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Summary: In this article, the issue of resilient filtering for descriptor nonhomogeneous Markovian jump cyber‐physical systems under hybrid cyber‐attacks has been studied. First, occurrences of both deception attacks and replay attacks are modeled as two mutually independent Bernoulli distributed random variables. To depress the ratio of sampled data transmissions over the network, the dynamic version of event‐triggered transmission policy is properly proposed. Then, criteria of stochastically admissible for the filtering error systems guaranteeing mixed H∞$$ {H}_{\infty } $$ and passive performance are addressed via linear matrix inequalities (LMIs). The gains of resilient filter and the dynamic event‐based transmission policy can be simultaneously gained by optimizing the LMIs. Finally, two examples that include a numerical example and a direct current motor system are provided to testify the feasibility and effectiveness of the developed technique. [ABSTRACT FROM AUTHOR]

Cyber-Physical-Social Systems (CPSS) bring together an assortment of resources from the physical network, cyberspace, and the social sphere. These systems rely on communication, computation, and control infrastructures, which usually comprise several tiers across the three realms, utilizing diverse resources such as sensors, actuators, computational resources, services, and human involvement. The proficient interaction of these resources is critical for the operational efficacy of cyber-physical-social systems. CPSS exhibits resource constraints, openness, and uncertainty, resulting in high uncertainty in its services, including quality of service (QoS) provision and significant security risks. Consequently, an efficient and dependable service composition approach is crucial when dealing with complex service requirements. Furthermore, preference learning plays a pivotal role in enhancing user experiences. Trust is viewed as a barrier to the integration of social attributes among smart objects, which is essential for forming reliable social connections and delivering dependable services. This review offers an in-depth analysis of trustworthiness management within Cyber-Physical Social Systems (CPSS). [ABSTRACT FROM AUTHOR]

Abstract Background Congenital portosystemic shunt (CPSS) is a vascular malformation in which portal blood drains toward the systemic circulation, leading to pulmonary hypertension. Case presentation A 10-year-old patient was brought for evaluation because of dyspnea on exertion. Echocardiography revealed a pulmonary hypertension of 75 mmHg, and multi-slice CT angiography revealed the presence of a CPSS. Closure was finally implemented using a muscular ventricular septal defect device. Follow-up of the patient revealed a gradual decline in pulmonary hypertension. Conclusions CPSS is an overlooked cause of reversible pulmonary hypertension (PH). Closure of such lesions and reversal pulmonary hypertension are possible via catheterization. The preferred device type depends largely on the intervening team. Plugs are the first choice for interventional radiologists, while ventricular and atrial septal occluder devices and duct occluders are preferred by pediatric cardiologists.

Vibrio parahaemolyticus, the leading cause of seafood-associated gastroenteritis, has a strong capacity to form biofilms on surfaces, which is strictly regulated by the CpsS–CpsR–CpsQ regulatory cascade. OpaR, a master regulator of quorum sensing, is a global regulator that controls multiple cellular pathways including biofilm formation and virulence. QsvR is an AraC-type regulator that works coordinately with OpaR to control biofilm formation and virulence gene expression of V. parahaemolyticus. QsvR and OpaR activate cpsQ transcription. OpaR also activates cpsR transcription, but lacks the detailed regulatory mechanisms. Furthermore, it is still unknown whether QsvR regulates cpsR transcription, as well as whether QsvR and OpaR regulate cpsS transcription. In this study, the results of quantitative real-time PCR and LacZ fusion assays demonstrated that deletion of qsvR and/or opaR significantly decreased the expression levels of cpsS and cpsR compared to the wild-type strain. However, the results of two-plasmid lacZ reporter and electrophoretic mobility-shift assays showed that both QsvR and OpaR were unable to bind the regulatory DNA regions of cpsS and cpsR. Therefore, transcription of cpsS and cpsR was coordinately and indirectly activated by QsvR and OpaR. This work enriched our knowledge on the regulatory network of biofilm formation in V. parahaemolyticus. [ABSTRACT FROM AUTHOR]

Background: Congenital portosystemic shunt (CPSS) is a vascular malformation in which portal blood drains toward the systemic circulation, leading to pulmonary hypertension. Case presentation: A 10-year-old patient was brought for evaluation because of dyspnea on exertion. Echocardiography revealed a pulmonary hypertension of 75 mmHg, and multi-slice CT angiography revealed the presence of a CPSS. Closure was finally implemented using a muscular ventricular septal defect device. Follow-up of the patient revealed a gradual decline in pulmonary hypertension. Conclusions: CPSS is an overlooked cause of reversible pulmonary hypertension (PH). Closure of such lesions and reversal pulmonary hypertension are possible via catheterization. The preferred device type depends largely on the intervening team. Plugs are the first choice for interventional radiologists, while ventricular and atrial septal occluder devices and duct occluders are preferred by pediatric cardiologists. [ABSTRACT FROM AUTHOR]

针对传统戏剧工作流程依赖于专家经验和人力劳动的问题，戏剧行业亟需从依赖个人经验和传统技巧的模式转变为基于智能技术和数据驱动的模式，以提升创作和运营效率、丰富艺术表现，从而更好地满足观众多样化的需求。为此，提出平行戏剧架构及系统，旨在实现人机协同创作和对戏剧工作全流程的智能管理。通过构建戏剧大模型、搭建戏剧计算实验平台、打造戏剧虚实闭环，平行戏剧将助力剧本创作、舞美设计、演出安排、观众互动以及市场营销等关键戏剧环节，实现自动化和智能化的创意设计与执行。此外，探讨了平行戏剧在教育和心理治疗的跨领域应用前景，分析了其在促进戏剧行业与其他领域融合、拓宽戏剧艺术影响力方面的潜力。作为一种创新的戏剧创作和管理模式，平行戏剧将为戏剧行业在智能时代的发展带来全新可能。

新疆夏尔希里自然保护区是一处珍稀生物资源与无法估量的生态价值共融的自然瑰宝，被誉为“中国最后的净土”和“珍稀的天然基因库”。为确保这一自然宝库的可持续发展，提出了“平行夏尔希里”概念，以智能化手段全方位助力其生态资源的精细化管理。核心思想是利用描述智能建立高精度模型，通过预测智能分析生态系统变化，借助引导智能科学地制定保护策略。探索了通过智能科技建立自然保护区管理与应用的新模式，以期推动保护区及其珍贵生物资源的可持续发展。

Parallel manufacturing is a new manufacturing form in industry, deeply integrating informalization, automation, and AI. In this paper we illustrated the procedure of non-standard mechanical design in parallel manufacturing, claiming it a nested parallel system. We proposed the ACP method based on the standard procedure and the emulated procedure, and defined the social-value-vector and trended-social-value-matrix so as to take the advantage of data in CPSS. We also presented Xinglun robot speed reducer as a case study of non-standard mechanical design in parallel design.

Introduction: Chronic pain after spinal surgery (CPSS), formerly known as failed back surgery syndrome, encompasses a variety of highly incapacitating chronic pain syndromes emerging after spinal surgery. The intractability of CPSS makes objective parameters that could aid classification and treatment essential. In this study, we investigated the use of cerebral diffusion-weighted magnetic resonance imaging. Methods: Cerebral 3T diffusion-weighted (DW-) MRI data from adult CPSS patients were assessed and compared with those of healthy controls matched by age and gender. Only imaging data without relevant artefacts or significant pathologies were included. Apparent diffusion coefficient (ADC) maps were calculated from the b0 and b1000 values using nonlinear regression. After skull stripping and affine registration of all imaging data, ADC values for fifteen anatomical regions were calculated and analyzed with independent samples T-tests. Results: A total of 32 subjects were included (sixteen CPSS patients and sixteen controls). The mean ADC value of the spinothalamic tract was found to be significantly higher in CPSS patients compared with in healthy controls (p = 0.013). The other anatomical regions did not show statistically different ADC values between the two groups. Conclusion: Our results suggest that patients suffering from CPSS are subject to microstructural changes, predominantly within the cerebral spinothalamic tract. Additional research could possibly lead to imaging biomarkers derived from ADC values in CPSS patients. [ABSTRACT FROM AUTHOR]

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]

Copyright of Chinese Journal of Intelligent Science & Technology is the property of Beijing Xintong Media Co., Ltd. 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.)

Abstract Background Congenital portosystemic shunts (CPSS) are rare vascular malformations and can be classified into extrahepatic and intrahepatic shunts. Extrahepatic CPSS, also termed Abernethy malformations are associated with severe long-term complications including portopulmonary hypertension, liver atrophy, hyperammoniemia and hepatic encephalopathy. We report a hitherto undescribed variant of Abernethy malformation requiring an innovative approach for interventional treatment. Case presentation We describe a 31-year-old patient following surgical repair of atrioventricular septal defect at the age of 6 years. In the long-term follow-up he showed persistent pulmonary hypertension which deteriorated despite dual pulmonary vasodilative treatment. When he developed arterial desaturation and symptomatic hyperammoniemia detailed reassessment revealed as underlying cause a hitherto undescribed variant of Abernethy malformation connecting the portal vein with the right lower pulmonary vein. Following interdisciplinary discussions we opted for an interventional approach. Since the malformation was un-accessible to interventional closure via antegrade venous or retrograde arterial access, a transhepatic percutaneous puncture of the portal vein was performed. Temporary balloon occlusion of the malformation revealed only a slight increase in portal venous pressure. Interventional occlusion of the large vascular connection was achieved via this transhepatic approach by successive implantation of two large vascular occluding devices. The postinterventional course was unremarkable and both ammonia levels and arterial saturation normalized at follow-up of 12 months. Conclusions Portal vein anomalies should be included in the differential diagnoses of pulmonary hypertension or pulmonary arterio-venous malformations. Based on careful assessment of the anatomy and testing of portal vein hemodynamics interventional therapy of complex Abernethy malformations can be performed successfully in specialized centers.

This research examined factors influencing levels of secondary traumatic stress (STS) in non-offending caregivers (NOCs) of children with histories of sexual or physical abuse. These factors included the nature of the abuse, NOCs' relationships with the initiators of the abuse, children's ages and genders, NOCs' trauma histories, and the elapsed time between children's disclosures of abuse and their trauma assessments. As a secondary objective, this research examined the interactions between children's self-reports of their own posttraumatic stress disorder (PTSD) symptomatology, NOCs' estimates of their children's PTSD symptomatology, and NOCs' self-reports of their own STS symptomatology. Participants from a clinical sample (N = 300, children = 150, NOCs = 150; child age M = 9.89, SD = 4.08; NOC age M = 37.87, SD = 9.23) completed structured intake interviews, the PTSD Checklist for the DSM-5 (PCL-5) and the Child PTSD Symptom Scale (CPSS; Child-Report and NOC-Report). Analytic strategies included point-biserial correlation coefficient calculations, linear regression analyses, and Analyses of Covariance (ANCOVAs). NOCs' levels of STS were impacted by their relationships with the initiators of the abuse and their own trauma histories. NOCs' self-reported STS symptomatology mirrored their estimates of their children's PTSD symptomatology. The discrepancy scores between children's self-reports of their PTSD symptomatology and NOCs' estimates of children's PTSD symptomatology were impacted by children's ages and genders. Clinical practitioners should note the importance of examining children's PTSD symptomatology and NOCs' STS symptomatology concurrently when making recommendations for trauma-informed evidence-based treatments. [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]

Background: Phenotypically identified Candida parapsilosis is actually a complex of 3 member species named Candida parapsilosis sensu stricto (CPSS), Candida orthopsilosis (CO), and Candida metapsilosis (CM), which can be identified only by molecular methods and automated methods such as MALDI-TOF mass spectrometry (MS). This study was undertaken to evaluate the VITEK MS, which uses the principle of MALDI-TOF MS for the identification of member species of C. parapsilosis complex (CPC). Methods: In this cross-sectional study, 126 presumptively identified and stocked isolates of CPC were included. Definite identification to species level was done by VITEK MS and PCR as the gold standard method. Clinico-demographic characters and risk factors were analyzed. Antifungal susceptibility testing was performed for fluconazole and voriconazole. Results: Twelve isolates were not identified as CPC either by VITEK MS or PCR and hence were excluded from the analysis. Out of 114 CPC isolates, 89 (78.1%), 18 (15.8%), and 7 (6.1%) isolates were identified as CPSS, CO, and CM, respectively, by VITEK MS. PCR identified 84 (79.2%), 15 (14.2%), and 7 (6.6%) isolates as CPSS, CO, and CM, respectively. However, PCR did not detect 8 isolates of CPSS detected by VITEK MS. VITEK MS showed 95.3% agreement in species identification and showed a kappa coefficient of 0.87, which is almost perfect agreement. Predominant isolations of all 3 species were from blood. Resistance was observed more in CPSS for both the azoles. Conclusion: MALDI-TOF MS can be used as a rapid, reliable, cost-effective method to identify the species of CPC. [ABSTRACT FROM AUTHOR]

Introduction In our previous study, we have developed the Child Psychosis-risk Screening System (CPSS), which incorporates psychological and behavioral characteristics of childhood into an algorithm, based on a retrospective survey. Objectives In this study, we actually tried to evaluate the risk of psychosis in pediatric and psychiatric outpatients using the CPSS. Methods We conducted an epidemiological study of 323 outpatients aged 6-18 years visiting pediatric and psychiatric departments using CBCL and clinical data (sex, age, winter birth, chief complaint, diagnosis, abuse, bullying, hikikomori). ROC analysis was used to assess the accuracy of CPSS predictions. Cross-sectional logistic regression analysis was performed on the clinical data to identify factors associated with risk groups exceeding the cutoff value. Results The results of the ROC analysis showed that the AUC (Area under the ROC Curve) was 80.3%, indicating that the CPSS has Moderate accuracy. The cutoff value was 98.11% (sensitivity: 0.857, specificity: 0.835), and 18% of the subjects were identified as risk groups above this value. Cross-sectional logistic regression analysis showed that schizophrenia diagnosis, no abuse, winter birth, and hikikomori were associated with the risk group, with respective odds ratios of 22.88, 10.76, 1.91, and 1.37. Conclusions The results of this study suggest that the CPSS can be applied to pediatric practice for early detection of risk for psychosis. The risk group is also present among pediatric patients with physical chief complaints. The factors suggested to be associated with risk groups may reflect the factors acting on the critical period of psychosis onset and the dynamic state. Disclosure No significant relationships.

Background and purposes: Stroke severity scales may expedite prehospital large vessel occlusion (LVO) stroke detection, but few are validated for paramedic use. We evaluated the feasibility of introducing the Cincinnati Stroke Triage Assessment Tool (C-STAT) in the field and its capacity to detect LVO stroke. Methods: We performed a prospective paramedic-based study assessing C-STAT in the field on patients currently redirected to two comprehensive stroke centers (CSC), based on a Cincinnati Prehospital Stroke Scale (CPSS) score of 3/3. C-STAT was administered by on-site paramedics with telephone guidance from trained centralized clinical support paramedics. Results: Between October 2018 and November 2019, C-STAT scores were obtained in 188/218 (86.2%) patients, among which 118/188 (62.8%) were positive. Paramedics reported performing the C-STAT in less than 5 minutes on 170/188 (90.4%) patients and noted no difficulties administering the scale in 151/188 (80.3%). A positive C-STAT identified 51/68 (75%) LVO strokes in the cohort, demonstrating a 43% (95% CI: 38%–48%) positive and 76% (95% CI: 66%–83%) negative predictive value for LVO stroke diagnosis. In a cohort of 100 patients with CPSS 3/3, requiring a positive C-STAT for redirection would decrease CSC patient volume by 37 but miss 9 of 36 LVO strokes. Conclusion: Prehospital administration of the C-STAT was feasible, using a model of minimal paramedic training and real-time telephone guidance. A protocol based on both a CPSS 3/3 and a positive C-STAT would decrease CSC redirected patient volume by one-third but would miss one-quarter of LVO strokes when compared to a CPSS-based protocol. [ABSTRACT FROM AUTHOR]

Summary: 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 device connected to the Internet has rapidly increased, bringing with it the current issues of flexibility, efficiency, availability, security, and scalability of the Internet‐of‐Things (IoT) network. These problems are caused by the key mechanism of large‐scale distribution to the IoT 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. CPSS has three complementary modules: application‐as‐a‐service (AAAS), network‐as‐a‐service (NAAS), and infrastructure‐as‐a‐service (IAAS). IAAS collects network information from a different set of controllers and devices and generates a domain‐wide network view. NAAS collects information from different domains and generates a global view, which helps data transmission between different domains and enables seamless switching between applications and infrastructure devices. AAAS provides personalized service for each user. To test CPSS, we develop a prototype system. Our experimental results show that CPSS can effectively control cyber‐physical system, and CPSS has greatly improved network latency, network throughput, and network reliability compared to traditional controller‐controlled CPS. [ABSTRACT FROM AUTHOR]

Copyright of Hungarian Medical Journal / Orvosi Hetilap is the property of Akademiai Kiado 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.)

Background: Congenital portosystemic shunts (CPSS) are rare vascular malformations and can be classified into extrahepatic and intrahepatic shunts. Extrahepatic CPSS, also termed Abernethy malformations are associated with severe long-term complications including portopulmonary hypertension, liver atrophy, hyperammoniemia and hepatic encephalopathy. We report a hitherto undescribed variant of Abernethy malformation requiring an innovative approach for interventional treatment.Case Presentation: We describe a 31-year-old patient following surgical repair of atrioventricular septal defect at the age of 6 years. In the long-term follow-up he showed persistent pulmonary hypertension which deteriorated despite dual pulmonary vasodilative treatment. When he developed arterial desaturation and symptomatic hyperammoniemia detailed reassessment revealed as underlying cause a hitherto undescribed variant of Abernethy malformation connecting the portal vein with the right lower pulmonary vein. Following interdisciplinary discussions we opted for an interventional approach. Since the malformation was un-accessible to interventional closure via antegrade venous or retrograde arterial access, a transhepatic percutaneous puncture of the portal vein was performed. Temporary balloon occlusion of the malformation revealed only a slight increase in portal venous pressure. Interventional occlusion of the large vascular connection was achieved via this transhepatic approach by successive implantation of two large vascular occluding devices. The postinterventional course was unremarkable and both ammonia levels and arterial saturation normalized at follow-up of 12 months.Conclusions: Portal vein anomalies should be included in the differential diagnoses of pulmonary hypertension or pulmonary arterio-venous malformations. Based on careful assessment of the anatomy and testing of portal vein hemodynamics interventional therapy of complex Abernethy malformations can be performed successfully in specialized centers. [ABSTRACT FROM AUTHOR]

The Clinical Ethics Center was asked to discuss the "ethics" of the prophylactic treatment of portosystemic shunts (CPSS). In order to meet this demand, semi-structured interviews were conducted with patients, parents, and doctors by a multidisciplinary team including laypersons. Doctors' perspectives seem opposed to patients' and parents' perspectives. Doctors wonder if they operate on CPSS too much or too early. Their questions arise from medical uncertainty. Patients and especially parents trust doctors but want to understand the situation to be able to take the best decision. They prefer CPSS to be cured in order to be relieved of the sword of Damocles. If international collegiality can help practitioners discuss therapeutic alternatives, the ethical reflection should take place more locally. In practice, ethics acting as a third party can help reflect on particular situations, taking into account psycho-socio-familial factors to decide whether to operate or not, now or later. [ABSTRACT FROM AUTHOR]

In this brief, we investigate the Cyber-Physical Systems (CPSs) with measurement channels under Denial-of-Service (DoS) attacks. An equivalent predictive control system model and attack model are constructed firstly. To compensate for the data loss due to the attacks, the predictive controller is designed based on linear quadratic form. Furthermore, through the design of an observer-based output predictor, a novel resilient observer-based predictive control framework is presented to improve the robustness of the LQ controller. By constructing the closed-loop system with proposed controller, the states of the control system are theoretically proved to be asymptotically stable under the influence of DoS attacks. Finally, the efficacy of the proposed control strategy is illustrated through a comparative simulation example. [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]

Timely response to changes of monitored objects is the key to ensuring the safety and reliability of cyberphysical systems (CPSs). There are two kinds of tasks in CPSs: update tasks and control tasks. Update tasks are responsible for updating the data in the system based on the state of the objects they monitor. Control tasks are responsible for making decisions based on the data in the system. The response time of the system to the change of a monitored object consists of two parts: the time taken by update tasks to reflect the change to the system, and the time taken by control tasks to make decisions according to the data in the system. Deadlines and periods of update tasks and control tasks directly affect the response time. Reasonable deadline and period assignment is the key to ensuring timely response to the changes of monitored objects. In this paper, we study the deadline and period assignment in CPSs. To the best of our knowledge, all existing work only focuses on the deadline and period assignment for update tasks with the goal of ensuring the freshness of the data in CPSs, and this is the first study focusing on the deadline and period assignment for both update tasks and control tasks with the goal of ensuring timely response to the changes of monitored objects. A new problem about response time control and system workload control is defined in this paper. Two deadline and period assignment methods are proposed to solve the defined problem. All the proposed methods can be used in the CPSs adopting the earliest deadline first (EDF) scheduling method. Experiments with randomly generated tasks are conducted to evaluate the performance of the proposed methods in terms of acceptance ratio and execution efficiency. [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]

The k-nearest neighbor (kNN) algorithm is a classic supervised machine learning algorithm. It is widely used in cyber-physical-social systems (CPSS) to analyze and mine data. However, in practical CPSS applications, the standard linear kNN algorithm struggles to efficiently process massive data sets. This paper proposes a distributed storage and computation k-nearest neighbor (D-kNN) algorithm. The D-kNN algorithm has the following advantages: First, the concept of k-nearest neighbor boundaries is proposed and the k-nearest neighbor search within the k-nearest neighbors boundaries can effectively reduce the time complexity of kNN. Second, based on the k-neighbor boundary, massive data sets beyond the main storage space are stored on distributed storage nodes. Third, the algorithm performs k-nearest neighbor searching efficiently by performing distributed calculations at each storage node. Finally, a series of experiments were performed to verify the effectiveness of the D-kNN algorithm. The experimental results show that the D-kNN algorithm based on distributed storage and calculation effectively improves the operation efficiency of k-nearest neighbor search. The algorithm can be easily and flexibly deployed in a cloud-edge computing environment to process massive data sets in CPSS.

Given two object sets P and Q, a k-closest pairs (k-CP) query finds k closest object pairs from P×Q. This operation is common in many real-life applications such as GIS, data mining and recommender systems. However, the k-CP problem has not been well studied in Dynamic Cyber-Physical-Social Systems (D-CPSS), where temporal information and multiple attributes are associated with each edge. In D-CPSS, people would like to specify multiple constraints on these attributes within a time interval to illustrate their requirements. In this paper, we study the temporal multiple constraints k closest pairs (TMC-k-CP) in D-CPSS, which is NP-Complete. We propose a divide-and-conquer cloud-based algorithm (DC) to find TMC-k-CP efficiently and effectively. To the best of our knowledge, DC is the first algorithm supporting the TMC-k-CP query in D-CPSS. The experimental results on eight real D-CPSS datasets demonstrate that our algorithm outperforms the state-of-the-art methods in terms of both efficiency and effectiveness.

The emergence of cyber-physical-social systems (CPSS) as a novel paradigm has revolutionized the relationship between humans, computers and the physical environment. CPSS extend cyber-physical systems (CPS) to include the social domain, which introduces a challenge of massive data processing. As a typically giant CPS, the Five-hundred-meter Aperture Spherical radio Telescope (FAST), the world's largest filled-aperture radio telescope, generates massive volume of data which poses a huge storage problem that CPSS face likewise and requires real-time data compressing to reduce data storage and movement overhead. The recently introduced Bitshuffle preprocessing algorithm is a novel approach towards exploiting spatial redundancy incorporation to improve the compression ratio with a specific compressor. However, the existing high-performance CPU-based solutions cannot satisfy the performance requirement and power budget requirement simultaneously. In the paper, we propose the implementation of this algorithm on Field Programmable Gate Array (FPGA) and present an unique data transformation strategy to turn raw FAST data in classic FITS format into another format to support huge file sizes, i.e. Hierarchical Data Format (HDF5). Evaluation results show that our implementation can achieve 3.2Gbyte/s throughput which can be equipped with LZ4 compressor to be high performance compressor. This makes Bitshuffle on FPGAs a candidate for meeting the computational and energy efficiency constraints of radio telescopes and provide reference for CPSS facing the same situation.

The rise of cyber-physical-social systems (CPSS) as a novel paradigm has revolutionized the relationship among humans, computers and physical environment. The key technologies to design CPSS directly related to multi-disciplinary technologies including cyber-physical systems (CPS) and cyber-social systems (CSS). Unfortunately, the design of CPSS is not an easier process because of the network heterogeneity, complex hardware and software entities. At the same time, fog computing is emerged as an expansion of cloud computing which efficiently addresses the abovementioned issue. Resource provisioning is a main technology involved in fog computing. This paper devises a novel fuzzy clustering with flower pollination algorithm called FCM-FPA as a resource provisioning model for fog computing. At the earlier stage, the resource attributes are standardized and normalized. Next, the fuzzy clustering with FPA is developed for partitioning the resources and the scalability of resource searching has been minimized. At last, the presented resource provisioning algorithm based on optimized fuzzy clustering has been devised. The performance of the proposed FCM-FPA model has been tested using a set of two benchmark Iris and Wine dataset. The experimental outcome ensured that the FCM-FPA model has shown proficient results over the compared methods by offering maximum user satisfaction and effective resource provisioning.

When planning the trajectory of a mobile robot, it is usually necessary to use sensors to collect a large amount of position information. Because traditional computing methods cannot effectively use huge data sources, an edge computing that implements edge intelligent services on the side of network edge near the data source is proposed, which speeds up the process of data processing. However, the data collected through sensors may contain gross errors. In general, the influence of gross errors on state estimation are rarely considered when using particle filter algorithms for state estimation. In fact, the measurements of dynamic systems are often affected by different types of gross errors in the actual application process. Therefore, it is a problem worth studying that how to detect and compensate for different types of gross errors. In this paper, an improved particle filter algorithm is proposed for the position estimation of mobile robot dynamic system. Firstly, the gross error identification method is used to identify the types of gross errors, and then the various gross errors are compensated. Finally, the particle filter algorithm based on the measurements compensation is obtained. Simulation experiments on the position estimation of mobile robots are carried out to verify the effectiveness of the proposed method in solving the measurements with gross errors. The precise position estimation of the mobile robot is achieved. Through the simulation experiments on the position estimation problem of mobile robots, it is verified that the proposed method is effective in solving the measurements with gross errors. And the accurate position estimation of the mobile robot is realized.

Existing intrusion detection and defense models for CPSS (Cyber-Physical-Social Systems) are based on analyzing the static intrusion characteristics, which cannot effectively detect large-scale Low-Rate Denial-of-Service (LR-DDoS) attacks, especially in the edge environment. In this paper, we firstly explore and enhance Mirai botnet to a sophisticated multi-targets low-rate TCP attack network, which makes edge LR-DDoS more powerful and obfuscates their activity. And then, we develop a novel intrusion detection and defense hybrid method for above CPSS LR-DDoS scenario in edge environment, which takes advantage of locality sensitive features extraction and Deep Convolution Neural Network (DCNN) to auto learn the optimal features of the original data distribution and employs deep reinforcement learning Q-network as the powerful decision maker to defend attacks. The experimental results in detection phase prove the proposed method can distinguish abnormal network attack flows with higher detection accuracy and faster response time than kinds of Support Vector Machines (SVM), K-means and Surface Learning Neural Network etc. Even more, it has a certain detection rate for unknown new attacks, which means the method is effective and suitable for the actual network environment. The experimental results in defense phase prove it can defense LR-DDoS attacks smoothly.

Cyber-Physical-Social System (CPSS) big data is specified as the global historical data which is usually stored in cloud, the local real-time data which is usually stored in the fog-edge server (FeS) of the mobile terminal devices or sensors, and the social data which is usually stored in the social data server (SdS), moreover adopts a centralized access control mechanism to offer users' access strategy which can easily cause CPSS big data to be tampered with and to be leaked. Therefore, a blockchain-based access control scheme called BacCPSS for CPSS big data is proposed. In BacCPSS, account address of the node in blockchain is used as the identity to access CPSS big data, the access control permission for CPSS big data is redefined and stored in blockchain, and processes of authorization, authorization revocation, access control and audit in BacCPSS are designed, and then a lightweight symmetric encryption algorithm is used to achieve privacy-preserving. Finally, a credible experimental model on EOS and Aliyun cloud is built. Results show that BacCPSS is feasible and effective, and can achieve secure access in CPSS while protecting privacy.

Cloud computing has emerged as a promising paradigm for the Internet of Things (IoT) and Cyber-Physical-Social Systems (CPSS). However, the problem of how to ensure the security of data transmission and data storage in CPSS is a key issue to address. We need to protect the confidentiality and privacy of users' data and users' identity during the transmission and storage process in CPSS. In order to avoid users' personal information leakage from IoT devices during the process of data processing and transmitting, we propose a certificateless encryption scheme, and conduct a security analysis under the assumption of Computational Diffie-Hellman(CDH) Problem. Furthermore, based on the proposed cryptography mechanism, we achieve a novel anonymous communication protocol to protect the identity privacy of communicating units in CPSS. In the new protocol, an anonymous communication link establishment method and an anonymous communication packet encapsulation format are proposed. The Diffie-Hellman key exchange algorithm is used to construct the anonymous keys distribution method in the new link establishment method. And in the new onion routing packet encapsulation format, the session data are firstly separated from the authentication data to decrease the number of cryptography operations. That is, by using the new onion routing packet we greatly reduces the encryption operations and promotes the forwarding efficiency of anonymous messages, implementing the privacy, security and efficiency in anonymous communication in cyber-physical-social systems.

Security issues of cyber-physical systems have become a timely and hot topic in recent years. In this brief, we investigate the power-constrained optimal denial-of-service (DoS) attack strategy with multiple power levels on state estimation over infinite-time horizon. We start from the two-level case and explicitly derive out the basic form of optimal attack strategy. In order to handle the intractability brought about by the larger number of levels, we develop an equivalent property for general multi-level cases that the multi-level scheduling can be viewed as a feasible combination of several sub-schedules with a designed stochastic-triggered manner, which provides a necessary condition of optimal solution. Moreover, based on the practical communication model, we give the condition under which the optimal schedule can be uniquely and explicitly determined for the two-pick strategy. Numerical simulations are accomplished to verify the effectiveness of our proposed methods. [ABSTRACT FROM AUTHOR]

Recent studies have shown that due to the hammer effect of the governor, hydropower units are easily creating negative damping torque at the common mode frequency (below 0.1 Hz). Therefore, there is a risk of ultra low frequency oscillations (ULFO) in hydropower-dominated systems. ULFO is a small-signal frequency oscillation problem, which is quite different from low frequency oscillations (LFO). A conventional power system stabilizer (CPSS) has less effect on suppressing ULFO. To solve this problem, this paper proposes a high-order polynomial structure to replace the CPSS, and combine it with a proportional resonance controller to form a novel PR-PSS. In order to ensure the robustness of PR-PSS, based on the characteristic analysis results of the PR-PSS, a deep reinforcement learning (DRL) algorithm asynchronous advantage actor-critic (A3C) is introduced to train an agent. After training, the proposed agent can provide optimal parameter settings for PR-PSS under various operating conditions. Simulation results verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Introduction: Chronic myelomonocytic leukemia (CMML) is a hematological malignancy that is extremely variable regarding its clinical course. It may present either as a chronic disorder with mild symptoms and low disease burden for several years, thereby justifying a watch-and-wait-strategy, or may soon progress to acute myeloid leukemia (AML) leaving allogeneic stem cell transplantation as the only curative treatment option.Areas Covered: Attempts have been made to integrate clinical, cytogenetic, and molecular parameters into scoring systems aiming at providing reliable prognostic information. In this article, we discuss several prognostic parameters and validate prognostic scores in a cohort of 645 patients with CMML.Expert Opinion: We show that the CPSS (CMML prognostic scoring system) is a useful prognostic tool. The integration of molecular data into the new CPSS-mol will further improve prognostic accuracy, primarily by identifying an increased proportion of higher-risk patients. [ABSTRACT FROM AUTHOR]

Abstract We propose to use an mean square error (MSE) transfer chart to evaluate the performance of the proposed belief propagation (BP)-based channel decoding and state estimation scheme. We focus on two models to evaluate the performance of BP-based channel decoding and state estimation: the sequential model and the iterative model. The numerical results show that the MSE transfer chart can provide much insight about the performance of the proposed channel decoding and state estimation scheme.

With the development of information technology, designing urban experience faces an unprecedented challenge: the combination of cyber information and physical facilities. Cyber-Physical Social System (CPSS) forms the connection between virtual and physical objects, public and private, activeness and passiveness, leading an innovative way to explore human-centred design practice in the context of city and community. The challenges faced by urban communities and extend the design practice of SPSS in combination with public participation community design and Living Lab method. Through three practical cases and related evaluation feedbacks, this study presents a Cyber-Physical Public Design (CPPD) model of a human city. The CPPD model is constructed based on CPSS with the human-centred concept. By combining physical touch points with virtual information world, it is possible to collect data on residents’ lives and improve the city's public facilities and space design, rebuild the urban communities to better meet the needs of the public.

This study proposes a stochastic Petri net model for evaluating the security and resilience of cyber-physical systems (CPSs) in the face of malicious attacks. The basic idea behind the proposed model is to evaluate the security of control loops equipped with intrusion detection systems (IDSs) faced with security attacks. The quantitative analysis is performed in terms of system-focused quantitative security measures, such as mean time-to-failure and availability. By using this model, one can investigate the effects of some attacks and defensive parameters, including the detection interval, the time to physical disruption, and the false-positive probability of IDSs. This evaluation results can help to improve the security countermeasures of CPSs.