Search Results (90)

In a rapidly changing era, education systems must empower learners as community innovators through Place-Based Education (PBE). While School–University partnerships are global drivers of reform, the specific administrative mechanisms required to support and scale these innovations within decentralized policy frameworks, such as Thailand’s Education Sandbox, remain underexplored. This Research and Development (R&D) study, integrated with a Design Thinking framework, investigated school-led administrative innovations across four diverse jurisdictions in the Songkhla Education Sandbox over 12 months. The study synthesized a collaborative administrative framework structured around four core pillars: Strategic Mentoring and Thinking Partnership, Place-Based Educational Ecosystems, Adaptive Governance and Resource Autonomy, and Collective Synergy and Iterative Development. Empirical findings indicate that this framework supported the development of “Innovative Co-creator” characteristics among students, generating high-value outcomes such as “Songkhla Mini Mango Coffee” and social innovations from water hyacinth. The study concludes that educational transformation thrives when administrative structures shift from compliance-driven mandates to flexible, context-responsive partnerships. By integrating university-led coaching with community assets, the framework offers a promising, contextually adaptable model for enhancing student learning outcomes while preserving local socio-cultural identity. This systematic approach supports the continuity of educational reform across diverse regional contexts. Full article

The rapid growth of Internet-of-Things (IoT) deployments has substantially expanded the attack surface of modern cyber–physical systems, making accurate and computationally feasible malware detection essential for enterprise and industrial environments. This study presents a large-scale, systematic comparison of 27 machine learning (ML) and 18 deep learning (DL) models for IoT malware detection across eight major malware categories: Trojan, Botnet, Ransomware, Rootkit, Worm, Spyware, Keylogger, and Virus. A realistic dataset was constructed using 50,000 executable samples collected from the Any.Run platform, including 8000 malware instances (1000 per class) and 42,000 benign samples. Each sample was executed in a sandbox to extract detailed static and behavioral telemetry. A targeted feature-selection pipeline reduced the feature space to 47 diagnostic features spanning static properties, behavioral indicators, process/file/registry activity, debug signals, and network telemetry, yielding a compact representation suitable for malware detection in IoT settings. Experimental results demonstrate that ensemble tree-based ML models consistently dominate performance on the engineered tabular feature set as 7 of the top 10 models are ML, with CatBoost and LightGBM achieving near-ceiling accuracy and low false-positive rates. Per-malware analysis further shows that optimal model choice depends on malware behavior. CatBoost is best for Trojan/Spyware, LightGBM for Botnet, XGBoost for Worm, Extra Trees for Rootkit, and Random Forest for Keylogger, while DL models are competitive only for specific categories, with TabNet performing best for Ransomware and FT-Transformer for Virus. In addition, an end-to-end computational time analysis across all 45 models reveals a clear efficiency advantage for boosted tree ensembles relative to most DL architectures, supporting deployment feasibility on commodity CPU hardware. Overall, the study provides actionable guidance for designing adaptive IoT malware detection frameworks, recommending gradient-boosted ensemble ML models as the primary deployment choice, with selective DL models only when category-specific gains justify additional computational cost. Full article

The global tourism industry is entering a phygital era, prompting renewed examination of the metaverse as an extension rather than a substitute for physical travel. This study investigates how metaverse technology operates across the Phygital Customer Journey within the Thai tourism context. Drawing on in-depth interviews with 12 experts from academic, multimedia development, and policy sectors, the data were analyzed using reflexive thematic analysis. The findings indicate that the metaverse assumes its most structurally significant role during the pre-trip phase. Immersive previews were described as recalibrating perceived risk by enabling advance assessment of accessibility, spatial configuration, and environmental conditions prior to commitment. This staged risk-calibration process operates through three interrelated mechanisms: Sensory Bridging, Psychological Risk Mitigation, and Physical Feasibility Testing, which are particularly relevant for secondary tourism destinations and demographic aging contexts. Building on these patterns, the study advances a four-layer architectural framework as an interpretive synthesis. Within this framework, the metaverse functions as a transactional and coordination layer that integrates booking systems, AI-enabled services, and real-time infrastructural data supported by IoT and Blockchain. The analysis further suggests that the state may assume an enabling role as an Infrastructure Architect through the development of a National Digital Highway and regulatory sandbox arrangements for SMEs. Sustainable adoption depends on hardware-agnostic, mobile-centric accessibility to mitigate digital exclusion. While grounded in Thailand, the framework offers analytical relevance for destinations facing comparable infrastructural and demographic conditions. Full article

The S-100 standard for Electronic Chart Display and Information Systems (ECDIS) uses Lua scripts to render electronic charts, yet lacks security specifications for script execution. This paper evaluates automated Static Application Security Testing (SAST) tools versus expert manual review for S-100-compliant software. Four SAST tools were applied alongside an expert review of OpenS100, a reference implementation for next-generation ECDIS. While automated tools identified numerous defects, they failed to detect 83% (19/23) of expert-identified vulnerabilities, including an unrestricted Lua interpreter flaw with a Common Vulnerability Scoring System (CVSS) score of 9.3. This vulnerability enables Remote Code Execution (RCE) via malicious portrayal catalogues, verified through Proof of Concept (PoC) development. The analysis demonstrates that SAST tools are constrained by limited maritime domain knowledge and challenges in analyzing cross-language semantic risks at the C++–Lua interface. The findings establish that identified vulnerabilities stem from specification gaps in the S-100 standard rather than isolated coding errors. These results indicate that functional safety certifications require supplementation to address design-level security risks. The evidence supports that the International Hydrographic Organization (IHO) incorporate security controls, such as script sandboxing and library restrictions, into the S-100 framework before the 2029 mandatory adoption deadline. Full article

Traditional signature-based anti-malware tools often fail to detect zero-day ransomware attacks due to their reliance on known patterns. This paper presents a real-time ransomware detection framework that models system behavior as a Reinforcement Learning (RL) environment. Behavioral features—including file entropy, CPU usage, and registry changes—are extracted from dynamic analysis logs generated by Cuckoo Sandbox. A (DQN) agent is trained to proactively block malicious actions by maximizing long-term rewards based on observed behavior. Experimental evaluation across multiple ransomware families such as WannaCry, Locky, Cerber, and Ryuk demonstrates that the proposed RL agent achieves a superior detection accuracy, precision, and F1-score compared to existing static and supervised learning methods. Furthermore, ablation tests and latency analysis confirm the model’s robustness and suitability for real-time deployment. This work introduces a behavior-driven, generalizable approach to ransomware defense that adapts to unseen threats through continual learning. Full article

Financial systems are quintessential complex adaptive systems, where stability emerges from the dynamic interactions among multiple subsystems and regulatory components. Grounded in systems theory, this study re-frames the establishment of China’s fintech regulatory sandbox as a systemic intervention within the broader financial governance framework. Utilizing this policy as a quasi-natural experiment, we employ a difference-in-differences (DID) model integrated with spatial econometric modeling to evaluate its impact on regional financial system risk—an emergent property of the system. The benchmark regression results indicate that this systemic policy innovation significantly enhances regional financial resilience, with effects that are both continuous and robust. Mechanism tests, analyzed through the lens of subsystem coordination, demonstrate that the policy curbs systemic risk by improving the synergy within economic inner cycles, outer cycles, and their dual-cycle integration, thereby optimizing the system’s internal structure and feedback loops. Further analysis reveals a significant negative spatial spillover effect, evidencing the policy’s role in reshaping inter-regional systemic linkages: it reduces financial risk in both implementing and neighboring regions, with the effect’s intensity following an inverted U-shaped pattern relative to distance. Heterogeneity analysis shows that the policy’s inhibitory effect varies significantly across different systemic configurations, including risk circulation patterns, macro–micro risk perspectives, financial inclusion coverage, government–market relationships, and the north–south regional divide. These findings provide critical insights for developing synergistic macro-prudential and micro-behavioral regulatory mechanisms, contributing to a more robust and adaptive financial security framework from a systems governance perspective. Full article

Technological advances are increasingly influencing how the public sector makes decisions according to citizens’ needs and the community’s problems. The need for a solution that facilitates the fast adaptation of administration to social context and people’s feedback becomes mandatory in order to ensure better services and implement projects that are in concordance with needs. In this paper, we propose a sandbox solution that helps public administration better understand community problems in real time, allocate public money more effectively to projects that really matter, and assess the administration’s performance. We started by collecting, filtering and analyzing social platforms posts and comments for 95 municipalities, and we extracted both the impressions/sentiment, but also the real problems that the communities are facing. Also, we categorized all cities depending on population, geographical area, and historical area to better identify common problems and create clusters of topics based on this split. We identified the most common issues communities face and integrated all the information into a sandbox that can be easily used by local administration for reactive decision-making and by central administration to provide a better overview of how public money is spent and whether the decisions align with needs. The results show that there is a real need for a sandbox to bring more clarity to the central and local administration layers and also better connect administrations with the people. Full article

We present WM-Classroom v1.0, a pedagogical multi-species agent-based model (ABM) designed for educational purposes in predator–prey–harvest systems. The model embeds a predator, two prey breeds, and human harvesters on a homogeneous 50 × 50 grid with weekly time steps, implementing random movement, abstract energetics, prey consumption, reproduction, legal harvest with species-specific cut-offs and seasons, optional predator control, and a poaching switch. After basic technical checks (energetic calibration, prey composition, herbivore viability), we explore the consistency of the model under illustrative scenarios including no hunting, single-prey harvest, hunter-density and season-length gradients, predator removal, and poaching. In the no-hunting baseline (n = 100), mean end-of-run abundances were 22 deer, 159 boar, and 45 wolves, with limited extinction events. Deer-only harvest often drove deer to very low end-of-run counts (mean 1–16) with extinctions in 2–7/10 replicates across cut-offs, whereas boar-only harvest showed higher persistence (mean 11–74) and boar extinctions occurred only at the lowest cut-off (3/10). Increasing hunter numbers or season length depressed prey and could indirectly reduce wolves via prey depletion. Legal predator control reduced predators as designed, while poaching had little effect under the implemented rules. Because interaction and prey-choice rules are simplified for transparency, outcomes should be interpreted as conditional on model assumptions. WM-Classroom v1.0 provides a didactic sandbox for courses, professional training, and outreach, with extensions (habitat heterogeneity, age/sex structure, probabilistic diet/kill success, and calibration/validation) outlined for future versions. Full article

This study draws on socio-technical transition theory to examine how multi-actor dynamics among producers, consumers, and the media within an experimental niche—Korea’s regulatory sandbox—shape policy responsiveness and the regulatory speed of governmental responses to emerging technologies, thereby influencing socio-technical transitions. We construct a longitudinal dataset of 2136 sandbox approvals between 2019 and 2025 and 1374 cases in which related legal or administrative adjustments have been completed. Changes in actor couplings before and after sandbox approval are first assessed using Pearson correlation analysis, while temporal lead–lag relationships are identified via vector autoregression (VAR) and Granger causality tests. Building on these dynamic analyses, the study subsequently investigates the determinants of regulatory response speed using ordered logistic regression, incorporating government policy orientation (progressive vs. conservative) as a moderating variable. The results show, first, that the strong producer–consumer coupling observed prior to sandbox approval weakens afterwards, whereas the consumer–media linkage becomes substantially stronger. Second, the time-series analysis of technologies within the regulatory sandbox reveals a typical technology-push pattern and a self-reinforcing feedback loop. Specifically, producer activity initiates the signal sequence, preceding consumer reactions; subsequently, media coverage significantly drives consumer engagement, and the resulting increase in consumer attention, in turn, stimulates further media coverage. Third, in the ordered logit model, media activity accelerates legal and regulatory reform, whereas consumer activity acts as a delaying factor, with producer activity showing no significant direct effect. Finally, government policy orientation systematically moderates the magnitude and direction of these effects. Overall, the study proposes an actor-centered mechanism in which learning generated in the sandbox is externalized through consumer–media channels and translated into regulatory pacing. Based on these findings, we derive practical implications for firms and regulators regarding proactive media engagement, transparent use of evidence, institutionalized channels for consumer input, and robust feedback standards that support sustainable commercialization of emerging technologies. Full article

Levees with double-layered foundations are characterized by a weakly permeable upper layer and a highly permeable sand layer beneath, which makes them susceptible to internal erosion, particularly backward erosion piping (BEP). Therefore, locating BEP channels before the failure of a levee is crucial for ensuring the safety of levee projects. In this study, a novel method is proposed for detecting BEP channels efficiently. This method involves applying the successive linear estimator (SLE) to fuse multipoint measured voltage to characterize the inner levee structure. Therefore, the BEP channels can be recognized from the details of the levee structure. This method is named three-dimensional geostatistical electrical resistivity tomography (3D GERT) in this study. To validate the performance of GERT, a custom-developed indoor sandbox device was used for physical BEP conductivity detection tests, and the results were analyzed via the SLE to assess the accuracy of channel engraving. The tests revealed that the surface sand was initially expelled from the piping exit, followed by the formation of a concentrated piping channel that extended upstream. The erosion depth at the piping exit was observed to be deeper than that of the main channel. This study demonstrated that 3D GERT, when the SLE was used as the inversion algorithm, detected BEP channels and achieved an internal erosion dimension deviation of less than 25.5% and a positional erosion dimension deviation within 16.5%. The accuracy of the SLE in mapping BEP channels improved with the use of a more comprehensive electrode distribution and an increased number of electrodes, thus yielding a more precise representation of the channel scale and pattern. The coefficient of determination (R²) between the acquired data and the simulated data generated by 3D GERT was greater than 0.85, demonstrating the capability of the simulated values to track and reproduce the variation trends observed in the acquired data. Thus, the SLE, when used as the inversion algorithm for 3D GERT, reliably represents BEP channels. Full article

Compact antennas with ultra-wideband operation and stable radiation are essential for portable and airborne ground-penetrating radar (GPR), yet miniaturization in the sub 3 GHz region is strongly constrained by the wavelength-driven aperture requirement and often leads to impedance discontinuity and radiation instability. This paper presents a compact aperture-slot antipodal Vivaldi antenna (AS-AVA) designed under a radiation stability-driven co-design strategy, where the miniaturization features are organized along the energy propagation path from the feed to the flared aperture. The proposed structure combines (i) aperture-slot current-path engineering with controlled meandering to extend the low-frequency edge, (ii) four tilted rectangular slots near the aperture to restrain excessive edge currents and suppress sidelobes, and (iii) back-loaded parasitic patches for coupling-based impedance refinement to eliminate residual mismatch pockets. A fabricated prototype on FR-4 (thickness 1.93 mm) occupies $111.15\times 156.82$ mm² and achieves a measured $S_{11}$ below $-10$ dB from 0.63 to 2.03 GHz (fractional bandwidth 105.26%). The measured realized gain increases from 2.1 to 7.5 dBi across the operating band, with stable far-field radiation patterns; the group delay measured over 0.6–2.1 GHz remains within 4–8 ns, indicating good time-domain fidelity for stepped-frequency continuous-wave (SFCW) operation. Finally, the antenna pair is integrated into an SFCW-GPR testbed and validated in sandbox and outdoor experiments, where buried metallic targets and a subgrade void produce clear B-scan signatures after standard processing. These results confirm that the proposed AS-AVA provides a practical trade-off among miniaturization, broadband matching, and radiation robustness for compact sub 3 GHz GPR platforms. Full article

This study examines how inclusive finance organisations are adapting to the European Union (EU)’s digital–green twin transition and how regulatory design can reinforce this alignment. Drawing on qualitative insights from 26 institutions—including microfinance organisations, small and medium-sized enterprise finance providers and socially oriented fintechs—across the EU and neighbouring countries, the analysis identifies how digitalisation, financial inclusion and environmental sustainability are being integrated into organisational strategies. The findings show that hybrid models, built on partnerships between nationally rooted microfinance institutions and cross-border fintech platforms, enable scalable, high-tech, high-touch ecosystems that align closely with sustainability objectives. The study argues that a coordinated EU-wide regulatory sandbox would advance inclusive, green financial innovation and build resilience across the inclusive finance ecosystem. Full article

Sustainable transport innovation often faces funding gaps, as traditional public and private sources rarely support early-stage or high-risk initiatives. Crowdfunding, enabled by digital transformation, is emerging as a complementary financing mechanism for this sector. This study presents a literature review combined with bibliometric mapping to examine the evolving research landscape on crowdfunding in transport. Three research questions guide the analysis: RQ1—What are the dominant research areas at the intersection of crowdfunding and transport? RQ2—What types of transport projects are financed via crowdfunding? RQ3—What research gaps and future directions emerge for transport innovation financing? Findings reveal three core research areas: (1) Sustainability and finance, (2) Fintech and blockchain, and (3) Management and consumer behavior. We propose a typology of crowdfunded transport projects comprising five categories: (1) Large-scale transport infrastructure, (2) Sustainable local mobility, (3) Innovative start-ups, (4) New business models, and (5) Advanced systems and technologies. This demonstrates crowdfunding’s versatility beyond traditional infrastructure, supporting high-risk innovations critical for decarbonization and technological transformation. The study highlights domain-specific challenges—such as integrating PPP models with digital finance and ensuring investor protection—and emphasizes crowdfunding’s role as an enabler of low-carbon transition aligned with global climate strategies (EU Green Deal, SDGs). Despite its potential, investor safety remains a major concern. Policy implications include sandbox regulation, standardized risk assessment, and operationalizing PPP–crowdfunding hybrids to unlock large-scale and innovative transport projects. Full article

Financial institutions increasingly rely on data-driven decision systems; however, many operational models remain purely predictive, failing to account for confounding biases inherent in observational data. In credit settings characterized by selective treatment assignment, this limitation can lead to erroneous policy assessments and the accumulation of “methodological debt”. To address this issue, we propose an “Estimate → Predict & Evaluate” framework that integrates Double Machine Learning (DML) with practical MLOps strategies. The framework first employs DML to mitigate selection bias and estimate unbiased Conditional Average Treatment Effects (CATEs), which are then distilled into a lightweight Target Model for real-time decision-making. This architecture further supports Off-Policy Evaluation (OPE), creating a “Causal Sandbox” for simulating alternative policies without risky experimentation. We validated the framework using two real-world datasets: a low-confounding marketing dataset and a high-confounding credit risk dataset. While uplift-based segmentation successfully identified responsive customers in the marketing context, our DML-based approach proved indispensable in high-risk credit environments. It explicitly identified “Sleeping Dogs”—customers for whom intervention paradoxically increased delinquency risk—whereas conventional heuristic models failed to detect these adverse dynamics. The distilled model demonstrated superior stability and provided consistent inputs for OPE. These findings suggest that the proposed framework offers a systematic pathway for integrating causal inference into financial decision-making, supporting transparent, evidence-based, and sustainable policy design. Full article

The global maritime industry faces a conflict between economic competition and sustainability standards. Economic pressure often incentivizes ship registries toward regulatory leniency, degrading environmental and social standards. Traditional static models often overlook how current flag choices impact future inspection risks and financing costs. To address this, we propose a Deep Reinforcement Learning framework that models flag selection as a sequential decision problem. Using a Markov Decision Process, we integrate economic, environmental, and social rewards. We analyze Port State Control records, AIS data, and 27 policy factors to quantify policy effectiveness within the simulation environment. The results show significant heterogeneity in policy performance. Reducing corporate income tax yielded the highest reward improvement (+131.37, p < 0.001). This suggests that, within the model, economic viability serves as a foundation for environmental investments. Enhanced safety standards also generate significant value (+58.35, p < 0.001) by reducing accident penalties and improving reputation metrics. Conversely, increasing tonnage taxes incentivizes the agent toward registries with lax oversight (−87.61, p < 0.001). These findings demonstrate that economic competitiveness and sustainability are mutually reinforcing. This framework provides maritime administrations with a “policy sandbox” for evidence-based decision-making, enabling a transition to sustainability without sacrificing competitiveness. Full article