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Abstract Purpose Convalescent plasma (CP) collected from people who recovered from COVID-19 became a rapidly available treatment modality in numerous countries, including the Czech Republic. The aims of our study were to evaluate the effectiveness and safety of CP in the treatment of COVID-19. Methods This retrospective observational study involved six Czech hospitals. This study enrolled patients with and without CP treatment who were hospitalized between April 2020 and April 2021. Propensity score matching and logistic regression analysis were performed to evaluate the influence of CP administration and its timing on the in-hospital survival of COVID-19 patients. Results A total of 1,498 patients were enrolled in the study; 406 (27%) were administered CP, and 1,092 (73%) were not treated with CP. The propensity score-matched control group consisted of 1,218 subjects. The survival of patients treated with CP was 79%, while that of patients in the matched control group was 62% (P

Understanding the effects of microhabitat changes on arthropod predator communities and their prey in agroecosystems is essential for field management and biocontrol. Few studies have investigated the trait composition of web-building spider communities in rice ecosystems. Here, we examined how temporal changes during the rice field development affect the abundance and traits of orb-web spiders, and how these effects consequently influence captured prey number and prey composition in irrigated rice ecosystems in southern Thailand. We used structural equation models to evaluate direct and indirect, spider-mediated effects of rice field development on captured prey numbers in each different guild. We found that the number of horizontal web-building spiders decreased during the rice field development, whereas there was no significant change in number of vertical web-building spiders. The number of captured detritivorous insects was positively related to the numbers of horizontal and vertical web-building spiders, while phytophagous insects and others were positively related only to the numbers of vertical web-building spiders. Moreover, the prey number captured by vertical web-building spiders seems to be indirectly increased through the decreasing number of horizontal web-building spiders in the late rice season. A fourth-corner analysis showed that spider species identity, spider traits (web type, web height and web diameter), vegetation height, and water level generally influenced the prey captured by web-building spiders. Horizontal web-building spider species with lower web placement during the flooding phase captured high numbers of detritus-feeding insects, while vertical web-building spider species with higher web placement captured high numbers of rice pests, predators and others. Our results suggest that the field development acted as an environmental factor that determined the species identity and traits of web-building spider communities. The findings of this study can help to predict the ecosystem services provided by the web-building spider community in rice ecosystems.

Abstract Detecting brain tumours is complex due to the natural variation in their location, shape, and intensity in images. While having accurate detection and segmentation of brain tumours would be beneficial, current methods still need to solve this problem despite the numerous available approaches. Precise analysis of Magnetic Resonance Imaging (MRI) is crucial for detecting, segmenting, and classifying brain tumours in medical diagnostics. Magnetic Resonance Imaging is a vital component in medical diagnosis, and it requires precise, efficient, careful, efficient, and reliable image analysis techniques. The authors developed a Deep Learning (DL) fusion model to classify brain tumours reliably. Deep Learning models require large amounts of training data to achieve good results, so the researchers utilised data augmentation techniques to increase the dataset size for training models. VGG16, ResNet50, and convolutional deep belief networks networks extracted deep features from MRI images. Softmax was used as the classifier, and the training set was supplemented with intentionally created MRI images of brain tumours in addition to the genuine ones. The features of two DL models were combined in the proposed model to generate a fusion model, which significantly increased classification accuracy. An openly accessible dataset from the internet was used to test the model's performance, and the experimental results showed that the proposed fusion model achieved a classification accuracy of 98.98%. Finally, the results were compared with existing methods, and the proposed model outperformed them significantly.

Abstract Heavy maternal alcohol drinking during pregnancy has been associated with altered neurodevelopment in the child but the effects of low-dose alcohol drinking are less clear and any potential safe level of alcohol use during pregnancy is not known. We evaluated the effects of prenatal alcohol on reward-related behavior and substance use in young adulthood and the potential sex differences therein. Participants were members of the European Longitudinal Study of Pregnancy and Childhood (ELSPAC) prenatal birth cohort who participated in its neuroimaging follow-up in young adulthood. A total of 191 participants (28–30 years; 51% men) had complete data on prenatal exposure to alcohol, current substance use, and fMRI data from young adulthood. Maternal alcohol drinking was assessed during mid-pregnancy and pre-conception. Brain response to reward anticipation and reward feedback was measured using the Monetary Incentive Delay task and substance use in young adulthood was assessed using a self-report questionnaire. We showed that even a moderate exposure to alcohol in mid-pregnancy but not pre-conception was associated with robust effects on brain response to reward feedback (six frontal, one parietal, one temporal, and one occipital cluster) and with greater cannabis use in both men and women 30 years later. Moreover, mid-pregnancy but not pre-conception exposure to alcohol was associated with greater cannabis use in young adulthood and these effects were independent of maternal education and maternal depression during pregnancy. Further, the extent of cannabis use in the late 20 s was predicted by the brain response to reward feedback in three out of the nine prenatal alcohol-related clusters and these effects were independent of current alcohol use. Sex differences in the brain response to reward outcome emerged only during the no loss vs. loss contrast. Young adult men exposed to alcohol prenatally had significantly larger brain response to no loss vs. loss in the putamen and occipital region than women exposed to prenatal alcohol. Therefore, we conclude that even moderate exposure to alcohol prenatally has long-lasting effects on brain function during reward processing and risk of cannabis use in young adulthood.

Optical biosensors that consist of a light source, optical elements, and a photodetector are used to detect chemical and biological species and pollutants. This Tutorial discusses the fundamental details of optical biosensing techniques that include materials, working principle, components, sensor configurations, parameters, and future prospects. Optical biosensing techniques include plasmonic [surface plasmon resonance (SPR) and localized SPR], fluorescence, luminescence, Raman scattering, colorimetric, and interferometric methods. Bioreceptor elements play a significant role in detecting the specific analyte that can be synthetic or natural. Surface functionalization techniques to bind the bioreceptor elements on the surface, to control the bioreceptor orientation, have been discussed in detail. The possibility of integration of techniques on a chip, to develop wearable, implantable sensors, and the associated challenges have been fully demonstrated. This Tutorial provides valuable insights into the present state and future directions of optical biosensors for various applications.

IntroductionAltered subjective visual sensitivity manifests as feelings of discomfort or overload elicited by intense and irritative visual stimuli. This can result in a host of visual aberrations including visual distortions, elementary visual hallucinations and visceral responses like dizziness and nausea, collectively referred to as “pattern glare.” Current knowledge of the underlying neural mechanisms has focused on overall excitability of the visual cortex, but the individual contribution of excitatory and inhibitory systems has not yet been quantified.MethodsIn this study, we focus on the role of glutamate and γ-aminobutyric acid (GABA) as potential mediators of individual differences in subjective visual sensitivity, measured by a computerized Pattern Glare Test—a series of monochromatic square-wave gratings with three different spatial frequencies, while controlling for psychological variables related to sensory sensitivity with multiple questionnaires. Resting neurotransmitter concentrations in primary visual cortex (V1) and right anterior insula were studied in 160 healthy participants using magnetic resonance spectroscopy.ResultsData showed significant differences in the perception of visual distortions (VD) and comfort scores between men and women, with women generally reporting more VD, and therefore the modulatory effect of sex was considered in a further examination. A general linear model analysis showed a negative effect of occipital glutamate on a number of reported visual distortions, but also a significant role of several background psychological traits. When assessing comfort scores in women, an important intervening variable was the menstrual cycle.DiscussionOur findings do not support that baseline neurotransmitter levels have a significant role in overreactivity to aversive stimuli in neurotypical population. However, we demonstrated that biological sex can have a significant impact on subjective responses. Based on this additional finding, we suggest that future studies investigate aversive visual stimuli while examining the role of biological sex.

The durability of LED bulbs ensures that the lights can operate for a long time without needing to be replaced or maintained too often. The brightness of high-power LED bulbs provides a strong and efficient light source, saves energy, and has a long lifespan. The traditional method of testing LED bulb life span has limitations such as long testing time, limited accuracy, high cost, inability to predict accurately, and failure to respond quickly. Therefore, the development of new and improved testing methods is necessary to effectively and accurately evaluate and ensure the durability of LED bulbs. This research paper proposes a hybrid Six Sigma method based on the recursive Kalman filter and Weibull distribution to estimate the lifespan of Bulb LEDs. The goal of the new method for evaluating LED bulb life is to provide accurate, reliable, and multi-dimensional information about the life of LED lamps, meeting the requirements of accuracy, diversity, time-saving, resources, credibility, and the ability to evaluate from a variety of perspectives. As a result of the research, the recursive Kalman filter method has strengths such as high accuracy, flexibility, saving computational resources, the ability to handle inaccurate data, and scalability. When applied to LED bulb life assessment, it provides an accurate and reliable estimate of LED lamp life. Research results on longevity testing were reduced from 1650 h to 710 h. The end-of-life test time is 710 h, and the aging time per life test cycle is 135 h. The cost of life testing is reduced from 2100.17 USD to 410.12 USD. This LED bulb lifespan testing method can be applied to similar types of LED products to improve the company's productivity and business efficiency.

Summary: Adar null mutant mouse embryos die with aberrant double-stranded RNA (dsRNA)-driven interferon induction, and Adar Mavs double mutants, in which interferon induction is prevented, die soon after birth. Protein kinase R (Pkr) is aberrantly activated in Adar Mavs mouse pup intestines before death, intestinal crypt cells die, and intestinal villi are lost. Adar Mavs Eifak2 (Pkr) triple mutant mice rescue all defects and have long-term survival. Adenosine deaminase acting on RNA 1 (ADAR1) and PKR co-immunoprecipitate from cells, suggesting PKR inhibition by direct interaction. AlphaFold studies on an inhibitory PKR dsRNA binding domain (dsRBD)-kinase domain interaction before dsRNA binding and on an inhibitory ADAR1 dsRBD3-PKR kinase domain interaction on dsRNA provide a testable model of the inhibition. Wild-type or editing-inactive human ADAR1 expressed in A549 cells inhibits activation of endogenous PKR. ADAR1 dsRNA binding is required for, but is not sufficient for, PKR inhibition. Mutating the ADAR1 dsRBD3-PKR contact prevents co-immunoprecipitation, ADAR1 inhibition of PKR activity, and co-localization of ADAR1 and PKR in cells.

This study utilizes the Kerr effect in the analysis of a pulsed electric field (intensity ~108 V/m, limited by the liquid dielectric strength) in deionized water at the sub-nanosecond time scale. The results provide information about voltage waveforms at the field-producing anode (160 kV peak, du/dt > 70 kV/ns). The analysis is based on detecting the phase shifts between measured and reference pulsed laser beams (pulse width, 35 ps; wavelength, 532 nm) using a Mach–Zehnder interferometer. The signal-to-noise ratio of the detected phase shift is maximized by an appropriate geometry of the field-producing anode, which creates a correctly oriented strong electric field along the interaction path and simultaneously does not electrically load the feeding transmission line. The described method has a spatial resolution of ~1 μm, and its time resolution is determined by the laser pulse duration.

White matter (WM) development has been studied extensively, but most studies used cross-sectional data, and to the best of our knowledge, none of them considered the possible effects of biological (vs. chronological) age. Therefore, we conducted a longitudinal multimodal study of WM development and studied changes in fractional anisotropy (FA) in the different WM tracts and their relationship with cortical thickness-based measures of brain aging in young adulthood. A total of 105 participants from the European Longitudinal Study of Pregnancy and Childhood (ELSPAC) prenatal birth cohort underwent magnetic resonance imaging (MRI) at the age of 23-24, and the age of 28-30 years. At both time points, FA in the different WM tracts was extracted using the JHU atlas, and brain age gap estimate (BrainAGE) was calculated using the Neuroanatomical Age Prediction using R (NAPR) model based on cortical thickness maps. Changes in FA and the speed of cortical brain aging were calculated as the difference between the respective variables in the late vs. early 20s. We demonstrated tract-specific increases as well as decreases in FA, which indicate that the WM microstructure continues to develop in the third decade of life. Moreover, the significant interaction between the speed of cortical brain aging, tract, and sex on mean FA revealed that a greater speed of cortical brain aging in young adulthood predicted greater decreases in FA in the bilateral cingulum and left superior longitudinal fasciculus in young adult men. Overall, these changes in FA in the WM tracts in young adulthood point out the protracted development of WM microstructure, particularly in men.

The demand for renewable energy has significantly increased over the last decade with increased attention to the preservation of the environment and sustainable, optimal resource management. As traditional sources of energy production are depleting at an alarming rate and causing long-lasting environmental damage, it is essential to explore green and cost-effective methodologies for meeting energy demand. With each country having different geographical, political, social, and natural factors, the problem arises of which renewable energy should be utilized for optimal resource management. This multi-criteria decision making (MCDM) challenge is tackled by applying a dynamic fuzzy hypersoft set-based Method for the evaluation of currently deployed Renewable Energy systems and providing a decision support system for the installation of new ones based on the factors mentioned above for Turkey. As the installation of new renewable energy projects and the evaluation of old ones is significantly influenced by human judgment, it leaves great room for uncertainty primarily because of the psychological factors of the expert. The novel concept of Fuzzy Hypersoft Sets (FHSs) and their Entropy (EN) and TOPSIS-based operations are first discussed with reference to the problem at hand. The presented structure is superior to the ones in the literature by allowing access to data parameters as sub-parametric values while utilizing the versatility of Fuzzy structures to deal with uncertainty. The technique has great potential to serve as a potential decision support system in any setting. For now, hypothetical expert ratings are used to illustrate the working of the dynamic structure along with a sensitivity analysis to investigate the primary criterion weights in sorting. The evaluation of currently deployed renewable energy systems using our methodology revealed an average improvement in system performance compared to traditional methods. Furthermore, the decision support system for the installation of new projects based on geographical, political, social, and natural factors exhibited a potential increase in overall system efficiency. These numeric outcomes highlight the effectiveness and practical applicability of our approach in optimizing resource management and fostering sustainable energy practices.

Induction heating is widely used in industrial furnaces due to its rapid response and energy efficiency. Computer-aided modelling and simulation are necessary for the design and optimisation of these furnaces. This article focuses on modelling and simulating induction heating of a one-dimensional spatial partial differential model for a rectangular carbon steel billet. The simulation considered temperature dynamics, including the skin effect and heat loss via radiation. The partial differential model was converted into a state-space model for designing a full-state feedback Linear Quadratic Regulator (LQR) with integral action. This controller was effectively applied to regulate the billet’s core temperature from 1000 °C to 1200 °C, under different input, R and state, Q, weighing matrices for the LQR, as well as in the presence of disturbances.

This study presents the development and examination of a streamlined $H_{\infty }$ loop-shaping continuous-time controller tailored for nonlinear Hard Disk Drive (HDD) systems. Using the Hankel-norm approximation technique, the complexity of the controller is systematically reduced, emphasizing preserving the system’s efficacy while enhancing computational efficiency. The dynamics of the HDD system and the performance of the proposed controller are investigated through MATLAB simulations. The effectiveness of the controller is quantitatively measured by employing the ITAE (Integral of Time multiplied by Absolute Error) criterion. The results indicate that a controller of the 10th-order provides a desirable compromise between maintaining robust control and minimizing disturbances and noise without significant loss in performance. This method illustrates the capacity for implementing more straightforward controllers in complex HDD systems, paving the way for their application in precision-critical industrial settings.

These days, the disease among different plants has been increasing day by day. It is a very hard task for government institutions and farmers to collect data on plant diseases from different distributed lands among regions. Therefore, data collection, disease detection, and processing are the key issues for plants when they are suffering from healthy and unhealthy issues in different lands. This article presents edge-cloud remote sensing data-based plant disease detection by exploiting deep neural networks with transfer learning. The objective is to solve the aforementioned issues, such as data collection at a wide range, disease detection, and processing them with higher accuracy and time on different machines. We suggest transfer learning commutative fuzzy deep convolutional neural network (FCDCNN) schemes based on combinatorial optimization problems. The convex function optimizes the processing time and learning rate of data training on different edge and cloud nodes to collect more and more data from different plants from distributed lands. In the concave function, we predict the diseases among different plants, such as sugarcane, blueberry, cotton, and cherry with images, videos, and numeric values. The plant disease detection app uses edge nodes and remote satellite point cloud nodes to gather and train data using transfer learning and make predictions using fuzzy DCNN schemes that are more accurate and take less time to process. Simulation results show that FCDCNN obtained higher accuracy by 98% with less processing time 25% and trained with a higher ratio of data than existing schemes.

Production costs are always the top concern of company managers in improving production and business efficiency. The cost of energy is one of the major costs that manufacturing companies must pay. This research paper proposes a Hybrid Six Sigma method based on fuzzy Multi-Attribute Decision Making (MADM), Industry 4.0, and digital numerical control (DNC). A fuzzy MADM method to select problems to improve and build an Industry 4.0 system with Internet of Things (IoT) devices, calling for automatic machining programs using Radio Frequency Identification (RFID) systems and management. Manage production equipment maintenance system using a digital numerical control (DNC) system. Measuring industry 4.0 system user satisfaction in manufacturing using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results of research on applying industry 4.0 techniques to the induction heat treatment process eliminate the dependence on worker skills and simplify the operation of the induction heat treatment process. Improve employee satisfaction with process operating conditions. Reduce the cost of electrical energy arising due to the coil maintenance system by applying the Industry 4.0 system. The result after the improvement is that the defect rate decreased from 47.2% to 4.9%. In terms of money, the reduction in losses due to defects is reduced from 6,593 USD per year to 549 USD per year. This research paper builds a sample continuous improvement model to apply to other production processes at other manufacturing companies in terms of applying industry 4.0 systems with IoT devices such as RFID and barcode readers in operations. automatically call the machining program of the machining machine and build an autonomous and preventive maintenance system using the industry 4.0 system to make improvements in process automation, smart data management, and analytics, using Internet of Things (IoT) to connect devices in the production process create a flexible production process.

Industry 4.0 technology is growing rapidly in the manufacturing industry and other businesses. Devices used in Industry 4.0 are manufactured with high-frequency switching functions and generate harmonics that negatively affect power quality. Choosing the direct or parallel connection method of the harmonic current absorber depends on the specific requirements of the system and the goal of improving power quality. Shunt Active Power Filter (SAPF) is the best device currently used to improve power quality. This study proposes to use the fuzzy-rough MARCOS method to make decisions on SAPF selection based on experts’ opinions to improve the quality of power sources at the source of smart manufacturing plants using Industry 4.0 devices. This study implements two decision-making methods in Multi-Criteria Decision-Making (MCDM). The first is the SWARA method (Stepwise Weight Assessment Ratio Analysis), and the second is the MARCOS method (Measurement Alternatives and Ranking According to Compromise Solution). The fuzzy-rough method is used to incorporate uncertain information into the results of decision-making and to use linguistic values. The analysis results of the fuzzy-rough SWARA method show that the price factor and power filter range has the greatest influence on the choice of SAPF for harmonic mitigation. Analysis results from the fuzzy-rough MARCOS method show that manufacturer Schneider Electric has the best features according to the evaluation results from decision makers. Sensitivity analysis methods were used to confirm the findings. The harmonic value THDi displayed in the field after installing the harmonic filter is, respectively, THDi $1=5$ %, THDi $2=6$ %, and THDi $3=5$ %, it meets the regulations of Circular 30/2019/TT-BCT. According to this circular, the requirement for total harmonic value (THDi) is below 12%. With THDi1, THDi2, and THDi3 values all below 12%. In operating electrical systems in production and business environments, using SAPF filters for harmonic mitigation helps improve power quality. The fuzzy-rough method is applied, and the decision maker’s decisions are used to adjust the intention to use the SAPF set to suit the conditions.

Non-invasive electrocardiography (NI-ECG) has become an indispensable tool for monitoring fetal and neonatal cardiac activity throughout the stages of pregnancy and postpartum care. This review emphasizes the distinct advantages of NI-ECG, including extended monitoring capabilities and valuable insights into fetal and neonatal health. The exploration of textile electrodes is highlighted as a promising alternative, offering improved comfort and reduced skin irritation compared to traditional adhesive electrodes. However, challenges in NI-ECG persist, with electrode placement, quantity, and noise removal being key considerations. The review underscores the significance of addressing interference sources, such as maternal and fetal body signals, motion artifacts, and electrode-skin contact. Additionally, the discussion extends to computer-aided diagnostics, presenting novel approaches for classifying fetal and neonatal health during pregnancy and delivery. Ongoing research aims to optimize electrode placement, develop advanced noise reduction algorithms, and explore sophisticated classification methodologies. These advancements hold the potential to enhance electronic monitoring, enabling early detection of abnormalities and promoting improved outcomes in prenatal and neonatal care.

Abstract Assessment of fetal heart rate (fHR) through non-invasive fetal electrocardiogram (fECG) is challenging task. This study compares the performance of five template subtraction (TS) methods on Labor (12 5-min recordings) and Pregnancy datasets (10 20-min recordings). The methods include TS without adaptation, TS using singular value decomposition (TS $$_{\textrm{SVD}}$$ SVD ), TS using linear prediction (TS $$_{\textrm{LP}}$$ LP ), TS using scaling factor (TS $$_{\textrm{SF}}$$ SF ), and sequential analysis (SA). The influence of the chosen maternal wavelet for the continuous wavelet transform (CWT) detector is also compared. The F1 score was used to measure performance. Each recording in both datasets consisted of four signals, resulting in a total comparison of 88 signals for the TS-based methods. The study reported the following results: F1 = 95.71% with TS, F1 = 95.93% with TS $$_{\textrm{SVD}}$$ SVD , F1 = 95.30% with TS $$_{\textrm{LP}}$$ LP , F1 = 95.82% with TS $$_{\textrm{SF}}$$ SF , and F1 = 95.99% with SA. The study identified gaus3 as the suitable maternal wavelet for fetal R-peak detection using the CWT detector. Furthermore, the study classified signals from the tested datasets into categories of high, medium, and low quality, providing valuable insights for subsequent fECG signal extraction. This research contributes to advancing the understanding of non-invasive fECG signal processing and lays the groundwork for improving fetal monitoring in clinical settings.

Implantation of germanium (Ge), gallium (Ga), and arsenic (As) into crystalline and preamorphized isotopically controlled silicon (Si) multilayer structures at temperatures between 153K and 973K was performed to study the mechanisms mediating ion-beam induced atomic mixing. Secondary-ionmass- spectrometry was applied to determine concentration-depth profiles of the stable isotopes before and after ion implantation. The intermixing is analytically described by a depth-dependent displacement function. The maximum displacement is found to depend not only on temperature and microstructure but also on the doping type of the implanted ion. Molecular dynamics calculations evaluate the contribution of cascade mixing, i.e., thermal-spike mixing, to the overall observed atomic mixing. Calculated and experimental results on the temperature dependence of ion-beam mixing in the amorphous and crystalline structures provide strong evidence for ion-beam induced enhanced crystallization and enhanced self-diffusion, respectively. On the other hand, the former process is confirmed by channeling Rutherford backscattering analyses of the amorphous layer thickness remaining after implantation, the latter process is consistently attributed to the formation of highly mobile Si di-interstitials formed under irradiation and in the course of damage annealing. The observed ionbeam mixing in Si is compared to recent results on ion-beam mixing of Ge isotope multilayers that, in contrast to Si, are fully described by thermal-spike mixing only. [ABSTRACT FROM AUTHOR]

Preservation of digestive structures in trilobites is generally rare; remains of the alimentary tract have been documented in about eighty of more than twenty thousand described species. Several tens of specimens belonging to five Cambrian and nine Ordovician species from the Barrandian area, Czech Republic, have markedly contributed to the knowledge of the trilobite digestive system. Here, we describe seven exceptionally preserved late holaspid trilobite specimens of species of the genus Dalmanitina collected from Upper Ordovician strata of the Barrandian area. All studied specimens are preserved as internal moulds and exhibit a narrow strip-like structure that runs sagitally or sub-sagitally under the axial lobe of the thoracopygon. These strip-like structures are markedly different from scavenger burrows and are interpreted as remains of the alimentary canal. A slightly expanded anterior-most part of the digestive system is preserved in the cephalic shield of six specimens and is interpreted as a remainder of the anterior midgut. A dark area is seen in the glabellar lobes of three specimens. These dark areas do not represent pyrite concentrations in the hypostome–glabellar cavity, because they contain only small amounts of Fe and S. These areas are interpreted as the remainder of cephalic digestive midgut diverticula, or the “perigastric organ”. Quantitative chemical analyses show that in species of Dalmanitina the digestive system is preserved as dark matter containing partly digested food particles and most probably also a higher amount of clay minerals. The described specimens constitute the first well-documented example of digestive structures in the subfamily Dalmanitininae.

Abstract Initiatives to minimise battery use, address sustainability, and reduce regular maintenance have driven the challenge to use alternative power sources to supply energy to devices deployed in Internet of Things (IoT) networks. As a key pillar of fifth generation (5G) and beyond 5G networks,IoT is estimated to reach 42 billion devices by the year 2025. Thermoelectric generators (TEGs) are solid state energy harvesters which reliably and renewably convert thermal energy into electrical energy. These devices are able to recover lost thermal energy, produce energy in extreme environments, generate electric power in remote areas, and power micro‐sensors. Applying the state of the art, the authorspresent a comprehensive review of machine learning (ML) approaches applied in combination with TEG‐powered IoT devices to manage and predict available energy. The application areas of TEG‐driven IoT devices that exploit as a heat source the temperature differences found in the environment, biological structures, machines, and other technologies are summarised. Based on detailed research of the state of the art in TEG‐powered devices, the authors investigated the research challenges, applied algorithms and application areas of this technology. The aims of the research were to devise new energy prediction and energy management systems based on ML methods, create supervised algorithms which better estimate incoming energy, and develop unsupervised and semi‐supervised approaches which provide adaptive and dynamic operation. The review results indicate that TEGs are a suitable energy harvesting technology for low‐power applications through their scalability, usability in ubiquitous temperature difference scenarios, and long operating lifetime. However, TEGs also have low energy efficiency (around 10%) and require a relatively constant heat source.

Abstract The paper presents evaluation of the proposed phonocardiography (PCG) measurement system designed primarily for heartbeat detection to estimate heart rate (HR). Typically, HR estimation is performed using electrocardiography (ECG) or pulse wave as one of the fundamental diagnostic methodologies for assessing cardiac function. The system includes novel both sensory part and data processing procedure, which is based on signal preprocessing using Wavelet Transform (WT) and Shannon energy computation and heart sounds classification using K-means. Due to the lack of standardization in the placement of PCG sensors, the study focuses on evaluating the signal quality obtained from 7 different sensor locations on the subject’s chest and investigates which locations are most suitable for recording heart sounds. The suitability of sensor localization was examined in 27 subjects by detecting the first two heart sounds (S1, S2). The HR detection sensitivity related to reference ECG from all sensor positions reached values over 88.9 and 77.4% in detection of S1 and S2, respectively. The placement in the middle of sternum showed the higher signal quality with median of the proper S1 and S2 detection sensitivity of 98.5 and 97.5%, respectively.

The analysis of biomedical signals is a very challenging task. This review paper is focused on the presentation of various methods where biomedical data, in particular vital signs, could be monitored using sensors mounted to beds. The presented methods to monitor vital signs include those combined with optical fibers, camera systems, pressure sensors, or other sensors, which may provide more efficient patient bed monitoring results. This work also covers the aspects of interference occurrence in the above-mentioned signals and sleep quality monitoring, which play a very important role in the analysis of biomedical signals and the choice of appropriate signal-processing methods. The provided information will help various researchers to understand the importance of vital sign monitoring and will be a thorough and up-to-date summary of these methods. It will also be a foundation for further enhancement of these methods.

Abstract In recent times, widely understood spine diseases have advanced to one of the most urgetn problems where quick diagnosis and treatment are needed. To diagnose its specifics (e.g. to decide whether this is a scoliosis or sagittal imbalance) and assess its extend, various kind of imaging diagnostic methods (such as X-Ray, CT, MRI scan or ST) are used. However, despite their common use, some may be regarded as (to a level) invasive methods and there are cases where there are contraindications to using them. Besides, which is even more of a problem, these are very expensive methods and whilst their use for pure diagnostic purposes is absolutely valid, then due to their cost, they cannot rather be considered as tools which would be equally valid for bad posture screening programs purposes. This paper provides an initial evaluation of the alternative approach to the spine diseases diagnostic/screening using inertial measurement unit and we propose policy-based computing as the core for the inference systems. Although the methodology presented herein is potentially applicable to a variety of spine diseases, in the nearest future we will focus specifically on sagittal imbalance detection.

Abstract Strong second harmonic generation (SHG) in silicon nitride has been extensively studied—among others, in terms of laser-induced SHG enhancement in Si3N4 waveguides. This enhancement has been ascribed to the all-optical poling induced by the coherent photogalvanic effect. Yet, an analogous process for Si3N4 thin films has not been reported. Our article reports on the observation of laser-induced threefold SHG enhancement in Si3N4 thin films. The observed enhancement has many features similar to all-optical poling, such as highly nonlinear power dependence, cumulative effect, or connection to the Si3N4–Si interface. However, identical experiments for low-oxygen silicon oxynitride thin films lead to complex behavior, including laser-induced SHG reduction. Following a thorough experimental study, including the effects of repetition rate or pulse length, the observed results were ascribed to heat-induced SHG variation. In addition to revealing a new mechanism of laser-induced SHG variation, our results also provide a means to identify this mechanism.

The paper presents a validation of novel multichannel ballistocardiography (BCG) measuring system, enabling heartbeat detection from information about movements during myocardial contraction and dilatation of arteries due to blood expulsion. The proposed methology includes novel sensory system and signal processing procedure based on Wavelet transform and Hilbert transform. Because there are no existing recommendations for BCG sensor placement, the study focuses on investigation of BCG signal quality measured from eight different locations within the subject's body. The analysis of BCG signals is primarily based on heart rate (HR) calculation, for which a J-wave detection based on decision-making processes was used. Evaluation of the proposed system was made by comparing with electrocardiography (ECG) as a gold standard, when the averaged signal from all sensors reached HR detection sensitivity higher than 95% and two sensors showed a significant difference from ECG measurement.

Aim of this paper is to evaluate short and long-term changes in T2 relaxation times after radiotherapy in patients with low and intermediate risk localized prostate cancer. A total of 24 patients were selected for this retrospective study. Each participant underwent 1.5T magnetic resonance imaging on seven separate occasions: initially after the implantation of gold fiducials, the required step for Cyberknife therapy guidance, followed by MRI scans two weeks post-therapy and monthly thereafter. As part of each MRI scan, the prostate region was manually delineated, and the T2 relaxation times were calculated for quantitative analysis. The T2 relaxation times between individual follow-ups were analyzed using Repeated Measures Analysis of Variance that revealed a significant difference across all measurements (F (6, 120) = 0.611, p << 0.001). A Bonferroni post hoc test revealed significant differences in median T2 values between the baseline and subsequent measurements, particularly between pre-therapy (M0) and two weeks post-therapy (M1), as well as during the monthly interval checks (M2 - M6). Some cases showed a delayed decrease in relaxation times, indicating the prolonged effects of therapy. The changes in T2 values during the course of radiotherapy can help in monitoring radiotherapy response in unconfirmed patients, quantifying the scarring process, and recognizing the therapy failure.

For the past decade, there has been a significant increase in customer usage of public transport applications in smart cities. These applications rely on various services, such as communication and computation, provided by additional nodes within the smart city environment. However, these services are delivered by a diverse range of cloud computing-based servers that are widely spread and heterogeneous, leading to cybersecurity becoming a crucial challenge among these servers. Numerous machine-learning approaches have been proposed in the literature to address the cybersecurity challenges in heterogeneous transport applications within smart cities. However, the centralized security and scheduling strategies suggested so far have yet to produce optimal results for transport applications. This work aims to present a secure decentralized infrastructure for transporting data in fog cloud networks. This paper introduces Multi-Objectives Reinforcement Federated Learning Blockchain (MORFLB) for Transport Infrastructure. MORFLB aims to minimize processing and transfer delays while maximizing long-term rewards by identifying known and unknown attacks on remote sensing data in-vehicle applications. MORFLB incorporates multi-agent policies, proof-of-work hashing validation, and decentralized deep neural network training to achieve minimal processing and transfer delays. It comprises vehicle applications, decentralized fog, and cloud nodes based on blockchain reinforcement federated learning, which improves rewards through trial and error. The study formulates a combinatorial problem that minimizes and maximizes various factors for vehicle applications. The experimental results demonstrate that MORFLB effectively reduces processing and transfer delays while maximizing rewards compared to existing studies. It provides a promising solution to address the cybersecurity challenges in intelligent transport applications within smart cities. In conclusion, this paper presents MORFLB, a combination of different schemes that ensure the execution of transport data under their constraints and achieve optimal results with the suggested decentralized infrastructure based on blockchain technology.

Abstract Industrial Internet of Things (IIoT) is the new paradigm to perform different healthcare applications with different services in daily life. Healthcare applications based on IIoT paradigm are widely used to track patients health status using remote healthcare technologies. Complex biomedical sensors exploit wireless technologies, and remote services in terms of industrial workflow applications to perform different healthcare tasks, such as like heartbeat, blood pressure and others. However, existing industrial healthcare technoloiges still has to deal with many problems, such as security, task scheduling, and the cost of processing tasks in IIoT based healthcare paradigms. This paper proposes a new solution to the above-mentioned issues and presents the deep reinforcement learning-aware blockchain-based task scheduling (DRLBTS) algorithm framework with different goals. DRLBTS provides security and makespan efficient scheduling for the healthcare applications. Then, it shares secure and valid data between connected network nodes after the initial assignment and data validation. Statistical results show that DRLBTS is adaptive and meets the security, privacy, and makespan requirements of healthcare applications in the distributed network.

Self-atom mixing induced by 310 keV gallium (Ga) ion implantation in crystalline and preamorphized germanium (Ge) at temperatures between 164 K and 623 K and a dose of 1 x 1015cm² is investigated using isotopic multilayer structures of alternating 70Ge and natGe layers grown by molecular beam epitaxy. The distribution of the implanted Ga atoms and the ion-beam induced depth-dependent self-atom mixing was determined by means of secondary ion mass spectrometry. Three different temperature regimes of self-atom mixing, i.e., low-, intermediate-, and high-temperature regimes are observed. At temperatures up to 423 K, the mixing is independent of the initial structure, whereas at 523 K, the intermixing of the preamorphized Ge structure is about twice as high as that of crystalline Ge. At 623 K, the intermixing of the initially amorphous Ge structure is strongly reduced and approaches the mixing of the crystalline material. The temperature dependence of ion-beam mixing is described by competitive amorphization and recrystallization processes. [ABSTRACT FROM AUTHOR]

The Industrial Internet of Healthcare Things (IIoHT) is the emerging paradigm in digital healthcare. Context-aware healthcare sensors, local intelligent watches, healthcare devices, wireless communication technologies, fog, and cloud computing are all parts of the IIoHT used in healthcare. The ubiquitous healthcare services it provides to its users in practice. However, the current IIoHT healthcare frameworks have security and failure issues in mobile fog and cloud networks where they are spread out. This paper presents the secure, fault-tolerant IIoHT Framework based on digital twin (DT) federated learning-enabled fog-cloud models. The DT is an effective technology that makes virtual copies of servers at different locations. DT integrated with federated learning inside the fog and cloud environments, where the failure of tasks and execution improved for healthcare sensor data. The study aims to reduce processing time and the risk of task failure. The study presents the Secure and Fault-Tolerant Strategies (SFTS)-enabled IIoHT framework that optimizes wearable sensor data and executes it with the minimum offloading and processing delays. Simulation results show that the proposed work minimized the security risk by 40%, failure risk of tasks risk by 50%, and the training and testing time by 39% for sensor data during the execution of mobile fog cloud networks.

This example-oriented article addresses the computation of regions of all robustly relatively stabilizing Proportional-Integral (PI) controllers under various robust stability margins α for Linear Time-Invariant (LTI) plants with unstructured multiplicative uncertainty, where the plant model with multiplicative uncertainty is built on the basis of the second-order plant with three uncertain parameters. The applied graphical method, adopted from the authors’ previous work, is grounded in finding the contour that is linked to the pairs of P–I coefficients marginally fulfilling the condition of robust relative stability expressed using the H∞ norm. The illustrative example in the current article emphasizes that the technique itself for plotting the boundary contour of robust relative stability needs to be combined with the precondition of the nominally stable feedback control system and with the line for which the integral parameter equals zero in order to get the final robust relative stability regions. The calculations of the robust relative stability regions for various robust stability margins α are followed by the demonstration of the control behavior for two selected controllers applied to a set of members from the family of plants.