Operational Settings Research Articles

A Bayesian framework for in-flight calibration and discrepancy reduction of spacecraft operational simulation models

Modeling and Simulation (M&S) tools have become indispensable for the comprehensive design, operations, and maintenance of products in the space industry. An example is the European Space Agency (ESA), which relies heavily on M&S throughout the entire lifecycle of a spacecraft. However, their use in operational settings poses significant challenges, mainly attributable to (i) the harsh, uncontrollable, and often unforeseen environmental conditions; (ii) the dramatic changes in operating conditions throughout a spacecraft’s lifespan, often beyond the intended designed-for lifetime; and (iii) the presence of epistemic and aleatoric uncertainty. This results in unavoidable discrepancies between the numerical simulations and real measurements, limiting their use for delicate operational tasks. To address those challenges, we present a Bayesian framework for simultaneous calibration of M&S tools, reduction of the model discrepancy, and quantification of the process and model uncertainties. The approach leverages the Kennedy and O’Hagan (KOH) calibration, tailored for a multi-objective problem. Its effectiveness is shown by its application to flying Earth observation spacecraft data and the operational simulation models.

The Future of Microreactors: Technological Advantages, Economic Challenges, and Innovative Licensing Solutions with Blockchain

This study details the unique advantages and challenges associated with microreactors. Microreactors offer rapid installation and flexible application capabilities, meeting energy needs in remote and inaccessible areas. Unlike large nuclear power plants, they can be set up and start generating energy within a few days, resulting in significant time and cost savings. Their small size and modular design reduce capital and operational costs while enhancing economic competitiveness. However, some technical and regulatory challenges persist for the widespread adoption of microreactors. Licensing processes designed for large nuclear power plants may delay the widespread adoption of microreactors. Blockchain technology can play a crucial role in overcoming these challenges by providing transparency and reliability in the licensing processes. The operational settings of microreactors should be carefully considered, and regulatory authorities must be effectively designated. Collaboration and coordination are vital in this process. Consequently, the flexibility and innovative solutions offered by microreactors highlight the importance of future research to examine the optimal conditions for their use.

Assessment of Surface Roughness of Non-Metallic Materials during Laser Processing

Experimental studies of the surface of non-metallic materials have been carried out to determine the roughness parameters of materials such as leather, bone, wood, plastic after processing the surface of materials with a laser beam. To assess the quality of the surface layer of materials, the depth of penetration of laser radiation into the material, the average value of the micro unevenness, and the mean square deviation of the micro unevenness were used. To describe the changes in the values of the micro unevenness, graphs of the correlation of the parameters of the micro unevenness and the modulus of elasticity of the surface of various non-metallic materials were used. Approximating polynomials are used to describe the correlations with the representation in a computer. A regression model is proposed that relates the properties of the material to the magnitude of the micro unevenness. Data on the depth of ablation for wood, bone, leather, plexiglass are presented, graphs of the correlation between the amount of surface micro unevenness and the density of materials, between the amount of surface micro-roughness and the modulus of elasticity of materials, the correlation coefficient between the amount of surface micro unevenness and the ignition temperature of organic materials. The obtained models make it possible to implement the proposed principle of laser processing of non-metallic materials, which consists in measuring the modulus of elasticity of the surface of the material and, based on the measurements obtained, control the modes of laser processing of products. The installation of laser surface treatment of non-metallic materials with the implementation of the principle of control of processing modes depending on the measured values of the modulus of elasticity of the surface of the material is proposed. To measure the elastic modulus of a material, a special sensor is used with indentation of the indenter and computer evaluation of the measurements obtained with the formation of solutions for controlling the modes of laser surface treatment of the material. The experimental results obtained made it possible to manufacture a number of products to ensure a given surface quality of non-metallic materials (a writing device, a gift for the newlyweds). Conducting experiments with changes in the power of laser radiation based on the results of measuring the modulus of elasticity of the surface of non-metallic materials has shown the effectiveness of operational setting of laser operating modes to ensure the quality of the surface of non-metallic materials.

Using chat GPT to evaluate police threats, risk and harm

General purpose artificial intelligence (GPAI) is a form of advanced AI system that includes the recently introduced ChatGPT. GPAI is known for its capacity to understand and emulate human responses, and potentially offers an opportunity to reduce human error when conducting tasks that involve analysis, judgement, and reasoning. To support officers to do this, the police presently use a range of decision-making support tools, one of which is called THRIVE (Threat, Harm, Risk, Investigation, Vulnerability, and Engagement). THRIVE is designed to provide police practitioners with a model to improve their identification and response to vulnerability. Despite the existence of such decision models, a 2020 meta-analysis of police cases resulting in death or serious injury identified contributory failures that included poor risk identification, risk management, failure to adhere to evidentiary processes, poor criminal investigations, and inadequate police engagement with victims, including the level of care and assistance provided (Allnock, et al, 2020). Importantly, this report outlined human error as being a major underpinning factor of the failures.Although GPAI offers an opportunity to improve analysis, judgement, and reasoning, such systems have not yet been tested in policing, a field where any reduction in human error, particularly in the assessment of threat, harm, risk, and vulnerability can potentially save lives. This study is the first attempt to do this by using the chain-of-thought prompt methodology to test the GPAI ChatGPT (3.5 vs 4) in a controlled environment using 30 life-like police scenarios, crafted, and analyzed by expert practitioners. In doing so, we identify that ChatGPT 4 significantly outperforms its 3.5 predecessor, indicating that GPAI presents considerable opportunity in policing. However, systems that use this technology require extensive directional prompting to ensure outputs that can be considered accurate, and therefore, potentially safe to utilize in an operational setting. The article concludes by discussing how practitioners and researchers can further refine police related chain-of-thought prompts or use application programming interfaces (APIs) to improve responses provided by such GPAI.

Commercially available activity monitors such as the fitbit charge and apple watch show poor validity in patients with gait aids after total knee arthroplasty

PurposeThe aim of this study is to determine the validity of consumer grade step counter devices during the early recovery period after knee replacement surgery.MethodsTwenty-three participants wore a Fitbit Charge or Apple Watch Series 4 smart watch and performed a walking test along a 50-metre hallway. There were 9 males and 14 females included in the study with an average age of 68.5 years and BMI of 32. Each patient wore both the Fitbit Charge and Apple Watch while completing the walking test and an observer counted the ground truth value using a thumb-push tally counter. This test was repeated pre-operatively with no gait aid, immediately post operatively with a walker, at 6 weeks follow up with a cane and at 6 months with no gait aid. Bland-Altman plots were performed for all walking tests to compare the agreement between measurement techniques.ResultsMean overall agreement of step count for pre-operative and at 6 months for subjects walking without gait aids was excellent for both the Apple Watch vs. actual and Fitbit vs. actual with bias values ranging from − 0.87 to 1.36 with limits of agreement (LOA) ranging between − 10.82 and 15.91. While using a walker both devices showed extremely little agreement with the actual step count with bias values between 22.5 and 24.37 with LOA between 11.7 and 33.3. At 6 weeks post-op while using a cane, both the Apple Watch and Fitbit devices had a range of bias values between − 2.8 and 5.73 with LOA between − 13.51 and 24.97.ConclusionsThese devices show poor validity in the early post operative setting, especially with the use of gait aids, and therefore results should be interpreted with caution.

Federated Transfer Fault Diagnosis Method Based on Variational Auto-Encoding with Few-Shot Learning

Achieving accurate equipment fault diagnosis relies heavily on the availability of extensive, high-quality training data, which can be difficult to obtain, particularly for models with new equipment. The challenge is further compounded by the need to protect sensitive data during the training process. This paper introduces a pioneering federated transfer fault diagnosis method that integrates Variational Auto-Encoding (VAE) for robust feature extraction with few-shot learning capabilities. The proposed method adeptly navigates the complexities of data privacy, diverse working conditions, and the cross-equipment transfer of diagnostic models. By harnessing the generative power of VAE, our approach extracts pivotal features from signals, effectively curbing overfitting during training, a common issue when dealing with limited fault samples. We construct a federated learning model comprising an encoder, variational feature generator, decoder, classifier, and discriminator, fortified with an advanced training strategy that refines federated averaging and incorporates regularization when handling non-independent data distributions. This strategy ensures the privacy of data while enhancing the model’s ability to discern subtleties in fault signatures across different equipment and operational settings. Our experiments, conducted across various working conditions and devices, demonstrate that our method significantly outperforms traditional federated learning techniques in terms of fault recognition accuracy. The innovative integration of VAE within a federated learning framework not only bolsters the model’s adaptability and accuracy but also upholds stringent data privacy standards.

Concept design, application and optimization of operational parameters for new forced safety injection tank system

Background The engineered safety systems are designed to execute fundamental safety functions encompassing reactivity confinement, reactivity control and decay heat removal. Failure of any one of these functions can result in severe accident conditions. Passive systems have been implemented as a better option for plant safety. This research proposes a modification of existing safety injection tanks to the new forced safety injection tanks (FSITs) that utilize the backpressure from the pressurizer or steam generator to drive coolant into the reactor core under high pressure conditions. Methods FSITs aim to extend the coping-time during accidents like Station Blackout, providing an extended timing window for deployment of FLEX systems. The mathematical design is proposed and implemented into the thermal-hydraulic input of a nuclear power plant to demonstrate the system’s applicability that was demonstrated by leveraging the conventional PRA approach and risk quantification. Results The suggested system useful when used in the accident scenario of Station black out in-coincident with turbine-driven pumps fail to run. As the proposed design is a conceptual design, the optimization of associated operational parameters and set points is necessary. This optimization is performed using the risk analysis and virtual control environment-based Dynamic Probabilistic Risk Assessment framework. The method and findings of this study affirm that the coping-time for Station Blackout can be significantly extended, ensuring a substantial margin for the effective deployment of the FLEX. Conclusions A concept design of a passive forced safety injection system was suggested and demonstrated by integrating the mathematical model into a thermal-hydraulic model of a nuclear power plant. The parameters were optimized and the results demonstrated that the new system was effective in recovering the nuclear power plant from the accidents such as SBO with turbine-driven pumps fail to operate.

Coupled CFD-DEM simulation of pin-type wet stirred media mills using immersed boundary approach and hydrodynamic lubrication force

Qualitative description of stressing energies between grinding beads/media in wet stirred media mills at different operation settings could provide a comprehensive understanding of the process parameter influence. In the current work, a modeling approach for wet stirred media mills is described, employing a coupled CFD-DEM framework. This study employs an immersed boundary methodology to emulate the pin-type stirrer rotation in CFD and utilizes a hydrodynamic lubrication force to model the squeezing out and sucking in of fluid between two grinding bead surfaces. The results indicate the influence of process parameters such as the stirrer rotation speed, bead diameter, and bead filling degree. Key findings include the necessity of coupled CFD-DEM with lubrication (i.e., comparison of results from DEM, and coupled CFD-DEM simulations). Dependence of system dynamics on the fluid properties is numerically analyzed using the properties of water–glycerol mixtures.

Integrating order picking and vehicle routing decisions in a dynamic e-commerce setting

Responding to e-commerce orders as quickly as possible is indispensable for companies competing in the current retailing landscape. After a customer orders online, the requested items should be picked in the warehouse and delivered to the customer’s location. Previous research showed the benefits of integrating the order picking and vehicle routing decisions in a static context. To achieve shorter customer response times, we propose multiple new algorithms in this study, able to handle the integrated problem of picking and delivery in a setting with dynamic order arrivals and considering more complex picking policies. We develop and implement four online large neighbourhood search algorithms, differing in their degree of integration between picking and routing decisions. The algorithms are tested on different operational settings. Differences between the algorithms and the impact of the operating context are analysed by use of an ANOVA. The results highlight the importance of integrated decision-making, as well as the large impact of the operating context on the operational efficiency. Furthermore, we demonstrate the environmental impact of stringent customer requests. These insights can be used by companies to optimise their operations.

A Method of Integrating Air Conditioning Usage Models to Building Simulations for Predicting Residential Cooling Energy Consumption

Great deviations in building energy consumption simulation are attributed to the simplified settings of occupants’ air conditioning (AC) usage schedules. This study was designed to develop a method to quantify the uncertainty and randomness of AC usage behavior and incorporate the model into simulations, in order to improve the prediction performance of AC energy consumption. Based on long-term onsite monitoring of household thermal environments and AC usage patterns, two stochastic models were built using unsupervised cluster and statistical methods. Based on the Monte Carlo method, the AC operation schedule was generated through AC opening duration, setpoints, and other relevant parameters, and was further incorporated into EnergyPlus. The results show that the ideally deterministic AC operation settings from the standard significantly overestimate the cooling energy consumption, where the value based on the fixed mode was 6.35 times higher. The distribution of daily AC energy consumption based on the stochastic modeling was highly consistent with the actual situation, thanks to the accurate prediction of the randomness and dynamics of residents’ AC usage patterns. The total cooling energy consumption based on two stochastic models was found to be much closer to the actual values. The work proposes a method of embedding stochastic AC usage models to EnergyPlus 22.1 benefits for an improvement in building energy consumption simulation and the energy efficiency evaluation regarding occupant behavior in the future.

A Radar-Based 10-Year Climatology of Convective Snow Events in Central Pennsylvania

Abstract Convective snow (CS) presents a significant hazard to motorists and is one of the leading causes of weather-related fatalities on Pennsylvania roadways. Thus, understanding environmental factors promoting CS formation and organization is critical for providing relevant and accurate information to those impacted. Prior research has been limited, mainly focusing on frontal CS bands often called “snow squalls”; thus, these studies do not account for the diversity of CS organizational modes that is frequently observed, highlighting a need for a robust climatology of broader CS events. To identify such events, a novel, radar-based CS detection algorithm was developed and applied to WSR-88D radar data from 10 cold seasons in central Pennsylvania, during which 159 cases were identified. Distinct convective organization modes were identified: linear (frontal) snow squalls, single cells, multicells, and streamer bands. Each algorithm-flagged radar scan containing CS was manually classified as one of these modes. Interestingly, the most-studied frontal mode only occurred <5% of the time, whereas multicellular modes dominated CS occurrence. Using the times associated with each CS mode, synoptic and local environmental information from model analyses was investigated. Key characteristics of CS environments compared to null cases include a 500-hPa trough in the vicinity, lower-tropospheric conditional instability, and sufficient moisture. Environments favorable for the different CS modes featured statistically significant differences in the 500-hPa trough axis position, surface-based CAPE, and the unstable layer depth, among others. These results provide insights into forecasting CS mode, explicitly presented in a forecasting decision tree. Significance Statement Convective snow events such as snow squalls are a leading cause of weather-related deaths on Pennsylvania roads. Research into these events is limited, thus negatively impacting forecast skill. To better understand convective snow event frequency of occurrence, inter- and intra-annual variability, and their supporting environments, we performed a 10-yr radar-based climatology of these events. We report the results of this climatology and on the statistically significant differences in their supporting environments. The latter are used to propose a forecasting framework for convective snow, which may improve the predictability of convective snow in an operational setting.

Bridging an Education Gap: Ultrasound-guided Peripheral IVs

PurposeTo provide ultrasound (US) education to anesthesia providers to increase usage for peripheral intravascular (IV) cannulation on difficult IV access patients. DesignThis project reflects a qualitative descriptive quality improvement project with outcomes measured. Data in this project include the overall knowledge and competency scores from the provider’s US survey before and after education and training were provided. MethodsA presentation was developed and provided to 38 certified registered nurse anesthetists (CRNA) via the hospital’s online platform. Next, in-person simulations were conducted to facilitate the skills of US utilization. Each CRNA participating in simulations successfully performed the skill. The impact and success of the education bundle were collected via the disbursement of a Likert-scale survey via an automated data collection software. FindingsSeventeen of the 38 (45%) participants responded to the survey. Of these respondents, 88% of providers indicated that their US skills and confidence had increased following the intervention, and 100% of participants stated that the intervention was helpful in learning US-guided peripheral IV insertion. ConclusionsEducation and hands-on skills sessions increased provider confidence in US use. While this project focused on educational training of CRNAs, this is applicable to all health care providers who are responsible for IV cannulation. The project team members will disseminate these findings and plan to improve perioperative nurses’ confidence in US-guided peripheral IV insertions; thus, improving efficiency in the operative setting.

BubbleID: A deep learning framework for bubble interface dynamics analysis

This paper presents BubbleID, a sophisticated deep learning architecture designed to comprehensively identify both static and dynamic attributes of bubbles within sequences of boiling images. By amalgamating segmentation powered by Mask R-CNN with SORT-based tracking techniques, the framework is capable of analyzing each bubble's location, dimensions, interface shape, and velocity over its lifetime and capturing dynamic events such as bubble departure. BubbleID is trained and tested on boiling images across diverse heater surfaces and operational settings. This paper also offers a comparative analysis of bubble interface dynamics prior to and post-critical heat flux conditions.

If not now, then when? The need for new evidence in the robotic management of upper tract urothelial carcinoma.

Current guidelines recommend radical nephroureterectomy with bladder cuff excision as the standard surgical treatment for high-risk upper tract urothelial carcinoma (UTUC). While large evidence is available regarding open and laparoscopic nephroureterectomy, data focusing on robotic nephroureterectomy (RNU) in UTUC are mostly limited with mixed results, especially in locally advanced disease. In light of the recent introduction of new robotic platforms, it is of utmost importance to further investigate oncologic outcomes associated with RNU. Moreover, stronger data exploring different operative settings (i.e. robotic arms and trocars placement) for the new robotic systems are eagerly warranted. To give an answer to such open clinical questions, the Junior ERUS/Young Academic Urologist Working Group on Robot-assisted Surgery designed a multicentric project involving different high-volume centers across the world. The aim of the study will be exploring surgical and oncologic outcomes of RNU, specifically focusing on several clinical unmet needs, such as best operative setting for new robotic platforms, lymph node dissection (LDN) template and robotic bladder cuff management.

Evaluating Factors Shaping Real-Time Internet-of-Things-Based License Plate Recognition Using Single-Board Computer Technology

Reliable and cost-efficient license plate recognition (LPR) systems enhance security, traffic management, and automated toll collection in real-world applications. This study addresses optimal unique configurations for enhancing LPR system accuracy and reliability by evaluating the impact of camera angle, object velocity, and distance on the efficacy of real-time LPR systems. The Internet of Things (IoT) LPR framework is proposed and utilized on single-board computer (SBC) technology, such as the Raspberry Pi 4 platform, with a high-resolution webcam using advanced OpenCV and OCR–Tesseract algorithms applied. The research endeavors to simulate common deployment scenarios of the real-time LPR system and perform thorough testing by leveraging SBC computational capabilities and the webcam’s imaging capabilities. The testing process is not just comprehensive, but also meticulous, ensuring the system’s reliability in various operational settings. We performed extensive experiments with a hundred repetitions at diverse angles, velocities, and distances. An assessment of the data’s precision, recall, and F1 score indicates the accuracy with which Thai license plates are identified. The results show that camera angles close to 180° significantly reduce perspective distortion, thus enhancing precision. Lower vehicle speeds (<10 km/h) and shorter distances (<10 m) also improve recognition accuracy by reducing motion blur and improving image clarity. Images captured from shorter distances (approximately less than 10 m) are more accurate for high-resolution character recognition. This study substantially contributes to SBC technology utilizing IoT-based real-time LPR systems for practical, accurate, and cost-effective implementations.

Managing menstruation while deployed operationally: experiences from the Australian emergency management sector

The circumstances faced during emergency incidents are characteristically physical, situational or time-critical, but little is known about how people manage their periods in these extreme settings. This study examined the attitudes, experiences and practices of managing menstruation by emergency services personnel in Australia while deployed operationally. Using a mixed-methods approach, a survey (n=287) collected data about operational roles, period characteristics, period management during operations and period stigmatisation. The findings show that navigating and solving the intersections between periods and the demanding circumstances of deployment is given substantial consideration by people who menstruate. Participants actively found solutions to the various routines, etiquettes and discomforts of menstruation to maintain service to their operational roles, despite problematic influences of period character and menstrual symptoms, menstrual products, hygiene, toileting, privacy and stigmatisation. Such self- determination suggests identity formation as competent first responders who also menstruate. However, externalities of menstruation that could be better accommodated in operational settings include toileting, bodily hygiene, field privacy, menstrual product supplies, used product disposal or cleaning, support, education and training. Attention to menstrual health in workplaces is increasing and should become a normalised aspect of emergency services.

Enhanced Adaptive Dynamic Surface Sliding Mode Control for Optimal Performance of Grid-Connected Photovoltaic Systems

This study presents an enhanced, adaptive, and dynamic surface sliding mode control (SMC), a cutting-edge method for improving grid-connected photovoltaic (PV) system performance. The suggested control approach uses dynamic SMC and adaptive approaches to enhance the robustness and efficiency of a system. Proportional–integral (PI) and SMC, two control systems for maximum power point tracking (MPPT) in PV systems, are compared in this paper. This study finds that the SMC system is a more effective and efficient MPPT approach for PV systems compared to the conventional PI control system. The SMC system’s unique feature is the capacity to stabilize grid voltage and attain a modulation index of less than one. An important component of power electronic system control is the index, which acts as a parameter representing the relationship between the output signal’s amplitude and the reference signal’s amplitude. The SMC method demonstrates improved robustness, efficiency, and stability, especially in dynamic operating settings with load and solar radiation changes. Compared to the PI control, the SMC exhibits a noteworthy 75% reduction in voltage fluctuations and an improvement in the power output of 5% to 10%. Regarding output power optimization, voltage stability, and accurate current tracking, the SMC system performs better than the PI control system. Furthermore, the SMC technique maintains a modulation index below one and guarantees grid voltage stability, both of which are essential for the efficiency and stability of power electrical systems.

Improving Instructional Decision‐Making Using Diagnostic Classification Models

AbstractIn recent years, educators, administrators, policymakers, and measurement experts have called for assessments that support educators in making better instructional decisions. One promising approach to measurement to support instructional decision‐making is diagnostic classification models (DCMs). DCMs are flexible psychometric models that facilitate fine‐grained reporting on skills that students have mastered. In this article, we describe how DCMs can be leveraged to support better decision‐making. We first provide a high‐level overview of DCMs. We then describe different methods for reporting results from DCM‐based assessments that support decision‐making for different stakeholder groups. We close with a discussion of considerations for implementing DCMs in an operational setting, including how they can inform decision‐making at state and local levels, and share future directions for research.

Prescriptive analytics for low and high water management in German waterways

Abstract The real-time management of multi-purpose storage reservoirs aims at an efficient operation of existing hydraulic infrastructure. This management process can be structured as a prescriptive analytics setup that considers both current and predicted system states to recommend actions and outline potential implications. In application to a reservoir and river system, it combines hydrological modelling components for the system schematization, observations and data assimilation for the identification of the current system state, meteorological and hydrological predictions as well as optimization-based techniques to support decision-making regarding reservoir operations. In this paper, we present the application of such a framework to the short-term management of the Eder and Diemel storage reservoirs. These reservoirs are operated by the German Federal Waterways and Shipping Administration (WSV) with the primary goal to support navigation in the River Weser during low flow periods. In addition, partially conflicting objectives such as flood protection, energy generation and recreation are considered. The implementation includes an explicit consideration of forecast uncertainty and its impact on the decision-making by using probabilistic forecasts in combination with a multi-stage stochastic optimization approach. We demonstrate the applicability of the approach based on low and high water use cases. Special attention is paid on the benefits of the probabilistic forecast in combination with the multi-stage stochastic optimization versus a deterministic setup. It provides an explicit translation of the forecast uncertainty in the decision variables, in this case the reservoir releases helping the operators to better anticipate the range of future release decisions. Furthermore, the stochastic approach is expected to provide more stable decisions in an operational setting, based on more stable forecasts by considering various possible realizations of the future instead of picking a single one, which gets random after 4–5 days.

Mini Review Article] Practicality of Piezo Surgery in Periodontics

The ongoing advances in periodontics, have led to the introduction of Piezosurgery, which has been rejuvenating in the field of osseous surgeries. Piezosurgery® device is based on the piezoelectric effect by ultrasonic vibrations. The linear vibrations and cavitation effect gives blood free zone for clear visibility and operating accuracy. The display unit guides the user to choose from a variety of operational settings that optimize the ultrasonic frequency for desired procedures. It has various applications in periodontics particularly in osteotomy procedures and implant dentistry.