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The article discusses the history, benefits, and challenges of neurofeedback therapy, a noninvasive treatment that aims to retrain the brain through repeated experiences of brain wave regulation. Despite its potential to treat various mental health issues and improve cognitive function, neurofeedback faces challenges such as lack of standardization, varying costs, and duration of treatment, as well as a shortage of rigorous clinical trials. Practitioners like Suruchi Chandra and Roxana Sasu advocate for neurofeedback, emphasizing its transformative potential and the need for early integration into treatment plans. Bernhard Wandernoth, a tech enthusiast, predicts that neurofeedback could become more widely recognized and utilized in the future, despite current organizational obstacles. [Extracted from the article]

Based on the hybrid event-triggered mechanism (HETM), the boundary stabilization issue for fractional-order nonlinear reaction–diffusion systems (FNRDSs) with time-varying delay is studied by using two kinds of measurements. First, when the system state is measurable, a event-triggered feedback controller (ETFC) is designed directly based on the average measured output. Secondly, for the case that the state is unmeasurable, an event-triggered feedback controller based on observer framework is constructed through the boundary point measurement information. Utilizing the Lyapunov method and Wirtinger's inequality, sufficient conditions for the asymptotic stability of the system are given in the form of linear matrix inequalities (LMIs), respectively, in which the Razumikhin theorem is used to deal with time-varying delay. Meanwhile, it is proved that Zeno behavior can be excluded by the designed HETM. Finally, numerical simulations demonstrate the validity and feasibility of the proposed control scheme. [ABSTRACT FROM AUTHOR]

This paper studies the dynamic output feedback (OF) control problem for unmanned aerial vehicles (UAVs) with limited network bandwidth by a genetic-algorithm-assisted design approach. In order to make full use of the limited network bandwidth, only partial measurement information is transmitted to the controller via the sensor-to-controller network. Round-Robin protocol is introduced to administrate the transmission rule. Sufficient conditions are presented to make sure that the closed-loop UAV system is exponentially stable with the required L 2 -gain performance. However, due to the nonlinear coupling terms of the dynamic OF controller parameters and some redundant variables in the system modeling, the established conditions are non-convex and difficult to be solved. Fortunately, genetic algorithms (GA) show good performance in solving optimization problem with nonlinear and non-convex constraints. Therefore, this paper skillfully combines GA with linear matrix inequality techniques to solve the established conditions. Finally, all the dynamic OF controller parameters and the minimal L 2 -gain can be obtained simultaneously. At the end of this paper, an UAV networked control example and a numerical example are performed to verify the effectiveness and superiorities of our method. [ABSTRACT FROM AUTHOR]

We present a new method for deriving functions that model the relationship between multiple signals in a physical system. The method, which we call dimensional function synthesis, applies to data streams where the dimensions of the signals (e.g., length, mass, etc.) are known. The method comprises two phases: a compile-time synthesis phase and a subsequent calibration using sensor data. We implement dimensional function synthesis and use the implementation to demonstrate efficiently summarizing multimodal sensor data for two physical systems using 90 laboratory experiments and 10,000 synthetic idealized measurements. The results show that our technique can generate models in less than 300 ms on average across all the physical systems we evaluated. This is a marked improvement when compared to an average of 16 s for training neural networks of comparable accuracy on the same computing platform. When calibrated with sensor data, our models outperform traditional regression and neural network models in inference accuracy in all the cases we evaluated. In addition, our models perform better in training latency (up to 1096× improvement) and required arithmetic operations in inference (up to 34× improvement). These significant gains are largely the result of exploiting information on the physics of signals that has hitherto been ignored. [ABSTRACT FROM AUTHOR]

Copyright of Plastverarbeiter is the property of Hüthig GmbH and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper studies the event-triggered (ET) L 2 -gain control issues for time-delay networked control systems (NCSs) subject to round-robin protocol. The considered NCS consists of n sensor nodes, where the communication from the sensors to the controller is scheduled by round-robin protocol. This protocol requires that, at every sampling instant, only one sensor is chosen to use the sensor/controller network and transmits its measurement information toward the controller. On the other hand, the ET strategy is adopted in the controller/actuator network, which means that the actuator will receive the updated control input signal in the situation that the predefined ET conditions are satisfied. The utilization of round-robin protocol and ET strategy will help to relieve the burden of network transmission. The network resources and bandwidth will be saved. By developing appropriate Lyapunov functional, sufficient conditions in matrix inequalities are established in order to achieve the stability and L 2 -gain of the studied systems. Then, a novel iterative algorithm is presented to solve the derived stability conditions so as to design the controllers and obtain the minimal L 2 -gain simultaneously. Two examples are executed at last to demonstrate the validness and benefits of our results. [ABSTRACT FROM AUTHOR]

In today's turbulent international situation, regional conflicts often occur, and the game between major countries has been continuing, which is not only reflected in the military strength, but also the war by means of information technology, especially the turmoil caused by the spread fake news. It is common to forge the statements of influential leaders or entrepreneurs called key populations forgery and spread them on the Internet, which causes bad influence. The number of key populations forgery belongs to a small group in the huge deep forgery number, and the conventional deep forgery detection methods have a great gap in the detection efficiency and performance of small group samples. Therefore, we use the forged information of a large number group to judge the forged information of a small number group, and propose a deep forgery detection method of information construction and comparison under information measurement. Firstly, we propose a sample coverage analysis method for the sample library boundary to explore the difference in the feature space between the small number of key population samples and the large number samples, so as to determine the sample boundary of small number groups from the information level. Secondly, we construct information of key groups and expand the sample scale to meet the data needs of improving the detection accuracy. Finally, we compare the forged features of the large number group with the feature of the expanded small number group, and measure the information coincidence of the two types features by the complexity of feature clusters. When the coincidence degree is high, it is considered that the small number group samples have gone through the forgery process and carried forged information. We have analyzed and tested the current mainstream deep forgery databases and collected a small number of population sample, and compare with some popular methods. The experimental results show that our proposed method achieves the state-of-the-art (SOTA). • We propose a sample boundary coverage analysis method to determine the coverage boundary by information measurement. • We integrate the triangular information into the GAN to achieve the expansion from multiple angles and expressions. • We measure the information coincidence of the two types features by the complexity of the feature cluster. [ABSTRACT FROM AUTHOR]

Constraints are very common for practical control systems. For logical systems, the existing technique of pre-feedback is an effective way of treating state-dependent constraints in control when the state is measurable. However, it is inapplicable for the case when measurement information is not available. In this situation, in order for the control input to not violate the state-dependent constraint, the control at each step must be selected from the common admissible controls of all possible states. Motivated by this observation, in this study, we propose a novel technique, termed the subset transition method, for finite-time controllability and stabilization of probabilistic logical dynamic control system (PLDCS) with a state-dependent control constraint. The main idea of this method is to construct an unconstrained deterministic logical control system over the power set of the state space, called the subset transition system (SubSTS), characterizing the transitional dynamics between subsets under common admissible controls. We prove that a control sequence is admissible with respect to all states in an initial subset if and only if it does not steer the SubSTS from the initial subset to the empty set. Based on this, necessary and sufficient conditions for set controllability and set stabilizability are obtained. Examples are presented to demonstrate the application of the obtained results. [ABSTRACT FROM AUTHOR]

The opening of the Arctic routes has great economic and strategic value, but the navigation environment is highly uncertain, unknown and complex. Aiming at the ambiguity and partial reliability of the uncertain information in the Arctic routes, based on the Z-number theory and the grey relational theory, this study establishes a grey relational optimization decision-making model for the comprehensive benefit evaluation of the Northeast Passage (NEP). First, the discrete Z-number is used to represent the comprehensive benefit evaluation information of channel, and Jensen Shannon divergence is introduced to measure the difference information between discrete Z-numbers. Then, on the basis of the grey relational degree of discrete Z-numbers, a new multi-dimensional fuzzy grey multi-criteria decision-making model is established and subsequently optimized using the PSO algorithm. Finally, the effectiveness and superiority of the new model are verified by comprehensively evaluating the benefit of using the Arctic NEP and the traditional route under different sea ice conditions. Results show that the comprehensive benefit of the Suez Passage is still the highest, but that of the NEP has been steadily increasing. Therefore, with future improvements in Arctic sea ice conditions and infrastructures, the NEP may become the best route for ocean shipping. [ABSTRACT FROM AUTHOR]

AI systems often need to deal with inconsistencies. One way of getting information about inconsistencies is by measuring the amount of information in the knowledgebase. In the past 20 years numerous inconsistency measures have been proposed. Many of these measures are syntactic measures, that is, they are based in some way on the minimal inconsistent subsets of the knowledgebase. Very little attention has been given to semantic inconsistency measures, that is, ones that are based on the models of the knowledgebase where the notion of a model is generalized to allow an atom to be assigned a truth value that denotes contradiction. In fact, only one nontrivial semantic inconsistency measure, the contension measure, has been in wide use. The purpose of this paper is to define a class of semantic inconsistency measures based on 3-valued logics. First, we show which 3-valued logics are useful for this purpose. Then we show that the class of semantic inconsistency measures can be developed using a graphical framework similar to the way that syntactic inconsistency measures have been studied. We give several examples of semantic inconsistency measures and show how they apply to three useful 3-valued logics. We also investigate the properties of these inconsistency measures and show their computation for several knowledgebases. [ABSTRACT FROM AUTHOR]

A recent study from the School of Public Health explores the use of directed acyclic graphs (DAGs) to represent measurement error and information bias in epidemiology. The research emphasizes the importance of utilizing DAGs to depict associations between empirically measured variables, which are often subject to error. By applying DAGs to map traditional epidemiological concepts onto causal graphical structures, the study aims to enhance the clarity with which epidemiologists analyze data-generating mechanisms. For more information, the full article can be accessed for free through the International Journal of Epidemiology. [Extracted from the article]

Copyright of TextilWirtschaft Online is the property of dfv Mediengruppe and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Researchers from the University of Fribourg in Switzerland have developed a new tablet-based measure to assess Theory of Mind (ToM) in children with autism spectrum disorder (ASD). The measure addresses the limitations of existing ToM measures, such as high verbal demands and lack of psychometric evaluations. Preliminary evaluations of the new measure suggest that it possesses good psychometric properties and is accessible and engaging for children. Further investigation with a larger group of participants is needed to confirm these initial results. The researchers plan to make the measure freely available to the scientific community. [Extracted from the article]

The Joint Commission has announced the availability of the Direct Data Submission Platform (DDSP) electronic clinical quality measure (eCQM) module. This platform allows healthcare organizations to upload their eCQM data for the calendar year 2024 reporting period. Key dates include August 5, 2024, as the start date for uploading and January 1, 2025, as the date when users can begin submitting eCQM data for all four quarters. Healthcare organizations are encouraged to submit the required performance measurement data well in advance of the March 17, 2025 deadline. The DDSP is a secure web-based platform that provides real-time feedback on data conformance and completeness, allowing organizations to review, purge, and re-upload their data as needed. More information on using the DDSP is available within the platform. [Extracted from the article]

OMRON Healthcare Co. Ltd. has been issued a patent for an electrocardiograph that aims to reduce power consumption while collecting electrocardiographic information. The electrocardiograph includes a measurement unit with electrodes and a circuit to measure the potential difference between the electrodes and collect electrocardiographic information. It also includes a physiological indicator measurement unit with a sensor and a circuit to measure a physiological indicator associated with the user's biological information. The electrocardiograph is designed to determine whether the user is in a relaxed state based on the physiological indicator, and control the measurement of electrocardiographic information accordingly. The patent was filed on February 15, 2021, and published online on August 27, 2024. [Extracted from the article]

This paper is concerned with event-triggered secure consensus for a class of linear multi-agent systems (MASs) under denial-of-service (DoS) attacks. Different from some existing methods, a multi-sensor multi-rate (MSMR) sampling mechanism is introduced to sample system states of agents. A class of multi-rate observer is devised to deal with some problems involved, such as the asynchrony and the incompleteness of several state sub-vectors, caused by the MSMR sampling mechanism. By using the partially updated state information of each agent, a novel multi-rate event-triggered mechanism is proposed, in which the continuous monitoring of the combined measurement information is avoided. Then, an event-based distributed secure consensus control protocol is presented against DoS attacks for the MAS under a directed communication topology. By taking into account the information on the duration and frequency of the DoS attacks, a sufficient condition is established to design suitable control protocols such that consensus can be achieved. Finally, a numerical example is provided to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper is concerned with a security problem about malicious integrity attacks in state estimation system, in which multiple smart sensors locally measure information and transmit it to a remote fusion estimator though wireless channels. A joint constraint is considered for the attacker behaviour in each channel to keep stealthiness under a residual-based detector on the remote side. In order to degrade the estimator performance, the attacker will maximize the trace of the remote state estimation error covariance which is derived based on Kalman filter theory. It is proved that the optimal linear attack strategy design problem is convex and finally turned into a semi-definite programming problem. In addition, the tendency of attack behaviour on recursive and fixed Kalman filter system is analyzed. Several examples are given to illustrate the theoretical results. [ABSTRACT FROM AUTHOR]

This article is concerned with the distributed Kalman filtering problem for discrete-time linear systems whose measurement information comes from a set of sensor nodes that can communicate with their direct neighbors. Two algorithms for distributed Kalman filtering are proposed, where the first algorithm is based on single-node measurement and the second algorithm is based on neighboring-node measurements. In order to improve the performance, a novel criterion is introduced to the algorithm design, where in the criterion, the proximity of matrix’s trace is used to determine the proximity of positive semidefinite matrix. We prove that all the proposed algorithms for distributed Kalman filtering are unbiased. Numerical examples are provided to demonstrate the correctness of the proposed theoretical method and the performance of the proposed new design technique. [ABSTRACT FROM AUTHOR]

Given a probability distribution, Shannon entropy can be used to measure its corresponding information volume. However, it is still an open issue to calculate the information volume of Z-number. In this paper, the information volume of the Z-number is presented using the proposed transformation of a Z-number into a mass function. In our proposed method, the mass function degenerates into probability distribution under the circumstance that the uncertainty of the Z-restriction is 0. In this case, the information volume degenerates into Shannon entropy. The information volume of a given Z-number increases approximately linearly with the unreliability of its Z-restriction. In addition, the information volume varies with the value of Z-restriction and fuzzy number A. Some illustrative examples are shown to demonstrate the properties of the proposed information volume of Z-number. A new Weighted Multiple Attribute Decision Making (WMADM) method is also proposed to illustrate the practical advantage of the proposed information volume. [ABSTRACT FROM AUTHOR]

In the increasingly complex environment of today, decision-making problems face general challenges involving the multi-dimensional relationships and the representation of uncertain information regarding performances, requirements, and designs. In this regard, this study combines the Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) method with the nested probabilistic linguistic term set (NPLTS) to rank the alternatives considering ease of use, cognitive information, and decreased complexity. We propose transformation functions with four kinds of NPLTSs to measure information scientifically, establish an NPLTSs-based PROMETHEE method, and apply it to a case study concerning the selection of marine fuels. The results show that renewable fuels are the preferred trend for marine transportation fuels, and the impact on the ranking of marine fuels is climate change (39.35%), technical availability (39.15%), cost (34.52%), and safety (18.55%). The proposed method considers cognitive-based multi-dimensional factors and makes full use of assessment information so that it is stable and suitable for complex and uncertain decision-making problems, and decision-makers can adjust the weights according to their preferences to obtain more reliable results. This study provides a novel method to deal with multi-dimensional decisions at both the theoretical and practical levels. [ABSTRACT FROM AUTHOR]

During an emergency or evacuation, individuals focus on the movement of their neighbors and follow those in sight, even without social affiliation, forming the undeclared Loose Leader-Follower (LLF) group, rather than the usual assumption of following certain rules in most of evacuation models. Inspired by the real-life experience and experimental data, the study develops a novel stochastic process-based evacuation model, integrated the LLF structure in collective motion to clarify the mechanism underlying individual interaction and refine the walking process according to attainable information. Transfer entropy (TE), a model-free measure of the direction of information flow, is used to detect the role of the individuals in the LLF structure from the time-series reconstructed motion information (e.g., the walking coordinate series). To reduce the error of the role detection, according to the 3σ principle for TE values, the detection threshold is decided based on the surrogate walking data. For simulation of their motion decision in the LLF structure, the TE is integrated into the walking behavior mode (TE-POMDP) using the Partially Observable Markov Decision Process (POMDP), that produces their motion behavior under the typical attained information condition. The results show that the TE-POMDP can provide more realistic trajectories. Additionally, we find that a 'Delta-like' formation of leading group distribution was presented and remained stable in the procession after a short period of confusion in early stage. [ABSTRACT FROM AUTHOR]

Copyright of Die Mediation is the property of Steinbeis-Stiftung fur Wirtschaftsforderung (StW) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A recent study conducted by researchers at Western Sydney University explores the use of hyperscanning in human neuroscience research. Hyperscanning allows for the simultaneous recording of brain activity from multiple individuals during social interactions. The study challenges the prevailing focus on interbrain synchrony in hyperscanning research, suggesting that it has limited sensitivity to information alignment across individuals. Instead, the researchers propose the use of representational analyses to capture indices of interpersonal information alignment. These findings offer a promising alternative for investigating interactive minds and highlight the limitations of current hyperscanning research. [Extracted from the article]

In graph embedding protection, deleting the embedding vector of a node does not completely disrupt its structural relationships. The embedding model must be retrained over the network without sensitive nodes, which incurs a waste of computation and offers no protection for ordinary users. Meanwhile, the edge perturbations do not guarantee good utility. This work proposed a new privacy protection and utility trade-off method without retraining. Firstly, since embedding distance reflects the closeness of nodes, we label and group user nodes into sensitive, near-sensitive, and ordinary regions to perform different strengths of privacy protection. The near-sensitive region can reduce the leaking risk of neighboring nodes connecting to sensitive nodes without sacrificing all of their utility. Secondly, we use mutual information to measure privacy and utility while adapting a single model-based mutual information neural estimator to vector pairs to reduce modeling and computational complexity. Thirdly, by keeping adding different noise to the divided regions and reestimating the mutual information between the original and noise-perturbed embeddings, our framework achieves a good trade-off between privacy and utility. Simulation results show that the proposed framework is superior to state-of-the-art baselines like LPPGE and DPNE. • Divided privacy protection according to nodes' embedding closeness to sensitive ones. • Region division classifies nodes into sensitive, near-sensitive and ordinary regions. • Mutual information neural estimator characterizes privacy and utility of embedding. • Tuning regional protections with mutual information for privacy utility trade-off. • Improved efficiency in publishing embedding privacy data and enhanced data utility. [ABSTRACT FROM AUTHOR]

Feature selection aims to choose the most relevant features from the dataset that can enhance the performance and efficiency of machine learning models. Although feature selection has been studied for many years, most existing methods focus on accuracy and efficiency while neglecting the interpretability of selected features. Therefore, inspired by the "Yin-Yang" philosophy, we introduce the concept of feature polarity for the first time and divide the features into positive and negative features. For example, by analyzing a patient's symptoms (features), we can obtain two sets of features to explain whether the patient has the flu. Positive features help us determine if the patient has the flu, while negative features can help us rule out the possibility of the flu. We introduce the PN (Positive and Negative) coefficient to measure the polarity of candidate features and develop a novel and explainable feature selection method based on feature polarity. Furthermore, we propose an ensemble classification framework that leverages both positive and negative features for each class to improve classification performance. Extensive experiments demonstrate the effectiveness of the PN coefficient compared to other information measurements. Moreover, our proposed classification framework performs excellently compared to some state-of-the-art feature selection methods. [ABSTRACT FROM AUTHOR]

A report from Heinrich-Heine-University Dusseldorf in Germany discusses the evaluation of measures to assess randomness in human-generated sequences. The study compares different measures of randomness, including those based on specific features of human behavior, information theory, and algorithmic complexity. The researchers aim to provide recommendations for selecting appropriate measures of randomness in psychological research. The study has been peer-reviewed and offers valuable insights for researchers in the field. [Extracted from the article]

In this paper, the problem of localizing and circumnavigating an unknown stationary target by multiple autonomous agents using bearing-only measurements is studied. We assume that all agents are anonymous in the sense that they are indistinguishable from each other, and each agent is not permitted to exchange its local measurements or state information. A novel collision avoidance mechanism is proposed to ensure collision-free system evolution, and a distributed control algorithm is developed to steer the group of agents to circumnavigate the target at a desired distance with any prescribed angular spaced formation. Global finite-time stability of the system is rigorously derived. The performance of the proposed algorithms is verified through numerical simulations. [ABSTRACT FROM AUTHOR]

A patent application by Omron Healthcare Co. Ltd. has been made available online for a biometric information measurement device. The device is designed to measure blood pressure and electrocardiographic waveform and is worn on the wrist. The device includes a blood pressure measurement unit, an electrocardiographic waveform measurement unit with multiple electrodes, an electrode contact state detection unit, a position detection unit, and a controller. The controller determines the correctness of the wrist position and electrode contact state and performs collective measurements of blood pressure and electrocardiographic waveform if the correctness is indicated. This invention aims to accurately measure blood pressure and electrocardiographic waveform in a portable device. [Extracted from the article]

A patent application has been published for a device developed by Omron Healthcare Co. Ltd. in Kyoto, Japan, that can measure blood pressure and other biological information. The device includes a belt portion that wraps around the measurement site, a fluid bag, a pump, a valve, a blood pressure measurement unit, a detection unit, and a biological information measurement unit. It allows for stable measurement with a simple and easy-to-manufacture configuration. The device can measure pulse waves, arterial oxygen saturation, or electrocardiographic waveforms. The patent application provides additional details and variations of the device. [Extracted from the article]

A patent application has been submitted by OMRON Healthcare Co. Ltd. for a "Biological Information Measurement Device." The device aims to measure a subject's blood pressure and electrocardiographic waveform. It includes a fluid bag, a pump, a valve, a blood pressure measurement unit, first and second electrodes, an electrocardiogram measurement unit, a belt portion, and a contact state stabilizer. The device is designed to provide stable measurement of biological information with a simple configuration. The patent application provides detailed descriptions and claims for the device. [Extracted from the article]

OMRON Healthcare Co. Ltd. has filed a patent application for a portable biological information measurement device that can measure blood pressure and electrocardiographic waveforms. The device, which can be worn on the wrist like a wristwatch, includes a controller that checks the position of the device on the wrist and the contact state of the electrodes. If the device is positioned correctly and the electrodes are in stable contact with the body, the controller can accurately measure both blood pressure and electrocardiographic waveforms. The patent application also includes a control method for the device. [Extracted from the article]

The concept of entropy has played a significant role in thermodynamics and information theory, and is also a current research hotspot. Information entropy, as a measure of information, has many different forms, such as Shannon entropy and Deng entropy, but there is no unified interpretation of information from a measurement perspective. To address this issue, this article proposes Generalized Information Entropy (GIE) that unifies entropies based on mass function. Meanwhile, GIE establishes the relationship between entropy, fractal dimension, and number of events. Therefore, Generalized Information Dimension (GID) has been proposed, which extends the definition of information dimension from probability to mass fusion. GIE plays a role in approximation calculation and coding systems. In the application of coding, information from the perspective of GIE exhibits a certain degree of particle nature that the same event can have different representational states, similar to the number of microscopic states in Boltzmann entropy. • Generalized Information Entropy is compatible with Shannon entropy, Deng entropy, and RPS entropy. • Generalized Information Dimension achieves approximate calculation of large number entropy. • The form of Generalized Information Entropy is closer to Boltzmann entropy. • The linearity of Deng entropy is further explained (Zhao et al., 2024). [ABSTRACT FROM AUTHOR]

An adaptive distributed control strategy is designed for the formation of multiple unmanned aerial vehicle (UAV) groups by using only relative measurement information. A distributed estimator is first designed to get the estimate of the consensus error. Then, an adaptive distributed controller is proposed to realize the formation tracking with the relative measurements. Based on the position and velocity information, a control barrier function (CBF) algorithm is applied to achieve the UAV formation under safe position constraints. In addition, in order to break up and reorganize multiple UAV groups, the proposed controller is designed based on the time-varying network topology. It is proved that the closed-loop system is uniformly asymptotically stability. Simulation and experiment results indicate that the proposed strategy can achieve formation control of multiple UAVs under safety constraints based on the measurement of relative measurement information. • The formation control issue of multiple UAV groups is investigated. • A distributed observer is developed to get the estimate of the consensus error. • A distributed adaptive control algorithm is proposed for the UAV. • Simulation and experiment results verify the effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

The neighborhood rough set model serves as an important tool for handling attribute reduction tasks involving heterogeneous attributes. However, measuring the relationship between conditional attributes and decision in the neighborhood rough set model is a crucial issue. Most studies have utilized neighborhood information entropy to measure the relationship between attributes. When using neighborhood conditional information entropy to measure the relationships between the decision and conditional attributes, it lacks monotonicity, consequently affecting the rationality of the final attribute reduction subset. In this paper, we introduce the concept of neighborhood granularity and propose a new form of relative neighborhood granularity to measure the relationship between the decision and conditional attributes, which exhibits monotonicity. Moreover, our approach for measuring neighborhood granularity avoids the logarithmic function computation involved in neighborhood information entropy. Finally, we conduct comparative experiments on 12 datasets using two classifiers to compare the results of attribute reduction with six other attribute reduction algorithms. The comparison demonstrates the advantages of our measurement approach. [ABSTRACT FROM AUTHOR]

A patent application for a body-attachable unit that measures biometric information has been made available online. The unit is designed to be attached to the body and can be used to monitor biometric information or provide treatment for chronic diseases such as diabetes. The unit includes a sensor that is inserted into the skin to measure blood glucose levels and a base unit that receives and outputs the biometric information. The invention aims to prevent contamination of the electrical connection between the sensor and the base unit and increase the unit's service life. [Extracted from the article]

Copyright of Técnica Contable y Financiera is the property of Wolters Kluwer Espana and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

For a multiple-channel system with sensor measurement losses, if the loss of sensor measurements can be observed by the estimator, the system is called a system with observable measurement losses; otherwise, it is called a system with unobservable measurement losses (a UML system). We first obtain the optimal linear estimator (OLE) for multiple-channel UML systems and then establish a necessary and sufficient condition for the stability and convergence of the OLE. We give a tight bound of the estimation performance loss caused by the unobservability of measurement losses. We show that the estimation performance is a monotonically increasing function of the measurement-loss rate and then analytically characterize the relation between the OLE-performance-loss rate and the measurement-loss rate. Finally, numerical examples are provided to show the effectiveness of the proposed OLE, when the measurement-loss status, that is, the private information of measurement losses, cannot be observed. [ABSTRACT FROM AUTHOR]

Copyright of Die Mediation is the property of Steinbeis-Stiftung fur Wirtschaftsforderung (StW) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article devises a photograph-based monitoring model to estimate the real-time PM2.5 concentrations, overcoming currently popular electrochemical sensor-based PM2.5 monitoring methods’ shortcomings such as low-density spatial distribution and time delay. Combining the proposed monitoring model, the photographs taken by various camera devices (e.g., surveillance camera, automobile data recorder, and mobile phone) can widely monitor PM2.5 concentration in megacities. This is beneficial to offering helpful decision-making information for atmospheric forecast and control, thus reducing the epidemic of COVID-19. To specify, the proposed model fuses Information Abundance measurement and Wide and Deep learning, dubbed as IAWD, for PM2.5 monitoring. First, our model extracts two categories of features in a newly proposed DS transform space to measure the information abundance (IA) of a given photograph since the growth of PM2.5 concentration decreases its IA. Second, to simultaneously possess the advantages of memorization and generalization, a new wide and deep neural network is devised to learn a nonlinear mapping between the above-mentioned extracted features and the groundtruth PM2.5 concentration. Experiments on two recently established datasets totally including more than 100 000 photographs demonstrate the effectiveness of our extracted features and the superiority of our proposed IAWD model as compared to state-of-the-art relevant computing techniques. [ABSTRACT FROM AUTHOR]

This paper considers the output feedback L 2 -gain control problem for continuous-time networked control systems subject to round-robin protocol. The sensors of the considered system are distributed over a network. Round-robin protocol is introduced to schedule the measurement information transmitted from sensors to the controller. Then the closed-loop system is modeled as a kind of time-delay system. According to Lyapunov–Krasovskii method, sufficient conditions are derived to guarantee the exponential stability and the prescribed L 2 -gain of the closed-loop system. The output feedback controller gains are determined efficiently by solving linear matrix inequalities. Finally, one example is reported to illustrate the advantages of the proposed design scheme. [ABSTRACT FROM AUTHOR]

This article provides a unified solution to a general coordination problem of multiagent systems with relative measurements, including uncertain transformations in translation, rotation, reflection, and scale. First, we introduce relative measurements described by a matrix group expressing such transformations in a unified way. Then, we derive a necessary, and sufficient condition to achieve a coordination task by relative, distributed control according to a network topology, and a class of measurement information. Especially, a strict class of all realizable coordination tasks is characterized with an orbit associated with the transformation matrix group on measurement. Next, we show that the network topology required to coordination is the clique rigidity. Then, the clique rigidity for concrete coordination tasks is associated with conventional connectivity, e.g., connectedness and rigidity. Moreover, an intuitive condition is derived as a connectivity condition of the intersection graph of the maximal cliques (i.e., complete subgraphs). Finally, the new method is applied to formation control with unknown, heterogeneous scale factors and its effectiveness is demonstrated through simulations for both 2-D and 3-D spaces. [ABSTRACT FROM AUTHOR]

The portfolio allocation problem is a hot topic in modern economics and management science. It involves many immeasurable criteria and uncertainties. Given the advantage of interval type-2 fuzzy sets (IT2FSs) in modeling complexities and uncertainties, this study provides an integrated methodology to deal with portfolio allocation problem based on the multiple criteria group decision-making (MCGDM) method considering the consensus reaching process in an interval type-2 fuzzy environment. First, some new information measures for IT2FSs include the weighted mean, and the weighted semi-absolute deviation measures are defined. They are used to describe the return and risk of the portfolio allocation, respectively. Second, a two-stage MCGDM framework is constructed for portfolio allocation: one is to achieve the group consensus, and the other is to obtain the optimal portfolio ratios. Finally, an example on calculating the optimal portfolio ratios for four newly listed stocks in China is offered to elaborate on the performance of our approach. Sensitivity and comparative analyses are also given to show the effectiveness and advantages of the proposed method. [ABSTRACT FROM AUTHOR]

• We propose an information entropy measure for intuitionistic fuzzy values (IFVs). • We propose the IIFEWA operator for IFVs. • We propose a new multiattribute decision making (MADM) method. • The proposed MADM method is based on the proposed IIFEWA operator of IFVs. • It provides us with a very useful way for MADM in the intuitionistic fuzzy context. This paper proposes the improved intuitionistic fuzzy Einstein weighted averaging (IIFEWA) operator of intuitionistic fuzzy values (IFVs). The proposed IIFEWA operator can overcome the drawbacks of the intuitionistic fuzzy Einstein improved weighted averaging (IFEIWA) operator, the intuitionistic fuzzy Hamacher improved weighted averaging (IFHIWA) operator, the intuitionistic fuzzy Hamacher weighted averaging (IFHWA) operator, the intuitionistic fuzzy Einstein weighted averaging (I F W A ω ε) operator, the intuitionistic fuzzy weighted averaging (IFWA) operator and the intuitionistic fuzzy Hamacher interactive ordered weighted averaging (IFHIOWA) operator of IFVs, where they have the drawbacks that (1) the membership grades and the non-membership grades of their obtained aggregating IFVs are indeterminated in some situations and (2) if there is only one IFV whose membership grade is equal to 1, then the membership grade of the aggregated IFV of n IFVs becomes 1; if there is only one IFV whose non-membership grade is equal to 0, then the non-membership grade of the aggregated IFV of n IFVs becomes 0. Based on the proposed IIFEWA operator, we propose a new multiattribute decision making (MADM) method. The proposed MADM method overcomes the drawbacks of the existing MADM methods, where they cannot distinguish the ranking orders of alternatives in some situations. [ABSTRACT FROM AUTHOR]

• Class-specific information measures are constructed by hierarchical decomposition. • Two-level systems of information measures exhibit a hierarchical isomorphism. • Informational class-specific reducts are proposed for class recognition optimization. • Informational class-specific reducts gain criss-cross connections with old modes. • Two-level and two-view reduct systems contain vertical and horizontal isomorphisms. Attribute reduction of rough set theory underlies knowledge acquisition and has two hierarchical types (classification-based and class-specific attribute reducts) and two perspectives from algebra and information theory; thus, there are four combined modes in total. Informational class-specific reducts are fundamental but lacking and are thus investigated by correspondingly constructing class-specific information measures. First, three types of information measures (i.e., information entropy, conditional entropy, and mutual information) are novelly established at the class level by hierarchical decomposition to acquire their hierarchical connection, systematical relationship, uncertainty semantics, and granulation monotonicity. Second, three types of informational class-specific reducts are correspondingly proposed to acquire their internal relationship, basic properties, and heuristic algorithm. Third, the informational class-specific reducts achieve their transverse connections, including the strength feature and consistency degeneration, with the algebraic class-specific reducts and their hierarchical connections, including the hierarchical strength and balance, with the informational classification-based reducts. Finally, relevant information measures and attribute reducts are effectively verified by decision tables and data experiments. Class-specific information measures deepen existing classification-based information measures by a hierarchical isomorphism, while the informational class-specific reducts systematically perfect attribute reduction by level and viewpoint isomorphisms; these results facilitate uncertainty measurement and information processing, especially at the class level. [ABSTRACT FROM AUTHOR]

A patent has been issued to OMRON Healthcare Co. Ltd. for an information management system that simplifies the process of registering a measuring device with an information terminal. The system uses ultrasonic waves to transmit identification information from the measuring device to the information terminal, which then displays the device for registration. This technology aims to reduce the burden on users and improve the accuracy of device registration. The patent provides a detailed description of the system and its components. [Extracted from the article]

• A new distance measure for pairwise flexible linguistic expressions is defined. • An FLODE-based method for determining personalized attribute weights is proposed. • A novel adaptive consensus model for flexible linguistic MAGDM is presented. • A flexible linguistic MAGDM approach with the novel CRP is developed. • A case study is presented to illustrate the feasibility of the proposed approach. As a novel linguistic representation tool, flexible linguistic expression (FLE) has substantial flexibility in expressing ambiguous and uncertain information from experts. However, FLEs pose the following challenges to developing information measurement and consensus mechanisms for FLEs-based multi-attribute group decision-making (MAGDM). (1) The current studies primarily use linguistic distribution approximation or triangular fuzzy numbers to measure the difference between FLEs indirectly without proposing an exact distance measure. (2) Few studies have been conducted on direct accurate consensus models incorporating adaptive consensus feedback mechanisms. To deal with these issues, the flexible linguistic ordinal Deng entropy (FLODE) is firstly proposed to measure the uncertainty of FLEs, and by combining it with linguistic scale functions, a new distance measure for FLEs is constructed, which can accurately measure the level of group consensus and the difference in pairwise FLEs. Subsequently, by integrating the defined FLODE with the classical entropy weight method, a new method for determining the experts' personalized attribute weights is constructed, fully considering the impact of experts' personalized attribute weights on decision-making results. Also, a new consensus model incorporating a minimum adjustment feedback mechanism and dynamic personalized attribute weights is developed, in which the feedback adjustment coefficients and personalized attribute weights are dynamically updated during the consensus reaching process (CRP). Lastly, an urban flooding risk assessment is applied to confirm the effectiveness of the proposed consensus model. [ABSTRACT FROM AUTHOR]

A patent application has been submitted for a wearable sensor that can measure various physiological parameters. The device, which can be easily attached and detached without special knowledge, includes electrodes for detecting electrocardiographic signals, a pulse wave sensor, and a vibration sensor. An analysis processing unit calculates heart-related data such as pre-ejection time and pulse transit time. The device also has the capability to measure blood pressure using a pressing cuff and a pressure sensor. The patent application provides detailed descriptions of the device's design and configuration, addressing the need for continuous and non-invasive measurement of biological information. [Extracted from the article]

A recent study conducted by researchers at King Saud University in Riyadh, Saudi Arabia, focused on the properties of the Renyi entropy and the residual Renyi entropy of k-record values in mathematics. The study explored the connection between the Renyi entropy order and stochastic and dispersive orders, as well as the aging properties of component lifetimes. The researchers also examined the estimation of the Renyi entropy of upper k-records using both real COVID-19 data and simulated data. For more information, readers can refer to the article published in AIMS Mathematics. [Extracted from the article]

A patent application for a "Biological Information Measurement Device" has been filed by inventors from Kyoto, Japan. The device aims to automatically switch the sampling frequency of measurement data based on the presence or absence of abnormalities in the organ being measured. This would allow for continuous measurement with low power consumption during normal times and high sampling frequency during abnormal times. The device is particularly suited for wearable devices with limited battery and storage capacity. The patent application provides detailed technical information and configurations for the device. [Extracted from the article]

A recent study conducted at MedStar Union Memorial Hospital in Baltimore, Maryland, examined the responsiveness of two patient-reported outcomes measures (PROMs) in a cohort of patients undergoing hand and upper extremity surgery. The study aimed to determine the minimal clinically important difference (MCID) for the brief Michigan Hand Outcomes Questionnaire (bMHQ) and the Patient-Reported Outcomes Measurement Information System (PROMIS) pain interference (PI). The researchers found that preoperative mental health status did not significantly impact the responsiveness of these measures. The study provides valuable information for designing future studies and interpreting study results in the context of patient mental health. [Extracted from the article]