A large part of our daily lives is spent with audio information. Massive obstacles are frequently presented by the colossal amounts of acoustic information and the incredibly quick processing times. This results in th...A large part of our daily lives is spent with audio information. Massive obstacles are frequently presented by the colossal amounts of acoustic information and the incredibly quick processing times. This results in the need for applications and methodologies that are capable of automatically analyzing these contents. These technologies can be applied in automatic contentanalysis and emergency response systems. Breaks in manual communication usually occur in emergencies leading to accidents and equipment damage. The audio signal does a good job by sending a signal underground, which warrants action from an emergency management team at the surface. This paper, therefore, seeks to design and simulate an audio signal alerting and automatic control system using Unity Pro XL to substitute manual communication of emergencies and manual control of equipment. Sound data were trained using the neural network technique of machine learning. The metrics used are Fast Fourier transform magnitude, zero crossing rate, root mean square, and percentage error. Sounds were detected with an error of approximately 17%;thus, the system can detect sounds with an accuracy of 83%. With more data training, the system can detect sounds with minimal or no error. The paper, therefore, has critical policy implications about communication, safety, and health for underground mine.展开更多
Light plays an essential role in psychobiological and psychophysiological processes,such as alertness.The alerting effect is influenced by light characteristics and the timing of interventions.This meta-analysis is th...Light plays an essential role in psychobiological and psychophysiological processes,such as alertness.The alerting effect is influenced by light characteristics and the timing of interventions.This meta-analysis is the first to systematically review the effect of light intervention on alertness and to discuss the optimal protocol for light intervention.In this meta-analysis,registered at PROSPERO(Registration ID:CRD42020181485),we conducted a systematic search of the Web of Science,PubMed,and PsycINFO databases for studies published in English prior to August 2021.The outcomes included both subjective and objective alertness.Subgroup analyses considered a variety of factors,such as wavelength,correlated color temperature(CCT),light illuminance,and timing of interventions(daytime,night-time,or all day).Twenty-seven crossover studies and two parallel-group studies were included in this meta-analysis,with a total of 1210 healthy participants(636(52%)male,mean age 25.62 years).The results revealed that light intervention had a positive effect on both subjective alertness(standardized mean difference(SMD)=-0.28,95%confidence interval(CI):-0.49 to-0.06,P=0.01)and objective alertness in healthy subjects(SMD=-0.34,95%CI:-0.68 to-0.01,P=0.04).The subgroup analysis revealed that cold light was better than warm light in improving subjective alertness(SMD=-0.37,95%CI:-0.65 to-0.10,P=0.007,I2=26%)and objective alertness(SMD=-0.36,95%CI:-0.66 to-0.07,P=0.02,I2=0).Both daytime(SMD=-0.22,95%CI:-0.37 to-0.07,P=0.005,I2=74%)and night-time(SMD=-0.32,95%CI:-0.61 to-0.02,P=0.04,I2=0)light exposure improved subjective alertness.The results of this meta-analysis and systematic review indicate that light exposure is associated with significant improvement in subjective and objective alertness.In addition,light exposure with a higher CCT was more effective in improving alertness than light exposure with a lower CCT.Our results also suggest that both daytime and night-time light exposure can improve subjective alertness.展开更多
Lanzhou Zhongchuan International Airport[International Civil Aviation Organization(ICAO)code ZLLL]is located in a wind shear prone area in China,where most low-level wind shear events occur in dry weather conditions.W...Lanzhou Zhongchuan International Airport[International Civil Aviation Organization(ICAO)code ZLLL]is located in a wind shear prone area in China,where most low-level wind shear events occur in dry weather conditions.We analyzed temporal distribution and synoptic circulation background for 18 dry wind shear events reported by pilots at ZLLL by using the NCEP final(FNL)operational global analysis data,and then proposed a lidar-based regional divergence algorithm(RDA)to determine wind shear intensity and location.Low-level wind shear at ZLLL usually occurs in the afternoon and evening in dry conditions.Most wind shear events occur in an unstable atmosphere over ZLLL,with changes in wind speed or direction generally found at 700 hPa and 10-m height.Based on synoptic circulations at 700 hPa,wind shear events could be classified as strong northerly,convergence,southerly,and weak wind types.The proposed RDA successfully identified low-level wind shear except one southerly case,achieving94%alerting rate compared with 82%for the operational system at ZLLL and 88%for the ramp detection algorithm(widely used in some operational alert systems)based on the same dataset.The RDA-unidentified southerly case occurred in a near neutral atmosphere,and wind speed change could not be captured by the Doppler lidar.展开更多
Objective To report the development,validation,and findings of the Multi-dimensional Attention Rating Scale(MARS),a self-report tool crafted to evaluate six-dimension attention levels.Methods The MARS was developed ba...Objective To report the development,validation,and findings of the Multi-dimensional Attention Rating Scale(MARS),a self-report tool crafted to evaluate six-dimension attention levels.Methods The MARS was developed based on Classical Test Theory(CTT).Totally 202 highly educated healthy adult participants were recruited for reliability and validity tests.Reliability was measured using Cronbach's alpha and test-retest reliability.Structural validity was explored using principal component analysis.Criterion validity was analyzed by correlating MARS scores with the Toronto Hospital Alertness Test(THAT),the Attentional Control Scale(ACS),and the Attention Network Test(ANT).Results The MARS comprises 12 items spanning six distinct dimensions of attention:focused attention,sustained attention,shifting attention,selective attention,divided attention,and response inhibition.As assessed by six experts,the content validation index(CVI)was 0.95,the Cronbach's alpha for the MARS was 0.78,and the test-retest reliability was 0.81.Four factors were identified(cumulative variance contribution rate 68.79%).The total score of MARS was correlated positively with THAT(r=0.60,P<0.01)and ACS(r=0.78,P<0.01)and negatively with ANT's reaction time for alerting(r=−0.31,P=0.049).Conclusion The MARS can reliably and validly assess six-dimension attention levels in real-world settings and is expected to be a new tool for assessing multi-dimensional attention impairments in different mental disorders.展开更多
In-cab alerts warn commercial vehicle drivers of upcoming roadway incidents, slowdowns and work zone construction activities. This paper reports on a study evaluating the driver response to in-cab alerts in Ohio. Driv...In-cab alerts warn commercial vehicle drivers of upcoming roadway incidents, slowdowns and work zone construction activities. This paper reports on a study evaluating the driver response to in-cab alerts in Ohio. Driver response was evaluated by measuring the statistical trends of vehicle speeds after the in-cab alerts were received. Vehicle speeds pre and post in-cab alert were collected over a 47 day period in the fall of 2023 for trucks traveling on interstate roadways in Ohio. Results show that approximately 22% of drivers receiving Dangerous Slowdown alerts had reduced their speeds by at least 5 mph 30 seconds after receiving such an alert. Segmenting this analysis by speed found that of vehicles traveling at or above 70 mph at the time of alerting, 26% reduced speeds by at least 5 mph. These speed reductions suggest drivers taking actional measures after receiving alerts. Future studies will involve further analysis on the impact of the types of alerts shown, roadway characteristics and overall traffic conditions on truck speeds passing through work zones.展开更多
Security Information and Event Management (SIEM) platforms are critical for organizations to monitor and manage their security operations centers. However, organizations using SIEM platforms have several challenges su...Security Information and Event Management (SIEM) platforms are critical for organizations to monitor and manage their security operations centers. However, organizations using SIEM platforms have several challenges such as inefficiency of alert management and integration with real-time communication tools. These challenges cause delays and cost penalties for organizations in their efforts to resolve the alerts and potential security breaches. This paper introduces a cybersecurity Alert Distribution and Response Network (Adrian) system. Adrian introduces a novel enhancement to SIEM platforms by integrating SIEM functionalities with real-time collaboration platforms. Adrian leverages the uniquity of mobile applications of collaboration platforms to provide real-time alerts, enabling a two-way communication channel that facilitates immediate response to security incidents and efficient SIEM platform management. To demonstrate Adrian’s capabilities, we have introduced a case-study that integrates Wazuh, a SIEM platform, to Slack, a collaboration platform. The case study demonstrates all the functionalities of Adrian including the real-time alert distribution, alert customization, alert categorization, and enablement of management activities, thereby increasing the responsiveness and efficiency of Adrian’s capabilities. The study concludes with a discussion on the potential expansion of Adrian’s capabilities including the incorporation of artificial intelligence (AI) for enhanced alert prioritization and response automation.展开更多
The use of machine learning algorithms to identify characteristics in Distributed Denial of Service (DDoS) attacks has emerged as a powerful approach in cybersecurity. DDoS attacks, which aim to overwhelm a network or...The use of machine learning algorithms to identify characteristics in Distributed Denial of Service (DDoS) attacks has emerged as a powerful approach in cybersecurity. DDoS attacks, which aim to overwhelm a network or service with a flood of malicious traffic, pose significant threats to online systems. Traditional methods of detection and mitigation often struggle to keep pace with the evolving nature of these attacks. Machine learning, with its ability to analyze vast amounts of data and recognize patterns, offers a robust solution to this challenge. The aim of the paper is to demonstrate the application of ensemble ML algorithms, namely the K-Means and the KNN, for a dual clustering mechanism when used with PySpark to collect 99% accurate data. The algorithms, when used together, identify distinctive features of DDoS attacks that prove a very accurate reflection of reality, so they are a good combination for this aim. Impressively, having preprocessed the data, both algorithms with the PySpark foundation enabled the achievement of 99% accuracy when tuned on the features of a DDoS big dataset. The semi-supervised dataset tabulates traffic anomalies in terms of packet size distribution in correlation to Flow Duration. By training the K-Means Clustering and then applying the KNN to the dataset, the algorithms learn to evaluate the character of activity to a greater degree by displaying density with ease. The study evaluates the effectiveness of the K-Means Clustering with the KNN as ensemble algorithms that adapt very well in detecting complex patterns. Ultimately, cross-reaching environmental results indicate that ML-based approaches significantly improve detection rates compared to traditional methods. Furthermore, ensemble learning methods, which combine two plus multiple models to improve prediction accuracy, show greatness in handling the complexity and variability of big data sets especially when implemented by PySpark. The findings suggest that the enhancement of accuracy derives from newer software that’s designed to reflect reality. However, challenges remain in the deployment of these systems, including the need for large, high-quality datasets and the potential for adversarial attacks that attempt to deceive the ML models. Future research should continue to improve the robustness and efficiency of combining algorithms, as well as integrate them with existing security frameworks to provide comprehensive protection against DDoS attacks and other areas. The dataset was originally created by the University of New Brunswick to analyze DDoS data. The dataset itself was based on logs of the university’s servers, which found various DoS attacks throughout the publicly available period to totally generate 80 attributes with a 6.40GB size. In this dataset, the label and binary column become a very important portion of the final classification. In the last column, this means the normal traffic would be differentiated by the attack traffic. Further analysis is then ripe for investigation. Finally, malicious traffic alert software, as an example, should be trained on packet influx to Flow Duration dependence, which creates a mathematical scope for averages to enact. In achieving such high accuracy, the project acts as an illustration (referenced in the form of excerpts from my Google Colab account) of many attempts to tune. Cybersecurity advocates for more work on the character of brute-force attack traffic and normal traffic features overall since most of our investments as humans are digitally based in work, recreational, and social environments.展开更多
文摘A large part of our daily lives is spent with audio information. Massive obstacles are frequently presented by the colossal amounts of acoustic information and the incredibly quick processing times. This results in the need for applications and methodologies that are capable of automatically analyzing these contents. These technologies can be applied in automatic contentanalysis and emergency response systems. Breaks in manual communication usually occur in emergencies leading to accidents and equipment damage. The audio signal does a good job by sending a signal underground, which warrants action from an emergency management team at the surface. This paper, therefore, seeks to design and simulate an audio signal alerting and automatic control system using Unity Pro XL to substitute manual communication of emergencies and manual control of equipment. Sound data were trained using the neural network technique of machine learning. The metrics used are Fast Fourier transform magnitude, zero crossing rate, root mean square, and percentage error. Sounds were detected with an error of approximately 17%;thus, the system can detect sounds with an accuracy of 83%. With more data training, the system can detect sounds with minimal or no error. The paper, therefore, has critical policy implications about communication, safety, and health for underground mine.
基金supported by the National Natural Science Foundation of China,No.82172530(to QT)Science and Technology Program of Guangdong,No.2018B030334001(to CRR)Guangzhou Science and Technology Project,No.202007030012(to QT).
文摘Light plays an essential role in psychobiological and psychophysiological processes,such as alertness.The alerting effect is influenced by light characteristics and the timing of interventions.This meta-analysis is the first to systematically review the effect of light intervention on alertness and to discuss the optimal protocol for light intervention.In this meta-analysis,registered at PROSPERO(Registration ID:CRD42020181485),we conducted a systematic search of the Web of Science,PubMed,and PsycINFO databases for studies published in English prior to August 2021.The outcomes included both subjective and objective alertness.Subgroup analyses considered a variety of factors,such as wavelength,correlated color temperature(CCT),light illuminance,and timing of interventions(daytime,night-time,or all day).Twenty-seven crossover studies and two parallel-group studies were included in this meta-analysis,with a total of 1210 healthy participants(636(52%)male,mean age 25.62 years).The results revealed that light intervention had a positive effect on both subjective alertness(standardized mean difference(SMD)=-0.28,95%confidence interval(CI):-0.49 to-0.06,P=0.01)and objective alertness in healthy subjects(SMD=-0.34,95%CI:-0.68 to-0.01,P=0.04).The subgroup analysis revealed that cold light was better than warm light in improving subjective alertness(SMD=-0.37,95%CI:-0.65 to-0.10,P=0.007,I2=26%)and objective alertness(SMD=-0.36,95%CI:-0.66 to-0.07,P=0.02,I2=0).Both daytime(SMD=-0.22,95%CI:-0.37 to-0.07,P=0.005,I2=74%)and night-time(SMD=-0.32,95%CI:-0.61 to-0.02,P=0.04,I2=0)light exposure improved subjective alertness.The results of this meta-analysis and systematic review indicate that light exposure is associated with significant improvement in subjective and objective alertness.In addition,light exposure with a higher CCT was more effective in improving alertness than light exposure with a lower CCT.Our results also suggest that both daytime and night-time light exposure can improve subjective alertness.
基金Supported by the National Natural Science Foundation of China(41275102)Science and Technology Project of the Northwest Air Traffic Management Bureau of Civil Aviation of China in 2017Special Fund for National Science and Technology Basic Research Program of China(2017FY100900).
文摘Lanzhou Zhongchuan International Airport[International Civil Aviation Organization(ICAO)code ZLLL]is located in a wind shear prone area in China,where most low-level wind shear events occur in dry weather conditions.We analyzed temporal distribution and synoptic circulation background for 18 dry wind shear events reported by pilots at ZLLL by using the NCEP final(FNL)operational global analysis data,and then proposed a lidar-based regional divergence algorithm(RDA)to determine wind shear intensity and location.Low-level wind shear at ZLLL usually occurs in the afternoon and evening in dry conditions.Most wind shear events occur in an unstable atmosphere over ZLLL,with changes in wind speed or direction generally found at 700 hPa and 10-m height.Based on synoptic circulations at 700 hPa,wind shear events could be classified as strong northerly,convergence,southerly,and weak wind types.The proposed RDA successfully identified low-level wind shear except one southerly case,achieving94%alerting rate compared with 82%for the operational system at ZLLL and 88%for the ramp detection algorithm(widely used in some operational alert systems)based on the same dataset.The RDA-unidentified southerly case occurred in a near neutral atmosphere,and wind speed change could not be captured by the Doppler lidar.
文摘Objective To report the development,validation,and findings of the Multi-dimensional Attention Rating Scale(MARS),a self-report tool crafted to evaluate six-dimension attention levels.Methods The MARS was developed based on Classical Test Theory(CTT).Totally 202 highly educated healthy adult participants were recruited for reliability and validity tests.Reliability was measured using Cronbach's alpha and test-retest reliability.Structural validity was explored using principal component analysis.Criterion validity was analyzed by correlating MARS scores with the Toronto Hospital Alertness Test(THAT),the Attentional Control Scale(ACS),and the Attention Network Test(ANT).Results The MARS comprises 12 items spanning six distinct dimensions of attention:focused attention,sustained attention,shifting attention,selective attention,divided attention,and response inhibition.As assessed by six experts,the content validation index(CVI)was 0.95,the Cronbach's alpha for the MARS was 0.78,and the test-retest reliability was 0.81.Four factors were identified(cumulative variance contribution rate 68.79%).The total score of MARS was correlated positively with THAT(r=0.60,P<0.01)and ACS(r=0.78,P<0.01)and negatively with ANT's reaction time for alerting(r=−0.31,P=0.049).Conclusion The MARS can reliably and validly assess six-dimension attention levels in real-world settings and is expected to be a new tool for assessing multi-dimensional attention impairments in different mental disorders.
文摘In-cab alerts warn commercial vehicle drivers of upcoming roadway incidents, slowdowns and work zone construction activities. This paper reports on a study evaluating the driver response to in-cab alerts in Ohio. Driver response was evaluated by measuring the statistical trends of vehicle speeds after the in-cab alerts were received. Vehicle speeds pre and post in-cab alert were collected over a 47 day period in the fall of 2023 for trucks traveling on interstate roadways in Ohio. Results show that approximately 22% of drivers receiving Dangerous Slowdown alerts had reduced their speeds by at least 5 mph 30 seconds after receiving such an alert. Segmenting this analysis by speed found that of vehicles traveling at or above 70 mph at the time of alerting, 26% reduced speeds by at least 5 mph. These speed reductions suggest drivers taking actional measures after receiving alerts. Future studies will involve further analysis on the impact of the types of alerts shown, roadway characteristics and overall traffic conditions on truck speeds passing through work zones.
文摘Security Information and Event Management (SIEM) platforms are critical for organizations to monitor and manage their security operations centers. However, organizations using SIEM platforms have several challenges such as inefficiency of alert management and integration with real-time communication tools. These challenges cause delays and cost penalties for organizations in their efforts to resolve the alerts and potential security breaches. This paper introduces a cybersecurity Alert Distribution and Response Network (Adrian) system. Adrian introduces a novel enhancement to SIEM platforms by integrating SIEM functionalities with real-time collaboration platforms. Adrian leverages the uniquity of mobile applications of collaboration platforms to provide real-time alerts, enabling a two-way communication channel that facilitates immediate response to security incidents and efficient SIEM platform management. To demonstrate Adrian’s capabilities, we have introduced a case-study that integrates Wazuh, a SIEM platform, to Slack, a collaboration platform. The case study demonstrates all the functionalities of Adrian including the real-time alert distribution, alert customization, alert categorization, and enablement of management activities, thereby increasing the responsiveness and efficiency of Adrian’s capabilities. The study concludes with a discussion on the potential expansion of Adrian’s capabilities including the incorporation of artificial intelligence (AI) for enhanced alert prioritization and response automation.
文摘The use of machine learning algorithms to identify characteristics in Distributed Denial of Service (DDoS) attacks has emerged as a powerful approach in cybersecurity. DDoS attacks, which aim to overwhelm a network or service with a flood of malicious traffic, pose significant threats to online systems. Traditional methods of detection and mitigation often struggle to keep pace with the evolving nature of these attacks. Machine learning, with its ability to analyze vast amounts of data and recognize patterns, offers a robust solution to this challenge. The aim of the paper is to demonstrate the application of ensemble ML algorithms, namely the K-Means and the KNN, for a dual clustering mechanism when used with PySpark to collect 99% accurate data. The algorithms, when used together, identify distinctive features of DDoS attacks that prove a very accurate reflection of reality, so they are a good combination for this aim. Impressively, having preprocessed the data, both algorithms with the PySpark foundation enabled the achievement of 99% accuracy when tuned on the features of a DDoS big dataset. The semi-supervised dataset tabulates traffic anomalies in terms of packet size distribution in correlation to Flow Duration. By training the K-Means Clustering and then applying the KNN to the dataset, the algorithms learn to evaluate the character of activity to a greater degree by displaying density with ease. The study evaluates the effectiveness of the K-Means Clustering with the KNN as ensemble algorithms that adapt very well in detecting complex patterns. Ultimately, cross-reaching environmental results indicate that ML-based approaches significantly improve detection rates compared to traditional methods. Furthermore, ensemble learning methods, which combine two plus multiple models to improve prediction accuracy, show greatness in handling the complexity and variability of big data sets especially when implemented by PySpark. The findings suggest that the enhancement of accuracy derives from newer software that’s designed to reflect reality. However, challenges remain in the deployment of these systems, including the need for large, high-quality datasets and the potential for adversarial attacks that attempt to deceive the ML models. Future research should continue to improve the robustness and efficiency of combining algorithms, as well as integrate them with existing security frameworks to provide comprehensive protection against DDoS attacks and other areas. The dataset was originally created by the University of New Brunswick to analyze DDoS data. The dataset itself was based on logs of the university’s servers, which found various DoS attacks throughout the publicly available period to totally generate 80 attributes with a 6.40GB size. In this dataset, the label and binary column become a very important portion of the final classification. In the last column, this means the normal traffic would be differentiated by the attack traffic. Further analysis is then ripe for investigation. Finally, malicious traffic alert software, as an example, should be trained on packet influx to Flow Duration dependence, which creates a mathematical scope for averages to enact. In achieving such high accuracy, the project acts as an illustration (referenced in the form of excerpts from my Google Colab account) of many attempts to tune. Cybersecurity advocates for more work on the character of brute-force attack traffic and normal traffic features overall since most of our investments as humans are digitally based in work, recreational, and social environments.
