Predicting prolonged postoperative ileus in gastric cancer patients based on bowel sounds using intelligent auscultation and machine learning

BACKGROUND Prolonged postoperative ileus(PPOI)delays the postoperative recovery of gastrointestinal function in patients with gastric cancer(GC),leading to longer hospitalization and higher healthcare expenditure.However,effective monitoring of gastrointestinal recovery in patients with GC remains challenging because of AIM To explore the risk factors for delayed postoperative bowel function recovery and evaluate bowel sound indicators collected via an intelligent auscultation system to guide clinical practice.METHODS This study included data from 120 patients diagnosed with GC who had undergone surgical treatment and postoperative bowel sound monitoring in the Department of General Surgery II at Shaanxi Provincial People's Hospital between January 2019 and January 2021.Among them,PPOI was reported in 33 cases.The patients were randomly divided into the training and validation cohorts.Significant variables from the training cohort were identified using univariate and multivariable analyses and were included in the model.RESULTS The analysis identified six potential variables associated with PPOI among the included participants.The incidence rate of PPOI was 27.5%.Age≥70 years,cTNM stage(Ⅰ and Ⅳ),preoperative hypoproteinemia,recovery time of bowel sounds(RTBS),number of bowel sounds(NBS),and frequency of bowel sounds(FBS)were independent risk factors for PPOI.The Bayesian model demonstrated good performance with internal validation:Training cohort[area under the curve(AUC)=0.880,accuracy=0.823,Brier score=0.139]and validation cohort(AUC=0.747,accuracy=0.690,Brier score=0.215).The model showed a good fit and calibration in the decision curve analysis,indicating a significant net benefit.CONCLUSION PPOI is a common complication following gastrectomy in patients with GC and is associated with age,cTNM stage,preoperative hypoproteinemia,and specific bowel sound-related indices(RTBS,NBS,and FBS).To facilitate early intervention and improve patient outcomes,clinicians should consider these factors,optimize preoperative nutritional status,and implement routine postoperative bowel sound monitoring.This study introduces an accessible machine learning model for predicting PPOI in patients with GC.

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.

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.

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.

Deep learning-based lung sound analysis for intelligent stethoscope

Auscultation is crucial for the diagnosis of respiratory system diseases.However,traditional stethoscopes have inherent limitations,such as inter-listener variability and subjectivity,and they cannot record respiratory sounds for offline/retrospective diagnosis or remote prescriptions in telemedicine.The emergence of digital stethoscopes has overcome these limitations by allowing physicians to store and share respiratory sounds for consultation and education.On this basis,machine learning,particularly deep learning,enables the fully-automatic analysis of lung sounds that may pave the way for intelligent stethoscopes.This review thus aims to provide a comprehensive overview of deep learning algorithms used for lung sound analysis to emphasize the significance of artificial intelligence(AI)in this field.We focus on each component of deep learning-based lung sound analysis systems,including the task categories,public datasets,denoising methods,and,most importantly,existing deep learning methods,i.e.,the state-of-the-art approaches to convert lung sounds into two-dimensional(2D)spectrograms and use convolutional neural networks for the end-to-end recognition of respiratory diseases or abnormal lung sounds.Additionally,this review highlights current challenges in this field,including the variety of devices,noise sensitivity,and poor interpretability of deep models.To address the poor reproducibility and variety of deep learning in this field,this review also provides a scalable and flexible open-source framework that aims to standardize the algorithmic workflow and provide a solid basis for replication and future extension:https://github.com/contactless-healthcare/Deep-Learning-for-Lung-Sound-Analysis.

Topology optimization of chiral metamaterials with application to underwater sound insulation

Chiral metamaterials have been proven to possess many appealing mechanical phenomena,such as negative Poisson's ratio,high-impact resistance,and energy absorption.This work extends the applications of chiral metamaterials to underwater sound insulation.Various chiral metamaterials with low acoustic impedance and proper stiffness are inversely designed using the topology optimization scheme.Low acoustic impedance enables the metamaterials to have a high and broadband sound transmission loss(STL),while proper stiffness guarantees its robust acoustic performance under a hydrostatic pressure.As proof-of-concept demonstrations,two specimens are fabricated and tested in a water-filled impedance tube.Experimental results show that,on average,over 95%incident sound energy can be isolated by the specimens in a broad frequency range from 1 k Hz to 5 k Hz,while the sound insulation performance keeps stable under a certain hydrostatic pressure.This work may provide new insights for chiral metamaterials into the underwater applications with sound insulation.

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.

Optimization for sound insulation of a sandwich plate with a corrugation and auxetic honeycomb hybrid core

A sandwich plate with a corrugation and auxetic honeycomb hybrid core is constructed,and its sound insulation and optimization are investigated.First,the motion governing equation of the sandwich plate is established by the third-order shear deformation theory(TSDT),and then combined with the fluid-structure coupling conditions,and the sound insulation is solved.The theoretical results are validated by COMSOL simulation results,and the effects of the structural parameter on the sound insulation are analyzed.Finally,the standard genetic algorithm is adopted to optimize the sound insulation of the sandwich plate.

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.

Vibration properties of Paulownia wood for Ruan sound quality using machine learning methods

As an important material for manufacturing resonant components of musical instruments,Paulownia has an important influence on the sound quality of Ruan.In this paper,a model for evaluating the sound quality of Ruan based on the vibration characteristics of wood is developed using machine learning methods.Generally,the selection of materials for Ruan manufacturing relies primarily on manually weighing,observing,striking,and listening by the instrument technician.Deficiencies in scientific theory have hindered the quality of the finished Ruan.In this study,nine Ruans were manufactured,and a prediction model of Ruan sound quality was proposed based on the raw material information of Ruans.Out of a total of 180 data sets,145 and 45 sets were chosen for training and validation,respec-tively.In this paper,typical correlation analysis was used to determine the correlation between two single indicators in two adjacent pairwise combinations of the measured objects in each stage of the production process in Ruan.The vibra-tion characteristics of the wood were tested,and a model for predicting the evaluation of Ruan’s acoustic qualities was developed by measuring the vibration characteristics of the resonating plate material.The acoustic quality of the Ruan sound board wood was evaluated and predicted using machine learning model generalized regression neural net-work.The results show that the prediction of Ruan sound quality can be achieved using Matlab simulation based on the vibration characteristics of the soundboard wood.When the model-predicted values were compared with the tradi-tional predicted results,it was found that the generalized regression neural network had good performance,achieving an accuracy of 93.8%which was highly consistent with the experimental results.It was concluded that the model can accurately predict the acoustic quality of the Ruan based on the vibration performance of the soundboards.

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.

识解理论视角下The Sound and the Fury汉译本中的认知偏离

翻译偏离通常源于语言的文化差异、译者的识解能力和方式的局限。兰盖克(Langacker)2019年最新提出的识解理论五维度,为认识The Sound and the Fury四个汉译本中的认知偏离现象提供了理论支撑。这些汉译本中的偏离现象虽遵循认知规律,但深层原因主要涉及译者的视角差异、场景选择、信息突显、动态性表达及想象性再现等多个层面。在语言认知加工过程中,译者的认知框架和识解方式,以及他们与源语文本、作者和读者之间的认知互动对意义的动态构建会产生显著的影响和制约。

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.

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.

A New Method for Deriving High-Vertical-Resolution Wind Vector Data from the L-Band Radar Sounding System in China

High-vertical-resolution radiosonde wind data are highly valuable for describing the dynamics of the meso-and microscale atmosphere. However, the current algorithm used in China's L-band radar sounding system for calculating highvertical-resolution wind vectors excessively smooths the data, resulting in significant underestimation of the calculated kinetic energy of gravity waves compared to similar products from other countries, which greatly limits the effective utilization of the data. To address this issue, this study proposes a novel method to calculate high-vertical-resolution wind vectors that utilizes the elevation angle, azimuth angle, and slant range from L-band radar. In order to obtain wind data with a stable quality, a two-step automatic quality control procedure, including the RMSE-F(root-mean-square error F) test and elemental consistency test are first applied to the slant range data, to eliminate continuous erroneous data caused by unstable signals or radar malfunctions. Then, a wind calculation scheme based on a sliding second-order polynomial fitting is utilized to derive the high-vertical-resolution radiosonde wind vectors. The evaluation results demonstrate that the wind data obtained through the proposed method show a high level of consistency with the high-resolution wind data observed using the Vaisala Global Positioning System and the data observed by the new Beidou Navigation Sounding System. The calculation of the kinetic energy of gravity waves in the recalculated wind data also reaches a level comparable to the Vaisala observations.

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.

Piecewise Acoustic Source Imaging with Unknown Speed of Sound Using a Level-Set Method

We investigate the following inverse problem:starting from the acoustic wave equation,reconstruct a piecewise constant passive acoustic source from a single boundary temporal measurement without knowing the speed of sound.When the amplitudes of the source are known a priori,we prove a unique determination result of the shape and propose a level set algorithm to reconstruct the singularities.When the singularities of the source are known a priori,we show unique determination of the source amplitudes and propose a least-squares fitting algorithm to recover the source amplitudes.The analysis bridges the low-frequency source inversion problem and the inverse problem of gravimetry.The proposed algorithms are validated and quantitatively evaluated with numerical experiments in 2D and 3D.

Flow and sound fields of scaled high-speed trains with different coach numbers running in long tunnel

Segregated incompressible large eddy simulation and acoustic perturbation equations were used to obtain the flow field and sound field of 1:25 scale trains with three,six and eight coaches in a long tunnel,and the aerodynamic results were verified by wind tunnel test with the same scale two-coach train model.Time-averaged drag coefficients of the head coach of three trains are similar,but at the tail coach of the multi-group trains it is much larger than that of the three-coach train.The eight-coach train presents the largest increment from the head coach to the tail coach in the standard deviation(STD)of aerodynamic force coefficients:0.0110 for drag coefficient(Cd),0.0198 for lift coefficient(Cl)and 0.0371 for side coef-ficient(Cs).Total sound pressure level at the bottom of multi-group trains presents a significant streamwise increase,which is different from the three-coach train.Tunnel walls affect the acoustic distribution at the bottom,only after the coach number reaches a certain value,and the streamwise increase in the sound pressure fluctuation of multi-group trains is strengthened by coach number.Fourier transform of the turbulent and sound pressures presents that coach number has little influence on the peak frequencies,but increases the sound pressure level values at the tail bogie cavities.Furthermore,different from the turbulent pressure,the first two sound pressure proper orthogonal decomposition(POD)modes in the bogie cavities contain 90%of the total energy,and the spatial distributions indicate that the acoustic distributions in the head and tail bogies are not related to coach number.