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The research paper is devoted to developing a mathematical approach for dealing with time-varying parameters in rolling window logit models for credit risk assessment. Forecasting coefficients yields a better model accuracy than a trivial approach of using computed past statistics parameters for the next time period. In this paper, a new method of dealing with time-varying parameters of scoring models is proposed, which is aimed at computing the default probability of a borrower. It was empirically shown that in a continuously changing economic environment factors’ influence on a target variable is also changing. Therefore, forecasting coefficients yields a better financial result than simply applying parameters obtained by accumulated statistics over past time periods. The paper develops a new theoretical approach, incorporating a combination of the ARIMA class model, the DCC-GARCH model and the state–space model, which is more accurate, than using only the ARIMA model. Rigorous simulation testing is provided to confirm the efficiency of the proposed method.

Unsupervised random-walk keyphrase extraction models mainly rely on global structural information of the word graph, with nodes representing candidate words and edges capturing the co-occurrence information between candidate words. However, using word embedding method to integrate multiple kinds of useful information into the random-walk model to help better extract keyphrases is relatively unexplored. In this paper, we propose a random-walk-based ranking method to extract keyphrases from text documents using word embeddings. Specifically, we first design a heterogeneous text graph embedding model to integrate local context information of the word graph (i.e., the local word collocation patterns) with some crucial features of candidate words and edges of the word graph. Then, a novel random-walk-based ranking model is designed to score candidate words by leveraging such learned word embeddings. Finally, a new and generic similarity-based phrase scoring model using word embeddings is proposed to score phrases for selecting top-scoring phrases as keyphrases. Experimental results show that the proposed method consistently outperforms eight state-of-the-art unsupervised methods on three real datasets for keyphrase extraction.

The dynamic, heterogeneous nature of Edge computing in the Internet of Things (Edge-IoT) and Industrial IoT (IIoT) networks brings unique and evolving cybersecurity challenges. This study maps cyber threats in Edge-IoT/IIoT environments to the Adversarial Tactics, Techniques, and Common Knowledge (ATT&CK) framework by MITRE and introduces a lightweight, data-driven scoring model that enables rapid identification and prioritization of attacks. Inspired by the Factor Analysis of Information Risk model, our proposed scoring model integrates four key metrics: Common Vulnerability Scoring System (CVSS)-based severity scoring, Cyber Kill Chain–based difficulty estimation, Deep Neural Networks-driven detection scoring, and frequency analysis based on dataset prevalence. By aggregating these indicators, the model generates comprehensive risk profiles, facilitating actionable prioritization of threats. Robustness and stability of the scoring model are validated through non-parametric correlation analysis using Spearman’s and Kendall’s rank correlation coefficients, demonstrating consistent performance across diverse scenarios. The approach culminates in a prioritized attack ranking that provides actionable guidance for risk mitigation and resource allocation in Edge-IoT/IIoT security operations. By leveraging real-world data to align MITRE ATT&CK techniques with CVSS metrics, the framework offers a standardized and practically applicable solution for consistent threat assessment in operational settings. The proposed lightweight scoring model delivers rapid and reliable results under dynamic cyber conditions, facilitating timely identification of attack scenarios and prioritization of response strategies. Our systematic integration of established taxonomies with data-driven indicators strengthens practical risk management and supports strategic planning in next-generation IoT deployments. Ultimately, this work advances adaptive threat modeling for Edge/IIoT ecosystems and establishes a robust foundation for evidence-based prioritization in emerging cyber-physical infrastructures.

Background Although the risk factors for positive follow-up blood cultures (FUBCs) in gram-negative bacteremia (GNB) have not been investigated extensively, FUBC has been routinely carried out in many acute care hospitals. We attempted to identify the risk factors and develop a predictive scoring model for positive FUBC in GNB cases. Methods All adults with GNB in a tertiary care hospital were retrospectively identified during a 2-year period, and GNB cases were assigned to eradicable and non-eradicable groups based on whether removal of the source of infection was possible. We performed multivariate logistic analyses to identify risk factors for positive FUBC and built predictive scoring models accordingly. Results Out of 1473 GNB cases, FUBCs were carried out in 1268 cases, and the results were positive in 122 cases. In case of eradicable source of infection, we assigned points according to the coefficients from the multivariate logistic regression analysis: Extended spectrum beta-lactamase-producing microorganism (+ 1 point), catheter-related bloodstream infection (+ 1), unfavorable treatment response (+ 1), quick sequential organ failure assessment score of 2 points or more (+ 1), administration of effective antibiotics (− 1), and adequate source control (− 2). In case of non-eradicable source of infection, the assigned points were end-stage renal disease on hemodialysis (+ 1), unfavorable treatment response (+ 1), and the administration of effective antibiotics (− 2). The areas under the curves were 0.861 (95% confidence interval [95CI] 0.806–0.916) and 0.792 (95CI, 0.724–0.861), respectively. When we applied a cut-off of 0, the specificities and negative predictive values (NPVs) in the eradicable and non-eradicable sources of infection groups were 95.6/92.6% and 95.5/95.0%, respectively. Conclusions FUBC is commonly carried out in GNB cases, but the rate of positive results is less than 10%. In our simple predictive scoring model, zero scores—which were easily achieved following the administration of effective antibiotics and/or adequate source control in both groups—had high NPVs. We expect that the model reported herein will reduce the necessity for FUBCs in GNB cases.

Federated learning (FL) is a decentralized and privacy-preserving machine learning technique that protects data privacy by learning models locally and not sharing datasets. However, due to limited computing resources on devices and highly heterogeneous data in practical situations, the training efficiency and resource utilization of federated learning is low. In order to resolve these challenges, we introduce a blockchain-assisted dynamic adaptive and personalized federated learning framework (TV-FedAvg) in the presence of restricted computing power resources and data heterogeneity. After each round of local training, we utilize an improved scoring model based on VIKOR and TOPSIS to comprehensively score the devices. The scores are then utilized to choose devices for participation in global aggregation and to carry out model aggregation through blockchain consensus. Furthermore, resources are reallocated for the next round to enhance resource efficiency, model fairness, and performance. Finally, we demonstrate through experimentation that TV-FedAvg outperforms other models such as pFedMe, FedAvg, Per-FedAvg, and TOPSIS in terms of both efficiency and performance.

Constructed response (CR) assessments allow students to demonstrate understanding of complex topics and provide teachers with deeper insight into student thinking. Computer scoring models (CSMs) remove the barrier of increased time and effort, making CR more accessible. As CSMs are commonly created using responses from research-intensive colleges and universities (RICUs), this pilot study examines the effectiveness of seven previously developed CSMs on diverse CRs from RICUs, two-year colleges (TYCs), and primarily undergraduate institutions (PUIs). We asked if accuracy of the CSMs was maintained with a new testing set of CRs and if CSM accuracy differed among different institutional types. A human scorer and the CSMs analytically categorized 444 CRs for the presence or absence of seven ideas relating to weight loss. Comparing human and CSM predictions revealed five CSMs maintained high agreement (Cohen’s kappa > 0.80); however, two CSMs demonstrated reduced agreement (Cohen’s kappa < 0.65). Seventy-one percent of these miscodes were false negatives. RICU responses were 1.4 times more likely to be miscoded than TYCs (p = 0.038) or PUIs (p = 0.047) across all seven CSMs. However, this increased frequency may result from the higher number of ideas in RICU responses in comparison to TYCs (p = 0.082) and PUIs (p = 0.013). Accounting for increased ideas removed the significant difference between RICUs and TYCs (p = 0.23) and PUIs (p = 0.54). Finally, qualitative examination of miscodes provides insight into reduced CSM performance. Collectively, these data support the utility of these CSMs across institutional types and with novel CRs.

In this paper, the disclosure of information about the scoring model is investigated. Some of the company’s customers find out their internal rating in the company. Such customers can change their behavior to increase their internal rating. The customers who are aware of the leakage are represented as players who can choose a strategy: whether to increase their internal rating and, if so, how much. The main goal is to find the Bayesian–Nash equilibrium in this game and find out how it depends on various parameters, such as the scale of the leakage, the distribution of ratings.

Although lead scoring is an essential component of lead management, there is a lack of a comprehensive literature review and a classification framework dedicated to it. Lead scoring is an effective and efficient way of measuring the quality of leads. In addition, as a critical Information Technology tool, a proper lead scoring model acts as an alleviator to weaken the conflicts between sales and marketing functions. Yet, little is known regarding lead scoring models and their impact on sales performance. Lead scoring models are commonly categorized into two classes: traditional and predictive. While the former primarily relies on the experience and knowledge of salespeople and marketers, the latter utilizes data mining models and machine learning algorithms to support the scoring process. This study aims to review and analyze the existing literature on lead scoring models and their impact on sales performance. A systematic literature review was conducted to examine lead scoring models. A total of 44 studies have met the criteria and were included for analysis. Fourteen metrics were identified to measure the impact of lead scoring models on sales performance. With the increased use of data mining and machine learning techniques in the fourth industrial revolution, predictive lead scoring models are expected to replace traditional lead scoring models as they positively impact sales performance. Despite the relative cost of implementing and maintaining predictive lead scoring models, it is still beneficial to supersede traditional lead scoring models, given the higher effectiveness and efficiency of predictive lead scoring models. This study reveals that classification is the most popular data mining model, while decision tree and logistic regression are the most applied algorithms among all the predictive lead scoring models. This study contributes by systematizing and recommending which machine learning method (i.e., supervised and/or unsupervised) shall be used to build predictive lead scoring models based on the integrity of different types of data sources. Additionally, this study offers both theoretical and practical research directions in the lead scoring field.

Iron deficiency anaemia (IDA) is a common condition during pregnancy. The aim of the present study was to determine the diagnostic accuracy measures and define the optimal parameters for diagnosing IDA. Simple parameters, such as erythrocyte count (E), iron (Fe), haematocrit (HCT), mean corpuscular volume of erythrocytes (MCV), and red blood cell distribution width (RDW), were included and several ratios were calculated (Fe/E, MCV/RDW, RDW/E, RDW/Fe, and RDW/HCT). Additionally, a scoring model was proposed. A total of 623 pregnant women were included in the present study, and the blood was obtained a day before or on the day of delivery. Simple parameters and calculated ratios were determined. The clinical criterion for IDA diagnosis was defined based on the World Health Organization threshold of haemoglobin <110 g/l, and pregnant women were classified as anaemic or non-anaemic based on this metric. The values of all assessed parameters were significantly different (P<0.001) between the two groups. A weak correlation was identified for MCV, Fe/E, and MCV/RDW; a moderate correlation for E, Fe, RDW/E, and RDW/Fe; and a strong correlation for HCT and RDW/HCT. No correlation was identified for RDW. Markedly high diagnostic accuracy for IDA diagnosis was obtained with an area under the curve (AUC) of 0.921 for the calculated parameter RDW/HCT >43.64 l/lx10-2, and an AUC of 0.973 for the simple parameter HCT ≤0.32 l/l. RDW/HCT and HCT demonstrated the highest diagnostic accuracy, and may be useful parameters in routine practice for the diagnosis of IDA.

Background Early identification of ventricular arrhythmia (VA) risk in children with acute myocarditis (AMC) is challenging, as existing tools lack pediatric targeting. AMC’s non-specific early symptoms in children lead to underdiagnosis. VA, a major complication of AMC, is linked to poor prognosis, but not all cases present at admission, highlighting the need for simple, accessible predictive indicators. Methods This retrospective single-center study included 312 children (1 month − 17 years) with AMC (2021–2024), divided into a training set ( n  = 208, 2021–2023) and validation set ( n  = 104, 2024). Eligibility required first-time AMC diagnosis without admission VA. Univariate analysis ( P  < 0.05) and binary logistic regression identified independent VA risk factors. Continuous variables were dichotomized via receiver operating characteristic curves, and a scoring model was constructed, with internal validation of discriminative performance and calibration. Results Forty-four children (14.1%) developed VA, mostly within the first week. Independent risk factors were cardiac troponin I (cTnI ≥ 0.1945 ng/mL, OR = 9.114), blood urea nitrogen (BUN ≥ 4.55 mmol/L, OR = 9.796), and left ventricular fraction shortening (LVFS ≤ 0.33, OR = 6.005). The model assigned 3 points to cTnI/BUN and 2 to LVFS (total 0–8 points). At cutoff ≥ 4 points: training set (sensitivity = 70.0%, specificity = 90.4%, AUC = 0.871); validation set (sensitivity = 78.6%, specificity = 94.4%, accuracy = 92.3%, AUC = 0.837). Calibration was acceptable (Hosmer-Lemeshow P  = 0.288). Conclusion The cTnI-, BUN-, and LVFS-based scoring model offers a simple, effective tool for early VA risk stratification in children with AMC. It aids targeted monitoring and intervention, improving clinical decision-making. Limitations include retrospective single-center design and small sample size; prospective multicenter validation is needed.