Search Results (501)

Crowdsourcing contest platforms provide enterprises with opportunities to seek external resources at a lower cost. Increasing the participation of solvers is the key to improving the success of crowdsourcing contests. Many previous studies attempt to use participation intention as a proxy for exploring participation behavior, using surveys to examine participation intention or continued participation intention. However, participation intention and participation behavior differ significantly, and the dynamic nature of influencing factors has a more complex effect on solvers’ participation. Therefore, based on social exchange theory, we use the dynamic data of winvk.com to construct a two-stage model of view and submission. The effect of dynamic competition on participation intention and participation behavior is explored. The results show that external competition has a consistent negative effect on both participation intention and participation behavior. However, the effect of internal competition is different. It has no significant effect on participation intention, but has a significant positive effect on participation behavior. In addition, rewards exacerbate the effect of competition on participation behavior. These findings provide empirical evidence for exploring differences in participation intention and behavior, and offer practical suggestions for enterprises and platforms to improve solvers’ participation in a short time. Full article

There is diminished transparency, fragmented information exchange, and lack of trust among geographically dispersed stakeholders, which increasingly challenge global supply chains. The classic centralized systems of supply chain management are not always capable of being able to offer real-time traceability and data integrity which is dependable and effective in contract enforcement. The proposed study is a blockchain-based smart contract design that is focused on ensuring increased transparency, traceability and trust in global supply chain management. The suggested framework will combine automated smart contracts, cryptographic provenance tracking, permissioned blockchain consensus, and a decentralized trust score evaluation mechanism to overcome some of the major operation and governance challenges. A simulated assessment with a multi-tier global supply chain setting of 15 blockchain nodes and 12,000 transactions was performed through experimentation. The findings show that the proposed system attained an average transaction delay of 210 ms, which is very low compared to centralized systems (520 ms), with throughput being raised to 120 transactions per minute. End-to-end traceability performance also improved significantly, with a reduction in trace-back time to 8 s compared with 95s this represents a 100% tampering detection rate. The consensus mechanism ensured that the ledger integrity failed only at a rate of less than 1.1%, even when more than 30% of nodes were faulty. Risk-wise, the trust evaluation algorithm dynamically enhanced reliable supplier scores up to 12%, which facilitated the selection of reliable partners. On the whole, the results prove that smart contracts based on blockchains can drastically enhance the efficiency of operations, data integrity, and confidence in global supply chains, with the platform capable of providing a resilient and scalable backbone for the future supply chain management model. Full article

With the rapid advancement in information technology, the digital twin and smart assembly process simulation have become an integral part of the design and manufacturing of high-precision products. However, conventional Tunnel Boring Machine (TBM) cutterhead design and on-site assembly planning remain largely experience-driven and fragmented, with limited interoperability between geological characterization, structural verification, and constructability validation. This study proposes a digital twin-driven framework for TBM cutterhead design optimization and assembly process simulation that integrates geology-aware design inputs, BIM-based information modelling, FEM-based structural assessment, and immersive virtual environments within a unified virtual–physical workflow. To ensure consistent data exchange across platforms, an IFC4.3-compliant ontology is established using a non-intrusive property-set (Pset) extension strategy to represent cutterhead components, geological parameters, FEM load cases/results, and assembly tasks. Tunnel-scale stress analysis and cutter–rock interaction modelling are used to define project-representative cutter loading envelopes, which are mapped to a high-fidelity cutterhead FEM model for iterative structural refinement. The optimized configuration is then transferred to a game-engine/VR environment to support full-scale design inspection and assembly rehearsal, followed by manufacturing and field deployment with bidirectional feedback. To validate the proposed framework, an implementation case study of a deep hard-rock tunnelling project is presented where five design iterations were tracked across BIM–FEM–VR and nine constructability issues detected and resolved prior to assembly. The results indicate that the proposed digital twin approach strengthens traceability from geology to loading to structural response, reduces localized stress concentration at critical interfaces, and improves assembly readiness for complex tunnelling projects. Full article

Artificial intelligence (AI) has the potential to transform healthcare by supporting more accurate diagnoses and personalized treatments. However, its adoption in practice remains constrained by fragmented data sources, strict privacy rules, and the technical complexity of building reliable clinical systems. To address these challenges, we introduce a model-driven engineering (MDE) framework designed specifically for healthcare AI. The framework relies on formal metamodels, domain-specific languages (DSLs), and automated transformations to move from high-level specifications to running software. At its core is the Medical Interoperability Language (MILA), a graphical DSL that enables clinicians and data scientists to define queries and machine learning pipelines using shared ontologies. When combined with a federated learning architecture, MILA allows institutions to collaborate without exchanging raw patient data, ensuring semantic consistency across sites while preserving privacy. We evaluate this approach in a multi-center cancer immunotherapy study. The generated pipelines delivered strong predictive performance, with best-performing models achieving up to 98.5% accuracy on selected prediction tasks, while substantially reducing manual coding effort. These findings suggest that MDE principles—metamodeling, semantic integration, and automated code generation—can provide a practical path toward interoperable, reproducible, and reliable digital health platforms. Full article

Cities worldwide face profound morphological changes due to population growth and urban densification. Coupled with climate change, this exacerbates the Urban Heat Island (UHI) effect and degrades outdoor thermal comfort. This paper introduces a novel simulation framework for climate-resilient urban design, transitioning from static planning standards to dynamic performance optimization. This research utilizes a multi-tiered data acquisition strategy, beginning with a PRISMA-guided Systematic Literature Review of 133 articles to identify key UHI mitigation variables. A high-fidelity, multi-physics Computational Fluid Dynamics (CFD) model was developed using the ANSYS Fluent solver, discretized with a poly-hexacore mesh of over 78 million cells. The simulation environment integrates multiscale data, including 2.5D urban geometry from GIS platforms, high-resolution satellite information (e.g., Copernicus and LiDAR) for surface and soil properties, and EUMETSAT weather files for boundary conditions. The model explicitly resolves aerodynamic and thermodynamic exchanges using Unsteady Reynolds-Averaged Navier–Stokes (URANS) equations, with vegetation represented via porous-medium parameterization. The core novelty lies in the development of a parameterized library of “Architectural Elements” (AEs) that introduces standardized material properties, derived from Ansys Granta Selector, directly with GIS-based street designs. This allows for iterative “what-if” scenario analyses over critical 24 h periods to assess the synergistic impact of green infrastructure (GI) and advanced materials. Validation against real-world monitoring data from the Grow-Green project confirmed the model’s accuracy, with a maximum error of only 0.22%. The results demonstrate that interconnecting isolated green areas and utilizing local porous materials can reduce UHI spot temperatures by 2–4 °C while significantly lowering building energy consumption. Full article

Background: Cutaneous squamous cell carcinoma (cSCC) represents one of the most common keratinocyte-derived malignancies encountered in clinical practice; however, its genomic landscape remains far less comprehensively characterized than that of other cutaneous cancers. This study aims to identify key molecular drivers and potential therapeutic targets by comprehensively characterizing the genomic landscape of cSCC using data from the American Association for Cancer Research (AACR) Project Genomics, Evidence, Neoplasia, Information, Exchange (GENIE) consortium. Methods: A retrospective cohort analysis of cSCC samples was performed utilizing AACR Project GENIE data accessed via the cBioPortal platform (v18.0-public) on 22 November 2025. Analyses included identification of recurrent somatic and copy-number alterations, pairwise gene–gene co-occurrence testing using Fisher’s exact tests with Benjamini–Hochberg False Discovery Rate (FDR) correction, and exploratory subgroup comparisons by sex and race, with statistical significance defined as p < 0.05. Results: Recurrent mutations were identified in TP53 (83.5%), NOTCH1 (56.3%), KMT2D (47.0%), CDKN2A (44.4%), TERT (41.4%), ROS1 (34.3%), FAT1 (33.3%), NOTCH2 (31.2%), ERBB4 (28.4%), and KMT2A (24.3%), reflecting disruption of the p53 pathway, cell-cycle control, Notch signaling, epigenetic regulation, telomere maintenance, RTK/MAPK pathways, and Wnt signaling. Statistically significant co-occurrence patterns were observed, and exploratory subgroup analyses evaluated mutation frequency differences by sex and race. Conclusions: This large, multi-institutional genomic analysis defines recurrent mutational and structural alterations in cSCC and highlights an integrated pattern of pathway disruption involving genomic integrity, differentiation, epigenetic control, and proliferative signaling. These findings enhance current understandings of the molecular architecture underlying this common yet genomically understudied malignancy and provide a foundation for future mechanistic studies and development of targeted diagnostic and therapeutic strategies. Full article

A digital twin (DT) is an automation strategy that integrates a physical plant with an adaptive, real-time simulation environment, with bidirectional communication between them. In process engineering, DTs promise real-time monitoring, prediction of future conditions, predictive maintenance, process optimization, and control. Dashboards for process monitoring are becoming increasingly relevant for tracking key metrics and supervising industrial units in real time. Supervisory Control and Data Acquisition (SCADA) systems are widely used for process automation, with ScadaBR, an open-source, freely licensed platform. This work presents the development of a computational tool that integrates the Aspen HYSYS/Python with the ScadaBR system for real-time monitoring and supervision of dynamic models. The virtual plant, which replicates the system’s physical behavior, was connected to the SCADA platform via the Modbus protocol, enabling bidirectional data exchange between the simulated model and the supervisory interface. The system supports operational analysis and control strategy validation. Two case studies were analyzed: (i) a simplified catalytic hydrocracking process, implemented in the Python environment, and (ii) a heat exchanger networks process, simulated using the HYSYS simulator. In the second case, the process was dynamically simulated, with real-time monitoring of a simple dynamic indicator that correlates the feed methane concentration with heat transfer fluids. The results demonstrate the feasibility and applicability of the proposed approach for educational purposes, operator training, and process engineering validation, fostering a more realistic and interactive simulation environment. Furthermore, the results show that the tool is promising for dynamic monitoring of environmental and energy indices, demonstrating that methane consumption relative to process feed can be evaluated and controlled over time. Full article

The COVID-19 outbreak significantly impacted global health, resulting in widespread infections and fatalities. In response, the World Health Organization (WHO) launched the Preparedness and Resilience for Emerging Threats (PRET) program, focusing on respiratory pathogens, to prepare for future pandemics and achieve sustainable well-being around the world. Moreover, the WHO also encouraged countries to establish a National Action Plan for Health Security (NAPHS) to address various disasters and threats. Therefore, governments should develop the necessary systems aligned with NAPHS while maintaining compliance with PRET. Thus, to maintain global health and wellness, a platform for sharing medical data on highly contagious diseases worldwide, free from interference from individual countries, is critical for effective border control. In this paper, we introduce an efficient approach, i.e., a two-layer decentralized blockchain architecture. Our mechanism streamlines integration between national sovereignty and the WHO, enabling the secure and supervised exchange of pandemic data via a single global blockchain and multiple local blockchains. This blockchain framework has strong potential to enhance nations’ ability to prepare for and respond to future pandemics, ensuring the safety and health of their citizens. The bottom-layer local chains facilitate the collection of pandemic immunity data within their own countries, and the upper-layer global chain, with the assistance of the WHO, facilitates the worldwide exchange of these data if needed. Given the characteristics of decentralization and transparency in blockchain technology, and the WHO’s oversight, no government should worry that sensitive medical data will be manipulated or abused by superpowers on the global blockchain. It also highlights the importance of global collaboration in preventing and controlling contagious diseases. Furthermore, the performance of this system is assessed based on several sets of real-world data and requirements. We demonstrate that this global information-sharing mechanism can improve international health safety, provided it is supported by feasible and reasonable resources to handle transactional demands around the world. Full article

Phacoemulsification is performed within a highly dynamic intraocular environment, in which fluid exchange, pressure regulation, and tissue biomechanics interact continuously. Although modern cataract surgery is considered safe and efficient, disruption of this delicate intraoperative microenvironment remains a major source of complications. Among fluidics-related events, post-occlusion break surge represents one of the most critical destabilizing factors of the anterior chamber. A surge occurs when the sudden release of an occluded aspiration port generates an abrupt pressure–volume imbalance that cannot be immediately compensated by infusion, leading to a transient collapse of the intraocular environment. This narrative review integrates current experimental and clinical evidence on the pathophysiology, quantification, and technological control of surge, framing it as a model of environmentally driven intraoperative stress. The evolution of phacoemulsification fluidics, from gravity-based systems to active, adaptive, and predictive platforms, is analyzed in relation to their ability to preserve a stable and physiologic intraocular environment. Comparative data from contemporary devices are reviewed, highlighting differences in surge volume, recovery time, and pressure restitution. Special emphasis is placed on the impact of surge on the microenvironments of both the anterior and posterior segments, including endothelial stress, capsular instability, vitreoretinal traction, and macular perfusion. Emerging strategies such as handpiece-integrated pressure sensors, predictive fluidics algorithms, intraoperative imaging, and artificial intelligence are reshaping environmental control during surgery. Despite substantial technological progress, the complete elimination of surge remains an unmet need. Continued innovation, standardized biomechanical models, and robust clinical validation will be essential to further protect the intraoperative intraocular environment and improve long-term visual outcomes. Full article

With rapid advances in nanotechnology and synthetic biology, biological nanonetworks are emerging for biomedical and environmental applications within the Internet of Bio-NanoThings. While they rely on molecular communication, experimental validation remains limited, especially for non-ideal effects such as molecular accumulation. In this work, we present a novel table-top experimental system that emulates the core functionalities of a biological nanonetwork and is straightforward to reproduce in standard laboratory environments, also making it suitable for educational demonstrations. To the best of our knowledge, this is the first experimental platform that incorporates two end nodes capable of acting interchangeably as transmitter and receiver, thereby enabling true bidirectional molecular communication. Information transfer is realized through controlled release, advection and diffusion of molecules, using molecular concentration coding analogous to concentration shift keying, while the receiver decodes messages by comparing measured concentrations against predefined thresholds. Based on the measurements reported herein, the drop-based algorithm substantially outperforms the threshold-based scheme. Specifically, it reduces first-message latency by more than 2.5× across the tested volumes and reduces latest-message latency by up to 71%, providing approximately 3.7× better message delivery. A key experimental outcome is the observation of channel saturation: beyond a certain operating period, residual molecules accumulate and effectively saturate the medium, inhibiting reliable further message exchange until sufficient clearance occurs. This saturation-induced “channel memory” emerges as a fundamental practical constraint on sustained communication and achievable data rates. Overall, the proposed platform provides a scalable, controllable, and experimentally accessible testbed for systematically studying signal degradation, saturation, clearance dynamics, and throughput limits, thereby bridging the gap between theoretical models and practical implementations in the Internet of Bio-NanoThings era. Full article

Enhanced Geothermal Systems (EGS) extend geothermal energy beyond conventional hydrothermal resources but face challenges in creating sustainable heat exchangers in low-permeability formations. This review synthesizes achievements from the Utah Frontier Observatory for Research in Geothermal Energy (FORGE), a field laboratory advancing EGS readiness in 175–230 °C granitic basement. From 2017 to 2025, drilling, multi-stage hydraulic stimulation, and monitoring established feasibility and operating parameters for engineered reservoirs. Hydraulic connectivity was created between highly deviated wells with ~300 ft vertical separation via hydraulic and natural fracture networks, validated by sustained circulation tests achieving 10 bpm injection at 2–3 km depth. Advanced monitoring (DAS, DTS, and microseismic arrays) delivered fracture propagation diagnostics with ~1 m spatial resolution and temporal sampling up to 10 kHz. A data infrastructure of 300+ datasets (>133 TB) supports reproducible ML. Geomechanical analyses showed minimum horizontal stress gradients of 0.74–0.78 psi/ft and N–S to NNE–SSW fractures aligned with maximum horizontal stress. Near-wellbore tortuosity, driving treating pressures to 10,000 psi, underscores completion design optimization, improved proppant transport in high-temperature conditions, and coupled thermos-hydro-mechanical models for long-term prediction, supported by AI platforms including an offline Small Language Model trained on Utah FORGE datasets. Full article

The Transport Layer Security (TLS) protocol is widely used nowadays to create secure communications over TCP/IP networks. Its purpose is to ensure confidentiality, authentication, and data integrity for messages exchanged between two endpoints. In order to facilitate its integration into widely used applications, the protocol is typically implemented through libraries, such as OpenSSL, BoringSSL, LibreSSL, WolfSSL, NSS, or mbedTLS. These libraries encompass functions that execute the specialized TLS handshake required for channel establishment, as well as the construction and processing of TLS records, and the procedures for closing the secure channel. However, these software libraries may contain vulnerabilities or errors that could potentially jeopardize the security of the TLS channel. To identify flaws or deviations from established standards within the implemented TLS code, a specialized tool known as TLS-Anvil can be utilized. This tool also verifies the compliance of TLS libraries with the specifications outlined in the Request for Comments documents published by the IETF. TLS-Anvil conducts numerous tests with a client/server configuration utilizing a specified TLS library and subsequently generates a report that details the number of successful tests. In this work, we exploit the results obtained from a selected subset of TLS-Anvil tests to generate rules used for anomaly detection in Suricata, a well-known signature-based Intrusion Detection System. During the tests, TLS-Anvil generates .pcap capture files that report all the messages exchanged. Such files can be subsequently analyzed with Wireshark, allowing for a detailed examination of the messages exchanged during the tests and a thorough understanding of their structure on a byte-by-byte basis. Through the analysis of the TLS handshake messages produced during testing, we develop customized Suricata rules aimed at detecting TLS anomalies that result from flawed implementations within the intercepted traffic. Furthermore, we describe the specific test environment established for the purpose of deriving and validating certain Suricata rules intended to identify anomalies in nodes utilizing a version of the OpenSSL library that does not conform to the TLS specification. The rules that delineate TLS deviations or potential attacks may subsequently be integrated into a threat detection platform supporting Suricata. This integration will enhance the capability to identify TLS anomalies arising from code that fails to adhere to the established specifications. Full article

In the context of tightening sustainability regulations and rising demands for transparent and responsible capital allocation, understanding how digital financial innovations influence market efficiency has become increasingly important. This study examines the impact of Financial Technology (FinTech) solutions and crowdfunding platforms on sustainable market efficiency, volatility dynamics, and risk structures in the United Kingdom. Using weekly data for the Financial Times Stock Exchange 100 (FTSE 100) index from January 2010 to June 2025, the analysis applies the Lo–MacKinlay variance ratio test to assess compliance with the Random Walk Hypothesis as a proxy for informational efficiency. Firm-level proxies for FinTech and crowdfunding activity are constructed using the Nomenclature of Economic Activities (NACE) and Standard Industrial Classification (SIC) systems. The empirical results indicate substantial deviations from random-walk behavior in crowdfunding-related market segments, where persistent positive autocorrelation and elevated volatility reflect liquidity constraints and informational frictions. By contrast, FinTech-dominated segments display milder inefficiencies and faster information absorption, pointing to more stable price-adjustment mechanisms. After controlling for structural distortions through heteroskedasticity-consistent corrections and volatility adjustments, variance ratios converge toward unity, suggesting a restoration of informational efficiency. The results provide relevant insights for investors, regulators, and policymakers seeking to align financial innovation with the objectives of sustainable financial systems. Full article

Reconfigurable mode switches can provide more flexible and advanced data exchange functions for complex on-chip optical networks. A reconfigurable mode-selective optical switch based on adiabatic progressive three-waveguide coupling (TWC) is proposed. As a proof of concept, the switching of E₀₀ (E₂₀)/E₁₀ (E₃₀) dual-mode channels was successfully implemented and demonstrated. At 1550 nm, the insertion losses for E₀₀/E₁₀ and E₂₀/E₃₀ mode switches were lower than 7.86 and 10.76 dB, respectively. These values include the loss of the mode demultiplexer. The crosstalk was lower than −22.84 (−18.28) dB at 1550 nm. The switching rise time (10–90%) and fall time (10–90%) were 0.86 ms and 0.64 ms, respectively. On the silica platform, the scalability of the structural scheme was also verified, and the arbitrary selection and switching of the E₀₀, E₁₀, E₂₀, and E₃₀ modes were achieved via the cascading of TWCs. The device can be used as an important component for the future large-scale integration and flexible switching of on-chip optical networks. Full article

Compliant cross-border data flows face persistent challenges from fragmented regulatory regimes, inconsistent enforcement, and limited trust among stakeholders. Current approaches typically rely on centralized oversight or excessive data disclosure, both compromising regulatory interoperability and operational security. This paper introduces a trust-oriented blockchain architecture that enables secure cross-border data exchange while ensuring verifiable compliance without revealing sensitive content. The architecture decouples policy enforcement, privacy-preserving validation, and cross-jurisdiction auditability, enabling entities to share cryptographically verifiable compliance proofs rather than raw data. To capture the behavioral dynamics across heterogeneous regulatory environments, we incorporate a strategic interaction layer that models how domestic firms, foreign enterprises, and cross-border data platforms adjust decisions under varying incentive structures. These insights guide the design of an adaptive compliance verification pipeline that maintains trust equilibrium across participants. Our design records only cryptographic digests and structured compliance evidence on-chain, while off-chain components execute privacy-preserving checks using secure computation and decentralized storage. Through a case-driven evaluation, we show that the proposed architecture reduces governance friction, enhances institutional trust, and achieves interoperable compliance validation with minimal disclosure overhead. Through component-level evaluation and architectural analysis, this work establishes a technical foundation for secure, transparent, and regulation-aligned cross-border data governance. The framework provides a blueprint for future multi-party pilot deployments in operational environments. Full article