ice Sheet, the role of snow on sea ice, and vertical redistribution. Notably, the sea ice cover has become more seasonal and more mobile, the mass loss of the Greenland Ice Sheet has increased in the 2010s (particularly in the west, north, 25 and south regions), and imported warm, salty Atlantic waters has shoaled. We show that the trend in Arctic freshwater content in the 2010s has stabilized relative to the 2000s, potentially due to an increased compensation between a freshening of the Beaufort Gyre and a reduction in freshwater in the rest of the Arctic Ocean. However, large inter-model spread across the ocean reanalyses and uncertainty in the observations used in this study prevent a definitive conclusion about the degree of this compensation. 30 1 Freshwater in the Arctic Ocean Rapid changes in the Arctic climate system are impacting marine resources and industries, coastal Arctic environments, and large-scale ocean and atmosphere circulations. The Arctic climate system is rapidly transitioning into a new regime with a reduction in the extent of sea ice (Stroeve and Notz, 2018), a thinning of the ice cover (Kwok, 2018), a warming and freshening Deleted: In this review paper w 35 Deleted: assess to what extent observations during the 2010-2019 period are sufficient to estimate the Arctic freshwater budget with greater certainty than previous assessments and Deleted: Deleted: 40 Deleted: Amerasian and Eurasian basins Deleted: Notably, the sea ice cover has become more seasonal and more mobile, the mass loss of the Greenland ice sheet has shifted from the western to the eastern part, and the import of subpolar waters into the Arctic has increased. 45
Public Goods Referenda without Perfectly Correlated Prices and Quantities

This paper examines the incentive properties of probabilistic referenda. In contrast to earlier research in which prices and quantities are perfectly correlated, we consider uncertain and potentially different outcomes for prices and quantities. We provide a theoretical analysis on incentive compatibility and an induced-value experimental test of this theory to gain new insights. First, using a standard design, our results confirm previous findings. Second, our results suggest that moving away from a perfectly correlated design undermines the incentive compatibility result found in other studies. Third, our experimental results are consistent with choices made by risk-averse agents in our theoretical analysis. Our findings would be important for survey design in practice as well as theoretical aspect of CV referenda.
Analysis of Liver Fibrosis Degree with APRI Score and FIB-4 Index on Patients with Non-Alcoholic Fatty Liver Disease

Liver fibrosis is the accumulation of Extracellular Matrix Protein (EMP) scar tissue after acute or chronic liver injury. Liver biopsy is the gold standard test for evaluating liver fibrosis in patients with Non-Alcoholic Fatty Liver Disease (NAFLD). This diagnostic method is invasive, painful, and complicated in rare cases; thus the noninvasive methods such as laboratory tests and radiology had been proposed to assess liver fibrosis in NAFLD and are expected to overcome the limitations of liver biopsy. This method consists of aspartate Aminotransferase to the Platelet Ratio Index (APRI) and fibrosis-4 (FIB-4). This research was retrospective cross-sectional performed at Dr. Wahidin Sudirohusodo Hospital Makassar involving 63 subjects from January to June 2018. The correlated variables were presented in categorical and numerical data and the Pearson correlation test was used to determine the compatibility of APRI score and FIB-4 index with Fibroscan result in patients with NAFLD. A highly significant positive correlation (p <0.001) was found between APRI score and Fibroscan (correlation value = 45.8%), APRI score and FIB-4 index (correlation value = 91.8%), FIB-4 index and Fibroscan (correlation value = 47.6%). The score of Aminotransferase to platelet ratio index score and FIB-4 index could be an alternative method instead of a liver biopsy to predict the degree of fibrosis in patients with NAFLD.
Evaluation of Potential and In-Vitro Antioxidant Activity of Mangrove Leaves Avicennia marina Ethanolic Extract

Mangroves thrive under stressful and extreme tropical environmental conditions such as high solar radiation, temperature, salinity, and anaerobic conditions that may have unfavourable effects on the photosynthesis of these plants. Mangrove is used in traditional medicine for the treatment of skin disorders, boils and wounds. The conventional methods are poor, timeconsuming as well as less systematic. The present study was carried out to A. marina and explores. This research may be useful for constituent (s)-based pharmacological activity. Plants are a rich wellspring of optional metabolites with intriguing organic exercises. The phytochemical analysis of A. marina reveals the presence of tannin, flavonoids, terpenoids, alkaloids, cardiac glycosides, and steroids. A. marina is a traditional medicinal plant and the leaves have tremendous medicinal values. In the current examination, ethanolic leaf concentrate of A. marina was analyzed using In-vitro antioxidant activity of Avicennia marina DPPH and H2O2 scavenging activity different concentration signification amount of Ascorbic acid compare each concentration with (Positive control).
Chinese Entity Recognition Based on BERT-BiLSTM-CRF Model

Chinese Entity Recognition Based on BERT-BiLSTM-CRF Model XIE Teng, YANG Jun-An, LIU Hui (College of Electronic Engineering, National University of Defense Technology, Hefei 230037, China) Abstract: Named Entity Recognition is a key technology in natural language processing, and the methods based on deep learning have been widely used in Chinese entity recognition. Most deep learning models focus on the feature extraction of words and characters, but ignore the semantic information of word context, therefore, they cannot represent polysemy, and the performance of entity recognition needs to be further improved. In order to solve this problem, this study proposes a method based on the BERT-BiLSTM-CRF model. First, word vectors based on context information are generated by the pretreatment of BERT model, and then the trained word vector is input into BiLSTM-CRF model for further training. The experimental result shows that the proposed model achieves sound results and reaches F1-score of 94.65% and 95.67% respectively in the MSRA corpus and People’s Daily.