Adverse Effects Of Noise Research Articles

A high‐precision timing and frequency synchronization algorithm for multi‐h CPM signals

AbstractIn the context of certain specific digital communication systems, where there are limitations such as spectral resources and energy availability, continuous phase modulation (CPM) emerges as an appealing choice among various modulation methods. Among CPM signals, multi‐h CPM is particularly noteworthy for its ability to address these constraints within the realm of single‐carrier and constant‐envelope waveforms. At the physical layer, the design of a multi‐h CPM receiver necessitates the efficient implementation of timing and frequency synchronization algorithm within a high dynamic environment. So this paper presents an innovative approach for achieving timing and frequency synchronization. To rectify timing offset and mitigate the adverse effects of noise in received signals, a re‐configurable local filter generation method is integrated into the timing synchronization algorithm. Simultaneously, an enhanced least mean square adaptive filter algorithm is applied to address frequency synchronization. A comprehensive series of simulations rigorously evaluates the outcomes of proposed novel synchronization methodology. These analyses demonstrate a notable proximity between the synchronization errors of proposed algorithm in this paper and the performance benchmark set by the modified Cramer–Rao bound. The proposed synchronization technology also exhibits the capability to substantially reduce the bit error rate, thereby effectively enhancing demodulation performance in multi‐h CPM receivers.

Cross-sectional study on the health of workers exposed to occupational noise in China.

This study aimed to understand the health of workers exposed to occupational noise and explore the influencing factors related to workers' health, especially the impact of noise on workers' hearing. This work can provide a basis for formulating relevant measures for occupational noise prevention and control in the future. On the basis of the key occupational disease monitoring project in Chongqing, China, in 2021, the data of 1125 workers exposed to occupational noise were analyzed. Data included demographic information, occupational history, clinical physical examination information, and noise detection information of the working environment. Chi-square test and multifactorial logistic regression were used for statistical analysis. The prevalence rates of abnormal electrocardiogram (ECG), blood pressure (BP), and pure tone audiometry (PTA) were 21.9% (246/1125), 27.8% (313/1125), and 18.0% (202/1125), respectively. Male workers accounted for 78.8%. Compared with male workers, female workers had a lower prevalence of abnormal PTA (OR = 0.28, 95% CI = 0.16-0.50). Workers working in medium enterprises had a lower prevalence of abnormal BP than workers in micro enterprises (OR = 0.36, 95% CI = 0.19-0.66). The prevalence of abnormal BP and PTA of workers increased with age. After adjusting for age, sex, and body mass index, the prevalence of abnormal ECG of mining workers was higher than that of manufacturing workers (OR = 1.54, 95% CI = 1.07-2.24), and the prevalence of abnormal PTA had a rising trend with the increase in noise exposure value. Noise-exposed workers have a high prevalence of abnormal ECG, BP, and PTA, and factors such as age, enterprise size, and workplace noise exposure are correlated with the aberrant health of workers. Governments, enterprises, and individuals need to attach great importance to the possible adverse effects of noise. They must also actively adopt various effective measures to protect the occupational safety and health of workers.

Auditory Sensory Gating: Effects of Noise.

Cortical auditory evoked potentials (CAEPs) indicate that noise degrades auditory neural encoding, causing decreased peak amplitude and increased peak latency. Different types of noise affect CAEP responses, with greater informational masking causing additional degradation. In noisy conditions, attention can improve target signals' neural encoding, reflected by an increased CAEP amplitude, which may be facilitated through various inhibitory mechanisms at both pre-attentive and attentive levels. While previous research has mainly focused on inhibition effects during attentive auditory processing in noise, the impact of noise on the neural response during the pre-attentive phase remains unclear. Therefore, this preliminary study aimed to assess the auditory gating response, reflective of the sensory inhibitory stage, to repeated vowel pairs presented in background noise. CAEPs were recorded via high-density EEG in fifteen normal-hearing adults in quiet and noise conditions with low and high informational masking. The difference between the average CAEP peak amplitude evoked by each vowel in the pair was compared across conditions. Scalp maps were generated to observe general cortical inhibitory networks in each condition. Significant gating occurred in quiet, while noise conditions resulted in a significantly decreased gating response. The gating function was significantly degraded in noise with less informational masking content, coinciding with a reduced activation of inhibitory gating networks. These findings illustrate the adverse effect of noise on pre-attentive inhibition related to speech perception.

Partial class activation mapping guided graph convolution cascaded U-Net for retinal vessel segmentation

Accurate segmentation of retinal vessels in fundus images is of great importance for the diagnosis of numerous ocular diseases. However, due to the complex characteristics of fundus images, such as various lesions, image noise and complex background, the pixel features of some vessels have significant differences, which makes it easy for the segmentation networks to misjudge these vessels as noise, thus affecting the accuracy of the overall segmentation. Therefore, accurately segment retinal vessels in complex situations is still a great challenge. To address the problem, a partial class activation mapping guided graph convolution cascaded U-Net for retinal vessel segmentation is proposed. The core idea of the proposed network is first to use the partial class activation mapping guided graph convolutional network to eliminate the differences of local vessels and generate feature maps with global consistency, and subsequently these feature maps are further refined by segmentation network U-Net to achieve better segmentation results. Specifically, a new neural network block, called EdgeConv, is stacked multiple layers to form a graph convolutional network to realize the transfer an update of information from local to global, so as gradually enhance the feature consistency of graph nodes. Simultaneously, in an effort to suppress the noise information that may be transferred in graph convolution and thus reduce adverse effects of noise on segmentation results, the partial class activation mapping is introduced. The partial class activation mapping can guide the information transmission between graph nodes and effectively activate vessel feature through classification labels, thereby improving the accuracy of segmentation. The performance of the proposed method is validated on four different fundus image datasets. Compared with existing state-of-the-art methods, the proposed method can improve the integrity of vessel to a certain extent when the pixel features of local vessels are significantly different, caused by objective factors such as inappropriate illumination and exudates. Moreover, the proposed method shows robustness when segmenting complex retinal vessels.

Actual versus perceived noise levels among critical care nurses and their related adverse effects: A cross-sectional study.

Critical care nurses should help in reducing noise to improve the well-being of patients and health care providers. To measure actual noise levels in intensive care units, identify nurses' perceived irritation levels of noise sources and examine the impact of noise on nurses' work performance and well-being. This cross-sectional study was conducted from January to April 2023 at a teaching hospital's six intensive care units. It involved three phases: mapping locations for noise measurement, actual noise measurements in decibels and a cross-sectional survey to identify nurses' perceptions and the adverse effects of noise. Actual noise was measured between patients' beds, nursing stations and beside 16 noise sources for 7 days 24 h a day. For nurse perceptions, the mean of perceived irritation levels by a Likert scale for each noise source was calculated to present perceived noise levels. Ranking of noise sources according to the actual measured and perceived irritation by nurses was done based on mean values. Curve estimation regression test was used to predict the relationship between actual and perceived noise and stepwise multiple linear regression identified factors associated with noise adverse effects. The mean noise level in the intensive care units was 65.55 dB. Nurses perceived mechanical ventilator alarms as the most irritating noise. However, the mean noise measurements showed that conversations among colleagues were the primary source of noise. There was no significant predictive relationship between the actual and perceived noise. Nurses reported feeling distressed, irritable, fatigued and less productive due to noise. There was a discrepancy between actual and perceived noise levels by nurses. Perceived noise levels had more impact on nurses than actual noise. By incorporating both objective measurements and subjective perceptions into efforts aimed at reducing noise, health care providers can create quieter intensive care units.

Distance Regression Enhanced with Temporal Information Fusion and Adversarial Training for Robot-Assisted Endomicroscopy.

Probe-based confocal laser endomicroscopy (pCLE) has a role in characterising tissue intraoperatively to guide tumour resection during surgery. To capture good quality pCLE data which is important for diagnosis, the probe-tissue contact needs to be maintained within a working range of micrometre scale. This can be achieved through micro-surgical robotic manipulation which requires the automatic estimation of the probe-tissue distance. In this paper, we propose a novel deep regression framework composed of the Deep Regression Generative Adversarial Network (DR-GAN) and a Sequence Attention (SA) module. The aim of DR-GAN is to train the network using an enhanced image-based supervision approach. It extents the standard generator by using a well-defined function for image generation, instead of a learnable decoder. Also, DR-GAN uses a novel learnable neural perceptual loss which combines for the first time spatial and frequency domain features. This effectively suppresses the adverse effects of noise in the pCLE data. To incorporate temporal information, we've designed the SA module which is a cross-attention module, enhanced with Radial Basis Function based encoding (SA-RBF). Furthermore, to train the regression framework, we designed a multi-step training mechanism. During inference, the trained network is used to generate data representations which are fused along time in the SA-RBF module to boost the regression stability. Our proposed network advances SOTA networks by addressing the challenge of excessive noise in the pCLE data and enhancing regression stability. It outperforms SOTA networks applied on the pCLE Regression dataset (PRD) in terms of accuracy, data quality and stability.

Optimizing support vector machine (SVM) by social spider optimization (SSO) for edge detection in colored images

Edge detection in images is a vital application of image processing in fields such as object detection and identification of lesion regions in medical images. This problem is more complex in the domain of color images due to the combination of color layer information and the need to achieve a unified edge boundary across these layers, which increases the complexity of the problem. In this paper, a simple and effective method for edge detection in color images is proposed using a combination of support vector machine (SVM) and the social spider optimization (SSO) algorithm. In the proposed method, the input color image is first converted to a grayscale image, and an initial estimation of the image edges is performed based on it. To this end, the proposed method utilizes an SVM with a Radial Basis Function (RBF) kernel, in which the model's hyperparameters are tuned using the SSO algorithm. After the formation of initial image edges, the resulting edges are compared with pairwise combinations of color layers, and an attempt is made to improve the edge localization using the SSO algorithm. In this step, the optimization algorithm's task is to refine the image edges in a way that maximizes the compatibility with pairwise combinations of color layers. This process leads to the formation of prominent image edges and reduces the adverse effects of noise on the final result. The performance of the proposed method in edge detection of various color images has been evaluated and compared with similar previous strategies. According to the obtained results, the proposed method can successfully identify image edges more accurately, as the edges identified by the proposed method have an average accuracy of 93.11% for the BSDS500 database, which is an increase of at least 0.74% compared to other methods.

Dual Self-Paced Cross-Modal Hashing

Cross-modal hashing~(CMH) is an efficient technique to retrieve relevant data across different modalities, such as images, texts, and videos, which has attracted more and more attention due to its low storage cost and fast query speed. Although existing CMH methods achieve remarkable processes, almost all of them treat all samples of varying difficulty levels without discrimination, thus leaving them vulnerable to noise or outliers. Based on this observation, we reveal and study dual difficulty levels implied in cross-modal hashing learning, \ie instance-level and feature-level difficulty. To address this problem, we propose a novel Dual Self-Paced Cross-Modal Hashing (DSCMH) that mimics human cognitive learning to learn hashing from ``easy'' to ``hard'' in both instance and feature levels, thereby embracing robustness against noise/outliers. Specifically, our DSCMH assigns weights to each instance and feature to measure their difficulty or reliability, and then uses these weights to automatically filter out the noisy and irrelevant data points in the original space. By gradually increasing the weights during training, our method can focus on more instances and features from ``easy'' to ``hard'' in training, thus mitigating the adverse effects of noise or outliers. Extensive experiments are conducted on three widely-used benchmark datasets to demonstrate the effectiveness and robustness of the proposed DSCMH over 12 state-of-the-art CMH methods.

Navigating through the noise: The effect of color‐coded performance feedback on decision‐making

AbstractMany companies use color codes in their internal performance reports to highlight how current performance compares to performance in a previous period. We examine whether the use of color coding affects managers' decision‐making in a resource allocation task. We argue that managers' decision accuracy will be lower if they receive noisier feedback, but that this detrimental effect of noise can be mitigated through color coding. Using two experiments, we find evidence consistent with our theory. Managers who receive reports in which performance increases are color‐coded green and performance decreases are color‐coded red are less affected by noise than managers who receive feedback reports without color coding. Supplemental analyses suggest that color coding induces managers to process feedback in a more holistic manner, which reduces the adverse effect of noise on managers' learning processes. Our findings have several important implications for research and practice.

A grey wolf optimization algorithm for solving partial destructive disassembly line balancing problem consider feasibility evaluation and noise pollution

Partial destructive disassembly (PDD) is a crucial tool for enhancing efficiency and reducing disassembly costs when recycling complex end-of-life (EOL) products. Evaluating the feasibility of PDD is crucial for determining its implementation. However, the existing partial destructive disassembly line balancing (PDDLB) problem prioritizes profitability and randomly selects components for destructive, disregarding the feasibility of PDD and the noise pollution resulting from destructive operations. To address this concern, this paper introduces the integration of feasibility evaluation and noise considerations into the PDDLB problem for the first time. Subsequently, a mathematical model is established for the PDDLB problem, aiming to minimize disassembly costs, achieve smoothness balance, minimize the number of stations, and mitigate the adverse effects of noise. Then, an improved grey wolf optimization algorithm (IGWOA) considering feasibility evaluation and noise limitations is developed. It non-linearizes the convergence factor to enhance accuracy in the search for optimality, introduces a Lévy flight operation to further expand the search solution space, and incorporates an interference factor to prevent local optimality. In addition, the superiority of the proposed algorithm in solving different cases is verified by comparing five algorithms for the 25-task example and seven algorithms for the 52-task example in the latest literature. Finally, the proposed method is applied to a real engine case. The obtained results are compared with several classical Swarm Intelligence methods. The comparison results show that IGWOA has advantages due to other group intelligence methods in solving small, medium, and large-scale example. Moreover, the disassembly cost is comparatively low.

A Novel Adaptive ANC Algorithm for Removal of Background Noise in Speech Applications

Noise is an unsafe mechanical toxin that causes serious hearing misfortune in the working environment of every nation. The working people in the military, mining, development, printing, and saw factories tend to lose their hearing performance due to the adverse effects of noise generated by the machines. They undergo elevated levels of noise, with various machinery producing greater levels of noise measured in decibels. These sounds may cause major health problems that may not allow the person to work in such conditions. Algorithms like Least Mean Square (LMS), Normalized Least Mean Squared (NLMS), Filtered-x Least Mean Squared (FxLMS) and Filtered-x Normalized Least Mean Squared (FxNLMS) are frequently being used for noise cancellation. Moreover, these filters have instability and poor noise reduction; slow convergence also requires a greater number of filter taps and less performance to identify the unknown system in the Active Noise Canceller (ANC). In this paper, a Précised FxNLMS (P-FxNLMS) algorithm is introduced for an ANC. This algorithm consists of dual adaptive filters, an updated Variable Step Size (VSS), a delay in the primary path, a slight improvement in the on-line secondary path, and a modified filter step size when compared to an existing ANC system, with the purpose of minimizing the demerits of existing algorithms. Initially, the P-FxNLMS algorithm was tested with Additive White Gaussian Noise (AWGN) and later tested with real noises from the NOISEUS dataset to check the noise reduction performance. The increase in Signal to Noise Ratio (SNR) segmentation for P-FxNLMS is around 1.45 dB to 4.07 dB and 38.46 % to 73.68 % of the Mean Square Error (MSE) as compared to the algorithms available for different sounds with different SNR input levels. From the performance results of MSE and SNR improvement (SNRi), we found improvements compared with existing algorithms

Hierarchical Graph Convolutional Networks With Latent Structure Learning for Mechanical Fault Diagnosis

Industrial sensor signals are essentially non-Euclidean graph structures due to the interplay between process variables; thus, graph convolutional networks (GCNs) have been widely studied and applied. However, most of the existing GCN-based methods may suffer from two drawbacks: 1) it is difficult to characterize multiple interactions among nodes and 2) the input graph constructed from the original data may contain errors and missing edges, which will degenerate the fault diagnosis performance. To address the abovementioned issues, this article designs a hierarchical GCN with latent structure learning for industrial fault diagnosis, which can organize hierarchical networks to collaboratively improve the quality of latent graph structure, and enhanced diagnostic performance can be guaranteed. First, a high-quality updated graph is formed by incorporating the original graph with the new graph in the graph constructing layer, which can not only eliminate the adverse effects of noise and outliers but also characterize the multiple interactions among nodes. Then, the updated graph is fed into the multilayer GCN layer for better feature learning and enhances the node representation through intra- and inter-layer convolutional operations simultaneously. After that, the produced node embeddings are used to guide the latent structure learning process for optimal graph. Finally, the proposed method is verified in both the simulated and real industrial processes. The experimental results demonstrate that the new approach has better fault diagnosis accuracy and practicability than state-of-the-art methods.

Transfer learning for clustering single-cell RNA-seq data crossing-species and batch, case on uterine fibroids.

Due to the high dimensionality and sparsity of the gene expression matrix in single-cell RNA-sequencing (scRNA-seq) data, coupled with significant noise generated by shallow sequencing, it poses a great challenge for cell clustering methods. While numerous computational methods have been proposed, the majority of existing approaches center on processing the target dataset itself. This approach disregards the wealth of knowledge present within other species and batches of scRNA-seq data. In light of this, our paper proposes a novel method named graph-based deep embedding clustering (GDEC) that leverages transfer learning across species and batches. GDEC integrates graph convolutional networks, effectively overcoming the challenges posed by sparse gene expression matrices. Additionally, the incorporation of DEC in GDEC enables the partitioning of cell clusters within a lower-dimensional space, thereby mitigating the adverse effects of noise on clustering outcomes. GDEC constructs a model based on existing scRNA-seq datasets and then applying transfer learning techniques to fine-tune the model using a limited amount of prior knowledge gleaned from the target dataset. This empowers GDEC to adeptly cluster scRNA-seq data cross different species and batches. Through cross-species and cross-batch clustering experiments, we conducted a comparative analysis between GDEC and conventional packages. Furthermore, we implemented GDEC on the scRNA-seq data of uterine fibroids. Compared results obtained from the Seurat package, GDEC unveiled a novel cell type (epithelial cells) and identified a notable number of new pathways among various cell types, thus underscoring the enhanced analytical capabilities of GDEC. Availability and implementation: https://github.com/YuzhiSun/GDEC/tree/main.

Otoprotective Effects of Quercetin Against Oxidative Damage in the Rat's Cochlea Induced by Noise and Silver Nanoparticles.

The aim of this study was to investigate the otoprotective effects of Quercetin (Que) against both noise-induced hearing loss (NIHL) and the ototoxicity of silver nanoparticles (SNPs) in rats. Forty-two male Wistar rats were divided into seven groups (n=6): control, SNPs, Que (100mg/kg) plus SNPs (100mg/kg), noise (104dB), Que plus noise, noise plus SNPs, and noise plus Que plus SNPs. In the weight change results, there was no significant difference between the groups exposed to noise plus SNPs and SNPs compared to the control group. However, animals had significant changes in DPOAE amplitude at 1 and 3days post-exposure when compared to baseline. Additionally, the DPOAE value of rats administered with Que plus SNPs was higher than in all other groups. Que also decreased the levels of TACT, MDA, IL-6, TNF-α, and NOX3 in the groups exposed to noise and SNPs and increased the SOD level and expression of myosin heavy chain VII (MYH7) and β-tubulin III (TUBB3) proteins. Furthermore, Que decreased structural changes in the animals' cochlea. Our findings indicate that pretreatment with Que efficiently counteracted the adverse effects of noise and SNPs on inner hair cell, outer hair cell, and nerve cells, which are responsible for high-frequency perception.

Obesity-related cardiometabolic indicators modify the associations of personal noise exposure with heart rate variability: A further investigation on the Study among Obese and Normal-weight Adults (SONA)

Elucidating the associations between environmental noise and heart rate variability (HRV) would be beneficial for the prevention and control of detrimental cardiovascular changes. Obese people have been found to manifest heightened susceptibility to the adverse effects of noise on HRV. However, the underlying mechanisms remain unclear. Based on 53 normal-weight and 44 obese young adults aged 18–26 years in Beijing, China, this study aimed to investigate the role of obesity-related cardiometabolic indicators for associations between short-term environmental noise exposure and HRV in the real-world context. The participants underwent personal noise exposure and ambulatory electrocardiogram monitoring using portable devices at 5-min intervals for 24 continuous hours. Obesity-related blood pressure, glucose and lipid metabolism, and inflammatory indicators were subsequently examined. Generalized mixed-effect models were used to estimate the associations between noise exposure and HRV parameters. The C-peptide, homeostasis model assessment of insulin resistance (HOMA-IR), and leptin levels were higher in obese participants compared to normal-weight participants. We observed amplified associations between short-term noise exposure and decreases in HRV among participants with higher C-peptide, HOMA-IR, and leptin levels. For instance, a 1 dB(A) increment in 3 h-average noise exposure level preceding each measurement was associated with changes of −0.20% (95%CI: −0.45%, 0.04%) and −1.35% (95%CI: −1.85%, −0.86%) in standard deviation of all normal to normal intervals (SDNN) among participants with lower and higher C-peptide levels, respectively (P for interaction <0.05). Meanwhile, co-existing fine particulate matter (PM2.5) could amplify the associations between noise and HRV among obese participants and participants with higher C-peptide, HOMA-IR, and leptin levels. The more apparent associations of short-term exposure to environmental noise with HRV and the effect modification by PM2.5 may be partially explained by the higher C-peptide, HOMA-IR, and leptin levels of obese people.

Effects of aircraft noise cessation on blood pressure, cardio- and cerebrovascular endothelial function, oxidative stress, and inflammation in an experimental animal model

Large epidemiological studies have shown that traffic noise promotes the development of cardiometabolic diseases. It remains to be established how long these adverse effects of noise may persist in response to a noise-off period. We investigated the effects of acute aircraft noise exposure (mean sound level of 72 dB(A) applied for 4d) on oxidative stress and inflammation mediating vascular dysfunction and increased blood pressure in male C57BL/6 J mice. 1, 2 or 4d of noise cessation after a 4d continuous noise exposure period completely normalized noise-induced endothelial dysfunction of the aorta (measured by acetylcholine-dependent relaxation) already after a 1d noise pause. Vascular oxidative stress and the increased blood pressure were partially corrected, while markers of inflammation (VCAM-1, IL-6 and leukocyte oxidative burst) showed a normalization within 4d of noise cessation. In contrast, endothelial dysfunction, oxidative stress, and inflammation of the cerebral microvessels of noise-exposed mice did not improve at all. These data demonstrate that the recovery from noise-induced damage is more complex than expected demonstrating a complete restoration of large conductance vessel function but persistent endothelial dysfunction of the microcirculation. These findings also imply that longer noise pauses are required to completely reverse noise-induced vascular dysfunction including the resistance vessels.

Novela nařízení vlády č. 272/2011 Sb., o ochraně zdraví před nepříznivými účinky hluku a vibrací

Novela nařízení vlády č. 272/2011 Sb., o ochraně zdraví před nepříznivými účinky hluku a vibrací

Deep Autoencoder Thermography for Defect Detection of Carbon Fiber Composites

Infrared thermography (IRT) is an economical nondestructive testing technique for structural health monitoring of composite materials. However, the nonlinear nature of the thermographic data and the adverse effects of noise and inhomogeneous backgrounds prevent it from achieving satisfactory results. Most of the existing thermographc data analysis methods are supervised and/or linear, which therefore are not favorable for nonlinear feature extraction of unlabeled thermograms. In this work, a deep autoencoder thermography (DAT) method is proposed for detecting subsurface defects in composite materials. The multi-layer network structure of DAT can handle nonlinear temperature profiles, and the output of the intermediate hidden layer is visualized to highlight defects. The layer-by-layer feature visualization reveals how the model extracts defect features. A loss inflection point scheme is utilized to determine a suitable depth of the model. Moreover, a new quantitative index is proposed to compare the defect detectability of different methods.

Sonar image target detection based on multi-region optimal selection strategy

To overcome the adverse effects of noise and shadow regions on target detection in side-scan sonar images, more precisely, it is difficult to accurately detect targets, a target detection technology based on a multi-region optimal selection strategy of spectral clustering combined with the entropy weight method is proposed in this study. First, the cluster numbers for spectral clustering are set in advance based on prior knowledge, and the pixels of the sonar image are clustered into several different regions. Second, the invariable features of translation, rotation and scaling up that each region is extracted and used to construct the feature criterion matrix for the multiple regions. Last, the entropy weight method is used to calculate the weights of each feature and the comprehensive weighted score of each region for this feature criterion matrix to obtain the final target region. Experimental results show that the proposed method can effectively overcome the adverse effects of noise and shadow regions in side-scan sonar images, but also achieve the selection of optimal target region among multiple regions after image clustering, thus verifying the feasibility and effectiveness of the proposed method in this study.

Effects of railway noise and vibrationson dissatisfaction of residents: case study of Iran.

In this study, the effects of noise and vibrations caused by railways on the lives of residents around the railways in districts 17 and 18 of Tehran, the capital of Iran which amounts to about 700,000 people, are investigated. In the first stage the actual level of noise was measured at various points which showed above standard levels. Afterward, the authors developed a questionnaireand the survey was conducted from 376 residents. The findings of this study show that the most important factor for the residents adjacent to the railway line is the need for action by the country's railway authorities to reduce and control the adverse effects of noise and vibration. Also, the analysis of inferential statistics performed by chi-square test shows that the variables of gender and proximity of residence or workplace or both to the railway do not affect any of the railway noise or vibration dissatisfaction. The age variable also has no effect on noise dissatisfaction but does impact on vibration. Finally, distance from the railway affects both disaffection from noise and vibration.