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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]

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]

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]

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: 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]

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]

There have been large, recent increases in the number of children and families migrating from Central America to the U.S. to escape regional cartel and gang violence. The rate of trauma exposure in recently immigrated youth from Central America is therefore alarmingly high and current trauma symptom measures have not been evaluated for use in this population. The broad goal of this study was to report on the psychometric properties of one such measure in two studies of recently immigrated youth. Data collected included self-report of recent immigrants from Central America attending public high school in the Southwestern U.S. and caregiver-report from a sample of Central American immigrants who arrived in the U.S. within the last 24 hours and reported on trauma symptoms in their children. Aims included providing descriptive data and examining inter-item correlations and factor structure. Results indicated trauma symptoms far exceeding published cutoff levels as well as adequate internal consistency and inter-item correlations. Findings of the current studies suggest a pressing need for the assessment of trauma symptoms among recently immigrated youth from Central America, in which the rates of exposure and symptoms were striking. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Securing information flow is essential to methods that must ensure confidentiality, but information-flow disruption is equally important because it points to an integrity vulnerability. A proposed security model addresses both aspects, accounting for cyber-physical systems' unique confidentiality and integrity vulnerabilities. [ABSTRACT FROM PUBLISHER]

Advances in embedded systems and communication technologies--along with the availability of low-cost sensors--have led to a pervasive presence of cyber-physical systems. However, several intelligent functionalities that are necessary to meet user and application demands are missing from current solutions. A homogeneous and integrated framework supporting intelligent mechanisms can fill this gap. [ABSTRACT FROM PUBLISHER]

The cloud computing infrastructure has the power to increase the dependability, interoperability, and scalability of emerging cyber-physical systems (CPSs). Integrating intelligent agents and semantic ontologies can help manage the complexity of such systems and enable the development of large-scale CPSs. [ABSTRACT FROM PUBLISHER]

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]

Background: The emergency medical service is designed to recognize and transfer critically ill patients. Evidence-based practice has rarely been emphasized in the emergency medical service field, especially in the dispatch center. Aims: To identify the effect of the Cincinnati Prehospital Stroke Scale (CPSS) on telephone triage of stroke patients by telephone triage nurses at the emergency medical dispatch center and to compare CPSS with the National Guidelines for Telephone Triage Tool (NGTT). Methods: A quasi-empirical study was conducted from June 2013 to June 2014. The setting of the study was the Mashhad dispatch center of the EMS. Two hundred and forty-six patients were randomly allocated to the CPSS intervention group (n = 121) and the NGTT control group (n = 125). True triage, triage error and odds ratio were statistically reported. Results: The mean age of the patients was 70.9 ± 12.7 years. Of all the cases, 77.7 and 65.6% of patients in the intervention and the control groups, respectively, were accurately triaged. Under-triage cases were 10.7 and 13.6% of the patients in the intervention and the control groups. Odds ratio was 1.14 (95% confidence interval 0.62–2.07) for the CPSS compared with the NGTT. Conclusion: CPSS is more efficient for use by telephone triage nurses in identifying stroke. The use of CPSS assists nurses by reducing the triage error and supports the evidence-based care. It needs to be developed to cover signs and symptoms of posterior-circulation stroke patients. [ABSTRACT FROM AUTHOR]

In this paper, an LHCPSS based on the Pierrot cell has been theoretically studied and demonstrated experimentally. Design guidelines are given and an accurate fabrication process has been developed. Measurements of a fabricated prototype exhibit performance compliant with the requirements for space applications over a limited bandwidth. Axial ratio and insertion loss smaller than 1 and 0.5 dB, respectively, over a 7% bandwidth have been achieved at 9.8 GHz. Wider bandwidths with low axial ratio are possible by relaxing the requirements in terms of insertion loss. [ABSTRACT FROM PUBLISHER]

Our society increasingly relies on the successful marriage of digital and physical systems to perform advanced automation and control tasks. Engineering these cyber-physical systems to ensure their efficiency, security, and dependability is a research area of tremendous importance. [ABSTRACT FROM PUBLISHER]

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]

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]

The objective of this paper is to investigate the power system damping enhancement via power system stabilizers (PSSs). However, the conventional power system stabilizers (CPSSs) have certain drawbacks. There are many techniques proposed in the literature for damping improvement of low frequency power system oscillations. In this paper, adaptive neuro-fuzzy inference system (ANFIS) technology has been proposed to coordinate the CPSSs in a multi-machine power system. The time-domain simulations are carried out in Matlab/Simulink environment to validate the effectiveness of the proposed control scheme under different operating conditions. [ABSTRACT FROM AUTHOR]

The psychometric properties of the Child PTSD Symptom Scale (CPSS) were examined in 2 samples. Sample 1 (N = 185, ages 6-17 years) consisted of children recruited from hospitals after accidental injury, assault, and road traffic trauma, and assessed 6 months posttrauma. Sample 2 (N = 68, ages 6-17 years) comprised treatment-seeking children who had experienced diverse traumas. In both samples psychometric properties were generally good to very good (internal reliability for total CPSS scores = .83 and .90, respectively). The point-biserial correlation of the CPSS with posttraumatic stress disorder (PTSD) diagnosis derived from structured clinical interview was .51, and children diagnosed with PTSD reported significantly higher symptoms than non-PTSD children. The CPSS demonstrated applicability to be used as a diagnostic measure, demonstrating sensitivity of 84% and specificity of 72%. The performance of the CPSS Symptom Severity Scale to accurately identify PTSD at varying cutoffs is reported in both samples, with a score of 16 or above suggested as a revised cutoff. [ABSTRACT FROM AUTHOR]

In this study, it has been analyzed the stability of a dynamic system consist of double synchronous generators connected to infinite powerbus against small signal disturbances which is low amplitude and low frequency. In the literature, many simplified models with different degree of synchronous generator have been proposed and used for studying the dynamic stability of power systems. The most widely one is third order model of Synchronous Generator connecting to Infinite Power Bus (SGIPB) in which are taken into consideration both rotor dynamics and stator dynamics but are neglected the affect of damping windings, the stator winding resistance and the stator flux variation. In this study; t-model of synchronous generator has been generated without neglecting stator resistant and stator flux variation as different from other studies in the literature and a dynamic model of double synchronous generators infinite bus system(DSGIB) has been simulated in Matlab /Simulink environment. On the other hand, Conventional Power System Stabilizer (CPSS) is designed by using the linearized model to increase the stability of synchronous generator. In this case, the system exhibits performance loses when the operating point have change. In this study, in order to get rid of the disadvantage, a Fuzzy logic based Power System Stabilizer (FPSS) having a robust structure has been additionally designed, which the loss of performance is not seen even if system parameters such as operation point and line impedance of the power system change. In the simulations, the performances across the small signal disturbance of the proposed FPSS system has been compared to those of CPSS system and the system including only Automatic Voltage Regulator (AVR). [ABSTRACT FROM AUTHOR]

Background: Although the liver has a large regenerative capacity, in many hepatopathies, these repair mechanisms fail. The therapeutic potential of hepatocyte growth factor (HGF) has been proven in numerous toxin-induced liver failure models in rodents, but never in spontaneously occurring liver diseases in larger animal models. Aim: The aim of this study was to induce liver growth in a hypoplastic liver by the administration of exogenous recombinant HGF. The natural hypoplastic liver model used is the canine congenital portosystemic shunt (CPSS) characterized by strongly reduced liver growth and function. Methods: Recombinant HGF (rHGF), 200 mg/kg, was given twice daily during 3 weeks by an intravenous injection in six dogs with CPSS. Liver volumes were determined by computed tomography before and at 1, 2, 3 and 7 weeks after the initiation of treatment.Portosystemic shunting was evaluated with an ammonia tolerance test and liver portal perfusion was quantified with scintigraphy. Simultaneously, blood parameters for liver function were assayed and liver biopsies were taken for histology, immunohistochemistry and gene expression measurements. Results: During 3 weeks of HGF treatment, hepatocyte proliferation increased and an increase in liver volume up to 44%was seen, persisting in two dogs up to 4 weeks after the termination of treatment. Ki-67 expression, gene expression of E2F1 and CDC6, phosphorylated-c-MET and phosphorylated-ERK1/2 protein levels confirmed increased hepatocyte proliferation and HGF signalling. The aberrant portal perfusion did not change during treatment. Conclusions: Transient in vivo liver growth is shown using CPPS as a naturally occurring large animal model, indicating the therapeutic potential of HGF in liver disease. [ABSTRACT FROM AUTHOR]

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]

The identification of the model discrepancy and skill is crucial when a forecast is issued. The characterization of the model errors for different cumulus parameterization schemes (CPSs) provides more confidence on the model outputs and qualifies which CPSs are to be used for better forecasts. Cases of good/bad skill scores can be isolated and clustered into weather systems to identify the atmospheric structures that cause difficulties to the forecasts. The objective of this work is to study the sensitivity of weather forecast, produced using the PSU-NCAR Mesoscale Model version 5 (MM5) during the launch of an Indian satellite on 5th May, 2005, to the way in which convective processes are parameterized in the model. The real-time MM5 simulations were made for providing the weather conditions near the launch station Sriharikota (SHAR). A total of 10 simulations (each of 48 h) for the period 25th April to 04th May, 2005 over the Indian region and surrounding oceans were made using different CPSs. The 24 h and 48 h model predicted wind, temperature and moisture fields for different CPSs, namely the Kuo, Grell, Kain-Fritsch and Betts-Miller, are statistically evaluated by calculating parameters such as mean bias, root-mean-squares error (RMSE), and correlation coefficients by comparison with radiosonde observation. The performance of the different CPSs, in simulating the area of rainfall is evaluated by calculating bias scores (BSs) and equitable threat scores (ETSs). In order to compute BSs and ETSs the model predicted rainfall is compared with Tropical Rainfall Measuring Mission (TRMM) observed rainfall. It was observed that model simulated wind and temperature fields by all the CPSs are in reasonable agreement with that of radiosonde observation. The RMSE of wind speed, temperature and relative humidity do not show significant differences among the four CPSs. Temperature and relative humidity were overestimated by all the CPSs, while wind speed is underestimated, except in the upper levels. The model predicted moisture fields by all CPSs show substantial disagreement when compared with observation. Grell scheme outperforms the other CPSs in simulating wind speed, temperature and relative humidity, particularly in the upper levels, which implies that representing entrainment/detrainment in the cloud column may not necessarily be a beneficial assumption in tropical atmospheres. It is observed that MM5 overestimates the area of light precipitation, while the area of heavy precipitation is underestimated. The least predictive skill shown by Kuo for light and moderate precipitation asserts that this scheme is more suitable for larger grid scale (>30 km). In the predictive skill for the area of light precipitation the Betts-Miller scheme has a clear edge over the other CPSs. The evaluation of the MM5 model for different CPSs conducted during this study is only for a particular synoptic situation. More detailed studies however, are required to assess the forecast skill of the CPSs for different synoptic situations. [ABSTRACT FROM AUTHOR]

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]

A cascaded circular‐polarisation‐selective surface (CPSS) based on bisected split rings is presented. The proposed CPSS allows the transmission of one sense of circular polarisation while reflecting the other sense. To validate the proposed design, a nine‐layer prototype that operates in the Ka band has been designed, fabricated and tested. Experimental results show that transmission coefficients higher than −1 dB and axial ratios lower than 3 dB are obtained for one sense of circular polarisation, whereas transmission coefficients lower than −20 dB are obtained for the opposite sense over the frequency band from 26.39 to 35.43 GHz. [ABSTRACT FROM AUTHOR]