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In this study, a new exploration of a classic problem in the field of music aesthetics was carried out through the research methods of cognitive neuroscience and the brain mechanism of the formation of artistic aesthetic value in piano performance based on the theory of multiple intelligences was analyzed. In terms of EEG signal preprocessing, an EEG signal preprocessing method based on baseline average was proposed. In terms of EEG feature extraction, a decision-level feature fusion method is proposed, which uses SVM as the base classifier to extract time-domain, frequency-domain and nonlinear features, respectively, train the base classifier, and integrate the output of multiple basic classifiers through decision fusion. A feature fusion method based on music and ERP was also proposed to predict the response of subjects after listening to a piano performance. Based on DS evidence theory, emotion recognition and classification were carried out using EEG and music features from the perspective of decision-making level fusion. The results showed that piano performance mainly activated the left inferior frontal gyrus, right precentral gyrus, left auxiliary motor area, right superior parietal gyrus, cerebellar Crus1 area, right lingual gyrus and left inferior occipital gyrus, with peak t values of 4.67, 4.8, 5.76, 5.16, 7.02, 7.12 and 8.3, respectively. The activation of auxiliary motor areas, cerebellum, inferior frontal gyrus and other areas indicates that the artistic beauty felt by listening to piano performance can enhance people’s creativity, and the activation of the right superior occipital gyrus can enhance people’s imagination.

We present extensive multifrequency Karl G. Jansky Very Large Array (VLA) and Very Long Baseline Array (VLBA) observations of the radio-bright supernova (SN) IIb SN 2004C that span ∼40–2793 days post-explosion. We interpret the temporal evolution of the radio spectral energy distribution in the context of synchrotron self-absorbed emission from the explosion’s forward shock as it expands in the circumstellar medium (CSM) previously sculpted by the mass-loss history of the stellar progenitor. VLBA observations and modeling of the VLA data point to a blastwave with average velocity ∼0.06 c that carries an energy of ≈1049 erg. Our modeling further reveals a flat CSM density profile ρ CSM ∝ R −0.03±0.22 up to a break radius R br ≈ (1.96 ± 0.10) × 1016 cm, with a steep density gradient following ρ CSM ∝ R −2.3±0.5 at larger radii. We infer that the flat part of the density profile corresponds to a CSM shell with mass ∼0.021 M ☉, and that the progenitor’s effective mass-loss rate varied with time over the range (50–500) × 10−5 M ☉ yr−1 for an adopted wind velocity v w = 1000 km s−1 and shock microphysical parameters ϵ e = 0.1, ϵ B = 0.01. These results add to the mounting observational evidence for departures from the traditional single-wind mass-loss scenarios in evolved, massive stars in the centuries leading up to core collapse. Potentially viable scenarios include mass loss powered by gravity waves and/or interaction with a binary companion.

Background: Two novel systemic inflammation indices, SII and SIRI, are associated with increased risk of cardiovascular diseases (CVD). However, SII and SIRI are prone to change over time and the association between changeable status and long-term outcome risk remains to be uncovered. This study aims to examine the association between the dynamic status of SII and SIRI and risk of CVD. Methods: This prospective study included a total of 45,809 subjects without MI, stroke and cancer prior to or in 2010 (baseline of this study). The dynamic status of SII and SIRI during 2006, 2008, and 2010 was assessed by dynamic trajectories (primary exposure), annual increase, and average value. The outcome was CVD incidence during 8.6 years' follow-up. Multiple Cox regression models were used to calculate the adjusted hazard ratios (HRs) and confidence intervals (95% CIs). Results: Four dynamic trajectories of SII and SIRI were identified as follows: low stable pattern, moderate stable pattern, increase pattern, and decrease pattern. For SII, compared with \"low stable pattern\", after controlling confounders and level of SII in 2006, adjusted HRs were 1.24 (95% CI = 1.02-1.51) for \"increase pattern\" and 1.11 (95% CI = 1.00-1.23) for \"moderate-stable pattern\" while the association was not significant for \"decrease pattern\". Additionally, the highest group of annual SII increase and average SII had respective HR of 1.20 (95% CI = 1.05-1.37) and 1.32 (95% CI = 1.13-1.55). The results were consistent for SIRI. \"Increase pattern\" and \"moderate stable pattern\" increased the risk of CVD by 38% (HR = 1.38, 95% CI = 1.17-1.63) and 12% (HR = 1.12, 95% CI = 1.01-1.25), while no significant association was found for \"decrease pattern\". The highest group of annual SIRI increase and average SIRI had respective HR of 1.25 (95% CI = 1.09-1.44) and 1.39 (95% CI = 1.19-1.63). Conclusion: Dynamic status of SII and SIRI was significantly associated with risk of CVD, which highlighted that we should focus on the dynamic change of SII and SIRI. Keywords: systemic inflammation, dynamic status, prospective study, cardiovascular diseases

Potentially hazardous element (PHE) contamination of aquifers is an issue of global concern, as this not only affects soil and plants but also exerts a negative impact on livestock. The current study assessed the extent of PHE (cadmium, copper, nickel, and lead) contamination of groundwater, soil, and forage crops in Shorkot, Punjab, Pakistan. Low concentrations of PHEs, particularly Cd and Cu, were found in drinking water which remained below detection limits. The concentrations of Ni and Pb in water samples were 0.1 and 0.06 mg L −1 , respectively. Calculated risk indices showed that there was a high carcinogenic and non-carcinogenic risk to livestock (sheep and cow/buffalo) from the ingestion of Ni- and Pb-contaminated water. Soil irrigation with contaminated water resulted in PHE accumulation (Cd: 0.4 mg kg −1 , Cu: 16.8 mg kg −1 , Ni: 17.6 mg kg −1 , Pb: 7.7 mg kg −1 ) in soil and transfer to forage crops. The potential impact of PHE contamination of the groundwater on fodder plants was estimated for animal health by calculating the average daily dose (ADD), the hazard quotient (HQ), and the cancer risk (CR). While none of the PHEs in forage plants showed any carcinogenic or non-carcinogenic risk to livestock, a high exposure risk occurred from contaminated water (HQ: 12.9, CR: 0.02). This study provides baseline data for future research on the risks of PHE accumulation in livestock and their food products. Moreover, future research is warranted to fully understand the transfer of PHEs from livestock products to humans.

Localizing small damages often requires sensors be mounted in the proximity of damage to obtain high Signal-to-Noise Ratio in system frequency response to input excitation. The proximity requirement limits the applicability of existing schemes for low-severity damage detection as an estimate of damage location may not be known a priori. In this work it is shown that spatial locality is not a fundamental impediment; multiple small damages can still be detected with high accuracy provided that the frequency range beyond the first five natural frequencies is utilized in the Frequency response functions (FRF) curvature method. The proposed method presented in this paper applies sensitivity analysis to systematically unearth frequency ranges capable of elevating damage index peak at correct damage locations. It is a baseline-free method that employs a smoothing polynomial to emulate reference curvatures for the undamaged structure. Numerical simulation of steel-beam shows that small multiple damages of severity as low as 5% can be reliably detected by including frequency range covering 5–10th natural frequencies. The efficacy of the scheme is also experimentally validated for the same beam. It is also found that a simple noise filtration scheme such as a Gaussian moving average filter can adequately remove false peaks from the damage index profile.

This study aimed to determine the status of groundwater contamination with faecal coliform and nitrate in the rural areas of Mardan district, Pakistan. Both analytes require regular monitoring according to the National Environmental Quality Standards (NEQS) of Pakistan. Groundwater samples (n = 100) were collected from 25 villages across four zones. Samples were analysed for physicochemical parameters including pH, electrical conductivity (EC), Escherichia coli (E. coli) contamination, nitrite, and nitrate (NO2- and NO3-). Whilst the average concentrations of NO3- in the water samples were within the permissible limits of 50 mg L−1 set by the World Health Organisation (WHO) and NEQS two villages exceeded the safety limits. Non-carcinogenic health risks of NO3- were estimated in terms of average daily dose (ADD) and hazard quotient (HQ). The HQ values for NO3- were > 1 for children signifying potential health risks; however, the adult population had HQ < 1 which indicates no risk. Groundwater samples tested positive for E. coli contamination in 13 villages, suggesting that residents may be living at risk of various microbial diseases due to drinking of contaminated water. The findings of this study provide valuable baseline data for groundwater researchers, policymakers, and the local public health department.

The increasing pressure on marine biodiversity emphasizes the importance of finding benchmarks against which to assess change. This is, however, a notoriously difficult task in estuarine ecosystems, where environmental gradients are steep, and where benthic biodiversity is highly variable in space and time. Although recent emphasis on diverse, healthy benthic communities in legislative frameworks has increased the number of indices developed for assessing benthic status, there is a lack of quantitative baselines in benthic diversity that would enable comparisons across broad spatial scales, encompassing different environmental settings and bioregions. By taking advantage of long-term monitoring data, spanning hundreds of stations over the past 40 years, we provide a comprehensive analysis of benthic α, β, and γ diversity, encompassing the entire salinity gradient of the open sea areas of the large, brackish-water Baltic Sea. Using a relatively simple measure, average regional diversity, we define area-specific reference conditions and acceptable deviation against which to gauge current conditions in benthic macrofaunal diversity. Results show a severely impaired condition throughout large areas of the Baltic for the assessment period 2001-2006. All ecosystems are plagued by baselines that shift in time and space, and their definition is not trivial, but average regional diversity may offer a transparent way to deal with such changes in low-diversity systems. Identifying baselines will be of increasing importance given the potential of climatic drivers to interact with local anthropogenic stressors to affect patterns of biodiversity. Our analysis provides an evaluation of the current condition in a system that has been heavily influenced by anthropogenic impact and changing oceanographic conditions, and it provides a basis for future impact assessment and ecosystem-based management.

Regulatory impact analyses of proposed environmental, occupational, and consumer product safety regulations often rely on a metric known as the Value per Statistical Case of Cancer (VSCC), that is, the public’s willingness to pay (WTP) for reductions in the risk of developing cancer. In this paper, we ask whether the VSCC depends on cancer survival prospects. We develop a simple theoretical model that shows that under standard assumptions the VSCC is decreasing in the chance of surviving cancer. We empirically test this prediction by means of a stated preference survey, where we ask subjects aged 45–60 from the general population in the Czech Republic to report information about their WTP for reductions in the risk of getting cancer. One half of the sample was told that, if they got cancer, the 5-year survival rate was 60 % (corresponding to the average survival chances across all types of cancer), while the other half was told that it was 75 %. Consistent with the theoretical model, we find that the VSCC is larger in the former group. The ratio between the VSCC of the two groups is approximately equal to the ratio between the conditional cancer mortality risks implied by the survey’s survival rates, suggesting that the VSCC is proportional to conditional cancer mortality. Our findings have important policy implications in the context of regulations that focus on pollutants linked to cancers with different chances of survival.

The ambient PM 2.5 concentration in Los Angeles (LA) County has been on a decreasing trend since LA County was designated as a nonattainment area in 2005. However, whether the nonattainment assignment is the underlying cause of the county’s reductions in PM 2.5 requires further empirical investigation. Traditional statistical approaches used to study the impact of nonattainment designation on air quality present problems involving indeterministic covariates, confoundedness, model misspecification, and undetected effects at the aggregate level. Our study successfully uses the Panel Data Approach for Program Evaluation (PAMPE) to compare the differences between the actual outcomes and counterfactual outcomes to reveal the treatment effects associated with nonattainment assignment without the burden associated with previous studies. Our results show that, at the monitor level, the air quality improvements obtained by the more-polluted areas were greater after LA County was designated as a nonattainment area. On average, the counterfactual reduction rates derived in our study range from − 0.01 to − 0.15%. Cases in which PM 2.5 levels increased occurred at two monitors that were fully or partially compliant during the study period, suggesting the regulatory oversight is indeed spatially heterogeneous.

Automatic electrocardiogram (ECG) signal enhancement has become a crucial pre-processing step in most ECG signal analysis applications. In this Letter, the authors propose an automated noise-aware dictionary learning-based generalised ECG signal enhancement framework which can automatically learn the dictionaries based on the ECG noise type for effective representation of ECG signal and noises, and can reduce the computational load of sparse representation-based ECG enhancement system. The proposed framework consists of noise detection and identification, noise-aware dictionary learning, sparse signal decomposition and reconstruction. The noise detection and identification is performed based on the moving average filter, first-order difference, and temporal features such as number of turning points, maximum absolute amplitude, zerocrossings, and autocorrelation features. The representation dictionary is learned based on the type of noise identified in the previous stage. The proposed framework is evaluated using noise-free and noisy ECG signals. Results demonstrate that the proposed method can significantly reduce computational load as compared with conventional dictionary learning-based ECG denoising approaches. Further, comparative results show that the method outperforms existing methods in automatically removing noises such as baseline wanders, power-line interference, muscle artefacts and their combinations without distorting the morphological content of local waves of ECG signal.