Amid accelerating global land degradation,establishing high-efficiency ecological restoration principles and frameworks is crucial.Here,we explore the application of threshold effects in the ecological restoration pro...Amid accelerating global land degradation,establishing high-efficiency ecological restoration principles and frameworks is crucial.Here,we explore the application of threshold effects in the ecological restoration process based on field experiments and globally available experimental data from 173 sites.Combining data integration analysis and meta-analysis,we collectively verified the universality of threshold effects in grasslands.The global grasslands’average nitrogen application threshold is 3.78 g·m^(-2)·yr^(−1),while the threshold value of degraded grassland(3.65 g·m^(-2)·yr^(−1))is lower than that of nondegraded grassland(5.90 g·m^(-2)·yr^(−1)).The low nitrogen-driven thresholds are affected by degradation status,climate(precipitation and temperature),and other site conditions,but not fertilization forms.Independent experiments further demonstrated that an increase in soil moisture content can lead to the disappearance of nitrogen threshold effects,revealing that ecological threshold effects are influenced by ecosystem stress factors.Following the significant increase in plant biomass triggered by the nitrogen threshold,the ecosystem undergoes systemic improvement.Soil organic carbon,urease activity,soil microbial diversity,and other soil properties are significantly enhanced.Soil nitrogen cycle-related microbial communities and soil physicochemical attributes are significantly activated.The results indicate that a threshold response pattern may develop before nitrogen saturation is reached,and low nitrogen input can boost productivity and improve the plant-soil-microbe system.Our findings reveal a nonprogressive path of restoration in degraded ecosystems,and thus,restoration based on threshold effects can offer an efficient and safe solution to combat ecological degradation.展开更多
An optimization model has been established and solved to determine the optimal threshold value for the event-triggered self-adaptive optimization strategy,which aims to strike a balance between optimization performanc...An optimization model has been established and solved to determine the optimal threshold value for the event-triggered self-adaptive optimization strategy,which aims to strike a balance between optimization performance and control load while ensuring continuous optimization.First,evaluation indicators are introduced to comprehensively analyze the impact of power fluctuations on the objective function and system voltage at both the system-wide and local levels.Based on these indicators,a multi-stage centralized optimization(MCO)is selectively applied,addressing system state deviations to achieve optimal operating states while maintaining a voltage security margin to ensure system safety.Then,distributed optimization(DO)is carried out at each bus with a renewable energy source or random load integration to accommodate short-term uncertainties using a self-adaptive reactive power algorithm.The optimal threshold value for event-triggered DO is calculated to balance control burden and optimization effectiveness.Utilizing the local state deviation evaluation indicator,unnecessary DOs are skipped when minor power fluctuations occur at the local level.Finally,following the linear superposition principle,event-triggered DOs executed at all distributed controllers collectively constitute the self-adaptive optimization strategy for the entire system.A case study on the IEEE New England 39-bus power system illustrates the effectiveness of the proposed strategy.展开更多
Understanding Cd contamination in the soil-rice ecosystem and the underlying its threshold and interaction effects is crucial for controlling Cd pollution and ensuring food safety.Although the quantitative relationshi...Understanding Cd contamination in the soil-rice ecosystem and the underlying its threshold and interaction effects is crucial for controlling Cd pollution and ensuring food safety.Although the quantitative relationships between Cd and environmental variables have been extensively studied,the threshold and interaction effects of multi-source environmental variables remain largely unexplored.This study employs a combination of random forest analysis and a human health risk model to investigate the effects of variables on Cd levels in rice grains,with the goal of quantifying their contributions and elucidating their relationships.The results indicated that the 15 selected variables collectively explained 47.36%of the variation in Cd content,with the top three variables being soil pH,distance from industrial park,and soil Zn.The majority of variables exhibited threshold effects on Cd levels in rice grains.By visualizing the interaction between Soil pH,distance from industrial park,and soil Zn with Cd levels in rice,we demonstrate the threshold effects of them on Cd level in rice grains,thereby providing further insight into the variation observed.Furthermore,oral intake of rice has been identified as the primary route of human exposure,significantly contributing to overall exposure pathways.Understanding these interactions is crucial for gaining insights into the underlying processes driving Cd pollution and fostering sustainable development within the industry.Our findings underscore the crucial need to consider multiple environmental variables and their interactions when managing heavy metals(HMs)contamination and mitigating health risks.展开更多
Sleep is a biological phenomenon with highly conserved evolutionary characteristics.The American Academy of Sleep Medicine and the Sleep Research Society recommend that adults get at least 7 hours of sleep per night[1...Sleep is a biological phenomenon with highly conserved evolutionary characteristics.The American Academy of Sleep Medicine and the Sleep Research Society recommend that adults get at least 7 hours of sleep per night[1].However,the stress caused by fast-paced life often leads to sleep deprivation(SD).SD is strongly associated with damage to the auditory system[2,3].Obstructive sleep apnea-hypopnea syndrome(OSAHS)is a common sleep disorder.Clinical observations indicate that some patients with OSAHS experience persistent hearing loss accompanied by tinnitus and other symptoms[4].More than 61.8%of patients with sudden deafness experienced SD[5].展开更多
Many machine learning-based Android malware detection often suffers from concept drift,where models trained on historical data fail to generalize to evolving threats.This paper proposes SCAN(Structural Clustering with...Many machine learning-based Android malware detection often suffers from concept drift,where models trained on historical data fail to generalize to evolving threats.This paper proposes SCAN(Structural Clustering with Adaptive thresholds for iNtelligent Android malware detection),a hybrid intelligent framework designed to mitigate concept drift without retraining.SCAN integrates Gaussian Mixture Models(GMMs)-based clustering with cluster-wise adaptive thresholding and supervised classifiers tailored to each cluster.A key challenge in clusteringbased malware detection is cluster-wise class imbalance,where clusters contain disproportionate distributions of benign and malicious samples.SCAN addresses this issue through adaptive thresholding,which dynamically adjusts the decision boundary of each cluster according to its malicious-to-benign ratio.In the final training stage,four supervised learning algorithms—Random Forest(RF),Support Vector Machine(SVM),k-NN,and XGBoost—are applied within the GMM-defined clusters.We train SCAN on Android applications collected from 2014-2017 and test it with applications from 2018-2023.Experimental results demonstrate that SCAN combined with RF consistently achieves superior performance,with both average accuracy and average F1-score exceeding 91%.These findings confirm SCAN’s robustness to concept drift and highlight its potential as a sustainable and intelligent solution for long-term Android malware detection in the real world.展开更多
To investigate the eco-economic thresholds of weeds and the critical period for their control,combining economic and ecological perspectives to achieve scientific weed management,four dominant weeds,Echinochloa crus-g...To investigate the eco-economic thresholds of weeds and the critical period for their control,combining economic and ecological perspectives to achieve scientific weed management,four dominant weeds,Echinochloa crus-galli(L.)P.Beauv,Chenopodium album L.,Digitaria sanguinalis(L.)Scop,and Commelina communis L.,were selected as experimental subjects,based on their common occurrence in spring maize planting areas in Northern China.A predictive model for maize yield loss caused by mixed weed populations was established.The study analyzed the eco-economic thresholds of weeds under different control measures and determined the optimal period for weed control by combining the critical control period.A logarithmic function model was developed to describe the relationship between mixed weed density and maize yield loss:y=5.9875ln(x)-6.5407(R^(2)=0.949,F=131.244,P=0.000).The optimal model for the critical period of competition between weeds and maize in maize fields was:y=-0.0027x^(2)+0.5624x-10.064(R2=0.968,F=30.513,P=0.032).When the weed density in maize fields reached 5.57 plants·m^(-2),manual weeding should be conducted promptly.When the weed density was 3.41 plants·m^(-2) or 3.48 plants·m^(-2),soil or foliar treatments should be applied,respectively.If the weed density reached 3.93 plants·m^(-2),a combination of soil and foliar treatment should be implemented.The critical period for manual weeding was 28.4 days after sowing,for soil treatment it was 19.9 days,for foliar treatment it was 21.8 days,and for the combined treatment of soil and foliar methods,it was 23.5 days after sowing.Retaining weeds for up to 15 days after maize sowing did not result in a yield loss and could even have a positive effect on maize yield.展开更多
Memristors have a synapse-like two-terminal structure and electrical properties,which are widely used in the construc-tion of artificial synapses.However,compared to inorganic materials,organic materials are rarely us...Memristors have a synapse-like two-terminal structure and electrical properties,which are widely used in the construc-tion of artificial synapses.However,compared to inorganic materials,organic materials are rarely used for artificial spiking synapses due to their relatively poor memrisitve performance.Here,for the first time,we present an organic memristor based on an electropolymerized dopamine-based memristive layer.This polydopamine-based memristor demonstrates the improve-ments in key performance,including a low threshold voltage of 0.3 V,a thin thickness of 16 nm,and a high parasitic capaci-tance of about 1μF·mm^(-2).By leveraging these properties in combination with its stable threshold switching behavior,we con-struct a capacitor-free and low-power artificial spiking neuron capable of outputting the oscillation voltage,whose spiking fre-quency increases with the increase of current stimulation analogous to a biological neuron.The experimental results indicate that our artificial spiking neuron holds potential for applications in neuromorphic computing and systems.展开更多
With the increasing popularity of blockchain applications, the security of data sources on the blockchain is gradually receiving attention. Providing reliable data for the blockchain safely and efficiently has become ...With the increasing popularity of blockchain applications, the security of data sources on the blockchain is gradually receiving attention. Providing reliable data for the blockchain safely and efficiently has become a research hotspot, and the security of the oracle responsible for providing reliable data has attracted much attention. The most widely used centralized oracles in blockchain, such as Provable and Town Crier, all rely on a single oracle to obtain data, which suffers from a single point of failure and limits the large-scale development of blockchain. To this end, the distributed oracle scheme is put forward, but the existing distributed oracle schemes such as Chainlink and Augur generally have low execution efficiency and high communication overhead, which leads to their poor applicability. To solve the above problems, this paper proposes a trusted distributed oracle scheme based on a share recovery threshold signature. First, a data verification method of distributed oracles is designed based on threshold signature. By aggregating the signatures of oracles, data from different data sources can be mutually verified, leading to a more efficient data verification and aggregation process. Then, a credibility-based cluster head election algorithm is designed, which reduces the communication overhead by clarifying the function distribution and building a hierarchical structure. Considering the good performance of the BLS threshold signature in large-scale applications, this paper combines it with distributed oracle technology and proposes a BLS threshold signature algorithm that supports share recovery in distributed oracles. The share recovery mechanism enables the proposed scheme to solve the key loss issue, and the setting of the threshold value enables the proposed scheme to complete signature aggregation with only a threshold number of oracles, making the scheme more robust. Finally, experimental results indicate that, by using the threshold signature technology and the cluster head election algorithm, our scheme effectively improves the execution efficiency of oracles and solves the problem of a single point of failure, leading to higher scalability and robustness.展开更多
Objective: To study the relationship between cortical auditory evoked potential (CAEP) thresholds and behavioral thresholds in pediatric populations with sensorineural hearing loss (SNHL). Methods: Fifteen children (m...Objective: To study the relationship between cortical auditory evoked potential (CAEP) thresholds and behavioral thresholds in pediatric populations with sensorineural hearing loss (SNHL). Methods: Fifteen children (mean age 6.8 years) with bilateral SNHL underwent behavioral pure-tone audiometry and CAEP testing at 0.5, 1, 2, and 4 kHz. CAEP thresholds were determined using tone bursts, and correlations between CAEP and pure-tone thresholds were analyzed using Pearson correlation and t-tests. Results: A strong positive correlation was observed between P1 thresholds and behavioral thresholds across all test frequencies: 0.5 kHz (r = 0.765, p Conclusion: The strong correlation between P1 and behavioral thresholds demonstrates the reliability of CAEP testing for estimating auditory thresholds in children. These findings support the use of CAEP testing as a reliable objective tool for threshold estimation, particularly in cases where behavioral responses cannot be reliably obtained. When adjusted with frequency-specific correction values, CAEP testing provides a reliable method for assessing hearing thresholds in pediatric populations.展开更多
To better capture the characteristics of asymmetry and structural fluctuations observed in count time series,this study delves into the application of the quantile regression(QR)method for analyzing and forecasting no...To better capture the characteristics of asymmetry and structural fluctuations observed in count time series,this study delves into the application of the quantile regression(QR)method for analyzing and forecasting nonlinear integer-valued time series exhibiting a piecewise phenomenon.Specifically,we focus on the parameter estimation in the first-order Self-Exciting Threshold Integer-valued Autoregressive(SETINAR(2,1))process with symmetry,asymmetry,and contaminated innovations.We establish the asymptotic properties of the estimator under certain regularity conditions.Monte Carlo simulations demonstrate the superior performance of the QR method compared to the conditional least squares(CLS)approach.Furthermore,we validate the robustness of the proposed method through empirical quantile regression estimation and forecasting for larceny incidents and CAD drug call counts in Pittsburgh,showcasing its effectiveness across diverse levels of data heterogeneity.展开更多
The societal risk related to rainfalltriggered rapid debris flows is commonly managed in urbanized areas by means of early warning systems based on monitoring of hydrological parameters(such as rainfall or soil moistu...The societal risk related to rainfalltriggered rapid debris flows is commonly managed in urbanized areas by means of early warning systems based on monitoring of hydrological parameters(such as rainfall or soil moisture) and thresholds values.In Alpine catchments,this type of landslides is recurrent and represent one of the major geohazards.Debris flows are typically initiated by high-intensity rainstorms,prolonged rainfall with moderate intensity or snow melting.They frequently happen in situations of temporary infiltration into soils that are initially unsaturated.During significant rainfall events,the rise in pore water pressure can become crucial for the stability of slopes in particular areas.This phenomenon relies on hydraulic and geotechnical characteristics,along with the thickness of the involved soils.This procedure can result in a local drop in shear strength,as both apparent cohesion and effective stress decline,while driving forces rise because of the increase in unit weight.Accordingly,this study estimates Intensity-Duration(I-D) rainfall thresholds at the site-specific and distributed scales by combining empirical and physics-based approaches and modeling of soil coverings involved in soil slips or debris slides inducing debris flows.The approach was tested for mountain slopes of the Valtellina valley(Lombardia region,northern Italy),which suffered several catastrophic landslide events in the last decades.The empirical approach was adopted to reconstruct physics-based slope models of representative source areas of past debris flows events.To such a scope,nonpunctual but distributed data of hydro-mechanical soil properties and thicknesses were considered.Thus,to reconstruct the unsaturated/saturated critical conditions leading the slope instability,a combined hydrological modeling and infinite-slope stability analysis was adopted.This combined hydromechanical numerical model was used to attempt to determine a three-dimensional Intensity-Duration threshold for landslide initiation considering plausible rainfall for the Valtellina valley.Due to the lack of reliable records of past landslide hindering a thorough empirical analysis,the presented approach can be considered as a feasible approach for establishing a warning standard in urbanized areas at risk of shallow landslides triggered by rainfall.Moreover,findings highlight the importance of having access to spatially distributed soil characteristics to define and enhance input data for physics-based modelling.Finally,the proposed approach can aid an early warning system for the onset of shallow landslides by utilizing real-time rainfall monitoring or now-casting through a meteorological radar technique.展开更多
Radio environment plays an important role in radio astronomy observations.Further analysis is needed on the time and intensity distributions of interference signals for long-term radio environment monitoring.Sample va...Radio environment plays an important role in radio astronomy observations.Further analysis is needed on the time and intensity distributions of interference signals for long-term radio environment monitoring.Sample variance is an important estimate of the interference signal decision threshold.Here,we propose an improved algorithm for calculating data sample variance relying on four established statistical methods:the variance of the trimmed data,winsorized sample variance,median absolute deviation,and median of the trimmed data pairwise averaged squares method.The variance and decision threshold in the protected section of the radio astronomy L-band are calculated.Among the four methods,the improved median of the trimmed data pairwise averaged squares algorithm has higher accuracy,but in a comparison of overall experimental results,the cleanliness rate of all algorithms is above 96%.In a comparison between the improved algorithm and the four methods,the cleanliness rate of the improved algorithm is above 98%,verifying its feasibility.The time-intensity interference distribution in the radio protection band is also obtained.Finally,we use comprehensive monitoring data of radio astronomy protection bands,radio interference bands,and interfered frequency bands to establish a comprehensive evaluation system for radio observatory sites,including the observable time proportion in the radio astronomy protection band,the occasional time-intensity distribution in the radio interference frequency band,and the intensity distribution of the interfered frequency band.展开更多
Low-pressure mercury lamps,with a main emission of about 254 nm,have been used for disinfecting air,water and surfaces for nearly a century.However,only a few studies on the corneal damage threshold at the wavelength ...Low-pressure mercury lamps,with a main emission of about 254 nm,have been used for disinfecting air,water and surfaces for nearly a century.However,only a few studies on the corneal damage threshold at the wavelength of 254nm exist.In this paper,the in vivo corneal damage threshold was determined inchinchilla rabbit model using a laser system at 254 nm.The irradiance of the laser spot was nearly flat-top distributed and the beam diameter on the animal corneal surface was about 3.44mm and 3.28 mm along the horizontal and vertical directions.Damage lesion determinations were performed at 12 h post-exposure using fluorescein sodium staining.TheED50 was17.7 mJ/cm^(2)with a 95%confidence interval of 15.3-20.1 mJ/cm^(2).The obtained results may contribute to the knowledge base for the refinement of the UV hazard function.展开更多
Atmospheric aerosols are the primary contributors to environmental pollution.As such aerosols are micro-to nanosized particles invisible to the naked eye,it is necessary to utilize LiDAR technology for their detection...Atmospheric aerosols are the primary contributors to environmental pollution.As such aerosols are micro-to nanosized particles invisible to the naked eye,it is necessary to utilize LiDAR technology for their detection.The laser radar echo signal is vulnerable to background light and electronic thermal noise.While single-photon LiDAR can effectively reduce background light interference,electronic thermal noise remains a significant challenge,especially at long distances and in environments with a low signal-to-noise ratio(SNR).However,conventional denoising methods cannot achieve satisfactory results in this case.In this paper,a novel adaptive continuous threshold wavelet denoising algorithm is proposed to filter out the noise.The algorithm features an adaptive threshold and a continuous threshold function.The adaptive threshold is dynamically adjusted according to the wavelet decomposition level,and the continuous threshold function ensures continuity with lower constant error,thus optimizing the denoising process.Simulation results show that the proposed algorithm has excellent performance in improving SNR and reducing root mean square error(RMSE)compared with other algorithms.Experimental results show that denoising of an actual LiDAR echo signal results in a 4.37 dB improvement in SNR and a 39.5%reduction in RMSE.The proposed method significantly enhances the ability of single-photon LiDAR to detect weak signals.展开更多
A clock bias data processing method based on interval correlation coefficient wavelet threshold denoising is suggested for minor mistakes in clock bias data in order to increase the efficacy of satellite clock bias pr...A clock bias data processing method based on interval correlation coefficient wavelet threshold denoising is suggested for minor mistakes in clock bias data in order to increase the efficacy of satellite clock bias prediction.Wavelet analysis was first used to break down the satellite clock frequency data into several levels,producing high and low frequency coefficients for each layer.The correlation coefficients of the high and low frequency coefficients in each of the three sub-intervals created by splitting these coefficients were then determined.The major noise region—the sub-interval with the lowest correlation coefficient—was chosen for thresholding treatment and noise threshold computation.The clock frequency data was then processed using wavelet reconstruction and reconverted to clock data.Lastly,three different kinds of satellite clock data—RTS,whu-o,and IGS-F—were used to confirm the produced data.Our method enhanced the stability of the Quadratic Polynomial(QP)model’s predictions for the C16 satellite by about 40%,according to the results.The accuracy and stability of the Auto Regression Integrated Moving Average(ARIMA)model improved up to 41.8%and 14.2%,respectively,whilst the Wavelet Neural Network(WNN)model improved by roughly 27.8%and 63.6%,respectively.Although our method has little effect on forecasting IGS-F series satellites,the experimental findings show that it can improve the accuracy and stability of QP,ARIMA,and WNN model forecasts for RTS and whu-o satellite clock bias.展开更多
基金supported by the Major Special Projects of the National Natural Science Foundation of China(Grants No.52374170 and 42377465)the Third Comprehensive Scientific Exploration in Xinjiang(Grant No.2022xjkk1005)+1 种基金the Special Technology Innovation Fund of Carbon Peak and Carbon Neutrality in Jiangsu Province(Grant No.BK20231515)the Shaanxi Shenmu Natural Field Observation and Research Station of Erosion and Environment,which provided the site and data on experimental conditions for field trials.
文摘Amid accelerating global land degradation,establishing high-efficiency ecological restoration principles and frameworks is crucial.Here,we explore the application of threshold effects in the ecological restoration process based on field experiments and globally available experimental data from 173 sites.Combining data integration analysis and meta-analysis,we collectively verified the universality of threshold effects in grasslands.The global grasslands’average nitrogen application threshold is 3.78 g·m^(-2)·yr^(−1),while the threshold value of degraded grassland(3.65 g·m^(-2)·yr^(−1))is lower than that of nondegraded grassland(5.90 g·m^(-2)·yr^(−1)).The low nitrogen-driven thresholds are affected by degradation status,climate(precipitation and temperature),and other site conditions,but not fertilization forms.Independent experiments further demonstrated that an increase in soil moisture content can lead to the disappearance of nitrogen threshold effects,revealing that ecological threshold effects are influenced by ecosystem stress factors.Following the significant increase in plant biomass triggered by the nitrogen threshold,the ecosystem undergoes systemic improvement.Soil organic carbon,urease activity,soil microbial diversity,and other soil properties are significantly enhanced.Soil nitrogen cycle-related microbial communities and soil physicochemical attributes are significantly activated.The results indicate that a threshold response pattern may develop before nitrogen saturation is reached,and low nitrogen input can boost productivity and improve the plant-soil-microbe system.Our findings reveal a nonprogressive path of restoration in degraded ecosystems,and thus,restoration based on threshold effects can offer an efficient and safe solution to combat ecological degradation.
基金supported in part by National Key R&D Program of China(2022YFF0610600).
文摘An optimization model has been established and solved to determine the optimal threshold value for the event-triggered self-adaptive optimization strategy,which aims to strike a balance between optimization performance and control load while ensuring continuous optimization.First,evaluation indicators are introduced to comprehensively analyze the impact of power fluctuations on the objective function and system voltage at both the system-wide and local levels.Based on these indicators,a multi-stage centralized optimization(MCO)is selectively applied,addressing system state deviations to achieve optimal operating states while maintaining a voltage security margin to ensure system safety.Then,distributed optimization(DO)is carried out at each bus with a renewable energy source or random load integration to accommodate short-term uncertainties using a self-adaptive reactive power algorithm.The optimal threshold value for event-triggered DO is calculated to balance control burden and optimization effectiveness.Utilizing the local state deviation evaluation indicator,unnecessary DOs are skipped when minor power fluctuations occur at the local level.Finally,following the linear superposition principle,event-triggered DOs executed at all distributed controllers collectively constitute the self-adaptive optimization strategy for the entire system.A case study on the IEEE New England 39-bus power system illustrates the effectiveness of the proposed strategy.
基金supported by the GDAS’Project of Science and Technology Development(No.2022GDASZH-2022010104-2)Guangdong Major Project of Basic and Applied Basic Research(No.2023B0303000006).
文摘Understanding Cd contamination in the soil-rice ecosystem and the underlying its threshold and interaction effects is crucial for controlling Cd pollution and ensuring food safety.Although the quantitative relationships between Cd and environmental variables have been extensively studied,the threshold and interaction effects of multi-source environmental variables remain largely unexplored.This study employs a combination of random forest analysis and a human health risk model to investigate the effects of variables on Cd levels in rice grains,with the goal of quantifying their contributions and elucidating their relationships.The results indicated that the 15 selected variables collectively explained 47.36%of the variation in Cd content,with the top three variables being soil pH,distance from industrial park,and soil Zn.The majority of variables exhibited threshold effects on Cd levels in rice grains.By visualizing the interaction between Soil pH,distance from industrial park,and soil Zn with Cd levels in rice,we demonstrate the threshold effects of them on Cd level in rice grains,thereby providing further insight into the variation observed.Furthermore,oral intake of rice has been identified as the primary route of human exposure,significantly contributing to overall exposure pathways.Understanding these interactions is crucial for gaining insights into the underlying processes driving Cd pollution and fostering sustainable development within the industry.Our findings underscore the crucial need to consider multiple environmental variables and their interactions when managing heavy metals(HMs)contamination and mitigating health risks.
文摘Sleep is a biological phenomenon with highly conserved evolutionary characteristics.The American Academy of Sleep Medicine and the Sleep Research Society recommend that adults get at least 7 hours of sleep per night[1].However,the stress caused by fast-paced life often leads to sleep deprivation(SD).SD is strongly associated with damage to the auditory system[2,3].Obstructive sleep apnea-hypopnea syndrome(OSAHS)is a common sleep disorder.Clinical observations indicate that some patients with OSAHS experience persistent hearing loss accompanied by tinnitus and other symptoms[4].More than 61.8%of patients with sudden deafness experienced SD[5].
基金supported in part by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Science and ICT(No.2021R1A2C2012574)in part by the IITP(Institute of Information&Communications Technology Planning&Evaluation)-ITRC(Information Technology Research Center)grant funded by the Korea government(Ministry of Science and ICT)(IITP-2025-RS-2023-00259967).
文摘Many machine learning-based Android malware detection often suffers from concept drift,where models trained on historical data fail to generalize to evolving threats.This paper proposes SCAN(Structural Clustering with Adaptive thresholds for iNtelligent Android malware detection),a hybrid intelligent framework designed to mitigate concept drift without retraining.SCAN integrates Gaussian Mixture Models(GMMs)-based clustering with cluster-wise adaptive thresholding and supervised classifiers tailored to each cluster.A key challenge in clusteringbased malware detection is cluster-wise class imbalance,where clusters contain disproportionate distributions of benign and malicious samples.SCAN addresses this issue through adaptive thresholding,which dynamically adjusts the decision boundary of each cluster according to its malicious-to-benign ratio.In the final training stage,four supervised learning algorithms—Random Forest(RF),Support Vector Machine(SVM),k-NN,and XGBoost—are applied within the GMM-defined clusters.We train SCAN on Android applications collected from 2014-2017 and test it with applications from 2018-2023.Experimental results demonstrate that SCAN combined with RF consistently achieves superior performance,with both average accuracy and average F1-score exceeding 91%.These findings confirm SCAN’s robustness to concept drift and highlight its potential as a sustainable and intelligent solution for long-term Android malware detection in the real world.
基金Supported by the National Key Research and Development Program of China(2023YFD1400502)。
文摘To investigate the eco-economic thresholds of weeds and the critical period for their control,combining economic and ecological perspectives to achieve scientific weed management,four dominant weeds,Echinochloa crus-galli(L.)P.Beauv,Chenopodium album L.,Digitaria sanguinalis(L.)Scop,and Commelina communis L.,were selected as experimental subjects,based on their common occurrence in spring maize planting areas in Northern China.A predictive model for maize yield loss caused by mixed weed populations was established.The study analyzed the eco-economic thresholds of weeds under different control measures and determined the optimal period for weed control by combining the critical control period.A logarithmic function model was developed to describe the relationship between mixed weed density and maize yield loss:y=5.9875ln(x)-6.5407(R^(2)=0.949,F=131.244,P=0.000).The optimal model for the critical period of competition between weeds and maize in maize fields was:y=-0.0027x^(2)+0.5624x-10.064(R2=0.968,F=30.513,P=0.032).When the weed density in maize fields reached 5.57 plants·m^(-2),manual weeding should be conducted promptly.When the weed density was 3.41 plants·m^(-2) or 3.48 plants·m^(-2),soil or foliar treatments should be applied,respectively.If the weed density reached 3.93 plants·m^(-2),a combination of soil and foliar treatment should be implemented.The critical period for manual weeding was 28.4 days after sowing,for soil treatment it was 19.9 days,for foliar treatment it was 21.8 days,and for the combined treatment of soil and foliar methods,it was 23.5 days after sowing.Retaining weeds for up to 15 days after maize sowing did not result in a yield loss and could even have a positive effect on maize yield.
基金support from the Beijing Natural Science Foundation-Xiaomi Innovation Joint Fund(No.L233009)National Natural Science Foundation of China(NSFC Nos.62422409,62174152,and 62374159)from the Youth Innovation Promotion Association of Chinese Academy of Sciences(No.2020115).
文摘Memristors have a synapse-like two-terminal structure and electrical properties,which are widely used in the construc-tion of artificial synapses.However,compared to inorganic materials,organic materials are rarely used for artificial spiking synapses due to their relatively poor memrisitve performance.Here,for the first time,we present an organic memristor based on an electropolymerized dopamine-based memristive layer.This polydopamine-based memristor demonstrates the improve-ments in key performance,including a low threshold voltage of 0.3 V,a thin thickness of 16 nm,and a high parasitic capaci-tance of about 1μF·mm^(-2).By leveraging these properties in combination with its stable threshold switching behavior,we con-struct a capacitor-free and low-power artificial spiking neuron capable of outputting the oscillation voltage,whose spiking fre-quency increases with the increase of current stimulation analogous to a biological neuron.The experimental results indicate that our artificial spiking neuron holds potential for applications in neuromorphic computing and systems.
基金supported by the National Natural Science Foundation of China(Grant No.62102449)the Central Plains Talent Program under Grant No.224200510003.
文摘With the increasing popularity of blockchain applications, the security of data sources on the blockchain is gradually receiving attention. Providing reliable data for the blockchain safely and efficiently has become a research hotspot, and the security of the oracle responsible for providing reliable data has attracted much attention. The most widely used centralized oracles in blockchain, such as Provable and Town Crier, all rely on a single oracle to obtain data, which suffers from a single point of failure and limits the large-scale development of blockchain. To this end, the distributed oracle scheme is put forward, but the existing distributed oracle schemes such as Chainlink and Augur generally have low execution efficiency and high communication overhead, which leads to their poor applicability. To solve the above problems, this paper proposes a trusted distributed oracle scheme based on a share recovery threshold signature. First, a data verification method of distributed oracles is designed based on threshold signature. By aggregating the signatures of oracles, data from different data sources can be mutually verified, leading to a more efficient data verification and aggregation process. Then, a credibility-based cluster head election algorithm is designed, which reduces the communication overhead by clarifying the function distribution and building a hierarchical structure. Considering the good performance of the BLS threshold signature in large-scale applications, this paper combines it with distributed oracle technology and proposes a BLS threshold signature algorithm that supports share recovery in distributed oracles. The share recovery mechanism enables the proposed scheme to solve the key loss issue, and the setting of the threshold value enables the proposed scheme to complete signature aggregation with only a threshold number of oracles, making the scheme more robust. Finally, experimental results indicate that, by using the threshold signature technology and the cluster head election algorithm, our scheme effectively improves the execution efficiency of oracles and solves the problem of a single point of failure, leading to higher scalability and robustness.
文摘Objective: To study the relationship between cortical auditory evoked potential (CAEP) thresholds and behavioral thresholds in pediatric populations with sensorineural hearing loss (SNHL). Methods: Fifteen children (mean age 6.8 years) with bilateral SNHL underwent behavioral pure-tone audiometry and CAEP testing at 0.5, 1, 2, and 4 kHz. CAEP thresholds were determined using tone bursts, and correlations between CAEP and pure-tone thresholds were analyzed using Pearson correlation and t-tests. Results: A strong positive correlation was observed between P1 thresholds and behavioral thresholds across all test frequencies: 0.5 kHz (r = 0.765, p Conclusion: The strong correlation between P1 and behavioral thresholds demonstrates the reliability of CAEP testing for estimating auditory thresholds in children. These findings support the use of CAEP testing as a reliable objective tool for threshold estimation, particularly in cases where behavioral responses cannot be reliably obtained. When adjusted with frequency-specific correction values, CAEP testing provides a reliable method for assessing hearing thresholds in pediatric populations.
基金supported by Social Science Planning Foundation of Liaoning Province(Grand No.L22ZD065)National Natural Science Foundation of China(Grand Nos.12271231,1247012719,12001229)。
文摘To better capture the characteristics of asymmetry and structural fluctuations observed in count time series,this study delves into the application of the quantile regression(QR)method for analyzing and forecasting nonlinear integer-valued time series exhibiting a piecewise phenomenon.Specifically,we focus on the parameter estimation in the first-order Self-Exciting Threshold Integer-valued Autoregressive(SETINAR(2,1))process with symmetry,asymmetry,and contaminated innovations.We establish the asymptotic properties of the estimator under certain regularity conditions.Monte Carlo simulations demonstrate the superior performance of the QR method compared to the conditional least squares(CLS)approach.Furthermore,we validate the robustness of the proposed method through empirical quantile regression estimation and forecasting for larceny incidents and CAD drug call counts in Pittsburgh,showcasing its effectiveness across diverse levels of data heterogeneity.
文摘The societal risk related to rainfalltriggered rapid debris flows is commonly managed in urbanized areas by means of early warning systems based on monitoring of hydrological parameters(such as rainfall or soil moisture) and thresholds values.In Alpine catchments,this type of landslides is recurrent and represent one of the major geohazards.Debris flows are typically initiated by high-intensity rainstorms,prolonged rainfall with moderate intensity or snow melting.They frequently happen in situations of temporary infiltration into soils that are initially unsaturated.During significant rainfall events,the rise in pore water pressure can become crucial for the stability of slopes in particular areas.This phenomenon relies on hydraulic and geotechnical characteristics,along with the thickness of the involved soils.This procedure can result in a local drop in shear strength,as both apparent cohesion and effective stress decline,while driving forces rise because of the increase in unit weight.Accordingly,this study estimates Intensity-Duration(I-D) rainfall thresholds at the site-specific and distributed scales by combining empirical and physics-based approaches and modeling of soil coverings involved in soil slips or debris slides inducing debris flows.The approach was tested for mountain slopes of the Valtellina valley(Lombardia region,northern Italy),which suffered several catastrophic landslide events in the last decades.The empirical approach was adopted to reconstruct physics-based slope models of representative source areas of past debris flows events.To such a scope,nonpunctual but distributed data of hydro-mechanical soil properties and thicknesses were considered.Thus,to reconstruct the unsaturated/saturated critical conditions leading the slope instability,a combined hydrological modeling and infinite-slope stability analysis was adopted.This combined hydromechanical numerical model was used to attempt to determine a three-dimensional Intensity-Duration threshold for landslide initiation considering plausible rainfall for the Valtellina valley.Due to the lack of reliable records of past landslide hindering a thorough empirical analysis,the presented approach can be considered as a feasible approach for establishing a warning standard in urbanized areas at risk of shallow landslides triggered by rainfall.Moreover,findings highlight the importance of having access to spatially distributed soil characteristics to define and enhance input data for physics-based modelling.Finally,the proposed approach can aid an early warning system for the onset of shallow landslides by utilizing real-time rainfall monitoring or now-casting through a meteorological radar technique.
基金supported by the Ministry of Science and Technology SKA Special Project(2020SKA0110202)the Special Project on Building a Science and Technology Innovation Center for South and Southeast Asia-International Joint Innovation Platform in Yunnan Province:“Yunnan Sino-Malaysian International Joint Laboratory of HF-VHF Advanced Radio Astronomy Technology”(202303AP140003)+4 种基金the National Natural Science Foundation of China(NSFC)Joint Fund for Astronomy(JFA)incubator program(U2031133)the International Partnership Program Project of the International Cooperation Bureau of the Chinese Academy of Sciences:“Belt and Road”Cooperation(114A11KYSB20200001)the Kunming Foreign(International)Cooperation Base Program:“Yunnan Observatory of the Chinese Academy of Sciences-University of Malaya Joint R&D Cooperation Base for Advanced Radio Astronomy Technology”(GHJD-2021022)the China-Malaysia Collaborative Research on Space Remote Sensing and Radio Astronomy Observation of Space Weather at Low and Middle Latitudes under the Key Special Project of the State Key R&D Program of the Ministry of Science and Technol ogy for International Cooperation in Science,Technology and Innovation among Governments(2022YFE0140000)the High-precision calibration method for low-frequency radio interferometric arrays for the SKA project of the Ministry of Science and Technology(2020SKA0110300).
文摘Radio environment plays an important role in radio astronomy observations.Further analysis is needed on the time and intensity distributions of interference signals for long-term radio environment monitoring.Sample variance is an important estimate of the interference signal decision threshold.Here,we propose an improved algorithm for calculating data sample variance relying on four established statistical methods:the variance of the trimmed data,winsorized sample variance,median absolute deviation,and median of the trimmed data pairwise averaged squares method.The variance and decision threshold in the protected section of the radio astronomy L-band are calculated.Among the four methods,the improved median of the trimmed data pairwise averaged squares algorithm has higher accuracy,but in a comparison of overall experimental results,the cleanliness rate of all algorithms is above 96%.In a comparison between the improved algorithm and the four methods,the cleanliness rate of the improved algorithm is above 98%,verifying its feasibility.The time-intensity interference distribution in the radio protection band is also obtained.Finally,we use comprehensive monitoring data of radio astronomy protection bands,radio interference bands,and interfered frequency bands to establish a comprehensive evaluation system for radio observatory sites,including the observable time proportion in the radio astronomy protection band,the occasional time-intensity distribution in the radio interference frequency band,and the intensity distribution of the interfered frequency band.
基金supported by the National Natural Science Foundation of China(No.61575221).
文摘Low-pressure mercury lamps,with a main emission of about 254 nm,have been used for disinfecting air,water and surfaces for nearly a century.However,only a few studies on the corneal damage threshold at the wavelength of 254nm exist.In this paper,the in vivo corneal damage threshold was determined inchinchilla rabbit model using a laser system at 254 nm.The irradiance of the laser spot was nearly flat-top distributed and the beam diameter on the animal corneal surface was about 3.44mm and 3.28 mm along the horizontal and vertical directions.Damage lesion determinations were performed at 12 h post-exposure using fluorescein sodium staining.TheED50 was17.7 mJ/cm^(2)with a 95%confidence interval of 15.3-20.1 mJ/cm^(2).The obtained results may contribute to the knowledge base for the refinement of the UV hazard function.
基金funded by the National Key R&D Program of China(Grant No.2022YFC3300705)the National Natural Science Foundation of China(Grant Nos.62203056,12202048,and 62201056).
文摘Atmospheric aerosols are the primary contributors to environmental pollution.As such aerosols are micro-to nanosized particles invisible to the naked eye,it is necessary to utilize LiDAR technology for their detection.The laser radar echo signal is vulnerable to background light and electronic thermal noise.While single-photon LiDAR can effectively reduce background light interference,electronic thermal noise remains a significant challenge,especially at long distances and in environments with a low signal-to-noise ratio(SNR).However,conventional denoising methods cannot achieve satisfactory results in this case.In this paper,a novel adaptive continuous threshold wavelet denoising algorithm is proposed to filter out the noise.The algorithm features an adaptive threshold and a continuous threshold function.The adaptive threshold is dynamically adjusted according to the wavelet decomposition level,and the continuous threshold function ensures continuity with lower constant error,thus optimizing the denoising process.Simulation results show that the proposed algorithm has excellent performance in improving SNR and reducing root mean square error(RMSE)compared with other algorithms.Experimental results show that denoising of an actual LiDAR echo signal results in a 4.37 dB improvement in SNR and a 39.5%reduction in RMSE.The proposed method significantly enhances the ability of single-photon LiDAR to detect weak signals.
基金2023 Liaoning Institute of Science and Technology Doctoral Program Launch fund(No.2307B29).
文摘A clock bias data processing method based on interval correlation coefficient wavelet threshold denoising is suggested for minor mistakes in clock bias data in order to increase the efficacy of satellite clock bias prediction.Wavelet analysis was first used to break down the satellite clock frequency data into several levels,producing high and low frequency coefficients for each layer.The correlation coefficients of the high and low frequency coefficients in each of the three sub-intervals created by splitting these coefficients were then determined.The major noise region—the sub-interval with the lowest correlation coefficient—was chosen for thresholding treatment and noise threshold computation.The clock frequency data was then processed using wavelet reconstruction and reconverted to clock data.Lastly,three different kinds of satellite clock data—RTS,whu-o,and IGS-F—were used to confirm the produced data.Our method enhanced the stability of the Quadratic Polynomial(QP)model’s predictions for the C16 satellite by about 40%,according to the results.The accuracy and stability of the Auto Regression Integrated Moving Average(ARIMA)model improved up to 41.8%and 14.2%,respectively,whilst the Wavelet Neural Network(WNN)model improved by roughly 27.8%and 63.6%,respectively.Although our method has little effect on forecasting IGS-F series satellites,the experimental findings show that it can improve the accuracy and stability of QP,ARIMA,and WNN model forecasts for RTS and whu-o satellite clock bias.
