Search Results (36,177)

Traditional scheduling theory optimizes initial task assignments under static assumptions, yet operational systems face repeated disruptions requiring both immediate rescheduling and long-term structural adaptation. Existing approaches treat each disruption independently, failing to capture how organizations learn and evolve through repeated challenges. This paper presents a unified framework bridging cascading rescheduling with network evolution, formally modeling how dependency structures adapt over time to improve resilience. The framework consists of three integrated components: (1) immediate rescheduling algorithms with provable complexity bounds—O(n) for tree-structured dependencies, fixed-parameter tractable for bounded treewidth—enabling real-time response; (2) five adaptation strategies (redundancy, buffering, decoupling, reshuffling, and control) with convergence guarantees showing exponential improvement rate $O\left(e^{-(\sigma -\lambda )t}\right)$; and (3) computable resilience metrics quantifying organizational capacity to absorb disruptions. Comprehensive validation through 5200 simulated weeks (52 weeks × 100 replications) demonstrates substantial performance improvements. Redundancy-based adaptation achieves 109% resilience improvement and 66% disruption reduction compared to non-adaptive baselines ($p<0.001$, Cohen’s $d>1.8$). The framework is implemented as Orange3 visual programming widgets, achieving 92% user acceptance among non-technical practitioners with 7-month payback periods. While the framework is domain-agnostic and applicable to any operational network with dependency constraints, validation focuses on healthcare scheduling contexts where disruption patterns are well documented. The approach demonstrates that organizations can systematically build resilience through principled adaptation rather than reactive responses, with quantifiable performance improvements and accessible implementation tools. Full article

This study examines multi-celebrity deployment as a destination branding practice, using Seoul as an empirical case. The analysis draws on 172 official tourism promotional videos released by the Seoul Tourism Organization between 2011 and 2025, featuring 67 identifiable celebrities and 438 destination references. A qualitative content analysis examines how celebrity endorsement is organized as a branding mechanism, focusing on who appears, what is represented, and how representations are communicated across media formats over time. The findings show that Seoul’s tourism promotion operates through a structured multi-celebrity branding system in which multiple endorsers are coordinated across campaigns and periods. Endorser selection is anchored in Hallyu-affiliated celebrities who function as primary carriers of destination meaning, while emerging, non-Hallyu, and heritage-linked figures occupy complementary roles that broaden representational scope and reduce reliance on individual figures. Celebrity endorsement continues to emphasize major and symbolically dense attractions, while also extending visibility to everyday neighborhoods and locally oriented urban landscapes. Long-term ambassador-led campaigns coexist with travel vlogs and other creative video formats, enabling variation in narrative tone and experiential framing. Theoretically, the study extends celebrity endorsement research by conceptualizing multi-celebrity deployment as a coordinated branding system. Practically, the findings show how destination marketing organizations can mobilize a broad pool of celebrity resources to structure endorsement portfolios over time. Coordinated use of celebrities with different levels of familiarity supports wider spatial representation, enables ongoing narrative renewal, and maintains promotional continuity across changing media environments. This configuration is most applicable to destinations with strong cultural visibility and an established celebrity ecosystem, and may be less transferable to destinations with limited access to influential figures. Full article

Background/Objectives: Post-cardiac arrest syndrome (PCAS) induces systemic ischemia–reperfusion injury accompanied by sepsis-like coagulopathy. This coagulopathy presents heterogeneously, yet distinct coagulation phenotypes and their impact on hypoxic–ischemic brain injury (HIBI) remain poorly defined. We aimed to identify coagulation phenotypes using latent class analysis (LCA) and assess their association with 6-month neurological outcomes. Methods: We retrospectively analyzed adult out-of-hospital cardiac arrest (OHCA) patients treated with targeted temperature management (TTM) between 2011 and 2019 from a prospective registry at a tertiary academic center. LCA was performed using coagulation biomarkers measured at admission and 24 h post-return of spontaneous circulation: D-dimer, fibrinogen, antithrombin III (ATIII), platelet count, and PT-INR. The primary outcome was poor neurological outcome (Cerebral Performance Category 3–5) at 6 months. Secondary outcomes included in-hospital mortality and cerebral edema severity assessed by gray-to-white matter ratio (GWR) on brain CT. Results: Among 325 patients, LCA identified three phenotypes: Class 1 (Preserved Coagulation, 36.9%), Class 2 (Hypercoagulable State, 41.5%) characterized by elevated D-dimer with preserved fibrinogen and ATIII, and Class 3 (Consumptive Coagulopathy, 21.5%) marked by profound D-dimer elevation with fibrinogen <150 mg/dL and ATIII <60%. Class 3 exhibited the lowest GWR and highest neuron-specific enolase levels. In multivariable analysis adjusting for age, low-flow time, initial rhythm, and lactate, Class 3 independently predicted poor neurological outcome (adjusted OR 4.52; 95% CI 2.15–9.48), whereas Class 2 did not. Conclusions: PCAS-related coagulopathy is heterogeneous. A consumptive coagulopathy phenotype identifies a high-risk subgroup associated with severe brain injury and poor long-term neurological outcomes. Early identification of this phenotype may enable targeted prognostication and guide future phenotype-specific interventional strategies.: Full article

This study developed an integrated vision–actuation system for precision weeding in indoor soil bin environments, with cabbage as a case example. The system integrates lightweight object detection, 3D co-ordinate mapping, path planning, and a three-axis synchronized conveyor-type actuator to enable precise weed identification and automated removal. By integrating ECA and CBAM attention mechanisms into YOLO11, we developed the YOLO11-WeedNet model. This integration significantly enhanced the detection performance for small-scale weeds under complex lighting and cluttered backgrounds. Based on the optimal model performance achieved during experimental evaluation, the model achieved 96.25% precision, 86.49% recall, 91.10% F1-score, and a mean Average Precision (mAP@0.5) of 91.50% calculated across two categories (crop and weed). An RGB-D fusion localization method combined with a protected-area constraint enabled accurate mapping of weed spatial positions. Furthermore, an enhanced Artificial Hummingbird Algorithm (AHA⁺) was proposed to optimize the execution path and reduce the operating trajectory while maintaining real-time performance. Indoor soil bin tests showed positioning errors of less than 8 mm on the X/Y axes, depth control within ±1 mm on the Z-axis, and an average weeding rate of 88.14%. The system achieved zero contact with cabbage seedlings, with a processing time of 6.88 s per weed. These results demonstrate the feasibility of the proposed system for precise and automated weeding at the cabbage seedling stage. Full article

To reduce water consumption and potential formation damage associated with conventional water-based fracturing fluids while improving the proppant-carrying and flow adaptability of CO₂-based systems without relying on specialized CO₂ thickeners, a CO₂–water polymer hybrid fracturing fluid was developed using an AM/AA copolymer (poly(acrylamide-co-acrylic acid), P(AM-co-AA)) as the thickening agent for the aqueous phase. Systematic experimental investigations were conducted under high-temperature and high-pressure conditions. Fluid-loss tests at different CO₂ volume fractions show that the CO₂–water polymer hybrid fracturing fluid system achieves a favorable balance between low fluid loss and structural continuity within the range of 30–50% CO₂, with the most stable fluid-loss behavior observed at 40% CO₂. Based on this ratio window, static proppant-carrying experiments indicate controllable settling behavior over a temperature range of 20–80 °C, leading to the selection of 60% polymer-based aqueous phase + 40% CO₂ as the optimal mixing ratio. Rheological results demonstrate pronounced shear-thinning behavior across a wide thermo-pressure range, with viscosity decreasing systematically with increasing shear rate and temperature while maintaining continuous and reproducible flow responses. Pipe-flow tests further reveal that flow resistance decreases monotonically with increasing flow velocity and temperature, indicating stable transport characteristics. Phase visualization observations show that the CO₂–water polymer hybrid fracturing fluid system exhibits a uniform milky dispersed appearance under moderate temperature or elevated pressure, whereas bubble-dominated structures and spatial phase separation gradually emerge under high-temperature and relatively low-pressure static conditions, highlighting the sensitivity of phase stability to thermo-pressure conditions. True triaxial hydraulic fracturing experiments confirm that the CO₂–water polymer hybrid fracturing fluid enables stable fracture initiation and sustained propagation under complex stress conditions. Overall, the results demonstrate that the AM/AA copolymer-based aqueous phase can provide effective viscosity support, proppant-carrying capacity, and flow adaptability for CO₂–water polymer hybrid fracturing fluid over a wide thermo-pressure range, confirming the feasibility of this approach without the use of specialized CO₂ thickeners. Full article

High resolution underwater mapping is fundamental to the sustainable development of the blue economy, supporting offshore energy expansion, marine habitat protection, and the monitoring of both living and non-living resources. This work presents a pose-graph SLAM and calibration framework specifically designed for 3D profiling sonars, such as the Coda Octopus Echoscope 3D. The system integrates a probabilistic scan matching method (3DupIC) for direct registration of 3D sonar scans, enabling accurate trajectory and map estimation even under degraded dead reckoning conditions. Unlike other bathymetric SLAM methods that rely on submaps and assume short-term localization accuracy, the proposed approach performs direct scan-to-scan registration, removing this dependency. The factor graph is extended to represent the sonar extrinsic parameters, allowing the sonar-to-body transformation to be refined jointly with trajectory optimization. Experimental validation on a challenging real world dataset demonstrates outstanding localization and mapping performance. The use of refined extrinsic parameters further improves both accuracy and map consistency, confirming the effectiveness of the proposed joint SLAM and calibration approach for robust and consistent underwater mapping. Full article

This paper addresses nonlinear time-varying cascade systems governed by differential equations with finite delay. Several sufficient conditions for asymptotic stability are derived, based on differing assumptions regarding the isolated subsystems and their interconnection. The cascade structure enables the treatment of a broad class of systems while simplifying stability analysis compared to conventional approaches. Moreover, it allows the stabilization problem to be decoupled: under suitable conditions, the asymptotic stability of the overall cascade system follows from the stability properties of its individual subsystems. These properties are typically verified using the direct Lyapunov method. In contrast to existing results, the theorems presented herein apply to an extended class of systems and impose relaxed conditions on the Lyapunov functions employed to establish uniform asymptotic stability. Additionally, new results are provided on semiglobal exponential stability and (non-uniform) asymptotic stability for time-varying cascade systems with delay. Collectively, these contributions broaden the applicability of the direct Lyapunov method to delayed cascade systems. 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

Literature reviews are a cornerstone of doctoral research in general, and of economic and business research, in particular. However, the exponential growth of scientific publications has made comprehensive and transparent reviews increasingly difficult. Conventional approaches, largely based on manual searches across a small number of databases, tend to be slow, error-prone, and incomplete. As a result, they constrain the scope of inquiry and, consequently, the robustness of theory development and empirical validation. This paper proposes and analyses an information system architecture driven by research questions and keyword taxonomies to automate core tasks of the literature search phase across multiple academic databases. Focusing on the domain of corporate and municipal financial distress, the authors employ a two-stage research design. First, the theoretical analysis integrates the literature on systematic reviews, automation, and financial distress prediction to derive a set of functional and non-functional requirements. Second, the experimental analysis documents a prototype front-end application designed to accelerate the literature review. The prototype is conceptualised as a socio-technical artefact that enhances IT competences and scientific resilience by enabling more efficient, reproducible, and extensible reviews. The authors conclude by discussing the scientific, technical, professional, and societal implications of the prototype, including opportunities for intellectual-property protection and avenues for future research. Full article

This article presents the results of research into the operation of retractable-type fall arresters used for fall protection in conjunction with flexible anchor points. The purpose of these devices is to enable the user to move freely in the vertical direction and safely arrest any fall from the workstation. Reports from users of such protective equipment and previous studies have indicated the occurrence of specific situations in which retractable-type fall arresters did not work properly. It was manifested by the sudden locking and unlocking of the device’s retractable lanyard, which means that the falling person was not stopped at the required distance. This is a highly dangerous phenomenon known as “jump action” that can cause serious injury or even death. Therefore, three different designs of retractable-type fall arresters and three loading conditions were investigated to analyze the jump action phenomenon. Based on the experimental results, a modification of the retractable type fall arresters was proposed in the form of an added electronic control. The proposed programmable control system will make it possible to eliminate the risks associated with “jump action” and expand the functionality of the studied fall arresters. Full article

The rapid integration of renewable energy sources (RES), particularly wind, together with fluctuating demand, has introduced significant uncertainty into power system operation, challenging traditional approaches for estimating Transmission Reliability Margin (TRM) and Available Transfer Capability (ATC). This paper proposes a fully adaptive TRM estimation framework that leverages rolling-window statistical analysis of net-load forecast errors to capture real-time uncertainty fluctuations. By continuously updating both the confidence factor and window length based on evolving forecast-error statistics, the method adapts to changing grid conditions. The framework is validated on the IEEE 30-bus system with 80 MW wind (42.3% penetration) and assessed for scalability on the IEEE 118-bus system (40.1% wind penetration). Comparative analysis against static TRM, fixed-confidence rolling-window, and Monte Carlo Simulation (MCS)-based methods shows that the proposed approach achieves 88.0% reliability coverage (vs. 81.8% for static TRM) while providing enhanced transfer capability for 31.5% of the operational day (7.5 h). Relative to MCS, it yields a 20.1% lower mean TRM and a 2.5% higher mean ATC, with an adaptation ratio of 18.8:1. Scalability assessment confirms preserved adaptation (12.4:1) with sub-linear computational scaling (1.82 ms to 3.61 ms for a 3.93× network size increase), enabling 1 min updates interval. Full article

Construction management plays a fundamental role in the sustainability of construction projects, as its primary objective is to enhance cost-effectiveness and efficient resource utilization. One of the main challenges encountered at the early stages of a project’s lifecycle, particularly during the planning phase, is the development and agreement of construction schedules among the stakeholders involved. The tools employed for time planning and scheduling during both the planning and construction phases should therefore be capable of modeling complex environments and supporting dynamic updates in response to resource constraints. Petri nets are known for their capability of modeling complex systems, such as resource management. Their use in project management is essential for resource constraint problems. This paper investigates the use of Petri Nets as a tool for the time scheduling of engineering and construction projects. A case study is presented and modeled using Timed Petri nets, enabling dynamic adaptation under time and resource constraints. Through simulation performed with the ROMEO (v3.10.6) software, the study identifies the critical paths and determines the total project duration under various scenarios of sensitivity by adjusting specific project parameters. The results demonstrate the effectiveness of Petri nets in project management and the benefits they offer when used in modeling complex systems, identifying critical activities and calculating resource constraints and time deadlines. Full article

To address image degradation in optical telescopes with fast focal ratios—a problem caused by the misalignment of optical elements during assembly and observation—this study proposes a high-precision calibration method for image quality detection and correction. The method substitutes parallel laser beams for starlight to generate the incident wavefront required for calibration. Low-order aberrations resulting from system misalignment are calculated from the centroid coordinate offsets of laser spots on defocused planes, thereby enabling feedback-controlled alignment adjustments. Simulations and experiments were conducted on a single parabolic mirror system with a diameter (D) of 500 mm and a focal ratio of $F/3$. The results indicate that for mirror tilt misalignments ranging from $-2^{\circ }$ to $+2^{\circ }$, the estimated error for the Zernike coefficients $Z_4$–$Z_6$ is below $0.1\lambda$ ($\lambda =650$ nm). This accuracy meets the alignment requirements for telescopes with fast focal ratios and eliminates the need for large flat mirrors and clear night skies, which are traditionally required for outdoor calibration. Consequently, the method provides a low-cost, high-precision solution for the real-time calibration of telescopes at remote sites, such as those in Antarctica. Full article

Public transportation systems in modern cities are transitioning from infrastructure- and technology-centric models to human-centered development. One emerging focus area is rider wellbeing, which integrates physical, emotional, and psychological dimensions of transit experiences. This study investigates rider wellbeing in the Dubai Metro system, leveraging a large-scale survey of 1409 users and analyzing the data using Generalized Structured Component Analysis (GSCA). The research identifies three latent constructs—Service Efficiency and Accessibility (SEA), Physical Environment and Passenger Comfort (PEPC), and Service Operations and Assurance (SOA)—as key determinants of rider wellbeing. The final model demonstrated strong fit (FIT = 0.639; AFIT = 0.621) and established a structural equation: Wellbeing = 0.216(SEA) + 0.513(SOA) + 0.318(PEPC) + ε. Findings reveal the need to prioritize speed, comfort, connectivity, and digital communication enhancements. Sustainable transportation planning is dependent on public transportation being not just available but also perceived as dependable, comfortable, and convenient to use. This study connects metro service characteristics to rider wellbeing and provides evidence to help guide service goals that promote rider retention and social sustainability. The study is unique in that it presents a latent-variable model that evaluates service features collectively (rather than individually) and converts them into interpretable planning levers using Dubai Metro survey data. By improving metro users’ experiences, the framework contributes to the sustainable mobility paradigm by enabling cities to maintain and expand public transportation use, an enabling solution for lowering vehicle dependency and associated negative impacts. This paradigm also benefits the environment by reducing emissions, increasing air quality, and promoting sustainable urban ecosystems. The proposed framework offers actionable insights for improving metro planning in Dubai and contributes broadly to global public transit development. Incorporating wellbeing into transportation planning supports smart city goals, enhances rider satisfaction, and fosters sustainable urban mobility. Full article

Unidirectional multilevel back-to-back (BTB) converters are widely employed in renewable energy generation systems and in motor drives for coal mining operations. However, the current zero-crossing distortion (CZCD) on the grid side and the neutral-point potential (NPP) imbalance on the common DC bus all restrict its applicability, such as in grids with stringent low harmonic requirements and in medium to high power situations. This paper proposes a coordinated control strategy to simultaneously address these issues theoretically. The study focuses on topology comprising a Vienna rectifier structure on the grid side and a three-level NPC inverter structure on the load side. In the proposed strategy, the current distortion angle, the manifestation of CZCD, is first eliminated by reactive current compensation on the Vienna rectifier side. Furthermore, the coupling between CZCD and NPP imbalance is resolved by reconstructing the neutral-point current target function. Ultimately, the optimal zero-sequence voltage (ZSV) is obtained using an interpolation function and then injected into the three-phase reference voltages of the inverter side to balance the NPP on the DC bus. The strategy transforms the influence of the rectifier on the NPP from an unknown coupling factor into a known disturbance and enables the inverter to actively compensate for variations in the overall converter system. An experimental platform was independently developed to verify the effectiveness of the proposed control strategy. Full article