Noise Intensity Level Research Articles

Tsunami Data Assimilation Using High‐Frequency Radar‐Derived Surface Currents by Considering Beam Angle‐Dependent Measurement Error Distributions

AbstractThe application of high‐frequency radar as an instrument for assimilating tsunami‐induced current fields is garnering increasing interest. The performance of surface current velocity measurements depends on the azimuthal differences between the crossing radar beams at the measurement points. This study aimed to incorporate the measurement error distributions of the east‐west and north‐south velocity components into tsunami data assimilation based on an optimal interpolation method, assuming Gaussian noise with the time‐invariant and a uniform standard deviation (STD = 5 cm/s) of radial velocity measurements. Through the empirical orthogonal function (EOF) analysis of radar‐derived surface currents in the Kii Channel, Japan, the velocities reconstructed using higher modes (EOFs 16–274) were associated with measurement errors, portraying nonuniform distribution depending on the crossing beam angle of two radar beams. Based on independent fifteen‐time assimilation experiments for two different tsunami scenarios, for a uniform water depth of 500 m, we observed a significant improvement of up to 29% and 0.9% in the assimilation performance (on average) over the along‐coast stations for scenarios with 1‐ and 5‐m maximum initial sea surface heights, respectively. The measurement errors dependent on the crossing beam angle reduced the error‐induced tsunamis, resulting in stable assimilations, with lower STDs in the fifteen‐time assimilation performances. When the STD of Gaussian noise varies with time, it is important to consider the temporal change in the radial velocity measurement errors and/or noise‐filtering techniques, to maintain a certain level of noise intensity.

The algorithm for denoising point clouds of annular forgings based on Grassmann manifold and density clustering

Abstract In the industrial sector, annular forgings serve as critical load-bearing components in mechanical equipment. During the production process, the precise measurement of the dimensional parameters of annular forgings is of paramount importance to ensure their quality and safety. However, owing to the influence of the measurement environment, the manufacturing process of annular forgings can introduce varying degrees of noise, resulting in inaccurate dimensional measurements. Therefore, researching methods for three-dimensional point cloud data to eliminate noise in annular forging point clouds is of significant importance for improving the accuracy of forging measurements. This paper presents a denoising approach for three-dimensional point cloud data of annular forgings based on Grassmann manifold and density clustering (GDAD). First, within the Grassmann manifold, the core points for density clustering are determined using density parameters. Second, density clustering is performed within the Grassmann manifold, with the Cauchy distance replacing the Euclidean distance to reduce the impact of noise and outliers on the analysis results. Finally, a search tree model was constructed to filter out incorrect point cloud clusters. The fusion of clustering results and the search tree model achieved denoising of point cloud data. Simulation experiments on annular forgings demonstrate that GDAD effectively eliminates edge noise in annular forgings and performs well in denoising point-cloud models with varying levels of noise intensity.

Data-driven methods for the inverse problem of suspension system excited by jump and diffusion stochastic track excitation

Following a lengthy tenure of service, suspension systems may exhibit inaccurate model parameters due to the presence of complex conditions, which can result in a deterioration of control and the potential for operational failures. It is therefore imperative to estimate the key parameters of suspension systems. This paper introduces two data-driven methods for addressing the inverse problem in suspension systems subjected to stochastic track excitations. By employing physics-informed neural networks (PINNs) and Monte Carlo (MC) simulation, we are able to address the resulting integro-differential equation that arises from stochastic jump processes, thus avoiding the necessity for mesh grids. In order to mitigate the numerical challenges that arise from the system parameters, a residual-based adaptive sampling method is proposed. These methods effectively infer unknown parameters, addressing scenarios where data is directly available as a probability density function (PDF) or only sparse trajectories are given. In the latter case, a novel loss function employing Kullback-Leibler divergence facilitates learning from stochastic trajectories. Both methods successfully obtain solutions to the forward Kolmogorov equation, as validated by numerical experiments testing their robustness against added noise. The results demonstrate accurate parameter estimation under varying noise intensities, highlighting the methods’ robustness.

Perancangan Sistem Alarm Kebisingan untuk Kapal Penangkap Ikan Berbasis Arduino Uno dengan Sensor KY-037

Fishermen are highly susceptible to hearing impairment due to prolonged exposure to the high-intensity noise of boat engines. While numerous studies have investigated the noise intensity levels of boat engines, no research has been conducted on noise warning alarm systems for fishing boats. Therefore, this study aims to develop a sound noise alarm system based on Arduino Uno with a KY-037 sensor for fishing boats, which is expected to reduce the potential for hearing impairment among fishermen. This research employs a quantitative descriptive approach, with data collected through experimental methods and analyzed by comparing noise level data at various engine speeds. The results of the noise level measurements of boat engines at the Fish Landing Base (PPI) in Cikidang, Pangadaran Regency, indicate that the majority of fishermen are safe from hearing impairment due to engine noise, as most boats there are small, with a Gross Tonnage (GT) capacity of under 5 GT. The risk of hearing impairment emerges for boats with a capacity of 11 GT running at a maximum engine speed of 1600 rpm, producing noise levels up to 84 dB. Thus, it is recommended that crew members use earplugs.

Hydrogeochemical characteristics and air quality risks associated with gold mining operations in Egypt using geochemical modeling and risk indices

The success of industrial operations depends on the effective identification, appraisal, and mitigation of possible hazards and associated environmental concerns. This report provides a complete review of environmental management techniques at the Sukari Gold Mine (SGM), located in the southeastern desert of Egypt. Extensive environmental measurements were taken to assess air and water quality, identify hazards, and analyze risks on the SGM premises. Air quality and noise intensity levels were measured at 39 places around the mine's working region. The findings found noncompliance with the Egyptian Environmental Law's (EEL4/94) noise exposure limitations, with the Power Generator House having the maximum noise levels at 107 dB. Remedial measures such as personal protective equipment (PPE) and exposure limit reduction strategies are being considered to address elevated noise levels. Measurements of particulate matter (PM10) and noxious gases (e.g., CO, SO2, NO2, HCN, and NH3) were conducted in workplace and ambient environments. Elevated PM10 concentrations were particularly concerning in underground regions, forcing the deployment of water depression techniques and improved PPE measures. While gas emissions from most activities remained under regulatory limits, select zones showed hydrogen cyanide (HCN) levels that exceeded permitted thresholds, necessitating specific control actions. Using hazard index (HI) and risk rating assessments, this study found different risk profiles across SGM's workplaces, focusing on high-risk regions for focused intervention. Additionally, a water assessment near a Tailing Storage Facility (TSF) was conducted to monitor the impact of mining activities on groundwater quality. The study revealed that groundwater in the region belongs to the Na–K–Cl–SO4 and Ca–Mg–Cl–SO4 water classes, with potential degradation attributed to high mineralization processes induced by aquifer materials and seawater intrusion. The findings underscore the importance of ongoing monitoring, control measures, and implementation of programs to ensure environmental sustainability and minimize risks associated with mining activities in the Sukari Gold Mines. This research highlights the imperative of continuous monitoring, proactive control measures, and the implementation of environmental initiatives to ensure the sustainability of mining operations within the Sukari Gold Mines.

Development of an artificial intelligence based occupational noise induced hearing loss early warning system for mine workers.

Occupational Noise Induced Hearing Loss (ONIHL) is one of the most prevalent conditions among mine workers globally. This reality is due to mine workers being exposed to noise produced by heavy machinery, rock drilling, blasting, and so on. This condition can be compounded by the fact that mine workers often work in confined workspaces for extended periods of time, where little to no attenuation of noise occurs. The objective of this research work is to present a preliminary study of the development of a hearing loss, early monitoring system for mine workers. The system consists of a smart watch and smart hearing muff equipped with sound sensors which collect noise intensity levels and the frequency of exposure. The collected information is transferred to a database where machine learning algorithms namely the logistic regression, support vector machines, decision tree and Random Forest Classifier are used to classify and cluster it into levels of priority. Feedback is then sent from the database to a mine worker smart watch based on priority level. In cases where the priority level is extreme, indicating high levels of noise, the smart watch vibrates to alert the miner. The developed system was tested in a mock mine environment consisting of a 67 metres tunnel located in the basement of a building whose roof top represents the "surface" of a mine. The mock-mine shape, size of the tunnel, steel-support infrastructure, and ventilation system are analogous to deep hard-rock mine. The wireless channel propagation of the mock-mine is statistically characterized in 2.4-2.5 GHz frequency band. Actual underground mine material was used to build the mock mine to ensure it mimics a real mine as close as possible. The system was tested by 50 participants both male and female ranging from ages of 18 to 60 years. Preliminary results of the system show decision tree had the highest accuracy compared to the other algorithms used. It has an average testing accuracy of 91.25% and average training accuracy of 99.79%. The system also showed a good response level in terms of detection of noise input levels of exposure, transmission of the information to the data base and communication of recommendations to the miner. The developed system is still undergoing further refinements and testing prior to being tested in an actual mine.

Intraindividual time-varying dynamic network of affects: linear autoregressive mixed-effects models for ecological momentary assessment.

An interesting recent development in emotion research and clinical psychology is the discovery that affective states can be modeled as a network of temporally interacting moods or emotions. Additionally, external factors like stressors or treatments can influence the mood network by amplifying or dampening the activation of specific moods. Researchers have turned to multilevel autoregressive models to fit these affective networks using intensive longitudinal data gathered through ecological momentary assessment. Nonetheless, a more comprehensive examination of the performance of such models is warranted. In our study, we focus on simple directed intraindividual networks consisting of two interconnected mood nodes that mutually enhance or dampen each other. We also introduce a node representing external factors that affect both mood nodes unidirectionally. Importantly, we disregard the potential effects of a current mood/emotion on the perception of external factors. We then formalize the mathematical representation of such networks by exogenous linear autoregressive mixed-effects models. In this representation, the autoregressive coefficients signify the interactions between moods, while external factors are incorporated as exogenous covariates. We let the autoregressive and exogenous coefficients in the model have fixed and random components. Depending on the analysis, this leads to networks with variable structures over reasonable time units, such as days or weeks, which are captured by the variability of random effects. Furthermore, the fixed-effects parameters encapsulate a subject-specific network structure. Leveraging the well-established theoretical and computational foundation of linear mixed-effects models, we transform the autoregressive formulation to a classical one and utilize the existing methods and tools. To validate our approach, we perform simulations assuming our model as the true data-generating process. By manipulating a predefined set of parameters, we investigate the reliability and feasibility of our approach across varying numbers of observations, levels of noise intensity, compliance rates, and scalability to higher dimensions. Our findings underscore the challenges associated with estimating individualized parameters in the context of common longitudinal designs, where the required number of observations may often be unattainable. Moreover, our study highlights the sensitivity of autoregressive mixed-effect models to noise levels and the difficulty of scaling due to the substantial number of parameters.

The Prevalence and Risk Factor of Noise-Induced Hearing Loss Among Industry Workers

Background: Noise-induced hearing loss (NIHL) has been gradually noticed for its insidious nature as an occupational hazard concomitant with various adverse personal health and industrial hygiene issues, including tinnitus, temporary threshold shift, hypertension, annoyance, elevated occupational accidents by aprosexia, stress, and poorer production performance. Objective: To assess prevalence and risk factor of noise-induced hearing loss among industry workers. Method: This cross sectional study was carried out at tertiary hospital from January 2021 to 2022, Dhaka. Where 97 workers exposed to noise and administrative controls were enrolled in study as a sample population. A structured questionnaire was developed by the expert-review method and applied to all the subjects with face-to-face interviews. Results: Out of 97 sample, 26 workers were having noise induced hearing loss (NIHL). Workers younger than 35 had a 20.59 percent NIHL incidence, while those older than 35 had a 41.38 percent NIHL incidence. The generator area of the plant had the loudest machinery (96-100 dB). As much as 46.67 percent of the generating crew had trouble hearing. Conclusion: We found out that the prevalence of noise induced hearing loss among industry workers. We also found out that workers are overexposed to noise and there is little protection accorded to the workers. It also shows that high noise intensity levels and exposure for long durations leads to hearing loss. Based on the study finding, implementation of hearing conservation programme through development and enforcement of regulations to identify and monitor occupational risk groups, restriction of importation of equipment, which emits dangerous levels of noise, are recommended.

Effect of intraoperative noise on postoperative pain in surgery patients under general anesthesia: evidence from a prospective study and mouse model.

Most patients are in a noisy environment during abdominal surgery under general anesthesia. This study included patients who underwent abdominal surgery under general anesthesia and established an animal model to determine whether intraoperative noise affects postoperative pain. This prospective study included 200 patients who underwent abdominal surgery under general anesthesia. Intraoperative noise and electroencephalograms were continuously recorded, and the mean level and time proportion of noise intensity of greater than 70dB were calculated. Maximum postoperative pain was assessed using a numerical rating scale at 0-12h and 12-24h after surgery, and postoperative analgesia consumption in patients receiving patient-controlled intravenous analgesia was recorded. Postoperative pain intensity and electroencephalogram amplitude were compared between patients with high-noise exposure (time proportion of noise intensity greater than 70dB ≥40%) and low-noise exposure (<40%). Mechanical pain sensitivity was tested in two groups of mice with plantar incisions exposed to 40dB or 70-100dB. The time proportion of noise intensity greater than 70dB was identified as an independent risk factor for postoperative pain intensity ( P <0.001). P ain numerical rating scale 0-12h (4.5±1.5 vs. 3.7±1.3, P =0.001) and 12-24h (3.9±1.5 vs. 3.2±1.1, P =0.004) after surgery in patients with high-noise exposure was significantly higher than in patients with low-noise exposure. The electroencephalogram amplitude of patients with high-noise exposure was significantly lower than that of patients with low-noise exposure ( P <0.05). In the mouse model, mechanical hyperalgesia in the 70-100dB group was significantly greater than that in the 40dB group ( P <0.001). High-level intraoperative noise exposure aggravates the degree of postoperative pain and analgesic needs of patients undergoing abdominal surgery, which may be related to the impact of noise on the neurophysiological activity of the brain and postoperative hyperalgesia.

Moment analysis of galloping energy harvesters with a parallel circuit under stochastic excitation

In this paper, the stochastic response performance of a galloping energy harvester with a parallel circuit under Gaussian white noise excitation is analyzed. To overcome the complexity of the distributed parameter model and the two-degree-of-freedom characteristics of the harvester, firstly, the exact closed expressions for the mean square displacement, mean square voltage and mean output power are derived by the equivalent linearization method. The results obtained by the direct Monte Carlo simulations verify the accuracy of the theoretical analysis. Then, the conditions for the occurrence of the galloping phenomenon can be identified. The influences of dimensionless parameters and noise intensity on the statistical moments are discussed. The results show that most of the parameters have the same tendency to affect the mean square displacement and the mean square voltage. However, the electromechanical coupling term has the opposite effect on both of them. It is also shown that the mean output power decreases as the coefficients of the electromechanical coupling term and mechanical damping ratio increase, however, it increases dramatically with increasing the wind speed and the linear galloping force coefficient. In addition, utilizing the stationary probability density of the stochastic harvester, the influences of important parameters and noise intensity on the harvester’s performance are studied. It is observed that a larger level of noise intensity results in the greater peak value of the stationary probability density.

Analysis of Noise Levels in the Engine Room &amp; St. Kernel Using the National Institute for Occupational Safety and Health (NIOSH) Method in PT. Karya Tanah Subur

Industry in Indonesia began to show progress and improvement marked by the use of sophisticated machines, almost all production processes in the industry caused noise. Noise can cause damage to hearing devices and cause psychological, physiological disorders that have the potential to cause disruption to work so as to reduce work productivity. PT. Karya Tanah Subur (KTS) is a company engaged in the business of plantation and processing of palm oil, the products produced in the form of crude palm oil (CPO) and kernels. The purpose of this study was to measure the level of noise that occurred in several PT operator rooms. The work of Fertile Land (KTS) which is directly adjacent to the machines that make noise. Then conduct a maximum time analysis of workers exposed to the noise generated based on the NIOSH calculation method. Selected noise sampling is the Engine Room Station (Turbine, Genset) and Kernel Station using the Sound level meter tool. From the results of this study, it shows that both points exceed the Threshold Value (NAB). The longest exposure time is at point 2 with a length of exposure time of 9 hours, while the shortest exposure time is at point 1 with a length of exposure time of 1.2 hours per day. In locations that have a risky noise intensity level, you should use earplugs/earmuffs to minimize the level of noise intensity and other supporting PPE for worker health and safety.

The Relationship between Noise Level and Work Stress

This study aims to determine the relationship between noise levels and work stress levels in employees of the Gotri Gentong Factory in Semarang. The research location in the working area of the Gotri Gentong Factory in Semarang in the administration and production department. The research sample was 36 samples in employees in the production department and 36 samples of employees in the administrative department. Analysis of the relationship between noise levels and work stress levels in employees of the Gotri Gentong Factory Semarang using the chi-square test. The results showed that there was a meaningful relationship between noise intensity and stress levels with a prevalence value of 0.000 (&lt; 0.05). For further research, it is necessary to use different types of treatment with different time periods.

Analyzing and measuring the diode laser’s linewidth affected by the driving current’s white noise

It is important to study the effect of injection current’s noise on the diode laser’s linewidth. In this paper, the effect of the white noise applied to the injection current on the linewidth of diode laser is investigated theoretically and experimentally. Theoretically, by adding the non-Markov noise term into the Langevin equation, the output laser’s linewidth of the diode laser with the white noise current is obtained. In experiment, an optical Fabry–Perot (F–P) cavity with a linewidth of ∼ 1.83 M H z and a finesse of ∼ 136 is employed to measure a 1560.5 nm distributed-feedback (DFB)-type diode laser’s linewidth. With an increase of the white noise intensity, the laser linewidth is broadened from ∼ 2 to ∼ 60 M H z gradually, and the line shape is characterized evolving from Lorentzian function to Gaussian function. At the same level of white noise intensity, the laser linewidth decreases with increase of the injection current. The accuracy of our measurements is verified based on the fiber-delayed and acousto-optic modulator (AOM)-shifted self-heterodyne scheme and the optical F–P cavity linewidth measurement method. The 1560.5 nm laser is boosted by using an erbium-doped fiber amplifier (EDFA) and is frequency doubled to 780.25 nm by a fiber-pigtailed, single-pass PPMgO:LN waveguide module, and the laser linewidth is visually observed and verified by broadening of the saturated absorption spectra of rubidium atoms. Clearly this linewidth manipulation method of diode lasers based on broadband white noise current coupling can be extended to other wavelength diode lasers and can be applied to laser measurement, laser communication, and other fields for different requirements.

Estimation of the present status of the species based on the theoretical bounds of environmental noise intensity: An illustration through a big abundance data and simulation

Sibly et al. (2005) described that most species have a fundamental characteristic to follow the theta-logistic growth trait with the convex downward trend. The fundamental yardstick of this research work builds under the deterministic setup, whereas the involvement of the external noise in any growth system is inevitable. But, the involvement of external affairs in any species growth can not be well judged only through its density dependence; it requires a further assessment. So, we frame the theta-logistic model with the stochastic analog in two directions, i.e., the discrete and continuous setup. The analysis of the discrete stochastic model is manifested by the bifurcation analysis, which shows that the attainment of the chaotic regime enhances with the increase in noise intensity level. Although the role of chaos in species extinction is debatable, a literature survey suggests that chaos with stochasticity accelerates the extinction of species. Similarly, in the case of the continuous version, we performed a theoretical study on the stochastic theta-logistic model to provide a critical value of the noise intensity parameter. This threshold magnitude acts as the sustainability criterion of any species environmental tolerability. In this connection, we use the data of four major taxonomic groups, i.e., Bird, Insect, Mammal, and Fish, from the GPDD database and classifies the species based on environmental sensitivity. The highly sensitive species have a low tolerance level, associated with the small magnitude of environmental noise intensity parameter. Moreover, the simulation prediction model on these four taxonomic classes also shows that the overall extinction probability of the considered birds in our research work is almost negligible for the current time window.

A mathematical model for the impact of noise on population dynamics of a single species experiencing Lombard effect

Noise is a form of pollution resulting from the undeniable increase in industrialization worldwide. Consequently, it is becoming increasingly important to understand the underlying mechanisms and potential effects of noise on ecosystems. In this study, we propose a deterministic mathematical model that uses a system of nonlinear, non-autonomous differential equations to describe the population dynamics of a single species exposed to noise. The Lombard effect is a phenomenon that involves increasing the intensity of acoustic signals in response to noise, which can mask and degrade acoustic signals and prevent them from being recognized or discriminated by their target receivers. However, when the anthropogenic noise is chronic and critical (i.e., that by its long duration and high intensity positively affects the mortality rate), the increase in the intensity of acoustic signals (due to the Lombard effect) only increases the chronic critical anthropogenic noise and also increase energetic, behavioral and predation costs. Therefore, the critical noise generated by the use of higher intensity acoustic signals (due to the Lombard effect) together with the chronic critical anthropogenic noise, negatively affect population survival. We analyzed the persistence of the population and found that our results are consistent with the observed ecological data as they suggest that, the maximum intensity level of critical chronic anthropogenic noise, consequently, by the Lombard effect, the maximum intensity of self generated acoustic signals, must decrease to ensure population persistence. However, when the maximum intensity level of critical chronic anthropogenic noise is uncontrollable, it is sufficient to reduce its mean intensity level to ensure persistence in the population mean. Furthermore, decreasing the degree to which noise affects the population favors the survival of the species. Finally, to validate our results, we performed numerical simulations.

Empirical Study of Data-Driven Evolutionary Algorithms in Noisy Environments

For computationally intensive problems, data-driven evolutionary algorithms (DDEAs) are advantageous for low computational budgets because they build surrogate models based on historical data to approximate the expensive evaluation. Real-world optimization problems are highly susceptible to noisy data, but most of the existing DDEAs are developed and tested on ideal and clean environments; hence, their performance is uncertain in practice. In order to discover how DDEAs are affected by noisy data, this paper empirically studied the performance of DDEAs in different noisy environments. To fulfill the research purpose, we implemented four representative DDEAs and tested them on common benchmark problems with noise simulations in a systematic manner. Specifically, the simulation of noisy environments considered different levels of noise intensity and probability. The experimental analysis revealed the association relationships among noisy environments, benchmark problems and the performance of DDEAs. The analysis showed that noise will generally cause deterioration of the DDEA’s performance in most cases, but the effects could vary with different types of problem landscapes and different designs of DDEAs.

A Boosted Minimum Cross Entropy Thresholding for Medical Images Segmentation Based on Heterogeneous Mean Filters Approaches.

Computer vision plays an important role in the accurate foreground detection of medical images. Diagnosing diseases in their early stages has effective life-saving potential, and this is every physician’s goal. There is a positive relationship between improving image segmentation methods and precise diagnosis in medical images. This relation provides a profound indication for feature extraction in a segmented image, such that an accurate separation occurs between the foreground and the background. There are many thresholding-based segmentation methods found under the pure image processing approach. Minimum cross entropy thresholding (MCET) is one of the frequently used mean-based thresholding methods for medical image segmentation. In this paper, the aim was to boost the efficiency of MCET, based on heterogeneous mean filter approaches. The proposed model estimates an optimized mean by excluding the negative influence of noise, local outliers, and gray intensity levels; thus, obtaining new mean values for the MCET’s objective function. The proposed model was examined compared to the original and related methods, using three types of medical image dataset. It was able to show accurate results based on the performance measures, using the benchmark of unsupervised and supervised evaluation.

Effects of wind and rainfall on ambient noise in the East Indian Ocean

The wind speed and rainfall rate above the sea surface have significant effects on the ocean ambient noise, and the noise level can be predicted by using the ocean ambient noise model combined with wind speed and rainfall rate parameters. In this work, we study the ambient noise characteristics in the East Indian Ocean and analyze the influences of wind speed and rainfall rate on the ocean ambient noise. The results show that the correlation coefficient between high-band noise level and wind speed could reach 0.59 in the absence of rainfall, that the correlation coefficient between high-band noise level and rainfall rate can reach 0.85 in the presence of rainfall, and that the strong rainfall can increase the high-band ambient noise level by more than 6 dB. The wind-generated noise source level formula is also modified to make it applicable to the deep-sea environment of the East Indian Ocean, and the overall error between the model prediction of noise intensity level and the experimental noise intensity level in intense rainfall is within 2 dB after the modification. When the model is validated by using small-scale rainfall noise data, it is found that the noise source model using a non-uniform rainfall noise source at the sea surface during small-scale rainfall is more accurate than using a uniform rainfall noise source. The modified prediction model of wind-generated and rain-generated noise is important for predicting the ocean-ambient-noise characteristics in the Indian Ocean.

A building carbon emission prediction model by PSO-SVR method under multi-criteria evaluation

Building carbon emission prediction plays an irreplaceable role in low-carbon economy development, public health protection and environmental sustainability. It is significant to identify influential factors mainly contributed to building emission and predict emission accurately in order to harness the growth from the source. In this paper, 11 influencing factors of building carbon emission are identified and a support vector regression (SVR) prediction model is proposed to forecast building carbon emission considering improvement the prediction accuracy, generalization, and robustness. In the SVR model, parameters are optimized by particle swarm optimization (PSO) algorithm with the aim to improve performance. Cases in Shanghai’s building sector are adopted to demonstrate practical applications of the proposed PSO-SVR prediction model. The results indicate that the presented prediction system has an outstanding performance in forecasting building carbon emission under multi-criteria evaluation. Furthermore, compared to the results from other four prediction models (e.g., linear regression, decision tree), it is shown that PSO-SVR model can achieve higher accuracy (e.g., improvement average of 1.01% R2 under training subset), better generalization (e.g., improvement average of 19.89% R2 under testing subset), and better robustness (e.g., improvement average of 18.93% R2 under different levels of noise intensity).

Development of auditory cognition in 5- to 10-year-old children: Focus on musical and verbal short-term memory.

Developmental aspects of auditory cognition were investigated in 5-to-10-year-old children (n=100). Musical and verbal short-term memory (STM) were assessed by means of delayed matching-to-sample tasks (DMST) (comparison of two four-item sequences separated by a silent retention delay), with two levels of difficulty. For musical and verbal materials, children's performance increased from 5 years to about 7 years of age, then remained stable up to 10 years of age, with performance remaining inferior to performance of young adults. Children and adults performed better with verbal material than with musical material. To investigate auditory cognition beyond STM, we assessed speech-in-noise perception with a four-alternative forced-choice task with two conditions of phonological difficulty and two levels of cocktail-party noise intensity. Partial correlations, factoring out the effect of age, showed a significant link between musical STM and speech-in-noise perception in the condition with increased noise intensity. Our findings reveal that auditory STM improves over development with a critical phase around 6-7 years of age, yet these abilities appear to be still immature at 10 years. Musical and verbal STM might in particular share procedural and serial order processes. Furthermore, musical STM and the ability to perceive relevant speech signals in cocktail-party noise might rely on shared cognitive resources, possibly related to pitch encoding. To the best of our knowledge, this is the first time that auditory STM is assessed with the same paradigm for musical and verbal material during childhood, providing perspectives regarding diagnosis and remediation in developmental learning disorders.