Singapore Management University

Cyber-physical systems are at the intersection of digital technology and engineering domains, rendering them high-value targets of sophisticated and well-funded cybersecurity threat actors. Prominent cybersecurity attacks on cyber-physical systems have brought attention to the vulnerability of these systems and the inherent weaknesses of critical infrastructure reliant on them. Security modelling for cyber-physical systems is an important mechanism to systematically identify and assess vulnerabilities, threats and risks throughout system lifecycles, and to ultimately ensure system resilience, safety and reliability. This survey delves into state-of-the-art research on security modelling for cyber-physical systems, encompassing both threat and attack modelling. While these terms are sometimes used interchangeably, they are different concepts. This article elaborates on the differences between threat and attack modelling, examining their implications for cyber-physical system security. We conducted a systematic search that yielded 449 papers, from which 32 were selected and categorised into 3 clusters: those focused on threat modelling methods, attack modelling methods and literature reviews. Specifically, we sought to examine what security modelling methods exist today, and how they address real-world cybersecurity threats and attacker capabilities throughout the lifecycle of cyber-physical systems, which typically span longer durations compared to traditional IT systems. This article also highlights several limitations in existing research, wherein security models adopt simplistic approaches that do not adequately consider the dynamic, multi-layer, multi-path and multi-agent characteristics of real-world cyber-physical attacks.

BackgroundIn silico evaluation (ISE) methods create a digital twin or a computer simulation of actual care pathways, enabling a broader assessment of the potential impact of algorithm-based clinical decision support systems (CDSS) before implementation. A programmatic search of several academic research databases showed at least 886 CDSS development and evaluation studies in the past 3 decades. However, fewer than 3% applied ISE to evaluate the potential impact on broader clinical care pathways.ObjectiveThis study aims to review the scope of proposed ISE methods to evaluate CDSS, with a focus on simulation modeling approaches, care pathway parameters considered, and outcomes evaluated within the ISE methodological domain.MethodsThis review followed the established scoping review methodological guidelines. We conceptualized a tailored search framework and conducted a 2-stage screening process on studies identified through automated searches of selected databases. Relevant information on CDSS study characteristics and the application of care pathway simulation modeling in CDSS evaluation was subsequently extracted.ResultsA small subset of studies on CDSS development conducted ISE. Most ISE studies were published after 2019, reflecting a more recent increase in the application of ISE. These studies frequently emphasized patient, process, and cost-effectiveness outcomes. Notably, the evaluation of outcomes directly related to care providers’ well-being is lacking, highlighting a critical gap in current ISE applications. Among the studies included in this review, various simulation modeling paradigms were used, including dynamic simulations and state-based models. Three themes were found among the different motivations and objectives for using ISE: (1) outcome comparison, (2) outcome comparison with sensitivity analysis, and (3) simulation-based optimization of the proposed CDSS. The first two approaches considered a decoupled CDSS model training followed by simulation-based evaluation of the trained CDSS model. The third approach iteratively improved the CDSS model’s decision-support capabilities through optimization based on care pathway simulations. These approaches can be broadly categorized as decoupled (1 and 2), where CDSS models are evaluated within seperately designed clinical workflow simulations, and integrated (3), where simulation iteratively optimizes CDSS performance.ConclusionsThe growing body of algorithm-based CDSS research underscores the need for evaluation approaches that are resource-efficient and account for systems-level workflow implications. This review highlights both the gaps and the potential of ISE, particularly care pathway simulation-based approaches, as a preimplementation strategy to strengthen evidence before significant resource allocations to pilot or full-scale implementations. ISE presents as a promising intermediary evaluation approach that bridges model-level performance and clinical workflow impact, allowing more contextualized and resource-efficient assessments prior to implementation.

Digital transformation (DT) presents both significant opportunities and challenges for entrepreneurial firms, necessitating a realignment of their strategies to integrate innovation and creativity. Aligning strategies with DT can foster innovation, drive business transformation, and enhance environmental sustainability. However, several obstacles hinder this alignment, impeding firms’ progress and threatening their long-term sustainability. Addressing these challenges is crucial to building a strong foundation for sustainable growth. This study investigates the obstacles to aligning digital capabilities in entrepreneurial firms for sustainable growth. A systematic literature review (SLR) and thematic analysis (TA) were conducted to identify the obstacles. Subsequently, a multi-layer grey decision-making trial and evaluation laboratory (G-DEMATEL)-based analytic network process (G-ANP) (G-DANP) approach is used to analyse these obstacles, determining their importance and interrelationships. The study identifies seven key barriers, with the most critical being firms’ lack of readiness, which drives all other challenges. In contrast, the absence of a coherent sustainability policy is the least influential. Notably, limited digital skills and the underuse of DT tools amplify other obstacles. By mapping the influence and dependency among barriers, the analysis reveals how managerial backing and policy coherence can significantly alter the entire landscape. The findings equip entrepreneurial firms with clear priorities—build capacity, engage leadership, and target readiness—to drive sustainable digital adoption.

The core of efficient on-demand ride-pooling lies in solving the Ride-Pool Matching Problem (RMP), which involves assigning multiple customer requests to single vehicles under various service constraints (e.g., pickup windows, detour allowances, and vehicle occupancy). A significant missed opportunity in most current RMP approaches is the assumption that passengers must be picked up and dropped off exactly at their requested locations. Allowing passengers to walk even short distances to meet vehicles could unlock substantial improvements in ride-pooling operations. Building upon the limitations of existing Ride-Pool Matching Problem (RMP) solutions that neglect passenger walkability, this paper introduces a novel matching method that strategically incorporates flexible pickup and drop-off locations. Our approach simultaneously determines the optimal assignment of vehicles to requests (one vehicle to potentially multiple requests and each request to at most one vehicle), identifies advantageous meeting points for passengers, and plans efficient vehicle routes. This comprehensive optimization respects all service constraints and considers the long-term implications of routing decisions. To achieve this integrated solution, we first employ a tree-based approach to enumerate all feasible pairings between passengers and vehicles. Subsequently, we calculate an optimal route for each of these feasible matches. Finally, we evaluate the quality of all possible assignments and select the most advantageous matching for implementation. In our experimental evaluation on city-scale taxi datasets, we demonstrate that our method improves the number of served requests by up to 13% and reduces the average vehicle travel distance by up to 21%. By serving more passengers with less driving distance, our approach achieves greater efficiency in a more sustainable manner — using fewer resources to deliver better service and creating a win-win outcome for all stakeholders, including customers, drivers, the aggregator, and the environment.

Developing new portfolio-management algorithms typically demands substantial programming effort, limiting rapid experimentation and excluding finance professionals without coding skills. Current robo-advisory tools offer pre-built but rigid strategies, restricting customization and experimentation. We introduce PortfolioPilot, an open-source, agentic platform that enables users to generate bespoke portfolio through natural-language descriptions. Leveraging the Anthropic Claude API, PortfolioPilot dynamically synthesizes executable TypeScript algorithms that run in the frontend with security validation. The system integrates real-time backtesting with historical market data, classical optimization algorithms (Markowitz, LSTM, ARIMA), and interactive performance visualizations.

Automated Program Repair (APR) tools have advanced in recent years, yet their effectiveness can improve when integrated into Continuous Integration/Continuous Deployment (CI/CD) pipelines. Motivated by this, we designed the Continuous Automatic Repair Framework (CARF), which detects build failures, routes each bug to the most suitable repair tool, and automatically commits the generated fix. During development, we identified a critical bottleneck: the need for instant bug classification within CI/CD. Accurate classification is essential to determine whether a fault lies in program code or the test suite, ensuring the defect is routed to the appropriate repair tool. Objectives: This study aims to design and evaluate a just-in-time bug classifier capable of distinguishing between program and test bugs during CI/CD execution, thereby enabling CARF to maintain workflow efficiency by directing defects to appropriate repair tools. We implemented a heuristic analysis tool that extracts discriminative structural features by comparing Abstract Syntax Trees (ASTs) between buggy and pre-buggy commits. These features are input to machine learning models for bug classification. Empirical validation demonstrated the accuracy and operational feasibility of the approach within CI/CD environments. Our approach achieved up to 73% accuracy in identifying regression bugs across 67 real-world projects, effectively distinguishing between program bugs and test bugs while requiring only 10% of the dataset for training. In contrast, prior studies reported an accuracy of 69% on artificially injected bugs derived from successive versions of only two projects, with 90% of the data used for training. CARF facilitates the integration of automated program repair into CI/CD pipelines, enabling faster, more accurate, and more flexible software maintenance. The just-in-time bug classifier demonstrates that defect classification can scale efficiently without symbolic execution or manual bug reports, providing a practical foundation for continuous automated repair. • Designed an orchestration layer to unify heterogeneous automated repair tools within CI/CD pipelines. • Identified the need for a bug classifier to distinguish between program and test bugs at build failure. • Developed a lightweight, heuristic-based bug classifier for fast and reliable bug differentiation.

Epidural analgesia has been the gold standard for pain management in minimally invasive esophagectomy (MIE), with paravertebral analgesia as a safe alternative. This cost-effectiveness analysis, conducted alongside a randomized controlled trial, evaluates the cost-effectiveness of paravertebral analgesia compared to epidural analgesia. This multicenter randomized controlled trial across four Dutch hospitals, including 192 patients, compared epidural and paravertebral analgesia in patients undergoing MIE. Cost-effectiveness was evaluated from a healthcare perspective, including in-hospital costs only, with time horizons from initial hospital stay and 90days postoperatively. Procedural costs were calculated with a bottom-up approach, and analgesia costs with data from the electronic case report form (eCRF). Other hospital costs were calculated using insurance claim data from hospital registries. Effectiveness was displayed as Quality of Recovery (QoR-40) scores. Bootstrapping was used to estimate uncertainty. Mean initial surgery costs were €10,469 for the epidural and €10,051 for the paravertebral group, primarily due to a shorter mean operating room time (mean difference 19min; 95% CI - 7 to 45). Mean postoperative (day 1-3) medication costs were €116 and €125, respectively. During the initial hospital stay, mean total in-hospital costs for uncomplicated patients were €8557 and €8646, and for complicated patients €28,244 and €28,387, respectively. When excluding complications, bootstrapping showed that 47.9% of iterations had lower costs and lower effectiveness for paravertebral analgesia, with the other iterations across all other quadrants. Cost differences between paravertebral and epidural analgesia were minimal, with cost-effectiveness primarily influenced by the slightly lower effectiveness of paravertebral analgesia.