The objective of this study was to evaluate biomarkers of heat stress (HS) from automatic milking system (AMS), the relationships between measurements of temperature-humidity index (THI) and reticulorumen ph and temperature and some automatic milking systems parameters in dairy cows (rumination time (RT), milk traits, body weight (BW) and consumption of concentrate (CC)) during summer period. The experiment was carried out on a dairy farm at 54.9587408, 23.784146. Lithuanian Black and White dairy cows (n=365) were selected. The cows were milked with Lely Astronaut® A3 milking robots with free traffic. The rations were calculated to meet physiological requirements of the animals. Daily milk yield, rumination time, body weight, milk fat and protein ratio were collected from the Lely T4C management program for analysis. The pH and temperature of the contents of cow reticulorumen were measured using specific smaX-tec boluses manufactured for animal care. The daily humidity and air temperature in the farm zone were obtained from the adjacent weather station (2 km away). According this study during HS, the higher THI had positive correlation with ML, which show tendencies to increase risk of mastitis, and decrease CC, RT, BW, MY, reticulorumen ph and F/P. Some biomarkers of HS can be milk yield, milk lactose, somatic cell count, concentrate conception, rumination time, body weight, reticulorumen ph and milk fat – protein ratio. We can recommend to monitoring these parameters in the herd management program to identify possibility of heat stress.

A theoretical nonlocal thermoelastic model for studying the effects of the thermal conductivity variability on a rotating nanobeam has been described in the present article. The theory of thermal stress is employed using the Euler–Bernoulli beam model and generalized heat conduction with phase lags. It is believed that the thermal conductivity of the current model varies linearly according to temperature. Due to variable harmonic heat, the considered nanobeam excited and was subjected to a time-varying exponential decay load. Using the Laplace transform process, the analytical solutions for displacement, deflection, thermodynamic temperature and bending moment of rotating nanobeams are provided in final forms and a numerical example has been taken to address the problem. A comparison of the stated results was displayed and additionally, the influences of non-local parameters and varying load were analyzed and examined. We also investigate how the linear changes in the temperature of physical properties can influence both the static and dynamic responses to the rotating nanobeam.

Relations between nutrient assimilation and growth rate in fishes may vary with abiotic factors such as temperature. The effects of feeding status, ontogeny and temperature regimes (15, 18 and 21 °C) on stable isotope (δ13C and δ15N) patterns were assayed and modelled in juveniles of the seahorse Hippocampus guttulatus. The use of effective day degrees (D°eff) and chronological time (age) were compared as development progress indices. Newborn seahorses were maintained at three temperature levels both deprived of food (5 days) or fed (30 days) on copepods or/and Artemia. Isotopic signatures in fed seahorses clearly differed from those in unfed juveniles. Temperature had a significant effect on δ13C values in fed juveniles throughout the experimental period. δ15N values also varied significantly with age, but not with temperature level. Faster growth and food assimilation in seahorses held at 18 and 21 °C were supported by faster variations in isotopic values. Our findings demonstrate that effective day degrees should be preferred over chronological time as index of developmental progress in temperature fluctuating scenarios or for comparative studies.

The coronavirus disease 2019 (COVID-19), the pandemic is an unprecedented health emergency never seen in the recorded history of humankind due to its sheer scale, rapid spread, and subsequent shock to the global economy. The past respiratory viral pandemics of the 21st century (SARS-CoV-2 in 2003, Influenza AH1N1 in 2009) have revealed seasonality in environmental factors to play a role in the dynamics of their spread. Here, we report the observed state-level relationship between environmental factors such as temperature (T), relative humidity (RH), specific humidity (SH), and solar radiation (SR) on the COVID-19 spread over the Indian region. The results show that T and RH have a significant impact on the disease growth rate and doubling time. Every degree rise in temperature corresponds to a 0.99 % decrease in the number of cases and an increase in doubling time by ~ 1.13 days implying a slowing down of spread. A similar analysis for RH reveals that more moisture leads to a higher growth rate and reduced doubling time. Lower SH and higher surface-reaching SR are found to reduce the spread and increase the doubling time similar to that of temperature. The range of average state-level T (RH) encountered during this period was between 24 and 35^oC (30 and 87%) which implies that environmental impact is still effective at all these T (RH) and is not limited to specific T (RH) ranges. The progression of the season towards monsoon, post-monsoon, and thereafter winter with a continuous reduction in temperature will prove a major challenge for health workers and policymakers attempting to enforce mitigation and control measures.

The pandemic threat SARS-CoV-2 is now beyond control though the country of origin of this virus had already been limited for the new infection. Number of infected people and countries have been increasing day by day. Considering the previous pandemic flues, it is hypothesizing that COVID-19 will be reduced with warming the global environmental temperature. Therefore, the current study was aimed to analyze the effect of temperature and relative humidity (RH) on spreading of SARS-CoV-2 infection. The COVID-19 confirmed cases of 31 different states in China and 70 cities of 11 countries were obtained from several online databases. The real time temperature and humidity of the respective regions were taken from an online weather forecasting data source. Correlation analyses showed that SARS-CoV-2 infectivity and spreading negatively correlated with temperature of most of the states of China or cities of the world or in a country. The effect of humidity on COVID-19 was found to be positively correlated inside the China and difference of humidity was not found among countries and/or various regions of the world. Moreover, a minimum number of COVID-19 cases have been confirmed in the temperate regions compared to regions/countries compared to regions/countries with relatively low temperature. In conclusion, the SARS-CoV-2 infection has been found in a wide range of temperatures. It might be hypothesized that comparatively elevated air temperature could play a detrimental effect for SARS-CoV-2 spread.

Precipitation and temperature are significant inputs for hydrological models. Currently, many satellite and reanalysis precipitation and air temperature datasets exist at different spatio-temporal resolutions at a global and quasi-global scale. This study evaluated the performances of three open-access precipitation datasets (gauge-adjusted research-grade Global Satellite Mapping of Precipitation (GSMaP_Gauge), Climate Hazards Group Infrared Precipitation with Station data (CHIRPS), Climate Forecast System Reanalysis(CFSR)) and CFSR air temperature dataset in driving the Soil and Water Assessment Tool (SWAT) model required for the monthly simulation of streamflow in the upper Shiyang River Basin of northwest China. After a thorough comparison of six model scenarios with different combinations of precipitation and air temperature inputs, the following conclusions were drawn: (1) Although the precipitation products had similar spatial patterns, however, CFSR differs significantly by showing an overestimation; (2) CFSR air temperature yielded almost identical performance in the streamflow simulation than the measured air temperature from gauge stations; (3) among the three open-access precipitation datasets, CHIRPS produced the best performance. These results suggested that the CHIRPS precipitation and CFSR air temperature datasets which are available at high spatial resolution (0.05), could be a promising alternative open-access data source for streamflow simulation in the case of limited access to desirable gauge data in the data-scarce area.

Sufficient evidence is currently available to demonstrate the reality of the warming of our planet's climate system. Global warming has different effects on climate at the regional and local levels. The detection of changes in extreme events using instrumental data provides further evidence of such warming and allows for the characterization of its local manifestations. The present study analyzes changes in temperature and precipitation extremes in the Mexican state of Zacatecas using climate change indices developed by the Expert Team on Climate Change Detection, Monitoring and Indices (ETCCDI). We studied a 40-year period (1976-2015) using annual and seasonal time scales. Maximum and minimum temperature data were used, as well as precipitation statistics from the Mexican climatology database (CLICOM) provided by the Mexican meteorological service. Weather stations with at least 80% of data availability for the selected study period were selected; these databases were subjected to quality control, homogenization, and data filling using Climatol, which runs in the R programming language. These homogenized series were used to obtain daily grides of the three variables at a resolution of 1.3 km. Results reveal important changes in temperature-related indices, such as the increase in maximum temperature and the decrease in minimum temperature. Irregular variability was observed in the case of precipitation, which could be associated with low-frequency oscillations such as the Pacific Decadal Oscillation and the El Niño–Southern Oscillation. The possible impact of these changes in temperature and the increased irregularity of precipitation could have a negative impact on the agricultural sector, especially given that the state of Zacatecas is the largest national bean producer. The most important problems in the short term will be related to the difficulty of adapting to these rapid changes and the new climate scenario, which will pose new challenges in the future.

Recently, Troyan et al (2019 arXiv:1908.01534) and Kong et al (2019 arXiv:1909.10482) extended near-room-temperature superconductors family by new yttrium superhydride polymorphs, YH_n (n = 4,6,7,9), which exhibit superconducting transition temperatures in the range of T_c = 210-243 K at pressure of P = 160-255 GPa. In this paper, temperature dependent upper critical field data, B_c2(T), for highly-compressed mixture of YH₄+YH₆ phases (reported by Kong et al 2019 arXiv:1909.10482) is analysed to deduce the ratio of T_c to the Fermi temperature, T_F. Our analysis shows that in all considered scenarios the YH₄+YH₆ mixture has the ratio 0.01 ≤ T_c/T_F ≤ 0.04. As the result, YH₄+YH₆ falls in the unconventional superconductors band in the Uemura plot. It is also found that the characteristic temperature of the order parameter amplitude fluctuations, T_fluc, in the YH₄+YH₆ mixture is only several percent above observed T_c, and thus the superconducting transition in yttrium superhydride polymorphs is fundamentally limited by thermodynamics fluctuations.

Study of spatiotemporal dynamics of temperature is vital to assess changes in climate, especially in the Himalayan region where livelihoods of billions of people living downstream depends on water coming from the melting of snow and glacier ice. To this end, temperature trend analysis is carried out in Narayani river basin, a major river basin of Nepal characterized by three climatic regions: tropical, subtropical and alpine. Temperature data from six stations located within the basin were analyzed. The elevation of these stations ranges from 460 to 3800 m a.s.l. and the time period of available temperature data ranges from 1960–2015. Multiple regression and empirical mode decomposition (EMD) methods were applied to fill in missing data and to detect trends. Annual as well as seasonal trends were analyzed and a Mann-Kendall test was employed to test the statistical significance of detected trends. Results indicate significant cooling trends before 1970s, and warming trends after 1970s in the majority of the stations. The warming trends range from 0.028 ∘" style="position: relative;">∘C year−1" style="position: relative;">−1 to 0.035 ∘" style="position: relative;">∘C year−1" style="position: relative;">−1 with a mean increasing trend of 0.03 ∘" style="position: relative;">∘C year−1" style="position: relative;">−1 after 1971. Seasonal trends show highest warming trends in the monsoon season followed by winter, pre-monsoon, and the post-monsoon season. However, difference in warming rates between different seasons was not significant. An average temperature lapse rate of −0.006 ∘" style="position: relative;">∘C m−1" style="position: relative;">−1 with the steepest value (−0.0064 ∘" style="position: relative;">∘C m−1" style="position: relative;">−1) in pre-monsoon season and least negative (−0.0052 ∘" style="position: relative;">∘C m−1" style="position: relative;">−1) in winter season was observed for this basin. A comparative analysis of the gap-filled data with freely available global climate datasets show reasonable correlation thus confirming the suitability of the gap filling methods.

In this paper, by developing a mathematical model, the operating temperature of perovskite solar cells (PSCs) under different operating conditions has been calculated. It is found that by reducing the density of tail states at the interfaces, acting as recombination centres, through some passivation mechanisms, the operating temperature can be reduced significantly at higher applied voltages. The results show that if the density of tail states at the interfaces is reduced by three orders of magnitude through some passivation mechanisms, then the active layer may not undergo any phase change up to an ambient temperature 300 K and it may not degrade up to 320 K. The calculated heat generation at the interfaces at different applied voltages with and without passivation shows that the heat generation can be reduced by passivating the interfaces. It is expected that this study may provide a deeper understanding of the influence of interface passivation on the operating temperature of PSCs.

The expansion of concrete subjected to extreme elevated temperature is linked with intricate micro-structural variations, such as the transformation of the constituent phases. This study proposes a model to predict the thermal expansion of cement paste and concrete considering micro-structural changes under elevated temperatures ranging from 20°C to 800°C. The model presented can consider characteristics of various aggregates in the calculation of thermal expansion for concrete. The model is a combination of a multi-scale stoichiometric model and a multi-scale composite model. At the cement paste level, the model satisfactorily predicted a test result. At concrete level, upper bounds from the model were matched relatively well with test results by previous researcher. If the mechanical properties, such as elastic modulus (E), Poisson’s ratio (ν), and thermal deformation, of the aggregates used in concrete are given, it is likely that the model will reasonably predict experimental results.

Carburizing of stainless steels at low temperatures (below 500°C) develops a high content carbon layer, known for its high hardness. X-ray Diffraction investigation indicates that carburization treatment does not impact the structure of substrate; however, introduced carbon causes expansion in the carburized layer through the increase in d-spacing. Characterization of carbon concentration and hardness profiles indicate that carbon content gradually decreases while moving further into the substrate; and the origin of the increased hardness of the expanded layer arises from the super-saturated carbon content, following the solid solution strengthening theory. An area that is not well understood is in regard to the carburized layer’s relation to the substrate and the significance in their differences. Using two grades of stainless steel, AISI 316L and PH 17-4, it was observed that the carburized layer is not a separate layer, but a higher carbon content version of the substrate,

New metastable β titanium alloys are receiving increasing attention due to their excellent biomechanical properties and machinability is critical to their uptake. In this study machining chip microstructure have been investigated to gain an understanding of strain and temperature fields during cutting. For higher cutting speeds, ≥60 m/min, the chips have segmented morphologies characterised by a serrated appearance. High levels of strain in the primary shear zone promote formation of expanded shear band regions between segments which exhibit intensive refinement of the β phase down to grain sizes below 100 nm. The presence of both α and β phases across the expanded shear band suggests that temperatures during cutting are in the range of 400–600 °C. For the secondary shear zone, very large strains at the cutting interface result in heavily refined and approximately equiaxed nanocrystalline β grains with sizes around 20–50 nm, while further from the interface the β grains become highly elongated in the shear direction. An absence of the α phase in the region immediately adjacent to the cutting interface indicates recrystallization during cutting and temperatures in excess of the 720 °C β transus temperature.

We investigate the ammonium removal abilities of natural and synthetic zeolites, which have distinct Si/Al ratios and various surface areas, to study how adsorption and ion-exchange processes in zeolites perform under different ammonium concentrations and different temperatures. Five zeolites including natural mordenite, chabazite, erionite, clinoptilolite and synthetic merlinoite were immersed in 20 mg/kg, 50 mg/kg and 100 mg/kg ammonium solutions. The results demonstrate that zeolites under high ammonium concentrations (100 mg/kg) possess higher physical adsorption capacity (0.398–0.468 meq/g), whereas those under lower ammonium concentrations (20 mg/kg) possess greater ion-exchange property (64–99%). The ion-exchange ability of zeolites are extremely dependent on the cation content of the zeolites, and the cation content is affected by the Si/Al ratio. The surface area of zeolites also has a partial influence on its physical adsorption ability. When the surface area is less than 100 m²/g, the adsorption ability of zeolite increases obviously with surface area; however, adsorption ability is saturated as the surface area becomes larger than this critical value of 100 m²/g. When we carried out the zeolites in 50 mg/kg ammonium concentration at different temperatures (5~50 ℃), we found that zeolites exhibit the highest ammonium removal ability at 30°C and the potassium release was enhanced at 30~40 ℃.

We conducted an in-situ crystal structure analysis of ferroselite at non-ambient conditions. The aim is to provide a solid ground to further the understanding of the properties of this material in a broad range of conditions. Ferroselite, marcasite-type FeSe2, was studied under high pressures up to 46 GPa and low temperatures, down to 50 K using single-crystal microdiffraction techniques. High pressure and low temperatures were generated using a diamond anvil cell and a cryostat. We found no evidences of structural instability in the explored P-T space. The deformation of the orthorhombic lattice is slightly anisotropic. As expected, the compressibility of the Se-Se dumbbell, the longer bond in the structure, is larger than that of the Fe-Se bonds. Less obvious is the behavior of the octahedral bonds, the shorter bond is the most compressible determining a small increase in the octahedron distortion with pressure. We also achieved a robust structural analysis of ferroselite at low temperature in the diamond anvil cell. Structural changes upon temperature decrease are small but qualitatively similar to those produced by pressure.

The global polyvinyldene flouride market is estimated to reach $937,278.5 thousand by 2019, therefore to develop new membranes and gain pioneering ideas, which could create innovative business opportunities, a fundamental knowledge about membrane properties fabricated from recent commercially available PVDF polymers is highly mandatory. In this study, we successfully prepared nine non-woven supported PVDF membranes using a phase inversion precipitation method starting from a 15 wt% PVDF solution in N-methyl-2-pyrrolidone. Various membrane morphologies were obtained by using (1) PVDF polymers with diverse molecular weight in a range from 300.000 Da to 700.000 Da and (2) different temperatures of the coagulation bath (20, 40, and 60 ±2°C) used for the films precipitation. Environmental Scanning Electron Microscope (ESEM) was used for surface and cross-section morphologies characterization. Atomic Force Microscope (AFM) was employed to investigate surface roughness, while Contact Angle (CA) instrument was used for membranes wettability studies. Fourier Transform Infrared Spectroscopy (FTIR) results show that the fabricated membranes are formed by a mixture of TGTG’ chains in α phase crystalline domains and all-TTTT trans planar zigzag chains characteristic to β phase. Moreover, generated results indicate that the phases content and membrane morphologies depend on the polymer molecular weight and conditions used for the membranes preparation. The diversity of fabricated membranes could be applied by the End User Industries for different applications.

Aquaculture is growing ever more important due to the decrease in natural marine resources and increase inworldwide demand. To avoid losses due to aging and abnormalweather, it is important to predict seawater temperature in order to maintain a more stable supply, particularly for high value added products, such as pearls and scallops. The increase in species extinction is a prominent societal issue. Furthermore, in order to maintain a stable quality of farmed fishery, water temperature should be measured daily and farming methods altered according to seasonal stresses. In this paper, we propose an algorithm to estimate seawater temperature in marine aquaculture by combining seawater temperature data and actual weather data.

Errors during manufacture of high value components are not acceptable nowadays in driving industries such as energy and transportation. Sectors such as aerospace, automotive, shipbuilding, nuclear power, large science facilities or wind power manufacture complex and accurate components that demand close measurements and fast feedback into manufacturing processes. New measuring technologies are already available in machine tools, including integrated touch probes and fast interface capabilities. They shall provide the possibility to measure the workpiece during or after the manufacturing process, maintaining the original setup of the workpiece and avoiding the manufacturing process from being interrupted to transport the workpiece to a measuring position. However, the traceability of the measurement process on a machine tool is not ensured yet and measurement data is still not fully reliable for process control or product validation. Due to the similarity between a coordinate measuring machine and a machine tool, some of the methods applied for a correct assessment of uncertainty in coordinate measuring machines are adapted to the challenges of a machine tool. The scientific objective is to determine the uncertainty on a machine tool measurement and, in this way, convert it into a machine integrated traceable measuring process. This paper reviews the fundamentals of machine tool metrology.

Remote sensing technologies are critical for analyzing the escalating impacts of global climate change and increasing urbanization, providing vital insights into land surface temperature (LST), land use and cover (LULC) changes, and the identification of urban heat island (UHI) and surface urban heat island (SUHI) phenomena. This research focuses on the nexus between LULC alterations and variations in LST and air temperature (Tair), with a specific emphasis on the intensified SUHI effect in Kharkiv, Ukraine. Employing an integrated approach, the study analyzes time-series data from Landsat and MODIS satellites, alongside Tair climate records, utilizing machine learning techniques and linear regression analysis. Key findings indicate a statistically significant upward trend in Tair and LST during summer months from 1984 to 2023, with a notable positive correlation between Tair and LST across both datasets. MODIS data exhibit a stronger correlation R² = 0.879, compared to Landsat R² = 0.663. The application of supervised classification through Random Forest algorithms and vegetation indices on LULC data reveals significant alterations, manifested as a 70.3% increase in urban land, concurrently with a decrement in vegetative cover, especially 15.5% reduction in dense vegetation and a 62.9% decrease in sparse vegetation. Change detection analysis elucidates a 24.6% conversion of sparse vegetation into urban land, underscoring a pronounced trajectory towards urbanization. Temporal and seasonal LST variations across different LULC classes were analyzed using kernel density estimation (KDE) and boxplot analysis. Urban areas and sparse vegetation had the smallest average LST fluctuations, at 2.09°C and 2.16°C, respectively, but recorded the most extreme LST values. Water and dense vegetation classes exhibited slightly larger fluctuations of 2.30°C and 2.24°C, with the bare land class showing the highest fluctuation 2.46°C, but fewer extremes. Quantitative analysis with the application of Kolmogorov-Smirnov tests across various LULC classes substantiated the normality of LST distributions p > 0.05 for both monthly and annual data sets. Conversely, the Shapiro-Wilk test validated the normal distribution hypothesis exclusively for monthly data, indicating deviations from normality in the annual data. Thresholded LST classifies urban and bare lands as warmest classes with 39.51°C, 38.20°C and water by 35.96°C and dense vegetation 35.52°C, sparse vegetation 37.71°C as coldest, a trend consistent annually and monthly. The analysis of SUHI effects demonstrates an increasing trend in UHI intensity, with statistical trends indicating a growth in average SUHI values over time. This comprehensive study underscores the critical role of remote sensing in understanding and addressing the impacts of climate change and urbanization on local and global climates, emphasizing the need for sustainable urban planning and green infrastructure to mitigate UHI effects.

Living walls (LWs) are a climate change adaptation strategy for cities, as they have a cooling effect. Previous studies of the cooling effect of LWs were carried out in different climatic zones. These studies differed in their experimental design, or simulated data via models. Plants’ cooling capacity is explained by shading and transpiration, and depends on physical plant parameters, environmental factors, and system-related influences. A three-year-long trial was carried out between 2017 and 2019 at an experimental garden in Geisenheim, Germany. We chose a textile-based LW system with high water demand and plants from a wet/fresh habitat. We assumed that this would achieve high evaporative cooling. The experimental setup included four experimental walls which were exposed to the north, south, east, and west, respectively. The plant choice was divided into three plant mix variants (Cascade, Ground cover, and Meadow) and a Control with no vegetation. We measured the temperature with sensors and a thermal imaging (IR) camera in different setups. The main results were that the measured vegetation temperature (TV) depends on air temperature (TA), measurement position, plant mix variant, and plant species. We could detect the cooling effect only at a small distance from the LW (microclimatic). Our methodological approaches should be continued in further studies.

Background: Wideband tympanometry (WBT) has the potential to distinguish various mechanical middle ear and inner ear pathologies noninvasively. This study investigated the diagnostic value of WBT in the diagnosis of enlarged vestibular aqueduct (EVA). Methods: The absorbance and resonance frequency (RF) of patients with EVA (40 ears, 25 patients) and matched population controls (39 ears, 28 subjects) were compared, alongside receiver operating characteristic (ROC) analysis. Correlations between VA width and RF were also examined. Results: Patients with EVA had higher absorbance at low frequencies (226–917 Hz) and lower absorbance at high frequencies (2,520–4,896 Hz) compared to controls. The RF of the EVA group was significantly lower versus controls (751 [391–1,165] vs. 933 [628–1,346] Hz). ROC analysis revealed area under the curve values of 0.771 and 0.801 respectively, for absorbance and RF. RF had a sensitivity, specificity, positive predictive value, and negative predictive value of 74.4%, 82.5%, 76.7%, and 80.6%, respectively, for diagnosing EVA. In the EVA group, the VA midpoint width (r = −0.334) and VA petrous width (r = −0.402) both significantly correlated with RF. Conclusion: Our findings support the utility of WBT for diagnosing EVA, with RF as the optimal index used.

The role of astro- and microglial cells in the pathogenesis of epilepsy is currently under active in-vestigation. It is proposed that the activity of these cells may be regulated by agonists of peroxi-some proliferator-activated nuclear receptors (PPARs). The objective of this study was to investi-gate the effects of the PPAR β/δ agonist GW0742 (Fitorine) on the behavior and gene expression of astro- and microglial proteins involved in the regulation of epileptogenesis in rat brain in a lithi-um-pilocarpine model of temporal lobe epilepsy (TLE). A seven-day administration of GW0742 at a dose of 5 mg/kg/day has been shown to suppress TLE-related behavioral hyperactivity in the Open Field test and to attenuate the TLE-induced increase in gene expression and protein levels of astro- and microglial activation markers Gfap and Aif1 in the dorsal hippocampus and temporal cortex. Furthermore, GW0742 reversed the TLE-related increase in Lcn2 gene expression (a marker of the A1 phenotype of astrocytes) in the temporal cortex, the increase in the ratio of Nos2/Arg1 gene expression (markers of the M1/M2 phenotypes of microglia), and the decrease in Arg1 gene ex-pression in the dorsal hippocampus. The results suggest that GW0742 may mitigate behavioral and biochemical abnormalities in a lithium-pilocarpine model of TLE.

This study advances the field of infectious disease forecasting by introducing a novel approach to micro-level contact modeling, leveraging human movement patterns to generate realistic temporal dynamic networks. Through the incorporation of human mobility models and parameter tuning, this research presents an innovative method for simulating micro-level encounters that closely mirror infection dynamics within confined spaces. Central to our methodology is the application of Bayesian optimization for parameter selection, which refines our models to emulate both the properties of real-world infection curves and the characteristics of network properties. By focusing on the distinct aspects of infection propagation within confined spaces, our approach significantly improves the realism of temporal-dynamic contact networks, offering a powerful tool for assessing the impact of specific locations on pandemic dynamics. The resulting models shed light on the role of spatial encounters in disease spread and strengthen the capability to forecast and respond to infectious disease outbreaks. This work not only contributes to the scientific understanding of micro-level transmission patterns but also offers practical insights for public health strategies and digital contact tracing efforts, aiming at more effective intervention and containment measures during pandemics.

This study was aim to assess the spatial-temporal trends of urban expansion with land use/land cover change dynamics over a period of 30 years in Addis Ababa. The source of data were Land sat satellite imagery freely available by the United States Geological Survey, Addis Ababa City plan map obtained from Addis Ababa city governance planning and development commission, and Ethiopia Space Science and Geospatial Agency. The data obtained were analysed by using QGIS, and ERDAS software. The finding showed that over the last thirty years Addis Ababa city was highly spreads with (Urban Expansion Intensity Index(UEII)=1.524%); building-up land increased faster (dynamic degree (K) = -6.22 and Annual Urban Expansion Rate(AUER) is 3.56. From the finding, Addis Ababa is spreading speedily.

Exploring the spatio-temporal differentiation characteristics and coupling coordination relationship of the composite ecosystem of urban agglomerations is of great significance for promoting the synergistic development and integration construction of urban agglomerations. This paper is based on LUCC data, DEM, temperature, precipitation and other multi-source data, taking the central Yunnan urban agglomeration as an example. The entropy weight method is first used to determine the comprehensive weights to evaluate the regional economic and social development level. Then, the InVEST (Integrated Valuation of Ecosystem Services and Trade-offs)model was used to quantitatively calculate and analyze the spatial and temporal evolution characteristics of the four ecosystem services, including water conservation, soil conservation, carbon sequestration services, and habitat quality. The coupling coordination model was used to quantitatively analyze the evolution of the coupling coordination of the composite ecosystems in the central Yunnan urban agglomeration during the period of 2010-2020, and to reveal its development law. The results show the following: ①During the study period, the socio-economic subsystems in central Yunnan urban agglomeration demonstrated an outward radiative growth from Kunming City, marked by underdeveloped sub-centers and prevalent low-level areas. ②Trends in ecosystem services varied, with water and soil conservation showing fluctuating increases, carbon sequestration remaining stable, and habitat quality declining. The integrated ecosystem services critically important zone is mainly located in the northeastern region and southwestern edge of the study area. ③Economic, social and ecological subsystems are highly coupled, with consistent overall development trends and strong interactions. ④The increase in the degree of harmonization amounted to 7.82%, with lagging subsystems varying in different degree of harmonization subregions. The study can provide a scientific basis for urban agglomerations to formulate scientific and reasonable development policies, promoting regional sustainable development and optimizing the spatial pattern of the national territory.

This study provides a comprehensive assessment of Derawan Island's coral reefs over two decades (2003, 2011, and 2021) from a machine learning classification perspective. Employing non-parametric algorithms like Random Forest (RF), Support Vector Machine (SVM), and Classification and Regression Tree (CART), our analysis primarily focused on spatial and temporal changes in coral habitats. RF emerged as the most accurate method, demonstrating an accuracy of 71.43% with Landsat 9, 73.68% with Sentinel 2, and 78.28% with Multispectral Aerial Photos. We found that the accuracy of our classification results was significantly influenced by the geographic resolution, as well as the quality of field and satellite/aerial image data. Through spatial clustering, the coral habitats exhibited an Nearest Neighbor Index (NNI) value of 0.8727, indicating specific patterns of distribution. The analysis revealed a decrease in coral reef extent from 2003 to 2011, shrinking to 16 hectares with varying densities, followed by a slight area increase but with more heterogeneous densities between 2011 and 2021. This study not only highlights the dynamic nature of coral reef habitats over two decades but also underscores the critical role of machine learning in environmental monitoring and conservation efforts.

At the beginning of 2019, the petroleum crisis impacted many economies dependent on this industry. The Mexican government started programs to identify points and government officials involved in the gasoline stealing from PEMEX (Petróleos Mexicanos), the country’s leading government petroleum company. The programs consisted of supervising and monitoring the Mexican country network of gasoline ducts to detect points where gasoline was being stolen. Consequently, large urban regions faced a lack and shortage of gasoline. This situation generated several reactions in social media and many open data in news media. Although the government provided open data about stealing gasoline locations related to crimes, it did not analyze the collected data to identify patterns, insights, and the spatio-temporal characterization of this phenomenon. This paper presents a study to deal with the regional semantics described in the social media locations of gasoline stealing. Thus, a framework to discover the trends that emerge from social media and how it is correlated with the government’s open data is also presented—the proposed methodology used machine learning techniques based on linguistic and semantic technologies. The analysis was applied to a dataset of 24,317 geo-referenced tweets. The obtained results reflected the Mexican thinking opinion regarding discovered social topics, polarization maps, and regional insights. According to discovered trends, there were long fuel lines between 1.5 and 5 kilometers (on average) at fuel stations in different Mexican states.

: Medial temporal lobe atrophy (MTA) remains an important imaging biomarker in brain magnetic resonance imaging (MRI) for diagnosing and evaluating Alzheimer's disease (AD). We compared the detection performance for determining significant MTA and the correlation with Scheltens' scale between two automatic volumetry software, NeuroQuant® and Aqua®, in non-AD and AD groups. Overall, 127 (non-AD, 54; AD, 73) individuals who underwent three-dimensional T1-weighted volumetric MRI in our hospital between January and August 2011 were enrolled and retrospectively analyzed using NeuroQuant® and Aqua®. The accuracy, sensitivity, and specificity for detecting significant MTA and the correlation for Scheltens' scale with the mean hippocampal volume of each software were evaluated. The study sample’s mean age was 78 (range, 59–97) years. Regarding accuracy and specificity, Aqua® was non-inferior to NeuroQuant®. Aqua® had significantly higher overall specificity than NeuroQuant® for detecting significant MTA. Additionally, the calculated mean hippocampal volume of Aqua® was more correlated with Scheltens' scale (Kendall's τ, - 0.48) than that of NeuroQuant® (Kendall's τ, - 0.20). The accuracy and specificity of Aqua® in detecting MTA are not inferior to those of NeuroQuant®. Additionally, Aqua® has a stronger correlation of mean hippocampal volume with Scheltens' scale than NeuroQuant®.

Temporal lobe epilepsy (TLE) is associated with reorganization of hippocampal neuronal net-works, gliosis, neuroinflammation, loss of integrity of the blood-brain barrier (BBB) in humans and animal models. More than 30% of epilepsies remain intractable and characterization of the molecular mechanisms involved in BBB dysfunction is essential to the identification of new thera-peutic strategies. In this work, we induced status epilepticus in rats by injection of the proconvul-sant drug pilocarpine that leads to TLE. Using RT-qPCR, double immunohistochemistry and con-focal imaging, we studied at different time points of epileptogenesis (latent phase, 3, 7, 14 days; chronic phase, 1 and 3 months) the regulation of reactive glia and vascular markers. In the hippo-campus, increased expression of mRNA encoding the glial proteins GFAP and Iba1 confirmed neu-roinflammatory status. We report for the first time the concomitant induction in endothelial cells, pericytes and basement membrane of the BBB of specific proteins CD31, PDGFR and ColIV, that peaks at the same time points as inflammation. The altered expression of these proteins occurs ear-ly in TLE, during the latent phase, suggesting that they could be associated with early rupture and pathogenicity of the BBB that will contribute to the chronic phase of epilepsy.

Temporal lobe epilepsy is a common, chronic disorder with spontaneous seizures that is often refractory to drug therapy. A potential cause of temporal lobe epilepsy is primary brain injury, making prevention of epileptogenesis after the initial event an optimal method of treatment. Despite this, no preventive therapy for epilepsy is currently available. The purpose of this study was to evaluate the effects of anakinra, lamotrigine, and their combination on epileptogenesis using the rat lithium-pilocarpine model of temporal lobe epilepsy. The study showed that the treated and untreated animals showed no significant difference in the number and duration of seizures. However, the severity of seizures was significantly reduced after treatment. Anakinra and lamotrigine, alone or in combination, significantly reduced neuronal loss in the CA1 hippocampus compared to the control group. However, the drugs administered alone were found to be more effective for CA3 than their combination. The treatment alleviated the impairments in activity level, exploratory behavior and anxiety, but had a relatively weak effect on TLE-induced impairments in social behavior and memory. The efficacy of the combination treatment did not differ from that of anakinra and lamotrigine monotherapy. These findings suggest that anakinra and lamotrigine, alone or in combination, may have clinical utility in preventing epileptogenesis.

The escalation of climate change and the increasing frequency of extreme weather events have amplified the importance of precise and timely lightning prediction. This predictive capability is pivotal for the preservation of life, protection of property, and maintenance of crucial infrastructure safety. Recently, the rapid advancement and successful application of data-driven deep learning across diverse sectors, particularly in computer vision and spatio-temporal data analysis, have opened up innovative avenues for enhancing both the accuracy and efficiency of lightning prediction. This article presents a comprehensive review of the broad spectrum of existing lightning prediction methodologies. Starting from traditional numerical forecasting techniques, we traverse the path to the most recent breakthroughs in deep learning research. We encapsulate these diverse methods, shedding light on their progression and summarizing their capabilities, while also predicting their future development trajectories. This exploration is designed to enhance our understanding of these methodologies, allowing us to better utilize their strengths, navigate their limitations, and potentially integrate these techniques to create novel and powerful lightning prediction tools. Through such endeavors, our aim is to bolster our preparedness against the growing unpredictability of our climate and ensure a proactive stance towards lightning prediction.

Humans learn from a lot of information sources to make decisions. Once this information is learned in the brain, spatio-temporal associations are made, connecting all these sources (variables) in space and time represented as brain connectivity. In reality, to make a decision, we usually have only part of the infor-mation, either as a limited number of variables, or limited time to make the decision, or both. The brain functions as spatio-temporal associative memory. Inspired by the ability of the human brain, a brain-inspired spatio-temporal associative memory was proposed earlier, that utilizes the NeuCube brain-inspired spiking neural network framework. Here we apply the STAM framework to develop STAM for neuroimaging data, on the cases of EEG and fMRI, resulting in STAM-EEG and STAM-fMRI. The paper shows that once a NeuCube STAM classification model is trained on a complete spatio-temporal EEG or fMRI data, it can be recalled using only part of the time series, or/and only part of the used variables. We evaluate accordingly the temporal association accuracy and spatial association accuracy. This is a pilot study that opens the field for the development of multimodal classification systems on other multimodal neuroimaging data, such as the also shown longitudinal MRI data, trained on complete data, but recalled on partial data collected across different settings, in different labs and clinics, that may vary in terms of variables, time of data collection, and other parameters. The proposed methods will allow also for brain diagnostic/prognostic marker discovery using spatio-temporal neuroimaging data.

In order to realize the performance degradation trend prediction accurately, a prediction method based on multi-domain features and temporal convolutional network (TCN) is proposed. Firstly, construct a high-dimensional feature set in the multi-domain of vibration signals, and use comprehensive evaluation indicators to preliminarily screen performance degradation indexes with good sensitivity and strong trend. Secondly, the kernel principal component analysis (KPCA) method is adopted to eliminate redundant information between multi-domain features, and construct a health index (HI) based on convolutional auto-encoder (CAE) network. Thirdly, a TCN-based performance degradation trend prediction model is constructed, and direct multi-step prediction is used to predict the performance degradation trend of the monitored object. On this basis, the validity of the proposed method is verified using the bearing public data, and it is successfully applied to performance degradation trend prediction of rolling contact fatigue specimen. The results show that the feature set can be reduced from 14 dimensions to 4 dimensions by using KPCA, while 98.33% of the information of the original feature set is retained. Furthermore, the method of constructing HI based on CAE network is effective. The change process of the HI constructed truly reflects the performance degradation process of the rolling contact fatigue specimen. Compared with the two commonly used HI construction methods, auto-encoding (AE) network and gaussian mixture model (GMM), this method has obvious advantages. At the same time, the prediction model based on TCN can accurately predict the performance degradation of the rolling contact fatigue specimen with the root mean square error 0.0146 and the mean absolute error 0.0105, which has better performance and higher prediction accuracy than the prediction model based on the long short-term memory (LSTM) network and the gated recurrent unit (GRU). This method has general significance and may be extended to the performance degradation prediction of other mechanical equipment/parts.

Water hyacinth (Eichhornia crassipes) is a potential accumulator of water pollutants in aquatic ecosystems, and its presence in water systems can affect water quality. This study used different field measurements and laboratory tests of Lake Water to determine the impact of water hyacinth phytoremediation capacity. A total of eight sampling stations used for the two lakes; Lake Koka and Lake Ziway. Sampling stations were selected from sites infested with water hyacinth (low, medium and high) and non-water hyacinth aquatic plants during wet and dry seasons to compare the effects of plants on water quality in the two lakes. All the sampled stations had various human interventions. The water samples were tested for the selected physico-chemical properties namely phosphate, nitrate, pH, electrical conductivity (EC), Biological oxygen demand (BOD5), temperature, and heavy metals (Chromium (Cr), Lead (Pb), Cadmium (Cd), Zinc (Zn), and Copper (Cu). These water quality variables were compared by means of ANOVA. Despite the COD of Lake Ziway, this study found no significant (p > 0.05) variation in the concentrations of Cu, EC, pH and temperature between wet and dry seasons in either lake. Variations in Zn concentration and other physico-chemical parameters (EC, BOD, COD, nitrate, phosphate) between low, medium and high levels of water hyacinth were significant in both lakes (p<0.05). Water hyacinth has shown significant phytoremediation nature during wet and dry seasons. The lowest average heavy metal, phosphate, and nitrate concentrations; and significant pH and temperature variations were observed in Lakes Koka and Lake Ziway, among water hyacinth and other grass-infested sites.

The gap junction-coupled astroglial network plays a central role in the regulation of neuronal activity and synchronization, but its involvement in the pathogenesis of neuronal diseases is not yet understood. Here we present the current state of knowledge about the impact of impaired glial coupling in the development and progression of epilepsy and discuss whether astrocytes represent alternative therapeutic targets. We focus mainly on temporal lobe epilepsy (TLE), which is the most common form of epilepsy in adults, characterized by high therapy resistance. Functional data from TLE patients and corresponding experimental models point to a complete loss of astrocytic coupling, but preservation of the gap junction forming proteins connexin43 (Cx43) and connexin30 (Cx30) in hippocampal sclerosis. Several studies further indicate that astrocyte uncoupling represents a causal event in the initiation of TLE, as it occurs very early in epileptogenesis, clearly preceding dysfunctional changes in neurons. However, more research is needed to fully understand the role of gap junction channels in epilepsy and to develop safe and effective therapeutic strategies targeting astrocytes.

Background: Evaluating muscle mass and function among stroke patients is important. However, evaluating muscle volume and function is not easy due to the disturbance of consciousness and paresis. Temporal muscle thickness (TMT) has been introduced as a novel surrogate marker for muscle mass, function, and nutritional status. We herein performed a narrative literature review on temporal muscle and stroke to understand the current meaning of the TMT in the clinical stroke practice. Methods: The search was performed in PubMed, last updated in October 2021. Report on temporal muscle morphomics and stroke-related diseases or clinical entities were collected. Results: Four studies reported on TMT and subarachnoid hemorrhage, 2 intracerebral hemorrhage, 2 ischemic stroke, 2 standard TMT values, and 2 nutritional status. TMT was reported as a prognostic factor for several diseases, surrogate markers for skeletal muscle mass, and an indicator of nutritional status. Computed tomography, magnetic resonance imaging, and ultrasonography were used to measure TMT. Conclusions: TMT is gradually used as a prognostic factor of stroke or surrogate marker for skeletal muscle mass and nutritional status. Establishing standard methods to measure TMT and large prospective studies to investigate the further relationship between TMT and diseases are needed.

Sea surface partial pressure of CO2 (pCO2) is a critical parameter in the quantification of air-sea CO2 flux, which plays an important role in calculating the global carbon budget and ocean acidification. In this study, we use chlorophyll-a concentration (Chla), sea surface temperature (SST), absorption due to dissolved and particulate detrital matter (Adg), diffuse attenuation coefficient of downwelling irradiance at 490nm (Kd) and mixed layer depth (MLD) as input data for retrieving the sea surface pCO2 in the North Atlantic based on a remote sensing empirical approach with the Categorical Boosting (CatBoost) algorithm. The results show that the root mean square error (RMSE) is 8.25μatm, the mean bias error (MAE) is 4.92μatm and the coefficient of determination (R2) can reach 0.946 in the validation set, which mean that the CatBoost model makes an improvement compared to other models in the published studies. In the further analysis of the spatial and temporal distribution of the sea surface pCO2 in the North Atlantic, it can be found that the North Atlantic sea surface pCO2 has a clear trend with latitude variations and have strong seasonal changes. Furthermore, the sea surface pCO2 in this area is mainly affected by sea temperature and salinity, and influenced by biological activities in some sub-regions.

Generative models for images, audio, text and other low-dimension data have achieved great success in recent years. Generating artificial human movements can also be useful for many applications, including improvement of data augmentation methods for human gesture recognition. The object of this research is to develop a generative model for skeletal human movement, allowing to control the action type of generated motion while keeping the authenticity of the result and the natural style variability of gesture execution. We propose to use a conditional Deep Convolutional Generative Adversarial Network (DC-GAN) applied to pseudo-images representing skeletal pose sequences using Tree Structure Skeleton Image format. We evaluate our approach on the 3D-skeleton data provided in the large NTU RGB+D public dataset. Our generative model can output qualitatively correct skeletal human movements for any of its 60 action classes. We also quantitatively evaluate the performance of our model by computing Frechet Inception Distances, which shows strong correlation to human judgement. Up to our knowledge, our work is the first successful class-conditioned generative model for human skeletal motions based on pseudo-image representation of skeletal pose sequences.

The purpose of this paper is to explore the presence of spatial and temporal effects on the calls for noise disturbance service reported to the Local Police of València (Spain) in the time period from 2014 to 2015, and investigate how some socio-demographic and environmental variables affect the noise phenomenon. The analysis is performed at the level of València's boroughs. It has been carried out using a logistic model after dichotomization of the noise incidents variable. The spatial effects consider first and second order neighbours. The temporal effects are included in the model by means of one and two weeks temporal lags. Our model confirms the presence of strong spatio-temporal effects. We also find significant associations between noise incidence and specific age groups, socio-economic status, land uses and recreational activities, among other variables. The results suggest that there is a problem of ``social'' noise in València that is not exclusively a consequence of coexistence between local residents. External factors such as the increasing number of people on the streets during weekend nights or during summer months increase severely the chances of expecting a noise incident.

The detection of seismic events at regional and teleseismic distances is critical to Nuclear Treaty Monitoring. Traditionally, detecting regional and teleseismic events has required the use of an expensive multi-instrument seismic array; however in this work, we present DeepPick, a novel seismic detection algorithm capable of array-like performance from a single trace. We achieve this directly, by training our single-trace detector against labeled events from an array catalog, and by utilizing a deep temporal convolutional neural network. The training data consists of all arrivals in the International Seismological Centre Catalog for seven seismic arrays over a five year window from 1 Jan 2010 to 1 Jan 2015, yielding a total training set of 608,362 detections. The test set consists of the same seven arrays over a one year window from 1 Jan 2015 to 1 Jan 2016. We report our results by training the algorithm on six of the arrays and testing it on the seventh, so as to demonstrate the transportability and generalization of the technique to new stations. Detection performance against this test set is outstanding. Fixing a type-I error rate of 1%, the algorithm achieves an overall recall rate of 73% on the 141,095 array beam picks in the test set, yielding 102,394 correct detections. This is more than 4 times the 23,259 detections found in the analyst-reviewed single-trace catalogs over the same period, and represents an 8dB improvement in detector sensitivity over current methods. These results demonstrate the potential of our algorithm to significantly enhance the effectiveness of the global treaty monitoring network.

Under optimal conditions, just 3–6 ms of visual stimulation suffices for humans to see motion. Motion perception on this time scale implies that the visual system under these conditions reliably encodes, transmits, and processes neural signals with near-millisecond precision. Motivated by in vitro evidence for high temporal precision of motion signals in the primate retina, we investigated how neuronal and perceptual limits of motion encoding relate. Specifically, we examined the correspondence between the time scale at which cat retinal ganglion cells in vivo represent motion information and temporal thresholds for human motion discrimination. The time scale for motion encoding by ganglion cells ranged from 4.6–91 ms, depended nonlinearly on temporal frequency but not on contrast. Human psychophysics revealed that minimal stimulus durations required for perceiving motion direction were similarly brief, 5.6–65 ms, similarly depended on temporal frequency but, above ~10%, not on contrast. Notably, physiological and psychophysical measurements corresponded closely throughout (r = 0.99), despite more than a 20-fold variation in both human thresholds and optimal time scales for motion encoding in the retina. These results demonstrate that neural circuits for motion vision in cortex can maintain and make use of the high temporal fidelity of the retinal output signals.

Effective discharge, which represents the flow, or range of flows, that transport the most sediment over long term, was determined based on the mean daily flow discharge and mean daily suspended sediment discharge recorded between 1994 and 2014 at four gauging stations along the Trotuș River. This study proposes an efficient method for the estimation of effective discharge based on observed values of the suspended sediment load. By employing this method the suspended sediment load is no longer either under- or overestimated as in the cases when the assessment is based on sediment rating curves. The assessment on effective discharge was performed at two distinct levels: for the entire data series during the investigated time spans and, subsequently, for flows less than the bankfull discharge. The effectiveness curves of the suspended sediment transport characteristics revealed highly multimodal characteristics with many peaks, indicating ample ranges for the effective discharges. The main effective discharge corresponded to large flood events, which are typical for the upper end of the discharge range, whereas the secondary effective discharges corresponded to sub-bankfull flows, which are more frequent. The changes that occurred in the channel bed are reflected by the temporal variations in the effective discharge.

The vegetation types on the Qinghai-Tibet Plateau are complex and diverse, and the ecological environment is fragile and sensitive. It is very important to study the dynamic changes in vegetation and the main factors related to these changes to grasp the present state of the regional ecosystem, maintain the balance of the ecosystem and promote the sustainable development of the ecosystem. Therefore, this paper is based on SPOT/VEG NDVI (normalized difference vegetation index) data, land use data, topographic data and meteorological data from 1999 to 2019. The spatiotemporal variation in the NDVI over the Tibetan Plateau in the last 21 years and its response to different driving factors were investigated by using the Sen slope method, Mann–Kendall mutation test, partial correlation analysis and geographical detector method. The results showed that (1) the vegetation coverage on the Qinghai-Tibet Plateau showed an increasing trend from 1999 to 2019, with increases in approximately 67.00% of the plateau area. (2) The spatial differences in vegetation coverage were large; notably, low-density vegetation areas decreased obviously, moderate-density vegetation areas accounted for approximately 50% of the total area, high-density vegetation areas were the least common, and the overall growth rate was significant. (3) The NDVI was positively correlated with temperature and precipitation, and a positive correlation was observed in more than 66% of the region. (4) The order of the influence of single driving factors on the NDVI was as follows: precipitation > soil type > altitude > temperature > gradient > slope > population density > GDP. (5) The effect of multiple factors was significantly higher than that of single driving factors, with a notable nonlinear influence. The interactions between meteorological factors, such as precipitation, and topographic factors, such as altitude, were important, with a q value over 0.79.

Neurofeedback (NF) shows promise in enhancing memory, but its application to the medial temporal lobe (MTL) still needs to be studied. Therefore, we aimed to develop an NF system for the memory function of the MTL and examine neural activity changes, and memory task score changes through NF training. We created a memory NF system using intracranial electrodes to acquire and visualise the neural activity of the MTL during memory encoding. Twenty trials of a tug-of-war game per session were employed for NF and designed to control neural activity bidirectionally (Up/Down condition). NF training was conducted with three patients with intractable epilepsy, and we observed an increasing difference in NF signal between conditions (Up−Down) as NF training progressed. Similarities and negative correlation tendencies between the transition of neural activity and the transition of memory function were also observed. Our findings demonstrate NF's potential to modulate MTL activity and memory encoding. Future research needs further improvements to the NF system to validate its effects on memory functions. Nonetheless, this study represents a crucial step in understanding NF's application to memory and provides valuable insights for developing more efficient memory enhancement strategies.

Accurate rainfall measurement is vital when investigating spatial and temporal precipitation variability at different scales. However, there are many regions around the world, such as North Darfur State in Sudan, where ground-based observations are few. Satellite-based precipitation products can fill such regions' spatial and temporal rainfall data gaps. Six satellite rainfall prod-ucts, namely the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), African Rainfall Climatology Version 2 (ARC2.0), Climate Hazards Group In-frared Precipitation with Station Data (CHIRPS2.0), the Integrated Multi-satellitE Retrievals for Global Precipitation Measurements (GPM) Final Run v 6 (GPM IMERG6), Precipitation Estima-tion from Remote Sensing Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), and the Tropical Applications of Meteorology using SATellite and ground-based observations (TAMSAT) v3.1 were evaluated to assess their accuracy in estimating rainfall amounts variability trends in the study area. The global-based satellite rainfall products were assessed at monthly and annual time scales by applying a point-to-pixel comparison with ground-based rain gauge data for the period 2000–2019. Based on the overall statistical results at monthly and temporal yearly scales, five satellite precipitation products (TMPA, CHIRPS, GPM IMERG6, PERSIANN-CDR, and TAMSATv3.1) overestimated rainfall amounts by values ranging from 1.49% to 82.69%. In contrast, the ARC2 product underestimated rainfall amounts by values ranging from-16.9% to-20.25%. The TAMSATv3.1, CHIRPS, and TMPA performed relatively better, showing stronger correlations and higher values of Nash-Sutcliffe efficiency. This study showed that the TAMSATv3.1 and CHIRPS products could reasonably estimate rainfall amounts in the North Darfur State.

A phenotype is the composite of an observable expression of a genome for traits in a given environment. The trajectories of phenotypes computed from an image sequence and timing of important events in a plant’s life cycle can be viewed as temporal phenotypes and indicative of the plant’s growth pattern and vigor. In this paper, we introduce a novel method called FlowerPhenoNet which uses deep neural networks for detecting flowers from multiview image sequences for high throughput temporal plant phenotyping analysis. Following flower detection, a set of novel flower-based phenotypes are computed, e.g., the day of emergence of the first flower in a plant’s life cycle, the total number of flowers present in the plant at a given time, the highest number of flowers bloomed in the plant, growth trajectory of a flower and the blooming trajectory of a plant. To develop a new algorithm and facilitate performance evaluation based on experimental analysis, a benchmark dataset is indispensable. Thus, we introduce a benchmark dataset called FlowerPheno which comprises image sequences of three flowering plant species, e.g., sunflower, coleus, and canna, captured by a visible light camera in a high throughput plant phenotyping platform from multiple view angles. The experimental analyses on the FlowerPheno dataset demonstrate the efficacy of the FlowerPhenoNet.

Growing interest surrounds the assessment of perivascular spaces (PVS) on magnetic resonance imaging (MRI) and their validation as a clinical biomarker of adverse brain health. Nonetheless, the limits of validity of current state-of-the-art segmentation methods are still unclear. Here, we propose an open-source three-dimensional computational framework comprising 3D digital reference objects and evaluate the performance of three PVS filtering methods under various spatiotemporal imaging considerations (including sampling, motion artefacts, and Rician noise). Specifically, we study the performance of the Frangi, Jerman and RORPO filters in enhancing PVS-like structures to facilitate segmentation. Our findings were three-fold. First, as long as voxels are isotropic, RORPO outperforms the other two filters, regardless of imaging quality. Unlike the Frangi and Jerman filters, RORPO’s performance does not deteriorate as PVS volume increases. Second, the performance of all “vesselness” filters is heavily influenced by imaging quality, with sampling and motion artefacts being the most damaging for these types of analyses. Third, none of the filters can distinguish PVS from other hyperintense structures (e.g. white matter hyperintensities, stroke lesions, or lacunes) effectively, the area under precision-recall curve dropped substantially (Frangi: from 94.21 [IQR 91.60, 96.16] to 43.76 [IQR 25.19, 63.38]; Jerman: from 94.51 [IQR 91.90, 95.37] to 58.00 [IQR 35.68, 64.87]; RORPO: from 98.72 [IQR 95.37, 98.96] to 71.87 [IQR 57.21, 76.63] without and with other hyperintense structures, respectively). The use of our computational model enables comparing segmentation methods and identifying their advantages and disadvantages, thereby providing means for testing and optimising pipelines for ongoing and future studies.

Event-based vision is an emerging field of computer vision that offers unique properties such as asynchronous visual output, high temporal resolutions, and dependence on brightness changes to generate data. These properties can enable robust high-temporal-resolution object detection and tracking when combined with frame-based vision. In this paper, we present a hybrid, high-temporal-resolution, object detection and tracking approach, that combines learned and classical methods using synchronized images and event data. Off-the-shelf frame-based object detectors are used for initial object detection and classification. Then, event masks, generated per each detection, are used to enable inter-frame tracking at varying temporal resolutions using the event data. Detections are associated across time using a simple low-cost association metric. Moreover, we collect and label a traffic dataset using the hybrid sensor DAVIS 240c. This dataset is utilized for quantitative evaluation using state-of-the-art detection and tracking metrics. We provide ground truth bounding boxes and object IDs for each vehicle annotation. Further, we generate high-temporal-resolution ground truth data to analyze the tracking performance at different temporal rates. Our approach shows promising results with minimal performance deterioration at higher temporal resolutions (48 – 384 Hz) when compared with the baseline frame-based performance at 24 Hz.

The South-to-North Water Transfer Jiangsu Water Supply Area (JWSA) is a mega inter-basin water transfer area (water source) that provides water resources from JiangHuai, combines drainage and flooding management, and regulates nearby rivers and lakes. Analyzing the spatiotemporal soil moisture dynamics in the area will inform agricultural drought and flood disaster assessment and early warning studies. Therefore, we evaluated the quality of European Space Agency Climate Change Initiative Soil moisture (ESA CCI_SM) data in the South-North Water Transfer JWSA. Then, we used ensemble empirical modal decomposition, Mann-Kendall tests, and regression analysis to study the spatiotemporal variation in soil moisture for the past 29 years. The CCI _SM data showed a high correlation with local soil measurements at nine sites. We then analyzed the CCI_SM data from three pumping stations (the Gaogang, Hongze, and Liushan stations) in the South-North Water Transfer JWSA. These stations had similar periodic characteristics of soil moisture, with significant periodic fluctuations around 3.1 d. The overall soil moisture at the three typical pumping stations showed an increasing trend. We then investigated whether there were abrupt soil moisture changes at each station. The spatial distribution of soil moisture in the South-North Water Transfer JWSA was characterized by “dry north and wet south”, with higher soil moisture in winter, followed by autumn, and low soil moisture in spring and summer. Although the linear trend of soil moisture in the South-North Water Transfer JWSA varied in significance, the overall soil moisture in the JWSA has increased over the past 29 years. The areas with significantly enhanced soil moisture are mainly distributed in the Yangzhou and Huai'an areas in the southeastern part of the study area. The areas with significantly decreased soil moisture are small in size and mainly located in northern Xuzhou.

The changing of land use and land cover (LULC) are both affected by climate and human activity and affect climate, biological diversity, and human well-being. Accurate and timely information about the LULC pattern and change is crucial for land management decision-making, ecosystem monitoring, and urban planning, especially in developing economies undergoing industrialization, urbanization, and globalization. Biodiversity degradation and urban expansion in eastern China are research hot-spots. However, the influence of LULC changes on the region remains largely unexplored. Here, an object-based and multi-temporal image analysis approach was developed to detect how LULC changes during 1985-2015 in the Tiaoxi watershed (Zhejiang province, eastern China) using Landsat TM and OLI data. The main objective of this study is to improve the accuracy of unsupervised change detection from object-based and multi-temporal images. To this end, a total of seven LULC maps are generated with multi-temporal images. A random stratified sample design was used for assessing change detection accuracy. The proposed method achieved an overall accuracy of 91.86%, 92.14%, 92.00%, and 93.86% for 2000, 2005, 2010, and 2015, respectively. Nevertheless, the proposed method, in conjunction with object-oriented and multi-temporal satellite images, offers a robust and flexible approach to LULC changes mapping that helps with emergency response and government management. Urbanization and agriculture efficiency are the main reasons for LULC changes in the region. We anticipate that this freely available data will improve the modeling for surface forcing, provide evidence of changes in LULC, and inform water-management decision-making.

Life cycle assessment (LCA) is used frequently as a decision support tool for evaluating different design choices of products based on their environmental impacts. A life cycle usually comprises several phases of varying timespan. The amount of emissions generated from different life cycle phases of a product could be significantly different from one another. In conventional LCA, the emissions generated from the life cycle phases of a product are aggregated at the inventory analysis stage, which is then used as an input for life cycle impact assessment. However, when the emissions are aggregated, the temporal variability of inventory data is ignored, which may result in inaccurate environmental impact assessment. Besides, the conventional LCA does not consider the environmental impact of circular products with multiple use cycles. It poses difficulties in identifying the hotspots of emission-intensive activities with the potential to mislead conclusions and implications for both practice and policy. To address this issue and to analyse the embedded temporal variations in inventory data in a CE context, the paper proposes to calculate the emission intensity for each life cycle phase. It is argued that calculating and comparing emission intensity, based on the timespan and amount of emissions for individual life cycle phases, at the inventory analysis stage of LCA offers a complementary approach to the traditional aggregate emission-based LCA approach. In a circular scenario, it helps to identify significant issues during different life cycle phases and the relevant environmental performance improvement opportunities through product, business model and supply chain design.

Currently the majority of studies on vision-based measurement has been conducted under ideal environments so that an adequate measurement performance and accuracy is ensured. However, vision-based systems may face some adverse influencing factors such as illumination change and fog interference, which can affect the measurement accuracy. This paper develops a robust vision-based displacement measurement method which can handle the two common and important adverse factors given above and achieve sensitivity at the subpixel level. The proposed method leverages the advantage of high-resolution imaging incorporating spatial and temporal context aspects. To validate the feasibility, stability and robustness of the proposed method, a series of experiments was conducted on a two-span three-lane bridge in the laboratory. The illumination change and fog interference are simulated experimentally in the laboratory. The results of the proposed method are compared to conventional displacement sensor data and current vision-based method results. It is demonstrated that the proposed method gives better measurement results than the current ones under illumination change and fog interference.

Global demand for blueberries has surged in recent years, primarily due to their health benefits. However, blueberries postharvest losses, particularly firmness loss and decay, remain significant challenges. This study applied high concentrations of CO2 (15% and 20%) for 3 days at 1 °C to highbush (Duke) and rabbiteye (Ochlockonee) blueberries to assess their effects on maintaining quality, especially firmness, during low-temperature storage. Various quality parameters were evaluated, including titratable acidity, pH, total soluble solids, weight loss, and decay. The impact of gaseous treatments on firmness was analysed through mechanical parameters and the expression of genes related to cell wall integrity (XTH23, PL8, PG, PM3, EXP4, and VcGH5). The effectiveness of the treatments varied between species. High CO2 levels successfully reduced decay in both cultivars, but only the highbush variety (Duke) showed improvements in firmness. In Duke, CO2 treatments influenced the expression of genes associated to cell wall integrity, such as XTH23, PL8, and GH5. In contrast, the roles of PG and PME in firmness maintenance were minimal, showing no significant differences between treatments. While CO2 did not enhance firmness in Ochlockonee, it effectively reduced weight loss, highlighting the need for tailored postharvest strategies for different blueberry cultivars.

In order to discover how formed the multiple oxides observed in the final external scales after long exposure of a low–Mn high–Cr Cantor’s alloy to hot air, oxidation tests in furnace were performed for seven different durations. Metallographic characterization was carried out concerning the oxidation products obtained after each test duration. The different oxides did not appear one after the other, but simultaneously, at the early beginning of the exposure to hot air and after. They thickened all progressively and the chemical composition of each also evolved with time, more or less. Globally the innermost oxide is almost chromia much richer in Cr than in Mn while the outermost one contains Mn principally. The interrupted tests also allowed specifying the mass gain kinetic, which is parabolic, twice faster than a chromia–forming alloy. Despite the lowered content in Mn, manganese still plays an important role in the oxidation phenomenon, starting very early.

The study delved into the interaction between corn starch and ethyl maltol during innovative repeated-continuous heat-moisture treatment (RCHMT), and its impact on the quality of fried chicken nuggets. The results revealed that the complexation ratio of ethyl maltol in the corn starch-ethyl maltol complex remained stable, and the complex created dense microporous structures. Native starch and complex samples exhibited an A-type crystal structure, while the physical mixture sample showed a Ca-type. Additionally, the peak of C-O-H def., CH2 of the complex sample was red-shifted, and the hydrogen bond structure was enhanced. Moreover, the complex exhibited a higher resistant starch content and lower hydrolysis rate than the physical mixture sample. The starch-ethyl maltol formed by RCHMT is a non-inclusion complex. It has been shown to reduce oil absorption and enhance the crispness of fried chicken nuggets, matching that of commercial products. This finding provides a direction for the development of innovative coating powders.

Research on high-volume fly ash concrete, which utilizes large quantities of fly ash, has been actively conducted to promote resource recycling and reduce carbon dioxide emissions. However, while cement mixed with fly ash exhibits different temperature sensitivities compared to traditional cement, its hydration and mechanical properties vary depending on curing conditions. Despite this, there is a significant gap in research on this topic. In this study, high-volume fly ash concrete was produced with fly ash replacement rates of 35 % and 55 %. Compression strength tests were performed at various curing ages under three different curing temperature conditions, and the results were analyzed. Additionally, an isothermal calorimeter was used to analyze hydration characteristics under different curing conditions. The results indicated that concrete with high fly ash content was more sensitive to curing temperature compared to ordinary Portland cement.

This study examines the combined influence of both meteorological and non-climatic factors on rice production in Bangladesh between 1990 and 2020, using the Autoregressive Distributed Lag (ARDL) model. The key variables consist of temperature, precipitation, urbanisation, fertiliser management (specifically potassium levels), and the area of permanent crops. The findings highlight the substantial detrimental impact of temperature on rice output over a prolonged period, whereas precipitation and urbanisation have a favourable affect on rice productivity. However, the utilisation of potassium fertiliser has a detrimental effect on crop productivity. Furthermore, it has been demonstrated that the development of permanent crop fields has a beneficial effect on rice output. These findings provide important information for policymakers and agricultural stakeholders, highlighting the need for adaptive strategies to improve the ability of rice production to withstand the impacts of climate change and urbanisation. The significance of these discoveries applies to rice-producing regions worldwide, offering a strategic blueprint for sustainable cultivation of rice and ensuring food security.

Faba bean is grown globally for human consumption and animal feed and is a significant rotation crop in cereal dominant areas in many countries including Australia. It is sensitive to several abiotic stresses, such as drought, frost and high temperatures, especially at the reproductive stage. However, little is known of the upper and lower temperature limits for successful podding. Experiments were conducted over two years at Breeza and Narrabri in north-western New South Wales to assess the impact of temperature on flowering and pod set of three genotypes; Doza, Cairo and IX1148f under field conditions. Flowering nodes were tagged from designated plants in the field and scored for podding, field temperatures were recorded. Optimum maximum temperature for podset ranged from 23.7-26.1 °C and the equivalent minimum temperature was 3.2-3.8 °C. Optimum average daily temperature for podset ranged from 12.7-13.9 °C. The strongest correlations between temperature and podding were observed at minimum temperatures, indicating that low temperatures are more important than high temperatures in determining yield in faba bean.

Biochar is a porous material produced by pyrolisis of biomass and can be modified using metal oxide to improve its adsorption performance, especially toward organics substances. The activated biochar was synthesized from patchouli biomass to study effect of calcination temperature on both structural properties and paracetamol drug adsorption of the composite was studied. Some process routes were conducted, including the patchouli biomass pyrolysis using CoCl2 activator, the activated biochar impregnation using 0.1 M FeCl3 solutions, biochar-FeCl3 calcination at various temperatures (400, 600, 800 oC) in the almost closed porcelain crucible, product characterizations using X-ray diffraction and FTIR spectrometry, and paracetamol adsorption test at various concentrations (10, 20, 30, 40, and 50 ppm). The paracetamol concentrations were analyzed using UV-Vis spectrophotometry at 243 nm. The adsorption data was treated using Langmuir, Freundlich, and Dubinin-Radushkevich (DR) models. The product diffractograms indicated α-Fe2O3, γ-Fe2O3, FeFe2O4 beside the carbon turbostratic structure. The metal oxide peak heights increased by increasing of the temperature. FTIR spectra significantly started to change at 600 oC. Adsorption test of paracetamol gave the highest adsorption capacity of 56.37 mg/g at 800 oC (Langmuir model) with correlation coefficient of 0.964. The highest adsorption energy was achieved at 800 oC, i.e 530.33 J/mol (physical adsorption) with a dimensionless factor, the RL value of 0.86 (favorable adsorption), and the n constant of 1.58 (favorable adsorption).

High temperature stress affects tomato production both in tropical and sub-tropical environments worldwide. To explore genetic variation for heat tolerance in tomato, 329 transcontinental tomato varieties were evaluated at The Ministry of Municipality and Environment (MME) greenhouses near Doha, Qatar. Preliminary phenotypic analysis identified a diverse set of 71 hybrid and pure line tomato varieties for more detailed studies. The selected subset was examined in the greenhouse in a randomized complete block design under heat stress. The materials were phenotyped for fruit size, fruit weight, fruit hardness, fruit locules, fruit set (%), total soluble solids (TSS), and fruit yield. Significant phenotypic differences among genotypes were observed for all the traits assessed. To explore the genetic basis of variation among varieties the subset was genotyped using 104 SNP markers identified in previous heat tolerance genome wide association studies (GWAS). Nineteen QTL associated SNP markers could reliably select heat tolerant varieties in terms of better fruit yield, fruit set, and TSS. These markers are located on Chromosome 1, 5, 6, 8, 9, and 12. Interestingly, two clusters of markers on chromosome 6 were linked to significant effects on yield, fruit set, and TSS under high temperature. Some markers located within these QTL regions were also reported as associated with heat tolerance in other studies. Eighteen out of nineteen SNP markers were mapped within a gene body. Based on the phenotypic and the genotypic analysis, an elite set of five varieties was selected for approval for heat stress environments in Qatar. These findings have significance not only for Qatar, but they are also valuable for wider application at other locations worldwide. The 19 markers presented here could be adopted by breeders for marker-assisted selection (MAS) for heat tolerance.

This research paper aims to evaluate and model the performance of rice husk gasifier cook stoves for household energy use, a byproduct of rice milling. The study aims to address environmental issues and promote the use of rice husk as a renewable energy source and pest repellent in stoves. The study compared insulated and non-insulated gasifier stoves using rice husk gasification. The study compared the start-up time of insulated and non-insulated gasifier stoves, where the insulated stove showed a lower start-up time, but there was no significant difference at a 5% probability level. The temperature change also was not significant, as heat transfer and temperature change started when the husk was ignited. The burner stoves had different size and volume. The maximum operated or flame temperature was 1,190.30oC at room or minimum temperature of 27.33 oC, whereas the average temperature was 758.916oC for the non-insulated type of gasifier stove. The insulated gasifier stove also observed maximum and average temperatures of 518.30 oC and 289.014oC respectively at the same room temperature. The operating time of stoves directly influences the fuel consumption rate, specific gasification rate, and combustion zone rate of stoves. The fuel consumption rate has a significant effect on the type of gasifier stove, with the maximum rates being 3.820 and 2.099 kg/hr. The time to consume rice husks to gasify them depends on the density of the rice husk, reactor volume, and fuel consumption rate. The average specific gasification rate for insulated and non-insulated stoves is 106.45 and 120.63 kg/m2h, respectively. The fuel or rice husk holding capacity of each type of stove is significant at a 5% level, with the amount of rice husk and biochar produced being 1.18kg and 0.27kg for insulated stoves, and 2.511kg and 0.688kg for non-insulated stoves, respectively. The performance of gasifier stoves varies based on the bio char conversion rate, with non-insulated stoves producing a higher bio char mass per rice husk. Multiple regression analysis was used to model the relationship between time elapse with temperature and heat energy. The Squirrel-2020 data logger was used to measure the temperature produced by burning rice husks in the reactor of the stove as well as related generated and losses of heat. A relationship between the heat energy produced and the operating time of gasifier stoves was conducted to estimate the model. The cubic polynomial model was found to be the best predictive model fit for all variables of this study.

In this paper we present a city-scale (possibly global) air pollution network made of low-cost sensors for particulate matter concentration in the air. The components of presented system are based on our research and experience from previous studies focused on air quality instruments and sensors for addressing urban air pollution. Sensor nodes are produced locally and distributed over a city of Sarajevo, Bosnia and Herzegovina. The system collects, calibrates and displays particulate metal concentrations in real-time. In comparison to the available measurements from governmental institutions, our system demonstrated good agreement of measured parameters, with several advantages including: higher resolution in space and time, lower costs, both horizontal and vertical measurements and many more. One of the capabilities of the system is the real-time air pollution city map with animations. By installing multiple sensor nodes over a slope (cable car), we receive vertical measurements of temperature, humidity and particulate matter concentration in real time, which gives a valuable insight into the dynamics of temperature inversion episodes and air pollution below the inversion layer.

The performance of Yttrium-Barium Copper Oxide (YBCO) high-temperature superconductors in high-frequency applications is significantly affected by the microwave surface resistance (Rs). The paper delves into the basics, measuring methods and factors affecting the resistance (Rs) in YBCO, highlighting its high critical temperature (Tc) and low resistance, positioning it as a promising material. YBCO's compatibility with epitaxial growth and microstructure engineering offers opportunities to reduce grain boundary effects and improve Rs, making it advantageous for high-frequency electronics, communication systems, and MRI coils. This is due to its high critical current density (Jc) and exceptional Rs at practical temperatures. Challenges remain in comprehending and managing Rs in YBCO, despite its favorable characteristics. Utilizing advanced fabrication methods and incorporating nanotechnology allow for customization of YBCO-based devices. Multi-scale modeling and simulation are essential for guiding experimental work and understanding YBCO's performance in high-frequency settings. This study highlights the promise of YBCO for future high-frequency technologies and stresses the importance of more research to overcome hurdles and fully exploit its capabilities, potentially transforming superconducting devices for practical use.

ABSTRACT Lubricants play a crucial role in reducing friction and wear between surfaces in relative motion. Engine oil, as a lubricant, is specifically designed to minimize friction and wear between the moving parts of different equipment and machinery. Engine oil blending serves the purpose of adjusting the variety of lubricants available to meet specific performance requirements. Through blending, it is possible to tailor the characteristics of the lubricating oil, such as viscosity, stability, and additive content, to optimize its effectiveness in reducing friction, protecting engine components, and enhancing overall equipment performance. By customizing lubricant formulations through blending, manufacturers can address the diverse lubrication needs of modern engines and machinery, ensuring smooth operation, longevity, and efficiency.To enhance the quality of the lubricant and achieve the desired high-performance multi-grade engine oil, a systematic approach was followed. The process began with testing the specific gravity of the oil using a hydrometer and thermometer to ensure accurate measurements. Subsequently, 5 liters of base oil were carefully measured and poured into a stainless steel reactor pot to serve as the blending vessel. Following this, 1 liter of paraffin oil (100N) was added to the base oil to introduce specific properties to the blend. To further optimize the formulation, 0.42 kg (7% of the total blend) of additives, including a selection of specific performance-enhancing compounds, was incorporated. These additives, such as Aspen, Tackifier, and other essential components like anti-wear, corrosion inhibitors, detergents, dispersants, and antioxidants, were meticulously measured and added to the mix. Through this precise blending process, the goal was to craft a high-quality multi-grade engine oil that meets the stringent performance requirements for various operating conditions and equipment types. After the melted Aspen and dissolved tackifier were added to the mixture, thorough stirring was carried out to ensure uniform distribution until a homogeneous blend was achieved. The mixture was then exposed to heat to facilitate the reaction between the base oil and additives, reaching a temperature of 70°C for optimal blending. Subsequently, 0.5 kg of viscosity index improver was introduced into the mixture and stirred continuously for a period of 5 minutes to ensure proper integration. Following this step, 5 grams of dye were carefully added to the mixture, and stirring was continued to evenly disperse the coloring agent throughout the blend. This meticulous process aimed to ensure that all components were effectively incorporated, facilitating the production of a high-quality, customized multi-grade engine oil with enhanced performance characteristics and optimal lubricating properties. After subjecting the mixture to a temperature of 70°C, the heat source was removed, allowing the product to gradually cool down to room temperature, which was estimated to be around 30°C. Following this cooling process, the product underwent a filtration step to remove any residual impurities present in the mixture. A sample of the purified product was then extracted for further analysis. Quality control tests were meticulously carried out on the sample to ensure that the product met the rigorous standards set by the Standard Organization of Nigeria (SON). The results obtained from the quality control tests were highly promising, indicating that the product indeed complied with the stringent specifications outlined by the regulatory body. This adherence to quality standards is essential in ensuring the product's reliability and performance in various applications. The successful outcome of the quality control test underscores the meticulous attention to detail and commitment to excellence in the production process. The rigorous quality control procedures implemented in the analysis of the product highlight the commitment to upholding high standards of quality and safety in the manufacturing process. By consistently meeting and exceeding industry regulations and standards, the product not only demonstrates its reliability but also reflects a dedication to customer satisfaction and product excellence. The adherence to the SON standards showcases a commitment to producing top-quality products that meet the needs and expectations of consumers and industry stakeholders alike.

The study aimed to determine the effect of heat stress (HS) on reproductive parameters [calving to first service (CTFS) and first service conception rate (FSCR)] and general physiological responses [rectal temperature (RT) and respiratory rate (RR)] in tropical Holstein Friesian dairy cows raised in Ratchaburi province, Thailand. Cows were divided into a moderate HS (n = 22) and a mild HS (n = 22) group. Cycling cows were detected and injected with PGF2α at the beginning of the experimental period. Reproductive and physiological parameters were recorded. The average temperature humidity indices experienced by the cows were 80.67 ± 0.79 and 77.81 ± 1.09 for the moderate and mild HS groups, respectively. Cows showed significantly lower RT and RR in the mild (38.47 ± 0.21°C and 41.04 ± 4.55 bpm, respectively) than in the moderate HS group (38.87 ± 0.15°C and 51.17 ± 10.52 bpm). Under mild HS, cows showed a better response to injection of PGF2α than those under moderate HS. The percentage of cows that ovulated after being induced by PGF2α and showed estrus signs was higher in the mild than the moderate HS groups (54.55% versus 18.18%). Furthermore, the FSCR of cows under mild HS tended to be higher than in the moderate HS group (54.55 % and 42.11%, respectively), while the average CTFS interval was significantly shorter under mild than moderate HS (69.47 ± 18.18 and 84.60 ± 27.68, respectively). These results indicate that HS impairs reproductive performance in cross-bred Holstein cows, especially in moderate HS conditions.

The aim of this research work is to study the influence of temperature, solar irradiation and wind dust on the production of solar energy. In addition, it aims to study the assessment of water quality, in particular groundwater used for cooling and cleaning photovoltaic panels (quality analysis). it's an important source, stable and sustainable can be used entirely for cooling and cleaning PV modules from the sales dust, in order to improve their performance. To date, all research studies carried out in this context of cooling and cleaning using water have not taken account of the nature of this water. We have taken a sample of water from a well in a rural area in the Darra Tafilalet region of Morocco. This region has an arid and semi-arid climate, with very high temperatures and dust-laden winds, particularly in summer. These meteorological characteristics influence the efficiency of cells, particularly photovoltaic (PV) panels, reducing their performance. Next, the water sample was analysed in the laboratory. The results obtained show that the groundwater in this area is very sensitive to cooling and cleaning photovoltaic panels. Moreover, these results indicate that the temperature, solar irradiation and wind-blown dust have a significant influence on energy production.

The ocean’s mixed layer depth (MLD) plays an important role in understanding climate dynamics, especially during extreme weather occurrences like hurricanes. This study investigates the effects of Hurricane Katrina (2005) on the MLD in the Gulf of Mexico, using the Delft3D modeling system. By integrating hydrodynamics and wave dynamics modules, we simulate the ocean’s response to extreme weather, focusing on temperature, salinity and MLD variations. Our analysis reveals significant cooling and mixing induced by Katrina, resulting in spatial and temporal fluctuations in temperature (~± 4°C) and salinity (~± 2.5 ppt). The MLD is estimated using a simple threshold method, revealing a substantial deepening to ~120 m on August 29th-30th during Hurricane Katrina in the middle of northern Gulf of Mexico, compared to an average MLD of ~20-40 m during pre-storm conditions. It took about 18-days to recover to ~84% of the pre-storm level after Katrina. Compared to the stand-alone FLOW model, the coupled FLOW+WAVE model yields a deeper MLD of ~5%. The MLD recovery and wave effect on the MLD provide insights from various scientific, environmental and operational perspectives, offering a valuable basis for ocean management, planning and applications, particularly during extreme weather events.

In this paper, we propose and demonstrate a network analysis optical frequency domain reflectometer (NA-OFDR) for distributed temperature measurements at high spatial (down to 3 cm) and temperature resolution. The system makes use of a frequency-stepped, continuous-wave (cw) laser, whose output light is modulated using a vector network analyzer. The latter is also used to demodulate the amplitude of the beat signal obtained by coherently mixing the Rayleigh backscattered light with a local oscillator. The system is capable of high measurand resolution (50 mK at 3-cm spatial resolution), thanks to the high sensitivity of coherent Rayleigh scattering to temperature. Furthermore, compared to the conventional optical-frequency domain reflectometry (OFDR), the proposed system does not rely on the use of a tunable laser, therefore it is less prone to limitations related to the laser coherence or sweep nonlinearity. Two configurations are analyzed, both numerically and experimentally, based on either a double sideband or single sideband modulated probe light. The results confirm the validity of the proposed approach.

Heat dissipation from Li-ion batteries is a potential safety issue for large-scale energy storage applications. Maintaining low and uniform temperature distribution, and low energy consumption of the battery storage is very important. We studied the fluid dynamics and heat transfer phenomena of a single cell, 16-cell modules, battery packs, and cabinet through computer simulations and experimental measurements. The results show that the temperature distribution of single cell is relatively uniform when the cell is facing the air flow. In a module, very high air flow rate and large gap between the cells can reduce the cell temperature. A pack with a good thermally conductive case is very important to ensure good heat dissipation of the battery module. Due to limited space in the cabinet, additional air inlets and outlets can be opened outside of the cabinet to slightly lower the pack temperature. Installing fins outside the cabinet can also slightly reduce the temperature inside the cabinet. Liquid cooling medium, such as water, is much better than the air-cooling medium.

We used ab initio molecular dynamics simulations to compare the activation of 2-propanol on the low index Co3O4 (111), (110) and (001) surfaces in dry conditions. The thermal and surface assisted decomposition of a film of 2-propanol to 2-propoxide on the B-termination of each surface was monitored and analyzed. The investigations suggest an activity order of Co3O4 (111) > (110) > (001). On the B-terminated (111) surface, full dissociation of all 2-propanol molecules at the interface is observed, accompanied by a Mars-van Krevelen-type mechanism upon hydroxylation of the surface. On the (110) surface, 2-propanol dissociation is driven by temperature, which activates the two-fold coordinatively unsaturated surface oxygens. The (001) surface on which almost no dissociation occurs is the least active. No formation of acetone is observed in the simulations conditions.

This study develops a wavelet maxima-based methodology to extract anomalous signals from microwave brightness temperature (MBT) observations for seismic estimation. MBT, acquired via satellite microwave radiometry, enables subsurface characterization penetrating clouds. Five earthquake categories were defined contingent on locale (oceanic/terrestrial) and ambient traits (soil hydration, vegetal covering). Continuous wavelet transform was applied to preprocess annualized MBT readings preceding and succeeding prototypical events of each grouping, utilizing optimized wavelet functions and orders tailored to individualized contexts. Wavelet maxima graphs visually portraying signal intensity variations facilitated identification of aberrant phenomena, including pre-seismic accrual, co-seismic perturbation, and postsismic remission signatures. Casework found 10GHz horizontal-polarized MBT optimally detected signals for aquatic and predominantly humid/vegetative settings, whereas 36GHz horizontal-polarized performed best for arid, vegetated landmasses. This preliminary investigation establishes an analytical framework, albeit reliant on qualitative appraisals. Quantitative machine learning methods are warranted to statistically define selection standards and augment empirical forecasting leveraging lithospheric stress state inferences from sensitive MBT parametrization.

The temperature-dependent intensities of the exciton (X) and biexciton (XX) peaks from single GaAs cone-shell quantum dots (QDs) are studied with micro photoluminescence (PL) at varied excitation power and QD size. The QDs are fabricated by filling of self-assembled nanoholes, which are drilled in an AlGaAs barrier by local droplet etching (LDE) during molecular beam epitaxy (MBE). This method allows the fabrication of strain-free QDs with sizes precisely controlled by the amount of material deposited for hole filling. Starting from the base temperature T = 3.2 K of the cryostat, single-dot PL measurements demonstrate a strong enhancement of the exciton emission up to a factor of five with increasing T. Both, the maximum exciton intensity and the temperature Tx,max of the maximum intensity depend on excitation power and dot size. At an elevated excitation power, Tx,max becomes larger than 30 K. This allows an operation using an inexpensive and compact Stirling cryocooler. Above Tx,max, the exciton intensity decreases strongly up to its disappearance. The experimental data are quantitatively reproduced by a model which considers the competing processes of exciton generation, annihilation, and recombination. Exciton generation in the QDs is given by the sum of direct excitation in the dot plus additional bulk excitons diffusing from the barrier layers into the dot. The thermally-driven bulk-exciton diffusion from the barriers causes the temperature enhancement of the exciton emission. Above Tx,max, the intensity decreases due to exciton annihilation processes. In comparison to the exciton, the biexciton intensity shows an only very weak enhancement, which is attributed to more efficient annihilation processes.

A widely used material for the photovoltaic (PV) arrays is crystalline silicon. The PV conversion losses of a power plant as a yearly average, include: light reflection losses (3,1%), low radiation and shadowing losses (3,2%), DC board losses (1,2%), DC/AC conversion losses (4%), mismatching losses (5,7%), temperature losses (7,6%), it can be stated that the major fraction of losses is related to the temperature increase of the sili-con solar cells. In real operating conditions, solar cells and modules operate at different temperatures, either due to changes in ambient temperature (atmospheric conditions changing with the seasons) and cooling rate, depending on wind speed and insolation, rain, snow, etc., or due to changes in the amount of heat (electrical power lost on the internal resistance of the cell), emitted during their operation. The possibility of operation of these devices in ground applications in the temperature range from -20 to +70C should be taken into account. The given range, of course, does not apply to work in the tropics. The detailed studies of the temperature on the electrical parameters of crystalline silicon solar cells have been presented. The theoretical justification of the temperature influence mechanism on the exploitation parameters of the silicon solar cells was submitted. Photovoltaic cells and modules are of better quality, the lower the value of temperature coefficients, in particular, attention should be paid to the decrease in maximum power with increasing temperature.The experimental results were compared with the theoretical predictions and the results, obtained by other authors and producers.

Present study focuses on the synthesis and characterisation of a lightweight ceramic material with electromagnetic interference (EMI) shielding properties, achieved using mullite containing micrometer-sized hollow spheres (cenospheres) and CoFe2O4 nanoparticles. The research explores compositions with varying CoFe2O4 content, ranging from 0 up to 20 wt.%. Сonventional sintering in an air atmosphere is carried out at a temperature between 1100-1300 °C. The addition of ferrite nanoparticles was found to enhance the sintering process of cenospheres, resulting in improved material density and mechanical properties. Furthermore, the study reveals a direct correlation between the concentration of ferrite nanoparticles and the electromagnetic properties of the. By increasing the concentration of ferrite nanoparticles, the electromagnetic shielding effect of the material (saturation magnetization (Ms) and remanent magnetization (Mr)) was observed to strengthen. These findings provide valuable insights into the design and development of lightweight ceramic materials with enhanced electromagnetic shielding capabilities. The synthesized ceramic material holds promise for various applications that require effective electromagnetic shielding, such as in electronics, telecommunications, and aerospace industries.

Meteorological observations over the last four decades are of paramount importance to investigate the ongoing climate change. The assessment of the reliability of any climatic time series is thus mandatory to draw correct conclusions. This evaluation involves homogeneity tests to detect artificial discontinuities whose identification is facilitated by metadata availability. In this work, daily minimum and maximum temperature measurements collected in Padua, Italy, between 1980 and 2022, are examined. Hourly observations began in 1993 and since the aim is to study long term behavior of the temperature, the focus is on daily averages and extremes. Over this period, the weather station of Padua center underwent many changes, in location or instrument; therefore, some tests have been used to identify and remove the effects of these variations and obtain a homogeneous time series. The homogeneity tests applied must be able to identify change-points both in the middle and at the extremes of the series. Some well-known absolute tests have been applied to investigate shift in the mean value: Standard Normal Homogeneity test (SNH), Buishand U and range tests, Pettitt test, F-test, STARS. Some relative tests have been applied too, which are generally more reliable than absolute tests, because they consider the information from neighboring stations. As relative tests rely on the homogeneity and quality of the reference series, several nearby stations and two reanalysis datasets (ERA5 and MERIDA) have been considered, to enhance the picture of the local situation and provide more robust conclusions. The applied tests identify change-points in the years in which a change in instrument or location of the station have occurred, confirming that these changes have compromised the homogeneity of the series. The sub-series obtained splitting the observations in correspondence of these change-points have been homogenized with respect a selected period; corrections must be applied also to future measurements to extend the time series properly.

Microstructured optical fibres (MOFs) are a new type of optical fibres that possess a wide range of optical properties and many advantages over common optical fibres. Those are provided by unique structures defined by a pattern of periodic or quasi-periodic arrangement of air holes that run through the fibre length. In recent years, MOFs have opened up new possibilities in the field of optics and photonics, enabling the development of advanced devices and novel optical systems for different applications. The key application areas of PCFs vary from telecommunications and high-power energy transmission to quantum optics and sensing. The stack-and-draw method is a standard manufacturing technique for MOFs, where a preform is first manually created and then drawn in a high-tech furnace into a fibre with the required final dimensions and position of the air holes. Since in the manufacturing process experimenters can control only a few parameters, mathematical models and numerical simulations of the drawing process are highly requested. They not only allow to deepen the understanding of physical phenomena occurring during the drawing process, but they also accurately predict the final cross-section shape and size of the fibre. In this manuscript, we assume thermal equilibrium between the furnace and the fibre and propose a functional form of the fibre temperature distribution. We utilise it with asymptotic mass, momentum, and evolution equations for free surfaces already available in the literature to describe the process of fibre drawing. By doing so, the complex heat exchange problem between the fibre and the furnace need not be solved. The numerical results of the whole asymptotic model overall agree well with experimental data available in the literature, both for the case of annular capillaries and for the case of holey fibres.

In Ethiopia, climate change risks are anticipated to have significant consequences for agriculture and food security. This study investigated the past (1981-2010) and the future (2041-2070) climate change trends and their influence on crop length of growing seasons in North-Western (NW) Ethiopian highlands. Climate data were obtained from National Meteorological Agency of Ethiopia and the most valid and high resolution CMIP5 rcp6 (Coupled models Intercomparison Project representative concentration path six) model data were extracted and applied for the analysis purpose. Standard statistical methods are then applied to compute soil water content as well as to evaluate climate variability and trends and their impact on crop Length of Growing Season (LGS). Maximum temperature (tasmax) and minimum temperature (tasmin) inter-annual variability anomalies show the region has experienced coolest years than hottest years during the past. However, in the future the coolest years will highly decrease by -1.2oC while the hottest years increase by +1.3oC. During the major rainfall season (JJAS), the area has received an adequate amount of rainfall in the past and is very likely to get similar rainfall in the future. Whereas the February to May (FMAM) season assists only for early planting and October to January (ONDJ) season for lengthen growing season of JJAS if properly utilized. Otherwise, the season will have the possibility to destroy crops before and during the harvesting time. The soil water content change in the future remains close to past condition, The length of growing seasons has less variable onset and cessation dates while the projected length of growing period (LGP) 174 to 177 days will be suitable for short, long cycle crops and double cropping that could benefit crop production yield of NW-Ethiopian highlands in the future.

Research has been actively conducted on the materials used to commercialize finished products that use hydrogen energy. Hydrogen embrittlement that occurs when hydrogen atoms penetrate metals is the largest problem that must be addressed first regarding safety. Because liquefied hydrogen has an 800 times higher density than hydrogen gas, hydrogen has been stored and transported through liquefaction at -253℃ using helium gas as a refrigerant. However, even if extreme temperatures below zero are maintained, natural vaporization loss that reduces liquefied hydrogen by 3 to 5% of the tank storage capacity per day occurs. In addition, when liquefied hydrogen is converted into high-pressure hydrogen gas for use, it is difficult to completely prevent the loss of the gas, even if sealing technology is applied to prevent gas leaks in transport and storage tanks. Therefore, methods to minimize the loss of hydrogen, establishment of assessment criteria for the embrittlement caused by a small amount of hydrogen gas, and related research are essential. We researched the hydrogen embrittlement resistance of SUS316L rods according to the tensile speed by conducting water electrolysis testing at room temperature, at which hydrogen gas is used to present hydrogen embrittlement assessment criteria for primary ring-shaped forged products.

: Flotation performance in plants can significantly be affected by seasonal variations in different parts of the world. This is generally attributed to the changes in water temperature and pulp temperature in flotation plants, type and concentration of dissolved ions in process water. Extreme temperature conditions may be the major factor affecting flotation performance and mask the influence of water chemistry. Therefore, the interactive effects of the temperature and water chemistry should be taken into consideration, particularly for mine sites experiencing extreme temperature conditions. In this paper, effects of temperature, sulphate (SO42-), thiosulphate (S2O32-) and calcium (Ca2+) ions on the flotation performance of a Cu-Pb-Zn complex sulfide ore were investigated using a statistical experimental design and modelling approach. Batch scale kinetic flotation tests were conducted using the sequential flotation flowsheet of a bulk Cu-Pb flotation stage followed by a zinc rougher flotation stage. The results of the zinc flotation section were used for the statistical evaluation since the zinc concentrate was the product with an economic value for the mine. The results were evaluated using ANOVA analysis and regression analysis to determine the significant parameters and derive individual regression models for each flotation response with Design Expert software version 6.0.8. Individual regression models were developed using the significant main effects and their interactions for each flotation response. Then, the models were used to determine the concentration of the dissolved ions and pulp temperature required to achieve the maximum zinc recovery, maximum zinc grade or the optimum zinc grade and recovery. The details of statistical experimental design, discussions on the effects of the experimental factors and their interactions on the flotation performance and development of regression models are discussed in this paper.

In order to apply visible light communication systems in different scenarios, this article utilizes an excellent temperature adjustable light source mixed with RGB LEDs, and applies it in a visible light communication system. It utilizes color division multiplexing technology to achieve three-channel communication, thereby improving the communication bandwidth of the system. The communication system adopts three constant current driving circuits to control the duty cycle of Pulse Width Modulation (PWM) of each channel, thereby changing the proportion of RGB LEDs, and obtaining different color temperatures to achieve the purpose of color control for mixed color LEDs. The experimental results show that when adjusting the color temperature, the change in luminous flux is very small, with fluctuations of less than 2.24%; When adjusting the brightness, the color temperature fluctuation is within 40K, which is less than the 50K color temperature limit that the human eye can distinguish, and the average color temperature error is 0.609%; Color tolerance less than 5.5×10-3 indicates good dimming effect, and the communication performance of the system is better in the high color temperature range, which is significantly superior to the low color temperature range. When the error rate is below 3.8×10-3, the total modulation bandwidth of the three channels reaches 11.7 MHz.

This paper deals with the analysis of the temperatures in a group of 36 African countries. By looking at the maximum, minimum and the range (the difference between the maximum and the minimum) and using a long memory model based on fractional integration and cointegration, we first show that all series display a long memory pattern, with a significant positive time trend in 29 countries for the maximum temperatures and in 33 for the minimum ones. Looking at the range, the estimated value for the order of integration is smaller than the one based on maximum or minimum temperatures in 17 countries. Performing fractional cointegration tests between the maximum and minimum temperatures, our results indicate that the two series cointegrate in the classical sense (i.e., with a short memory equilibrium relationship) in a group of 11 countries, and there is another group of eight countries displaying cointegration in a fractional sense. The remaining 17 countries with no evidence of cointegration are therefore at the very high risk of climate change due to the absence of long-term comovement in their maximum and minimum temperatures. Findings in this paper are of tremendous interpretations and relevance for the analysis and climate projections in Africa.

Plasma plays an important role in semiconductor processes. With the recent miniature and integration, the control of plasma became essential for the success in critical dimension of a few nanometers and etch narrow and deep holes with high aspect ratios. Recently, the etching process has reached physical limitations due to a significant increase in wafer surface temperature under the elevated amount of RF power, affecting not only the warpage phenomenon but also etching uniformity and etching profiles. Therefore, the plasma characteristics are identified using invasive single Langmuir probe (SLP) for wafer temperature diagnosis. Optical data is obtained through a non-invasive optical emission spectroscopy (OES) and the plasma parameters are derived to compare and verify with the SLP. Two variables, electron temperature and electron density, are substituted for the heat flux formula to derive the heat flux according to its location. Using a wafer-type temperature sensor, check the derived heat flux values and compare trends. Such studies are expected to be able to calculate heat flux values in real time, anticipate wafer temperatures, and solve existing problems.

Iapetus, a Saturn moon, shows the most differentiated albedo dichotomy of the Solar System. The dark leading side has a lower albedo than the bright trailing side. Spectral data on the visible light reveal the existence of two types of materials on the surface. The darkening in the leading side is thought to be originated by the presence of organic material and carbonaceous compounds on surface, while the trailing side is covered by water ice due to migration processes from the dark side. On airless bodies like Iapetus, the surface escape speed is greater than the speed of water molecules, resulting in the retention of a H2O atmosphere that allows some species to get diffused through it. Here, there were performed simulations of the evolution of the surface temperature for each of the two hemispheres of Iapetus. The results showed a slow yet steady increment of temperatures for both sides, with a steeper slope for the dark hemisphere. It was also simulated how much energy budget can be accumulated in both sides and its consequences. Finally, we calculated the diffusion coefficients for ammonia, methane, and water ice. The results let us infer how these compounds could evolve over time.

Graphene nanostructures (GNS) are among the most promising materials for creating supercapacitors. However, GNS are still not used in creating supercapacitors due to the impossibility of obtaining large volumes of high-quality material at an acceptable cost. In this work, we investigated the efficiency of using few-layer graphene (FLG) with the number of layers is not more than 5 synthesized under Self-propagating high-temperature synthesis (SHS) conditions as basic material for supercapacitors. Using the SHS process makes it possible to synthesize large volumes of FLG without Stone-Wales defects. It was found that the synthesized FLG makes it possible to obtain better results than using classical materials, namely activated carbon (AC). It was found that the sample based on FLG has a higher specific capacitance – 65 F×g-1 compared to the sample from AC, the specific capacitance of which is 35 F×g-1, for a speed of 5 mV×s-1 these values are 170 and 64 F×g-1, respectively. The drop in capacitance over 1000 cycles was 4%, which indicates a sufficiently high stability of FLG and allows us to consider FLG as perspective material for use in supercapacitors.

The mountains in northern Xinjiang of China were studied during the snowmelt season. Multi-source fusion live data Chinese Land Data Assimilation System (CLDAS,0.05°× 0.05°,hourly data) were used as real data, and Central Meteorological Observatory guidance forecast (SCMOC,0.05°× 0.05 °,forecasting the following 10 days in 3 h intervals) was used as forecast data, both issued by the China Meteorological Administration. The dynamic linear regression and the average filter correction algorithms were selected to revise the original forecast products SCMOC.Based on conventional temperature forecast information,we designed four temperature rise prediction algorithms for essential factors affecting snowmelt.Temperature rise prediction algorithms included the daily maximum temperature algorithm, daily temperature rise range algorithm, snowmelt temperature algorithm, and daily snowmelt duration algorithm.Four temperature rise prediction algorithms values were calculated of each prediction product.Root-mean-square error algorithm and temperature prediction accuracy algorithm were used to compare and test each prediction algorithm value from the time sequence and spatial distribution.Comprehensive tests show that the forecast product revised by the average filter algorithm was superior to revised by the dynamic linear regression algorithm as well as the original forecast product.Through these algorithms, the more suitable temperature rise forecast value for each grid point in the study area can be obtained at different prediction times. The comprehensive and accurate temperature forecast value in mountainous snowmelt season can provide an accurate theoretical basis for effective prediction of the runoff in snowmelt areas and prevention of snowmelt flooding.

We propose a method of cooling nuclear spin systems of solid-state nanostructures by application of a time-dependent magnetic field synchronized with spin fluctuations. Optical spin noise spectroscopy is considered as the method of fluctuation control. Depending on the mutual orientation of the oscillating magnetic field and the probe light beam, cooling might be either provided by dynamic spin polarization in an external static field or result from population transfer between spin levels without build-up of a net magnetic moment (“true cooling”).

(1) Background: This article presents a study that aims to provide a precise understanding of the temperature distribution within a Whole-Body Cryostimulation (WBC) chamber, whether it is empty or occupied by one or several individuals.; (2) Methods: The study employs a mixed numerical and experimental approach, utilizing simplified Computational Fluid Dynamics (CFD) simulations and an experimental analysis that employs thermocouples to determine the 3D thermal field in actual conditions.; (3) Results: The results reveal a non-negligible temperature difference between the setpoint and actual temperature in the middle of the cryochamber. Furthermore, it is shown that the presence of individuals inside the chamber results in both an average temperature rise and a more heterogeneous thermal behavior, which appear to be associated with the number of individuals present.; (4) Conclusions: The findings of this study emphasize the need for further research to establish temperature guidelines and standardize measurement methods for effective WBC treatment.

Substantial reductions of the human and economic activities like road traffic for several months in 2020 were one of the consequences of the Coronavirus disease. This unprecedented change in urban metabolism also affected temperature and air pollutants. This study investigates the effects of the first COVID-19 lockdown over 43 cities in Europe. It determines the influence of anthropogenic activities on nitrogen dioxide (NO2), ozone (O3), and particulate matter (PM2.5) as well as on land surface temperature (LST), and the surface urban heat island intensity (SUHII), using satellite, modelled, and mobility data. Our findings show that there are great temporal and spatial differences and distinct patterns between the cities regarding the magnitude of change of the variables under study. In general, the results indicate a significant decrease in NO2 concentrations in most of the studied cities compared to the reference period 2015-2019. However, reductions could not be attributed to mobility changes like less traffic at transit stations, contrary to the results of previous studies. O3 levels increased during the first lockdown mainly influenced by the decreasing NO2 concentrations. The PM pattern was inconsistent over time and space. Similar as the NO2 results, no relation to the altered mobility behavior was found. No clear signal could be detected for LST and the SUHII, likely due to dominating meteorological influences. Therefore, single city case studies may be misleading.

To overcome the temperature effect of NaI(Tl) detectors for energy spectrometry without additional hardware, a new correction method was put forward based on pulse deconvolution, trapezoidal shaping and amplitude correction, named DTSAC. To verify this method, actual pulses from a NaI(Tl)-PMT detector were measured at different temperatures from -20℃ to 50℃. Pulse processing and spectrum synthesis showed that the position drift of the 662 keV peak was less than 3 keV, and the corresponding resolution at 662 keV of the sum spectra ranges from 6.9% to 10.60% with trapezoidal width set as 1000 ns to 100 ns. The DTSAC method corrects temperature effect by pulse processing, and need no reference peak, reference spectrum and additional circuits. The method solves the problem of correction of pulse shape and pulse amplitude at the same time, and can be used even at high counting rate.

Near space environment is the airspace at an altitude of 20 km-100 km, where complex conditions such as low temperature, low pressure, high wind speed and solar radiation exist. As one of the important meteorological parameters, temperature is crucial for space activities, but the influence of the complex environment makes the error of conventional temperature measurement methods large. Therefore, a new microbridge temperature sensor was designed that can reduce solar radiation and achieve a fast response. And through simulation analysis, the three factors influencing the temperature errors of Joule heat, solar radiation heat and aerodynamic heat were investigated. And the influence of temperature error is reduced by optimizing the installation position of the sensor. Through the temperature error model, the error value in the actual measurement value is removed, to realize the high accuracy detection of near-space temperature.

Chitosan (CTS) is widely used in agriculture as a soil amendment, plant growth regulator and promoter of cold resistance. However, the effects of CTS on strawberry seedlings under the combined stress of high-temperature and high-light are unclear. In this study, Xuelixiang strawberry was used as a test material, and 100 mg·kg-1 CTS solution was sprayed onto the leaves. We measured the chlorophyll content, membrane peroxidation, reactive oxygen species content, antioxidant enzyme content, AsA-GSH cycle activity and other related indicators of strawberry seedling leaves under the combined stress of high-temperature and high-light and found that CTS can increase the contents of antioxidants, increase the contents of osmotic adjustment substances, increase the activity of antioxidant enzymes activity,; remove reactive oxygen species over time, reduce the damage of reactive oxygen species to photosynthetic organs and cell membranes, and improve the photosynthesis efficiency of leaves of strawberry plants under high-temperature and high-light stress, thereby reducing damage to strawberry seedlings caused by high-temperature and high-light.

This article presents a case study on the structural assessment of a reinforced concrete (RC) foundation exposed to low temperatures. The foundation supports a 19,500 m³-capacity tank with low-temperature (-89°C) ethane. Icing and bubbling were observed on the tank’s surface soon after it started operations. Condensation was also observed at the bottom of the 0.8 m-depth RC slab, which raised concerns about the structural condition of the concrete. This study provides details of the field and analytical investigations conducted to assess the structural condition of the foundation. Heat transfer finite element (FE) analyses were performed to examine the concrete sections subjected to low temperatures. It was found that the ethane leakage produced a low temperature on the top side of the concrete foundation of +9.7°C. Overall, the temperatures calculated by the FE analyses were in good agreement with actual field measurements, within a ±5% accuracy. The simplified heat transfer equation for porous media used in this study was sufficiently accurate to model the effects of the ethane leakage in the concrete foundation, provided the ambient temperature at the site is taken into account in the analysis. The results also confirm that reinforcing bars can be neglected in the thermal analysis of massive concrete slabs. The results from the field measurements and FE analyses confirmed that the structural integrity of the RC foundation was never compromised. The approaches, methods and techniques discussed in this article are deemed suitable to solve the practical and scientific challenges involved in the structural assessment and repairs of large special structures. Accordingly, they can serve as useful reference and guidance for engineers and practitioners working in the field of forensic engineering.

The varved sediments of the Pyrenean Lake Montcortès (Pallars Sobirà, Lleida) embody a unique continuous high-resolution (annual) paleoarchive of the last 3000 years for the circum-Mediterranean region. A variety of paleoclimatic and paleoecological records have been retrieved from these uncommon sediments that have turned the lake into a regional reference. Present-day geographical, geological, ecological and limnological features of the lake and its surroundings are reasonably well known but the lack of a local weather station has prevented characterization of current climate, which is important to develop modern-analog studies for paleoclimatic reconstruction and to forecast the potential impacts of future global warming. Here, the local climate of the Montcortès area for the period 1955-2020 is characterized using a network of nearby stations situated along an elevational transect in the same river basin of the lake. The finding of statistically significant elevational gradients for annual and monthly average temperature and precipitation has enabled to estimate these parameters and their seasonal regime for the lake site. A representative climograph has been shaped with these data that can serve as a synthetic descriptive and comparative climatic tool. The same analysis has provided climatic data for modern-analog studies useful to improve the interpretation of sedimentary records in climatic and ecological terms. In addition, the seasonal slope shifting of the climatic elevational gradients has been useful to gain insights about possible future climatic trends under a warming scenario.

It is proven that the Clapeyron-Mendeleev equation, originally discovered for gases with large entropy, describes the state of dark matter and energy due to enormous self-gravitation. It is shown that the dark matter exists as compact cold neutrino stars at positive temperature with size twice the Schwarzschild radius. In contrast, the dark energy is dark matter at temperature below the absolute zero, which is locked entirely in black holes, where the pressure is negative as required for the Universe intrinsic expansion. The derived dark phase diagram predicts negative critical temperatures for the Big Bang and the Big Rip of black holes.

The vehicular technology has integrated many features in the system, which enhances the safety and comfort of the user. Among these features, heating, ventilation, and air conditioning (HVAC) is the only feature that maintains the set cabin air temperature (CAT). The user’s command drives the set CAT, and the thermostat provides feedback to the HVAC to maintain the set CAT. The CAT is increased by extracting the heat from the engine surface produced by the fuel combustion, whereas the CAT is reduced by the known processes of the air conditioning system (ACS). Therefore, the CAT driven by the user’s command may not be optimal, and estimating the optimal CAT is still unsolved. In this work, the user was allowed to input a range for CAT instead of a single value. Optimal HVAC criteria were defined, and the CAT was estimated by performing iterative analysis in the user-selected range satisfying the criteria. The HVAC criteria were defined based on two measurable parameters: air conditioning refrigerant fluid pressure (ACRFP) and engine surface temperature (EST) empirically defined as the vector CATOP. In this article, a NARX DL model by mapping the vehicle-level vectors (VLV) to predict the CATOP in real-time using field data obtained from a 2020 Cadillac CT5 test vehicle. Utilising the DL model, CATOP for future time steps were predicted by varying the CAT in the definite range and applying HVAC criteria. Thus, an optimal set CAT was estimated, corresponding to the optimal CATOP defined by the HVAC criteria. We performed the validation of the DL model for multiple datasets using traditional statistical techniques, namely, signal-to-noise ratio (SNR) values, first-order derivatives (FOD), and root-mean-square error (RMSE).

Oil palm is one of the most important crops in Malaysia. Lately, the production of oil palm has been reduced due to a variety of factors, including the weather and climate. Temperature, wind speed, relative humidity, sunshine, and rainfall distribution all have an impact on palm tree growth and development, which in turn has an impact on oil palm production. This paper aims to investigate the effects of some weather elements (temperature, wind speed, relative humidity, sunshine, and rainfall) on oil palm production in Peninsular Malaysia. Data were analyzed using the Statistical Package for Social Sciences (SPSS 20.0 version), with descriptive statistics, and multiple linear regression (MLR). The MLR model determined the strength of the relationship between oil palm yield (dependent variable) and the changing variables of temperature, sunshine, wind speed, relative humidity, and rainfall (independent variables). The findings revealed that temperature, wind speed, relative humidity, sunshine, and rainfall have a low impact on oil palm production and yield turnover. The R2 value of 0.202 shows that the independent variables explained only 20.2% of the fluctuation in palm oil production. The study recommends working within an integrated approach involving scientific research, planting, improving variety, improving regional academic leadership, and engaging private and public stakeholders, emphasized collaborative efforts with researchers in consumer countries, and strengthening the capacity of growers to best agroecological practices.

To design properly and optimizate liquid-assisted processes such as reactive infiltration for fabricating light weight and corrosion resistant SiC/TiSi2 composites, the interfacial phenomena taking place when liquid Si-rich Si-Ti alloys are in contact with glassy carbon (GC) were investigated for the first time by wetting tests performed by both the sessile and pendant drop methods at T = 1450°C. Specifically, two different Si-rich Si-Ti alloys were selected, and the obtained results in terms of contact angle values, spreading kinetics, reactivity, and developed interface microstructures were compared with experimental observations previously obtained for the liquid Si-rich Si-Ti eutectics processed under the same operating conditions. The increase of the Si content did not affected the final contact angle values. Contrarily, the final developed microstructure at the interface as well as the spreading kinetics were observed as weakly dependent on the composition. From the practical point of view, Si-Ti alloy compositions with a Si-content falling in the simple eutectic region of the phase diagram might be potentially used as infiltrant materials of C- and SiC-based composites.

This paper explores the effect of climate change on the regression of the Argan tree (Argania spinosa L. Skeels) woodland, focusing on the Argan Biosphere Reserve and especially in the Souss plain (Western Morocco). Rainfall and temperature data of four sites within the Argan Biosphere Reserve were analyzed over the last 60 years to assess any climatic change. Regression curves applied to the dataset showed an important decrease in rainfall (18 to 26 %) in the four locations as well as an increase in temperature (1 to 2 °C). These changes may have a detrimental effect on the Argan woodland although human factors have been reported to be the main factor of its regression. It can therefore be concluded that the reduction in rainfall and the increase in temperature should now be considered as factors of Argan woodland regression.

Currently, a large part of the wells for agricultural use located in the state of Sonora are overexploited, which generates a high degree of saline intrusion and abandonment by nearby communities. In this paper the temperature effect on the final concentration of permeate water was evaluated through voltage and intel concentration variations in a batch electrodialysis reversal process (EDR), in order to identify optimal operating conditions with emphasis on the reduction of energy consumption and cost of desalinated water. Thirty-six samples were prepared: eighteen 2,000 mg/L total dissolved solids (TDS) samples and eighteen 5,000 mg/L TDS samples; brackish well water of 639 mg/L TDS and synthetic salt were mixed to obtain those concentrations. 3 different temperatures (25, 30, 35 °C) and 2 different voltages (10 and 20 V) were tested for each sample. The best salt removal occurred in the 20 V arrays, with 18.34% higher removal for 2,000 mg/L TDS experiments and 25.05% for 5,000 mg/L experiments (average between the 25 to 35 °C tests). Temperature positively affected EDR, especially in the experiments at 10 V voltage, where increasing 10 °C increased its efficiency by 10.83% and 24.69% for 2,000 and 5,000 mg/L TDS, respectively. Energy consumption was lower with increasing temperature (35 °C), as it decreased by 1.405% and 1.613% for 2,000 and 5,000 mg/L TDS concentrations, respectively (average between 10 and 20 V tests), decreasing the cost per m3 of water.

The organic solar cells (OSCs) have drawn attention in the past decade due to its cynosure in industrial manufacturing because of its promising properties such as low weight, highly flexible and low cost production. However, low η restricts the utilization of OSCs for potential application such as low cost energy harvesting devices. In this paper, OSCs structure based on triple junction tandem scheme is reported with three different absorber materials with the objective to enhance the absorption of photons which in turn improves the η, as well as its correlating performance parameters. The investigated structure gives the higher value of η = 14.33% with Jsc=16.87 (mA/m2), Voc=1.0 (V), and FF=84.97% by utilizing a stack of three different absorber layers with different band energies. The proposed structure is tested under 1.5 (AM) with 1 sun (W/m2). The impact of top, middle and bottom sub cells thickness on η is analyzed with a terse to find the optimum thickness for three sub cells to extract high η. The optimized structure is then tested with different electrode combination and the highest η is recorded with FTO/Ag. Moreover, the influence of upsurge temperature is also demonstrated on the proposed structure and observed that the upsurge temperature has greatly affected the electrical parameters of the device and η decreases from 14.33% to 11.40% when the temperature of the device rises from 300-400 K.