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Fishing vessels are important contributors to global emissions in terms of greenhouse gases and air pollutants. However, few studies have addressed the emissions from fishing vessels on fishing grounds. In this study, a framework for estimating fishing vessel emissions, using a bottom-up dynamic method based on the big data from the Beidou VMS (vessel monitoring system) of fishing vessels, is proposed and applied to a survey of fishing vessel emissions in the East China Sea. The results of the study established a one-year emission inventory of fishing vessels in the East China Sea. This study was the first to use VMS data to estimate fishing vessel emissions in a fishing area, and the results will help to support the management of their carbon emissions.

Illegal, unreported, and unregulated (IUU) fishing significantly threatens marine ecosystems, disrupts the ecological balance of the oceans, and poses serious challenges to global fisheries management. This contribution presents the efficacy of China’s summer fishing moratorium using BeiDou vessel monitoring system (VMS) data from 2805 fishing vessels in the East China Sea and Yellow Sea, integrated with a deep learning framework for spatiotemporal analysis. A preprocessing protocol addressing multidimensional noise in raw VMS datasets was developed, incorporating velocity normalization and gap filling to ensure data reliability. The CNN-BiLSTM hybrid model emerged as optimal for fishing behavior classification, achieving 89.98% accuracy and an 87.72% F1 score through synergistic spatiotemporal feature extraction. Spatial analysis revealed significant policy-driven reductions in fishing intensity during the moratorium (May–August), with hotspot areas suppressed to sporadic coastal distributions. However, concentrated vessel activity in Zhejiang’s nearshore waters suggested potential illegal fishing. Post-moratorium, fishing hotspots expanded explosively, peaking in October and clustering in Yushan, Zhoushan, and Yangtze River estuary fishing grounds. Quarterly patterns identified autumn–winter 2021 as peak fishing seasons, with hotspots covering >80% of East China Sea grounds. The framework enables real-time fishing state detection and adaptive spatial management via dynamic closure policies. The findings underscore the need for strengthened surveillance during moratoriums and post-ban catch regulation to mitigate overfishing risks.

The complex interdependencies among numerous safety risk factors influencing oil tanker loading/unloading operations constitute a focal point in academic research. To enhance safety management in oil port operations, this study conducts a risk analysis of oil tanker berthing and cargo transfer safety. Initially, safety risk factors are identified based on the Wu-li Shi-li Ren-li (WSR) systems methodology. Subsequently, a hybrid weighting approach integrating the Fuzzy Analytic Hierarchy Process (FAHP), G2 method, and modified CRITIC technique is employed to calculate indicator weights. These weights are then synthesised into a combined weight (GVW) using cooperative game theory and variable weight theory. Further, by integrating grey theory with the cloud model (GCM), a risk assessment is performed using Tianjin Port as a case study. Results indicate that the higher-risk indicators for Tianjin Port include vessel traffic density, safety of berthing/unberthing operations, safety of cargo transfer operations, safety of pipeline transfer operations, psychological resilience, proficiency of pilots and captains, and emergency management capability. The overall comprehensive risk evaluation value for Tianjin Port is 0.403, corresponding to a “Moderate Risk” level. Comparative experiments demonstrate that the results generated by this model align with those obtained through Fuzzy Comprehensive Evaluation Methods. However, the proposed GVW-GCM framework provides a more objective and accurate reflection of safety risks during tanker operations. Based on the computational outcomes, targeted recommendations for risk mitigation are presented. The integrated weighting model—incorporating game theory and variable weight concepts—coupled with the grey cloud methodology, establishes an interpretable and reusable analytical framework for the safety assessment of oil port operations under diverse port conditions. This approach provides critical decision support for constructing comprehensive management systems governing oil tanker loading/unloading operations.

In response to the increasing frequency of maritime traffic accidents along China’s coast, this study develops an accident-cause analysis framework that integrates an optimized Bidirectional Encoder Representations from Transformers (BERT) with a Bidirectional Long Short-Term Memory network (BiLSTM), combined with the Apriori association rule algorithm. Systematic performance comparisons demonstrate that the BERT + BiLSTM architecture achieves superior unstructured-text-processing capability, attaining 89.8% accuracy in accident-cause classification. The hybrid framework enables comprehensive investigation of complex interactions among human factors, vessel characteristics, environmental conditions, and management practices through multidimensional analysis of accident reports. Our findings identify improper operations, fatigue-related issues, illegal modifications, and inadequate management practices as primary high-risk factors while revealing that multi-factor interaction patterns significantly influence accident severity. Compared with traditional single-factor analysis methods, the proposed framework shows marked improvements in Natural Language Processing (NLP) efficiency, classification precision, and systematic interpretation of cross-factor correlations. This integrated approach provides maritime authorities with scientific evidence to develop targeted accident prevention strategies and optimize safety management systems, thereby enhancing maritime safety governance along China’s coastline.

Background There is controversy regarding the optimal treatment for stage IIIA-N2 non-small cell lung cancer (NSCLC). We aimed to address this crucial issue through a frequentist network meta-analysis. Methods We conducted a literature database search for randomized controlled trials comparing the following treatment modalities before March 1st, 2023: surgery, radiotherapy, chemotherapy, targeted therapy, immunotherapy, and various combinations of these treatments. Summary data on overall survival (OS) and treatment - related deaths (trDeath) were analyzed using frequentist methods. Results Twenty - two randomized controlled trials (RCTs) with 3269 participants were included, covering 17 treatment regimens. In terms of overall survival, surgery followed by adjuvant targeted therapy (S - T), neoadjuvant targeted therapy followed by surgery and adjuvant targeted therapy (T-S-T), and neoadjuvant chemotherapy followed by surgery and adjuvant chemotherapy (C-S-C) were relatively more advantageous than other treatment regimens. Overall, S-T is the most likely treatment option to prolong OS, with a 59.8% likelihood, while immunotherapy plus chemotherapy followed by surgery and adjuvant chemotherapy (IC - S - C) demonstrates good safety. Conclusion S-T and T-S-T treatments have the greatest potential to be the optimal overall survival treatments for stage IIIA-N2 NSCLC patients with positive driver genes, demonstrating significant clinical application prospects. While for patients with negative driver genes, C-S-C treatments benefit the most. The protocol was registered in the Prospective Register of Systematic Reviews, PROSPERO (CRD42022372711).

Non-small cell lung cancer (NSCLC) is one of the most prevalent cancers. As reported, long non-coding RNAs (lncRNAs) induce various biological behaviors in cancers. LncRNA PCGEM1 prostate-specific transcript (PCGEM1) is reported to exert carcinogenic effect on certain cancers. Our research aimed to explore the role of PCGEM1 in NSCLC. We enrolled forty NSCLC patients to explore PCGEM1 expression in clinical NSCLC tissues. Colony formation assay, CCK-8, Transwell assay were conducted to reveal cell proliferation, viability, migration and invasion. Luciferase reporter assay, RNA pull down, and RIP assay were performed to investigate the downstream axis of PCGEM1. PCGEM1 was significantly upregulated in NSCLC cells and tissues. Subsequently, in vitro loss-of-function experiments illustrated the carcinogenic role of PCGEM1 in NSCLC through promoting viability, proliferation, migration, and invasion. MiR-590-3p was confirmed to be a downstream gene of PCGEM1. Furthermore, SRY-box transcription factor 11 (SOX11) was verified to be a target of miR-590-3p. Additionally, rescue experiments indicated that miR-590-3p inhibitor or pcDNA3.1/SOX11 rescued the impacts of downregulated PCGEM1 on NSCLC cell proliferation, viability, migration and invasion. LncRNA PCGEM1 aggravated proliferative and migrative abilities in NSCLC via the miR-590-3p/SOX11 axis.

Background Accurate preoperative identification of high-risk pathological features in early-stage lung adenocarcinoma is critical for guiding surgical decisions and improving patient outcomes. This study aimed to develop and validate a nomogram to predict high-risk pathology in clinical stage IA lung adenocarcinoma located in the left upper lobe (LUL), an anatomical site with distinct surgical implications. Methods We retrospectively reviewed 545 patients with clinical stage IA LUL adenocarcinoma who underwent surgery between January 2018 and May 2022. The cohort was randomly divided into training (80%) and validation (20%) sets. Independent predictors were identified via multivariate logistic regression and further validated using LASSO regression. A nomogram was constructed and evaluated using ROC curves, calibration plots, decision curve analysis (DCA), and bootstrap resampling. Results High-risk pathology, defined by the presence of solid/micropapillary predominant patterns, complex glandular architecture, STAS, or LVI, was observed in 19.1% of patients. Four independent preoperative predictors were identified: elevated CEA levels, larger CT-measured tumor size, invasive histology on frozen section, and higher mean CT value. The nomogram demonstrated excellent discriminative ability, with AUCs of 0.837 in the training set and 0.865 in the validation set. Internal validation by bootstrap resampling confirmed model stability. Conclusion The proposed nomogram integrates routinely available clinical, radiologic, and intraoperative variables to enable individualized preoperative risk assessment for high-risk pathology in stage IA LUL adenocarcinoma. This tool may assist surgeons in tailoring surgical approaches and identifying patients who may benefit from more extensive resection or adjuvant therapy. Prospective external validation is required to confirm generalizability.

Background Neuron-specific enolase (NSE) has become a widely used and easily attainable laboratory assay of small cell lung cancer (SCLC). However, the prognostic value of NSE for SCLC patients remains controversial. The aim of the study was to evaluate the correlation between elevated serum NSE before therapy and survival of SCLC patients. Methods We performed a systematic review and meta-analysis. A systematic literature search was conducted in PubMed, Embase, and the Cochrane Central Register from the inception dates to December 2019. Eligible articles were included according to inclusion and exclusion criteria; then, data extraction and quality assessment were performed. The primary outcome was overall survival (OS), and the secondary endpoint was progression-free survival (PFS). Results We identified 18 studies comprising 2981 patients. Pooled results revealed that elevated NSE was associated with worse OS (HR = 1.78, 95% CI 1.55–2.06, p < 0.001) and PFS (HR = 1.50, 95% CI 1.16–1.93, p = 0.002). In subgroup analysis, elevated NSE did not predict worse OS in patients who received only chemotherapy (HR 1.22, 95% CI 0.96–1.55, p = 0.10) or part of whom received surgical resection before chemotherapy and radiotherapy (HR = 2.16, 95% CI 0.82–5.69, p = 0.12). Conclusion Elevated serum NSE before any therapy of SCLC patients may be a negative prognostic factor for OS and PFS. The prognostic value of NSE for OS was particularly observed in patients treated by standard management.