Early identification of viruses leads to more efficient disease management and control, and is extremely important. A possible new approach for creating virus sensors is the Electromagnetic echo effect (EMEE). An important feature is that the signal from EMEE is highly dependent on the state of the irradiated body. This makes it possible to control ongoing reactions, even if these reactions are invisible to the human eye or other equipment. This article shows the possibility of registering reaction occurring in the presence of an avian coronavirus causing infectious bronchitis, strain Massachusetts. The same methodology can be applied for other types of viruses as well.

Background: According to EWGSOP2, the diagnosis of sarcopenia is confirmed when in addition to low muscle strength, low muscle quantity or quality is present. Echo intensity (EI) determined by muscle ultrasound (US) has been proposed as an efficient method for the assessment of muscle quality. The effect of changing various US parameter settings on EI remains unclear. Therefore, the aim of this study was to assess the differences between EI values obtained by adjusting parameter settings over their entire range in a sample of middle-aged healthy subjects. Methods: Thirty-two repeated US scans of rectus femoris (RF) and rectus abdominis (RA) muscles were taken in eight men and three women with a portable Mindray M7 premium US machine, working in an Extended field-of-view, B-Mode setup, equipped with a linear 5.0-10.0 MHz transducer. The following US parameters and settings were fixed: gain 60dB, depth 6.5cm, and frequency 10 MHz. Readily adjustable parameters were dynamic range (DR), gray map (GM), line density, persistence, and IClear. A default setting DR65 was chosen as the reference setting. For each of the parameters, the settings of one single parameter of interest was changed over its entire range following a standardized protocol. The EI values were calculated using the open-source software ImageJ. Repeated measures analyses were performed to evaluate the effect of parameter settings on EI. Linear interpolation was used to determine non-significant ranges across a given parameter. Results: For the RF muscle, the EI values were significantly different across DR (p<0.001), GM (p<0.001), and IClear (p<0.001). Post hoc analysis confirmed the differences within these three settings. Echo intensity values within the range of DR55 to DR80 were not significantly different. For the RA muscle, the EI values were significantly different across DR (p=0.004) and GM (p=0.030). Post hoc pairwise comparisons revealed no significant difference with the default setting, except for DR150 (p=0.042). Conclusion: We showed that EI values differ across the DR and GM range, especially in the RF. We suggest using a DR setting within its midrange to minimize the effect of machine setting-dependent factors on EI values. These findings reconfirm the need for standardization of ultrasound echo intensity settings when applied for diagnostic purposes of muscle quality.

Currently there is no multi-hazard risk assessment tool for determining the level of complexity to swiftwater and flood rescue incidents. Traditionally, the International Scale of River Difficulty is used but it is primarily for paddlers for use in a recreational context, without much consideration to the multitude of hazards faced in swiftwater and flood rescue environments. In response to this gap, the ECHO risk assessment tool has been developed and undergone initial testing. This tool provides for simple and rapid codification of multiple hazards and response considerations and is globally applicable. The tool also assigns a final risk assessment colour making the interpretation of the assessment easy to understand and communicate. Though the proposed tool shows potential, further research is needed before it should be operationalised.

Using the unique combination of atomically resolved atom probe tomography (APT) and volume averaged neutron (resonance) spin echo (NRSE and NSE) experiments, the influence of nano-scaled clusters on the spin relaxation in spin glasses was studied. For this purpose, the phase transition from the paramagnetic phase to the spin glass phase in a Fe-Cr spin glass with a composition of Fe_17.8Cr_82.2 was studied in detail by means of NRSE. The microstructure was characterised by APT measurements, which show local concentration fluctuations of Fe and Cr on a length scale of 2 to 5 nm, which lead i) to the coexistence of ferro- and anti-ferromagnetic clusters and ii) a change of the magnetic properties of the whole sample, even in the spin glass phase, where spins are supposed to be randomly frozen. We show that a generalized spin glass relaxation function, which was successfully used to describe the phase transition in diluted spin glasses, can also be used for fitting the spin dynamics in spin glasses with significant concentration fluctuations.

In this paper, a high-performance antenna array system model is presented to analyze moving-object-skin-returns and track them in the presence of stationary objects using frequency modulated continuous wave (FMCW). The main features of the paper are bonding the aspects of antenna array and electromagnetic (EM) wave multi-skin-return modeling and simulation (M&S) with the aspects of algorithm and measurement/tracking system architecture. The M&S aspect models both phase and amplitude of the signal waveform from a transmitter to the signal processing in a receiver. In the algorithm aspect, a novel scheme for FMCW signal processing is introduced by combining time- and frequency-domain methods, including a vector moving target indication filter and a vector direct current canceller in time-domain, and a constant false alarm rate detector and a mono-pulse digital beamforming angle tracker in frequency-domain. In addition, unlike previous designs of using M×N fast Fourier transform (FFT) for an M×N array, only four FFTs are used, which tremendously saves time and space in hardware. With the presented model, the detection of the moving-target-skin-return in stationary objects under a noisy environment is feasible. Therefore, to track long range and high-speed objects, the proposed technique is promising. Using a scenario having 1) a target with 17 dBm2 radar cross section (RCS) at about 40 km range with 5.93 Mach speed and 11.6 dB post processing signal to noise ratio, and 2) a strong stationary clutter with 37 dBm2 RCS located at the proximity of the target, it demonstrates that the root-mean-square errors of range, angle and Doppler measurements are about 26 meters, 0.68 degree and 1100 Hz, respectively.

Ultrasonic transducers are widely used for non-destructive testing (NDT) and evaluation in various industries. While capacitive micromachined ultrasonic transducers (CMUTs) have emerged as significant novel transducers, their application in NDT has been limited. This study designed and fabricated a novel two-element CMUT device for NDT applications. The CMUT’s structural parameters were introduced, and the device was fabricated using silicon fusion wafer-bonding technology. The transducer’s sensitivity, bandwidth, and pulse-echo response performance were tested and analyzed. Testing with aluminum blocks demonstrated that the two-element transducer can transmit and receive ultrasonic waves, with central operating frequencies of 3.35 MHz. In addition, at 3.4 MHz, the receive sensitivities of both elements were –197.434 dB and –197.413 dB, respectively, meeting the requirements for NDT systems. In pulse-echo response testing, the CMUT successfully detected signals from the aluminum block interface and located the interface’s position. This study highlights the potential of CMUTs for NDT testing and evaluation applications.

Little is known about skeletal muscle thickness, echogenicity, and muscle quality (handgrip strength /muscle thickness) in breast cancer-related lymphedema (BCRL). Using B-mode ultrasound, we recorded the raw radiofrequency data of the affected (AA) and unaffected (UA) arms of women (n=20) with BCRL, and in 20 control women with no lymphedema (CA). The data were converted into images and measurements of skin, subcutaneous fat and muscle thickness were obtained. Within a designated region of interest, muscle echo-intensity was assessed using computer graded grey scale and muscle thickness was measured using ImageJ2. Handgrip strength was measured using standard dynamometry. We found that forearm muscle thickness was similar among AA, UA, and CA. When compared to the unaffected and control arms, the affected arm had a significantly higher (p 0.013) muscle echo-intensity. When compared to the AA, handgrip strength was similar to UA (p 0.401) but lower than CA (p 0.05). We conclude that the use of B-mode ultrasound is a reliable method to assess muscle thickness and EI in BCRL. When comparing AA to UA, the elevated levels of muscle EI in AA did not influence strength or muscle quality. The elevated EI in AA is a marker of abnormal health that needs to be further explored.

Background: This study presents a graph-based and purely structural analysis to detect echo chambers on Twitter. Echo chambers are a concern as they can spread misinformation and reinforce harmful stereotypes and biases in social networks. Methods: The study recorded the German-language Twitter stream over two months, recording about 180.000 accounts and their interactions. The study focuses on retweet interaction patterns in the German-speaking Twitter stream and found that the greedy modularity maximization and HITS metric are the most effective methods for identifying echo chambers. Results: The purely structural detection approach was able to identify an echo chamber (red community) that was focused on a few topics with a triad of Anti-Covid, right-wing populism, and pro-Russian positions (very likely reinforced by Kremlin-orchestrated troll accounts). In contrast, a blue community was much more heterogeneous and showed "normal" communication interaction patterns. Conclusions: The study highlights the effects of echo chambers as they can make political discourse dysfunctional and foster polarization in open societies. The presented results contribute to identifying problematic interaction patterns in social networks often involved in the spread of disinformation by problematic actors. It is important to note that not the content but only the interaction patterns would be used as a decision criterion, thus avoiding problematic content censorship.

Social media platforms are not serving only as tools of communication rather they are acting like intricate systems managing our interactions. A major setback to our interactions occur when the flow of information in these networks gets distorted through polarization. It resembles a society where every debate turns into divide. Our research focuses exclusively on quantifying polarization as well as solving this issue head-on. In order to accurately gauge polarization, we’ve developed a new method that takes into consideration majority of its influencing factors. This helps us in correctly quantifying the polarization value of a given network and then by proposing it as an optimization problem we are attempting to maximally decrease polarization while staying within the set budget. We test our method on synthetic and real-world data sets, and find out that polarization is declining, and diversity is increasing. This proves that our recommendation engine has a penchant for finding the ideal connections between the nodes to start a dialogue. Our research presents a successful tactic to quantifying and reducing polarization in social media networks. The results indicate that our novel metric and intervention technique are effective in dismantling echo chambers, thereby promoting diversity in these networks.

Polarization in online social networks has gathered a significant amount of attention in the research community and in the public sphere due to stark disagreements with millions of participants in topics surrounding politics, climate, the economy and other areas where an agreement is required. There are multiple approaches to investigating the scenarios in which polarization occurs and given that polarization is not a new phenomenon but that its virality may be supported by the low cost and latency messaging offered by online social media platforms; an investigation into the intrinsic dynamics of online opinion evolution is presented for complete networks. Extending a model which utilizes the Binary Voter Model (BVM) to examine the effect of the degree of freedom for selecting contacts based upon homophily, simulations show that different opinions are reinforced for a period of time when users have a greater range of choice for association. The facility of discussion threads and groups formed upon common views further delays the rate in which a consensus can form between all members of the network. This can temporarily incubate members from interacting with those who can present an alternative opinion where a voter model would then proceed to produce a homogeneous opinion based upon pairwise interactions.

Recurrent neural networks (RNNs) transmit information over time through recurrent connections. In contrast, biological neural networks use many other temporal processing mechanisms. One of these mechanisms is the inter-neuron delays caused by varying axon properties. Recently, this feature was implemented in echo state networks (ESNs), a type of RNN, by assigning spatial locations to neurons and introducing distance-dependent inter-neuron delays. These delays were shown to significantly improve ESN task performance. However, thus far it is still unclear why distance-based delay networks (DDNs) perform bettern than ESNs. In this paper, we show that by optimizing inter-node delays, the memory capacity of the network matches the memory requirements of the task. As such, networks concentrate their memory capabilities to the points in the past which contain the most information for the task at hand. Moreover, we show that DDNs have more total linear memory capacity, with the same amount of non-linear processing power.

Background: Although electronic health records (EHR) provide useful insights into disease patterns and patient treatment optimisation, their reliance on unstructured data presents a difficulty. Because of their narrative structure, echocardiography reports, which provide extensive pathology information for cardiovascular patients, are particularly challenging to extract and analyse. Although natural language processing (NLP) has been utilised successfully in a variety of medical fields, it is not commonly used in echocardiography analysis. Objectives: To develop an NLP-based approach for extracting and categorizing data from echocardiography reports by accurately converting continuous (e.g. LVOT VTI, AV VTI, and TR Vmax) and discrete (e.g. Regurgitation severity) outcomes in semi-structured narrative format into structured and categorized format, allowing for future research or clinical use. Methods: 135,062 Trans-Thoracic Echocardiogram (TTE) reports were derived from 146967 baseline Echocardiogram reports and split into three cohorts: Training and Validation (n = 1075), Test Dataset (n = 98) and Application Dataset (n = 133,889). The NLP system was developed and iteratively refined using medical expert knowledge. The system was used to curate a moderate-fidelity database from extractions of 133,889 reports. A hold-out validation set of 98 reports was blindly annotated and extracted by two clinicians for comparison with the NLP extraction. Agreement, discrimination, accuracy and calibration of outcome measure extractions were evaluated. Results: Continuous outcomes including LVOT VTI, AV VTI, and TR Vmax exhibited perfect inter-rater reliability using intra-class correlation scores (ICC=1.00, P< 0.05) alongside high R2 values, demonstrating an ideal alignment between the NLP system and clinicians. Good level (ICC =0.75-0.9, P<0.05) of inter-rater reliability were observed for outcomes such as LVOT Diam, Lateral MAPSE, Peak E Velocity, Lateral E' Velocity, PV Vmax, Sinuses of Valsalva, and Ascending Aorta diameters. Furthermore, the accuracy rate for discrete outcome measures was 91.38% in the confusion matrix analysis, indicating effective performance. Conclusions: The NLP-based technique yielded good results when it came to extracting and categorising data from echocardiography reports. The system demonstrated a high degree of agreement and concordance with clinician extractions. This study contributes to the effective use of semi-structured data by providing a useful tool for converting semi-structured text to structured echo report that can be used for data management. Additional validation and implementation in healthcare settings can improve data availability and support research and clinical decision-making.

This research aimed to improve the interpretation of electrical resistivity (ER) results in concrete bridge decks by utilizing results from multiple nondestructive evaluations (NDE) techniques in machine learning algorithms. To achieve this, a parametric study was first conducted using numerical simulations to investigate the effect of various parameters on ER measurements, such as the degree of saturation, corrosion length, delamination depth, concrete cover, and the moisture condition of delamination. A data set from this study was used to build a machine-learning algorithm based on the Random Forest methodology. This algorithm was then implemented on data collected from a bridge deck in the BEAST® facility. The presented results demonstrate an improvement in the interpretation of ER measurements using data from other NDE technologies.

Recently, a layer-stacked ESN model named deep echo state Network (DeepESN) has been established. As an interactional model of recurrent neural network and deep neural network, investigations of DeepESN are of significant importance in both areas. Optimizing the structure of neural networks remains a common task in artificial neural networks, and the question of how many neurons should be used in each layer of DeepESN must be stressed. In this paper, our aim is to solve the problem of choosing the optimized size of DeepESN. Inspired by the sensitive iterative pruning algorithm, a neuronal similarity-based iterative pruning merging algorithm (NS-IPMA) is proposed to iteratively prune or merge the most similar neurons in DeepESN. Two chaotic time series prediction tasks are applied to demonstrate the effectiveness of NS-IPMA. The results show that the DeepESN pruned by NS-IPMA outperforms unpruned DeepESN with the same network size, and NS-IPMA is a feasible and superior approach to improving the generalization performance of DeepESN.

During the last years our society was permanently disturbed by the coherent information waves of high amplitudes. These are waves of huge social energy. Often they are of the destructive character, a kind of information tsunami. But, they can carry as well positive improvements in the human society, as waves of decision making matching rational recommendations of societal institutes. The main distinguishing features of these waves are their high amplitude, coherence (homogeneous character of social actions generated by them), and short time needed for their generation and relaxation. We show that such social phenomenon can be modeled on the basis of the recently developed social laser theory. This theory can be used to model stimulated amplification of coherent social actions. ``Actions'' are treated very generally, from mass protests to votes and other collective decisions, as, e.g., acceptance (often unconscious) of some societal recommendations. We point to the main distinguishing features of the modern society simplifying social lasing: a) transformation of humans into social atoms - lost of individuality; b) generation by mass-media of powerful information fields leading to information overload of social atoms; c) creation of powerful social resonators based on internet Echo Chambers. In this paper, we analyze in very detail their functioning leading to increasing of the power fo the quantum information field as well as its coherence.

Fatigue-related subchondral bone injury of the third metacarpal/ metatarsal (McIII/ MtIII) bone is a common cause of wastage and welfare concern in racehorses. Better understanding of bone health and strength would improve animal welfare and be of benefit for the racing industry. Porosity index (PI) of bone is an indirect measure of osseous pore size and number and is therefore an interesting indicator of bone strength. MRI of compact bone using traditional methods even with short echo times fails to generate enough signal to assess bone architecture as the water protons are tightly bound. Ultrashort echo time (UTE) sequences aim to increase the amount of signal detected in the equine McIII/MtIII condyle. Cadaver specimens were imaged using a novel dual-echo UTE MRI technique and PI was calculated and validated against quantitative CT-derived bone mineral density (BMD). BMD and PI are inversely correlated in the equine distal Mc/ MtIII with a mean r value of -0.29. The r value is significantly different between fore- and hindlimbs. Further work is needed to assess how correlation patterns behave in different areas of the bone and to evaluate PI in horses with and without clinically relevant stress injury.

Several factors impact the durability of concrete bridge decks, including traffic loads, fatigue, temperature changes, environmental stress, and maintenance activities. Detecting problems such as corrosion, delamination, or concrete degradation early on can lower maintenance costs. Nondestructive evaluation (NDE) techniques can detect these issues at early stages. Each NDE method, meanwhile, has limitations that reduce the accuracy of the assessment. In this study, multiple NDE technologies were combined with machine learning algorithms to improve the interpretation of half-cell potential (HCP) and electrical resistivity (ER) measurements. A parametric study was performed to analyze the influence of five parameters on HCP and ER measurements, such as degree of saturation, corrosion length, delamination depth, concrete cover, and moisture condition of delamination. The results were obtained through finite element simulations and used to build two machine learning algorithms, a classification algorithm and a regression algorithm, based on the Random Forest methodology. The algorithms were tested using data collected from a bridge deck in the BEAST® facility. Both machine learning algorithms were effective in improving the interpretation of ER and HCP measurements using data from multiple NDE technologies.

This paper presents a study to accurately evaluate defects in reinforced concrete decks using ultrasonic pulse-echo signals. Two validation specimens were designed and tested where reinforced concrete deck slab specimens included planned voids and defects. A commercial Ultrasonic Pulse-Echo (UPE) device obtains 2-D images of the void/defect locations of the reinforced concrete deck. The UPE is based on the ultrasonic shear-wave test method using dry-point-contact transmitting and receiving transducers and employing a synthetic aperture focusing technique. The authors analyzed the recorded UPE A-scan data to enhance the accuracy of estimating the defects' locations using the synthetic aperture focusing technique imaging method. The analysis benefitted from an advanced denoising approach and defect echo peak extraction based on empirical modal decomposition, Hurst exponent characterization, and Hilbert envelope estimation. While output from conventional UPC devices can only provide qualitative results, the new method provides quantitative information on the anomalies inside the reinforced concrete deck. The developed approach accurately assists with assessing the location and depth of the voids/defects in the reinforced concrete deck slabs.

This paper presents an in-depth study of the stress wave behavior propagating in a Rayleigh–Love rod with a sudden cross-sectional area change. The analytical solutions of stress waves are derived for the reflection and transmission propagation behavior at the interface of the cross-sectional area change in the rod, considering inertia and Poisson’s effects. Examples solved by the finite element method are provided to verify the correctness of the analytical results. In addition to the forward analysis of Rayleigh–Love wave propagation in rods impacted by a striker rod, a nondestructive testing method is proposed to conduct the defect assessment in the rod type of the structure component with a sudden cross-sectional area change within a cover medium by using the measured signals at the measurable zone of the rod to be inspected.

Oxygen extraction fraction (OEF), the fraction of oxygen that tissue extracts from blood, is an es-sential biomarker for directly assessing tissue viability and function in neurologic disorders. For quantitative mapping of OEF, an integrative model of quantitative susceptibility mapping and quantitative blood oxygen level-dependent magnitude (QSM+qBOLD or QQ) was recently pro-posed. However, QQ assumes Gaussian noise in both susceptibility and multi-echo gradient echo (mGRE) magnitude signals for OEF estimation. This assumption is unreliable in low sig-nal-to-noise ratio (SNR) regions like disease-related lesions, risking inaccurate OEF estimation and potentially impacting clinical decisions. Addressing this, our study presents a novel multi-echo complex QQ (mcQQ) that models realistic noise in mGRE complex signals. The proposed mcQQ was implemented using a deep learning framework (mcQQ-NET) and compared with the existing deep learning-based QQ (QQ-NET) in simulations, ischemic stroke patients, and healthy subjects. Both mcQQ-NET and QQ-NET used identical training and testing datasets and schemes for a fair comparison. In simulations, mcQQ-NET provided more accurate OEF than QQ-NET. In the sub-acute stroke patients, mcQQ-NET showed a lower average OEF ratio in lesions relative to unaf-fected contralateral normal tissue than QQ-NET. In the healthy subjects, mcQQ-NET provided uniform OEF maps, similar to QQ-NET, but without unrealistically high OEF outliers in areas of low SNR. Therefore, mcQQ-NET improves OEF accuracy by better reflecting realistic data noise characteristics.