Real-time Conditions Research Articles

Concordance of HER2 status between primary tumor and circulating tumor cells in breast cancer

BackgroundDuring tumor progression, HER2 expression undergoes dynamic changes. Circulating tumor cells (CTCs) can be used to monitor HER2 expression in real-time and hold potential for clinical application. This study aimed to evaluate the consistency of HER2 expression between primary tumors and CTCs in patients with breast cancer (BC).MethodsWe used a previously established telomerase reverse transcriptase-based CTC detection method (TBCD) combined with anti-HER2 antibody to detect CTC and HER2-positive CTC (HER2 + CTC) in 4 ml of peripheral blood from patients with breast cancer prior to radiotherapy. The results indicated that the status of HER2 in CTC was inconsistent with the histological results.ResultsDiscordance in HER2 status between primary tumor and CTC was observed in 32.6% of patients (kappa value = 0.325, p = 0.03). And among patients with histologically HER2-negative breast cancer, the detection rate of HER2 + CTC was 32.1% (9/28).ConclusionsWe found that the HER2 status of CTC in peripheral blood was inconsistent with the histological findings. Further research should explore the clinical significance of detecting HER2-positive CTCs, and it is desired that real-time HER2 status testing of CTCs could hold potential value for patients with breast cancer.

A Hybrid Model for Freight Train Air Brake Condition Monitoring

The Digital Freight Train is expected to revolutionise the rail freight industry. A critical aspect of this transformation is real-time condition monitoring of air brake systems, which are among the leading causes of train malfunctions. To achieve this goal, advanced algorithms for air brake modelling are required. This paper introduces a computationally efficient air brake model tailored for real-time diagnostic applications. A hybrid approach, integrating both empirical data and simplified fluid-dynamic equations, has been adopted. Compared to other air brake models found in the literature, the innovative contributions of the presented model are the reduction of the number of required parameters and the estimation of the brake cylinder pressure directly from the main brake pipe pressure using a feed-forward approach. Moreover, a new approach in the evaluation of the first braking phase and the brake cylinder pressure build-up as the saturation of the brake mode is presented. The model input includes the main brake pipe pressure, the weighing valve pressure, and the brake mode, and the output includes the pressure at the brake cylinder. The air brake model has been validated using data from a previous experimental campaign. The model’s accuracy in replicating the air brake system mechanism makes it well-suited for future development of model-based algorithms designed for air brake fault detection.

Degradation-considering adaptive equivalent consumption minimization strategy for fuel cell electric vehicles

In order to improve the economy and durability of fuel cell vehicle, a degradation-considering adaptive equivalent consumption minimization strategy (D-ECMS) was proposed for fuel cell/lithium battery hybrid power system. Aiming at enhancing the adaptability of the equivalent consumption minimum strategy, four different vehicle speed conditions were considered including low speed, medium speed, high speed, and ultra-high speed. Particle swarm optimization (PSO) algorithm was used to optimize the penalty factor under different vehicle speed conditions, and BP neural network was used to identify real-time conditions to form an adaptive equivalent consumption minimum strategy (A-ECMS). The voltage decay model of the fuel cell was established according to the voltage decay rate of the fuel cell under the three adverse conditions of idle speed, high power and dynamic load cycle. The voltage decay of the fuel cell was transformed to hydrogen consumption in the cost function of the A-ECMS strategy to obtain the D-ECMS considering durability degradation. The simulation and the hardware-in-the-loop (HIL) test based on the dSPACE real-time platform was carried out. The results show that: compared with A-ECMS, the hydrogen consumption of the D-ECMS increased by 5.2%, 9.7%, 10.2%, and 0.2% respectively under WLTC, NEDC, UDDS, and HWFET cycles. However, D-ECMS can reduce the voltage decay by 38.3%, 59.7%, 35.2%, and 26% under the above four test conditions, and their total comprehensive cost loss is reduced by 8.7%, 6.7%, 9.6%, and 6.3% respectively. As result, the D-ECMS can reduces the voltage attenuation of fuel cells and improve the durability of fuel cells significantly with a small hydrogen consumption increase,which can reduce the operating cost of fuel cells finally. Furthermore, the average error between the results of HIL test and offline simulation results is less than 5%, which validates the adaptability and real-time application feasibility of D-ECMS.

Study on Instability Mechanism and Compensation Strategy for Distributed Energy Storage Systems

Distributed energy storage systems (DESSs), which would become key components in a new power system, can flexibly deliver peak load shaving and demand management. With the popularization of distributed renewable energy generation in a distribution network, the grid impedance varies and DESSs thus have to face stability issues. In order to enhance the system’s stability, a compensation strategy is proposed for the inverter in a DESS. First, a stability analysis model is developed to show the main factors that affect system stability. Then, an improved compensation strategy is proposed for the phase-locked loop (PLL) in a DESS, in which control parameters are adaptively tuned on-line according to real-time conditions to improve the stability of a grid-tied DESS. Simulation and hardware-in-the-loop (HIL) experimental results are given to validate the effectiveness of the proposed strategy. Simulation and experimental results show that the proposed strategy significantly increases the system’s tolerance to grid impedance variations, maintains total harmonic distortion (THD) below 5% during normal operation, and effectively reduces low-order harmonic content caused by impedance fluctuations. Moreover, the strategy is demonstrated to enhance system stability under low state-of-charge (SOC) conditions, showcasing its robustness and adaptability across various operating scenarios.

A multi-scale and multi-modal convolutional neural network for condition monitoring of transmission line

Abstract Monitoring the fatigue damage of transmission lines is crucial for stable power system operation. However, existing model-driven methods face challenges such as high computational complexity and reliance on expert knowledge, while data-driven methods require large amounts of abnormal state data. To address these issues, a multi-scale and multi-modal convolutional neural network (CNN) is proposed for real-time condition monitoring of transmission lines. Key steps include: firstly, empirical Fourier decomposition is used to decompose the original signals, extracting multi-scale state information at different frequency scales. Then, time-domain, frequency-domain, and time–frequency domain analyses are performed on the decomposed signals to capture multi-modal information. Based on this, a multi-modal fusion network is proposed based on a CNN to extract shallow and deep features, with a fully connected layer used for multi-modal feature fusion. Notably, the algorithm is implemented on a microprocessor for practical application. Experimental results show that the proposed model achieves a diagnostic accuracy of 93.06%, outperforming classical networks. It also surpasses models trained solely on time, frequency, or time–frequency features by 25.18%, 21.8%, and 19.3%, respectively.

Integrated modeling of boron powder injection for real-time plasma-facing component conditioning

An integrated modeling framework for investigating the application of solid boron (B) powder injection for real-time surface conditioning of plasma-facing components (PFCs) in tokamak environments is presented. Utilizing the DIII-D impurity powder dropper (IPD) setup, this study simulates B powder injection scenarios ranging from milligrams to tens of milligrams per second, corresponding to boron flux rates of 1020–1021 B/s in standard L-mode conditions. The comprehensive modeling approach combines EMC3-EIRENE for simulating the deuterium plasma background and the Dust Injection Simulator (DIS) for the ablation and transport of the boron powder particles. EMC3 trace impurity fluid modeling results show substantial boron transport to the inboard lower divertor, predominantly influenced by the main ion plasma flow. The dependency on powder particle size (5–250μm) was found to be insignificant for the scenario considered. The effects of erosion and redeposition were considered to reconcile the discrepancies with experimental observations, which saw substantial deposition on the outer divertor plasma-facing components. For this purpose, the WallDYN3D code was updated to include boron sources within the plasma domain and integrated into the modeling framework. The mixed-material migration modeling shows evolving boron deposition patterns, suggesting the formation of mixed B-C layers or predominantly B coverage depending on the powder mass flow rate. While the modeling outcomes at lower B injection rates tend to align with DIII-D experimental observations, the prediction of near-pure boron layers at higher rates has yet to be experimentally verified in the carbon environment of the DIII-D tokamak. The extensive reach of boron layers found in the modeling suggests the need for modeling that encompasses the entire wall geometry for more accurate experimental correlations. This integrated approach sets a precedent for analyzing and applying real-time in-situ boron coating techniques in advanced tokamak scenarios, potentially extendable to ITER.

A Bayesian-interference approach to quantify degradation parameters in a water-cooled variable speed screw compressor chiller

Abstract Variable-speed screw chillers can provide effective capacity modulations in commercial buildings to reduce energy consumption, match the load requirements, and contribute to lower equivalent carbon emissions. However, variable-speed operation can also yield to degradation phenomena inside the compressor. Assessing the real-time health status of chillers and detecting anomalies are important aspects to ensure long-term operation and improve reliability. To this end, an automated accelerated life test (ALT) procedure was developed and applied to a 513 kW (145.9 RT) water-cooled variable-speed screw chiller in a laboratory test facility to experimental assess performance degradation over time. After every 1,000 operating hours of ALT, steady-state performance tests at 30%, 50%, 75% and 100% load were conducted according to AHRI Standard 550/590. Additional operating conditions were included while conducting the tests to provide a more complete assessment of the degradation trends compared to the initially measured baseline. The experimental data set was then used to quantify the performance degradation based on the impact on the screw compressor operation. A Bayesian-inference identification approach has been developed to identify changes of sensitive parameters that statistically provide evidence for the likelihood of a performance degradation of the compressor. The information was used to implement the parameters in a dynamic model which was used to perform studies to predict the degradations of a chiller. The outcomes of this study help to improve the maintenance schedule of a compressor and even the whole chiller so that the system can be operated more economically and unplanned downtime can be minimized.

Dynamic Seat Management Algorithm for Optimizing Sleeper Train Capacity

Abstract: In this paper, we present a Dynamic Day-Night Seat Allocation Algorithm designed to optimize the utilization of sleeper train berths during long-duration journeys. The algorithm dynamically reallocates berths based on real-time conditions, optimizing seat usage for daytime sitting and nighttime sleeping. The objective is to enhance passenger comfort, prioritize elderly and family passengers for lower berths, and maximize train capacity through intelligent seat management. Our approach incorporates greedy algorithms to efficiently assign passengers to the most appropriate berth based on time of day and available space.

Evaluation of a Probabilistic Framework for Traffic Volume Forecasting Using Deep Learning and Traditional Models

Forecasting is a very important issue in the present times to optimize urban traffic management systems and their proper planning. It is often found that traditional forecasting models cannot fully approximate the non-linear and dynamic nature of traffic patterns. The presented paper proposes a framework that integrates multiple traditional models such as Autoregression (AR), Support Vector Regression (SVR), Exponential Smoothing, Historical Average, Random Walk and Artificial Neural Network (ANN) with deep learning models, especially Stacked Autoencoders, and enhances their predictive efficiency phenomenally. The forecast accuracy is then further enhanced through a dynamic probabilistic model integration strategy that adapts to real-time traffic conditions by dynamically weighting the models based on their performance. It is found that the hybrid framework proposed in the present paper performs much better than the traditional models in predicting traffic volume. Deep learning models with RMSE of 26.9976, AR (RMSE: 22.6679) and SVR (RMSE: 28.2221) provided remarkable prediction accuracy. Based on the results, the authors are confident that the integrated models are useful for real-time traffic management applications. However, they still have great potential for further refinement and optimization.

Ga-doped ZnO nanoparticles for enhanced CO2 gas sensing applications

Gallium-doped zinc oxide (GZO) has demonstrated significant potential in gas-sensing applications due to its enhanced electrical and chemical properties. This study focuses on the synthesis, characterization, and gas-sensing performance of GZO nanoparticles (NPs), specifically targeting CO₂ detection, which is crucial for environmental monitoring and industrial safety. The GZO samples were synthesized using a sol–gel method, and their crystal structure was determined through X-ray diffraction (XRD), confirming the successful incorporation of gallium into the ZnO lattice. X-ray photoelectron spectroscopy (XPS) was employed to analyze the samples’ elemental composition and chemical state, revealing the presence of Ga in the ZnO matrix and providing insights into the doping effects. Transmission electron microscopy (TEM) combined with energy-dispersive X-ray spectroscopy (EDS) was used to confirm the purity and elemental distribution of the synthesized samples, ensuring the homogeneity of the Ga doping. In-situ TEM measurements were also conducted on one of the three samples, with the smallest size. The experiment involved exposing the sample to argon (Ar) as a reference gas and carbon dioxide (CO₂) as the target gas to evaluate the sensor’s response under real-time conditions. The in-situ TEM provided nanoscale observation of changes in the crystal structure parameters, particularly the d-spacing, which exhibited significant alterations exceeding 3.2% when exposed to CO₂ and Ar gases. Furthermore, electron paramagnetic resonance (EPR) and optical joint density of states (OJDS) analyses were performed to examine the presence of paramagnetic defects and to comprehensively understand the electronic structure within the GZO sample, respectively.

Research on the Resource-Allocation-Optimization Strategy for Offshore Wind Power Construction Considering Complex Influencing Factors

The construction process of offshore wind farms involves multiple complexities, which is very complex to be scheduled manually, and the coordinating and optimized scheduling not only decreases project construction costs but also increases the construction speed. The impact of meteorological conditions on offshore wind power construction has been considered, and optimizing resource-allocation strategies under complex influencing factors has been analyzed. Then, a comprehensive strategy optimization index system is developed, which includes key indicators, such as the minimum working hours, resource-allocation-optimization rate, window period utilization rate, and cost–benefit ratio. Additionally, an offshore wind power resource-allocation-optimization model is formulated based on discrete event simulation (DES). A statistical analysis of each optimization index was performed using this model to assess the impact of resource-allocation strategies. The simulation results demonstrate that the model can not only simulate the construction process of offshore wind farms and monitor the state of wind turbines, personnel, and meteorological conditions in real time but also accurately calculate key indicators, such as the minimum working hours, resource-allocation-optimization rate, window period utilization rate, and cost–benefit ratio. This strategy effectively enhances resource-allocation efficiency during the wind farm installation phase and improves the overall construction process efficiency.

Digital Analysis Methods in Business-Processes and Projects in Digital Twins at Investment-Construction Companies

The article analyzes modeling of investment-construction company work from graph models to digital analogues. Such analysis is necessary in order to build economically grounded twin of the investment-construction company. The need to use advanced digital methods of business-analysis in conditions of real time was substantiated. It was demonstrated that elaboration of digital methods of business-analysis in projects and business-processes in investment-construction work is acute and leans against numerous factors. They are as follows: positive overseas experience in projecting and controlling processes; adjustment of this experience to specific features of project and business-process organization in Russia; use of digital imitation models of investment-construction enterprise in conditions of real time; introduction of digital methods of strategic and operative business-analysis of projects and business-processes into imitation model and digital twins at investment-construction enterprises; use of promising digital technologies in digital methods of business-analysis in projects and business-processes, for instance, analysis of big data, artificial neuron nets, cloud technologies, internet of things, virtual reality, augmented reality, technologies of register distribution, automation and robotics of business-processes, etc. Typical features of digital analysis built in digital twin for effective decision-making in management were identified. The article provided results of modeling visualization and digital analysis of business-processes.

Temperature-Responsive Hydrogel System Integrating Wound Temperature Monitoring and On-demand Drug Release for Sequentially Inflammatory Process Regulation of Wound Healing.

Wound healing faces challenges like inflammation, infection, and limited monitoring capabilities, and traditional dressings often lack the ability to promote healing or provide real-time wound status updates. Early pro-inflammatory responses help clear pathogens and damaged tissue, while timely anti-inflammatory modulation aids tissue regeneration, making sequential inflammation regulation crucial. Additionally, wound temperature, a key infection biomarker, enables real-time monitoring for effective management. We propose a temperature-responsive hydrogel dressing capable of two-stage sequential drug release and wound temperature monitoring. The hydrogel, composed of poly(N-isopropylacrylamide) (PNIPAM) and dopamine/methacrylated-modified hyaluronic acid (HA-MA-DA), allows temperature-based drug release control, sequential regulating pro-inflammatory and anti-inflammatory stages in wound healing. Interleukin-8 (IL-8), a pro-inflammatory molecule, is encapsulated into hydrogel matrix and rapidly released to trigger the initial inflammatory response. Furthermore, photothermally responsive and erastin-loaded polydopamine@PNIPAM nanoparticles (E-PD NPs) are incorporated to release the anti-inflammatory drug erastin upon near-infrared light exposure, terminating inflammation through cytosolic burial, and thus achieve anti-inflammatory effects at the second stage of wound healing. Furthermore, a bluetooth module enables real-time wound temperature monitoring. Combining sequential drug release with temperature monitoring, our hydrogel dressing addresses significant gaps in current wound healing technologies and offers new insights into personalized therapeutic interventions.

Automatic Classification of Rotating Rectifier Faults in Brushless Synchronous Machines

This paper presents an advanced automatic fault classification method for detecting rotating rectifier faults in brushless synchronous machines (BSMs). The proposed approach employs a multilayer perceptron (MLP) neural network to classify the operational states of the rotating rectifier, including healthy conditions and common fault types: open-diode (OD), shorted-diode (SD), and open-phase (OP). Key machine measurements, available on an ordinary basis in the industry, such as active power (P), reactive power (Q), stator voltage (U), and excitation current (Ie), are used as inputs for this model, allowing for non-invasive, real-time fault detection. This model achieved an overall classification accuracy of 93.4%, with a precision of 94.9% for fault detection and strong recall performance across multiple fault types. The neural network’s robustness is enhanced by advanced data processing techniques, including Gaussian filtering and class balancing through the synthetic minority over-sampling technique (SMOTE). Experimental testing on a modified 5-kVA BSM setup, where rectifier faults were systematically induced, was used to train the network and validate the model’s performance. This method provides a promising tool for real-time condition monitoring of BSMs, improving machine reliability and minimizing downtime in industrial applications.

Geophysical methods of ensuring technological sovereignty and national security of Russia in the Arctic

The article discusses the achievements and problems of geological exploration in the Arctic offshore areas. The authors of the article obtained fundamentally new knowledge about the prospects for oil and gas content of the sedimentary cover and heterogeneous basement, as well as gas saturation in the free and hydrated states in the upper part of sedimentary deposits. A complex of new technologies has been developed, including 4D seismic exploration in near real time conditions. For the first time, seismic refraction data were used to map the distribution of frozen and thawed ground on the shelf of the seas of Eastern Siberia. A fundamentally new justification has been given for the gas-dynamic genesis of the formation of giant craters in the Arctic, which implies the formation of gas-saturated cavities in the masses of ground ice under the influence of endogenous processes. According to remote Yamal Peninsula, as well as coastal parts of the Kara Sea, 6022 zones of intense (explosive) degassing with the formation of craters (pockmarks) were revealed. For the first time, according to RS data from space, large mud-volcanic edifices with pronounced craters were discovered at the bottom of the Arctic thermokarst lakes. Causes and consequences of catastrophic man-made gas blowouts and self-ignition during drilling of exploratory wells at a number of fields in the Arctic were investigated.

Finite-Time Control for Automatic Berthing of Pod-Driven Unmanned Surface Vessel with an Event-Triggering Mechanism

Coastal water accidents have occurred frequently in recent years, and human factors are still the main cause of these accidents. The purpose of this study is to provide a better and safer solution for the symmetry problem of unmanned surface vessels during automatic berthing in coastal waters. In automatic berthing, the symmetry problem refers to whether the USVs can maintain a stable state during motion and positioning, including dynamic symmetry and environmental symmetry. A finite-time controller based on a global nonsingular terminal sliding mode is designed to improve response speed and state consistency. Dynamic uncertainty and disturbance are considered in the design process, which can optimize the control law and effectively ensure the symmetry of the vessel in different states. Then, an event-triggering mechanism based on a dynamic threshold is adopted. In practice, there is excessive operation of the actuator. This mechanism ensures that the actuator is triggered only when the threshold is reached. USVs can adaptively adjust control strategies based on the real-time status, thereby improving symmetry during berthing. In simulation analysis, a pod-driven unmanned surface vessel with good maneuverability is taken as the research object. The results indicate that this control strategy can ensure rapid and consistent response of the vessel when subjected to external disturbances, which helps to maintain the symmetry of the automatic berthing motion under different conditions.

Single-shot 3 × 3 Mueller matrix microscopy with color polarization encoding.

Snapshot retrieval of linear Stokes parameters in polarized light microscopy has benefited from recent advances in nanotechnology which have made polarization image sensors commercially available. However, real-time retrieval of the 3 × 3 Mueller matrix (MM) is limited by the need for time-sequential change in polarization states in the input. In the present Letter, we propose a cost-effective technique for the real-time retrieval of the 3 × 3 Mueller matrix by encoding linear polarization states in each of the three color channels of the source and analyzing light passing through the sample using a color polarization sensor placed in the imaging plane of a transmission microscope. Validation experiments for samples under real-time imaging conditions are presented. To the best of our knowledge, this is the first report on the snapshot 3 × 3 Mueller matrix element retrieval in polarized light microscopy by combining division of focal plane (DoFP) color polarimetric sensing and polarization color encoding of the source.

Coupled theoretical modelling for the photoelectric performance by the concave-bent flexible PV module

Flexible PV module can fit with complex building skins and provide electricity for the building users. However, for the concave bent PV module, the special structure will cast shadow on itself, resulting in power loss during the working conditions, which is rarely investigated in previous studies. Combined optical-electrical-thermal model is decoupled to describe the realistic physical field and predict the concave module's electrical power output and working temperature. The beam shading ratio (BSR) and the diffuse shading ratio (DSR) are proposed based on the microfacet subdivision, which describes the real-time solar irradiance distribution and quantify the influence of self-shadow on the photoelectric performance by the concave-bent module under dynamic real-time working conditions. The concave PV module with the longitudinal/lateral layout is constructed and outdoor experiments are conducted to collect the data for the theoretical model verification. The parametric analysis is carried out to investigate the influence of inclination, curvature, and azimuth, demonstrating that increasing inclination and curvature angles raise power loss due to shading. Weather data of four cities are imported into the model to conduct annual performance simulation with longitudinal/lateral installation respectively. The model established in this paper has significant value for performance prediction in complex surface photovoltaic systems.

Gradual Failure of a Rainfall-Induced Creep-Type Landslide and an Application of Improved Integrated Monitoring System: A Case Study.

Landslides cause severe damage to life and property with a wide-ranging impact. Infiltration of rainfall is one of the significant factors leading to landslides. This paper reports on a phase creep landslide caused by long-term rainfall infiltration. A detailed geological survey of the landslide was conducted, and the deformation development pattern and mechanism of the landslide were analyzed in conjunction with climatic characteristics. Furthermore, reinforcement measures specific to the landslide area were proposed. To monitor the stability of the reinforced slope, a Beidou intelligent monitoring and warning system suitable for remote mountainous areas was developed. The system utilizes LoRa Internet of Things (IoT) technology to connect various monitoring components, integrating surface displacement, deep deformation, structural internal forces, and rainfall monitoring devices into a local IoT network. A data processing unit was established on site to achieve preliminary processing and automatic handling of monitoring data. The monitoring results indicate that the reinforced slope has generally stabilized, and the improved intelligent monitoring system has been able to continuously and accurately reflect the real-time working conditions of the slope. Over the two-year monitoring period, 13 early warnings were issued, with more than 90% of the warnings accurately corresponding to actual conditions, significantly improving the accuracy of early warnings. The research findings provide valuable experience and reference for the monitoring and warning of high slopes in mountainous areas.

Simultaneous Structural Monitoring over Optical Ground Wire and Optical Phase Conductor via Chirped-Pulse Phase-Sensitive Optical Time-Domain Reflectometry.

Optimizing the use of existing high-voltage transmission lines demands real-time condition monitoring to ensure structural integrity and continuous service. Operating these lines at the current capacity is limited by safety margins based on worst-case weather scenarios, as exceeding these margins risks bringing conductors dangerously close to the ground. The integration of optical fibers within modern transmission lines enables the use of Distributed Fiber Optic Sensing (DFOS) technology, with Chirped-Pulse Phase-Sensitive Optical Time-Domain Reflectometry (CP-ΦOTDR) proving especially effective for this purpose. CP-ΦOTDR measures wind-induced vibrations along the conductor, allowing for an analysis of frequency-domain vibration modes that correlate with conductor length and sag across spans. This monitoring system, capable of covering distances up to 40 km from a single endpoint, enables dynamic capacity adjustments for optimized line efficiency. Beyond sag monitoring, CP-ΦOTDR provides robust detection of external threats, including environmental interference and mechanical intrusions, which could compromise cable stability. By simultaneously monitoring the Optical Phase Conductor (OPPC) and Optical Ground Wire (OPGW), this study offers the first comprehensive, real-time evaluation of both structural integrity and potential external aggressions on overhead transmission lines. The findings demonstrate that high-frequency data offer valuable insights for classifying mechanical intrusions and environmental interferences based on spectral content, while low-frequency data reveal the diurnal temperature-induced sag evolution, with distinct amplitude responses for each cable. These results affirm CP-ΦOTDR's unique capacity to enhance line safety, operational efficiency, and proactive maintenance by delivering precise, real-time assessments of both structural integrity and external threats.