System Simulation Research Articles

Impact of the horizontal resolution of GEOS-Chem on land‒ocean and tropic‒extratropic partitioning and seasonal cycle in CO2 inversion

Abstract Credible regional carbon budget estimates from atmospheric CO2 measurements rely on the accuracy of atmospheric transport models (ATMs). However, the atmospheric transport in ATMs is usually simplified and spatiotemporally averaged, leading to systematic biases in simulating the atmospheric CO2 and estimating surface CO2 fluxes. We show that forward simulations of global CO2 using an ATM, GEOS-Chem, at a native resolution of 0.5°×0.625° and a coarse resolution of 4°×5° differ significantly near the surface in the Northern Hemisphere and the polar vortex, mainly because of advection in GEOS-Chem. Comparing observing system simulation experiments that assimilate synthetic observations sampled from the forward simulations, we separate the impact of coarse-resolution GEOS-Chem on regional flux estimates. The results suggest that a significant amount of annual carbon uptake from the ocean and tropics is improperly redistributed to the land and northern and southern extratropics, respectively. In addition, these errors lead to an underestimated seasonal amplitude in the northern extratropical land and a reversed sign of the seasonal cycle in the northern extratropical ocean. The reversed sign of the seasonal cycle has also been observed in a real data assimilation experiment and several state-of-the-art inversions, suggesting that reasonable ocean flux estimates depend strongly on the accuracy of the ATM.

National Addiction Workshop: A Virtual Adaptation to Support Competency Development in Opioid Use Disorder Management.

Accessible, manualized, skill-based training ready for wide dissemination is needed to prepare healthcare staff to meet the needs of people impacted by the opioid epidemic. A 2-day workshop and simulation training was designed by an interprofessional substance use disorder (SUD) specialty care team, adapted to a virtual platform, manualized, and offered to healthcare staff and trainees from a large healthcare system. The workshop was offered 6 times over the course of 10 months with a total of 177 participants from across the United States enrolled in the training. Interactive experiential learning strategies including games designed to test knowledge, small-group case discussions, video demonstrations of skills, patient panels, and 3 simulations of a patient with chronic pain who developed opioid use disorder in the context of long-term opioid therapy were utilized in efforts to build skills and confidence managing SUDs in primary care and general mental health settings. Of those who completed the post-workshop survey, most found both content and training structure useful, particularly content related to medication management, stigma, and collaborative care. In addition, overall confidence scores in assessing, diagnosing, and treating SUD increased. Skill building exercises, such as interprofessional team simulations, were highlighted as most beneficial. The workshop received national attention leading to a partnership with the healthcare system's simulation center for wider dissemination. Expanding access to SUD treatment requires training healthcare staff to effectively change attitudes, increase knowledge, and improve key skills. This 2-day interprofessional workshop was well-received by participants who reported high acceptability and satisfaction scores and demonstrated improved confidence in the management of SUDs. This type of manualized, collaborative, skill-based learning experience can foster staff preparedness and willingness to conceptualize SUD as a chronic condition amenable to treatment in different healthcare settings.

Enhancing Daylight Comfort with Climate-Responsive Kinetic Shading: A Simulation and Experimental Study of a Horizontal Fin System

This study employs both simulation and experimental methodologies to evaluate the effectiveness of bi-sectional horizontal kinetic shading systems (KSS) with horizontal fins in enhancing daylight comfort across various climates. It emphasizes the importance of optimizing daylight levels while minimizing solar heat gain, particularly in the context of increasing energy demands and shifting climatic patterns. The study introduces a custom-designed bi-sectional KSS, simulated in three distinct climates—Wroclaw, Tehran, and Bangkok—using climate-based daylight modeling methods with the Ladybug and Honeybee tools in Rhino v.7 software. Standard daylight metrics, such as Useful Daylight Illuminance (UDI) and Daylight Glare Probability (DGP), were employed alongside custom metrics tailored to capture the unique dynamics of the bi-sectional KSS. The results were statistically analyzed using box plots and histograms, revealing UDI300–3000 medians of 78.51%, 88.96%, and 86.22% for Wroclaw, Tehran, and Bangkok, respectively. These findings demonstrate the KSS’s effectiveness in providing optimal daylight conditions across diverse climatic regions. Annual simulations based on standardized weather data showed that the KSS improved visual comfort by 61.04%, 148.60%, and 88.55%, respectively, compared to a scenario without any shading, and by 31.96%, 54.69%, and 37.05%, respectively, compared to a scenario with open static horizontal fins. The inclusion of KSS switching schedules, often overlooked in similar research, enhances the reproducibility and clarity of the findings. A physical reduced-scale mock-up of the bi-sectional KSS was then tested under real-weather conditions in Wroclaw (latitude 51° N) during June–July 2024. The mock-up consisted of two Chambers ‘1’ and ‘2’ equipped with the bi-sectional KSS prototype, and the other one without shading. Stepper motors managed the fins’ operation via a Python script on a Raspberry Pi 3 minicomputer. The control Chamber ‘1’ provided a baseline for comparing the KSS’s efficiency. Experimental results supported the simulations, demonstrating the KSS’s robustness in reducing high illuminance levels, with illuminance below 3000 lx maintained for 68% of the time during the experiment (conducted from 1 to 4 PM on three analysis days). While UDI and DA calculations were not feasible due to the limited number of sensors, the Eh1 values enabled the evaluation of the time illuminance to remain below the threshold. However, during the June–July 2024 heat waves, illuminance levels briefly exceeded the comfort threshold, reaching 4674 lx. Quantitative and qualitative analyses advocate for the broader application and further development of KSS as a climate-responsive shading system in various architectural contexts.

Dynamics of Two Planets near a 2:1 Resonance: Case Studies of Known and Synthetic Exosystems on a Grid of Initial Configurations

The distribution of period ratios for 580 known two-planet systems is apparently nonuniform, with several sharp peaks and troughs. In particular, the vicinity of the 2:1 commensurability seems to have a deficit of systems. Using Monte Carlo simulations and an empirically inferred population distribution of period ratios, we prove that this apparent dearth of near-resonant systems is not statistically significant. The excess of systems with period ratios in the wider vicinity of the 2:1 resonance is significant, however. Long-term WHFast integrations of a synthetic two-planet system on a grid period ratios from 1.87 through 2.12 reveal that the eccentricity and inclination exchange mechanism between non-resonant planets represents the orbital evolution very well in all cases, except at the exact 2:1 mean motion resonance. This resonance destroys the orderly exchange of eccentricity, while the exchange of inclination still takes place. Additional simulations of the Kepler-113 system on a grid of initial inclinations show that the secular periods of eccentricity and inclination variations are well fitted by a simple hyperbolic cosine function of the initial mutual inclination. We further investigate the six known two-planet systems with period ratios within 2% of the exact 2:1 resonance (TOI-216, KIC 5437945, Kepler-384, HD 82943, HD 73526, HD 155358) on a grid of initial inclinations and for two different initial periastron longitudes corresponding to the aligned and anti-aligned states. All these systems are found to be long-term stable except HD 73526, which is likely a false positive. The periodic orbital momentum exchange is still at work in some of these systems, albeit with much shorter cycling periods of a few years.

Enhancing Task-Oriented Dialogue Systems through Synchronous Multi-Party Interaction and Multi-Group Virtual Simulation

This paper presents two innovative approaches: a synchronous multi-party dialogue system that engages in simultaneous interactions with multiple users, and multi-group simulations involving virtual user groups to evaluate the resilience of this system. Unlike most other chatbots that communicate with each user independently, our system facilitates information gathering from multiple users and executes 17 administrative tasks for group requests adeptly by leveraging a state machine-based framework for complete control over dialogue flow and a large language model (LLM) for robust context understanding. Assessing such a unique dialogue system poses challenges, as it requires many groups of users to interact with the system concurrently for an extended duration. To address this, we simulate various virtual groups using an LLM, each comprising 10–30 users who may belong to multiple groups, in order to evaluate the efficacy of our system; each user is assigned a persona and allowed to interact freely without scripts. As a result, our system shows average success rates of 87% for task completion and 89% for natural language understanding. Comparatively, our virtual simulation, which has an average success rate of 80%, is juxtaposed with a group of 15 human users, depicting similar task diversity and error trends. To our knowledge, it is the first work to show the LLM’s potential in both task execution and the simulation of a synchronous dialogue system to fully automate administrative tasks.

MSTAN: multi-scale spatiotemporal attention network with adaptive relationship mining for remaining useful life prediction in complex systems

Abstract In the era of smart manufacturing and advanced industrial systems, the high degree of integration and intelligence of equipment demands higher reliability and safety from systems. Existing methods often rely on historical data for Remaining Useful Life (RUL) prediction to achieve Prognostic and Health Management (PHM). However, the internal units of complex equipment exhibit significant spatial correlation and temporal diversity, making PHM for complex equipment a multidimensional challenge involving both temporal and spatial information, thereby severely limits the effectiveness of RUL prediction for complex systems. Addressing these challenges, this study introduces a multi-scale spatiotemporal attention network with adaptive relationship mining, specifically designed for the remaining useful life (RUL) prediction of such equipment. The core of the proposed method lies in the multi-scale feature perception module, which adeptly extracts varied scale features from multidimensional sensor data. Following this, an innovative adaptive relationship mining module is integrated to uncover multi-order coupling relationships between diverse sensors, enhancing the model’s predictive accuracy. Furthermore, a spatiotemporal attention module is employed to discern and emphasize crucial spatiotemporal correlations. To validate the effectiveness and superiority of the proposed method, the Commercial Modular Aero-propulsion System Simulation (C-MAPSS) dataset is employed for comprehensive performance evaluation, the IEEE 2012 PHM bearing dataset is also adopted to demonstrate the generalization and robustness of the proposed method. The results not only show a notable improvement over existing methods but also offer a more intuitive understanding through visual representations, marking a significant stride in enhancing the safety and efficiency of complex systems.

Dynamic Fault Tree Generation and Quantitative Analysis of System Reliability for Embedded Systems Based on SysML Models

As embedded systems become increasingly complex, traditional reliability analysis methods based on text alone are no longer adequate for meeting the requirements of rapid and accurate quantitative analysis of system reliability. This article proposes a method for automatically generating and quantitatively analyzing dynamic fault trees based on an improved system model with consideration for temporal characteristics and redundancy. Firstly, an “anti-semantic” approach is employed to automatically explore the generation of fault modes and effects analysis (FMEA) from SysML models. The evaluation results are used to promptly modify the system design to meet requirements. Secondly, the Profile extension mechanism is used to expand the SysML block definition diagram, enabling it to describe fault semantics. This is combined with SysML activity diagrams to generate dynamic fault trees using traversal algorithms. Subsequently, parametric diagrams are employed to represent the operational rules of logic gates in the fault tree. The quantitative analysis of dynamic fault trees based on probabilistic models is conducted within the internal block diagram of SysML. Finally, through the design and simulation of the power battery management system, the failure probability of the top event was obtained to be 0.11981. This verifies that the design of the battery management system meets safety requirements and demonstrates the feasibility of the method.

Growth and nitrogen uptake modeling in composite and hybrid corn varieties

Corn is a strategic commodity in Indonesia's agricultural and economic development. The problem of corn production can be overcome with proper usage of nitrogen fertilization. Dynamic modeling can be used as a tool to solve this problem. This study aimed to develop a dynamic model for the growth and nitrogen uptake of composite and hybrid corn varieties. Stages in the model construction: (1) identifying system components, (2) model construction, (3) simulation, and (4) validation. The growth model is a construction of the plant's ecophysiological response to solar radiation and temperature in photosynthesis. A growth model in composite and hybrid corn varieties with simulated dry weight as output has been constructed validly. The model input components, such as light use efficiency, extinction coefficient, specific leaf area, and carbohydrate partition coefficient varied at each development phase for each variance. The actual total dry weight on the Pioneer 27 variety was 6,406 kg ha-1 with a simulated total dry weight was 6,228 kg ha-1, and the actual total dry weight on the Bisma variety was 5,127 kg ha-1 with a simulated total dry weight was 4,864 kg ha-1. The level of validity of the Pioneer 27 variety growth model reached 83.5% and the Bisma variety reached 80%. Keywords: Bisma; dry weight; growth model; nitrogen uptake; Pioneer 27; simulation; validation

Optimizing Numerical Weather Prediction Utility of the Maryland Mesonet with Observing System Simulation Experiments

Abstract The Maryland Mesonet Project will construct a network of 75 surface observing stations with aims that include mitigating the statewide impact of severe convective storms and improving analyses of record. The spatial configuration of mesonet stations is expected to affect the utility newly provided observations will have via data assimilation, making it desirable to study the effects of mesonet configuration. Furthermore, the impact associated with any observing system configuration is constrained by errors inherent to the prediction systems used to generate forecasts, which may change with future advances in data assimilation methodology, physical parameterization schemes, and resource availability. To address such possibilities, we perform sets of observing system simulation experiments using a high-resolution regional modeling system to assess the expected impact of four candidate mesonet configurations. Experiments cover seven 18-hour case-study events featuring moist convective regimes associated with severe weather over the state of Maryland and are performed using two versions of our experimental modeling system: a ’standard-uncertainty’ configuration tuned to be representative of existing convective-allowing prediction systems, and a ’constrained-uncertainty’ configuration with reduced boundary condition and model error that reflects a possible trajectory for future prediction systems. We find that the assimilation of mesonet data produces definitive improvements to analysis fields below 1000 m that are mediated by modeling system uncertainty. Conversely, mesonet impact on forecast verification is inconclusive and strongly variable across verification metrics. The impact of mesonet configuration appears limited by a saturation effect that caps local analysis improvements past a minimal density of observing stations.

Polynomial filter-based adaptive fault-tolerant control for a class of nonlinear systems with multiplicative faults

In this article, the problem of fault-tolerant control is investigated for polynomial nonlinear systems (PNSs) subject to multiplicative faults and measurement noise. Firstly, a matrix Taylor expansion technique is applied to transform the PNS into a linear parameter varying system. Then the polynomial filter is constructed to get estimations of states and unmatched disturbances, which reduces the effect of measurement noise. A novel adaptive fault-tolerant controller is proposed to compensate for multiplicative faults and unmatched disturbances. Furthermore, the condition of the parameter-dependent linear matrix inequality (PDLMI) is deduced, which guarantees the boundedness of tracking errors and estimation errors, and the sum of squares (SOS) decomposition technique is utilized to solve PDLMI. Finally, the simulation of a rotary steerable drilling tool system confirms the feasibility of the proposed adaptive fault-tolerant control framework.

Direct torque control of bearingless induction motor based on super-twisting sliding mode control

A bearingless induction motor (BIM) is an innovative magnetic levitation motor that incorporates torque system and suspension systems. Aiming to improve the anti-interference and levitation performance of the BIM, a second-order super-twisting sliding mode direct torque control strategy (STSM-DTC) is proposed. Initially, the basic principles and convergence conditions of the super-twisting algorithm are analysed. Subsequently, corresponding sliding mode surfaces are constructed according to speed, torque, and stator flux error, and the corresponding super-twisting sliding mode controllers (STSMC) are designed in the torque system. The STSMCs are designed based on the rotor radial displacement error and further enhanced by incorporating a differential loop in the suspension system. The hyperbolic tangent function tanh(x) is used as the switching function. Simulation and experimental of the BIM STSM-DTC system show that the STSM-DTC strategy effectively improves the anti-interference ability and suspension performance of the BIM, and show better dynamic response.

Mesoscale Convective Systems Represented in High Resolution E3SMv2 and Impact of New Cloud and Convection Parameterizations

AbstractIn this study, we evaluate mesoscale convective system (MCS) simulations in the second version of U.S. Department of Energy (DOE) Energy Exascale Earth System Model (E3SMv2). E3SMv2 atmosphere model (EAMv2) is run at the uniform 0.25° horizontal resolution. We track MCSs consistently in the model and observations using PyFLEXTRKR algorithm, which defines MCSs based on both cloud top brightness temperature (Tb) and surface precipitation. Results from using only Tb to define MCSs are also discussed to understand the impact of different MCS tracking algorithms on MCS evaluation and provide additional insights into model errors in simulating MCSs. Our results show that EAMv2 simulated MCS precipitation is largely underestimated in tropical and extratropical regions. This is mainly attributed to the underestimated MCS genesis and underestimated precipitation intensity in EAMv2. Comparing the two MCS tracking methods, simulated MCS precipitation is increased if MCSs are defined with only cloud top Tb. The Tb‐based MCS tracking method, however, includes cloud systems with very weak precipitation. This illustrates the model issues in simulating heavy precipitation even though the convective cloud shield is overall well simulated from the moist convective processes. Furthermore, sensitivity experiments are performed to examine the impact of new cloud and convection parameterizations developed for EAMv3 on simulated MCSs. The new physics parameterizations help increase the relative contribution of convective precipitation to total precipitation in the tropics, but the simulated MCS properties are not significantly improved. This suggests that simulating MCSs still remain a challenge for the next version of E3SM.

Assimilation of ground-based GNSS data using a local ensemble Kalman filter

Tropical cyclones become increasingly nonlinear and dynamically unstable in high-resolution models. The initial conditions are typically sub-optimal, leaving scope to improve the accuracy of forecasts with improved data assimilation. Simultaneously, the lack of real ground-based GNSS observations over the ocean poses significant challenges when evaluating the assimilation results in oceanic regions. In this study, an Observation System Simulation Experiment is carried out based on a tropical cyclone case. Assimilation experiments using the WRF-PDAF framework are conducted. Conventional and GNSS observation operators are implemented. A diverse array of synthetic observations, encompassing temperature (T), wind components (U and V), precipitable water (PW), and zenith total delay (ZTD), are assimilated utilizing the Local Error-Subspace Transform Kalman filter (LESTKF). The findings highlight the improvement in forecast accuracy achieved through the assimilation process over the ocean. Multiple observation types further improve the forecast accuracy. The study underscores the crucial role of GNSS data assimilation techniques. The assimilation of GNSS data presents potential for advancing weather forecasting capabilities. Thus, the construction of ground-based GNSS observation stations over the ocean is promising.

Enhancing safety feedback to the design of small, unmanned aircraft by joint assessment of impact area and human fatality.

Advantages of commercial UAS-based services come with the disadvantage of posing third party risk (TPR) to overflown population on the ground. Especially challenging is that the imposed level of ground TPR tends to increase linearly with the density of potential customers of UAS services. This challenge asks for the development of complementary directions in reducing ground TPR. The first direction is to reduce the rate of a UAS crash to the ground. The second direction is to reduce overflying in more densely populated areas by developing risk-aware UAS path planning strategies. The third direction is to develop UAS designs that reduce the product in case of a crashing UAS, where is the size of the crash impact area on the ground, and is the probability of fatality for a person in the crash impact area. Because small UAS accident and incident data are scarce, each of these three developments is in need of predictive models regarding their contribution to ground TPR. Such models have been well developed for UAS crash event rate and risk-aware UAS path planning. The objective of this article is to develop an improved model and assessment method for the product In literature, the model development and assessment of the latter two terms is accomplished along separate routes. The objective of this article is to develop an integrated approach. The first step is the development of an integrated model for the product . The second step is to show that this integrated model can be assessed by conducting dynamical simulations of Finite Element (FE) or Multi-Body System (MBS) models of collision between a UAS and a human body. Application of this novel method is illustrated and compared to existing methods for a DJI Phantom III UAS crashing to the ground.

A Comparative Study of Data-Driven Prognostic Approaches under Training Data Deficiency

In industrial system health management, prognostics play a crucial role in ensuring safety and enhancing system availability. While the data-driven approach is the most common for this purpose, they often face challenges due to insufficient training data. This study delves into the prognostic capabilities of four methods under the conditions of limited training datasets. The methods evaluated include two neural network-based approaches, Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) networks, and two similarity-based methods, Trajectory Similarity-Based Prediction (TSBP) and Data Augmentation Prognostics (DAPROG), with the last being a novel contribution from the authors. The performance of these algorithms is compared using the Commercial Modular Aero-Propulsion System Simulation (CMAPSS) datasets, which are made by simulation of turbofan engine performance degradation. To simulate real-world scenarios of data deficiency, a small fraction of the training datasets from the original dataset is chosen at random for the training, and a comprehensive assessment is conducted for each method in terms of remaining useful life prediction. The results of our study indicate that, while the Convolutional Neural Network (CNN) model generally outperforms others in terms of overall accuracy, Data Augmentation Prognostics (DAPROG) shows comparable performance in the small training dataset, being particularly effective within the range of 10% to 30%. Data Augmentation Prognostics (DAPROG) also exhibits lower variance in its predictions, suggesting a more consistent performance. This is worth highlighting, given the typical challenges associated with artificial neural network methods, such as inherent randomness, non-intuitive decision-making processes, and the complexities involved in developing optimal models.

Performance simulation and energy efficiency analysis of multi-energy complementary HVAC system based on TRNSYS

The failure rate and operating cost of the C-B (chiller-gas boiler) system, which has been in use for a long time, are increasing year by year, but there is a lack of reasonable programs and effective measures on how to retrofit the C-B system. In order to explore the prospect of ground-source heat pumps applied to the retrofit of C-B systems in HSCW (hot summer and cold winter) zone, this study designed a G-C-B (GSHP-Chiller-Boiler) system in a partial retrofit context and constructed a TRNSYS numerical simulation model of the retrofit system. Secondly, the performance and energy efficiency of the system is analyzed as well as different control strategies for the ground source heat pump cooling tower are discussed. Finally, the energy conversion efficiency of the chiller is compared based on the energy flow of the system. Studies have shown that the G-C-B system can achieve a seasonal performance factor of 5.3 during the cooling season and 4.1 during the heating season. Compared to the existing C-B system, year-round electricity consumption was reduced by 21 %, gas consumption by 92 %, and combined energy demand by 49.6 %. With the addition of GSHP, the energy conversion efficiency of the chiller has also increased by 21 %. The soil temperature of the G-C-B system increased by less than 0.5 °C after ten years of continuous operation. This paper can provide a valuable theoretical basis for the energy form modification of the C-B system in the HSCW zone.

A wide-output buck DC-DC power management IC

-This article designs and develops a wide-input voltage, high-efficiency, small-size, and peak current-mode control step-down DC-DC converter. The Cadence Spectre simulation tool is used for system simulation to verify the performance of the chip. The overall research content of the article includes the function of the output under heavy load, light load and the stability of the output under transient load changes. The specific content of the research is buck synchronous step-down DC-DC converter chip with pulse modulated. It is provided with an input-voltage range of 6 V–80 V and maximum output-voltage range 72 V. The chip possesses wide operating temperature range of −20 °C to 130 °C. The 92 % high-efficiency can be achieved by using a PWM/PFM hybrid modulation method. When achieving transient load jump, the output voltage change shall not exceed 150 mV. The maximum load current of the chip is 1 A. Furthermore, the chip is packaged and samples can be obtained, and the output light load/heavy load, and other functions are tested through the circuit board. In addition, the chip achieved tape out by using 0.18 μm CMOS process with size of 2027 μm × 2020 μm. The converter features current mode control to simplify external compensation and optimize transient response through a wide range of inductors and output capacitors. It can be adopted user-programmable soft-start time to prevent inrush current during startup. It also includes thermal shutdown protection to provide safe and smooth operation in operating conditions.

High fidelity adaptive mirror simulations with reduced order models

In the design process of large adaptive mirrors numerical simulations represent the first step to evaluate the system design compliance in terms of performance, stability and robustness. For the next generation of Extremely Large Telescopes increased system dimensions and bandwidths lead to the need of modeling not only the deformable mirror alone, but also all the system supporting structure or even the full telescope. The capability to perform the simulations with an acceptable amount of time and computational resources is highly dependent on finding appropriate methods to reduce the size of the resulting dynamic models. In this paper we present a framework developed together with the company Microgate to create a reduced order structural model of a large adaptive mirror as a preprocessing step to the control system simulations. The reduced dynamic model is then combined with the remaining system components allowing to simulate the full adaptive mirror in a computationally efficient way. We analyze the feasibility of our reduced models for Microgate’s prototype of the adaptive mirror of the Giant Magellan Telescope.

Prediction of Remaining Useful Life of Aero-engines Based on CNN-LSTM-Attention

Accurately predicting the remaining useful life (RUL) of aircraft engines is crucial for maintaining financial stability and aviation safety. To further enhance the prediction accuracy of aircraft engine RUL, a deep learning-based RUL prediction method is proposed. This method possesses the potential to strengthen the recognition of data features, thereby improving the prediction accuracy of the model. First, the input features are normalized and the CMAPSS (Commercial Modular Aero-Propulsion System Simulation) dataset is utilized to calculate the RUL for aircraft engines. After extracting attributes from the input data using a convolutional neural network (CNN), the extracted data are input into a long short-term memory (LSTM) network model, with the addition of attention mechanisms to predict the RUL of aircraft engines. Finally, the proposed aircraft engine model is evaluated and compared through ablation studies and comparative model experiments. The results indicate that the CNN-LSTM-Attention model exhibits superior prediction performance for datasets FD001, FD002, FD003, and FD004, with RMSEs of 15.977, 14.452, 13.907, and 16.637, respectively. Compared with CNN, LSTM, and CNN-LSTM models, the CNN-LSTM model demonstrates better prediction performance across datasets. In comparison with other models, this model achieves the highest prediction accuracy on the CMAPSS dataset, showcasing strong reliability and accuracy.

The Intersection of Health Policy and Technology: Applying Quantum Computing for Optimizing Healthcare System Simulations and Predictions

The convergence of fitness coverage and generation is a vital frontier in present-day healthcare, giving probabilities for innovation and optimization that have no means to visible earlier. The cause of this paper is to research the capacity for quantum computing to enhance computer simulations and forecasts of healthcare systems. To revolutionise customized medication processes, provided a progressive approach that everybody termed Efficient Personalised Medicine through Quantum Machine Learning Analysis (EPM-QMLA). This approach combines the abilities of quantum computing with device learning. The significance of this work is living inside the fact that it has the potential to substantially beautify the results of healthcare by way of making it possible to expand remedy plans which are greater correct and individualized. Nevertheless, there are limitations, inclusive of the fact that quantum computing programs in healthcare are still in their infant degrees, which necessitates the introduction of stable infrastructure and algorithmic advancements. The modern capacity of EPM-QMLA is verified through simulated analyses in a lot of healthcare situations. This demonstrates the capability of EPM-QMLA to optimise useful resource allocation, predict infection progression, and personalize remedies to particular patient profiles. Through this examine, the promise of quantum computing as a sturdy device for advancing healthcare coverage and exercise is highlighted. This will pave the manner for a healthcare device this is greater efficient and targeted on the patient.