Comprehensive Process Research Articles

Implementation and Impacts of California Senate Bill 1152 on Homeless Discharge Protocols.

In recent decades, there has been a growing focus on addressing social needs in healthcare settings. California has been at the forefront of making state-level investments to improve care for patients with complex social and medical needs, including patients experiencing homelessness (PEH). Examples include Medicaid 1115 waivers such as the Whole Person Care pilot program and California Advancing and Innovating Medi-Cal (CalAIM). To date, California is also the only state to have passed a legislative mandate to address concerns related to the hospital discharge of PEH who lack sufficient resources to support self-care. To this end, California enacted Senate Bill 1152 (SB 1152), a unique legislative mandate that requires hospitals to standardize comprehensive discharge processes for PEH by providing (and documenting the provision of) social and preventive services. Understanding the implementation and impact of this law will help inform California and other states considering legislative investments in healthcare activities to improve care for PEH. To understand health system stakeholders' perceived impact of SB 1152 on hospital discharge processes and key barriers and facilitators to SB 1152's implementation, we conducted 32 semi-structured interviews with key informants across 16 general acute care hospitals in Humboldt and Los Angeles counties. Study data were coded and analyzed using thematic analysis informed by the Consolidated Framework for Implementation Research. Participants perceived several positive impacts of SB 1152, including streamlined services, increased accountability, and more staff awareness about homelessness. In parallel, participants also underscored concerns about the law's limited scope and highlighted multiple implementation challenges, including lack of clarity about accountability measures, scarcity of implementation supports, and gaps in community resources. Our findings suggest that SB 1152 was an important step toward the goal of more universal safe discharge of PEH. However, there are also several addressable concerns. Recommendations to improve future legislation include adding targeted funding for social care staff and improving implementation training. Participants' broader concerns about the parallel need to increase community resources are more challenging to address in the immediate term, but such changes will also be necessary to improve the overall health outcomes of PEH.

Neural network predictions of [formula omitted] reaction cross sections at 18.5±3 MeV using the Levenberg-Marquardt algorithm

In recent developments, artificial neural networks (ANNs) have demonstrated their capability to predict reaction cross-sections based on experimental data. Specifically, for predicting (α,n) reaction cross-sections, we meticulously fine-tuned the neural network’s performance by optimizing its parameters through the Levenberg-Marquardt algorithm. The effectiveness of this approach is corroborated by notable correlation coefficients; an R-value of 0.90928 for overall correlation, 0.98194 for validation, 0.99981 for testing, and 0.94116 for the comprehensive network prediction. We conducted a rigorous comparison between the results and theoretical computations derived from the TALYS 1.95 nuclear code to validate the predictive accuracy. The mean square error value for artificial neural network results is 7620.92, whereas for TALYS 1.95 calculations, it has been found to be 50,312.74. This comprehensive evaluation process validates the reliability of the ANN based on the Levenberg-Marquardt algorithm in approximating the reaction sections, thus demonstrating its potential for comprehensive investigations. These recent developments confirm the feasibility of using ANN models to gain insight into (α,n) reaction cross-sections.

Design And Analysis of a Pipe Inspection Caterpillar Robot Using Different Materials

The development of a pipe inspection robot employing caterpillar locomotion and constructed with PLA and ABS materials entails a comprehensive process. Beginning with the delineation of specific requirements, such as size and inspection capabilities, the conceptual design is conceived, outlining the robot's features. The subsequent phase involves meticulous 3D modelling using software like SolidWorks, followed by ANSYS analysis to simulate and optimize the robot's structural integrity, stress distribution, and overall performance. PLA and ABS are chosen as 3D printing materials, with PLA offering ease of printing and biodegradability, while ABS contributes durability and temperature resistance. The prototype is then constructed, incorporating motors, sensors, and the caterpillar locomotion system. ANSYS analysis is employed to assess the robot's behaviour under varying conditions, ensuring robustness and reliability. The iterative process of testing and refining the design leads to the optimization of the robot's performance. The final steps involve thorough documentation, regulatory compliance checks, and potential scalability for diverse applications, culminating in the deployment of the caterpillar robot for effective pipe inspection tasks.
 This paper delves into the design and static structural analysis of a caterpillar robot model utilizing ANSYS, with a specific emphasis on assessing two distinct materials: Acrylonitrile Butadiene Styrene (ABS) and Polylactic acid (PLA). The study involves a comprehensive comparison of various parameters between the two materials, aiming to provide insights into their respective performance and suitability for the caterpillar robot's structural components.

Innovation and Practice of Educational Management Path Building in Colleges and Universities in the Context of Big Data

Abstract In this paper, a digital education management information system (e-MIS) is constructed based on big data, which realizes the comprehensive processing of education administration and school management data. Through the fuzzy comprehensive evaluation model, the e-MIS system was evaluated and optimized. Then, the e-MIS system was practically applied to the fields of teaching effectiveness evaluation, teachers’ work assessment, scholarship evaluation and dormitory management. Finally, the significant impact of the e-MIS system on the enhancement aspects of new student enrollment education was empirically verified. The results show that the average score of freshmen’s school rules and discipline education is 4.35, learning culture education is 4.53, mental health education is 4.41, and life guidance education is 4.31, all of which are in the middle-upper level, as calculated by the e-EMIS evaluation model. This paper is a positive exploration of improving the efficiency of student education and management in local colleges and universities and promoting the overall development of students.

A dual-branch selection method with pseudo-label based self-training for semi-supervised smoke image segmentation

In recent years, there has been significant progress in fully supervised learning for smoke image segmentation. However, these methods rely heavily on manually annotated labels, which is difficult for the field of smoke segmentation that lacks public datasets. In this work, we address this issue by employing a semi-supervised semantic segmentation approach that iteratively assigns pseudo-labels to unlabeled data, reducing the need for smoke-labeled data. However, traditional self-training methods tend to prioritize reliable pseudo-labels and overlook the potential of unreliable pseudo-labels. At the same time, there may be false reliable pseudo-labels that introduce false supervisory signals, leading to the confirmation bias problem. To alleviate these problems, we propose a novel method called the Dual-Branch Pseudo-label Selection (DBPS). DBPS focuses on two aspects: selecting more reliable pseudo-labels and exploiting the potential of unreliable pseudo-labels. Intuitively, one branch identifies potentially incorrect regions within reliable pseudo-labels, while the other identifies potentially correct regions within unreliable pseudo-labels. This dual-branch approach ensures a more comprehensive and balanced pseudo-label selection process. In addition, we propose a multi-scale decoder representation head that leverages pixel-level contrastive learning to learn discriminative feature representations. This allows us to enable the gathering of similar samples and the scattering of dissimilar samples. The experimental results on the synthetic smoke dataset demonstrate the effectiveness of our proposed method in mitigating the noise of pseudo-labels and achieving outstanding performance, particularly in challenging scenarios with a limited number of labeled data.

Issues of understanding a literary text from a psycholinguistic perspective

The article describes psycholinguistic methods used to model the processes of modern schoolchildren’s perception and comprehension of the text of A. P. Chekhov’s short story "The Death of a Government Clerk". We postulate that the processes of perceiving and understanding fictional texts are conditioned by several factors. These include the quality of the text itself, its size, the closeness of the work to recipients, recipients’ gender- and age-specific qualities, and the peculiarities of their conceptual systems. The research data are the results of two-stage psycholinguistic experiments conducted with the participation of Omsk secondary and vocational school students. The first stage was performed according to the method of semantic differential. It aimed to diagnose the process of emotional perception in the experimental subjects. In the second stage, the students had to answer questions concerning the basic semantic lines of the text. The first stage revealed an emotional attitude to the text and the influence of the gender factor. The former was manifested in different intensities of the evaluation parameters concentrated in the area of negative coefficients. The second stage showed the results of the comprehension process as the totality of gender-, socially, and psychologically determined interpretations of the semantic content of the text. The comprehension process is associated with choosing certain supports such as particular textual signs, the socio-psychological model of the author, the plot, and others.

D2WFP: A Novel Protocol for Forensically Identifying, Extracting, and Analysing Deep and Dark Web Browsing Activities

The use of the unindexed web, commonly known as the deep web and dark web, to commit or facilitate criminal activity has drastically increased over the past decade. The dark web is a dangerous place where all kinds of criminal activities take place, Despite advances in web forensic techniques, tools, and methodologies, few studies have formally tackled dark and deep web forensics and the technical differences in terms of investigative techniques and artefact identification and extraction. This study proposes a novel and comprehensive protocol to guide and assist digital forensic professionals in investigating crimes committed on or via the deep and dark web. The protocol, named D2WFP, establishes a new sequential approach for performing investigative activities by observing the order of volatility and implementing a systemic approach covering all browsing-related hives and artefacts which ultimately resulted in improving the accuracy and effectiveness. Rigorous quantitative and qualitative research has been conducted by assessing the D2WFP following a scientifically sound and comprehensive process in different scenarios and the obtained results show an apparent increase in the number of artefacts recovered when adopting the D2WFP which outperforms any current industry or opensource browsing forensic tools. The second contribution of the D2WFP is the robust formulation of artefact correlation and cross-validation within the D2WFP which enables digital forensic professionals to better document and structure their analysis of host-based deep and dark web browsing artefacts.

SCInter: a comprehensive single-cell transcriptome integration database for human and mouse

Single-cell RNA sequencing (scRNA-seq), which profiles gene expression at the cellular level, has effectively explored cell heterogeneity and reconstructed developmental trajectories. With the increasing research on diseases and biological processes, scRNA-seq datasets are accumulating rapidly, highlighting the urgent need for collecting and processing these data to support comprehensive and effective annotation and analysis. Here, we have developed a comprehensive Single-Cell transcriptome integration database for human and mouse (SCInter, https://bio.liclab.net/SCInter/index.php), which aims to provide a manually curated database that supports the provision of gene expression profiles across various cell types at the sample level. The current version of SCInter includes 115 integrated datasets and 1016 samples, covering nearly 150 tissues/cell lines. It contains 8016,646 cell markers in 457 identified cell types. SCInter enabled comprehensive analysis of cataloged single-cell data encompassing quality control (QC), clustering, cell markers, multi-method cell type automatic annotation, predicting cell differentiation trajectories and so on. At the same time, SCInter provided a user-friendly interface to query, browse, analyze and visualize each integrated dataset and single cell sample, along with comprehensive QC reports and processing results. It will facilitate the identification of cell type in different cell subpopulations and explore developmental trajectories, enhancing the study of cell heterogeneity in the fields of immunology and oncology.

Measuring Care-Related Regret Intensity: Reliability and Validation of a 10-item Scale Among Chinese Nursing Interns

AimThe 10-item Care-Related Regret Intensity Scale (RIS) assesses the experience and intensity of care-related regrets. The aim of this study was to validate a Chinese version of the RIS and examine its reliability and validity when applied to Chinese nursing interns. BackgroundNursing students, the future healthcare workforce, often face clinical errors and adverse events due to limited knowledge and experience. Assessing care-related regret during their internships is crucial for their development. MethodsThrough a process involving translation, back-translation and cross-cultural adaptation, a Chinese version of the Care-Related Regret Intensity Scale was developed. A convenience sampling method was employed to survey 529 nursing interns to validate the reliability and validity of the scale. ResultsThe Chinese version of the scale, consisting of 10 items, demonstrated a single dimension, explaining 66.77% of the total variance according to exploratory factor analysis. Content validity for the 10 items ranged from 0.800 to 1.000, with a scale-level content validity of 0.980. Confirmatory factor analysis indicated a well-fitting model (CFI = 0.968, GFI = 0.921, NFI = 0.952, IFI = 0.969, TLI = 0.957, RMSEA = 0.088). Convergent validity was supported by an Average Variance Extracted (AVE) of 0.636 and a Composite Reliability (CR) of 0.946. The scale also exhibited strong reliability with a Cronbach's α coefficient of 0.945, a split-half reliability coefficient of 0.903 and a test-retest reliability of 0.901. ConclusionsThis comprehensive translation and validation process confirms the suitability of the Chinese version of the Care-Related Regret Intensity Scale for use among Chinese nursing interns.

Leveraging the functionality of Research Electronic Data Capture (REDCap) to enhance data collection and quality in the Opioid Analgesic Reduction Study.

The Opioid Analgesic Reduction Study is a double-blind, prospective, clinical trial investigating analgesic effectiveness in the management of acute post-surgical pain after impacted third molar extraction across five clinical sites. Specifically, Opioid Analgesic Reduction Study examines a commonly prescribed opioid combination (hydrocodone/acetaminophen) against a non-opioid combination (ibuprofen/acetaminophen). The Opioid Analgesic Reduction Study employs a novel, electronic infrastructure, leveraging the functionality of its data management system, Research Electronic Data Capture, to not only serve as its data reservoir but also provide the framework for its quality management program. Within the Opioid Analgesic Reduction Study, Research Electronic Data Capture is expanded into a multi-function management tool, serving as the hub for its clinical data management, project management and credentialing, materials management, and quality management. Research Electronic Data Capture effectively captures data, displays/tracks study progress, triggers follow-up, and supports quality management processes. At 72% study completion, over 12,000 subject data forms have been executed in Research Electronic Data Capture with minimal missing (0.15%) or incomplete or erroneous forms (0.06%). Five hundred, twenty-three queries were initiated to request clarifications and/or address missing data and data discrepancies. Research Electronic Data Capture is an effective digital health technology that can be maximized to contribute to the success of a clinical trial. The Research Electronic Data Capture infrastructure and enhanced functionality used in Opioid Analgesic Reduction Study provides the framework and the logic that ensures complete, accurate, data while guiding an effective, efficient workflow that can be followed by team members across sites. This enhanced data reliability and comprehensive quality management processes allow for better preparedness and readiness for clinical monitoring and regulatory reporting.

Assessment of Co-Gasification Methods for Hydrogen Production from Biomass and Plastic Wastes

In recent decades, economic development and population growth has been accompanied by the generation of billions of tonnes of solid residues or municipal “wastes”, a substantial portion of which is composed of plastics and biomass materials. Combustion-based waste-to-energy is a viable and mature method of extracting calorific value from these end-of-life post-recyclable materials that are otherwise landfilled. However, alternative thermochemical methods, such as gasification, are becoming attractive due to the ability to synthesize chemical precursors for supply chain recirculation. Due to the infancy of gasification technology deployment, especially in the context of anthropogenic CO2 emission reduction, additional systems engineering studies are necessary. Herein, we conduct an attributional life cycle analysis to elucidate the syngas production and environmental impacts of advanced thermochemical gasification methods for the treatment of biomass and plastic wastes obtained from municipal solid wastes, using a comprehensive thermodynamic process model constructed in AspenTech. Feedstock composition, process parameters, and gasification methods are varied to study the effects on syngas quality, yield, power generation potential, and overall greenhouse gas emissions. Steam-based gasification presents up to 38% reductions in CO2 emissions when compared to conventional thermochemical methods. Using gasifier-active materials, such as metal hydroxides, can also further reduce CO2 emissions, and realizes a capture load of 1.75 tonnes of CO2 per tonne of plastic/stover feedstock. This design alteration has implications for reductions in CAPEX due to the mode of CO2 capture utilized (e.g., solid sorbent vs. liquid SELEXOL). The use of renewable energy to provide a method to generate steam for this process could make the environmental impact of such MSW gasification processes lower by between 60–75% tonnes of CO2 per tonne of H2. Overall, these results can be used to inform the guidance of advanced waste gasification methods as a low-carbon transition towards a circular economy.

An Overview of Management Status and Recycling Strategies for Plastic Packaging Waste in China

Given their exceptional performance, plastic packaging products are widely used in daily life, and the dramatic expansion in plastic packaging waste (PPW) has exacerbated environmental problems. Many countries have enacted laws and developed recycling technologies to manage plastic packaging waste in consideration of the nature of PPW as both garbage and a resource. As the world’s largest producer and consumer of plastics, China has also taken measures to address this issue. This paper presents the latest management regulations and recycling strategies for PPW in China. Based on an analysis of the current management status of PPW and recycling technologies and their carbon emission impacts, some management suggestions and a comprehensive full-chain recycling process were put forward. We supposed that management challenges that need to be overcome in the future can be solved through the improvement of green designs for plastic packaging, manufacturing technology updates, consumption concept changes, and the high-value utilization of PPW. This paper aims to provide valuable references for government decisions on PPW management and, furthermore, to set up an economically sensible and industrially feasible PPW solution and boost the development of PPW recycling.

How smart health leaders make intuitive decisions.

A rational decision-making process enables a leader to process information clearly and logically and thus allows for accurate perception and interpretation of the event. It is believed this process prevents leaders from excessively distorting reality and being impacted by cognitive biases, both of which are possible, particularly under stressful conditions. But what happens when the decision-making environment is rapidly changing and the leader does not have time to deploy a thorough, comprehensive rational decision-making process? In time-compressed decision-making environments, leaders must often make quick, accurate decisions, with incomplete, inaccurate, or rapidly changing information, under extremely stressful conditions. To improve the ability of a leader to make high-stress, time-compressed decisions under rapidly changing conditions, we offer the intuitive decision-making process as an alternative to rational decision-making and discuss five components essential to improve intuitive decision-making outcomes.

Exploring the Release of Elastin Peptides Generated from Enzymatic Hydrolysis of Bovine Elastin via Peptide Mapping.

To enhance the understanding of enzymatic hydrolysis and to accelerate the discovery of key bioactive peptides within enzymatic products, this research focused on elastin as the substrate and investigated the variations in peptide profiles and the production of key bioactive peptides (those exceeding 5% of the total) and their impacts on the biological activity of the hydrolysates. Through the application of advanced analytical techniques, such as stop-flow two-dimensional liquid chromatography and ultra-high-performance liquid chromatography-tandem mass spectrometry, the research tracks the release and profiles of peptides within elastin hydrolysates (EHs). Despite uniform peptide compositions, significant disparities in peptide concentrations were detected across the hydrolysates, hinting at varying levels of bioactive efficacy. A comprehensive identification process pinpointed 403 peptides within the EHs, with 18 peptides surpassing 5% in theoretical maximum content, signaling their crucial role in the hydrolysate's bioactivity. Of particular interest, certain peptides containing sequences of alanine, valine, and glycine were released in higher quantities, suggesting Alcalase® 2.4L's preference for these residues. The analysis not only confirms the peptides' dose-responsive elastase inhibitory potential but also underscores the nuanced interplay between peptide content, biological function, and their collective synergy. The study sets the stage for future research aimed at refining enzymatic treatments to fully exploit the bioactive properties of elastin.

National Health and Medical Research Council statement on electronic cigarettes: 2022 update.

Electronic cigarette (e-cigarette) use in Australia has rapidly increased since the 2017 National Health and Medical Research Council (NHMRC) Chief Executive Officer (CEO) statement on e-cigarettes. The type of products available and the demographic characteristics of people using these products have changed. New evidence has been published and there is growing concern among public health professionals about the increased use, particularly among young people who do not currently smoke combustible cigarettes. The combination of these issues led NHMRC to review the current evidence and provide an updated statement on e-cigarettes. In this article, we describe the comprehensive process used to review the evidence and develop the 2022 NHMRC CEO statement on electronic cigarettes. E-cigarettes can be harmful; all e-cigarette users are exposed to chemicals and toxins that have the potential to cause adverse health effects. There are no health benefits of using e-cigarettes if you do not currently smoke tobacco cigarettes. Adolescents are more likely to try e-cigarettes if they are exposed to e-cigarettes on social media. Short term e-cigarette use may help some smokers to quit who have been previously unsuccessful with other smoking cessation aids. There are other proven safe and effective options available to help smokers to quit. The evidence base for the harms of e-cigarette use has strengthened since the previous NHMRC statement. Significant gaps in the evidence base remain, especially about the longer term health harms of using e-cigarettes and the toxicity of many chemicals in e-cigarettes inhaled as an aerosol.

Unveiling the role of transition metal catalysts in low-temperature, liquid-phase isopropanol oxidation

Driven by the increasing need to optimize industrial chemical reactions through effective catalysts, this study reports the comprehensive oxidation process of isopropanol under liquid phase conditions at subambient temperatures, investigating the catalytic potential of transition metal oxides. The complete oxidation of isopropanol within the liquid phase at reduced temperatures was investigated, employing transition metal oxides as catalysts (Cr2O3, Fe2O3, NiO, and ZnO). These solids were prepared using the sol–gel method and the precipitation method. The results from structural (FTIR, XRD) and textural (BET) analyses were consistent with those reported in the literature. The employed solid substances exhibited favorable catalytic activity for the full oxidation of isopropanol, maintaining a consistent selectivity of 100 %, regardless of the temperature conditions under examination (40, 60, and 80 °C). These catalytic tests reveal that chromium oxide is a typical catalyst for this type of reaction. Furthermore, a kinetic analysis of the reaction was conducted, revealing that the reaction proceeds according to a sequential A-B-C mechanistic pathway. The low values of apparent activation energy for all the solids align with the high initial reaction rates in the studied temperature range. This study revealed the effective role of transition metal oxides, particularly chromium oxide, as catalysts for the total oxidation of isopropanol under liquid phase conditions at subambient temperatures, offering vital insights to enhance efficiency in chemical industrial processes.

What makes inferences reliable? The unpredictable relationship between pragmatic inference and truth

One of the main tenets of Gricean pragmatics is the distinction between what is said and what is implicated; that is, between truth-conditional and non-truth-conditional meaning. The main contribution of post-Gricean pragmatics was a new definition of what is said as a pragmatic and truth-conditional meaning (Carston, 2002; Recanati, 2010; Moeschler, 2018a). However, implicated meaning (mainly conversational implicature) is still defined as non-truth-conditional because it is cancellable without contradiction. The main consequence is that a speaker can deny having intended her implicature, which results in the hearer having no guarantee of the truthfulness of his inference. This paper explores the relationship between truth and pragmatic implicated meaning and posits that the assessment of a truth value of an inference is a crucial step in the comprehension process. To demonstrate this hypothesis, the paper develops a relevance-based approach to inference, which is coupled with Kahneman's dual cognitive system. I argue that the triggering of an inference is an intuitive and automatic process controlled by System 1, whereas its assessment as a true or false assumption is controlled by System 2. In other words, System 2 is responsible for what is defined in relevance theory as epistemic vigilance.

Relative clause comprehension in Cantonese-speaking children with and without developmental language disorder.

Developmental Language Disorder (DLD), present in 2 out of every 30 children, affects primarily oral language abilities and development in the absence of associated biomedical conditions. We report the first experimental study that examines relative clause (RC) comprehension accuracy and processing (via looking preference) in Cantonese-speaking children with and without DLD, testing the predictions from competing domain-specific versus domain-general theoretical accounts. We compared children with DLD (N = 22) with their age-matched typically-developing (TD) children (AM-TD, N = 23) aged 6;6-9;7 and language-matched (and younger) TD children (YTD, N = 21) aged 4;7-7;6, using a referent selection task. Within-subject factors were: RC type (subject-RCs (SRCs) versus object-RCs (ORCs); relativizer (classifier (CL) versus relative marker ge3 RCs). Accuracy measures and looking preference to the target were analyzed using generalized linear mixed effects models. Results indicated Cantonese children with DLD scored significantly lower than their AM-TD peers in accuracy and processed RCs significantly slower than AM-TDs, but did not differ from the YTDs on either measure. Overall, while the results revealed evidence of a SRC advantage in the accuracy data, there was no indication of additional difficulty associated with ORCs in the eye-tracking data. All children showed a processing advantage for the frequent CL relativizer over the less frequent ge3 relativizer. These findings pose challenges to domain-specific representational deficit accounts of DLD, which primarily explain the disorder as a syntactic deficit, and are better explained by domain-general accounts that explain acquisition and processing as emergent properties of multiple converging linguistic and non-linguistic processes.

Composition of bacterial blends for fermentation-induced pea protein emulsion gels using multi-property screening of lactic acid bacteria

In this study, a comprehensive screening and identification process to pinpoint lactic acid bacteria (LAB) strains capable of enhancing gel firmness and elevating the flavor profile in pea protein-based protein gels was conducted, aiming to demonstrate how tailored starter cultures for specific applications can provide high-quality sensory attributes in fermented plant-based cheese. Thirteen single strains were selected from a poll of 90 different LAB based on their acidification performance, contribution to gel firmness, removal of off-flavors and production of dairy-associated volatile organic compounds. They were combined in 64 different blends of five strains each and screened again for the same parameters. Fast acidifying strains were prioritized for food preservation reasons to inhibit undesirable spoilage. Fermentation of pea protein emulsions with Streptococcus thermophilus, Lactobacillus, and Lactococcus blends resulted in a significant reduction in volatile organic compounds associated with beany and green off-flavor, a production of dairy-associated compounds, and satisfactorily contributed to gel firmness.

The carbon budget of China: 1980–2021

As one of the world’s largest emitters of greenhouse gases, China has set itself the ambitious goal of achieving carbon peaking and carbon neutrality. Therefore, it is crucial to quantify the magnitude and trend of sources and sinks of atmospheric carbon dioxide (CO2), and to monitor China’s progress toward these goals. Using state-of-the-art datasets and models, this study comprehensively estimated the anthropogenic CO2 emissions from energy, industrial processes and product use, and waste along with natural sources and sinks of CO2 for all of China during 1980–2021. To recognize the differences among various methods of estimating greenhouse emissions, the estimates are compared with China’s National Greenhouse Gas Inventories (NGHGIs) for 1994, 2005, 2010, 2012, and 2014. Anthropogenic CO2 emissions in China have increased by 7.39 times from 1980 to 12.77 Gt CO2 a−1 in 2021. While benefiting from ecological projects (e.g., Three Norths Shelter Forest System Project), the land carbon sink in China has reached 1.65 Gt CO2 a−1 averaged through 2010–2021, which is almost 15.81 times that of the carbon sink in the 1980s. On average, China’s terrestrial ecosystems offset 14.69% ± 2.49% of anthropogenic CO2 emissions through 2010–2021. Two provincial-level administrative regions of China, Xizang and Qinghai, have achieved carbon neutrality according to our estimates, but nearly half of the administrative regions of China have terrestrial carbon sink offsets of less than 10% of anthropogenic CO2 emissions. This study indicated a high level of consistency between NGHGIs and various datasets used for estimating fossil CO2 emissions, but found notable differences for land carbon sinks. Future estimates of the terrestrial carbon sinks of NGHGIs urgently need to be verified with process-based models which integrate the comprehensive carbon cycle processes.