The increasing sophistication of cyber risks has made it challenging for organizations to assess their business impacts. The key challenge is the technical and language “barrier” between cybersecurity teams and business teams who make strategic investment decisions on cybersecurity. This often leads to delays, budget issues that prevent timely responses to cyber incidents. Existing research lacks a transparent, traceable, and reproducible method to communicate cyber risks and their impacts on businesses. We introduce a novel cyber risk economics model for organization-wide risk management (CYREM-ORM) that captures complex cyber risks and expresses them using financial terms. This is achieved by mapping Cyber Threat Intelligence (CTI) to the Factor Analysis of Information Risk (FAIR) model, enriched by cyber cost typologies. CYREM-ORM provides a traceable workflow that links organisation-related CTI to FAIR factor estimation, cost breakdowns, and ultimately to monetary loss amounts and prioritised risk scenarios. This design improves transparency in risk management, helps organisations prioritise mitigations in line with strategic business objectives, and enables stakeholders to assess the rationale behind results when needed. By grounding risk parameters in CTI, the model also facilitates proactive screening of organisation-relevant threats, instead of reactive, control-gap reporting. We evaluate the CYREM-ORM through three complementary case studies: the 2017 Equifax breach case proves its feasibility with historical data and open-source CTI, while the Small and Medium Enterprise (SME) education company and the large retail company cases show its effectiveness in communicating cyber risks at an organizational-wide strategic level within real-world contexts.

QRS complex detection is a crucial preprocessing step in ECG-based arrhythmia recognition. Traditional methods often fail to maintain stability when confronted with noisy interference, baseline drift, and high inter-patient variability in ECG signals. Although deep learning approaches achieve superior detection performance, their high computational complexity hinders deployment on mobile and edge devices. Therefore, this study proposes a lightweight encoder-decoder network named MSCRAG for robust QRS complex detection. The architecture integrates joint time and frequency analysis with multi-scale convolutional to enable adaptive frequency decomposition, and incorporates a channel attention to enhance QRS time and frequency feature representation. In the encoder and decoder, an improved GhostV2Block (GhostV2BlockMS) reduces model parameters and computational cost while preserving feature extraction efficiency. Furthermore, the Multi-Scale Dynamic Feature Convolution Plus (MSDFC+) module which introduces in the bottleneck enhances the global receptive field and provides dynamic feature modulation, allowing the network to adaptively focus on critical temporal regions and capture long-range dependencies in ECG signals. Evaluations on standard ECG datasets demonstrate that our method achieves 99.1 % accuracy, 97.2 % sensitivity, 99.6 % positive predictive value, and 99.3% F1-score, outperforming existing state-of-the-art approaches. Its high accuracy, lightweight design, and low computational cost render it suitable for both static analysis and real-time cardiac monitoring in dynamic and wearable applications, offering significant potential for clinical and portable healthcare scenarios.

The increasing sophistication of cyberattacks and the evolution of security risks make it challenging for organizations to understand their impact on businesses.The habitual reliance on the judgment of cyber security experts and communication gaps between cyber security team and board members, responsible for making strategic cyber security investment decisions further weaken organization's capability to respond to cyber threats.Existing research lacks a transparent approach to quantify security risks and their impact on businesses.This paper introduces a novel CYSEM that express security risk in financial terms, through integrating Cyber Threat Intelligence (CTI) with the Factor Analysis of Information Risk (FAIR) model, elaborated with cyber security cost typologies.CYSEM facilitates communication among multi-stakeholders and improves transparency and quality of investment decision-making at the strategic level.We evaluate the CYSEM using a case study, which has showed its effectiveness in understanding the impact of cyber threat from an economics perspective. CCS Concepts• Security and privacy → Human and societal aspects of security and privacy; Economics of security and privacy.

Pre-trained language models (PLMs) leverage chains-of-thought (CoT) to simulate human reasoning and inference processes, achieving proficient performance in multi-hop QA. However, a gap persists between PLMs’ reasoning abilities and those of humans when tackling complex problems. Psychological studies suggest a vital connection between explicit information in passages and human prior knowledge during reading. Nevertheless, current research has given insufficient attention to linking input passages and PLMs’ pre-training-based knowledge from the perspective of human cognition studies. In this study, we introduce a Prompting Explicit and Implicit knowledge (PEI) framework, which uses prompts to connect explicit and implicit knowledge, aligning with human reading process for multi-hop QA. We consider the input passages as explicit knowledge, employing them to elicit implicit knowledge through unified prompt reasoning. Furthermore, our model incorporates type-specific reasoning via prompts, a form of implicit knowledge. Experimental results show that PEI performs comparably to the state-of-the-art on HotpotQA. Ablation studies confirm the efficacy of our model in bridging and integrating explicit and implicit knowledge.

Sinus node ischemia is mainly characterized by slow heart rate, which is caused by ischemia-induced changes in electrophysiological properties and ion homeostasis leading to prolonged pacing cycle length. According to the available data, research on the mechanism of sinus node ischemia has been reported, but the report on the drug treatment of the disease is scarce. In order to reveal the effect of Chinese medicine (Yiqi Tongyang) in sinus node ischemia, this paper uses rabbit sinus node center and periphery models to simulate the effects of medium and high doses of the Chinese medicine in ischemia at the sub-cellular, cellular and tissue levels, and to simulate the changes in cellular action potentials and tissue pacing functions to predict the drug efficacy. Simulation results showed that 1) the Chinese medicine can effectively shorten the pacing cycle, with negligible effect on the duration of cellular action potential and maximal diastolic potential, but can cause a decrease in the maximal depolarization velocity; 2) it accelerated the activation of ischemic sinus node-atrum tissue, so that the electrical excitatory activity of the tissue return to the normal state; 3) by comparing the simulation results of medium and high doses of the Chinese medicine, it was shown that the high dose group did not increase the heart rate significantly, while the medium dose group had a significant effect on the regulation of heart rate, indicating that the Chinese medicine (Yiqi Tongyang) can effectively treat sinus node ischemic disease.

Sinus node ischemia is mainly characterized by slow heart rate, which is caused by ischemia-induced changes in electrophysiological properties and ion homeostasis leading to prolonged pacing cycle length. According to the available data, research on the mechanism of sinus node ischemia has been reported, but the report on the drug treatment of the disease is scarce. In order to reveal the effect of Chinese medicine (Yiqi Tongyang) in sinus node ischemia, this paper uses rabbit sinus node center and periphery models to simulate the effects of medium and high doses of the Chinese medicine in ischemia at the sub-cellular, cellular and tissue levels, and to simulate the changes in cellular action potentials and tissue pacing functions to predict the drug efficacy. Simulation results showed that 1) the Chinese medicine can effectively shorten the pacing cycle, with negligible effect on the duration of cellular action potential and maximal diastolic potential, but can cause a decrease in the maximal depolarization velocity; 2) it accelerated the activation of ischemic sinus node-atrum tissue, so that the electrical excitatory activity of the tissue return to the normal state; 3) by comparing the simulation results of medium and high doses of the Chinese medicine, it was shown that the high dose group did not increase the heart rate significantly, while the medium dose group had a significant effect on the regulation of heart rate, indicating that the Chinese medicine (Yiqi Tongyang) can effectively treat sinus node ischemic disease.

It has been verified that biological pacemaker could be produced based on ventricular myocytes (VMs) by overexpressing HCN gene which codes the expression of hyperpolarization-activated current (If). Clinically, xenograft is in common use by which one specie' stem cell is infected with another specie's HCN gene so that the stem cell could transfer into cardiac pacemaker cell. The difference of HCN gene between species affects Ifproperties, but how the Ifproperties influence pacemaker creation is not easy to be qualified in biological experiments. In this study, we build an engineered biological pacemaker model based on a ventricular myocyte model by incorporating Ifformulation and simulated the membrane potential of biological pacemaker. The Ifof different species is simulated by modifying average half-maximal activation voltage (V1/2) of Ifactivation gate and Ifconductance (Gf). Based on the modified pacemaker model, the effect of Ifproperties on pacemaking stability and frequency is evaluated. Simulation results indicate that pacemaking ability is influenced dramatically by Ifproperties. In addition, the spontaneous pacemaking mechanism showed both membrane-clock and Ca2+-clock and its deep reason is analyzed in this paper. This study may provide a subcellular perspective for the clinical use of biological pacemaker.

The identified genetic short QT syndrome (SQTS) is associated with an increased risk of arrhythmia and sudden death. This study was to investigate the potential effects of propafenone on KCNH2-linked short QT syndrome (SQT1) using a multi-scale biophysically detailed model of the heart developed by ten Tusscher and Panfilov. The ion electrical conductivities were reduced by propafenone in order to simulate the pharmacological effects in healthy and SQT1 cells. Based on the experimental data of McPate et al., the pharmacological effect of propafenone was modelled by dose-dependent IKrblocking. Action potential (AP) profiles and 1D tissue level were analyzed to predict the effects of propafenone on SQT1. Both low- and high- dose of propafenone prolonged APD and QT interval in SQT1 cells. It suggests the superior efficacy of high dose of propafenone on SQT1. However, propafenone did not significantly alter the healthy APD or QT interval at low dose, whereas markedly shortened them at high dose. Our simulation data show that propafenone has a dose-dependently anti-arrhythmic effect on SQT1, and a pro-arrhythmic effect on healthy cells. These computer simulations help to better understand the underlying mechanisms responsible for the initiation or termination of arrhythmias in healthy or SQT1 patients using propafenone.