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In Cyber–Physical–Social Systems (CPSS), the interactions among various entities form complex graphs. Many tasks can be formulated as instances of node classification. Node classification on graphs has attracted increasing research interest. However, the performance of existing graph neural networks for few-shot node classification has not achieved satisfactory results due to the limitation of the number of labeled instances. Therefore, we propose a graph-weighted prototype scaling network (GWPSN) based on deep metric learning for addressing the graph few-shot node classification problem. Specifically, we first extract node representations for the attributed graph via the simplifying graph convolutional network. At the same time, learning the importance of each node in the attributed graph is used to aggregate class prototypes. Finally, the class of the test node can be predicted by comparing the scaled metric distance between the test node and the class prototype. Experiments indicate that GWPSN can achieve superior performance on three real-world datasets and thus can provide enhanced few-shot classification services for CPSS. • Extracting node representations by the simplifying graph convolutional network • A new approach to aggregating class prototypes by using a few samples of each class • First introduction of metric scaling parameters in graph few-shot node classification • Proving the effectiveness of the proposed method on three real-world datasets [ABSTRACT FROM AUTHOR]

• An EEG emotion recognition method is proposed for Cyber-Physical-Social Systems. • An emotion recognition model based on brain connectivity reservoir is established. • A training algorithm based on valence lateralization is proposed. • The proposed method improves the average recognition accuracy to 85.55 As an important application of pattern recognition, emotion recognition can make Cyber-Physical-Social Systems (CPSS) provide more efficient services for humans. In order to improve the recognition accuracy, this paper proposes an electroencephalogram (EEG) emotion recognition method based on brain connectivity reservoir (BCR) and valence lateralization (VL) for CPSS. First, for the purpose of comprehensively considering the temporality, nonlinearity, and correlation of EEG signals, an emotion recognition model based on BCR is established. Specifically, according to the connectivity index, the correlation between EEG channels is calculated to determine the brain connectivity structure of BCR, and the features of EEG signals are represented through BCR, then the classification result is obtained by the fully connected neural network according to the feature representation. Second, for the purpose of enhancing the feature representation capability of BCR, a training algorithm of BCR based on VL is proposed. Specifically, BCR is divided into two parts, i.e., the left hemi-BCR and the right hemi-BCR. These two parts are trained separately, so that the lateralization characteristic of the brain is better reflected. Finally, the experimental results on DEAP demonstrate that the proposed method achieves a recognition accuracy of 85.55% which is higher than the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Intelligent analysis of user generated content (UGC) plays an important role in ensuring urban natural gas safety and controlling process risks. However, most existing analysis methods are single-task driven and ignore the spatio-temporal information of gas sensing data. To address these problems, we propose a Transformer-based cyber-physical social security system (CPSS) for UGC analysis. Specifically, this unified system integrates multiple tasks, i.e. quality assessment and control of gas data, security factors of user consumption, and spatio-temporal abnormal gas signal detection. In the developed Transformer-based model, a time-space cross-attention module is embedded for combining the long-range spatio-temporal dependences of gas data. Moreover, a feature memory block module is introduced for abnormal feature enhancement and high-level representation of gas quality. Experimental results on related gas datasets demonstrate that this Transformer-based method achieves state-of-the-art performance, and the security system significantly improves the safety factor of natural gas use in smart cities, providing a robust framework for risk management and safety enhancement. • A Transformer based spatiotemporal assessment model is designed based on our personally annotated gas user datasets. • A Time-Space Cross Attention (TSCA) module is designed for abnormal feature enhancement and high-level representation of gas quality. • Based on the proposed Transformer, a unified CPSS security system is designed for the gridded management of gas supply chain and the cultivation of security culture. • The proposed method achieves SOTA on the gas security datasets compared with previous spatiotemporal or sequential deep learning methods. [ABSTRACT FROM AUTHOR]

Big data has been continuously generated from the rapidly developing of cloud/fog/edge computing, Internet of Things (IoT) and 5G technology. This big data not only brings great benefits and opportunities to human beings but also brings many risks and challenges. One major challenge is how to represent and treat higher-order and heterogeneous data from multi-sources. Tensors are emerging as powerful tools for representation and modeling of this data. Tensor decomposition can be used to extract potentially useful information from this data. Thus, it has attracted much attention from the big data community. The main target of this paper is to propose a novel data fusion framework to solve several main challenges of CPSS data applications, including CPSS big data representation, fusion, efficient computing, storage, robustness, and security issues. In this paper, we use many graphics to visualize complex tensor decomposition and transformation processes. The visualization may help the readers better understand tensor and tensor decomposition. It also provides a general guideline and a good starting point for those who are interested in tensor and tensor decomposition. Specifically, we first introduce the most extensively used matrix and tensor decomposition methods. Second, the current popular data fusion methods are reviewed and summarized. Third, we propose a novel tensor-network-conversion-based data fusion approach which can simultaneously analyze multiple matrices and multiple tensors. To better understand this approach, we give a brief review of tensor network. Fourth, based on the proposed approach, a novel CPSS big data fusion framework is proposed in this paper. Meanwhile, we verify it by a concise case study. Finally, some challenges and open problems of the proposed framework are discussed. The discussion also includes some exciting future research directions in the big data fusion field. • We review popular matrix decomposition, data fusion and tensor decomposition methods. • A novel Tensor-Network-Conversion-based data fusion approach (TNC) is proposed in this paper. • Our approach can simultaneously analyze multiple matrices and multiple tensors. • Based on the proposed approach, we propose a novel CPSS big data fusion framework. • A concise case study is used to verify the proposed framework. [ABSTRACT FROM AUTHOR]

The modern technological advancement influences the growth of the cyber–physical system and cyber–social system to a more advanced computing system cyber–physical–social system (CPSS). Therefore, CPSS leads the data science revolution by promoting tri-space information resource from a single space. The establishment of CPSSs increases the related privacy concerns. To provide privacy on CPSSs data, various privacy-preserving schemes have been introduced in the recent past. However, technological advancement in CPSSs requires the modifications of previous techniques to suit its dynamics. Meanwhile, differential privacy has emerged as an effective method to safeguard CPSSs data privacy. To completely comprehend the state-of-the-art developments and learn the field's research directions, this article provides a comprehensive review of differentially private data fusion and deep learning in CPSSs. Additionally, we present a novel differentially private data fusion and deep learning Framework for Cyber–Physical–Social Systems , and various future research directions for CPSSs. • We review the design requirements of differential privacy for CPSS applications. • We review the data fusion in CPSS, differentially private deep learning in CPSS. • We propose Differentially Private Data Fusion and Deep Learning Framework for CPSS. • We discuss the future research directions for CPSSs. [ABSTRACT FROM AUTHOR]

As the concept of Industries 5.0 develops, industrial metaverses are expected to operate in parallel with the actual industrial processes to offer "Human-Centric" Safe, Secure, Sustainable, Sensitive, Service, and Smartness "6S" manufacturing solutions. Industrial metaverses not only visualize the process of productivity in a dynamic and evolutional way, but also provide an immersive laboratory experimental environment for optimizing and remodeling the process. Besides, the customized user needs that are hidden in social media data can be discovered by social computing technologies, which introduces an input channel for building the whole social manufacturing process including industrial metaverses. This makes the fusion of multi-source data cross Cyber–Physical–Social Systems (CPSS) the foundational and key challenge. This work firstly proposes a multi-source-data-fusion-driven operational architecture for industrial metaverses on the basis of conducting a comprehensive literature review on the state-of-the-art multi-source data fusion methods. The advantages and disadvantages of each type of method are analyzed by considering the fusion mechanisms and application scenarios. Especially, we combine the strengths of deep learning and knowledge graphs in scalability and parallel computation to enable our proposed framework the ability of prescriptive optimization and evolution. This integration can address the shortcomings of deep learning in terms of explainability and fact fabrication, as well as overcoming the incompleteness and the challenges of construction and maintenance inherent in knowledge graphs. The effectiveness of the proposed architecture is validated through a parallel weaving case study. In the end, we discuss the challenges and future directions of multi-source data fusion cross CPSS for industrial metaverses and social manufacturing in Industries 5.0. [ABSTRACT FROM AUTHOR]

• This paper analyzes data collection and representation in CPSS and proposes to use tensors to represent CPSS data. • This paper reviews some representative data fusion methods related to CPSS. • This paper proposes a series of tensor based data fusion methods for CPSS data. • This paper reviews the design of data fusion frameworks and proposes a comprehensive data fusion framework for CPSS. Cyber-Physical-Social systems (CPSSs) are the extension of Cyber-Physical systems (CPS), which seamlessly integrate cyber space, physical space and social space. CPSSs promote the information resource from single space to tri-space, so as to lead a revolution in data science (DS). This paper aims to provide a comprehensive review of data fusion in CPSSs for readers. We firstly analyze data collection and representation in CPSS and propose to use tensors to represent CPSS data, then a general definition of CPSS data fusion is proposed to clarify the concept of information fusion in CPSS. After that, some representative data fusion methods related to CPSS are reviewed. Furthermore, we propose a series of tensor based data fusion methods for CPSS data. Also, we review the design of data fusion frameworks and propose a comprehensive data fusion framework for CPSS. Some challenges and future works are discussed as well. [ABSTRACT FROM AUTHOR]

Abstract The Cyber Physical System (CPS) has become a promising direction that can enrich the interaction between people and people, people and objects, objects and objects in the physical world and the virtual world. Because of the development of CPS, the number and types of smart devices connected to the Internet have rapidly increased, bringing with it the current issues of scalability and efficiency of the Internet of Things(IoT) network. These problems are caused by the key mechanism of large-scale distribution to the Internet of Things network. In this article, we propose CPSS inspired by software-defined networking (SDN) and service-oriented architecture, which is a powerful software-defined cyber physical system to solve the challenges of broader CPS adoption. To test CPSS, we develop a prototype system. Our experimental results show that CPSS can effectively control CPS, and CPSS has greatly improved latency and throughput compared to traditional controller-controlled CPS. [ABSTRACT FROM AUTHOR]

Although specific prognostic models for chronic myelomonocytic leukemia (CMML) exist, few are based on large series of patients. MD Anderson prognostic score (MDAPS) has been the most useful for CMML risk assessment. Due to recent emergence of CMML-specific prognostic scoring system (CPSS) and Mayo prognostic model, we compared the three scores. One hundred forty-six CMML patients diagnosed between 1998 and 2014 were retrospectively analyzed. Univariate analysis was performed to assess prognostic impact on overall survival (OS) and leukemia-free survival (LFS) of the variables composing the scores and all items showed prognostic value on OS with the exception of the presence of circulating immature myeloid cells. Regarding LFS, only CPSS variables, bone marrow blast ≥10% and an absolute monocyte count >10 × 10 9 /L had an impact. When the scores were applied, all showed an impact on OS and retained their significance in multivariate analysis. By using ROC curves and C-index, CPSS showed a slightly better predictive value for mortality and leukemia transformation. Variables composing the three indexes were compared in multivariate analysis and only CPSS parameters and platelets < 100 × 10 9 /L retained their significance. Based on these findings, by adding platelet count to CPSS, a new score was implemented ( CPSS-P ) showing the best risk prediction capability in our series. This study reinforces the validity of the tested scores. [ABSTRACT FROM AUTHOR]

Since 2001, chronic myelomonocytic leukemia (CMML) is classified by the WHO as myeloproliferative/myelodysplastic neoplasm. Herein we tried to better describe CMML patients with regard to hematological characteristics and prognosis using data of the Duesseldorf registry. We created 6 CMML subgroups, by dividing dysplastic and proliferative CMML at the cut-off of white blood cell count of 13,000/μL and splitting these two groups into 3 subgroups: CMML 0 with <5% blasts ( n = 101), CMML I with 5–9% blasts ( n = 204) and CMML II with 10–19% blasts ( n = 81). For comparison we included patients with RCMD, RAEB I and II. The newly created CMML 0 group had better prognosis than CMML I and II, median survival times were 31 months (ms), 19 ms and 13 ms, respectively ( p < 0.001). Median survival times between the corresponding dysplastic and proliferative subgroups 0 and 1 differed significantly: CMML 0 dysplastic 48 ms and CMML 0 proliferative 17 ms ( p = 0.03), CMML I dysplastic 29 ms and CMML I proliferative 15 ms ( p = 0.008), CMML II dysplastic 17 ms and CMML II proliferative 10 ms ( p = 0.09). Outcome of CMML patients worsens with increasing medullary blasts and when presenting as proliferative type. Therefore it is justified to separate CMML with <5% medullary blasts. [ABSTRACT FROM AUTHOR]

Background and Purpose: Screening scales are recommended to assist field-based triage of acute stroke patients to designated stroke centers. Cincinnati prehospital stroke scale (CPSS) is a commonly used prehospital stroke screening tool and has been validated to identify large vessel occlusion (LVO). This study addresses the impact of county-based CPSS implementation to triage suspected LVO patients to a comprehensive stroke center (CSC).Materials and Methods: Dekalb County in Atlanta, Georgia, implemented CPSS-based protocol with score of 3 and last seen normal time < 24 h mandating transfer to the nearest CSC if the added bypass time was <15 min. Frequency of stroke codes, LVO, IV-tPA use, and thrombectomy treatment were compared six months before and after protocol change (November 1, 2020).Results: During the study period, 907 stroke patients presented to the CSC by EMS, including 289 (32%) with CPSS score 3. There was an increase in monthly ischemic stroke volume (pre-16 ± 2 vs.19 ± 3 p = 0.03), LVO (pre-4.3 ± 1.7 vs. post-7.0 ± 2.4; p = 0.03), EVT (pre-15% vs. post-30%; p = 0.001), without significant increase in stroke mimic volume or delay in mean time from last seen normal to IV-tPA (pre-165 ± 66, post-158 ± 49 min; p = 0.35). CPSS score 3 was associated with increased likelihood of LVO diagnosis (OR 8.5, 95% CI 5.0-14.4; p = 0.001) and decreased the likelihood of stroke mimics (OR 0.66, 95% CI 0.50-0.88; p = 0.004).Conclusion: CPSS is a quick, easy to implement, and reliable prehospital severity scale for EMS to triage LVO to CSC without delaying IV-tPA treatment or significantly increasing stroke mimics. [ABSTRACT FROM AUTHOR]

Highlights: [•] We examined the diagnostic utility of the CPSS with the CAPS-CA. [•] CPSS reached medium effect sizes, good sensitivity, but low specificity. [•] Diagnostic inter-test agreement between the two instruments was low. [Copyright &y& Elsevier]

Abstract: In this paper, a comparative transient performance of two types of single machine power system is analyzed. The two types of system configurations are viz. (i) automatic voltage regulator (AVR) loop with single input conventional power system stabilizer (CPSS) combined with automatic generation control (AGC) loop, (ii) AVR with CPSS combined with capacitive energy storage (CES) unit-based AGC loop. For AGC loop both thermal unit and hydro unit are individually considered. The thermal unit is considered with either single or double stage reheat turbine, hydro unit is considered with mechanical or electric governor. Integral controller is provided in the AGC loop. It is shown that the CES-based integral controlled AGC loop along with CPSS assists in the best transient performance of the power system in all cases under different operating conditions. Parameters of the CPSS installed in AVR loop, gains of the integral controller in AGC loop are optimized with the help of a novel particle swarm optimization, developed by the authors, called as craziness-based particle swarm optimization (CRPSO). Transient performance is carried out with 1% step perturbation in either reference voltage or load torque. [Copyright &y& Elsevier]

Abstract: Nonlinear data reconciliation problem are inherently difficult to solve with conventional optimization methods because of the existence of a multimodal function with differentiated solutions. In this paper, the genetic algorithm (GA) of Wasanapradit [Wasanapradit, T. (2000). Solving nonlinear mixed integer programming using genetic algorithm. Master Thesis, King Mongkut University of Technology Thonburi, Bangkok, Thailand. Available: fengtcs@ku.ac.th] based on modified cross-generational probabilistic survival selection (CPSS) is explored for solving the steady state nonlinear data reconciliation (DR) problem. The DR problem is defined by a redescending estimator as the objective function, which is both a non-convex and discontinuous function. In the GA method, first the appropriate GA parameters are found and then the algorithm must be validated with the problem. The results show that the GA solves the redescending function without the complex calculations required by conventional optimization methods, but the calculation time is longer. [Copyright &y& Elsevier]

Data center is a fundamental infrastructure of computers and networking equipment to collect, store, process, and distribute huge amounts of data for a variety of applications such as Cyber–Physical–Social Systems, business enterprises and social networking. As the demands of remote data services keep increasing, both the workload of the data center and its power consumption are rapidly rising. An indispensable part of a data center is the cooling system which provides a suitable operation environment, and accounts for around 30% of the power consumption of the data center. Therefore, optimized energy management of data center's cooling system is a highly profitable research area. Generally, a cooling system is made up of a mechanical refrigeration sub-system and a terminal cooling sub-system. Heat generated during operation of the data center will be absorbed by the latter one, and transferred into the outdoor environment via the former one. Depending on the cooling principle, current cooling solutions can be classified into air-cooling, liquid-cooling or free cooling technology. Although air-cooling is widely used in most existing data centers, the other two solutions have attracted more interests due to their excellent cooling effectiveness and higher energy efficiencies. Among the different cooling equipment, the chillers and fans are the major power consumers of the entire cooling system. Therefore, modeling of their power consumption is important for energy management of the cooling system, which can be classified into mechanism-based methods and data-driven methods. Based on the aforementioned models, optimization strategies for the operation management of cooling equipment are proposed to reduce the power consumption of the cooling system, which mainly includes the model predictive control-based methods and reinforcement learning-based methods. This paper is an overview of the data center's cooling system, which mainly includes the mainstream cooling solutions, the power consumption modeling methods and the optimization control strategies. In addition, several current challenges and future work in the data center's cooling system are described. [ABSTRACT FROM AUTHOR]

• CPSS caregiver report can be used in Spanish-speaking populations • CPSS caregiver reports demonstrated adequate concurrent and convergent validity • More research is needed to improve discriminant validity of caregiver reports of youth trauma In recent years, there has been a large increase in the number of youth and families emigrating from Central America to the United States to escape extreme violence and crime victimization. As a result, the rate of trauma-exposure and trauma-related distress among this population is alarmingly high and necessitates representation of this population in psychological research, particularly related to trauma symptom measurement. The broad aim of the current study was to examine the psychometric performance of caregiver-reported data on one such instrument intended to measure youth trauma symptoms. Specifically, we sought to document the psychometric performance of the Child PTSD Symptoms Scale (CPSS) with recently immigrated Spanish-Speaking youth and caregivers from Central America by examining the convergent, divergent, and concurrent validity of the caregiver-report form. Overall, the measure showed psychometric promise as it broadly demonstrated adequate concurrent and convergent. Results also suggested that further research is needed to better understand how to reduce the comorbidity that is often depicted in caregiver reports, which can lead to poor discriminant validity. Regardless, current findings suggest the appropriateness of incorporating caregiver reports of youth trauma in Spanish-speaking, recently immigrated youth. [ABSTRACT FROM AUTHOR]

• The study sets out from user's personality influence the way in which the work environment affects designer's creativity. • A practical experiment has been performed, using 3 environments during the design process: arousing, relaxing and neutral. • Personality profiles were classified based on NEO-FFI personality test. • Results point that there is an influence between personality and work environment, mainly in achieving useful results. Creativity is one of the fundamental elements to be taken into consideration in the conceptual design phases of the development of new industrial products. It is widely agreed that every individual is creative by nature and that his or her creative capacity may be conditioned by different variables within the work environment. Yet, studies that analyse these variables often treat users in a generalised manner, without paying attention to their specific characteristics. This study sets out from the hypothesis that the user's psychological profile influences the way in which the work environment affects the designer's creativity when addressing the conceptual design phase. Thus, the aim of this study will be to determine whether developing a conceptual design proposal in a "relaxing" or an "arousing" work environment will affect all designers equally or, conversely, it will have a greater effect on some of them depending on their psychological profile. Consequently, the first stage of the study consisted in characterising each member of the control group using the NEO-FFI test. The different users were then distributed into four groups according to the results obtained. In a second phase, each designer developed conceptual proposals for a problem in each of the environments defined for the study. A neutral environment was added as a control element. Subsequently, the CPSS methodology was used to evaluate the creativity of each of the proposals designed in each of the environments. The conclusions from the study show that there is a relationship between some personality profiles and the level of creativity achieved by the designer according to the work environment in which the conceptual proposals have been developed. [ABSTRACT FROM AUTHOR]