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This paper introduces RWA-BFT, a reputation-weighted asynchronous Byzantine Fault Tolerance (BFT) consensus algorithm designed to address the scalability and performance challenges of blockchain systems in large-scale IoT scenarios. Traditional centralized IoT architectures often face issues such as single points of failure and insufficient reliability, while blockchain, with its decentralized and tamper-resistant properties, offers a promising solution. However, existing blockchain consensus mechanisms struggle to meet the high throughput, low latency, and scalability demands of IoT applications. To address these limitations, RWA-BFT adopts a two-layer blockchain architecture; the first layer leverages reputation-based filtering to reduce computational complexity by excluding low-reputation nodes, while the second layer employs an asynchronous consensus mechanism to ensure efficient and secure communication among high-reputation nodes, even under network delays. This dual-layer design significantly improves performance, achieving higher throughput, lower latency, and enhanced scalability, while maintaining strong fault tolerance even in the presence of a substantial proportion of malicious nodes. Experimental results demonstrate that RWA-BFT outperforms HB-BFT and PBFT algorithms, making it a scalable and secure blockchain solution for decentralized IoT applications.

In order to build a better understanding of the relationship between depositing mode and porosity, microstructure, and properties in wire + arc additive manufacturing (WAAM) 2319-Al components, several Al-6.3%Cu deposits were produced by WAAM technique with cold metal transfer (CMT) variants, pulsed CMT (CMT-P) and advanced CMT (CMT-ADV). Thin walls and blocks were selected as the depositing paths to make WAAM samples. Porosity, microstructure and micro hardness of these WAAM samples were investigated. Compared with CMT-P and thin wall mode, CMT-ADV and block process can effectively reduce the pores in WAAM aluminum alloy. The microstructure varied with different depositing paths and CMT variants. The micro hardness value of thin wall samples was around 75 HV from the bottom to the middle, and gradually decreased toward the top. Meanwhile, the micro hardness value ranged around 72–77 HV, and varied periodically in block samples. The variation in micro hardness is consistent with standard microstructure characteristics.

Federated learning enables multiple intelligent devices to collaboratively perform machine learning tasks while preserving local data privacy. However, traditional FL architectures face challenges such as centralization and lack of effective defense mechanisms against malicious nodes, particularly in large-scale edge computing scenarios, which can lead to system instability. To address these challenges, this paper proposes a cross-zone secure federated learning method with blockchain and credibility mechanism, named CS-FL. By constructing a dual-layer blockchain network and introducing a blockchain ledger between zone servers, CS-FL establishes a decentralized trust mechanism for index detection and model aggregation. In node selection, CS-FL considers multiple dimensions, including node quality, communication resources, and historical credibility, and employs a three-stage mechanism that introduces lightweight probe tasks to assess node status before formal FL training, ensuring high-quality nodes participate. Additionally, the credibility incentive mechanism penalizes nodes that bypass probe mechanism and engage in malicious behaviors, effectively mitigating the impact of deceptive attacks. Experimental results show that CS-FL significantly improves the defense performance of FL, reducing attack success rates from 75–85% to below 5–20% when facing different types of threats, and effectively maintaining the training accuracy of the FL model. This demonstrates the potential of CS-FL to enhance the security and stability of FL systems in complex edge computing scenarios.

Signet-ring cell carcinoma (SRCC) has specific epidemiology and oncogenesis in gastric cancer, however, with no systematical investigation for prognostic genomic features. Here we report a systematic investigation conducted in 1868 Chinese gastric cancer patients indicating that signet-ring cells content was related to multiple clinical characteristics and treatment outcomes. We thus perform whole-genome sequencing on 32 pairs of SRC samples, and identify frequent CLDN18-ARHGAP26/6 fusion (25%). With 797 additional patients for validation, prevalence of CLDN18-ARHGAP26/6 fusion is noticed to be associated with signet-ring cell content, age at diagnosis, female/male ratio, and TNM stage. Importantly, patients with CLDN18-ARHGAP26/6 fusion have worse survival outcomes, and get no benefit from oxaliplatin/fluoropyrimidines-based chemotherapy, which is consistent with the fact of chemo-drug resistance acquired in CLDN18-ARHGAP26 introduced cell lines. Overall, this study provides insights into the clinical and genomic features of SRCC, and highlights the importance of frequent CLDN18-ARHGAP26/6 fusions in chemotherapy response for SRCC. Signet-ring cell carcinoma (SRCC) is a unique type of gastric cancer with no prognostic features. Here, the authors report a CLDN18-ARHGAP26/6 gene fusion in patients with a high signet-ring cell content, poor survival outcomes, and who experience no benefit from platinum/fluoropyrimidines-based chemotherapy.

100-seed weight (100-SW) is a critical determinant of soybean yield. The identification and functional characterization of its underlying genes are therefore essential for the genetic improvement of seed and yield-related traits. A residual heterozygous line (RHL) segregating for 100-SW was derived from a recombinant inbred line (RIL) population generated by crossing small-seed (19.75 ± 1.93 g) and large-seed (26.20 ± 0.82 g) soybean parents. Phenotypic segregation of 100-SW was analyzed, and Chi-square test was used to verify the segregation ratio. Bulked segregant analysis combined with whole-genome sequencing (BSA-seq) was performed using both Euclidean distance and index algorithms to map the target gene. Functional annotation, molecular marker validation, and germplasm resequencing were conducted to identify the key candidate gene and its haplotypes. Phenotypic analysis showed significant segregation and normal distribution of 100-SW in the RHL, with a Chi-square-verified 1:2:1 segregation ratio, indicating control by a single nuclear gene. BSA-seq mapped the gene to a 5.46 Mb region on chromosome 19, where 74 non-synonymous SNPs in coding sequences were identified (including one causing initiation codon loss), distributed across 36 genes. GmDt1 ( Glycine max Determinant stem 1 ) was confirmed as the key candidate gene, with a G-to-T non-synonymous mutation in its first exon as the functional locus (validated in the original RIL population). Resequencing of diverse germplasm classified GmDt1 into five haplotypes; the large-seed haplotype GmDt1-H2 was absent in wild soybeans, present in 9.07% of landraces, and 15.83% of cultivated soybeans. The gradual increase in the frequency of GmDt1-H2 from wild to cultivated soybeans suggests that this haplotype has been positively selected during soybean breeding. Identification of GmDt1 and its functional mutation provides a valuable molecular target for the genetic improvement of soybean seed traits and yield.

Digital transformation (DT) has become pervasive in all aspects of our society, forcing businesses, industries, governments, and individuals to discover new ways and modes of operating. The connections among DT, economic growth, and ecological systems are complex and multifaceted, and they are particularly influenced by market dynamics and spatial elements. Considering spatial dependence, this study analyzed the nexuses between DT and major environmental pollutants in China at the prefecture (i.e., city) level. Using spatial Durbin models, the empirical findings indicate that DT exerts a significant nonlinear impact on various environmental pollutants; moreover, inverted U-shaped patterns of the spatial spillover effect were observed at the nexus between DT and CO2. Furthermore, we examined three valid channels (i.e., technological progress, industrial structural upgrading, and green finance) that form a transmission mechanism between DT and environmental pollutants. In addition, a heterogeneity analysis revealed that DT imposes a relatively stronger impact on CO2, carbon intensity, and PM2.5 in developed regions, suggesting that the impact of DT on environmental pollution is sensitive according to regional development status. The empirical evidence presented in this article can guide policy directions for different governments worldwide that are experiencing rapid industrial transitions and technological expansion.

A novel method was proposed to improve the bond behavior of new-to-old concrete interface, which was beneficial to introduce the fiber-reinforced concrete only at the old concrete interface. This study investigated the effect of the fiber addition, strength grade of new concrete, interfacial angle, and surface treatment types on the bond behavior in terms of the new-to-old concrete through the axial tensile tests. The three-dimensional digital image correlation technique (3D-DIC) and scanning electron microscope were adopted to evaluate the variation of specimen surface strain distributions and microstructure of fiber-reinforced concrete and bond interface between new-to-old concrete. The experimental results indicated that interfacial angle and surface treatment type were significantly promoted bond behaviors, while the specimen cooperating with steel fibers had the highest bond strength. Besides, the maximum strain locations obtained from 3D-DIC method were the same as the location of the specimen failure, which indicated the 3D-DIC method can be adopted to forecast the structural failure. The microcrack strain located in the major crack was decreased with the development of the major crack. Ample crystals and Ca(OH)2 were generated in the interface between the new-to-old concrete to weaken the bond strength. Moreover, this paper provided the mechanics-driven and machine learning method to predict the bond strength. This study provides a new interface bonding method for the fabricated and large span structure to effectively avoid cracking of new-to-old concrete.

PurposeThis study aims to investigate the extent to which overeducation imposes wage effects on university graduates, taking into account the individual heterogeneity due to skills and innate ability.Design/methodology/approachUsing Graduates Occupation and Mobility Survey (GOMS) 2019 and Korea Dictionary of Occupations (KDOT) 2019, the overeducated and adequately educated graduates are differentiated by the job analysis (JA) measure. To unveil the masked results, the unconditional quantile regression (UQR) accompanying skills and field of study mismatches is adopted to explore the wage effects of overeducation across the overall wage distribution.FindingsEmpirical evidence shows that the incidence of overeducation is high; however, overeducated graduates only suffer a 6.5% wage loss relative to their adequately matched peers. The findings indicate that regardless of being derived from either overskilled or field of study mismatch, genuine overeducation impose a higher wage penalty at all percentiles relative to the apparent overeducation. Meanwhile, high-ability men suffer lower-wage penalties than their low-ability peers, whereas the inverted “U” pattern is exhibited for women. The theoretical hypotheses differ depending on the estimated results by gender.Research limitations/implicationsEach measure of educational mismatch has been criticized for its insurmountable shortcoming. The recent graduates are likely to overstate the job requires of skills.Originality/valueThis paper contributes to the insufficient evidence on the multiple aspects of wage effects of overeducation by providing new and rigorous examinations and by focusing on the country experiencing rapid economic growth, industrial upgrading and educational expansion.

Purpose In online purchase for dietary supplements, due to the lack of professional advice from pharmacists, electronic word-of-mouth (eWOM) has become an important source of information for consumers to make purchase decisions. How can firms use eWOM resources to increase sales? The purpose of this paper is to provide practical methods for firms by exploring the effects of eWOM on sales and developing a sales prediction model based on eWOM. Design/methodology/approach The data came from 120 dietary supplements on Tmall.com. The authors extracted the product sales as dependent variable and 11 eWOM factors as independent variables. The multicollinearity was tested by using variance inflation factor and least absolute shrinkage and selection operator. The multiple linear regression was used to investigate the effects of eWOM on sales. Drawing on white- and black-box approaches, six models were developed. Comparing the root mean square error, the authors selected the optimal one as their target sales prediction model. Findings Product ratings, total reviews and favorites are positively and strongly associated with sales. Questions and additional reviews have negative effects on sales. The random forest model has the best prediction performance. Originality/value The research focuses on eWOM of dietary supplement. First, the authors show that easily accessible eWOM from online platforms can be used to evaluate effects and predict sales. Second, the authors introduce white- and black-box models through machine learning to assess eWOM. Firms could use the described models to foster their marketing initiatives.

Fibroblast growth factor receptor (FGFR) signalling is a crucial regulator of endothelial metabolism and vascular development. The role of fibroblasts in vascular development The development of blood vessel networks involves the growth and spread of endothelial cells. Recent studies suggest that these processes are affected by changes in cellular metabolism, but the role of fibroblast growth factors (FGFs) is poorly understood. Michael Simons and colleagues identify FGF receptor signalling as a crucial regulator of vascular development andendothelial cell proliferation in adult tissues. They explore the molecular basis of this effect and find that FGFs control endothelial cell glycolysis through MYC-dependent regulation of hexokinase 2 expression. The authors suggest that understanding this pathway may guide investigations into targeted therapies for diseases associated with irregular vascular growth. Blood and lymphatic vasculatures are intimately involved in tissue oxygenation and fluid homeostasis maintenance. Assembly of these vascular networks involves sprouting, migration and proliferation of endothelial cells. Recent studies have suggested that changes in cellular metabolism are important to these processes 1 . Although much is known about vascular endothelial growth factor (VEGF)-dependent regulation of vascular development and metabolism 2 , 3 , little is understood about the role of fibroblast growth factors (FGFs) in this context 4 . Here we identify FGF receptor (FGFR) signalling as a critical regulator of vascular development. This is achieved by FGF-dependent control of c-MYC (MYC) expression that, in turn, regulates expression of the glycolytic enzyme hexokinase 2 (HK2). A decrease in HK2 levels in the absence of FGF signalling inputs results in decreased glycolysis, leading to impaired endothelial cell proliferation and migration. Pan-endothelial- and lymphatic-specific Hk2 knockouts phenocopy blood and/or lymphatic vascular defects seen in Fgfr1 / Fgfr3 double mutant mice, while HK2 overexpression partly rescues the defects caused by suppression of FGF signalling. Thus, FGF-dependent regulation of endothelial glycolysis is a pivotal process in developmental and adult vascular growth and development.