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Abstract Using generalizability (G-) theory and qualitative feedback analysis, this study evaluated the role of ChatGPT in enhancing English-as-a-foreign-language (EFL) writing assessments in classroom settings. The primary objectives were to assess the reliability of the holistic scores assigned to EFL essays by ChatGPT versions 3.5 and 4 compared to college English teachers and to evaluate the relevance of the qualitative feedback provided by these versions of ChatGPT. The study analyzed 30 College English Test Band 4 (CET-4) essays written by non-English majors at a university in Beijing, China. ChatGPT versions 3.5 and 4, along with four college English teachers, served as raters. They scored the essays holistically following the CET-4 scoring rubric and also provided qualitative feedback on the language, content, and organization of these essays. The G-theory analysis revealed that the scoring reliability of ChatGPT3.5 was consistently lower than that of the teacher raters; however, ChatGPT4 demonstrated consistently higher reliability coefficients than the teachers. The qualitative feedback analysis indicated that both ChatGPT3.5 and 4 consistently provided more relevant feedback on the EFL essays than the teacher raters. Furthermore, ChatGPT versions 3.5 and 4 were equally relevant across the language, content, and organization aspects of the essays, whereas the teacher raters generally focused more on language but provided less relevant feedback on content and organization. Consequently, ChatGPT versions 3.5 and 4 could be useful AI tools for enhancing EFL writing assessments in classroom settings. The implications of adopting ChatGPT for classroom writing assessments are discussed.

IntroductionSepsis poses a serious threat to individual life and health. Early and accessible diagnosis and targeted treatment are crucial. This study aims to explore the relationship between microbes, metabolic pathways, and blood test indicators in sepsis patients and develop a machine learning model for clinical diagnosis.MethodsBlood samples from sepsis patients were sequenced. α-diversity and β-diversity analyses were performed to compare the microbial diversity between the sepsis group and the normal group. Correlation analysis was conducted on microbes, metabolic pathways, and blood test indicators. In addition, a model was developed based on medical records and radiomic features using machine learning algorithms.ResultsThe results of α-diversity and β-diversity analyses showed that the microbial diversity of sepsis group was significantly higher than that of normal group (p < 0.05). The top 10 microbial abundances in the sepsis and normal groups were Vitis vinifera, Mycobacterium canettii, Solanum pennellii, Ralstonia insidiosa, Ananas comosus, Moraxella osloensis, Escherichia coli, Staphylococcus hominis, Camelina sativa, and Cutibacterium acnes. The enriched metabolic pathways mainly included Protein families: genetic information processing, Translation, Protein families: signaling and cellular processes, and Unclassified: genetic information processing. The correlation analysis revealed a significant positive correlation (p < 0.05) between IL-6 and Membrane transport. Metabolism of other amino acids showed a significant positive correlation (p < 0.05) with Cutibacterium acnes, Ralstonia insidiosa, Moraxella osloensis, and Staphylococcus hominis. Ananas comosus showed a significant positive correlation (p < 0.05) with Poorly characterized and Unclassified: metabolism. Blood test-related indicators showed a significant negative correlation (p < 0.05) with microorganisms. Logistic regression (LR) was used as the optimal model in six machine learning models based on medical records and radiomic features. The nomogram, calibration curves, and AUC values demonstrated that LR performed best for prediction.DiscussionThis study provides insights into the relationship between microbes, metabolic pathways, and blood test indicators in sepsis. The developed machine learning model shows potential for aiding in clinical diagnosis. However, further research is needed to validate and improve the model.

Abstract Objective The aim of present study was to evaluate the combined effect of hypertension and activities of daily living (ADL)/instrumental activities of daily living (IADL) with the risk of CVD, stroke and cardiac events. Methods A total of 14,083 participants aged 45 years or older from the China Health and Retirement longitudinal study were included in current study. Participants were divided into 4 groups according to hypertension and ADL/IADL status. Cox proportional hazards regression model was used to explore the associations between hypertension, ADL/IADL and new-onset CVD, stroke and cardiac events. Results During the 7-year follow-up, a total of 2,324 respondents experienced CVD (including 783 stroke and 1,740 cardiac events). Individuals with limitations in ADL alone, or with hypertension alone, or with both limitations in ADL and hypertension were associated with increased risk of CVD, with the adjusted hazard ratios (95% confidence intervals) were 1.17(1.00-1.35), 1.36(1.24–1.49) and 1.44(1.23–1.68), respectively. Those with limitations in ADL and hypertension also had higher risk of stroke (hazard ratios = 1.64; 1.26–2.14) and cardiac events (hazard ratios = 1.37; 1.14–1.64). Similarly, individuals with both limitations in IADL and hypertension were associated with increased risk of CVD (hazard ratios = 1.34; 1.15–1.57), stroke (hazard ratios = 1.50; 1.17–1.95) and cardiac events (hazard ratios = 1.27; 1.06–1.53). Conclusion Hypertension and limitations in ADL/IADL jointly increased the risk of CVD, stroke and cardiac events.

BackgroundTelomere length, crucial for genomic stability, have been implicated in various inflamm-aging diseases, but their role in sarcoidosis remains unexplored.ObjectiveThis study aims to explore the casual effects between TL and sarcoidosis via a bidirectional Mendelian Randomization (MR) study.MethodsWe examined single nucleotide polymorphisms (SNPs) associated with TL and sarcoidosis, utilizing available open-access genome-wide association study (GWAS) databases from the UK Biobank and FinnGen. We employed five MR techniques, including Inverse Variance Weighted (IVW), MR Egger, weighted median (WM), Robust adjusted profile score (RAPS), and Maximum likelihood, to assess causal relationships and explore pleiotropy.ResultsSummary data extracted from GWAS datasets of TL (n = 472,174) and (n = 217,758) of European ancestry. Employing 130 SNPs with genome-wide significance as instrumental factors for TL, we detect a significant negative correlation between TL and sarcoidosis (OR: 0.682, 95% confidence interval: 0.524–0.888, p: 0.0045). Similarly, utilizing 6 SNPs with genome-wide significance as instrumental factors for sarcoidosis, we fail to identify a noteworthy association between sarcoidosis and TL (OR: 0.992, 95% confidence interval: 0.979–1.005, p: 0.2424).ConclusionOur results suggest that longer telomeres may reduce the risk of sarcoidosis, highlighting TL as a potential biomarker for diagnosis and long-term monitoring. Understanding the critical role of telomere shortening enables more effective focus on diagnosing, treating, and curing sarcoidosis linked to telomeres. Clinical investigations into treatments that enhance TL are warranted.

Abstract BackgroundThe integration of chatbots in nursing education is a rapidly evolving area with potential transformative impacts. This narrative review aims to synthesize and analyze the existing literature on chatbots in nursing education. ObjectiveThis study aims to comprehensively examine the temporal trends, international distribution, study designs, and implications of chatbots in nursing education. MethodsA comprehensive search was conducted across 3 databases (PubMed, Web of Science, and Embase) following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagram. ResultsA total of 40 articles met the eligibility criteria, with a notable increase of publications in 2023 (n=28, 70%). Temporal analysis revealed a notable surge in publications from 2021 to 2023, emphasizing the growing scholarly interest. Geographically, Taiwan province made substantial contributions (n=8, 20%), followed by the United States (n=6, 15%) and South Korea (n=4, 10%). Study designs varied, with reviews (n=8, 20%) and editorials (n=7, 18%) being predominant, showcasing the richness of research in this domain. ConclusionsIntegrating chatbots into nursing education presents a promising yet relatively unexplored avenue. This review highlights the urgent need for original research, emphasizing the importance of ethical considerations.

Background: Most respiratory viruses can cause serious lower respiratory diseases at any age. Therefore, timely and accurate identification of respiratory viruses has become even more important. This study focused on the development of rapid nucleic acid testing techniques for common respiratory infectious diseases in the Chinese population.Methods: Multiplex fluorescent quantitative polymerase chain reaction (PCR) assays were developed and validated for the detection of respiratory pathogens including the novel coronavirus (SARS-CoV-2), influenza A virus (FluA), parainfluenza virus (PIV), and respiratory syncytial virus (RSV).Results: The assays demonstrated high specificity and sensitivity, allowing for the simultaneous detection of multiple pathogens in a single reaction. These techniques offer a rapid and reliable method for screening, diagnosis, and monitoring of respiratory pathogens.Conclusion: The implementation of these techniques might contribute to effective control and prevention measures, leading to improved patient care and public health outcomes in China. Further research and validation are needed to optimize and expand the application of these techniques to a wider range of respiratory pathogens and to enhance their utility in clinical and public health settings.

Abstract For around half of the pediatric B-lineage acute lymphoblastic leukemia (B-ALL) patients, the molecular mechanism of relapse remains unclear. To fill this gap in knowledge, here we characterize the chromatin accessibility landscape in pediatric relapsed B-ALL. We observe rewired accessible chromatin regions (ACRs) associated with transcription dysregulation in leukemia cells as compared with normal B-cell progenitors. We show that over a quarter of the ACRs in B-ALL are in quiescent regions with high heterogeneity among B-ALLs. We identify subtype-specific and allele-imbalanced chromatin accessibility by integrating multi-omics data. By characterizing the differential ACRs between diagnosis and relapse in B-ALL, we identify alterations in chromatin accessibility during drug treatment. Further analysis of ACRs associated with relapse free survival leads to the identification of a subgroup of B-ALL which show early relapse. These data provide an advanced and integrative portrait of the importance of chromatin accessibility alterations in tumorigenesis and drug responses.

Emotion is a significant factor influencing education and teaching, closely intertwined with learners’ cognitive processing. Conducting analysis of learners’ emotions based on cross-modal data is beneficial for achieving personalized guidance in intelligent educational environments. Currently, due to factors such as data scarcity and environmental noise, data imbalances have led to incomplete or missing emotional information. Therefore, this study proposes a collaborative analysis model based on attention mechanisms. The model extracts features from various types of data using different tools and employs multi-head attention mechanisms for parallel processing of feature vectors. Subsequently, through a cross-modal attention collaborative interaction module, effective interaction among visual, auditory, and textual information is facilitated, significantly enhancing comprehensive understanding and the analytical capabilities of cross-modal data. Finally, empirical evidence demonstrates that the model can effectively improve the accuracy and robustness of emotion recognition in cross-modal data.

Wearing masks is an effective protective measure for residents to prevent respiratory infectious diseases when going out. Due to issues such as a small target size, target occlusion leading to information loss, false positives, and missed detections, the effectiveness of face mask-wearing detection needs improvement. To address these issues, an improved YOLOv7 object detection model is proposed. Firstly, the C2f_SCConv module is introduced in the backbone network to replace some ELAN modules for feature extraction, enhancing the detection performance of small targets. Next, the SPPFCSPCA module is proposed to optimize the spatial pyramid pooling structure, accelerating the model convergence speed and improving detection accuracy. Finally, the HAM_Detect decoupled detection head structure is introduced to mitigate missed and false detections caused by target occlusion, further accelerating model convergence and improving detection performance in complex environments. The experimental results show that improved YOLOv7 achieved an mAP of 90.1% on the test set, a 1.4% improvement over the original YOLOv7 model. The detection accuracy of each category improved, effectively providing technical support for mask-wearing detection in complex environments.

Efficient and accurate flood inundation predictions can provide useful information for flood risk mitigation and water resource management. In this paper, we propose a new modelling method, LoHy + , which can be applied to efficiently simulate the spatiotemporal evolution of flood inundation with reasonable accuracy. The method integrates a low-fidelity two-dimensional (2D) hydrodynamic model and a mapping module to estimate water depth in a catchment during floods. The performance of the proposed modelling method was evaluated using a real-world catchment of approximate 2,000 km2, in the Southern Murray–Darling Basin, Australia. The results show that there is a good agreement between flood inundation obtained from the proposed method and that simulated using a high-fidelity 2D hydrodynamic model. The proposed method is much more efficient than the high-fidelity 2D hydrodynamic model, which makes it an alternative method for applications requiring many model runs or long simulation durations. Also, the LoHy+ model has the potential to be applied in flood inundation forecast, flood risk mitigation design water resource management, etc. HIGHLIGHTS A new method, LoHy+, is developed to efficiently simulate flood inundation with reasonable accuracy.; The existing hydrodynamic model is exploited to achieve higher computational efficiency.; Relationships between flood water evolutions in the river and floodplain are revealed and applied to better achieve the computational efficiency while still guaranteeing the simulation accuracy with the use of LoHy + .;

Ensuring consistent high water quality is paramount in water management planning. This paper addresses this objective by proposing an intelligent edge-cloud framework for water quality monitoring within the water distribution system (WDS). Various scenarios—cloud computing, edge computing, and hybrid edge-cloud computing—are applied to identify the most effective platform for the proposed framework. The first scenario brings the analysis closer to the data generation point (at the edge). The second and third scenarios combine both edge and cloud platforms for optimised performance. In the third scenario, sensor data are directly sent to the cloud for analysis. The proposed framework is rigorously tested across these scenarios. The results reveal that edge computing (scenario 1) outperforms cloud computing in terms of latency, throughput, and packet delivery ratio obtaining 20.33 ms, 148 Kb/s, and 97.47%, respectively. Notably, collaboration between the edge and cloud enhances the accuracy of classification models with an accuracy of up to 94.43%, this improvement was achieved while maintaining the energy consumption rate at the lowest value. In conclusion, our study demonstrates the effectiveness of the proposed intelligent edge-cloud framework in optimising water quality monitoring, and the superior performance of edge computing, coupled with collaborative edge-cloud strategies, underscores the practical viability of this approach.

Abstract Previous studies had reported the mutual relation between depression and chronic kidney diseases (CKD). This study aimed to investigate potential bidirectional relationships between depression and CKD. Participants more than 45 years from the China Health and Retirement Longitudinal Study (CHARLS) were included in present study. In study I, we tended to assess the association between baseline depression with the risk of subsequent CKD. In study II, we aimed to examine whether the onset of CKD could predict the development of depression. Multivariate logistic regression models were used to calculate odds ratios (ORs) and 95% confidence intervals (95% CIs) in study I and study II, respectively. In study I, 301 (6.16%) respondents experienced CKD in participants without depression, and 233 (8.48%) respondents experienced CKD in participants with depression. Participants with depression had higher risk of developing CKD with the corresponding ORs (95% CIs) was 1.38(1.08–1.76). In study II, 1333 (22.29%) subjects in the non-CKD group and 97 (27.17%) in CKD group developed depressive symptoms. Individuals with CKD had higher risk of developing depression than those without CKD, with the multivariate ORs (95% CIs) was 1.48(1.23–1.78). Significant bidirectional relationships remained in both sensitivity and subgroup analyses. Findings demonstrate bidirectional relationships between depression and CKD. Individuals with depression were associated with increasing risk of CKD; in addition, CKD patients had higher risk of developing depression.

Daily streamflow forecasting is a major determinant of ecological processes in running waters, healthy stream ecology and surrounding environment, and accurate streamflow forecasting provides a powerful foundation for ecological assessment, management, and decision-making. Recently, data-driven models for different flow regimes have shown excellent potential in streamflow forecasting. However, the boundaries between different flow regimes were selected arbitrarily without considering the changes in boundaries that often occur over time in the real world. Therefore, in this paper, an integrated modelling approach that couples a dynamic classification method with a long short-term memory networks (LSTM) model without data transformation (the DC-LSTM model) and an LSTM with Box-Cox data transformation (the DC-B-LSTM model) is developed to improve the performance of streamflow forecasting considering different flow regimes. The boundaries of dynamic classification are dynamic changing interval values of related hydrological variables improved from traditional classification method just using static single-variable threshold, so dynamic classification can more fully explore the relationship and information of hydrological data. The performance of both the DC-LSTM and DC-B-LSTM models is compared to that of the LSTM model without data classification (the traditional LSTM model) and with data classification using a traditional static method (the C-LSTM model) based on data from 8 stations within 4 river basins in different climate regions in the United States. The results show that both the DC-LSTM and DC-B-LSTM models out-perform the traditional LSTM models (with or without static data classification) for all river basins considered. Furthermore, the DC-B-LSTM model displays better performance than the DC-LSTM model in arid areas.

Abstract The Casitas B-lineage lymphoma (Cbl) family proteins are E3 ubiquitin ligases implicated in the regulation of various immune cells. However, their function in dendritic cells (DCs) remains unclear. To investigate the role of Cbl family members in DCs, we created dendritic cell double-deficient Casitas B lymphoma-b (Cbl-b) and Casitas B lineage lymphoma (c-Cbl) mice by crossing Cbl-b−/− mice with c-Cblflox/flox CD11c-Cre+ mice. We found that specific deletion of Cbl-b and c-Cbl in CD11c+ cells, predominantly in DCs, led to liver fibrosis, cirrhosis, and accumulation of systemic conventional Type I DCs (cDC1s) due to enhanced cell proliferation and decreased cell apoptosis. In addition to a change in DC number, double knockout (dKO) cDC1s exhibited a partially activated status as indicated by high basal expression levels of certain cytokines and possessed an enhanced capacity to prime T cells. After adoptive transfer, dKO cDC1s could drive liver fibrosis too. In further experiments, we demonstrated that Cbl-b and c-Cbl could target signal transducer and activator of transcription 5 (STAT5), a transcriptional repressor for the pro-apoptotic protein Bim, to promote ubiquitination-mediated degradation and cell apoptosis in cDC1s. Further extensive experiments revealed that Cbl-b mediated K27-linked ubiquitination of lysine 164 of STAT5a while c-Cbl induced K29-linked ubiquitination of lysine 696 of STAT5a and K27-linked ubiquitination of lysine 140 and 694 of STAT5b. Thus, our findings indicate a functional redundancy of Cbl-b and c-Cbl in cDC homeostasis and maturation.

Background. Type 2 diabetes mellitus increases the risk of sarcopenia, which is characterized by decreased muscle mass, strength, and function. However, there are no effective drugs to treat diabetic sarcopenia, and its underlying mechanism remains unknown. Here, we aimed to determine whether the GLP-1 receptor agonist (GLP-1RA) dulaglutide (Dul) affects the progression of diabetic sarcopenia. Methods. db/db mice were injected intraperitoneally with 0.6 mg/kg dulaglutide for 10 weeks. Mouse muscle tissues were then pathologically evaluated and stained with F4/80 or MPO to detect macrophages and neutrophils, respectively. In addition, inflammatory factors and FNDC5 in the muscle tissues were detected using qRT-PCR. Moreover, C2C12 cells were induced to enable their differentiation into skeletal muscle cells, and muscle factor levels were then detected. Furthermore, changes in muscle factor levels were detected at various glucose concentrations (11 mM, 22 mM, and 44 mM). Results. In vivo, dulaglutide alleviated muscle tissue injury; reduced levels of the inflammatory factors, IL-1β, IL-6, CCL2, and CXCL1; and reversed the level of FNDC5 in the muscle tissues of db/db mice. In vitro, a C2C12 cell differentiation model was established through the observation of cell morphology and determination of myokine levels. Upon stimulation with high glucose, the differentiation of C2C12 cells was inhibited. Dulaglutide improved this inhibitory state by upregulating the levels of both FNDC5 mRNA and protein. Conclusions. Treatment with the GLP-1RA dulaglutide protects db/db mice against skeletal muscle injury by inhibiting inflammation and regulating the differentiation of myoblasts. High glucose inhibited the differentiation of C2C12 cells and decreased the mRNA and protein levels of myokines. Dulaglutide could reverse the differentiation state induced in C2C12 cells by high glucose.

Abstract Long non-coding RNAs (lncRNAs) play a crucial role in macrophage development but little is known about their role in asthma. Here, we investigated the role of lncRNA lncTRPM2-AS in asthma and found that lncTRPM2-AS participates in the promotion of macrophage inflammation. Downregulation of lncTRPM2-AS promoted apoptosis and inhibited proliferation and production of cytokines including IL-1β, IL-4, IL-6, IL-10, TNF-α, and TGF-β. RNA-immunoprecipitation and mass spectrometry indicated that the protein TRPM2 interacted with both lncTRPM2-AS and the E3 ubiquitin ligase TRIM21. LncTRPM2-AS silencing enhanced the interaction between TRIM21 and TRPM2, resulting in elevated levels of ubiquitin-related degradation of TRPM2. Mutation analysis indicated that TRPM2 K1218 is a key site for TRIM21-dependent ubiquitination. Downregulation of lncTRPM2-AS significantly decreased intracellular calcium levels by restraining TRPM2 protein expression, which in turn decreased ROS levels and increased autophagy to promote macrophage apoptosis and reduce cytokine production, together inhibiting macrophage inflammation. Taken together, our findings demonstrate that lncTRPM2-AS blocks the ubiquitination of TRPM2 via TRIM21 and inhibits autophagy-induced apoptosis which may contribute to macrophage inflammation in asthma.

Abstract Introduction: Previous studies had reported the impact of weight changes in middle age on the incidence of cardiovascular disease (CVD), but the results were inconsistent. In present study, we aimed to investigated the impact of a 4-year weight change on the risk of CVD in middle-aged and elderly Chinese individuals. Methods: Using nationally representative data from the China Health and Retirement Longitudinal Study, 7,530 participants (age: 58.2 ± 8.9 years) were included. Weight change was calculated by subtracting weight at baseline from that at 4-year follow-up. Weight change over 4 years was divided into five categories (loss ≥5 kg; loss 2–5 kg; stable (change ≤ 2 kg); gain 2–5 kg; and gain ≥5 kg). Results: During the follow-up period, a total of 758 respondents experienced CVD (including 319 stroke and 477 cardiac events). The multivariable ORs of CVD for gain ≥5 kg compared to stable weight (change ≤2 kg) was 1.50 (95% CI, 1.14–1.97) versus 1.41(1.09-1.83) for losing ≥5 kg. Multivariable-adjusted logistic regression model with restricted cubic splines showed a U-shaped association between weight change and the risk of CVD (p for nonlinearity

BackgroundEarly mobilization in the intensive care unit (ICU) is a hotspot. This study aims to provide a bibliometric perspective of the progress in this field.MethodsWe extracted publications on ICU early mobilization published in the Web of Science Core Collection database from 2000 to 2021. VOSviewer was used to construct co-occurrence and co-citation relationships for authors, references, and keywords; Citespace was used to visualize knowledge mapping of subject categories, countries, and keywords with the strongest citation bursts.ResultsA total of 4,570 publications were analyzed, with a steady increase in publications in the field of ICU early mobilization. From a macro perspective, research on ICU early mobilization involves multidisciplinary involvement, including critical care medicine, neurology, and nursing; as for the meso perspective, the United States is the major contributor. Needham DM and Schweickert WD are the key researchers in this field. Moreover, the core journal is Critical Care Medicine, with the most publications and citations. The microscopic level, dominated by references and keywords, illustrates that the hotspot and frontier of research on ICU early mobilization focus on ICU-acquired weakness, delirium, the prognosis of critical illness, and severe COVID-19.ConclusionThis study presents a research landscape of ICU early mobilization from different perspectives. These findings will contribute to a better understanding of the current state of research in critical care medicine and provide the available information for future research ideas.

Background: Immune dysregulation, polyendocrinopathy, enteropathy, X-linked (IPEX) syndrome is a rare disorder caused by mutation of the forkhead box protein 3 (FOXP3) gene, often leading to intractable and life-threatening diarrhea. Fecal microbiota transplantation (FMT), has been regarded in recent years as an available approach to reconstruct disrupted gut microbiome and successfully used to attenuates diarrhea induced by different underlying diseases. Therefore, FMT may have curative potential on the symptoms of enteropathy in patients with IPEX syndrome. Methods: Physical and laboratory examinations were performed, and clinical data were collected. FMT was administered via frozen fecal microbial solution, and the fecal microbiota composition was analyzed using 16S rDNA sequencing before and after FMT. Results: The patient was diagnosed with IPEX syndrome with a mutation detected in the FOXP3 gene, which was identified as c.767T > C (p.M256T). He presented with recurrent watery diarrhea and respiratory infections after birth and developed a significant failure to thrive. Disturbances in the gut microbiota composition and marked decreased bacterial diversity were observed to be involved in the persistent and refractory diarrhea. After receiving FMT treatment, the patient responded with remission of the diarrhea without apparent side effects. His stool output significantly decreased, corresponding to increased microbial diversity and modification of his microbiota composition. The patient finally achieved full recovery after hematopoietic stem cell transplantation (HSCT). Conclusion: Our data suggest an association between the gut microbiota and clinical symptoms of patient with IPEX syndrome and demonstrate FMT as an alternative therapy for severe diarrhea unresponsive to routine therapy in these patients.

Introduction: Diabetic kidney disease (DKD), one of the leading causes of end-stage renal disease, has complex pathogenic mechanisms and few effective clinical therapies. DKD progression is accompanied by the loss of renal resident cells, followed by chronic inflammation and extracellular matrix deposition. Necroptosis is a newly discovered form of regulated cell death and is a major form of intrinsic cell loss in certain diabetic complications such as cardiomyopathy, intestinal disease, and retinal neuropathy; however, its significance in DKD is largely unknown. Methods: In this study, the expression of necroptosis marker phosphorylated MLKL (p-MLKL) in renal biopsy tissues of patients with DKD was detected using immunofluorescence and semiquantified using immunohistochemistry. The effects of different disease-causing factors on necroptosis activation in human HK-2 cells were evaluated using immunofluorescence and western blotting. db/db diabetic mice were fed a high-fat diet to establish an animal model of DKD with significant renal tubule damage. Mice were treated with RIPK1 inhibitor RIPA-56 to evaluate its renal protective effects. mRNA transcriptome sequencing was used to explore the changes in signaling pathways after RIPA-56 treatment. Oil red O staining and electron macroscopy were used to observe lipid droplet accumulation in renal biopsy tissues and mouse kidney tissues. Results: Immunostaining of phosphorylated RIPK1/RIPK3/MLKL verified the occurrence of necroptosis in renal tubular epithelial cells (RTECs) of patients with DKD. The level of the necroptosis marker p-MLKL correlated positively with the severity of renal functional, pathological damages, and lipid droplet accumulation in patients with DKD. High glucose and fatty acids were the main factors causing necroptosis in human renal tubular HK-2 cells. Renal function deterioration and renal pathological injury were accelerated, and the necroptosis pathway was activated in db/db mice fed a high-fat diet. Application of RIPA-56 effectively reduced the degree of renal injury, inhibited the necroptosis pathway activation, and reduced necroinflammation and lipid droplet accumulation in the renal tissues of db/db mice fed a high-fat diet. Conclusion: The present study revealed the role of necroptosis in the progression of DKD and might provide a new therapeutic target for the treatment of DKD.

Due to the instability of the characteristics of materials, fabrication processes and user handling, newly designed and fabricated wireless passive surface acoustic wave (WP-SAW) sensor nodes have inconsistent sensing performance. Furthermore, ambient environmental interferences aggravate inconsistences under complex working conditions. In this paper, a multi-iteration enhanced two-point simple moving average (MI-2P-SMA) method is proposed for sensing error reduction of a WP-SAW reflective delay line water temperature and pressure sensor. This method is improved from the traditional 2P-SMA method for better performance on error reduction. The results show: the MI-2P-SMA method does not change the original characteristics of experimental data; it can reduce relative errors of the WP-SAW reflective delay line water temperature and pressure sensor and has better performance than a traditional 2P-SMA method; it reduces the number of data points and the extent of this reduction is dependent on iteration time.

BackgroundThe intracellular pathogen Legionella pneumophila (L. pneumophila) is a causative agent of pneumonia and does great harm to human health. These bacteria are phagocytosed by alveolar macrophages and survive to replicate within the macrophages. Despite macrophage infectivity potentiator (MIP) protein serving as an essential virulence factor during the invasion process of L. pneumophila, the regulatory mechanism of MIP protein in the process of bacterial infection to host cells is not yet completely understood. This research thus aims to explore the interaction between MIP and macrophage phagocytosis.MethodsThrough the experiment of the co-culture of RAW264.7 macrophages with different concentrations of MIP, the chemotactic activity of macrophages was detected and the phagocytosis was determined by a neutral red uptake assay. The expression of long noncoding RNA (lncRNA) GAS5, microRNA-21 (miR-21), and suppressor of cytokine signaling (SOCS)6 was determined by qRT-PCR. Target genes were detected by dual luciferase assay.ResultsMIP could reduce the phagocytosis and improve the chemotaxis of RAW264.7 macrophages. The expression of both lncRNA GAS5 and SOCS6 was increased whereas the expression of miR-21 was decreased when macrophages were treated with MIP. Dual luciferase assay revealed that lncRNA GAS5 could interact with miR-21, and SOCS6 served as the target of miR-21. After GAS5 overexpression, the phagocytosis of RAW264.7 treated with MIP was increased whereas the chemotaxis was decreased. In contrast, the opposite results were found in RAW264.7 following GAS5 interference.ConclusionsThe present results revealed that MIP could influence RAW264.7 macrophages on phagocytic and chemotactic activities through the axis of lncRNA GAS5/miR-21/SOCS6.

Abstract Compound weather and climate events are combinations of climate drivers and/or hazards that contribute to societal or environmental risk. Studying compound events often requires a multidisciplinary approach combining domain knowledge of the underlying processes with, for example, statistical methods and climate model outputs. Recently, to aid the development of research on compound events, four compound event types were introduced, namely (a) preconditioned, (b) multivariate, (c) temporally compounding, and (d) spatially compounding events. However, guidelines on how to study these types of events are still lacking. Here, we consider four case studies, each associated with a specific event type and a research question, to illustrate how the key elements of compound events (e.g., analytical tools and relevant physical effects) can be identified. These case studies show that (a) impacts on crops from hot and dry summers can be exacerbated by preconditioning effects of dry and bright springs. (b) Assessing compound coastal flooding in Perth (Australia) requires considering the dynamics of a non‐stationary multivariate process. For instance, future mean sea‐level rise will lead to the emergence of concurrent coastal and fluvial extremes, enhancing compound flooding risk. (c) In Portugal, deep‐landslides are often caused by temporal clusters of moderate precipitation events. Finally, (d) crop yield failures in France and Germany are strongly correlated, threatening European food security through spatially compounding effects. These analyses allow for identifying general recommendations for studying compound events. Overall, our insights can serve as a blueprint for compound event analysis across disciplines and sectors.

Current codes and guidelines for the dynamic design of footbridges often only specify the pedestrian-induced excitations. However, earthquakes may occur during the passing stage of pedestrians in earthquake-prone regions. In addition, modern footbridges tend to be slender and are sensitive to vertical ground motions. Therefore, we investigate the effects of vertical ground motion on pedestrian-induced vibrations of footbridges. A total of 138 footbridges with different materials, dimensions, and structural types are considered as the target structures. The classical social force model combined with the pedestrian-induced load is used to simulate crowd loads for the scenarios with six typical pedestrian densities. Furthermore, 59 vertical ground motions with four seismic intensities are taken as the seismic inputs. An amplification factor is introduced to quantify the amplification effects of vertical ground motion on human-induced vibrations of footbridges. Four machine learning (ML) algorithms are used to predict the amplification factor. The feature importance indicates that the scaled peak ground acceleration, the pedestrian density, and the bridge span are the three most important parameters influencing the amplification factor. Finally, the vibration serviceability of the footbridge subjected to both crowd load and vertical ground motion is assessed.

Many studies have applied optimization to the planning, design, rehabilitation or operation of water distribution systems. Recent reviews of the research literature in this area have identified hundreds of papers that address these topics. The objectives considered include variables measuring direct impact of the system such as cost, energy, greenhouse gas emissions, as well as performance variables such as pressure deficit and system reliability. Very few of these studies have considered the effects of the various sources of uncertainty on the objectives considered. The sources of uncertainty include model related uncertainty such as uncertainty in model structure and parameters (e.g., pipe roughness and chemical reaction rates for water quality studies), data related uncertainty such as uncertainty in water demand due to natural variability in the short-term or population growth and/or climate change in the long-term, and human related uncertainty such as lack of knowledge about the physical network as well as modelling errors. This paper is aimed at reviewing the relative importance of these various sources of uncertainty on the key optimization objectives. It also summarizes the key literature in this area and identifies areas where there have been few publications.

The development of intelligent information technology provides an effective way for cross-modal learning analytics and promotes the realization of procedural and scientific educational evaluation. To accurately capture the emotional changes of learners and make an accurate evaluation of the learning process, this paper takes the evaluation of learners’ emotional status in the behavior process as an example to construct an intelligent analysis model of learners’ emotional status in the behavior process, which provides effective technical solutions for the construction of cross-modal learning analytics. The effectiveness and superiority of the proposed method are verified by experiments. Through the analysis of learners’ cross-mode learning behavior, it innovates the evaluation method of classroom teaching in the intelligent age and improves the quality of modern teaching.

Flooding in coastal areas is a major global hazard, made worse during compound flood events, which occur when multiple flood-drivers, such as tide, sea surge, and fluvial and pluvial flooding, coincide. We use 12 sea-level, 2065 rainfall, and 81 river-flow records to assess the dependence of (1) extreme skew-surge and extreme rainfall (pluvial/surface runoff) and (2) extreme skew-surge and extreme river-flow (fluvial discharge) in New Zealand. We found that (1) skew-surge and rainfall and (2) skew-surge and river-flow are significantly, but not strongly, correlated in NZ. When spatially averaged to within 30 km of sea-level gauge location, the correlation was generally significant and positive, but weak with Kendall’s rank correlation coefficient τ < 0.3. We identify the weather types driving regional patterns of dependence. Trough weather types were the dominant driver of individual and coincident extreme events. Blocking weather types were associated with the highest extreme skew-surge and rainfall events along the northeast coast of the North Island and, consequently, were associated with a high proportion of coincident skew-surge/rainfall and skew-surge/river-flow events there. These findings have important implications for flood management, emergency response, and the insurance sector because impacts and losses may be correlated in space. Our findings add to a growing understanding of compound flooding worldwide for different geographical and meteorological settings. The positive dependence observed suggests that more attention to compound event probabilities is warranted when undertaking localized coastal-flood modelling.

Hydraulic models of water distribution systems (WDSs) need to be calibrated, so they can be used to help to make informed decisions. Usually, hydraulic model calibration follows an iterative process of comparing the simulation results from the model with field observations and making adjustments to model parameters to make sure an acceptable level of agreement between predicted and measured values (e.g., water pressure) has been achieved. However, the manual process can be time-consuming, and the termination criterion relies on the modeler’s judgment. Therefore, various optimization-based calibration methods have been developed. In this study, three different optimization methods, i.e., Sequential Least Squares Programming (SLSQP), a Genetic Algorithm (GA) and Differential Evolution (DE), are compared for calibrating the pipe roughness of WDS models. Their performance is investigated over four different decision variable set formulations with different levels of discretization of the search space. Results obtained from a real-world case study demonstrate that compared to traditional engineering practice, optimization is effective for hydraulic model calibration. However, a finer search space discretization does not necessarily guarantee better results; and when multiple methods lead to similar performance, a simpler method is better. This study provides guidance on method and formulation selection for calibrating WDS models.

The obesity epidemic represents a critical public health issue worldwide, as it is a vital risk factor for many diseases, including type 2 diabetes (T2D) and cardiovascular disease. Obesity is a complex disease involving excessive fat accumulation. Proper adipose tissue accumulation and function are highly transcriptional and regulated by many genes. Recent studies have discovered that post-transcriptional regulation, mainly mediated by RNA-binding proteins (RBPs), also plays a crucial role. In the lifetime of RNA, it is bound by various RBPs that determine every step of RNA metabolism, from RNA processing to alternative splicing, nucleus export, rate of translation, and finally decay. In humans, it is predicted that RBPs account for more than 10% of proteins based on the presence of RNA-binding domains. However, only very few RBPs have been studied in adipose tissue. The primary aim of this paper is to provide an overview of RBPs in adipogenesis and adipose function. Specifically, the following best-characterized RBPs will be discussed, including HuR, PSPC1, Sam68, RBM4, Ybx1, Ybx2, IGF2BP2, and KSRP. Characterization of these proteins will increase our understanding of the regulatory mechanisms of RBPs in adipogenesis and provide clues for the etiology and pathology of adipose-tissue-related diseases.

The water distribution system has deployed several low-power IoT devices on an uneven surface where battery power is a major concern. Therefore, this paper focuses on using a UAV-enabled wireless powered communication network capable of directing energy to a target location and using it for communication, thereby reducing battery issues. In this paper, a static optimization was applied to find the initial height values using 3D clustering and beamforming method and dynamic optimization using extremum seeking method was applied to find the optimized height. The optimized height values were calculated and Travelling Salesman Problem (TSP) was applied to create the trajectory of the UAV. The overall energy consumption of the UAV was minimized by integrating dynamic optimization and dome packing method, which can find an optimal position and trajectory where the UAV will be hovering to direct energy and collect data. Moreover, we also minimized the total flight time of the UAV.

Fast and accurate modelling of flood inundation has gained increasing attention in recent years. One approach gaining popularity recently is the development of emulation models using data driven methods, such as artificial neural networks. These emulation models are often developed to model flood depth for each grid cell in the modelling domain in order to maintain accurate spatial representation of the flood inundation surface. This leads to redundancy in modelling, as well as difficulties in achieving good model performance across floodplains where there are limited data available. In this paper, a spatial reduction and reconstruction (SRR) method is developed to (1) identify representative locations within the model domain where water levels can be used to represent flood inundation surface using deep learning models; and (2) reconstruct the flood inundation surface based on water levels simulated at these representative locations. The SRR method is part of the SRR-Deep-Learning framework for flood inundation modelling and therefore, it needs to be used together with data driven models. The SRR method is programmed using the Python programming language and is freely available from https://github.com/yuerongz/SRR-method. • The SRR method identifies locations which are representative of flood inundation behavior in surrounding areas. • The representative locations selected following the SRR method have sufficient flood data for developing emulation models. • Flood inundation surfaces can be reconstructed using the SRR method with a detection rate of above 99%.

In this study, the correlations between microbial communities with physicochemical properties and volatile flavor compounds (VFCs) during the fermentation of traditional tomato sour soup (CTN) are explored. The results of high-throughput sequencing (HTS) of CTN showed that Lacticaseibacillus (28.67%), Enterobacter (12.37%), and Providencia (12.19%) were the dominant bacteria in the first round of fermentation, while Lacticaseibacillus (50.11%), Enterobacter (13.86%), and Providencia (8.61%) were the dominant bacteria in the second round of fermentation. Additionally, the dominant fungi genera of the first fermentation were Pichia (65.89%) and Geotrichum (30.56%), and the dominant fungi genera of the second fermentation were Pichia (73.68%), Geotrichum (13.99%), and Brettanomyces (5.15%). These results indicate that Lacticaseibacillus is one of the main dominant bacteria in CTN. Then, the dominant strain Lacticaseibacillus casei H1 isolated from CTN was used as a culture to ferment tomato sour soup to monitor dynamic changes in the physicochemical properties and VFCs during enhanced fermentation of tomato sour soup (TN). The physicochemical analysis showed that, compared with CTN, the TN group not only produced acid faster but also had an earlier peak of nitrite and a lower height. The results of the GC–IMS analysis showed that the ester and alcohol contents in the TN group were 1.26 times and 1.8 times that of the CTN group, respectively. Using an O2PLS-DA analysis, 11 bacterial genera and 18 fungal genera were identified as the functional core flora of the CTN group flavor production, further verifying the importance of dominant bacteria for the production of VFCs. This study proved that enhanced fermentation not only shortens the fermentation cycle of tomato sour soup, but also significantly improves its flavor quality, which has great value in the industrial production of tomato sour soup and in the development of a vegetable fermentation starter.

The development of many intelligent technologies, such as artificial intelligence and the Internet of Things, has brought new opportunities for water industry intelligence. Based on intelligent pressure regulation technology, this paper built an intelligent management platform, designed an intelligent pressure-regulating device, and combined both to form an intelligent pressure-regulating vehicle (IPRV). The IPRV has the functions of developing a pressure-regulating scheme, equipment selection, pressure reduction potential analysis, etc. It can bring convenience to the field test of the water supply network. In the field test, an intelligent pressure-regulating device was used to obtain the network data in the pilot site called S-cell. After utilizing the intelligent management platform to analyze the measured data, the water usage pattern and pressure reduction potential of the S-cell were obtained, and an optimal pressure-regulating strategy was formulated. The water pressure at the critical node always met the water demand at the critical node during the field test. In addition, no complaints were received from other users. The results show that the IPRV is not only convenient for utility managers to make decisions on building pressure-reducing stations, but also meets user needs, realizing a win–win situation for both users and companies.

Water Distribution System (WDS) threats have significantly grown following the Maroochy shire incident, as evidenced by proofed attacks on water premises. As a result, in addition to traditional solutions (e.g., data encryption and authentication), attack detection is being proposed in WDS to reduce disruption cases. The attack detection system must meet two critical requirements: high accuracy and near real-time detection. This drives us to propose a two-stage detection system that uses self-supervised and unsupervised algorithms to detect Cyber-Physical (CP) attacks. Stage 1 uses heuristic adaptive self-supervised algorithms to achieve near real-time decision-making and detection sensitivity of 66% utilizing Boss. Stage 2 attempts to validate the detection of attacks using an unsupervised algorithm to maintain a detection accuracy of 94% utilizing Isolation Forest. Both stages are examined against time granularity and are empirically analyzed against a variety of performance evaluation indicators. Our findings demonstrate that the algorithms in stage 1 are less favored than those in the literature, but their existence enables near real-time decision-making and detection reliability. In stage 2, the isolation Forest algorithm, in contrast, gives excellent accuracy. As a result, both stages can collaborate to maximize accuracy in a near real-time attack detection system.

The severity and frequency of flood and drought events have increased in recent decades. These climate change-induced and anthropogenic stressors on water resources represent the leading water-related hazards to communities. Further, the increasing exposure of the population and infrastructure to such events has heightened the risks. Assessing the impact scope of these events in different subfields towards comprehensively evaluating the risks requires an unbiased systematic approach. This paper combines content analysis and science mapping to investigate the existing multidisciplinary body of knowledge on analyzing flood and drought together. Searching the literature using selected search terms yielded a sample of 119 publications. Initially, various contents, such as the authors’ keywords, applied methods and indices, and study scale, were extracted from these articles. These contents were then incorporated into the science mapping technique to form communicative networks. Analyzing these publications revealed 13 major research themes, with a sustained focus on hydrological issues. However, a more diverse range of themes was recently revealed, including economy, sociology, insurance, and policymaking. Producing such computational and visual networks explained informative insights that can help further develop both existing and new frameworks to support the management, design and policymaking sectors in responding to both flood and drought events.

Communication campaigns to promote the importance of water as a vital but limited resource have evolved in many ways. Nowadays, the resources, techniques and skills to deliver effective communication campaigns are far greater than ever before. Over the past decades, there has been a significant body of research towards improving water conservation campaign communication but with limited success in promoting more resilient behaviours on behalf of water consumers. While the media and technology have rapidly evolved and awareness among consumers may have increased, this has not been sufficient to make the communication effective in changing behaviour. Communications to promote resilience among consumers need to reach a wide audience, capture audiences’ attention, build awareness and motivate water consumers to consume water sustainably. This represents a subject in need of further theoretical and conceptual investigation. This research reviews various approaches to effective communication and through a synthesis of the concepts aims to present a new, socio-psychological water conservation conceptual framework. The present conceptual framework integrates emotional appeal, for use on social media platforms and in order to foster more water resilient communities. This framework represents a potentially major contribution in providing guidelines for water sectors to deliver effective video communications on social media platforms.

This work develops a toolbox called WDSchain on MATLAB that can simulate blockchain on water distribution systems (WDS). WDSchain can import data from Excel and EPANET water modelling software. It extends the EPANET to enable simulation blockchain of the hydraulic data at any intended nodes. Using WDSchain will strengthen network automation and the security in WDS. WDSchain can process time-series data with two simulation modes: (1) static blockchain, which takes a snapshot of one-time interval data of all nodes in WDS as input and output into chained blocks at a time, and (2) dynamic blockchain, which takes all simulated time-series data of all the nodes as input and establishes chained blocks at the simulated time. Five consensus mechanisms are developed in WDSchain to provide data at different security levels using PoW, PoT, PoV, PoA, and PoAuth. Five different sizes of WDS are simulated in WDSchain for performance evaluation. The results show that a trade-off is needed between the system complexity and security level for data validation. The WDSchain provides a methodology to further explore the data validation using Blockchain to WDS. The limitations of WDSchain do not consider selection of blockchain nodes and broadcasting delay compared to commercial blockchain platforms.

Objective This study was performed to compare the clinical value of the second-generation Shikani optical stylet with that of the Macintosh laryngoscope for tracheal intubation of patients undergoing cerebral aneurysm embolization. Methods Thirty-six patients who underwent cerebral aneurysm embolization were included. The intubation time, intubation success rate, blood oxygen saturation, heart rate, systolic blood pressure (SBP), and diastolic blood pressure (DBP) were measured. Adverse reactions during tracheal intubation and the local tissue injury rate were recorded. Comparisons between the groups were performed with one-way analysis of variance. Results The heart rate, SBP, and DBP upon tracheal intubation and at 1 and 3 minutes were significantly higher in the Macintosh laryngoscope group than in the Shikani optical stylet group. The time to completion of tracheal intubation was significantly shorter and the tissue injury rate was significantly lower in the Shikani optimal stylet group than in the Macintosh laryngoscope group. Conclusions The second-generation Shikani optical stylet is a simple, safe, and reliable tool for tracheal intubation in critically ill patients undergoing cerebral aneurysm embolization.

This paper aims to assess and analyze the health impact of consuming contaminated drinking water in a water distributed system (WDS). The analysis was based on qualitative simulation performed in two different models named hydraulic and water quality in a WDS. The computation focuses on quantitative analysis for chemically contaminated water impacts by analyzing the dose level in various locations in the water network and the mass of the substance that entered the human body. Several numerical experiments have been applied to evaluate the impact of water pollution on human life. They analyzed the impact on human life according to various factors, including the location of the injected node (pollution occurrence) and the ingested dose level. The results show a significant impact of water contaminant on human life in multiple areas in the water network, and the level of this impact changed from one location to another in WDSs based on several factors such as the location of the pollution occurrence, the contaminant concentration, and the dose level. In order to reduce the impact of this contaminant, water quality sensors have been used and deployed on the water network to help detect this contaminant. The sensors were optimally deployed based on the time-detection of water contamination and the volume of polluted water consumed. Numerical experiments were carried out to compare water pollution’s impact with and without using water quality sensors. The results show that the health impact was reduced by up to 98.37% by using water quality sensors.

We present a statistical method to quantify the contribution of urbanization to precipitation changes during 1958–2017 across the greater Beijing–Tianjin–Hebei metropolitan region in northern China. We find distinct trends in precipitation in the past six decades: decreasing in annual and summer while increasing in other seasons. The spatial patterns of precipitation show discernible terrain-induced characteristics with high values in the buffer zones of plain and mountain areas and low values in the northwestern mountainous regions. Our results indicate that although urbanization has limited impacts on the trends and spatial patterns of precipitation, it has a positive contribution to the changes in precipitation for about 80% of the comparisons conducted, especially in autumn (100%), with the negative contribution being dominant in summer (66.67%). In addition, these results are sensitive to the classifications of urban and rural stations, suggesting that how to classify urban/rural areas is a crucial step to estimate the potential contribution of urbanization to precipitation changes. These findings also support that urbanization can diversify and enhance the variations in precipitation, with urban areas becoming a secondary center along with more increasing or less decreasing trends in precipitation.

Recent advances in Internet of Things (IoT) enabled technologies allow the intelligent sensor systems to effectively and efficiently observe and identify human behaviour in many applications, particularly in energy consumption and healthcare sectors. One typical case is that how to use IoT technologies to understand human water use behaviour for improved and sustained hygiene practice. Traditionally, static behaviour intervention models are widely utilised to simulate behaviour intervention process over time. These static methods can predict targeted human behaviour reasonably well, but lack of capabilities on understanding and responding behaviour change process in IoT environments. In this study, the authors proposed a dynamic behaviour intervention model for predicting household water user behaviour for improved hygiene practices. This model is based on an expanded theory of planned behaviour (ETPB), and adopted structure equation model approach and control engineering concept. A case study of household water consumption model using artificial neural network is utilised to evaluate intervention trend of proposed ETPB dynamic behaviour model with system parameter identification. The ETPB dynamic model has been proved to be effective for modelling human behaviour intervention process.

Secondary pollution by microorganisms and substances peeling off from the “growth ring” causes clean water deterioration during the water distribution process. In order to reduce the secondary pollution, our previous research investigated the best settings of a two-phase flow flushing method for pipeline cleaning in water distribution systems experimentally, and a case study was carried out for comparison of the efficiencies between two-phase and single-phase flow methods. In this paper, based on the results of the experimental study, numerical modelling and a simulation study are carried out by FLUENT to evaluate the performance of the two-phase flow flushing method for removal of the “growth ring”. Results: the simulation results match the experimental results; pressure, water-phase flow velocity and water-phase volume ratio distributions in a section of pipe are simulated and analysed; the shear force against time in a period is obtained; elbow pipes cause flushing energy loss, and therefore, at most one section of elbow pipe is flushed in one flushing period.

Dependence between extreme rainfall and storm surge can have significant implications for coastal floods, which are often caused by joint occurrence of these flood drivers (through pluvial or fluvial processes). The effect of multiple drivers leading to a compound flood event poses higher risk than those caused by a single flood-driving process. There is strong evidence that compound floods caused by joint occurrence of extreme storm surge and heavy rainfall are related to meteorological forcing (e.g. large scale pressure systems and wind) and climate phenomena (e.g. the El Niño Southern Oscillation or ENSO). Therefore, understanding how climate phenomena affect the co-occurrence of coastal flood drivers is an important step towards understanding future coastal flood risk under climate change. Here we examine the impact of one of the most important climate phenomena—ENSO—on dependence between storm surge and rainfall in Australia, using both observed surge and modelled surge from a linked ocean-climate model—the Regional Ocean Modeling System. Our results show that ENSO has a significant impact on the dependence between extreme rainfall and storm surge, thus flood risk resulted from these drivers. The overall dependence is largely driven by La Niña in Australia, with increased dependence observed during La Niña along most of the Australian coastline. However, there can be increased dependence during El Niño in some locations. The results demonstrate dependence is contributed by unequally-weighted mechanisms due to the interaction between climate phenomena and local features, indicating the need for greater understanding of composition of compound flood risk. Where climate phenomena are anticipated to change into the future, it is possible to use integrated process-driven models to establish a better understanding of whether extremes are more likely to co-occur and exacerbate compound flood risk.

Sorghum grains with different applications had different phenolic profiles, which were corresponded to various antioxidant capacities. In this study, total phenolic, proanthocyanidins and flavonoids contents, as well as contents of individual phenolic compounds from sorghum grains with various applications were determined, and their antioxidant capacities were evaluated. Total phenolic contents (TPC) and total proanthocyanidins contents (TPAC) showed strong correlation with antioxidant activities (r > 0.95, p < 0.01). Hongyingzi (S-1), one of the brewing sorghums, showed the highest level of TPC and TPAC, while white grain sorghum (S-8) had the lowest. Except for black grain sorghum (S-7), that contained the highest contents of ferulic acid, brewing sorghum grains contained the higher contents of the most individual phenolic compounds, especially the variety S-1. The correlation among individual phenolic compounds and antioxidant activities indicated that the free forms of protocatechuic acid (r = 0.982 of FRAPassay, p < 0.01) and taxifolin (r = 0.826 of FRAP assay, p < 0.01) may be the main functional compounds. These results indicate that brewing sorghum grains can also be utilized as effective materials for functional foods.

The deterioration of drinking water during distribution process is caused by many factors. The microorganisms and substances peeling off from the “growth-ring” make the secondary pollution in drinking water distribution systems. To reduce the secondary pollution, two-phase pulse flushing technology is introduced to quickly remove the “growth-ring”. In this study, experiment is undertaken for investigating the efficiency of the two-phase pulse flushing and finding the best setting combination. A case study is undertaken to compare the efficiencies between the two-phase pulse and the single-phase flushing. The best setting combination of the two-phase pulse flushing is at the frequency 4 s–6 s (air inflow time is 4 s and air cut off time is 6 s) and the round air inflow nozzle is set at the bottom of the pipe. Two-phase pulse flushing technology can save 95% of water and 6 h 40 min flushing time.