Miracle-dz/UniFAR-FTUs huggingface dataset:
Aritificial Inteligence Challenges in COPD management: a review

Machine learning algorithms have been drawing attention in lung disease research. However, due to their algorithmic learning complexity and the variability of their architecture, there is an ongoing need to analyze their performance. This study reviews the input parameters and the performance of machine learning applied to diagnosis of chronic obstructive pulmonary disease (COPD). One research focus of this study was on clearly identifying problems and issues related to the implementation of machine learning in clinical studies. Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) protocol, 179, 1032, and 36,500 titles were identified from the PubMed, Scopus, and Google Scholar databases respectively. Studies that used machine learning to detect COPD and provided performance measures were included in our analysis. In the final analysis, 24 studies were included. The analysis of machine learning methods to detect COPD reveals the limited usage of the methods and the lack of standards that hinder the implementation of machine learning in clinical applications. The performance of machine learning for diagnosis of COPD was considered satisfactory for several studies; however, given the limitations indicated in our study, further studies are warranted to extend the potential use of machine learning to clinical settings.
A survey of business network integration: implications for quality and productivity performance

The increasing levels of globalisation have introduced complexities into organisations that require a focus on integrating business networks. Business network integration is critical not only as a result of current competitive challenges but also due to business disruptions created by natural and man-made disasters. Network relationships, productivity, and risk management are key aspects of integrating networks and although supply chain integration literature has touched this topic, these issues deserve further review. The objective of this paper is to provide a research model based on literature review of three theory streams: business network theory; business process redesign theory; and supply chain risk management theory. We present a research model which defines the drivers, practices and outcomes of business network integration based on this framework. We frame propositions to clarify the interrelationships of these theory streams and present theoretical and managerial implications for quality and productivity performance.
Hand Gesture Recognition Using CNN & Publication of World's Largest ASL Database

Sign language is used throughout the world by the hearing impaired to communicate. Recent advancements in Computer Vision and Deep Learning has given rise to many machine learning based translators. In this research paper, a solution to recognize the English alphabet presented as static signs in the American Sign Language (ASL) is proposed. The classifications are achieved by a four layer CNN. The model is trained and tested on a dataset created for this paper. This dataset will be published as a contribution to the community and is currently the world's largest ASL database consisting of 624,000 images. Split into two sections, the database contains images in both the IR and RGB spectrum. Classifications on both sets of data achieve state-of-the-art results when compared to similar research. An accuracy of 99.89% and 99.91 % are achieved when classifying the IR and RGB datasets respectively.
Predicting Reliability of an Embedded Vehicle System by modeling Coincident Failures and Usage-Profiles

Abstract The increasingly ubiquitous use of software systems has increased the need to determine their reliability and the extent to which they can be depended upon. Structured models of systems allow us to do this, yet there are numerous challenges that need to be overcome to obtain meaningful results. This paper is an experiment to model and analyze the Anti-lock Braking System of a passenger vehicle using Stochastic Petri Nets. Special emphasis is laid on modeling extra-functional characteristics like coincident failures among components, severity of failure and usage-profiles of the system. Components generally interact with each other during operation, and a faulty component can affect the probability of failure of other components. The severity of a failure also has an impact on the operation of the system, as does the usage profile - failures which occur during active use of the system are the only failures considered (i.e., in reliability calculations). This paper gives emphasis to the importance of the extra-functional properties mentioned above, the challenges incurred in modeling, a detailed description of the models developed, and the results of the analysis carried out for realistically predicting the reliability of system components.
Semi-Automatic Left Ventricle Model Generation

Most cardiac diseases and disorders occur in the left ventricle. Numerical methods can give an insight into the mechanical response of the left ventricle under different conditions, before the execution of clinical trials and experiments. Before we use the finite element method to analyze the behavior of the left ventricle, a geometrical model has to be generated. In our work, we generated a left ventricle model from echocardiographic data. We manually extracted contours of the inner and outer surface of the left ventricle and applied our algorithm to generate the 3D model. This semi-automatic model generation enables the usage of patient-specific geometries for finite element analysis of the left ventricle.
The influence of calf birth weight on selected beef quality characteristics

cattle nutrition and type have been the focus of much attention, little information is available on the impact of pre-natal growth on beef quality. The objective of this preliminary study was to determine the effect of birth-weight of single-born calves on meat quality. Data were available from two studies. In Experiment 1, 36 heifers of similar breed, were reared according to a 20-month beef production system. In Experiment 2, 30 bulls, the progeny of one of two Charolais sires were reared similarly and slaughtered at 16 months of age. Within each experiment, all animals were slaughtered on the same day. Post-slaughter, carcass weight and fat grade were recorded. Two days post-mortem, pH, colour and drip loss of longissimus dorsi (LD) was measured and after vacuum storage for an additional 12 days, tenderness was measured instrumentally and organoleptic characteristics were assessed by a trained sensory panel. Within each treatment in each experiment, animals were categorised as having low (L) or high (H) birth-weight. Data were subjected to ANOVA using a model that had birth-weight, experiment and treatment (within production system) as main effects. For L and H, birth-weight averaged 41.4, and 54.1 kg (P<0.001), respectively. The corresponding carcass weight was 315 and 336 kg (P<0.05), and carcass fat score was 3.83 and 3.54 (P<0.1). The fat concentration in LD was 28 and 20 g/kg (P<0.05) for L and H, respectively. It is concluded that within the limitations of this study, high birth-weight resulted in heavier carcasses which tended to be leaner but birth-weight did not influence beef quality.
Comparison of Machine Learning Algorithms for Priority-Based Network Slicing in 5G Systems

Network slicing is a technique to enable multi-tenant operation in future 5G systems. Efficient implementation of slicing at the air interface requires comprehensive optimization algorithms characterized by high execution complexity. To address this issue in the paper, we first present a priority-based mechanism enabling performance isolation between slices competing for resources. Then, to speed up the resource arbitration process under high traffic conditions, when resource shares need to be re-calculated in sub-second timescales, we propose and compare several machine learning techniques: linear regression, polynomial regression, a random forest regressor, and a two-layer artificial neural network. The techniques' performance is assessed by utilizing the mean squared error. Our results show that a high order polynomial regression provides the desired balance between computational complexity and accuracy, outperforming both the simpler linear regression and the more complex random forest and neural network algorithms.
An Approach for Steam Pipe Network Pressure Prediction Based on CNN and BiLSTM with Attention Mechanism

The steam pipe network is a complex nonlinear network structure system with a large time delay, and the accurate prediction of its pipeline pressure is the prerequisite for energy dispatch and guarantee. Considering the complicated changes in pipeline pressure and many influencing factors, this paper proposes a steam pipe network pressure prediction model based on the CNN and BiLSTM with attention mechanism. This method can strengthen the connection between various users, reduce the loss of historical information, and achieve the higher-precision steam pipe network pressure prediction. Compared with other models, the MAPE of this method is reduced to 0.55%, and the RMSE is only 0.006 MPa. This method can improve the management level of the steam pipe network and is helpful to the construction of energy saving and emission reduction.
FGIC44 - Fully Controllable Immittance Converter: Chip Performance Evaluation

In signal processing and mainly frequency filter design the general immittance converter (GIC) is commonly seen as a circuit used to implement a synthetic inductor. As the fractional-order approach recently started to impact also the electrical engineering area, the idea of impedance conversion may be successfully broaden. In this paper we continue in our previous research achievements and present the performance and key parameters of the FGIC44 - the Fully controllable immittance converter that was designed at our workplace as custom integrated circuit using the $0.18\ \mu\mathrm{m}$ TSMC CMOS process. The experimentally determined properties of OTA amplifiers forming the core of the FGIC44 circuit are presented. The electronic controllability of the structure, its wide frequency range and also the possibility of compensation of parasitic internal conductances of the terminals are demonstrated.
An efficient representation of nonstationary signals using mixed-transforms with applications to speech

A useful technique is presented to efficiently represent nonstationary signals by combining the wavelet and mixed-transforms. First, the signal is split into subbands using the discrete wavelet transform. Specifically, the subband splitting is performed by employing the scaling and the wavelet functions to low-pass and high-pass filter the signal. Next, each of these subbands is represented using superimposed partial sets of basis functions of different transforms, which are in general, mutually nonorthogonal. This is termed the mixed-transforms representation. The residual error, which is the difference between the original subband and the reconstructed subband is properly formulated. Adaptive algorithms are developed to minimize this error and to maximize the Signal to Noise ratio (SNR) of the reconstructed subband for a fixed number of transform components. An optimization strategy is also proposed to select the dominant components from the various domains for adaptation. These efficiently represented subbands are finally combined to reconstruct the signal. Sample results for representing voiced and unvoiced speech signals are given to illustrate the accuracy of the proposed method. A specific class of scaling and wavelet functions are employed in conjunction with the mixed Fourier/Haar transforms. It is verified that for a given number of coefficients, the proposed technique yields a significantly higher SNR of the reconstructed signal than using mixed-transforms alone or the wavelet transform followed by a single transform. Performance comparison is also presented for two different orders of scaling functions belonging to the same family. >
Direction of Arrival for Uniform Circular Array Using Directional Antenna Elements

In the past few decades, the needs for multimedia content increased. This caused an increase in the demands of the wireless communications. Also, it opened the way for mobile devices that need smart antenna technology as well as array signal processing to estimate the direction of incoming signal which known as direction-of-arrival (DOA). This paper investigates both the DOA of the received signal in Wi-Fi band (2.4-2.48 GHz) in azimuth and elevation based on a famous DOA algorithm named multiple signal classification (MUSIC) using four modified patch antenna elements (2.434 GHz) by adding parasitic, sleeves and slots as the building block of the uniform circular array (UCA), and the beamforming technique based on the least mean square algorithm (LMS). The modified antenna is analyzed carefully using CST-microwave studio software to improve the S-parameters, gain, directivity, and radiation pattern. A UCA with new patch antenna elements assures that system design will be straight forward. Further, such system will be able to provide DOA of the received signal using DOA algorithm then guide the maximum radiation pattern to a desire location using beamforming techniques.
Taking a Peek: An Evaluation of Anomaly Detection Using System calls for Containers

The growth in the use of virtualization in the last ten years has contributed to the improvement of this technology. The practice of implementing and managing this type of isolated environment raises doubts about the security of such systems. Considering the host's proximity to a container, approaches that use anomaly detection systems attempt to monitor and detect unexpected behavior. Our work aims to use system calls to identify threats within a container environment, using machine learning based strategies to distinguish between expected and unexpected behaviors (possible threats).
Cyber-Physical Disaster Response of Power Supply Using a Centralised-to-Distributed Framework

This paper proposes a cyber-physical cooperative recovery framework to maintain critical power supply, enhancing power systems resilience under extreme events such as earthquakes and hurricanes. Extreme events can possibly damage critical infrastructure in terms of power supply, on both cyber and physical layers. Microgrid (MG) has been widely recognised as the physical-side response to such blackouts, however, the recovery of cyber side is yet fully investigated, especially the cooperatively recovery of cyber-physical power supply. Therefore, a centralised-to-distributed resilient control framework is designed to maintain the power supply of critical loads. In such resilient control, controller-to-controller (C2C) wireless network is utilised to form the emergency distributed communication without a centralised base station. Owing to the limited reliable bandwidth that can be employed in C2C networks, the inevitable delay is considered in designing a discrete control framework, and the corresponding stability criteria are given quantitatively. Finally, the cyber-physical recovery framework is demonstrated effectively through simulations in MATLAB/Simulink.
A Technique for Detecting the Substitution of a Java-Module of an Information System Prone to Pharming with Using a Hidden Embedding of a Digital Watermark Resistant to Decompilation

The relevance of the problem under study is due to the rapid development of the java programming language, a large number of commercial applications written in this programming language. The purpose of the article is to develop a methodology that allows you to determine the java-module of the information system, which does not contain a hidden digital watermark. It is necessary that the digital watermark is not damaged during the decompilation of the entire system in order to replace the java module, and that the class files and the entire information system function unchanged. Thus, at first, a digital watermark is hidden in all java-modules of the information system. Next, we check the resistance of the digital watermark to decompilation attacks with block substitution. The materials of the article can be useful in the implementation of hidden embedding of a digital watermark in the class files of a large java module.
Harmonization of Multi-site Dynamic Functional Connectivity Network Data

Neuroscience studies have begun to benefit from combining large numbers of data from different sites to increase statistical power. Pooling data from various sites into a single analysis introduces additional variability from site-effects due to differences in scanner protocols, imaging protocol, and acquisition methods, among others. These site-effects can reduce statistical power or lead to erroneous conclusions. Harmonization is the process of combining data aiming at reducing site variability. One recent approach for harmonizing data called ComBat has been shown to be helpful in the context of functional MRI and static functional connectivity. However, ComBat has not been applied to the analysis of dynamic functional network connectivity (dFNC). Here we explore the impact of ComBat harmonization on dFNC data collected from two different mild traumatic brain injury (mTBI) studies. Results show that ComBat harmonization of dFNC can reduce site effects producing a more robust analysis of patient effects across sites.
Compact circularly-polarised slot antenna for UWB applications

A simple compact broadband circularly-polarised square slot antenna (CPSSA) fed by a coplanar waveguide is presented. A CP bandwidth larger than 38.2% can be attained simply by embedding two inverted-L grounded strips around two opposite corners whereas the impedance bandwidth can be greatly improved through the tuning stubs. The designed CPSSA antenna is 25×25×0.8 mm3, which is 56% smaller than previous CPSSA structures operating over the frequency band between 2.9 and 11.2 GHz for VSWR<2.
Monitoring system for over-temperature detection of water cooled rotor poles of hydro electrical generators installed at the Danube River Iron Gates Power Plant

In the field of temperature measurement, infra-red measuring methods, the only one which gives full information on surface temperature distribution of the observed objects, proves to be even more valuable when it is necessary to measure and monitor temperatures of moving objects. The advantage of being a passive method, with no interference on the thermal balance of the observed object, i.e. no changing influence of the temperatures to be measured, is again, even more appreciated in the case of moving objects, by the elimination of rather complex, stuffy and expensive system of probes, signals wiring and transmission devices, required in the case of conventional methods (thermocouple/thermoresistance). Based on a dedicated application, the article shows the criteria considered in the design stage, as well as in that of the implementation of a monitoring system for over-temperature detection of water-cooled rotor poles of hydro-electrical generators (HEG), all done in normal working conditions, i.e. with the HEG in movement. Two possible fixed implementations of a monitoring system, one of them in operation for over three years at the Danube River Power Plant, as well as a portable device, with same designation and practical similar results, are presented with field results which are discussed.
DC Power Distribution System and Its Protection Configuration

In order to absorb renewable energy and electric vehicles, the typical design and protection scheme of AC/DC power distribution system are studied. Firstly, the advantages of DC distribution are analyzed from the point of view of power grid and users. Secondly, the energy router is configured as the system energy supply center and conversion hub, and the typical topology structure is designed. Finally, in order to solve the problems of fault location, isolation and self-healing, the fault current precise control and selective cutting technology based on source and load characteristics are adopted, and the multi-voltage level DC distribution network frame is constructed for verification.
Determination of the propagation constant of coupled lines on chips based on high frequency measurements

A new method has been developed to determine the propagation constant of symmetrical coupled lossy lines. The results are based on high frequency measurements of the scattering parameters of only two coupled two line systems of different lengths. The mathematical derivation of the method is be given. The proposed method is related to an eigenvalue calculation. Results obtained from measurements are presented and discussed. A comparison between the measured and calculated results is given and shows excellent agreement.
Changes in NK Cell Subsets and Receptor Expressions in HIV-1 Infected Chronic Patients and HIV Controllers

Natural killer (NK) cells are major effectors of the innate immune response and purported to play an influential role in the spontaneous control of HIV infection. In the present study, we compared the phenotypes of NK cells in the peripheral blood of three groups of subjects with chronic HIV-1 infection, HIV controllers, and healthy donors. The results showed that CD56+/CD16- NK cell subsets decreased in chronic patients and remained unchanged in controllers. Notably, we found that people living with chronic HIV-1 infection had suppressed NKp80, NKp46, and NKG2D expressions on NK cells compared to healthy donors, while HIV controllers remained unchanged. In contrast, NKG2D expression was substantially higher in controllers than in chronic patients (M=97.67, p<0.001). There were no significant differences in inhibitory receptors KIR3DL1 and KIR2DL1 expressions. In addition, plasma cytokine IFN-γ, TNF-α and IL-12showed higher levels in HIV controllers compared to chronic patients. Overall, our study revealed that, as compared to chronic patients, HIV controllers show an increased activating receptors expression and higher number ofCD56+/CD16-NK cell subset, with increased expression levels of plasma cytokines, suggesting that higher immune activation in controllers may have a key role in killing and suppressing HIV.