Search Results (151)

The piston pin operates under severe mechanical and thermal conditions, making accurate lubrication prediction essential for engine durability. This study presents a comprehensive digital engineering framework for piston pin bearings, built upon a fully coupled thermo-elasto-hydrodynamic (TEHD) formulation. The framework integrates: (1) a Reynolds-equation hydrodynamic solver with temperature-/pressure-dependent viscosity and cavitation; (2) elastic deformation obtained from FEA (finite element analysis)-based compliance matrices; (3) a break-in module that iteratively adjusts surface profiles before steady-state simulation; (4) a three-body heat transfer model resolving heat conduction, convection, and solid–liquid interfacial heat exchange. Applied to a heavy-duty diesel engine, the framework reproduces experimentally observed behaviors, including bottom-edge rounding at the small end and the slow unidirectional drift of the floating pin. By integrating multi-physics modeling with design-level flexibility, this work aims to provide a robust digital twin for the piston-pin system, enabling virtual diagnostics, early-stage failure prediction, and data-driven design optimization for engine development. Full article

The financial services sector is experiencing unprecedented transformation through the adoption of virtualization technologies, encompassing cloud computing and edge computing digitalization initiatives that fundamentally alter operational paradigms and competitive dynamics within the industry. This systematic literature review employed a comprehensive methodology, analyzing peer-reviewed articles, systematic reviews, and industry reports published between 2016 and 2025 across three primary technological domains, utilizing thematic content analysis to synthesize findings and identify key implementation patterns, performance outcomes, and emerging challenges. The analysis reveals consistent evidence of positive long-term performance outcomes from virtualization technology adoption, including average transaction processing time reductions of 69% through edge computing implementations, substantial operational cost savings and efficiency improvements through cloud computing adoption, while simultaneously identifying critical challenges related to regulatory compliance, security management, and organizational transformation requirements. Virtualization technology offers transformative potential for financial services through improved operational efficiency, enhanced customer experience, and competitive advantage creation, though successful implementation requires sophisticated approaches to standardization, regulatory compliance, and change management, with future research needed to develop integrative frameworks addressing technology convergence and emerging applications in decentralized finance and digital currency systems. Full article

The development of KRAS G12D inhibitors represents an effective therapeutic strategy for treating oncological pathologies. Existing quantitative structure-activity relationship (QSAR) models for KRAS G12D inhibitors have several limitations, primarily the lack of applicability domain determination and virtual screening implementation. In this study, we propose a set of regression QSAR models for KRAS G12D inhibitors by employing various molecular descriptors and machine learning methods. Our consensus model achieved a Q² test value of 0.70 on an external test set, covering 78% of the data within the applicability domain. We integrated this consensus model into our Python-based framework KRASAVA. The platform predicts inhibitory activity while considering the applicability domain, assesses compounds for compliance with Muegge’s bioavailability rules, and identifies PAINS, toxicophores, and Brenk filters. Furthermore, we structurally interpreted the QSAR models to propose several promising inhibitors and performed molecular docking on these candidates using GNINA. For the reference inhibitor MRTX1133, we reproduced the crystal structure pose with an RMSD of 0.76 Å (PDB ID: 7T47). The key interactions with amino acid residues Asp12, Asp69, His95, Arg68, and Gly60, identified for both MRTX1133 and our proposed compounds, demonstrate a strong consistency between the molecular docking and QSAR results. Full article

Grid-forming power converters play a foundational role in modern microgrids and inverter-dominated distribution systems by establishing voltage and frequency references during islanded or low-inertia operation. However, when subjected to nonlinear or impulsive impact-type loads, these converters often suffer from severe harmonic distortion and transient current overshoot, leading to waveform degradation and protection-triggered failures. While virtual impedance control has been widely adopted to mitigate these issues, conventional implementations rely on fixed or rule-based tuning heuristics that lack adaptivity and robustness under dynamic, uncertain conditions. This paper proposes a novel reinforcement learning-based framework for real-time virtual impedance scheduling in grid-forming converters, enabling simultaneous optimization of harmonic suppression and impact load resilience. The core of the methodology is a Soft Actor-Critic (SAC) agent that continuously adjusts the converter’s virtual impedance tensor—comprising dynamically tunable resistive, inductive, and capacitive elements—based on real-time observations of voltage harmonics, current derivatives, and historical impedance states. A physics-informed simulation environment is constructed, including nonlinear load models with dominant low-order harmonics and stochastic impact events emulating asynchronous motor startups. The system dynamics are modeled through a high-order nonlinear framework with embedded constraints on impedance smoothness, stability margins, and THD compliance. Extensive training and evaluation demonstrate that the learned impedance policy effectively reduces output voltage total harmonic distortion from over 8% to below 3.5%, while simultaneously limiting current overshoot during impact events by more than 60% compared to baseline methods. The learned controller adapts continuously without requiring explicit load classification or mode switching, and achieves strong generalization across unseen operating conditions. Pareto analysis further reveals the multi-objective trade-offs learned by the agent between waveform quality and transient mitigation. Full article

A Virtual Organization (VO) unites companies or independent individuals to achieve a shared, short-term objective by leveraging information technologies for communication and coordination in personalized product creation. Despite extensive research, existing VO management architectures lack alignment with Industry 4.0 standards, do not incorporate intelligent requirement-gathering mechanisms, and are not based on the RAMI 4.0 framework. These limitations hinder support for Autonomous Virtual Organizations (AVOs) in evaluation, risk management, and continuity, often excluding small and medium-sized enterprises (SMEs) during the partner selection process. This study proposes a comprehensive architecture for AVO management, grounded in ACODAT (Autonomous Cycle of Data Analysis Tasks) and RAMI 4.0 principles. The methodology includes a literature review, an architectural design, and a detailed specification of the ACODAT for the digital supply chain design. A prototype was developed and applied in a case study involving a virtual organization within an editorial consortium. Evaluation addressed core service performance, scalability of the batch selection algorithm, resource-use efficiency, and accessibility/SEO compliance. Benchmarking demonstrated that the prototype met or exceeded thresholds for scalability, efficiency, and accessibility, with minor performance deviations attributed to the testing environment. The results highlight significant time savings and improved automation in requirement identification, partner selection, and supply chain configuration, underscoring the architecture’s effectiveness and inclusivity. Full article

In smart home projects, building data management at the design stage increasingly relies on digital-twin assets delivered via game engines. Without a clear governance workflow, however, these practices tend to generate non-standard building data on the consumption side, causing broken data chains and increasing construction and management risks. To address this problem, this study proposes a traceability-oriented governance workflow that strengthens IfcGUID compliance and automatically detects and converts inconsistent digital-twin assets into IFC-compliant, auditable data, thereby reducing data chain breakage and improving cross-system traceability in building data management. The workflow uses IfcGUID as a cross-system primary key and is evaluated in a virtual smart home project through a pre-test–repair–post-test experiment at the design stage. We examine four indicators of IfcGUID quality—completeness, validity, uniqueness, and stability—together with a bridge recognition rate that reflects game engine interoperability on the consumption side. The results show that all four IfcGUID indicators converge towards 1 after applying the workflow, and the bridge recognition rate approaches 100%, indicating that the risk of data chain breakage, measured on an IFC basis, is substantially reduced. Within existing toolchains, this workflow provides design teams, visualization teams, clients, and auditors with a low-cost and reproducible path for standardizing design-stage digital-twin assets and establishing a traceable, auditable baseline for cross-system interoperability and lifecycle building data management and data reuse. Full article

The construction industry remains among the most hazardous sectors globally, facing persistent safety challenges despite advancements in occupational health and safety OHS) measures. The objective of this study is to systematically analyze the use of Artificial Intelligence (AI) in construction safety management and to identify the most effective techniques, data modalities, and validation practices. The method involved a systematic review of 122 peer-reviewed studies published between 2016 and 2025 and retrieved from major academic databases. The selected studies were classified by AI technologies including Machine Learning (ML), Deep Learning (DL), Computer Vision (CV), Natural Language Processing (NLP), and the Internet of Things (IoT), and by their applications in real-time hazard detection, predictive analytics, and automated compliance monitoring. The results show that DL and CV models, particularly Convolutional Neural Network (CNN) and You Only Look Once (YOLO)-based frameworks, are the most frequently implemented for personal protective equipment recognition and proximity monitoring, while ML approaches such as Support Vector Machines (SVM) and ensemble algorithms perform effectively on structured and sensor-based data. Major challenges identified include data quality, generalizability, interpretability, privacy, and integration with existing workflows. The paper concludes that explainable, scalable, and user-centric AI integrated with Building Information Modeling (BIM), Augmented Reality (AR) or Virtual Reality (VR), and wearable technologies is essential to enhance safety performance and achieve sustainable digital transformation in construction environments. Full article

Construction quality failures impose substantial costs on the industry, with traditional quality assurance (QA) methods operating reactively by detecting problems after they occur rather than preventing them during planning and design phases. Limited research exists on the systematic integration of immersive technologies (IMTs) for proactive quality failure prevention across construction project lifecycles. This study investigates how IMTs can systematically prevent specific quality failure categories through enhanced spatial visualization and virtual verification processes. A qualitative approach was employed, combining scoping literature review, two purposively selected case studies, and autoethnographic analysis to capture both performance metrics and implementation insights. Case Study 1 achieved 8% improvement in solar panel placement efficiency (optimizing from 82 to 90 modules) and 1.7% increase in useful energy production (85.8% vs. 84.1%) through BIM-Unreal Engine integration for shadow analysis and spatial optimization. Case Study 2 demonstrated virtual site mobilization using the Revit–Twinmotion workflow, eliminating spatial conflicts and safety clearance violations during pre-construction planning. Findings revealed that IMT applications systematically address quality failure root causes by preventing design coordination errors, measurement mistakes, and regulatory non-compliance through virtual verification before physical implementation. This paper establishes IMTs as transformative QA platforms that fundamentally shift construction quality management from reactive detection to proactive prevention, offering measurable improvements in project delivery efficiency and quality outcomes. Full article

This study presents an analytical investigation of the thermomechanical stability of hyperbolic doubly curved shells reinforced with graphene origami auxetic metamaterials (GOAMs) and resting on a Pasternak elastic foundation. The proposed model integrates shell geometry, thermal–mechanical loading, and architected auxetic reinforcement to capture their coupled influence on buckling behavior. Stability equations are derived using the First-Order Shear Deformation Theory (FSDT) and the principle of virtual work, while the effective thermoelastic properties of the GOAM phase are obtained through micromechanical homogenization as functions of folding angle, mass fraction, and spatial distribution. Closed-form eigenvalue solutions are achieved with Navier’s method for simply supported boundaries. The results reveal that GOAM reinforcement enhances the critical buckling load at low folding angles, whereas higher folding induces compliance that diminishes stability. The Pasternak shear layer significantly improves buckling resistance up to about 46% with pronounced effects in asymmetrically graded configurations. Compared with conventional composite shells, the proposed GOAM-reinforced shells exhibit tunable, folding-dependent stability responses. These findings highlight the potential of origami-inspired graphene metamaterials for designing lightweight, thermally stable thin-walled structures in aerospace morphing skins and multifunctional mechanical systems. Full article

The increasing deployment of islanded microgrids in disaster-prone and infrastructure-constrained regions has elevated the importance of resilient energy storage systems capable of supporting autonomous operation. Grid-forming energy storage (GFES) units—designed to provide frequency reference, voltage regulation, and black-start capabilities—are emerging as critical assets for maintaining both energy adequacy and dynamic stability in isolated environments. However, conventional storage planning models fail to capture the interplay between uncertain renewable generation, time-coupled operational constraints, and control-oriented performance metrics such as virtual inertia and voltage ride-through. To address this gap, this paper proposes a novel distributionally robust optimization (DRO) framework that jointly optimizes the siting and sizing of GFES under renewable and load uncertainty. The model is grounded in Wasserstein-metric DRO, allowing worst-case expectation minimization over an ambiguity set constructed from empirical historical data. A multi-period convex formulation is developed that incorporates energy balance, degradation cost, state-of-charge dynamics, black-start reserve margins, and stability-aware constraints. Frequency sensitivity and voltage compliance metrics are explicitly embedded into the optimization, enabling control-aware dispatch and resilience-informed placement of storage assets. A tractable reformulation is achieved using strong duality and solved via a nested column-and-constraint generation algorithm. The framework is validated on a modified IEEE 33-bus distribution network with high PV penetration and heterogeneous demand profiles. Case study results demonstrate that the proposed model reduces worst-case blackout duration by 17.4%, improves voltage recovery speed by 12.9%, and achieves 22.3% higher SoC utilization efficiency compared to deterministic and stochastic baselines. Furthermore, sensitivity analyses reveal that GFES deployment naturally concentrates at nodes with high dynamic control leverage, confirming the effectiveness of the control-informed robust design. This work provides a scalable, data-driven planning tool for resilient microgrid development in the face of deep temporal and structural uncertainty. Full article

The rapid global expansion of photovoltaic (PV) generation has increased the prevalence of PV-dominated weak-grid systems, where wideband oscillations (WBOs) pose a significant challenge to secure and reliable operation. Unlike conventional electromechanical oscillations, WBOs originate from inverter control loops and multi-inverter interactions, spanning sub-Hz to kHz ranges. This review provides a PV-focused and mitigation-oriented analysis that addresses this gap. First, it clarifies the mechanisms of WBOs by mapping oscillatory drivers such as phase-locked loop dynamics, constant power control, converter–grid impedance resonance, and high-frequency switching effects to their corresponding frequency bands, alongside their engineering implications. Second, analysis methods are systematically evaluated, including eigenvalue and impedance-based models, electromagnetic transient simulations, and measurement- and data-driven techniques, with a comparative assessment of their strengths, limitations, and practical applications. Third, mitigation strategies are classified across converter-, plant-, and system-levels, ranging from adaptive control and virtual impedance to coordinated PV-battery energy storage systems (BESS) operation and grid-forming inverters. The review concludes by identifying future directions in grid-forming operation, artificial intelligence (AI)-driven adaptive stability, hybrid PV-BESS-hydrogen integration, and the establishment of standardized compliance frameworks. By integrating mechanisms, methods, and mitigation strategies, this work provides a comprehensive roadmap for addressing oscillatory stability in PV-dominated weak grids. Full article

Driving in snowy conditions challenges both human drivers and autonomous systems. Snowfall and ice accumulation impair vehicle control and affect driver perception and performance. Road markings are often obscured, forcing drivers to rely on intuition and memory to stay in their lane, which can lead to encroachment into adjacent lanes or sidewalks. Current lane detectors assist in lane keeping, but their performance is compromised by visual disturbances such as ice reflection, snowflake movement, fog, and snow cover. Furthermore, testing these systems with users on actual snowy roads involves risks to driver safety, equipment integrity, and ethical compliance. This study presents a low-cost virtual reality simulation for evaluating winter lane detection in controlled and safe conditions from a human-in-the-loop perspective. Participants drove in a simulated snowy scenario with and without the detector while quantitative and qualitative variables were monitored. Results showed a 49.9% reduction in unintentional lane departures with the detector and significantly improved user experience, as measured by the UEQ-S (p = 0.023, Cohen’s d = 0.72). Participants also reported higher perceived safety, situational awareness, and confidence. These findings highlight the potential of vision-based lane detection systems adapted to winter environments and demonstrate the value of immersive simulations for user-centered testing of ADASs. Full article

Telerehabilitation is an effective, accessible addition or alternative to conventional rehabilitation for fracture management, especially in older adults after hip fractures, leveraging video visits, mHealth apps, virtual reality (VR), and wearable sensors to deliver exercise, education, and monitoring at home with high satisfaction and adherence. Across non-surgical and surgical contexts, telemedicine shows feasibility and cost benefits, with mixed superiority but consistent non-inferiority for functional outcomes versus in-person care. In hip fracture populations, randomized and non-randomized studies indicate improvements in functional independence measure (FIM), Timed Up and Go test (TUG), Activities of Daily Living/Instrumental Activities of Daily Living (ADLs/IADLs), and quality of life, with some evidence for reduced anxiety and depression, while effects on mobility, pain, and adverse events remain uncertain overall. In patients with upper-limb fractures, telerehabilitation appears to improve function and pain, though strength gains may lag compared with in-person therapy in some trials; adjuncts like motor imagery and virtual reality may enhance outcomes and motivation. Application is facilitated by user-friendly platforms, caregiver involvement, and simple modalities such as structured phone follow-up. Limitations include small samples, heterogeneous protocols, scarce long-term data, and a predominance of non-inferiority or complementary designs, warranting larger, definitive trials. This technology can lead to improved patient management at home, effortlessly verifying treatment compliance, efficacy, and safety, while simultaneously reducing the need for hospitalization, promoting a more peaceful recovery. Here, we have undertaken a narrative review of the medical–scientific literature in this field. Full article

As power systems decentralize, Virtual Power Plants (VPPs) offer a promising approach to coordinate distributed energy resources (DERs) and enhance grid flexibility. However, real-world validation of VPP performance in Indonesia remains limited, especially regarding internationally aligned test standards. This study presents the design and experimental validation of a cluster-based VPP framework integrated with a centralized VPP Management System (VMS). Each cluster integrates solar photovoltaic (PV) system, battery energy storage system (BESS), and controllable load. A Local Control Unit (LCU) manages cluster operations, while the VMS coordinates power export–import dispatch, cluster-level aggregation, and grid compliance. The framework proposes a scalable VPP architecture and presents the first comprehensive experimental verification of key VPP performance indicators, including response time, adjustment rate, and accuracy, in the Indonesian context. Testing was conducted in alignment with the IEC TS 63189-1:2023 international standard. Results suggest real time responsiveness and indicate that, even at smaller scales, VPPs may contribute effectively to voltage control while exhibiting minimal influence on system frequency in interconnected grids. These findings confirm the capability of the proposed VPP framework to provide reliable real time control, ancillary services, and aggregated energy management. Its cluster-based architecture supports scalability for broader deployment in complex grid environments. Full article

In recent years, corporate perceptions of environmental sustainability have shifted from viewing it as a compliance burden to recognizing it as a strategic driver of innovation and competitive advantage, prompting a demand for effective sustainable design tools. Traditionally, tools like the Life Cycle Assessment (LCA) tool have been used to evaluate environmental impacts, yet their complexity, cost, and retrospective focus make them impractical for driving early-stage, disruptive innovation. Although biomimicry has emerged as a promising approach, adopting this novel interdisciplinary design practice within a corporate setting requires significant resources and time, disrupting established processes. Therefore, the biomimicry Life Principles (LPs) tool, a guiding sustainable design tool of the practice, provides an opportunity to lower the barrier to the entry of biomimicry within a corporate setting and potentially increases adoption of the broader practice. This comparative study seeks to explore the creative potential, and practical value of the biomimicry LPs tool compared to the traditional LCA approach while exploring the intrinsic motivation of R&D practitioners to implement these tools within a virtual product redesign workshop. To derive our conclusions, we employed a mixed-methods approach comprising a 23-item survey designed to assess practitioners’ intrinsic motivation and perceived practical value of the implemented tool alongside an external evaluation of the creativity of all generated design concepts. Together, these methods provide empirical evidence of the biomimicry LPs tool’s potential to enhance creative output, require minimal adoption effort, and act as a catalyst for whole-systems thinking in sustainable innovation. These findings offer compelling evidence to support their strategic addition to existing R&D toolkits and workflows. By highlighting the efficacy, accessibility, and intrinsic motivation of R&D professionals to use biomimicry LPs, the results underscore the viability of this tool to streamline the integration of biomimicry design thinking into real-world workflows. As such, they represent a pragmatic and scalable pathway to catalyze broader and deeper engagement with biomimicry across corporate contexts. Full article