Unveiling the activity origin of electrochemical oxygen evolution on heteroatom-decorated carbon matrix.

Chemical modification via functional dopants in carbon materials holds great promise for elevating catalytic activity and stability. To gain comprehensive insights into the pivotal mechanisms and establish structure-performance relationships, especially concerning the roles of dopants, remains a pressing need. Herein, we employ computational simulations to unravel the catalytic function of heteroatoms in the acidic oxygen evolution reaction (OER), focusing on a physical model of high-electronegative F and N co-doped carbon matrix. Theoretical and experimental findings elucidate that the enhanced activity originates from the F and pyridinic-N (Py-N) species that achieve carbon activation. This activated carbon significantly lowers the conversion energy barrier from O* to OOH*, shifts the potential-limiting step from OOH* formation to O* generation, and ultimately optimizes the energy barrier of the potential-limiting step. This wok elucidates that the critical role of heteroatoms in catalyzing the reaction and unlocks the potential of carbon materials for acidic OER.

Efficient optimization parameter calibration method-based DEM simulation for compacted loess slope under dry-wet cycling.

Dry-wet cycles can cause significant deterioration of compacted loess and thus affect the safety of fill slopes. The discrete element method (DEM) can take into account the non-homogeneous, discontinuous, and anisotropic nature of the geotechnical medium, which is more capable of reflecting the mechanism and process of instability in slope stability analysis. Therefore, this paper proposes to use the DEM to analyze the stability of compacted loess slopes under dry-wet cycles. Firstly, to solve the complex calibration problem between macro and mesoscopic parameters in DEM models, an efficient parameter optimization method was proposed by introducing the chaotic particle swarm optimization with sigmoid-based acceleration coefficients algorithm (CPSOS). Secondly, during the parameter calibration, a new indicator, the bonding ratio (BR), was proposed to characterize the development of pores and cracks in compacted loess during dry-wet cycles, to reflect the impact of dry-wet action on the degradation of bonding between loess aggregates. Finally, according to the results of parameter calibration, the stability analysis model of compacted loess slope under dry-wet cycling was established. The results show that the proposed optimization calibration method can accurately reflect the trend of the stress-strain curve and strength of the actual test results under dry-wet cycles, and the BR also reflects the degradation effect of dry-wet cycles on compacted loess. The slope stability analysis shows that the DEM reflects the negative effect of dry-wet cycles on the safety factor of compacted loess slopes, as well as the trend of gradual stabilization with dry-wet cycles. The comparison with the finite element analysis results verified the accuracy of the discrete element slope stability analysis.

Diffuse tumors: Molecular determinants shared by different cancer types.

Most cancer types have both diffuse and non-diffuse subtypes, which have rather distinct morphologies, namely scattered tiny tumors vs. one solid tumor, and different levels of aggressiveness. However, the causes for forming such distinct subtypes remain largely unknown. Using the diffuse and non-diffuse gastric cancers (GCs) as the illustrative example, we present a computational study based on the transcriptomic data from the TCGA and GEO databases, to address the following questions: (i) What are the key molecular determinants that give rise to the distinct morphologies between diffuse and non-diffuse cancers? (ii) What are the main reasons for diffuse cancers to be generally more aggressive than non-diffuse ones of the same cancer type? (iii) What are the reasons for their distinct immunoactivities? And (iv) why do diffuse cancers on average tend to take place in younger patients? The study is conducted using the framework we have previously developed for elucidation of general drivers cancer formation and development. Our main discoveries are: (a) the level of (poly-) sialic acids deployed on the surface of cancer cells is a significant factor contributing to questions (i) and (ii); (b) poly-sialic acids synthesized by ST8SIA4 are the key to question (iii); and (c) the circulating growth factors specifically needed by the diffuse subtype dictate the answer to question (iv). All these predictions are substantiated by published experimental studies. Our further analyses on breast, prostate, lung, liver, and thyroid cancers reveal that these discoveries generally apply to the diffuse subtypes of these cancer types, hence indicating the generality of our discoveries.

NSUN4 mediated RNA 5-methylcytosine promotes the malignant progression of glioma through improving the CDC42 mRNA stabilization.

5-Methylcytosine (m5C) methylation is a significant post-transcriptional modification that play a crucial role in the development and progression of numerous cancers. Whereas the functions and molecular mechanisms underlying m5C methylation in gliomas remain unclear. This study dedicated to explore changes of m5C levels and the clinical significance of the m5C writer NSUN4 in gliomas. We found that high m5C levels were negatively related to prognosis of patients with glioma. Moreover, gain- and loss-of-function experiments revealed the role of NSUN4 in enhancing m5C modification of mRNA to promote the malignant progression of glioma. Mechanistically speaking, NSUN4-mediated m5C alterations regulated ALYREF binding to CDC42 mRNA, thereby impacting the mRNA stability of CDC42. We also demonstrated that CDC42 promoted glioma proliferation, migration, and invasion by activating the PI3K-AKT pathway. Additionally, rescue experiments proved that CDC42 overexpression weaken the inhibitory effect of NSUN4 knockdown on the malignant progression of gliomas in vitro and in vivo. Our findings elucidated that NSUN4-mediated high m5C levels promote ALYREF binding to CDC42 mRNA and regulate its stability, thereby driving the malignant progression of glioma. This provides theoretical support for targeted the treatment of gliomas.

PLK-3-mediated phosphorylation of BAP1 prevents diabetic retinopathy.

Diabetic retinopathy (DR) is a microvascular complication of diabetes mellitus, and its main clinical manifestation is retinal vascular dysfunction. DR causes blindness and is a problem with significant global health implications. However, treating DR is still challenging. In this study, we aimed to explore the role of polo-like kinase-3 (PLK-3) and the potential regulatory mechanism in DR. Sprague-Dawley rats were injected intraperitoneally with streptozotocin (STZ, 60 mg/kg) to induce a rat model of DR, and rat retinal microvascular endothelial cells (RRMECs) were treated with high glucose (HG, 25 mmol/L glucose) to develop a cell model of DR. We found that PLK-3 was significantly downregulated in the retinal tissues of STZ-induced diabetic rats and HG-induced RRMECs. Lentivirus-mediated PLK-3 overexpression alleviated the histological damages in DR rats. After HG stimulation, cell proliferation, migration, and angiogenesis in RRMECs were inhibited after PLK-3 upregulation. By using label-free proteomics, we identified 82 differentially expressed proteins downstream of PLK-3, including BRCA1-associated protein 1 (BAP1), which was significantly upregulated in PLK-3-overexpressed RRMECs compared to control cells under the HG condition. In vivo and in vitro assays indicated that the forced expression of PLK-3 increased the phosphorylation of BAP1 at serine 592 and caspase-8 expression. Detailed evidence showed that BAP1-shRNA-mediated knockdown restored the cell function in HG-treated RRMECs when PLK-3 was overexpressed. Collectively, this study shows that PLK-3 alleviates retinal vascular dysfunction in DR by inhibiting the phosphorylation of BAP1. Thus, PLK-3 may develop as a promising target for the therapy of DR.

PSMD11 promotes the proliferation of hepatocellular carcinoma by regulating the ubiquitination degradation of CDK4.

BACKGROUND: The 26S proteasome non-ATPase regulatory subunit 11 is a multiprotein complex involved in the ATP-dependent degradation of ubiquitinated proteins, and PSMD11 plays a key role in the regulation of embryonic stem cell proteasome activity. However, the role of PSMD11 in hepatocellular carcinoma has not been studied. In this study, it was found that the expression of PSMD11 in HCC tissues was significantly higher than that in para-cancerous tissues, and was associated with poor prognosis. The results of in vitro experiments showed that PSMD11 knockdown could effectively inhibit the proliferation and apoptosis of hepatoma cell lines, and flow cytometry showed that the G0/G1 phase was significantly prolonged. Through protein spectrometry, immunoprecipitation and in vitro experiments, it was found that PSMD11 can promote the proliferation of hepatocellular carcinoma through regulating the ubiquitination of CDK4 and enhancing its protein stability. This study explores the mechanism of action of PSMD11 in hepatocellular carcinoma and provides new insights for the treatment of hepatocellular carcinoma.

Urban network noise control based on road grade optimization considering comprehensive traffic environment benefit.

A double-decision optimization model based on the road grade optimization strategy and considered comprehensive traffic environment benefit is proposed to control the traffic noise. The upper-level model maximizes the comprehensive traffic environment benefit, including network noise emission and traffic efficiency. Adjusting the emphasis on noise optimization benefits and traffic efficiency in road network planning through setting weights. The lower-level resolves the question of network traffic flow assignment using a stochastic user-equilibrium model. The increase of traffic environment demand, network noise emissions decrease and travel time rises. In the case, with a low environmental requirement (weighting with 1.1), the sound pressure emission of the network decreases by 9.23% with only a 4.01% increase in travel time. Under the high environmental requirement (weighting with 0.2), the sound pressure decreases by 26.8%, but the travel time rises by as high as 30.9%. The network is optimized towards road grade degradation and is the first to optimize the arterial roads. In addition, it is found that the influence of speed on traffic noise is greater than that of traffic volume through case validation. This method proposing traffic noise optimization strategies at the road network planning level provides technical support for the proactive governance of traffic noise pollution and the improvement of traffic sound environment quality.

sTREM-1 as a Predictive Biomarker for Disease Severity and Prognosis in COVID-19 Patients.

Background: Research on biomarkers associated with the severity and adverse prognosis of COVID-19 can be beneficial for improving patient outcomes. However, there is limited research on the role of soluble TREM-1 (sTREM-1) in predicting the severity and prognosis of COVID-19 patients. Methods: A total of 115 COVID-19 patients admitted to the emergency department of Beijing Youan Hospital from February to May 2023 were included in the study. Demographic information, laboratory measurements, and blood samples for sTREM-1 levels were collected upon admission. Results: Our study found that sTREM-1 levels in the plasma of COVID-19 patients increased with the severity of the disease (moderate vs mild, p=0.0013; severe vs moderate, p=0.0195). sTREM-1 had good predictive value for disease severity and 28-day mortality (area under the ROC curve was 0.762 and 0.805, respectively). sTREM-1 also exhibited significant correlations with age, body temperature, respiratory rate, PaO2/FiO2, PCT, CRP, and CAR. Ultimately, through multivariate logistic regression analysis, we determined that sTREM-1 (OR 1.008, 95% CI: 1.002-1.013, p=0.005), HGB (OR 0.966, 95% CI: 0.935-0.998, p=0.036), D-dimer (OR 1.001, 95% CI: 1.000-1.001, p=0.009), and CAR (OR 1.761, 95% CI: 1.154-2.688, p=0.009) were independent predictors of 28-day mortality in COVID-19 patients. The combination of these four markers yielded a strong predictive value for 28-day mortality in COVID-19 cases with an AUC of 0.919 (95% CI: 0.857 -0.981). Conclusion: sTREM-1 demonstrated good predictive value for disease severity and 28-day mortality, serving as an independent prognostic factor for adverse patient outcomes. In the future, we anticipate conducting large-scale multicenter studies to validate our research findings.

Comprehensive assessment of enhancing dewaterability of dredged sediments by starch-based flocculant.

Dredged sediment poses significant challenges for transportation and subsequent treatment due to its high water content and large volume. Coagulation, a common method of dewatering, can significantly enhance the dewatering performance of dredged sediment. This study synthesized a cationic starch-based flocculant [starch-3-chloro-2-hydroxypropyl trimethylammonium chloride (St-CTA)] through etherification for the flocculation dewatering of dredged sediment. The effectiveness and mechanism of St-CTA as a dewatering flocculant for dredged sediment were investigated. The results demonstrated that when the dosage of St-CTA was 12 mg g-1 TSS (total suspended solids), the dehydration property of dredged sediment substantially improved, with the specific resistance to filtration (SRF) decreasing by 93.3%, the capillary suction time (CST) by 93.5%, and the water content of the filter cake (WC) by 9.7%. The removal rate of turbidity of the supernatant from the conditioned dredged sediment reached 99.6%, accelerating the settling speed and effectively capturing and separating fine particles from the sediment. St-CTA significantly increased the median particle size (D50), altered the microstructure and extracellular polymeric substances (EPS) of the flocs, and increased the fractal dimension of the flocs, making them more compact and conducive to the formation of drainage channels. These findings confirm the feasibility of using potentially environmentally friendly St-CTA as a rapid dewatering conditioning agent for sediment.

APEX-pHLA: A novel method for accurate prediction of the binding between exogenous short peptides and HLA class I molecules.

Human leukocyte antigen (HLA) molecules play critically significant role within the realm of immunotherapy due to their capacities to recognize and bind exogenous antigens such as peptides, subsequently delivering them to immune cells. Predicting the binding between peptides and HLA molecules (pHLA) can expedite the screening of immunogenic peptides and facilitate vaccine design. However, traditional experimental methods are time-consuming and inefficient. In this study, an efficient method based on deep learning was developed for predicting peptide-HLA binding, which treated peptide sequences as linguistic entities. It combined the architectures of textCNN and BiLSTM to create a deep neural network model called APEX-pHLA. This model operated without limitations related to HLA class I allele variants and peptide segment lengths, enabling efficient encoding of sequence features for both HLA and peptide segments. On the independent test set, the model achieved Accuracy, ROC_AUC, F1, and MCC is 0.9449, 0.9850, 0.9453, and 0.8899, respectively. Similarly, on an external test set, the results were 0.9803, 0.9574, 0.8835, and 0.7863, respectively. These findings outperformed fifteen methods previously reported in the literature. The accurate prediction capability of the APEX-pHLA model in peptide-HLA binding might provide valuable insights for future HLA vaccine design.

Mitophagy in mammalian follicle development and health.

Mitophagy, the cellular process that removes damaged mitochondria, plays a crucial role in maintaining normal cell functions. It is deeply involved in the entire process of follicle development and is associated with various ovarian diseases. This review aims to provide a comprehensive overview of mitophagy regulation, emphasizing its role at different stages of follicular development. Additionally, the study illuminates the relationship between mitophagy and ovarian diseases, including ovary aging (OA), primary ovarian insufficiency (POI), and polycystic ovary syndrome (PCOS). A detailed understanding of mitophagy could reveal valuable insights and novel strategies for managing female ovarian reproductive health.

Discovery of Sovleplenib, a Selective Inhibitor of Syk in Clinical Development for Autoimmune Diseases and Cancers.

Herein we describe the medicinal chemistry efforts that led to the discovery of the clinical-staged Syk inhibitor sovleplenib (41) via a structure-activity relationship investigation and pharmacokinetics (PK) optimization of a pyrido[3,4-b]pyrazine scaffold. Sovleplenib is a potent and selective Syk inhibitor with favorable preclinical PK profiles and robust anti-inflammation efficacy in a preclinical collagen-induced arthritis model. Sovleplenib is now being developed for treating autoimmune diseases such as immune thrombocytopenic purpura and warm antibody hemolytic anemia as well as hematological malignancies.

The Value of Chemokine and Chemokine Receptors in Diagnosis, Prognosis, and Immunotherapy of Hepatocellular Carcinoma.

Background: Chemokines and chemokine receptors (CCRs) are involved in a variety of anti-tumour and pro-tumour immune processes in vivo, such as angiogenesis, metastasis, proliferation and invasiveness, and influence patient prognosis and response to therapy. Methods: CCRs differentially expressed in HCC and associated with prognosis were extracted from TCGA and GEO databases, and the obtained CCRs were then used to construct signature genes, and the signature gene were selected for expression validation as well as functional experiments to explore the role of CCRs in the treatment and prognosis of HCC. Results: We constructed a prognostic model including five CCRs (CCL20, CCL23, CCR3, CCR10, and CXCR3) and validated the expression of signature genes. The model's risk score is an independent prognostic factor for HCC. We have also developed prognostic model nomograms for clinical use. In addition, we validated that CCR3 expression is associated with poor prognosis in HCC, and the proliferation and migration ability of HCC cells was significantly inhibited after interfering with the expression of CCR3 in MHCC-LM3. We also looked at differences in pathway enrichment, immune infiltration and immune checkpoints. Finally, we found that risk scores were also correlated with drug sensitivity, the high-risk group had a better sensitivity to sorafenib. Conclusion: The CCRs-related gene signature may better assess HCC prognosis and response to immunotherapy and tyrosine kinase inhibitors such as sorafenib in HCC, providing prospective solutions for diagnosis and treatment.

Presepsin as a prognostic biomarker in COVID-19 patients: combining clinical scoring systems and laboratory inflammatory markers for outcome prediction.

BACKGROUND: There is still limited research on the prognostic value of Presepsin as a biomarker for predicting the outcome of COVID-19 patients. Additionally, research on the combined predictive value of Presepsin with clinical scoring systems and inflammation markers for disease prognosis is lacking. METHODS: A total of 226 COVID-19 patients admitted to Beijing Youan Hospital's emergency department from May to November 2022 were screened. Demographic information, laboratory measurements, and blood samples for Presepsin levels were collected upon admission. The predictive value of Presepsin, clinical scoring systems, and inflammation markers for 28-day mortality was analyzed. RESULTS: A total of 190 patients were analyzed, 83 (43.7%) were mild, 61 (32.1%) were moderate, and 46 (24.2%) were severe/critically ill. 23 (12.1%) patients died within 28 days. The Presepsin levels in severe/critical patients were significantly higher compared to moderate and mild patients (p < 0.001). Presepsin showed significant predictive value for 28-day mortality in COVID-19 patients, with an area under the ROC curve of 0.828 (95% CI: 0.737-0.920). Clinical scoring systems and inflammation markers also played a significant role in predicting 28-day outcomes. After Cox regression adjustment, Presepsin, qSOFA, NEWS2, PSI, CURB-65, CRP, NLR, CAR, and LCR were identified as independent predictors of 28-day mortality in COVID-19 patients (all p-values < 0.05). Combining Presepsin with clinical scoring systems and inflammation markers further enhanced the predictive value for patient prognosis. CONCLUSION: Presepsin is a favorable indicator for the prognosis of COVID-19 patients, and its combination with clinical scoring systems and inflammation markers improved prognostic assessment.

PAFAH1B3 is a KLF9 target gene that promotes proliferation and metastasis in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal human malignancies. Uncontrolled cell proliferation, invasion and migration of pancreatic cancer cells are the fundamental causes of death in PDAC patients. Our previous studies showed that KLF9 inhibits the proliferation, invasion and migration of pancreatic cancer cells. However, the underlying mechanisms are not fully understood. In this study, we found that platelet-activating factor acetylhydrolase IB3 (PAFAH1B3) is highly expressed in pancreatic cancer tissues and cells. In vitro and in vivo studies showed that overexpression of PAFAH1B3 promoted the proliferation and invasion of pancreatic cancer cells, while downregulation of PAFAH1B3 inhibited these processes. We found that KLF9 expression is negatively correlated with PAFAH1B3 expression in pancreatic cancer tissues and cells. Western blotting revealed that KLF9 negatively regulates the expression of PAFAH1B3 in pancreatic cancer tissues and cells. Rescue experiments showed that overexpression of PAFAH1B3 could partially attenuate the suppression of pancreatic cancer cell proliferation, invasion and migration induced by KLF9 overexpression. Finally, chromatin immunoprecipitation (ChIP) and dual-luciferase reporter assays were carried out, and the results showed that KLF9 directly binds to the promoter of PAFAH1B3 and inhibits its transcriptional activity. In conclusion, our study indicated that KLF9 can inhibit the proliferation, invasion, migration and metastasis of pancreatic cancer cells by inhibiting PAFAH1B3.

Ice sheet and precession controlled subarctic Pacific productivity and upwelling over the last 550,000 years.

The polar oceans play a vital role in regulating atmospheric CO2 concentrations (pCO2) during the Pleistocene glacial cycles. However, despite being the largest modern reservoir of respired carbon, the impact of the subarctic Pacific remains poorly understood due to limited records. Here, we present high-resolution, 230Th-normalized export productivity records from the subarctic northwestern Pacific covering the last five glacial cycles. Our records display pronounced, glacial-interglacial cyclicity superimposed with precessional-driven variability, with warm interglacial climate and high boreal summer insolation providing favorable conditions to sustain upwelling of nutrient-rich subsurface waters and hence increased export productivity. Our transient model simulations consistently show that ice sheets and to a lesser degree, precession are the main drivers that control the strength and latitudinal position of the westerlies. Enhanced upwelling of nutrient/carbon-rich water caused by the intensification and poleward migration of the northern westerlies during warmer climate intervals would have led to the release of previously sequestered CO2 from the subarctic Pacific to the atmosphere. Our results also highlight the significant role of the subarctic Pacific in modulating pCO2 changes during the Pleistocene climate cycles, especially on precession timescale ( ~ 20 kyr).

An O/N/S-rich porous Fe-based metal-organic framework (MOF) for gold recovery from the aqueous phase with excellent performance.

Recovering gold from wastewater has both economic and environmental benefits. However, how to effectively recover it is challenging. In this work, a novel Fe-based metal-organic framework (MOF) was synthesized and decorated with 2,5-thiophenedicarboxylic acid to have a well-developed porous architecture to effectively recover Au(III) from water. The maximum Au(III) sorption capacity by the finally-synthesized porous material MIL-101(Fe)-TDCA reached 2350 mg/g at pH = 6.00 ± 0.15, which is one of the highest among all literature-reported relevant materials including MOFs, and high sorption strength can be maintained within a wide pH range from 2.0 to 10.0. Besides, Au(III) sorption efficiency at low concentrations (i.e., 3.5 × 104 mg/mL) reached over 99%. Mechanically, outstanding Au(III) sorption by MIL-101(Fe)-TDCA resulted from the O/N/S-containing moieties on its surface, large surface area and porosity. The N- and S-containing functionalities (CS, CONH) served as electron donors to chelate Au(III). The O-containing (FeOFe, COFe, COOH, and coordinated H2O) and N-containing (CONH) moieties on MIL-101(Fe)-TDCA interacted with OH groups on the hydrolyzed species of Au(III) (AuCl3(OH)-, AuCl2(OH)2-, and AuCl(OH)3-) by hydrogen bond, which further increased Au(III) sorption. Furthermore, about 45.71% of Au(III) was reduced to gold nanoparticles by CS groups on the decorated 2,5-dithiophene dicarboxylic acid during sorption on MIL-101(Fe)-TDCA. Over 98.35% of Au(III) was selectively sorbed on MIL-101(Fe)-TDCA at pH 4.0, much higher than that of the coexisting heavy metal ions including Cu(II), Zn(II), Pb(II), and Ni(II) (< 5%), despite their same concentration at 0.01 mg/mL. Although sorption selectivity of a noble metal Pt(IV) by MIL-101(Fe)-TDCA is relatively poor (68.23%), it could be acceptable. Moreover, reusability of MIL-101(Fe)-TDCA is also excellent, since above 90.5% Au(III) still can be sorbed after two sorption-desorption cycles. Overall, excellent sorption performance and the roughly-calculated gold recycling benefits (26.30%) highlight that MIL-101(Fe)-TDCA is a promising porous material for gold recovery from the aqueous phase.

Iron-enhanced microscale laboratory aerated filters in the treatment of artificial mariculture wastewater: A study on nitrogen removal performance and the impact on microbial community structure.

This study investigates the nitrogen removal efficacy and microbial community dynamics in seawater aquaculture effluent treatment using three different substrate combinations of microscale laboratory aerated filters (MFs) - MF1 (LECA), MF2 (LECA/Fe-C), and MF3 (LECA/Pyrite). The findings indicated that the COD removal exceeded 95% across all MFs, with higher removal efficiencies in MF2 and MF3. In terms of nitrogen removal performance, MF2 exhibited the highest average nitrogen removal of 93.17%, achieving a 12.35% and 3.56% increase compared to MF1 (80.82%) and MF3 (89.61%), respectively. High-throughput sequencing analysis revealed that the Fe-C substrate significantly enhanced the diversity of the microbial community. Notably, in MF2, the salinophilic denitrifying bacterium Halomonas was significantly enriched, accounting for 42.6% of the total microbial community, which was beneficial for nitrogen removal. Moreover, an in-depth analysis of nitrogen metabolic pathways and microbial enzymes indicated that MF2 and MF3 possessed a high abundance of nitrification and denitrification enzymes, related to the high removal rates of NH4+-N and NO3--N. Therefore, the combination of LECA with iron-based materials significantly enhances the nitrogen removal efficiency from mariculture wastewater.

The Variation Patterns of the Martensitic Hierarchical Microstructure and Mechanical Properties of 35Si2MnCr2Ni3MoV Steel at Different Austenitizing Temperatures.

This study investigates the influence of varying austenitizing temperatures on the microstructure and mechanical properties of 35Si2MnCr2Ni3MoV steel, utilizing Charpy impact testing and microscopic analysis techniques such as scanning electron microscopy (SEM) and electron backscatter diffraction (EBSD). The findings reveal that optimal combination of strength and toughness is achieved at an austenitizing temperature of 980 °C, resulting in an impact toughness of 67.2 J and a tensile strength of 2032 MPa. The prior austenite grain size initially decreases slightly with increasing temperature, then enlarges significantly beyond 1100 °C. The martensite blocks' and packets' structures exhibit a similar trend. The proportion of high-angle grain boundaries, determined by the density of the packets, peaks at 980 °C, providing maximal resistance to crack propagation. The amount of retained austenite increases noticeably after 980 °C; beyond 1200 °C, the coarsening of packets and a decrease in density reduce the likelihood of trapping retained austenite. Across different austenitizing temperatures, the steel demonstrates superior crack initiation resistance compared to crack propagation resistance, with the fracture mode transitioning from ductile dimple fracture to quasi-cleavage fracture as the austenitizing temperature increases.

Artificial Intelligence-Assisted RFID Tag-Integrated Multi-Sensor for Quality Assessment and Sensing.

Radio frequency identification (RFID) is well known as an identification, track, and trace approach and is considered to be the key physical layer technology for the industrial internet of things (IIoT). However, IIoT systems have to introduce additional complex sensor networks for pervasive monitoring, and there are still challenges related to item-level sensing and data recording. To overcome the shortage, this work proposes an artificial intelligence (AI)-assisted RFID-based multi-sensing technology. Both passive and semi-passive RFID tag-integrated multi-sensors are developed. The main contributions and the novelty of this investigation are as follows. A UHF RFID tag-integrated multi-sensor with a boosted charge pump is proposed; it enables high RF signal sensitivity and a long operational range. The whole hardware design, including the antenna and energy harvester, are studied. Moreover, a demonstration with real-world ham product sensing data is conducted. This work also proposes and successfully demonstrates the integration of machine learning algorithms, specifically the NARX neural network, with RFID sensing data for food product quality assessment and sensing (QAS). This application of machine learning to RFID-generated data for quality assessment is also a novel aspect of the research. The deployment of an autoregressive model with an exogenous input (NARX) neural network model, tailored for nonlinear processes, emerges as the most effective, achieving a root mean square error (RMSE) of 0.007 and an R-squared value of 0.99 for ham product QAS. By deploying the technology, low-cost, timely, and flexible product QAS can be achieved in manufacturing industries, which helps product quality improvement and the optimization of the manufacturing line and supply chain.