Most popular statistical models in epidemic evolution focus on the dynamics of average relevant quantities and overlooks the role of small fluctuations on the model parameters. Models for Covid-19 are no exception. In this paper we show that the role of time-correlated fluctuations, far from being negligible, can in fact determine the spreading of an epidemic and, most importantly, the resurgence of the exponential diffusion in the presence of time-limited episodes in promiscuity behaviours.

High level of noise is a disturbance to the human environment. Noise in industries is also an occupational hazard because of its attendant effects on workers’ health. Noise presents health and social problems in industrial operations, and the source is related to the machineries used in the industries. One of the unique features of the noise associated with wood machinery is the level of exposure and duration. Equipment used in a factory can be extremely loud. They can produce noise at decibels high enough to cause environmental health and safety concerns. The mechanically driven transport and handling equipment, cutting, milling, shaping and dust extractor installations in the wood industry generate noise. The sources of noise pollution have increased due to non-compliance with basic safety practices. The increased use of locally fabricated machine in the industry has increased the level of noise and vibration. The effects of industrial noise pollution as discussed include: increase in blood pressure; increased stress; fatigue; vertigo; headaches; sleep disturbance; annoyance; speech problems; dysgraphia, which means reading/learning impairment; aggression; anxiety and withdrawal. As presented in this paper, noise control techniques include; sound insulation, sound absorption, vibration damping and Vibration isolation.

Breast cancer is a leading cause of death among women. Conventional screening methods, such as mammography, and ultrasound diagnosis are expensive and have significant limitations. Digital Image Elasto Tomography (DIET) is a new noninvasive breast cancer screening system that has a potential to be a low cost and reliable breast cancer screening tool. It is based on modal analysis of the breast mass, and stereographic 3D image analysis to detect the stiffer abnormal tissues. However, camera sensor noise, especially Gaussian noise is a major source of Optical Flow (OF) error in this approach to tumor detection. This work studies the performance of different conventional filters, including the standard Gaussian filter tool to remove this noise and produce more robust screening results. A radical approach, Multiple Frame Noise Removal (MFNR) is proposed, for use in this type of medical image processing instead of a Gaussian filter or other typical image noise removal tools. Its a multiple frame noise removal method where Probability Density Function (PDF) of noise is extracted from the multiple images by characterizing the same pixel positions in multiple images. The noise becomes deterministic, and hence easily removed. The proposed algorithm was applied to a data set from 10 phantom breast tests with a prototype DIET system, and 10 in-vivo samples from healthy women. Comparisons were made to an optimal Gaussian filter form that is commonly used. Reductions in OF error using these digitally imaged data sets was used to compare performance. Refinement of the images for medical applications requires higher PSNR, which was successfully achieved by using MFNR algorithm. In this study, the algorithm was used to improve the imaging results of a DIET system. The conventional wisdom that states that noise removal and detail preservation are contrasting effects is

Land surface temperature (LST) variations and anomalies associated with tectonic plate movements are common precursors to earthquakes. Spatially extended and dynamic linear zones of high temperature anomalies at the Earth's surface coinciding with faults in the Earth's crust can be used as a predictor of an approaching earthquake. LST contrast boundary concentration maps are proposed as an indicator for analyzing temperature changes before and after a series of earthquakes. The concentration of LST contrast boundaries is estimated from Landsat 8 data for the East Anatolian Fault zone in the vicinity of epicenters of the destructive earthquakes with magnitudes up to 7.8 Mw on the moment magnitude scale occurred in February 2023. A spatial relationship between earthquake epicenters and the maximum concentration of LST boundaries at azimuths of 0° and 90° was found to strengthen as the earthquake approaches and weaken after it. It was found that 92% of epicenters are located at up to 5 km distance from zones of maximum LST boundary concentration. The found regularity allows to conclude the possibility of using the proposed indicator as additional data for seismic hazard risk assessment.

Abstract Purpose: This study aims to evaluate the potential of multimodal ultrasound techniques in improving the visibility of liver lesions that are indistinct under conventional ultrasound. Specifically, we will explore the application of fusion imaging, contrast-enhanced ultrasound (CEUS), and high-frequency ultrasound in enhancing the visibility of liver masses. These techniques will be utilized to assist in visualizing liver lesions and to accurately localize them for the purpose of performing percutaneous tissue biopsy. Lastly, we will compare the biopsy results with the final diagnosis to assess whether multimodal techniques can enhance the success rate of percutaneous biopsy for liver masses that are not clearly visible under conventional ultrasound. Furthermore, the potential impact of these techniques on the development of personalized treatment strategies for patients was also discussed. Materials and Results: A retrospective analysis was conducted on 144 patients with liver masses who underwent conventional ultrasound examinations at our hospital from October 2018 to January 2024. These patients had poor visibility on conventional ultrasound, but the tumor location was visible on abdominal computerized tomography (CT) or magnetic resonance imaging (MRI) scans. We excluded 71 patients who did not undergo percutaneous liver tumor biopsy and 8 patients who remained non-visualized under multimodal ultrasound. Finally, 95 patients met the inclusion criteria and their clinical and radiological data were selected for analysis. Computerized tomography or magnetic resonance imaging-ultrasound (CT/MR-US) fusion imaging was performed in 55 patients, CEUS was conducted in 95 patients, and high-frequency ultrasound examination was performed in 21 patients. The visibility of the lesions using these three techniques was evaluated, and the consistency between the biopsy pathology and the final diagnosis was analyzed. Results: In the study, a total of 39 cases were identified as solitary liver lesions, 34 cases as multiple lesions, and an additional 23 cases as diffuse hepatocellular carcinoma. Among them, there were 13 cases (13.7%) of lesions located in the left lobe, 73 cases (76.8%) in the right lobe, and 9 cases (9.4%) at the junction of the left and right lobes. The average maximum diameter of the lesions was 2.7 cm (ranging from 0.7 cm to 10.0 cm). The detection rates of lesions using CT/MR-US fusion imaging, contrast-enhanced ultrasound (CEUS), and high-frequency ultrasound examination were 49.1%, 96.8%, and 76.2%, respectively. All patients in the entire cohort underwent percutaneous ultrasound-guided biopsy after confirming the target location of the lesions, including 9 patients underwent percutaneous microwave ablation. No severe complications were observed. The accuracy rate of the biopsies was 91.6%, and the positive concordance rate was 91.1%. The biopsy pathology included 39 cases of hepatocellular carcinoma (HCC), 16 cases of intrahepatic cholangiocarcinoma (ICC), 22 cases of metastatic adenocarcinoma, 3 cases of hepatic adenoma (HA), 1 case of vascular sarcoma, and 1 case of mixed HCC-ICC. Among the 13 patients with negative pathology findings after biopsy, 8 cases were considered false negatives (based on follow-up laboratory tests and imaging results consistent with malignant tumor characteristics), resulting in a false negative rate of 8.9%. Conclusions: The application of multimodal ultrasound technology can significantly improve the success rate of liver lesion biopsies that are difficult to visualize using conventional ultrasound. Furthermore, it will facilitate the guidance of microwave ablation therapy for lesions using multimodal ultrasound imaging, enabling collaborative precision treatment planning for patients with clinical physicians.

As most of the videos are destined for human perception, many techniques have been designed to improve video coding based on how the human visual system perceives video quality. In this paper, we propose the use of two perceptual coding techniques, namely contrast masking and texture masking, jointly operating under the High Efficiency Video Coding (HEVC) standard. These techniques aim to improve the subjective quality of the reconstructed video at the same bit rate. For contrast masking, we propose the use of a specific weighting matrix for each block size (from 4×4 up to 32×32). We achieve average Bjøntegaard Delta-Rate (BD-rate) gains of between 2.5% and 4.48%, depending on the perceptual metric and coding mode used. The texture masking scheme is based on the classification of each coding unit, using its mean directional variance features as input to a support vector machine model. According to this classification, the block’s energy, the type of coding unit, and its size, an over-quantization is computed to provide a QP offset (DQP) for this coding unit. By applying both techniques in the HEVC Reference Software, an overall average of 5.79% BD-rate gain is achieved.

A fern, Polypodium aureum, possesses a specialized spore-releasing mechanism like a catapult induced by quick expansion of vaporized bubbles. This study introduces lipid-coated perfluorocarbon droplets to enable repeatable vaporization-condensation cycles, inspired by the repeatable vaporization of Polypodium aureum. Lipid-perfluorocarbon droplets have been considered not to exhibit repeatable oscillations due to bubble collapse by low surface tension of lipid layers. However, a single lipid-dodecafluoropentane droplet with a diameter of 9.17 µm shows expansion-contraction oscillations over 4,000 cycles by changing lipid composition and applying low power of 1.7 MHz ultrasound to induce partial vaporization of droplets. The optimal combinations of shell composition, droplet fabrication, and acoustic conditions can minimize damage on shell structure and quick recovery of damaged shell layers. The highly-expanding oscillatory microbubbles provide a new direction for fuel-free micro or nanobots as well as biomedical applications of contrast agents and drug delivery.

Photon-counting Computed Tomography (PCCT) is a promising cardiovascular-imaging technique. Using detectors that count the number and energy of photons in multiple bins, PCCT offers several advantages over conventional CT, including a higher image quality, reduced contrast agent volume, radiation doses, and artifacts. Although PCCT is well established for cardiac imaging in assessing coronary artery disease, its application in vascular imaging remains limited. This review summarizes the available literature and provides an overview of the current use of PCCT for the diagnosis of vascular conditions. Of the 13 records selected in the initial search, 7 articles were included in the present review: 6 cohort studies and 1 case report. This review synthesized the available literature on PCCT in vascular imaging, focusing mainly on endoleaks detection and characterization after endovascular aneurysm repair (EVAR), contrast dose volume, and radiation exposure reduction, particularly in patients with chronic kidney disease and in those requiring follow-up CT.

In this study dual PET and contrast enhanced MRI were combined to investigate their correlation per voxel in patients at initial diagnosis with suspected glioblastoma. Correlation with contrast enhancement (CE) as an indicator of BBB leakage was further used to evaluate whether PET signal is likely caused by BBB disruption alone, or rather attributable to specific binding after BBB passage. PET images with [18F]GE180 and the amino acid [18F]FET were acquired and normalized to healthy background (TBR). Contrast enhanced images were normalized voxel by voxel with the pre-contrast T1-weighted MRI to generate relative CE values (rCE). Voxel-wise analysis revealed a high PET signal even within the sub-volumes without detectable CE. No to moderate correlation of rCE with TBR voxel-values and a small overlap as well as a larger distance of the hotspots delineated in rCE and TBR-PET images were detected. In contrast, voxel-wise correlation between both PET modalities was strong for most patients and hotspots showed a moderate overlap and distance. The high PET signal in tumor sub-volumes without CE observed in voxel-wise analysis as well as the discordant hotspots emphasize the specificity of the PET signals and the relevance of combined differential information from dual PET and MRI images.

An evaluation of image contrast in a controlled foggy environment has been made by comparison between intensity imaging and different modes of polarimetric imaging. A small-scale fog chamber has been designed and constructed to create the necessary controlled foggy environment. A linear polarimetric camera of division of focal plane and a linearly polarized light source has been used for performing the experiments with polarized light. In order to evaluate the image contrast of the different imaging modes, the Michelson’s contrast of samples of different materials relative to their background has been calculated. The higher the image contrast, the easier is to detect and segment the targets of interest that are surrounded by fog. It has been proved quantitatively that in the studied situations polarimetric images present an improvement in contrast when compared to conventional intensity images.

An ultra-low power (ULP), power gated static random access memory (SRAM) is presented for Internet of Things (IoT) applications, which operates in sub-threshold voltage ranges from 300mV to 500mV. The proposed SRAM has tendency to operate in low supply voltages with high static and dynamic noise margins. The IoT application involves battery enabled low leakage memory architecture in subthreshold regime which has low power consumption. Therefore, to improve power consumption along with better cell stability, a power gated 10T SRAM is presented. The proposed cell uses a power gated p-MOS transistor to reduce the leakage power or static power in standby mode. Moreover, due to the schmitt triggering and read decoupling of 10T SRAM the static and dynamic behavior in read, write and standby mode has shown enhanced tolerance at different process, voltage and temperature (PVT) conditions. The proposed SRAM shows better results in terms of leakage power, read static noise margin (RSNM), write static noise margin (WSNM), write-ability or write trip point (WTP), read-write energy and dynamic read margin (DRM). Further, these parameters are observed at 8-Kilo bit (Kb) and compared with already existing SRAM architectures. It is observed that the leakage power is reduced by 1/81×, 1/75× of the conventional 6T (C6T) SRAM and read decoupled 8T (RD8T) SRAM, respectively at 300mV VDD. On the contrary, RSNM, WSNM, WTP and DRM values are improved by 3×, 2×, 11.11% and 31.8% as compared to C6T SRAM, respectively. Similarly, proposed 10T has 1.48×, 25% and 9.75% better RSNM, WSNM and WTP values as compared to RD8T SRAM, respectively at 300mV VDD.

Large Language Models (LLMs) have proved significant proficiency when comes to code generation especially in Structured Query Language (SQL) for databases and recent successful Text-to-SQL method involves fine-tuning pre-trained LLMs for SQL generation tasks. Transforming natural language text into SQL queries, has been attempted to solve with various learning techniques including Few-shot learning[1], fine tuning. In this paper we propose Supervised fine-tuning (SFT) as a better alternative for learning technique for text-to-SQL generation task using Code-Llama that pushes state of art accuracy on spider test suite to 89.6% on dev set which represent first instance of surpassing the earlier best-in-class with 5.5% higher score and 86.8% of exact match accuracy on dev set. Furthermore, we demonstrate that properly prompted LLM along with SFT provides far fewer hallucinations and much more robust LLM that can be used as a general tool for any text-to-SQL generation use case.

Motivated by important applications to the analysis of complex noise-induced phenomena, a problem of the constructive description of randomly forced equilibria for nonlinear systems with multiplicative noise is considered. Using apparatus of the first approximation systems, we construct an approximation of mean square deviations that explicitly takes into account the presence of multiplicative noise. This approximation is compared with the widely used approximation based on the stochastic sensitivity technique. The general mathematical results are illustrated with the example of the van der Pol oscillator with hard excitement.

Quantum computing is tipped to lead the future of global technological progress. However, the obstacles related to quantum software development are an actual challenge to overcome. In this scenario, this work presents an implementation of the quantum search algorithm in Atos Quantum Assembly Language (AQASM) using the quantum software stack my Quantum Learning Machine (myQLM). We present the creation of a virtual quantum processor whose configurable architecture allows the analysis of induced quantum noise effects on the quantum algorithms. The codes are available throughout the manuscript so that readers can replicate them and apply the methods discussed in this article to solve their own quantum computing projects. The presented results are consistent with theoretical predictions and demonstrate that AQASM and myQLM are powerful tools for building, implementing, and simulating quantum hardware.

Highly-doped graphene samples show the conductance reduced and the shot-noise power enhanced compared to standard ballistic systems in two-dimensional electron gas. These features can be understood within a model assuming incoherent scattering of Dirac electrons between two interfaces separating the sample and the leads. Here we find, by adopting the above-mentioned model for the edge-free (Corbino) geometry and by means of the computer simulation of quantum transport, that another graphene-specific feature should be observable when the current flow through a doped disk is blocked by high magnetic field. In case the conductance drops to zero, the Fano factor approaches the value of $F\approx{}0.56$, with a very weak dependence on the disk radii ratio. The role of finite source-drain voltages and the system behavior upon tuning the electrostatic potential barrier from a rectangular to parabolic shape are also discussed.

Text-based CAPTCHA remains the most widely adopted security scheme, which is the first barrier to securing websites. Deep learning methods, especially Convolutional Neural Networks (CNNs) are the mainstream approach for text-CAPTCHA recognition, which are widely used in CAPTCHA vulnerability assessment and data collection. However, verification code recognizers are mostly deployed on the CPU platform as part of a web crawler and security assessment, they are required to have both low complexity and high recognition accuracy. Due to the specifically designed anti-attack mechanisms like noise, interference, geometric deformation, twisting, rotation, and character adhesion in text CAPTCHAs, some characters are difficult to efficiently identify with high accuracy in these complex CAPTCHA images. This paper proposed a recognition model named Adaptive-CAPTCHA with a CRNN module and trainable and configurable filtering networks, which effectively handle the interference and learn the correlation between characters in CAPTCHAs to enhance recognition accuracy. Experimental results on two datasets of different complexity show that compared with the baseline model Deep-CAPTCHA, the number of parameters of our proposed model is reduced by about 70% and the recognition accuracy is improved by more than 10 percentage points in the two datasets. In addition, the proposed model has a faster training convergence speed.

Time evolution of the quantum correlation of the hybrid qubit-qutrit system in multilocal and global dephasing under the effect of a classical noises environment is presented. We considered the qutrit in z direction and the qubit x direction. In addition, our system presents two configurations: the different and the common environment and our discussion involves three types of noises: A Static Noise (SN), Random Telegraph Noise (RTN) and Ornstein- Uhlenbeck noise (OU). By using the density matrix approach to determine the evolution of the state of the system, the found that the static noise is fatal for the quantum entanglement, the quantum correlation and for RTN and OU noises the quantum entanglement, the quantum correlation decreases monotonically with a sufficiently long time. Therefore, we can say that, entanglement and the quantum correlation are more robust to the noise precisely those Random telegraph noise and Ornstein-Uhlenbeck noise.

This study proposes a customized industrial nozzle for generating impulsive air jets. It is installed on an automatic machine where wine caps are stacked and shot consecutively into a rotating cylinder by an air jet. This process is very noisy; hence, this study aimed to investigate possible geometric variations of the nozzle that can reduce the emitted noise. First, the nozzle was tested in a laboratory to measure the air consumption at different supply pressures. Subsequently, 3D models of the nozzle and its variations were created and used for computational fluid dynamics simulations. Different boundary conditions were set, first to validate the model and compare it with the experimental test results, and then to simulate the real working conditions and determine the geometry that is less noisy while maintaining the velocity peak. Among the various possibilities, shortening the final ducts of the nozzle appears to be the most promising solution. These modified nozzles could be easily added to current machines to provide immediate benefits, and this study represents a promising start for action on other machines where this type of device is present.

Wolfenden and his coworkers observed the astronomical numbers for the catalytic proficiency of some enzymes. We connected that pinnacle of biological evolution to the universal thermodynamic evolution. We added or multiplied a random noise with chosen rate constants to explore the correlation between dissipation and enzyme efficiency for ten enzymes: beta-galactosidase, glucose isomerase, β-lactamases from three bacterial strains, ketosteroid isomerase, triosephosphate isomerase, and carbonic anhydrase I, II, and T200H. The turnover number kcat and catalytic efficiency kcat/KM are proportional to the overall entropy production – the main parameter from irreversible thermodynamics. For most enzymes with the Michaelis-Menten type cycle kinetics, the best increase in the forward kcat/KM follows after increasing the equilibrium constant of substrate-enzyme association. The Discussion section emphasizes the role of biological evolution in harvesting order (high enzyme efficiency) from disorder (high noise and dissipation). It also connects the applications of the maximum partial entropy production theorem in optimizing enzyme kinetics (D. Juretić "Bioenergetics - A Bridge across Life and Universe") with the present total entropy production role analysis. De novo enzyme design and various attempts to speed up the rate-limiting catalytic steps may profit from our theoretical insights.

This paper presents an approach to enhancing the clarity and intelligibility of speech in digital communications compromised by various background noises. Utilizing deep learning techniques, specifically a Variational Autoencoder (VAE) with 2D convolutional filters, we aim to suppress background noise in audio signals. Our method focuses on four simulated environmental noise scenarios: storms, wind, traffic, and aircraft. Training dataset has been obtained from public sources (TED-LIUM 3 dataset, which includes audio recordings from the popular TED-TALK series) combining with these background noises. The audio signals were transformed into 2D power spectrograms, upon which our VAE model was trained to filter out the noise and reconstruct clean audio. Our results demonstrate that the model outperforms existing state-of-the-art solutions in noise sup-pression. Although differences in noise types were observed, it was challenging to definitively conclude which background noise most adversely affects speech quality. Results have been assessed with objective methods (mathematical metrics) and subjective (listening to a set of audios by humans). Notably, wind noise showed the smallest deviation between the noisy and cleaned audio, perceived subjectively as the most improved scenario. Future work involves refining the phase calculation of the cleaned audio and creating a more balanced dataset to minimize differences in audio quality across scenarios. Additionally, practical ap-plications of the model in real-time streaming audio are envisaged. This research contributes significantly to the field of audio signal processing by offering a deep learning solution tailored to various noise conditions, enhancing digital communication quality.

This paper exposes the initial part of a larger research on the impact of the automotive on human health and the quality of life and environment. After a brief introduction on the need for responsible and sustainable approaches and few questions on the subject, some considerations and statistical data are presented, based on a study of the literature on the impact of road traffic and vehicles noise and vibration on people's comfort and health and their quality of life. The research results show, based on official statistics, the significant harmful impact of noise and vibration from motor vehicles and road transport on people's health and quality of life, especially in urban areas during the day. Also, the significant increase in the number of electric and hybrid vehicles from one year to another, a reality and a necessity nowadays, and the awareness that electric vehicles are not perfectly quiet and comfortable, open new research opportunities and require the development of new standards, materials, tools, equipment and test methods in the field of NVH, in a sustained synergistic approach from all stakeholders, to meet the needs and demands of today's consumers, and to comply with existing regulations and standards on environmental protection and sustainable development.

This study deals with two different aspects of the high-strength low-alloyed 1100 MC steel. The first is associated with the remarkable heterogeneity in the surface state produced during sheet rolling with respect to the sheet width. The variable-thickness surface layer exhibits a microstructure different from that of the deeper bulk. Variation of the thickness of the thermally softened near-surface region strongly affects Barkhausen noise, as well. This technique can be considered a reliable tool for monitoring the aforementioned heterogeneity. It can also be reported that the opposite sides of the sheet are different with respect to the surface state, heterogeneity distribution, and corresponding Barkhausen noise. These aspects indicate the different conditions during hot rolling followed by rapid quenching on the upper and lower rollers. The second aspect is related to the remarkable asymmetry of Barkhausen noise emission with respect to two consecutive bursts. This asymmetry is due to the presence of remnant magnetisation in the sheet produced during manufacturing. The remnant magnetisation is coupled to the magnetic field produced by the excitation coil of the Barkhausen noise sensor and strongly contributes to the aforementioned asymmetry. As soon as sufficient removal of this remnant magnetisation is carried out in the vanishing magnetic field (demagnetisation), the aforementioned remarkable asymmetry is fully lost.

Most existing data synthesis methods are designed to tackle problems such as dataset imbalance, data anonymization and insufficient sample size. There is a lack of effective synthesis methods for the limited number of datasets which contain a large of features and unknown noise to expand the size of the dataset. We propose a data synthesis method, named Adaptive Subspace Interpolation for Sample Optimization (ASISO). The idea is to divide the original feature space into several subspaces with an equal number of samples, and then perform interpolation for the samples in the adjacent subspaces. This method can adaptively adjust the size of the dataset containing unknown noise, and the expanded data typically contain minimal error with actual. Moreover, it adjusts the structure of the samples, which can significantly reduce the proportion of samples with large errors. In addition, the hyperparameters of this method have an intuitive explanation and usually require little calibration. Experimental results on artificial data and benchmark data sets demonstrate that ASISO is a robust and stable method to optimize samples.

Analyzing the records of Advanced LIGO and Virgo gravitational observatories, we found a specific time-domain asymmetry, inherent only to the signals of their gravitational detectors. Experiments with different periodic signals, Gaussian and non-Gaussian noises made it possible to conclude that the noise of gravitational detectors is an unusual mixture of signals. The gravitational-wave signals have been detected and recognized using a specialized Pearson correlation analyzer. It turned out that the detector signals include a significant (– 6 dB) component, which has the properties of records of reliably recognized gravitational waves. This allows one to argue that the gravitational noise is largely due to the processes of merging astronomical objects. Since the specific signal is registered by the detectors continuously, the field of gravitational oscillations of the sub-kilohertz band can be considered as detected. A method of analysis has also been developed to estimate the contribution of the gravitational noise component to the total signal energy. With its help it will be possible not only to pass to the radio-frequency estimation of the magnitude of gravitational disturbances but also, possibly, to construct a map of the gravitational noise of the sky.

In research applications across several areas, noise removal is indispensable for accuracy of final results. Noise is caused due to physical principals, such as background electronic noise, quantum effect, and wave rebound effect to name a few. Noise removal can help improve results in medical, astronomy, defense, and numerous other fields. Addressing this limitation would result in potentially low cost, automatic, and reliable systems. In this paper, a generalized new approach i.e. Multi-Frame Noise Removal (MFNR) is proposed for noise removal. Given any type of data, the probability density function (PDF) of the noise can be determined. Herein, we extracted the noise PDF parameters using KDE (Kernel Density Estimation). Because the data is corrupted by “deterministic” noise, hence can be cleaned. This could be used as a general purpose noise removal tool. The data point with same position in multiple frames helps us determine the noise PDF characteristics and hence making it possible to remove noise. The conventional wisdom which states that noise removal and detail preservation are contrary to each other is not true for MFNR. Experimental results validate our proposed method which showed practically complete noise reduction based on number of frames used, as compared to existing benchmark methods.

Population concentration in cities brings new risks as an increase in pollution, which causes urban health problems. In order to address this problem, traffic reduction measures are being implemented, as pedestrianization areas and the definition of Low Emissions Zones (LEZ). When the effectiveness of these types of measures is in doubt, smart city tools provide data that can be used to scientifically asses their impact. This article analyzes the situation of Madrid Central (Spain), a LEZ subject to controversy. We apply statistical and regression analyses to evaluate the effectiveness of this measure to reduce air pollution and outdoor noise. According to the results, this LEZ was able to significantly reduce NO$_2$, PM$_{2.5}$, and PM$_{10}$ concentration locally, having the same positive impact in the rest of the city. In terms of noise, this measure is able to mitigate background noise levels generated by the road traffic.

We discuss some of the fundamental practical limitations of the Fluctuation-Enhanced Sensing of odors and gases. We address resolution, measurement speed, reproducibility, memory and other problems such as humidity. Various techniques and ideas are presented to overcome these problems. Circuit solutions are also discussed.

The new WHO Environmental Noise Guidelines for the European Region have recommendations for limiting noise exposure associated with adverse health effects. The limits are said to be based on a systematic review of existing evidence. This paper gives a systematic assessment of the presented evidence with respect to aircraft noise annoyance and demonstrates that the new guidelines are based on an arbitrary selection of existing studies comprising an imperfect and faulty set of data not representative for the general airport population.

Personalized recommendation is an important part of ecommerce platform. In the recommendation system, the neural network is used to enhance collaborative filtering to accurately capture user preferences, so as to obtain better recommendation performance. Traditional recommendation methods focused on the results of a single user behavior, ignoring the modeling of multiple interactive behaviors of users, such as click, add to cart and purchase. Although many studies had also focused on Multi-Behavior modeling, two important challenges remained. 1)Since multiple behavioral context information was ignored, it was still a challenge to identify the multimodal relationships of behaviors. 2) Surveillance signals were still sparse. In order to solve this problem, this paper proposes Two-path Multi-behavior Sequence Modeling(TP_MB). First, a two-path learning strategy is introduced to maximize the multiple interaction information of user items learned by the two paths, which effectively enhances the robustness of the model. Second, a multi-behavior dependent encoder is designed. Contextual information is obtained through behavior dependencies in the interaction of different user. In addition, three contrastive learning methods are designed, which not only obtain additional auxiliary supervision signals, but also alleviate the problem of sparse supervision signals. Extensive experiments on two real datasets demonstrate that our method outperforms state-of-the-art multi-behavior recommendation methods.

The optical response of bulk germanium sulfide (GeS) is investigated systematically using different polarization-resolved experimental techniques, such as photoluminescence (PL), reflectance contrast (RC), and Raman scattering (RS). It is shown that while the low-temperature (T=5 K) optical band-gap absorption is governed by a single resonance related to the neutral exciton, the corresponding emission is dominated by the disorder/impurity- and/or phonon-assisted recombination processes. Both the RC and PL spectra are found to be linearly polarized along the armchair direction. The low and room (T=300 K) temperature RS spectra consist of six Raman peaks identified with the help of Density Fuctional Theory (DFT) calculations: Ag1, Ag2, Ag3, Ag4, B1g1, and B1g2, which polarization properties are studied under four different excitation energies. We found that the polarization orientations of the Ag2 and Ag4 modes under specific excitation energy can be useful tools to determine the GeS crystallographic directions: armchair and zigzag.

Electron Channeling Contrast Imaging (ECCI) is becoming a powerful tool in Materials Science for characterizing deformation defects. Dislocations observed by ECCI in Scanning Electron Microscope, exhibit several features depending on the crystal orientation relative to the incident beam (white/black line on a dark/bright background). In order to bring new insights concerning these contrasts, we report an original theoretical approach based on the dynamical diffraction theory. Our calculations led, for the first time, to an explicit formulation of the backscattered intensity as a function of various physical and practical parameters governing the experiment. Intensity profiles are modeled for dislocations parallel to the sample surface for different channeling conditions. All theoretical predictions are consistent with experimental results.

Recently, environmental degradation has become an international issue due to the extensive economic activities around the globe. This aspect demands the policymakers' and new researchers’ emphasis. Hence, the present article examines the impact of corporate social responsibilities (CSR) to community and environment on the environmental performance (ENP) of the manufacturing industry in China. The article also investigates the mediating role of green innovation and environmental strategy among CSR to community, CSR to environment and ENP of manufacturing industry in China. The article gets the primary data from the selected employees of the manufacturing industry using questionnaires. The article also tests the data's validity and reliability, and association among variables using smart-PLS. The outcomes indicated that CSR to environment has a positive association with ENP. The outcomes also revealed that green innovation and environmental strategy significantly mediate among CSR to community, CSR to environment and ENP. The article guides the policymakers in making policies related to improve ENP using CSR to environment and green innovation.

Background/Objectives: It is known that balance disorders involve occupational hazards. However, the inverse relationship (between certain occupations and an increased incidence of vertigo or dizziness) has been scarcely studied. The objective of this work was to analyze the occupation of a group of patients with vertigo, compared to economically active general population, and to evaluate the prevalence of occupational noise and/or vibration-exposure in both groups. Methods: A cross-sectional, observational, case-control study was carried out including 461 patients (220: Meniere’s disease; 73: vestibular migraine; 28: vestibular neuritis; 140: BPPV) (310 women and 151 men). These patients were compared to a control group from the general population, obtained from 6th EWCS-Spain (2015). Possible differences regarding sex, age, occupation, exposure to noise, and exposure to mechanical vibrations were analyzed. Results: Differences in distribution of occupations between patients with vertigo and general population were observed (Chi-square, p=6.142 e-30). Patients with vertigo were significantly more exposed to noise (p=8.19 e-12; OR=2,127, CI95% (1.720;2.630)) and vibrations (p=1.11 e-12; OR=2.288, CI95% (1.835;2.853)) than the control group. These differences were observed both between men and women. Conclusions: A relationship between occupational exposure to noise and/or vibrations and the presence of vertigo was observed. Protective and preventive measures could help prevent the occurrence of some diseases involving vertigo.

The acoustic environment in learning spaces significantly influences learning effectiveness and occupant well-being. Recent studies have shown that a series of architectural factors within physical learning environments can impact students' educational experiences. This research delves into the relationship between architectural characteristics and the perceived acoustic environment among 467 primary students in 19 learning spaces across 9 schools in Chile. The survey comprises a student questionnaire, a checklist evaluating space characteristics, and acoustic measurements of indoor noise levels during occupied conditions. Examined topics include Indoor Environmental Quality (IEQ) comfort perception, noise annoyance, hearing ability, and acoustic preference. Architectural characteristics considered encompass flexible and acoustic space design parameters. The results indicated that noise discomfort was a common issue, especially in traditional learning spaces without acoustic treatment. The type of learning space (traditional or flexible settings) has a greater impact on acoustic perception particularly concerning noise discomfort and acoustic preference. However, poorly acoustic spaces lacking absorbing materials or exhibiting lower spatial density (m2 per student) contribute to increased indoor noise levels affecting noise annoyance caused by children's talking inside. The study emphasizes the importance of considering both architectural design and acoustic conditioning for optimizing the acoustic experience in educational spaces.

The abilities of quantitative description of noise are restricted due to its origin and only the statistical and spectral analysis methods can be applied while an exact time evolution cannot be defined or predicted. It emphasizes the challenges faced in many applications including communication systems where noise can play, on one hand, a vital role impacting signal-to-noise ratio but possessing, on the other hand, unique properties such as infinite entropy (infinite information capacity), exponentially decaying correlation function and so on. Despite the deterministic nature of chaotic systems, the predictability of chaotic signals is limited for a short time window putting them close to random noise. In this article, we propose and experimentally verify an approach to achieve Gaussian-distributed chaotic signals by processing the outputs of chaotic systems. The mathematical criteria, on which the main idea of the study is based on, is the Central Limit Theorem which states that the sum of a large number of independent random variables with similar variances approaches a Gaussian distribution. The study involves more than 40 mostly three-dimensional continuous-time chaotic systems (Chua’s, Lorenz’s, Sprott’s, memristor-based, etc.) whose output signals are analyzed according to criteria that encompass the probability density functions of a chaotic signal itself and its envelope and phase, statistical and entropy-based metrics such as skewness, kurtosis, and entropy power. We found that two chaotic signals of Chua’s and Lorenz’s systems exhibited superior performance across the chosen metrics. Furthermore, our focus extended to determining the minimum number of independent chaotic signals necessary to yield a Gaussian-distributed combined signal. The analytic and experimental results indicate that the sum of at least three non-Gaussian chaotic signals closely approximates Gaussian distribution. It allows generation of reproducible Gaussian-distributed deterministic chaos by modeling simple chaotic systems.

We propose a method that enables precise determination of the number of atoms in a Dick-noise-free optical lattice clock, by effectively addressing quantum projection noise. Our approach relies on conducting stability measurements at three distinct parameter sets, allowing us to differentiate between quantum projection noise, photon shot noise, and technical noise. Importantly, our method enables accurate extraction of the atom number, even in the presence of photon shot noise and technical noise. To validate the effectiveness of our approach, we employ numerical simulations and optimize the modulation parameters to achieve the smallest uncertainty possible. The numerical simulations show the validity of our method and demonstrate a measurement uncertainty in the atom number that is below 4% across a wide range of atom numbers, provided that the standard deviation of atom number fluctuation is kept below 0.23 times the average atom number.

Discretization in time and in the state space of the system leads to the necessity to solve the parameter identification problems for dynamic systems in a limited time (at a finite time interval) using observations obtained under the influence of unknown-but-bounded noise. Finding the solution in this case is more difficult compared to traditional identification problem setting which considers random independent zero-mean noise. For system parameter identification problem under unknown-but-bounded noise, a randomized stochastic optimization algorithm is given in the paper, estimates for the mean square values of the residuals for a finite observation interval are obtained. An example of application of the given method to the problem of tuning the parameters of a multi-mirror telescope is considered.

In forensic acoustics, a possible area of analysis is represented by unwanted sound that 1 is perceived as a source of intrusion or disturbance within a certain auditory context. This context 2 is defined as the "auditory scene" and refers to the set of sounds present in a specific environment. 3 The presence of unwanted sounds in the auditory scene can cause a wide range of negative effects, 4 including disturbance, discomfort, moral or immoral harm, and other types of negative impact on the 5 health and well-being of individuals exposed to noise. In 2022, the technical specification UNI/TS 6 11844:2022 dedicated to the measurement and analysis of intrusive noise was published. The standard 7 introduces the concept of intrusive noise and defines its calculation methods based on environmental 8 measurements. The purposes of this technical specification is to provide an objective support to 9 methods already in used in acoustic disputes, where the assessment of the annoyance of a noise is 10 often a subjective evaluation of the technician. This work delves into the application to some real 11 cases, identifying the potentiality and the limits of the standardized method.

These years, disease-causing and disabling diseases have caused great concern. Neurological, musculoskeletal disorders are diverse and affect people of a wide range of ages. And the lack of comprehensive diagnostic methods places a huge burden on healthcare systems and social economies. In this paper, the current status of clinical research on neuromuscular diseases is introduced, and the advantages of magnetic field measurement compared with clinical diagnostic methods are illustrated. The paper makes a comprehensive description about the related technology of superconducting quantum interference devices (SQUIDs), magnetic field detection noise suppression scheme, the development trend of sensor detection system, and the application and model establishment of neuromuscular magnetic field. The current researches and developments in the worldwide are compared simultaneously in this paper and finally the conclusions and outlook are put forward. This review will be very interesting and helpful to the scientific community.

The frequency synthesizer is a critical component in Cs beam clock technology. In this paper, we present a demonstration of a direct microwave frequency-synthesis chain for a cesium-beam atomic clock, which utilizes frequency multiplication and a dual-phase-locked loop mode. A detailed analysis of the frequency-synthesis chain is conducted, and a mathematical model is established. The phase settling time and system stability are simulated, measured, and verified. The experimental results for the phase settling time align with the simulation outcomes. The phase settling time can be adjusted within the range of 644.5 µs to 1.5 ms, and the absolute phase noise values are -63.7 dBc/Hz, -75.7 dBc/Hz, -107.1 dBc/Hz, and -122.5 dBc/Hz at 1 Hz, 10 Hz, 1 kHz, and 10 kHz offset frequencies, respectively. Additionally, the Ramsey fringes are detected, and the Allan deviations of the 10 MHz output from the cesium-beam atomic clock are measured to be 2.99×10−12 at 1s and 8.02×10−14 at 10,000 s.

We propose a method of cooling nuclear spin systems of solid-state nanostructures by application of a time-dependent magnetic field synchronized with spin fluctuations. Optical spin noise spectroscopy is considered as the method of fluctuation control. Depending on the mutual orientation of the oscillating magnetic field and the probe light beam, cooling might be either provided by dynamic spin polarization in an external static field or result from population transfer between spin levels without build-up of a net magnetic moment (“true cooling”).

An algorithm for the treatment of images affected by both blurring and salt&pepper noise is proposed with a cost only proportional to the number of pixels. The methodology uses a discretization scheme for the Laplace operator multiplied by a suitable nonlinear term depending on the gradient. Even if this approach resembles a diffusion type algorithm, only one step of the procedure is applied, leading to significant time savings. The procedure is successfully tested on some standard black&white natural images.

Graphene quality for use in biosensors was assessed in fabricated chips by a set of methods that includes atomic force microscopy (AFM), Raman spectroscopy, and low-frequency noise. It is shown that local areas of residues on the graphene surface, which arisen due to the interaction of graphene with a photoresist at the initial stage of chips development, leads to a spread of chips resistance (R) within 1-10 kΩ and to an increase in the root mean square (RMS) roughness up to 10 times that can significantly impair reproducibility of graphene parameters in biosensors chips. It was observed that the control of the photoresist residues after photolithography (PLG) by AFM and subsequent additional cleaning allow reducing the spread of R values in chips to 1–1.6 kΩ and obtaining RMS roughness similar to the roughness in pristine graphene before PLG. Monitoring of the spectral density of low-frequency voltage fluctuation (SU), which provides integral information about the defect system and quality of the material, makes it possible to identify chips with low graphene quality and with inhomogeneously distributed compressive stresses areas by the type of frequency dependence SU (f).

Multirotors are gaining great importance in the layout of innovative and more agile mobility. In this framework, a possible solution to developing an aircraft complying with the stringent size requirements characterizing this type of application can be a coaxial rotor configuration. To exploit the several possibilities linked to coaxial rotors, a scaled experimental model is designed to evaluate the performances of the counter-rotating propellers system, concerning the distance between the two propellers. Both thrust and noise are considered as parameters of interest. Two brushless motors are deployed whereas the propellers' angular velocity, in terms of round per minute (rpm), is controlled by an external control system. Tests are conducted on both single isolated propellers as well as on the counter-rotating system: the two propellers and their respective motors have been characterized concerning the thrust. Furthermore, a comparison with a numerical model is performed. Noise evaluation on the single propeller has shown a motor contribution prevalence at a low rpm regime (1140-1500 rpm) and a propeller prevalence for angular velocities higher than 1860 rpm. By varying the distances between the propellers a sensitivity analysis is performed with the aim of identifying the optimum configuration taking into account both noise and thrust performances.

Digital medicine based on the integration of all medical data of a particular patient, has become a reality today, thanks to information technology. Traditional medical examinations can be supple-mented by assessment results of the oxidative-anti-oxidative (OAO) status of the body [1]. Elec-trochemical sensors are able to not only determine the integral indicators of the OAO system of the body, but also to depict details of the processes occurring in the system. The main obstacle to the widespread use of electrochemical sensors in medical diagnostics is the extremely small amount of the received information in comparison with tens of thousands of known human dis-eases. The problem can be eliminated only by rethinking the purpose of electrochemical measure-ment within the framework of thermodynamics of information processes and information theory. In the information paradigm of electrochemical analysis of biological fluids, a sample is considered as an electrochemical message created by a sensor. The purpose of electrochemical measurement is to obtain information in a volume sufficient to identify the sample composition within the range of possible concentrations of its components. The fundamentals of the thermodynamics of infor-mation processes are considered and conclusions that are of practical importance for the devel-opment of electrochemical sensors and analyzers are derived. It is shown that potentiostatic con-trol of the sensor is physically impacted by the electromechanical instability of the electrical double layer, which is the main source of sensor signal noise. Estimates are of a minimum amount of an-alytical signal information required for identification of a sample of a known composition, such as a biological fluid, are provided. Examples of highly informative analytical signals for flowing and stationary samples are presented. Problems related to the visualization of such signals are noted.

Amplitude modulation (AM) is a characteristic feature of wind farm noise and has the potential to contribute to annoyance and sleep disturbance. This study aimed to develop an AM detection method using a random forest approach. The method was developed and validated on 6,000 10-second samples of wind farm noise manually classified by a scorer via a listening experiment. Comparison between the random forest method and other widely-used methods showed that the proposed method consistently demonstrated superior performance. This study also found that a combination of low-frequency content features and other unique characteristics of wind farm noise play an important role in enhancing AM detection performance. Taken together, these findings support that using machine learning-based detection of AM is well suited and effective for in-depth exploration of large wind farm noise data sets for potential legislative and research purposes.

Plants are such important keys of biological part of our environment, supply the human life and creatures. Understanding how the plant’s functions react with our surroundings, helps us better to make plant growth and development of food products. It means the plant phenotyping gives us bio information which needs some tools to reach the plant knowledge. Imaging tools is one of the phenotyping solutions which consists of imaging hardware such as the camera and image analysis software analyses the plant images changings such as plant growth rates. In this paper, we proposed a preprocessing algorithm to eliminate the noise and separate foreground from the background which results the plant image to help the plant image segmentation. The preprocessing is one of important levels has effect on better image segmentation and finally better plant’s image labeling and analysis. Our proposed algorithm is focused on removing noise such as converting the color space, applying the filters and local adaptive binarization step such as Niblack. Finally, we evaluate our algorithm with other algorithms by testing a variety of binarization methods.

One of the most common limits to gas sensor performance is the presence of unwanted interference fringes or etalons arising, for example, from multiple reflections between surfaces in the optical path. Additionally, since the amplitude and the frequency of these interference depend on the distance and alignment of the optical elements, they are affected by temperature changes and mechanical disturbances, giving rise to a drift of the signal. In this work, we present a novel semi-parametric algorithm which allows the extraction of a signal, like the spectroscopic absorption line of a gas molecule, from a background containing arbitrary disturbances, without having to make any assumption on the functional form of these disturbances. The algorithm is applied first to simulated data and then to oxygen absorption measurements in presence of strong fringes.To the best of the authors' knowledge, the algorithm enables an unprecedented accuracy particularly if the fringes have a free spectral range and amplitude comparable to those of the signal to be detected. The described method presents the advantage of being based purely on post processing, and to be of extremely straightforward implementation if the functional form of the Fourier transform of the signal is known. Therefore it has the potential to enable interference-immune absorption spectroscopy. Finally, its relevance goes beyond absorption spectroscopy for gas sensing since it can be applied to any kind of spectroscopic data.

Distributed Energy Resources might have a severe influence on Power Line Communications, as they can generate interfering signals and high frequency emissions or supraharmonics that may cause loss of metering and control data. In this paper, the influence of various energy resources on Narrowband Power Line Communications is described and analyzed through several test measurements performed in a real microgrid. Accordingly, the paper describes the effects on smart metering communications through MAC layer analysis. Results show that the commutation frequency of inverters and the presence of battery chargers are remarkable sources of disturbance in low voltage distribution networks. In this sense, the results presented can contribute to efforts towards standardization and normative of emissions at higher frequencies higher, such as CENELEC EN 50160 and IEC/TS 62749.

Steels studs are an inevitable part of drywall construction as they are lightweight and offer the required structural stability. However, the studs act as sound bridges between the plasterboards reducing the overall sound insulation of the wall. Overcoming this often calls for wider cavity walls and complex stud decoupling fixtures that increase the installation cost while reducing the floor area. As an alternative approach, this research reveals the potential of perforated studs to improve the acoustic insulation of drywall partitions. The acoustic and structural performance is characterized using a validated finite element model that acted as a prediction tool in reducing the number of physical tests required. The results established that an acoustic numerical model featuring fluid-structure-interaction can predict the weighted sound reduction index of a stud wall assembly at an accuracy of ±1 dB. The model was used to analyze six perforated stud designs and found to outperform the sound insulation of non-perforated drywall partitions by reducing the sound bridging. Overall, the best performing perforated stud design was found to offer improvements in acoustic insulation of up to 4 dB, while being structurally compliant.

Current Micro-Computed Tomography (Micro-CT) protocols for microvasculature analysis often necessitate organ dissection and non-clearable contrast agents. We describe a non-destructive technique for achieving high-resolution images of the human coronary microvasculature. Formalin-fixed human hearts were cannulated using venogram catheters designed for balloon occlusion. Catheters were anchored at the aortic root. Hearts were placed in sealable, radiolucent containers filled with insulating polyurethane foam. Iodine potassium iodide (IKI, Lugol's solution;6.3% Potassium Iodide, 4.1% Iodide) was injected into the coronary arteries. Contrast distribution was monitored using a North Star Imaging X3000 Micro-CT scanner with low-radiation settings, followed by high-radiation scans (3600 rad, 60 kV, 900 mA). We successfully imaged two intact human hearts with chronic total coronary occlusions of the right coronary artery. The scans enabled detailed analysis of the vasa vasorum surrounding stenosed and occluded segments. After imaging, both hearts were cleared of iodine and excess polyurethane foam and then returned to their initial formalin-fixed state for indefinite storage. Conclusions: This method allows for the non-destructive, high-resolution micro-CT imaging of coronary microvasculature in intact human hearts, paving the way for 3D microvascular reconstruction in a macrovascular context.

Blind image quality assessment (BIQA) aims to evaluate image quality in a way that closely matches human perception. To achieve this goal, the strengths of deep learning and the characteristics of human visual system (HVS) can be combined. In this paper, inspired by the ventral pathway and dorsal pathway of HVS, a dual-pathway convolutional neural network is proposed for BIQA task. The proposed method consists of two pathways: “what” pathway, which mimics the ventral pathway of HVS to extract the content features of distorted images, and the “where” pathway, which mimics the dorsal pathway of HVS to extract the global shape features of distorted images. Then, the features from the two pathways are fused and mapped to an image quality score. Additionally, the gradient images weighted by the contrast sensitivity are used as the input to the “where” pathway, allowing it to extract global shape features that are more sensitive to human perception. Moreover, a dual-pathway multi-scale feature fusion module is designed to fuse the multi-scale features of the two pathways, enabling the model to capture both global features and local details, thus improving the overall performance of the model. The experiments conducted on six databases show that the proposed method achieves the state-of-the-art performance.

The log interpretation and evaluation of Chang 8 tight sandstone reservoir in Longdong West area, Ordos Basin, is facing great challenges due to the existence of low resistivity contrast oil pays. To better guide the exploration of oil resources in this area, the characteristics and control mechanism of low resistivity contrast oil pays were studied in this research. Firstly, according to the relative value of apparent resistivity increase rate of the target formation, the reservoir was divided into two types: low resistivity contrast oil pay (LRCP) and high resistivity oil pay (HRP). Then, the reservoir characteristics were studied by comparing and analyzing the experimental data, formation water data and logging data collected from the two reservoir types. On this basis, the control mechanism of LRCP was studied and summarized into reservoir micro factors and regional macro factors, respectively. It is found that the reservoir rock composition between LRCP and HRP is basically the same. Compared with HRP reservoir, the average porosity and permeability of LRCP reservoir are relatively better, but the pore structure is relatively poorer because of the high content of micro pores. When the filling pressure of crude oil is sufficient enough, the high oil saturation can be formed in LRCP reservoir. The high irreducible water saturation and high formation water salinity are the main micro factors reduced the reservoir resistivity. Besides, the difference of hydrocarbon expulsion capacity of source rock and the regional difference of formation water salinity are the macro factors influenced the distribution of LRCP and HRP from vertical and horizontal of the region. The formation of LRCP is controlled by the comprehensive influence of reservoir micro factors and regional macro factors. And the comprehensive consideration of the influence of micro factors and macro factors on LRCP is suggested during the log interpretation and evaluation.

Magnetic Resonance Imaging (MRI) is a powerful, non-invasive and nondestructive tool, capable of providing three-dimensional (3D) images of living organisms. The use of magnetic contrast agents has allowed clinical researchers and analysts to enormously increase the sensitivity and specificity of MRI since these substances change the intrinsic properties of the tissues within a living body, increasing the information present in the images. The advances in nanotechnology and materials science as well as the research of new magnetic effects have been the driving forces that propel the use of magnetic nanostructures as promising alternatives to the commercial contrast agents used in MRI. This review discusses the principles associated with the use of contrast agents in MRI as well as the most recent reports focused on nanostructured contrast agents. The potential applications of gadolinium (Gd) and manganese Mn-based nanomaterials and iron oxide nanoparticles in this imaging technique are discussed as well, from their magnetic behavior to the mainly used materials and nanoarchitectures. Then, it is also addressed the recent efforts made to develop new types of contrast agents based on synthetic antiferromagnetic and high-aspect ratio nanostructures. Furthermore, the application of these materials in theragnosis, either as contrast agents and controlled drug release, contrast agents and thermal therapy or contrast agents and radiosensitizers, is also presented.

A new attack against the Kirchhoff-Law-Johnson-Noise (KLJN) key distribution system is introduced. The attack is based on 1) Utilizing the dc-voltage-source - which we put at Alice’s end in our mathematical modeling of the attack-that could exist due to errors, imbalances, Electromagnetic interference and etc.2) On studying the number of samples per bit in the security key that the measured Alice/Bob voltages exceeds or falls below a threshold voltage, respectively. The threshold voltage is the average between dc voltages across low and high resistors- generated by a dc-voltage source. We count the number of samples the voltage at Bob’s end (containing both the noise and dc components) exceeds the threshold voltage and how many times it falls below the threshold to judge whether the resistor can be guessed as low or high for every cycle. Also for a pre-specified key-length we count the number of high resistance estimations per bit –at Bob’s end-according to the previous criterion and the non-successful estimations per bit to judge the final guessed resistor value at Alice’s end and Bob’s end, where if we have more bits with most of its measured samples are above the threshold voltage then we will predict Bob’s resistance as high resistance, otherwise we predict Bob’s resistance to be low resistance. The Simulation was conducted and the attack proved that it is successful unless the temperature increased dramatically to ranges more than a threshold temperature ~ Kelvin that increases when the number of samples per bit increases.

The research examines the relationship between self-rated health situation and personal percep-tion of heat waves, and how social linkage of communities would be a moderator variable in residents’ perception of heat waves in Taiwan. This study uses the questionnaire conducted by Sinica “Responsive Capacity under Heat Wave: The Perspectives of the Locals”(2019), using OLS method for estimating the unknown parameters in multiple regression model. The author finds that the correlation of self-rated health situation and perception toward heat is significantly posi-tive. Also, social linkage in communities affects strongly as a moderator variable: While the sat-isfaction to with their community could reduce negative reaction to heat, contacts with neighbors could increase possibility people feel uncomfortable in high-temperature situation. This study ex-hibits the effects of social environment on community, and expects further related researches or practices to strengthen capability to resist heat wavesƒ for Taiwanese residents.

Amebiasis is a disease caused by the unicellular parasite Entamoeba histolytica. In most cases, the infection is asymptomatic but when symptomatic, the infection can cause dysentery and invasive extraintestinal complications. In the gut, E.histolytica feeds on bacteria. Increasing evidences support the role of the gut microbiota in the development of the disease. In this review we will discuss the consequences of E.histolytica infection on the gut microbiota. We will also discuss new evidences about the role of the gut microbiota in regulating the resistance of the parasite to oxi-dative stress and its virulence.

Objective: To explore three novel fasting blood glucose (FBG)-based novel indicators, including FBG-to-albumin ratio (FAR), FBG-to-lymphocytes ratio (FLR), and FBG-to-hemoglobin ratio (FHR), in predicting prognosis and detecting metastasis for patients with pancreatic neuroendocrine neoplasms after resection. Materials and methods: A total of 178 pNEN patients underwent surgical resection were included in this study. Receiver operating characteristic (ROC) curves were used to evaluate the diagnosis values of FAR, FLR, and FHR, and the cut-off values were obtained for further analyses. Univariate and multivariate analyses were conducted to determine the independent predictors. The Kaplan-Meier method was used to evaluate the progression-free survival (PFS) and overall survival (OS) of the pNEN patients. Results: The optimal cut-off values of FAR, FLR, and FHR were 0.17, 2.85, and 0.028, respectively. As for PFS, the area under the curve (AUC) was 0.693 for FAR, 0.690 for FLR, and 0.661 for FHR, respectively. The AUC was 0.770, 0.692, and 0.715 accordingly for OS. The groups with lower FAR, FLR, and FHR were significantly associated with prolonged PFS and OS (p < 0.05). In patients with metastasis, the lower FAR group was correlated with significantly longer PFS and OS (p = 0.022 and 0.002, respectively). The FLR was an independent predictor of PFS in pNEN patients, and the FAR was a predictor of OS. FAR was an independent indicator of PFS in patients with metastasis. Conclusion: Preoperative FAR, FLR, and FHR are effective in predicting the prognosis of pNEN patients and detecting the synchronous metastases.

Objective: The current study aimed to delineate the relationship of furin with inflammation while cervical intraepithelial neoplasia progresses to cancer. Study Design:This cross-sectional study included 81 women who required colposcopic examinations. The study groups were formed based on pathological results: Group I included women with cervical intraepithelial neoplasia (CIN) I (n=30); Group II included women with CIN II-III (n=28); and Group III included women with cervical cancer (CC) (n=23). Furin, ki-67, and p16 levels were evaluated based on immunostaining intensity. The inflammatory indices were calculated parallel with the literature from routine blood samples retrieved within one week before the procedure. Results:Furin expression gradually increased from CIN I to CIN II-III and from CIN II-III to CC, respectively (p < .001, p = .005). The NLR, MLR, PLR, and SII were significantly higher in the CC group (p < .001). ROC curve analysis unveiled that NLR, MLR, PLR and SII predicted the presence of CC with a cutoff value of 2.39 for NLR (sensitivity: 91.3%, specificity: 63.8%, AUROC: 0.79, p < .001); a cutoff value of 0.27 for MLR (sensitivity: 78.3%, specificity: 72.4%, AUROC: 0.77, p = .009); a cutoff value of 123 for PLR (sensitivity: 100%, specificity: 41.4%, AUROC: 0.70, p = .04); and a cutoff value of 747 for SII (sensitivity: 69.6%, specificity: 90.7%, AUROC: 0.71, p = .014). Conclusion:Furin expression increased gradually in parallel to the severity of cervical intraepithelial neoplasia. The inflammatory indices were higher in the presence of CC and denoted good discrimination ability for predicting cervical cancer.

The architectural configuration of the space plays a crucial role in the acoustics of Neonatal Intensive Care Units (NICUs). The design of the environment, the materials used in construction, and the organisation of noise sources within the room significantly influence the noise levels present in these critical areas. For this research, noise levels found in two hospitals, with different architectural configurations and sizes but similar construction materials, have been analyzed. Data were recorded at 1-second intervals over a period of more than 24 hours. The data collected in these hospitals confirm the magnitude of the noise problem in NICUs, highlighting the urgent need to address it effectively to ensure an appropriate and safe environment for the recovery of neonates. Good architectural planning can help mitigate noise, while poor configuration can exacerbate acoustic problems, negatively affecting both patients and healthcare staff. It is crucial to involve architects, acoustic engineers, and healthcare staff in the design and renovation of NICUs to create spaces that not only meet medical needs but also consider the auditory well-being of neonates.

The presence of phase diffusion can lead to the loss of the advantages of quantum measurements. Squeezing is considered to be an effective method to reduce the detrimental effect of phase diffusion on the measurement. The Mach-Zehnder (MZ) interferometer has been exploited as a generic tool for precise phase measurement. Describing the reduction in quantum advantage caused by phase diffusion in an MZ interferometer that can be mitigated by squeezing is not easy to handle analytically, because the input state will change from a pure state to a mixed state after experiencing the diffusion noise in the MZ interferometer. We introduce a truncated MZ interferometer that can achieve the same potential phase sensitivity as the conventional MZ interferometer. This scheme can theoretically explain how phase diffusion reduces phase estimation and why squeezing counteracts in the presence of phase diffusion. Using the Gaussian property of the input state and the characteristic of Gaussian operation in the squeezing, the two orthogonal field quantities of the quantum state are squeezed and anti-squeezed to different degrees, and the analytic results are obtained. The truncated MZ interferometer is simpler to build and operate than the conventional MZ interferometer, and also mitigates the phase diffusion noise by the squeezing operation, thus making it useful for applications in precision metrology.

This study tests the utilization of Bluetooth noise-canceling headphones in improving the quality of eye exams in patients with hearing loss. University of Texas Medical Branch patients above 55 years of age were screened for hearing loss using SoundCheck audiometry. All 29 subjects (18 red-zone and 11 yellow-zone) answered pre-recorded Amsler grid and patient education questions that solicited precise responses. As controls, subjects were randomly administered half of the questions via headphones and half via a smartphone at normal speech volume (60 decibels). Points were awarded for responses demonstrating comprehension, and a post-exam survey was collected. Collectively, the mean score was 1.79 with headphones versus 0.96 with control on the Amsler grid segment and 1.90 with headphones versus 0.97 with control on education questions (p=0.001). Between the red and yellow zone hearing loss patients, the red-zone group answered significantly better in both Amsler (1.78 versus 0.50; p=0.0003) and education questions (1.88 versus 0.44; p&lt;0.0001) with headphones. The yellow zone group answered better with headphones overall but failed to reach significance. Post-exam survey indicated that 28 of 29 patients (97%) preferred the headphones during ophthalmic exams. Patients with severe hearing loss demonstrated better comprehension with the noise-canceling headphones; thus these devices show great promise in improving effective, compassionate communication between providers and hearing-loss patients.

Environmental pollution by noise is one of the most serious health threats nowadays. The impact of noise on the human body depends not only on the sound level but also on its spectral distribution. Reliable measurements of the environmental noise spectrum are often hampered by the very high price of top quality measuring devices. This paper explores the possibility of using much cheaper audio recorders for frequency analysis. Comparative research were performed in laboratory and field conditions, which showed that, with some limitations, these devices can be useful for the needs of environmental noise frequency analysis. This fact gives an opportunity for reduce the cost of noise analysis experimental work.

This paper explores the use of different deep learning models for predicting precipitation in 56 meteorological stations in Jilin Province, China. The models used include Stacked-LSTM, Transformer, and SVR, and Gaussian noise is added to the data to improve their robustness. Results show that the Stacked-LSTM model performs the best, achieving high prediction accuracy and stability. The study also conducts variable attribution analysis using LightGBM and finds that temperature, dew point, precipitation in previous days, and air pressure are the most important factors affecting precipitation prediction, which is consistent with traditional meteorological theory. The paper provides detailed information on the data processing, model training, and parameter settings, which can serve as a reference for future precipitation prediction tasks. The findings suggest that adding Gaussian noise to the dataset can improve the model's generalization ability, especially for predicting days with zero precipitation. Overall, this study provides useful insights into the application of deep learning models in precipitation prediction and can contribute to the development of meteorological forecasting and applications.

Target recognition mainly includes three approaches: optical image-based, echo detection-based, and passive signal analysis-based methods. Among them, the passive signal-based method is closely integrated with practical applications due to its strong environmental adaptability. Based on passive radar signal analysis, we design an "end-to-end" model that cascades a noise estimation network with a recognition network to identify working modes in noise environment. The noise estimation network is implemented based on U-Net, which adopts a method of feature extraction and reconstruction to adaptively estimate the noise mapping level of the sample, which can help the recognition network to reduce noise interference. Focusing on the characteristics of radar signal, the recognition network is realized based on Multi-Scale Convolutional Attention Network (MSCANet). Firstly, the deep group convolution is used to isolate the channel interaction in the shallow network. Then, through the multi-scale convolution module, finer-grained features of the signal are extracted without increasing the complexity of the model. Finally, the self-attention mechanism is used to suppress the influence of low-correlation and negative-correlation channels and spaces. This method overcomes the problem that the conventional method is seriously disturbed by noise. We validated the proposed method in 81 kinds of noise environments, achieving an average accuracy of 94.65%. Additionally, we discussed the performance of six machine learning algorithms and four deep learning algorithms. Compared to these methods, proposed MSCANet achieved an accuracy improvement of approximately 17%. Our method demonstrates better generalization and robustness.

EMG analysis is becoming increasingly important in many research and clinical applications, including muscle fatigue detection, control of robotic mechanisms and prostheses, clinical diagnosis of neuromuscular diseases and quantification of force. However, electromyographic signals can be contaminated by various types of noise, interference and artifacts, which can lead to misinterpretation of the data acquired using this method. Even assuming best practices, the collected signal may still be altered by such contaminants. The aim of this paper is to review methods employed to reduce contamination of single channel EMG signals. This review is limited to methods performed directly on the measured EMG signal and those that allow total reconstruction of the EMG signal. Subtraction methods used in the time domain, denoising methods performed after signal decomposition and hybrid methods are assessed. It is defended that individual methods may be more or less suitable for a particular application depending on contaminant(s) present in the signal and on the specific requirements of the application.

Pulsed thermography is a commonly used non-destructive testing method, and is increasingly studied for advanced materials such as carbon fiber-reinforced polymer (CFRP) evaluation. Different processing approaches are proposed to detect and characterize anomalies that may be generated in structures during the manufacturing cycle or service period. In this study, we used a type of matrix decomposition using Robust-PCA via Inexact-ALM in our experiment. We investigate this method as a pre-and post-processing method on thermal data acquired by pulsed thermography. We employed state-of-the-art methods, i.e., PCT, PPT, and PLST, as the main process. The results indicate that pre-processing on thermal data can elevate the defect detectability while post-processing, in some cases, can deteriorate the results.

Titanium’s accelerating usage in global markets is attributable to its distinctive combination of physical and metallurgical properties. The key to best utilizing titanium is to exploit these characteristics, especially as they complement one another in a given application, rather than to just directly substitute titanium for another metal. Titanium alloy are extensively used in aerospace applications such as components in aero-engines and space shuttles, mainly due to their superior strength to weight ratio. For these demanding applications functionality and reliability of components are of great importance. To increase flight safety, higher sensitivity inspections are sought for rotating parts. Increased sensitivity can be applied at the billet stage, the forging stage, or both. Inspection of the forging geometry affords the opportunity to apply the highest sensitivity due to the shorter material paths when compared to those required for billet inspections. Forging inspection is typically performed for titanium (Ti) rotating parts with immersion inspection and fixed-focus, single-element transducers. Increased gain is required with depth because the ultrasonic beam attenuates with distance and diverges beyond the focus position that is placed near the surface. The higher gain that is applied with depth has the effect of increasing the UT noise with depth. The relationships between the UT noise, selection of the examination technique and the smallest detectable defect are presented in this material.

Wildlands are increasingly lost to human development. Conservation scientists repeatedly call for protecting the remaining wildlands and expanding the land area protected in reserves. Despite these calls, conservation reserves can be eliminated through legislation that demotes their conservation status. For example, legislation introduced to the Congress of the United States recently would demote 29 Wilderness Study Areas (WSAs) from the protections afforded by their existing status. The legislation suggests that the 29 areas are not suitable for a promotion and future inclusion in the National Wilderness Preservation System based on decades-old local evaluations. Local evaluations, notwithstanding, it may be important to consider the value of lands from a national perspective. Without a national perspective, local evaluations alone may overlook the national significance of lands. With this in mind, I used five qualities of wildland value (wildness, light pollution, noise pollution, intactness of mammals, and intactness mammal carnivores of conservation concern) to compare the 29 WSAs to all national parks and wilderness areas located within the contiguous United States. The pool of 29 WSAs were similar to the pool of national parks and wilderness with respect to the five qualities assessed, and some of the WSAs were characterized by higher values than most of national parks and wilderness areas. This analysis demonstrates the national significance of the WSAs targeted for conservation status demotion. Such an approach could be used in future land management legislation and planning to ensure that a national perspective on conservation value is brought to bear on decisions facing federally-managed lands.

This paper presents a 612–1152 MHz Injection Locked Frequency Multiplier (ILFM). The proposed ILFM is only used for sending an input signal to the receiver in the I/Q mismatch calibration mode. Using the Phase-Locked Loop (PLL) to calibrate the receiver places a burden on this system due to the extra area required and power consumption. Instead of the PLL, to satisfy high frequency, low jitter, and low area, a Ring Oscillator is proposed. The free-running frequency of the ILFM is automatically digitally calibrated to reflect the frequency of the injected signal from the harmonics of the reference clock. To control the frequency of the ILFM, the load current is digitally tuned with 6-bit digital control signal. The proposed ILFM locks to the target frequency using a digitally controlled Frequency Locked Loop (FLL). This chip is fabricated using 1-poly 6-metal 0.18 µm CMOS and achieve the wide tuning range of 612–1152 MHz. The power consumption is 0.95 mW from a supply voltage of 1.8 V. The measured phase noise of the ILFM is −108 dBc/Hz at a 1 MHz offset.

A turbomachinery is essential part in the power generation cycle. But, it is main noise source to annoy workers and users and to make environmental problem. Thus it is important to reduce this noise for operating the power generation cycle. This noise is created by flow instability on rotor blade trailing edge. An airfoil that becomes a section of a rotor blade of a rotating machine is manufactured as a blunt trailing edge (TE) with a round or flatback shape rather than the ideal sharp TE shape for the purposes of producibility and durability. This increases the tonal noise and flow-induced vibration at low frequency owing to vortex shedding behind TE when compared with a sharp TE. In order to overcome this problem, this study investigates the oblique TE shape using numerical simulation. In order to do so, the flow was simulated using large eddy simulation (LES) and the noise was analysed by acoustic analogy coupled with LES result. Once the simulation results were verified using the flatback airfoil measurements of Sandia National Laboratories, numerical prediction was performed for airfoils modified to have oblique trailing edge angles of 60°, 45°, and 30° to analyse the flow and noise characteristics. From the simulation results for an airfoil having an oblique TE, it could be seen that the vortex shedding frequency moves in accordance with the oblique angle and that the vortex shedding noise characteristics change according to this angle when compared to the flatback TE airfoil. Therefore, it is considered that modifying the flatback TE airfoil to have an appropriate oblique angle can reduce noise and change the tonal frequency to a bandwidth that is suitable for mechanical systems.

Intelligent fault diagnosis techniques have replaced the time-consuming and unreliable human analysis, increasing the efficiency of fault diagnosis. Deep learning model can improve the accuracy of intelligent fault diagnosis with the help of its multilayer nonlinear mapping ability. This paper has proposed a novel method named Deep Convolutional Neural Networks with Wide First-layer Kernels (WDCNN). The proposed method uses raw vibration signals as input (data augmentation is used to generate more inputs), and uses the wide kernels in first convolutional layer for extracting feature and suppressing high frequency noise. Small convolutional kernels in the preceding layers are used for multilayer nonlinear mapping. AdaBN is implemented to improve the domain adaptation ability of the model. The proposed model addresses the problem that currently, the accuracy of CNN applied to fault diagnosis is not very high. WDCNN can not only achieve 100% classification accuracy on normal signals, but also outperform state of the art DNN model which is based on frequency features under different working load and noisy environment.

Abstract Purpose Despite a considerable amount of literature on dual-energy-CT(DECT) iodine uptake of the head and neck, the physiologic iodine uptake of this region has not been defined yet. This study aims to establish reference values for the iodine uptake of healthy organs to facilitate clinical application. Material and Methods: Consecutive venous DECT scans of head and neck were reviewed, and unremarkable exams were included (n=617). 35 region of interest measurements were performed in 16 anatomical regions. Iodine uptake was compared among different organs/tissues and subgroup analysis was performed (male [n=403] vs female [n=214]; young [n=207] vs middle-aged [n=206] vs old [n=204]; normal weight [n=314] vs overweight [n=196] vs obese [n=107]]). Results: Overall mean iodine uptake values ranged between 0.5 and 9.4mg/ml. Women showed higher iodine concentration in the cervical vessels and higher uptake for the parotid gland, masseter muscle, submandibular glands, sublingual glands, palatine tonsils, tongue body, thyroid gland, and the sternocleidomastoid muscle than men (p0.04). With increasing age, intravascular iodine concentrations increased as well as iodine uptake for cerebellum and thyroid gland, while values for tongue and palatine tonsils were lower compared to younger subjects (p0.03). Iodine concentrations for parotid glands and sternocleidomastoid muscles decreased with higher BMI (p0.004), while normal-weighted patients showed higher iodine values inside the jugular veins, other cervical glands, and tonsils versus patients with higher BMI (p0.04). Conclusion: Physiologic iodine uptake values of cervical organs and tissues show gender-, age-, and BMI-related differences, which should be considered in clinical routine of head and neck DECT.

Quantitative differential phase contrast (qDPC) imaging has become an important method of optical measurement and life science research in microscopy because of its high reconstruction resolution and non-invasive, high-contrast and quantitative imaging of biological samples. Despite the continuous development of the principle and algorithm, the frame rate of the existing qDPC algorithm is still much lower than that of camera acquisition, so it is hardly applied to real-time image the fast-moving biological samples. In this paper, based on color-coded multiplexing strategy, a compact real-time quantitative phase imaging system is designed to realize multi-mode imaging. The system employs a programmable LED array to illuminate directly, and the phase reconstruction algorithm is deployed in the graphics processing unit (GPU) of the laptop to accelerate the calculation. The system can achieve high-speed quantitative phase imaging of non-stained biological samples, and the frame rate can reach 60fps. The device has the advantages of compact structure, low cost and portability. Thus, it is suitable for mobile medical applications.

(1) Background: Little research has been done on professionals' perceptions of institutions and governments during epidemics. We aim to create a profile of physicians who feel they can raise public health issues with relevant institutions during a pandemic. (2) Methods: A total of 1285 Romanian physicians completed an online survey as part of a larger study. We used binary logistic regression to profile physicians who felt able to raise public health issues with relevant institutions; (3) Results: Five predictors could differentiate between respondents who tended to agree with the trust statement and those who tended to disagree: feeling safe at work during the pandemic, considering the financial incentive worth the risk, receiving training on the use of protective equipment, having the same values as colleagues, and enjoying work as much as before the pandemic; (4) Conclusions: Physicians who trust the system to raise public health issues with the appropriate institutions feel they share the same values as their colleagues, say they were trained in the use of protective equipment used during the pandemic, felt they were safe at work during the pandemic, enjoyed their work as much as before the pandemic, and felt the financial bonus justified the risk.

The properties of the annual, semiannual and triennial oscillations (AO, SAO and TO) in the middle atmosphere have been investigated using the TIMED/SABER temperature data. The Lomb-Scargle and wavelet spectra were used to determine the dominant oscillations in the background temperature field. The AO is prominent at the mid-latitudes. The AO amplitudes present an asymmetry between the two Hemispheres, being larger in the mesosphere than in the stratosphere. The SAO dominates the tropical regions, with three amplitude maxima at altitudes of 45, 75, and 85 km. The SAOs in the upper mesosphere (75 km) are out of phase with those in the mesopause (85 km) in the tropical regions, which can generate an enhancement of 11 K at each equinox, contributing to the lower mesospheric inversion layer. The TO is significant in the tropical region, with amplitude being maximum at 35, 45 and 85 km. Result shows that there may be potential interaction by the TO with SAO at 85km at the equator. The relation between ENSO and TO has also been discussed. The ENSO signal may modulate the amplitude of the TO, mainly in the lower stratosphere. The real origin of the TO may lie in the wave-mean-flow interaction.

Economic development and population growth lead to increased production of chicken manure (CM), which is a problematic organic waste for its amount, environmental threats, and moisture content. There are different ways of CM, namely anaerobic digestion, composting, combustion, and direct land spreading. Hydrothermal carbonization (HTC) is another emerging way, however. In this study, the HTC of CM was performed to produce energy-rich material called hydrochar (HC). The effects of HTC temperature (180, 240, 300 C) and process time (30, 90, 180 min) were summarized. Proximate and ultimate analysis, as well as heating values (HHV, LHV), have been performed both on raw CM and derived HC. Additionally, the process performance has been examined. The obtained results show that HTC is a feasible method for CM disposal and valorization. Although process time did not influence considerably fuel properties of CM, higher temperature led to significantly higher HHV, reaching 23,880.6734.56 Jg-1 at 300 C and 180 min with an improvement of 8,329 Jg-1 compared with raw CM (15,551.67 Jg-1). The process conducted at 240 C in 30 min has been specified as the most favorable, due to the highest energy gain of HC and relatively low energy consumption.

The increase in the emission of greenhouse gases (GHG) is one of the most important world problems. Decreasing of GHG emission is a big challenge in the future. Transportation sector uses a significant part of petroleum production in the world and it leads to an increase in the emission of GHG. The result of this issue is that population of the world befoul environment of transportation system automatically. Electric Vehicles (EV) have a potentiality to solve a big part of the GHG emission and energy efficiency issues such as stability and reality of energy. The energy actors and their research team determine some targets for 2050; hence, they hope to decrease the world temperature to 6 °C in the best and 2 °C in the normal condition. Fulfillment of these scenarios needs suitable grid infrastructure but in most of the countries, the grid does not have a suitable background to apply those scenarios. In this paper, some problems about energy scenarios, energy storage systems, grid infrastructure and communication systems in supply and demand side of the grid and its solutions have been investigated.

Vehicle to vehicle (V2V) and Vehicle to infrastructure (V2I) or briefly V2X communications are the one of hot topics in automotive industry. Therefore, this situation is providing many advantages of connected vehicles and infrastructures which bring to human life. For instance, vehicles and road infrastructures which shares information with each other, provides a neat flow regulation, more ordered traffic flow and therefore jammed traffic dependent accident’s percentage will be decreased. On the other hand, security is the most important issue for these systems because the operation of V2X networks is completely dependent on uninterrupted and accurate information sharing. In the light of these information, in this paper we review security issues and current solution architectures. We also propose some open problems in this lively field.

Sustaining learners’ motivation to keep learning has been concerned for over 60 years in education and SLA. Most research focusing on Japanese university students has contributed lots of theoretical and practical developments, but research focusing on motivations of non-traditional adult learners and those under 18-year-old to learn Chinese is still little even though previous research findings have proved age is one of the key variables influencing learning besides nationalities and Chinese language levels. In order to find an effective treatment for sustainable Chinese language learning in Japan, this study utilized a survey method to analyze the types of Japanese learners’ motivation to learn Chinese via analysis of a moment structures (AMOS), and to compare the differences of the motivation via one-way ANOVA. This study revealed that Japanese learners’ motivation to learn Chinese consists of eight common types, which are “instrumental motivation”, “personal orientation”, “identified regulation”, “Chinese cultural productions”, “integrating into Chinese community”, “external regulation”, “social responsibility”, and “Chinese for academic purposes”. Also, there are similarities and differences existing in the eight motivation types among the Japanese children, adolescents, (non-) traditional adult learners of Chinese as a foreign language (CFL). The findings contribute the understanding of motivation types and differences among the four aged Japanese learners to teachers of teaching Chinese to speakers of other languages (TCSOL), and to future research further exploring how differently aged Japanese learners can have and use a higher motivation as a main drive to learn Chinese language as a lifelong business.

Machine learning has gained significant recognition within healthcare organizations as a potent technology for medical image analytics. Among various medical image modalities, Contrast-Enhanced CT (CECT) is widely utilized to acquire additional diagnostic information that can help to better visualize and evaluate certain structures or abnormalities in the human body. Accordingly, there is an increasing demand for machine learning systems for disease analytics using CECT. However, developing such systems presents two significant challenges: high technical complexity and high development effort. This paper presents a software platform that can effectively remedy these challenges. This platform embodies a unified software process for CECT-specific disease diagnosis including oncological diseases, cardiovascular diseases, and gastrointestinal diseases. It integrates multiple types of machine learning models into the disease diagnosis process, where the models can diagnose lesions, malignancies, tumors, medical features of tumors, temporal transitions with a contrast agent, and disease progress. The platform has been implemented in Python using Scikit-learn and TensorFlow libraries. To validate its applicability and reusability, a Hepatocellular Carcinoma (HCC) diagnosis system has been implemented.

Contrast agents developed for X-ray computed tomography (CT) offer extremely valuable tools and techniques in diagnostics via contrast enhancements. Heavy metal-based nanoparticles (NPs) can provide high contrast in CT images. These result from a high density of heavy metal atoms with high X-ray attenuation coefficients that exceed that of iodine (I), which is currently used in hydrophilic organic CT contrast agents. Nontoxicity and colloidal stability are vital characteristics in designing heavy metal-based NPs as CT contrast agents. In addition, the particle size must be small for in vivo renal excretion. In vitro phantom imaging studies can provide X-ray attenuation efficiency, which is a critical parameter for CT contrast agents, and the imaging performance of CT contrast agents can be demonstrated via in vivo experiments. This review focuses on in vitro and in vivo studies of various heavy metal-based NPs (metal: Au, Pt, Pd, Ag, Ce, Gd, Dy, Ho, Yb, Ta, W, and Bi) and provides an outlook on their use as high-performance CT contrast agents.

Optimum design and accurate control of the internal structure of the laser target materials remain an important goal for various laser physics experiments, especially for generating high flux photon and neutron beams. The low-density material is considered to be one of the most difficult for X-ray study due to its high transparency and imperceptible contrast. To produce clear visualization of foam containing sparse structures we used a high-quality monochromatic X-ray beam of synchrotron radiation source PETRA-III at DESY. The X-ray beam parameters allow tomographic scanning with application of phase contrast retrieval algorithms. A series of 3D images of foam-suspended glass microsphere inside the plastic cylinder were obtained with the quality high enough to observe the internal structure and to visualize uniformity, displacement and surface roughness on both sides of the microsphere. The main object under investigation was a CH-plastic capillary including 10 mg/cc CHO-foam with the centered glass microsphere. The results of this work demonstrate that tomographic visualization based on a high quality X-ray radiation and phase contrast analysis is an effective and useful technique for development of new laser targets containing structured low density materials.

A community-level nutrition intervention was implemented among tribal children (3 to 6 years) in Telangana, India. The one-year intervention involved six nutrient-rich formulations of millet-pulse-groundnut based products, suited to local taste preferences. Anthropometric measurements of height, weight and Mid-Upper-Arm -Circumference (MUAC) along with haemoglobin (Hb) levels were monitored at baseline and endline. The treatment group showed considerable gain in height (3.2 cm), weight (1.68 kg) and MUAC (0.33 cm), over the control group. Paired t-test indicated significant differences (p<.01) between pre- and post-intervention anthropometric measurements. Positive shifts were observed in wasting (WHZ; -1.2 ±1.3 to -0.9± 1), stunting (HAZ; -1.8±1.6 to -0.3±1.3) and underweight (WAZ; -1.9±1.2 to -0.7±1) among treatment group. Hb levels in the treatment group also improved significantly from 9.70 ± 0.14 g/dl (moderately-anaemic) to 11.08 ± 0.13 g/dl (non-anaemic). Post-intervention Focus Group Discussions (FGDs) involving mothers and teachers confirmed the positive impact. Thus, nutritional intervention formulated using climate resilient millets, pulses and groundnut, promotes dietary diversity and improves the nutrition & health status of children.

Measuring sound pressure is a complex and tedious task in industrial environments. Classical methods can be costly, inaccurate, and potentially dangerous for workers. Integrating robotics and computer vision into acoustic measurement processes offers an innovative solution to these problems. This paper uses a Doosan Robotic Vision System to investigate an innovative automatic acoustic sound pressure measurement solution. The Doosan robot, equipped with a scanning camera and microphone, captures acoustic data and produces a map of the machine’s acoustic radiation using computer vision algorithms. The measurement technique is accurate, efficient, and safe for workers. It can also be used for continuous monitoring of the industrial acoustic environment. The developed measurement method offers a promising way to automate acoustic measurement, which can be performed faster and with better accuracy to help industries improve their product sound quality and reduce noise levels. The method was compared to analytical predictions. A 3D acoustic mapping was conducted to visualize the spatial distribution of sound sources within the environment. The experimental results show a good correlation with the theory.

Deep underground laboratories offer advantages for conducting high-precision observations of weak geophysical signals, benefitting from a low background noise level. It is both valuable and feasible to enhance strong, noisy ground electromagnetic (EM) field data using synchronously recorded underground EM signals, which typically exhibit a high signal-to-noise ratio. In this study, we propose an EM field reconstruction method employing a Long Short-Term Memory (LSTM) recurrent neural network with referenced deep underground EM observations. Initially, a deep learning model was developed to capture the time-varying features of underground multi-component EM fields using the LSTM recurrent neural network. Subsequently, this model was applied to process synchronously observed strong, noisy data from other conventional observation systems, such as those at surface, to achieve noise suppression through signal reconstructions. Both theoretical analysis and practical observational data suggest that the proposed method effectively suppresses noise and reconstructs clean EM signals. This method is efficient and time-saving, representing an effective approach to fully utilizing the advantages of deep underground observation data. Furthermore, this method could be extended to the processing and analysis of other geophysical data.

Interactions between mobile robots and human operators in common areas require a high safety especially in terms of trajectory planning, obstacle avoidance and mutual cooperation. In this connection the crossings of planned trajectories, their uncertainty based on model fluctuations, system noise and sensor noise,play an outstanding role. This paper discusses the calculation of expected areas of interactions du ring human-robot navigation with respect to fuzzy and noisy information. Expected crossing points of possible trajectories are nonlinearily associated with positions and orientations of robot and human. The nonlinear transformation of a noisy system input, such as directions of motion of human and robot, to a system output, the expected area of intersection of their trajectories, is done by two methods: statistical linearization and the sigma-point-transformation. For both approaches fuzzy approximations are presented and the inverse problem is discussed where the input distribution parameters are computed from given output distribution parameters.

As the lifespan of porous asphalt pavement extends, it invariably experiences performance degradation and an increase in tire/pavement noise compared with the original surface when it is newly built. To understand the medium-term performance of porous asphalt pavement during service and reveal the influence of different pavement performance indicators on tire/pavement noise generation, a seven-year continuous observation and data analysis study was conducted. Porous asphalt with different designed material parameters was taken into account, including thin-layer porous asphalt overlay with a maximum nominal aggregate size of 10mm (PUC-10), porous asphalt with a maximum nominal aggregate size of 13mm (PAC-13), and a two-layer porous asphalt incorporating both PUC-10 and PAC-13 (PUC-10+PAC-13). Using automated pavement technology testing equipment, key performance indexes, including damage rate (DR), rut depth (RD), international roughness index (IRI), and sideway force coefficient (SFC), were measured and calculated. The on-board sound intensity (OBSI) method was used to test noise levels in three types of porous asphalt pavement structures and on a dense graded easy compact asphalt concrete (ECA) for comparison. The progressions of the medium-term performances were analyzed according to the measured results. The correlations between tire/pavement noise levels and testing speed, testing lane, pavement structure, and road condition indicators were investigated by regression. The research results revealed that the DR and SFC of the three types of porous asphalt pavement displayed more significant attenuation in the service period, while the IRI and RD showed small changes. Among them, the SFC demonstrated significant attenuation over the first 4 years, decreasing between 13.8% and 17.0%. From the 3-7 years of operation, the transverse crack disease in porous asphalt pavement progressed rapidly. The three porous asphalt pavement structures tested exhibited commendable noise reduction capabilities. Compared to ECA, the OBSI noise levels of these structures were lower by 2.09 dB, 1.53 dB, and 2.88 dB, respectively. Compared to other pavement condition indicators, RD had a more significant impact on the OBSI value of the PUC-10 porous asphalt pavement. For the PUC-10+PAC-13 porous asphalt pavement, there was a significant linear relationship between the OBSI value and SFC, mainly due to polishing of the coarse aggregate surface. It leads to a decrease of the micro-texture, which lies in increasing tire/pavement noise at high-frequency ranges and decreasing SFC. This study will have guiding significance for the research of the attenuated law and durability improvement of porous asphalt tire/pavement noise.

Noise model selection criteria has a significant impact on identifying the stochastic noise proper-ties of any GNSS daily coordinate time series. The low-frequency random walk noise existing in these time series could lead to overestimation of the tectonic rate, so it is of great significance to accurately detect the random walk component. This study focuses on noise model estimation cri-terion (BIC_tp) derived from the AIC and the BIC by introducing 2π factors. It is more sensitive to abnormal steps (random jumps). Using observation data from 72 GNSS stations from 1992 to 2022 and simulated data, four combined noise models are used to explore the impacts of time se-ries lengths (ranging from2 to 24 years) and data loss (between 2% and 30%) on noise models and velocity estimation. The results show that as the time length increased, the selected optimal noise model, and the estimated uncertainty of the tectonic trend with different data gap, gradually con-verge. When the time length is short (less than 8 years), it could lead to the FNRWWN, FNWN, and PLWN models being mistakenly estimated as GGMWN models, thereby affecting the accura-cy of determining the station velocity parameters. When the time length is 12 years, the RW noise component is more probably detected, As the time length increases, the impact of RW on velocity uncertainty is weakened. Finally, we conclude that for a time series with a minimum time length of 12 years, both the selection of the optimal stochastic noise model and the estimation of the ve-locity parameters are reliable.

A simple underwater noise model suitable for use with explosive severance of well conductors and piles during the decommissioning of oil and gas subsea structures is introduced and evaluated against data from five projects in the US. This study focuses on a novel model for the determination of sound exposure levels. The model has been developed to enable determination of impact areas for marine mammals and fish. Simulated received underwater sound exposure levels were significantly correlated with measurements for all scenarios. The maximum total error achieved between simulations and measurements was 2.6%, suggesting that predictions are accurate to within 3% of the average measurement. A low relative bias was observed in the simulations when compared to measured values, suggesting only a small systematic underestimate (≤ 0.5% of average measurement) for most severance operations and a small overestimate (0.14%) for open water blasts.

Interleaved power factor correction (PFC) is widely used circuit topology due to good efficiency and power density for single-switch boost PFC. As the differential mode (DM) electromagnetic interference (EMI) noise magnitude depends upon the input current ripple, this research details a comprehensive study of DM EMI filter design for interleaved boost PFC with the aim of minimizing the component size. It is also demonstrated that the different numbers of interleaved stages and switching frequency influence the filter attenuation requirement and, thus, the EMI filter size. First, an analytical model is derived on the basis of the Norton equivalent circuit model for the differential mode noises of interleaved boost PFC within the frequency range of 9-500 kHz. The derived model can help identify the optimal phase shifting among the interleaved boost converters in order to minimize the considered differential mode noises at the filter design frequency. So, a novel phase-shift method is developed to get a minimized attenuation required by a filter in Band B. Further, a volume optimization of the required DM filter was introduced based on the calculated filter attenuation and volumetric component parameters. Based on the obtained results, unconventional and conventional phase shifts have demonstrated a good performance in decreasing the EMI filter volume in Band B and Band A, respectively. A 2-kW interleaved PFC case study is presented to verify the theoretical analyses and the impact of phase-shifting on EMI filter size.

The sound field properties for non-cavitating marine propellers are studied using FW-H (Ffowcs William-Hawkings) acoustic analogy and BEM (Boundary Element Method) approach. For the first time, the FW-H formula to calculate acoustic pressure generated by the propeller wake sheet is proposed. The corresponding sound signal can be dominant up to several kilometres. Secondly, non-physical fictitious volume flux is observed when the permeable FW-H approach is used together with BEM. The reason is explained and a virtual source correction method is proposed to solve this problem. Furthermore, analytical analyse show that the second thickness term and the first two loading terms in Farassat 1A formula are important, and the others are negligible. The numerical studies show that the permeable FW-H approach produces underestimation when compared to the direct FW-H method, and the underestimation is severer for larger permeable surface and higher frequency. It is also found that the first loading term in Farassat 1A formula is the dominant source term in the direct FW-H method, while the first thickness term is dominant when the permeable FW-H approach is used.

In recent years, intelligent fault diagnosis algorithms using deep learning method have achieved much success. However, the signals collected by sensors contain a lot of noise, which will have a great impact on the accuracy of the diagnostic model. To address this problem, we propose a one-dimensional convolutional neural network with multi-scale kernels (MSK-1DCNN) and apply this method to bearing fault diagnosis. We use a multi-scale convolution structure to extract different fault features in the original signal, and use the ELU activation function instead of the ReLU function in the multi-scale convolution structure to improve the anti-noise ability of MSK-1DCNN; then we use the training set with pepper noise to train the network to suppress overfitting. We use the Western Reserve University bearing data to verify the effectiveness of the algorithm and compare it with other fault diagnosis algorithms. Experimental results show that the improvements we proposed have effectively improved the diagnosis performers of MSK-1DCNN under strong noise and the diagnosis accuracy is higher than other comparison algorithms.

We present a high-throughput nanoindentation study of in-situ bending effects on incipient plastic deformation behavior of polycrystalline and single-crystalline pure aluminum and pure copper at ultra-nano depths (<200nm). We find that hardness displays a statistically inverse dependence on in-plane stress for indentation depths smaller than 10nm, and the dependence disappears for larger indentation depths. In addition, plastic noise in the nanoindentation force and displacement displays statistically robust noise features, independently of applied stresses. Our experimental results suggest the existence of a regime in FCC crystals where ultra-nano hardness is sensitive to residual applied stresses, but plasticity pop-in noise is insensitive to it.

Noise pollution is an environmental problem due to its effect on hearing and other related health issues. The elevated noise levels above standard limits cause hearing loss and other attendant problems. In Nigeria, viewing centers where football matches are watched via satellite are known as ‘Mini Stadium’. In this study, the noise pollution of a viewing center was assessed. To do this, five top teams each from the English Premier League (EPL), La Liga, Bundesliga, League 1, and Serie A were selected for the assessment. A sound level meter (GB: 2266204, made in China) was used for the monitoring. The range of noise levels in dBA showed as follow: EPL (56-108); La Liga (46-106); Bundesliga (54-102); Serie A (49-101); and Ligue 1 (54-101). The results when compared with the standard limits, it was observed that part of the results exceeded the limits, this means that the viewers and others within the viewing center may be prone to the hearing problem. It is recommended that acoustic insulators and hearing aids should be used to reduce the attendant problems associated with noise.

The unique porous crystal structure of zeolite offers various important utilizations, it is one of the considerations in selecting zeolite at this study as component of composite for restraining noise. It so happens, previous experiments show that banana stem has porous structure, fibrous, high flexibility and can be applied as material for many various products including as component of acoustic material. The combination of both is alleged that it has capability in absorbing noise. This paper presents an investigation on the composite that it was synthesized of Activated Zeolite and Banana Stem Fibre in various weight for determining its sound absorption coefficient alpha (a). Activating natural zeolite was conducted by using 6M HCl in order for enlarging zeolite pores. The sound absorption coefficient was measured in the frequency range between 125 Hz up to 6000 Hz. The results show that the different weight of banana stem fibre as component of the synthesized composite affects the value of alpha and shifts the frequency area.

Using coherent optical detection and digital signal processing, laser phase noise and equalization enhanced phase noise can be effectively mitigated using the feed-forward and feed-back carrier phase recovery approaches. In this paper, theoretical analyses of feed-back and feed-forward carrier phase recovery methods have been carried out in the long-haul high-speed n-level phase shift keying (n-PSK) optical fiber communication systems, involving a one-tap normalized least-mean-square (LMS) algorithm, a block-wise average algorithm, and a Viterbi-Viterbi algorithm. The analytical expressions for evaluating the estimated carrier phase and for predicting the bit-error-rate (BER) performance (such as the BER floors) have been presented and discussed in the n-PSK coherent optical transmission systems by considering both the laser phase noise and the equalization enhanced phase noise. The results indicate that the Viterbi-Viterbi carrier phase recovery algorithm outperforms the one-tap normalized LMS and the block-wise average algorithms for small phase noise variance (or effective phase noise variance), while the one-tap normalized LMS algorithm shows a better performance than the other two algorithms for large phase noise variance (or effective phase noise variance). In addition, the one-tap normalized LMS algorithm is more sensitive to the level of modulation formats.

Access to medicines is one of the essential problems in Public Health of low- and middle-income countries (LMICs). The World Health Organization (WHO) defines access to medicines as the possibility of "having continuously accessible and affordable medicines in public or private health facilities that are within a kilometer of the place of residence." Access to medicines, as defined by the WHO, is not fully guaranteed in many LMICs and even in many regions of high-resource countries. The WHO identifies several factors as determinants of limitations in the access to medicines: rational selection, affordable prices, sustainable financing, and reliable health services. The action on these factors makes it possible to improve universal access to medicines with consequent improvement in Public Health. Adequate access to antibiotics and vaccination will avoid a large part of the deaths caused by infectious diseases in the LMICs. However, the emergence of resistance and the difficulties in vaccination campaigns due to socio-political or cultural problems make it challenging to fight many easily treatable infectious diseases. The use and abuse of antibiotics are inevitably associated with the appearance of resistances that make them ineffective. Thus, whereas limited access to antibiotics raises mortality rates from infectious diseases, generalized open access to them ends up eliminating their clinical value. Moreover, the contraction of research in this field for many years has reduced the success in discovering new drugs. Additionally, local market regulations, inadequate selection, inaccessible prices, especially for those of second and third-generation, inefficient health systems, and difficulties of administration and control of prescription compliance, especially in the case of combined therapies, are additional obstacles to universal access to antibiotics. In order to simultaneously improve access to antibiotics and keep resistances under control, it is necessary to develop training and education activities at different social levels (from patients to various Health Care Providers) to complement the national or supranational strategic plans.

Only languages with abundant resources are given the opportunity to build speech recognition systems, whereas low resource languages are disregarded. This project attempts to create a system that can normally convert speech to text in the Myanmar (Burmese) language, a language part of low resource languages. The Burmese language is extremely intricate and is made up of vowels, consonants, tones, and stress. There are 10 numbers, 12 vowels, and 33 letters in the Myanmar alphabet. Deep learning allows for the evolution of numerous algorithms while eradicating ignorance and even low-resource languages may be turned into speech-to-text systems with a small quantity of data. The procedures required to construct a successful speech recognition system that can assist both the corporate sector and society will be covered in this project, including data collecting, preprocessing, modeling, prototyping, assessment, and ultimately deployment.

Poultry farming in Slovakia has a tradition and an irreplaceable position to ensure rational nutrition for the population. In terms of a nutritional value, poultrymeat (Galliformes) contains a high amount of proteins, essential amino acids, essential unsaturated fatty acids, minerals, vitamins and a low fat content. Poultrymeat production has a relatively short reproductive cycle and thus allows regulating the demand and supply. The poultry market also influences the development of domestic consumption. According to the Statistical Office of the Slovak Republic, the consumption of meat has witnessed a persistent increase over the last 10 years. [1] According to the 2023 AVEC (The Voice of European Meat Sector) annual report, the consumption of poultrymeat in Slovakia was 20.0 kg per person per year. The average consumption in the EU-27 (2021) was 23.4 kg, with the countries with the highest consumption being Portugal (37.8 kg), Ireland (36.0 kg), Hungary (33.5 kg) and Spain (29.9 kg). On the other hand, the countries with the lowest consumption of poultrymeat, apart from Slovakia, also include Austria and Greece with a consumption of 21.5, resp. 21.0 kg [2]. The breeding of broiler chickens, as well as the production and placing on the market of poultrymeat must comply with the requirements laid down by legislation, while the compliance is regularly checked by the competent authorities.