Central Temperature Research Articles

Comprehensive Representation of Tropical–Extratropical Teleconnections Obstructed by Tropical Pacific Convection Biases in CMIP6

Abstract The central role of tropical sea surface temperature (SST) variability in modulating Northern Hemisphere (NH) extratropical climate has long been known. However, the prevailing pathways of teleconnections in observations and the ability of climate models to replicate these observed linkages remain elusive. Here, we apply maximum covariance analysis between atmospheric circulation and tropical SST to reveal two coexisting tropical–extratropical teleconnections albeit with distinctive spatiotemporal characteristics. The first mode, resembling the Pacific–North American (PNA) pattern, favors a tropical–Arctic in-phase (warm Pacific–warm Arctic) teleconnection in boreal spring and winter. However, the second mode, with a slight seasonal preference of summer, is manifested as an elongated Rossby wave train emanating from the tropical eastern Pacific that features an out-of-phase relationship (cold Pacific–warm Arctic) between tropical central Pacific SSTs and temperature variability over the Arctic (referred to as the PARC mode). While climate models participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6) appear to successfully simulate the PNA mode and its temporal characteristics, the majority of models’ skill in reproducing the PARC mode is obstructed to some extent by biases in simulating low-frequency SST and rainfall variability over the tropical eastern Pacific and the climatological mean flow over the North Pacific during boreal summer. Considering the contribution of the PARC mode in shaping low-frequency climate variations over the past 42 years from the tropics to the Arctic, improving models’ capability to capture the PARC mode is essential to reduce uncertainties associated with decadal prediction and climate change projection over the NH. Significance Statement This study focuses on the skill of models in phase 6 of the Coupled Model Intercomparison Project (CMIP6) in simulating two leading observed Northern Hemisphere (NH) teleconnections that show distinctive spatial and temporal characteristics. The first one, the Pacific–North American (PNA) mode, exhibits a warm Pacific–warm Arctic pattern in boreal spring and winter, and the second one, the Pacific–Arctic (PARC) mode, features a cold Pacific–warm Arctic out-of-phase relationship. We find that models are skillful in simulating the PNA mode but not the PARC mode. This limitation may be rooted in unrealistic simulations of the mean state of winds and the low-frequency sea surface temperature variability in the tropical eastern Pacific. These biases call for caution when interpreting current models’ projections of extratropical circulations on multidecadal time scales.

Optimisation of initial velocity distribution of jets for entrainment and diffusion control using deep reinforcement learning

The control of the entrainment and diffusion of the heat and mass ejected along the jet flow requires the control of fluid motion. In the present study, optimal initial velocity distributions for suppressing and promoting jet entrainment and diffusion were identified using deep reinforcement learning under constant initial jet flow rate conditions. Two-dimensional jet flow simulations using OpenFOAM were combined with a deep Q-network as the learning algorithm. The spatial distribution of the initial jet (velocity and ejection angle) changed. The results show that to suppress the entrainment of the jet, an optimal initial velocity distribution shape in which the velocity in the centre is almost 0, with an inward velocity just outside of it, and a large outward velocity at the outer edge, is desirable. However, to promote entrainment, a shape with a large velocity in the centre portion and small velocity at the outer edge, with an overall inward angle, is desirable. To suppress the diffusion of the jet, an optimal initial velocity distribution shape with a large velocity in the centre portion, a small velocity at the outer edge, and an overall inward angle is desirable. Conversely, to promote diffusion, a shape with a large outward velocity at both the centre and outer edges is desirable. It is also indicated that, when the initial velocity distribution of the jet has a special shape, and the relationship between flow rate, centre velocity, and centre temperature that is normally observed in a jet flow may not hold. In other words, by changing the initial velocity distribution, the entrainment and diffusion of the jet can be separated and controlled, which is usually difficult to achieve using conventional engineering methods.

Analysis of Spatial Pattern and Geographical Divergence Characteristics of Traditional Villages Oriented to Digital Landscape Technology

Abstract In order to study the spatial pattern and geographical differentiation characteristics of traditional villages, this paper combines digital landscape technology to analyze the influencing factors of the distribution of traditional villages. Based on multi-source data, this paper reveals the spatial pattern and geographical differentiation characteristics of traditional villages with the help of GIS, RS and mathematical and statistical methods and systematically explores the inner mechanism and laws. It also combines digital landscape technology, which is in line with the trend of the times, and designs an image visual optimization model based on digital landscape technology so as to make the experimental analysis of the distribution of traditional villages more convenient. It is obtained that the 3D modeling errors using digital landscape technology are all lower than 1.12%, the 3D modeling errors of the overhead GIS 3D modeling method are between 3.21% and 4.58%, and the 3D modeling errors of the digital photography 3D modeling method are all higher than 4.82%. The distance from the urban center, population density, ethnic distribution, and average annual temperature was the main influencing factors, all of which exceeded the explanatory power of 0.4. Average annual rainfall, administrative center, and GDP per capita were the secondary influencing factors, with explanatory power over 0.15. Based on this study, the spatial distribution density of traditional socio-economic villages was more influenced by socio-economic factors, followed by physical geographic factors.

Can Machine Learning Models Be a Suitable Tool for Predicting Central European Cold Winter Weather on Subseasonal to Seasonal Time Scales?

Abstract Skillful weather prediction on subseasonal to seasonal time scales is crucial for many socioeconomic ventures. But forecasting, especially extremes, on these time scales is very challenging because the information from initial conditions is gradually lost. Therefore, data-driven methods are discussed as an alternative to numerical weather prediction models. Here, quantile regression forests (QRFs) and random forest classifiers (RFCs) are used for probabilistic forecasting of central European mean wintertime 2-m temperatures and cold wave days at lead times of 14, 21, and 28 days. ERA5 reanalysis meteorological predictors are used as input data for the machine learning models. For the winters of 2000/01–2019/20, the predictions are compared with a climatological ensemble obtained from E-OBS observational data. The evaluation is performed as full distribution predictions for continuous values using the continuous ranked probability skill score and as binary categorical forecasts using the Brier skill score. We find skill at lead times up to 28 days in the 20-winter mean and for individual winters. Case studies show that all used machine learning models are able to learn patterns in the data beyond climatology. A more detailed analysis using Shapley additive explanations suggests that both random forest (RF)-based models are able to learn physically known relationships in the data. This underlines that RF-based data-driven models can be a suitable tool for forecasting central European mean wintertime 2-m temperatures and the occurrence of cold wave days. Significance Statement Because of the chaotic nature of weather, it is very complicated to make predictions with traditional numerical methods 2–4 weeks in advance. Therefore, we use alternative, interpretable methods that “learn” to find statistically relevant patterns in meteorological data that can be used for forecasting central European mean surface wintertime temperatures and cold wave days. These methods are part of the so-called machine learning methods that do not rely on the traditional numerical equations anymore. We test our methods for 20 winters between 2000/01 and 2019/20 against a static weather prediction consisting of the past 30 winters. For single winters and in a mean over the 20 predicted winters, we find improved predictions up to 4 weeks in advance.

Penerapan Metode Overall Equipment Effectiveness (OEE) dan Six Big Losses untuk Mengukur Efektivitas Mesin Packing pada PT. Surya Tsabat Mandiri

This research aims to investigate the risk of six big losses and the effectiveness of packing machines at PT Surya Tsabat Mandiri in South Lampung using monthly data on production of bread products from March 2021 to February 2022 used in the research. Research was carried out on packing machine objects using quantitative data or data that can be measured systematically. The Overall Equipment Effectiveness (OEE) method is applied to measure the total performance of equipment as a whole according to what must be done and to assess the dominant factors of the Six Big Losses which are dominantly used by the fishbone diagram approach. The research results show that the average OEE value of packing machines is 78.17% and is still below world standards (≥85%). Apart from that, evidence was found that the most dominant six big losses factors were idling and minor stoppage losses with a value of 16.85% and reduced speed losses with a value of 7.85%. Based on the fishbone diagram, the aspects that cause speed losses on packing machines are humans (lack of operator knowledge, lack of workers guarding bread baskets), machines (repair of damaged machines, unstable center and end seal temperatures), environment (unstable production room temperature) and method (non-periodic machine maintenance). The recommendation given to reduce these two losses is to use the TPM pillars, namely education and training, autonomous maintenance and planned maintenance.

Physics-informed learning for thermophysical field reconstruction and parameter measurement in a nano-porous insulator's heat transfer problem

To deeply explore aerogel's insulation performance, we proposed a thermophysical field reconstruction based on physics-informed learning with limited parameters measurement. It's the first time that the model has been utilized to make high-precision measurements of thermophysical parameters in heat transfer problem. In this work, the monolithic aerogel's center temperature response is measured, and the thermal conductivity is extracted at small (< 15 K), medium and large (≥ 400 K) temperature differences, respectively. By leveraging the physics-informed learning method, the temperature field can be reconstructed and thermophysical parameters are precisely identified collaboratively, yielding the majority error within 5% and 1%, respectively. Additionally, our method solves the inverse problem of nonlinear heat transfer. The heat conduction and the overall transport attenuation coefficient of thermal radiation are precisely identified at large temperature differences (≥ 400 K), and the majority of errors are <2% and 4%, respectively. Moreover, the opacifier-doped aerogel displays the lowest thermal radiation contribution of 6.36% at temperature differences (≈ 700 K), whereas pure aerogel shows the highest contribution of 35.40%. Notably, the temperature field exhibits significant nonlinearity currently. The proposed study of experiments and model provides a novelty method to make high-precision measurements, which make up the gap of the classical numerical methods.

Analysis on Thermo-Electrical Principal Parametric Resonance of an Axially Moving Piezoelectric Thin Plate

In this paper, principal parametric resonance of an axially moving piezoelectric rectangular thin plate under thermal and electric field is investigated. Based on Kirchhoff–Love plate theory and Von Karman theory, the transverse vibration differential equation of a piezoelectric rectangular thin plate under thermal and electric field is derived by using Hamilton’s principle. The dimensionless vibration equation of piezoelectric rectangular thin plate with parametric excitations is discretized by Galerkin’s method. Then, the multiple scales method is applied to derive amplitude-frequency response equation and the stability conditions of the steady-state solution are obtained by Lyapunov stability theory. Numerical method is used to find the influences of specific parameters on the vibration performance and stability of the system. Based on the global bifurcation diagram and corresponding response diagram, the influences of bifurcation control parameters on the nonlinear dynamic characteristics of the system are discussed. Numerical results illustrate that the system amplitude frequency characteristic curve presents soft spring characteristics. There are periodic and chaotic motions with the increase of velocity and the central temperature difference, and the decrease of plate thickness and velocity will result in the decrease of chaotic threshold. The results also show that increase the velocity perturbation amplitude can prolong the chaotic motion.

Influences of fracture toughness and thermal conductivity on the ablation behavior of SiC-ZrC-MPCFs composite modified by mesophase-pitch-based carbon fibers

A novel SiC-ZrC-MPCFs composite was successfully prepared by introducing short-cut mesophase-pitch-based carbon fibers (MPCFs) into SiC-ZrC ceramics. Results show that the MPCFs improved the fracture toughness of SiC-ZrC-MPCFs composite to 4.97 ± 0.47 MPa m1/2, which was 58.06 % higher than that of SiC-ZrC ceramics. Due to the reinforcement of MPCFs, no cracks were formed on the composite during ablation, thus preventing the direct diffusion of oxidizing atmospheres into the interior. Moreover, the MPCFs also worked as thermally conductive channels and provided a thermal conductivity of 68.10 ± 0.68 W/(m K) for the composite, compared to 39.48 ± 1.25 W/(m K) for SiC-ZrC ceramics. The heat could rapidly transfer from ablation center to other areas through the MPCFS, leading to low ablation center temperatures. The COMSOL simulation indicates that the ablation center temperature of SiC-ZrC-MPCFs composite was 1094 °C after 2300 °C plasma ablation for 20 s, while that of SiC-ZrC ceramics was up to 1543 °C, which was confirmed by the actual back temperatures. Therefore, the improved fracture toughness and high thermal conductivity ensured the excellent ablation property of SiC-ZrC-MPCFs composite. After ablation for 20 s, the ablation rates of the composite were − 0.048 ± 0.007 mg/s and − 0.715 ± 0.056 µm/s, respectively.

Comparing the ocular surface temperature and dry eye condition of keratoconus with normal eyes using infrared thermal imaging.

This study was conducted to compare the ocular surface temperature in keratoconus eyes with that in normal eyes. A total of 27 participants were enrolled, with 10 and 17 participants in the keratoconus and control groups, respectively. Participants in the control group underwent an ophthalmic slit lamp examination and ocular thermography, while an additional corneal tomography was performed for those in the keratoconus group. For patients with keratoconus, the mean upper eyelid temperature (UET) was 32.36 ± 1.02°C, inner canthus temperature (ICT) was 34.25 ± 0.83°C, outer canthus temperature (OCT) was 33.62 ± 0.96°C, initial central corneal temperature (initial CCT) was 33.04 ± 1.03 °C, sixth-second CCT (6s-CCT) was 32.67 ± 1.19°C, and the mean change in CCT measured within 6s (change in CCT within 6s) was 0.36 ± 0.26°C. For controls, the values for UET, ICT, OCT, initial CCT, 6s-CCT, and change in CCT within 6s were 32.35 ± 1.13°C, 34.14 ± 0.91°C, 33.51 ± 1.02°C, 33.22 ± 1.01°C, 32.99 ± 1.01°C, and 0.22 ± 0.17°C, respectively. Except for the change in CCT within 6s (p = 0.022), no significant differences were observed in UET (p = 0.973), ICT (p = 0.659), OCT (p = 0.697), initial CCT (p = 0.556) or 6s-CCT (p = 0.310) between the two groups. The keratoconus eyes showed faster changes in CCT and evaporation of tear film after opening the eyes. Therefore, the keratoconus eyes had a higher incidence of dry eye conditions.

Characteristics and Evolution of the Response of the Lower Atmosphere to the Tonga Volcanic Eruption

Research concerning the response characteristics of lower atmosphere to volcanic eruption is a key and hot topic in the field of volcanic environment research. Against the background of a submarine volcano in the South Pacific island country of Hunga Tonga–Hunga Ha’apai (HTHH) on 15 January 2022, this paper explores the response characteristics of this volcanic eruption on environmental factors in the lower atmosphere region using a priori data such as ERA5 reanalysis data, water vapor data from GNSS inversion and surface temperature data from Landsat inversion for the Tonga Islands region. Among them, (1) The amount of precipitable water (PWV) in Tonga was abnormally high on 15 January. (2) The water vapor flux was mainly in the lower space below 850 hPa. (3) The average surface temperature in December 2021 was higher. In February 2022, the average surface temperature was lower. (4) There was a low-pressure center near 30° S on the south side of Tonga volcano on 14 January, and a new low-pressure center was formed on the east side of Tonga volcano after the eruption of Tonga volcano on 15 January. Furthermore, the precipitation area of Tonga increased in January and decreased in February 2022. The PWV values, water vapor fluxes, temperature and circulation response characteristics, and precipitation characteristics show that the volcanic eruption affected part of the atmospheric and oceanic circulation, and water vapor was transported to the low-pressure center along the direction of atmospheric circulation. With the continuous water vapor transport, precipitation formed in Tonga, and the intensity and area of precipitation in Tonga increased significantly in January. Thus, the volcanic eruption could have significantly triggered the response between the low-pressure center, PWV, precipitation and surface temperature in the lower atmosphere, which influenced the environmental characteristics of this eruption.

Sous vide cooking improved the physicochemical parameters of hot-boned bovine semimembranosus muscles

The physicochemical parameters of hot-boned bovine semimembranosus muscles after sous vide cooking were investigated. Hot-boned or wet-aged steaks were collected, and cooked by different cooking methods, including sous vide (57 °C, 11 h, SV), grilling (at 200 °C to the central temperature of 72 °C, GR) or boiling (100 °C, 2 h, BO). The meat color, tenderness, water-holding capacity, degree of oxidation, myoglobin denaturation and sensory quality traits were determined, as well as the changes in the microstructure. Compared to other cooking methods, SV reduced the degree of oxidation and muscle shortening, and significantly improved the water holding capacity (WHC), tenderness, connective tissue content and overall acceptability for both hot-boned and wet-aged steaks. The oxidation and muscle shortening were reduced in hot-boned SV steaks (P < 0.05), and the water-holding capacity and sensory scores for juiciness, connective tissue content and overall acceptability were increased (P < 0.05) compared to the wet-aged steaks. The combination of hot-boning and SV cooking resulted in an acceptable tenderness, better overall sensory acceptability and higher WHC than other combinations of muscle states and cooking methods. Therefore, it is a good choice to cook hot-boned semimembranosus muscles using SV to improve the eating quality, which can eliminate the need for aging, benefiting the beef industry.

Non-Oberbeck–Boussinesq effects in two-dimensional Rayleigh–Bénard convection of different fluids

Non-Oberbeck–Boussinesq (NOB) effects in three representative fluids are quantitatively investigated in two-dimensional Rayleigh–Bénard convection. Numerical simulations are conducted in air, water, and glycerol with Prandtl numbers of Pr=0.71,4.4, and 2547, respectively. We consider Rayleigh number Ra∈[106,109] involving temperature difference (Δθ̃) of up to 60 K. The velocity and temperature profiles are found to be top-bottom antisymmetric under NOB conditions. As Pr increases, the time-averaged temperature of the cavity center ⟨θc⟩t increases under NOB conditions and the value of ⟨θc⟩t is only weakly influenced by Ra for all fluids. For Pr = 4.4 and 2547, with the enhancement of NOB effects, ⟨θc⟩t linearly increases and the maximum θ rms decreases/increases, and its location shifts toward/away from the wall near the bottom/top wall. Dispersed ⟨θc⟩t points and opposite phenomenon are observed in Pr = 0.71. The Nusselt number (Nu) and thermal boundary layer thickness at hot and cold walls (λ¯h,cθ) of the three fluids are comparable, and the Reynolds number (Re) significantly decreases as Pr increases. Under the NOB conditions with Pr = 4.4 and 2547, Nu decreases, Re increases, and λ¯hθ (λ¯cθ) thins (thickens) in an approximately linear fashion. Furthermore, the NOB effects on Nu, Re, and λ¯h,cθ are relatively small for Pr = 0.71 and 4.4, whereas the modifications caused by NOB effects at Pr = 2547 are more significant. The power-law scaling factors of Nu, Re, and λ¯h,cθ are demonstrated to be robust to Pr, as well as NOB effects.

Large-Scale Anomalous Cyclone in the Western North Pacific

Abstract The large-scale anomalous anticyclone in the western North Pacific (WNP) has been extensively studied, but the large-scale anomalous cyclone has not received much attention in the past years. In this study, we use observational data to find that the occurrence numbers of the anomalous cyclone and anticyclone in the WNP have been roughly the same from 1979 to 2020. Our analyses indicate that the WNP anomalous cyclone is an interannual circulation anomaly in the WNP, which can persist from boreal autumn to the subsequent spring during a La Niña year and from spring to summer during a developing El Niño year. To confirm the roles of the central equatorial Pacific, tropical Indian Ocean, and central WNP sea surface temperatures, we perform a suite of model experiments using an atmospheric general circulation model. The model experiments demonstrate that central equatorial Pacific warming contributes to the WNP anomalous cyclone during a developing El Niño year. Cooling in the central equatorial Pacific or the tropical Indian Ocean alone cannot induce the WNP anomalous cyclone, but the combination of central equatorial Pacific cooling, tropical Indian Ocean cooling, and central WNP warming can jointly induce the WNP anomalous cyclone during a La Niña year. Similar to the WNP anomalous anticyclone, the WNP anomalous cyclone and its climatic impacts deserve attention.

550 CSF Leak or Ear Wax? Logical Thinking in Emergency Management of a Collapsed Patient with Heat Stroke

Abstract A man in his 60s presented to RESUS by ambulance on the hottest day of the year with suspected STEMI and collapse at home. The patient was intubated and unresponsive (GCS 5), with global rigors. Hospital ECG showed sinus tachycardia. Body temperature was in excess of 41°C, confirmed on central temperature monitoring. Bilateral clear fluid was noted to be discharging from both ear canals. Query CSF leak was indication for CT head, which was unremarkable. Considering this, efforts were focused on optimising body temperature. This was done with the use of ice packs and fans as well as continuous misting of cool water over the exposed patient. Improvement was shown with temperatures decreasing to 38°C. In questioning the cause of the “clear fluid,” a swab of it placed on an icepack swiftly returned the fluid to its natural consistency of ear wax. As a result of the extraordinarily high body temperatures, ear wax had melted in both ear canals and started to leak out, giving the impression of a CSF leak. This was simply identified at the bedside to refute the idea of a CSF leak. As CSF leak was the indication for a CT head, this could have been avoided with rapid cooling initiated earlier. Unfortunately, this patient later died due to heat stroke. Whilst avoiding the delay of a CT head most likely would not have changed the prognosis for this patient, it is useful to note the importance of logical thinking in an emergency setting.

Effects of Perioperative Hypothermia on Extubation, Recovery Time, and Postoperative Shivering in Breast Surgery.

Women undergoing breast surgery seem to be under the risk for hypothermia (central body temperature <36°) due to the uncertainty caused by the preoperative preparation time and the variety of operations, leading to neglect of warming precautions. The study examines the perioperative hypothermia (PH) in breast surgery and the relationships between the depth of decrease in body temperature and individual or clinical variables. This prospective, cross-sectional, observational study includes 120 female patients 18-65 years of age who were given general anesthesia for breast surgery. The incidence of hypothermia was 68.1%. The given patients were discriminated as body temperature <36°, hypothermia was significantly related with lower weight, body mass index, American Society of Anesthesiology (ASA) score, baseline body temperatures, higher extubation, and recovery times. When patients' temperature decreases by >1 or 1.5°, lower age and longer durations of operation and anesthesia were also significant variables. The incidence of shivering is quite high at temperatures below 36°C or at 1 and 1.5°C reductions from baseline (72.7%, 84%, 94.1%, respectively). Body temperature changes of the patients had no effect on nausea-vomiting and pain scores. It turned out that the incidence of PH and shivering is high in women who underwent breast surgery. We think that the depth of decrease in body temperature should be taken into account when evaluating the predictors or clinical consequences of hypothermia, except for the 36°C limit for body temperature.

Could cooling dialysate improve inflammatory and nutritional status of hemodialysis patients?

BackgroundIt has been shown that dialysate cooling (lowering the dialysate temperature to 0.5 °C below central body temperature) reduces the incidence of intradialytic hypotension. Other influences on hemodialysis patients, however, have not been adequately investigated. The purpose of this study was to determine the impact of individualized dialysate cooling on nutritional and inflammatory parameters in chronic hemodialysis (HD) patients.MethodsSeventy HD patients were separated into two groups: group A: (control group) standard dialysate temperature was 37 °C, and group B: (intervention group) dialysate temperature was 0.5 °C below core body temperature. In addition to routine laboratory tests, blood pressure, anthropometric measurements, inflammatory markers, and the malnutrition inflammation score (MIS) were calculated.ResultsAfter six months of dialysate cooling, intradialytic hypotension episodes were much less prevalent in the intervention group (p = 0.001). Serum ferritin, transferrin saturation (TSAT), high sensitive C-reactive protein (HS-CRP), and Interleukin-6 (IL-6) reduced following dialysate cooling, whereas serum albumin rose. In the control group, IL-6 dropped but serum ferritin, TSAT, albumin, and HS-CRP rose. In both groups, hemoglobin levels dropped, and erythrocyte sedimentation rate (ESR) rose, both groups’ midarm muscle circumference and MIS worsened.ConclusionCold dialysate decreased intradialytic hypotension with no significant improvement of the nutritional and inflammatory surrogates. However, more studies including larger number of patients with longer duration of follow up are required to adequately assess its effect on inflammation and nutrition in chronic hemodialysis patients.

Experimental study on a novel vacuum sublimation-rehydration thawing of frozen potatoes.

To realize a quick thawing of frozen potatoes, the experimental investigation of thawing performance was conducted by using a novel vacuum sublimation-rehydration thawing (VSRT) in this study. Frozen diced potatoes (20mm × 20mm × 20mm) with a total mass of 1.5kg were selected as the thawing object. The center temperature of the frozen diced potato was raised from -18°C to 5°C to assess the beginning and end of thawing. The effects of sublimation time, heating plate temperature, and rehydration temperature on thawing time of frozen potatoes were experimentally studied. The VSRT and vacuum steam thawing (VST) were compared in terms of thawing time, hardness, and specific energy consumption. The results showed that the conditions of sublimation time of 25min, heating plate temperature of 30°C, and rehydration temperature of 100°C could effectively shorten the thawing time of VSRT for thawing frozen potatoes. The thawing time of VSRT was only 49% of that of VST. Compared to the hardness of frozen potatoes thawed by VST, the hardness of frozen potatoes thawed by VSRT was closer to that of blanched (unfrozen) potatoes. The specific energy consumption of VSRT was lower than that of VST. PRACTICAL APPLICATION: The quality of frozen potatoes is directly affected by the thawing method used. A novel vacuum sublimation-rehydration thawing was conducted in this study, which can provide a new idea for a reasonable, effective, and quick thawing method for frozen potatoes.

Artificial Intelligence-Based Temperature Twinning and Pre-Control for Data Center Airflow Organization

Green and low-carbon has become the main theme of global energy development. Data centers are the core of the digital age, carrying huge arithmetic demand. Data centers must implement green low-carbon energy efficiency management to improve energy efficiency, reduce energy waste and carbon emissions, and achieve sustainable development. As a result, an intelligent management strategy for dynamic energy efficiency of data center networks with Artificial Intelligence (AI) fitting control is proposed. Firstly, a Long Short-Term Memory (LSTM) network is used for long sequence trend prediction to predict the temperature of the data center in the next sequence using the temperature of the past 15 sequences and the power consumption of the equipment as parameters. Then, based on the prediction results, the intelligent air conditioning controller based on Deep Q-Network (DQN) is designed to update the parameters by using the gradient of double-Q network and error backpropagation, and the optimal control action is selected by using the ε-greedy strategy to ensure that the prediction of the hotspot does not occur. Experiments show that the average absolute errors of temperature prediction for supply air, return air, cold aisle as well as hot aisle are 0.32 °C, 0.21 °C, 0.36 °C and 0.19 °C, respectively. The Power Usage Effectiveness (PUE) and Water Usage Effectiveness (WUE) decreased by an average of 2.6% and 2.5%, respectively. The method achieves the purpose of predicting future temperatures and intelligently controlling the output so that the data center can satisfy the premise of normal operation and thus achieve more efficient energy use.

Simulated and Verification of Mass and Heat Transfer Coupled Model of Jujube Slices Dried by Hot Air Combined with Radio Frequency Heat Treatment at Different Drying Stages.

This study investigates the impact of radio frequency (RF) heat treatment on heat and mass transfer during the hot air drying of jujube slices. Experiments were conducted at different drying stages, comparing single-hot air drying with hot air combined with RF treatment. Numerical models using COMSOL Multiphysics® were developed to simulate the process, and the results were compared to validate the models. The maximum difference between the simulated value of the center temperature and the experimental value was 6.9 °C, while the minimum difference was 0.1 °C. The maximum difference in average surface temperature was 1.7 °C, with a minimum of 0.3 °C. The determination coefficient (R2) between the simulated experimental values of HA and the early (E-HA + RF), middle (M-HA + RF), and later (L-HA + RF) groups was 0.964, 0.987, 0.961, and 0.977, respectively. The study demonstrates that RF treatment reduces drying time, enhances internal temperature, promotes consistent heat and mass transfer, and accelerates moisture diffusion in jujube slices. Furthermore, the later the RF treatment is applied, the greater the increase in internal temperature and the faster the decrease in moisture content. This research elucidates the mechanism by which RF heat treatment influences heat transfer in hot air-dried jujube slices.

Isotope dependence of transport in ST40 hot ion mode plasmas

The ST40 compact, high-field spherical tokamak, operating at 2.1 T and with 1.8 MW of neutral beam heating, achieved central carbon impurity ion temperatures in excess of 10 keV, surpassing their business milestone of 100 M ∘C (8.6 keV). The high temperature discharges were in the hot ion mode, with , and they were achieved in both hydrogen and deuterium plasmas with deuterium neutral beam injection. In order to achieve the high temperature scenarios, careful wall conditioning and scenario optimization was carried out in , , and finally in plasmas. The TRANSP transport code was employed to study the dependence of confinement and transport on isotopic mass, and the conditions that led to the high measured central ion temperatures in the and plasmas. The kinetic profiles input into TRANSP were inferred from line-of-sight and limited radial measurements as well as consistency with a number of other experimental constraints. The TRANSP results first showed that the main species central ion temperature was only 0.5–1 keV lower than the measured carbon impurity temperature, and thus in the high performance plasmas also surpassed the 100 M ∘C level. TRANSP also showed that while the electron thermal conduction loss was dominant, reductions in central ion transport, and an effective decoupling of the ions from the electrons, led to an increase in central ion temperatures with increasing plasma mass. In fact, the associated confinement times exhibited a strong dependence on the isotopic mass of the thermal plasma. These preliminary results are foundations for dedicated experiments with full kinetic profile measurements on ST40 in the next run campaign.