Hubei Polytechnic University

Abstract As the pivotal component of refrigeration systems, the performance of refrigerator compressors directly determines the overall energy efficiency, reliability, and lifespan of refrigerators. The cylinder block, a critical element of compressors, significantly influences operational efficiency, mechanical losses, and noise control. Traditional compressors typically employ integrally molded cylinder blocks, which suffer from constrained machining accuracy, high manufacturing costs, and complex assembly processes. Split-type cylinder block designs are increasingly gaining attention for their modular structure, which optimizes manufacturing and assembly while enhancing overall compressor performance. This study investigates the impact of split cylinder block structural characteristics on refrigerator compressor performance through three key aspects: Comparative analysis of design differences between split-type and monolithic cylinder blocks, highlighting advantages in machining precision, material utilization efficiency, and assembly convenience. Examination of mechanical stress distribution during compressor operation using finite element analysis (FEA) to validate structural reliability. Experimental results demonstrate that split-type blocks improve component fit accuracy and reduce frictional losses, thereby enhancing operational efficiency. Identification of modular design’s critical role in reducing production costs, increasing part interchangeability, and improving maintenance accessibility. In conclusion, split-type cylinder blocks exhibit significant advantages in improving compressor performance, reducing manufacturing expenses, and boosting assembly efficiency, demonstrating broad application potential. This research provides a theoretical foundation for structural optimization of refrigerator compressors.

Dear Editor, I am writing to express my perspectives on the article entitled “Identifying the minimum number of retrieved lymph nodes in node-negative duodenal adenocarcinoma: a retrospective analysis of real-world data and the SEER database” by Zhenxing Jiang et al[1]. This study addresses a critical clinical challenge in the management of duodenal adenocarcinoma (DA), aiming to define the optimal number of retrieved lymph nodes (rLNs) for accurate N-stage assessment. By leveraging dual-cohort data (real-world data and SEER database), the authors established stratified rLN thresholds for node-negative (N0) and node-positive (N1) DA patients, providing valuable insights for optimizing surgical and postoperative therapeutic strategies[1]. Nevertheless, after a careful review of the manuscript, we identified several critical points that require further clarification and improvement to enhance the validity and generalizability of the conclusions. First, the retrospective design inherently carries selection bias, and temporal confounding factors may have compromised the consistency of the results. This study relies on historical data with a 10-year follow-up period, during which significant advancements have been made in DA diagnostic techniques (e.g., enhanced imaging modalities) and treatment protocols (e.g., refined surgical approaches and adjuvant therapy regimens). Such dynamic changes in the clinical management system are likely to reduce the homogeneity of research data across different periods, thereby undermining the stability of the study conclusions. Notably, Figure 3B of the external validation cohort clearly demonstrates an upward trend in rLN counts over time. This observation directly corroborates the interference of time-related confounding factors, suggesting that variations in diagnosis and treatment practices across the study period may have biased the core research indicator (rLN count). Second, the lack of detailed information on surgical approaches precludes clarification of the causal relationship between rLNs and prognosis. The manuscript fails to specify the surgical procedures adopted for the included patients. Extant literature has well established that standard surgical approaches for DA are heterogeneous, including distal gastrectomy, pancreaticoduodenectomy (PD), and segmental/local excision[2]. These surgical approaches not only exert a direct and significant impact on patient prognosis but also indirectly affect prognostic analyses by altering rLN counts. For instance, Sakamoto et al confirmed that patients undergoing PD have significantly higher rLN counts than those receiving limited resection[2]. Consequently, the current study cannot distinguish between two potential causal pathways: whether rLN count itself is a prognostic factor, or whether different surgical approaches indirectly influence prognosis by modifying rLN counts through a mediating effect. We recommend the authors supplement detailed surgical data and perform mediation effect analysis to clarify the true relationship between these core variables. Third, the prognostic association pattern of rLNs remains unclear, and the methods used to determine cut-off values have inherent flaws. On one hand, existing studies have reported an association between increased rLN counts and decreased survival rates in certain tumor types[3]. However, this study did not explore whether such a specific association exists in DA, leading to an incomplete understanding of the rLN-prognosis relationship. On the other hand, the authors employed the Contal and O’Quigley method and X-tile method to determine optimal rLN cut-off values, which have three major limitations: (1) forced stratification of the continuous rLN variable (e.g., binary or ternary classification) results in loss of continuous distribution information; (2) inability to capture potential non-linear associations between rLNs and prognosis; and (3) data-driven segmentation is susceptible to outliers, leading to high overfitting risk and poor result stability. If cut-off value analysis is retained, strict statistical validation (e.g., cross-validation, multiple comparison correction, and external cohort validation) is essential to reduce bias and ensure result reliability and generalizability. A more robust alternative is to adopt restricted cubic splines or the K-Adaptive Partitioning for Survival Data method, which preserve variable continuity, accurately characterize non-linear relationships, and enhance result scientificity[4]. In conclusion, Jiang et al’s study explores an important clinical question, but the aforementioned limitations may affect the validity of its conclusions. Addressing these issues will significantly improve the study’s academic value and clinical implications.

Based on the resource-based view (RBV), this study investigates prefabricated construction supply chains using survey data from 265 respondents. Employing Structural Equation Modeling (SEM) and fuzzy-set Qualitative Comparative Analysis (fsQCA), it examines the joint effects of IT resources (IT infrastructure, IT human resources) and supply chain collaborative capability on supply chain cooperative performance, alongside the moderating role of government support. The study found that the interaction between IT infrastructure and supply chain collaborative capabilities, and the interaction between IT human resources and supply chain collaborative capabilities, both significantly positively affect supply chain cooperative performance. Government support strengthened the joint effect of information technology and supply chain collaborative. The results of fsQCA showed that there were three core configurations: the combination of IT infrastructure and IT human resources, the combination of IT infrastructure, supply chain collaborative capabilities, and government support, and the combination of IT human resources, supply chain collaborative capabilities, and government support. This study enriches the related research in the prefabricated building field, expands the framework of the resource-based view, and provides insights for enterprises to optimize resource allocation, utilize government support, and dynamically adjust strategies.

This paper proposes a manufacturing task allocation model based on bi-level planning to address the multi-agent distributed manufacturing task allocation problem within the equipment group. This model considers the group headquarters as the upper-level decision-making entity and its subsidiaries as the lower-level decision-making entity. It comprehensively considers key factors such as time, cost, and quality, and constructs a bi-level optimization objective function. The model is solved using the NSGA-II algorithm, achieving a scientific and rational allocation of manufacturing tasks. Case analysis results show that the proposed method effectively optimizes resource allocation, reduces production costs, shortens production cycles, improves product quality, and enhances the manufacturing capabilities and customer service satisfaction of subsidiaries, providing an effective solution for manufacturing task allocation within the equipment group.

ABSTRACT The existing literature lacks studies on the boundaries of digital inclusive finance on small and medium-sized enterprises’ credit accessibility (SCA). Based on 8395 samples from 2011–2022, this study finds that digital inclusive finance significantly promotes SCA, financial technology (FinTech) suppresses this effect, and financial ecosystem can mitigate the negative impact of FinTech on the relationship between digital inclusive finance and SCA. This study extends the boundaries under which digital inclusive finance promotes SCA.

Emerging evidence suggests that the onset or worsening of psoriasis can occur following COVID-19 infection or vaccination. Additionally, individuals with psoriasis may be more susceptible to COVID-19. However, the underlying mechanisms driving this phenomenon remain unclear. This study aims to explore the potential shared mechanisms and the complex interplay between psoriasis and COVID-19. Gene expression profiles for COVID-19 (GSE162183) and psoriasis (GSE54456) were obtained from the Gene Expression Omnibus (GEO) database. Common differentially expressed genes (DEGs) were identified, followed by functional annotation, protein–protein interaction (PPI) network construction, and hub gene identification. Validation of hub genes was performed using independent datasets (GSE152075 and GSE157103 for COVID-19; GSE121212 and GSE78097 for psoriasis). Receiver operating characteristic (ROC) curves were used to assess the predictive value of the hub genes. Gene set enrichment analysis (GSEA) and immune infiltration analysis were conducted, and expression patterns of the hub genes were further explored using a single-cell RNA sequencing dataset. A total of 66 common DEGs (all upregulated) were identified. The influenza A and NOD-like receptor signaling pathways were enriched in both COVID-19 and psoriasis. OAS2, MX1, IRF7, RSAD2, OASL, IFIT1, IFIT3, and ISG15 were identified as hub genes after validation, with all are under the curve (AUC) > 0.7 for COVID-19 and AUC > 0.9 for psoriasis. Upregulation of these hub genes was associated with increased infiltration of neutrophils and Th17 cells. Single-cell analysis showed that the hub genes were primarily expressed in epithelial cells in COVID-19 and keratinocytes in psoriasis. This study reveals shared pathogenic mechanisms between psoriasis and COVID-19 and provides insights into how COVID-19 may exacerbate psoriasis. The identified common pathways, hub genes, and associated cell types offer valuable guidance for future research and potential therapeutic targets.