Miracle-dz/UniFAR-FTUs huggingface dataset:
Calibration method of flow rate of the extracorporeal membrane oxygenation system based on a blood-mimicking fluid

To address the issue of lacking suitable measuring media for calibrating ultrasound flow sensors used in monitoring the blood pump flow within the Extracorporeal Membrane Oxygenation (ECMO) system, a blood-mimicking fluid was prepared. This fluid replicated the viscosity and acoustic characteristics of human blood under different shear rate conditions within the clinically relevant range of blood pump flow. It was achieved by combining glycerol-water mixtures of varying concentrations with macro-molecular organic substances and inorganic salts. The adjustment methods for viscosity and acoustic characteristics during the preparation of the blood-mimicking fluid were studied, allowing for fine adjustments of viscosity within the range of (3.6 ± 0.5) mPa•s and fine adjustments of sound velocity within the range of (1570 ± 30) m/s. The prepared blood-mimicking fluid was validated by comparing its flow measurement results with those obtained using blood as the measuring medium under the same flow rate setting, and both showed good consistency.
Crack detection and spatial marking of ancient buildings by near-view and far-view combination

In the protection of ancient buildings, not only crack parameters need to be detected but also crack positions need to be known in the spatial structure. As the existing image detection methods cannot consider both detection accuracy and field of view scope, a detection scheme that combines near-view images and far-view images is proposed. Near-view image realizes crack identification and parameter extraction. A region growth algorithm based on local Otsu and local mean is proposed to improve the efficiency of crack extraction. A mapping method based on ASIFT and homography matrix is proposed to realize the crack mapping from near-view images to far-view images and complete the crack marking of far-view images. The 3D reconstruction of the processed far-view images is carried out by the multi-view geometry method to realize the marking of cracks in the spatial structure. Through the actual measurement of two ancient buildings in the Da Ci’en Temple in Xi’an, the effectiveness of crack identification is verified. The detection accuracy of the average crack width is less than 0.1 mm, and the spatial marking of cracks is realized. The crack detection of ancient buildings on stereo vision is realized, which can effectively guide the restoration of ancient buildings and has important reference value for the protection of ancient buildings.
Deep Learning Techniques for EEG-Based BCI: Analysis and Applications

Brain-Computer Interface enables people to communicate with each other or with computing systems without any movements even speech. Recent advancements in BCI mostly use EEG as their channels to achieve connections between the brain and computer, leaving EEG signal decoding a challenge to be solved. Similar complex problems have been given satisfying solutions using Deep Learning techniques, due to its capacity to learn patterns hidden in raw data and its ability to be generalized. However, at present, people usually use some methods that are not state-of-the-art DL architectures for EEG decoding. Besides, some open challenges and limitations have also been exposed in the previous research yet haven’t been solved. In this paper, some basic but critical concepts and challenges in EEG-Based BCI systems are mentioned, as well as prospects of further development in combination with DL and EEG analysis are arisen. The author expects that the investigation presented in this paper will help researchers to have the basic and hands-on concepts for constructing EEG-Based BCI and prompt more recent DL techniques to be used in designing an effective EEG-BCI system.
Deep Learning-Based Classification of Eye Conditions

Diabetic eye disease, affecting those with diabetes, underscores the importance of timely screening for prompt treatment and preventing permanent vision loss. Retinal fundus images aid ophthalmologists in diagnosing retinal issues. Manual detection is arduous and time-intensive, prompting the need for automated diagnostic systems to ease ophthalmologist workload. Utilizing Deep Learning (DL), image classification of retinal fundus, whether healthy or infected, has shown impressive outcomes. Hence, this study aims to automate classification of diabetic eye disease (DIED). Employing own designed Convolutional Neural Network (CNN) models with Adam optimizer achieved 89.9% accuracy for classification of DIED.
Finding Context-Based Positive Influence on Twitter: A Conceptualization

The popularity of online social networks has attracted many business worlds to leverage this popularity in their favor. Thus to advertise their products, advertisers started targeting the most influential users on online social networks. There were mainly two methods to find the users who are influencers. The first one used centrality, which is the network of users' connectivity, to find their influence. The second method used the user's activity, which is the engagement ratio of the user, to find the user's influence. We are taking into account the context of a business as the topic for which the top influential users need to be listed. We are also taking into account the sentiment of the user for the given context. To label the tweets extracted from twitter with the sentiment and the topic, the textblob and LDA (Latent Dirichlet Allocation) algorithms are used respectively. Here in this concept paper, we are considering the user's connectivity as well as the activity of the user based on the context and the sentiment of the user's engagement in the network. We finally derive the list of influential users who have positive sentiments towards the topic or the context.
SEFCL-DNN Based Multi-Grade Brain Tumor Prediction Model with Survival Time Analysis

Among adults and children, one of the most dangerous and deadly cancers is Brain Tumor (BT). For treating the tumor effectively, BTs’ early identification along with classification into their specific grade is very important. For minute feature extraction as well as precise classification, prevailing techniques suffer from inherent challenges. Thus, this paper proposes a novel Squared Exponential Function activated Charbonnier Loss employed Deep Neural Network (SEFCL-DNN) and Fuzzy Inference System (FIS)-based survival time analysis of detected BT grades. Primarily, pre-processing is conducted, where bias correction, noise removal, along with edge detection and enhancement are executed. Thereafter, by utilizing Reniy’s Entropy-based Morphological Operation (REMO), the morphological operation is performed. Afterward, by employing the Cauchy Distributed-Ai-Biruni Earth radius Optimization Algorithm (CD-AiEBOA), the tumors are segmented. Next, grounded on the abnormalities of tumors and necrosis, the segmented images are clustered using Sorensen Dice Based Gaussian mixture model clustering (SDB-GMMC). Following this clustering, the features of tumors are extracted. Then, by utilizing the Symmetrical Uncertainty Principal Component Analysis (SU-PCA), the important features are selected. For classifying the grades of tumors, the selected features are fed into SEFCL-DNN. Grounded on the risk score, the survival time is analyzed for the classified grades. As per the experimental outcomes, the proposed technique withstands maximum accuracy as compared to prevailing methodologies.
Cloud-Powered Precision: Revolutionizing Fault Detection in Thermal Power Plants

Electric current plays a vital role in human existence as it underpins numerous aspects of our daily lives. Without current, life as we know it would cease to exist. To meet the ever-growing demand for electricity, various types of power stations have been developed. Among these, thermal power stations stand out as a major contributor, accounting for approximately 70% of current production. However, ensuring the smooth functioning of different sections within a thermal power station goes beyond simply calculating current production. Continuous monitoring of these sections is imperative to guarantee their optimal performance. To address this need, we propose the implementation of an expert system that can monitor the various sections of the power station seamlessly, eliminating the need for manual intervention. Additionally, this system would transmit data wirelessly, enhancing convenience and efficiency. In a thermal power plant, coal combustion in the boiler serves to convert water into steam within the boiler tubes. This steam, characterized by high pressure and temperature, flows into the turbine, causing it to rotate. The turbine shaft is connected to the generator shaft, which also rotates as a result. This rotational motion generates power. Safeguarding the operational parameters of the power system is crucial for maintaining the health of power-generating equipment and ensuring the efficient utilization of electricity by customers. Therefore, continuous monitoring and frequent inspections of both the customer's equipment and the power plant itself are essential. By implementing an expert system for continuous monitoring, the thermal power plant can achieve enhanced operational efficiency, improved equipment reliability, and effective utilization of resources. This proactive approach helps identify potential issues promptly, reducing the risk of equipment failures and optimizing power generation processes.
Empowering the Tribal people with the use of big data processing expert system in animal Husbandry and Poultry Farming application

The population of the tribal people has been decreased day by day in India due to the lack of awareness in health related issues and there is a series challenges in their sustainable livelihood. The Particularly Vulnerable People from tribal groups (PVTGs) engaged in animal husbandry and poultry farming as their primary source of income, which improves their standard of living. Maintain and safeguard poultry and animals from diseases is a cumbersome process. Providing enough medical facility is still a challenging task due to the geographical location and unavailability of the infrastructure and human resources. The proposed framework uses Apache Kafka-Apache Storm-NoSQL Mongo DB architecture to process enormous volume of sensor data in real time and it receives the sensor data and uses it to create the various disease identification models. The processed data are stored in Mongo DB as a historical data. The system provides a Web-based monitoring system for continuos monitoring the health conditions of cattles and poultry through the Smart Health Care Centre. Smartness in operation is performed through System on Chip (SoC) IoT system, the proposed big data expert system model transcends from the traditional functionalities of disease identification by the real time field visit analysis by the medical professionals. The proposed system is more suitable for the remote hill area. Smart Health Care system improves the disease identification accuracy and provides a powerful Big Data architecture for data analytics and data storage. The big data expert system frame work is underwent successful functional testing of "SoC-IoT smart devices" connected with the network and the performance of the network in terms of CPU, memory usage and the network delay is analyzed. Further the frame work uses the big data processing with the machine learning approach "Hybrid diseases identification Model" with the combination of DBSCAN for outlier detection together with Random Forest classification, which improves the disease identification accuracy of the various disease attacked the cattles and poultry.
Speech Emotion Recognition Using Deep Learning

Speech emotion recognition is the task of automatically detecting the emotional state of a speaker from their spoken words. It is a growing area of research that has applications in various fields such as human computer interaction, education, and psychology. There are several approaches to speech emotion recognition, including the use of machine learning algorithms, which can be trained on large datasets of annotated speech samples to recognize patterns associated with different emotions. Other approaches include the use of linguistic features, prosodic features, and physiological signals such as facial expressions and heart rate. One challenge in speech emotion recognition is the variability in the expression of emotions across individuals and cultural groups. Another challenge is the need to accurately identify the underlying emotion, as opposed to simply recognizing the presence or absence of an emotion. Overall, speech emotion recognition has the potential to improve communication between humans and machines, and to provide insights into the emotional states of individuals.
Comparison of Anthropometry and Cognitive Function Before and During Ramadhan Fasting among Overweight and Obese Adults in

Ramadhan fasting is one of the five pillars in Islam. During these 29-30 days of holistic and spiritual days, Muslims abstain from drinking and eating from dawn to sunset. This alteration of dietary behavior could affect not only on anthropometry but also cognitive function. This prospective observational study aimed to determine the effect of Ramadhan fasting on anthropometry and cognitive function and its correlation between overweight and obese adult populations. This study recruited adults aged between 19 to 65 years old with a BMI of ≥23.0 kg/m2 live in Kuala Nerus. Sociodemographic, anthropometry, and cognitive function data were assessed twice; Before Ramadhan (two weeks prior to Ramadhan) and During Ramadhan (week 3 and 4 during Ramadhan). Anthropometry data including body weight, height, body mass index (BMI), waist circumference, body fat percentage, muscle mass, and total body water were measured using a weighing scale, stadiometer, measuring tape, and bioelectrical impedance analysis (BIA) while the cognitive function was assessed using the Malay Version of the Auditory Verbal Learning Test (MAVLT), Trail Making Test (TMT) A and B, and Stroop Task. A total of 146 participants were recruited with old 54.8% were male while 45.2% were female. The mean age of participants was 42.6±19.4 years. Analysis on the anthropometry data showed that weight, BMI, waist circumference, and body fat percentage were significantly reduced during Ramadhan with p<0.001. Meanwhile, the cognitive function data showed that the completion time for Stroop Congruent and Incongruent Test, and TMT B were significantly reduced during Ramadhan with p<0.001 signifying a better executive performance. The MAVLT learning and recognition scores were significantly increased during Ramadhan (p<0.001) indicating an improvement in memory. The correlational analysis showed the negative correlation between the changes in waist circumference and MAVLT learning during Ramadhan (p=0.031) indicating that reduced waist circumference results in increased MAVLT learning score during Ramadhan. This study’s limitation includes the number of participants that did not reach the required sample size. In conclusion, the current study showed the significant potential of Ramadhan fasting to improve anthropometry outcomes and cognitive function among overweight and obese adults.
Combining Pop-up Cards with Conductive Ink Printing

We propose a tangible user interface combining pop-up cards with conductive ink printing technique. To fulﬁll our purpose, we developed software that can automatically calculate the circuit layouts on the pop-up card. The circuit layout is made to have the shortest path by considering the thickness of the layout, the space between circuit lines, and that the layout should not crossover cut lines. The crease pattern and layouts are printed separately when the layouts are calculated. By attaching electronic elements, we can make an interactive pop-up card. In a use trial, we conﬁrmed that even novice could create original interactive pop-up card with our software and use such card to build an application. Furthermore, we demonstrated the eﬀectiveness of our method by making and showing example applications using the interactive pop-up card interface.
Pulmonary Vascular Disease in the Single-Ventricle Patient: Is it Really Pulmonary Hypertension and if So, How and When Should We Treat it?

Despite significant improvements in the surgical and postoperative care for patients with single-ventricle physiology culminating in the Fontan circulation, significant late morbidity and mortality remains. In the setting of passive (ie, non-“pump” driven) pulmonary blood flow, pulmonary vascular resistance (PVR) plays a key role in determining cardiac output, and even slight elevations in PVR may result in significant morbidity. There is now great interest to treat Fontan patients with pulmonary vasodilators in an attempt to “optimize” PVR (and by extension, quality of life) and/or improve an elevated PVR. This review discusses the hemodynamic implications of the Fontan circulation, the evidence for use of pulmonary vasodilator therapy, and possible target physiologic mechanisms.
An Area-Efficient Recurrent Neural Network Core for Unsupervised Time-Series Anomaly Detection

SUMMARY Since most sensor data depend on each other, time-series anomaly detection is one of practical applications of IoT devices. Such tasks are handled by Recurrent Neural Networks (RNNs) with a feedback structure, such as Long Short Term Memory. However, their learning phase based on Stochastic Gradient Descent (SGD) is computationally expensive for such edge devices. This issue is addressed by executing their learning on high-performance server machines, but it introduces a communication overhead and additional power consumption. On the other hand, Recursive Least-Squares Echo State Network (RLS-ESN) is a simple RNN that can be trained at low cost using the least-squares method rather than SGD. In this paper, we propose its area-e ﬃ cient hardware implementation for edge devices and adapt it to human activity anomaly detection as an example of interdependent time-series sensor data. The model is implemented in Verilog HDL, synthesized with a 45 nm process technology, and evaluated in terms of the anomaly capability, hardware amount, and performance. The evaluation results demonstrate that the RLS-ESN core with a feed-back structure is more robust to hyper parameters than an existing Online Sequential Extreme Learning Machine (OS-ELM) core. It consumes only 1.25 times larger hardware amount and 1.11 times longer latency than the existing OS-ELM core.
Development of a Non-Contact Sensor System for Converting 2D Images into 3D Body Data: A Deep Learning Approach to Monitor Obesity and Body Shape in Individuals in Their 20s and 30s

This study demonstrates how to generate a three-dimensional (3D) body model through a small number of images and derive body values similar to the actual values using generated 3D body data. In this study, a 3D body model that can be used for body type diagnosis was developed using two full-body pictures of the front and side taken with a mobile phone. For data training, 400 3D body datasets (male: 200, female: 200) provided by Size Korea were used, and four models, i.e., 3D recurrent reconstruction neural network, point cloud generative adversarial network, skinned multi-person linear model, and pixel-aligned impact function for high-resolution 3D human digitization, were used. The models proposed in this study were analyzed and compared. A total of 10 men and women were analyzed, and their corresponding 3D models were verified by comparing 3D body data derived from 2D image inputs with those obtained using a body scanner. The model was verified through the difference between 3D data derived from the 2D image and those derived using an actual body scanner. Unlike the 3D generation models that could not be used to derive the body values in this study, the proposed model was successfully used to derive various body values, indicating that this model can be implemented to identify various body types and monitor obesity in the future.
Automatic Osteoporosis Screening System Using Radiomics and Deep Learning from Low-Dose Chest CT Images

Objective: Develop two fully automatic osteoporosis screening systems using deep learning (DL) and radiomics (Rad) techniques based on low-dose chest CT (LDCT) images and evaluate their diagnostic effectiveness. Methods: In total, 434 patients who underwent LDCT and bone mineral density (BMD) examination were retrospectively enrolled and divided into the development set (n = 333) and temporal validation set (n = 101). An automatic thoracic vertebra cancellous bone (TVCB) segmentation model was developed. The Dice similarity coefficient (DSC) was used to evaluate the segmentation performance. Furthermore, the three-class Rad and DL models were developed to distinguish osteoporosis, osteopenia, and normal bone mass. The diagnostic performance of these models was evaluated using the receiver operating characteristic (ROC) curve and decision curve analysis (DCA). Results: The automatic segmentation model achieved excellent segmentation performance, with a mean DSC of 0.96 ± 0.02 in the temporal validation set. The Rad model was used to identify osteoporosis, osteopenia, and normal BMD in the temporal validation set, with respective area under the receiver operating characteristic curve (AUC) values of 0.943, 0.801, and 0.932. The DL model achieved higher AUC values of 0.983, 0.906, and 0.969 for the same categories in the same validation set. The Delong test affirmed that both models performed similarly in BMD assessment. However, the accuracy of the DL model is 81.2%, which is better than the 73.3% accuracy of the Rad model in the temporal validation set. Additionally, DCA indicated that the DL model provided a greater net benefit compared to the Rad model across the majority of the reasonable threshold probabilities Conclusions: The automated segmentation framework we developed can accurately segment cancellous bone on low-dose chest CT images. These predictive models, which are based on deep learning and radiomics, provided comparable diagnostic performance in automatic BMD assessment. Nevertheless, it is important to highlight that the DL model demonstrates higher accuracy and precision than the Rad model.