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Abstract Objective Given the frequency of disasters worldwide, there is growing demand for efficient and effective emergency responses. One challenge is to design suitable retrospective charts to enable knowledge to be gained from disasters. This study provides comprehensive understanding of published retrospective chart review templates for designing and updating retrospective research. Methods We conducted a systematic review and text analysis of peer-reviewed articles and grey literature on retrospective chart review templates for reporting, analysing, and evaluating emergency responses. The search was performed on PubMed, Cochrane, and Web of Science and pre-identified government and non-government organizational and professional association websites to find papers published before July 1, 2022. Items and categories were grouped and organised using visual text analysis. The study is registered in PROSPERO (374,928). Results Four index groups, 12 guidelines, and 14 report formats (or data collection templates) from 21 peer-reviewed articles and 9 grey literature papers were eligible. Retrospective tools were generally designed based on group consensus. One guideline and one report format were designed for the entire health system, 23 studies focused on emergency systems, while the others focused on hospitals. Five papers focused specific incident types, including chemical, biological, radiological, nuclear, mass burning, and mass paediatric casualties. Ten papers stated the location where the tools were used. The text analysis included 123 categories and 1210 specific items; large heterogeneity was observed. Conclusion Existing retrospective chart review templates for emergency response are heterogeneous, varying in type, hierarchy, and theoretical basis. The design of comprehensive, standard, and practicable retrospective charts requires an emergency response paradigm, baseline for outcomes, robust information acquisition, and among-region cooperation.

Although questionnaire surveys are widely applied in second-language research, there is still little research on the application of questionnaire among early career researchers. Problems such as the lack of details concerning why the questionnaire survey is adopted, how the questionnaire is constructed, and how the survey is administered and reported are prevalent in student teachers’ research proposals or theses, which greatly impairs the quality of their academic work. However, few studies have been conducted to address these problems. This study aims to explore the real situation of the questionnaire survey application by examining student teachers’ perceptions of a questionnaire survey and their application of this research method to their theses. Using a mixed-method approach, the study collected quantitative and qualitative data from 138 student teachers, including 103 fresh undergraduates and 35 post-graduates. Quantitative data analysis used descriptive statistics, while qualitative data analysis used semi-structured interviews and content analysis. The study found that: (1) student teachers had some basic but unsystematic perceptions of the questionnaire survey. They had insufficient awareness of the rules of writing good questions, piloting questionnaires, sampling strategies, doing reliability, and validity analysis, etc. (2) Most student teachers developed questionnaires based on existing questionnaires without explaining reasons and adjustments. They introduced questionnaire design in their theses in an incomplete way, with piloting, selecting sample strategies, and statistical analysis methods often missing. Thus, some pedagogical implications can be drawn to improve student teachers’ awareness and competencies of the questionnaire survey.

Abstract Background This study aimed to explore the association between the triglyceride-glucose (TyG) index and the risk of in-hospital mortality in critically ill patients with sepsis. Methods This was a retrospective observational cohort study and data were obtained from the Medical Information Mart for Intensive Care-IV (MIMIC IV2.2) database. The participants were grouped into three groups according to the TyG index tertiles. The primary outcome was in-hospital all-cause mortality. Multivariable logistics proportional regression analysis and restricted cubic spline regression was used to evaluate the association between the TyG index and in-hospital mortality in patients with sepsis. In sensitivity analysis, the feature importance of the TyG index was initially determined using machine learning algorithms and subgroup analysis based on different subgroups was also performed. Results 1,257 patients (56.88% men) were included in the study. The in-hospital, 28-day and intensive care unit (ICU) mortality were 21.40%, 26.17%, and 15.43% respectively. Multivariate logistics regression analysis showed that the TyG index was independently associated with an elevated risk of in-hospital mortality (OR 1.440 [95% CI 1.106–1.875]; P = 0.00673), 28-day mortality (OR 1.391; [95% CI 1.52–1.678]; P = 0.01414) and ICU mortality (OR 1.597; [95% CI 1.188–2.147]; P = 0.00266). The restricted cubic spline regression model revealed that the risks of in-hospital, 28-day, and ICU mortality increased linearly with increasing TyG index. Sensitivity analysis indicate that the effect size and direction in different subgroups are consistent, the results is stability. Additionally, the machine learning results suggest that TyG index is an important feature for the outcomes of sepsis. Conclusion Our study indicates that a high TyG index is associated with an increased in-hospital mortality in critically ill sepsis patients. Larger prospective studies are required to confirm these findings.

Abstract This paper proposes a method to enhance the accuracy of power load forecasting by considering the variability in the impact of multi‐dimensional meteorological information on power load in diverse regions. The proposed method employs spatio‐temporal fusion (SF) of multi‐dimensional meteorological information and applies the Copula theory to analyze the non‐linear coupling of meteorological information from multiple stations with power load to achieve SF in the spatial dimension. To enhance the accuracy of load forecasting in the time dimension, this paper improves the core parameters of the variational mode decomposition (VMD) using the marine predators algorithm (MPA) and utilizes the weighted permutation entropy (WPE) to construct the MPA‐VMD fitness function for the adaptive decomposition of the load sequence. Moreover, this paper constructs input sets for the long short‐term memory model and the MPA‐LSSVM model by combining each component of the time dimension and each meteorological information of the spatial dimension to obtain the prediction results of each component. The prediction model corresponding to each component is selected according to the evaluation index and reconstructed to obtain the overall prediction results. The analysis results demonstrate that the proposed forecasting method outperforms the traditional forecasting method and effectively enhances the accuracy of power load forecasting.

In recent years, carbon emission reduction in the shipping sector has increasingly garnered scholarly attention. This study delves into the pathways for carbon emission reduction in shipping across the Yangtze River, emphasizing fuel alternatives. It initiates by introducing a novel ship carbon emission calculation methodology predicated on voyage data, followed by the development of a predictive model for ship carbon emissions tailored to specific voyages. Then, emission reduction scenarios for various voyage categories are designed and exemplary alternative fuels selected to assess their potential for emission mitigation. Subsequently, scenario analysis is employed to scrutinize the CO2 emission trajectories under diverse conditions, pinpointing the most efficacious route for carbon emission abatement for inland vessels. Finally, the proposed method is applied to the middle and lower reaches of the Yangtze River. The results indicate that accelerating the adoption of alternative fuels for long-distance cargo ships would greatly accelerate the development of environmentally friendly shipping. Under a scenario prioritizing zero-carbon growth, emissions from inland vessels are anticipated to reach their zenith by 2040. These findings can provide theoretical guidance for emission reductions in inland shipping and effectively promote the green and sustainable development of the shipping sector.

[This corrects the article DOI: 10.1371/journal.pone.0259516.].

This paper proposes a new optimization algorithm for backpropagation (BP) neural networks by fusing integer-order differentiation and fractional-order differentiation, while fractional-order differentiation has significant advantages in describing complex phenomena with long-term memory effects and nonlocality, its application in neural networks is often limited by a lack of physical interpretability and inconsistencies with traditional models. To address these challenges, we propose a mixed integer-fractional (MIF) gradient descent algorithm for the training of neural networks. Furthermore, a detailed convergence analysis of the proposed algorithm is provided. Finally, numerical experiments illustrate that the new gradient descent algorithm not only speeds up the convergence of the BP neural networks but also increases their classification accuracy.

It is well recognized that carbon dioxide and air pollutants share similar emission sources so that synergetic policies on climate change mitigation and air pollution control can lead to remarkable co-benefits on greenhouse gas reduction, air quality improvement, and improved health. In the context of carbon peak, carbon neutrality, and clean air policies, this perspective tracks and analyzes the process of the synergetic governance of air pollution and climate change in China by developing and monitoring 18 indicators. The 18 indicators cover the following five aspects: air pollution and associated weather-climate conditions, progress in structural transition, sources, inks, and mitigation pathway of atmospheric composition, health impacts and benefits of coordinated control, and synergetic governance system and practices. By tracking the progress in each indicator, this perspective presents the major accomplishment of coordinated control, identifies the emerging challenges toward the synergetic governance, and provides policy recommendations for designing a synergetic roadmap of Carbon Neutrality and Clean Air for China.

Abstract Background In this study we explored whether one pleural catheter plus single chest tube drainage could achieve a noninferior drainage effect when compared with the traditional two chest tubes in uniportal video‐assisted thoracoscopic surgery (VATS) for an upper pulmonary lobectomy. Methods Patients that underwent an upper pulmonary lobectomy from January to November 2020 were enrolled in this single‐center, randomized, open‐label, noninferiority trial. Prior to closure, patients were randomized to an intervention group who received an improved drainage strategy involving one pleural catheter with one chest tube (24 Fr), while traditional double chest tube drainage was applied for the control group. Results A total of 390 patients entered the study, although 190 were excluded for changing nonuniportal surgical approaches or opting for nonlobectomy resections. Finally, 200 patients were randomized (100 in the intervention group and 100 in the control group). The baseline demographic and clinical characteristics were comparable between the groups. The incidence of pneumothorax in the intervention and control groups was similar on postoperative Day 1 (noninferiority, 10% vs. 13%, p = 0.658). In addition, there were no significant differences in secondary outcomes such as incidence of pneumothorax by Day 30, postoperative chest tube/pleural catheter removal time, amount of drainage on Day 1, total amount of drainage after operation, or postoperative hospitalization. A significantly lower pain score was observed in the intervention group (3.33 ± 0.68 vs. 3.68 ± 0.94, p = 0.003). Conclusions The new strategy is noninferior to double chest tube drainage after an upper pulmonary lobectomy offers superior pain control, and is recommended for an upper lobectomy by uniportal VATS.

Graph Convolutional Networks (GCNs) have received considerable attention in the field of artificial machine intelligence (AMI) and natural language processing research because they can build more sophisticated accompanying graph structures than traditional neural networks for feature engineering. Graph is used as feature in neural network because it is easy to find relations among nodes. In text classification applications, a GCN can create complex and rich relation-based adjacent matrix graphs as features to be trained. The existing methods, on the other hand, only generated adjacent matrix graphs in GCN at the word-document and word-word levels as features. In this paper, we propose a document-relational GCN to achieve superior accuracy in text classification by adding cumulative term frequency-inverse document frequency (TF-IDF) document-document relations as features. The performance of the proposed method is evaluated using five popular benchmark databases. In addition, different hidden nodes and proportions of document-document features are tested to achieve an advantageous outcome.

The user demographic prediction problem is one of the critical processes in the construction of user profiles, which is of great significance for understanding users’ characteristics and attributes. Most of the prior works on this problem either used only single-source data or employed a hard-matching method to handle multi-source data. These methods will result in a great loss of data and information in many circumstances, which may affect the model’s accuracy as well as the application scenarios. In order to solve these problems, this paper proposes a framework for user demographic prediction based on mobile and survey data, and presents a Deep Structured Fusion Model (DSFM) using neural networks with attention mechanisms to perform data fusion by comparing user similarity between two heterogeneous datasets. We examine the effectiveness of the framework and the fusion model on a real-world mobile dataset with almost one billion users, using a survey dataset containing 29,809 users’ questionnaire results as an additional information source to predict users’ age and gender. Our framework achieves excellent results on these datasets, increasing the prediction accuracy of gender and age by up to 3.23% and 5.21% compared to the best baseline model.

In recent decades, the influence of climate change and human activities on the ecosystem services (ES) in the Qinghai–Tibet Plateau (QTP) has been extensively investigated. However, few studies focus on linear traffic corridor area, which is heavily affected by human activities. Taking the Golmud–Lhasa national highway corridor as a case, this study investigated the land-use and land-cover change (LUCC) and spatiotemporal variations of ES indicators using ecosystem indices of fractional vegetation cover (FVC), leaf area index (LAI), evapotranspiration (ET), and net primary productivity (NPP) from 2000 to 2020. The results indicated that LUCC was faster in the last decade, mostly characterized by the conversion from grassland to unused land. In buffer within 3000 m, the proportions of productive areas represented the increased trends with distance. In terms of ES variations, the improved areas outweighed the degraded areas in terms of FVC, LAI, and NPP from 2000 to 2020, mostly positioned in the Qinghai Province. In addition, FVC, LAI, and NPP peaked at approximately 6000 m over time. With regard to influencing factors, precipitation (20.54%) and temperature (14.19%) both positively influenced the spatiotemporal variation of FVC. Nearly 60% of the area exhibited an increased NPP over time, especially in the Qinghai Province, which could be attributed to the temperature increase over the last two decades. In addition, the distance effects of climatic factors on ES indicators exhibited that the coincident effects almost showed an opposite trend, while the reverse effects showed a similar trend. The findings of this study could provide a reference for the ecological recovery of traffic corridors in alpine fragile areas.

Information exchange between building information modelling (BIM) and geographic information system (GIS) is problematic, especially in terms of semantic information. Graph-based technologies, such as the resource description framework (RDF) and the labelled property graph (LPG), are promising in solving this problem. These two technologies are different but have not been systematically investigated in the context of BIM/GIS integration. This paper presents our systematic investigation into these two technologies, trying to propose the proper one for BIM/GIS data integration. The main findings are as follows. (1) Both LPG-based databases and RDF-based databases can be generally considered graph databases, but an LPG-based database is considered a native graph database, while an RDF-based database is not. (2) RDF suits applications focusing more on linking data and sharing data, and (3) LPG-based graph database suits applications focusing more on data query and analysis. An LPG-based graph database is thus proposed for BIM/GIS data integration. This review can facilitate the use of graph technology in BIM/GIS integration.

In order to fulfill the commitment of China to achieve carbon peak by 2030 and carbon neutrality by 2060, all industries have been taking their respective carbon reduction actions. The transportation industry accounts for 11% of CO2 emission of the whole society, and its energy conservation and carbon reduction benefit is of great significance to the national carbon reduction process. New energy vehicles are undoubtedly one of the most important means of carbon emission reduction in the transportation sector. However, electric vehicles still have CO2 emissions, as the fossil fuel use comes from upstream power. To systematically and comprehensively evaluate the CO2 emissions of HEV, PHEV and BEV in the whole process, this study introduces the life-cycle method to research on the past and current situations, and predict future scenarios for ICEV and EV light-duty vehicles at the national and regional levels, by deeply analyzing the generation mix and generating efficiency from the WTT stage, and fuel economy from the TTW stage. The study shows that compared with ICEV, HEV and PHEV could reduce around 30% of CO2 emissions. Currently, BEV could reduce 37% of CO2 emission in the region where the proportion of coal-fired power is high, and 90% of CO2 emission in the region where the proportion of hydro power is high. This study discusses the impact of the proportion of renewable energy application on the carbon emissions from electric vehicles, analyzes the environmental benefits of promoting electric vehicles in different regions, and lays a foundation for the promotion strategy of electric vehicles for different regions in the future.

This paper proposes a new linear combination model to predict the closing prices on multivariate financial data sets. The new approach integrates two delays of deep learning methods called the two-delay combination model. The forecasts are derived from three different deep learning models: the multilayer perceptron (MLP), the convolutional neural network (CNN) and the long short-term memory (LSTM) network. Moreover, the weight combination of our proposed model is estimated using the differential evolution (DE) algorithm. The proposed model is built and tested for three high-frequency stock data in financial markets—Microsoft Corporation (MSFT), Johnson & Johnson (JNJ) and Pfizer Inc. (PFE). The individual and combination forecast methods are compared using the root mean square error (RMSE) and the mean absolute percentage error (MAPE). The state-of-the-art combination models used in this paper are the equal weight (EW), the inverse of RMSE (INV-RMSE) and the variance-no-covariance (VAR-NO-CORR) methods. These comparisons demonstrate that our proposed approach using DE weight’s optimization has significantly lower forecast errors than the individual model and the state-of-the-art weight combination procedures for all experiments. Consequently, combining two delay deep learning models using differential evolution weights can effectively improve the stock price prediction.

This study proposes an optimisation approach to improve multiple-criteria aspiration-level public transportation performance by combining public transport criteria matrix analytic hierarchy process (PTCM-AHP) models and multi-aspiration-level goal programming. The approach uses the PTCM-AHP to calculate the system weights. Based on the weight values, the approach combines the multi-aspiration goal-level selection process in three different ways. The proposed approach was used to optimise public transportation networks in Bayswater, Cockburn, and Stonnington, Australia, to demonstrate the public transportation network performance optimisation process. By controlling the criteria goal value interval, this new approach combines decision-making plans and strategies to optimise various scenarios. The optimisation outcomes can be applied to provide guidelines for improving the performance of public transportation networks.

Ship exhaust emissions have been considered as a significant source of air pollution that has an adverse impact on the global climate and human health. It is of vital importance to create an accurate ship emission inventory for the purpose of formulating effective control measures. A wide range of inventory compilation methods have been proposed around the globe, and there has long been a pressing need to analyze and compare these methods in depth. This study sorted out and categorized inventory compilation methods of ship emissions in recent decades. Five main methods were compared and analyzed by their applicability, complexity, time of calculation, accuracy of results, etc. In addition, a new method was proposed to develop an emission inventory based on a vessel energy consumption reporting system. This method is believed to have the potential advantages to produce results of higher accuracy and temporal and spatial resolutions. To perform the validation, three cases at different scales were selected in part of China and surrounding maritime waters (large-scale), the Yangtze River Delta region (medium-scale), and Tianjin Port (small-scale), respectively. The analysis results show that: each of methods have different technical characteristics. Computed results significantly between methods, with the maximum deviation of up to 87%. It is advisable that the optimal method should be chosen based on the actual needs in inventory compilation and the data available. In terms of accuracy of results, Methods 1 and 5 offer moderately high accuracy; Method 2 provides average accuracy; while Methods 3 and 4 produce low accuracy. In terms of resolution of results, Methods 1 and 5 provide high-resolution temporal and spatial distribution of ship emissions; Method 2 delivers low-resolution spatial distribution; while Methods 3 and 4 are incapable of spatial distribution. In terms of applicability, Method 1 applies to the calculation of inventories of varying scales; Method 2 is more applicable to small-scale calculations, such as a port; Methods 3, 4, and 5 are more desirable for large-scale calculations, such as a country. The author recommends Methods 5, 1, 3, and 2/4 in a descending order of preference for large-scale ship emissions inventory compilations; recommends Method 5 (if accuracy is the first priority) or Method 1 (if temporal and spatial resolutions are given first priority), followed by Methods 2, 3, and 4 in a descending order of preference for small/medium-scale ship emissions inventory compilations. These results may serve to help inventory compilers choose an applicable method and support improvements in inventory compilation methods.

Objective: Functional dysphonia can impair the language expression ability and adversely affect the career development of some patients. Therefore, an active exploration of effective treatment options is imperative. This study investigated the effect of Akson therapy on acoustic parameters in patients with functional dysphonia. Materials and Methods: In this retrospective analysis, 79 patients with functional dysphonia who received conventional voice correction training from June 2020 to June 2021 were included in the reference group (RG). Our hospital has implemented Akson therapy since July 2021. Correspondingly, 72 patients with functional dysphonia who underwent Akson therapy from July 2021 to July 2022 were enrolled in the observation group (OG). The acoustic parameters such as fundamental frequency (F0), jitter, shimmer, and normalized noise energy (NNE); the aerodynamic parameters including maximum phonation time (MPT), mean airflow rate (MFR), and Voice Handicap Index-10 (VHI-10) score; and the Grade, Roughness, Breathiness, Asthenia, and Strain scale (GRBAS) score were measured before and after treatment and compared between the two groups. Results: The F0, jitter, shimmer, NNE, MPT, and MFR values as well as the VHI-10 score and the grade (G), roughness (R), and breathiness (B) scores on the GRBAS did not significantly differ between the two groups before treatment (P > 0.05). However, significantly lower F0, jitter, shimmer, NNE, and MFR values and higher MPT levels were found in the OG compared to the RG after treatment (P < 0.001). Furthermore, the VHI-10 score and the G, R, and B scores were significantly lower in the OG than in the RG after treatment (P<0.001), whereas the asthenia (A) and strain (S) scores remained at 0 before and after treatment. Conclusion: Akson therapy can improve the acoustic parameters of patients with functional dysphonia to a certain extent, indicating its potential application value. [ABSTRACT FROM AUTHOR]

Objective The composition and degradability of dissolved organic matter (DOM) play a critical role in evaluating biogenic gas production during shale gas exploration. A simple and effective method needs to be developed to assess the composition, functional groups and microbial activity of DOM, which will provide crucial valuable information concerning the gas production mechanism in the field of biogenic shale gas. Methods Shale samples were collected from depths of 1 219 m to 1 309 m from the ST1 exploration well located at the edge of the Sebei No.1 gas field in the Qaidam Basin. According to the 3D fluorescence of the extraction and emission matrix (EEM) and the UV visible absorption spectrum characteristics of chromophoric dissolved organic matter (CDOM), the composition, functional group and degradation characteristics of CDOM were analysed to determine the distribution of methanol formation substrates. Results The DOM in the samples mainly consisted of the small molecular tryptophan-like fluorescent components C1 and C3, which are readily degradable by biotic processes and account for 70.54% of the total fluorescence intensity. The fluorescence components, fluorescence parameters (HIX, BIX), and absorbance ratios (E253/E203, SUVA254) confirmed that the core layers at depths of 1 219-1 222 m and 1 285-1 301 m were potential effective gas-generating formations. These layers contain low amounts of humified organic matter, weak aromaticity and high amounts of methanol functional groups. The active gas-generating formations are generally consistent with those in previous reports. Conclusion The results indicated that the CDOM spectrum is a valuable method for exploring the methanol formation substrate distribution and microbial activity. The CDOM-EEM method has the potential to serve as an effective indicator of the methanol formation characteristics of DOM in mud shale and provides an effective and essential way to assess geochemical-based gas production capacity in hydrocarbon source rocks.

With major signal analytical elements situated away from the measurement environment, extended gate (EG) ion-sensitive field-effect transistors (ISFETs) offer prospects for whole chip circuit design and system integration of chemical sensors. In this work, a highly sensitive and power-efficient ISFET was proposed based on a metal–ferroelectric–insulator gate stack with negative capacitance–induced super-steep subthreshold swing and ferroelectric memory function. Along with a remotely connected EG electrode, the architecture facilitates diverse sensing functions for future establishment of smart biochemical sensor platforms.