Temperature Maximum Research Articles

Numerical investigation in an indirect concentrated solar thermal dryer incorporated with discrete mirrors

The objective of this study is to promote the production for small scale drying industries by reducing the drying time of agricultural products. Discrete mirrors are arranged in a parabolic fashion and used as reflectors to concentrate the incoming solar radiation. The available solar flux is doubled by using this discrete arrangement of mirrors. Optical simulations are performed using Monte Carlo Ray tracing algorithm and various effects were studied to select the best arrangement of mirrors. The concentrated type dryer has been numerically simulated and no-load testing is performed using ANSYS Fluent. Simulation is carried out for various inlet velocities from 0.1 to 1 m s−1. The concentrated setup operates between 0.4 and 1 m s−1 of velocity to obtain temperature in the range of 40–70 °C. The maximum and minimum increase in outlet temperature is 23.52% and 2.87%, and the Nusselt number enhancement is 18.94% and 4.15%, respectively.

Podnebne projekcije temperature zraka in padavin za porečja Ledave, Pesnice in Vipave do konca 21. stoletja

As part of the project ‚CeVoTak‘, Integrated Management of Small Water Retention and Soil Erosion Prevention Measures in Agricultural Catchments, we studied changing temperature and precipitation conditions up to the year 2100. The study was conducted on agricultural lands in the catchments of the Ledava and Pesnica rivers in the sub-Pannonian region and the Vipava river in the sub-Mediterranean region. A common climate database was used to create the climate projections - RCM (Regional Climate Model) simulations from the project EURO-CORDEX and scenarios RCP (Representative Concentration Pathway), RCP2.6, 4.5 and 8.5. The projections were prepared for three time periods 2011-2040; 2041-2070 and 2071-2100 for 6 different regional climate models for average, minimum and maximum air temperatures and precipitation. Analysis of the ensemble of model simulations for all scenarios shows similar results for the basin of all rivers, an increase in temperature (maximum in winter, minimum in spring), with high confidence for all scenarios and periods. Projections of precipitation are less reliable, but show an increase in annual precipitation due to the winter increase. The use of climate change projections with expert interpretation is essential for determining the vulnerability of individual areas and building resilience through the implementation of climate change adaptation.

Influence of Meteorological Parameters on the Prevalence of TEE Detected Left Atrial Appendage Thrombi.

(1) Background: Meteorological factors seem to exert various effects on human health, influencing the occurrence of diseases such as thromboembolic events and strokes. Low atmospheric pressure in summer may be associated with an increased likelihood of ischemic stroke. The aim of this study was to investigate the potential impact of meteorological conditions on left atrial appendage (LAA) thrombus formation. (2) Methods: A total of 131 patients were included, diagnosed with a first instance of thrombus via 3D transesophageal echocardiography (TEE) between February 2009 and February 2019. Months with frequent thrombus diagnoses of at least 10 thrombi per month were categorized as frequent months (F-months), while months with fewer than 10 thrombus diagnoses per month were labelled as non-frequent months (N-months). The analysis focused on differences in meteorological parameters in two-week and four-week periods before the diagnosis. (3) Results: F-months were predominantly observed in spring and summer (April, May, June, and July), as well as in February and November. During F-months, a higher absolute temperature difference, lower relative humidity, longer daily sunshine duration, and greater wind speed maximum were observed in the two- and four-week periods rather than for N-months. In the two-week period, average temperatures, equivalent temperatures, and temperature maxima were also significantly higher during F-months than N-months. (4) Conclusion: Thrombi in the left atrial appendage are more prevalent during periods characterized by high absolute temperature differences, low relative humidity, and long daily sunshine duration.

Temperature and palaeolake evolution during a Middle Pleistocene interglacial–glacial transition at the Palaeolithic locality of Schöningen, Germany

The Middle Pleistocene Reinsdorf sequence at the Lower Palaeolithic sites of Schöningen offers the opportunity to reconstruct a rarely well‐preserved post‐Holsteinian environmental transition from an interglacial into a glacial phase along with its highly dynamic interjacent climatic oscillations. Combining biological proxies, element composition and stable isotope ratios of two lakeshore sequences at excavation site 13 II, we demonstrate repeated variations in climate, hydrology and catchment vegetation cover. New ostracod‐based quantitative mean summer and winter air temperature reconstructions with the Mutual Ostracod Temperature Range (MOTR) method provide the first detailed information about the temperature evolution. The interglacial temperature maximum, probably corresponding to Marine Isotope Stage 9e, is followed by a first dry phase and, during the younger part of the Reinsdorf sequence, by a second dry period. Both were marked by lower precipitation/evaporation ratios, reduced vegetation cover in the catchment and increased surface inflows from springs. Temperature reconstructions of these two steppe (open woodland) phases yield very narrow ranges for mean January (−4–0 °C) and July (+17–19 or +17–21 °C) air temperatures, demonstrating that, while summers were similar to those of today, winters were at least 1 °C colder, hinting at a more pronounced continental climate. Precise temperature estimates for the interjacent woodland and steppe (woodland) phase are hindered by generally wider ranges produced by the MOTR method (January mean −4–3 °C, July mean +15–21 °C). The development of a more extensive vegetation cover, reducing surface runoff and erosion in favour of increased river and groundwater discharge, as indicated by a shift in microfossil and stable isotope records, suggests generally more humid climates with higher precipitation/evaporation ratios as well as reduced seasonal temperature variations.

Evaluating The Thickness Of A Cyclopentane Hydrate Film Over A Sessile Water Drop Using PLIF And PIV

The accumulation of hydrates presents an important flow-assurance challenge. Herein, we performed an experimental study to quantify the thickness of a hydrate layer growing laterally along the interface of a water drop submerged in the bulk phase of cyclopentane at sub-zero temperatures. Spatially-resolved velocity and temperature measurements in both liquid phases were taken independently using particle image velocimetry, one- or two-color planar laser-induced fluorescence. We found that the appearance of hydrates leads to a displacement of the temperature maximum away from the phase boundary, which is typically observed very close to the interface due to the recirculating motion of water inside the drop. Making an assumption that, when hydrates are absent, this recirculating motion is retained in time and the maximum position is, thus, unchanged, we conclude that this shift should indicate the hydrate layer should mostly protrude into the drop, and its thickness was evaluated to be around 0.38 mm.

Meteorological data from Badwater, Death Valley National Park 1998 to 2019

AbstractWe installed a meteorological recording system at Badwater (elev. −75 m), the lowest point in Death Valley, California and recorded data over the period 1998–2019. A second station (the Outhouse Station) was established nearby from 2014 to 2019. Here, we report on and publicly archive the data from these two stations. Of interest was the comparison between two air temperature measurements at the Badwater Station, the first with an aspirated platinum resistance temperature device and the second with a thermistor probe in a passive sun shield. During the hottest periods of the summer when temperatures were typically between 30°C at night and 50°C daily peak, the passively shielded sensor indicated up to 0.5°C warmer than the aspirated temperature sensor due to radiative effects. The data suggest a correction for radiative heating of (T–35)/30, for T > 35°C, where, T, is the uncorrected temperature reading of a passively shielded sensor subtracted after any calibration at lower temperatures. Our station was the first precision temperature measurements at Badwater. A longer record exists for the reporting station near the visitor's centre at the Furnace Creek. The summer temperature maxima at the Badwater site correlate well with the values the same day from the Furnace Creek site. The daily maximum temperatures in winter at the Badwater site appear to be about 1°C lower than at the Furnace Creek site. The largest differences are in the minimum temperatures for which the Badwater site averages about 2–3°C warmer than the Furnace Creek site.

The Hawai‘i Climate Data Portal (HCDP)

Abstract The Hawai‘i Climate Data Portal (HCDP) is designed to facilitate streamlined access to a wide variety of climate data and information for the State of Hawai‘i. Prior to the development of the HCDP, gridded climate products and point datasets were fragmented, outdated, not easily accessible, and not available in near–real time. To address these limitations, HCDP researchers developed the cyberinfrastructure necessary to 1) operationalize data acquisition and product production in a near-real-time environment and 2) make data and products easily accessible to a wide range of users. The HCDP hosts several high-resolution (250 m) gridded products including monthly rainfall and daily temperature (maximum, minimum, and mean), station data, and gridded future projections of rainfall and temperature. HCDP users can visualize both gridded and point data, create and download custom maps, and query station and gridded data for export with relative ease. The “virtual station” feature allows users to create a climate time series at any grid point. The primary objective of the HCDP is to promote sharing and access to data and information to streamline research activities, improve awareness, and promote the development of tools and resources that can help to build adaptive capacities. The HCDP products have the potential to serve a wide range of users including researchers, resource managers, city planners, engineers, teachers, students, civil society organizations, and the broader community.

Statistical-based spatial analysis on urban water management under changing environments: a case study of Hawassa, Ethiopia

Hawassa characterizes a typical developing city in Ethiopia, owning to rapid urban growth and demographic trends. The combined effect of climate change and urban expansion is increasing the challenge to the environment and the services it provides. Relating changing environments with urban water management (UWM) is required to build resilience in the urban environment. This research analyzed local climate change and urban growth and linked it to UWM. The historical period 1990–2021 of daily rainfall, temperature variables, four satellite imageries, and DEM were analyzed. Changes in rainfall (annual and daily maximum) and temperature (maximum and minimum) trends are detected and projected to 2051 using a statistical-based model. With geospatial techniques sub-watersheds are delineated, and the urban cover change is quantified. The trend detection result implies an upward trend of annual and daily maximum rainfalls however a significance is insufficient (p > 0.05) to associate it with climate change during the study period. Maximum and minimum temperatures change indicate a positive and significant trend. The forecasting result suggests an increment of both temperatures (0.5 °C–1.5 °C) to the projected period compared to historical scenario. The land cover analysis results show the built-up area changed from 11.6 km2 (7.2%) to 42.5 km2 (26.5%) during the historical period, where the rate varies spatially. The surface runoff increased by 30.7% in the urban watersheds. With a growth rate of 8.9% built-up, the urban area will cover 73.6 km2 (45.9%) for the predicted period. The research finding justifies the potential to reorganize the relationship between the spatial effect of climate change and urban growth on UWM. Considering distinct characteristics of urban watershed, exposure to flooding risk, access to water demand and resilient to climate change have spatial variation. Thus, a local-specific planning approach will support effective UWM and climate adaptation for sustainable city development.

Design and research of heat dissipation system of electric vehicle lithium-ion battery pack based on artificial intelligence optimization algorithm

This research focuses on the design of heat dissipation system for lithium-ion battery packs of electric vehicles, and adopts artificial intelligence optimization algorithm to improve the heat dissipation efficiency of the system. By integrating genetic algorithms and particle swarm optimization, the research goal is to optimize key design parameters of the cooling system to improve temperature control and extend battery life. In the process of algorithm implementation, genetic algorithm improves the diversity of population through crossover and mutation operations, thus enhancing the global search ability. Particle swarm optimization (PSO) improves local search accuracy and convergence speed by dynamically adjusting inertia weight and learning factor. The effects of different design schemes on heat dissipation performance were systematically evaluated by using computational fluid dynamics (CFD) software. The experimental results show that the efficiency of the cooling system is significantly improved after the application of the optimization algorithm, especially in the aspects of temperature distribution uniformity and maximum temperature reduction. The optimization algorithm also successfully shortens the thermal response time of the system and improves the adaptability and stability of the system under different working conditions. The computational complexity and execution time of these algorithms are also analyzed, which proves the efficiency and feasibility of these algorithms in practical applications. This study demonstrates the practicability and effectiveness of artificial intelligence optimization algorithm in the design of heat dissipation system of lithium-ion battery pack for electric vehicles, and provides valuable reference and practical guidance for the progress of heat dissipation technology of electric vehicles in the future.

Spatiotemporal variability and trends in rainfall and temperature extremes using ERA5 reanalysis and CORDEX–Africa model in Southern Ethiopia

Rainfall and temperature are characterized by spatial and temporal variability in Ethiopia. However, less attention was given for the analysis of climate variability using advanced techniques and multiple sets of data. This study was conducted to examine spatiotemporal variability and trends in rainfall and temperature extremes in Ghibe III Dam watershed. Observational, ERA5, and regional simulation model data sets were used. The coefficient of variation (CV) and precipitation concentration index (PCI) were employed. The trends in rainfall and temperature extremes were examined using the modified Mann-Kendall test and the Sen Slope estimator in R-ClimDex in R 4.2.2 software. The warmest days exhibited temperature from 24.6°C to 40°C in Bele, 28.2°C to 35.43°C in Wolaita Sodo, 33.6 °C–44 °C in Areka, 31.64 °C–36.8 °C in Gesuba, and 29.19 °C–36.15 °C in Gena Bosa. The warmest nights showed temperature ranging from 14 °C to 18.74 °C in Bele and Gena Bosa, respectively. Annual warm days (TX90p) ranges from 11.34 to 57.1 days, with higher heating in the southern parts. The cool days (TX10p) range from 2.79 to 8.41, while the cool nights (TN10p) range from 0.04 to 8.26 days. The areal average temperature maximum and minimum range between 26.37 °C and 13.81 °C, respectively, with mean precipitation of 1446.92 mm.The rainfall extremes indices showed increasing and decreasing trends. The extreme temperature indices showed an overall warming trend. Based on ERA estimates, the rainfall in winter showed higher variability (CV = 72.4%–99.3 %) than the annual rainfall (CV = 33%–79.8 %). PCI showed a moderately (12 %) to very erratic (19.4 %) rainfall distribution. The climate model estimate showed high variability (CV = 20.65 %) in Climate Limited Area Modeling Community (CCLM) under representative concentration paths (RCP) 4.5 and 8.5 and extremely high variability (CV = 93.49 %) in the Regional Atmospheric Climate Model (RACMO) under RCP 4.5. Policymakers should design appropriate adaptation strategies applicable to farmers.

Comparison on thermal and hydraulic performances of transverse mini-channel based on a novel comprehensive evaluation factor

To disclose the application value of cold plate with transverse layout used in thermal management system, a novel dimensionless factor, Comprehensive Index of Thermal Management System (CITMS), is proposed, which involves three most important parameters: pumping power Etol, temperature dispersion Tσ and maximum temperature Tmax. Results show that compared to 15 types of mini-channels (longitudinal parallel, serpentine, bifurcated, u-turn, annular, zigzag, cobweb, elm leaf, diverged, rhombus spoiler, gridding, wave, honeycomb, multistage tesla-valve, pease types), transverse parallel mini-channel shows superior CITMS, which declines by an average of 80.26 % to 99.61 % under weighting coefficient w = 1 and expected operating temperature Texp = 320.15 K. Meanwhile, zigzag, wave, pease and multistage tesla-valve mini-channels with transverse layout show significantly better CITMS than longitudinal layout, which decline by an average of 78.61 % to 89.03 %. This is mainly due to the decline in Tσ and Etol. Furthermore, based on CITMS, an appropriate mass flow rate can be achieved for a specific cold plate. Under w = 30 and Texp = 298.15 K, the optimal value of CITMS for transverse parallel mini-channel occurs at 0.5 g/s. In view of better comprehensive performance and same process difficulty, transverse layout could undoubtedly become an effective design concept.

Comprehensive evaluation of extreme hydrometeorological events coincidence and their interrelationships in the Hanjiang River Basin, China

Extreme hydrometeorological events are often highly destructive and can cause huge damage to the natural environment and social economy. As climate change and human activities intensify, the frequency and intensity of such extreme events continue to increase. This phenomenon is also shown in the Hanjiang River Basin (HRB) in recent decades. In this study, we focus on the coincidence occurrence patterns of extreme hydrometeorological events. First, we used the standardized index method to extract the annual maximum and minimum values of runoff, precipitation, maximum and minimum temperatures from 1961 to 2019 in the control area of the Baihe (BH) hydrological station, with time scales from 1 month to 12 months. Then through the Copula and nested structure Copula functions, the joint occurrence probability of bivariate and multivariate were analyzed. The results show that too high a maximum temperature will inhibit runoff, while an increase in minimum temperature will promote an increase in runoff to a certain extent. Both the maximum and minimum temperatures in the basin shown a significant increasing trend, and the intensity of hydrological drought also shows an increasing trend. An increase in maximum temperature will aggravate hydrological drought, as the combined joint occurrence probability of them is the greatest. Analysis through the nested structure Copula functions show that when extreme hydrometeorological events both occurred at the median of their intervals, their coincidence occurrence probability is the maximum, and when the time scale is eight months, their coincidence occurrence probability is the highest. This study can provide a reference for joint probability analysis of extreme hydrometeorological events.

Weather Parameter’s Impact on Natural Parasitization by Campoletis chlorideae Uchida in Chickpea Ecosystem - New Alluvial Zone of West Bengal

Background: A major biotic constraint in chickpea (Cicer arietinum L.) production is gram pod borer, Helicoverpa armigera (Hübner). However, the population of this pest is reduced to some extent by a solitary endo-larval parasitoid, Campoletis chlorideae Uchida under field conditions. Climate change has a greater impact on natural enemies’ effectiveness used in field-level pest management. Therefore, the present study was conducted to understand the impact of weather factors on C. chlorideae parasitization rate of H. armigera in chickpea grown in the new alluvial agro-climatic zone of West Bengal. Methods: Chickpea was grown in a randomized complete block design (RCBD) with three replications during Rabi 2017-18, 2018-19 and 2019-2020, at Mondouri farm, Bidhan Chandra Krishi Visawavidayala, Mohanpur, West Bengal. To know the most significant weather parameter which influences C. chlorideae parasitization or parasitoid incidence, H. armigera larvae (2nd instar) collected from the field experiment were reared until cocoon formation and parasitization % was correlated with weather parameters and subsequently subjected to stepwise regression analysis. Result: The larval endo-parasitoid, C. chlorideae marked its first appearance from 3rd SMW after the appearance of H. armigera and its activity was high (68% to 72% parasitization) during the flowering to pod initiation stage and thereafter declined gradually. Among the weather variables, only temperature (maximum and minimum) had a consistent and significant negative correlation with C. chlorideae incidence and was responsible for nearly 50% C. chlorideae incidence. An increase in 1°C temperature results in a decrease of 4% parasitoid incidence. Thus, climate change in the near future will have a considerable influence on the overall survival, development and rate of parasitization of parasitoids.

ANN for Temperature and Irradiation Prediction and Maximum Power Point Tracking Using MRP-SMC

Currently, artificial intelligence (AI) is emerging as a dominant force in various technologies, owing to its unparalleled efficiency. Among the plethora of AI techniques available, neural networks (NNs) have garnered significant attention due to their adeptness in addressing diverse challenges, particularly for prediction tasks. This study offers a comprehensive review of predominant AI-based approaches to photovoltaic (PV) energy forecasting, with a particular emphasis on artificial neural networks (ANNs). We introduce a revolutionary methodology that amalgamates the predictive capabilities of ANN with the precision control afforded by the minimum-risk problem and sliding mode control (MRP-SMC), thereby revolutionizing the PV panel performance enhancement. Building upon this methodology, our hybrid approach utilizes the ANN as a proficient weather forecaster, accurately predicting the temperature and solar radiation levels impacting the panels. These forecasts serve as guiding principles for the MRP-SMC algorithm, enabling the proactive determination of the Maximum Power Point (MPP). Unlike conventional methods that grapple with weather unpredictability, the MRP-SMC algorithm transforms stochastic optimization challenges into controllable deterministic risk problems. Our method regulates the boost converter’s work cycle dynamically. This dynamic adaptation, guided by environmental predictions from ANNs, unlocks the full potential of PV panels, maximizing energy recovery efficiency. To train the model, we utilized a large dataset comprising 60,538 temperature and solar radiation readings from the Department of Systems Engineering and Automation at the Faculty of Engineering in Vitoria (University of the Basque Country). Our approach demonstrates a high regression coefficient (R = 0.99) and low mean square error (MSE = 0.0044), underscoring its exceptional ability to predict real energy values. In essence, this study proposes a potent fusion of artificial intelligence and control mechanisms that unleash the untapped potential of photovoltaic panels. By utilizing forecasts to guide the converter, we are paving the way for a future where solar energy shines brighter than ever.

Max-convolution processes with random shape indicator kernels

In this paper, we introduce a new class of models for spatial data obtained from max-convolution processes based on indicator kernels with random shape. We show that these models have appealing dependence properties including tail dependence at short distances and independence at long distances. We further consider max-convolutions between such processes and processes with tail independence, in order to separately control the bulk and tail dependence behaviors, and to increase flexibility of the model at longer distances, in particular, to capture intermediate tail dependence. We show how parameters can be estimated using a weighted pairwise likelihood approach, and we conduct an extensive simulation study to show that the proposed inference approach is feasible in relatively high dimensions and it yields accurate parameter estimates in most cases. We apply the proposed methodology to analyze daily temperature maxima measured at 100 monitoring stations in the state of Oklahoma, US. Our results indicate that our proposed model provides a good fit to the data, and that it captures both the bulk and the tail dependence structures accurately.

Connecting past, present, and future trends of hydraulic and phosphorus loading in the Bay of Quinte tributaries, Ontario, Canada

Study regionThe Bay of Quinte watershed, located on the northeastern shore of Lake Ontario, Canada. Special focus is placed on the Napanee River and Wilton Creek catchments, where the presence of a subterranean network of conduits that facilitate groundwater-surface water interactions is conducive to an increase in the delivery of nutrients during storm events and can exacerbate the risk of eutrophication. Study focusDevelopment of a rigorous modelling framework that can advance our understanding of past and present trends of streamflow and tributary phosphorus (P) concentration and loads. We also analyze future streamflow rates and P export trends in order to guide the long-term watershed management in the area. Our analysis is carried out through a comprehensive combination of data-driven and process-based models. Novel hydrological insights for the regionRetrospective analysis provides evidence of a recent increase in the impact of nonpoint-source P pollution. General Circulation Models suggest a future increase in both temperature minima and maxima relative to present conditions (2002–2018). Precipitation projections are indicative of increased frequency of occurrence of high extreme precipitation events during the summer and mid-fall, when the Bay of Quinte is more susceptible to undesirable ecological shifts. Our study predicts a seasonality shift in the streamflow, with the spring freshet occurring earlier in the year accompanied by higher flow rates in the local creeks for the winter and early spring. We also examined the relationship between precipitation and streamflow across varying levels of flow regulation. Our analysis indicates that our predictive capacity declines with increased flow regulation as well as when streamflow rates are predicted by contemporaneous precipitation values.

A warmer climate impairs the growth performance of Central Europe's major timber species in lowland regions

Recent hot droughts have caused tree vitality decline and increased mortality in many forest regions on earth. Most of Central Europe's important timber species have suffered from the extreme 2018/2019 hot drought, confronting foresters with difficult questions about the choice of more drought- and heat-resistant tree species. We compared the growth dynamics of European beech, sessile oak, Scots pine and Douglas fir in a warmer and a cooler lowland region of Germany to explore the adaptive potential of the four species to climate warming (24 forest stands). The basal area increment (BAI) of the two conifers has declined since about 1990–2010 in both regions, and that of beech in the warmer region, while oak showed positive BAI trends. A 2 °C difference in mean temperatures and a higher frequency of hot days (temperature maximum >30 °C) resulted in greater sensitivity to a negative climatic water balance in beech and oak, and elevated sensitivity to summer heat in Douglas fir and pine. This suggests to include hot days in climate-growth analyses. Negative pointer years were closely related to dry years. Nevertheless, all species showed growth recovery within one to three years. We conclude that all four species are sensitive to a deteriorating climatic water balance and hot temperatures, and have so far not been able to successfully acclimate to the warmer climate, with especially Douglas and beech, but also Scots pine, being vulnerable to a warming and drying climate.

Small oases below the canopy: The cooling effects of water-filled tree holes on the local microclimate in oak-dominated stands

The relevance of forests in mitigating biodiversity loss and the negative effects of climate change on the biota is largely shaped by below-canopy microclimatic conditions, which are buffered, cooler and more humid compared to adjoining open habitats. Water-filled tree holes (dendrotelms, DTs) are special tree-related microhabitats that host semi-aquatic communities and are important substrates or water sources for numerous taxa (e.g., birds, mammals, insects, fungi, bryophytes). Despite their microrefugia-role might be more pronounced under climate change-induced drought events, little is known about their microclimate and its potential drivers.In an abandoned Turkey oak-dominated coppice stand in Hungary, the microclimates of 80 paired dendrotelms and adjacent forest plots were measured in the summer of 2021. Compared to nearby open areas, under closed forests, air temperature maxima were up to 5 °C cooler, DTs had an additional negative offset of 2.5 °C and increased air humidity by 15 percentage points (5% vs. 20 %). The local microclimate of DTs and the adjacent closed forest differed significantly in means and extremes of air temperature, relative humidity and vapour pressure deficit, which was mainly driven by DT entrance area and water volume and seemed independent from local stand structure. On the contrary, nearby below-canopy microclimate was shaped by the distance from forest edge, incoming light and tree density. Water volume in DTs also influenced local forest microclimates up to 0.5–1 m from the entrances.As the frequency and severity of extreme weather events such as heatwaves are likely to intensify due to climate change, the importance of microhabitats and habitat trees for invertebrates and vertebrates will increase further, and will additionally contribute to the microclimatic refugia role of forests. Active protection of veteran trees under the scheme of continuous cover forestry or conservation-oriented management is essential facing climate change to mitigate its negative effects on forest biodiversity.

Machine Learning Analysis of Enhanced Biodegradable Phoenix dactylifera L./HDPE Composite Thermograms.

Worldwide, environmental groups and policymakers are focusing on waste recycling to create economic value and on the decomposition of waste by leveraging on scarce resources. This work, therefore, explores the thermal decomposition of enhanced biodegradable polymer matrices made from a mixture of discarded Phoenix dactylifera L./high-density polyethylene (PD/HDPE) using the machine learning analysis of experimental data. The experimental results of these samples were obtained via thermogravimetric (TGA) analysis under an oxidation-free environment, with heating rates of 10, 20, and 40 °C·min-1 and a degradation temperature range from 25 to 600 °C. The TGA analyses revealed the continued dependence of the actual percentage weight loss by these materials as a test function of the degradation temperature, shifting thermograms to temperature maxima consistent with increasing heating rates. Although high-density polyethylene (HDPE) materials were found to be thermally more stable than Phoenix dactylifera L. (PD) materials, PD/HDPE composite materials contained a significant amount of residual ash. Using a machine learning deep neural network approach for this process, significantly improved learning algorithms have been developed, which reduces the overall cost function (residual error) to almost zero (0.025) after just over a million iterations (epochs) and provides predictions that overlap with the experimental results (R2~1). Learning algorithms, along with optimized synaptic weights and biases, were employed to predict the behaviour of PD materials based on experimental thermograms conducted at higher degradation temperatures, typically ranging between 600 and 1000 °C. Predicted data using the enhanced learning algorithms completely overlapped the experiments (R2~1) for these higher degradation temperatures with near unity correlation if the decomposition of the materials continued until the residue was attained. With this approach, it is possible to predict and optimize the thermal characteristics of PD and HDPE with greater efficiency, which reduces the need for multiple design iterations and experimentation.

Petroleum Potential Prospectivity of Muglad Basin, South Sudan: A Case of Source Rocks Characterisation in the Drilled Kaikang West-1 Well

This study focuses on Prospectivity of Petroleum Potential of Muglad basin, South Sudan, with the help of petroleum source rocks characterisation in the drilled Kaikang West-1 Well based on the organic matter quantity, quality, and the thermal maturation generation capability of the organic matter disseminated in the analysed rock samples. Muglad basin is a major petroliferous Western and Central African Rifts System (WCARS) member, with estimated reserves of 2,053 mm bbl. Muglad Basin comprises thick sandstones of Aradeiba and Bentiu Formations, considered to be the main petroleum prospectivity targets, with trapping mechanism being structural faults and lithological anticlines. The organic matter contents were determined directly from laboratory analyses of the source rock samples with the help of seismic and gamma ray profiles, total organic carbon (TOC wt %), maturity indices (OI, HI and PI), temperature maximum (Tmax °C), porosities and other sedimentological parameters. This study involved a series of analytical geochemistry and petro-physical studies to ascertain a number of effective source rock samples from the well cores which were then analysed in terms of TOC, OI, PI, HI, S2, S1 and Tmax to determine oil/gas prone samples (resource areas). These were then distinguished from strata with very high organic matter samples as well as very high TOC to help identify for possible source rock characterizations within the lithology pertaining to the well in terms of potential source-reservoir-seals combinations. The Rock-Eval pyrolysis data were useful in assessing and evaluating the type of organic matter, thermal maturity, and the generation capability of source rocks for hydrocarbon exploration rationale. The analyses revealed that some strata, within the sampled well data, have high hydrocarbon generation potential with the existence of commercial hydrocarbon production