Shutdown Period Research Articles

Shutdown dose rates in-cryostat outside the EU-DEMO vacuum vessel

Future fusion reactors using deuterium–tritium fuel will exhibit high fluences of high-energy neutrons inside and around the reactor vacuum vessel (VV). As well as causing material damage, fusion neutrons will activate materials, the decay of which leads to radiation fields in and around the reactor after shutdown. Gamma-ray emission from activated materials is a particular radiological hazard during periods of reactor shutdown. This must be accounted for in the design of the reactor shielding to ensure that risks are reduced as low as reasonably achievable.Recent neutronics work has evaluated the shutdown dose rates (SDDRs) in the EU DEMOnstration power plant (DEMO) around the ports and throughout the cryostat, incorporating prospective shielding improvements to the VV and ports. Prior to the proposed shielding design improvements, calculations for the model including the helium-cooled pebble bed (HCPB) blanket showed that radiation leakage through the blanket and VV leads to biological-equivalent SDDRs (following 12 days’ decay) above 103μSv/h throughout the cryostat, ignoring additional contribution from radiation streaming through the port openings. Inclusion of the proposed VV changes reduces this dose rate to below 100 μSv/h. The work finds an approximate order-of-magnitude reduction in SDDR throughout the cryostat when all proposed shielding improvements are applied, leading to dose rates in the cryostat in the range of hundreds to thousands of μSv/h for the full model. The work shows that to further reduce dose rates inside the cryostat, improving the shielding performance of the ports is required, with particular emphasis on the lower port and the equatorial electron–cyclotron launcher which currently dominate the dose rates.

Transcranial and Transcutaneous Stimulation for Pain: What Have We Learned From the COVID-19 Pandemic Shutdown?

BACKGROUND: Transcranial magnetic stimulation (TMS) and transcutaneous magnetic stimulation (tMS) offer a novel noninvasive treatment option for chronic pain. While the recent COVID-19 pandemic caused by the SARS-CoV-2 virus resulted in a temporary interruption of the treatments for patients, it provided an excellent opportunity to assess the long-term sustainability of the treatment, and the feasibility of resuming the treatments after a brief period of interruption as no such data are available in current literature. METHODS: First, a list of patients whose pain/headache conditions have been stably controlled with either treatment for at least 6 months prior to the 3-month pandemic-related shutdown was generated. Those who returned for treatments after the shutdown were identified and their underlying pain diagnoses, pre- and posttreatment Mechanical Visual Analog Scale (M-VAS) pain scores, 3-item Pain, Enjoyment, and General Activity (PEG-3), and Patient Health Questionnaire-9 scores were assessed in 3 phases: Phase I (P1) consisted of a 6-month pre-COVID-19 period in which pain conditions were stably managed with either treatment modality; Phase II (P2) consisted of the first treatment visit period immediately after COVID-19 shutdown; and Phase III (P3) consisted of a 3-4 month post-COVID-19 shutdown period patients received up to 3 sessions of either treatment modality after the P2 treatment. RESULTS: For pre- and posttreatment M-VAS pain scores, mixed-effect analyses for both treatment groups demonstrated significant (P < 0.01) time interactions across all phases. For pretreatment M-VAS pain scores, TMS (n = 27) between-phase analyses indicated a significant (F = 13.572, P = 0.002) increase from 37.7 ± 27.6 at P1 to 49.6 ± 25.9 at P2, which then decreased significantly (F = 12.752, P = 0.001) back to an average score of 37.1 ± 24.7 at P3. Similarly, tMS (n = 25) between-phase analyses indicated the mean pretreatment pain score (mean ± standard deviation [SD]) increased significantly (F = 13.383, P = 0.003) from 34.9 ± 25.1 at P1 to 56.3 ± 27.0 at P2, which then decreased significantly (F = 5.464, P = 0.027) back to an average score of 41.9 ± 26.4 at P3. For posttreatment pain scores, the TMS group between-phase analysis indicated the mean posttreatment pain score (mean ± SD) increased significantly (F = 14.206, P = 0.002) from 25.6 ± 22.9 at P1 to 36.2 ± 23.4 at P2, which then significantly decreased (F = 16.063, P < 0.001) back to an average score of 23.2 ± 21.3 at P3. The tMS group between-phase analysis indicates a significant (F = 8.324, P = 0.012) interaction between P1 and P2 only with the mean posttreatment pain score (mean ± SD) increased from 24.9 ± 25.7 at P1 to 36.9 ± 26.7 at P2. The combined PEG-3 score between-phase analyses demonstrated similar significant (P < 0.001) changes across the phases in both treatment groups. CONCLUSIONS: Both TMS and tMS treatment interruptions resulted in an increase of pain/headache severity and interference of quality of life and functions. However, the pain/headache symptoms, patients’ quality of life, or function can quickly be improved once the maintenance treatments were restarted. KEY WORDS: Pain, headache, transcranial magnetic stimulation (TMS), transcutaneous magnetic stimulation (tMS), COVID_19 pandemic

Thermal performance evaluation of a compact two-fluid finned heat exchanger integrated with cold latent heat energy storage

To alleviate the mismatch between thermal energy supply and demand, the use of latent heat thermal energy storage of phase change materials is a reliable and effective solution in thermal systems. The present work aims to introduce an innovative finned-plate air-liquid heat exchanger with phase change material partially embedded in the fins' gap providing airside cooling by thermal energy storage during coolant shutdown periods. The thermal performance and accompanying phase change process are studied using a three-dimensional computational fluid dynamic approach. The developed model is validated with corresponding experimental data at the same geometry and operating conditions. The system performance is examined time-dependently considering the charging/discharging processes of the phase change material. The effect of air mass flow rate variation is studied, and the results are discussed in terms of fluids temperature, heat transfer rates, and the phase change material phase transition process. The results demonstrate that the heat exchanger stores excess thermal energy during the charging process, which is later discharged for air cooling during demand periods. It is observed that the use of phase change material provides a cooling load of 188 kJ and over 9 min of extra cooling time for the airside in the discharging process. The share of latent heat transfer in the discharging process is calculated at an average of 54% of the total heat transfer. Obtained results reveal that increasing the air mass flow rate can reduce the rate of discharged cold thermal energy and cooling time. The presented thermal management system offers a unique solution to manage passenger thermal comfort in hybrid and electric vehicles with a start-stop function. The introduced system could provide efficient cabin air cooling and enable effective energy savings during short periods of engine shutdown.

‘Though the earth gives way’: An autoethnographic account of autistic grief

Through an autoethnographic account of my mother’s death, I reveal how grief and the desolation it brings the autistic person is vastly different to neurotypical models of grief (such as the Kübler-Ross model) determining when, where and how I was permitted to feel my emotions. Grief takes those with autism spectrum disorder (ASD) a greater amount of time to work through, often resulting in periods of meltdown and shutdown (heightened anger and fear) as well as an unawareness of the emotion they are feeling. However, I will also show how parental death can also be transformative, providing a powerful reason to persevere.

Factors Predicting Hotel Recommendations: A Comparison of Guest Feedback Before and After the Hotel Closures During the COVID-19 Pandemic

ABSTRACT The COVID-19 pandemic adversely impacted the hospitality industry. The current study explored potential factors (i.e. cleanliness, location, room, service, and value) influencing guests’ hotel recommendations before the shutdown of hotels due to COVID-19 and after the reopening of hotels. This study employed secondary data and random forest analysis to test the hypotheses. The results indicated that before COVID-19, the value factor had the most significant effect on a guest’s willingness to recommend, followed by service, cleanliness, room, and location. A year after the shutdown period, the value factor still had the most significant effect, followed by service, room, and cleanliness, while location had an insignificant effect. Hotel managers can utilize the findings to create new strategies to attract guests and allocate resources to better address guests’ evolving expectations.

Status report of the n_TOF facility after the 2nd CERN long shutdown period

During the second long shutdown period of the CERN accelerator complex (LS2, 2019-2021), several upgrade activities took place at the n_TOF facility. The most important have been the replacement of the spallation target with a next generation nitrogen-cooled lead target. Additionally, a new experimental area, at a very short distance from the target assembly (the NEAR Station) was established. In this paper, the core commissioning actions of the new installations are described. The improvement in the n_TOF infrastructure was accompanied by several detector development projects. All these upgrade actions are discussed, focusing mostly on the future perspectives of the n_TOF facility. Furthermore, some indicative current and future measurements are briefly reported.

Design and Analysis of Iot-Based Remote Load Monitoring and Outage Management System

An essential instrument for the operation of a power system is to monitor and analyze the data to find the fault and rectify it before the system collapses completely. This paper intents to utilize the idea to create a control system that will fulfill objectives like monitoring vital parameters controlling the power distribution, outage management by fault detection based on the variation of Voltage, Frequency, and Current & protection of the Circuit against any significant incidents by isolating the load from utility and flagging the information through feedback to the utility authority. The Project implements the high-end technology for IoT applications that will connect with the Microcontroller to receive the data at regular intervals and post the timestamped data in a cloud platform for remote monitoring and archiving. This innovation can reduce human dependency and overcome the whole outage situation by building a two-way communication that means electricity and information are traded between consumers and utilities to capitalize on the efficiency of a grid system. The proposed model offers intelligent monitoring of the timestamped data with remote access & historical data archiving for a better load profile. An outage management system is designed for the proposed system by intellectual fault detection through Voltage, Frequency & current variation, and isolation of the faulty part from the entire system, maintaining an auto circuit recloser application before the permanent isolation for reducing the human interaction & flagging the outage information & supply emergency backup during the shutdown period, and in the process, helping the power grid from total power outage or blackout.

An investigation of PM2.5 concentration changes in Mid-Eastern China before and after COVID-19 outbreak

With the Chinese government revising ambient air quality standards and strengthening the monitoring and management of pollutants such as PM2.5, the concentrations of air pollutants in China have gradually decreased in recent years. Meanwhile, the strong control measures taken by the Chinese government in the face of COVID-19 in 2020 have an extremely profound impact on the reduction of pollutants in China. Therefore, investigations of pollutant concentration changes in China before and after COVID-19 outbreak are very necessary and concerning, but the number of monitoring stations is very limited, making it difficult to conduct a high spatial density investigation. In this study, we construct a modern deep learning model based on multi-source data, which includes remotely sensed AOD data products, other reanalysis element data, and ground monitoring station data. Combining satellite remote sensing techniques, we finally realize a high spital density PM2.5 concentration change investigation method, and analyze the seasonal and annual, the spatial and temporal characteristics of PM2.5 concentrations in Mid-Eastern China from 2016 to 2021 and the impact of epidemic closure and control measures on regional and provincial PM2.5 concentrations. We find that PM2.5 concentrations in Mid-Eastern China during these years is mainly characterized by “north-south superiority and central inferiority”, seasonal differences are evident, with the highest in winter, the second highest in autumn and the lowest in summer, and a gradual decrease in overall concentration during the year. According to our experimental results, the annual average PM2.5 concentration decreases by 3.07 % in 2020, and decreases by 24.53 % during the shutdown period, which is probably caused by China's epidemic control measures. At the same time, some provinces with a large share of secondary industry see PM2.5 concentrations drop by more than 30 %. By 2021, PM2.5 concentrations rebound slightly, rising by 10 % in most provinces.

Long-term ultrasonic monitoring of concrete affected by alkali-silica reaction

This article presents continuous monitoring results of alkali-silica reaction (ASR) development in concrete specimens for over 400 days using ultrasonic testing and expansion measurements. Eight concrete specimens with nonreactive aggregate (Control), reactive coarse aggregate, and reactive fine aggregates were cast with two reinforced confinement conditions. The specimens were conditioned in an environmental chamber with high temperature and humidity (38°C and 90% relative humidity) to accelerate the ASR development. A multichannel ultrasonic monitoring system was developed to collect ultrasonic signals automatically, and the expansions in three directions were measured periodically. Results showed that the relative velocity change could detect the ASR initiation in all reactive specimens and show a correlation with expansion in the early stage. However, these correlations are inconsistent for different ASR specimens, and the velocity change becomes less sensitive to ASR damage in the late stage (after 300 days). Irrecoverable velocity drop was observed during every chamber shutdown period, especially in specimens with higher levels of ASR damage. This phenomenon suggests that the nonlinear ultrasonic response caused by the ambient temperature variation may indicate the ASR damage.

Towards the Choice of Better Social Media Platform for Knowledge Delivery: Exploratory Study in University of Ilorin

Rising from the outbreak of COVID-19 pandemic, all schools in Nigeria were put under lock and key, like other countries. This was to curtail the rapid spread of the virus, while the medical practitioners were in the laboratories. But due to longer period of shutdown to formal education, most higher institutions instantaneously keyed-in to any available social media platform (SMP) without understudying its efficiency and effectiveness for knowledge transfer. The consequences of this include non-satisfactory knowledge transfer, most especially, by the learners; hence, aim of knowledge impact is jeopardized. This study therefore is aimed to fill the gap to correct the existing damage, and prevent the likelihood of not-afore-put-to-test syndrome in the subsequent choice of SMP. To achieve this, University of Ilorin is chosen as the territory survey for being the most sought-after University for admission in Nigeria. Quantitative research methods were adopted, being the most appropriate for this study in the literature. A total of 200 respondents participated voluntarily with the adoption of Slovin’s formula for the sampling techniques; 156 students and 44 lecturers. The data collection process was performed through a survey comprising demographic, dichotomous, multiple choice, and open-ended questionnaire to capture detail information for the research work. Simple descriptive statistics was employed for the analysis. Our findings reveal that out of the seventeen SMPs put to test, zoom platform ranks the most effective and efficient for the knowledge delivery and acquisition by the lecturers and students of the University respectively, based on the designed principles. Therefore, making Zoom platform the most preferred for e-learning. This study recommends that choice of satisfactory SMP for knowledge delivery should be utmost concern of all institutions before embarking on virtual learning system

Application of Optical Coherence Tomography (OCT) to Analyze Membrane Fouling under Intermittent Operation

There is increasing interest in membrane systems powered by renewable energy sources, including solar and wind, that are suitable for decentralized water supply in islands and remote regions. These membrane systems are often operated intermittently with extended shutdown periods to minimize the capacity of the energy storage devices. However, relatively little information is available on the effect of intermittent operation on membrane fouling. In this work, the fouling of pressurized membranes under intermittent operation was investigated using an approach based on optical coherence tomography (OCT), which allows non-destructive and non-invasive examination of membrane fouling. In reverse osmosis (RO), intermittently operated membranes were investigated by OCT-based characterization. Several model foulants such as NaCl and humic acids were used, as well as real seawater. The cross-sectional OCT images of the fouling were visualized as a three-dimensional volume using Image J. The OCT images were used to quantitatively measure the thickness of foulants on the membrane surfaces under different operating conditions. The results showed that intermittent operation retarded the flux decrease due to fouling compared to continuous operation. The OCT analysis showed that the foulant thickness was significantly reduced by the intermittent operation. The decrease in foulant layer thickness was found to occur when the RO process was restarted in intermittent operation.

High-Temperature Low Cycle Fatigue of Nickel-Based Superalloy IN738LC

Polycrystalline cast nickel-based superalloy IN738LC is employed for critical parts of gas turbine components in the power industry, aircraft engines, and the marine sector. These components undergo severe degradation by low cycle fatigue caused by thermal gradients, particularly during start-up and shut-down periods. The present work reports the cyclic deformation behaviour and fatigue damage of IN738LC during high-temperature isothermal fatigue. Cylindrical specimens were cyclically deformed under strain control with constant total strain amplitude in symmetrical cycling at 800 °C and 950 °C in air. The microstructure is typical of coarse dendritic grains with carbides, eutectic, and shrinkage pores. SEM imaging revealed a γ matrix with coherent L12 γ′ precipitates with bimodal morphology. Cyclic hardening/softening curves, cyclic stress-strain response, and fatigue life diagrams were determined. An increase in testing temperature is associated with a significant decrease in stress amplitude and an increase in plastic strain amplitude. The fatigue life gradually decreases with increasing temperature. The fracture surfaces and polished sections parallel to the specimen axis were examined to study damage mechanisms in cyclic loading at high temperatures.

Lessons Learned: A Disruption in Care Leads to Increased Rates of Proximal Amputations

BACKGROUND: In March 2020, due to the COVID-19 pandemic, hospitalizations in New York state were restricted to emergency purposes. Non-COVID related cases involving lower extremity wounds were only admitted for acute infections and limb salvage. Patients with these conditions were placed at higher risk for eventual limb loss. PURPOSE: To understand the impact of COVID-19 on amputation rates. METHODS: A retrospective review of lower limb institution-wide amputations was conducted at Northwell Health from January 2020 to January 2021. The amputation rates during the COVID-19 shutdown period were compared to the pre-pandemic, post-shutdown, and reopening period. RESULTS: The pre-pandemic period had 179 amputations, of which 8.38% were proximal. 86 amputations were performed during shutdown, with a greater proportion being proximal (25.58%, p=0.0009). Following the shutdown period, amputations returned to baseline. The proportion of proximal amputations during post-shutdown was 18.5% and during reopening was 12.06%. Patients had 4.89 times higher odds of undergoing a proximal amputation during the shutdown period. CONCLUSIONS: The effect of COVID-19 on amputation rates demonstrates an increase in proximal amputation during the initial shutdown. This study suggests an indirect negative effect of COVID-19 hospital restrictions on surgeries during the initial shutdown period.

Machine Learning applications for Data Quality Monitoring and Data Certification within CMS

The Compact Muon Solenoid (CMS) detector is getting ready for datataking in 2022, after a long shutdown period. LHC Run-3 is expected to deliver an ever-increasing amount of data. To ensure that the recorded data has the best quality possible, the CMS Collaboration has dedicated Data Quality Monitoring (DQM) and Data Certification (DC) working groups. These working groups are made of human shifters and experts who carefully watch and investigate histograms generated from different parts of the detector. However, the current workflow is not granular enough and prone to human errors. On the other hand, several techniques in Machine Learning (ML) can be designed to learn from large collections of data and make predictions for the data quality at an unprecedented speed and granularity. Hence, the data certification process can be considered as a perfect problem for ML techniques to tackle. With the help of ML, we can increase the granularity and speed of the DQM workflow and assist the human shifters and experts in detecting anomalies during data-taking. In this presentation, we present preliminary results from incorporating ML to highly granular DQM information for data certification.

Fault Detection and Classification of CIGS Thin-Film PV Modules Using an Adaptive Neuro-Fuzzy Inference Scheme.

The use of artificial intelligence to automate PV module fault detection, diagnosis, and classification processes has gained interest for PV solar plants maintenance planning and reduction in expensive inspection and shutdown periods. The present article reports on the development of an adaptive neuro-fuzzy inference system (ANFIS) for PV fault classification based on statistical and mathematical features extracted from outdoor infrared thermography (IRT) and I-V measurements of thin-film PV modules. The selection of the membership function is shown to be essential to obtain a high classifier performance. Principal components analysis (PCA) is used to reduce the dimensions to speed up the classification process. For each type of fault, effective features that are highly correlated to the PV module's operating power ratio are identified. Evaluation of the proposed methodology, based on datasets gathered from a typical PV plant, reveals that features extraction methods based on mathematical parameters and I-V measurements provide a 100% classification accuracy. On the other hand, features extraction based on statistical factors provides 83.33% accuracy. A novel technique is proposed for developing a correlation matrix between the PV operating power ratio and the effective features extracted online from infrared thermal images. This eliminates the need for offline I-V measurements to estimate the operating power ratio of PV modules.

A two-phase online air balancing method for multi-zone ventilation systems based on distributed finite-time control

Air balancing (AB) is a commonly used technique to control and balance the air flow of multi-zone ventilation systems. The performance of air balancing is associated with occupant thermal comfort, indoor air quality (IAQ) and system energy consumption, which is critical to the ventilation system. However, the existing air balancing methods suffer low convergence and some even require shutdown period of the ventilation system for offline operation, causing big inconvenience and degraded ventilation performance. To fix the above issues, this paper proposes a novel air balancing method based on distributed finite-time control (DFTC). The proposed DFTC-AB method is an online method which can be performed during system operation without interrupting the ventilation service. It achieves the balance of air flow by two phases, i.e. to first proportion the ventilation system and then finely regulate the absolute quantity of each terminal flow. A revised DFTC control protocol is proposed to accelerate the convergence of terminal flow given any initial state, which significantly improves the efficiency of the air balancing process. To validate the proposed DFTC-AB method, a 5-terminal duct system is fabricated in the lab. The experimental results demonstrate that the proposed DFTC-AB method can balance the 5-termial ventilation system under various cases with satisfactory dynamic performance. The whole process of air balancing can be fulfilled within 20 min, with the highest accuracy achieving 3.2% among the tested cases.

Research on safety early warning of abnormal A-annular pressure of “three-high” gas wells based on data mining

A major safety accident will be triggered when the A-annular pressure value of high pressure, high productivity and high sulfur gas well exceeded the maximum allowable value. The A-annular pressure value of high pressure high productivity and high sulfur gas well once exceeded the allowable value will trigger a major safety accident. Therefore, this paper proposed a data mining based the gas well early warning strategy by analyzing the annular pressure mechanism and the change in the pattern of instantaneous gas volume, well temperature, and annular pressure in various conditions. To summarize, the law of gas well abnormal A-annular pressure is aimed at constructing a gas well safety warning rule for gas well stable production stage and shutdown period where the initial parameters setting in the early warning rule and adjustment optimization mechanism is also determined. Lastly, with the use of historical abnormal samples, the gas well early warning strategy bought up in this paper was verified. The example shown that compared with the traditional DCS system warning strategy, the gas well safety early warning strategy can identify the abnormal A-annular pressure phenomenon 82 h in advance thus achieving production safety management.

Letter: Research Productivity of Physicians in the Pandemic and Post-pandemic Era: A "Productive Outbreak'".

Letter: Research Productivity of Physicians in the Pandemic and Post-pandemic Era: A "Productive Outbreak'".

Economic ordering quantity inventory model with verhulst’s demand under fuzzy uncertainty for geographical market

Inventory plays an important role in the production process. One of the primary reasons why inventory management modeling is essential for the industry is because it will suffer immensely if there are insufficient food products to stock during the shutdown period. By determining the combined optimal cost of the retailers and wholesalers, this research significantly improves the service of the supply chain from wholesaler to retailer. The stochastic number for the imperfect perishable items is provided in this inventory study. By altering the parameter values, the uniform distribution is used to calculate these damaged items. This approach identifies the backordering quantity for both regular and uncertain fish band circumstances. The cost of maintaining the inventory will rise significantly of increased wastage due to a rise in deteriorating, which will result in the loss of perishable food items. The primary goal of this research paper is to transport them without being destroyed until they reach their desired consumers. By determining the back ordering quantity during a shutdown, one can decrease the overall expenses incurred by the retailers. These computational complexity measures are proven in a fuzzy uncertain environment. The main goal of this paper is to analyze the variation of demand during the unanticipated period and find the optimum total cost of the perishable products. The growth of production in a particular area at a particular time, interconnect with another large number of products in the same area and is calculated by Verhulst’s demand with time depended on proficiency rate. Concerning the existing Verhulst’s demand pattern for the production process, this paper introduced that for perishable items in a fuzzy unanticipated situation. A bountiful system analysis is performed to find the cost function under fuzzy environment and the sensitivity analysis is carried out to perform the key representation constant.

Повышение выработки запасов конденсата методом периодических остановок скважин

Introduction. During the development of gas condensate deposits for depletion, with a decrease in reservoir pressure, condensate falls out in the bottomhole zone of wells (BWZ) and a zone with increased condensate saturation is formed. Materials and methods of research. The considered method of non-stationary operating mode was justified by the example of the development of the Achimov deposits of the Urengoyskoye field using the tNavigator software package. Research results and their discussion. The work studies a method of periodical shutting down a gas condensate well in order to increase the additional recovery of retrograde condensate from the reservoir. The optimal well shutdown period is substantiated through the use of hydrodynamic modeling. The paper also analyzes the factors aimed at maintaining the maximum effect from the periodic operation of the well, the main of which is the mode of operation after the well is launched. The considered method of non-stationary operating mode was justified by the example of the development of the Achimov deposits of the Urengoyskoye field using the tNavigator software package. The initial information for modeling was the following: monthly operational reports (MER); results of gas condensate studies; downhole and wellhead measurement data. Conclusion. Based on the studies performed to determine the optimal duration of well shutdown during the development of the Achimov deposits of the Urengoyskoye field, it was revealed that the shutdown duration (more than 183 days) will have a negative impact on the potential condensate content in the formation gas. It has been substantiated that the efficiency of periodic shutdowns is affected by the technological mode of well operation after start-up. Also, the drawdown reduction makes it possible to preserve the effect of the reverse dissolution of condensate through the use of the natural energy of the reservoir.