Mindfulness-Based Cognitive Therapy: A Case Study on Experiences of Healthy Behaviors by Clients in Psychiatric Care

The aim of the study was for the first time to investigate patients’ own experiences of developing healthy behavior in connection with their participation in Mindfulness-Based Cognitive Therapy (MBCT). Healthy behaviors were defined as those which aimed to improve the individual’s well-being and physical function. Two women, diagnosed with bipolar illness or depressive episodes, were recruited from a group of clients in psychiatric care who both had been treated according to MBCT. The two clients shared their views of what changes they experienced during the treatment in semi structured interviews. Data analysis was performed according to the Empirical Phenomenological Psychological (EPP) method. The analysis generated five main themes which were shown to create a causal chain where paths to healthy behavior contributed to a process of change which was mirrored in comprehensive distancing, which in turn facilitated a reduced tendency of illness-identity and the acquiring of new proficiencies which could be generalized to different situations in daily life which led to insights about healthier behavior.

Mindfulness-based cognitive therapy in the intervention of psychiatric disorders: A review

Mindfulness-based cognitive therapy(MBCT)is frequently used for psychiatric disorders.Despite MBCT’s considerable potential for improving psychological health for patients,there is little empirical evidence to support its practical application in Chinese.This review will define meditation and mindfulness,provide an overview of the development of MBCT,identify the evidence for the effectiveness of MBCT,and offer recommendations to medical personnels on how to provide support for patients receiving mindfulness intervention.

A Mindfulness-Based Cognitive Therapy (MBCT) Intervention to Improve Resilience and Mitigate Symptoms of Burnout Syndrome in Critical Care Nurses: Results of a Randomized Trial

Objective: Critical care nurses work in a challenging intensive care (ICU) environment that results in work-related psychological distress. Our objective was to pilot an in-person or virtual mindfulness-based cognitive therapy (MBCT) program enhanced resilience and a similarly designed attention control group. Methods: We randomized ICU nurses with symptoms of burnout syndrome and decreased resilience to an MBCT program or a similarly formatted book club control. Our primary outcome was change in resilience as measured by the Connor-Davidson Resilience Scale (CD-RISC). Results: One-hundred one nurses completed study-related procedures. Overall, 70% had baseline symptoms of anxiety and 26% had symptoms of depression. For the in-person cohorts, there was no statistical difference between intervention and control groups regarding the total number of sessions attended (3.85 days ± 1.4 versus 3.75 days ± 0.15;p = 0.64). Using the Client/Patient Satisfaction Questionnaire-8 (CSQ-8), satisfaction scores were higher in the intervention group for weeks two through four of the program: p = 0.03, 0.0003, 0.007 respectively. There was no difference in the change in CD-RISC scores between the two groups (mean difference: treatment = 5.0, control = 7.0;p = 0.30). The online intervention cohort had greater improvements in the change of their median emotional exhaustion burnout scores when compared to the in-person intervention cohorts (-5 [-8 to -1.5] vs. 2 [-5 to 8], p = 0.049). Conclusions: We developed a feasible and acceptable in-person and online MBCT-ICU intervention that did not increase resilience scores in ICU nurses when compared to an attention control group. These results could help guide the proper design of larger trials to determine the efficacy of other resilience interventions.

Effect of Mindfulness-based Cognitive Training on Sleep Quality and Mindfulness Cognitive Level of College Students

Objective:To study the effect of mindfulness-based cognitive training on sleep quality and mindfulness cognitive level of college students who score below the critical value of sleep disorders.Methods:The subjects were freshmen of a university in Shanghai who had scored below the critical value of sleep disorders.They were divided into the control group and experimental group by a random number table method,with 35 students in each group.No intervention was provided in the control group,and the mindfulness-based cognitive training(1 hour per day,5 days per week for 8 consecutive weeks)was performed in the intervention group.Eight weeks later,the Pittsburgh Sleep Quality Index(PSQI)and Five Facet Mindfulness Questionnaire(FFMQ)scores were compared between the two groups before and after the intervention for changes in the sleep quality and mindfulness cognitive level.Results:The sleep quality and daytime dysfunction were significantly improved,the sleep latency was shortened,the sleep duration was prolonged(P<0.05),and the mindfulness level of the subjects was significantly improved(P<0.05)in the intervention group compared with the control group.Conclusion:Mindfulness-based cognitive training can significantly improve the sleep quality of college students who scored below the critical value of sleep disorders.Furthermore,its psychological mechanism may be associated with the improvement of the mindfulness level of college students.

Blockchain-Based Cognitive Computing Model for Data Security on a Cloud Platform

Cloud storage is widely used by large companies to store vast amounts of data and files,offering flexibility,financial savings,and security.However,information shoplifting poses significant threats,potentially leading to poor performance and privacy breaches.Blockchain-based cognitive computing can help protect and maintain information security and privacy in cloud platforms,ensuring businesses can focus on business development.To ensure data security in cloud platforms,this research proposed a blockchain-based Hybridized Data Driven Cognitive Computing(HD2C)model.However,the proposed HD2C framework addresses breaches of the privacy information of mixed participants of the Internet of Things(IoT)in the cloud.HD2C is developed by combining Federated Learning(FL)with a Blockchain consensus algorithm to connect smart contracts with Proof of Authority.The“Data Island”problem can be solved by FL’s emphasis on privacy and lightning-fast processing,while Blockchain provides a decentralized incentive structure that is impervious to poisoning.FL with Blockchain allows quick consensus through smart member selection and verification.The HD2C paradigm significantly improves the computational processing efficiency of intelligent manufacturing.Extensive analysis results derived from IIoT datasets confirm HD2C superiority.When compared to other consensus algorithms,the Blockchain PoA’s foundational cost is significant.The accuracy and memory utilization evaluation results predict the total benefits of the system.In comparison to the values 0.004 and 0.04,the value of 0.4 achieves good accuracy.According to the experiment results,the number of transactions per second has minimal impact on memory requirements.The findings of this study resulted in the development of a brand-new IIoT framework based on blockchain technology.

A DYNAMIC SPECTRUM ACCESS NETWORK BASED ON COGNITIVE RADIO

Dynamic spectrum access technologies based on Cognitive Radio(CR) is under intensive research carried out by the wireless communication society and is expected to solve the problem of spectrum scarcity.However,most enabling technologies related to dynamic spectrum access are con-sidered individually.In this paper,we consider these key technologies jointly and introduce a new implementation scheme for a Dynamic Spectrum Access Network Based on Cognitive Radio(DSAN-BCR).We start with a flexible hardware platform for DSAN-BCR,as well as a flexible protocol structure that dominates the operation of DSAN-BCR.We then focus on the state of the art of key technologies such as spectrum sensing,spectrum resources management,dynamic spectrum access,and routing that are below the network layer in DSAN-BCR,as well as the development of technologies related to higher layers.Last but not the least,we analyze the challenges confronted by these men-tioned technologies in DSAN-BCR,and give the perspectives on the future development of these technologies.The DSAN-BCR introduced is expected to provide a system level guidance to alleviate the problem of spectrum scarcity.

Research Findings Using Mindfulness-Based Interventions for Chronic Pain

Chronic pain is a complex condition that is very detrimental to physical and psychological wellbeing. It carries a significant level of disability and economic burden. Pain patients frequently experience comorbid mental illness (e.g. depression, anxiety, PTSD, insomnia) and often require psychotherapeutic interventions in addition to medication management. Mindfulness-based interventions (MBIs) have emerged as a means to treat several chronic conditions (e.g. chronic pain, depression, anxiety, substance abuse, stress, insomnia). The objective of this review is to evaluate the current research on the use of MBIs in chronic pain managment. Although there are several controlled trials on the use of MBIs in chronic pain management, only a few studies were found that demonstrated significant effects on pain intensity, quality of life, as well as physical and psychological functioning. Therefore, the current evidence is mixed and there are insufficient data to definitively confirm the full impact of the use of MBIs in chronic pain conditions such as fibromyalgia, chronic low back pain, rheumatoid arthritis, and chronic musculoskeletal pain. The lack of compelling evidence at this time signals a demand for higher quality investigations in this area. Research examining MBIs and concomitant CBT may be of great value in order to synergize and strengthen patient outcomes.

Cooperative Spectrum Sensing for Cognitive Radio-Wireless Sensors Network Based on OR Rule Decision to Enhance Energy Consumption in Greenhouses

Wireless sensors networks (WSNs) combined with cognitive radio have developed and solved the limited space of the frequency spectrum. In this paper, we propose different types of spectrums sensing and their own decisions depend on the probabilities that applied into fusion center, and how these probabilities’ techniques help to enhance the energy consumption of WSNs. In the same way, the importance of designing balanced distribution between the wireless sensors networks and their own sinks. This research also provides an overview of security issues in CR-WSN, especially in Spectrum Sensing Data Falsification (SSDF) attacks that enforces harmful effects on spectrum sensing and spectrum sharing. We adopt OR rule as four types of CRSN sensing protocolin greenhouses application by using Matlab and Netsim simulators. Our results show that the designing balanced wireless sensors and their sinks in greenhouses are very significant to decrease the energy, which is due to the traffic congestion in the sink range area. Furthermore, by applying OR rule has enhanced the energy consumption, and improved the sensors network lifetime compared to cognitive radio network.

On Cognitive Style in English Web-based Autonomous Learning

Since Henry Holec first put forward the term‘Autonomy'in 1980's, autonomous learning has been drawing the universal attention of scholars both at home and abroad. Promoting learners' ability of self-regulated learning has been taken as one of the important goals of modern education. College English autonomous learning based on network environment does not mean free study without any restraints or monitoring, but rather involves the self-monitoring and external monitoring. Meanwhile, different learners may have different cognitive styles in their learning processes, which may have an influence on the improvement of the learners' efficiency in the autonomous language learning. Proper monitoring models coordinating with the students' different field cognitive styles.

A Cognitive Science Framework for the Analysis of Knowledge-Based Systems

The paper presents a cognitive science framework for the analysis of knowledge-based systems,including people, media. simulation and expert systems, resulting in a practical model for the procedures ofknowledge engineering. Starting with the construct of a social organization model driven by anticipationand thed differentiating this into pesonal scientists with diverse relations to people and their internal andexternal communication, it provides powerful and general model of society. people, and the roles of peoplein society. This model extends naturally ic the role of conventional media in the knowledge processes ofsociety and the new roles of computer-based simulation and expert systems. In particular it provides amodel of knowledge transfer that enables the processes of knowledge engineering to be analyzed andautomated.

A MULTI-RESOLUTION SPECTRUM SENSING (MRSS) SCHEME UNDER MEASUREMENT-BASED CHANNEL MODELS IN COGNITIVE RADIO

Spectrum sensing is one of the key technologies in Cognitive Radios(CRs).Previous works are accomplished under simple channel models,which may lead to unreliable results when it applied to the over-the-air systems.In this paper,we investigate the performance of a Multi-Resolution Spectrum Sensing(MRSS) algorithm under measurement-based channel models in China.MRSS is a wavelet based algorithm which is suitable for non-stationary,wideband signal analysis.Using statistical mod-eling,measurement-based channel models are presented under typical urban and suburban scenarios in Shanghai,China.Then,the performance of the MRSS algorithm is evaluated under the measure-ment-based channel models.Simulation results show that,using MRSS,the performance is always better in the scenarios where Line-Of-Sight(LOS) path exist;also,in LOS scenarios,rich scattering effect helps to increase the performance.

Multi-criteria user selection scheme for learning-based multiuser MIMO cognitive radio networks

For multiuser multiple-input-multiple-output （MIMO） cognitive radio （CR） networks a four-stage transmiision structure is proposed. In learning stage, the learning-based algorithm with low overhead and high flexibility is exploited to estimate the channel state information （ CSI ） between primary （PR） terminals and CR terminals. By using channel training in the second stage of CR frame, the channels between CR terminals can be achieved. In the third stage, a multi-criteria user selection scheme is proposed to choose the best user set for service. In data transmission stage, the total capacity maximization problem is solved with the interference constraint of PR terminals. Finally, simulation results show that the multi-criteria user selection scheme, which has the ability of changing the weights of criterions, is more flexible than the other three traditional schemes and achieves a tradeoff between user fairness and system performance.

Cognition Based Adaptive Control Mechanism

According to the basic functions and objectives of Cognitive Radio (CR) systems, the cognition-based adaptive control mechanism is the generalization of the research contents and approaches of cognitive radio systems. Therefore, the mechanism is described by a cognition loop, which contains the following parts: environment, inner structure of intelligent systems, observation and action.

Cognition Based Reconfigurable Wireless Network Technology

The End-to-End Reconfigurability (E2R) project aims at realizing the convergence of the heterogeneous radio networks and the optimal utilization of the radio resources. With the continuous development of E2R technology and cognitive theory, the evolution from existing radio networks to future reconfigurable radio networks with the cognitive ability becomes possible. Nowadays the research aspects of E2R include the system architecture of reconfigurable radio networks and some key technologies for their evolution.

Quantum Computational Techniques for Prediction of Cognitive State of Human Mind from EEG Signals

The utilization of quantum states for the representation of information and the advances in machine learning is considered as an efficient way of modeling the working of complex systems.The states of mind or judgment outcomes are highly complex phenomena that happen inside the human body.Decoding these states is significant for improving the quality of technology and providing an impetus to scientific research aimed at understanding the functioning of the human mind.One of the key advantages of quantum wave-functions over conventional classical models is the existence of configurable hidden variables,which provide more data density due to its exponential state-space growth.These hidden variables correspond to the amplitudes of each probable state of the system and allow for the modeling of various intricate aspects of measurable and observable physical quantities.This makes the quantum wave-functions powerful and felicitous to model cognitive states of the human mind,as it inherits the ability to efficiently couple the current context with past experiences temporally and spatially to approach an appropriate future cognitive state.This paper implements and compares some techniques like Variational Quantum Classifiers(VQC),quantum annealing classifiers,and hybrid quantum-classical neural networks,to harness the power of quantum computing for processing cognitive states of the mind by making use of EEG data.It also introduces a novel pipeline by logically combining some of the aforementioned techniques,to predict future cognitive responses.The preliminary results of these approaches are presented and are very encouraging with upto 61.53%validation accuracy.

IDSS: Designing to Extend the Cognitive Limits

The paper presents the conceptual and operational basis of the creation of IDSS based on our recent research experience. In this paper, an intelligent decision support system, IDSS is defined as: any interactive system that is specially designed to improve the decision making of its user by extending the user's cognitive decision making abilities. As a result, this view of man-machine joint cognitive system stresses the need to use computational technology to aid the user in the decision making process. And the human's role is to achieve total systems's objectives. The paper outlines the designing procedure in successive steps. First, the decision maker's cognitive needs for decision support are identified. Second, the computationally realizable support functions are defined that could be provided by IDSS. Then, the specific techniques that would best fill the decision needs are discussed. And finally, for system implementation the modern computational technology infrastructure is emphasized.

Artificial Intelligence and Human Intelligence——On Human-Computer Competition from the Five-Level Theory of Cognitive Science

It is generally accepted that the human mind and cognition can be viewed at five levels; nerves, psychology, language, thinking and culture. Artificial intelligence(AI) simulates human intelligence at all five levels of human cognition, however, AI has yet to outperform human intelligence, although it is making progress. Presently artificial intelligence lags far behind human intelligence in higher-order cognition, namely, the cognitive levels of language, thinking and culture. In fact, artificial intelligence and human intelligence fall into very different intelligence categories. Machine learning is no more than a simulation of human cognitive ability and therefore should not be overestimated. There is no need for us to feel scared even panic about it. Put forward by John R. Searle, the"Chinese Room"argument, a famous AI model and standard, is not yet out of date. According to this argument, a digital computer will never acquire human intelligence. Given that, no artificial intelligence will outperform human intelligence in the foreseeable future.

Adaptive Spectr um Decision Method for Heterogeneous Cognitive Radio Networks

To improve spectrum utilization and minimize interference to Primary User (PU), an adaptive spectrum decision method is proposed for Secondary User (SU), while taking traffic load balancing and spectrum heterogeneity into consideration. Long-term statistics and current sensing results are integrated into the proposed decision method of spectrum access. Two decision methods, namely probability based and sensing based, are presented, compared and followed by performance analysis in terms of delay. For probability based spectrum decision, Short-Time-Job-First (STJF) priority queuing discipline is employed to minimize average residual time and theoretical conclusion is derived in a novel way. For sensing based decision we treat the interrupted service of SU as newly incoming and re-decision process is initialized to find available spectrum in a First-Available-First-Access (FAFA) fashion. Effect of sensing error in PHY layer is also analyzed in terms of extended average residual time. Simulation results show that, for relatively low arriving rate of SU traffic, the proposed spectrum decision method yields at least a delay reduction of 39.5% compared with non-adaptive method. The proposed spectrum decision can significantly improve delay performance even facing sensing errors, which cause performance degeneration to both PU and SU.