Low Noise Level Research Articles

Electric tugboat deployment in maritime transportation: detailed analysis of advantages and disadvantages

Purpose This study aims to provide a comprehensive review of electric tugboat deployment in maritime transportation, including an in-depth assessment of its advantages and disadvantages. Along with the identification of advantages and disadvantages of electric tugboat deployment, the present research also aims to provide managerial insights into the economic viability of different tugboat alternatives that can guide future investments in the following years.Design/methodology/approach A detailed literature review was conducted, aiming to gain broad insights into tugboat operations and focusing on different aspects, including tugboat accidents and safety issues, scheduling and berthing of tugboats, life cycle assessment of diesel tugboats and their alternatives, operations of electric and hybrid tugboats, environmental impacts and others. Moreover, a set of interviews was conducted with the leading experts in the electric tugboat industry, including DAMEN Shipyards and the Port of Auckland. Econometric analyses were performed as well to evaluate the financial viability and economic performance of electric tugboats and their alternatives (i.e. conventional tugboats and hybrid tugboats).Findings The advantages of electric tugboats encompass decreased emissions, reduced operating expenses, improved energy efficiency, lower noise levels and potential for digital transformation through automation and data analytics. However, high initial costs, infrastructure limitations, training requirements and restricted range need to be addressed. The electric tugboat alternative seems to be the best option for scenarios with low interest rate values as increasing interest values negatively impact the salvage value of electric tugboats. It is expected that for long-term planning, the electric and hybrid tugboat alternatives will become preferential since they have lower annual costs than conventional diesel tugboats.Practical implications The outcomes of this research provide managerial insights into the practical deployment of electric tugboats and point to future research needs, including battery improvements, cost reduction, infrastructure development, legislative and regulatory changes and alternative energy sources. The advancement of battery technology has the potential to significantly impact the cost dynamics associated with electric tugboats. It is essential to do further research to monitor the advancements in battery technology and analyze their corresponding financial ramifications. It is essential to closely monitor the industry’s shift toward electric tugboats as their prices become more affordable.Originality/value The maritime industry is rapidly transforming and facing pressing challenges related to sustainability and digitization. Electric tugboats represent a promising and innovative solution that could address some of these challenges through zero-emission operations, enhanced energy efficiency and integration of digital technologies. Considering the potential of electric tugboats, the present study provides a comprehensive review of the advantages and disadvantages of electric tugboats in maritime transportation, extensive evaluation of the relevant literature, interviews with industry experts and supporting econometric analyses. The outcomes of this research will benefit governmental agencies, policymakers and other relevant maritime transportation stakeholders.

DIY adapting SEM for low-voltage TEM imaging.

Electron microscopy is essential for examining materials and biological samples at microscopic levels, providing detailed insights. Achieving high-quality imaging is often challenged by the potential damage high-energy beams can cause to sensitive samples. This study compares scanning electron microscopy (SEM) and transmission electron microscopy (TEM) to evaluate image quality, noise levels, and the ability to preserve delicate specimens. We used a modified SEM system with a transmitted electrons conversion accessory, allowing it to operate like a TEM but at lower voltages, thereby reducing sample damage. Our analysis included quantitative assessments of noise levels and texture characteristics such as entropy, contrast, dissimilarity, homogeneity, energy, and correlation. This comprehensive evaluation directly compared traditional TEM and the adapted SEM system across various images. The results showed that TEM provided images with higher clarity and significantly lower noise levels, reinforcing its status as the preferred method for detailed studies. However, the modified SEM system also produced high-quality images at very low acceleration voltages, which is crucial for imaging samples sensitive to high-energy exposure. The texture metrics analysis highlighted the strengths and limitations of each method, with TEM images exhibiting lower entropy and higher homogeneity, indicating smoother and more uniform textures. This study emphasizes the importance of selecting the appropriate electron microscopy method based on research needs, such as sample sensitivity and required detail level. With its conversion accessory, the modified SEM system is a versatile and valuable tool, offering a practical alternative to TEM for various applications. This research enhances our understanding of the capabilities and limitations of SEM and TEM. It paves the way for further innovations in electron microscopy techniques, improving their applicability for studying sensitive materials. RESEARCH HIGHLIGHTS: Our study introduces a modified SEM adapter enabling TEM-like imaging at reduced voltages, effectively minimizing sample damage without compromising image resolution. Through comparative analysis, we found that images from the modified SEM closely match the quality of traditional TEM, showcasing significantly lower noise levels. This advancement underscores the SEM's enhanced capability for detailed structural analysis of sensitive materials, broadening its utility across materials science and biology.

Prediction of noise generated by rod-airfoil configuration: An investigation based on experiments and machine learning

This study investigated the effects of various parameters on the SPL (Sound Pressure Level) levels of rod-airfoil configurations. An experimental study was performed to investigate the effects of the rod parameters, such as the configuration of the rod, the distance between the rod and the airfoil, the diameter effect of the rod, and the geometry of the rod, on the performance of the rod-airfoil configuration. An Artificial Neural Network (ANN) model was then developed and applied to accurately predict the SPL of rod-airfoil configurations. The results of the study revealed that the Levenberg-Marquardt (LM) algorithm with 2 hidden neurons produced the best performance in predicting the SPL level, with a training R-squared value of 0.9998 and a testing R-squared value of 0.998715. The findings also indicated that increasing rod diameter increases sound pressure level while reducing gap width increases SPL levels and decreases frequency values. This method offers a more precise and effective technique to forecast the SPL levels of rod-airfoil designs, allowing designers to enhance their creations and lower noise levels. The findings of this study can also be utilized to direct future research in this area and offer important information for a better understanding of the mechanism of rod-airfoil noise creation. To the best of the authors’ knowledge, this is the first study to look into rod-airfoil design predictions made using machine learning approaches.

A Polymer Thick Film on an Organic Substrate Grid Electrode and an Open-Source Recording System for UHF MRI: An Imaging Study.

Electrocorticography (ECoG) is a critical tool in preclinical neuroscience research for studying global network activity. However, integrating ECoG with functional magnetic resonance imaging (fMRI) has posed challenges, due to metal electrode interference with imaging quality and heating around the metallic electrodes. Here, we introduce recent advancements in ECoG grid development that utilize a polymer-thick film on an organic substrate (PTFOS). PTFOS offers notable advantages over traditional ECoG grids. Firstly, it significantly reduces imaging artifacts, ensuring minimal interference with MR image quality when overlaying brain tissue with PTFOS grids. Secondly, during a 30-min fMRI acquisition, the temperature increase associated with PTFOS grids is remarkably low, measuring only 0.4 °C. These findings suggest that utilizing ECoG with PTFOS grids has the potential to enhance the safety and efficacy of neurosurgical procedures. By providing clearer imaging results and mitigating risk factors such as excessive heating during MRI scans, PTFOS-based ECoG grids represent a promising advancement in neurosurgical technology. Furthermore, we describe a cutting-edge open-source system designed for simultaneous electrophysiology and fMRI. This system stands out due to its exceptionally low input noise levels (<0.6 V peak-to-peak), robust electromagnetic compatibility (it is suitable for use in MRI environments up to 9.4 teslas), and the inclusion of user-programmable real-time signal-processing capabilities. The open-platform software is a key feature, enabling researchers to swiftly implement and customize real-time signal-processing algorithms to meet specific experimental needs. This innovative system has been successfully utilized in several rodent EEG/fMRI studies, particularly at magnetic field strengths of 4.7 and 9.4 teslas, focusing on the somatosensory system. These studies have allowed for detailed observation of neural activity and responses within this sensory system, providing insights that are critical for advancing our understanding of neurophysiological processes. The versatility and high performance of our system make it an invaluable tool for researchers aiming to integrate and analyze complex datasets from advanced imaging and electrophysiological recordings, ultimately enhancing the depth and scope of neuroscience research.

Predictions in biometric models

One of the domains of genetic enhancement that has extensively employed both simulation and authentic data is Biometrics. Selecting efficient models for the Genome-Wide Selection (GWS) process using molecular markers (SNPs) presents several challenges. Among these challenges is the effective identification of the optimal model for fitting a given dataset. To contribute to this endeavor, this paper's primary objective is to assess the predictive accuracy of nine (9) distinct models, each following different paradigms within the realm of Biometrics. The data employed in this study were generated through simulation, encompassing the primary issues encountered in this field of research, including high dimensionality, nonlinearity, and multicollinearity. As the primary findings, notable observations include the enhancement of predictive efficiency as data noise decreases, the predominance of the tree paradigm (for low noise levels, BOO), and the efficacy of the neural network paradigm (for high noise levels, RBF).

Ratcheting by Stochastic Resetting With Fat-Tailed Time Distributions.

We investigated both numerically and analytically the drift of a Brownian particle in a ratchet potential under stochastic resetting with fat-tailed distributions. As a study case we chose a Pareto time distribution with tail index β. We observed that for rectification occurs even if for the mean resetting time is infinite. However, for rectification is completely suppressed. For low noise levels, the drift speed attains a maximum for β immediately above 1, that is for finite but large mean resetting times. In correspondence with such an optimal drift the particle diffusion over the ratchet potential turns from normal to superdiffusive, a property also related to the fat tails of the resetting time distribution.

Usability of Community Seismic Network recordings for ground-motion modeling

A source of ground-motion recordings in urban Los Angeles that has seen limited prior application is the Community Seismic Network (CSN), which uses low-cost, micro–electro–mechanical system (MEMS) sensors that are deployed at much higher densities than stations for other networks. We processed CSN data for the 29 earthquakes with M &gt; 4 between July 2012 and January 2023 that produced CSN recordings, including selection of high- and low-pass corner frequencies ( fcHP and fcLP, respectively). Each record was classified as follows: (1) Broadband Record (BBR)—relatively broad usable frequency range from fcHP &lt; 0.5 to fcLP &gt; 10 Hz; (2) Narrowband Record (NBR)—limited usable frequency range relative to those for BBR; and (3) Rejected Record (REJ)—noise-dominated. We compare recordings from proximate (within 3 km) CSN and non-CSN stations (screened to only include cases of similar surface geology and favorable CSN instrument housing). We find similar high- to medium-frequency ground motions (i.e. peak ground acceleration (PGA) and Sa for T &lt; 5 s) from CSN BBR and non-CSN stations, whereas NBRs have lower amplitudes. We examine PGA distributions for BBR and REJ records and find them to be distinguished, on average across the network, at 0.005 g, whereas 0.0015 g was found to be the threshold between usable records (BBR and NBR) and pre-event noise. Recordings with amplitudes near or below these thresholds are generally noise-dominated, reflecting environmental and anthropogenic ground vibrations and instrument noise. We find nominally higher noise levels in areas of high-population density and lower noise levels by a factor of about 1.5 in low-population density areas. By applying the 0.0015 g threshold, limiting distances for the network-average site condition, based on the expected fifth-percentile ground-motion levels, are 89, 210, 280, and 370 km for M 5, 6, 7, and 8 events, respectively.

(Invited) MoS2 Photodetectors for Near-Infrared Biosensing Applications

Device researchers have been actively developing novel diagnostic biosensors using metallic nanoparticle-based plasmonic immunoassays.[1] In these biosensing devices, photodetectors with high photoresponsivity and low internal noise levels are integrated to detect the marginal optical signals change induced by biomarker binding events (i.e., Antigen-antibody binding). Recently, atomically thin layers of molybdenum disulfide (MoS2) have been integrated with plasmonic components to enable fast and sensitive colorimetric monitoring of disease-related biomarkers.[2] However, such biosensors are mostly operated in the ultraviolet/visible range, which needs a purification step to separate out a variety of unwanted biomaterials that absorb visible light and takes several hours of preparation steps. Thus, it would be amenable to develop biosensors for biomolecular detections or immunoassays in the whole blood (WB) analytes without any sample preparations.[3] To address the issues mentioned above, recent studies have demonstrated the engineered gold nanoparticles (AuNPs) that have localized surface plasmonic resonance (LSPR) shift around near-infrared (NIR) wavelength region. However, a high-sensitivity photodetector under NIR region is still required to detect marginal signal changes due to the target biomarker binding events. Meanwhile, the MoS2 material has been reported to exhibits superior photo-response characteristics.[4] MoS2 is a transition metal dichalcogenide material of which single- and multi-layer films exhibit an efficient electron-hole pair generation rate under photoexcitation and therefore high photo absorption as compared to silicon. Therefore, a systematical study on MoS2 photoconductors to optimize the optoelectronic properties under near-infrared (NIR) (λ = 650 nm) operation can enable zero-preparation WB immunoassays combined with specially engineered AuNPs.In this work, we study the photo-response properties (i.e., Photoresponsivity spectrum) of in-plane MoS2 photodetectors as the function of their geometric dimensions and fabrication conditions. Recent study shows that an annealing temperature after exfoliation can etch the upper layers of MoS2 flakes and clean stains on the device and therefore improve device performances (e.g., mobility). [5] This work enables the NIR operation capabilities of plasmonic colorimetric biosensing by introducing the optimized MoS2 photodetector fabrication practice. This approach reduces assay preparation times and mitigate background interference even with WB analytes and thereby enable the WB point-of-care immunoassays.

Tripartite evolutionary game and simulation analysis of electric bus charging facility sharing under the governmental reward and punishment mechanism

Electric vehicles (EVs) are environmentally friendly with the features of low noise levels and zero tailpipe emissions. However, limited charging facilities have restricted the large-scale development of EVs, and electric bus charging facility sharing is a promising solution to this issue. While reducing charging time and improving facility utilization, this measure involves the interests of the government, bus operators, and electric car owners, requiring a systemic analysis of their interactions. Therefore, this study constructs a tripartite evolutionary game model under the governmental reward and punishment mechanism. The model examines the strategic decision-making process, influential factors, and evolutionary stability of three players, followed by simulation analysis. The results show that: (1) The evolutionary system may exhibit three stable states: (0,0,0), (1,0,0) and (1,1,1), and enhance the initial ratio of the three stakeholders will help evolve towards the ideal state. (2) Increasing rewards and penalties can both be meaningful, when penalties exceed rewards, the measures can be more effective. (3) Bus operators are more sensitive to incentive and penalty measures than electric car owners, and governments should pay more attention to them in the implementation process. The findings can provide references for the policy formulation on electric bus charging facility sharing.

Low Frequency Electronic Noise Characterization of Au-W/β-Ga2O3 Schottky Diodes

Ultra-wide bandgap (UWBG) semiconductors have attracted tremendous amount of interest for applications in high-power electronics and solar-blind UV photodetectors. In particular, β-Ga2O3 has attracted significant research attention in recent years due to its promising electrical and optical characteristics including a large bandgap, high breakdown electrical field, and large Baliga’s figure of merit compared to well-established wide-bandgap technologies such as GaN and SiC [1].Low frequency electronic noise, which is present in all electronic devices, can be used as a diagnostic tool for semiconductor material and device characterization. By studying the low frequency noise in semiconductor materials and devices, the physics associated with the noise properties of bulk and interface defects and traps can be investigated [2]. Measurement of low frequency noise is also crucial for evaluating material quality and device reliability.In this work, we investigate the forward-bias diode parameters and low-frequency electronic noise in Au-W/β-Ga2O3 Schottky barrier diodes. The Schottky contact is formed by depositing 20 nm of Tungsten (W) using dc sputtering followed by 340 nm of Gold (Au) using e-beam evaporation, and subsequent annealing [3].We first measure the I-V characteristics and extract important diode parameters, such as the Schottky barrier height, ideality factor, and series resistance from room temperature forward-bias current-voltage characteristics. The extracted Schottky barrier height for these devices is in the range 0.7-0.8 eV and the ideality factor is approximately 1.2.We next measure the low-frequency electronic noise in Au-W/β-Ga2O3 Schottky diodes at different forward bias voltages at room temperature. We find that the current noise spectral density exhibits 1/f-type behavior for all forward bias voltages at low frequency with an exponent ranging from 0.75 to 1.15, indicating that the low frequency noise of these devices is dominated by excess (flicker) noise. No Lorentzian-shaped generation-recombination (G-R) noise is observed. At low frequencies, the shot noise contribution is found to be negligible for the values of the current in our devices, and the total noise is 1/f-limited.We also investigate the dependence of the current noise spectral density on forward bias current, revealing different relationships in different current regimes. At low currents, the noise spectral density is found to scale approximately as the square of current, whereas at high currents, the noise spectral density begins to saturate. Such noise behavior can be attributed to different noise sources dominating in different current regimes. Furthermore, using the noise figure of merit adapted for ultra-wide bandgap semiconductor devices [4], we find that the Au-W/β-Ga2O3 devices have lower noise levels compared to other emerging UWBG materials and similar noise levels as well-established technologies such as GaN.These results provide important insights into the electronic noise properties of Au-W/β-Ga2O3 Schottky diodes and show that low frequency noise characterization is an important diagnostic tool for assessing the quality and reliability of gallium oxide based wide-bandgap materials and device technologies.

Acoustic emission monitoring of composite marine propellers in submerged conditions using embedded piezoelectric sensors

Flexible composite marine propellers can aid the marine industry in reducing carbon emissions and underwater radiated noise pollution. The structural integrity of the blades can be assessed using structural health monitoring. One of these methods is the measurement and analysis of damage-induced acoustic emission signals. This paper experimentally investigates the feasibility of using embedded piezoelectric sensors for the measurement of acoustic emissions throughout a submerged flexible composite marine propeller blade. A full-scale glass-fibre reinforced polymer blade has been manufactured with 24 embedded sensors. While suspended in artificial seawater, acoustic emissions were simulated on the blade. The measurements show that the embedded piezoelectric sensors can measure acoustic emissions while the blade is submerged. Further, the distance from source to sensor over which the acoustic emission is measurable was investigated. For a noise level of 40 dB and a source amplitude of 70 dB between 100 and 250 kHz, an average maximum measurable distance of 124 mm was obtained. For higher frequencies, the distance drops and for lower noise levels the distance increases.

An Improved CH4 Profile Retrieving Method for Ground-Based Differential Absorption Lidar

Range-resolved CH4 concentration measurement is important prior data for atmospheric physical and chemical models. Ground-based differential absorption lidar (DIAL) can measure the vertical distribution of CH4 concentration in the atmosphere. The traditional method uses lidar observational data and the lidar equation to calculate profiles, but the inversion accuracy is greatly affected by noise. Although some denoising methods can improve accuracy at low altitudes, the low signal-to-noise ratio caused by the effect of aerosol Mie scattering and lower aerosol concentrations at high altitudes cannot be solved. Here, an improved cubic smoothing spline fitting CH4 concentration profile inversion method is proposed to address this challenge. By adding a penalty term of the second derivative of the conventional cubic spline function to the objective function, this penalty term acts to smooth the fitting, allowing the fitting function to avoid necessarily passing through those noisy sampling points. This avoids the large fluctuations caused by noisy sampling points, effectively suppresses noise, captures signals with lower noise levels, and thereby enhances the inversion accuracy of the profiles. Simulations and case studies demonstrated the superiority of the proposed method. Compared with the traditional method, cubic smoothing spline fitting can reduce the mean error of the whole CH4 profile by 85.54%. The standard deviation of CH4 concentration retrieved is 3.59 ppb–90.29 ppb and 0.01 ppb–6.75 ppb smaller than the traditional method and Chebyshev fitting, respectively. Three real cases also indicate that the CH4 concentration retrieved by cubic smoothing spline fitting is more consistent with in-situ measurements. In addition, long-term DIAL observations have also revealed notable diurnal and seasonal trends in CH4 concentration at observation sites.

Automatic Weight Redistribution Ensemble Model Based on Transfer Learning to Use in Leak Detection for the Power Industry.

Creating an effective deep learning technique for accurately diagnosing leak signals across diverse environments is crucial for integrating artificial intelligence (AI) into the power plant industry. We propose an automatic weight redistribution ensemble model based on transfer learning (TL) for detecting leaks in diverse power plant environments, overcoming the challenges of site-specific AI methods. This innovative model processes time series acoustic data collected from multiple homogeneous sensors located at different positions into three-dimensional root-mean-square (RMS) and frequency volume features, enabling accurate leak detection. Utilizing a TL-driven, two-stage learning process, we first train residual-network-based models for each domain using these preprocessed features. Subsequently, these models are retrained in an ensemble for comprehensive leak detection across domains, with control weight ratios finely adjusted through a softmax score-based approach. The experiment results demonstrate that the proposed method effectively distinguishes low-level leaks and noise compared to existing techniques, even when the data available for model training are very limited.

Assembling and testing optoelectronic system to record and process signals from fiber-optic sensors

The given research presents assembling and testing optoelectronic system to record and process signals from fiber-optic sensors. The main optoelectronic systems to record and process the signals from fiber-optic sensors are light source controller and optical power detector. There was assembled controller diagram, which apart from light source includes current source for its adequate operation, as well as the systems necessary for stabilizing its working point. The scheme was modelled for specifying nominal and maximum operation criteria. Construction has been designed in the way, that light source controller includes structures of the current regulation and stabilization super luminescent diode (SLED) and temperature stabilization. Apart from that, there was assembled the microsystem of optical power detector additionally to the light detector, which includes the microsystems of intensification and filtration of the signal measured, processing analog data into digital form, microcontroller, used for preliminary data analysis. Data of optoelectronic systems diagram to record and process the signals from fiber-optic sensors has high response speed, low noise level and sufficient progress.

Optimizing spatial accuracy in electroencephalography reconstruction through diffuse optical tomography priors in the auditory cortex.

Diffuse optical tomography (DOT) enhances the localization accuracy of neural activity measured with electroencephalography (EEG) while preserving EEG's high temporal resolution. However, the spatial resolution of reconstructed activity diminishes for deeper neural sources. In this study, we analyzed DOT-enhanced EEG localization of neural sources modeled at depths ranging from 11-25 mm in simulations. Our findings reveal systematic biases in reconstructed depth related to DOT channel length. To address this, we developed a data-informed method for selecting DOT channels to improve the spatial accuracy of DOT-enhanced EEG reconstruction. Using our method, the average absolute reconstruction depth errors of DOT reconstruction across all depths are 0.9 ± 0.6 mm, 1.2 ± 0.9 mm, and 1.2 ± 1.1 mm under noiseless, low-level noise, and high-level noise conditions, respectively. In comparison, using fixed channel lengths resulted in errors of 2.6 ± 1.5 mm, 5.0 ± 2.6 mm, and 7.3 ± 4.5 mm under the same conditions. Consequently, our method improved the depth accuracy of DOT reconstructions and facilitated the use of more accurate spatial priors for EEG reconstructions, enhancing the overall precision of the technique.

Noise barriers as a mitigation measure for highway traffic noise: Empirical evidence from three study cases

Noise barriers are path interventions between noise sources and human receivers used mainly along road corridors to improve the acoustic environment for affected residents. Despite their widespread use, the impact of these interventions on community perception is still insufficiently investigated. This paper presents findings from a longitudinal study evaluating the efficacy of noise barriers in three residential areas alongside highways, compared to a reference case in a relatively quiet area. Noise exposure was objectively quantified via acoustic measurements and noise modelling, while socio-acoustic surveys measured the residents' subjective response in terms of noise annoyance as well as other aspects of quality of life. While noise exposure (Lday) decreased on average by 4.4–11.7 dBA at near-barrier points, direct reductions in pre to post-intervention noise annoyance were observed only in one case. Additionally, only in this particular case were the appraisals of the acoustic environment restored to a condition similar to low-level noise emissions (reference case). Contextual factors potentially downgrading the interventions' effectiveness are discussed, such as the history of complaints and coping, mistrust towards road authorities, high expectations, and the impacts of the COVID-19 pandemic. While noise exposure reductions did not directly lead to noise annoyance decreases, an ordinal regression pooling all cases revealed that larger reductions in noise exposure were associated with a higher likelihood of residents reporting decreased traffic noise annoyance in the post-survey. No evidence was found regarding noise barriers’ impact on the subjective assessment of other aspects of quality of life, such as health complaints, concentration disturbance, and sleep quality.

JointNet: Multitask Learning Framework for Denoising and Detecting Anomalies in Hyperspectral Remote Sensing

One of the significant challenges with traditional single-task learning-based anomaly detection using noisy hyperspectral images (HSIs) is the loss of anomaly targets during denoising, especially when the noise and anomaly targets are similar. This issue significantly affects the detection accuracy. To address this problem, this paper proposes a multitask learning (MTL)-based method for detecting anomalies in noisy HSIs. Firstly, a preliminary detection approach based on the JointNet model, which decomposes the noisy HSI into a pure background and a noise–anomaly target mixing component, is introduced. This approach integrates the minimum noise fraction rotation (MNF) algorithm into an autoencoder (AE), effectively isolating the noise while retaining critical features for anomaly detection. Building upon this, the JointNet model is further optimized to ensure that the noise information is shared between the denoising and anomaly detection subtasks, preserving the integrity of the training data during the anomaly detection process and resolving the issue of losing anomaly targets during denoising. A novel loss function is designed to enable the joint learning of both subtasks under the multitask learning model. In addition, a noise score evaluation metric is introduced to calculate the probability of a pixel being an anomaly target, allowing for a clear distinction between noise and anomaly targets, thus providing the final anomaly detection results. The effectiveness of the proposed model and method is validated via testing on the HYDICE and San Diego datasets. The denoising metric results of the PSNR, SSIM, and SAM are 41.79, 0.91, and 4.350 and 42.83, 0.93, and 3.558 on the HYDICE and San Diego datasets, respectively. The anomaly detection ACU is 0.943 and 0.959, respectively. The proposed method outperforms the other algorithms, demonstrating that the reconstructed images using this method exhibited lower noise levels and more complete image information, and the JointNet model outperforms the mainstream HSI anomaly detection algorithms in both the quantitative evaluation and visual effect, showcasing its improved detection capabilities.

Commuter Experience: An Assessment of Metro-Train Comfort Amidst Operational Vibroacoustic Conditions

This study investigates the impact of different passenger rolling stock structures and train configurations on vibrations and noise generated during operation. Vibroacoustic measurements were performed during acceleration, constant speed, and braking phases to analyze the effects using statistical analysis according to the relevant standards, revealing a statistically significant relationship between the equivalent noise level and vibration dose. In the context of the expanding Warsaw metro network, which is adding new lines and modern rolling stock, trends in the development of metro rolling stock were analyzed using five different types of metro vehicles, from the oldest to the newest designs. Vehicle performance was ranked in the context of ride comfort using standards and combined vibration and noise measures. The research results allowed for a collective comparative assessment of the construction of individual types of passenger rolling stock in terms of vibroacoustic phenomena, thanks to which it is possible to assess the impact of modern solutions and the justification for investing in modern rolling stock. Newer trains generally record significantly lower vibration and noise levels. The difference between the oldest and the most modern vehicle types is 57% for the vibration acceleration level (0.08 ms−2 RMS) and 66% for the noise level (2.2 dB LAeq).

Optimal electric bus scheduling considering battery degradation effect and charging facility capacity

ABSTRACT Electric buses are environmentally friendly with the features of low noise levels and zero-emissions. However, higher upfront costs due to the battery degradation effect, charging facility capacity and operational issues are the main obstacles to large-scale application of electric buses. This study proposes a mixed-integer nonlinear model for optimizing electric bus scheduling with a partial charging strategy, battery degradation and constraints of charging facility capacity. A genetic algorithm is then proposed to solve the model. Last, a case study based on a real transit network in Nanjing, China is conducted. The experimental results show that compared with the existing scheduling scheme, the optimal scheduling model can reduce the total system costs by 8.20%, which validates the effectiveness of the proposed model. The findings in this paper can provide a reference for operators to formulate electric bus scheduling schemes and promote the sustainable development of public transport.

Investigating the impact of cavity parameters on an ultrashort pulse in a fiber laser

Fiber lasers have numerous applications in various fields due to their compact design, low noise levels, high efficiency, and robust stability. We present a study focusing on the impact of cavity parameters on ultrashort pulses in fiber lasers, proposing an ultrafast laser that utilizes a MoTe2 saturable absorber (SA). Here numerical simulations allowed us to analyze the diverse pulse dynamics within a laser cavity, incorporating a SA, a segment of passive fiber, and erbium-doped fiber (EDF). Key factors of second-order dispersion, nonlinearity, gain saturation energy, small signal gain coefficient, and modulation depth and saturable energy of the SA were simulated. According to the simulations, we obtained the parameter settings for the minimum output pulse duration using a neural network and genetic algorithm. Then we proposed a normal dispersion laser with a MoTe2 mode-locker, capable of emitting dissipative soliton with a pulse duration of 824 fs and a spectrum bandwidth of 13.2 nm. These simulations provide valuable guidance for the design and optimization of fiber lasers, presenting a versatile and practical approach for applications.