Success Index Research Articles

Thirty days clinical outcomes following stent implantation with aspirin-free prasugrel monotherapy in non-ST segment elevation myocardial infarction. Interim report from ASET-Japan pilot study

Abstract Background Dual antiplatelet therapy with a P2Y12 inhibitor, in addition to aspirin, is the standard therapy after percutaneous coronary intervention (PCI) for non-ST-segment elevation acute coronary syndrome (NSTE-ACS) to prevent ischemic cardiovascular events. The Acetyl-Salicylic Elimination Trial (ASET) pilot studies conducted in Japan in patients with NSTE-ACS investigated the safety and feasibility of a low-dose prasugrel monotherapy after PCI. Purpose To ensure the safety of the patients, enrolment in the study was implemented only after confirmation of a successful procedure. The abstract reports our interim analysis, aimed at evaluating preliminary safety and feasibility. Methods The ASET-Japan pilot study was designed to explore the feasibility and safety of a low dose of prasugrel monotherapy (3.75mg/day), without adjunctive aspirin, after optimal PCI using Everolimus eluting stent (EES) in Japanese patients with non-ST-segment elevation acute coronary syndromes. The primary endpoint in the ASET-Japan NSTE-ACS (Phase 2) is a 12-month clinical outcome. The study is currently in the follow-up phase. All the target lesions were treated with a platinum-chromium everolimus-eluting stent. The primary ischemic endpoint was the composite of cardiac death, spontaneous target vessel myocardial infarction, or definite stent thrombosis, and the primary bleeding endpoint was Bleeding Academic Research Consortium types 3 and 5 bleeding up to 12-month. If more than 3 events occurred, trial enrollment would have to be terminated by the data and safety monitoring board. Results One hundred one patients were enrolled after successful index PCI and completed a 30 days follow-up. The mean age was 69.1±12.3 years and 75.2% was male. The mean anatomical SYNTAX score was 7.9±4.7. Unstable angina (UAP) and NSTEMI were seen in 24.8% (25/101) and 75.2% (75/101) patients, respectively. According to the Braunwald classification, 8 percent of the patients were categorised as Class IA UAP, 48% as Class IB, 20% as Class IIB, 4% as Class IIIA and 20% as Class IIIB. In NSTEMI patients, the median (IQR) pre-procedural troponin value (cardiac Troponin T or I) was 2.79 (1.12 – 22.27) times the Upper Limit of Normal. According to the PRECISE DAPT score, 36.6% of patients were categorised as High Bleeding Risk (HBR) with scores above 25. At one month, the primary ischemic endpoints of cardiac death, target-vessel spontaneous MI or definite Stent thrombosis did not occur. Two patients (2.0%) had BARC type 3a bleeding. Conclusion Prasugrel monotherapy in the first 30 days following platinum-chromium everolimus-eluting stent deployment is a feasible and safe strategy in NSTE-ACS patients with simple anatomy and successful PCI.

Precipitation Retrieval from FY-3G/MWRI-RM Based on SMOTE-LGBM

Using the FY-3G/MWRI-RM observations, this paper proposes a precipitation retrieval method that combines the Synthetic Minority Over-sampling Technique with Light Gradient Boosting Machine (SMOTE-LGBM) and analyzes the impact of MWRI-RM channel settings on precipitation retrieval. The SMOTE-LGBM-based model consists of two LGBM models for precipitation identification and estimation, respectively. The SMOTE method is used to address the imbalance between precipitation and non-precipitation samples. Using the Integrated Multi-Satellite Retrievals for the Global Precipitation Measurement (IMERG) product as a reference, we validate the retrieved precipitation by the SMOTE-LGBM-based model with an independent testing dataset. The critical success indexes are 0.483 and 0.526, and the Pearson correlation coefficients are 0.611 and 0.645 for the ocean and land regions, respectively. The spatial distributions of the retrieved and IMERG accumulated precipitation in the testing dataset are similar. In addition, we visualize and analyze the cases of Meiyu and two typhoons. The results indicate that the SMOTE-LGBM-based model effectively represents the spatial distribution characteristics of precipitation and achieves high agreement with IMERG precipitation products. Overall, the SMOTE-LGBM-based model successfully retrieves precipitation from MWRI-RM and provides accurate precipitation products for FY-3G/MWRI-RM for the first time.

A rainfall prediction model based on ERA5 and Elman neural network

The heightened frequency and intensity of heavy rainfall, brought about by global climate warming, significantly affect various regions. Rainfall prediction plays a pivotal role in ensuring societal security and fostering development. There has been limited exploration of the impact of input parameters on the accuracy of rainfall predictions. Despite the advantages of Elman neural network models in handling non-linear relationships and spatiotemporal data, their application in weather forecasting is restricted. This paper assesses the accuracy of the European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) with a dataset from meteorological stations. It introduces a novel rainfall forecasting model based on the Elman neural network and ERA5 reanalysis data. The study also identifies optimal input parameters through factor correlation analysis. Experimental results showcase the precision and stability of ERA5 across various rainfall conditions. Meteorological parameters, such as Precipitable Water Vapor (PWV), exhibit noticeable correlations with temporal variables and precipitation volume. The seven-factor model, including PWV, Zenith Tropospheric Delay, temperature, relative humidity, day-of-year, and hour of day, outperforms in precision evaluation. It achieves a mean critical success index of 57.06 %, a correct forecast rate of 91.39 %, and a false alarm rate of 39.48 %. This rainfall forecasting model introduces a novel approach and empirical research to enhance predictive accuracy, holding significant implications for the amelioration of meteorological alert systems, risk mitigation, and the safeguarding of life and property.

Comparison of CAMS and CMAQ analyses of surface-level PM2.5 and O3 over the conterminous United States (CONUS)

To reduce economic and health impacts from poor air quality (AQ) in the U.S., the National Air Quality Forecasting Capability (NAQFC) at the National Oceanic and Atmospheric Administration (NOAA) produces forecasts of surface-level ozone (O3), fine particulate matter (PM2.5), and other pollutants so that advance notice and warning can be issued to help individuals and communities limit their exposure. The NAQFC uses the U.S. Environmental Protection Agency (EPA) Community Multiscale Air Quality (CMAQ) model for operational forecasts. This study is a first step in proposing a potential upgrade to the current operational NAQFC bias-correction system, by examining potential candidates for a gridded analysis (“truth”) dataset.In this paper, we compare the performance of the “analysis” time series over the period of August 2020–December 2021 at EPA AirNow stations for both PM2.5 and O3 from raw Copernicus Atmosphere Monitoring Service (CAMS) reanalyses, raw CAMS near real-time forecasts, raw near real-time CMAQ forecasts, bias-corrected CAMS forecasts, and bias-corrected CMAQ forecasts (CMAQ FC BC). This 17-month period spans two wildfire seasons, to assess model “analysis” performance in high-end AQ events. In addition to determining the best-performing gridded product, this process allows us to benchmark the performance of CMAQ forecasts against other global datasets (CAMS reanalysis and forecasts). For both PM2.5 and O3, the bias correction algorithm employed here greatly improved upon the raw model time series, and CMAQ FC BC was the best-performing model “analysis” time series, having the lowest RMSE, smallest bias error, and largest critical success index at multiple thresholds.

A Data-Driven Multi-Step Flood Inundation Forecast System

Inundation maps that show water depths that occur in the event of a flood are essential for protection. Especially information on timings is crucial. Creating a dynamic inundation map with depth data in temporal resolution is a major challenge and is not possible with physical models, as these are too slow for real-time predictions. To provide a dynamic inundation map in real-time, we developed a data-driven multi-step inundation forecast system for fluvial flood events. The forecast system is based on a convolutional neural network (CNN), feature-informed dense layers, and a recursive connection from the predicted inundation at timestep t as a new input for timestep t + 1. The forecast system takes a hydrograph as input, cuts it at desired timesteps (t), and outputs the respective inundation for each timestep, concluding in a dynamic inundation map with a temporal resolution (t). The prediction shows a Critical Success Index (CSI) of over 90%, an average Root Mean Square Error (RMSE) of 0.07, 0.12, and 0.15 for the next 6 h, 12 h, and 24 h, respectively, and an individual RMSE value below 0.3 m, for all test datasets when compared with the results from a physically based model.

Fruit resources shape sexual selection processes in a lek mating system.

The degree to which within-population variation in sexual trait expression relates to resource heterogeneity remains poorly explored. This is particularly true in lek-mating species, where genetic explanations for male phenotypic variance and mating success are dominant. Here, we demonstrate a link between fine-scale fruit resource availability and indices of male mating success in the white-bearded manakin (Manacus manacus), a lek-mating frugivorous bird that produces energetically costly courtship displays. We used motion-activated camera traps to monitor male display behaviour and female visitation at male courts while concurrently conducting twice-monthly fruit surveys around courts. We observed significant variability in ripe fruit biomass among display courts and leks, and mean fruit biomass at courts significantly predicted male display rates. In turn, male display rate was the strongest predictor of female visitation to courts. Causal modelling supported the hypothesis that hyper-local fruit availability indirectly affects female visitation via its direct effects on male display rate. The demonstration that resource availability at fine spatial scales predicts display rate in a lekking organism-for which resource-related variables are typically not considered to play important roles in shaping male reproductive variance-has implications for the expression, honesty and maintenance of sexually selected traits under fluctuating ecological conditions.

Evaluating the performance of herd-specific Long Short-Term Memory models to identify automated health alerts associated with a ketosis diagnosis in early lactation cows

The growing use of automated systems in the dairy industry generates a vast amount of cow-level data daily, creating opportunities for using these data to support real-time decision-making. Currently, various commercial systems offer built-in alert algorithms to identify cows requiring attention. To our knowledge, no work has been done to compare the use of models accounting for herd-level variability on their predictive ability against automated systems. Long Short-Term Memory (LSTM) models are machine learning models capable of learning temporal patterns and making predictions based on time series data. The objective of our study was to evaluate the ability of LSTM models to identify a health alert associated with a ketosis diagnosis (HAK) using deviations of daily milk yield, milk FPR, number of successful milkings, rumination time, and activity index from the herd median by parity and DIM, considering various time series lengths and numbers of d before HAK. Additionally, we aimed to use Explainable Artificial Intelligence method to understand the relationships between input variables and model outputs. Data on daily milk yield, milk fat-to-protein ratio (FPR), number of successful milkings, rumination time, activity, and health events during 0 to 21 d in milk (DIM) were retrospectively obtained from a commercial Holstein dairy farm in northern Indiana from February 2020 to January 2023. A total of 1,743 cows were included in the analysis (non-HAK = 1,550; HAK = 193). Variables were transformed based on deviations from the herd median by parity and DIM. Six LSTM models were developed to identify HAK 1, 2, and 3 d before farm diagnosis using historic cow-level data with varying time series lengths. Model performance was assessed using repeated stratified 10-fold cross-validation for 20 repeats. The Shapley additive explanations framework (SHAP) was used for model explanation. Model accuracy was 83, 74, and 70%, balanced error rate was 17 to 18, 26 to 28, and 34%, sensitivity was 81 to 83, 71 to 74, and 62%, specificity was 83, 74, and 71%, positive predictive value was 38, 25 to 27, and 21%, negative predictive value was 97 to 98, 95 to 96, and 94%, and area under the curve was 0.89 to 0.90, 0.80 to 0.81, and 0.72 for models identifying HAK 1, 2, and 3 d before diagnosis, respectively. Performance declined as the time interval between identification and farm diagnosis increased, and extending the time series length did not improve model performance. Model explanation revealed that cows with lower milk yield, number of successful milkings, rumination time, and activity, and higher milk FPR compared with herdmates of the same parity and DIM were more likely to be classified as HAK. Our results demonstrate the potential of LSTM models in identifying HAK using deviations of daily milk production variables, rumination time, and activity index from the herd median by parity and DIM. Future studies are needed to evaluate the performance of health alerts using LSTM models controlling for herd-specific metrics against commercial built-in algorithms in multiple farms and for other disorders.

Validation of Cool-Season Snowfall Forecasts at a High-Elevation Site in Utah’s Little Cottonwood Canyon

Abstract Producing a quantitative snowfall forecast (QSF) typically requires a model quantitative precipitation forecast (QPF) and snow-to-liquid ratio (SLR) estimate. QPF and SLR can vary significantly in space and time over complex terrain, necessitating fine-scale or point-specific forecasts of each component. Little Cottonwood Canyon (LCC) in Utah’s Wasatch Range frequently experiences high-impact winter storms and avalanche closures that result in substantial transportation and economic disruptions, making it an excellent testbed for evaluating snowfall forecasts. In this study, we validate QPFs, SLR forecasts, and QSFs produced by or derived from the Global Forecast System (GFS) and High-Resolution Rapid Refresh (HRRR) using liquid precipitation equivalent (LPE) and snowfall observations collected during the 2019/20–2022/23 cool seasons at the Alta–Collins snow-study site (2945 m MSL) in upper LCC. The 12-h QPFs produced by the GFS and HRRR underpredict the total LPE during the four cool seasons by 33% and 29%, respectively, and underpredict 50th, 75th, and 90th percentile event frequencies. Current operational SLR methods exhibit mean absolute errors of 4.5–7.7. In contrast, a locally trained random forest algorithm reduces SLR mean absolute errors to 3.7. Despite the random forest producing more accurate SLR forecasts, QSFs derived from operational SLR methods produce higher critical success indices since they exhibit positive SLR biases that offset negative QPF biases. These results indicate an overall underprediction of LPE by operational models in upper LCC and illustrate the need to identify sources of QSF bias to enhance QSF performance. Significance Statement Winter storms in mountainous terrain can disrupt transportation and threaten life and property due to road snow and avalanche hazards. Snow-to-liquid ratio (SLR) is an important variable for snowfall and avalanche forecasts. Using high-quality historical snowfall observations and atmospheric analyses, we developed a machine learning technique for predicting SLR at a high mountain site in Utah’s Little Cottonwood Canyon that is prone to closure due to winter storms. This technique produces improved SLR forecasts for use by weather forecasters and snow-safety personnel. We also show that current operational models and SLR techniques underforecast liquid precipitation amounts and overforecast SLRs, respectively, which has implications for future model development.

Enhanced rainfall nowcasting of tropical cyclone by an interpretable deep learning model and its application in real-time flood forecasting

Reliable Tropical Cyclone (TC) rainfall and flood forecasts play an important role in disaster prevention and mitigation. Numerous studies have demonstrated the promising performance of deep learning in hydrometeorological forecasts. However, few studies have investigated the potential enhancement of advanced TC track forecasts in predicting rainfall and induced flood. In this study, a novel rainfall nowcasting model (TCRainNet) is developed by fusing TC track characteristics with antecedent rainfall in a Convolution LSTM to predict hourly rainfall with a lead time of 6 h. The nowcasts are subsequently used to drive an event-based Xin’anjiang hydrological model for real-time flood forecasting. The model performance is interpretated by the occlusion sensitivity approach, and the propagation of errors from TC track forecasts to flood forecasts is quantified. The results underscore the superiority of TC track characteristics as input features for rainfall nowcasts, as indicated by a Mutual Information value of up to 0.51. The generated nowcasts are found to have averaged Probability of Detection (POD) and Critical Success Index (CSI) greater than 0.27 and 0.2 respectively. The Mean Absolute Error (MAE) of the nowcasts falls below 2.6 mm, which is only 46 % of the ECMWF operational high-resolution forecasts. The rainfall-driven flood forecasts have NSE greater than 0.7 and PBIAS smaller than 20 % with lead time up to + 4 h. It is shown that the position error of 0.45° and intensity error of 10 hPa&7.8 m/s in TC track forecasts generally result in 0.9 mm degradation in rainfall forecasts and 10% decline in the accuracy of rainfall-driven flood forecasts. The effectiveness of our method presents favorable applicability in advancing disaster mitigation efforts.

Multi-sensor fault detection and correction for automated IAQ monitoring in smart buildings through attention-aware autoencoders with spatial prediction module

The integration of interconnected monitoring and control devices has significantly enhanced the management of indoor building environments, notably in underground subway stations. In these sophisticated settings, the accuracy of monitoring systems is critical, as data-driven control systems rely extensively on sensor feedback. However, sensor faults arising from hardware damage, wear, and cyberattacks compromise the reliability of these complex systems. Despite extensive efforts, existing methods still face challenges in accurately detecting and reconstructing faults, particularly when dealing with multiple sensor failures. This study introduces a hybrid sensor validation framework that combines a multi-head attention-based denoising autoencoder (AE) with a deep learning prediction module. The performance assessment of the proposed gated recurrent unit (GRU) integrated attention-based fault detection, and reconstruction network (GADRN) shows a significant 33 % and 21 % improvement in critical success index compared to Independent Component Analysis and GRU-AE frameworks. GADRN consistently identified faulty sensors across various fault periods, achieving the lowest false alarm rate of 11 % against several conventional and machine learning-based models. A ventilation control analysis highlights the significance of incorporating a sensor validation framework in modern buildings. The outputs by the GADRN led to a 22.5 % decrease in energy consumption by preventing unnecessary resource use attributed to sensor faults.

Gerotranscendence, Hope, and Coherence in the Face of Life Adversity.

Background and Purpose: Older adults are living longer and becoming more diverse. The current study examined the relationship between traumatic life events, hope, coherence, and successful aging in Black and White older adults with at least one chronic health condition, and the influence of life events on gerotranscendence. Methods: Fifty older adults from two senior centers participated. Participants completed the Successful Aging Inventory, Life Events Checklist, Herth Hope Index, and Sense of Coherence Scale. Results: Significant correlations were found between successful aging and gerotranscendence (r = .290; p = .048) and hope (r = .585; p = <.001). Simple linear regression found that Successful Aging Index (SAI) scores significantly predicted gerotranscendence (R2 = .10, F(1, 46) = 5.157, p = .028) and Herth Hope Index scores (R2 = .36, F(1, 46) = 25.850, p <.001). Higher Sense of Coherence (SoC) and Gerotranscendence Scale scores among those with no firsthand trauma experience suggest that experiencing traumatic events firsthand may adversely affect the aging process. Implications: Therefore, exploration of trauma experiences, with mental health referrals as appropriate are clinical implications to consider.

Assessments of various precipitation product performances and disaster monitoring utilities over the Tibetan Plateau

The Tibetan Plateau, often referred to as Asia’s water tower, is a focal point for studying spatiotemporal changes in water resources amidst global warming. Precipitation is a crucial water resource for the Tibetan Plateau. Precipitation information holds significant importance in supporting research on the Tibetan Plateau. In this study, we estimate the performance and applicability of Climate Prediction Center Merged Analysis of Precipitation (CMAP), Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG), Global Land Data Assimilation System (GLDAS), and Global Precipitation Climatology Project (GPCP) precipitation products for estimating precipitation and different disaster scenarios (including extreme precipitation, drought, and snow) across the Tibetan Plateau. Extreme precipitation and drought indexes are employed to describe extreme precipitation and drought conditions. We evaluated the performance of various precipitation products using daily precipitation time series from 2000 to 2014. Statistical metrics were used to estimate and compare the performances of different precipitation products. The results indicate that (1) Both CMAP and IMERG showed higher fitting degrees with gauge precipitation observations in daily precipitation. Probability of detection, False Alarm Ratio, and Critical Success Index values of CMAP and IMERG were approximately 0.42 to 0.72, 0.38 to 0.56, and 0.30 to 0.42, respectively. Different precipitation products presented higher daily average precipitation amount and frequency in southeastern Tibetan Plateau. (2) CMAP and GPCP precipitation products showed relatively great and poor performance, respectively, in predicting daily and monthly precipitation on the plateau. False alarms might have a notable impact on the accuracy of precipitation products. (3) Extreme precipitation amount could be better predicted by precipitation products. Extreme precipitation day could be badly predicted by precipitation products. Different precipitation products showed that the bias of drought estimation increased as the time scale increased. (4) GLDAS series products might have relatively better performance in simulating (main range of RMSE: 2.0–4.5) snowfall than rainfall and sleet in plateau. G-Noah demonstrated slightly better performance in simulating snowfall (main range of RMSE: 1.0–2.1) than rainfall (main range of RMSE: 2.0–3.8) and sleet (main range of RMSE: 1.5–3.8). This study’s findings contribute to understanding the performance variations among different precipitation products and identifying potential factors contributing to biases within these products. Additionally, the study sheds light on disaster characteristics and warning systems specific to the Tibetan Plateau.

Efficacy and Safety of Topical Roflumilast for the Treatment of Psoriasis: A Systematic Review and Meta-Analysis of Randomized Controlled Trials.

Plaque psoriasis is commonly treated topically with glucocorticoids and vitamin D derivatives. However, potential side effects such as skin atrophy underscore the need for safe and effective alternative topical therapies. Recently, the US Food and Drug Administration (FDA) and Health Canada approved roflumilast 0.3% cream as an option for treating this disease. A systematic review and meta-analysis of randomized controlled trials (RCTs) was conducted to assess the efficacy and safety of topical roflumilast 0.3% compared with vehicle for plaque psoriasis. PubMed, Embase, ClinicalTrials.gov, and Cochrane databases were searched from inception to 1 May 2024, assessing the outcomes of Investigator's Global Assessment (IGA) or body-IGA success (clear or almost clear status plus an at least 2-grade improvement from baseline), Psoriasis Area and Severity Index (PASI)-50, PASI-75, PASI-90, intertriginous-IGA success (clear or almost clear status on the intertriginous-IGA plus an at least 2-grade improvement from baseline), and adverse events (AEs). Statistical analysis was performed using Review Manager, R software, and RStudio. Heterogeneity was determined using the Cochran Q test and I2 statistics. Four RCTs were included, comprising a total of 1403 patients, of whom 885 (63.1%) received topical roflumilast 0.3% and 518 (36.9%) received vehicle. At week 8, the achievement of IGA or body-IGA success was significantly higher among those treated with topical roflumilast than in the vehicle group [relative risk (RR) 5.07; 95% confidence interval (CI) 3.55-7.23; p < 0.01]. Similar findings were observed at week 8 for PASI-50 (RR 2.73; 95% CI 2.27-3.29; p < 0.01), PASI-75 (RR 4.48; 95% CI 2.26-8.89; p < 0.01), and PASI-90 (RR 5.61; 95% CI 2.57-12.25; p < 0.01). Corresponding outcomes were found at weeks 2, 4, and 6. Additionally, a higher percentage of patients treated with topical roflumilast 0.3% once daily achieved intertriginous-IGA success, compared with those receiving vehicle, at week 8 (71.9% versus 20.5%; RR 3.32; 95% CI 2.11-5.22; p < 0.01), with similar findings at weeks 2, 4, and 6. While a significant difference was observed in the overall incidence of AEs between the topical roflumilast and vehicle groups, there was no difference in treatment-related AEs, serious AEs, or AEs leading to study discontinuation. These findings support the superiority of topical roflumilast 0.3% over vehicle and suggest its use as a valuable asset for the treatment of plaque psoriasis. International Prospective Register of Systematic Reviews (PROSPERO), CRD42023456494.

Precipitable water vapor retrieval for rainfall forecasting using BDS-3 PPP-B2b signal in the coastal region of China

Abstract Currently, rainfall cannot be accurately forecast because of poor network communication at the ocean. The advantage of the BeiDou Global Navigation Satellite System (BDS-3) precise point positioning (PPP-B2b) signal, which does not rely on network communication to receive data, is that it can provide precipitable water vapor (PWV) retrieval application services for the open seas in eastern China, where the communication system presents difficulties. In this study, data from stations in the coastal region of China were used to establish a rainfall forecasting method for monitoring extreme weather on the sea. First, the service performance of PPP-B2b was explored. Then, based on 17 Chinese coastal stations, the PWV accuracy was evaluated. Finally, based on an analysis of the relationship between PWV and actual rainfall, a threshold rainfall forecasting method based on a sliding window was constructed. The experimental results show that the PWV accuracy varies slightly depending on the geographic location, in which the mean absolute error in the North Sea region is the smallest (2.1 mm), that of the South China Sea region is the largest (2.60 mm), and that of the East China Sea region is in the middle (2.48 mm). The optimal predictors of the constructed 12 h sliding-window threshold rainfall prediction method are a PWV maximum of 49 mm, PWV increase of 5 mm, and PWV increase rate of 1.2 mm h−1. The prediction results can reach a critical success index value of more than 45%, indicating high prediction accuracy and applicability to the coastal region of China during the same period.

Mentorship program to elevate journal quality and rankings in Indonesia: a case study

This article explores the best practices of mentorship programs in all journals at Universitas Airlangga. The university has established a journal mentoring team, as mandated by the rector’s regulation, which is responsible for guiding journals through preparation, submission, management, policy, and overall quality improvement. A case study was conducted to explore the mentoring mechanisms at Universitas Airlangga. Mentors were selected from among experienced editors at the university, each with a distinguished background in managing their own journals. The mentorship program successfully led to the indexing of 14 journals in Scopus, one in Web of Science (WoS), 85 in the Science and Technology Index (SINTA), and 60 in Directory of Open Access Journals (DOAJ). The strategies used can be shared with other universities to assist their journal editors. The mentorship program at Universitas Airlangga has significantly improved the quality and international visibility of its academic journals. This is evidenced by the successful indexing of numerous journals in prestigious databases including Scopus, WoS, SINTA, and DOAJ. The structured mentoring, clear targets, and comprehensive institutional support were instrumental in achieving these results. This model serves as a scalable best practice for other universities seeking to improve their journal quality and global standing.

Bias correction of the hourly satellite precipitation product using machine learning methods enhanced with high-resolution WRF meteorological simulations

Accurate precipitation data are crucial in atmospheric and hydrological studies, especially for water resource management and disaster early warning. Satellite precipitation product (SPP) with high spatiotemporal resolution has been regarded as a valuable alternative precipitation source to ground observations. However, the hourly SPP generally performs poorly compared to daily SPP, thereby bias correction is urgently required. This study investigates the viability of utilizing machine learning methods to correct the bias of the hourly Integrated Multi-satellitE Retrievals for Global Precipitation Measurement-Early (IMERG-E) product. Meanwhile, the Weather Research and Forecasting (WRF) model is utilized to generate high-resolution fields of four hourly meteorological variables, namely, temperature at 2 m (TEMP2), specific humidity at 2 m (Q2), wind direction at 10 m (WDIR10), and wind speed at 10 m (WSPD10), which further serve as covariates in machine learning models to enhance the correction process. Four machine learning models were developed, i.e., Random Forest (RF) and Bidirectional Long Short-Term Memory Networks (Bi-LSTM) without WRF-simulated covariates, and RF-WRF and Bi-LSTM-WRF with meteorological covariates. The results demonstrated that incorporating WRF-simulated meteorological covariates improved model performance. Specifically, correlation coefficient (CC) values increased from 0.47 (RF) to 0.51 (RF-WRF) and rose from 0.55 (Bi-LSTM) to 0.60 (Bi-LSTM-WRF), along with reduced root mean square error (RMSE) and increased critical success index (CSI) values. Furthermore, two Bi-LSTM models consistently outperformed two RF models. Overall, the Bi-LSTM-WRF model emerged as the most effective correction method, which increased CC from 0.43 (IMERG-E) to 0.60, reduced RMSE from 1.91 mm to 1.08 mm, and enhanced CSI from 0.34 to 0.41. This study underscores the potential of integrating high-resolution WRF meteorological outputs into machine learning frameworks for correcting hourly SPPs, contributing significantly to the advancement of precipitation estimation in meteorological and hydrological applications.

Automatic topology and capacity generation framework for urban drainage systems with deep learning-based land use segmentation and hydrological characterization

This paper proposed a comprehensive and automated framework that integrates the entire urban drainage design process, from refined land use segmentation, to catchment subdivision and characterization, to network topology generation, and finally to hydraulic estimation of pipeline capacity, based on multiple open-source datasets. The method incorporated deep learning-based semantic segmentation, GIS-based hydrological characterization, topology generation and hydraulic calculation algorithms. The performance and applicability of the method were validated through case studies of two cities. The results showed that the deep learning model with prior knowledge could accurately segment urban land use to obtain the detailed shape and boundary description, and achieved higher detection accuracy for water bodies, buildings, and roads. The stormwater drainage topology and conveyance capacity were automatically generated based on subcatchment division and hydrological characterization under a set of topological parameter conditions. In the optimal Critical Success Index (CSI) scenario, only 8% and 21% of the generated pipelines were inconsistent with the expert-designed networks. Compared to the traditional manual design process, the automated approach can save significant time and resources and reduce the reliance on field surveys and empirical/expert work. At the same time, the tool can be employed for system layout and capacity optimization, providing important support for the automation, design, and rehabilitation of stormwater drainage systems.

A User-Focused Approach to Evaluating Probabilistic and Categorical Forecasts

Abstract A user-focused verification approach for evaluating probability forecasts of binary outcomes (also known as probabilistic classifiers) is demonstrated that (i) is based on proper scoring rules, (ii) focuses on user decision thresholds, and (iii) provides actionable insights. It is argued that when categorical performance diagrams and the critical success index are used to evaluate overall predictive performance, rather than the discrimination ability of probabilistic forecasts, they may produce misleading results. Instead, Murphy diagrams are shown to provide a better understanding of the overall predictive performance as a function of user probabilistic decision threshold. We illustrate how to select a proper scoring rule, based on the relative importance of different user decision thresholds, and how this choice impacts scores of overall predictive performance and supporting measures of discrimination and calibration. These approaches and ideas are demonstrated using several probabilistic thunderstorm forecast systems as well as synthetic forecast data. Furthermore, a fair method for comparing the performance of probabilistic and categorical forecasts is illustrated using the fixed risk multicategorical (FIRM) score, which is a proper scoring rule directly connected to values on the Murphy diagram. While the methods are illustrated using thunderstorm forecasts, they are applicable for evaluating probabilistic forecasts for any situation with binary outcomes. Significance Statement Recently, several papers have presented verification results for probabilistic forecasts using so-called categorical performance diagrams, which summarize multiple verification metrics. While categorical performance diagrams measure discrimination ability, we demonstrate how they can potentially lead to incorrect conclusions when evaluating overall predictive performance of probabilistic forecasts. By reviewing recent advances in the statistical literature, we show a comprehensive approach for the meteorological community that (i) does not reward a forecaster who “hedges” their forecast, (ii) focuses on the importance of the forecast user’s decision threshold(s), and (iii) provides actionable insights. Additionally, we present an approach for fairly comparing the skill of categorical forecasts to probabilistic forecasts.

Maternal emotion regulation abilities affect adolescent depressive symptoms by mediating their emotion regulation ability: An ERP study

Adolescents with major depressive disorder (MDD) experience significant difficulties in emotion regulation. This study aimed to explore emotion regulation in adolescents with depression using an emotion regulation paradigm combined with event-related potentials (ERP) while investigating the relationship between maternal emotion regulation and adolescent depressive symptoms through a mediation model. Overall, 38 healthy controls (HC) and 57 adolescents with depression (MDD) rated the pictures they saw according to aversive reappraisal (reappraisal of an aversive picture or down-regulate aversive emotions), aversive watch, and neutral conditions. Adolescents with depression gave more negative ratings to aversive images, and the emotional regulation success index (ERSI) of adolescents with depression was lower than that of healthy individuals. ERP data revealed an elevation in late positive potential (LPP) amplitude during the aversive reappraisal and aversive watch conditions compared with that in the neutral condition in the MDD group. Compared with the HC group, adolescents with depression showed larger LPP amplitudes under aversive watch conditions. The aversive reappraisal condition evoked a larger LPP than that in the other conditions in the HC group in the late time windows. The ΔLPP (separating the variability in the ERP wave associated with emotion regulation) was larger in the HC group than in the MDD group. Mediation analysis revealed that maternal emotion regulation influenced adolescent depression levels through its effect on the adolescent’s emotion regulation. These findings provide important insights into the emotion regulation process in adolescents with depression and offer suggestions for clinical interventions.

Precipitation nowcasting method based on Spatial-Temporal Dual Discriminators

Abstract The prediction of radar echo sequence images is a spatiotemporal sequence forecasting problem, which is one of the main challenges in precipitation nowcasting. Addressing issues such as poor extraction of spatiotemporal features by previous models and blurry image sequence predictions, this study proposes a Spatial-Temporal dual Discriminator Precipitation Nowcasting Model (STD-SNGAN) based on spectral normalization generative adversarial networks (SNGAN). The model utilizes multi-scale convolution modules (Inception) to extract spatial features from radar echo images and convolutional gated recurrent units (ConvGRU) to extract temporal features between feature maps. By introducing a hidden feature sampler to enhance the extraction capability of the convolutional gated recurrent units and designing spatial-temporal dual discriminators to constrain the generator’s predicted samples, the spatiotemporal forecasting ability is enhanced. Experimental results demonstrate that the proposed STD-SNGAN model outperforms other algorithms in critical success index (CSI) and probability of detection (POD) for high echo intensity and long-term regions.