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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.

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.

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.