High Noise Research Articles

NOISE POLLUTION AND ITS EFFECTS ON HUMAN HEALTH: A REVIEW

Noise pollution is a pervasive environmental issue with significant adverse effects on human health. This abstract explores the impact of noise pollution on both physical and mental well-being, emphasizing its role in increasing risks for cardiovascular diseases, hearing loss, sleep disturbances, and cognitive impairments. Vulnerable populations, including children and the elderly, are particularly affected by high noise levels, which exacerbate stress, anxiety, and depression, and impair cognitive development in children. The economic implications of noise pollution are considerable, with substantial costs associated with healthcare and productivity losses, as well as negative effects on community cohesion and quality of life. Effective noise management is essential, requiring robust regulations, improved urban planning, and heightened public awareness. Further research is needed to understand the long-term health impacts, refine noise reduction technologies, and assess effects on specific populations. Interdisciplinary studies will be crucial in developing comprehensive strategies to mitigate the health impacts of noise pollution. KEYWORDS: Noise Pollution; Health Impacts; Noise Reduction Technologies; Economic Costs; Interdisciplinary Research

GraphPCA: a fast and interpretable dimension reduction algorithm for spatial transcriptomics data

The rapid advancement of spatial transcriptomics technologies has revolutionized our understanding of cell heterogeneity and intricate spatial structures within tissues and organs. However, the high dimensionality and noise in spatial transcriptomic data present significant challenges for downstream data analyses. Here, we develop GraphPCA, an interpretable and quasi-linear dimension reduction algorithm that leverages the strengths of graphical regularization and principal component analysis. Comprehensive evaluations on simulated and multi-resolution spatial transcriptomic datasets generated from various platforms demonstrate the capacity of GraphPCA to enhance downstream analysis tasks including spatial domain detection, denoising, and trajectory inference compared to other state-of-the-art methods.

Low-noise 2-GHz figure-9 fiber laser based on passive harmonic mode-locking

We have proposed and demonstrated the generation of a high-repetition-rate ultrashort pulse with long-term stability and low noise based on a harmonic mode-locked (HML) figure-9 fiber laser. Different HML orders from the 2nd to the 13th are generated by adjusting the pump power, net dispersion, and wave plate angles. A 2-GHz HML pulse is obtained with a 155-MHz fundamental repetition rate in a Yb-doped fiber laser, and the corresponding supermode suppression level is as high as 50 dB. The average power of the output pulse is nearly 200 mW, and the compressed pulse duration is 535 fs. To the best of our knowledge, 2 GHz and 196 mW represent, respectively, the highest repetition rate and output power in a figure-9 fiber laser. This work offers a potential pathway to achieve high-repetition-rate ultrashort pulses in the GHz range with high power and low noise levels.

An automated pheochromocytoma and paraganglioma lesion segmentation AI-model at whole-body 68Ga- DOTATATE PET/CT.

Somatostatin receptor (SSR) targeting radiotracer 68Ga-DOTATATE is used for Positron Emission Tomography (PET)/Computed Tomography (CT) imaging to assess patients with Pheochromocytoma and paraganglioma (PPGL), rare types of Neuroendocrine tumor (NET) which can metastasize thereby becoming difficult to quantify. The goal of this study is to develop an artificial intelligence (AI) model for automated lesion segmentation on whole-body 3D DOTATATE-PET/CT and to automate the tumor burden calculation. 132 68Ga-DOTATATE PET/CT scans from 38 patients with metastatic and inoperable PPGL, were split into 70, and 62 scans, from 20, and 18 patients for training, and test sets, respectively. The training set was further divided into patient-stratified 5 folds for cross-validation. 3D-full resolution nnUNet configuration was trained with 5-fold cross-validation. The model's detection performance was evaluated at both scan and lesion levels for the PPGL test set and two other clinical cohorts with NET (n = 9) and olfactory neuroblastoma (ONB, n = 5). Additionally, quantitative statistical analysis of PET parameters including SUVmax, total lesion uptake (TLU), and total tumor volume (TTV), was conducted. The nnUNet AI model achieved an average 5-fold validation dice similarity coefficient of 0.84 at the scan level. The model achieved dice similarity coefficients (DSC) of 0.88, 0.6, and 0.67 at the scan level, the sensitivity of 86%, 61.13%, and 61.64%, and a positive predictive value of 89%, 74%, and 86.54% at the lesion level for the PPGL test, NET and ONB cohorts, respectively. For PPGL cohorts, smaller lesions with low uptake were missed by the AI model (p < 0.001). Anatomical region-based failure analysis showed most of the false negative and false positive lesions within the liver for all the cohorts, mainly due to the high physiologic liver background activity and image noise on 68Ga- DOTATATE PET scans. The developed deep learning-based AI model showed reliable performance for automated segmentation of metastatic PPGL lesions on whole-body 68Ga-DOTATATE-PET/CT images, which may be beneficial for tumor burden estimation for objective evaluation during therapy follow-up. https://www. gov/study/NCT03206060 , https://www. gov/study/NCT04086485 , https://www. gov/study/NCT05012098 .

Impact of Adverse Weather and Image Distortions on Vision-Based UAV Detection: A Performance Evaluation of Deep Learning Models

Unmanned aerial vehicle (UAV) detection in real-time is a challenging task despite the advances in computer vision and deep learning techniques. The increasing use of UAVs in numerous applications has generated worries about possible risks and misuse. Although vision-based UAV detection methods have been proposed in recent years, a standing open challenge and overlooked issue is that of adverse weather. This work is the first, to the best of our knowledge, to investigate the impact of adverse weather conditions and image distortions on vision-based UAV detection methods. To achieve this, a custom training dataset was curated with images containing a variety of UAVs in diverse complex backgrounds. In addition, this work develops a first-of-its-kind dataset, to the best of our knowledge, with UAV-containing images affected by adverse conditions. Based on the proposed datasets, a comprehensive benchmarking study is conducted to evaluate the impact of adverse weather and image distortions on the performance of popular object detection methods such as YOLOv5, YOLOv8, Faster-RCNN, RetinaNet, and YOLO-NAS. The experimental results reveal the weaknesses of the studied models and the performance degradation due to adverse weather, highlighting avenues for future improvement. The results show that even the best UAV detection model’s performance degrades in mean average precision (mAP) by −50.62 points in torrential rain conditions, by −52.40 points in high noise conditions, and by −77.0 points in high motion blur conditions. To increase the selected models’ resilience, we propose and evaluate a strategy to enhance the training of the selected models by introducing weather effects in the training images. For example, the YOLOv5 model with the proposed enhancement strategy gained +35.4, +39.3, and +44.9 points higher mAP in severe rain, noise, and motion blur conditions respectively. The findings presented in this work highlight the advantages of considering adverse weather conditions during model training and underscore the significance of data enrichment for improving model generalization. The work also accentuates the need for further research into advanced techniques and architectures to ensure more reliable UAV detection under extreme weather conditions and image distortions.

Magnetic Field Enhanced Frequency‐Selective Terahertz Detection via 3D Printing Micro‐Helical Stepped and MoTe2 Coated Arrays

AbstractRecent advancements in wireless communication have markedly increased data throughput and decreased latency in progressing toward sixth‐generation (6G) networks, wherein the terahertz (THz) waveband offers significant potential. However, conventional THz sensors often suffer from high noise, low responsivity, and low spectrum efficiency, limiting their effectiveness for THz applications. Here, to address these challenges, a magnetic‐enhanced frequency‐selective is developed THz sensor by depositing an Au‐enhanced active layer of MoTe2 on a designed 3D printing high‐depth micro‐helical stepped structure. This sensor, featuring four characteristic frequency points and an effective area of 107.12 mm2, demonstrated noticeable improvements under a 0.145 mT magnetic field at 0.1 THz: optical responsivity improved by 413.23% (from 3.32 to 17.03 MV W−1), noise equivalent power decreased by 80.51% (from 49.16 to 9.58 pW Hz−1/2), and the detectivity reached 3.30 × 1010 cm·Hz1/2·W−1. This work highlights the potential of integrating 3D microstructures with novel topological materials for practical 6G communication.

A Likelihood-Based Triangulation Method for Uncertainties in Through-Water Depth Mapping

Coastal environments, which are crucial for economic and strategic reasons, heavily rely on accurate bathymetry for safe navigation and resource monitoring. Recent advancements in through-water photogrammetry have shown promise in mapping shallow waters efficiently. However, robust uncertainty modeling methods for these techniques, especially in challenging coastal environments, are lacking. This study introduces a novel likelihood-based approach for through-water photogrammetry, focusing on uncertainties associated with camera pose—a key factor affecting depth mapping accuracy. Our methodology incorporates probabilistic modeling and stereo-photogrammetric triangulation to provide realistic estimates of uncertainty in Water Column Depth (WCD) and Water–Air Interface (WAI) height. Using simulated scenarios for both drone and airborne surveys, we demonstrate that viewing geometry and camera pose quality significantly influence resulting uncertainties, often overshadowing the impact of depth itself. Our results reveal the superior performance of the likelihood ratio statistic in scenarios involving high attitude noise, high flight altitude, and complex viewing geometries. Notably, drone-based applications show particular promise, achieving decimeter-level WCD precision and WAI height estimations comparable to high-quality GNSS measurements when using large samples. These findings highlight the potential of drone-based surveys in producing more accurate bathymetric charts for shallow coastal waters. This research contributes to the refinement of uncertainty quantification in bathymetric charting and sets a foundation for future advancements in through-water surveying methodologies.

An Enhanced Diffractive Neural Network for Metasurface Holograms With High Resolution, Low Noise, and Uniform Intensity

An Enhanced Diffractive Neural Network for Metasurface Holograms With High Resolution, Low Noise, and Uniform Intensity

Using cross-domain knowledge augmentation to explore comorbidity in electronic health records data

Research concentrating on specific diseases or employing single datasets, such as medical histories and thematic data, has garnered considerable attention. However, there has been limited investigation into comorbidities. Although some ad-hoc methods have been utilized in the medical field, there is a scarcity of systematic approaches to address this challenge. The task of expressing patient features using heterogeneous and cross-domain data presents considerable difficulties. Directly mapping this data into a matrix frequently results in issues such as high dimensionality, sparsity, redundancy, and noise. Additionally, given the critical role of supervisory information in medicine, acquiring accurate information is paramount. To address these issues, we propose an enhanced clustering method that capitalizes on cross-domain knowledge augmentation. This method can iteratively learn clustering outcomes and cross-domain knowledge. The cross-domain knowledge matrix produced by our approach can be interpreted as a measure of similarity between instances across domains. We validate our proposed model in real-world electronic health records (EHR) data, and achieve significant performance improvement compared to the baseline method, successfully completing the task of exploring comorbidity. Due to the privacy of EHR data, we also conduct extensive experiments on the publicly available datasets DBLP and UCI. The experimental results show that our algorithm is superior to the baseline algorithm and has strong generality.

Programmed DNA tile response system enabled accurate detection of ATP for clinical diagnosis

Adenosine triphosphate (ATP), as a crucial signaling molecule, is of high potential diagnosis value for multiple diseases. However, current technologies for ATP detection struggle to satisfy the harsh conditions required for biospecimen tests, existing several limitations for clinical practical application. In this study, the DNA tile response system was designed and constructed by integrating nucleic acid aptamer, hybrid chain reaction (HCR), and DNA tile self-assembly technology to achieve accurate detection of ATP in body fluid specimens. The DNA tile response system, possessing high-order structure characteristic, could remove HCR false-positive products and conquer the high noise background to enhance the detection sensitivity. Under the optimal reaction parameters, this system achieved the low limit of detection (LOD) of 189.23 nM over a wide linear range and splendid detection specificity faced with multiple interference terms. Furthermore, this system, possessing simple storage conditions, long-term detection stability, and superior anti-interference performance, was sufficient to satisfy the detection requirements for complicated biological specimens in multi-scenarios. Ultimately, the DNA tile response system showed excellent diagnostic efficiency that could easily distinguish urinary tract infection patients and healthy controls through ATP quantification, as well as hypertension patients and healthy ones. This system provides a high-potential rapid detection tool for early diagnosis and dynamic monitoring of urinary tract infections and hypertension, holding enormous potential for clinical translation due to the superiorities of simple operation, general applicability, and low instrument requirements.

Investigating the Need for Urban Air Mobility in Tehran City

The increase in urbanization has led to the creation of dense population in urban areas, along with economic development and the increase in vehicles, which has led to an increase in demand and consequently an increase in many challenges regarding mobility and infrastructure. Solving these challenges or reducing the effects of these challenges can have significant effects on people's daily lives. Various solutions have always been proposed to solve these challenges, some of which have been successful and some of which have not. In the city of Tehran, too, challenges have arisen due to the rapid growth of urbanization, population, and vehicles, and solutions have been proposed to solve these challenges or improve the conditions, but due to the current conditions in Tehran, these solutions have not been able to appear as successful as they should be. In this paper, by examining the conditions of Tehran in terms of noise pollution, energy and fuel consumption, air pollution, traffic conditions, public and road transportation, it is proved that Tehran needs a new mode of transportation. According to today's needs of Tehran, this new mode of transportation can be an urban air mobility (UAM) that can be used in areas such as passenger and goods transportation. The presence of this mode of transportation in the transportation system of Tehran, in addition to improving the level of transportation of this city, helps to improve and even solve the challenges of today's transportation in Tehran, such as the high noise pollution of this city.

Super-resolution imaging quality enhancement method for distributed array infrared camera

Abstract To address issues related to low resolution and high noise in infrared cameras, a distributed array infrared camera imaging system utilizing four cameras is proposed. The four cameras are arranged in an unconstrained array, and the combination algorithm of Projections onto Convex Sets (POCS) and Real-Enhanced Super-Resolution Generative Adversarial Networks (Real-ESRGAN) is applied to achieve high-quality super-resolution infrared imaging. The wavelet fusion algorithm is used to preprocess four low-resolution infrared images to reduce noise. Then, the POCS algorithm is used to reconstruct the preprocessed image. Finally, the Real-ESRGAN is employed for image reconstruction, resulting in an ultra-high-resolution infrared image. The results show that compared to single infrared camera imaging, the resolution of images reconstructed using the distributed infrared camera array is increased by 0.58 times, with the Modulation Transfer Function (MTF) increased by 1.2 times. Additionally, the entropy is increased by 18.87%, the standard deviation is increased by 13.51%, and the Naturalness Image Quality Evaluator (NIQE) is reduced by 18.87%. This demonstrates a significant enhancement in the super-resolution imaging quality of the distributed infrared camera array.

Assessment of cervical spine CT by an image quality audit using qualitative and quantitative methods.

To study the feasibility and assess the correlation of qualitative and quantitative methods for an image quality (IQ) audit of a Cervical spine CT. Five radiologists retrospectively performed a blinded visual grading analysis (VGA) on 20 studies (10 from Protocol 1 and 10 from Protocol 2), using the RANZCR CT IQ Self-Audit worksheet. A Visual Grading Analysis Score (VGAS) and Area under the curve using Visual Grading Characteristics (AUCVGC) were the figures of merit. Quantitative metrics for noise and contrast were correlated to the qualitative assessment. No statistically significant difference was observed in the IQ, VGASProtocol1 = 0.65, 95% CI [0.54, 0.75] and VGASProtocol 2 = 0.73, 95% CI [0.67, 0.79] and AUCVGC = 0.548, 95% CI [0.40, 0.69]. Protocol 2 indicated a statistically significant average dose reduction of 35% in CTDIvol (P = 0.020) and a higher noise; however, the difference was statistically insignificant. There was a moderate correlation between the manual noise measurements in soft tissue and air (P = 0.035) and a strong correlation between the manual and automated noise measurements (P < 0.001). The contrast resolution-based quantitative parameter, EdgeGradientSoft, correlated to the qualitative scores (P = 0.031). Validated VGA tools can be used for IQ audits; however, tailoring the image criteria and rating scale to the clinical practice is suggested. The use of contrast-based IQ metrics showed encouraging results, and further larger-scale studies are needed to explore their potential use in quality management.

Statistical Modelling of Traffic Noise Using Smart PLS-SEM In Urban Roads

The objectives of this study are to assess the traffic noise levels in Shah Alam, Selangor, Malaysia, and to propose traffic noise model among influential factors. Five factors and 20 attributes were measured at three locations using a sound level meter and analyzed using the PLS-SEM approach. The results of this study showed that high traffic noise levels on urban roads exceeded the maximum permissible limit of 60 dBA during the day, according to Malaysian guidelines. However, the integrated traffic noise prediction model approach has specific indirect effects: built environment -&gt; climate conditions -&gt; traffic flow.

Random-Coupled Neural Network

Improving the efficiency of current neural networks and modeling them on biological neural systems have become prominent research directions in recent years. The pulse-coupled neural network (PCNN) is widely used to mimic the computational characteristics of the human brain in computer vision and neural network fields. However, PCNN faces limitations such as limited neural connections, high computational costs, and a lack of stochastic properties. This study proposes a random-coupled neural network (RCNN) to address these limitations. RCNN employs a stochastic inactivation process, selectively inactivating neural connections using a random inactivation weight matrix. This method reduces the computational burden and allows for extensive neural connections. RCNN encodes constant stimuli as periodic spike trains and periodic stimuli as chaotic spike trains, reflecting the information encoding characteristics of biological neural systems. Our experiments applied RCNN to image segmentation and fusion tasks, demonstrating its robustness, efficiency, and high noise resistance. Results indicate that RCNN surpasses traditional methods in performance across these applications.

Decoding Brain Signals from Rapid-Event EEG for Visual Analysis Using Deep Learning

The perception and recognition of objects around us empower environmental interaction. Harnessing the brain’s signals to achieve this objective has consistently posed difficulties. Researchers are exploring whether the poor accuracy in this field is a result of the design of the temporal stimulation (block versus rapid event) or the inherent complexity of electroencephalogram (EEG) signals. Decoding perceptive signal responses in subjects has become increasingly complex due to high noise levels and the complex nature of brain activities. EEG signals have high temporal resolution and are non-stationary signals, i.e., their mean and variance vary overtime. This study aims to develop a deep learning model for the decoding of subjects’ responses to rapid-event visual stimuli and highlights the major factors that contribute to low accuracy in the EEG visual classification task.The proposed multi-class, multi-channel model integrates feature fusion to handle complex, non-stationary signals. This model is applied to the largest publicly available EEG dataset for visual classification consisting of 40 object classes, with 1000 images in each class. Contemporary state-of-the-art studies in this area investigating a large number of object classes have achieved a maximum accuracy of 17.6%. In contrast, our approach, which integrates Multi-Class, Multi-Channel Feature Fusion (MCCFF), achieves a classification accuracy of 33.17% for 40 classes. These results demonstrate the potential of EEG signals in advancing EEG visual classification and offering potential for future applications in visual machine models.

‘TLDetect’: AI-Based Application for Detection and Correction of Anomalous TLD Glow Curves

This research reviews a novel artificial intelligence (AI)-based application called TLDetect, which filters and classifies anomalous glow curves (GCs) of thermoluminescent dosimeters (TLDs). Until recently, GC review and correction in the lab were performed using an old in-house software, which uses the Microsoft Access database and allows the laboratory technician to manually review and correct almost all GCs without any filtering. The newly developed application TLDetect uses a modern SQL database and filters out only the necessary GCs for technician review. TLDetect first uses an artificial neural network (ANN) model to filter out all regular GCs. Afterwards, it automatically classifies the rest of the GCs into five different anomaly classes. These five classes are defined by the typical patterns of GCs, i.e., high noise at either low or high temperature channels, untypical GC width (either wide or narrow), shifted GCs whether to the low or to the high temperatures, spikes, and a last class that contains all other unclassified anomalies. By this automatic filtering and classification, the algorithm substantially reduces the amount of the technician’s time spent reviewing the GCs and makes the external dosimetry laboratory dose assessment process more repeatable, more accurate, and faster. Moreover, a database of the class anomalies distribution over time of GCs is saved along with all their relevant statistics, which can later assist with preliminary diagnosis of TLD reader hardware issues.

Mechanical equipment fault diagnosis method based on improved deep residual shrinkage network.

Fault diagnosis of mechanical equipment can effectively reduce property losses and casualties. Bearing vibration signals, as one of the effective sources of diagnostic information, are often overwhelmed by substantial environmental noise. To address this issue, we present a fault diagnosis method, CCSDRSN, which exhibits strong noise resistance. This method enhances the soft threshold function in the traditional deep residual shrinkage network, allowing it to extract useful information from the fault signal to the maximum extent, thus significantly improving diagnostic accuracy. Additionally, we have developed a novel activation function that can nonlinearly transform the time frequency map across multiple dimensions and the entire region. In pursuit of network optimization and parameter reduction, we have strategically incorporated depthwise separable convolutions, effectively replacing conventional convolutional layers. This architectural innovation streamlines the network. By verifying the effectiveness of the proposed method using Case Western Reserve University datasets, the results demonstrate that the proposed method not only possesses strong noise resistance in high noise environments but also achieves high diagnostic accuracy and good generalization performance under different load conditions.

A qubit-efficient variational selected configuration-interaction method

Abstract Finding the ground-state energy of molecules is an important and challenging computational problem for which quantum computing can potentially find efficient solutions. The variational quantum eigensolver (VQE) is a quantum algorithm that tackles the molecular groundstate problem and is regarded as one of the flagships of quantum computing. Yet, to date, only very small molecules were computed via VQE, due to high noise levels in current quantum devices. Here we present an alternative variational quantum scheme that requires significantly less qubits than VQE. The reduction in the qubit number allows for shallower circuits to be sufficient, rendering the method more resistant to noise. The proposed algorithm, termed variational quantum selected-configuration-interaction (VQ-SCI), is based on: (a) representing the target groundstate as a superposition of Slater determinant configurations, encoded directly upon the quantum computational basis states; and (b) selecting a-priory only the most dominant configurations. This is demonstrated through a set of groundstate calculations of the H2, LiH, BeH2, H2O, NH3 and C2H4 molecules in the sto-3g basis set, performed on IBM quantum devices. We show that the VQ-SCI reaches the full configuration interaction energy within chemical accuracy using the lowest number of qubits reported to date. Moreover, when the SCI matrix is generated ‘on the fly’, the VQ-SCI requires exponentially less memory than classical SCI methods. This offers a potential remedy to a severe memory bottleneck problem in classical SCI calculations. Finally, the proposed scheme is general and can be straightforwardly applied for finding the groundstate of any Hermitian matrix, outside the chemical context.

Study on the Identification Method of Planar Geological Structures in Coal Mines Using Ground-Penetrating Radar

The underground detection environment in coal mines is complex, with numerous interference sources. Traditional ground-penetrating radar (GPR) methods suffer from limited detection range, high noise levels, and weak deep signals, making it extremely difficult to accurately identify geological structures without stable feature feedback. During research, it was found that the detection energy of the same target significantly changes with the antenna direction. Based on this phenomenon, this paper proposes a geological radar advanced detection method using spatial scanning. This method overcomes constraints imposed by the underground coal mine environment on detection equipment, enhancing both detection range and accuracy compared to traditional approaches. Experiments using this method revealed pea-shaped response characteristics of planar geological structures in radar images, and the mechanisms behind their formation were analyzed. Additionally, this paper studied the changes in response characteristics under changes in target inclination, providing a basis for understanding the spatial distribution of geological structures. Finally, application experiments in underground coal mine environments explored the practical potential of this method. Results indicate that, compared to drilling data, this method achieves identification accuracies of 91.88%, 90.42%, and 78.72% for the depth and spatial extent of geological structures, providing effective technical support for coal mining operations.