Short-term Temperature Research Articles

Resilience mechanisms of Trichopria drosophilae (Hymenoptera: Diapriinae) under global extreme cooling: insights into parasitic response and physiological adaptation.

Global climate warming and frequent extreme low-temperature events have made it essential to investigate the impact of low temperatures on parasitic wasps to protect and strengthen farmland biodiversity, which in turn enhances the biological control potential of natural enemies such as parasitic wasps. We systematically examined how low-temperature stress affects the parasitic functional response of Trichopria drosophilae to Drosophila suzukii (Diptera: Drosophilidae) pupae. Our findings indicate that the parasitic behavior of T. drosophilae towards D. suzukii pupae aligns with the Holling II functional response model following exposure to different temperatures. Within the temperature range of 8 °C to -8 °C, lower temperatures correlated decreased instantaneous attack rate of T. drosophilae and an increase in processing time. The search constant Q initially increased and then decreased with declining temperatures. Short-term low-temperature stress negatively impacted the parasitic and searching abilities of T. drosophilae but did not alter its parasitic functional response model. Notably, short-term low-temperature stress had minimal effects on the water content, protein content, and total sugar content of male and female T. drosophilae adults. However, as temperatures decreased, the activities of key enzymes, including GAPDH, SOD, T-AOC, and malondialdehyde (MDA), exhibited an initial increase followed by a decrease. Conversely, the activities of LDH and HOAD decreased, while the activities of CAT and POD increased. Further study on the effect of short-term low temperature on T. drosophilae can provide a research basis for the large-scale production and low-temperature refrigeration technology of T. drosophilae, and provide a scientific basis for its efficient use in the field.

Extreme weather has variable effects on reproductive success of grassland songbirds at the northern extent of their range

Abstract Grassland songbirds breeding in Canada and the United States have experienced significant population declines likely because of habitat loss and degradation. Many climate change models predict an increase in the frequency, intensity, and duration of extreme precipitation and temperature events that could place further pressures on declining species. We monitored the fate of 1,868 individual nesting attempts of 7 grassland songbird species in response to various precipitation and temperature measures over a 10-yr period (1997 to 2002 and 2004 to 2008) in Saskatchewan, Canada. Daily nest survival rates of 5 species, including 3 at-risk species, were negatively influenced by high levels of precipitation, although the amount of precipitation where declines in daily nest survival occurred varied. Daily nest survival rates of 2 species were negatively correlated with high temperatures. We failed to detect any relationship between precipitation or temperature and the number of fledglings produced from successful nests. Extreme weather events could add additional stressors to declining populations of grassland birds in Canada. Increases in the frequency and intensity of extreme weather, specifically extreme precipitation events and short-term high temperatures, will likely lead to lower reproductive success for several species compared to current levels. This may be especially problematic for management of Anthus spragueii (Sprague’s Pipit) and Centronyx bairdii (Baird’s Sparrow), where a large proportion (> 75%) of the breeding population occurs near the northern edge of the Great Plains. The continuing loss and degradation of northern grasslands may limit the ability of these species to disperse and find favorable climate conditions.

Forecasting sea surface temperature during typhoon events in the Bohai Sea using spatiotemporal neural networks

Advanced neural network methods can effectively combine temporal and spatial information, allowing us to exploit complex relationships in the prediction of sea surface temperature (SST). Therefore, the authors have developed an attention-based context fusion network model recently, known as ACFN, to enhance the underlying spatiotemporal correlations in SST data. In a previous preliminary long-term evaluation, ACFN has demonstrated substantial improvements over several state-of-the-art baseline models, such as Convolutional Long-Short Term Memory (ConvLSTM) and Predictive Recurrent Neural Network (PredRNN). However, these methods have not been widely used for predicting SST during typhoons, and their performance has not been thoroughly evaluated. This study focuses on three specific typhoons, In-fa (2021), Lekima (2019), and Rumbia (2018), as case studies. The results show that ACFN consistently outperforms ConvLSTM and PredRNN, as demonstrated during Typhoon In-fa with a mean absolute error of 0.27 °C and a root-mean-square error of 0.33 °C for a 1-day forecast time. This study also examines the impact of forecast time on predictive skill, revealing that the performance of ACFN, as expected, gradually decreases with longer forecast times. This thorough evaluation of the ACFN model provides a valuable reference for using deep learning in predicting regional typhoon SST. Plain language summaryIn previous work, we developed an attention-based context fusion network model. This spatiotemporal neural network model has been successfully applied to short-term sea surface temperature (SST) prediction in the Bohai Sea, utilizing only current and previous 1- to 10-day SST data as input to forecast SST for the subsequent 1- to 10-day period. In this study, we further evaluated the capability of the model to predict SST during typhoons in the Bohai Sea, and the results were very promising. When predicting SST during Typhoon In-fa, the present spatiotemporal neural network model had a mean absolute error of only 0.27 °C for a 1-day forecast time, and a correlation coefficient of 0.74. On the other hand, comparison with baseline spatiotemporal neural network models shows that the present model is more effective in extracting input information. The current model has resolved the constraint that its predictive capability does not diminish as the forecast time increases, thereby enhancing the rationality of the prediction outcomes. In conclusion, the present spatiotemporal neural network model demonstrates its efficacy in forecasting typhoon-induced SST in the Bohai Sea, offering valuable insights for applications in meteorology and oceanography.

The ZcVg3 Gene Regulates the Reproduction and Lifespan of Female Zeugodacus cucurbitae (Coquillett) Mediated by Short-Term High Temperatures.

Zeugodacus cucurbitae (Coquillett) is a significant pest affecting fruit and vegetables in tropical and subtropical regions, and its development and reproduction are enhanced after exposure to short-term high-temperature stress at 45 °C. Vitellogenin (Vg) is an essential precursor of yolk protein formation in eggs and plays a vital role in the ovarian development of insects. Interfering with the Z. cucurbitae vitellogenin receptor (ZcVgR) gene in short-term high-temperature conditions decreases the fecundity of female adults, while the transcription level of the ZcVg3 gene increases. To elucidate the reproductive function of the ZcVg3 gene and the synergistic relationship among the ZcVgs genes under short-term high temperatures, this study injected siRNA to interfere with the ZcVg3 gene after subjecting Z. cucurbitae to a 1 h treatment at 45 °C and 25 °C. The expression of the ZcVg3 gene was suppressed, leading to the upregulation of the ZcVg1 and ZcVg2 genes, and the expression of the ZcVgR gene was initially decreased and then increased. Silencing the ZcVg3 gene after a 1 h treatment at 45 °C resulted in a reduction of approximately 84.7% and 75.9% in the fecundity and spawning days of female adults compared to the control. The development rate of their ovaries and the ovarian diameter significantly decreased, and their lifespan was reduced by 71%. The ZcVg3 gene plays a crucial role in the reproduction of Z. cucurbitae in short-term high-temperature conditions. The results of this study provide potential targets for the development of RNAi-based techniques for the control of Z. cucurbitae.

Coarse particulate air pollution and mortality in a multidrug-resistant tuberculosis cohort

RationaleThe association between ambient coarse particulate matter (PM2.5–10) and mortality in multi-drug resistant tuberculosis (MDR-TB) patients has not yet been studied. The modifying effects of temperature and humidity on this association are completely unknown. ObjectivesTo evaluate the effects of long-term PM2.5–10 exposures, and their modifications by temperature and humidity on mortality among MDR-TB patients. MethodsA Chinese cohort of 3469 MDR-TB patients was followed up from diagnosis until death, loss to follow-up, or the study's end, averaging 2567 days per patient. PM2.5–10 concentrations were derived from the difference between PM10 and PM2.5. Cox proportional hazard models estimated hazard ratios (HRs) per 3.74 μg/m3 (interquartile range, IQR) exposure to PM2.5–10 and all-cause mortality for the full cohort and individuals at distinct long-term and short-term temperature and humidity levels, adjusting for other air pollutants and potential covariates. Exposure-response relationships were quantified using smoothed splines. ResultsHazard ratios of 1.733 (95% CI, 1.407, 2.135) and 1.427 (1.114, 1.827) were observed for mortality in association with PM2.5–10 exposures for the full cohort under both long-term and short-term exposures to temperature and humidity. Modifying effects by temperature and humidity were heterogenous across sexes, age, treatment history, and surrounding environment measured by greenness and nighttime light levels. Nonlinear exposure-response curves suggestes a cumulative risk of PM2.5–10-related mortality starting from a low exposure concentration around 15 μg/m3. ConclusionLong-term exposure to PM2.5–10 poses significant harm among MDR-TB patients, with effects modified by temperature and humidity. Immediate surveillance of PM2.5–10 is crucial to mitigate the progression of MDR-TB severity, particularly due to co-exposures to air pollution and adverse weather conditions.

Effect of waste engine oil and warm mix additive on the physical, rheological, and short-term aging attributes of Styrene–Butadiene Rubber-modified asphalt binders

Styrene-butadiene rubber (SBR) latex is used widely to increase the low-temperature resistance of asphalt binders. However, the high content of butadiene copolymer in SBR-treated asphalt causes oxidation and aging. Furthermore, following the SBR modification, the compatibility and thermal storage stability of SBR-modified asphalt is not satisfactory. Therefore, the best approach to improve the quality of short-term aged SBR-modified asphalt is to lower the manufacturing temperatures of the SBR-modified asphalt mixture. In this study, to reduce the preparation temperatures of SBR-modified asphalt mixes and improve their performance after short-term aging, several additives i.e., waste engine oil (WEO), warm mix agent i.e., ZycoTherm, and WEO/ZycoTherm combination (WEO+ ZycoTherm) were selected. The rheological properties and aging performance of binder samples were evaluated through the rational viscosity, dynamic shear rheometer, bending beam rheometer and Fourier transform infrared spectroscopy tests. The results showed that adding WEO together with ZycoTherm into an SBR-modified binder contributed to reducing the compaction and mixing temperatures. The chemical analysis showed that the three additives i.e., WEO, ZycoTherm, and ZycoTherm/WEO declined the aromaticity and chemical aging indicators of aged SBR-modified binders. Furthermore, it was demonstrated that both the ZycoTherm and WEO/ZycoTherm improved the asphalt resistance to rutting at elevated temperatures in both conditions i.e., before and after short-term aging. In addition, the findings revealed ZycoTherm /WEO compound yielded the best performance to improve the minimal-temperature cracking resistance of the SBR-modified binder, resulting in lower short-term aging temperatures. In summary, this study highlights the necessity of lowering preparation temperatures in asphalt mixes containing SBR to enhance binder resistance to rutting and cracking and reduce susceptibility to oxidation during short-term aging.

Flies on the rise: acclimation effect on mitochondrial oxidation capacity at normal and high temperatures in Drosophila melanogaster.

Increased average temperatures and extreme thermal events (such as heatwaves) brought forth by climate change impose important constraints on aerobic metabolism. Notably, mitochondrial metabolism, which is affected by both long- and short-term temperature changes, has been put forward as an important determinant for thermal tolerance of organisms. This study examined the influence of phenotypic plasticity on metabolic and physiological parameters in Drosophila melanogaster and the link between mitochondrial function and their upper thermal limits. We showed that D. melanogaster acclimated to 15°C have a 0.65°C lower critical thermal maximum (CTmax) compared with those acclimated to 24°C. Drosophila melanogaster acclimated to 15°C exhibited a higher proportion of shorter saturated and monounsaturated fatty acids, concomitant with lower proportions of polyunsaturated fatty acids. No mitochondrial quantitative changes (fractional area and number) were detected between acclimation groups, but changes of mitochondrial oxidation capacities were observed. Specifically, in both 15°C- and 24°C-acclimated flies, complex I-induced respiration was increased when measured between 15 and 24°C, but drastically declined when measured at 40°C. When succinate and glycerol-3-phosphate were added, this decrease was however compensated for in flies acclimated to 24°C, suggesting an important impact of acclimation on mitochondrial function related to thermal tolerance. Our study reveals that the use of oxidative substrates at high temperatures is influenced by acclimation temperature and strongly related to upper thermal tolerance as a difference of 0.65°C in CTmax translates into significant mitochondrial changes.

Research on the residual compressive properties of concrete with preload after sustained high temperatures

The containment vessel of nuclear power plants, concrete structures near the furnaces and roller lines, and chimneys, are exposed to high temperatures (120 °C to 350 °C) for long term. Research on the compressive properties of concrete after short-term high temperatures (usually within 3 h) has been widely conducted, while that of concrete after sustained high temperatures is scarce. In this research, the effects of temperature (120 °C to 350 °C), heating duration (3 h to 32 h) and initial stress level (0.2 to 0.6, ratio of initial applied stress to compressive strength at room temperature) on the failure mode, compressive strength, peak strain and stress-strain relationship of concrete after sustained high temperatures were studied. The results show that with increasing temperature and heating duration, concrete compressive strength gradually decreases, while concrete peak strain gradually increases. At heating duration of 24 h, the properties of concrete have basically stabilised. Preloading helps the concrete to resist the adverse effects of high temperatures by inhibiting the dehydration of cement matrix and the cracking of interfacial transition zone. For concrete with preload, transient thermal strain and short-term creep at high temperatures could not be recovered after cooling, which causes a significant increase in the peak strain of concrete. A compressive stress-strain model for concrete with preload after sustained high temperatures was developed, which consists of pre-creep ascending segment, creep segment, post-creep ascending segment and descending segment. Comparison of the prediction results with the test results proves that the model has high accuracy.

Climate change induces shifts in coastal Baltic Sea surface water microorganism stress and photosynthesis gene expression.

The world's oceans are challenged by climate change linked warming with typically highly populated coastal areas being particularly susceptible to these effects. Many studies of climate change on the marine environment use large, short-term temperature manipulations that neglect factors such as long-term adaptation and seasonal cycles. In this study, a Baltic Sea 'heated' bay influenced by thermal discharge since the 1970s from a nuclear reactor (in relation to an unaffected nearby 'control' bay) was used to investigate how elevated temperature impacts surface water microbial communities and activities. 16S rRNA gene amplicon based microbial diversity and population structure showed no difference in alpha diversity in surface water microbial communities, while the beta diversity showed a dissimilarity between the bays. Amplicon sequencing variant relative abundances between the bays showed statistically higher values for, e.g., Ilumatobacteraceae and Burkholderiaceae in the heated and control bays, respectively. RNA transcript-derived activities followed a similar pattern in alpha and beta diversity with no effect on Shannon's H diversity but a significant difference in the beta diversity between the bays. The RNA data further showed more elevated transcript counts assigned to stress related genes in the heated bay that included heat shock protein genes dnaKJ, the co-chaperonin groS, and the nucleotide exchange factor heat shock protein grpE. The RNA data also showed elevated oxidative phosphorylation transcripts in the heated (e.g., atpHG) compared to control (e.g., atpAEFB) bay. Furthermore, genes related to photosynthesis had generally higher transcript numbers in the control bay, such as photosystem I (psaAC) and II genes (psbABCEH). These increased stress gene responses in the heated bay will likely have additional cascading effects on marine carbon cycling and ecosystem services.

Investigating the synergistic anti-aging effects of Sasobit and recycled engine oil in styrene-butadiene rubber modified asphalt

During the preparation of asphalt mixtures, styrene-butadiene rubber (SBR) polymers are susceptible to thermal decomposition, which can significantly impair the binder’s low-temperature performance. This study explores the potential of combining warming agents with waste materials to enhance the low-temperature properties and aging resistance of the binder. Specifically, it examines the synergistic impact of Sasobit/recycled engine oil (Sasobit/REO) composites on the rheological and physical attributes of styrene-butadiene rubber asphalt binder (SBRAB). Utilizing fluorescence microscopy (FM), bending beam rheometer (BBR), and dynamic shear rheometer (DSR), the study assesses the aging resistance and modification mechanisms of Sasobit/REO on SBRAB. The findings indicate that the incorporation of Sasobit/REO composites more effectively reduces the mix preparation temperature than either component alone. The preparation of Sasobit/REO warm mix asphalt mixtures is feasible at temperatures 20°C lower than those required for traditional hot mixtures. These composites also enhance the performance of SBRAB at both high and low temperatures, counteracting the adverse effects associated with the individual use of Sasobit or REO. This reduced short-term aging temperature is beneficial in lessening the negative impact of high temperatures on SBRAB’s performance. Moreover, the addition of Sasobit/REO composites significantly improves the thermal cracking resistance of SBRAB mixtures. The study also demonstrates that Sasobit/REO enhances the short-term and long-term aging resistance of SBRAB, paving the way for the broader application of this novel warm mix additive in the asphalt industry.

Directed preparation of N-(1-deoxy-d-ribulos-1-yl)-glutathione during short-term high temperature dehydration: Suppressing the side reactions of glutathione

The Amadori rearrangement product (GR-ARP) derived from glutathione/ribose mixture (GSH/Rib) was prepared through spray drying characterized by short-term high temperature dehydration. The water activity of spray-dried samples containing GR-ARP decreased continuously with an intensified degree of dehydration. Lower water activity (from 0.23 to 0.13) and higher GR-ARP yield (from 46.56% to 64.02%) in the spray-dried products were found with the increase of inlet temperature, the slowdown of feed speed, and the alkalization of stock solution. Moreover, during the spray drying and formation of powders, the intensified dissipation of bound water to free water promoted intermolecular condensation that compensated bound water, favoring the directed conversion of GSH/Rib into GR-ARP. Compared to rotary evaporation at 80 °C, spray drying enabled a stronger restraint of amide cleavage and sulfhydryl oxidation in GSH, which reduced the yield of GSH-derived byproducts by 16.39%. Thus, GR-ARP was achieved a peak yield of 64.02% at an initial pH of 7.00 based on more efficient intermolecular condensation and controlled side reactions of GSH when the inlet temperature was set at 170 °C. The spray-dried sample containing richer GR-ARP and other flavor precursors showed a more similar flavor profile to the heated GSH/Rib mixture (complete Maillard reaction products) than other GR-ARP-related thermal processing samples, such as vacuum-dehydrated, GR-ARP, GR-ARP + GSH, and GR-ARP + Rib samples, revealing a great potential of the ARP mixture prepared by spray drying for flavor enhancement.

Yellow hypergiant V509 Cas: Stable in the ‘yellow void’

Context. The yellow hypergiant star V509 Cas is currently undergoing an extreme phase of evolution. Having experienced eruptive mass-loss outbursts in the 20th century, the star’s effective temperature reached record high values in the early 2000s. However, since then, the star’s behaviour has displayed an unprecedented level of stability. In spite of that, the star could be traversing through the ‘yellow void’ instability region. Aims. To describe the current evolutionary state of V509 Cas, we analysed its variability using photometric and spectroscopic data collected over recent years. By comparing our findings with historical records, we aim to determine whether the star’s surface shows signs of stabilisation. Additionally, we investigate the variability of emission components in the wings of certain spectral lines to highlight the contribution of the circumstellar gaseous disc to this phenomenon. Methods. Our spectroscopic monitoring observations were carried out at Tartu Observatory over the course of seven years, supplemented by echelle spectra obtained at the Nordic Optical Telescope, as well as publicly available photometric data from Gaia, AAVSO, and AAVSO’s Bright Star Monitor programme. We estimated the variability of effective temperature and radial velocity from the spectral time series and correlated it with the brightness variability of V509 Cas. Results. The results indicate that the star’s average brightness level has remained stable throughout the observed period, with an amplitude of variability ∼0.1 mag. While the amplitude of short-term temperature fluctuations has decreased compared to the early 2000s, the variability of the radial velocity remains similar to historical values from the early 20th century. Moreover, we show how the variable radial velocity affects the emission components in some absorption lines (e.g. Sc II) and how that follows the hypothesis of a disc surrounding the star.

단기 전압-온도 스트레스에 의한 LTPS TFT의 Subthreshold Swing 변화

단기 전압-온도 스트레스에 의한 LTPS TFT의 Subthreshold Swing 변화

Characterization of ascorbate-glutathione cycle response in Zostera marina seedlings under short-term temperature surge

The ascorbate-glutathione (AsA-GSH) cycle plays a critical role in scavenging hydrogen peroxide in plants and contributes significantly to plant stress tolerance. This study examines the cycle’s response in Zostera marina seedlings to warming, specifically under conditions of abnormal sea temperature increase. Three temperature gradients were established: 18°C (control group), 23°C (high-temperature group), and 28°C (abnormally high-temperature group). Results after 7 days of exposure to mild high temperature (23°C) showed decreased MDA content in the HT group, increased AsA/DHA ratio, and enhanced activity of enzymes related to the AsA-GSH cycle. However, exposure to extreme high temperatures resulted in increased oxidative damage and redox imbalance in the AHT group. Initially, enzymes associated with the AsA-GSH cycle, such as APX, MDHAR, GPX, and γ-ECS, increased but significantly decreased later under stress. In contrast, DHAR and GaILDH levels significantly rose on the seventh day. Transcriptome analysis revealed upregulation of APX, MDHAR, DHAR, GR, and γ-ECS genes in the HT group, with a decline in other enzyme gene expressions by the seventh day, except for APX. Under extreme high temperatures, APX expression was downregulated early in the stress period, while DHAR was upregulated, indicating Z. marina seedlings can mitigate oxidative damage under short-term high temperatures by activating the AsA-GSH cycle. Conversely, extreme high temperatures may inhibit this cycle, disrupt redox balance, and adversely affect Z. marina seedling establishment, potentially leading to their demise.

A functional autoregressive approach for modeling and forecasting short-term air temperature

A precise forecast of atmospheric temperatures is essential for various applications such as agriculture, energy, public health, and transportation. Modern advancements in technology have led to the development of sensors and other tools to collect high-frequency air temperature data. However, accurate forecasts are challenging due to their specific features including high dimensionality, non-linearity, seasonal dependency, etc. To address these forecasting challenges, this study proposes a functional modeling framework based on the components estimation technique by partitioning the air temperature time series into deterministic and stochastic components. The deterministic component that comprises daily and yearly seasonalities is modeled and forecasted using generalized additive modeling techniques. Similarly, the stochastic component that accounts for the short-term dynamics of the process is modeled and forecasted by a functional autoregressive model, autoregressive integrated moving average, and vector autoregressive models. To evaluate the performance of models, hourly air temperature data are collected from Islamabad, Pakistan, and one-day-ahead out-of-sample forecasts are obtained for a complete year. The forecasting results from all models are compared using the root mean squared error, mean absolute error, and mean absolute percentage error. The results suggest that the proposed FAR model performs relatively well compared to ARIMA and VAR models, resulting in lower out-of-sample forecasting errors. The findings of this research can facilitate informed decision-making across sectors, optimize resource allocation, enhance public safety, and promote socio-economic resilience.

The association of short-term increases in ambient PM2.5 and temperature exposures with stillbirth: racial/ethnic disparities among Medicaid recipients.

Racial/ethnic disparities in the association between short-term (eg, days, weeks), ambient fine particulate matter (PM2.5) and temperature exposures and stillbirth in the United States have been understudied. A time-stratified, case-crossover design using a distributed lag nonlinear model (0- to 6-day lag) was used to estimate stillbirth odds due to short-term increases in average daily PM2.5 and temperature exposures among 118 632 Medicaid recipients from 2000 to 2014. Disparities by maternal race/ethnicity (Black, White, Hispanic, Asian, American Indian) and zip code-level socioeconomic status (SES) were assessed. In the temperature-adjusted model, a 10μg m-3 increase in PM2.5 concentration was marginally associated with increased stillbirth odds at lag 1 (0.68%; 95% CI, -0.04% to 1.40%) and lag 2 (0.52%; 95% CI, -0.03 to 1.06) but not lag 0-6 (2.80%; 95% CI, -0.81 to 6.45). An association between daily PM2.5 concentrations and stillbirth odds was found among Black individuals at the cumulative lag (0-6days: 9.26% 95% CI, 3.12%-15.77%) but not among other races or ethnicities. A stronger association between PM2.5 concentrations and stillbirth odds existed among Black individuals living in zip codes with the lowest median household income (lag 0-6: 14.13%; 95% CI, 4.64%-25.79%). Short-term temperature increases were not associated with stillbirth risk among any race/ethnicity. Black Medicaid enrollees, and especially those living in lower SES areas, may be more vulnerable to stillbirth due to short-term increases in PM2.5 exposure. This article is part of a Special Collection on Environmental Epidemiology.

Adaptive SARIMA modelling for continuous chamber temperature tracking in ultra-low temperature freezers

Ultra-low temperature (ULT) freezers are vital for storing perishable biological materials, requiring continuous monitoring of chamber thermal conditions to ensure sample integrity. A reliable dynamic data-driven model is key for smart proactive surveillance of the ULT freezers through real-time status tracking. However, the dynamic nature of ULT freezers, influenced by external disturbances, demands adaptive models to sustain performance. Complex models thus face challenges due to time-consuming parameter updates. To ensure model prediction performance while minimising model updating time, this study proposes a Seasonal Autoregressive Integrated Moving Average (SARIMA) model-based approach for short-term ULT freezer chamber temperature prediction. This method solely relies on historical chamber temperature data, eliminating the need for additional measurements. Moreover, two adaptive duty cycle length (DCL) selection strategies and a one-step ahead DCL estimation method are developed to address auto-correlation issues caused by recurring duty cycles. Comparative analyses with reference models demonstrate that the SARIMA model, incorporating future DCL effects, excels in one-step prediction and multi-step forecast under stable and variant DCL periods. Moreover, it outperforms the reference models in auto-correlation representation, affirming its reliability in predicting future states. The proposed modelling approach promises an effective method for freezing chamber temperature prediction, enabling continuous status tracking and early fault detection. Moreover, it holds great potential for modelling other critical temperatures in refrigeration systems and facilitating flexible energy operations.

Environmental exposure and element-by-element analysis of pandemic waves at the city level: Exposome algorithm for analysis of short-term temperature drops on surges in COVID-19-associated hospitalizations

Amid the COVID-19 pandemic, it is crucial to understand the complex network of factors that contribute to its pathogenesis and epidemiology in order to be able to prevent, predict, and minimize the consequences of the burden on urban services, particularly the critical surge in hospitalized patients.Based on a hypothesis of compromised airway epithelium, the principle of wave superposition, and mechanistic and causal interface approaches, this study aims to design a concept of element-by-element analysis of pandemic waves to isolate and analyze the impact of short-term drops in ambient temperature (as one important factor) on the spikes in hospitalizations due to the exacerbation of the disease course in SARS-CoV-2 infected individuals during the Omicron-associated wave.A new concept and analysis algorithm were proposed and tested. Results showed that every short-term drop in daily temperature of more than 3 °C led to an increase in the number of hospitalizations due to a worsening of disease progression in infected individuals with a 1–3 day lag. The analysis also revealed an influence with a 7.6-day lag, which may indicate an incubation period in patients at risk, such as the elderly and patients with comorbidities.The findings have implications for public health initiatives and urban planning to prevent overwhelming the healthcare system. Further research is recommended.

The distribution of self-incompatibility systems in angiosperms: the relationship between mating system diversity, life span, growth habit and latitude in a changing global environment.

There is ample theoretical and experimental evidence that angiosperms harbouring self-incompatibility (SI) systems are likely to respond to global changes in unique ways relative to taxa with other mating systems. In this paper, we present an updated database on the prevalence of SI systems across angiosperms and examine the relationship between the presence of SI and latitude, biomes, life-history traits and management conditions to evaluate the potential vulnerability of SI taxa to climate change and habitat disturbance. We performed literature searches to identify studies that employed controlled crosses, microscopic analyses and/or genetic data to classify taxa as having SI, self-compatibility (SC), partial self-compatibility (PSC) or self-sterility (SS). Where described, the site of the SI reaction and the presence of dimorphic versus monomorphic flowers were also recorded. We then combined this database on the distribution of mating systems with information about the life span, growth habit, management conditions and geographic distribution of taxa. Information about the geographic distribution of taxa was obtained from a manually curated version of the Global Biodiversity Information Facility database, and from vegetation surveys encompassing 9 biomes. We employed multinomial logit regression to assess the relationship between mating system and life-history traits, management condition, latitude and latitude-squared using self-compatible taxa as the baseline. Additionally, we employed LOESS regression to examine the relationship between the probability of SI and latitude. Finally, by summarizing information at the family level, we plotted the distribution of SI systems across angiosperms including information about the presence of SI or dioecy, the inferred reaction site of the SI system when known, as well as the proportion of taxa in a family for which information is available. We obtained information about the SI status of 5686 hermaphroditic taxa, of which 55% exhibited SC, and the remaining 45% harbour SI, self-sterility (SS), or PSC. Highlights of the multinomial logit regression include that taxa with PSC have a greater odds of being short- (OR=1.3) or long- (OR=1.57) lived perennials relative to SC ones, and that SS/SI taxa (pooled) are less likely to be annuals (OR=0.64) and more likely to be long-lived perennials (OR=1.32). SS/SI taxa had a greater odds of being succulent (OR=2.4) or a tree (OR=2.05), and were less likely to be weeds (OR=0.34). Further, we find a quadratic relationship between the probability of being SI with latitude: SI taxa were more common in the tropics, a finding that was further supported by the vegetation surveys which showed fewer species with SS/SI in temperate and northern latitudes compared to mediterranean and tropical biomes. We conclude that in the short-term habitat fragmentation, pollinator loss and temperature increases may negatively impact plants with SI systems, particularly long-lived perennial and woody species dominant in tropical forests. In the longer term, these and other global changes are likely to select for self-compatible or partially self-compatible taxa which, due to the apparent importance of SI as a driver of plant diversification across the angiosperm tree of life, may globally influence plant species richness.

Short-term office temperature forecasting through a data-driven approach integrated with bidirectional gated recurrent neural network

Accurate indoor temperature forecasting can facilitate the energy savings, thus improving the building energy efficiency. Based on the discontinuous sensor data collection in this research, a novel gated recurrent neural network model, bidirectional Gated Recurrent Unit (Bi-GRU) is developed, achieving superior performances and optimizing the forecasting accuracy, compared to other state-of-the-art models. The developed data-driven approach based on integrating data pre-processing, feature engineering, Kalman time-series smoothing, and neural network models is applied for indoor temperature forecasting, calibrated on two test scenarios. The prediction results are examined by error performance metrics, and the relative prediction residuals are visually compared in time-series plots and quantile–quantile (Q-Q) plots. Kalman smoothing has effectively improved the forecasting accuracy of regression models by reducing their prediction errors and upgrading the prediction accuracies with approximately 4 ∼ 9 % and 6 ∼ 19 % in single-step- and multi-step-ahead forecasting tasks, respectively. Bi-GRU with Kalman smoothing is verified to be the most efficacious forecasting solution by yielding the accuracy of 94.28 % and 91.58 % in Test scenario I, and 91.58 % and 83.82 % in Test scenario II for single-step- and multi-step-ahead predictions, respectively.