Important Measure Research Articles

Importance Measure for Multilevel Inspections of Multistate Systems: A Value of Information Perspective

Importance Measure for Multilevel Inspections of Multistate Systems: A Value of Information Perspective

Chronic Obstructive Pulmonary Disease Mortality in Bladder Cancer Patients: A SEER-Based Competing Risk Analysis.

This study was designed to evaluate risk of mortality from chronic obstructive pulmonary disease (COPD) in patients with bladder cancer (BC). Data on patients diagnosed with BC by pathology between 2000 and 2016 were collected from the Surveillance, Epidemiology, and End Results (SEER) database. Based on reference data from the general population, the standardized mortality rate (SMR) is calculated. Nelson-Aalen cumulative hazard curves were used for assessment of the risk of COPD mortality in BC patients. Multivariable competing risk models were conducted. The proportional hazards assumption was tested using Schoenfeld residuals, which were scaled and plotted over time for each risk factor. A total of 237,563 BC patients were identified for further analysis from the SEER database, 5,198 of these patients experienced COPD mortality; the overall SMR for COPD mortality in BC patients was 1.58 (95% CI: 1.54-1.63). Age, race, year of diagnosis, histologic type, summary stage, surgery, marital status, college education level, and median household income independently predicted COPD mortality in BC patients. In comparison to the general population, the risk of COPD mortality is significantly higher in patients with BC. Pre-identification of high-risk groups and respiratory care provisions are important measures to effectively improve survival in this group of patients.

Association of Contrast Sensitivity with Eye Disease and Vision-Related Quality of Life

To investigate contrast sensitivity (CS) as a screening tool to detect eye disease and assess its association with both eye disease and vision-related quality of life. Cross-sectional study. Setting and population: Adults receiving care from a free clinic and a Federally Qualified Health Center in Michigan. Screening positive for eye disease and Visual Function Questionnaire (VFQ) score. Participants received a vision exam reviewed via telemedicine for disease, completed a demographic survey, and the 9-item VFQ. The ability of CS to predict eye disease was explored and area under the curve (AUC) is reported. Logistic and linear regression were used to investigate the continuous effect of CS on the probability of screening positive for eye disease and VFQ score, respectively, adjusting for age and visual acuity. 1159 included participants were, on average, 54.9 ± 14.5 years old, 62% identified as female, 34% as White, 54% as Black, 10% as Hispanic/Latino, and reported mean VFQ score of 79.7 ± 15.3. CS ranged from 0.00 to 1.95 log units (mean = 1.54 ± 0.24), 21% of eyes had glaucoma, 19% cataract, 6% DR, and 2% AMD. AUCs were 0.53 to 0.73. A 0.3 log unit decrease in better eye CS was associated with increased odds of glaucoma (odds ratio [OR] = 1.35, confidence interval [CI] = 1.09-1.67), cataract (OR = 1.35, CI = 1.05-1.72), DR (OR = 2.05, CI = 1.51-2.77), and AMD (OR = 2.08, CI = 1.10-3.91). A 0.3 log unit increase in better eye CS was associated with a 5.9 unit increase in VFQ. While CS alone is not sufficient to identify people with eye disease, it is an important measure of visual function that can add value to comprehensive eye screening.

Epidemiological and mycological profile of superficial mycoses in diabetic patient

Introduction: Superficial mycoses, especially those of nails, scalp and skin, are very common in various countries. The presence of specific terrain, as well as other factors, can affect the prevalence of these. In this sense, diabetic patients are more susceptible to infections especially fungal infections because of changes in the immune system and macro and microvascular changes. The objective of our study was to determine the epidemiological and mycological profile of superficial mycoses in diabetic patients at the Mohammed VI Hospital in Oujda (Morocco). Material and methods: This is a retrospective, observational and descriptive study spread over 34 months, from January 2021 to October 2023 at the Parasitology-Mycology laboratory of the Mohammed VI Hospital Center of Oujda. We collected demographic and mycological data for the included patients. In the laboratory, upon reception of the samples, a direct examination (DE) of the sample was obtained after clarification by KOH to better visualize the mycelial filaments or yeasts and culture on both media: Sabouraud with chloramphenicol and Sabouraud with chloramphenicol and cycloheximide is systematically carried out. Results: We collected 136 diabetic patients referred to our laboratory for suspicion of superficial mycosis. The latter was confirmed in 46 of the 136 diabetic patients (33.82%) referred to our laboratory, which corresponds to 50 lesions among the 166 sites taken, a prevalence of 30.12%. At the mycological examination, the DE allowed the confirmation of the diagnosis of mycoses in 64% (n=32) and showed most often mycelial filaments (n=21; 65.62%) especially at the level of the feet and yeasts were observed in particular at the level of the nails of the hands (n=11; 34.37%). While culture was positive in all cases without exception (100%; n=50), dermatophytes were the most frequently isolated fungi (n=30; 60%), followed by yeasts (n=20; 40%). By species, T. rubrum (n=30; 60%) was the only isolated dermatophyte species, followed by Candida albicans (n=17; 34%). Conclusion: In recent years, we have seen an increase in the prevalence of superficial mycoses in diabetic patients which is why we conducted this study. The results show on the one hand that the most isolated fungi in the superficial mycoses of these patients are dominated by dermatophytes, as T. rubrum represents the most frequently encountered species. Furthermore, the high prevalence of the latter demonstrates the significance of implementing important prevention measures.

Phage cocktail superimposed disinfection: A ecological strategy for preventing pathogenic bacterial infections in dairy farms

Bovine mastitis (BM) is mainly caused by bacterial infection that has a highly impact on dairy production, affecting both economic viability and animal well-being. A cross-sectional study was conducted in dairy farms to investigate the prevalence and antimicrobial resistance patterns of bacterial pathogens associated with BM. The analysis revealed that Staphylococcus (49%), Escherichia (16%), Pseudomonas (11%), and Klebsiella (6%) were the primary bacterial pathogens associated with mastitis. A significant proportion of Staphylococcus strains displayed multiple drug resistance. The use of disinfectants is an important conventional measure to control the pathogenic bacteria in the environment. Bacteriophages (Phages), possessing antibacterial properties, are natural green and effective disinfectants. Moreover, they mitigate the risk of generating harmful disinfection byproducts, which are commonly associated with traditional disinfection methods. Based on the primary bacterial pathogens associated with mastitis in the investigation area, a phage cocktail, named SPBC-SJ, containing seven phages capable of lysing S. aureus, E. coli, and P. aeruginosa was formulated. SPBC-SJ exhibited superior bactericidal activity and catharsis effect on pollutants (glass surface) compared to chemical disinfectants. Clinical trials confirmed that the SPBC-SJ-based superimposed disinfection group (phage combined with chemical disinfectants) not only cut down the dosage of disinfectants used, but significantly reduced total bacterial counts on the ground and in the feeding trough of dairy farms. Furthermore, SPBC-SJ significantly reduced the abundance of Staphylococcus and Pseudomonas in the environment of the dairy farm. These findings suggest that phage-based superimposed disinfection is a promising alternative method to combat mastitis pathogens in dairy farms due to its highly efficient and environmentally-friendly properties.

Exact Algorithms and Lowerbounds for Multiagent Path Finding: Power of Treelike Topology

In the Multiagent Path Finding (MAPF for short) problem, we focus on efficiently finding non-colliding paths for a set of k agents on a given graph G, where each agent seeks a path from its source vertex to a target. An important measure of the quality of the solution is the length of the proposed schedule l, that is, the length of a longest path (including the waiting time). In this work, we propose a systematic study under the parameterized complexity framework. The hardness results we provide align with many heuristics used for this problem, whose running time could potentially be improved based on our Fixed-Parameter Tractability (FPT) results. We show that MAPF is W[1]-hard with respect to k (even if k is combined with the maximum degree of the input graph). The problem remains NP-hard in planar graphs even if the maximum degree and the makespan l are fixed constants. On the positive side, we show an FPT algorithm for k+l. As we continue, the structure of G comes into play. We give an FPT algorithm for parameter k plus the diameter of the graph G. The MAPF problem is W[1]-hard for cliquewidth of G plus l while it is FPT for treewidth of G plus l.

Estimating Tomato Plant Leaf Area Using Multiple Images from Different Viewing Angles

The estimation of leaf area is an important measure for understanding the growth, development, and productivity of tomato plants. In this study, we focused on the leaf area of a potted tomato plant and proposed methods, namely, NP, D2, and D3, for estimating its leaf area. In the NP method, we used multiple tomato plant images from different viewing angles to reduce the estimation error of the leaf area, whereas in the D2 and D3 methods, we further compensated for the perspective effects. The performances of the proposed methods were experimentally assessed using 40 “Momotaro Peace” tomato plants. The experimental results confirmed that the NP method had a smaller mean absolute percentage error (MAPE) on the test set than the conventional estimation method that uses a single tomato plant image. Likewise, the D2 and D3 methods had a smaller MAPE on the test set than the conventional method that did not compensate for perspective effects.

Average trapping time on horizontally divided 3-dimensional 3-level Sierpinski gasket network

An important characteristic of random wandering is the average trapping time, which is a hot issue in current research. The average trapping time is an important measure of the transmission efficiency of random wandering in a network. In this paper, we construct a 3-dimensional 3-level Sierpinski gasket network divided horizontally by the horizontal division plane P s , that is, the division coefficients s. We study the capture problem on the network and obtain an analytical expression for the average trapping time (ATT). Then, by adjusting the number of iterations and the values of the division coefficients, we obtained the relationship between ATT and them. As can be seen from our numerical simulation plots, ATT is affected by s. The larger s is, the more the self-similar structure of the three-dimensional residual network gradually transforms towards the structure of the two-dimensional complete Sierpinski gasket network. Meanwhile, the shorter ATT is, that is, the more efficient the transmission on the network.

A Novel Identification Approach Using RFECV–Optuna–XGBoost for Assessing Surrounding Rock Grade of Tunnel Boring Machine Based on Tunneling Parameters

In order to solve the problem of the poor adaptability of the TBM digging process to changes in geological conditions, a new TBM digging model is proposed. An ensemble learning prediction model based on XGBoost, combined with Optuna for hyperparameter optimization, enables the real-time identification of surrounding rock grades. Firstly, an original dataset was established based on the TBM tunneling parameters under different surrounding rock grades based on the KS tunnel. Subsequently, the RF–RFECV was employed for feature selection and six features were selected as the optimal feature subset according to the importance measure of random forest features and used to construct the XGBoost identification model. Furthermore, the Optuna framework was utilized to optimize the hyperparameters of XGBoost and validated by applying the established TBM dataset of the KS Tunnel. In order to verify the applicability and efficiency of the proposed model in surrounding rock grade identification, the prediction results of five commonly used machine learning models, Optuna–XGBoost, Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Decision Tree (DT), XGBoost, and PSO–XGBoost, were compared and analyzed. The main conclusions are as follows: the feature selection method based on RF–RFECV improved the accuracy by 8.26%. Among the optimal feature subset, T was the most essential feature for the model’s input, while PR was the least important. The Optuna–XGBoost model proposed in this paper had higher accuracy (0.9833), precision (0.9803), recall (0.9813), and F1 score (0.9807) than other models and could be used as an effective means for the lithological identification of surrounding rock grade.

Downscale Inversion of Soil Moisture during Vegetation Growth Period in Ebinur Lake Watershed

Soil moisture content is an important measure of soil health, and high-precision soil moisture trend analysis is essential for understanding regional ecological quality in the context of climate change, flood monitoring, and water cycle processes. However, in the arid regions of Central Asia, where data are severely lacking, obtaining high-spatial-resolution, continuous soil moisture data is difficult due to the scarcity of stations. Moreover, because soil moisture is easily affected by evaporation time, surface morphology, and anthropogenic factors, mature theoretical models or empirical or semiempirical models to measure soil moisture are also lacking. To investigate the distribution and trend of soil moisture in the Ebinur Lake water, in this study, microwave remote sensing and visible remote sensing data were selected as inputs, and the Global Land Data Assimilation System (GLDAS-2.2) data products were downscaled using the GTWR model, which increased the spatial scale from 27,830 m × 27,830 m to 30 m × 30 m. The phenomena involved in the soil moisture change cycle, spatial distribution, temporal variation, and internal randomness distance were analyzed in the study area through wavelet analysis, Theil–Sen trend analysis, the Mann–Kendall (MK) test, and a variogram. This study obtained high-resolution continuous soil moisture data in the arid and data-scarce region in Central Asia, thus broadening the field of multisource remote sensing analysis and providing a theoretical basis for the construction of precision agriculture in northwest China.

Feature importance measures for flood forecasting system design

ABSTRACT Effective proxy selection in hydrological processes is crucial in several applications. This study investigates the role of sub-basins in hydrological response, which remains unclear. Our focus is on exploring feature importance measures to identify influential sub-basins in a flood forecasting system. We use the Tiber River basin as a case study and employ a synthetic flood hydrograph dataset, consisting in approximately 20 000 simulated annual maximum hydrographs across 39 sub-basins and the basin outlet. Through this study, we present a proof of concept for ranking sub-basins based on their contribution to basin response using six feature importance measures. The results reveal eight influential sub-basins and provide guidance for strategically installing measurement instrumentation for an efficient and cost-effective flood early warning system.

Influence Analysis of Real Exchange Rate Fluctuations on Trade Balance Data Using Feature Important Evaluation Methods

This study delves into the intricate relationship between fluctuations in the real exchange rate and the trade balance, situated within the framework of a ‘two-country’ trade theory model. Despite a wealth of prior research on the impact of exchange rates on international trade, the precise extent of this influence remains a contentious issue. To bridge this gap, our research adopts a pioneering approach, employing three distinct artificial intelligence-based influence measurement methods: Mean Decrease Impurity (MDI), Permutation Importance Measurement (PIM), and Shapley Additive Explanation (SHAP). These sophisticated techniques provide a nuanced and differentiated perspective, enabling specific and quantitative measurements of the real exchange rate’s impact on the trade balance. The outcomes derived from the application of these innovative methods shed light on the substantial contribution of the real exchange rate to the trade balance. Notably, the real exchange rate (RER) emerges as the second most influential factor within the ‘two-country’ trade model. This empirical evidence, drawn from a panel dataset of 78 nations over the period 1992–2021, addresses crucial gaps in the existing literature, offering a finer-grained understanding of how real exchange rates shape international trade dynamics. Importantly, our study implies that policymakers should recognize the pivotal role of the real exchange rate as a key determinant of trade flow.

Consecutive Fertilization-Promoted Soil Nutrient Availability and Altered Rhizosphere Bacterial and Bulk Fungal Community Composition

Fertilization is an important measure to quickly supplement the soil nutrients required for plantation productivity. However, the response patterns of the microbial community and functional taxa in Larix plantation root, rhizosphere, and bulk soil to short-term and consecutive fertilization have rarely been reported. In this study, we assessed Larix root, rhizosphere, and bulk soil microbial community on days 0, 5, 15, and 30 after the first inorganic fertilization and after three consecutive years of fertilization. The bacterial 16S and fungal ITS high-throughput sequencing technology were used to monitor changes in microbial community composition and potential functional groups, as well as changes in soil nutrient content and enzyme activity to evaluate the status of plantation soil productivity. Consecutive fertilization treatment significantly increased the available nitrogen, phosphorus, and potassium (NPK) content and soil enzyme activity. The nonmetric multidimensional scaling (NMDS) and analysis of similarities (ANOSIM) results showed that there were significant differences in microbial community composition in root samples, rhizosphere soil, and bulk soil samples. The dominant microbial taxa were different between root and soil microbial community composition. Consecutive fertilization treatment had little effect on endophytic microbial community but significantly increased the abundance of Gaiellales in rhizosphere soil and Mortierella in bulk soil. The redundancy analysis (RDA) and co-occurrence network analyses showed that Gaiellales and Mortierellales had significant positive correlations with soil nutrient content and enzyme activity. The fungal functional group compositions were significantly affected by consecutive fertilization treatment and the proportions of ectomycorrhizal and saprotroph significantly decreased, but the proportion of endophyte significantly increased in bulk soil samples. Our results suggested that consecutive fertilization may promote soil nutrient availability by increasing the abundance of Gaiellales and Mortierella. Consecutive fertilization maintained the balance of the soil microbiota under Larix plantation and had a positive effect on promoting soil nutrient availability. This study provided a theoretical basis for consecutive fertilization to promote soil nutrient availability through specific microbial groups.

Tillage Depth Regulation and the Effect of Straw Return on Soil Respiration in Farmland

Straw return and tillage depth treatments are one of the most important agricultural management measures that affect farmland soil respiration, but the mechanism of their interaction affecting farmland soil respiration remains unclear. Therefore, 116 published research articles were used through Meta-analysis technology for dryland farmland ecosystems in China to explore the effects of straw return and tillage depth treatments and their interaction on farmland soil respiration and its regulatory factors, which will provide important data support and a theoretical basis for achieving "carbon neutrality" in farmland ecosystems. The results showed that no tillage reduced soil respiration by 8.3%, and the effects of shallow and deep tillage treatments on soil respiration were not significant, but the increase in soil respiration still showed a trend of deep tillage>shallow tillage>no tillage. However, both shallow and deep tillage had relatively small effects on soil respiration and soil organic carbon (SOC), whereas no tillage reduced soil respiration by 8.3% and increased SOC by 7.05%. Therefore, implementing no tillage measures is of great significance for soil carbon sequestration and emission reduction in farmland ecosystems. In addition, tillage depth significantly regulated the impact of straw return on soil respiration, and the increase in soil respiration showed a trend of deep tillage straw return>shallow tillage straw return>no tillage straw return, with an overall average increase of 14.51%. The increase in soil respiration under different tillage depth treatments after straw return was closely related to the change in soil bulk density, crop yield, SOC, soil temperature, and moisture, and the contribution to the increase in soil respiration showed a trend of soil bulk density>crop yield>soil organic carbon>soil moisture>soil temperature. However, SOC increased by 29.32%, 10.12%, and 23.94%, respectively, in the deep tillage straw return, shallow tillage straw return, and no tillage straw return treatments, whereas soil respiration increased by 29.32% and 18.92%, respectively, in the deep tillage straw return and shallow tillage straw return treatments, and it only increased by 1.2% in the no tillage straw return treatment. Therefore, no tillage straw return was also beneficial to soil carbon sequestration and emission reduction in farmland ecosystems. Thus, in the dryland farmland ecosystem of China, tillage depth treatments regulated the impact of straw return on soil respiration, which was mainly related to soil physical and chemical properties, especially being closely related to soil bulk density. Moreover, no tillage and no tillage straw return are important agricultural management measures that are conducive to soil carbon sequestration and emission reduction.

Forecasting East and West Coast Gasoline Prices with Tree-Based Machine Learning Algorithms

This study aims to forecast New York and Los Angeles gasoline spot prices on a daily frequency. The dataset includes gasoline prices and a big set of 128 other relevant variables spanning the period from 17 February 2004 to 26 March 2022. These variables were fed to three tree-based machine learning algorithms: decision trees, random forest, and XGBoost. Furthermore, a variable importance measure (VIM) technique was applied to identify and rank the most important explanatory variables. The optimal model, a trained random forest, achieves a mean absolute percent error (MAPE) in the out-of-sample of 3.23% for the New York and 3.78% for the Los Angeles gasoline spot prices. The first lag, AR (1), of gasoline is the most important variable in both markets; the top five variables are all energy-related. This paper can strengthen the understanding of price determinants and has the potential to inform strategic decisions and policy directions within the energy sector, making it a valuable asset for both industry practitioners and policymakers.

Characteristic Analysis of Carbon Sink Capacity Changes in Xinjiang’s Terrestrial Ecosystem Based on EEMD

Net Ecosystem Productivity (NEP) is an important measure to assess the carbon balance and dynamics of ecosystems, providing a direct measure of carbon source–sink dynamics in terrestrial ecosystems and finding widespread applications in carbon cycle research. However, the nonlinear characteristics of NEP in Xinjiang’s terrestrial ecosystems remain unclear. Additionally, the influence of land use patterns, temperature, and precipitation variations on carbon sink capacity remains unclear. Ensemble Empirical Mode Decomposition (EEMD) is used to investigate the nonlinear variation of NEP in Xinjiang. Landscape pattern analysis of Xinjiang’s land use patterns from 1981 to 2019 is conducted using a 30 km moving window, and the interannual relationships between NEP, land use patterns, and meteorological factors are investigated through EEMD detrending analysis and Pearson correlation. The findings indicate that: (1) NEP exhibits interannual variations, primarily concentrated in the foothills of the Tianshan Mountains, with a three-year cycle. (2) Although NEP changes in most regions are not significant, urban clusters on the northern slopes of the Tianshan Mountains show noteworthy trends, with initial decrease followed by an increase, covering around 34.87% of the total area. Areas at risk of NEP decline constitute approximately 7.32% of the total area. (3) Across Xinjiang, we observe a widespread rise in patch fragmentation and complexity, coupled with a decline in patch connectivity and the size of the dominant patch. Additionally, there is a notable increase in both the diversity and evenness of land use types. However, the correlation between land use patterns and NEP is generally found to be insignificant in the majority of areas, with a percentage exceeding 85%. (4) Approximately 62% of regions in Xinjiang have NEP that is positively correlated with temperature, with significance observed in 33% of these areas. Furthermore, almost 95% of regions demonstrate that NEP is positively correlated with precipitation, with significance noted in 83% of these regions. It appears that precipitation exerts a more pronounced influence on NEP fluctuations in Xinjiang when compared to temperature.

Metabolite signatures associated with microRNA miR-143-3p serve as drivers of poor lung function trajectories in childhood asthma

Lung function trajectories (LFTs) have been shown to be an important measure of long-term health in asthma. While there is a growing body of metabolomic studies on asthma status and other phenotypes, there are no prospective studies of the relationship between metabolomics and LFTs or their genomic determinants. We utilized ordinal logistic regression to identify plasma metabolite principal components associated with four previously-published LFTs in children from the Childhood Asthma Management Program (CAMP) (n=660). The top significant metabolite principal component (PCLF) was evaluated in an independent cross-sectional child cohort, the Genetic Epidemiology of Asthma in Costa Rica Study (GACRS) (n=1151) and evaluated for association with spirometric measures. Using meta-analysis of CAMP and GACRS, we identified associations between PCLF and microRNA, and SNPs in their target genes. Statistical significance was determined using an false discovery rate-adjusted Q-value. The top metabolite principal component, PCLF, was significantly associated with better LFTs after multiple-testing correction (Q-value=0.03). PCLF is composed of the urea cycle, caffeine, corticosteroid, carnitine, and potential microbial (secondary bile acid, tryptophan, linoleate, histidine metabolism) metabolites. Higher levels of PCLF were also associated with increases in lung function measures and decreased circulating neutrophil percentage in both CAMP and GACRS. PCLF was also significantly associated with microRNA miR-143-3p, and SNPs in three miR-143-3p target genes; CCZ1 (P-value=2.6×10-5), SLC8A1 (P-value=3.9×10-5); and TENM4 (P-value=4.9×10-5). This study reveals associations between metabolites, miR-143-3p and LFTs in children with asthma, offering insights into asthma physiology and possible interventions to enhance lung function and long-term health. Molecular data for CAMP and GACRS via the Trans-Omics in Precision Medicine (TOPMed) program was supported by the National Heart, Lung, and Blood Institute (NHLBI).

Real-world experience of direct oral anticoagulant use in a single pediatric center.

Pediatric venous thromboembolism has increased by 130%-200%, specifically in hospitalized children, and direct oral anticoagulants (DOACs) offer several therapeutic advantages. This study aims to evaluate the real-world epidemiological and outcome data from a retrospective review of pediatric patients treated with DOACs from January 1, 2013 to December 31, 2022. In this single-center, IRB-approved study, 65 patients were identified and analyzed using SPSS statistical software, and a descriptive statistical analysis was conducted. Of the 65 patients, 37% were on apixaban, 61.5% were on rivaroxaban, and 1.5% were on dabigatran. Per the 2023 ISTH outcome definitions, one (2%) patient had a major bleeding episode, six (9%) had clinically relevant non-major bleeding, three (5%) patients had patient-important heavy menstrual bleeding (HMB), and one (1.5%) patient had minor bleeding. Seven (19%) of 37 postmenarchal patients had evidence of HMB. Six (9.2%) patients had recurrent venous thromboembolism while on a DOAC (one was on apixaban, and five were on rivaroxaban) and were transitioned to other forms of anticoagulation. Thus, bleeding rates after DOAC therapy are comparable to previous DOAC trials, as well as other anticoagulants in pediatrics. HMB is an important outcome measure and should continue to be investigated. This study reports a higher rate of recurrent thrombosis (9.2%) compared to other trials. However, this observation may be attributed to patients who had ongoing risk factors, as well as a longer duration of study follow-up. Additional multicentered outcome studies evaluating DOAC use in children are needed to determine long-term recurrence and HMB risks.

A Multi-scale Attention-Based Transfer Model for Cross-bearing Fault Diagnosis

Bearings are key components of mechanical equipment, and fault diagnosis is a necessary and important measure to ensure bearing safety. Driven by industrial big data and deep learning (DL), intelligent fault diagnosis (IFD) has made great progress in recent years. However, most of the existing methods mainly focus on the fault diagnosis of individual bearings, and the feature extraction and fault classification rely on traditional networks and expert experience, which cannot meet the diagnostic requirements of cross-bearing conditions. To fill this research gap, this paper proposes a multi-scale attention-based transfer model (MSATM). First, the collected vibration signals are converted into time–frequency maps as samples, and the proposed MSATM employs multi-scale residual learning and attention mechanism to adaptively extract sensitive fault features, and recognizes faults of new bearings by deep transfer learning using the trained MSATM. A large number of experimental results based on a bearing benchmark validate the effectiveness and superiority of the proposed method and provide a promising tool for cross-bearing fault diagnosis.

Hypersensitive Reactions During Hemodialysis Treatment: What Do We Need to Know?

Kidney replacement therapies (KRTs) including hemodialysis (HD) are one of the treatment options for most of the patients with end-stage kidney disease. Although HD is vital for these patients, it is not hundred percent physiological, and various adverse events including hypersensitivity reactions may occur. Fortunately, these reactions are rare in total and less when compared to previous decades, but it is still very important for at least two reasons: First, the number of patients receiving kidney replacement treatment is increasing globally; and the cumulative number of these reactions may be substantial. Second, although most of these reactions are mild, some of them may be very severe and even lead to mortality. Thus, it is very important to have basic knowledge and skills to diagnose and treat these reactions. Hypersensitivity reactions can occur at any component of dialysis machinery (access, extracorporeal circuit, medications, etc.). The most important preventive measure is to avoid the allergen. However, even with very specific test, sometimes the allergen cannot be found. In mild conditions, HD can be contained with non-specific treatment (topical creams, antihistaminics, corticosteroids). In more severe conditions, treatment must be stopped immediately, blood should not be returned to patient, drugs must be stopped, and rules of general emergency treatment must be followed.