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Background: The management of patients with disorders of consciousness (DOC) presents substantial challenges in clinical practice. Deep brain stimulation (DBS) has emerged as a potential therapeutic approach, but the lack of standardized regulatory parameters for DBS in DOC hinders definitive conclusions., Objective: This comprehensive review aims to provide a detailed summary of the current issues concerning patient selection, target setting, and modulation parameters in clinical studies investigating the application of DBS for DOC patients., Methods: A meticulous systematic analysis of the literatures was conducted, encompassing articles published from 1968 to April 2023, retrieved from reputable databases (PubMed, Embase, Medline, and Web of Science)., Results: The systematic analysis of 21 eligible articles, involving 146 patients with DOC resulting from acquired brain injury or other disorders, revealed significant insights. The most frequently targeted regions were the Centromedian-parafascicular complex (CM-pf) nuclei and central thalamus (CT), both recognized for their role in regulating consciousness. However, other targets have also been explored in different studies. The stimulation frequency was predominantly set at 25 or 100 Hz, with pulse width of 120 μs, and voltages ranged from 0 to 4 V. These parameters were customized based on individual patient responses and evaluations. The overall clinical efficacy rate in all included studies was 39.7%, indicating a positive effect of DBS in a subset of DOC patients. Nonetheless, the assessment methods, follow-up durations, and outcome measures varied across studies, potentially contributing to the variability in reported efficacy rates., Conclusion: Despite the challenges arising from the lack of standardized parameters, DBS shows promising potential as a therapeutic option for patients with DOC. However, there still remains the need for standardized protocols and assessment methods, which are crucial to deepen the understanding and optimizing the therapeutic potential of DBS in this specific patient population., (© 2023 The Authors. CNS Neuroscience & Therapeutics published by John Wiley & Sons Ltd.)

Combatting climate change depends on demand-side mitigation strategies related to food, which is in turn contingent on explicit estimation and management of dish-level emissions. Here, on the basis of a bottom-up integrated emissions framework, we first estimate the greenhouse gas emissions of 540 dishes from 36 cuisines using data from over 800,488 restaurants in China's provincial capital cities. By mining residents' dietary preferences, we then design various dietary change strategies to explicitly link food emissions to the Paris Agreement pledges. The results show that China's food system greenhouse gas emissions were approximately 4.64 GtCO 2 eq in 2020, accounting for 37% of total emissions, with average per-dish emissions of 8.44 kgCO 2 eq. Current emission patterns of food consumption in China may not be consistent with the attainment of the 1.5 °C and 2 °C climate targets, but transitioning towards low-emission cuisines and dishes could change that by reducing emissions by 38-69%., (© 2024. The Author(s), under exclusive licence to Springer Nature Limited.)

This study analyzes the performance of the Shanghai Composite Index, S&P 500 index, WTI oil price, and LBMA gold price when wars took place, especially the Russia-Ukraine conflict. We employ empirical methods to explore the stability, instantaneous shock, and short-term shock regarding the abovementioned financial assets. We first adopt the event study method to ascertain whether the cumulative abnormal returns of the selected assets are significant when wars break out. Then, we use the permutation test to examine the significance of price level changes. Results show that only the Shanghai Composite Index is relatively stable. Second, the difference-in-differences model indicates that the 3 unstable assets all suffered positive shocks in their price levels within several days after the Russia-Ukraine conflict broke out. The parallel trend test confirms the validity of establishing the difference-in-differences model. Third, regression discontinuity is designed to measure the impact in a longer event window, suggesting the robustness of conclusions of the difference-in-differences model and revealing an upward trend before the conflict and a downward trend after the conflict of the financial assets. The study suggests that investors consider adjustments to investment strategies and governments take precautions to diminish the risk of the outbreak of wars., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors.)

The coronavirus disease (COVID-19) pandemic has already caused enormous damage to the global economy and various industries worldwide, especially the tourism industry. In the post-pandemic era, accurate tourism demand recovery forecasting is a vital requirement for a thriving tourism industry. Therefore, this study mainly focuses on forecasting tourist arrivals from mainland China to Hong Kong. A new direction in tourism demand recovery forecasting employs multi-source heterogeneous data comprising economy-related variables, search query data, and online news data to motivate the tourism destination forecasting system. The experimental results confirm that incorporating multi-source heterogeneous data can substantially strengthen the forecasting accuracy. Specifically, mixed data sampling (MIDAS) models with different data frequencies outperformed the benchmark models., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 Elsevier Ltd. All rights reserved.)

2D-material-based van der Waals heterostructures (vdWhs) have shown great potential in next-generation multi-functional microelectronic devices. Thanks to their sharp interface and ultrathin thickness, 2D p-n junctions with high rectification properties have been established by combining p-type monochalcogenides with n-type transition metal dichalcogenides. However, the anisotropic rectification together with the charge transfer and gate effect has not been clarified. Herein, the electrical anisotropy of p-SnS/n-MoS 2 diodes was studied. Optimum ideality factors within 1.08-1.18 have been achieved for the diode with 6.6 nm thick SnS on monolayer MoS 2 , and a high rectification ratio of 3.1 × 10 4 with strong in-plane anisotropy is observed along the zigzag direction of SnS. A strong gate effect on the anisotropic series resistance has been verified and an effective tuning over the transport length of the SnS channel can be established through adjustment of the current orientation and gate voltage. A thickness-dependent minority carrier transport mechanism has also been demonstrated for the reverse drain current, and Fowler-Nordheim tunneling and direct tunneling are proposed for the increase of the reverse current of the thicker and thinner diodes, respectively. This work will provide another strategy for high-performance diodes based on vdWhs via the control of the current orientation and the gate effect.

The need to make more accurate grain demand (GD) forecasting has become a major topic in the current international grain security discussion. Our research aims to improve short-term GD prediction by establishing a multi-factor model that integrates the key factors: shifts in dietary structures, population size and age structure, urbanization, food waste, and the impact of COVID-19. These factors were not considered simultaneously in previous research. To illustrate the model, we projected China's annual GDP from 2022 to 2025. We calibrated key parameters such as conversion coefficients from animal foods to feed grain, standard person consumption ratios, and population size using the latest surveys and statistical data that were either out of date or missing in previous research. Results indicate that if the change in diets continued at the rate as observed during 2013-2019 (scenario 1), China's GD is projected to be 629.35 million tons in 2022 and 658.16 million tons in 2025. However, if diets shift to align with the recommendations in the Dietary Guideline for Chinese Residents 2022 (scenario 2), GD would be lower by 5.9-11.1% annually compared to scenario 1. A reduction in feed grain accounts for 68% of this change. Furthermore, for every 1 percentage point increase in the population adopting a balanced diet, GD would fall by 0.44-0.73 million tons annually during that period. Overlooking changes in the population age structure could lead to an overprediction of annual GDP by 3.8% from 2022 to 2025. With an aging population, China's GD would fall slightly, and adopting a balanced diet would not lead to an increase in GD but would have positive impacts on human health and the environment. Our sensitivity analysis indicated that reducing food waste, particularly cereal, livestock, and poultry waste, would have significant effects on reducing GD, offsetting the higher demand due to rising urbanization and higher incomes. These results underscore the significance of simultaneous consideration of multiple factors, particularly the dietary structure and demographic composition, resulting in a more accurate prediction of GD. Our findings should be useful for policymakers concerning grain security, health, and environmental protection.

Industry redistribution is a common economic phenomenon that involves a dynamic configuration of the production location across a region, country, or the world. However, measurements of the associated pollutant emission effects have not been well conducted at the domestically regional level. Here, we calculate the CO 2 emission changes induced by China's domestic inter-provincial industry redistribution during 2002-2017 using a counterfactual approach in the multi-regional input-output framework. We find that China's domestic industry redistribution decreased CO 2 emissions during 2002-2017 and has considerable potential to continuously mitigate CO 2 emissions in the future. We emphasize that the pollution haven effect may accompany the process of industry redistribution but can be weakened by effective policies, including stringent access thresholds in the regions undertaking industry relocation and regional industry structural upgrading. This paper provides policy recommendations for strengthening regional coordination to achieve China's transformation to carbon neutrality., Competing Interests: The authors declare no competing interests., (© 2023 The Author(s).)

Studies have shown that the soaring demand for air conditioners in recent years is closely related to the worsening global warming; however, little evidence has been provided for China. This study uses weekly data of 343 Chinese cities to investigate how air conditioner sales respond to climate variability. We detected a U-shaped relationship between air-conditioning and temperature. An additional day with average temperature above 30°C increases weekly sales by 16.2%. Heterogeneity analysis shows that the adoption of air-conditioning is different for south and north China. By combining our estimates with shared socioeconomic pathway scenarios, we project China's mid-century air conditioner sales and the resulting electricity demand. Under the fossil-fueled development scenario, air conditioner sales in the Pearl River Delta would rise by 71% (65.7%-87.6%) in summer. On average, the per capita electricity demand for air-conditioning will surge by 28% (23.2%-35.4%) in China by mid-century., Competing Interests: The authors declare no competing interests., (© 2023 The Authors.)

Strict carbon emission regulations are set with respect to countries' territorial seas or shipping activities in exclusive economic zones to meet their climate change commitment under the Paris Agreement. However, no shipping policies on carbon mitigation are proposed for the world's high seas regions, which results in carbon intensive shipping activities. In this paper, we propose a Geographic-based Emission Estimation Model (GEEM) to estimate shipping GHG emission patterns on high seas regions. The results indicate that annual emissions of carbon dioxide equivalent (CO 2 -e) in shipping on the high seas reached 211.60 million metric tonnes in 2019, accounting for about one-third of all shipping emissions globally and exceeding annual GHG emissions of countries such as Spain. The average emission from shipping activities on the high seas is growing at approximately 7.26% per year, which far surpasses the growth rate of global shipping emission at 2.23%. We propose implementation of policies on each high seas region with respect to the main emission driver identified from our results. Our policy evaluation results show that carbon mitigation policies could reduce emissons by 25.46 and 54.36 million tonnes CO 2 -e in the primary intervention stage and overall intervention stage, respectively, with 12.09% and 25.81% reduction rates in comparison to the 2019 annual GHG emissions in high seas shipping., (© The Author(s) 2022. Published by Oxford University Press on behalf of China Science Publishing & Media Ltd.)

This study investigates granger causal linkages among six Asian emerging stock markets and the US market over the period 2002-2020, taking into account several crisis periods. The pairwise Granger causality tests for investigating the short-run causality show significant bi- and uni-directional causal relationships in those markets and evidence that they have become more internationally integrated after every crisis period. An exception is Bangladesh with almost no significant short-term causal linkages with other markets. For understanding, how the financial linkages amplify volatility spillover effects, we apply the GARCH-M model and find that volatility and return spillovers act very inversely over time. However, market interface is weak before the crisis periods and becomes very strong during the financial crisis and US-China economic policy uncertainty periods. The US market plays a dominant role during the financial crisis and COVID-19 periods. Further analysis using the VAR model shows that a large proportion of the forecast variance of the Asian emerging stock markets is affected by the S&P 500 and that market shock starts to rise notably from the 1 to 10 period. The overall findings could provide important policy implications in the six countries under study regarding hedging, trading strategies, and financial market regulation., Competing Interests: The authors have declared that no competing interests exist.

This article considers impacts from innovation, defined in terms of research and development expenditure, on carbon emissions. We relate our study to scholarship about the Environmental Kuznets Curve and the Pollution Haven Hypothesis, situating this analysis within literature about the compatibility of broadly capitalist systems and combating climate change. We thus incorporate scholarship surrounding themes such as climate capitalism and ecological modernization. There are three main research questions. First, what is the impact of increasing levels of innovation on emissions? Second, how does the level of economic development affect impacts from greater innovation on emissions? Third, does this analysis generate evidence to support the Pollution Haven Hypothesis? To test these questions, and three parallel hypotheses, we initially deployed a panel data model, based on World Bank data, incorporating control variables covering economic, spatial and environmental factors. We then split the country sample into two GDP-based cohorts to test for variations in effects related to economic development. Subsequently, a multi-input regional-output model was deployed to incorporate analysis of a pollution haven effect. Our analysis suggests that whilst greater innovation diminished carbon dioxide emissions for high-income countries, this effect could not be identified elsewhere. Furthermore, the multi-input regional-output model implied that explanations for these contrasting results might lie in a pollution haven effect. Overall, this study implied some acutely limited support for climate capitalism and ecological modernization, constructed on data from high-income countries alone., (Copyright © 2022. Published by Elsevier Ltd.)

With unconventional oil and gas booming in commercial development, its inevitable environmental damage has aroused the public's vigilance. To support the regulation improvement and early-warning system building, it is of great need to learn the regular patterns in recurrent violations both for practitioners and governments. In this respect, we utilized the "Oil and Gas Compliance Report" from the Pennsylvania Department of Environmental Protection from 2000 to 2019, a total of 5737 violation records, to dig out the historical violation patterns. Through LDA (Latent Dirichlet Allocation) analysis combined with the decision tree model, our research attained the following conclusions: first, we find that the LDA themes of violations as "Erosion and sediment" and "Water pollution" are critical factors for "Failed" enforcement results. Therefore, policymakers and practitioners should pay more attention to those two types of accidents. Second, it is noted that counties are also one of the essential features that matter the enforcement results. Third, we need to consider the role of economic punishment dialectically, while it is not a significant feature for successful enforcement results. That is to say, a monetary penalty may not necessarily improve the effectiveness of the company's measurements., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Regional trade agreements (RTAs) have been widely adopted to facilitate international trade and cross-border investment and promote economic development. However, ex ante measurements of the environmental effects of RTAs to date have not been well conducted. Here, we estimate the CO 2 emissions burdens of the Regional Comprehensive Economic Partnership (RCEP) after evaluating its economic effects. We find that trade among RCEP member countries will increase significantly and economic output will expand with the reduction of regional tariffs. However, the results show that complete tariff elimination among RCEP members would increase the yearly global CO 2 emissions from fuel combustion by about 3.1%, doubling the annual average growth rate of global CO 2 emissions in the last decade. The emissions in some developing members will surge. In the longer run, the burdens can be lessened to some extent by the technological spillover effects of deeper trade liberalization. We stress that technological advancement and more effective climate policies are urgently required to avoid undermining international efforts to reduce global emissions., (© 2022. The Author(s).)

The shift of China's economy since 2013, dubbed the "new normal", has caused its production and consumption emissions to plateau, with the country seeming to embody the tantalizing promise of decoupling its economic growth from carbon emissions. By using multi-region input-output analysis, we find that China's relative decoupling in the new normal is technology driven, evidenced by the narrowing gap between its technology-adjusted and non-adjusted consumption emissions. By applying structural decomposition analysis, we further explore the driving forces behind the slowdown in China's imported emissions growth, finding that it is attributable to restructuring of import patterns resulting from changes in the structures of domestic demand. These changes could have been caused by China moving along the global value chain and rebalancing its industrial linkages toward trade in carbon-efficient goods to avoid transferring emissions-intensive production to other regions, indicating a shift to less emissions-intensive trade rather than pure outsourcing., Competing Interests: The authors declare no competing interests., (© 2021 The Author(s).)

Broke out at the end of 2019, the novel coronavirus pneumonia (COVID-19) has been spreading throughout the world, leading to more than 87 million confirmed infections and 1.88 million fatalities. Motivated by this, we evaluate the economic impacts of COVID-19 outbreak on both national and industrial levels by employing quarterly computable general equilibrium (CGE) model. Our results reveal that the epidemic may lower China's economic growth in 2020 by 3.5%, versus 4.4% for final consumption (relative to baseline). The service industry suffers the most from the outbreak, and the Accommodation-Food-Beverage service, Wholesale-Retail Trade, and Transport-Storage-Post are identified as the most vulnerable sectors, with the negative impact on output reaching as high as 14.6%. When moving to 2021, the hit to economy shrinks to 2% (1.2-2.7%), with industry estimated to be the most affected sector instead. This study indicates that implementing effective measures for preventing and controlling the epidemic and policies for post-disease economic recovery play critical role in curbing the potential economic damage., Competing Interests: None., (© 2021 Elsevier Inc. All rights reserved.)

Given the increasing interest in keeping global warming below 1.5°C, a key question is what this would mean for China's emission pathway, energy restructuring, and decarbonization. By conducting a multimodel study, we find that the 1.5°C-consistent goal would require China to reduce its carbon emissions and energy consumption by more than 90 and 39%, respectively, compared with the "no policy" case. Negative emission technologies play an important role in achieving near-zero emissions, with captured carbon accounting on average for 20% of the total reductions in 2050. Our multimodel comparisons reveal large differences in necessary emission reductions across sectors, whereas what is consistent is that the power sector is required to achieve full decarbonization by 2050. The cross-model averages indicate that China's accumulated policy costs may amount to 2.8 to 5.7% of its gross domestic product by 2050, given the 1.5°C warming limit., (Copyright © 2021 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)

The growing energy consumption and associated carbon emission of Bitcoin mining could potentially undermine global sustainable efforts. By investigating carbon emission flows of Bitcoin blockchain operation in China with a simulation-based Bitcoin blockchain carbon emission model, we find that without any policy interventions, the annual energy consumption of the Bitcoin blockchain in China is expected to peak in 2024 at 296.59 Twh and generate 130.50 million metric tons of carbon emission correspondingly. Internationally, this emission output would exceed the total annualized greenhouse gas emission output of the Czech Republic and Qatar. Domestically, it ranks in the top 10 among 182 cities and 42 industrial sectors in China. In this work, we show that moving away from the current punitive carbon tax policy to a site regulation policy which induces changes in the energy consumption structure of the mining activities is more effective in limiting carbon emission of Bitcoin blockchain operation.

Recently, applications of complex-valued neural networks (CVNNs) to real-valued classification problems have attracted significant attention. However, most existing CVNNs are black-box models with poor explanation performance. This study extends the real-valued group method of data handling (RGMDH)-type neural network to the complex field and constructs a circular complex-valued group method of data handling (C-CGMDH)-type neural network, which is a white-box model. First, a complex least squares method is proposed for parameter estimation. Second, a new complex-valued symmetric regularity criterion is constructed with a logarithmic function to represent explicitly the magnitude and phase of the actual and predicted complex output to evaluate and select the middle candidate models. Furthermore, the property of this new complex-valued external criterion is proven to be similar to that of the real external criterion. Before training this model, a circular transformation is used to transform the real-valued input features to the complex field. Twenty-five real-valued classification data sets from the UCI Machine Learning Repository are used to conduct the experiments. The results show that both RGMDH and C-CGMDH models can select the most important features from the complete feature space through a self-organizing modeling process. Compared with RGMDH, the C-CGMDH model converges faster and selects fewer features. Furthermore, its classification performance is statistically significantly better than the benchmark complex-valued and real-valued models. Regarding time complexity, the C-CGMDH model is comparable with other models in dealing with the data sets that have few features. Finally, we demonstrate that the GMDH-type neural network can be interpretable.

To meet the growing electricity demand, China's power generation sector has become an increasingly large source of air pollutants. Specific control policymaking needs an inventory reflecting the overall, heterogeneous, time-varying features of power plant emissions. Due to the lack of comprehensive real measurements, existing inventories rely on average emission factors that suffer from many assumptions and high uncertainty. This study is the first to develop an inventory of particulate matter (PM), SO 2 and NO X emissions from power plants using systematic actual measurements monitored by China's continuous emission monitoring systems (CEMS) network over 96-98% of the total thermal power capacity. With nationwide, source-level, real-time CEMS-monitored data, this study directly estimates emission factors and absolute emissions, avoiding the use of indirect average emission factors, thereby reducing the level of uncertainty. This dataset provides plant-level information on absolute emissions, fuel uses, generating capacities, geographic locations, etc. The dataset facilitates power emission characterization and clean air policy-making, and the CEMS-based estimation method can be employed by other countries seeking to regulate their power emissions.

Utilizing a scientometric review of global trends and structure from 388 bibliographic records over two decades (1999-2018), this study seeks to advance the building of comprehensive knowledge maps that draw upon global travel demand studies. The study, using the techniques of co-citation analysis, collaboration network and emerging trends analysis, identified major disciplines that provide knowledge and theories for tourism demand forecasting, many trending research topics, the most critical countries, institutions, publications, and articles, and the most influential researchers. The increasing interest and output for big data and machine learning techniques in the field were visualized via comprehensive knowledge maps. This research provides meaningful guidance for researchers, operators and decision makers who wish to improve the accuracy of tourism demand forecasting., Competing Interests: The authors declare that there is no conflict of interests regarding the publication of this paper., (© 2020 Elsevier Ltd. All rights reserved.)

Countries have sought to stop the spread of coronavirus disease 2019 (COVID-19) by severely restricting travel and in-person commercial activities. Here, we analyse the supply-chain effects of a set of idealized lockdown scenarios, using the latest global trade modelling framework. We find that supply-chain losses that are related to initial COVID-19 lockdowns are largely dependent on the number of countries imposing restrictions and that losses are more sensitive to the duration of a lockdown than its strictness. However, a longer containment that can eradicate the disease imposes a smaller loss than shorter ones. Earlier, stricter and shorter lockdowns can minimize overall losses. A 'go-slow' approach to lifting restrictions may reduce overall damages if it avoids the need for further lockdowns. Regardless of the strategy, the complexity of global supply chains will magnify losses beyond the direct effects of COVID-19. Thus, pandemic control is a public good that requires collective efforts and support to lower-capacity countries.

In the field of business research method, a literature review is more relevant than ever. Even though there has been lack of integrity and inflexibility in traditional literature reviews with questions being raised about the quality and trustworthiness of these types of reviews. This research provides a literature review using a systematic database to examine and cross-reference snowballing. In this paper, previous studies featuring a generalized autoregressive conditional heteroskedastic (GARCH) family-based model stock market return and volatility have also been reviewed. The stock market plays a pivotal role in today's world economic activities, named a "barometer" and "alarm" for economic and financial activities in a country or region. In order to prevent uncertainty and risk in the stock market, it is particularly important to measure effectively the volatility of stock index returns. However, the main purpose of this review is to examine effective GARCH models recommended for performing market returns and volatilities analysis. The secondary purpose of this review study is to conduct a content analysis of return and volatility literature reviews over a period of 12 years (2008-2019) and in 50 different papers. The study found that there has been a significant change in research work within the past 10 years and most of researchers have worked for developing stock markets.

Since 2013, China has implemented a large-scale initiative to systematically deploy solar photovoltaic (PV) projects to alleviate poverty in rural areas. To provide new understanding of China's targeted poverty alleviation strategy, we use a panel dataset of 211 pilot counties that received targeted PV investments from 2013 to 2016, and find that the PV poverty alleviation pilot policy increases per-capita disposable income in a county by approximately 7%-8%. The effect of PV investment is positive and significant in the year of policy implementation and the effect is more than twice as high in the subsequent two to three years. The PV poverty alleviation effect is stronger in poorer regions, particularly in Eastern China. Our results are robust to alternative specifications and variable definitions. We propose several policy recommendations to sustain progress in China's efforts to deploy PV for poverty alleviation.

With the frequent occurrence of irregular events in recent years, the tourism industry in some areas, such as Hong Kong, has suffered great volatility. To enhance the predictive accuracy of tourism demand forecasting, a decomposition-ensemble approach is developed based on the complete ensemble empirical mode decomposition with adaptive noise, data characteristic analysis, and the Elman's neural network model. Using Hong Kong tourism demand as an empirical case, this study firstly investigates how data characteristic analysis is used in a decomposition-ensemble approach. The empirical results show that the proposed model outperforms other models in both point and interval forecasts for different prediction horizons, indicating the effectiveness of the proposed approach for forecasting tourism demand, especially for time series with complexity., (© 2020 Elsevier Ltd. All rights reserved.)

In this paper, some privacy-preserving features for distributed subgradient optimization algorithms are considered. Most of the existing distributed algorithms focus mainly on the algorithm design and convergence analysis, but not the protection of agents' privacy. Privacy is becoming an increasingly important issue in applications involving sensitive information. In this paper, we first show that the distributed subgradient synchronous homogeneous-stepsize algorithm is not privacy preserving in the sense that the malicious agent can asymptotically discover other agents' subgradients by transmitting untrue estimates to its neighbors. Then a distributed subgradient asynchronous heterogeneous-stepsize projection algorithm is proposed and accordingly its convergence and optimality is established. In contrast to the synchronous homogeneous-stepsize algorithm, in the new algorithm agents make their optimization updates asynchronously with heterogeneous stepsizes. The introduced two mechanisms of projection operation and asynchronous heterogeneous-stepsize optimization can guarantee that agents' privacy can be effectively protected.

Objective: To develop a formula to compute mean pulmonary arterial pressure (MPAP) by chest computerized tomography (CT), and to verify its accuracy and reliability., Methods: Eighty-five patients who had taken chest CT and right heart catheterization (RHC) were recruited. The pulmonary arterial systolic pressure (PASP), pulmonary arterial diastolic pressure (PADP), and MPAP of each subject were measured and recorded by RHC. The diameters of the ascending aorta (dAA), descending aorta (dDA) and main pulmonary artery (dMPA), Cobb angle, diameters of right ventricle (dRV), diameters of left ventricle (dLV) were measured by means of chest CT scans. Systolic blood pressure (SBP) was measured by using electronic sphygmomanometer. A linear regression equation was generated in 56 patients to estimate PAP based on chest CT values, 29 patients were used to test the accuracy of the formula., Results: The computed equation for analyzing MPAP is: MPAP = 9.011 + 34.195 × dMPA/dAA - 0.319 × SBP + 0.402 × Cobb angle. AUC of equation with three variables (dMPA/dAA, SBP, and Cobb angle) was 0.923 with 95% CI (0.863-0.982). The mean ± SD of predicted values and RHC values had no statistical difference., Conclusions: Ratio of dMAP/dAA, Cobb angle, and SBP can be reliably used to estimate MPAP and predict severity of PH., (© 2016 John Wiley & Sons Ltd.)

This paper proposes a new time-varying coefficient vector autoregressions (VAR) model, in which the coefficient is a linear function of dynamic lagged correlation. The proposed model allows for flexibility in choices of dynamic correlation models (e.g. dynamic conditional correlation generalized autoregressive conditional heteroskedasticity (GARCH) models, Markov-switching GARCH models and multivariate stochastic volatility models), which indicates that it can describe many types of time-varying causal effects. Time-varying causal relations between West Texas Intermediate (WTI) crude oil and the US Standard and Poor's 500 (S&P 500) stock markets are examined by the proposed model. The empirical results show that their causal relations evolve with time and display complex characters. Both positive and negative causal effects of the WTI on the S&P 500 in the subperiods have been found and confirmed by the traditional VAR models. Similar results have been obtained in the causal effects of S&P 500 on WTI. In addition, the proposed model outperforms the traditional VAR model., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

A right upper lung mass was incidentally found on a chest radiograph in a routine health examination in a 56-year-old man. The mass showed mild heterogeneous enhancement on contrast chest CT images and intense FDG uptake on PET/CT images. Pathological examination demonstrated hepatoid adenocarcinoma in the lung.

Purpose: To investigate the potential of diffusion tensor imaging (DTI) and T2 measurements in the evaluation of radiation-induced peripheral nerve injury (RIPNI)., Materials and Methods: RIPNI was produced in a randomly selected side of sciatic nerve in each of 21 rabbits while the contralateral side served as the control. The limb function and MR parameters were evaluated over a 4-month period. Fractional anisotropy (FA), axial diffusivity (λ∥ ), radial diffusivity (λ⊥ ) and T2 values were obtained using 3T MR for quantitative analysis. Two animals were randomly killed for histological evaluation at each timepoint., Results: The T2 value of irradiated nerve increased at 1 day (63.95 ± 15.60, P = 0.012) and was restored at 1 month (52.34 ± 5.38, P = 0.105). It increased progressively at 2 to 4 months (60.39 ± 10.60, 66.96 ± 6.08, 75.51 ± 7.39, all P < 0.01). λ⊥ significantly increased at 1 day (0.82 ± 0.44, P = 0.046) and slightly decreased at 1 month (0.61 ± 0.17, P < 0.001). It increased gradually from 2 to 3 months (0.84 ± 0.29, 1.13 ± 0.33, both P < 0.001) followed by a decline at 4 months (0.83 ± 0.17, P < 0.001). FA was statistically lower than the contralateral sides at 1 to 4 months (0.72 ± 0.08, 0.60 ± 0.12, 0.51 ± 0.11, 0.62 ± 0.06, all P < 0.01). Changes in FA and λ⊥ correlated well with the functional and pathological changes in irradiated nerve., Conclusion: DTI may be a more sensitive and accurate method to evaluate RIPNI compared with T2 measurements. FA and λ⊥ are promising quantitative indices in monitoring RIPNI. J. Magn. Reson. Imaging 2016;43:1492-1499., (© 2015 Wiley Periodicals, Inc.)

Novelty detection models aim to find the minimum volume set covering a given probability mass. This paper proposes a robust single-class support vector machine (SSVM) for novelty detection, which is mainly based on the worst case conditional value-at-risk minimization. By assuming that every input is subject to an uncertainty with a specified symmetric support, this robust formulation results in a maximization term that is similar to the regularization term in the classical SSVM. When the uncertainty set is l1 -norm, l∞ -norm or box, its training can be reformulated to a linear program; while the uncertainty set is l2 -norm or ellipsoidal, its training is a tractable second-order cone program. The proposed method has a nice consistent statistical property. As the training size goes to infinity, the estimated normal region converges to the true provided that the magnitude of the uncertainty set decreases in a systematic way. The experimental results on three data sets clearly demonstrate its superiority over three benchmark models.