Spatial-temporal changes and driving factors of eco-environmental quality in the Three-North region of China

Eco-environmental quality is a measure of the suitability of the ecological environment for human survival and socioeconomic development.Understanding the spatial-temporal distribution and variation trend of eco-environmental quality is essential for environmental protection and ecological balance.The remote sensing ecological index(RSEI)can quickly and objectively quantify eco-environmental quality and has been extensively utilized in regional ecological environment assessment.In this paper,Moderate Resolution Imaging Spectroradiometer(MODIS)images during the growing period(July-September)from 2000 to 2020 were obtained from the Google Earth Engine(GEE)platform to calculate the RSEI in the three northern regions of China(the Three-North region).The Theil-Sen median trend method combined with the Mann-Kendall test was used to analyze the spatial-temporal variation trend of eco-environmental quality,and the Hurst exponent and the Theil-Sen median trend were superimposed to predict the future evolution trend of eco-environmental quality.In addition,ten variables from two categories of natural and anthropogenic factors were analyzed to determine the drivers of the spatial differentiation of eco-environmental quality by the geographical detector.The results showed that from 2000 to 2020,the RSEI in the Three-North region exhibited obvious regional characteristics:the RSEI values in Northwest China were generally between 0.2 and 0.4;the RSEI values in North China gradually increased from north to south,ranging from 0.2 to 0.8;and the RSEI values in Northeast China were mostly above 0.6.The average RSEI value in the Three-North region increased at an average growth rate of 0.0016/a,showing the spatial distribution characteristics of overall improvement and local degradation in eco-environmental quality,of which the areas with improved,basically stable and degraded eco-environmental quality accounted for 65.39%,26.82%and 7.79%of the total study area,respectively.The Hurst exponent of the RSEI ranged from 0.20 to 0.76 and the future trend of eco-environmental quality was generally consistent with the trend over the past 21 years.However,the areas exhibiting an improvement trend in eco-environmental quality mainly had weak persistence,and there was a possibility of degradation in eco-environmental quality without strengthening ecological protection.Average relative humidity,accumulated precipitation and land use type were the dominant factors driving the spatial distribution of eco-environmental quality in the Three-North region,and two-factor interaction also had a greater influence on eco-environmental quality than single factors.The explanatory power of meteorological factors on the spatial distribution of eco-environmental quality was stronger than that of topographic factors.The effect of anthropogenic factors(such as population density and land use type)on eco-environmental quality gradually increased over time.This study can serve as a reference to protect the ecological environment in arid and semi-arid regions.

Fast Detection and Classification of Dangerous Urban Sounds Using Deep Learning

Video analytics is an integral part of surveillance cameras. Comparedto video analytics, audio analytics offers several benefits, includingless expensive equipment and upkeep expenses. Additionally, the volume ofthe audio datastream is substantially lower than the video camera datastream,especially concerning real-time operating systems, which makes it lessdemanding of the data channel’s bandwidth needs. For instance, automaticlive video streaming from the site of an explosion and gunshot to the policeconsole using audio analytics technologies would be exceedingly helpful forurban surveillance. Technologies for audio analytics may also be used toanalyze video recordings and identify occurrences. This research proposeda deep learning model based on the combination of convolutional neuralnetwork (CNN) and recurrent neural network (RNN) known as the CNNRNNapproach. The proposed model focused on automatically identifyingpulse sounds that indicate critical situations in audio sources. The algorithm’saccuracy ranged from 95% to 81% when classifying noises from incidents,including gunshots, explosions, shattered glass, sirens, cries, and dog barking.The proposed approach can be applied to provide security for citizens in openand closed locations, like stadiums, underground areas, shopping malls, andother places.

Introduction to the Special Issue on Perspectives on Soundscape and Challenges of Noise Pollution: A Multidisciplinary Approach to Sustainable Environmental Solutions

Over the past two decades,research on the subject of noise pollution and urban soundscapes has seen significant growth[1,2].The goal of these studies was to gain a better understanding of the urban acoustic environment by employing various methodologies and techniques to delve into the complexity of this topic.These research efforts have primarily revolved around two fundamental axes[3].On one hand,the first axis focused on combating noise pollution[4–6],emphasizing the reduction of unwanted sounds and compliance with sound levels set by environmental and health protection organizations[7,8].

Recognition System for Diagnosing Pneumonia and Bronchitis Using Children’s Breathing Sounds Based on Transfer Learning

Respiratory infections in children increase the risk of fatal lung disease,making effective identification and analysis of breath sounds essential.However,most studies have focused on adults ignoring pediatric patients whose lungs are more vulnerable due to an imperfect immune system,and the scarcity of medical data has limited the development of deep learning methods toward reliability and high classification accuracy.In this work,we collected three types of breath sounds from children with normal(120 recordings),bronchitis(120 recordings),and pneumonia(120 recordings)at the posterior chest position using an off-the-shelf 3M electronic stethoscope.Three features were extracted from the wavelet denoised signal:spectrogram,mel-frequency cepstral coefficients(MFCCs),and Delta MFCCs.The recog-nition model is based on transfer learning techniques and combines fine-tuned MobileNetV2 and modified ResNet50 to classify breath sounds,along with software for displaying analysis results.Extensive experiments on a real dataset demonstrate the effectiveness and superior performance of the proposed model,with average accuracy,precision,recall,specificity and F1 scores of 97.96%,97.83%,97.89%,98.89%and 0.98,respectively,achieving superior performance with a small dataset.The proposed detection system,with a high-performance model and software,can help parents perform lung screening at home and also has the potential for a vast screening of children for lung disease.

Spectral Soundscapes in Elfriede Jelinek’s Post-Holocaust Novel The Children of the Dead and Its 2019 Film Adaptation

How should we approach Die Kinder der Toten by the Austrian Nobel Prize winner Elfriede Jelinek today?And how does the 2019 film adaptation by the Nature Theatre of Oklahoma change the text’s reception through focus on intermedial and intertextual elements?So far,the most insightful reviews have centered on the conceptual,contextual and textual-and thus also political aspects of this work.By focusing on intertextual and intermedial components,I hope to illustrate a few aspects of the novel that have yet to be analyzed in the scholarship on Jelinek.Drawing on Derrida’s Specters of Marx and on elements of sound studies,literature studies,and film studies,I hope to demonstrate how sound can have a significant spectral presence that connects with other literary texts and media,different world regions in the past,the present and the future.

Can urban forests provide acoustic refuges for birds?Investigating the influence of vegetation structure and anthropogenic noise on bird sound diversity

As a crucial component of terrestrial ecosystems,urban forests play a pivotal role in protecting urban biodiversity by providing suitable habitats for acoustic spaces.Previous studies note that vegetation structure is a key factor influencing bird sounds in urban forests;hence,adjusting the frequency composition may be a strategy for birds to avoid anthropogenic noise to mask their songs.However,it is unknown whether the response mechanisms of bird vocalizations to vegetation structure remain consistent despite being impacted by anthropogenic noise.It was hypothesized that anthropogenic noise in urban forests occupies the low-frequency space of bird songs,leading to a possible reshaping of the acoustic niches of forests,and the vegetation structure of urban forests is the critical factor that shapes the acoustic space for bird vocalization.Passive acoustic monitoring in various urban forests was used to monitor natural and anthropogenic noises,and sounds were classified into three acoustic scenes(bird sounds,human sounds,and bird-human sounds)to determine interconnections between bird sounds,anthropogenic noise,and vegetation structure.Anthropogenic noise altered the acoustic niche of urban forests by intruding into the low-frequency space used by birds,and vegetation structures related to volume(trunk volume and branch volume)and density(number of branches and leaf area index)significantly impact the diversity of bird sounds.Our findings indicate that the response to low and high frequency signals to vegetation structure is distinct.By clarifying this relationship,our results contribute to understanding of how vegetation structure influences bird sounds in urban forests impacted by anthropogenic noise.

Experimental and numerical investigations on acoustic damping of monoclinic crystalline wideband sound absorbing structures

In order to overcome the limitations of traditional microperforated plate with narrow sound absorption bandwidth and a single structure,two multi-cavity composite sound-absorbing materials were designed based on the shape of monoclinic crystals:uniaxial oblique structure(UOS)and biaxial oblique structure(BOS).Through finite element simulation and experimental research,the theoretical models of UOS and BOS were verified,and their sound absorption mechanisms were revealed.At the same time,the influence of multi-cavity composites on sound absorption performance was analyzed based on the theoretical model,and the influence of structural parameters on sound absorption performance was discussed.The research results show that,in the range of 100-2000 Hz,UOS has three sound absorption peaks and BOS has five sound absorption peaks.The frequency range of the half-absorption bandwidth(α>0.5)of UOS and BOS increases by 242% and 229%,respectively.Compared with traditional microperforated sound-absorbing structures,the series and parallel hybrid methods significantly increase the sound-absorbing bandwidth of the sound-absorbing structure.This research has guiding significance for noise control and has broad application prospects in the fields of transportation,construction,and mechanical design.

ST-LSTM-SA:A New Ocean Sound Velocity Field Prediction Model Based on Deep Learning

The scarcity of in-situ ocean observations poses a challenge for real-time information acquisition in the ocean.Among the crucial hydroacoustic environmental parameters,ocean sound velocity exhibits significant spatial and temporal variability and it is highly relevant to oceanic research.In this study,we propose a new data-driven approach,leveraging deep learning techniques,for the prediction of sound velocity fields(SVFs).Our novel spatiotemporal prediction model,STLSTM-SA,combines Spatiotemporal Long Short-Term Memory(ST-LSTM) with a self-attention mechanism to enable accurate and real-time prediction of SVFs.To circumvent the limited amount of observational data,we employ transfer learning by first training the model using reanalysis datasets,followed by fine-tuning it using in-situ analysis data to obtain the final prediction model.By utilizing the historical 12-month SVFs as input,our model predicts the SVFs for the subsequent three months.We compare the performance of five models:Artificial Neural Networks(ANN),Long ShortTerm Memory(LSTM),Convolutional LSTM(ConvLSTM),ST-LSTM,and our proposed ST-LSTM-SA model in a test experiment spanning 2019 to 2022.Our results demonstrate that the ST-LSTM-SA model significantly improves the prediction accuracy and stability of sound velocity in both temporal and spatial dimensions.The ST-LSTM-SA model not only accurately predicts the ocean sound velocity field(SVF),but also provides valuable insights for spatiotemporal prediction of other oceanic environmental variables.

Experimental realization of fractal fretwork metasurface for sound anomalous modulation

Natural creatures and ancient cultures are full of potential sources to provide inspiration for applied sciences.Inspired by the fractal geometry in nature and the fretwork frame in ancient culture,here we design the acoustic metasurface to realize sound anomalous modulation,which manifests itself as an incident-dependent propagation behavior:sound wave propagating in the forward direction is allowed to transmit with high efficiency while in the backward direction is obviously suppressed.We quantitatively investigate the dependences of asymmetric transmission on the propagation direction,incident angle and operating frequency by calculating sound transmittance and energy contrast.This compact fractal fretwork metasurface for acoustic anomalous modulation would promote the development of integrated acoustic devices and expand versatile applications in acoustic communication and information encryption.

Characterization of Aquifers in Crystalline and Crystallophyll Basement Zones Using the Electrical Resistivity Method (Trails and Electrical Soundings) in the Gagnoa Region, (Central-Western Côte d’Ivoire)

Introduction: Located in the central-western part of Côte d’Ivoire, the subsoil of the Gagnoa region is made up of sedimentary volcano formations and granitoids with developed fracturing. This complex Precambrian basement contains most of the region’s water resources. This is at the origin of the high failure rate during the various hydrogeological prospecting campaigns. Methodology: The database consists of resistivities from 42 holes and 51 trails drilled as part of the implementation of high-throughput drilling in the study area. The objective of this study is to deepen the knowledge of the fissured basement by interpreting profile curves and electrical soundings. It will be a question of classifying the different types of anomalies obtained on the profiles and their shapes. The orientation of the lineaments observed on the profiles was determined. Results: The interpretation of the geophysical data revealed various anomalies, the main ones being of the CC (Conductor Compartment) and CEDP (Contact between two bearings) types. These types of anomalies are mainly expressed in various forms: the “V”, “W” and “U” shapes. From these anomalies and the appearance of the electrical profiles, lineaments and their orientations were identified with N90-100, N130-140, N170-180 as major orientations. Conclusion: These results could contribute to a better understanding of the fractured environment of the Gagnoa region.

Diagnostic Accuracy of Computerized Bowel Sound Analysis with Non-Invasive Devices for Irritable Bowel Syndrome:A Systematic Review and Meta-Analysis

Objective To assess the diagnostic accuracy of bowel sound analysis for irritable bowel syndrome(IBS)with a systematic review and meta-analysis.Methods We searched MEDLINE,Embase,the Cochrane Library,Web of Science,and IEEE Xplore databases until September 2023.Cross-sectional and case-control studies on diagnostic accuracy of bowel sound analysis for IBS were identified.We estimated the pooled sensitivity,specificity,positive likelihood ratio,negative likeli-hood ratio,and diagnostic odds ratio with a 95% confidence interval(CI),and plotted a summary receiver operat-ing characteristic curve and evaluated the area under the curve.Results Four studies were included.The pooled diagnostic sensitivity,specificity,positive likelihood ratio,nega-tive likelihood ratio,and diagnostic odds ratio were 0.94(95%CI,0.87‒0.97),0.89(95%CI,0.81‒0.94),8.43(95%CI,4.81‒14.78),0.07(95%CI,0.03‒0.15),and 118.86(95%CI,44.18‒319.75),respectively,with an area under the curve of 0.97(95%CI,0.95‒0.98).Conclusions Computerized bowel sound analysis is a promising tool for IBS.However,limited high-quality data make the results'validity and applicability questionable.There is a need for more diagnostic test accuracy studies and better wearable devices for monitoring and analysis of IBS.

Train-induced aerodynamic characteristics of vertical sound barriers influenced by several factors

Investigations into the aerodynamic properties of vertical sound barriers exposed to high-speed operations employ computational fluid dynamics.The primary focus of this research is to evaluate the influence of train speed and the distance(D)from the track centerline under various operating conditions.The findings elucidate a marked elevation in the aerodynamic effect amplitude on sound barriers as train speeds increase.In single-train passages,the aerodynamic effect amplitude manifests a direct relationship with the square of the train speed.When two trains pass each other,the aerodynamic amplitude intensifies due to an additional aerodynamic increment on the sound barrier.This increment exhibits an approximate quadratic correlation with the retrograde train speed.Notably,the impact of high-speed trains on sound barrier aerodynamics surpasses that of low-speed trains,and this discrepancy amplifies with larger speed differentials between trains.Moreover,the train-induced aerodynamic effect diminishes significantly with greater distance(D),with occurrences of pressure coefficient(CP)exceeding the standard thresholds during dual-train passages.This study culminates in the formulation of universal equations for quantifying the influence of train speed and distance(D)on sound barrier aerodynamic characteristics across various operational scenarios.

The influence of reflections from the train body and the ground on the sound radiation from a railway rail

Purpose – The vibration of the rails is a significant source of railway rolling noise, often forming the dominantcomponent of noise in the important frequency region between 400 and 2000 Hz. The purpose of the paper is toinvestigate the influence of the ground profile and the presence of the train body on the sound radiation fromthe rail.Design/methodology/approach – Two-dimensional boundary element calculations are used, in which therail vibration is the source. The ground profile and various different shapes of train body are introduced in themodel, and results are observed in terms of sound power and sound pressure. Comparisons are also made withvibro-acoustic measurements performed with and without a train present.Findings – The sound radiated by the rail in the absence of the train body is strongly attenuated by shieldingdue to the ballast shoulder. When the train body is present, the sound from the vertical rail motion is reflectedback down toward the track where it is partly absorbed by the ballast. Nevertheless, the sound pressure at thetrackside is increased by typically 0–5 dB. For the lateral vibration of the rail, the effects are much smaller. Oncethe sound power is known, the sound pressure with the train present can be approximated reasonably well withsimple line source directivities.Originality/value – Numerical models used to predict the sound radiation from railway rails have generallyneglected the influence of the ground profile and reflections from the underside of the train body on the soundpower and directivity of the rail. These effects are studied in a systematic way including comparisons with measurements.

An improved algorithm based on equivalent sound velocity profile method at large incident angle

With the development of ultra-wide coverage technology,multibeam echo-sounder(MBES)system has put forward higher requirements for localization accuracy and computational efficiency of ray tracing method.The classical equivalent sound speed profile(ESSP)method replaces the measured sound velocity profile(SVP)with a simple constant gradient SVP,reducing the computational workload of beam positioning.However,in deep-sea environment,the depth measurement error of this method rapidly increases from the central beam to the edge beam.By analyzing the positioning error of the ESSP method at edge beam,it is discovered that the positioning error increases monotonically with the incident angle,and the relationship between them could be expressed by polynomial function.Therefore,an error correction algorithm based on polynomial fitting is obtained.The simulation experiment conducted on an inclined seafloor shows that the proposed algorithm exhibits comparable efficiency to the original ESSP method,while significantly improving bathymetry accuracy by nearly eight times in the edge beam.

Sound event localization and detection based on deep learning

Acoustic source localization(ASL)and sound event detection(SED)are two widely pursued independent research fields.In recent years,in order to achieve a more complete spatial and temporal representation of sound field,sound event localization and detection(SELD)has become a very active research topic.This paper presents a deep learning-based multioverlapping sound event localization and detection algorithm in three-dimensional space.Log-Mel spectrum and generalized cross-correlation spectrum are joined together in channel dimension as input features.These features are classified and regressed in parallel after training by a neural network to obtain sound recognition and localization results respectively.The channel attention mechanism is also introduced in the network to selectively enhance the features containing essential information and suppress the useless features.Finally,a thourough comparison confirms the efficiency and effectiveness of the proposed SELD algorithm.Field experiments show that the proposed algorithm is robust to reverberation and environment and can achieve higher recognition and localization accuracy compared with the baseline method.

Sound Transmission Loss Analysis of a Double Plate-Acoustic Cavity Coupling System with In-Plane Functionally Graded Materials

In this paper, the isogeometric analysis (IGA) is employed to develop an acoustic radiation model for a double plate-acoustic cavity coupling system, with a focus on analyzing the sound transmission loss (STL). The functionally graded (FG) plate exhibits a different material properties in-plane, and the power-law rule is adopted as the governing principle for material mixing. To validate the harmonic response and demonstrate the accuracy and convergence of the isogeometric modeling, ANASYS is utilized to compare with numerical examples. A plane wave serves as the acoustic excitation, and the Rayleigh integral is applied to discretize the radiated plate. The STL results are compared with the literature, confirming the reliability of the coupling system. Finally, the investigation is conducted to study impact of cavity depth and power-law parameter on the STL.

Research Progress of Underwater Soundabsorbing Material

This article provides an overview of underwater sound-absorbing materials mainly applied with polyurethane matrix.It mainly elaborates on the underwater sound mecha-nism,commonly used underwater sound-absorbing materials and structures,as well as new underwater sound-absorbing material structures derived from local resonance pho-nonic crystals,such as phononic crystals,local resonance phonon wood piles,and meta-material sound-absorbing structures.This provides a broader development space and direction for the future development of underwater sound-absorbing materials.

Land cover change and eco-environmental quality response of different geomorphic units on the Chinese Loess Plateau

Land cover in the Chinese Loess Plateau has undergone dramatic changes since the late 1980s.Revealing the trend in land cover change and eco-environmental quality response of different geomorphic units in this stage is a realistic requirement for promoting sustainable development of the Chinese Loess Plateau.Based on the data of geomorphic units and land cover in 1990,2000,2010 and 2018 of the Chinese Loess Plateau,we studied the trend of land cover change and eco-environmental quality response of different geomorphic units by using a significance index of land cover change,a proportion index of land cover change and an eco-environmental response model.The results indicated that from 1990 to 2018,the areas of forestland and construction land substantially increased,whereas those of cropland,grassland,wetland and unused land considerably decreased.Land cover change exhibited large geomorphic differences,and the main conversion of land cover was from cropland into other land types.Unstable trend of land cover change in the loess tablelands and sandy loess hills declined,whereas the unstable trends in the other geomorphic units enhanced.Eco-environmental quality varied among different geomorphic units.The expansion of construction land and degradation of forestland,grassland and wetland resulted in the deterioration of eco-environmental quality.The conversion of cropland and unused land into forestland and grassland,and the conversion of grassland into forestland were the main factors that drove the improvement of eco-environmental quality.The findings of this study may provide theoretical reference and support decision making for the optimization of land use structure and the improvement of eco-environmental quality on the Chinese Loess Plateau.

Quantitative Evaluation of Sustainable Development and Eco-Environmental Carrying Capacity in Water-Deficient Regions:A Case Study in the Haihe River Basin,China

Quantitative assessment of development sustainability could be a challenge to regional management and planning, especially for areas facing great risks of water shortage. Surface-water decline and groundwater over-pumping have caused serious environmental problems and limited economic development in many regions all around the world. In this paper, a framework for quantitatively evaluating development sustainability was established with water-related eco-environmental carrying capacity （EECC） as the core measure. As a case study, the developed approach was applied to data of the Haihe River Basin, China, during 1998 through 2007. The overall sustainable development degree （SDD） is determined to be 0.39, suggesting that this rate of development is not sustainable. Results of scenario analysis revealed that overshoot, or resource over- exploitation, of the Basin＇s EECC is about 20% for both population and economy. Based on conditions in the study area in 2007, in order to achieve sustainable development, i.e., SDD〉0.70 in this study, the EECC could support a population of 108 million and gross domestic product （GDP） of 2.72 trillion CNY. The newly developed approach in quantifying ecoenvironmental carrying capacity is anticipated to facilitate sustainable development oriented resource management in waterdeficient areas.

Ultrastructure of stridulating organ of Xylotrechus rusticus L. (Coleoptera, Cerambycidae) and behavioral responses to alarm sounds

We used scanning electron microscope （SEM） to observe the ultrastructure of stridulating organs in Xylotrechus rusticus L. We com- pared the morphological structure and size of stridulating organs, the numbers of a tooth-like part used in stridulation and its presence in fe- males and males of this beetle. The alarm sound of X.. rusticus was re- corded first when it was stimulated, then we tested behavioral responses to this alarm sound. The alarm sound ofX. rusticus has a warning effect on conspecifics.