Search Results (250)

Elastic cloud systems are increasingly employing machine learning (ML) to automate resource scaling in response to variable workloads and stringent service-level objectives. However, current ML-based autoscalers are fragmented across different platforms, objectives, and evaluation frameworks. This survey examines 60 primary studies conducted between 2015 and 2025, categorising them according to a five-dimensional taxonomy that includes goal, decision logic, scaling mode, control scope, and deployment. This study classifies supervised, unsupervised, and reinforcement learning approaches and analyzes their integration into practical frameworks, including Kubernetes-based controllers and cloud provider services. This paper summarizes the application of machine learning to workload prediction, proactive and hybrid horizontal–vertical scaling, and adaptive policy optimization. Additionally, it synthesises common evaluation practices, encompassing workloads, metrics, and benchmarks. The analysis identifies ongoing challenges: actuation delays and telemetry lag, the intricacies of hybrid scaling, coordination across multi-service and edge-cloud deployments, and the constrained joint consideration of cost, SLO, and energy objectives. The identified gaps necessitate additional research on unified machine learning-driven orchestration, multi-agent and federated control, standardised benchmarks, and sustainability-aware autoscaling. Full article

This study addresses the impact-induced failure of drilling pump valves caused by uncontrolled disc–seat collisions by proposing a novel valve design incorporating a two-stage buffering mechanism. The design employs a wave spring as the primary buffer and an elastic sealing ring as the secondary buffer, effectively mitigating impact through staged energy dissipation. A nonlinear stiffness model of the wave spring, accounting for the transition between line and surface contact modes, was developed. Strong fluid–structure interaction transients were simulated using dynamic meshing and user-defined functions. A parametric study was conducted by systematically varying cylindrical spring stiffness (7.7–15 N/mm), preload (110–160 N), and wave spring type (D85 to D110). Results show that, compared to a conventional valve, the two-stage mechanism reduces impact velocity by 24.2%, accelerates opening response by 17.9%, and extends the closing phase by 0.28%. Increasing wave spring stiffness (from D85 to D110) decreases opening delay time by 98.7% and attenuates peak velocity by 44.4%. Optimized hybrid spring parameters can minimize closing delay height by 27.3%. By reducing seat erosion and suppressing vibration-induced failure, the two-stage buffering mechanism effectively extends valve service life and enhances operational reliability in high-cycle drilling operations. Full article

Virtual agents powered by large language models are increasingly deployed in digital mental health services, yet the influence of avatar appearance on users’ emotional, cognitive, and physiological responses remains insufficiently understood. This study was conducted between March and April 2024 and examined how three avatar designs—animal-like, human-like, and object-like—shape affective experience, user evaluation, autonomic activity, and attentional allocation during virtual doctor interactions. Forty-two participants completed a within-subjects experiment involving self-reported affect ratings, multidimensional user-experience assessments, heart rate variability (HRV) measures, and eye-tracking indicators. The avatar type did not yield statistically significant differences in changes in positive or negative affect across conditions. However, physiological data revealed clear divergences. The animal-like avatar elicited the strongest parasympathetic activation, reflected by significant increases in the root mean square of successive differences (RMSSD) and high-frequency (HF) power, whereas the object-like avatar produced a sympathetic-dominant response. Across six user-experience dimensions, the animal-like avatar consistently received the highest evaluations. Eye-tracking results showed faster first fixation and a longer face-directed fixation duration for the animal-like avatar, indicating stronger social attention. The human-like avatar demonstrated slightly delayed initial fixation, consistent with subtle yet nonsignificant uncanny-valley tendencies. These findings underscore the critical role of avatar visual design in shaping emotional safety, engagement, and social processing in virtual mental-health interactions. Full article

This paper addresses the stability and $H_{\infty }$ performance of a single-area discrete-time power system with time-varying transmission delays under Denial-of-Service (DoS) attacks. First, the power system is modeled as a discrete-time delay system that integrates both DoS-induced delays and transmission delays, with PI controllers incorporated for Load Frequency Control (LFC). Using advanced summation inequality techniques, a Lyapunov–Krasovskii Functional (LKF) is constructed to capture comprehensive system state information, enabling the derivation of less conservative stability criteria. The proposed stability criterion based on linear matrix inequalities (LMI) ensures asymptotic stability and meets the $H_{\infty }$ performance index, while considering norm-bounded external load disturbances. Two convex optimization algorithms are designed to obtain optimal controller gains, either for a given $H_{\infty }$ index or by searching within a specified index range. Numerical examples and MATLAB simulations validate the effectiveness of the method. The results demonstrate that the maximum allowable delay upper bounds (MADUBs) estimated by the proposed criterion are larger than those obtained by existing methods, with an increase of at least 1 s. This indicates a reduction in conservatism. Simulation trajectories of frequency deviation ($\Delta f$) and area control error (ACE) confirm that the system remains stable under DoS attacks, with responses converging to zero after transient oscillations. Full article

Lymphatic filariasis (LF) is a mosquito-borne neglected tropical disease that causes substantial morbidity and social exclusion. Global efforts under the World Health Organization’s Global Programme to Eliminate Lymphatic Filariasis have markedly reduced prevalence, and several Pacific Island Countries and Territories (PICTs) have achieved elimination of the disease as a public health problem. However, post-validation surveillance (PVS), essential for detecting resurgence and enabling early response, has rarely been implemented, and barriers to its delivery remain poorly understood. We used two complementary qualitative approaches to identify systemic barriers and enablers to LF PVS in PICTs. First, we conducted a Nominal Group Technique followed by a structured expert elicitation involving program managers and technical staff. Data were analysed thematically and triangulated across sources. Participants identified 70 challenges which were consolidated into ten thematic domains. Pertinent barriers relate to limited leadership understanding of LF and surveillance options, inconsistent technical and financial support, and a lack of context-appropriate operational guidance. Additional challenges included limited field-ready diagnostics, procurement delays, the absence of formal mandates, and low community engagement. Enablers included embedding PVS within existing health services, leveraging trusted community networks, strengthening regional frameworks, and co-developing practical tools with countries. Sustaining LF elimination in the Pacific will require political commitment, regional collaboration, and integrated, programmatic approaches informed by recent PVS experience. Full article

Coastal ecological restoration is a key approach to enhancing ecosystem resilience; however, the stage-wise evolution of restoration outcomes and the underlying driving mechanisms remain insufficiently quantified. Using Qingdao City as the study area, this research integrates remote sensing inversion, the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, and time-series data from 2010 to 2020 to develop a comprehensive evaluation system for ecological restoration effectiveness, comprising 17 indicators across five dimensions: vegetation, biology, hydrology, economy, and climate. Based on this system, the entropy-weighted method is applied to conduct a dynamic assessment of restoration outcomes. The results indicate that (i) the composite evaluation score in the study area decreased from 0.36 in 2010 to 0.19 in 2015 and then increased to 0.74 in 2020, forming a “V-shaped” nonlinear trajectory with 2015 as a turning point, which is temporally consistent with a delayed response of ecological restoration outcomes following the implementation of major anthropogenic interventions. (ii) Dimension-specific analysis indicates that the decline in the composite score during 2010–2015 was mainly associated with the hydrological dimension, within which chemical oxygen demand (COD) and ammonia nitrogen emissions showed marked increases and were among the highest-weighted indicators. After 2015, following the intensive implementation of regional and system-oriented restoration projects such as the Blue Bay Initiative, pollutant emissions were observed to be effectively controlled, and Bare land area showed a continuous decline. These changes coincided with the rapid rebound of the composite score, within which Bare land area, as the highest-weighted indicator, played a prominent regulatory role. Marked differences were observed among dimensional responses: the biological and vegetation dimensions showed sustained improvement throughout the study period, whereas the hydrological dimension exhibited greater variability over time and stronger temporal alignment with policy-related phases. (iii) Robustness tests indicate that, after completely excluding climate-related variables, the composite score still increased from 0.36 and 0.24 to 0.77, with the “V-shaped” recovery pattern remaining unchanged. This result suggests that the observed improvement in restoration effectiveness in 2020 was more closely associated with systematic human interventions, rather than with short-term climatic fluctuations. This study provides a quantitative and transferable methodological framework for the dynamic evaluation and stage-oriented analysis of coastal ecological restoration effectiveness. Full article

Ambulance offload delay (AOD) occurs when incoming ambulance patients cannot be transferred promptly from paramedics to emergency department (ED) staff, usually due to ED and hospital congestion. This study empirically examines how AOD affects ambulance system efficiency in Nova Scotia, Canada. Using 12 months of call data from an integrated provincial EMS system and the electronic patient care reporting system, the analysis quantifies AOD impacts on the number of ambulances at EDs, turnaround time, total call time, response time, and ambulance availability across all regions. Findings show that AOD in the Central Region negatively affects all performance measures locally and in adjacent regions, prolonging turnaround and total call times, lengthening response times, and reducing ambulance availability where resources are shared. These results highlight the scale of AOD’s system-wide impact and provide a generalizable methodological framework that other EMS operators can adapt to assess and manage AOD in their specific operational contexts, recognizing that region-specific factors significantly influence outcomes. Full article

As a major water conservation region and ecological security barrier in China, the Three-River Source Region (TRSR) of the Qinghai–Tibet Plateau (QTP) is underlain by extensive permafrost. However, how permafrost degradation alters regional water conservation, particularly the existence of critical thresholds and time-lagged responses, remains insufficiently understood. To clarify these issues, spatiotemporal variations in water conservation (1990–2020) were quantified, and their nonlinear, lagged, and spatially heterogeneous responses to active layer thickness (ALT) were assessed. Using multi-source remote sensing and in situ observations from 1990 to 2020, spatiotemporal variations in water conservation were quantified with the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, and responses to permafrost degradation were examined by integrating Sen’s slope, GeoDetector, geographically weighted regression (GWR), and structural equation modeling (SEM) methods. The results showed that water conservation increased overall during 1990–2020 and exhibited a pronounced southeast–northwest gradient (higher in the southeast and lower in the northwest); the rates of change in the Lancang, Yellow, and Yangtze headwaters were 63.5, 56.5, and 31.0 mm a⁻¹, respectively. GeoDetector results indicate that precipitation was the dominant control on the spatial heterogeneity of water conservation (q = 0.704), and its interaction with active layer thickness (ALT) further increased explanatory power (q = 0.736). ALT also interacted with vegetation (q = 0.224) and topography (q = 0.157), suggesting that permafrost effects are modulated by vegetation condition and topographic setting in addition to water inputs. Piecewise regression identified a potential threshold at ALT = 1.77 m, indicating a shift in the ALT–water conservation relationship across this threshold. A 5–7-year lag in the response of water conservation to ALT was also detected, particularly apparent in continuous permafrost zones. Overall, water conservation exhibits a clear southeast–northwest gradient and a delayed response to ALT changes. In addition, the response exhibits clear spatial clustering, with the strongest sensitivity observed in areas with ice-rich permafrost overlain by alpine meadow, and a potential ALT breakpoint further suggests nonlinear permafrost–water conservation coupling. Full article

Sustainable urban development increasingly relies on hyperlocal environmental analytics created by smart city platforms that combine stationary and mobile sensors, Earth observations, meteorology, and land-use data. However, accurate spatio-temporal resolution can provide indirect identification and amplify cybersecurity threats. This article proposes the regulatory and technical mapping that implements the General Data Protection Regulation (GDPR) and the Network and Information Security Directive (NIS2) throughout the lifecycle of environmental data—reception, transport, storage, analytics, sharing, and publication. The methods combine doctrinal legal analysis, a review of the scope of recent research, formalized compliance modeling, modeling with synthetic city-scale datasets, expert identification, and demonstration of integrated analytics. The demonstration links deep evaluation of neural abnormalities (convolutional plus recurrent layers), short-term Fourier transformation of sensor signals, byte-to-image telemetry fingerprints, and protocol event counters, thereby tracking detection to explanatory evidence and to control actions. Deliverables include a matrix aligning lifecycle stages with GDPR principles and rights, as well as with the responsibilities of NIS2; a checklist for assessing the impact on data protection, which takes into account the risks of fairness and stigmatization; a basic set of controls for identification and access, secure design, monitoring, continuity, supplier assurance, and incident reporting; as well as a multi-layered publishing strategy that combines transparency with privacy through aggregation, delayed release, differentiated privacy budgets, and research enclaves. The visualization confirms that technical signals can be included in audit-ready reporting and automated response, while the guidelines legally clarify the relevant bases for common use cases such as air quality assurance networks, noise mapping, citizen sensor applications, and mobility and exposure modeling. The effects of the policy emphasize shared services for small municipalities, supply chain security, and ongoing review to counteract the mosaic effect. Overall, the study shows how cities can maximize environmental and social value based on environmental data, while maintaining privacy, sustainability, and equity by design. Full article

Background: Rapid helicopter air ambulance (HAA) response is a cornerstone of emergency medical logistics, yet the “time-to-care” metric remains highly sensitive to uncertainties in base posture, readiness, and operational disruptions. This study evaluates how these factors jointly influence response-time reliability and identifies strategies for improving service performance. Methods: A Monte Carlo simulation was developed to model the end-to-end HAA mission chain, including dispatch, wheels-up delay, en-route flight, and patient handoff, while accounting for uncertainty from weather, airspace congestion, and flight dynamics. Scenario experiments incorporated training improvements and alternative response protocols (Ground vs. Airborne Standby). Results: Simulation results indicate that operational factors reduced mean and tail response times, with Airborne Standby reducing the probability of exceeding a 45 min threshold by over 90% in urban night scenarios. Performance gains were most prominent in rural service areas and night operations, where disruption risks were highest. Conclusions: The findings offer evidence-based guidance for EMS logistics planners by clarifying how standby policies and readiness enhancements mitigate logistical risks. Full article

Understanding the coupling mechanisms between vegetation phenology and carbon productivity is essential for assessing ecosystem responses to climate change and guiding sustainable grassland management. This study focuses on stable alpine grasslands on the southern slope of the Qilian Mountains from 2001 to 2020, a climatically sensitive but relatively under-investigated transition zone on the northeastern Tibetan Plateau. We utilized MODIS NDVI time-series (MOD13Q1) and the latest PML V2 gross primary productivity (GPP) product at 500 m resolution to quantify changes in the start (SOS), end (EOS), and length (LOS) of the growing season. A pixel-wise linear regression approach was applied to evaluate the sensitivity of GPP to phenological metrics, explicitly characterizing how much GPP changes in response to unit shifts in SOS, EOS and LOS. Compared with previous studies that mainly described large-scale correlations between phenology and GPP or relied on coarser GPP products, this study provides a pixel-level, sensitivity-based assessment of phenology–carbon coupling in alpine grasslands using a long-term, phenology–GPP dataset tailored to the Qilian alpine region. The results revealed trends of earlier SOS, delayed EOS, and extended LOS, accompanied by a gradual increase in GPP. However, phenology–GPP coupling exhibited notable spatial heterogeneity. In mid- and low-altitude areas, extended growing seasons enhanced GPP, whereas high-altitude zones showed limited or even negative responses, likely due to climatic constraints such as cold stress and thermal–moisture mismatches. To better understand these spatial differences, we constructed a three-dimensional phenology–GPP sensitivity space and applied k-means clustering to delineate three ecological functional zones: (1) high carbon sink potential, (2) ecologically fragile regions, and (3) neutral buffers. This sensitivity-based functional zonation moves beyond traditional correlation analyses and provides a process-oriented and spatially explicit framework for ecosystem service assessment, carbon sink enhancement and adaptive land-use strategies in sensitive mountain environments. Full article

The dynamic response accuracy of suspension servo actuators directly determines the vibration-reduction performance of active-suspension systems. However, during long-term service, the system is prone to the influence of compound-degradation faults, such as internal leakage and time delay, leading to a significant decline in control performance. To address this issue, this paper proposes a collaborative control framework combining model-free adaptive control with a differential term of tracking error (DE-MFAC) and an improved particle swarm optimization (IPSO) algorithm. Firstly, to overcome the limitations of traditional model-free adaptive control (MFAC), a DE-MFAC strategy is constructed by implicitly handling the time-delay term and introducing the differential term of tracking error and dynamic weight factor into the performance index. Secondly, to enhance the parameter-tuning effect, the traditional particle swarm optimization (PSO) algorithm is improved (IPSO) by incorporating a dynamic inertia weight and an out-of-bounds random reflection mechanism, thereby strengthening the global optimization capability. On this basis, a suspension servo actuator system model incorporating internal leakage and time-delay faults is established based on the co-simulation platform of Simulink and AMESim, and the proposed method is validated. The simulation results show that, compared with the optimized traditional MFAC, the DE-MFAC tuned by IPSO exhibits superior position-tracking accuracy, faster response speed, and stronger overshoot-suppression capability under various compound-fault conditions. Further analysis indicates that the Integral of Absolute Cubic Error (IACE) function, due to its higher sensitivity to large deviations, can more effectively suppress overshoot and is suitable for engineering scenarios with strict requirements on dynamic performance. In addition, the optimization of control parameters using the IPSO algorithm can effectively compensate for the performance degradation caused by degradation faults, providing a feasible technical approach for extending the service life of actuators through adaptive adjustment. Full article

Ensuring production safety and enabling rapid emergency response in complex industrial environments remains a critical challenge. Traditional inspection robots are often limited by perception delays when confronted with sudden dynamic threats. This paper presents a vision-driven dynamic digital twin system designed to enhance real-time monitoring and emergency management capabilities. The framework constructs high-fidelity 3D models using SolidWorks 2024, Scaniverse 5.0.0, and 3ds Max 2024, and integrates them into a unified digital twin environment via the Unity 3D engine. Its core contribution is a vision-driven dynamic mapping mechanism: robots operating on the Robot Operating System (ROS) and equipped with ZED stereo cameras and embedded YOLOv5m models perform real-time detection, such as personnel and fire sources. Recognized targets trigger the dynamic instantiation of corresponding virtual models from a pre-built library, enabling automated, real-time reconstruction within the digital twin. An integrated service platform further supports early warning, status monitoring, and maintenance functions. Experimental validation confirms that the system satisfies key performance metrics, including data collection completeness exceeding 99.99%, incident detection accuracy of 80%, and state synchronization latency below 90 milliseconds. The system improves the dynamic updating efficiency of digital twins and demonstrates strong potential for proactive safety assurance and efficient emergency response in dynamic industrial settings. Full article

Background/Objectives: Emergency department short-stay units (ED SSUs) manage patients requiring short-term observation and treatment. For a small number of patients, a longer hospital admission is required. Care for these patients is provided by an inpatient team and the responsibility for managing acute clinical deterioration falls to a rapid response team, activated by an emergency call. While emergency calls have primarily been a feature of the inpatient setting, admitted patients are increasingly boarding within ED SSUs and the occurrence and impact of emergency calls in this setting remains largely unreported. This study aimed to determine the incidence and characteristics of emergency calls within an ED SSU, describing patient demographics, clinical triggers, and outcomes. Methods: This retrospective cohort study utilised the Tasmanian Emergency Care Outcomes Registry (TECOR) to analyse emergency calls in the ED SSU of a tertiary emergency department between 1 February 2024 and 28 February 2025. Inclusion criteria were defined as adult patients (≥14 years) admitted to an inpatient service who had emergency calls whilst in the ED SSU. Descriptive statistics were used to characterise this cohort. Results: Of 83,238 ED presentations, 11,775 adult patients were transferred to the ED SSU. 1464 (12.4%) of these patients were subsequently admitted under an inpatient service but remained boarding in the ED SSU, with 54 emergency calls occurring in 38 unique patients (2.6%). The median age was 81.5 years (IQR 65–86), older than both the main ED cohort with a median age of 71 years, and median ages of 65 to 69.5 years reported in ward-based cohorts. Most calls were medical emergency team (MET) activations (52, 96.30%) with only 2 (3.7%) code blues. The most common triggers were hypotension (20, 37.04%), reduced level of consciousness (7, 12.96%) and serious concern (7, 12.96%). Delays occurred in 18.52% of calls (mean 82 min). The median ED SSU length of stay for patients having an emergency call was 40.15 h, substantially exceeding the intended ED SSU admission criteria threshold of 24 h. Goals of care remained incomplete in 33.33% of calls, even after emergency team review. Conclusions: ED SSU emergency calls are infrequent but clinically significant, involving an elderly, vulnerable population with late sign triggers and prolonged boarding. These findings highlight fundamental mismatches between patient acuity and ED SSU environment capabilities, emphasising the need for improved monitoring, more selective admission criteria, and enhanced systems for recognising deterioration for patients boarding in ED SSUs. Full article

Microservice deployment and request routing can help improve server efficiency and the performance of large-scale mobile edge computing (MEC). However, the joint optimization of microservice deployment and request routing is extremely challenging, as dynamic request routing easily results in asymmetric network structures and imbalanced microservice workloads. This article proposes multi-objective joint optimization for microservice deployment and request routing based on structural symmetry. Firstly, the structural symmetry of microservice deployment and request routing is defined, including spatial symmetry and temporal symmetry. A constrained nonlinear multi-objective optimization model was constructed to jointly optimize microservice deployment and request routing, where the structural symmetric metrics take into account the flow-aware routing distance, workload balancing, and request response delay. Secondly, an improved artificial plant community algorithm is designed to search for the optimal route to achieve structural symmetry, including the environment preparation and dependency installation, service packaging and image orchestration, arrangement configuration and dependency management, deployment execution and status monitoring. Thirdly, a benchmark experiment is designed to compare with baseline algorithms. Experimental results show that the proposed algorithm can effectively optimize structural symmetry and reduce the flow-aware routing distance, workload imbalance, and request response delay, while the computational overhead is small enough to be easily deployed on resource-constrained edge computing devices. Full article