Multi-Parameter Physiological State Monitoring in Target Detection Under Real-World Settings

Mental state changes induced by stimuli under experimental settings or by daily events in real life affect task performance and are entwined with physical and mental health. In this study, we developed a physiological state indicator with five parameters that reflect the subject’s real-time physiological states based on online EEG signal processing. These five parameters are attention, fatigue, stress, and the brain activity shifts of the left and right hemispheres. We designed a target detection experiment modified by a cognitive attention network test for validating the effectiveness of the proposed indicator, as such conditions would better approximate a real chaotic environment. Results demonstrated that attention levels while performing the target detection task were significantly higher than during rest periods, but also exhibited a decay over time. In contrast, the fatigue level increased gradually and plateaued by the third rest period. Similar to attention levels, the stress level decreased as the experiment proceeded. These parameters are therefore shown to be highly correlated to different stages of the experiment, suggesting their usage as primary factors in passive brain-computer interfaces (BCI). In addition, the left and right brain activity indexes reveal the EEG neural modulations of the corresponding hemispheres, which set a feasible reference of activation for an active BCI control system, such as one executing motor imagery tasks. The proposed indicator is applicable to potential passive and active BCI applications for monitoring the subject’s physiological state change in real-time, along with providing a means of evaluating the associated signal quality to enhance the BCI performance.

The Voice of Silence: Patterns of Digital Participation Among Palestinian Women in East Jerusalem

Facebook is one of the world’s largest social networks, with more than 2,7 billion active users globally. It is also one of the most dominant platforms and one of the platforms most commonly used by Arabs. However, connecting via Facebook and sharing content cannot be taken for granted. While many studies have focused on the role played by networked platforms in empowering women in the Arab world in general and on feminist movements in the Arab Spring, few have explored Palestinian women’s use of Facebook. During and after the Arab Spring, social media was used as a tool for freedom of expression in the Arab world. However, Palestinians in East Jerusalem using social media witnessed a decrease in freedom of expression, especially after the Gaza war in 2014. This article focuses on the Facebook usage patterns and political participation of young adult Palestinian women living in the contested space of East Jerusalem. These women live under dynamic power struggles as they belong to a traditionally conservative society, live within a situation of intractable conflict, and are under state control as a minority group. Qualitative thematic analysis of 13 in-depth interviews reveals three patterns of usage, all related to monitoring: state monitoring, kinship monitoring, and self-monitoring. The article conceptualises these online behaviours as “participation avoidance,” a term describing users’ (non-)communicative practices in which the mundane choices of when, why, and how to <em>participate </em>also mirror users’ choices of when, why, and how to <em>avoid</em>.

Correction: Huang et al. Deep-Learning Based Label-Free Classification of Activated and Inactivated Neutrophils for Rapid Immune State Monitoring. Sensors 2021, 21, 512

The authors wish to make the following correction to their paper [...]

A fusion data security protection scheme for sensitive E-documents in the open network environment

E-documents are carriers of sensitive data, and their security in the open network environment has always been a common problem with the field of data security. Based on the use of encryption schemes to construct secure access control, this paper proposes a fusion data security protection scheme. This scheme realizes the safe storage of data and keys by designing a hybrid symmetric encryption algorithm, a data security deletion algorithm, and a key separation storage method. The scheme also uses file filter driver technology to design a user operation state monitoring method to realize real-time monitoring of user access behavior. In addition, this paper designs and implements a prototype system. Through the verification and analysis of its usability and security, it is proved that the solution can meet the data security protection requirements of sensitive E-documents in the open network environment.

Analysis of air pollution in the City of Odesa using automated observation data

The work presents the analysis of air pollution in the City of Odesa using, among others, the data of automated observations. The air basin state was also evaluated using individual parameters of sustainable development. According to the official data of the recent years the City of Odesa belongs to the most polluted cities of Ukraine in terms of air pollution. Based on the ranking results it was established that the level of atmospheric pollution in the City of Odesa can be classified as high for the most of pollutants. It is classified as acceptable for some substances (sulfur dioxide and nitrogen oxide) and as extremely high for formaldehyde concentration. After comparing the observational data related to content of individual pollutants at the OSENU's observation point and the data of long-term observations in the city it was found that the content of nitrogen dioxide generally corresponds to the average long-term values. The observations conducted at the points of the city network indicated that the content of carbon monoxide is two orders of magnitude lower and the content of PM10 is one order of magnitude lower than dust concentrations. The increased content of certain pollutants in the air (nitrogen dioxide, carbon monoxide, etc.) is observed in the summer-autumn period and caused by the traffic intensification. The evaluation of the city's air basin state using individual parameters of the environmental measurement index showed that we observed better conditions in 2014 and 2016. The conditions for sustainable development are characterized by average indicators, however, towards worsening of the situation. The results obtained in this paper form a basis for extending the implementation of the Resolution of the Cabinet of Ministers of Ukraine on introducing a new procedure for state monitoring of the atmospheric air in Ukraine. The existing laboratory base of the observation points requires radical re-equipment. It is also necessary to conduct an air pollution survey for identification of high priority pollutants and, based thereon, development of monitoring programs with consideration of the necessity for keeping certain impurities under control.

Cyber-Physical LPG Debutanizer Distillation Columns: Machine-Learning-Based Soft Sensors for Product Quality Monitoring

Refineries execute a series of interlinked processes, where the product of one unit serves as the input to another process. Potential failures within these processes affect the quality of the end products, operational efficiency, and revenue of the entire refinery. In this context, implementation of a real-time cognitive module, referring to predictive machine learning models, enables the provision of equipment state monitoring services and the generation of decision-making for equipment operations. In this paper, we propose two machine learning models: (1) to forecast the amount of pentane (C5) content in the final product mixture; (2) to identify if C5 content exceeds the specification thresholds for the final product quality. We validate our approach using a use case from a real-world refinery. In addition, we develop a visualization to assess which features are considered most important during feature selection, and later by the machine learning models. Finally, we provide insights on the sensor values in the dataset, which help to identify the operational conditions for using such machine learning models.