Explore the vast range of titles available.

Most proteins exert their functions by interacting with other proteins, making the identification of protein-protein interactions (PPI) crucial for understanding biological activities, pathological mechanisms, and clinical therapies. Developing effective and reliable computational methods for predicting PPI can significantly reduce the time-consuming and labor-intensive associated traditional biological experiments. However, accurately identifying the specific categories of protein-protein interactions and improving the prediction accuracy of the computational methods remain dual challenges. To tackle these challenges, we proposed a novel graph neural network method called GNNGL-PPI for multi-category prediction of PPI based on global graphs and local subgraphs. GNNGL-PPI consisted of two main components: using Graph Isomorphism Network (GIN) to extract global graph features from PPI network graph, and employing GIN As Kernel (GIN-AK) to extract local subgraph features from the subgraphs of protein vertices. Additionally, considering the imbalanced distribution of samples in each category within the benchmark datasets, we introduced an Asymmetric Loss (ASL) function to further enhance the predictive performance of the method. Through evaluations on six benchmark test sets formed by three different dataset partitioning algorithms (Random, BFS, DFS), GNNGL-PPI outperformed the state-of-the-art multi-category prediction methods of PPI, as measured by the comprehensive performance evaluation metric F1-measure. Furthermore, interpretability analysis confirmed the effectiveness of GNNGL-PPI as a reliable multi-category prediction method for predicting protein-protein interactions.

Long waiting time in outpatient departments is a crucial factor in patient dissatisfaction. We aim to analytically interpret the waiting times predicted by machine learning models and provide patients with an explanation of the expected waiting time. Here, underestimating waiting times can cause patient dissatisfaction, so preventing this in predictive models is necessary. To address this issue, we propose a framework considering dissatisfaction for estimating the waiting time in an outpatient department. In our framework, we leverage asymmetric loss functions to ensure robustness against underestimation. We also propose a dissatisfaction-aware asymmetric error score (DAES) to determine an appropriate model by considering the trade-off between underestimation and accuracy. Finally, Shapley additive explanation (SHAP) is applied to interpret the relationship trained by the model, enabling decision makers to use this information for improving outpatient service operations. We apply our framework in the endocrinology metabolism department and neurosurgery department in one of the largest hospitals in South Korea. The use of asymmetric functions prevents underestimation in the model, and with the proposed DAES, we can strike a balance in selecting the best model. By using SHAP, we can analytically interpret the waiting time in outpatient service (e.g., the length of the queue affects the waiting time the most) and provide explanations about the expected waiting time to patients. The proposed framework aids in improving operations, considering practical application in hospitals for real-time patient notification and minimizing patient dissatisfaction. Given the significance of managing hospital operations from the perspective of patients, this work is expected to contribute to operations improvement in health service practices.

Risk assessments are common in multiple domains, from finance to medicine. They require evaluating an event’s potential severity and likelihood. We investigate the possible dependence of likelihood and severity within the domain of impact-based weather forecasting (IBF), following predictions derived from considering asymmetric loss functions. In a collaboration between UK psychologists and partners from four meteorological organisations in Southeast Asia, we conducted two studies ( N  = 363) eliciting weather warnings from forecasters. Forecasters provided warnings denoting higher likelihoods for high severity impacts than low severity impacts, despite these impacts being described as having the same explicit numerical likelihood of occurrence. This ‘Severity effect’ is pervasive, and we find it can have a continued influence even for an updated forecast. It is additionally observed when translating warnings made on a risk matrix to numerical probabilities.

Mixture distributions are naturally extra attractive to model the heterogeneous environment of processes in reliability analysis than simple probability models. This focus of the study is to develop and Bayesian inference on the 3-component mixture of power distributions. Under symmetric and asymmetric loss functions, the Bayes estimators and posterior risk using priors are derived. The presentation of Bayes estimators for various sample sizes and test termination time (a fact of time after that test is terminated) is examined in this article. To assess the performance of Bayes estimators in terms of posterior risks, a Monte Carlo simulation along with real data study is presented.

In manufacturing sectors, product performance evaluation is crucial. The lifetime performance index, denoted as CL, is widely used in product evaluation, where L signifies the lower specification limit. This study aims to refine the estimation of CL by employing maximum-likelihood and Bayesian methodologies, where symmetric and asymmetric loss functions are utilized. The analysis is conducted on progressive type II censored data, a requirement often imposed by budgetary constraints or the need for expedited testing. The data are assumed to follow the Ishita distribution, whose conforming rate is also evaluated. Furthermore, a hypothesis testing framework is employed to validate whether component lifetimes meet predefined standards. The theoretical findings are corroborated using real data collected from glass strength in aircraft windows. The numerical analysis emphasizes the goodness of fit of the Ishita distribution to model the data, thereby demonstrating the applicability of the proposed distribution.

This paper proposes a deep learning solution for estimating the compressive strength of rice husk ash-blended concrete. The deep learning models are trained by the adaptive moment estimation (Adam) optimizer. A dataset consisting of 527 data samples has been used to establish the deep learning method. Experimental results show that the deep learning model is able to achieve the most desired performance with a root-mean-square error (RMSE) of 3.21, a mean absolute percentage error (MAPE) of 7.01%, and a coefficient of determination ( R 2 ) of 0.97. This outcome is significantly better than that obtained from the benchmark models, including the Levenberg–Marquardt neural network and the multivariate adaptive regression splines. Additionally, the asymmetric loss function has been used to diminish the number of overestimated compressive strengths and enhance the reliability of the prediction model. The newly proposed approach can help reduce the percentage of overestimated cases from 48 to 34%.

In recent years, the use of recycled aggregate (RA) in roller-compacted concrete (RCC) for pavement construction has been increasingly attractive due to various environmental and economic benefits. Early determination of the compressive strength (CS) is crucial for the construction and maintenance of pavement. This paper presents the idea of combining metaheuristics and an advanced gradient boosting regressor for estimating the compressive strength of roller-compacted concrete containing RA. A dataset, including 270 samples, has been collected from previous experimental works. Recycled aggregates of construction demolition waste, reclaimed asphalt pavement, and industrial slag waste are considered in this dataset. The extreme gradient boosting machine (XGBoost) is employed to generalize a functional mapping between the CS and its influencing factors. A recently proposed gradient-based optimizer (GBO) is used to fine-tune the training phase of XGBoost in a data-driven manner. Experimental results show that the hybrid GBO-XGBoost model achieves outstanding prediction accuracy with a root mean square error of 2.64 and a mean absolute percentage error less than 8%. The proposed method is capable of explaining up to 94% of the variation in the CS. Additionally, an asymmetric loss function is implemented with GBO-XGBoost to mitigate the overestimation of CS values. It was found that the proposed model trained with the asymmetric loss function helped reduce overestimated cases by 17%. Hence, the newly developed GBO-XGBoost can be a robust and reliable approach for predicting the CS of RCC using RA.

In order to represent the data with non-monotonic failure rates and produce a better fit, a novel distribution is created in this study using the alpha power family of distributions. This distribution is called the alpha-power Kum-modified size-biased Lehmann type II or, in short, the AP-Kum-MSBL-II distribution. This distribution is established for modeling bounded data in the interval (0,1). The proposed distribution’s moment-generating function, mode, quantiles, moments, and stress–strength reliability function are obtained, among other attributes. To estimate the parameters of the proposed distribution, estimation methods such as the maximum likelihood method and Bayesian method are employed to estimate the unknown parameters for the AP-Kum-MSBL-II distribution. Moreover, the confidence intervals, credible intervals, and coverage probability are calculated for all parameters. The symmetric and asymmetric loss functions are used to find the Bayesian estimators using the Markov chain Monte Carlo (MCMC) method. Furthermore, the proposed distribution’s usefulness is demonstrated using three real data sets. One of them is a medical data set dealing with COVID-19 patients’ mortality rate, the second is a trade share data set, and the third is from the engineering area, as well as extensive simulated data, which were applied to assess the performance of the estimators of the proposed distribution.

In this paper, we calculate premiums that are based on the Conditional Tail Expectation (CTE) and asymmetric loss functions to account for the risk of both underestimation and overestimation losses. After selecting an appropriate loss function, the premium is calculated as the quantity minimizing an objective function related to the conditional tail expectation of the loss. The premium satisfies desirable properties, i.e., it is a coherent risk measure, and it helps the practitioner to quantify the global risk of the insurer. Finally, this methodology is applied to quantify the risks associated to the total claims amount that are modelled via composite models and comparisons with the usual risk measures, i.e., Value-at-Risk (VaR) and Tail Value-at-Risk (TVaR) are carried out.

Sustainable and green waste management has become increasingly crucial due to the rising volume of waste driven by urbanization and population growth. Deep learning models based on image recognition offer potential for advanced waste classification and recycling methods. However, traditional image recognition approaches usually rely on single-label images, neglecting the complexity of real-world waste occurrences. Moreover, there is a scarcity of recognition efforts directed at actual municipal waste data, with most studies confined to laboratory settings. Therefore, we introduce an efficient Query2Label (Q2L) framework, powered by the Vision Transformer (ViT-B/16) as its backbone and complemented by an innovative asymmetric loss function, designed to effectively handle the complexity of multi-label waste image classification. Our experiments on the newly developed municipal waste dataset “Garbage In, Garbage Out”, which includes 25,000 street-level images, each potentially containing up to four types of waste, showcase the Q2L framework’s exceptional ability to identify waste types with an accuracy exceeding 92.36%. Comprehensive ablation experiments, comparing different backbones, loss functions, and models substantiate the efficacy of our approach. Our model achieves superior performance compared to traditional models, with a mean average precision increase of up to 2.39% when utilizing the asymmetric loss function, and switching to ViT-B/16 backbone improves accuracy by 4.75% over ResNet-101.